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Remote Sensing of Soil Erosion

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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 December 2020) | Viewed by 50603

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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. Athos Agapiou

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Department of Civil Engineering and Geomatics, Cyprus University of Technology, Saripolou 2-8, Limassol 3036, Cyprus
Interests: remote sensing for cultural heritage; optical image processing analysis
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Dr. Antonios Mouratidis

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Department of Physical and Environmental Geography, School of Geology, Aristotle University of Thessaloniki, Thessaloniki, Greece
Interests: geomorphology; environmental Geography; earth observation/remote sensing; GIS; GNSS
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) approaches (satellite remote sensing, field spectroscopy, UAVs, LiDAR, SAR, and aerial photos) will be investigated for their potential and impact on 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 remote sensing experts about the use, perspective, and current limits of EO and the associated geospatial science and technology in monitoring and modeling soil erosion both at a 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 modelling (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. Athos Agapiou
Dr. Antonios Mouratidis
Guest Editors

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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.

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Research

38 pages, 10781 KiB

Open AccessArticle

Analysis of the Influence of DTM Source Data on the LS Factors of the Soil Water Erosion Model Values with the Use of GIS Technology

by Anna Fijałkowska

Remote Sens. 2021, 13(4), 678; https://doi.org/10.3390/rs13040678 - 13 Feb 2021

Cited by 6 | Viewed by 2582

Abstract

Counteracting soil degradation is one of the strategic priorities for sustainable development. One of the most important current challenges is effective management of available resources. Multiple studies in various aspects of soil water erosion are conducted in many research institutions in the world. They concern, among others, the development of risk estimation models and the use of new data for modelling. The aim of the presented research was a discussion on the impact of the accuracy and detail of elevation data sources on the results of soil water erosion topographic factors modelling. Elevation data for this research were chosen to reflect various technologies of data acquisition, differences in the accuracy and detail of field forms mapping and, consequently, the spatial resolution of the digital terrain models (DTMs). The methodology of the universal soil loss equation USLE/RUSLE was used for the L and S factors modelling and calculation. The research was carried out in three study areas located in different types of geographical regions in Poland: uplands, highlands and lake districts. The proposed methodology consisted of conducting detailed comparative elevation and slope value assessments, calculating the values of topographical factors of the universal soil loss equation: slope length (L) and slope (S) and a detailed analysis of the total LS factors values. An approach to assess LS factors values within homogeneous areas such as agricultural plots has also been proposed. The studies draw the conclusion that the values of topographical factors obtained from various DTM sources were significantly different. It was shown that the choice of the right modelling data has a significant impact on the L and S factors values and, thus, also, on the decision-making process. The conducted research has definitely shown that data of the highest accuracy and detail should be used to study local phenomena (inter alia erosion), even analysing a large area. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Erosion)

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17 pages, 31827 KiB

Open AccessArticle

Mapping Soil Degradation on Arable Land with Aerial Photography and Erosion Models, Case Study from Danube Lowland, Slovakia

by Marián Jenčo, Emil Fulajtár, Hana Bobáľová, Igor Matečný, Martin Saksa, Miroslav Kožuch, Michal Gallay, Ján Kaňuk, Vladimír Píš and Veronika Oršulová

Remote Sens. 2020, 12(24), 4047; https://doi.org/10.3390/rs12244047 - 10 Dec 2020

Cited by 8 | Viewed by 3501

Abstract

The presented study uses the recent colour aerial photographs, historical black and white aerial photographs, and detailed digital elevation model to assess the spatial distribution and long-term temporal dynamics of soil loss in agriculturally intensively exploited loess hilly land with a subcontinental temperate climate. The strongly eroded soils appear in the studied area as bright patterns, surrounded by darker soils, and they are well visible on aerial photos. Three approaches of interpretation of aerial photographs were tested: visual interpretation, pixel-based image classification, and object-based image classification. All three methods provided detailed maps of soil redistribution patterns. The bright areas as the areas of soil degradation characterized by erosion increased from 1949 until 2011 by 76%. A detailed map of areal erosion patterns was used for the validation of water erosion models. LS-factor of USLE and ED’ index of USPED were selected for expressing the relation of real erosion to the terrain. The relationship between surface morphology and real erosion is very complex, and the tested water erosion models do not express it sufficiently. Therefore, the first and second-order directional derivative of the surface elevations with respect to the tillage direction has been tested. The absolute value of the first-order directional derivative showed better results and better corresponded with the real erosion pattern than the other morphometric characteristics. The findings suggest that tillage is the dominant erosion factor in the area. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Erosion)

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20 pages, 3685 KiB

Open AccessArticle

Assessment of Intra-Annual and Inter-Annual Variabilities of Soil Erosion in Crete Island (Greece) by Incorporating the Dynamic “Nature” of R and C-Factors in RUSLE Modeling

by Christos Polykretis, Dimitrios D. Alexakis, Manolis G. Grillakis and Stelios Manoudakis

Remote Sens. 2020, 12(15), 2439; https://doi.org/10.3390/rs12152439 - 29 Jul 2020

Cited by 37 | Viewed by 3777

Abstract

Under the continuously changing conditions of the environment, the exploration of spatial variability of soil erosion at a sub-annual temporal resolution, as well as the identification of high-soil loss time periods and areas, are crucial for implementing mitigation and land management interventions. The main objective of this study was to estimate the monthly and seasonal soil loss rates by water-induced soil erosion in Greek island of Crete for two recent hydrologically contrasting years, 2016 (dry) and 2019 (wet), as a result of Revised Universal Soil Loss Equation (RUSLE) modeling. The impact of temporal variability of the two dynamic RUSLE factors, namely rainfall erosivity (R) and cover management (C), was explored by using rainfall and remotely sensed vegetation data time-series of high temporal resolution. Soil, topographical, and land use/cover data were exploited to represent the other three static RUSLE factors, namely soil erodibility (K), slope length and steepness (LS) and support practice (P). The estimated rates were mapped presenting the spatio-temporal distribution of soil loss for the study area on a both intra-annual and inter-annual basis. The identification of high-loss months/seasons and areas in the island was achieved by these maps. Autumn (about 35 t ha⁻¹) with October (about 61 t ha⁻¹) in 2016, and winter (about 96 t ha⁻¹) with February (146 t ha⁻¹) in 2019 presented the highest mean soil loss rates on a seasonal and monthly, respectively, basis. Summer (0.22–0.25 t ha⁻¹), with its including months, showed the lowest rates in both examined years. The intense monthly fluctuations of R-factor were found to be more influential on water-induced soil erosion than the more stabilized tendency of C-factor. In both years, olive groves in terms of agricultural land use and Chania prefecture in terms of administrative division, were detected as the most prone spatial units to erosion. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Erosion)

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29 pages, 11466 KiB

Open AccessArticle

Soil Conservation Service Spatiotemporal Variability and Its Driving Mechanism on the Guizhou Plateau, China

by Linan Niu and Quanqin Shao

Remote Sens. 2020, 12(14), 2187; https://doi.org/10.3390/rs12142187 - 08 Jul 2020

Cited by 18 | Viewed by 2726

Abstract

The Guizhou Plateau has an extremely fragile ecological environment with prominent soil and water losses. Since 2000, conservation policies and ecological restoration projects, e.g., the Grain for Green Project (GGP), have been implemented on the Guizhou Plateau to control soil/water losses which have achieved notable accomplishments. Using the Revised Universal Soil Loss Equation (RUSLE) to estimate the soil conservation service (SCS) on the Guizhou Plateau, this study analyzed the dynamic characteristics of its spatiotemporal variation based on multiyear (2000–2018) meteorological and remote sensing data to determine its driving mechanisms. Residual analysis of the meteorological and remote sensing data was used to evaluate the effect of anthropogenic activities. Results showed a clear upward trend (1.39 t ha⁻¹ yr⁻¹) of SCS on the Guizhou Plateau during 2000–2018, and areas with a highly improved positive effect on SCS were distributed primarily in karst landform regions. Precipitation and vegetation fractional coverage (VFC) were found to be positively correlated with SCS on the Guizhou Plateau. Specifically, the highest proportion of significant positive correlation between precipitation and SCS was related to the Wildlife Conservation Nature Reserve (WCNR), and the highest proportion of significant positive correlation between VFC and SCS was related to the GGP, i.e., 76.59% and 53.02%, respectively. Residual analysis revealed a significant positive role of anthropogenic activity on SCS improvement via ecological engineering in areas with a poor ecological background, e.g., the GGP in western areas where the ecological environment is fragile and the problem of water/soil loss is serious. In areas with a more robust ecological background, e.g., the engineering area of the WCNR, the effect of anthropogenic activity has had a largely negative effect on SCS. The findings of this study could make an important contribution to the development of ecological management projects and the work to control soil/water losses on the Guizhou Plateau. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Erosion)

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21 pages, 4436 KiB

Open AccessArticle

Assessing the Accuracy and Feasibility of Using Close-Range Photogrammetry to Measure Channelized Erosion with a Consumer-Grade Camera

by Fangzhou Zheng, Rene Wackrow, Fan-Rui Meng, David Lobb and Sheng Li

Remote Sens. 2020, 12(11), 1706; https://doi.org/10.3390/rs12111706 - 27 May 2020

Cited by 3 | Viewed by 3034

Abstract

Water-induced channel is one of the main forms of soil erosion in cultivated fields. Channelized erosion is often measured by the volume of the channels. Traditionally, the measurements were conducted with rulers or measuring tapes. However, these traditional methods are generally time- and labor-consuming and can cause soil surface disturbance. Close-range photogrammetry with a Consumer-Grade Camera (CGC-CRP) provides an alternative way of measuring channel volume and can overcome limitations of traditional methods and provides much higher spatial resolution. However, quantitative information on the accuracy of this technique is rare. In this study, the accuracy of the CGC-CRP method under different settings were examined with an in-house experiment and validated with a field experiment. In the in-house experiment, a wood board surface with Artificial Channels (AC) of different shapes, orientations, and sizes were built. These ACs were surveyed using the CGC-CRP method with a series of settings of shooting angles and image overlapping rates. Selected cross-sectional areas were extracted to compare against manual measurements to assess the absolute and relative errors of the CGC-CRP method. The applicability of the CGC-CRP method with different settings was evaluated by comparing time consumption and the size of detection areas. The results indicated that in order to maintain an acceptable accuracy level, the image overlapping rate should be ≥70%, and the shooting angle should be in the range of 60° to 80°. For the channel shape, the accuracy for V-channel was ~15% higher than that for U-channel. For the U-channel, the impact of the channel orientation on the accuracy was not significant when the shooting angle was relatively high, whereas for the V-channel, the vertically oriented channel had higher accuracy than horizontal or angle channels. Last, channel size did not strongly affect accuracy when the channel was vertically orientated, and the shooting angle and image overlapping rate were set in the optimum ranges. However, when the shooting angle or image overlapping rate was low, or when the channel was angled or horizontally orientated, the accuracy was lower with larger channel size. In the field experiment, under the optimum camera setting, the error for the ten cross-sectional areas was about 1.6%. This result suggests that the CGC-CRP method is promising in volumetric assessment of rill and gully erosion. The quantitative information on the accuracy provided in this study can help researchers to select the setting of CGC-CRP methods to achieve their required accuracy level. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Erosion)

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21 pages, 5965 KiB

Open AccessFeature PaperArticle

A Soil Erosion Indicator for Supporting Agricultural, Environmental and Climate Policies in the European Union

by Panos Panagos, Cristiano Ballabio, Jean Poesen, Emanuele Lugato, Simone Scarpa, Luca Montanarella and Pasquale Borrelli

Remote Sens. 2020, 12(9), 1365; https://doi.org/10.3390/rs12091365 - 26 Apr 2020

Cited by 100 | Viewed by 15789

Abstract

Soil erosion is one of the eight threats in the Soil Thematic Strategy, the main policy instrument dedicated to soil protection in the European Union (EU). During the last decade, soil erosion indicators have been included in monitoring the performance of the Common Agricultural Policy (CAP) and the progress towards the Sustainable Development Goals (SDGs). This study comes five years after the assessment of soil loss by water erosion in the EU [Environmental science & policy 54, 438–447 (2015)], where a soil erosion modelling baseline for 2010 was developed. Here, we present an update of the EU assessment of soil loss by water erosion for the year 2016. The estimated long-term average erosion rate decreased by 0.4% between 2010 and 2016. This small decrease of soil loss was due to a limited increase of applied soil conservation practices and land cover change observed at the EU level. The modelling results suggest that, currently, ca. 25% of the EU land has erosion rates higher than the recommended sustainable threshold (2 t ha⁻¹ yr⁻¹) and more than 6% of agricultural lands suffer from severe erosion (11 t ha⁻¹ yr⁻¹). The results suggest that a more incisive set of measures of soil conservation is needed to mitigate soil erosion across the EU. However, targeted measures are recommendable at regional and national level as soil erosion trends are diverse between countries which show heterogeneous application of conservation practices. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Erosion)

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24 pages, 4236 KiB

Open AccessArticle

Morphometric Analysis for Soil Erosion Susceptibility Mapping Using Novel GIS-Based Ensemble Model

by Alireza Arabameri, John P. Tiefenbacher, Thomas Blaschke, Biswajeet Pradhan and Dieu Tien Bui

Remote Sens. 2020, 12(5), 874; https://doi.org/10.3390/rs12050874 - 09 Mar 2020

Cited by 57 | Viewed by 10970

Abstract

The morphometric characteristics of the Kalvārī basin were analyzed to prioritize sub-basins based on their susceptibility to erosion by water using a remote sensing-based data and a GIS. The morphometric parameters (MPs)—linear, relief, and shape—of the drainage network were calculated using data from the Advanced Land-observing Satellite (ALOS) phased-array L-type synthetic-aperture radar (PALSAR) digital elevation model (DEM) with a spatial resolution of 12.5 m. Interferometric synthetic aperture radar (InSAR) was used to generate the DEM. These parameters revealed the network’s texture, morpho-tectonics, geometry, and relief characteristics. A complex proportional assessment of alternatives (COPRAS)-analytical hierarchy process (AHP) novel-ensemble multiple-criteria decision-making (MCDM) model was used to rank sub-basins and to identify the major MPs that significantly influence erosion landforms of the Kalvārī drainage basin. The results show that in evolutionary terms this is a youthful landscape. Rejuvenation has influenced the erosional development of the basin, but lithology and relief, structure, and tectonics have determined the drainage patterns of the catchment. Results of the AHP model indicate that slope and drainage density influence erosion in the study area. The COPRAS-AHP ensemble model results reveal that sub-basin 1 is the most susceptible to soil erosion (SE) and that sub-basin 5 is least susceptible. The ensemble model was compared to the two individual models using the Spearman correlation coefficient test (SCCT) and the Kendall Tau correlation coefficient test (KTCCT). To evaluate the prediction accuracy of the ensemble model, its results were compared to results generated by the modified Pacific Southwest Inter-Agency Committee (MPSIAC) model in each sub-basin. Based on SCCT and KTCCT, the ensemble model was better at ranking sub-basins than the MPSIAC model, which indicated that sub-basins 1 and 4, with mean sediment yields of 943.7 and 456.3

m^{3} {km}^{- 2} {year}^{- 1}

, respectively, have the highest and lowest SE susceptibility in the study area. The sensitivity analysis revealed that the most sensitive parameters of the MPSIAC model are slope (R² = 0.96), followed by runoff (R² = 0.95). The MPSIAC shows that the ensemble model has a high prediction accuracy. The method tested here has been shown to be an effective tool to improve sustainable soil management. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Erosion)

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24 pages, 6286 KiB

Open AccessArticle

Proposing a Novel Predictive Technique for Gully Erosion Susceptibility Mapping in Arid and Semi-arid Regions (Iran)

by Alireza Arabameri, Artemi Cerda, Jesús Rodrigo-Comino, Biswajeet Pradhan, Masoud Sohrabi, Thomas Blaschke and Dieu Tien Bui

Remote Sens. 2019, 11(21), 2577; https://doi.org/10.3390/rs11212577 - 02 Nov 2019

Cited by 48 | Viewed by 5225

Abstract

Gully erosion is considered to be one of the main causes of land degradation in arid and semi-arid territories around the world. In this research, gully erosion susceptibility mapping was carried out in Semnan province (Iran) as a case study in which we tested the efficiency of the index of entropy (IoE), the Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, and their combination. Remote sensing and geographic information system (GIS) were used to reduce the time and costs needed for rapid assessment of gully erosion. Firstly, a gully erosion inventory map (GEIM) with 206 gully locations was obtained from various sources and randomly divided into two groups: A training dataset (70% of the data) and a validation dataset (30% of the data). Fifteen gully-related conditioning factors (GRCFs) including elevation, slope, aspect, plan curvature, stream power index, topographical wetness index, rainfall, soil type, drainage density, distance to river, distance to road, distance to fault, lithology, land use/land cover, and soil type, were used for modeling. The advanced land observing satellite (ALOS) digital elevation model with a spatial resolution of 30 m was used for the extraction of the above-mentioned topographic factors. The tolerance (TOL) and variance inflation factor (VIF) were also included for checking the multicollinearity among the GRCFs. Based on IoE, we concluded that soil type, lithology, and elevation were the most significant in terms of gully formation. Validation results using the area under the receiver operating characteristic curve (AUROC) showed that IoE (0.941) reached a higher prediction accuracy than VIKOR (0.857) and VIKOR-IoE (0.868). Based on our results, the combination of statistical (IoE) models along with remote sensing and GIS can convert the multi-criteria decision-making (MCDM) models into efficient and powerful tools for gully erosion prediction. We strongly suggest that decision-makers and managers should use these kinds of results to develop more consistent solutions to achieve sustainable development on degraded lands such as in the Semnan province. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Erosion)

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