Remote Sensing

21 pages, 7074 KiB

Open AccessEditor’s ChoiceArticle

Fire Vulnerability, Resilience, and Recovery Rates of Mediterranean Pine Forests Using a 33-Year Time Series of Satellite Imagery

by Esther Peña-Molina^1,*

, Daniel Moya¹

, Eva Marino²

, José Luis Tomé²

, Álvaro Fajardo-Cantos¹

, Javier González-Romero³

, Manuel Esteban Lucas-Borja¹

and Jorge de las Heras¹

¹ Forest Ecology Research Group (ECOFOR), High Technical School of Agricultural and Forestry Engineering and Biotechnology, University of Castilla-La Mancha, University Campus, s/n, 02071 Albacete, Spain

² AGRESTA Sociedad Cooperativa, c/Duque de Fernán Núñez 2, 28012 Madrid, Spain

³ Department of Forestry and Environmental Engineering and Management, Technical University of Madrid, 28040 Madrid, Spain

Remote Sens. 2024, 16(10), 1718; https://doi.org/10.3390/rs16101718 - 13 May 2024

Cited by 1 | Viewed by 1871

Abstract

The modification of fire regimes and their impact on vegetation recovery, soil properties, and fuel structure are current key research areas that attempt to identify the thresholds of vegetation’s susceptibility to wildfires. This study aimed to evaluate the vulnerability of Mediterranean pine forests [...] Read more.

The modification of fire regimes and their impact on vegetation recovery, soil properties, and fuel structure are current key research areas that attempt to identify the thresholds of vegetation’s susceptibility to wildfires. This study aimed to evaluate the vulnerability of Mediterranean pine forests (Pinus halepensis Mill. and Pinus pinaster Aiton) to wildfires, analyzing two major forest fires that occurred in Yeste (Spain) in 1994 and 2017, affecting over 14,000 and 3200 hectares, respectively. Four recovery regions were identified based on fire severity—calculated using the delta Normalized Burn Ratio (dNBR) index—and recurrence: areas with high severity in 2017 but not in 1994 (UB94-HS17), areas with high severity in 1994 but not in 2017 (HS94-UB17), areas with high severity in both fires (HS94-HS17), and areas unaffected by either fire (UB94-UB17). The analysis focused on examining the recovery patterns of three spectral indices—the Normalized Difference Vegetation Index (NDVI), Normalized Moisture Index (NDMI), and Normalized Burn Ratio (NBR)—using the Google Earth Engine platform from 1990 to 2023. Additionally, the Relative Recovery Indicator (RRI), the Ratio of Eighty Percent (R80P), and the Year-on-Year average (YrYr) metrics were computed to assess the spectral recovery rates by region. These three spectral indices showed similar dynamic responses to fire. However, the Mann–Kendall and unit root statistical tests revealed that the NDVI and NDMI exhibited distinct trends, particularly in areas with recurrence (HS94-HS17). The NDVI outperformed the NBR and NDMI in distinguishing variations among regions. These results suggest accelerated vegetation spectral regrowth in the short term. The Vegetation Recovery Capacity After Fire (VRAF) index showed values from low to moderate, while the Vulnerability to Fire (V2FIRE) index exhibited values from medium to high across all recovery regions. These findings enhance our understanding of how vegetation recovers from fire and how vulnerable it is to fire. Full article

(This article belongs to the Special Issue Land Use/Cover Mapping and Trend Analysis Using Google Earth Engine)

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46 pages, 25578 KiB

Open AccessEditor’s ChoiceReview

Remote Sensing and Modeling of the Cryosphere in High Mountain Asia: A Multidisciplinary Review

by Qinghua Ye^1,2

, Yuzhe Wang³

, Lin Liu⁴

, Linan Guo¹, Xueqin Zhang²

, Liyun Dai⁵, Limin Zhai^6,7, Yafan Hu^1,7, Nauman Ali^1,7, Xinhui Ji^1,7, Youhua Ran⁵

, Yubao Qiu⁸

, Lijuan Shi⁸, Tao Che⁵

, Ninglian Wang⁹, Xin Li¹

and Liping Zhu^1,*

¹ State Key Laboratory Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China

² Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

³ College of Geography and Environment, Shandong Normal University, Jinan 250358, China

⁴ Earth and Environmental Sciences Programme, Faculty of Science, The Chinese University of Hong Kong, Hong Kong 999077, China

⁵ Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China

⁶ Key Lab of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China

⁷ University of Chinese Academy of Sciences, Beijing 100049, China

⁸ Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

⁹ College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China

Remote Sens. 2024, 16(10), 1709; https://doi.org/10.3390/rs16101709 - 11 May 2024

Cited by 1 | Viewed by 2335

Abstract

Over the past decades, the cryosphere has changed significantly in High Mountain Asia (HMA), leading to multiple natural hazards such as rock–ice avalanches, glacier collapse, debris flows, landslides, and glacial lake outburst floods (GLOFs). Monitoring cryosphere change and evaluating its hydrological effects are [...] Read more.

Over the past decades, the cryosphere has changed significantly in High Mountain Asia (HMA), leading to multiple natural hazards such as rock–ice avalanches, glacier collapse, debris flows, landslides, and glacial lake outburst floods (GLOFs). Monitoring cryosphere change and evaluating its hydrological effects are essential for studying climate change, the hydrological cycle, water resource management, and natural disaster mitigation and prevention. However, knowledge gaps, data uncertainties, and other substantial challenges limit comprehensive research in climate–cryosphere–hydrology–hazard systems. To address this, we provide an up-to-date, comprehensive, multidisciplinary review of remote sensing techniques in cryosphere studies, demonstrating primary methodologies for delineating glaciers and measuring geodetic glacier mass balance change, glacier thickness, glacier motion or ice velocity, snow extent and water equivalent, frozen ground or frozen soil, lake ice, and glacier-related hazards. The principal results and data achievements are summarized, including URL links for available products and related data platforms. We then describe the main challenges for cryosphere monitoring using satellite-based datasets. Among these challenges, the most significant limitations in accurate data inversion from remotely sensed data are attributed to the high uncertainties and inconsistent estimations due to rough terrain, the various techniques employed, data variability across the same regions (e.g., glacier mass balance change, snow depth retrieval, and the active layer thickness of frozen ground), and poor-quality optical images due to cloudy weather. The paucity of ground observations and validations with few long-term, continuous datasets also limits the utilization of satellite-based cryosphere studies and large-scale hydrological models. Lastly, we address potential breakthroughs in future studies, i.e., (1) outlining debris-covered glacier margins explicitly involving glacier areas in rough mountain shadows, (2) developing highly accurate snow depth retrieval methods by establishing a microwave emission model of snowpack in mountainous regions, (3) advancing techniques for subsurface complex freeze–thaw process observations from space, (4) filling knowledge gaps on scattering mechanisms varying with surface features (e.g., lake ice thickness and varying snow features on lake ice), and (5) improving and cross-verifying the data retrieval accuracy by combining different remote sensing techniques and physical models using machine learning methods and assimilation of multiple high-temporal-resolution datasets from multiple platforms. This comprehensive, multidisciplinary review highlights cryospheric studies incorporating spaceborne observations and hydrological models from diversified techniques/methodologies (e.g., multi-spectral optical data with thermal bands, SAR, InSAR, passive microwave, and altimetry), providing a valuable reference for what scientists have achieved in cryosphere change research and its hydrological effects on the Third Pole. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

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18 pages, 51367 KiB

Open AccessEditor’s ChoiceArticle

Drone-Acquired Short-Wave Infrared (SWIR) Imagery in Landscape Archaeology: An Experimental Approach

by Jesse Casana^1,*

and Carolin Ferwerda²

¹ Department of Anthropology, Dartmouth College, Hanover, NH 03755, USA

² Spatial Archaeometry Lab (SPARCL), Dartmouth College, Hanover, NH 03755, USA

Remote Sens. 2024, 16(10), 1671; https://doi.org/10.3390/rs16101671 - 9 May 2024

Cited by 1 | Viewed by 1745

Abstract

Many rocks, minerals, and soil types reflect short-wave infrared (SWIR) imagery (900–2500 nm) in distinct ways, and geologists have long relied on this property to aid in the mapping of differing surface lithologies. Although surface archaeological features including artifacts, anthrosols, or structural remains [...] Read more.

Many rocks, minerals, and soil types reflect short-wave infrared (SWIR) imagery (900–2500 nm) in distinct ways, and geologists have long relied on this property to aid in the mapping of differing surface lithologies. Although surface archaeological features including artifacts, anthrosols, or structural remains also likely reflect SWIR wavelengths of light in unique ways, archaeological applications of SWIR imagery are rare, largely due to the low spatial resolution and high acquisition costs of these data. Fortunately, a new generation of compact, drone-deployable sensors now enables the collection of ultra-high-resolution (<10 cm), hyperspectral (>100 bands) SWIR imagery using a consumer-grade drone, while the analysis of these complex datasets is now facilitated by powerful imagery-processing software packages. This paper presents an experimental effort to develop a methodology that would allow archaeologists to collect SWIR imagery using a drone, locate surface artifacts in the resultant data, and identify different artifact types in the imagery based on their reflectance values across the 900–1700 nm spectrum. Our results illustrate both the potential of this novel approach to exploring the archaeological record, as we successfully locate and characterize many surface artifacts in our experimental study, while also highlighting challenges in successful data collection and analysis, largely related to current limitations in sensor and drone technology. These findings show that as underlying hardware sees continued improvements in the coming years, drone-acquired SWIR imagery can become a powerful tool for the discovery, documentation, and analysis of archaeological landscapes. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Landscape Archaeology)

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24 pages, 20771 KiB

Open AccessEditor’s ChoiceArticle

Overestimated Fog-Top Entrainment in WRF Simulation Leading to Unrealistic Dissipation of Sea Fog: A Case Study

by Li Zhang^†

, Hao Shi^†

, Shanhong Gao^*

and Shun Li

¹ Key Laboratory of Physical Oceanography, College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China

^† These authors contributed equally to this work.

Remote Sens. 2024, 16(10), 1656; https://doi.org/10.3390/rs16101656 - 7 May 2024

Viewed by 1254

Abstract

Entrainment at the top of the planetary boundary layer (PBL) is of significant importance because it controls the upward growth of the PBL height. An option called ysu_topdown_pblmix, which provides a parameterization of fog-top entrainment, has been proposed for valley fog modeling and [...] Read more.

Entrainment at the top of the planetary boundary layer (PBL) is of significant importance because it controls the upward growth of the PBL height. An option called ysu_topdown_pblmix, which provides a parameterization of fog-top entrainment, has been proposed for valley fog modeling and introduced into the YSU (Yonsei University) PBL scheme in the Weather Research and Forecasting (WRF) model. However, enabling this option in simulations of sea fog over the Yellow Sea typically results in unrealistic dissipation near the fog bottom and even within the entire fog layer. In this study, we theoretically examine the composition of the option ysu_topdown_pblmix, and then argue that one term in this option might be redundant for sea-fog modeling. The fog-top variables are employed in this term to determine the basic entrainment in the dry PBL, which is already parameterized by the surface variables in the original YSU PBL scheme. This term likely leads to an overestimation of the fog-top entrainment rate, so we refer to it as redundant. To explore the connection between the redundant term and unrealistic dissipation, a widespread sea-fog episode over the Yellow Sea is employed as a case study based on the WRF model. The simulation results clearly attribute the unrealistic dissipation to the extra entrainment rate that the redundant term induces. Fog-top entrainment is unexpectedly overestimated due to this extra entrainment rate, resulting in a significantly drier and warmer bias within the interior of sea fog. When sea fog develops and reaches a temperature lower than the sea surface, the sea surface functions as a warming source to heat the fog bottom jointly with the downward heat flux brought by the fog-top entrainment, leading the dissipation to initially occur near the fog bottom and then gradually expand upwards. We suggest a straightforward method to modify the option ysu_topdown_pblmix for sea-fog modeling that eliminates the redundant term. The improvement effect of this method was supported by the results of sensitivity tests. However, more sea-fog cases are required to validate the modification method. Full article

(This article belongs to the Special Issue Severe Weather Observations and Meteorology Modeling Development Using Remote Sensing)

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12 pages, 3256 KiB

Open AccessEditor’s ChoiceArticle

Miniaturizing Hyperspectral Lidar System Employing Integrated Optical Filters

by Haibin Sun^1,2, Yicheng Wang³, Zhipei Sun⁴, Shaowei Wang¹

, Shengli Sun¹, Jianxin Jia²

, Changhui Jiang², Peilun Hu², Haima Yang⁵, Xing Yang³, Mika Karjalnen², Juha Hyyppä² and Yuwei Chen^2,3,*

¹ Key Laboratory of Intelligent Infrared Perception Chinese Academy Science (CAS), Shanghai Institute of Technical Physics, Chinese Academy Science (CAS), Shanghai 200043, China

² Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, FI-02150 Espoo, Finland

³ State Key Laboratory of Pulsed Power Laser Technology, Hefei 230037, China

⁴ Department of Electronics and Nanoengineering, Aalto University, FI-02150 Espoo, Finland

⁵ Department of Measurement and Information Engineering, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Remote Sens. 2024, 16(9), 1642; https://doi.org/10.3390/rs16091642 - 4 May 2024

Cited by 1 | Viewed by 1573

Abstract

Hyperspectral LiDAR (HSL) has been utilized as an efficacious technique in object classification and recognition based on its unique capability to obtain ranges and spectra synchronously. Different kinds of HSL prototypes with varied structures have been promoted and measured its performance. However, almost [...] Read more.

Hyperspectral LiDAR (HSL) has been utilized as an efficacious technique in object classification and recognition based on its unique capability to obtain ranges and spectra synchronously. Different kinds of HSL prototypes with varied structures have been promoted and measured its performance. However, almost all of these HSL prototypes employ complex and large spectroscopic devices, such as an Acousto-Optic Tunable Filter and Liquid-Crystal Tunable Filter, which makes this HSL system bulky and expensive, and then hinders its extensive application in many fields. In this paper, a smart and smaller spectroscopic component, an intergraded optical filter (IOF), is promoted to miniaturize these HSL systems. The system calibration, range precision, and spectral profile experiments were carried out to test the HSL prototype. Although the IOF employed here only covered a wavelength range of 699–758 nm with a six-channel passband and showed a transmittance of less than 50%, the HSL prototype showed excellent performance in ranging and spectral profile collecting. The spectral profiles collected are well in accordance with those acquired based on the AOTF. The spectral profiles of the fruits, vegetables, plants, and ore samples collected by the HSL based on an IOF can effectively reveal the status of the plants, the component materials, and ore species. Finally, we also showed the integrated design of the HSL based on a three-dimensional IOF and combined with a detector. The performance and designs of this HSL system based on an IOF show great potential for miniaturizing in some specific applications. Full article

(This article belongs to the Special Issue Remote Sensing and Lidar Data for Forest Monitoring)

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29 pages, 2637 KiB

Open AccessEditor’s ChoiceArticle

Four Years of Atmospheric Boundary Layer Height Retrievals Using COSMIC-2 Satellite Data

by Ginés Garnés-Morales^1,2, Maria João Costa^3,4

, Juan Antonio Bravo-Aranda^1,5

, María José Granados-Muñoz^1,5

, Vanda Salgueiro^3,4

, Jesús Abril-Gago^1,5

, Sol Fernández-Carvelo^1,5

, Juana Andújar-Maqueda^1,5

, Antonio Valenzuela^1,5

, Inmaculada Foyo-Moreno^1,5

, Francisco Navas-Guzmán^1,5

, Lucas Alados-Arboledas^1,5

, Daniele Bortoli^3,4

and Juan Luis Guerrero-Rascado^1,5,*

¹ Andalusian Institute for Earth System Research (IISTA-CEAMA), 18006 Granada, Spain

² Regional Atmospheric Modeling Lab (G-MAR), Department of Physics, Regional Campus of International Excellent ‘Campus Mare Nostrum’ (CEIR), University of Murcia, 30100 Murcia, Spain

³ Institute of Earth Sciences (ICT) and Earth Remote Sensing Laboratory (EaRSLab), 7000-671 Évora, Portugal

⁴ Department of Physics, University of Évora, 7000-671 Évora, Portugal

⁵ Department of Applied Physics, University of Granada, 18071 Granada, Spain

Remote Sens. 2024, 16(9), 1632; https://doi.org/10.3390/rs16091632 - 3 May 2024

Viewed by 1738

Abstract

This work aimed to study the atmospheric boundary layer height (ABLH) from COSMIC-2 refractivity data, endeavoring to refine existing ABLH detection algorithms and scrutinize the resulting spatial and seasonal distributions. Through validation analyses involving different ground-based methodologies (involving data from lidar, ceilometer, microwave [...] Read more.

This work aimed to study the atmospheric boundary layer height (ABLH) from COSMIC-2 refractivity data, endeavoring to refine existing ABLH detection algorithms and scrutinize the resulting spatial and seasonal distributions. Through validation analyses involving different ground-based methodologies (involving data from lidar, ceilometer, microwave radiometers, and radiosondes), the optimal ABLH determination relied on identifying the lowest refractivity gradient negative peak with a magnitude at least

τ

% times the minimum refractivity gradient magnitude, where

τ

is a fitting parameter representing the minimum peak strength relative to the absolute minimum refractivity gradient. Different

τ

values were derived accounting for the moment of the day (daytime, nighttime, or sunrise/sunset) and the underlying surface (land or sea). Results show discernible relations between ABLH and various features, notably, the land cover and latitude. On average, ABLH is higher over oceans (≈1.5 km), but extreme values (maximums > 2.5 km, and minimums < 1 km) are reached over intertropical lands. Variability is generally subtle over oceans, whereas seasonality and daily evolution are pronounced over continents, with higher ABLHs during daytime and local wintertime (summertime) in intertropical (middle) latitudes. Full article

(This article belongs to the Special Issue Observation of Atmospheric Boundary-Layer Based on Remote Sensing)

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18 pages, 7831 KiB

Open AccessEditor’s ChoiceArticle

Landslide Mapping in Calitri (Southern Italy) Using New Multi-Temporal InSAR Algorithms Based on Permanent and Distributed Scatterers

by Nicola Angelo Famiglietti¹

, Pietro Miele^1,*

, Marco Defilippi², Alessio Cantone², Paolo Riccardi², Giulia Tessari² and Annamaria Vicari¹

¹ Istituto Nazionale di Geofisica e Vulcanologia, Sezione Irpinia, 83035 Grottaminarda, Italy

² SarmapSA, 6987 Caslano, Switzerland

Remote Sens. 2024, 16(9), 1610; https://doi.org/10.3390/rs16091610 - 30 Apr 2024

Cited by 3 | Viewed by 2267

Abstract

Landslides play a significant role in the morpho-evolutional processes of slopes, affecting them globally under various geological conditions. Often unnoticed due to low velocities, they cause diffuse damage and loss of economic resources to the infrastructure or villages built on them. Recognizing and [...] Read more.

Landslides play a significant role in the morpho-evolutional processes of slopes, affecting them globally under various geological conditions. Often unnoticed due to low velocities, they cause diffuse damage and loss of economic resources to the infrastructure or villages built on them. Recognizing and mapping mass movements is crucial for mitigating economic and social impacts. Conventional monitoring techniques prove challenging for large areas, necessitating resource-intensive ground-based networks. Leveraging abundant synthetic aperture radar (SAR) sensors, satellite techniques offer cost-effective solutions. Among the various methods based on SAR products for detecting landslides, multi-temporal differential interferometry SAR techniques (MTInSAR) stand out for their precise measurement capabilities and spatiotemporal evolution analysis. They have been widely used in several works in the last decades. Using information from the official Italian landslide database (IFFI), this study employs Sentinel-1 imagery and two new processing chains, E-PS and E-SBAS algorithms, to detect deformation areas on the slopes of Calitri, a small town in Southern Italy; these algorithms assess the cumulated displacements and their state of activity. Taking into account the non-linear trends of the scatterers, these innovative algorithms have helped to identify a dozen clusters of points that correspond well with IFFI polygons. Full article

(This article belongs to the Special Issue Landslide Inventory Mapping and Monitoring Using Remote Sensing Techniques)

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17 pages, 27416 KiB

Open AccessEditor’s ChoiceArticle

Landsat 8 and 9 Underfly International Surface Reflectance Validation Collaboration

by Joshua Mann^1,*

, Emily Maddox¹

, Mahesh Shrestha¹

, Jeffrey Irwin²

, Jeffrey Czapla-Myers³

, Aaron Gerace⁴

, Eon Rehman⁴, Nina Raqueno⁴, Craig Coburn⁵

, Guy Byrne^{6,7,8,9,10,11}

, Mark Broomhall^{6,7,8,9,10,11}

and Andrew Walsh^{6,7,8,9,10,11}

¹ KBR Contractor to U.S. Geological Survey Earth Resources Observation and Science Center, Sioux Falls, SD 57198, USA

² U.S. Geological Survey Earth Resources Observation and Science Center, Sioux Falls, SD 57198, USA

³ Remote Sensing Group, Wyant College of Optical Sciences, University of Arizona, 1630 E University Blvd, Tucson, AZ 85721, USA

⁴ Digital Imaging and Remote Sensing Laboratory (DIRS), Rochester Institute of Technology, 54 Lomb Memorial Drive, Rochester, NY 14623, USA

⁵ Department of Geography and Environment, Faculty of Arts and Science, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada

⁶ Geoscience Australia, Canberra, ACT 2601, Australia

⁷ School of Earth Sciences, University of Western Australia, Perth, WA 6009, Australia

⁸ Frontier SI, Docklands, VIC 3008, Australia

⁹ CSIRO Oceans and Atmosphere, Townsville, QLD 4811, Australia

¹⁰ Queensland Department of Environment and Science, Manly, QLD 4179, Australia

¹¹ School of the Environment, University of Queensland, Brisbane, QLD 4072, Australia

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Remote Sens. 2024, 16(9), 1492; https://doi.org/10.3390/rs16091492 - 23 Apr 2024

Viewed by 1948

Abstract

During the launch and path to its final orbit, the Landsat 9 satellite performed a once in a mission lifetime maneuver as it passed beneath Landsat 8, resulting in near coincident data collection. This maneuver provided ground validation teams from across the globe [...] Read more.

During the launch and path to its final orbit, the Landsat 9 satellite performed a once in a mission lifetime maneuver as it passed beneath Landsat 8, resulting in near coincident data collection. This maneuver provided ground validation teams from across the globe the opportunity of collecting surface in situ data to compare directly to Landsat 8 and Landsat 9 data. Ground validation teams identified surface targets that would yield reflectance and/or thermal values that could be used in Landsat Level 2 product validation and set out to collect at these locations using surface validation methodologies the teams developed. The values were collected from each team and compared directly with each other across each of the different bands of both Landsat 8 and 9. The results proved consistency across the Landsat 8 and 9 platforms and also agreed well in surface reflectance underestimation of the Coastal Aerosol, Blue, and SWIR2 bands. Full article

(This article belongs to the Special Issue Landsat 9 Pre-launch, Commissioning, and Early On-Orbit Imaging Performance)

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24 pages, 10745 KiB

Open AccessEditor’s ChoiceArticle

Modeling Land Use Transformations and Flood Hazard on Ibaraki’s Coastal in 2030: A Scenario-Based Approach Amid Population Fluctuations

by Mohammadreza Safabakhshpachehkenari^*

and Hideyuki Tonooka

Graduate School of Science and Engineering, Ibaraki University, Hitachi 316-8511, Ibaraki, Japan

Remote Sens. 2024, 16(5), 898; https://doi.org/10.3390/rs16050898 - 3 Mar 2024

Cited by 2 | Viewed by 1629

Abstract

Coastal areas, influenced by human activity and natural factors, face major environmental shifts, including climate-induced flood risks. This highlights the importance of forecasting coastal land use for effective flood defense and ecological conservation. Japan’s distinct demographic path necessitates flexible strategies for managing its [...] Read more.

Coastal areas, influenced by human activity and natural factors, face major environmental shifts, including climate-induced flood risks. This highlights the importance of forecasting coastal land use for effective flood defense and ecological conservation. Japan’s distinct demographic path necessitates flexible strategies for managing its urban development. The study examines the Ibaraki Coastal region to analyze the impacts of land-use changes in 2030, predicting and evaluating future floods from intensified high tides and waves in scenario-based forecasts. The future roughness map is derived from projected land-use changes, and we utilize this information in DioVISTA 3.5.0 software to simulate flood scenarios. Finally, we analyzed the overlap between simulated floods and each land-use category. The results indicate since 2020, built-up areas have increased by 52.37 sq. km (39%). In scenarios of constant or shrinking urban areas, grassland increased by 28.54 sq. km (42%), and urban land cover decreased by 7.47 sq. km (5.6%) over ten years. Our research examines two separate peaks in water levels associated with urban flooding. Using 2030 land use maps and a peak height of 4 m, which is the lower limit of the maximum run-up height due to storm surge expected in the study area, 4.71 sq. km of residential areas flooded in the urban growth scenario, compared to 4.01 sq. km in the stagnant scenario and 3.96 sq. km in the shrinkage scenario. With the upper limit of 7.2 m, which is the extreme case in most of the study area, these areas increased to 49.91 sq. km, 42.52 sq. km, and 42.31 sq. km, respectively. The simulation highlights future flood-prone urban areas for each scenario, guiding targeted flood prevention efforts. Full article

(This article belongs to the Special Issue Future Prediction and Scenario Analysis of Urbanization Using Remote Sensing and GIS)

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17 pages, 9275 KiB

Open AccessEditor’s ChoiceArticle

Mapping Soil Organic Carbon Stock Using Hyperspectral Remote Sensing: A Case Study in the Sele River Plain in Southern Italy

by Nicolas Francos^1,2,*

, Paolo Nasta³

, Carolina Allocca³

, Benedetto Sica³, Caterina Mazzitelli³

, Ugo Lazzaro³, Guido D’Urso³

, Oscar Rosario Belfiore³

, Mariano Crimaldi³

, Fabrizio Sarghini³

, Eyal Ben-Dor¹

and Nunzio Romano³

¹ The Remote Sensing Laboratory, Tel Aviv University, Tel Aviv 699780, Israel

² Sydney Institute of Agriculture & School of Life & Environmental Sciences, The University of Sydney, Sydney, NSW 2015, Australia

³ Department of Agricultural Sciences, University of Naples Federico II, Portici, 80055 Naples, Italy

Remote Sens. 2024, 16(5), 897; https://doi.org/10.3390/rs16050897 - 3 Mar 2024

Cited by 2 | Viewed by 3286

Abstract

Mapping soil organic carbon (SOC) stock can serve as a resilience indicator for climate change. As part of the carbon dioxide (CO₂) sink, soil has recently become an integral part of the global carbon agenda to mitigate climate change. We used [...] Read more.

Mapping soil organic carbon (SOC) stock can serve as a resilience indicator for climate change. As part of the carbon dioxide (CO₂) sink, soil has recently become an integral part of the global carbon agenda to mitigate climate change. We used hyperspectral remote sensing to model the SOC stock in the Sele River plain located in the Campania region in southern Italy. To this end, a soil spectral library (SSL) for the Campania region was combined with an aerial hyperspectral image acquired with the AVIRIS–NG sensor mounted on a Twin Otter aircraft at an altitude of 1433 m. The products of this study were four raster layers with a high spatial resolution (1 m), representing the SOC stocks and three other related soil attributes: SOC content, clay content, and bulk density (BD). We found that the clay minerals’ spectral absorption at 2200 nm has a significant impact on predicting the examined soil attributes. The predictions were performed by using AVIRIS–NG sensor data over a selected plot and generating a quantitative map which was validated with in situ observations showing high accuracies in the ground-truth stage (OC stocks [RPIQ = 2.19, R² = 0.72, RMSE = 0.07]; OC content [RPIQ = 2.27, R² = 0.80, RMSE = 1.78]; clay content [RPIQ = 1.6 R² = 0.89, RMSE = 25.42]; bulk density [RPIQ = 1.97, R² = 0.84, RMSE = 0.08]). The results demonstrated the potential of combining SSLs with remote sensing data of high spectral/spatial resolution to estimate soil attributes, including SOC stocks. Full article

(This article belongs to the Special Issue Remote Sensing of Carbon Fluxes and Stocks II)

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22 pages, 4906 KiB

Open AccessEditor’s ChoiceArticle

Using Remote Sensing Multispectral Imagery for Invasive Species Quantification: The Effect of Image Resolution on Area and Biomass Estimation

by Manuel de Figueiredo Meyer¹, José Alberto Gonçalves^1,2

and Ana Maria Ferreira Bio^1,*

¹ Interdisciplinary Centre of Marine and Environmental Research (CIIMAR/CIMAR), University of Porto, 4099-002 Porto, Portugal

² Department of Geosciences Environment and Spatial Planning, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal

Remote Sens. 2024, 16(4), 652; https://doi.org/10.3390/rs16040652 - 9 Feb 2024

Cited by 1 | Viewed by 1966

Abstract

This study assesses the applicability of different-resolution multispectral remote sensing images for mapping and estimating the aboveground biomass (AGB) of Carpobrotus edulis, a prominent invasive species in European coastal areas. This study was carried out on the Cávado estuary sand spit (Portugal). [...] Read more.

This study assesses the applicability of different-resolution multispectral remote sensing images for mapping and estimating the aboveground biomass (AGB) of Carpobrotus edulis, a prominent invasive species in European coastal areas. This study was carried out on the Cávado estuary sand spit (Portugal). The performance of three sets of multispectral images with different Ground Sample Distances (GSDs) were compared: 2.5 cm, 5 cm, and 10 cm. The images were classified using the supervised classification algorithm random forest and later improved by applying a sieve filter. Samples of C. edulis were also collected, dried, and weighed to estimate the AGB using the relationship between the dry weight (DW) and vegetation indices (VIs). The resulting regression models were evaluated based on their coefficient of determination (R²), Normalised Root Mean Square Error (NRMSE), p-value, Akaike information criterion (AIC), and the Bayesian information criterion (BIC). The results show that the three tested image resolutions allow for constructing reliable coverage maps of C. edulis, with overall accuracy values of 89%, 85%, and 88% for the classification of the 2.5 cm, 5 cm, and 10 cm GSD images, respectively. The best-performing VI-DW regression models achieved R² = 0.87 and NRMSE = 0.09 for the 2.5 cm resolution; R² = 0.77 and NRMSE = 0.12 for the 5 cm resolution; and R² = 0.64 and NRMSE = 0.15 for the 10 cm resolution. The C. edulis area and total AGB were 3441.10 m² and 28,327.1 kg (with an AGB relative error (RE) = 0.08) for the 2.5 cm resolution; 3070.04 m² and 29,170.8 kg (AGB RE = 0.08) for the 5 cm resolution; and 2305.06 m² and 22,135.7 kg (AGB RE = 0.11) for the 10 cm resolution. Spatial and model differences were analysed in detail to determine their causes. Final analyses suggest that multispectral imagery of up to 5 cm GSD is adequate for estimating C. edulis distribution and biomass. Full article

(This article belongs to the Special Issue Remote Sensing for 2D/3D Mapping)

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25 pages, 25438 KiB

Open AccessEditor’s ChoiceArticle

Geomorphology, Mineralogy, and Chronology of Mare Basalts in the Oceanus Procellarum Region

by Cheng Zhang^1,2, Jianping Chen^1,2,*, Yiwen Pan^1,2, Shuangshuang Wu^1,2, Jian Chen³

, Xiaoxia Hu¹, Yue Pang^1,2, Xueting Liu^1,2 and Ke Wang^1,2

¹ School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China

² Beijing Key Laboratory of Development and Research for Land Resources Information, Beijing 100083, China

³ Shandong Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment, School of Space Science and Physics, Institute of Space Sciences, Shandong University, Weihai 264209, China

Remote Sens. 2024, 16(4), 634; https://doi.org/10.3390/rs16040634 - 8 Feb 2024

Cited by 2 | Viewed by 1875

Abstract

Mare basalts on the lunar surface are tangible expressions of the complex thermal evolution and geological processes that have occurred within the lunar interior. These basaltic manifestations are highly important because they provide invaluable insights into lunar geological evolution. Notably, the Oceanus Procellarum [...] Read more.

Mare basalts on the lunar surface are tangible expressions of the complex thermal evolution and geological processes that have occurred within the lunar interior. These basaltic manifestations are highly important because they provide invaluable insights into lunar geological evolution. Notably, the Oceanus Procellarum region, which is renowned for its extensive and long-lasting basaltic volcanism, is a premier location to investigate late-stage lunar thermal evolution. The primary aim of this research is to elucidate the geomorphological, compositional, and temporal attributes that define the mare basalts within the Oceanus Procellarum region. To achieve this aim, we comprehensively analyzed the geomorphological features present within the region, leveraging Kaguya/SELENE TC images and digital elevation models. Specifically, these geomorphological features encompass impact craters, wrinkle ridges, sinuous rilles, and volcanic domes. Subsequently, we thoroughly examined the mineralogical attributes of basalts in the Oceanus Procellarum region, leveraging Kaguya/SELENE MI data and compositional map products. To more accurately reflect the actual ages of the mare basalts in the Oceanus Procellarum region, we carefully delineated the geological units within the area and employed the latest crater size-frequency distribution (CSFD) technique to precisely determine their ages. This refined approach allowed for a more comprehensive and accurate understanding of the basaltic rocks in the study area. Overall, our comprehensive study included an in-depth analysis of the volcanic activity and evolution of the Oceanus Procellarum region, along with an examination of the correlation between the mineralogical composition and ages of mare basalts. The findings from this exhaustive investigation reveal a definitive age range for basalt units within the Oceanus Procellarum region from approximately 3.69 Ga to 1.17 Ga. Moreover, the latest mare basalts that formed were pinpointed north of the Aristarchus crater. Significantly, the region has experienced at least five distinct volcanic events, occurring approximately 3.40 Ga, 2.92 Ga, 2.39 Ga, 2.07 Ga, and 1.43 Ga, leading to the formation of multiple basalt units characterized by their unique mineral compositions and elemental abundances. Through the application of remote sensing mineralogical analysis, three primary basalt types were identified: low-titanium, very-low-titanium, and intermediate-titanium basalt. Notably, the younger basalt units exhibit an elevated titanium proportion, indicative of progressive olivine enrichment. Consequently, these younger basalt units exhibit more intricate and complex mineral compositions, offering valuable insights into the dynamic geological processes shaping the lunar surface. Full article

(This article belongs to the Special Issue Planetary Remote Sensing and Applications to Mars and Chang’E-6/7)

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23 pages, 3468 KiB

Open AccessEditor’s ChoiceReview

Review of Satellite Remote Sensing and Unoccupied Aircraft Systems for Counting Wildlife on Land

by Marie R. G. Attard¹

, Richard A. Phillips¹, Ellen Bowler¹

, Penny J. Clarke^1,2, Hannah Cubaynes¹, David W. Johnston³

and Peter T. Fretwell^1,*

¹ British Antarctic Survey, Natural Environment Research Council, High Cross, Madingley Road, Cambridge CB3 0ET, UK

² School of Engineering, The University of Edinburgh, Sanderson Building, Robert Stevenson Road, The King’s Buildings, Edinburgh EH9 3FB, UK

³ Division of Marine Science and Conservation, Nicholas School of the Environment, Duke University Marine Laboratory, 135 Duke Marine Lab Road, Beaufort, NC 28516, USA

Remote Sens. 2024, 16(4), 627; https://doi.org/10.3390/rs16040627 - 8 Feb 2024

Cited by 2 | Viewed by 3142

Abstract

Although many medium-to-large terrestrial vertebrates are still counted by ground or aerial surveys, remote-sensing technologies and image analysis have developed rapidly in recent decades, offering improved accuracy and repeatability, lower costs, speed, expanded spatial coverage and increased potential for public involvement. This review [...] Read more.

Although many medium-to-large terrestrial vertebrates are still counted by ground or aerial surveys, remote-sensing technologies and image analysis have developed rapidly in recent decades, offering improved accuracy and repeatability, lower costs, speed, expanded spatial coverage and increased potential for public involvement. This review provides an introduction for wildlife biologists and managers relatively new to the field on how to implement remote-sensing techniques (satellite and unoccupied aircraft systems) for counting large vertebrates on land, including marine predators that return to land to breed, haul out or roost, to encourage wider application of these technological solutions. We outline the entire process, including the selection of the most appropriate technology, indicative costs, procedures for image acquisition and processing, observer training and annotation, automation, and citizen science campaigns. The review considers both the potential and the challenges associated with different approaches to remote surveys of vertebrates and outlines promising avenues for future research and method development. Full article

(This article belongs to the Special Issue Towards Biodiversity Conservation: Remote Sensing Applications in Ecological Modeling)

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28 pages, 11516 KiB

Open AccessEditor’s ChoiceArticle

Segmentation of Individual Tree Points by Combining Marker-Controlled Watershed Segmentation and Spectral Clustering Optimization

by Yuchan Liu^1,2, Dong Chen^1,*

, Shihan Fu¹, Panagiotis Takis Mathiopoulos³

, Mingming Sui¹, Jiaming Na¹

and Jiju Peethambaran⁴

¹ College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China

² School of Geographic Information and Tourism, Chuzhou University, Chuzhou 239000, China

³ Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, 15784 Athens, Greece

⁴ Department of Mathematics and Computing Science, Saint Mary’s University, Halifax, NS B3P 2M6, Canada

Remote Sens. 2024, 16(4), 610; https://doi.org/10.3390/rs16040610 - 6 Feb 2024

Cited by 4 | Viewed by 2406

Abstract

Accurate identification and segmentation of individual tree points are crucial for assessing forest spatial distribution, understanding tree growth and structure, and managing forest resources. Traditional methods based on Canopy Height Models (CHM) are simple yet prone to over- and/or under-segmentation. To deal with [...] Read more.

Accurate identification and segmentation of individual tree points are crucial for assessing forest spatial distribution, understanding tree growth and structure, and managing forest resources. Traditional methods based on Canopy Height Models (CHM) are simple yet prone to over- and/or under-segmentation. To deal with this problem, this paper introduces a novel approach that combines marker-controlled watershed segmentation with a spectral clustering algorithm. Initially, we determined the local maxima within a series of variable windows according to the lower bound of the prediction interval of the regression equation between tree crown radius and tree height to preliminarily segment individual trees. Subsequently, using this geometric shape analysis method, the under-segmented trees were identified. For these trees, vertical tree crown profile analysis was performed in multiple directions to detect potential treetops which were then considered as inputs for spectral clustering optimization. Our experiments across six plots showed that our method markedly surpasses traditional approaches, achieving an average Recall of 0.854, a Precision of 0.937, and an F1-score of 0.892. Full article

(This article belongs to the Special Issue Advances in Understanding and 3D Semantic Modeling of Large-Scale Urban Scenes from Point Clouds (Second Edition))

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16 pages, 5457 KiB

Open AccessEditor’s ChoiceArticle

Edge Effects in Amazon Forests: Integrating Remote Sensing and Modelling to Assess Changes in Biomass and Productivity

by Luise Bauer^1,*, Andreas Huth^1,2,3, André Bogdanowski¹, Michael Müller¹ and Rico Fischer^1,4

¹ Department of Ecological Modelling, Helmholtz Centre for Environmental Research—UFZ Leipzig, Permoserstr. 15, 04318 Leipzig, Germany

² Institute for Environmental Systems Research, University of Osnabrück, Barbara Straße 12, 49074 Osnabrück, Germany

³ iDiv German Centre for Integrative Biodiversity Research Halle-Jena-Leipzig, Puschstraße 4, 04103 Leipzig, Germany

⁴ Institute for Forest Protection, Julius Kühn-Institute (JKI)—Federal Research Center for Cultivated Plants, Erwin-Baur-Str. 27, 06484 Quedlinburg, Germany

Remote Sens. 2024, 16(3), 501; https://doi.org/10.3390/rs16030501 - 28 Jan 2024

Cited by 1 | Viewed by 2902

Abstract

The tropical forests in the Amazon store large amounts of carbon and are still considered a carbon sink. There is evidence that deforestation can turn a forest landscape into a carbon source due to land use and forest degradation. Deforestation causes fragmented forest [...] Read more.

The tropical forests in the Amazon store large amounts of carbon and are still considered a carbon sink. There is evidence that deforestation can turn a forest landscape into a carbon source due to land use and forest degradation. Deforestation causes fragmented forest landscapes. It is known from field experiments that forest dynamics at the edge of forest fragments are altered by changes in the microclimate and increased tree mortality (“edge effects”). However, it is unclear how this will affect large fragmented forest landscapes, and thus the entire Amazon region. The aim of this study is to investigate different forest attributes in edge and core forest areas at high resolution, and thus to identify the large-scale impacts of small-scale edge effects. Therefore, a well-established framework combining forest modelling and lidar-generated forest structure information was combined with radar-based forest cover data. Furthermore, forests were also analyzed at the landscape level to investigate changes between highly fragmented and less-fragmented landscapes. This study found that the aboveground biomass in forest edge areas is 27% lower than in forest core areas. In contrast, the net primary productivity is 13% higher in forest edge areas than in forest core areas. In the second step, whole fragmented landscapes were analyzed. Nearly 30% of all forest landscapes are highly fragmented, particularly in the regions of the Arc of Deforestation, on the edge of the Andes and on the Amazon river banks. Less-fragmented landscapes are mainly located in the central Amazon rainforest. The aboveground biomass is 28% lower in highly fragmented forest landscapes than in less-fragmented landscapes. The net primary productivity is 13% higher in highly fragmented forest landscapes than in less-fragmented forest landscapes. In summary, fragmentation of the Amazon rainforest has an impact on forest attributes such as biomass and productivity, with mostly negative effects on forest dynamics. If deforestation continues and the proportion of highly fragmented forest landscapes increase, the effect may be even more intense. By combining lidar, radar and forest modelling, this study shows that it is possible to map forest structure, and thus the degree of forest degradation, over a large area and derive more detailed information about the carbon dynamics of the Amazon region. Full article

(This article belongs to the Special Issue Lidar for Environmental Remote Sensing: Theory and Application)

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33 pages, 56873 KiB

Open AccessEditor’s ChoiceArticle

An Open Benchmark Dataset for Forest Characterization from Sentinel-1 and -2 Time Series

by Sarah Hauser^1,2,*,†

, Michael Ruhhammer^1,†, Andreas Schmitt^1,2,†

and Peter Krzystek^1,2,†

¹ Geoinformatics Department, Hochschule München University of Applied Sciences, Karlstraße 6, D-80333 Munich, Germany

² Institute for Applications of Machine Learning and Intelligent Systems (IAMLIS), Lothstraße 34, D-80335 Munich, Germany

^† These authors contributed equally to this work.

Remote Sens. 2024, 16(3), 488; https://doi.org/10.3390/rs16030488 - 26 Jan 2024

Viewed by 2345

Abstract

Earth observation satellites offer vast opportunities for quantifying landscapes and regional land cover composition and changes. The integration of artificial intelligence in remote sensing is essential for monitoring significant land cover types like forests, demanding a substantial volume of labeled data for effective [...] Read more.

Earth observation satellites offer vast opportunities for quantifying landscapes and regional land cover composition and changes. The integration of artificial intelligence in remote sensing is essential for monitoring significant land cover types like forests, demanding a substantial volume of labeled data for effective AI model development and validation. The Wald5Dplus project introduces a distinctive open benchmark dataset for mid-European forests, labeling Sentinel-1/2 time series using data from airborne laser scanning and multi-spectral imagery. The freely accessible satellite images are fused in polarimetric, spectral, and temporal domains, resulting in analysis-ready data cubes with 512 channels per year on a 10 m UTM grid. The dataset encompasses labels, including tree count, crown area, tree types (deciduous, coniferous, dead), mean crown volume, base height, tree height, and forested area proportion per pixel. The labels are based on an individual tree characterization from high-resolution airborne LiDAR data using a specialized segmentation algorithm. Covering three test sites (Bavarian Forest National Park, Steigerwald, and Kranzberg Forest) and encompassing around six million trees, it generates over two million labeled samples. Comprehensive validation, including metrics like mean absolute error, median deviation, and standard deviation, in the random forest regression confirms the high quality of this dataset, which is made freely available. Full article

(This article belongs to the Special Issue Remote Sensing for Forest Morphological and Physiological Traits Monitoring)

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23 pages, 12227 KiB

Open AccessEditor’s ChoiceArticle

3D Reconstruction of Ancient Buildings Using UAV Images and Neural Radiation Field with Depth Supervision

by Yingwei Ge¹

, Bingxuan Guo^1,*, Peishuai Zha¹, San Jiang²

, Ziyu Jiang³ and Demin Li^1,4

¹ The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

² School of Computer Science, China University of Geosciences, Wuhan 430074, China

³ School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

⁴ School of Artificial Intelligence, Zhejiang College of Security Technology, Wenzhou 325016, China

Remote Sens. 2024, 16(3), 473; https://doi.org/10.3390/rs16030473 - 25 Jan 2024

Cited by 6 | Viewed by 2754

Abstract

The 3D reconstruction of ancient buildings through inclined photogrammetry finds a wide range of applications in surveying, visualization and heritage conservation. Unlike indoor objects, reconstructing ancient buildings presents unique challenges, including the slow speed of 3D reconstruction using traditional methods, the complex textures [...] Read more.

The 3D reconstruction of ancient buildings through inclined photogrammetry finds a wide range of applications in surveying, visualization and heritage conservation. Unlike indoor objects, reconstructing ancient buildings presents unique challenges, including the slow speed of 3D reconstruction using traditional methods, the complex textures of ancient structures and geometric issues caused by repeated textures. Additionally, there is a hash conflict problem when rendering outdoor scenes using neural radiation fields. To address these challenges, this paper proposes a 3D reconstruction method based on depth-supervised neural radiation fields. To enhance the representation of the geometric neural network, the addition of a truncated signed distance function (TSDF) supplements the existing signed distance function (SDF). Furthermore, the neural network’s training is supervised using depth information, leading to improved geometric accuracy in the reconstruction model through depth data obtained from sparse point clouds. This study also introduces a progressive training strategy to mitigate hash conflicts, allowing the hash table to express important details more effectively while reducing feature overlap. The experimental results demonstrate that our method, under the same number of iterations, produces images with clearer structural details, resulting in an average 15% increase in the Peak Signal-to-Noise Ratio (PSNR) value and a 10% increase in the Structural Similarity Index Measure (SSIM) value. Moreover, our reconstruction model produces higher-quality surface models, enabling the fast and highly geometrically accurate 3D reconstruction of ancient buildings. Full article

(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)

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23 pages, 2229 KiB

Open AccessEditor’s ChoiceReview

Remote Sensing-Based 3D Assessment of Landslides: A Review of the Data, Methods, and Applications

by Hessah Albanwan^1,2, Rongjun Qin^2,3,4,5,*

and Jung-Kuan Liu⁶

¹ Civil Engineering Department, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait

² Geospatial Data Analytics Lab, The Ohio State University, Columbus, OH 43210, USA

³ Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH 43210, USA

⁴ Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA

⁵ Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA

⁶ U.S. Geological Survey (USGS), Center of Excellence for Geospatial Information Science, Denver, CO 80225, USA

Remote Sens. 2024, 16(3), 455; https://doi.org/10.3390/rs16030455 - 24 Jan 2024

Cited by 6 | Viewed by 3537

Abstract

Remote sensing (RS) techniques are essential for studying hazardous landslide events because they capture information and monitor sites at scale. They enable analyzing causes and impacts of ongoing events for disaster management. There has been a plethora of work in the literature mostly [...] Read more.

Remote sensing (RS) techniques are essential for studying hazardous landslide events because they capture information and monitor sites at scale. They enable analyzing causes and impacts of ongoing events for disaster management. There has been a plethora of work in the literature mostly discussing (1) applications to detect, monitor, and predict landslides using various instruments and image analysis techniques, (2) methodological mechanics in using optical and microwave sensing, and (3) quantification of surface geological and geotechnical changes using 2D images. Recently, studies have shown that the degree of hazard is mostly influenced by speed, type, and volume of surface deformation. Despite available techniques to process lidar and image/radar-derived 3D geometry, prior works mostly focus on using 2D images, which generally lack details on the 3D aspects of assessment. Thus, assessing the 3D geometry of terrain using elevation/depth information is crucial to determine its cover, geometry, and 3D displacements. In this review, we focus on 3D landslide analysis using RS data. We include (1) a discussion on sources, types, benefits, and limitations of 3D data, (2) the recent processing methods, including conventional, fusion-based, and artificial intelligence (AI)-based methods, and (3) the latest applications. Full article

(This article belongs to the Special Issue Ground Deformation Detection and Geomatic Applications by InSAR and GNSS Techniques II)

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29 pages, 8367 KiB

Open AccessEditor’s ChoiceArticle

X- and Ku-Band SAR Backscattering Signatures of Snow-Covered Lake Ice and Sea Ice

by Katriina Veijola^1,*

, Juval Cohen¹, Marko Mäkynen¹

, Juha Lemmetyinen¹, Jaan Praks²

and Bin Cheng¹

¹ Finnish Meteorological Institute, P.O. Box 503, FI-00101 Helsinki, Finland

² Department of Electronics and Nanoengineering, Aalto University, P.O. Box 15500, FI-00076 Espoo, Finland

Remote Sens. 2024, 16(2), 369; https://doi.org/10.3390/rs16020369 - 16 Jan 2024

Viewed by 1793

Abstract

In this work, backscattering signatures of snow-covered lake ice and sea ice from X- and Ku-band synthetic aperture radar (SAR) data are investigated. The SAR data were acquired with the ESA airborne SnowSAR sensor in winter 2012 over Lake Orajärvi in northern Finland [...] Read more.

In this work, backscattering signatures of snow-covered lake ice and sea ice from X- and Ku-band synthetic aperture radar (SAR) data are investigated. The SAR data were acquired with the ESA airborne SnowSAR sensor in winter 2012 over Lake Orajärvi in northern Finland and over landfast ice in the Bay of Bothnia of the Baltic Sea. Co-incident with the SnowSAR acquisitions, in situ snow and ice data were measured. In addition, time series of TerraSAR-X images and ice mass balance buoy data were acquired for Lake Orajärvi in 2011–2012. The main objective of our study was to investigate relationships between SAR backscattering signatures and snow depth over lake and sea ice, with the ultimate objective of assessing the feasibility of retrieval of snow characteristics using X- and Ku-band dual-polarization (VV and VH) SAR over freshwater or sea ice. This study constitutes the first comprehensive survey of snow backscattering signatures at these two combined frequencies over both lake and sea ice. For lake ice, we show that X-band VH-polarized backscattering coefficient (

σ^{o}

) and the Ku-band VV/VH-ratio exhibited the highest sensitivity to the snow depth. For sea ice, the highest sensitivity to the snow depth was found from the Ku-band VV-polarized

σ^{o}

and the Ku-band VV/VH-ratio. However, the observed relations were relatively weak, indicating that at least for the prevailing snow conditions, obtaining reliable estimates of snow depth over lake and sea ice would be challenging using only X- and Ku-band backscattering information. Full article

(This article belongs to the Special Issue Emerging Remote Sensing Techniques for Monitoring Glaciers and Snow)

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33 pages, 6092 KiB

Open AccessEditor’s ChoiceReview

Mapping Compound Flooding Risks for Urban Resilience in Coastal Zones: A Comprehensive Methodological Review

by Hai Sun^1,2,3

, Xiaowei Zhang¹, Xuejing Ruan⁴, Hui Jiang⁵ and Wenchi Shou^3,*

¹ College of Engineering, Ocean University of China, Qingdao 266100, China

² Key Laboratory of Marine Environment and Ecology, Ministry of Education, Ocean University of China, Qingdao 266100, China

³ School of Engineering, Design and Built Environment, Western Sydney University, Sydney 2745, Australia

⁴ College of Civil Engineering and Architecture, Qingdao Agricultural University, Qingdao 266109, China

⁵ Guangdong Provincial Seismological Bureau, Guangzhou 510070, China

Remote Sens. 2024, 16(2), 350; https://doi.org/10.3390/rs16020350 - 16 Jan 2024

Cited by 5 | Viewed by 4253

Abstract

Coastal regions, increasingly threatened by floods due to climate-change-driven extreme weather, lack a comprehensive study that integrates coastal and riverine flood dynamics. In response to this research gap, we conducted a comprehensive bibliometric analysis and thorough visualization and mapping of studies of compound [...] Read more.

Coastal regions, increasingly threatened by floods due to climate-change-driven extreme weather, lack a comprehensive study that integrates coastal and riverine flood dynamics. In response to this research gap, we conducted a comprehensive bibliometric analysis and thorough visualization and mapping of studies of compound flooding risk in coastal cities over the period 2014–2022, using VOSviewer and CiteSpace to analyze 407 publications in the Web of Science Core Collection database. The analytical results reveal two persistent research topics: the way to explore the return periods or joint probabilities of flood drivers using statistical modeling, and the quantification of flood risk with different return periods through numerical simulation. This article examines critical causes of compound coastal flooding, outlines the principal methodologies, details each method’s features, and compares their strengths, limitations, and uncertainties. This paper advocates for an integrated approach encompassing climate change, ocean–land systems, topography, human activity, land use, and hazard chains to enhance our understanding of flood risk mechanisms. This includes adopting an Earth system modeling framework with holistic coupling of Earth system components, merging process-based and data-driven models, enhancing model grid resolution, refining dynamical frameworks, comparing complex physical models with more straightforward methods, and exploring advanced data assimilation, machine learning, and quasi-real-time forecasting for researchers and emergency responders. Full article

(This article belongs to the Special Issue GIS and Remote Sensing in Ocean and Coastal Ecology)

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20 pages, 3372 KiB

Open AccessEditor’s ChoiceReview

The Rising Concern for Sea Level Rise: Altimeter Record and Geo-Engineering Debate

by Jim Gower¹ and Vittorio Barale^2,*

¹ The Pillars of Hercules Foundation, Sidney, BC V8L 4B2, Canada

² Tethys Research Institute, 20121 Milan, Italy

Remote Sens. 2024, 16(2), 262; https://doi.org/10.3390/rs16020262 - 9 Jan 2024

Cited by 2 | Viewed by 2410

Abstract

The Oceans from Space V Symposium, held in Venice, Italy, on 24–27 October 2022, devoted special sessions to sea level rise, as described by a series of satellite altimeters, and to remediations of consequent calamities in vulnerable mediterranean seas. It emerged that various [...] Read more.

The Oceans from Space V Symposium, held in Venice, Italy, on 24–27 October 2022, devoted special sessions to sea level rise, as described by a series of satellite altimeters, and to remediations of consequent calamities in vulnerable mediterranean seas. It emerged that various aspects of climate change can be modelled in time as a Single Exponential Event (SEE), with a similar trend (a 54–year e–folding time) for CO₂ concentration in the Earth’s atmosphere, global average sea surface temperature, and global average sea level. The sea level rise record, combining tide gauges data starting in 1850, as well as more recent altimeter data, for the last 30 years, is already 25 cm above historical values. If the curve continues to follow the exponential growth of the simple SEE model, it will reach about 40 cm by the year 2050, 1 m by 2100, and 2.5 m by 2150. As a result, dramatic impacts would be expected for most coastal areas in the next century. Decisive remediations, based on geo-engineering at the basin scale, are possible for semi-enclosed seas, such as the Mediterranean and Black Seas. Damming the Strait of Gibraltar would provide an alternative to the conclusion that coastal sites such as the City of Venice are inevitably doomed. Full article

(This article belongs to the Special Issue Oceans from Space V)

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17 pages, 8577 KiB

Open AccessEditor’s ChoiceArticle

A Feasibility Study of Nearshore Bathymetry Estimation via Short-Range K-Band MIMO Radar

by Giovanni Ludeno^1,*

, Matteo Antuono², Francesco Soldovieri¹

and Gianluca Gennarelli¹

¹ Institute for the Electromagnetic Sensing of the Environment, National Research Council of Italy, Via Diocleziano 328, 80124 Napoli, Italy

² Institute of Marine Engineering, National Research Council of Italy, Via di Vallerano 139, 00128 Rome, Italy

Remote Sens. 2024, 16(2), 261; https://doi.org/10.3390/rs16020261 - 9 Jan 2024

Cited by 1 | Viewed by 2342

Abstract

This paper provides an assessment of a 24 GHz multiple-input multiple-output radar as a remote sensing tool to retrieve bathymetric maps in coastal areas. The reconstruction procedure considered here exploits the dispersion relation and has been previously employed to elaborate the data acquired [...] Read more.

This paper provides an assessment of a 24 GHz multiple-input multiple-output radar as a remote sensing tool to retrieve bathymetric maps in coastal areas. The reconstruction procedure considered here exploits the dispersion relation and has been previously employed to elaborate the data acquired via X-band marine radar. The estimation capabilities of the sensor are investigated firstly on synthetic radar data. With this aim, case studies referring to sea waves interacting with a constant and a spatially varying bathymetry are both considered. Finally, the reconstruction procedure is tested by processing real data recorded at Bagnoli Bay, Naples, South Italy. The preliminary results shown here confirm the potential of the radar sensor as a tool for sea wave monitoring. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series — Recent Advances in Ocean Radar)

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26 pages, 12608 KiB

Open AccessEditor’s ChoiceArticle

Your Input Matters—Comparing Real-Valued PolSAR Data Representations for CNN-Based Segmentation

by Sylvia Hochstuhl^1,2,*,†

, Niklas Pfeffer^1,2,†, Antje Thiele^1,2, Horst Hammer² and Stefan Hinz¹

¹ Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany

² Fraunhofer IOSB, 76275 Ettlingen, Germany

^† These authors contributed equally to this work.

Remote Sens. 2023, 15(24), 5738; https://doi.org/10.3390/rs15245738 - 15 Dec 2023

Cited by 2 | Viewed by 1249

Abstract

Inspired by the success of Convolutional Neural Network (CNN)-based deep learning methods for optical image segmentation, there is a growing interest in applying these methods to Polarimetric Synthetic Aperture Radar (PolSAR) data. However, effectively utilizing well-established real-valued CNNs for PolSAR image segmentation requires [...] Read more.

Inspired by the success of Convolutional Neural Network (CNN)-based deep learning methods for optical image segmentation, there is a growing interest in applying these methods to Polarimetric Synthetic Aperture Radar (PolSAR) data. However, effectively utilizing well-established real-valued CNNs for PolSAR image segmentation requires converting complex-valued data into real-valued representations. This paper presents a systematic comparison of 14 different real-valued representations used as CNN input in the literature. These representations encompass various approaches, including the use of coherency matrix elements, hand-crafted feature vectors, polarimetric features based on target decomposition, and combinations of these methods. The goal is to assess the impact of the choice of PolSAR data representation on segmentation performance and identify the most suitable representation. Four test configurations are employed to achieve this, involving different CNN architectures (U-Net with ResNet-18 or EfficientNet backbone) and PolSAR data acquired in different frequency bands (S- and L-band). The results emphasize the importance of selecting an appropriate real-valued representation for CNN-based PolSAR image segmentation. This study’s findings reveal that combining multiple polarimetric features can potentially enhance segmentation performance but does not consistently improve the results. Therefore, when employing this approach, careful feature selection becomes crucial. In contrast, using coherency matrix elements with amplitude and phase representation consistently achieves high segmentation performance across different test configurations. This representation emerges as one of the most suitable approaches for CNN-based PolSAR image segmentation. Notably, it outperforms the commonly used alternative approach of splitting the coherency matrix elements into real and imaginary parts. Full article

(This article belongs to the Section Remote Sensing Image Processing)

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26 pages, 11888 KiB

Open AccessEditor’s ChoiceArticle

Remotely Sensed Agroclimatic Classification and Zoning in Water-Limited Mediterranean Areas towards Sustainable Agriculture

by Ioannis Faraslis^1,*

, Nicolas R. Dalezios², Nicolas Alpanakis², Georgios A. Tziatzios²

, Marios Spiliotopoulos²

, Stavros Sakellariou¹

, Pantelis Sidiropoulos³

, Nicholas Dercas⁴, Alfonso Domínguez⁵

, José Antonio Martínez-López⁵

, Ramón López-Urrea⁶, Fadi Karam⁷, Hacib Amami⁸ and Radhouan Nciri⁹

¹ Department of Environmental Sciences, University of Thessaly, 41500 Larisa, Greece

² Department of Civil Engineering, University of Thessaly, 38221 Volos, Greece

³ Laboratory of Hydraulic Works and Environmental Management, School of Rural and Surveying Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

⁴ Department of Natural Resources and Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece

⁵ University of Castilla-La Mancha (UCLM), Technical School of Agricultural and Forestry Engineering and Biotechnology, Regional Centre of Water Research (CREA), Campus Universitario s/n, 02071 Albacete, Albacete, Spain

⁶ Desertification Research Centre (CIDE), CSIC-UV-GVA, Carretera CV 315, km 10.7, 46113 Moncada, Valencia, Spain

⁷ Department of Environmental Engineering, Faculty of Agriculture, Lebanese University, Dekwaneh P.O. Box 6573/14, Lebanon

⁸ National Research Institute for Rural Engineering, Water, and Forestry (INRGREF), Hédi Karrai Street, P.O. Box 10, Ariana 2080, Tunisia

⁹ National Institute of Field Crops (INGC), B.P. 120, Bousalem 8170, Tunisia

Remote Sens. 2023, 15(24), 5720; https://doi.org/10.3390/rs15245720 - 13 Dec 2023

Cited by 4 | Viewed by 1698

Abstract

Agroclimatic classification identifies zones for efficient use of natural resources leading to optimal and non-optimal crop production. The aim of this paper is the development of a methodology to determine sustainable agricultural zones in three Mediterranean study areas, namely, “La Mancha Oriental” in [...] Read more.

Agroclimatic classification identifies zones for efficient use of natural resources leading to optimal and non-optimal crop production. The aim of this paper is the development of a methodology to determine sustainable agricultural zones in three Mediterranean study areas, namely, “La Mancha Oriental” in Spain, “Sidi Bouzid” in Tunisia, and “Bekaa” valley in Lebanon. To achieve this, time series analysis with advanced geoinformatic techniques is applied. The agroclimatic classification methodology is based on three-stages: first, the microclimate features of the region are considered using aridity and vegetation health indices leading to water-limited growth environment (WLGE) zones based on water availability; second, landform features and soil types are associated with WLGE zones to identify non-crop-specific agroclimatic zones (NCSAZ); finally, specific restricted crop parameters are combined with NCSAZ to create the suitability zones. The results are promising as compared with the current crop production systems of the three areas under investigation. Due to climate change, the results indicate that these arid or semi-arid regions are also faced with insufficient amounts of precipitation for supporting rainfed annual crops. Finally, the proposed methodology reveals that the employment and use of remote sensing data and methods could be a significant tool for quickly creating detailed, and up to date agroclimatic zones. Full article

(This article belongs to the Special Issue Remote Sensing for Agrometeorology II)

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22 pages, 19803 KiB

Open AccessEditor’s ChoiceArticle

MSISR-STF: Spatiotemporal Fusion via Multilevel Single-Image Super-Resolution

by Xiongwei Zheng^1,2, Ruyi Feng^1,3, Junqing Fan^1,3, Wei Han^1,3

, Shengnan Yu^1,3 and Jia Chen^1,3,*

¹ School of Computer Science, China University of Geoscience, Wuhan 430074, China

² China Geological Survey, Beijing 100037, China

³ Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China

Remote Sens. 2023, 15(24), 5675; https://doi.org/10.3390/rs15245675 - 8 Dec 2023

Cited by 4 | Viewed by 1601

Abstract

Due to technological limitations and budget constraints, spatiotemporal image fusion uses the complementarity of high temporal–low spatial resolution (HTLS) and high spatial–low temporal resolution (HSLT) data to obtain high temporal and spatial resolution (HTHS) fusion data, which can effectively satisfy the demand for [...] Read more.

Due to technological limitations and budget constraints, spatiotemporal image fusion uses the complementarity of high temporal–low spatial resolution (HTLS) and high spatial–low temporal resolution (HSLT) data to obtain high temporal and spatial resolution (HTHS) fusion data, which can effectively satisfy the demand for HTHS data. However, some existing spatiotemporal image fusion models ignore the large difference in spatial resolution, which yields worse results for spatial information under the same conditions. Based on the flexible spatiotemporal data fusion (FSDAF) framework, this paper proposes a multilevel single-image super-resolution (SISR) method to solve this issue under the large difference in spatial resolution. The following are the advantages of the proposed method. First, multilevel super-resolution (SR) can effectively avoid the limitation of a single SR method for a large spatial resolution difference. In addition, the issue of noise accumulation caused by multilevel SR can be alleviated by learning-based SR (the cross-scale internal graph neural network (IGNN)) and then interpolation-based SR (the thin plate spline (TPS)). Finally, we add the reference information to the super-resolution, which can effectively control the noise generation. This method has been subjected to comprehensive experimentation using two authentic datasets, affirming that our proposed method surpasses the current state-of-the-art spatiotemporal image fusion methodologies in terms of performance and effectiveness. Full article

(This article belongs to the Special Issue Advances and Challenges on Multisource Remote Sensing Image Fusion: Datasets, New Technologies, and Applications)

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26 pages, 29208 KiB

Open AccessEditor’s ChoiceArticle

A Green Fingerprint of Antarctica: Drones, Hyperspectral Imaging, and Machine Learning for Moss and Lichen Classification

by Juan Sandino^1,2,*

, Barbara Bollard^3,4

, Ashray Doshi³, Krystal Randall^3,4

, Johan Barthelemy^3,5

, Sharon A. Robinson^3,4

and Felipe Gonzalez^1,2

¹ Securing Antarctica’s Environmental Future, Queensland University of Technology, 2 George St, Brisbane City, QLD 4000, Australia

² QUT Centre For Robotics, Queensland University of Technology, 2 George St, Brisbane City, QLD 4000, Australia

³ Securing Antarctica’s Environmental Future, University of Wollongong, Northfields Ave, Wollongong, NSW 2522, Australia

⁴ School of Earth, Atmospheric and Life Sciences, University of Wollongong, Northfields Ave, Wollongong, NSW 2522, Australia

⁵ NVIDIA, Santa Clara, CA 95051, USA

Remote Sens. 2023, 15(24), 5658; https://doi.org/10.3390/rs15245658 - 7 Dec 2023

Cited by 3 | Viewed by 4536

Abstract

Mapping Antarctic Specially Protected Areas (ASPAs) remains a critical yet challenging task, especially in extreme environments like Antarctica. Traditional methods are often cumbersome, expensive, and risky, with limited satellite data further hindering accuracy. This study addresses these challenges by developing a workflow that [...] Read more.

Mapping Antarctic Specially Protected Areas (ASPAs) remains a critical yet challenging task, especially in extreme environments like Antarctica. Traditional methods are often cumbersome, expensive, and risky, with limited satellite data further hindering accuracy. This study addresses these challenges by developing a workflow that enables precise mapping and monitoring of vegetation in ASPAs. The processing pipeline of this workflow integrates small unmanned aerial vehicles (UAVs)—or drones—to collect hyperspectral and multispectral imagery (HSI and MSI), global navigation satellite system (GNSS) enhanced with real-time kinematics (RTK) to collect ground control points (GCPs), and supervised machine learning classifiers. This workflow was validated in the field by acquiring ground and aerial data at ASPA 135, Windmill Islands, East Antarctica. The data preparation phase involves a data fusion technique to integrate HSI and MSI data, achieving the collection of georeferenced HSI scans with a resolution of up to 0.3 cm/pixel. From these high-resolution HSI scans, a series of novel spectral indices were proposed to enhance the classification accuracy of the model. Model training was achieved using extreme gradient boosting (XGBoost), with four different combinations tested to identify the best fit for the data. The research results indicate the successful detection and mapping of moss and lichens, with an average accuracy of 95%. Optimised XGBoost models, particularly Model 3 and Model 4, demonstrate the applicability of the custom spectral indices to achieve high accuracy with reduced computing power requirements. The integration of these technologies results in significantly more accurate mapping compared to conventional methods. This workflow serves as a foundational step towards more extensive remote sensing applications in Antarctic and ASPA vegetation mapping, as well as in monitoring the impact of climate change on the Antarctic ecosystem. Full article

(This article belongs to the Special Issue Antarctic Remote Sensing Applications)

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26 pages, 37177 KiB

Open AccessEditor’s ChoiceArticle

An Integrated Approach for 3D Solar Potential Assessment at the City Scale

by Hassan Waqas¹

, Yuhong Jiang^2,*, Jianga Shang^1,2, Iqra Munir³ and Fahad Ullah Khan⁴

¹ National Engineering Research Center for Geographic Information System, School of Geography, China University of Geosciences, Wuhan 430074, China

² School of Computer Science, China University of Geosciences, Wuhan 430074, China

³ State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China

⁴ School of Civil and Environmental Engineering, University of Engineering and Technology, Peshawar Campus III Bannu, Bannu 28100, Pakistan

Remote Sens. 2023, 15(23), 5616; https://doi.org/10.3390/rs15235616 - 3 Dec 2023

Cited by 2 | Viewed by 2905

Abstract

The use of solar energy has shown the fastest global growth of all renewable energy sources. Efforts towards careful evaluation are required to select optimal locations for the installation of photovoltaics (PV) because their effectiveness is strongly reliant on exposure to solar irradiation. [...] Read more.

The use of solar energy has shown the fastest global growth of all renewable energy sources. Efforts towards careful evaluation are required to select optimal locations for the installation of photovoltaics (PV) because their effectiveness is strongly reliant on exposure to solar irradiation. Assessing the shadows cast by nearby buildings and vegetation is essential, especially at the city scale. Due to urban complexity, conventional methods using Digital Surface Models (DSM) overestimate solar irradiation in dense urban environments. To provide further insights into this dilemma, a new modeling technique was developed for integrated 3D city modeling and solar potential assessment on building roofs using light detection and ranging (LiDAR) data. The methodology used hotspot analysis to validate the workflow in both site and without-site contexts (e.g., trees that shield small buildings). Field testing was conducted, covering a total area of 4975 square miles and 10,489 existing buildings. The results demonstrate a considerable impact of large, dense trees on the solar irradiation received by smaller buildings. Considering the site’s context, a mean annual solar estimate of 99.97 kWh/m²/year was determined. Without considering the site context, this value increased by 9.3% (as a percentage of total rooftops) to 109.17 kWh/m²/year, with a peak in July and troughs in December and January. The study suggests that both factors have a substantial impact on solar potential estimations, emphasizing the importance of carefully considering the shadowing effect during PV panel installation. The research findings reveal that 1517 buildings in the downtown area of Austin have high estimated radiation ranging from 4.7 to 6.9 kWh/m²/day, providing valuable insights for the identification of optimal locations highly suitable for PV installation. Additionally, this methodology can be generalized to other cities, addressing the broader demand for renewable energy solutions. Full article

(This article belongs to the Special Issue 3D City Modelling and Remote Sensing: Advances, Challenges, and New Technologies)

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38 pages, 11952 KiB

Open AccessEditor’s ChoiceArticle

NOAA MODIS SST Reanalysis Version 1

by Olafur Jonasson^1,2,*, Alexander Ignatov¹, Boris Petrenko^1,2, Victor Pryamitsyn^1,2

and Yury Kihai^1,2

¹ STAR, NOAA Center For Weather and Climate Prediction (NCWCP), College Park, MD 20740, USA

² Global Science and Technology, Inc., Greenbelt, MD 20770, USA

Remote Sens. 2023, 15(23), 5589; https://doi.org/10.3390/rs15235589 - 30 Nov 2023

Cited by 1 | Viewed by 1450

Abstract

The first NOAA full-mission reanalysis (RAN1) of the sea surface temperature (SST) from the two Moderate Resolution Imaging Spectroradiometers (MODIS) onboard Terra (24 February 2000–present) and Aqua (4 July 2002–present) was performed. The dataset was produced using the NOAA Advanced Clear-Sky Processor for [...] Read more.

The first NOAA full-mission reanalysis (RAN1) of the sea surface temperature (SST) from the two Moderate Resolution Imaging Spectroradiometers (MODIS) onboard Terra (24 February 2000–present) and Aqua (4 July 2002–present) was performed. The dataset was produced using the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) enterprise SST system from Collection 6.1 brightness temperatures (BTs) in three MODIS thermal emissive bands centered at 3.7, 11, and 12 µm with a spatial resolution of 1 km at nadir. In the initial stages of reprocessing, several instabilities in the MODIS SST time series were observed. In particular, Terra SSTs and corresponding BTs showed three ‘steps’: two on 30 October 2000 and 2 July 2001 (due to changes in the MODIS operating mode) and one on 25 April 2020 (due to a change in its nominal blackbody temperature, BBT, from 290 to 285 K). Additionally, spikes up to several tenths of a kelvin were observed during the quarterly warm-up/cool-down (WUCD) exercises, when the Terra MODIS BBT was varied. Systematic gradual drifts of ~0.025 K/decade were also seen in both Aqua and Terra SSTs over their full missions due to drifting BTs. These calibration instabilities were mitigated by debiasing MODIS BTs using the time series of observed minus modeled (‘O-M’) BTs. The RAN1 dataset was evaluated via comparisons with various in situ SSTs. The data meet the NOAA specifications for accuracy (±0.2 K) and precision (0.6 K), often by a wide margin, in a clear-sky ocean domain of 19–21%. The long-term SST drift is typically less than 0.01 K/decade for all MODIS SSTs, except for the daytime ‘subskin’ SST, for which the drift is ~0.02 K/decade. The MODIS RAN1 dataset is archived at NOAA CoastWatch and updated monthly in a delayed mode with a latency of two months. Additional archival with NASA JPL PO.DAAC is being discussed. Full article

(This article belongs to the Special Issue VIIRS 2011–2021: Ten Years of Success in Earth Observations)

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17 pages, 9435 KiB

Open AccessEditor’s ChoiceArticle

Spatial and Spectral Translation of Landsat 8 to Sentinel-2 Using Conditional Generative Adversarial Networks

by Rohit Mukherjee^1,*,†

and Desheng Liu^2,†

¹ School of Geography, Development, and Environment, The University of Arizona, Tucson, AZ 85721, USA

² Department of Geography, The Ohio State University, Columbus, OH 43210, USA

^† These authors contributed equally to this work.

Remote Sens. 2023, 15(23), 5502; https://doi.org/10.3390/rs15235502 - 25 Nov 2023

Viewed by 1716

Abstract

Satellite sensors like Landsat 8 OLI (L8) and Sentinel-2 MSI (S2) provide valuable multispectral Earth observations that differ in spatial resolution and spectral bands, limiting synergistic use. L8 has a 30 m resolution and a lower revisit frequency, while S2 offers up to [...] Read more.

Satellite sensors like Landsat 8 OLI (L8) and Sentinel-2 MSI (S2) provide valuable multispectral Earth observations that differ in spatial resolution and spectral bands, limiting synergistic use. L8 has a 30 m resolution and a lower revisit frequency, while S2 offers up to a 10 m resolution and more spectral bands, such as red edge bands. Translating observations from L8 to S2 can increase data availability by combining their images to leverage the unique strengths of each product. In this study, a conditional generative adversarial network (CGAN) is developed to perform sensor-specific domain translation focused on green, near-infrared (NIR), and red edge bands. The models were trained on the pairs of co-located L8-S2 imagery from multiple locations. The CGAN aims to downscale 30 m L8 bands to 10 m S2-like green and 20 m S2-like NIR and red edge bands. Two translation methodologies are employed—direct single-step translation from L8 to S2 and indirect multistep translation. The direct approach involves predicting the S2-like bands in a single step from L8 bands. The multistep approach uses two steps—the initial model predicts the corresponding S2-like band that is available in L8, and then the final model predicts the unavailable S2-like red edge bands from the S2-like band predicted in the first step. Quantitative evaluation reveals that both approaches result in lower spectral distortion and higher spatial correlation compared to native L8 bands. Qualitative analysis supports the superior fidelity and robustness achieved through multistep translation. By translating L8 bands to higher spatial and spectral S2-like imagery, this work increases data availability for improved earth monitoring. The results validate CGANs for cross-sensor domain adaptation and provide a reusable computational framework for satellite image translation. Full article

(This article belongs to the Special Issue Remote Sensing Data Fusion and Applications)

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18 pages, 5728 KiB

Open AccessEditor’s ChoiceArticle

SatelliteSkill5—An Augmented Reality Educational Experience Teaching Remote Sensing through the UN Sustainable Development Goals

by Eimear McNerney¹, Jonathan Faull², Sasha Brown^2,†, Lorraine McNerney³, Ronan Foley², James Lonergan⁴, Angela Rickard⁵

, Zerrin Doganca Kucuk⁵

, Avril Behan^1,†, Bernard Essel², Isaac Obour Mensah², Yeray Castillo Campo², Helen Cullen², Jack Ffrench⁶, Rachel Abernethy⁶, Patricia Cleary⁶, Aengus Byrne⁷ and Conor Cahalane^2,*

¹ School of Surveying and Construction Innovation, Technological University Dublin, D01 K822 Dublin, Ireland

² Department of Geography, Maynooth University, W23 F2H6 Maynooth, Ireland

³ Tailte Éireann-Surveying, D08 F6E4 Dublin, Ireland

⁴ Society of Chartered Surveyors Ireland, D02 EV61 Dublin, Ireland

⁵ Department of Education, Maynooth University, W23 F2H6 Maynooth, Ireland

⁶ Esri Ireland, D15 NP9Y Dublin, Ireland

⁷ St. Brendans National School, R42 KN92 Birr, Ireland

^† Current contact info: [email protected] (A.B.)

Remote Sens. 2023, 15(23), 5480; https://doi.org/10.3390/rs15235480 - 23 Nov 2023

Cited by 1 | Viewed by 1645

Abstract

Advances in visualisation techniques provide new ways for us to explore how we introduce complex topics like remote sensing to non-specialist audiences. Taking inspiration from the popularity of augmented reality (AR) apps, a free, mobile digital AR app titled SatelliteSkill5, has been [...] Read more.

Advances in visualisation techniques provide new ways for us to explore how we introduce complex topics like remote sensing to non-specialist audiences. Taking inspiration from the popularity of augmented reality (AR) apps, a free, mobile digital AR app titled SatelliteSkill5, has been developed for both Androids and iPhones in Unity AR. SatelliteSkill5 helps users conceptualise remote sensing (RS) theory and technology by showcasing the potential of datasets such as multispectral images, SAR backscatter, drone orthophotography, and bathymetric LIDAR for tackling real-world challenges, with examples tackling many of the United Nations’ Sustainable Development Goals (SDGs) as the focus. Leveraging tried and tested pedagogic practices such as active learning, game-based learning, and targeting cross-curricular topics, SatelliteSkill5 introduces users to many of the fundamental geospatial data themes identified by the UN as essential for meeting the SDGs, imparting users with a familiarity of concepts such as land cover, elevation, land parcels, bathymetry, and soil. The SatelliteSkill5 app was piloted in 12 Irish schools during 2021 and 2022 and with 861 students ranging from 12 to 18 years old. This research shows that both students and teachers value learning in an easy-to-use AR environment and that SDGs help users to better understand complex remote sensing theory. Full article

(This article belongs to the Collection Teaching and Learning in Remote Sensing)

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25 pages, 6644 KiB

Open AccessEditor’s ChoiceArticle

Vegetation Stress Monitor—Assessment of Drought and Temperature-Related Effects on Vegetation in Germany Analyzing MODIS Time Series over 23 Years

by Ursula Gessner^1,*

, Sophie Reinermann²

, Sarah Asam¹

and Claudia Kuenzer^1,2

¹ German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany

² Department of Remote Sensing, Institute of Geography and Geology, University of Würzburg, 97074 Würzburg, Germany

Remote Sens. 2023, 15(22), 5428; https://doi.org/10.3390/rs15225428 - 20 Nov 2023

Cited by 2 | Viewed by 2215

Abstract

Over the past two decades, and particularly since 2018, Central Europe has experienced several droughts with strong impacts on ecosystems and food production. It is expected that under accelerating climate change, droughts and resulting vegetation and ecosystem stress will further increase. Against this [...] Read more.

Over the past two decades, and particularly since 2018, Central Europe has experienced several droughts with strong impacts on ecosystems and food production. It is expected that under accelerating climate change, droughts and resulting vegetation and ecosystem stress will further increase. Against this background, there is a need for techniques and datasets that allow for monitoring of the timing, extent and effects of droughts. Vegetation indices (VIs) based on satellite Earth observation (EO) can be used to directly assess vegetation stress over large areas. Here, we use a MODIS Enhanced Vegetation Index (EVI) time series to analyze and characterize the vegetation stress on Germany’s croplands and grasslands that has occurred since 2000. A special focus is put on the years from 2018 to 2022, an extraordinary 5-year period characterized by a high frequency of droughts and heat waves. The study reveals strong variations in agricultural drought patterns during the past major drought years in Germany (such as 2003 or 2018), as well as large regional differences in climate-related vegetation stress. The northern parts of Germany showed a higher tendency to be affected by drought effects, particularly after 2018. Further, correlation analyses showed a strong relationship between annual yields of maize, potatoes and winter wheat and previous vegetation stress, where the timing of strongest relationships could be related to crop-specific development stages. Our results support the potential of VI time series for robustly monitoring and predicting effects of climate-related vegetation development and agricultural yields. Full article

(This article belongs to the Special Issue Women’s Special Issue Series: Remote Sensing 2023-2024)

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26 pages, 55211 KiB

Open AccessEditor’s ChoiceArticle

Assessing the Hazard of Deep-Seated Rock Slope Instability through the Description of Potential Failure Scenarios, Cross-Validated Using Several Remote Sensing and Monitoring Techniques

by Charlotte Wolff^1,*, Michel Jaboyedoff¹

, Li Fei¹, Andrea Pedrazzini², Marc-Henri Derron¹, Carlo Rivolta³ and Véronique Merrien-Soukatchoff^1,4

¹ Risk Analysis Group, Institute of Earth Science, University of Lausanne, 1015 Lausanne, Switzerland

² Sezione Forestale—Repubblica e Cantone Ticino, 6500 Bellinzona, Switzerland

³ Ellegi Srl—Via Bandello 5, 20123 Milano, Italy

⁴ Laboratoire Géomatique & Foncier, Conservatoire National des Arts et Métiers, 75013 Paris, France

Remote Sens. 2023, 15(22), 5396; https://doi.org/10.3390/rs15225396 - 17 Nov 2023

Cited by 2 | Viewed by 1704

Abstract

Foreseeing the failure of important unstable volumes is a major concern in the Alps, especially due to the presence of people and infrastructures in the valleys. The use of monitoring and remote sensing techniques is aimed at detecting potential instabilities and the combination [...] Read more.

Foreseeing the failure of important unstable volumes is a major concern in the Alps, especially due to the presence of people and infrastructures in the valleys. The use of monitoring and remote sensing techniques is aimed at detecting potential instabilities and the combination of several techniques permits the cross-validation of the detected movements. Supplemented with field mapping and structural analysis, it is possible to define possible scenarios of rupture in terms of volume, mechanisms of failure and susceptibility. A combined observation strategy was applied to the study of major instability located in the Ticinese Alps (Switzerland), Cima del Simano, where the monitoring started in 2006 with the measurement of opened cracks with extensometers. Since 2021, the monitoring has been completed by LiDAR, satellite and GB-InSAR observations and structural analysis. Here, slow but constant movements of about 7 mm/yr were detected along with rockfall activities near the Simano summit. Eight failure scenarios of various sizes ranging from 2.3 × 10⁵ m³ to 51 × 10⁶ m³, various mechanisms (toppling, planar, wedge and circular sliding) and various occurrence probabilities were defined based on the topography and the monitoring results and by applying a Slope Local Base Level (SLBL) algorithm. Weather acquisition campaigns by means of thermologgers were also conducted to suggest possible causes that lead to the observed movements and to suggest the evolution of the instabilities with actual and future climate changes. Full article

(This article belongs to the Special Issue Landslide Studies Integrating Remote Sensing and Geophysical Data)

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32 pages, 8640 KiB

Open AccessEditor’s ChoiceArticle

Characterizing Snow Dynamics in Semi-Arid Mountain Regions with Multitemporal Sentinel-1 Imagery: A Case Study in the Sierra Nevada, Spain

by Pedro Torralbo^1,2,*

, Rafael Pimentel^1,2

, Maria José Polo^1,2

and Claudia Notarnicola³

¹ Fluvial Dynamics and Hydrology Research Group, Andalusian Institute for Earth System Research, University of Córdoba, Campus Rabanales, Edificio Leonardo da Vinci, Área de Ingeniería Hidráulica, 14071 Córdoba, Spain

² Department of Agronomy, Unit of Excellence María de Maeztu (DAUCO), University of Córdoba, 14071 Córdoba, Spain

³ Institute for Earth Observation, EURAC Research, 39100 Bolzano, Italy

Remote Sens. 2023, 15(22), 5365; https://doi.org/10.3390/rs15225365 - 15 Nov 2023

Cited by 5 | Viewed by 1932

Abstract

Monitoring snowmelt dynamics in mountains is crucial to understand water releases downstream. Sentinel-1 (S-1) synthetic-aperture radar (SAR) has become one of the most widely used techniques to achieve this aim due to its high frequency of acquisitions and all-weather capability. This work aims [...] Read more.

Monitoring snowmelt dynamics in mountains is crucial to understand water releases downstream. Sentinel-1 (S-1) synthetic-aperture radar (SAR) has become one of the most widely used techniques to achieve this aim due to its high frequency of acquisitions and all-weather capability. This work aims to understand the possibilities of S-1 SAR imagery to capture snowmelt dynamics and related changes in streamflow response in semi-arid mountains. The results proved that S-1 SAR imagery was able not only to capture the final spring melting but also all melting cycles that commonly appear throughout the year in these types of environments. The general change detection approach to identify wet snow was adapted for these regions using as reference the average S-1 SAR image from the previous summer, and a threshold of −3.00 dB, which has been assessed using Landsat images as reference dataset obtaining a general accuracy of 0.79. In addition, four different types of melting-runoff onsets depending on physical snow condition were identified. When translating that at the catchment scale, distributed melting-runoff onset maps were defined to better understand the spatiotemporal evolution of melting dynamics. Finally, a linear connection between melting dynamics and streamflow was found for long-lasting melting cycles, with a determination coefficient (R²) ranging from 0.62 to 0.83 and an average delay between the melting onset and streamflow peak of about 21 days. Full article

(This article belongs to the Special Issue Advanced Microwave Remote Sensing Technologies for Hydrology)

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22 pages, 7537 KiB

Open AccessEditor’s ChoiceArticle

High-Resolution Real-Time Coastline Detection Using GNSS RTK, Optical, and Thermal SfM Photogrammetric Data in the Po River Delta, Italy

by Massimo Fabris^1,*

, Mirco Balin² and Michele Monego¹

¹ Department of Civil, Environmental and Architectural Engineering, University of Padua, 35131 Padua, Italy

² Brenta Lavori srl, 35010 Vigonza, Italy

Remote Sens. 2023, 15(22), 5354; https://doi.org/10.3390/rs15225354 - 14 Nov 2023

Cited by 3 | Viewed by 1436

Abstract

High-resolution coastline detection and monitoring are challenging on a global scale, especially in flat areas where natural events, sea level rise, and anthropic activities constantly modify the coastal environment. While the coastline related to the 0-level contour line can be extracted from accurate [...] Read more.

High-resolution coastline detection and monitoring are challenging on a global scale, especially in flat areas where natural events, sea level rise, and anthropic activities constantly modify the coastal environment. While the coastline related to the 0-level contour line can be extracted from accurate Digital Terrain Models (DTMs), the detection of the real-time, instantaneous coastline, especially at low tide, is a challenge that warrants further study and evaluation. In order to investigate an efficient combination of methods that allows to contribute to the knowledge in this field, this work uses topographic total station measurements, Global Navigation Satellite System Real-Time Kinematic (GNSS RTK) technique, and the Structure from Motion (SfM) approach (using a low-cost drone equipped with optical and thermal cameras). All the data were acquired at the beginning of 2022 and refer to the areas of Boccasette and Barricata, in the Po River Delta (Northeastern of Italy). The real-time coastline obtained from the GNSS data was validated using the topographic total station measurements; the correspondent polylines obtained from the photogrammetric data (using both automatic extraction and manual restitutions by visual inspection of orhophotos) were compared with the GNSS data to evaluate the performances of the different techniques. The results provided good agreement between the real-time coastlines obtained from different approaches. However, using the optical images, the accuracy was strictly connected with the radiometric changes in the photos and using thermal images, both manual and automatic polylines provided differences in the order of 1–2 m. Multi-temporal comparison of the 0-level coastline with those obtained from a LiDAR survey performed in 2018 provided the detection of the erosion and accretion areas in the period 2018–2022. The investigation on the two case studies showed a better accuracy of the GNSS RTK method in the real-time coastline detection. It can be considered as reliable ground-truth reference for the evaluation of the photogrammetric coastlines. While GNSS RTK proved to be more productive and efficient, optical and thermal SfM provided better results in terms of morphological completeness of the data. Full article

(This article belongs to the Special Issue Advances in Remote Sensing in Coastal Geomorphology Ⅱ)

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18 pages, 8328 KiB

Open AccessEditor’s ChoiceArticle

Evaluation of Tree-Growth Rate in the Laurentides Wildlife Reserve Using GEDI and Airborne-LiDAR Data

by Adriana Parra^*

and Marc Simard

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA

Remote Sens. 2023, 15(22), 5352; https://doi.org/10.3390/rs15225352 - 14 Nov 2023

Cited by 4 | Viewed by 1649

Abstract

Loss of forest cover and derived effects on forest ecosystems services has led to the establishment of land management policies and forest monitoring systems, and consequently to the demand for accurate and multitemporal data on forest extent and structure. In recent years, spaceborne [...] Read more.

Loss of forest cover and derived effects on forest ecosystems services has led to the establishment of land management policies and forest monitoring systems, and consequently to the demand for accurate and multitemporal data on forest extent and structure. In recent years, spaceborne Light Detection and Ranging (LiDAR) missions, such as the Global Ecosystem Dynamics Investigation (GEDI) instrument, have facilitated the repeated acquisition of data on the vertical structure of vegetation. In this study, we designed an approach incorporating GEDI and airborne LiDAR data, in addition to detailed forestry inventory data, for estimating tree-growth dynamics for the Laurentides wildlife reserve in Canada. We estimated an average tree-growth rate of 0.32 ± 0.23 (SD) m/year for the study site and evaluated our results against field data and a time series of NDVI from Landsat images. The results are in agreement with expected patterns in tree-growth rates related to tree species and forest stand age, and the produced dataset is able to track disturbance events resulting in the loss of canopy height. Our study demonstrates the benefits of using spaceborne-LiDAR data for extending the temporal coverage of forestry inventories and highlights the ability of GEDI data for detecting changes in forests’ vertical structure. Full article

(This article belongs to the Special Issue Lidar for Environmental Remote Sensing: Theory and Application)

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25 pages, 32152 KiB

Open AccessEditor’s ChoiceArticle

Assessing Planet Nanosatellite Sensors for Ocean Color Usage

by Mark D. Lewis^1,*, Brittney Jarreau¹, Jason Jolliff¹

, Sherwin Ladner¹, Timothy A. Lawson¹, Sean McCarthy¹, Paul Martinolich² and Marcos Montes³

¹ Naval Research Laboratory, Building 1009, Stennis Space Center, MS 39529, USA

² Peraton, Building 1103, Stennis Space Center, MS 39529, USA

³ Naval Research Laboratory, Building 2, Washington, DC 20375, USA

Remote Sens. 2023, 15(22), 5359; https://doi.org/10.3390/rs15225359 - 14 Nov 2023

Cited by 1 | Viewed by 1447

Abstract

An increasing number of commercial nanosatellite-based Earth-observing sensors are providing high-resolution images for much of the coastal ocean region. Traditionally, to improve the accuracy of normalized water-leaving radiance (

n L_{w}

) estimates, sensor gains are computed using in-orbit vicarious calibration methods. [...] Read more.

An increasing number of commercial nanosatellite-based Earth-observing sensors are providing high-resolution images for much of the coastal ocean region. Traditionally, to improve the accuracy of normalized water-leaving radiance (

n L_{w}

) estimates, sensor gains are computed using in-orbit vicarious calibration methods. The initial series of Planet nanosatellite sensors were primarily designed for land applications and are missing a second near-infrared band, which is typically used in selecting aerosol models for atmospheric correction over oceanographic regions. This study focuses on the vicarious calibration of Planet sensors and the duplication of its red band for use in both the aerosol model selection process and as input to bio-optical ocean product algorithms. Error measurements show the calibration performed well at the Marine Optical Buoy location near Lanai, Hawaii. Further validation was performed using in situ data from the Aerosol Robotic Network—Ocean Color platform in the northern Adriatic Sea. Bio-optical ocean color products were generated and compared with products from the Visual Infrared Imaging Radiometric Suite sensor. This approach for sensor gain generation and usage proved effective in increasing the accuracy of

n L_{w}

measurements for bio-optical ocean product algorithms. Full article

(This article belongs to the Section Ocean Remote Sensing)

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31 pages, 6937 KiB

Open AccessEditor’s ChoiceArticle

Data-Driven Landslide Spatial Prediction and Deformation Monitoring: A Case Study of Shiyan City, China

by Yifan Sheng¹, Guangli Xu^1,2, Bijing Jin², Chao Zhou³

, Yuanyao Li^1,* and Weitao Chen⁴

¹ Institute of Geological Survey, China University of Geosciences, Wuhan 430074, China

² Faculty of Engineering, China University of Geosciences, Wuhan 430074, China

³ School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China

⁴ School of Computer Science, China University of Geosciences, Wuhan 430074, China

Remote Sens. 2023, 15(21), 5256; https://doi.org/10.3390/rs15215256 - 6 Nov 2023

Cited by 8 | Viewed by 2529

Abstract

Landslide susceptibility mapping (LSM) is significant for landslide risk assessment. However, there remains no consensus on which method is optimal for LSM. This study implements a dynamic approach to landslide hazard mapping by integrating spatio-temporal probability analysis with time-varying ground deformation velocity derived [...] Read more.

Landslide susceptibility mapping (LSM) is significant for landslide risk assessment. However, there remains no consensus on which method is optimal for LSM. This study implements a dynamic approach to landslide hazard mapping by integrating spatio-temporal probability analysis with time-varying ground deformation velocity derived from the MT-InSAR (Multi-Temporal InSAR) method. Reliable landslide susceptibility maps (LSMs) can inform landslide risk managers and government officials. First, sixteen factors were selected to construct a causal factor system for LSM. Next, Pearson correlation analysis, multicollinearity analysis, information gain ratio, and GeoDetector methods were applied to remove the least important factors of STI, plan curvature, TRI, and slope length. Subsequently, information quantity (IQ), logistic regression (LR), frequency ratio (FR), artificial neural network (ANN), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) methods were performed to construct the LSM. The results showed that the distance to a river, slope angle, distance from structure, and engineering geological rock group were the main factors controlling landslide development. A comprehensive set of statistical indicators was employed to evaluate these methods’ effectiveness; sensitivity, F1-measure, and AUC (area under the curve) were calculated and subsequently compared to assess the performance of the methods. Machine learning methods’ training and prediction accuracy were higher than those of statistical methods. The AUC values of the IQ, FR, LR, BP-ANN, RBF-ANN, RF, SVM, and CNN methods were 0.810, 0.854, 0.828, 0.895, 0.916, 0.932, 0.948, and 0.957, respectively. Although the performance order varied for other statistical indicators, overall, the CNN method was the best, while the BP-ANN and RBF-ANN method was the worst among the five examined machine methods. Hence, adopting the CNN approach in this study can enhance LSM accuracy, catering to the needs of planners and government agencies responsible for managing landslide-prone areas and preventing landslide-induced disasters. Full article

(This article belongs to the Special Issue Remote Sensing for Geohazards Monitoring: Towards Refined Risk Analysis and Management)

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27 pages, 5790 KiB

Open AccessEditor’s ChoiceArticle

A New Approach for Feeding Multispectral Imagery into Convolutional Neural Networks Improved Classification of Seedlings

by Mohammad Imangholiloo^1,*

, Ville Luoma¹

, Markus Holopainen^1,2, Mikko Vastaranta³

, Antti Mäkeläinen⁴, Niko Koivumäki²

, Eija Honkavaara²

and Ehsan Khoramshahi^2,5

¹ Department of Forest Sciences, University of Helsinki, P.O. Box 27, 00014 Helsinki, Finland

² Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute (FGI), National Land Survey of Finland (NLS), Geodeetinrinne 2, 02430 Masala, Finland

³ School of Forest Sciences, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland

⁴ MosaicMill Oy, Presently AFRY Management Consulting, Jaakonkatu 3, 01620 Vantaa, Finland

⁵ School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 14174-66191, Iran

Remote Sens. 2023, 15(21), 5233; https://doi.org/10.3390/rs15215233 - 3 Nov 2023

Cited by 1 | Viewed by 1731

Abstract

Tree species information is important for forest management, especially in seedling stands. To mitigate the spectral admixture of understory reflectance with small and lesser foliaged seedling canopies, we proposed an image pre-processing step based on the canopy threshold (C_th) applied on [...] Read more.

Tree species information is important for forest management, especially in seedling stands. To mitigate the spectral admixture of understory reflectance with small and lesser foliaged seedling canopies, we proposed an image pre-processing step based on the canopy threshold (C_th) applied on drone-based multispectral images prior to feeding classifiers. This study focused on (1) improving the classification of seedlings by applying the introduced technique; (2) comparing the classification accuracies of the convolutional neural network (CNN) and random forest (RF) methods; and (3) improving classification accuracy by fusing vegetation indices to multispectral data. A classification of 5417 field-located seedlings from 75 sample plots showed that applying the C_th technique improved the overall accuracy (OA) of species classification from 75.7% to 78.5% on the C_th-affected subset of the test dataset in CNN method (1). The OA was more accurate in CNN (79.9%) compared to RF (68.3%) (2). Moreover, fusing vegetation indices with multispectral data improved the OA from 75.1% to 79.3% in CNN (3). Further analysis revealed that shorter seedlings and tensors with a higher proportion of C_th-affected pixels have negative impacts on the OA in seedling forests. Based on the obtained results, the proposed method could be used to improve species classification of single-tree detected seedlings in operational forest inventory. Full article

(This article belongs to the Special Issue Novel Applications of UAV Imagery for Forest Science)

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20 pages, 7584 KiB

Open AccessEditor’s ChoiceArticle

A Learning Strategy for Amazon Deforestation Estimations Using Multi-Modal Satellite Imagery

by Dongoo Lee^†

and Yeonju Choi^*,†

¹ Korea Aerospace Research Institute, Daejeon 34133, Republic of Korea

^† These authors contributed equally to this work.

Remote Sens. 2023, 15(21), 5167; https://doi.org/10.3390/rs15215167 - 29 Oct 2023

Cited by 1 | Viewed by 3370

Abstract

Estimations of deforestation are crucial as increased levels of deforestation induce serious environmental problems. However, it is challenging to perform investigations over extensive areas, such as the Amazon rainforest, due to the vast size of the region and the difficulty of direct human [...] Read more.

Estimations of deforestation are crucial as increased levels of deforestation induce serious environmental problems. However, it is challenging to perform investigations over extensive areas, such as the Amazon rainforest, due to the vast size of the region and the difficulty of direct human access. Satellite imagery can be used as an effective solution to this problem; combining optical images with synthetic aperture radar (SAR) images enables deforestation monitoring over large areas irrespective of weather conditions. In this study, we propose a learning strategy for multi-modal deforestation estimations on this basis. Images from three different satellites, Sentinel-1, Sentinel-2, and Landsat 8, were utilized to this end. The proposed algorithm overcomes visibility limitations due to a long rainy season of the Amazon by creating a multi-modal dataset using supplementary SAR images, achieving high estimation accuracy. The dataset is composed of satellite data taken on a daily basis with relatively less monthly generated, ground truth masking data, which is called the many-to-one-mask condition. The Normalized Difference Vegetation Index and Normalized Difference Soil Index bands are selected to comprise the datasets. This yields better detection performance and a shorter training time than datasets consisting of RGB or all bands. Multiple deep neural networks are independently trained for each modality and an appropriate fusion method is developed to detect deforestation. The proposed method utilizes the distance similarity of the predicted deforestation rate to filter prediction results. The elements with high degrees of similarity are merged into the final result with average and denoising operations. The performances of five network variants of the U-Net family are compared, with Attention U-Net observed to exhibit the best prediction results. Finally, the proposed method is utilized to estimate the deforestation status of novel queries with high accuracy. Full article

(This article belongs to the Special Issue Deforestation Detection with Deep Learning from Multispectral and Hyperspectral Satellite Images)

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20 pages, 3798 KiB

Open AccessEditor’s ChoiceArticle

Machine Learning Downscaling of SoilMERGE in the United States Southern Great Plains

by Kenneth Tobin^1,*, Aaron Sanchez¹, Daniela Esparza¹, Miguel Garcia¹, Deepak Ganta²

and Marvin Bennett²

¹ Center for Earth and Environmental Studies, Texas A&M International University, Laredo, TX 78041, USA

² School of Engineering, Texas A&M International University, Laredo, TX 78041, USA

Remote Sens. 2023, 15(21), 5120; https://doi.org/10.3390/rs15215120 - 26 Oct 2023

Viewed by 1274

Abstract

SoilMERGE (SMERGE) is a root-zone soil moisture (RZSM) product that covers the entire continental United States and spans 1978 to 2019. Machine learning techniques, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Gradient Boost (GBoost) downscaled SMERGE to spatial resolutions straddling the field [...] Read more.

SoilMERGE (SMERGE) is a root-zone soil moisture (RZSM) product that covers the entire continental United States and spans 1978 to 2019. Machine learning techniques, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Gradient Boost (GBoost) downscaled SMERGE to spatial resolutions straddling the field scale domain (100 to 3000 m). Study area was northern Oklahoma and southern Kansas. The coarse resolution of SMERGE (0.125 degree) limits this product’s utility. To validate downscaled results in situ data from four sources were used that included: United States Department of Energy Atmospheric Radiation Measurement (ARM) observatory, United States Climate Reference Network (USCRN), Soil Climate Analysis Network (SCAN), and Soil moisture Sensing Controller and oPtimal Estimator (SoilSCAPE). In addition, RZSM retrievals from NASA’s Airborne Microwave Observatory of Subcanopy and Surface (AirMOSS) campaign provided a nearly spatially continuous comparison. Three periods were examined: era 1 (2016 to 2019), era 2 (2012 to 2015), and era 3 (2003 to 2007). During eras 1 and 2, RF outperformed XGBoost and GBoost, whereas during era 3 no model dominated. Performance was better during eras 1 and 2 as opposed to the pre-L band era 3. Improvements across all eras, regions, and models realized from downscaling included an increase in correlation from 0.03 to 0.42 and a decrease in ubRMSE from −0.0005 to −0.0118 m³/m³. This study demonstrates the feasibility of SMERGE downscaling opening the prospect for the development of a long-term RZSM dataset at a more desirable field-scale resolution with the potential to support diverse hydrometeorological and agricultural applications. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

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26 pages, 13995 KiB

Open AccessEditor’s ChoiceArticle

Evaluation of C and X-Band Synthetic Aperture Radar Derivatives for Tracking Crop Phenological Development

by Marta Pasternak^† and Kamila Pawłuszek-Filipiak^*,†

¹ Institute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, 50-375 Wroclaw, Poland

^† These authors contributed equally to this work.

Remote Sens. 2023, 15(20), 4996; https://doi.org/10.3390/rs15204996 - 17 Oct 2023

Cited by 2 | Viewed by 2935

Abstract

Due to the expanding population and the constantly changing climate, food production is now considered a crucial concern. Although passive satellite remote sensing has already demonstrated its capabilities in accurate crop development monitoring, its limitations related to sunlight and cloud cover significantly restrict [...] Read more.

Due to the expanding population and the constantly changing climate, food production is now considered a crucial concern. Although passive satellite remote sensing has already demonstrated its capabilities in accurate crop development monitoring, its limitations related to sunlight and cloud cover significantly restrict real-time temporal monitoring resolution. Considering synthetic aperture radar (SAR) technology, which is independent of the Sun and clouds, SAR remote sensing can be a perfect alternative to passive remote sensing methods. However, a variety of SAR sensors and delivered SAR indices present different performances in such context for different vegetation species. Therefore, this work focuses on comparing various SAR-derived indices from C-band and (Sentinel-1) and X-band (TerraSAR-X) data with the in situ information (phenp; pgy development, vegetation height and soil moisture) in the context of tracking the phenological development of corn, winter wheat, rye, canola, and potato. For this purpose, backscattering coefficients in VV and VH polarizations

(σ_{V V}^{0}

,

σ_{V H}^{0}

), interferometric coherence, and the dual pol radar vegetation index (DpRVI) were calculated. To reduce noise in time series data and evaluate which filtering method presents a higher usability in SAR phenology tracking, signal filtering, such as Savitzky–Golay and moving average, with different parameters, were employed. The achieved results present that, for various plant species, different sensors (Sentinel-1 or TerraSAR-X) represent different performances. For instance,

σ_{V H}^{0}

of TerraSAR-X offered higher consistency with corn development (r = 0.81), while for canola

σ_{V H}^{0}

of Sentinel-1 offered higher performance (r = 0.88). Generally,

σ_{V V}^{0}

,

σ_{V H}^{0}

performed better than DpRVI or interferometric coherence. Time series filtering makes it possible to increase an agreement between phenology development and SAR-delivered indices; however, the Savitzky–Golay filtering method is more recommended. Besides phenological development, high correspondences can be found between vegetation height and some of SAR indices. Moreover, in some cases, moderate correlation was found between SAR indices and soil moisture. Full article

(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment, 2nd Edition)

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17 pages, 4711 KiB

Open AccessEditor’s ChoiceArticle

Evaluation and Comparison of ICESat-2 and GEDI Data for Terrain and Canopy Height Retrievals in Short-Stature Vegetation

by Xiaoxiao Zhu^1,2,3,4, Sheng Nie^4,5,*

, Yamin Zhu⁶, Yiming Chen⁷, Bo Yang⁷ and Wang Li²

¹ State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing 100029, China

² State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

³ Key Laboratory of Seismic and Volcanic Hazards, Institute of Geology, China Earthquake Administration, Beijing 100029, China

⁴ Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

⁵ International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China

⁶ Zhejiang Academy of Surveying and Mapping, Hangzhou 311100, China

⁷ China Forestry Group Corporation, Beijing 100036, China

Remote Sens. 2023, 15(20), 4969; https://doi.org/10.3390/rs15204969 - 15 Oct 2023

Cited by 6 | Viewed by 2902

Abstract

Two space-borne light detection and ranging (LiDAR) missions, Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), have demonstrated high capabilities in extracting terrain and canopy heights in forest environments. However, there have been limited studies evaluating their performance [...] Read more.

Two space-borne light detection and ranging (LiDAR) missions, Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), have demonstrated high capabilities in extracting terrain and canopy heights in forest environments. However, there have been limited studies evaluating their performance for terrain and canopy height retrievals in short-stature vegetation. This study utilizes airborne LiDAR data to validate and compare the accuracies of terrain and canopy height retrievals for short-stature vegetation using the latest versions of ICESat-2 (Version 5) and GEDI (Version 2). Furthermore, this study also analyzes the influence of various factors, such as vegetation type, terrain slope, canopy height, and canopy cover, on terrain and canopy height retrievals. The results indicate that ICESat-2 (bias = −0.05 m, RMSE = 0.67 m) outperforms GEDI (bias = 0.39 m, RMSE = 1.40 m) in terrain height extraction, with similar results observed for canopy height retrievals from both missions. Additionally, the findings reveal significant differences in terrain and canopy height retrieval accuracies between ICESat-2 and GEDI data under different data acquisition scenarios. Error analysis results demonstrate that terrain slope plays a pivotal role in influencing the accuracy of terrain height extraction for both missions, particularly for GEDI data, where the terrain height accuracy decreases significantly with increasing terrain slope. However, canopy height has the most substantial impact on the estimation accuracies of GEDI and ICESat-2 canopy heights. Overall, these findings confirm the strong potential of ICESat-2 data for terrain and canopy height retrievals in short-stature vegetation areas, and also provide valuable insights for future applications of space-borne LiDAR data in short-stature vegetation-dominated ecosystems. Full article

(This article belongs to the Special Issue LiDAR Remote Sensing for Forest Mapping)

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28 pages, 19592 KiB

Open AccessEditor’s ChoiceArticle

Evaluating the Uncertainty in Coherence-Change-Detection-Based Maps of Torrential Sediment Transport in Arid Environments

by Joan Botey i Bassols^1,2,3,*, Carmen Bedia¹

, María Cuevas-González⁴, Sonia Valdivielso¹, Michele Crosetto⁴

and Enric Vázquez-Suñé¹

¹ Institute of Environmental Assessment and Water Research (IDAEA), CSIC, C. Jordi Girona 18-26, 08034 Barcelona, Spain

² Department of Civil and Environmental Engineering (DECA), Universitat Politècnica de Catalunya, C. Jordi Girona 1-3, 08034 Barcelona, Spain

³ Building, Architecture and Town Planning (BATir) Department, Université Libre de Bruxelles (ULB), Av. F. Roosevelt 50, CPI 194/02, 1050 Brussels, Belgium

⁴ Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Geomatics Research Unit, Av. Gauss 7, 08860 Castelldefels, Spain

Remote Sens. 2023, 15(20), 4964; https://doi.org/10.3390/rs15204964 - 14 Oct 2023

Viewed by 1425

Abstract

InSAR coherence-change detection (CCD) is a promising remote sensing technique that is able to map areas affected by torrential sediment transport triggered by flash floods in arid environments. CCD maps the changes in the interferometric coherence between synthetic aperture radar images (InSAR coherence), [...] Read more.

InSAR coherence-change detection (CCD) is a promising remote sensing technique that is able to map areas affected by torrential sediment transport triggered by flash floods in arid environments. CCD maps the changes in the interferometric coherence between synthetic aperture radar images (InSAR coherence), a parameter that measures the stability of the radar signal between two different SAR images, i.e., data acquisitions. In arid environments, such changes are mainly due to changes in the surface. However, the residual effect of other factors on the InSAR coherence cannot be completely excluded. Therefore, CCD-based maps contain the uncertainty of whether the detected changes are actual changes in the observed surface or just errors related to those residual effects. Thus, in this paper, the results of four CCD mapping methods, with different degrees of complexity and sensitivity to the different factors affecting the InSAR coherence, are compared in order to evaluate the existence of the errors and their importance. The obtained CCD maps are also compared with changes in satellite optical images and a field campaign. The results lead to the conclusion that CCD maps are reliable in the identification of the zones affected by sediment transport, although the precision in the delimitation of the affected area remains an open issue. However, highly rugged relief areas still require a thorough analysis of the results in order to discard the geometric effects related to the perpendicular baseline. Full article

(This article belongs to the Special Issue Applications of SAR for Environment Observation Analysis)

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19 pages, 12278 KiB

Open AccessEditor’s ChoiceArticle

Multi-Scale Influence Analysis of Urban Shadow and Spatial Form Features on Urban Thermal Environment

by Liqun Lin^1,2, Yangyan Deng¹, Man Peng^3,*, Longxiang Zhen¹ and Shuwei Qin¹

¹ Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China

² Hubei Key Laboratory of Regional Development and Environmental Response, Wuhan 430062, China

³ The State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

Remote Sens. 2023, 15(20), 4902; https://doi.org/10.3390/rs15204902 - 10 Oct 2023

Cited by 5 | Viewed by 2120

Abstract

In urban thermal environment research (UTE), urban shadows formed by buildings and trees contribute to significant variations in thermal conditions, particularly during the mid-day period. This study investigated the multi-scale effects of indicators, including urban shadows, on UTE, focusing specifically on the mid-day [...] Read more.

In urban thermal environment research (UTE), urban shadows formed by buildings and trees contribute to significant variations in thermal conditions, particularly during the mid-day period. This study investigated the multi-scale effects of indicators, including urban shadows, on UTE, focusing specifically on the mid-day hours. It integrated field temperature measurements and drone aerial data from multiple city blocks. Considering both urban shadows and direct solar radiation, a linear mixed-effects model was employed to study the multi-scale effects of urban morphological indicators. Results showed that: (1) UTE is a multi-scale, multi-factor phenomenon, with thermal effects manifesting at specific scales. Under shadow conditions, smaller scales (10–20 m) of landscape heterogeneity and larger scales (300–400 m) of landscape consistency better explained temperature variations mid-day. Conversely, under direct sunlight, temperature was primarily influenced by larger scales (150–300 m). (2) Trees significantly reduced temperature; 100% tree canopy cover within a 10-m radius reduced air temperatures by approximately 2 °C mid-day. However, there is no significant correlation between temperature and green spaces. (3) Building area and height were significantly correlated with temperature. Specifically, an increase in building area beyond 150 m, especially within a 300-m radius, leads to higher temperatures. Conversely, building height within a 10–20 m range exhibits significant cooling effects. These findings provide crucial reference data for micro-scale UTE investigations during mid-day hours and offer new strategies for urban planning and design. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Interaction between Urbanization and Eco-Environment)

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23 pages, 39874 KiB

Open AccessEditor’s ChoiceArticle

Crop Water Productivity from Cloud-Based Landsat Helps Assess California’s Water Savings

by Daniel Foley^1,*

, Prasad Thenkabail¹

, Adam Oliphant¹

, Itiya Aneece¹

and Pardhasaradhi Teluguntla^1,2

¹ U.S. Geological Survey, 2255 N. Gemini Rd., Flagstaff, ZA 86001, USA

² Bay Area Environmental Research Institute, NASA Ames Research Park, Building 18 Room 101, Moffett Field, CA 94035, USA

Remote Sens. 2023, 15(19), 4894; https://doi.org/10.3390/rs15194894 - 9 Oct 2023

Cited by 1 | Viewed by 2594

Abstract

Demand for food and water are increasing while the extent of arable land and accessible fresh water are decreasing. This poses global challenges as economies continue to develop and the population grows. With agriculture as the leading consumer of water, better understanding how [...] Read more.

Demand for food and water are increasing while the extent of arable land and accessible fresh water are decreasing. This poses global challenges as economies continue to develop and the population grows. With agriculture as the leading consumer of water, better understanding how water is used to produce food may help support the increase of Crop Water Productivity (CWP; kg/m³), the ratio of crop output per unit of water input (or crop per drop). Previous large-scale CWP studies have been useful for broad water use modeling at coarser resolutions. However, obtaining more precise CWP, especially for specific crop types in a particular area and growing season as outlined here are important for informing farm-scale water management decision making. Therefore, this study focused on California’s Central Valley utilizing high-spatial resolution satellite imagery of 30 m (0.09 hectares per pixel) to generate more precise CWP for commonly grown and water-intensive irrigated crops. First, two products were modeled and mapped. 1. Landsat based Actual Evapotranspiration (ET_a; mm/d) to determine Crop Water Use (CWU; m³/m²), and 2. Crop Productivity (CP; kg/m²) to estimate crop yield per growing season. Then, CWP was calculated by dividing CP by CWU and mapped. The amount of water that can be saved by increasing CWP of each crop was further calculated. For example, in the 434 million m² study area, a 10% increase in CWP across the 9 crops analyzed had a potential water savings of 31.5 million m³ of water. An increase in CWP is widely considered the best approach for saving maximum quantities of water. This paper proposed, developed, and implemented a workflow of combined methods utilizing cloud computing based remote sensing data. The environmental implications of this work in assessing water savings for food and water security in the 21st century are expected to be significant. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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28 pages, 24166 KiB

Open AccessEditor’s ChoiceArticle

Semi-Supervised Learning Method for the Augmentation of an Incomplete Image-Based Inventory of Earthquake-Induced Soil Liquefaction Surface Effects

by Adel Asadi^1,*

, Laurie Gaskins Baise¹, Christina Sanon¹

, Magaly Koch²

, Snehamoy Chatterjee³

and Babak Moaveni¹

¹ Geohazards Research Lab, Civil and Environmental Engineering Department, School of Engineering, Tufts University, Medford, MA 02155, USA

² Center for Remote Sensing, Earth and Environment Department, Boston University, Boston, MA 02215, USA

³ Geological and Mining Engineering and Sciences Department, Michigan Technological University, Houghton, MI 49931, USA

Remote Sens. 2023, 15(19), 4883; https://doi.org/10.3390/rs15194883 - 9 Oct 2023

Cited by 5 | Viewed by 2338

Abstract

Soil liquefaction often occurs as a secondary hazard during earthquakes and can lead to significant structural and infrastructure damage. Liquefaction is most often documented through field reconnaissance and recorded as point locations. Complete liquefaction inventories across the impacted area are rare but valuable [...] Read more.

Soil liquefaction often occurs as a secondary hazard during earthquakes and can lead to significant structural and infrastructure damage. Liquefaction is most often documented through field reconnaissance and recorded as point locations. Complete liquefaction inventories across the impacted area are rare but valuable for developing empirical liquefaction prediction models. Remote sensing analysis can be used to rapidly produce the full spatial extent of liquefaction ejecta after an event to inform and supplement field investigations. Visually labeling liquefaction ejecta from remotely sensed imagery is time-consuming and prone to human error and inconsistency. This study uses a partially labeled liquefaction inventory created from visual annotations by experts and proposes a pixel-based approach to detecting unlabeled liquefaction using advanced machine learning and image processing techniques, and to generating an augmented inventory of liquefaction ejecta with high spatial completeness. The proposed methodology is applied to aerial imagery taken from the 2011 Christchurch earthquake and considers the available partial liquefaction labels as high-certainty liquefaction features. This study consists of two specific comparative analyses. (1) To tackle the limited availability of labeled data and their spatial incompleteness, a semi-supervised self-training classification via Linear Discriminant Analysis is presented, and the performance of the semi-supervised learning approach is compared with supervised learning classification. (2) A post-event aerial image with RGB (red-green-blue) channels is used to extract color transformation bands, statistical indices, texture components, and dimensionality reduction outputs, and performances of the classification model with different combinations of selected features from these four groups are compared. Building footprints are also used as the only non-imagery geospatial information to improve classification accuracy by masking out building roofs from the classification process. To prepare the multi-class labeled data, regions of interest (ROIs) were drawn to collect samples of seven land cover and land use classes. The labeled samples of liquefaction were also clustered into two groups (dark and light) using the Fuzzy C-Means clustering algorithm to split the liquefaction pixels into two classes. A comparison of the generated maps with fully and manually labeled liquefaction data showed that the proposed semi-supervised method performs best when selected high-ranked features of the two groups of statistical indices (gradient weight and sum of the band squares) and dimensionality reduction outputs (first and second principal components) are used. It also outperforms supervised learning and can better augment the liquefaction labels across the image in terms of spatial completeness. Full article

(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide II)

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19 pages, 11429 KiB

Open AccessEditor’s ChoiceArticle

A Laboratory for the Integration of Geomatic and Geomechanical Data: The Rock Pinnacle “Campanile di Val Montanaia”

by Luca Tavasci^1,*

, Alessandro Lambertini¹

, Davide Donati¹

, Valentina Alena Girelli¹

, Giovanni Lattanzi²

, Silvia Castellaro²

, Stefano Gandolfi¹

and Lisa Borgatti¹

¹ Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), Alma Mater Studiorum University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy

² Department of Physics and Astronomy (DIFA), Alma Mater Studiorum University of Bologna, Viale Berti Pichat 6/2, 40127 Bologna, Italy

Remote Sens. 2023, 15(19), 4854; https://doi.org/10.3390/rs15194854 - 7 Oct 2023

Cited by 2 | Viewed by 1672

Abstract

This work describes a procedure for building a high-quality 3D model of a rocky pinnacle in the Dolomites, Italy, using Structure from Motion (SfM) techniques. The pinnacle, known as “Campanile di Val Montanaia”, is challenging to survey due to its high elevation and [...] Read more.

This work describes a procedure for building a high-quality 3D model of a rocky pinnacle in the Dolomites, Italy, using Structure from Motion (SfM) techniques. The pinnacle, known as “Campanile di Val Montanaia”, is challenging to survey due to its high elevation and sub-vertical cliffs. The construction of the 3D model is the first step in a multi-disciplinary approach to characterize the rock mass and understand its behavior and evolution. This paper discusses the surveying operations, which involved climbing the pinnacle to collect Ground Control Points (GCPs) and using a UAV to capture aerial imagery. The photographs were processed using SfM software to generate point clouds, mesh, and texture, which were then used for rock mass discontinuity mapping. The study compares models of different qualities and point densities to determine the optimal trade-off between processing time and accuracy in terms of discontinuity mapping. The results show that higher quality models allow for more detailed mapping of discontinuities, with some drawbacks due to noise in the case of the densest solution (e.g., increase in frequency of outliers across the point cloud). These pros and cons are also discussed in relation to the computational cost necessary to build the models. The study also examines the limitations and challenges of performing discontinuity mapping in the different models, including subjectivity in interpretation. A further element of interest is the publication of a high-quality 3D georeferenced model of the “Campanile di Val Montanaia” to be used for several potential further applications, such as stability analyses and numerical modeling. Full article

(This article belongs to the Special Issue Geomatics and Natural Hazards)

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25 pages, 21548 KiB

Open AccessEditor’s ChoiceArticle

Impact of Atmospheric Correction on Classification and Quantification of Seagrass Density from WorldView-2 Imagery

by Victoria J. Hill^1,*

, Richard C. Zimmerman¹

, Paul Bissett², David Kohler³, Blake Schaeffer⁴, Megan Coffer^5,6

, Jiang Li⁷

and Kazi Aminul Islam⁸

¹ Department of Ocean and Earth Sciences, Old Dominion University, Norfolk, VA 23529, USA

² Eathon Intelligence LLC, 2210 US Hwy 301 S, Suite 100, Tampa, FL 33619, USA

³ Trimble, Inc., 10368 Westmoor Drive, Westminster, CO 80021, USA

⁴ Office of Research and Development, U.S. Environmental Protection Agency, Durham, NC 27709, USA

⁵ Global Science & Technology, Inc., Greenbelt, MD 20770, USA

⁶ NOAA/NESDIS Center for Satellite Applications and Research, College Park, MD 20740, USA

⁷ Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA

⁸ Department of Computer Science, Kennesaw State University, Marietta, GA 30060, USA

Remote Sens. 2023, 15(19), 4715; https://doi.org/10.3390/rs15194715 - 26 Sep 2023

Cited by 1 | Viewed by 1844

Abstract

Mapping the seagrass distribution and density in the underwater landscape can improve global Blue Carbon estimates. However, atmospheric absorption and scattering introduce errors in space-based sensors’ retrieval of sea surface reflectance, affecting seagrass presence, density, and above-ground carbon (AGC_seagrass) estimates. This [...] Read more.

Mapping the seagrass distribution and density in the underwater landscape can improve global Blue Carbon estimates. However, atmospheric absorption and scattering introduce errors in space-based sensors’ retrieval of sea surface reflectance, affecting seagrass presence, density, and above-ground carbon (AGC_seagrass) estimates. This study assessed atmospheric correction’s impact on mapping seagrass using WorldView-2 satellite imagery from Saint Joseph Bay, Saint George Sound, and Keaton Beach in Florida, USA. Coincident in situ measurements of water-leaving radiance (L_w), optical properties, and seagrass leaf area index (LAI) were collected. Seagrass classification and the retrieval of LAI were compared after empirical line height (ELH) and dark-object subtraction (DOS) methods were used for atmospheric correction. DOS left residual brightness in the blue and green bands but had minimal impact on the seagrass classification accuracy. However, the brighter reflectance values reduced LAI retrievals by up to 50% compared to ELH-corrected images and ground-based observations. This study offers a potential correction for LAI underestimation due to incomplete atmospheric correction, enhancing the retrieval of seagrass density and above-ground Blue Carbon from WorldView-2 imagery without in situ observations for accurate atmospheric interference correction. Full article

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23 pages, 8488 KiB

Open AccessEditor’s ChoiceArticle

Unexpected Expansion of Rare-Earth Element Mining Activities in the Myanmar–China Border Region

by Emmanuel Chinkaka^1,2,*

, Julie Michelle Klinger¹, Kyle Frankel Davis^1,3

and Federica Bianco^4,5,6

¹ Department of Geography and Spatial Sciences, University of Delaware, Newark, DE 19716, USA

² Department of Earth Sciences, Malawi University of Science and Technology, Limbe P.O. Box 5196, Malawi

³ Department of Plant and Soil Sciences, University of Delaware, Newark, DE 19716, USA

⁴ Department of Physics and Astronomy, University of Delaware, Newark, DE 19716, USA

⁵ Joseph R. Biden, Jr. School of Public Policy and Administration, University of Delaware, Newark, DE 19716, USA

⁶ Data Science Institute, University of Delaware, Newark, DE 19716, USA

Remote Sens. 2023, 15(18), 4597; https://doi.org/10.3390/rs15184597 - 19 Sep 2023

Cited by 3 | Viewed by 8060

Abstract

Mining for rare earth elements is rapidly increasing, driven by current and projected demands for information and energy technologies. Following China’s Central Government’s 2012 strategy to shift away from mining in favor of value-added processing, primary extraction has increased outside of China. Accordingly, [...] Read more.

Mining for rare earth elements is rapidly increasing, driven by current and projected demands for information and energy technologies. Following China’s Central Government’s 2012 strategy to shift away from mining in favor of value-added processing, primary extraction has increased outside of China. Accordingly, changes in mineral exploitation in China and Myanmar have garnered considerable attention in the past decade. The prevailing assumption is that mining in China has decreased while mining in Myanmar has increased, but the dynamic in border regions is more complex. Our empirical study used Google Earth Engine (GEE) to characterize changes in mining surface footprints between 2005 and 2020 in two rare earth mines located on either side of the Myanmar–China border, within Kachin State in northern Myanmar and Nujiang Prefecture in Yunnan Province in China. Our results show that the extent of the mining activities increased by 130% on China’s side and 327% on Myanmar’s side during the study period. We extracted surface reflectance images from 2005 and 2010 from Landsat 5 TM and 2015 and 2020 images from Landsat 8 OLI. The Normalized Vegetation Index (NDVI) was applied to dense time-series imagery to enhance landcover categories. Random Forest was used to categorize landcover into mine and non-mine classes with an overall accuracy of 98% and a Kappa Coefficient of 0.98, revealing an increase in mining extent of 2.56 km², covering the spatial mining footprint from 1.22 km² to 3.78 km² in 2005 and 2020, respectively, within the study area. We found a continuous decrease in non-mine cover, including vegetation. Both mines are located in areas important to ethnic minority groups, agrarian livelihoods, biodiversity conservation, and regional watersheds. The finding that mining surface areas increased on both sides of the border is significant because it shows that national-level generalizations do not align with local realities, particularly in socially and environmentally sensitive border regions. The quantification of such changes over time can help researchers and policymakers to better understand the shifting geographies and geopolitics of rare earth mining, the environmental dynamics in mining areas, and the particularities of mineral extraction in border regions. Full article

(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)

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16 pages, 3066 KiB

Open AccessEditor’s ChoiceTechnical Note

Mapping the National Seagrass Extent in Seychelles Using PlanetScope NICFI Data

by C. Benjamin Lee^1,*

, Lucy Martin^2,3

, Dimosthenis Traganos¹

, Sylvanna Antat⁴

, Stacy K. Baez⁵, Annabelle Cupidon⁶, Annike Faure⁷, Jérôme Harlay⁴, Matthew Morgan⁶, Jeanne A. Mortimer^6,8

, Peter Reinartz⁹

and Gwilym Rowlands²

¹ German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF), Rutherfordstr. 2, 12489 Berlin, Germany

² Department of Biology, University of Oxford, Oxford OX1 3RB, UK

³ Ecospan Environmental Ltd. (Part of Ocean Ecology), Unit 8 Strashleigh View, Lee Mill Industrial Estate, Lee Mill, Plymouth PL21 9GS, UK

⁴ Blue Economy Research Institute (BERI), University of Seychelles, Anse Royale, Victoria P.O. Box 1348, Mahé, Seychelles

⁵ The Pew Charitable Trusts, Washington, DC 20004, USA

⁶ Island Conservation Society (ICS), Pointe Larue, Mahé, Seychelles

⁷ The Seychelles Conservation and Climate Adaptation Trust (SeyCCAT), Room 109, Oceangate House, Flamboyant Avenue, Victoria, Mahé, Seychelles

⁸ Department of Biology, University of Florida, Gainesville FL 32601, USA

⁹ German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF), 82234 Wessling, Germany

Remote Sens. 2023, 15(18), 4500; https://doi.org/10.3390/rs15184500 - 13 Sep 2023

Cited by 3 | Viewed by 2956

Abstract

Seagrasses provide ecosystem services worth USD 2.28 trillion annually. However, their direct threats and our incomplete knowledge hamper our capabilities to protect and manage them. This study aims to evaluate if the NICFI Satellite Data Program basemaps could map Seychelles’ extensive seagrass meadows, [...] Read more.

Seagrasses provide ecosystem services worth USD 2.28 trillion annually. However, their direct threats and our incomplete knowledge hamper our capabilities to protect and manage them. This study aims to evaluate if the NICFI Satellite Data Program basemaps could map Seychelles’ extensive seagrass meadows, directly supporting the country’s ambitions to protect this ecosystem. The Seychelles archipelago was divided into three geographical regions. Half-yearly basemaps from 2015 to 2020 were combined using an interval mean of the 10th percentile and median before land and deep water masking. Additional features were produced using the Depth Invariant Index, Normalised Differences, and segmentation. With 80% of the reference data, an initial Random Forest followed by a variable importance analysis was performed. Only the top ten contributing features were retained for a second classification, which was validated with the remaining 20%. The best overall accuracies across the three regions ranged between 69.7% and 75.7%. The biggest challenges for the NICFI basemaps are its four-band spectral resolution and uncertainties owing to sampling bias. As part of a nationwide seagrass extent and blue carbon mapping project, the estimates herein will be combined with ancillary satellite data and contribute to a full national estimate in a near-future report. However, the numbers reported showcase the broader potential for using NICFI basemaps for seagrass mapping at scale. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Land-Sea Ecosystems)

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