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

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

Deadline for manuscript submissions: 31 January 2022.

Special Issue Editors

Dr. Dimitrios D. Alexakis
E-Mail Website
Guest Editor
GeoSAT ReSearch Lab, Institute for Mediterranean Studies, Foundation for Research and Technology Hellas (FORTH), 74100 Rethymno, Crete, Greece
Interests: earth observation; geographical information systems; geomorphology; natural hazards; landscape ecology; landscape archaeology
Special Issues, Collections and Topics in MDPI journals
Dr. Christos Polykretis
E-Mail Website
Guest Editor
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 papers will be 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 2500 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
  • Earth Observation
  • GIS, Spatial Scale

Published Papers (3 papers)

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Research

Article
Soil Erosion Susceptibility Prediction in Railway Corridors Using RUSLE, Soil Degradation Index and the New Normalized Difference Railway Erosivity Index (NDReLI)
Remote Sens. 2022, 14(2), 348; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020348 - 12 Jan 2022
Viewed by 554
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 [...] Read more.
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
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Article
Assessing Post-Fire Effects on Soil Loss Combining Burn Severity and Advanced Erosion Modeling in Malesina, Central Greece
Remote Sens. 2021, 13(24), 5160; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245160 - 19 Dec 2021
Viewed by 691
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 [...] Read more.
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
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Communication
Towards the Assessment of Soil-Erosion-Related C-Factor on European Scale Using Google Earth Engine and Sentinel-2 Images
Remote Sens. 2021, 13(24), 5019; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245019 - 10 Dec 2021
Viewed by 582
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 [...] Read more.
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 (CEUROPE) values concerning the island of Crete (Greece) were compared with relevant values estimated for the island (CCRETE) 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
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