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Remote Sensing of Soil Erosion in Forest Area
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Remote Sensing of Soil Erosion in Forest Area

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

Deadline for manuscript submissions: 15 June 2024 | Viewed by 9021

Special Issue Editors

Dr. Pei Leng

E-Mail Website
Guest Editor
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: satellite soil moisture retrieval; hydrological modeling; satellite vegetation parameter retrieval
Dr. Hong-Yuan Huo

E-Mail Website
Guest Editor
Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100083, China
Interests: Soil erosion; Land use and land cover; Wetland remote sensing
Prof. Dr. Walter Chen

E-Mail Website
Guest Editor
Department of Civil Engineering, National Taipei University of Technology, No. 1, Sec. 3, Chung-Hsiao E. Road, Taipei 10608, Taiwan
Interests: soil erosion; machine learning; geotechnical engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Min-Cheng Tu

E-Mail Website
Guest Editor
Department of Civil Engineering, National Taipei University of Technology, Taipei City 106, Taiwan
Interests: surface water hydrology; hydrological modeling; low impact development; remote sensing

Special Issue Information

Dear Colleagues,

Soil erosion is currently one of the most important environmental problems worldwide. Specifically, with the aggravation of global climate change and human activities, forest has been suffering an increasing risk of soil erosion. As a consequence, many forestry ecosystem functions such as carbon exchange and water/soil conservation would be seriously affected.

In recent decades, the development of quantitative remote sensing allows for the generation of many key land surface/atmospheric parameters (such as soil moisture, precipitation, forest canopy cover, leaf area index, etc.) and associated remote-sensing-based soil erosion models and has provided an unprecedented opportunity to monitor soil erosion over forest areas. It is the right time to summary the achievements and to further guide the future research directions in this field and especially to promote new methods in the monitoring of forest soil erosion, since many new technologies (such as LIDAR and P-band radar) have been introduced to detect land surface parameters in forest areas.

In the context of “Remote Sensing of Soil Erosion in Forest Area”, this Special Issue seeks contributions reflecting the present innovative research progress in this field. The topics can range from the satellite retrieval methods for key factors of soil erosion, the remote-sensing-based soil erosion models, the effects of climate change and human activities on soil erosion, as well as the system development for soil erosion assessment. Specifically, research articles and review papers are warmly welcomed in this Special Issue.

Dr. Pei Leng
Dr. Hong-Yuan Huo
Dr. Walter Chen
Dr. Min-Cheng Tu
Guest Editors

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

  • soil erosion model
  • satellite soil moisture and vegetation cover
  • forest degradation monitoring
  • soil erosion modeling using InSAR
  • IoT-based soil erosion monitoring
  • soil erosion susceptibility with machine learning
  • soil erosion sensitivity mapping
  • soil erodibility and risk factors
  • USLE/RUSLE
  • USPED
  • WaTEM/SEDEM
  • WEPP/GeoWEPP

Published Papers (6 papers)

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24 pages, 13116 KiB  
Article
Applicability Comparison of GIS-Based RUSLE and SEMMA for Risk Assessment of Soil Erosion in Wildfire Watersheds
by Seung Sook Shin, Sang Deog Park and Gihong Kim
Remote Sens. 2024, 16(5), 932; https://doi.org/10.3390/rs16050932 - 06 Mar 2024
Viewed by 1063
Abstract
The second-largest wildfire in the history of South Korea occurred in 2022 due to strong winds and dry climates. Quantitative evaluation of soil erosion is necessary to prevent subsequent sediment disasters in the wildfire areas. The erosion rates in two watersheds affected by [...] Read more.
The second-largest wildfire in the history of South Korea occurred in 2022 due to strong winds and dry climates. Quantitative evaluation of soil erosion is necessary to prevent subsequent sediment disasters in the wildfire areas. The erosion rates in two watersheds affected by the wildfires were assessed using the revised universal soil loss equation (RUSLE), a globally popular model, and the soil erosion model for mountain areas (SEMMA) developed in South Korea. The GIS-based models required the integration of maps of the erosivity factor, erodibility factor, length and slope factors, and cover and practice factors. The rainfall erosivity factor considering the 50-year and 80-year probability of rainfall increased from coastal to mountainous areas. For the LS factors, the traditional version (TV) was initially used, and the flow accumulation version (FAV) was additionally considered. The cover factor of the RUSLE and the vegetation index of the SEMMA were calculated using the normalized difference vegetation index (NDVI) extracted from Sentinel-2 images acquired before and after the wildfire. After one year following the wildfire, the NDVI increased compared to during the year of the wildfire. Although the RUSLE considered a low value of the P factor (0.28) for post-fire watersheds, it overestimated the erosion rate by from 3 to 15 times compared to the SEMMA. The erosion risk with the SEMMA simulation decreased with the elapsed time via the vegetation recovery and stabilization of topsoil. While the FAV of RUSLE oversimulated by 1.65~2.31 times compared to the TV, the FAV of SEMMA only increased by 1.03~1.19 times compared to the TV. The heavy rainfall of the 50-year probability due to Typhoon Khanun in 2023 generated rill and gully erosions, landslides, and sediment damage in the post-fire watershed on forest roads for transmission tower construction or logging. Both the RUSLE and SEMMA for the TV and FAV predicted high erosion risks for disturbed hillslopes; however, their accuracy varied in terms of the intensity and extent. According to a comparative analysis of the simulation results of the two models and the actual erosion situations caused by heavy rain, the FAV of SEMMA was found to simulate spatial heterogeneity and a reasonable erosion rate. Full article
(This article belongs to the Special Issue Remote Sensing of Soil Erosion in Forest Area)
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21 pages, 56215 KiB  
Article
Landscape Pattern of Sloping Garden Erosion Based on CSLE and Multi-Source Satellite Imagery in Tropical Xishuangbanna, Southwest China
by Rui Tan, Guokun Chen, Bohui Tang, Yizhong Huang, Xianguang Ma, Zicheng Liu and Junxin Feng
Remote Sens. 2023, 15(23), 5613; https://doi.org/10.3390/rs15235613 - 03 Dec 2023
Cited by 1 | Viewed by 1048
Abstract
Inappropriate soil management accelerates soil erosion and thus poses a serious threat to food security and biodiversity. Due to poor data availability and fragmented terrain, the landscape pattern of garden erosion in tropical Xishuangbanna is not clear. In this study, by integrating multi-source [...] Read more.
Inappropriate soil management accelerates soil erosion and thus poses a serious threat to food security and biodiversity. Due to poor data availability and fragmented terrain, the landscape pattern of garden erosion in tropical Xishuangbanna is not clear. In this study, by integrating multi-source satellite imagery, field investigation and visual interpretation, we realized high-resolution mapping of gardens and soil conservation measures at the landscape scale. The Chinese Soil Loss Equation (CSLE) model was then performed to estimate the garden erosion rates and to identify critical erosion-prone areas; the landscape pattern of soil erosion was further discussed. Results showed the following: (1) For the three major plantations, teas have the largest degree of fragmentation and orchards suffer the highest soil erosion rate, while rubbers show the largest patch area, aggregation degree and soil erosion ratio. (2) The average garden erosion rate is 1595.08 t·km−2a−1, resulting in an annual soil loss of 9.73 × 106 t. Soil erosion is more susceptible to elevation and vegetation cover rather than the slope gradient. Meanwhile, irreversible erosion rates only occur in gardens with fraction vegetation coverage (FVC) lower than 30%, and they contribute 68.19% of total soil loss with the smallest land portion, indicating that new plantations are suffering serious erosion problems. (3) Garden patches with high erosion intensity grades and aggregation indexes should be recognized as priorities for centralized treatment. For elevations near 1900 m and lowlands (<950 m), the decrease in the fractal dimension index of erosion-prone areas indicates that patches are more regular and aggregated, suggesting a more optimistic conservation situation. Full article
(This article belongs to the Special Issue Remote Sensing of Soil Erosion in Forest Area)
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22 pages, 12482 KiB  
Article
Temperature/Emissivity Separation of Typical Grassland of Northwestern China Based on Hyper-CAM and Its Potential for Grassland Drought Monitoring
by Pengfei Liu, Hongyuan Huo, Li Guo, Pei Leng and Long He
Remote Sens. 2022, 14(19), 4809; https://doi.org/10.3390/rs14194809 - 26 Sep 2022
Viewed by 1442
Abstract
Research on grassland monitoring based on temperature/emissivity separation based on hyperspectral thermal infrared (HTIR) remote sensing is rare. Based on the longwave TIR instrument (Hyper-CAM), this study designed two experiments to collect HTIR datasets, separate the temperature and emissivity of different vegetation of [...] Read more.
Research on grassland monitoring based on temperature/emissivity separation based on hyperspectral thermal infrared (HTIR) remote sensing is rare. Based on the longwave TIR instrument (Hyper-CAM), this study designed two experiments to collect HTIR datasets, separate the temperature and emissivity of different vegetation of grassland, and analyze the relationship between the emissivity of vegetation and soil moisture content. First, we collected the HTIR remotely sensed dataset of different kinds of vegetation and used the temperature/emissivity separation algorithm to separate the temperature and emissivity of seven types of vegetation. The temperature and emissivity of these types of vegetation were separated. Then, the absorption characteristics of the emissivity spectral curves of each type of grass were analyzed. The distribution and differences of the temperature and specific emissivity in different parts of these seven grassland vegetation types were quantitatively analyzed, and the relationship between their changes and vegetation leaf moisture and vegetation health status was also analyzed. Second, to monitor the drought of grassland vegetation, a second experiment was designed to measure the changes in the emissivity under different soil water contents. This observation experiment took Artemisia frigida as the research object. From the results of the separation of the temperature and emissivity, we found that the emissivity of Artemisia frigida has significantly changed with the increase in the water content, and the emissivity showed an overall increasing trend. We also quantitatively analyzed the differences in the temperature and specific emissivity between Artemisia frigida and Artemisia subulata Nakai, both belonging to the genus Artemisia, under different water content conditions. The overall waveform characteristics and their similarities and differences at 850–1280 cm−1 were compared and analyzed. The experimental results shows that Hyper-CAM can effectively obtain the emissivity of various types of grassland vegetation as the absorption characteristics of grassland vegetation in the thermal infrared spectral region were quite notable, which shows the significant potential ability of identification and discrimination of different types of grassland vegetation. Full article
(This article belongs to the Special Issue Remote Sensing of Soil Erosion in Forest Area)
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19 pages, 7100 KiB  
Article
Fuzzy Logic Modeling of Land Degradation in a Loess Plateau Watershed, China
by Ang Lu, Peng Tian, Xingmin Mu, Guangju Zhao, Qingyu Feng, Jianying Guo and Wenlong Xu
Remote Sens. 2022, 14(19), 4779; https://doi.org/10.3390/rs14194779 - 24 Sep 2022
Cited by 9 | Viewed by 1624
Abstract
Various land degradation processes have led to land productivity reduction, food insecurity and ecosystem destruction. The Loess Plateau (LP) suffered from severe land degradation, such as vegetation degradation, soil erosion and desertification. This study assessed land degradation changes by considering different land degradation [...] Read more.
Various land degradation processes have led to land productivity reduction, food insecurity and ecosystem destruction. The Loess Plateau (LP) suffered from severe land degradation, such as vegetation degradation, soil erosion and desertification. This study assessed land degradation changes by considering different land degradation types including vegetation degradation, soil erosion, aridity, loss of soil organic carbon and desertification in the Huangfuchuan watershed of the northern LP. A comprehensive land degradation index (LDI) was developed by combining different degradation processes using the fuzzy logic modeling method. Our results showed significant land use transitions from bare land and sandy area to grass land and forest land from 1990 to 2018, which were consistent with an obvious increase in vegetation cover from 31.24% to 40.72%. The soil erosion rate predicted by the RUSLE model decreased by 51.95% during 1990–2018. The basin-average LDI decreased from 0.68 in 1990 to 0.51 in 2018, suggesting the great success of land degradation prevention in a fragile ecological environment region on the LP during the past decades. This study proposed an integrated framework for land degradation assessment in the high erodible area. The results can provide good references for the improvement of ecological environment in the future. Full article
(This article belongs to the Special Issue Remote Sensing of Soil Erosion in Forest Area)
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23 pages, 4077 KiB  
Article
Response of Sediment Connectivity to Altered Convergence Processes Induced by Forest Roads in Mountainous Watershed
by Qinghe Zhao, Yaru Jing, An Wang, Zaihui Yu, Yi Liu, Jinhai Yu, Guoshun Liu and Shengyan Ding
Remote Sens. 2022, 14(15), 3603; https://doi.org/10.3390/rs14153603 - 27 Jul 2022
Cited by 6 | Viewed by 1562
Abstract
Forest roads significantly affect sediment connectivity in mountainous catchments by contributing to the production of and disturbing the confluence of sediment-loaded runoff. This study considered forest roads as pathways and sinks of sediment-loaded runoff to understand the effects of forest roads on the [...] Read more.
Forest roads significantly affect sediment connectivity in mountainous catchments by contributing to the production of and disturbing the confluence of sediment-loaded runoff. This study considered forest roads as pathways and sinks of sediment-loaded runoff to understand the effects of forest roads on the confluence characteristics and sediment connectivity in mountainous a catchment using a scenario simulation. In order to determine the contribution and spatial relationship between sediment connectivity and influencing factors, this study utilized buffer analysis, an extremely randomized tree model, and multiscale geographically weighted regression. The results show that the presence of forest roads significantly changes the transport process and connectivity of runoff and sediment in the mountainous catchment. Specifically, flow length increases, but flow accumulation, upslope contributing area, and topographic index decrease with increasing distance from roads and streams. Meanwhile, the effects of roads on convergence characteristics and sediment connectivity are mainly manifested within a certain threshold that varies with different confluence characteristics. Moreover, sediment connectivity increases when considering roads as pathways and sinks of sediment-loaded runoff, especially on the upper hillslopes intercepted by roads and at the road–stream crossings. In addition, the closer the distance to the roads, the greater the impact of road on the confluence characteristics and sediment connectivity. Change in flow length is the most important factor affecting the sediment connectivity among all of the other convergence, terrain, and spatial distance characteristics. The longer the flow length, the lower the sediment connectivity. In conclusion, this study demonstrates that the altered confluence processes by roads increases the possibility that sediment-loaded runoff will be transported to the catchment outlet, which is of significance for the proper management of forest roads in mountainous catchments. Full article
(This article belongs to the Special Issue Remote Sensing of Soil Erosion in Forest Area)
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15 pages, 2994 KiB  
Technical Note
A Physics-Based Method for Retrieving Land Surface Emissivities from FengYun-3D Microwave Radiation Imager Data
by Fangcheng Zhou, Xiuzhen Han, Shihao Tang, Guangzhen Cao, Xiaoning Song and Binqian Wang
Remote Sens. 2024, 16(2), 352; https://doi.org/10.3390/rs16020352 - 16 Jan 2024
Viewed by 635
Abstract
The passive microwave land surface emissivity (MLSE) plays a crucial role in retrieving various land surface and atmospheric parameters and in Numerical Weather Prediction models. The retrieval accuracy of MLSE depends on many factors, including the consistency of the input data acquisition time. [...] Read more.
The passive microwave land surface emissivity (MLSE) plays a crucial role in retrieving various land surface and atmospheric parameters and in Numerical Weather Prediction models. The retrieval accuracy of MLSE depends on many factors, including the consistency of the input data acquisition time. The FengYun-3D (FY-3D) polar-orbiting meteorological satellite, equipped with passive microwave and infrared bands, offers time-consistent data crucial for MLSE retrieval. This study proposes a physics-based MLSE retrieval algorithm using all the input data from the FY-3D satellite. Based on the retrieved MLSE, the spatial distribution of the MLSE is closely correlated with the land cover types and topography. Lower emissivities prevailed over barren or sparsely vegetated regions, river basins, and coastal areas. Higher emissivities dominated densely vegetated regions and mountainous areas. Moderate emissivities dominated grasslands and croplands. Lower-frequency channels showed larger emissivity differences with different polarizations than those of higher-frequency channels in barren or sparsely vegetated regions. The MLSE across densely vegetated land areas, mountainous areas, and deserts showed small seasonal variations. However, woody savannas, grasslands, croplands, and seasonal snow-covered areas showed noticeable seasonal variations. For most land cover types, the differences between vertically and horizontally polarized emissivities remained relatively constant across seasons. However, certain grasslands in eastern Inner Mongolia and southern Mongolia showed clear seasonal variations. It is very difficult to verify the MLSE on a large scale. Consequently, the possible error sources in the retrieved MLSE were analyzed, including the brightness temperature errors (correlation coefficient ranging from 0.92 to 0.99) and the retrieved land surface temperature errors (Root Mean Square Error was 3.34 K and relation coefficient was 0.958). Full article
(This article belongs to the Special Issue Remote Sensing of Soil Erosion in Forest Area)
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