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Integrated Use of Earth Observation and GIS Approaches for Soil Erosion Assessment in Local, Regional and Global Scale

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

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 26492

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Special Issue Editors

Dr. Dimitrios D. Alexakis

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Institute for Mediterranean Studies, Foundation for Research and Technology Hellas (FORTH), 74100 Rethymno, Crete, Greece
Interests: remote sensing; GIS; geomorphology; landscape ecology; landscape archaeology; soil erosion; land cover/land use change; natural hazards monitoring
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Dr. Christos Polykretis

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Lab of Geophysical - Satellite Remote Sensing and Archaeo-environment (GeoSat ReSeArch), Institute for Mediterranean Studies (IMS), Foundation for Research and Technology Hellas (FORTH), 74100 Rethymno, Crete, Greece
Interests: GIS; remote sensing; spatial analysis; (geo)statistical analysis; environmental modeling; natural hazard assessment; landslides; soil erosion; land use/land cover monitoring; social sciences; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Soil erosion is considered a major environmental problem, as it seriously threatens natural resources, agriculture, and the environment. This Special Issue aims to assess the impact of a changing climate, land use, soil moisture, hydrology, topography, and vegetation cover on the soil erosion processes. Thus, several innovative Earth observation (EO) (satellite remote sensing, field spectroscopy, UAVs, LiDAR, SAR, and aerial photos) and geospatial approaches will be investigated for their potential in monitoring soil properties and the corresponding soil erosion phenomena. Remote sensing offers a unique opportunity to map, monitor, quantify, and analyze, in detail, the processes that contribute to soil loss as a result of water erosion. The main aim of this Special Issue is to raise a dialogue between Geoinformatics and soil experts about the use, perspective, and current limits of EO and the associated geospatial science and technology in monitoring and modeling soil erosion at both the local and regional scale. In addition, this Special Issue can include topics related to soil loss and erosion as a result of climate change, land degradation, current and future land use, and agricultural practices, as well as the associated educational aspects. Authors are encouraged to submit articles on, but not limited to, the following subjects:

Soil Erosion;
Remote Sensing (Both Optical and SAR);
UAVs;
LiDAR;
Climate Change;
Land Use;
Geomorphology;
Hydrology;
Landscape Ecology;
Land Degradation;
Conservation Practices;
GIS Modeling (RUSLE, G2, etc.);
High-Resolution Land Topography;
Remote Sensing Education, Training, Capacity Building and Outreach Practices and Activities Related to Soil Erosion.

Dr. Dimitrios D. Alexakis
Dr. Christos Polykretis
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.

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Keywords

Soil Erosion
Earth Observation
GIS, Spatial Scale

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Published Papers (7 papers)

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Research

18 pages, 3578 KiB

Open AccessArticle

Coupled Thorens and Soil Conservation Service Models for Soil Erosion Assessment in a Loess Plateau Watershed, China

by Changjia Li, Tong Lu, Shuai Wang and Jiren Xu

Remote Sens. 2023, 15(3), 803; https://doi.org/10.3390/rs15030803 - 31 Jan 2023

Cited by 8 | Viewed by 2200

Abstract

Assessing soil erosion in China’s severely eroded Loess Plateau is urgently needed but is usually limited by suitable erosion models and long-term field measurements. In this study, we coupled the Thorens and Soil Conservation Service (SCS) models to evaluate runoff and sediment yield during the 1980s and 2010s in the Xiaolihe watershed on the Loess Plateau. Results showed the proposed model framework had a satisfactory performance in modelling spatially distributed runoff and sediment yield. The Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS) and the root mean square error-measured standard deviation ratio (RSR) were 0.93, 4.42% and 0.27 for monthly runoff; and 0.31, 62.31% and 0.82 for monthly sediment yield. The effects of land use changes on runoff and sediment yield were well captured by the SCS and Thorens models. The proposed modelling framework is distributed with a simple structure, requires relatively little data that can be obtained from public datasets, and can be used to predict runoff and sediment yield in other similar ungagged or poorly monitored watersheds. This work has important implications for runoff and erosion assessment in other arid and semi-arid regions, to derive runoff and erosion rates across large areas with scarce field measurements. Full article

(This article belongs to the Special Issue Integrated Use of Earth Observation and GIS Approaches for Soil Erosion Assessment in Local, Regional and Global Scale)

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26 pages, 10842 KiB

Open AccessArticle

Insights for Estimating and Predicting Reservoir Sedimentation Using the RUSLE-SDR Approach: A Case of Darbandikhan Lake Basin, Iraq–Iran

by Arsalan Ahmed Othman, Salahalddin S. Ali, Sarkawt G. Salar, Ahmed K. Obaid, Omeed Al-Kakey and Veraldo Liesenberg

Remote Sens. 2023, 15(3), 697; https://doi.org/10.3390/rs15030697 - 24 Jan 2023

Cited by 7 | Viewed by 2352

Abstract

Soil loss (SL) and its related sedimentation in mountainous areas affect the lifetime and functionality of dams. Darbandikhan Lake is one example of a dam lake in the Zagros region that was filled in late 1961. Since then, the lake has received a considerable amount of sediments from the upstream area of the basin. Interestingly, a series of dams have been constructed (13 dams), leading to a change in the sedimentation rate arriving at the main reservoir. This motivated us to evaluate a different combination of equations to estimate the Revised Universal Soil Loss Equation (RUSLE), Sediment Delivery Ratio (SDR), and Reservoir Sedimentation (RSed). Sets of Digital Elevation Model (DEM) gathered by the Shuttle Radar Topography Mission (SRTM), Tropical Rainfall Measuring Mission (TRMM), Harmonized World Soil Database (HWSD), AQUA eMODIS NDVI V6 data, in situ surveys by echo-sounding bathymetry, and other ancillary data were employed for this purpose. In this research, to estimate the RSed, five models of the SDR and the two most sensitive factors affecting soil-loss estimation were tested (i.e., rainfall erosivity (R) and cover management factor (C)) to propose a proper RUSLE-SDR model suitable for RSed modeling in mountainous areas. Thereafter, the proper RSed using field measurement of the bathymetric survey in Darbandikhan Lake Basin (DLB) was validated. The results show that six of the ninety scenarios tested have errors <20%. The best scenario out of the ninety is Scenario #18, which has an error of <1%, and its RSed is 0.46458 km³·yr⁻¹. Moreover, this study advises using the Modified Fournier index (MIF) equations to estimate the R factor. Avoiding the combination of the Index of Connectivity (IC) model for calculating SDR and land cover for calculating the C factor to obtain better estimates is highly recommended. Full article

(This article belongs to the Special Issue Integrated Use of Earth Observation and GIS Approaches for Soil Erosion Assessment in Local, Regional and Global Scale)

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24 pages, 5537 KiB

Open AccessArticle

Effects of Vegetation Change on Soil Erosion by Water in Major Basins, Central Asia

by Kaixuan Qian, Xiaofei Ma, Yonghui Wang, Xiuliang Yuan, Wei Yan, Yuan Liu, Xiuyun Yang and Jiaxin Li

Remote Sens. 2022, 14(21), 5507; https://doi.org/10.3390/rs14215507 - 1 Nov 2022

Cited by 9 | Viewed by 3588

Abstract

The uncertainties in soil erosion (SE) are further intensified by various factors, such as global warming, regional warming and humidification, and vegetation cover changes. Moreover, quantitative evaluations of SE in major basins of Central Asia (CA) under changing environments have rarely been conducted. This study conducted quantitative evaluation of SE in four major basins (Syr Darya Basin (SDB), Amu Darya Basin (ADB), Ili River Basin (IRB) and Tarim River Basin (TRB) using the Revised Universal Soil Loss Equation (RUSLE) and analyzed the main driving factors. SE quantities in the basins presented relatively consistent upward fluctuating trends from 1982 to 2017. Vegetation cover variation fluctuated significantly from 1982 to 2017. Specifically, vegetation cover decreased continuously in SDB, ADB, and IRB, but increased gradually in TRB. Pixels with positive spatial variation of vegetation mainly occurred around lakes and oases near rivers. The Normalized Difference Vegetation Index (NDVI) showed higher correlation with precipitation (80.5%) than with temperature (48.3%). During the study period, the area of arable land (AL) exhibited the largest change among all land use types in CA. Under long-term human activities, the proportion of NDVI of other land types converting to AL was the highest. In the structural equation model (SEM), precipitation, temperature, Shannon Diversity Index (SHDI), and NDVI strongly influenced SE. Overall, the major basins in CA were jointly affected by climate, human activities, and vegetation. Specifically, climatic factors exerted the strongest influence, followed by SHDI (human activities). SE was found to be relatively serious in ADB, SDB, and IRB, with SE in SDB even approaching that in the Loess Plateau. Under the background of global changes, appropriate water and land resource management and optimization configurations should be implemented in CA with reference to TRB in order to relieve local SE problems. Full article

(This article belongs to the Special Issue Integrated Use of Earth Observation and GIS Approaches for Soil Erosion Assessment in Local, Regional and Global Scale)

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17 pages, 5869 KiB

Open AccessArticle

Dynamic Modelling of Water and Wind Erosion in Australia over the Past Two Decades

by Mingxi Zhang, Raphael A. Viscarra Rossel, Qinggaozi Zhu, John Leys, Jonathan M. Gray, Qiang Yu and Xihua Yang

Remote Sens. 2022, 14(21), 5437; https://doi.org/10.3390/rs14215437 - 29 Oct 2022

Cited by 8 | Viewed by 3408

Abstract

Soil erosion caused by water and wind is a complicated natural process that has been accelerated by human activity. It results in increasing areas of land degradation, which further threaten the productive potential of landscapes. Consistent and continuous erosion monitoring will help identify the location, magnitude, and trends of soil erosion. This information can then be used to evaluate the impact of land management practices and inform programs that aim to improve soil conditions. In this study, we applied the Revised Universal Soil Loss Equation (RUSLE) and the Revised Wind Erosion Equation (RWEQ) to simulate water and wind erosion dynamics. With the emerging earth observation big data, we estimated the monthly and annual water erosion (with a resolution of 90 m) and wind erosion (at 1 km) from 2001 to 2020. We evaluated the performance of three gridded precipitation products (SILO, GPM, and TRMM) for monthly rainfall erosivity estimation using ground-based rainfall. For model validation, water erosion products were compared with existing products and wind erosion results were verified with observations. The datasets we developed are particularly useful for identifying finer-scale erosion dynamics, where more sustainable land management practices should be encouraged. Full article

(This article belongs to the Special Issue Integrated Use of Earth Observation and GIS Approaches for Soil Erosion Assessment in Local, Regional and Global Scale)

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27 pages, 66387 KiB

Open AccessArticle

Soil Erosion Susceptibility Prediction in Railway Corridors Using RUSLE, Soil Degradation Index and the New Normalized Difference Railway Erosivity Index (NDReLI)

by Yashon O. Ouma, Lone Lottering and Ryutaro Tateishi

Remote Sens. 2022, 14(2), 348; https://doi.org/10.3390/rs14020348 - 12 Jan 2022

Cited by 6 | Viewed by 4466

Abstract

This study presents a remote sensing-based index for the prediction of soil erosion susceptibility within railway corridors. The empirically derived index, Normalized Difference Railway Erosivity Index (NDReLI), is based on the Landsat-8 SWIR spectral reflectances and takes into account the bare soil and vegetation reflectances especially in semi-arid environments. For the case study of the Botswana Railway Corridor (BRC), the NDReLI results are compared with the RUSLE and the Soil Degradation Index (SDI). The RUSLE model showed that within the BRC, the mean annual soil loss index was at 0.139 ton ha⁻¹ year⁻¹, and only about 1% of the corridor area is susceptible to high (1.423–3.053 ton ha⁻¹ year⁻¹) and very high (3.053–5.854 ton ha⁻¹ year⁻¹) soil loss, while SDI estimated 19.4% of the railway corridor as vulnerable to soil degradation. NDReLI results based on SWIR1 (1.57–1.65 μm) predicted the most vulnerable areas, with a very high erosivity index (0.36–0.95), while SWIR2 (2.11–2.29 μm) predicted the same regions at a high erosivity index (0.13–0.36). From empirical validation using previous soil erosion events within the BRC, the proposed NDReLI performed better than the RUSLE and SDI models in the prediction of the spatial locations and extents of susceptibility to soil erosion within the BRC. Full article

(This article belongs to the Special Issue Integrated Use of Earth Observation and GIS Approaches for Soil Erosion Assessment in Local, Regional and Global Scale)

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18 pages, 4998 KiB

Open AccessArticle

Assessing Post-Fire Effects on Soil Loss Combining Burn Severity and Advanced Erosion Modeling in Malesina, Central Greece

by Ioanna Tselka, Pavlos Krassakis, Alkiviadis Rentzelos, Nikolaos Koukouzas and Issaak Parcharidis

Remote Sens. 2021, 13(24), 5160; https://doi.org/10.3390/rs13245160 - 19 Dec 2021

Cited by 10 | Viewed by 4546

Abstract

Earth’s ecosystems are extremely valuable to humanity, playing a key role ecologically, economically, and socially. Wildfires constitute a significant threat to the environment, especially in vulnerable ecosystems, such as those that are commonly found in the Mediterranean. Due to their strong impact on the environment, they provide a crucial factor in managing ecosystems behavior, causing dramatic modifications to land surface processes dynamics leading to land degradation. The soil erosion phenomenon downgrades soil quality in ecosystems and reduces land productivity. Thus, it is imperative to implement advanced erosion prediction models to assess fire effects on soil characteristics. This study focuses on examining the wildfire case that burned 30 km² in Malesina of Central Greece in 2014. The added value of remote sensing today, such as the high accuracy of satellite data, has contributed to visualizing the burned area concerning the severity of the event. Additional data from local weather stations were used to quantify soil loss on a seasonal basis using RUSLE modeling before and after the wildfire. Results of this study revealed that there is a remarkable variety of high soil loss values, especially in winter periods. More particularly, there was a 30% soil loss rise one year after the wildfire, while five years after the event, an almost double reduction was observed. In specific areas with high soil erosion values, infrastructure works were carried out validating the applied methodology. The approach adopted in this study underlines the significance of using remote sensing and geoinformation techniques to assess the post-fire effects of identifying vulnerable areas based on soil erosion parameters on a local scale. Full article

(This article belongs to the Special Issue Integrated Use of Earth Observation and GIS Approaches for Soil Erosion Assessment in Local, Regional and Global Scale)

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9 pages, 1899 KiB

Open AccessCommunication

Towards the Assessment of Soil-Erosion-Related C-Factor on European Scale Using Google Earth Engine and Sentinel-2 Images

by Dimitrios D. Alexakis, Stelios Manoudakis, Athos Agapiou and Christos Polykretis

Remote Sens. 2021, 13(24), 5019; https://doi.org/10.3390/rs13245019 - 10 Dec 2021

Cited by 18 | Viewed by 3942

Abstract

Soil erosion is a constant environmental threat for the entirety of Europe. Numerous studies have been published during the last years concerning assessing soil erosion utilising Remote Sensing (RS) and Geographic Information Systems (GIS). Such studies commonly employ empirical erosion models to estimate soil loss on various spatial scales. In this context, empirical models have been highlighted as major approaches to estimate soil loss on various spatial scales. Most of these models analyse environmental factors representing soil-erosion-influencing conditions such as the climate, topography, soil regime, and surface vegetation coverage. In this study, the Google Earth Engine (GEE) cloud computing platform and Sentinel-2 satellite imagery data have been combined to assess the vegetation-coverage-related factor known as cover management factor (C-factor) at a high spatial resolution (10 m) considering a total of 38 European countries. Based on the employment of the RS derivative of the Normalised Difference Vegetation Index (NDVI) for January and December 2019, a C-factor map was generated due to mean annual estimation. National values were then calculated in terms of different types of agricultural land cover classes. Furthermore, the European C-factor (C_EUROPE) values concerning the island of Crete (Greece) were compared with relevant values estimated for the island (C_CRETE) based on Sentinel-2 images being individually selected at a monthly time-step of 2019 to generate a series of 12 maps for the C-factor in Crete. Our results yielded identical C-factor values for the different approaches. The outcomes denote GEE’s high analytic and processing abilities to analyse massive quantities of data that can provide efficient digital products for soil-erosion-related studies. Full article

(This article belongs to the Special Issue Integrated Use of Earth Observation and GIS Approaches for Soil Erosion Assessment in Local, Regional and Global Scale)

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