Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Climate climate	3.0	5.5	2013	21.9 Days	CHF 1800
Drones drones	4.4	5.6	2017	21.7 Days	CHF 2600
Forests forests	2.4	4.4	2010	16.9 Days	CHF 2600
Land land	3.2	4.9	2012	17.8 Days	CHF 2600
Remote Sensing remotesensing	4.2	8.3	2009	24.7 Days	CHF 2700
ISPRS International Journal of Geo-Information ijgi	2.8	6.9	2012	36.2 Days	CHF 1700

18 pages, 4745 KiB

Open AccessArticle

The Link between Surface Visible Light Spectral Features and Water–Salt Transfer in Saline Soils—Investigation Based on Soil Column Laboratory Experiments

by Shaofeng Qin, Yong Zhang, Jianli Ding, Jinjie Wang, Lijing Han, Shuang Zhao and Chuanmei Zhu

Remote Sens. 2024, 16(18), 3421; https://doi.org/10.3390/rs16183421 - 14 Sep 2024

Viewed by 560

Abstract

Monitoring soil salinity with remote sensing is difficult, but knowing the link between saline soil surface spectra, soil water, and salt transport processes might help in modeling for soil salinity monitoring. In this study, we used an indoor soil column experiment, an unmanned [...] Read more.

Monitoring soil salinity with remote sensing is difficult, but knowing the link between saline soil surface spectra, soil water, and salt transport processes might help in modeling for soil salinity monitoring. In this study, we used an indoor soil column experiment, an unmanned aerial vehicle multispectral sensor camera, and a soil moisture sensor to study the water and salt transport process in the soil column under different water addition conditions and investigate the relationship between the soil water and salt transport process and the spectral reflectance of the image on the soil surface. The observation results of the soil column show that the soil water and salt transportation process conforms to the basic transportation law of “salt moves together with water, and when water evaporates, salt is retained in the soil weight”. The salt accumulation phenomenon increases the image spectral reflectance of the surface layer of the soil column, while soil temperature has no effect on the reflectance. As the water percolates down, water and salt accumulate at the bottom of the soil column. The salinity index decreases instantly after the addition of brine and then tends to increase slowly. The experimental results indicate that this work can capture the relationship between the water and salt transport process and remote sensing spectra, which can provide theoretical basis and reference for soil water salinity monitoring. Full article

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

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30 pages, 10837 KiB

Open AccessArticle

Twenty Years of Land Accounts in Europe

by Eva Ivits, Erika Orlitova, Roger Milego, Gergely Maucha, Barbara Kosztra, Emanuele Mancosu, Jaume Fons, Mirko Gregor, Manuel Löhnertz and Gerard Hazeu

Land 2024, 13(9), 1350; https://doi.org/10.3390/land13091350 - 24 Aug 2024

Viewed by 760

Abstract

Land use and its change impact food security, carbon cycling, biodiversity, and, hence, the condition of ecosystems to mitigate and adapt to climate change, support economic prosperity, and human well-being. To support and guide policy actions between the economy and the environment, harmonized [...] Read more.

Land use and its change impact food security, carbon cycling, biodiversity, and, hence, the condition of ecosystems to mitigate and adapt to climate change, support economic prosperity, and human well-being. To support and guide policy actions between the economy and the environment, harmonized time series datasets, transparent methodologies, and easily interpretable statistics are needed. Therefore, monitoring of the function and condition of lands and their change, along with properly agreed methodologies and freely accessible data, are essential. The Copernicus Land Monitoring Service has produced over 20 years of Corine Land Cover datasets for 39 countries in Europe, which allows continental-wide harmonized and comparable monitoring and accounting of land cover and land use change at a high thematic resolution and in a long time series (2000–2018). With the upcoming 2024 update, the time series will reach a unique product worldwide in terms of time series length, spatial resolution, extent, and thematic detail, enabling policymakers and the scientific community to address the main anthropogenic drivers of land and ecosystem degradation. This paper describes a unified approach for producing continental-wide land accounts that aligns with internationally agreed-upon standards for measuring the environment and its relationship with the economy. Furthermore, the study provides a harmonized time series of geospatial data for deriving land accounts and provides statistics of land cover and land use status and changes for a twenty-year period. All geospatial data and statistics presented in this paper are freely accessible and downloadable to serve other studies. Full article

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

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17 pages, 6323 KiB

Open AccessTechnical Note

Accelerated Atmospheric to Hydrological Spread of Drought in the Yangtze River Basin under Climate

by Chengyuan Zhang, Zhiming Han, Shuo Wang, Jiankun Wang, Chenfeng Cui and Junrong Liu

Remote Sens. 2024, 16(16), 3033; https://doi.org/10.3390/rs16163033 - 18 Aug 2024

Viewed by 980

Abstract

Persistent droughts pose a threat to agricultural production, and the changing environment worsens the risk of drought exposure. Understanding the propagation of drought in changing environments and assessing possible impact factors can help in the early detection of drought, guiding agricultural production practices. [...] Read more.

Persistent droughts pose a threat to agricultural production, and the changing environment worsens the risk of drought exposure. Understanding the propagation of drought in changing environments and assessing possible impact factors can help in the early detection of drought, guiding agricultural production practices. The current study cannot reflect the propagation status of drought to the total terrestrial hydrological drought, so this work creatively investigated the atmospheric to hydrological drought propagation time in the Yangtze River Basin under the dynamic and static perspectives based on the Standardized Precipitation Evapotranspiration Index and the Terrestrial Water Storage Anomalous Drought Index, fine-tuned the time scale to the seasonal scale, and explored the contributing capacity of the variable interactions. The results show that: (1) under the dynamic perspective, while the propagation time is decreasing in the annual scale, the spring season shows the opposite trend; and (2) large variability exists in the timing of drought propagation at spatial scales, with elevation playing the most important influential role, and bivariate interactions contributing stronger explanations compared to single variables. This study highlights the importance of considering the impact of variable interactions and contributes to our understanding of the response of secondary droughts to upper-level droughts, providing valuable insights into the propagation of droughts to total terrestrial hydrologic drought. Full article

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

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21 pages, 5176 KiB

Open AccessArticle

Combining Drone LiDAR and Virtual Reality Geovisualizations towards a Cartographic Approach to Visualize Flooding Scenarios

by Ermioni Eirini Papadopoulou and Apostolos Papakonstantinou

Drones 2024, 8(8), 398; https://doi.org/10.3390/drones8080398 - 15 Aug 2024

Viewed by 1353

Abstract

This study aims to create virtual reality (VR) geovisualizations using 3D point clouds obtained from airborne LiDAR technology. These visualizations were used to map the current state of river channels and tributaries in the Thessalian Plain, Greece, following severe flooding in the summer [...] Read more.

This study aims to create virtual reality (VR) geovisualizations using 3D point clouds obtained from airborne LiDAR technology. These visualizations were used to map the current state of river channels and tributaries in the Thessalian Plain, Greece, following severe flooding in the summer of 2023. The study area examined in this paper is the embankments enclosing the tributaries of the Pineios River in the Thessalian Plain region, specifically between the cities of Karditsa and Trikala in mainland Greece. This area was significantly affected in the summer of 2023 when flooding the region’s rivers destroyed urban elements and crops. The extent of the impact across the entire Thessalian Plain made managing the event highly challenging to the authorities. High-resolution 3D mapping and VR geovisualization of the embarkments encasing the main rivers and the tributaries of the Thessalian Plain essentially provides information for planning the area’s restoration processes and designing prevention and mitigation measures for similar disasters. The proposed methodology consists of four stages. The first and second stages of the methodology present the design of the data acquisition process with airborne LiDAR, aiming at the high-resolution 3D mapping of the sites. The third stage focuses on data processing, cloud point classification, and thematic information creation. The fourth stage is focused on developing the VR application. The VR application will allow users to immerse themselves in the study area, observe, and interact with the existing state of the embankments in high resolution. Additionally, users can interact with the 3D point cloud, where thematic information is displayed describing the classification of the 3D cloud, the altitude, and the RGB color. Additional thematic information in vector form, providing qualitative characteristics, is also illustrated in the virtual space. Furthermore, six different scenarios were visualized in the 3D space using a VR app. Visualizing these 3D scenarios using digital twins of the current antiflood infrastructure provides scenarios of floods at varying water levels. This study aims to explore the efficient visualization of thematic information in 3D virtual space. The goal is to provide an innovative VR tool for managing the impact on anthropogenic infrastructures, livestock, and the ecological capital of various scenarios of a catastrophic flood. Full article

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

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20 pages, 11614 KiB

Open AccessArticle

Spatio-Temporal Change and Drivers of the Vegetation Trends in Central Asia

by Moyan Li, Junqiang Yao and Jianghua Zheng

Forests 2024, 15(8), 1416; https://doi.org/10.3390/f15081416 - 13 Aug 2024

Viewed by 660

Abstract

The impact of changing climate on vegetation in dryland is a prominent focus of global research. As a typical arid region in the world, Central Asia is an ideal area for studying the associations between climate and arid-area vegetation. Utilizing data from the [...] Read more.

The impact of changing climate on vegetation in dryland is a prominent focus of global research. As a typical arid region in the world, Central Asia is an ideal area for studying the associations between climate and arid-area vegetation. Utilizing data from the European Centre for Medium-Range Weather Forecasts fifth-generation reanalysis (ECMWF ERA-5) and normalized difference vegetation index (NDVI) datasets, this study investigates the spatio-temporal variation characteristics of the NDVI in Central Asia. It quantitatively assesses the contribution rates of climatic factors to vegetation changes and elucidates the impact of an increased vapor pressure deficit (VPD) on vegetation changes in Central Asia. The results indicate that the growing seasons’ NDVI exhibited a substantial increase in Central Asia during 1982–2015. Specifically, there was a pronounced “greening” process (0.012/10 yr, p < 0.05) from 1982 to 1998. However, an insignificant “browning” trend was observed after 1998. Spatially, the vegetation NDVI in the growing seasons exhibited a pattern of “greening in the east and browning in the west” of Central Asia. During spring, the dominant theme was the “greening” of vegetation NDVI, although there was noticeable “browning” observed in southwest region of Central Asia. During summer, the “browning” of vegetation NDVI further expanded eastward and impacted the entire western Central Asia in autumn. According to the estimated results computed via the partial differential equation method, the “browning” trend of vegetation NDVI during the growing seasons was guided by increased VPD and decreased rainfall in western Central Asia. Specifically, the increased VPD contributed 52.3% to the observed vegetation NDVI. Atmospheric drought depicted by the increase in VPD significantly lowers the “greening” trend of vegetation NDVI in arid regions, which further aggravates the “browning” trend of vegetation NDVI. Full article

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

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15 pages, 5720 KiB

Open AccessArticle

Spatial Pattern of Forest Age in China Estimated by the Fusion of Multiscale Information

by Yixin Xu, Tao Zhou, Jingyu Zeng, Hui Luo, Yajie Zhang, Xia Liu, Qiaoyu Lin and Jingzhou Zhang

Forests 2024, 15(8), 1290; https://doi.org/10.3390/f15081290 - 24 Jul 2024

Viewed by 783

Abstract

Forest age is one of most important biological factors that determines the magnitude of vegetation carbon sequestration. A spatially explicit forest age dataset is crucial for forest carbon dynamics modeling at the regional scale. However, owing to the high spatial heterogeneity in forest [...] Read more.

Forest age is one of most important biological factors that determines the magnitude of vegetation carbon sequestration. A spatially explicit forest age dataset is crucial for forest carbon dynamics modeling at the regional scale. However, owing to the high spatial heterogeneity in forest age, accurate high-resolution forest age data are still lacking, which causes uncertainty in carbon sink potential prediction. In this study, we obtained a 1 km resolution forest map based on the fusion of multiscale age information, i.e., the ninth (2014–2018) forest inventory statistics of China, with high accuracy at the province scale, and a field-observed dataset covering 6779 sites, with high accuracy at the site scale. Specifically, we first constructed a random forest (RF) model based on field-observed data. Utilizing this model, we then generated a spatially explicit forest age map with a 1 km resolution (random forest age map, RF map) using remotely sensed data such as tree height, elevation, meteorology, and forest distribution. This was then used as the basis for downscaling the provincial-scale forest inventory statistics of the forest ages and retrieving constrained maps of forest age (forest inventory constrained age maps, FIC map), which exhibit high statistical accuracy at both the province scale and site scale. The main results included the following: (1) RF can be used to estimate the site-scale forest age accurately (R² = 0.89) and has the potential to predict the spatial pattern of forest age. However, (2) owing to the impacts of sampling error (e.g., field-observed sites are usually located in areas exhibiting relatively favorable environmental conditions) and the spatial mismatch among different datasets, the regional-scale forest age predicted by the RF model could be overestimated by 71.6%. (3) The results of the downscaling of the inventory statistics indicate that the average age of forests in China is 35.1 years (standard deviation of 21.9 years), with high spatial heterogeneity. Specifically, forests are older in mountainous and hilly areas, such as northeast, southwest, and northwest China, than in southern China. The spatially explicit dataset of the forest age retrieved in this study encompasses synthesized multiscale forest age information and is valuable for the research community in assessing the carbon sink potential and modeling carbon dynamics. Full article

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

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19 pages, 4458 KiB

Open AccessArticle

Construction of Ecological Security Patterns and Evaluation of Ecological Network Stability under Multi-Scenario Simulation: A Case Study in Desert–Oasis Area of the Yellow River Basin, China

by Wenhao Cheng, Caihong Ma, Tongsheng Li and Yuanyuan Liu

Land 2024, 13(7), 1037; https://doi.org/10.3390/land13071037 - 10 Jul 2024

Cited by 1 | Viewed by 735

Abstract

Land use change has a significant impact on the sustainability of ecosystems, and ecological security patterns (ESPs) can improve environmental quality through spatial planning. This study explored a multi-scenario ESP framework by integrating future land use simulation (FLUS) and minimum cumulative resistance (MCR) [...] Read more.

Land use change has a significant impact on the sustainability of ecosystems, and ecological security patterns (ESPs) can improve environmental quality through spatial planning. This study explored a multi-scenario ESP framework by integrating future land use simulation (FLUS) and minimum cumulative resistance (MCR) for urban agglomeration along the Yellow River Basin (YRB) in Ningxia. The research involved simulating land use change in 2035 under four development scenarios, identifying ecological security networks, and evaluating network stability for each scenario. The study revealed that the ecological sources under different development scenarios, including a natural development scenario (NDS), an economic development scenario (EDS), a food security scenario (FSS), and an ecological protection scenario (EPS), were 834.82 km², 715.46 km², 785.56 km², and 1091.43 km², respectively. The overall connectivity values (O_G) for these scenarios were 0.351, 0.466, 0.334, and 0.520, respectively. It was found that under an EPS, the ESPs had the largest area of ecological sources and the most stable ecological network structure, which can effectively protect natural habitats. This study provides a valuable method for identifying ESPs that can respond to diversity and the uncertainty of future development. It can assist decision-makers in enhancing the ecological quality of the study area while considering various development scenarios. Full article

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

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24 pages, 17475 KiB

Open AccessArticle

Spatio-Temporal Land-Use/Cover Change Dynamics Using Spatiotemporal Data Fusion Model and Google Earth Engine in Jilin Province, China

by Zhuxin Liu, Yang Han, Ruifei Zhu, Chunmei Qu, Peng Zhang, Yaping Xu, Jiani Zhang, Lijuan Zhuang, Feiyu Wang and Fang Huang

Land 2024, 13(7), 924; https://doi.org/10.3390/land13070924 - 25 Jun 2024

Viewed by 1037

Abstract

Jilin Province is located in the northeast of China, and has fragile ecosystems, and a vulnerable environment. Large-scale, long time series, high-precision land-use/cover change (LU/CC) data are important for spatial planning and environmental protection in areas with high surface heterogeneity. In this paper, [...] Read more.

Jilin Province is located in the northeast of China, and has fragile ecosystems, and a vulnerable environment. Large-scale, long time series, high-precision land-use/cover change (LU/CC) data are important for spatial planning and environmental protection in areas with high surface heterogeneity. In this paper, based on the high temporal and spatial fusion data of Landsat and MODIS and the Google Earth Engine (GEE), long time series LU/CC mapping and spatio-temporal analysis for the period 2000–2023 were realized using the random forest remote sensing image classification method, which integrates remote sensing indices. The prediction results using the OL-STARFM method were very close to the real images and better contained the spatial image information, allowing its application to the subsequent classification. The average overall accuracy and kappa coefficient of the random forest classification products obtained using the fused remote sensing index were 95.11% and 0.9394, respectively. During the study period, the area of cultivated land and unused land decreased as a whole. The area of grassland, forest, and water fluctuated, while building land increased to 13,442.27 km² in 2023. In terms of land transfer, cultivated land was the most important source of transfers, and the total area share decreased from 42.98% to 38.39%. Cultivated land was mainly transferred to grassland, forest land, and building land, with transfer areas of 7682.48 km², 8374.11 km², and 7244.52 km², respectively. Grassland was the largest source of land transfer into cultivated land, and the land transfer among other feature types was relatively small, at less than 3300 km². This study provides data support for the scientific management of land resources in Jilin Province, and the resulting LU/CC dataset is of great significance for regional sustainable development. Full article

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

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20 pages, 8093 KiB

Open AccessArticle

Analysis of Spatial and Temporal Pattern Evolution and Decoupling Relationships of Land Use Functions Based on Ecological Protection and High-Quality Development: A Case Study of the Yellow River Basin, China

by Hanwen Du, Zhanqi Wang, Haiyang Li and Chen Zhang

Land 2024, 13(6), 862; https://doi.org/10.3390/land13060862 - 15 Jun 2024

Viewed by 728

Abstract

With rapid industrialization and urbanization, the contradiction between the human exploitation of land production and living functions and natural ecosystem service functions has intensified. The issues of how to coordinate the exploitation and conservation functions of land and guide the rational distribution of [...] Read more.

With rapid industrialization and urbanization, the contradiction between the human exploitation of land production and living functions and natural ecosystem service functions has intensified. The issues of how to coordinate the exploitation and conservation functions of land and guide the rational distribution of human activities have become important for global sustainable development, especially considering the realization that multifunctional land use is an effective way to relieve land pressure and improve land use efficiency, that land multifunction has significant spatio-temporal heterogeneity, and that there is a mutual promotion and stress relationship between multifunctional land use. However, few existing studies have discussed the decoupling relationship among land use functions. In this study, a system of 10 sub-functions and 25 indicators was established based on the production function (PDF), living function (LVF), and ecological function (ELF) for 59 cities in the Yellow River Basin (YRB). There are both subjective and objective procedures employed to determine the weights, while an exploratory spatial data analysis is used to analyze the time-based and territorial changes in various functions of land use in the study area from 2000 to 2020. The decoupling relationship between the three functions is detected utilizing the theoretical foundation of the decoupling analysis. The results show that land use is multifunctional, LUFs develop unevenly, and their spatial distribution varies substantially. The results of the decoupling analysis demonstrate that the predominant types of correlations among the land use ELF and PDF and LVF over the research period are strong decoupling and strong negative decoupling correlations, with the former being a dilemma and the latter being a sustainable type of development. 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, 9680 KiB

Open AccessArticle

Spatio-Temporal Dynamics and Drivers of Ecosystem Service Bundles in the Altay Region: Implications for Sustainable Land Management

by Suyan Yi, Hongwei Wang, Ling Xie, Can Wang and Xin Huang

Land 2024, 13(6), 805; https://doi.org/10.3390/land13060805 - 6 Jun 2024

Cited by 1 | Viewed by 760

Abstract

Understanding the dynamics of ecosystem services (ESs) in arid landscapes and socio-ecological systems is crucial for sustainable development and human well-being. This study uses the Invest model to quantify the spatio-temporal changes in four key ecosystems services in Altay from 1990 to 2020: [...] Read more.

Understanding the dynamics of ecosystem services (ESs) in arid landscapes and socio-ecological systems is crucial for sustainable development and human well-being. This study uses the Invest model to quantify the spatio-temporal changes in four key ecosystems services in Altay from 1990 to 2020: water yield (water yield), carbon stock (carbon stock), soil retention (soil retention), and habitat quality (habitat quality). The trade-offs/synergies between different ESs were investigated via Spearman’s correlation analysis. Ecosystem service bundles (ESBs) were mapped using self-organizing mapping (SOM), and the key drivers of ES relationships and the spatio-temporal dynamics of ESBs were revealed through redundancy analysis. The results showed that water yield increased by 33.7% and soil retention increased by 1.2%, while carbon stock and habitat quality decreased by 3.5% and 1.24%, respectively. The spatial distribution pattern had a clear zonal pattern, with the northern mountainous areas higher than the southern desert areas. The six pairs of ESs, in general, showed mainly low trade-off and high synergistic relationships, with trade-offs between water yield and carbon stock, soil retention and habitat quality, and a decreasing trend of trade-offs over time. Four types of ESBs were distinguished, and the compositional differences and spatial distribution within each ESB were determined by interactions between ESs and landscape types. There are complex non-linear relationships between the drivers and the four ESBs in different years. Before 2010, ecological factors were the key drivers influencing the spatio-temporal changes in ESBs, whereas social and environmental factors combined to drive changes in ESB allocations after 2010. Additionally, this study found that the implementation of conservation measures, such as reforestation and sustainable land management practices, positively influenced the provision of ecosystem services in the Altay region. These findings underscore the importance of integrating conservation efforts into land use planning and decision-making processes to ensure the sustainable delivery of ecosystem services in arid landscapes. Full article

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

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21 pages, 6726 KiB

Open AccessArticle

Research on Rural Environments’ Effects on Well-Being: The Huizhou Area in China

by Xingmeng Ma, Xin Su, Yanlong Guo and Linfu Zhang

ISPRS Int. J. Geo-Inf. 2024, 13(6), 189; https://doi.org/10.3390/ijgi13060189 - 6 Jun 2024

Cited by 1 | Viewed by 1105

Abstract

The Huizhou region is an important area of traditional Chinese culture, and currently, the state of the village’s surroundings in this area is still not perfect. In this study, seven districts (counties) in the Huizhou region were selected for research. The Rural Habitat [...] Read more.

The Huizhou region is an important area of traditional Chinese culture, and currently, the state of the village’s surroundings in this area is still not perfect. In this study, seven districts (counties) in the Huizhou region were selected for research. The Rural Habitat Environment (RHES) Indicator Program is based on the concept of Socio-Economic-Natural Complex Ecosystems (SENCE) and constructs 18 metrics in three dimensions. Trends and influencing factors were analyzed using entropy weight TOPSIS and a Grey Relational Analysis (GRA) for the years 2013–2022, and spatial and temporal evolution was measured using Geographic Information Systems (GISs). The findings show that the composite index for the Huizhou region grew from 2013 (0.3197) to 2022 (0.6806). Second, the Tunxi District belongs to the high index–high economy category. The Shexian, Xiuning, and Qimen counties belong to the high index–low economy category. Huizhou District and Huangshan District belong to the low index–high economy category. Yixian County belongs to the low index–low economy category. Third, all districts (counties) show an upward trend, and Huangshan District has the best RHES condition. Shexian County ranks relatively low in the comprehensive index. Full article

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

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27 pages, 15877 KiB

Open AccessArticle

The Performance of Landsat-8 and Landsat-9 Data for Water Body Extraction Based on Various Water Indices: A Comparative Analysis

by Jie Chen, Yankun Wang, Jingzhe Wang, Yinghui Zhang, Yue Xu, Ou Yang, Rui Zhang, Jing Wang, Zhensheng Wang, Feidong Lu and Zhongwen Hu

Remote Sens. 2024, 16(11), 1984; https://doi.org/10.3390/rs16111984 - 31 May 2024

Cited by 2 | Viewed by 1296

Abstract

The rapid and accurate extraction of water information from satellite imagery has been a crucial topic in remote sensing applications and has important value in water resources management, water environment monitoring, and disaster emergency management. Although the OLI-2 sensor onboard Landsat-9 is similar [...] Read more.

The rapid and accurate extraction of water information from satellite imagery has been a crucial topic in remote sensing applications and has important value in water resources management, water environment monitoring, and disaster emergency management. Although the OLI-2 sensor onboard Landsat-9 is similar to the well-known OLI onboard Landsat-8, there were significant differences in the average absolute percentage change in the bands for water detection. Additionally, the performance of Landsat-9 in water body extraction is yet to be fully understood. Therefore, it is crucial to conduct comparative studies to evaluate the water extraction performance of Landsat-9 with Landsat-8. In this study, we analyze the performance of simultaneous Landsat-8 and Landsat-9 data for water body extraction based on eight common water indices (Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), Augmented Normalized Difference Water Index (ANDWI), Water Index 2015 (WI₂₀₁₅), tasseled cap wetness index (TCW), Automated Water Extraction Index for scenes with shadows (AWEI_sh) and without shadows (AWEI_nsh) and Multi-Band Water Index (MBWI)) to extract water bodies in seven study sites worldwide. The Otsu algorithm is utilized to automatically determine the optimal segmentation threshold for water body extraction. The results showed that (1) Landsat-9 satellite data can be used for water body extraction effectively, with results consistent with those from Landsat-8. The eight selected water indices in this study are applicable to both Landsat-8 and Landsat-9 satellites. (2) The NDWI index shows a larger variability in accuracy compared to other indices when used on Landsat-8 and Landsat-9 imagery. Therefore, additional caution should be exercised when using the NDWI for water body analysis with both Landsat-8 and Landsat-9 satellites simultaneously. (3) For Landsat-8 and Landsat-9 imagery, ratio-based water indices tend to have more omission errors, while difference-based indices are more prone to commission errors. Overall, ratio-based indices exhibit greater variability in overall accuracy, whereas difference-based indices demonstrate lower sensitivity to variations in the study area, showing smaller overall accuracy fluctuations and higher robustness. This study can provide necessary references for the selection of water indices based on the newest Landsat-9 data. The results are crucial for guiding the combined use of Landsat-8 and Landsat-9 for global surface water mapping and understanding its long-term changes. Full article

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

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13 pages, 2839 KiB

Open AccessArticle

Annual and Seasonal Dynamics of CO₂ Emissions in Major Cities of China (2019–2022)

by Yue Zhao, Yuning Feng, Mingyi Du and Klaus Fraedrich

ISPRS Int. J. Geo-Inf. 2024, 13(6), 181; https://doi.org/10.3390/ijgi13060181 - 29 May 2024

Cited by 1 | Viewed by 1016

Abstract

To control the growth of CO₂ emissions and achieve the goal of carbon peaking, this study carried out a detailed spatio-temporal analysis of carbon emissions in major cities of China on a city-wide and seasonal scale, used carbon emissions as an indicator [...] Read more.

To control the growth of CO₂ emissions and achieve the goal of carbon peaking, this study carried out a detailed spatio-temporal analysis of carbon emissions in major cities of China on a city-wide and seasonal scale, used carbon emissions as an indicator to explore the impact of COVID-19 on human activities, and thereby studied the urban resilience of different cities. Our research re-vealed that (i) the seasonal patterns of CO2 emissions in major cities of China could be divided into four types: Long High, Summer High, Winter High, and Fluctuations, which was highly related to the power and industrial sectors. (ii) The annual trends, which were strongly affected by the pan-demic, could be divided into four types: Little Impact, First Impact, Second Impact, and Both Impact. (iii) The recovery speed of CO2 emissions reflected urban resilience. Cities with higher levels of de-velopment had a stronger resistance to the pandemic, but a slower recovery speed. Studying the changes in CO2 emissions and their causes can help to make timely policy adjustments during the economic recovery period after the end of the pandemic, provide more references to urban resilience construction, and provide experience for future responses to large-scale emergencies. Full article

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

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22 pages, 14629 KiB

Open AccessArticle

Mechanism of Vegetation Greenness Change and Its Correlation with Terrestrial Water Storage in the Tarim River Basin

by Tingting Xia, Xuan Xue, Haowei Wang, Zhen Zhu, Zhi Li and Yang Wang

Land 2024, 13(5), 712; https://doi.org/10.3390/land13050712 - 18 May 2024

Viewed by 868

Abstract

The response of dryland vegetation to climate change is particularly sensitive in the context of global climate change. This paper analyzes the characteristics of spatial and temporal dynamics of vegetation cover in the Tarim River Basin, China, and its driving factors in order [...] Read more.

The response of dryland vegetation to climate change is particularly sensitive in the context of global climate change. This paper analyzes the characteristics of spatial and temporal dynamics of vegetation cover in the Tarim River Basin, China, and its driving factors in order to investigate the response of vegetation growth to water storage changes in the basin. The Enhanced Vegetation Index (EVI), the GRACE gravity satellite, and meteorological data from 2002 to 2022 are used to decipher the characteristics of the response of water storage changes to vegetation changes, which is of great significance to the realization of regional ecological development and sustainable development. The results of the study show the following: (1) The vegetation in the Tarim River Basin has an overall increasing trend, which is mainly distributed in the Aksu Basin and the Weigangkuche River Basin and is spatially distributed in the form of a ring. (2) Vegetation distribution greatly improved during the 20-year study period, dominated by high-cover vegetation, with a change rate of 200.36%. Additionally, vegetation changes are centered on the watersheds and expand to the surrounding area, with a clear increase in vegetation in the Kumukuri Basin. Areas with a vegetation Hurst index of <0.5 account for 63.27% of the study area, and the areas with a continuous decrease were mainly located in the outer contour area of the Tarim River and Kumu Kuri Basins. (3) There are obvious spatial differences in the correlation between EVI and temperature and precipitation elements. The proportion of areas with positive correlation with temperature within the study area is 64.67%. EVI tends to be consistent with the direction of migration of the center of gravity of the population and GDP, and the areas with positive correlation between vegetation and terrestrial water reserves are mainly distributed in the northern slopes of the Kunlun Mountains, with an area proportion of about 50.513%. The Kumukuli Basin also shows significantly positive correlation. 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, 4480 KiB

Open AccessArticle

Toward a More Robust Estimation of Forest Biomass Carbon Stock and Carbon Sink in Mountainous Region: A Case Study in Tibet, China

by Guanting Lyu, Xiaoyi Wang, Xieqin Huang, Jinfeng Xu, Siyu Li, Guishan Cui and Huabing Huang

Remote Sens. 2024, 16(9), 1481; https://doi.org/10.3390/rs16091481 - 23 Apr 2024

Cited by 1 | Viewed by 1437

Abstract

Mountainous forests are pivotal in the global carbon cycle, serving as substantial reservoirs and sinks of carbon. However, generating a reliable estimate remains a considerable challenge, primarily due to the lack of representative in situ measurements and proper methods capable of addressing their [...] Read more.

Mountainous forests are pivotal in the global carbon cycle, serving as substantial reservoirs and sinks of carbon. However, generating a reliable estimate remains a considerable challenge, primarily due to the lack of representative in situ measurements and proper methods capable of addressing their complex spatial variation. Here, we proposed a deep learning-based method that combines Residual convolutional neural networks (ResNet) with in situ measurements, microwave (Sentinel-1 and VOD), and optical data (Sentinel-2 and Landsat) to estimate forest biomass and track its change over the mountainous regions. Our approach, integrating in situ measurements across representative elevations with multi-source remote sensing images, significantly improves the accuracy of biomass estimation in Tibet’s complex mountainous forests (R² = 0.80, root mean squared error = 15.8 MgC ha⁻¹). Moreover, ResNet, which addresses the vanishing gradient problem in deep neural networks by introducing skip connections, enables the extraction of complex spatial patterns from limited datasets, outperforming traditional optical-based or pixel-based methods. The mean value of forest biomass was estimated as 162.8 ± 21.3 MgC ha⁻¹, notably higher than that of forests at comparable latitudes or flat regions in China. Additionally, our findings revealed a substantial forest biomass carbon sink of 3.35 TgC year⁻¹ during 2015–2020, which is largely underestimated by previous estimates, mainly due to the underestimation of mountainous carbon stock. The significant carbon density, combined with the underestimated carbon sink in mountainous regions, emphasizes the urgent need to reassess mountain forests to better approximate the global carbon budget. Full article

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

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23 pages, 10293 KiB

Open AccessArticle

Spatial-Temporal Evolution Characteristics and Driving Force Analysis of NDVI in Hubei Province, China, from 2000 to 2022

by Peng Chen, Hongzhong Pan, Yaohui Xu, Wenxiang He and Huaming Yao

Forests 2024, 15(4), 719; https://doi.org/10.3390/f15040719 - 19 Apr 2024

Cited by 1 | Viewed by 1324

Abstract

Exploring the characteristics of vegetation dynamics and quantitatively analyzing the potential drivers and the strength of their interactions are of great significance to regional ecological environmental protection and sustainable development. Therefore, based on the 2000–2022 MODIS NDVI dataset, supplemented by climatic, topographic, surface [...] Read more.

Exploring the characteristics of vegetation dynamics and quantitatively analyzing the potential drivers and the strength of their interactions are of great significance to regional ecological environmental protection and sustainable development. Therefore, based on the 2000–2022 MODIS NDVI dataset, supplemented by climatic, topographic, surface cover, and anthropogenic data for the same period, the Sen+Mann–Kendall trend analysis, coefficient of variation, and Hurst exponent were employed to examine the spatial and temporal characteristics and trends of NDVI in Hubei Province, and a partial correlation analysis and geographical detector were used to explore the strength of the influence of driving factors on the spatial differentiation of NDVI in vegetation and the underlying mechanisms of interaction. The results showed that (1) the mean NDVI value of vegetation in Hubei Province was 0.762 over 23 years, with an overall increasing trend and fluctuating upward at a rate of 0.01/10a (p < 0.005); geospatially, there is a pattern of “low east and high west”; the spatial change in NDVI shows a trend of “large-scale improvement in the surrounding hills and mountains and small-scale degradation in the middle plains”; it also presents the spatial fluctuation characteristics of “uniform distribution in general, an obvious difference between urban and rural areas, and a high fluctuation of rivers and reservoirs”, (2) the future trend of NDVI in 70.76% of the region in Hubei Province is likely to maintain the same trend as that of the 2000–2022 period, with 70.78% of the future development being benign and dominated by sustained improvement, and (3) a combination of partial correlation analysis and geographical detector analysis of the drivers of vegetation NDVI change shows that land cover type and soil type are the main drivers; the interactions affecting the distribution and change characteristics of NDVI vegetation all showed two-factor enhancement or nonlinear enhancement relationships. This study contributes to a better understanding of the change mechanisms in vegetation NDVI in Hubei Province, providing support for differentiated ecological protection and project implementation. Full article

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

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15 pages, 7440 KiB

Open AccessArticle

Characteristics of Foehn Wind in Urumqi, China, and Their Relationship with EI Niño and Extreme Heat Events in the Last 15 Years

by Maoling Ayitikan, Xia Li, Yusufu Musha, Qing He, Shuting Li, Yuting Zhong and Kai Cheng

Climate 2024, 12(4), 56; https://doi.org/10.3390/cli12040056 - 19 Apr 2024

Viewed by 1387

Abstract

Dry and hot Foehn wind weather often occurs in Urumqi, China, due to its canyon terrain. This directly impacts the lives and health of local people. Using surface meteorological variables (including the hourly wind, temperature, humidity, and pressure) measured in situ at the [...] Read more.

Dry and hot Foehn wind weather often occurs in Urumqi, China, due to its canyon terrain. This directly impacts the lives and health of local people. Using surface meteorological variables (including the hourly wind, temperature, humidity, and pressure) measured in situ at the Urumqi Meteorological Station and ERA5 reanalysis from the European Centre for Medium-Range Weather Forecasts in the past 15 years (2008–2022), the characteristics of Foehn wind and their relationship with EI Niño and extreme high-temperature events in Urumqi are analyzed. The results show that the annual distributions of Foehn wind present a fluctuating pattern, and the highest frequency occurred in 2015. Compared to the summer (July) and winter (February) seasons, Foehn wind occurs most frequently in spring (March, April, May) and autumn (September, October, and November). Daily variations in Foehn wind occur most frequently from 9:00 a.m. to 14:00 p.m. In particular, high levels are found at 10:00 a.m. and 11:00 a.m. in April and May. In 2011, 2012, and 2014, the average wind speed of FW exceeded 6 m/s, and the lowest average wind speed was 3.8 m/s in 2021. The temperature and relative humidity changes (ΔT and ΔRH) caused by Foehn wind are the most significant in winter and when Foehn wind begins to occur. The high-temperature hours related to Foehn wind weather in Urumqi represented 25% of the total in the past 15 years. During the EI Niño period, the amount of Foehn wind in Urumqi significantly increased; The correlation coefficient beteewn slide anomaly of Foehn days and the Oceanic Niño Index is as high as 0.71. Specifically, Foehn wind activity aggravates extreme high-temperature events. This study provides indications for Foehn wind weather forecasting in Urumqi. Full article

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

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18 pages, 8566 KiB

Open AccessArticle

The Impact of Urbanization on Spatial–Temporal Variation in Vegetation Phenology: A Case Study of the Yangtze River Delta, China

by Enyan Zhu, Dan Fang, Lisu Chen, Youyou Qu and Tao Liu

Remote Sens. 2024, 16(5), 914; https://doi.org/10.3390/rs16050914 - 5 Mar 2024

Cited by 2 | Viewed by 1235

Abstract

The response of vegetation phenology to urbanization has become a growing concern. As impervious surfaces change as urbanization advances, the variation in vegetation phenology at the dynamic urbanization level was analyzed to significantly quantify the impact of urbanization processes on vegetation phenology. Based [...] Read more.

The response of vegetation phenology to urbanization has become a growing concern. As impervious surfaces change as urbanization advances, the variation in vegetation phenology at the dynamic urbanization level was analyzed to significantly quantify the impact of urbanization processes on vegetation phenology. Based on the MOD13Q1 vegetation index product from 2001 to 2020, vegetation phenology parameters, including the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (GSL), were extracted, and the spatial–temporal variation in vegetation phenology, as well as its response to urbanization, was comprehensively analyzed. The results reveal that (1) from 2001 to 2020, the average rates of change for the SOS, EOS, and GSL were 0.41, 0.16, and 0.57 days, respectively. (2) The vegetation phenology changes showed significant spatial–temporal differences at the urbanization level. With each 10% increase in the urbanization level, the SOS and EOS were advanced and delayed by 0.38 and 0.34 days, respectively. (3) The urban thermal environment was a major factor in the impact of urbanization on the SOS and EOS. Overall, this study elucidated the dynamic reflection of urbanization in phenology and revealed the complex effects of urbanization on vegetation phenology, thus helping policymakers to develop effective strategies to improve urban ecological management. Full article

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

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19 pages, 10631 KiB

Open AccessArticle

Improving the Detection Effect of Long-Baseline Lightning Location Networks Using PCA and Waveform Cross-Correlation Methods

by Ting Zhang, Jiaquan Wang, Qiming Ma and Liping Fu

Remote Sens. 2024, 16(5), 885; https://doi.org/10.3390/rs16050885 - 2 Mar 2024

Cited by 1 | Viewed by 1089

Abstract

Ultra-long-distance and high-precision lightning location technology is an important means to realize low-cost and wide-area lightning detection. This paper carried out research on the high-precision location technology of very-low-frequency (VLF) lightning electromagnetic pulse based on the Asia-Pacific Lightning Location Network (APLLN) deployed in [...] Read more.

Ultra-long-distance and high-precision lightning location technology is an important means to realize low-cost and wide-area lightning detection. This paper carried out research on the high-precision location technology of very-low-frequency (VLF) lightning electromagnetic pulse based on the Asia-Pacific Lightning Location Network (APLLN) deployed in 2018. Two key technologies are proposed in this paper: one is the calculation method of signal arrival time using very-low-frequency lightning electromagnetic pulse waveform, and the other is the compression transmission technology of lightning electromagnetic pulse waveform based on a signal principal component analysis. The results of a comparison and evaluation of the improved APLLN with the ADTD system show that the APLLN has a relative location efficiency of 69.1% and an average location error within the network of 4.5 km. Full article

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

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17 pages, 20307 KiB

Open AccessArticle

Analysis of Changes in Ecological Environment Quality and Influencing Factors in Chongqing Based on a Remote-Sensing Ecological Index Mode

by Yizhuo Liu, Tinggang Zhou and Wenping Yu

Land 2024, 13(2), 227; https://doi.org/10.3390/land13020227 - 12 Feb 2024

Cited by 5 | Viewed by 1790

Abstract

Chongqing is a large municipality in southwestern China, having the characteristics of a vast jurisdiction, complex topography, and a prominent dual urban–rural structure. It is vitally important to optimize the spatial layout of land and efficiency of natural resource allocation, achieve sustainable development, [...] Read more.

Chongqing is a large municipality in southwestern China, having the characteristics of a vast jurisdiction, complex topography, and a prominent dual urban–rural structure. It is vitally important to optimize the spatial layout of land and efficiency of natural resource allocation, achieve sustainable development, and conduct influence assessment and causation analysis in this region. Here, using the Google Earth Engine platform, we selected Landsat remote-sensing (RS) images from the period 2000–2020 and constructed a remote-sensing ecological index (RSEI) model. Considering the urban spatial pattern division in Chongqing, the Sen + Mann–Kendall analytical approach was employed to assess the fluctuating quality of the ecological environment in different sectors of Chongqing. Subsequently, single-factor and interaction detectors in the Geodetector software tool were used to conduct causation analysis on the RSEI, with the use of eight elements: elevation, slope, aspect, precipitation, temperature, population, land use, and nighttime lighting. Findings indicate that, over the course of the investigation period, the eco-quality in Chongqing displayed a pattern of degradation, succeeded by amelioration. The RSEI decreased from 0.700 in 2000 to 0.590 in 2007, and then gradually recovered to 0.716 in 2018. Overall, the eco-environment quality of Chongqing improved. Spatially, changes in the RSEI were consistent with the planning and positioning of the urban spatial pattern. The main new urban area and periphery of the central urban area showed a slight deterioration, while other regions showed marked improvement. The combined effect of any two elements enhanced the explanatory power of a single factor, with elevation, temperature, and land use being the strongest explanatory elements of eco-quality in Chongqing. The most influential factor explaining the spatial variation of the RSEI was determined to be the combined impact of elevation and land use. At the temporal scale, elements related to human activities showed the most evident trend in explanatory power. 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, 2249 KiB

Open AccessArticle

Monitoring Soil Salinity Classes through Remote Sensing-Based Ensemble Learning Concept: Considering Scale Effects

by Huifang Chen, Jingwei Wu and Chi Xu

Remote Sens. 2024, 16(4), 642; https://doi.org/10.3390/rs16040642 - 9 Feb 2024

Viewed by 1884

Abstract

Remote sensing (RS) technology can rapidly obtain spatial distribution information on soil salinization. However, (1) the scale effects resulting from the mismatch between ground-based “point” salinity data and remote sensing pixel-based “spatial” data often limit the accuracy of remote sensing monitoring of soil [...] Read more.

Remote sensing (RS) technology can rapidly obtain spatial distribution information on soil salinization. However, (1) the scale effects resulting from the mismatch between ground-based “point” salinity data and remote sensing pixel-based “spatial” data often limit the accuracy of remote sensing monitoring of soil salinity, and (2) the same salinity RS monitoring model usually provides inconsistent or sometimes conflicting explanations for different data. Therefore, based on Landsat 8 imagery and synchronously collected ground-sampling data of two typical study regions (denoted as N and S, respectively) of the Yichang Irrigation Area in the Hetao Irrigation District for May 2013, this study used geostatistical methods to obtain “relative truth values” of salinity corresponding to the Landsat 8 pixel scale. Additionally, based on Landsat 8 multispectral data, 14 salinity indices were constructed. Subsequently, the Correlation-based Feature Selection (CFS) method was used to select sensitive features, and a strategy similar to the concept of ensemble learning (EL) was adopted to integrate the single-feature-sensitive Bayesian classification (BC) model in order to construct an RS monitoring model for soil salinization (Nonsaline, Slightly saline, Moderately saline, Strongly saline, and Solonchak). The research results indicated that (1) soil salinity exhibits moderate to strong variability within a 30 m scale, and the spatial heterogeneity of soil salinity needs to be considered when developing remote sensing models; (2) the theoretical models of salinity variance functions in the N and S regions conform to the exponential model and the spherical model, with

R^{2}

values of 0.817 and 0.967, respectively, indicating a good fit for the variance characteristics of salinity and suitability for Kriging interpolation; and (3) compared to a single-feature BC model, the soil salinization identification model constructed using the concept of EL demonstrated better potential for robustness and effectiveness. Full article

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

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20 pages, 14108 KiB

Open AccessArticle

Machine-Learning-Assisted Characterization of Regional Heat Islands with a Spatial Extent Larger than the Urban Size

by Yin Du, Zhiqing Xie, Lingling Zhang, Ning Wang, Min Wang and Jingwen Hu

Remote Sens. 2024, 16(3), 599; https://doi.org/10.3390/rs16030599 - 5 Feb 2024

Cited by 2 | Viewed by 1910

Abstract

Surface urban heat islands (SUHIs) can extend beyond the urban boundaries and greatly affect the thermal environment of continuous regions over an agglomeration. Traditional urban-rural dichotomy depending on the built-up and non-urban lands is challenged in characterizing regional SUHIs, such as how to [...] Read more.

Surface urban heat islands (SUHIs) can extend beyond the urban boundaries and greatly affect the thermal environment of continuous regions over an agglomeration. Traditional urban-rural dichotomy depending on the built-up and non-urban lands is challenged in characterizing regional SUHIs, such as how to accurately quantify the intensity, spatial pattern, and scales of SUHIs, which are vulnerable to SUHIs, and what the optimal scale for conducting measures to mitigate the SUHIs. We propose a machine-learning-assisted solution to address these problems based on the thermal similarity in the Yangtze River Delta of China. We first identified the regional-level SUHI zone of approximately 42,328 km² and 38,884 km² and the areas that have no SUHI effects from the annual cycle of land surface temperatures (LSTs) retrieved from Terra and Aqua satellites. Defining SUHI as an anomaly on background condition, random forest (RF) models were further adopted to fit the LSTs in the areas without the SUHI effects and estimate the LST background and SUHI intensity at each grid point in the SUHI zone. The RF models performed well in fitting rural LSTs with a simulation error of approximately 0.31 °C/0.44 °C for Terra/Aqua satellite data and showed a good generalization ability in estimating the urban LST background. The RF-estimated daytime Aqua/SUHI intensity peaked at approximately 6.20 °C in August, and the Terra/SUHI intensity had two peaks of approximately 3.18 and 3.81 °C in May and August, with summertime RF-estimated SUHIs being more reliable than other SUHI types owing to the smaller simulation error of less than 1.0 °C in July–September. This machine-learning-assisted solution identified an optimal SUHI scale of 30,636 km² and a zone of approximately 23,631 km² that is vulnerable to SUHIs, and it provided the SUHI intensity and statistical reliability for each grid point identified as being part of the SUHI. Urban planners and decision-makers can focus on the statistically reliable RF-estimated summertime intensities in SUHI zones that have an LST annual cycle similar to that of large cities in developing effective strategies for mitigating adverse SUHI effects. In addition, the selection of large cities might strongly affect the accuracy of identifying the SUHI zone, which is defined as the areas that have an LST annual cycle similar to large cities. Water bodies might reduce the RF performance in estimating the LST background over urban agglomerations. Full article

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

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21 pages, 7054 KiB

Open AccessArticle

Analysis and Prediction of Spatial and Temporal Evolution of Ecosystem Service Value on the Northern Slopes of the Kunlun Mountains Based on Land Use

by Zhichao Zhang, Yang Wang, Haisheng Tang and Zhen Zhu

Land 2023, 12(12), 2123; https://doi.org/10.3390/land12122123 - 30 Nov 2023

Viewed by 1094

Abstract

The ecological environment in the mountainous areas of southern Xinjiang is very sensitive and fragile, and identifying the ecological asset retention within the mountainous areas is a top priority at the current stage in the context of comprehensive environmental management in arid zones. [...] Read more.

The ecological environment in the mountainous areas of southern Xinjiang is very sensitive and fragile, and identifying the ecological asset retention within the mountainous areas is a top priority at the current stage in the context of comprehensive environmental management in arid zones. This study examines the conversion and ecosystem service values between different land types within the mountainous areas based on a time series of land-use data from 1990 to 2020, and the results show that: (1) The value of ecosystem services on the northern slopes of the Kunlun Mountains shows an overall increasing trend. It increased from CNY 308.645 billion in 1990 to CNY 326.550 billion in 2020. Among them, the value of ecosystem services increased significantly between 2000 and 2010, with an increase of CNY 39.857 billion. Regulatory services accounted for more than 66% of the value of each ecosystem service. (2) Land use on the northern slopes of the Kunlun Mountains has changed significantly since 1990. The areas of cropland, forest land, grassland, watershed, and construction land have all shown an upward trend, with the greatest increase in construction land. The area of unutilized land, on the other hand, has slightly decreased. (3) The value of ecosystem services within the northern slopes of the Kunlun Mountains was spatially high in the south, low in the north, and higher in the west than in the east. The study also found a significant positive spatial correlation between ecosystem service values. In the spatial distribution, the increasing areas were mainly distributed in the southeast, and the decreasing areas were in the north. Changes in land types are expected to include an increase in the area of grassland and woodland, a decrease in unutilized land and cropland, and an overall improvement in the ecological environment of the northern slopes of the Kunlun Mountains in the next decade. This study also provides lessons and references for sustainable development and ecological protection in ecologically fragile regions. Full article

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

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25 pages, 6814 KiB

Open AccessArticle

Assessing the Effects of Subjective and Objective Measures on Housing Prices with Street View Imagery: A Case Study of Suzhou

by Jin Zhu, Yao Gong, Changchang Liu, Jinglong Du, Ci Song, Jie Chen and Tao Pei

Land 2023, 12(12), 2095; https://doi.org/10.3390/land12122095 - 22 Nov 2023

Cited by 4 | Viewed by 1668

Abstract

The price of a house is affected by both the subjective and objective factors of the street environment in a neighborhood. However, the relationships between these factors and housing prices are not fully understood. Street view imagery (SVI) has recently emerged as a [...] Read more.

The price of a house is affected by both the subjective and objective factors of the street environment in a neighborhood. However, the relationships between these factors and housing prices are not fully understood. Street view imagery (SVI) has recently emerged as a new data source for housing price studies. The SVI contains both objective and subjective information and can be used to extract objective measurements describing the physical environment and subjective measurements depicting human perceptions. Compared to conventional methods, there is consistency between subjective and objective information extracted from SVIs, and the two types of information are acquired from the perspective of the human visual perceptual system. Therefore, using both objective and subjective information extracted from street view images to study their relationship with housing prices has several advantages. In this study, focusing on the city of Suzhou, China, we extracted subjective perception and objective view indices from SVIs and systematically assessed their effects on housing prices. The global ordinary least squares (OLS) regression model and the local geographically weighted regression (GWR) model were used to model the correlations between these measures and housing prices. The OLS reveals that overall objective measures have stronger explanatory power, and built environment factors have a greater impact on housing prices. GWR shows that subjective factors can explain more variance in housing prices on the local scale and that home buyers care more about the subjective perceptions of the neighborhood’s surroundings. The map of the GWR local coefficients demonstrates that the perception indicators have both positive and negative effects on housing prices in different places. In addition, a Monte Carlo test was performed to verify the spatially varying relationships between these measures. Our findings provide important references for urban designers and guide various applications, such as safe neighborhood design and sustainable city planning. 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, 10830 KiB

Open AccessTechnical Note

Dynamics of Spring Snow Cover Variability over Northeast China

by Taotao Zhang and Xiaoyi Wang

Remote Sens. 2023, 15(22), 5330; https://doi.org/10.3390/rs15225330 - 12 Nov 2023

Cited by 1 | Viewed by 1207

Abstract

Spring snow cover variability over Northeast China (NEC) has a profound influence on the local grain yield and even the food security of the country, but its drivers remain unclear. In the present study, we investigated the spatiotemporal features and the underlying mechanisms [...] Read more.

Spring snow cover variability over Northeast China (NEC) has a profound influence on the local grain yield and even the food security of the country, but its drivers remain unclear. In the present study, we investigated the spatiotemporal features and the underlying mechanisms of spring snow cover variability over NEC during 1983–2018 based on the satellite-derived snow cover data and atmospheric reanalysis products. The empirical orthogonal function (EOF) analysis showed that the first EOF mode (EOF1) explains about 50% of the total variances and characterizes a coherent snow cover variability pattern over NEC. Further analyses suggested that the formation of the EOF1 mode is jointly affected by the atmospheric internal variability and the sea surface temperature (SST) anomaly at the interannual timescale. Specifically, following a negative phase of the atmospheric teleconnection of the Polar–Eurasian pattern, a prominent cyclonic circulation appears over NEC, which increases the snowfall over the east of NEC by enhancing the water vapor transport and decreases the air temperature through reducing the solar radiation and intensifying the cold advection. As a result, the snow cover has increased over NEC. Additionally, the tripole structure of the North Atlantic spring SST anomaly could excite a wave-train-type anomalous circulation propagating to NEC that further regulates the snow cover variability by altering the atmospheric dynamic and thermodynamic conditions and the resultant air temperature and snowfall. Our results have important implications on the understanding of the spring snow cover anomaly over NEC and the formulation of the local agricultural production plan. Full article

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

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19 pages, 26073 KiB

Open AccessArticle

Influence of Climate, Topography, and Hydrology on Vegetation Distribution Patterns—Oasis in the Taklamakan Desert Hinterland

by Lei Peng, Yanbo Wan, Haobo Shi, Abudureyimu Anwaier and Qingdong Shi

Remote Sens. 2023, 15(22), 5299; https://doi.org/10.3390/rs15225299 - 9 Nov 2023

Cited by 2 | Viewed by 2269

Abstract

Vegetation in natural desert hinterland oases is an important component of terrestrial ecosystems. Determining how desert vegetation responds to natural variability is critical for a better understanding of desertification processes and their future development. The aim of this study is to characterize the [...] Read more.

Vegetation in natural desert hinterland oases is an important component of terrestrial ecosystems. Determining how desert vegetation responds to natural variability is critical for a better understanding of desertification processes and their future development. The aim of this study is to characterize the spatial distribution of vegetation in the natural desert hinterland and to reveal how different environmental factors affect vegetation changes. Taking a Taklamakan Desert hinterland oasis as our research object, we analyzed the effects of different environmental factors on desert vegetation using a time-series normalized difference vegetation index (NDVI) combined with meteorological, topographic, and hydrological data, including surface water and groundwater data. Vegetation was distributed in areas with high surface water frequency, shallow groundwater levels, relatively flat terrain, and dune basins. NDVI datasets show greening trends in oasis areas over the past 20 years. The frequency of surface water distribution influences water accessibility and effectiveness and shapes topography, thus affecting the spatial distribution pattern of vegetation. In this study, areas of high surface water frequency corresponded with vegetation distribution. The spatial distribution of groundwater depth supports the growth and development of vegetation, impacting the pattern of vegetation growth conditions. Vegetation is most widely distributed in areas where the groundwater burial depth is 3.5–4.5 m. This study provides data for restoring riparian vegetation, ecological water transfer, and sustainable development. Full article

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

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17 pages, 2374 KiB

Open AccessArticle

Production–Living–Ecological Spatial Function Identification and Pattern Analysis Based on Multi-Source Geographic Data and Machine Learning

by Ziqiang Bu, Jingying Fu, Dong Jiang and Gang Lin

Land 2023, 12(11), 2029; https://doi.org/10.3390/land12112029 - 7 Nov 2023

Cited by 3 | Viewed by 1426

Abstract

Land use cannot be simply understood as land cover. The same land may carry different functions, such as production, living, and ecological applications; the dominant function of land will affect and restrict other uses. Disorderly urbanization and industrialization have led to an intensification [...] Read more.

Land use cannot be simply understood as land cover. The same land may carry different functions, such as production, living, and ecological applications; the dominant function of land will affect and restrict other uses. Disorderly urbanization and industrialization have led to an intensification of conflicts among the production, living, and ecological functions of land, which is a major constraint on regional sustainable development. This paper took the perspective of land-use function and used multi-source data such as Sentinel remote-sensing imagery, VIIRS night-time light data, and POIs to classify land-use functions on a large scale in the Beijing–Tianjin–Hebei (BTH) urban agglomeration. The specific research process was as follows. Firstly, the BTH region was multi-scale-segmented based on Sentinel remote-sensing data. Then, the spectral, texture, shape, and socio-economic features of each small area after segmentation were extracted. Moreover, a PLES land-use classification system oriented towards land-use function was established, and a series of representative samples were selected. Subsequently, a random forest model was trained using these samples; then, the trained model was used for the large-scale analysis of land use in the entire BTH region. Finally, the spatial distribution patterns and temporal–spatial evolution characteristics of PLES in the BTH region from 2016 to 2021 were analyzed from the macro level to the micro level. Full article

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

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14 pages, 3640 KiB

Open AccessArticle

Homogenous Climatic Regions for Targeting Green Water Management Technologies in the Abbay Basin, Ethiopia

by Degefie Tibebe, Mekonnen Adnew Degefu, Woldeamlak Bewket, Ermias Teferi, Greg O’Donnell and Claire Walsh

Climate 2023, 11(10), 212; https://doi.org/10.3390/cli11100212 - 23 Oct 2023

Cited by 1 | Viewed by 2232

Abstract

Spatiotemporal climate variability is a leading environmental constraint to the rain-fed agricultural productivity and food security of communities in the Abbay basin and elsewhere in Ethiopia. The previous one-size-fits-all approach to soil and water management technology targeting did not effectively address climate-induced risks [...] Read more.

Spatiotemporal climate variability is a leading environmental constraint to the rain-fed agricultural productivity and food security of communities in the Abbay basin and elsewhere in Ethiopia. The previous one-size-fits-all approach to soil and water management technology targeting did not effectively address climate-induced risks to rain-fed agriculture. This study, therefore, delineates homogenous climatic regions and identifies climate-induced risks to rain-fed agriculture that are important to guide decisions and the selection of site-specific technologies for green water management in the Abbay basin. The k-means spatial clustering method was employed to identify homogenous climatic regions in the study area, while the Elbow method was used to determine an optimal number of climate clusters. The k-means clustering used the Enhancing National Climate Services (ENACTS) daily rainfall, minimum and maximum temperatures, and other derived climate variables that include daily rainfall amount, length of growing period (LGP), rainfall onset and cessation dates, rainfall intensity, temperature, potential evapotranspiration (PET), soil moisture, and AsterDEM to define climate regions. Accordingly, 12 climate clusters or regions were identified and mapped for the basin. Clustering a given geographic region into homogenous climate classes is useful to accurately identify and target locally relevant green water management technologies to effectively address local-scale climate-induced risks. This study also provided a methodological framework that can be used in the other river basins of Ethiopia and, indeed, elsewhere. Full article

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

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22 pages, 12575 KiB

Open AccessArticle

Identifying Major Diurnal Patterns and Drivers of Surface Urban Heat Island Intensities across Local Climate Zones

by Yongjuan Guan, Jinling Quan, Ting Ma, Shisong Cao, Chengdong Xu and Jiali Guo

Remote Sens. 2023, 15(20), 5061; https://doi.org/10.3390/rs15205061 - 21 Oct 2023

Cited by 2 | Viewed by 1747

Abstract

Deepening the understanding of diurnal characteristics and driving mechanisms of surface urban heat islands (SUHIs) across different local climate zones (LCZs) and time scales is of great significance for guiding urban surface heat mitigation. However, a comprehensive investigation of SUHIs from the diurnal, [...] Read more.

Deepening the understanding of diurnal characteristics and driving mechanisms of surface urban heat islands (SUHIs) across different local climate zones (LCZs) and time scales is of great significance for guiding urban surface heat mitigation. However, a comprehensive investigation of SUHIs from the diurnal, local, multi-seasonal, and interactive perspectives remains a large gap. Here, we generalized major diurnal patterns of LCZ-based SUHI intensities (SUHIIs) throughout 2020 over the urban area of Beijing, China, based on diurnal temperature cycle modeling, block-level LCZ mapping, and hierarchical clustering. A geographical detector was then employed to explore the individual and interactive impacts of 10 morphological, socioeconomic, and meteorological factors on the multi-temporal spatial differentiations of SUHIIs. Results indicate six prevalent diurnal SUHII patterns with distinct features among built LCZ types. LCZs 4 and 5 (open high- and mid-rise buildings) predominantly display patterns one, two, and five, characterized by an afternoon increase and persistently higher values during the night. Conversely, LCZs 6, 8, and 9 (open, large, and sparsely built low-rise buildings) mainly exhibit patterns three, four, and six, with a decrease in SUHII during the afternoon and lower intensities at night. The maximum/minimum SUHIIs occur in the afternoon–evening/morning for patterns 1–3 but in the morning/afternoon for patterns 5–6. In all four seasons, the enhanced vegetation index (EVI) and gross domestic product (GDP) have the top two individual effects for daytime spatial differentiations of SUHIIs, while the air temperature (TEM) has the largest explanatory power for nighttime differentiations of SUHIIs. All factor interactions are categorized as two-factor or nonlinear enhancements, where nighttime interactions exhibit notably greater explanatory powers than daytime ones. The strongest interactions are EVI ∩ GDP (q = 0.80) during the day and TEM ∩ EVI (q = 0.86) at night. The findings of this study contribute to an improved interpretation of the diurnal continuous dynamics of local SUHIIs in response to various environmental conditions. Full article

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

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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, Julie Michelle Klinger, Kyle Frankel Davis and Federica Bianco

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

Cited by 2 | Viewed by 7375

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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26 pages, 18934 KiB

Open AccessArticle

A Comparison of Six Forest Mapping Products in Southeast Asia, Aided by Field Validation Data

by Bin Liu, Xiaomei Yang, Zhihua Wang, Yaxin Ding, Junyao Zhang and Dan Meng

Remote Sens. 2023, 15(18), 4584; https://doi.org/10.3390/rs15184584 - 18 Sep 2023

Cited by 1 | Viewed by 1418

Abstract

Currently, many globally accessible forest mapping products can be utilized to monitor and assess the status of and changes in forests. However, substantial disparities exist among these products due to variations in forest definitions, classification methods, and remote sensing data sources. This becomes [...] Read more.

Currently, many globally accessible forest mapping products can be utilized to monitor and assess the status of and changes in forests. However, substantial disparities exist among these products due to variations in forest definitions, classification methods, and remote sensing data sources. This becomes particularly conspicuous in regions characterized by significant deforestation, like Southeast Asia, where forest mapping uncertainty is more pronounced, presenting users with challenges in selecting appropriate datasets across diverse regions. Moreover, this situation impedes the further enhancement of accuracy for forest mapping products. The aim of this research is to assess the consistency and accuracy of six recently produced forest mapping products in Southeast Asia. These products include three 10 m land cover products (Finer Resolution Observation and Monitoring Global LC (FROM-GLC10), ESA WorldCover 10 m 2020 (ESA2020), and ESRI 2020 Land Cover (ESRI2020)) and three forest thematic mapping products (Global PALSAR-2 Forest/Non-Forest map (JAXA FNF2020), global 30 m spatial distribution of forest cover in 2020 (GFC30_2020), and Generated_Hansen2020, which was synthesized based on Hansen TreeCover2010 (Hansen2010) and Hansen Global Forest Change (Hansen GFC) for the year 2020). Firstly, the research compared the area and spatial consistency. Next, accuracy was assessed using field validation points and manual densification points. Finally, the research analyzed the geographical environmental and biophysical factors influencing consistency. The results show that ESRI2020 had the highest overall accuracy for forest, followed by ESA2020, FROM-GLC10, and Generated_Hansen2020. Regions with elevations ranging from 200 to 3000 m and slopes below 15° or above 25° showed high spatial consistency, whereas other regions showed low consistency. Inconsistent regions showed complex landscapes heavily influenced by human activities; these regions are prone to being confused with shrubs and cropland and are also impacted by rubber and oil palm plantations, significantly affecting the accuracy of forest mapping. Based on the research findings, ESRI2020 is recommended for mountainous areas and abundant forest regions. However, in areas significantly affected by human activities, such as forest and non-forest edges and mixed areas of plantations and natural forests, caution should be taken with product selection. The research has identified areas of forest inconsistency that require attention in future forest mapping. To enhance our understanding of forest mapping and generate high-precision forest cover maps, it is recommended to incorporate multi-source data, subdivide forest types, and increase the number of sample points. Full article

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

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22 pages, 11112 KiB

Open AccessEditor’s ChoiceArticle

An Evaluation of Sun-Glint Correction Methods for UAV-Derived Secchi Depth Estimations in Inland Water Bodies

by Edvinas Tiškus, Martynas Bučas, Diana Vaičiūtė, Jonas Gintauskas and Irma Babrauskienė

Drones 2023, 7(9), 546; https://doi.org/10.3390/drones7090546 - 23 Aug 2023

Cited by 3 | Viewed by 1968

Abstract

This study investigates the application of unoccupied aerial vehicles (UAVs) equipped with a Micasense RedEdge-MX multispectral camera for the estimation of Secchi depth (SD) in inland water bodies. The research analyzed and compared five sun-glint correction methodologies—Hedley, Goodman, Lyzenga, Joyce, and threshold-removed glint—to [...] Read more.

This study investigates the application of unoccupied aerial vehicles (UAVs) equipped with a Micasense RedEdge-MX multispectral camera for the estimation of Secchi depth (SD) in inland water bodies. The research analyzed and compared five sun-glint correction methodologies—Hedley, Goodman, Lyzenga, Joyce, and threshold-removed glint—to model the SD values derived from UAV multispectral imagery, highlighting the role of reflectance accuracy and algorithmic precision in SD modeling. While Goodman’s method showed a higher correlation (0.92) with in situ SD measurements, Hedley’s method exhibited the smallest average deviation (0.65 m), suggesting its potential in water resource management, environmental monitoring, and ecological modeling. The study also underscored the quasi-analytical algorithm (QAA) potential in estimating SD due to its flexibility to process data from various sensors without requiring in situ measurements, offering scalability for large-scale water quality surveys. The accuracy of SD measures calculated using QAA was related to variability in water constituents of colored dissolved organic matter and the solar zenith angle. A practical workflow for SD acquisition using UAVs and multispectral data is proposed for monitoring inland water bodies. Full article

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

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