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Remote Sensing Applications for Land Surface Properties and Processes

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 16968

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Prof. Dr. Wenge Ni‐Meister

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Department of Geography and Environmental Science, Hunter College The City University of New York, 695 Park Ave., New York, NY 10065, USA
Interests: remote sensing; forest structure and carbon estimates; ecosystem modeling
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Special Issue Information

Dear Colleagues,

This Special Issue calls for studies using remote sensing data to understand land surface properties and processes. High-quality multi-sensor data with unique characteristics provide rich land surface information required for diverse mapping, modeling, and monitoring of land properties and processes. Advancements in data science have resulted in various innovative data fusion methods to take advantage of each unique remote sensing data type. Furthermore, the Google Earth Engine platform has made vast remote sensing data easily accessible. These advancements expedite the applications of remote sensing data to advance our understanding of land characterization, mapping, and monitoring from the local to the global scale. Topics for this Special Issue include but are not limited to:

Using multispectral remote sensing data to study anthropogenic and natural impacts on land cover and land use;
Innovative approaches to use passive optical multispectral imagery, lidar or radar imaging data to understand vegetation structure, the role of vegetation structure on terrestrial carbon stocks, and fluxes at various spatial and temporal scales;
Fusion of hyperspectral and lidar remote sensing to understand the characteristics of vegetation traits and vegetation structure and their impact on vegetation growth;
Using multisensor remote sensing data to understand snow–vegetation interaction and the role of vegetation structure in surface energy and water balance, and the snowmelt process;
Innovative data fusion methods to fuse passive multispectral, hyperspectral data with lidar or radar remote sensing data for land applications.

This Special Issue is open to all types of research of land applications from local to regional and continental scales using a multiremote sensing data approach

Prof. Dr. Wenge Ni‐Meister
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Data fusion
Multispectral
Hyperspectral remote sensing
Radar
Lidar
Land applications

Published Papers (5 papers)

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Research

16 pages, 3824 KiB

Open AccessArticle

Mapping Land Cover Types for Highland Andean Ecosystems in Peru Using Google Earth Engine

by Samuel Edwin Pizarro, Narcisa Gabriela Pricope, Daniella Vargas-Machuca, Olwer Huanca and Javier Ñaupari

Remote Sens. 2022, 14(7), 1562; https://doi.org/10.3390/rs14071562 - 24 Mar 2022

Cited by 10 | Viewed by 5821

Abstract

Highland Andean ecosystems sustain high levels of floral and faunal biodiversity in areas with diverse topography and provide varied ecosystem services, including the supply of water to cities and downstream agricultural valleys. Google (™) has developed a product specifically designed for mapping purposes (Earth Engine), which enables users to harness the computing power of a cloud-based solution in near-real time for land cover change mapping and monitoring. We explore the feasibility of using this platform for mapping land cover types in topographically complex terrain with highly mixed vegetation types (Nor Yauyos Cochas Landscape Reserve located in the central Andes of Peru) using classification machine learning (ML) algorithms in combination with different sets of remote sensing data. The algorithms were trained using 3601 sampling pixels of (a) normalized spectral bands between the visible and near infrared spectrum of the Landsat 8 OLI sensor for the 2018 period, (b) spectral indices of vegetation, soil, water, snow, burned areas and bare ground and (c) topographic-derived indices (elevation, slope and aspect). Six ML algorithms were tested, including CART, random forest, gradient tree boosting, minimum distance, naïve Bayes and support vector machine. The results reveal that ML algorithms produce accurate classifications when spectral bands are used in conjunction with topographic indices, resulting in better discrimination among classes with similar spectral signatures such as pajonal (tussock grass-dominated cover) and short grasses or rocky groups, and moraines, agricultural and forested areas. The model with the highest explanatory power was obtained from the combination of spectral bands and topographic indices using the random forest algorithm (Kappa = 0.81). Our study presents a first approach of its kind in topographically complex Cordilleran terrain and we show that GEE is particularly useful in large-scale land cover mapping and monitoring in mountainous ecosystems subject to rapid changes and conversions, with replicability and scalability to other areas with similar characteristics. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Land Surface Properties and Processes)

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24 pages, 2244 KiB

Open AccessArticle

Temperate Grassland Afforestation Dynamics in the Aguapey Valuable Grassland Area between 1999 and 2020: Identifying the Need for Protection

by Melisa Apellaniz, Niall G. Burnside and Matthew Brolly

Remote Sens. 2022, 14(1), 74; https://doi.org/10.3390/rs14010074 - 24 Dec 2021

Viewed by 3221

Abstract

Temperate grasslands are considered the most endangered terrestrial ecosystem worldwide; the existent areas play a key role in biodiversity conservation. The Aguapey Valuable Grassland Area (VGA), one of the most well-preserved temperate grassland areas within Argentina, is currently threatened by the anthropogenic expansion of exotic tree plantations. Little is known about the impacts of afforestation over temperate grassland landscape structures; therefore, the aim of this study is to characterize Aguapey VGA landscape structural changes between 1999 and 2020 based on remotely sensed data. This involves the generation of land cover maps for four annual periods based on unsupervised classification of Landsat 5 TM and 8 OLI images, the estimation of landscape metrics, and the transition analysis between land cover types and annual periods. The area covered by temperate grassland is shown to have decreased by almost 22% over the 20 year-period studied, due to the expansion of tree plantation cover. The afforestation process took place mainly between 1999 and 2007 in the northern region of the Aguapey VGA, which led first to grassland perforation and subsequently to grassland attrition; however, Aguapey’s cultural tradition of cattle ranching could have partially inhibited the expansion of exotic trees over the final years of the study. The evidence of grassland loss and fragmentation within the Aguapey VGA should be considered as an early warning to promote the development of sustainable land use policies, mainly focused towards the Aguapey VGA’s southern region where temperate grassland remains the predominant land cover type. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Land Surface Properties and Processes)

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18 pages, 4963 KiB

Open AccessArticle

Drivers and Environmental Impacts of Vegetation Greening in a Semi-Arid Region of Northwest China since 2000

by Zhenzong Wu, Jian Bi and Yifei Gao

Remote Sens. 2021, 13(21), 4246; https://doi.org/10.3390/rs13214246 - 22 Oct 2021

Cited by 3 | Viewed by 1662

Abstract

The dynamics of terrestrial vegetation have changed a lot due to climate change and direct human interference. Monitoring these changes and understanding the mechanisms driving them are important for better understanding and projecting the Earth system. Here, we assessed the dynamics of vegetation in a semi-arid region of Northwest China for the years from 2000 to 2019 through satellite remote sensing using Vegetation Index (VI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and analyzed the interannual covariation between vegetation and three climatic factors—air temperature, precipitation, and vapor pressure deficit (VPD)—at nine meteorological stations. The main findings of this research are: (1) herbaceous land greened up much more than forests (2.85%/year vs. 1.26%/year) in this semi-arid region; (2) the magnitudes of green-up for croplands and grasslands were very similar, suggesting that agricultural practices, such as fertilization and irrigation, might have contributed little to vegetation green-up in this semi-arid region; and (3) the interannual dynamics of vegetation at high altitudes in this region correlate little with temperature, precipitation, or VPD, suggesting that factors other than temperature and moisture control the interannual vegetation dynamics there. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Land Surface Properties and Processes)

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22 pages, 4069 KiB

Open AccessArticle

The Interplay between Canopy Structure and Topography and Its Impacts on Seasonal Variations in Surface Reflectance Patterns in the Boreal Region of Alaska—Implications for Surface Radiation Budget

by Bibhash Nath and Wenge Ni-Meister

Remote Sens. 2021, 13(16), 3108; https://doi.org/10.3390/rs13163108 - 06 Aug 2021

Cited by 4 | Viewed by 2328

Abstract

Forests play an essential role in maintaining the Earth’s overall energy balance. The variability in forest canopy structure, topography, and underneath vegetation background conditions create uncertainty in modeling solar radiation at the Earth’s surface, particularly for boreal regions in high latitude. The purpose of this study is to analyze seasonal variation in visible, near-infrared, and shortwave infrared reflectance with respect to land cover classes, canopy structures, and topography in a boreal region of Alaska. We accomplished this investigation by fusing Landsat 8 images and LiDAR-derived canopy structural data and multivariate statistical analysis. Our study shows that canopy structure and topography interplay and influence reflectance spectra in a complex way, particularly during the snow season. We observed that deciduous trees, also tall with greater rugosity, are more dominant on the southern slope than on the northern slope. Taller trees are typically seen in higher elevations regardless of vegetation types. Surface reflectance in all studied wavelengths shows similar relationships with canopy cover, height, and rugosity, mainly due to close connections between these parameters. Visible and near-infrared reflectance decreases with canopy cover, tree height, and rugosity, especially for the evergreen forest. Deciduous forest shows more considerable variability of surface reflectance in all studied wavelengths, particularly in March, mainly due to the mixing effect of snow and vegetation. The multivariate statistical analysis demonstrates a significant tree shadow effect on surface reflectance for evergreen forests. However, the topographic shadow effect is prominent for deciduous forests during the winter season. These results provide great insight into understanding the role of vegetation structure and topography in surface radiation budget in the boreal region. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Land Surface Properties and Processes)

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22 pages, 52824 KiB

Open AccessArticle

Relationship between Fire Events and Land Use Changes in the State of São Paulo, Brazil

by Sheena Philogene and Wenge Ni-Meister

Remote Sens. 2021, 13(15), 2853; https://doi.org/10.3390/rs13152853 - 21 Jul 2021

Cited by 1 | Viewed by 2696

Abstract

This study investigated the land use and land cover changes in the state of São Paulo, Brazil, for the period of 2002 through 2017, to determine if forested areas were burned or converted to other land uses, to analyze the use of fire as a catalyst and mechanism for land cover change, and to determine if there was a relationship between land use changes and gross domestic product (GDP). MapBiomas classifications and MODIS data were analyzed using the Google Earth Engine. The results of the analysis found that there were minimal changes in the forested areas in São Paulo during the study period; however, there was a 5% increase in natural forest and a 75% increase in planted forest cover. On the other hand, there was a 128% increase in sugarcane, and nearly a 50% decrease in pasture land coverage, suggesting that land was converted from pasture to more profitable agricultural land. Finally, there was a strong positive correlation (r = 0.96) between the increase in sugarcane and the GDP, and a negative correlation between the frequency of fire events and economic production (r = −0.62). Overall, there was a decline in fire events in São Paulo, with fire events occurring in less than 2% of the total observed land area by 2017. This overall declining trend in fire events are likely the direct result of increases in green harvest methods, which prevent the need for pre-harvest burning. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Land Surface Properties and Processes)

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