Land Degradation Assessment Research Articles

Using remote sensing data to study anthropogenic land degradation in Khulna Division, Bangladesh for SDG indicator 15.3.1

The Sustainable Development Goals (SDGs) include a goal on land degradation: indicator 15.3.1 (proportion of degraded land over total land). It is not always easy to monitor the SDGs, and remote sensing could be an effective tool for monitoring several SDGs. This study assessed land degradation in Bangladesh's Khulna Division over the past two decades. The Trends.Earth toolset was used to assess land degradation during the baseline period (2001–2015) and the reporting period (2016–2020). Inputs include data from the United Nations Convention on Desertification, and outputs include three sub-indicators: land productivity, land cover change, and soil organic carbon (SOC) stocks. Over the past 20 years, the land use and land cover, land productivity, and SOC content of the study area have undergone substantial changes. A significant change was observed in croplands, water bodies, and built-up areas. Croplands have been converted into settlements and tree cover. Nonetheless, there is an increase in land productivity in the area (>64 %) accompanied by a small percentage of decreasing productivity (approximately 9 %). Accordingly, the SOC in major land areas (84.68 %) is stable with 66,475 tons of carbon lost from croplands. Overall, this area reveals substantial progress in SDG indicator 15.3.1 with a clear transformation of degraded land (from 10.38 % to 8.46 %) into stable land (32.09 %–64.01 %). Land degradation is mostly seen in Khulna, Bagerhat, Satkhira, Kushtia, and Jashore areas. Land covers change for urbanisation, developments, water logging, and salinity intrusion cause land degradation. Despite poor representation of the SOC and normalised difference vegetation index datasets in the waterlogged areas, the Trends.Earth-generated results are informative and stand alone. With the results of this study, policymakers may be able to develop more appropriate land management plans by better understanding the complex interconnections of land change processes.

Dynamic Land Degradation Assessment: Integrating Machine Learning with Landsat 8 OLI/TIRS for Enhanced Spectral, Terrain, and Land Cover Indices

Dynamic Land Degradation Assessment: Integrating Machine Learning with Landsat 8 OLI/TIRS for Enhanced Spectral, Terrain, and Land Cover Indices

Identifying vegetation patterns for a qualitative assessment of land degradation using a cellular automata model and satellite imagery.

We aim to identify the spatial distribution of vegetation and its growth dynamics with the purpose of obtaining a qualitative assessment of vegetation characteristics tied to its condition, productivity and health, and to land degradation. To do so, we compare a statistical model of vegetation growth and land surface imagery derived vegetation indices. Specifically, we analyze a stochastic cellular automata model and data obtained from satellite images, namely using the normalized difference vegetation index and the leaf area index. In the experimental data, we look for areas where vegetation is broken into small patches and qualitatively compare it to the percolating, fragmented, and degraded states that appear in the cellular automata model. We model the periodic effect of seasons, finding numerical evidence of a periodic fragmentation and recovery phenomenology if the model parameters are sufficiently close to the model's percolation transition. We qualitatively recognize these effects in real-world vegetation images and consider them a signal of increased environmental stress and vulnerability. Finally, we show an estimation of the environmental stress in land images by considering both the vegetation density and its clusterization.

Rural Land Degradation Assessment through Remote Sensing: Current Technologies, Models, and Applications

Rural Land Degradation Assessment through Remote Sensing: Current Technologies, Models, and Applications

Digital mapping of soil quality and salt-affected soil indicators for sustainable agriculture in the Nile Delta region

The study addresses the challenge of sustainable land management, which is crucial for agricultural production and soil quality (SQ), in the face of land degradation that negatively impacts crop production and SQ. The goal of the current work is to assess SQ using digital soil mapping (DSM) in Kafr El-Sheikh province, Egypt, to develop a framework employing two methods for soil quality index (SQI) assessment: the total data set (SQI-TDS) and a selected minimum data set (SQI-MDS) to choose indicators, along with a weighted additive SQI (SQIw), and a Random Forest (RF) model to predict and map the SQI, as well as the salt-affected soil indicators (EC, pH, and ESP). This framework uses remote sensing data: time series of Sentinel-1 (S-1) and Sentinel-2 (S-2) greenest pixel composite. Additionally, we incorporated environmental covariates derived from S-1 and S-2 imagery to understand their influence on SQ, which in turn informs land management practices, land degradation assessment, and crop productivity. The findings reveal a clear negative impact of salinity and alkalinity on SQ. We demonstrate the importance of Variance Inflation Factor (VIF) and Sequential Feature Selection (SFS) techniques for improving the performance of the RF model used for prediction. Notably, the greenest pixel composite imagery proved promising for SQI assessment using DSM beneath vegetation cover, crop mapping, and land-use dynamics. The precise SQI obtained is essential for decision-makers to detect land degradation, develop sustainable agricultural management strategies, and assess their appropriateness for developing plans and strategies to increase agricultural productivity.

Exploration of the utilization of a new land degradation index in Lake Ebinur Basin in China

Land degradation significantly impacts regional economic development and food security, particularly in arid river basins where soil and water conservation is crucial. Understanding the extent and causes of land degradation is pivotal for effectively prevention and management. This study employs the soil adjusted vegetation index (SAVI), the temperature vegetation dryness index (TVDI), and the salinization detection index (SDI), combined with the analytic hierarchy process and the entropy weight method, to construct a comprehensive land degradation index (LDI). Sen’s slope trend analysis and the Mann-Kendall significance test were used to analyze land degradation trends in the Ebinur Lake watershed from 2002 to 2022. Additionally, the optimal parameters-based geographical detector was used to examine the underlying mechanisms of land degradation. The results indicate the following: (1) From 2002 to 2012, the degree of land degradation in the Ebinur Lake watershed worsened, particularly in the eastern and southeastern parts, as well as in the southern region of Toli County. From 2012 to 2022, land degradation significantly improved, with a notable reduction in degraded land area. (2) Over the period of 2002-2022, 93.08%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$93.08\\%$$\\end{document} of the land in the research region exhibited a declining LDI trend, 3.95%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3.95\\%$$\\end{document} showed no change, and only 2.96%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2.96\\%$$\\end{document} showed an increasing LDI trend. (3) Moderate, severe, and very severe degradation mainly occurred on grassland and unused land, while light degradation and non-degradation primarily occurred on forest land and cultivated land. (4) Unreasonable land use and overgrazing were identified as the primary factors influencing land degradation, with elevation being a secondary factor. The interaction between land use and other factors was found to be most significant, followed by the synergistic effects of grazing quantity with elevation, annual average temperature, gross domestic product, soil moisture, and elevation with annual average precipitation, and temperature. The results of this study offer an empirical basis and taking decisions assistance for land degradation control in the Ebinur Lake Basin, as well as examples and references for assessing land degradation in other places.

Integrated use of the CA–Markov model and the Trends.Earth module to enhance the assessment of land cover degradation

This study aims to demonstrate the potential of assessing future land cover degradation status by combining the forecasting capabilities of the Cellular-Automata and Markov chain (CA-Markov) models in Idris Selva with the land cover degradation (LCD) model in the Trends.Earth module. The study focuses on the upper Zambezi Basin (UZB) in southern Africa, which is one of the regions with high rates of land degradation globally. Landsat satellite imagery is utilised to generate historical (1993–2023) land cover and land use (LCLU) maps for the UZB, while the global European Space Agency Climate Change Initiative (ESA CCI) LCLU maps are obtained from the Trends.Earth module. The CA-Markov chain model is employed to predict future LCLU changes between 2023 and 2043. The LCD model in the Trends.Earth module in QGIS 3.32.3 is then used to assess the historical and forecasted land cover degradation status. The findings reveal that land cover degradation maps produced from local LCLU classifications provide more detailed information compared to those produced from the global ESA CCI LCLU product. Between 2023 and 2043, the UZB is predicted to experience a net reduction of approximately 3.2 million hectares of forest cover, with an average annual reduction rate of − 0.13%. In terms of land cover degradation, the UZB is forecasted to remain generally stable, with 87% and 96% of the total land cover area expected to be stable during the periods 2023–2033 and 2033–2043, respectively, relative to the base years 2023 and 2033. Reduction in forest cover due to the expansion of grassland, human settlements, and cropland is projected to drive land cover degradation, while improvements in forest cover are anticipated through the conversion of grassland and cropland into forested areas. It appears that using locally produced LCLU with high-resolution images provides better assessments of land degradation in the Trends.Earth module than using global LCLU products. By leveraging the opportunities offered by models with capacity to predict LCLU such as the CA–Markov model and the capabilities of the LCD model, as evidenced in this study, valuable forecasted information can be effectively obtained for monitoring land cover degradation. This information can then be used to implement targeted interventions that align with the objective of realising the United Nations' land degradation neutral world target by 2030.

Monitoring Sustainable Development Goal Indicator 15.3.1 on Land Degradation Using SEPAL: Examples, Challenges and Prospects

A third of the world’s ecosystems are considered degraded, and there is an urgent need for protection and restoration to make the planet healthier. The Sustainable Development Goals (SDGs) target 15.3 aims at protecting and restoring the terrestrial ecosystem to achieve a land degradation-neutral world by 2030. Land restoration through inclusive and productive growth is indispensable to promote sustainable development by fostering climate change-resistant, poverty-alleviating, and environmentally protective economic growth. The SDG Indicator 15.3.1 is used to measure progress towards a land degradation-neutral world. Earth observation datasets are the primary data sources for deriving the three sub-indicators of indicator 15.3.1. It requires selecting, querying, and processing a substantial historical archive of data. To reduce the complexities, make the calculation user-friendly, and adapt it to in-country applications, a module on the FAO’s SEPAL platform has been developed in compliance with the UNCCD Good Practice Guidance (GPG v2) to derive the necessary statistics and maps for monitoring and reporting land degradation. The module uses satellite data from Landsat, Sentinel 2, and MODIS sensors for primary productivity assessment, along with other datasets enabling high-resolution to large-scale assessment of land degradation. The use of an in-country land cover transition matrix along with in-country land cover data enables a more accurate assessment of land cover changes over time. Four different case studies from Bangladesh, Nigeria, Uruguay, and Angola are presented to highlight the prospect and challenges of monitoring land degradation using various datasets, including LCML-based national land cover legend and land cover data.

Geospatial based soil loss rate and land degradation assessment in Debre Berhan Regio-Politan city, Upper Blue Nile Basin, Central Ethiopia

In Ethiopia’s central highlands, soil erosion and degradation are the most challenging and persistent issues, owing to their rough topography and human activities. Planning land use and managing natural resources, as well as agricultural output, depend on the evaluation of land degradation (LD). Thus, this study was carried out with the objective to quantify and evaluate soil loss rates, sediment yield (SY), and identify degradation hotspot areas in the Debre Berhan Regio-Politan City, Central Highlands of Ethiopia. The city has an area of 127191.7 ha. The Revised Universal Soil Loss Equation (RUSLE) model was applied to generate soil erosion severity map. An empirical equation based on topography was utilized to calculate sediment yield, in addition to the Analytical Hierarchy Process (AHP) method for evaluating land LD. The results showed that the city’s average annual estimated soil loss was 38.5 t ha−1 yr−1. Serious risks of soil loss are especially present in large portions of the city’s hilly eastern, northwest, and north peripheral areas. The city’s yearly average SY varies from 0–32 t ha−1 yr−1, with plain areas generally contributing 0 tons, while the hilly/barren areas contributing 32 tons. The LD severity class indicated that about 15197.65 ha (11.91%), 38018.49 ha (29.85%), 40287.86 ha (31.67%), 19869.97 ha (15.61%), and 13817.73 ha (10.96%) portion is categorized as very low, low, moderate, high, and very high, respectively. To prevent soil loss and LD and increase agricultural productivity, it is crucial to implement nature-based climate-smart agricultural practices and provide capacity-building training in line with the SDGs, Africa Agenda 2063, and national strategies and policies of the country.

Bringing satellite and nanotechnologies together: unifying strengths against pollution and climate change

Nowadays, we witness remarkable technological progress alongside unprecedented challenges that threaten the delicate balance of our planet’s ecological system. Environmental contamination plays a central role in this, with rapid urbanization, industrialization, mining and agricultural practices intensifying the introduction of pollutants into the environment. This article highlights the potential synergy between two fields operating at vastly different scales: satellite technology and nanotechnology. This article delves into the offerings of each of these disciplines and examines how they can mutually contribute to the detection, prevention and mitigation of environmental pollution. Satellites play a crucial role in identifying and monitoring large-scale polluted areas, offering comprehensive insights into environmental challenges. They are indispensable in tracking air, water pollution levels, assessing land degradation, and monitoring changes in ocean health with relatively high spatial and temporal resolution. Nanotechnology leverages the unique properties of materials at sub-micron scale by offering amplified chemical reactivity and new optical, electronic, and magnetic attributes, enabling selective and sensitive sensors and rapid and efficient contaminant capture/degradation strategies. Emerging nanomaterials, along with nature-inspired and self-powered or self-sustaining designs, broaden capabilities for efficient solutions. Advanced nanocharacterization techniques deepen material understanding and quantification, while nanofabrication allows precise design of functional nano-devices. We believe the synergistic relationship between both fields can yield cooperative solutions, expediting effective measures and greatly influencing policy decisions. This article advocates for the collaboration between these two disciplines to foster impactful progress in facing global challenges.

Quantitative Soil Quality Assessment by Using Visual Soil-Field Assessment Tools (VS-FAST)

Unplanned land use reflects the changes from agricultural to urbanization and decreasing of green spaces in cities and agricultural lands surrounding cities. The Soil Adjusted Vegetation Index (SAVI), Dry Bare - Soil Index (DBSI) and Normalized Difference Built-up Index (NDBI) indices were derived to evaluate the rate of the land use/cover changes due to uncontrolled urban sprawl for the years 2001 and 2020. Field indicators were also used to assess soil properties’ degradation by using Land Degradation Assessment in Drylands (LADA) under the FAO Global Environment Facility (GEF) project. A set of 30 soil samples were randomly collected from two locations: unplanned built-up and vegetated sites in Babil Governorate, Iraq. The results showed that soil texture and salinity were the most varied indicators. The poorer areas (unplanned urban areas) were low and assessed as poor quality, while the vegetated site was assessed as moderate quality with high total score values. The findings from spectral indices show a significant rise in the built-up land use/cover category, expanding from 10.367 square kilometres in 2001 to 50.883 square kilometres in 2020. In contrast, the areas of bare-soils and vegetated decreased from 29.29 and 35.474 square kilometres in 2001 to 12.511 and 19.601 square kilometres in 2020, respectively. Various analytical techniques proved instrumental in distinguishing the different land use/cover categories in this research.

Analysis of trends in productivity metrics in assessing land degradation: A case study in the Campania region of southern Italy

Land degradation is a critical issue at a global level and its progressive increasing greatly reduces soil ecosystem services. In this context, the 2030 Agenda for Sustainable Development, adopted by all United Nations Member States in 2015, defined the Sustainable Development Goals (SDGs) and indicated some targets of particular interest for a territory to be integrated into short- and medium-term national programs. Target 15.3, which aims to end desertification and restore degraded lands, is currently monitored by indicator 15.3.1, measured as the combination of three sub-indicators (trends in land cover change, land productivity and carbon stocks) as suggested by the United Nations Convention to Combat Desertification (UNCCD), the custodian agency for the SDG indicator. In our opinion, this assessment shows some weakness that are generally caused by a lack of information from direct field observations. The greatest limitation regards land productivity dynamics linked to the NDVI trajectory adopted by the UNCCD methodological approach. For this reason, the paper proposes an alternative approach that consists of using annual maximum NDVI value assessments instead of annual mean values for trajectory calculation. To come to these conclusions, the study addresses a reliability assessment by using remote sensing techniques via the Google Earth Engine (GEE) and analysing the NDVI evolution over time at 450 locations spread around the Campania region (southern Italy). To this end, a customised Graphical User Interface (GUI) was built on the GEE platform and a Google Earth time slider tool was applied to visualize land cover changes which occurred at each location over a period of 18 years (2001–2018). The survey was carried out on MODIS and Landsat 7 collections and showed that the new approach had a better performance than the UNCCD approach (90 % vs 62 % of successful reliability tests, up to 96 % considering results from Landsat images). The application of maximum NDVI values to assess productivity dynamics spatially shows, with regard to UNCCD data, more than double the percentages of degraded and stable lands and a drastic reduction in improved areas within the Campania region. Overall, this innovative approach appears to agree more closely with ground truth and the use of finer resolution data is more suitable for investigating land degradation processes within a regional context.

Integrated Universal Soil Loss Equation (USLE) and Geospatial Approach to Assess Soil Erosion in Machhu Sub-watershed, Morbi, India

Abstract Soil erosion is a severe and rapidly rising issue in many parts of the world due to human activities such as farming practices, land excavation for development and deforestation. Moreover, it can negatively impact water availability, agricultural growth, and ultimately, countries’ long-term economy. A quantitative and consistent land degradation assessment is vital for proper planning of soil conservation activities in a catchment or watershed. The Universal Soil Loss Equation (USLE) model is applied in this study to address the issue of soil erosion in the Machhu-sub watershed (24769.63 ha), located in Saurashtra, India. The landscape feature of the study area includes basalt type rock and water body. In this USLE model study we have used input parameters such as rainfall erosivity (R), soil erosivity (K), cover management (C), slope length and steepness (LS), and conservation practice (P) integrated with Geographical Information System (GIS) to analyze and obtain the estimated annual soil loss. Results indicated that the overall soil loss in the study area can be classified into five categories: Very Low (0-1), Low (1-3), Low moderate (3-5), Moderate (5-10), and High ( >10 tons/ha/year). The finding includes the overall soil potential loss of the Machhu sub-watershed is 14.90 tons/ha/year. Furthermore, 60.86 % of the agricultural area is affected by soil erosion. Therefore, the necessary soil conservation methods can be planned in the Machu-sub watershed area based on the USLE analysis. These findings may assist researchers, scientists, and policymakers in building a concrete strategy for sustainable development of not only study area but other catchments also.

Assessing the extent of land degradation in the eThekwini municipality using land cover change and soil organic carbon

ABSTRACT More than 75% of the global land has already suffered degradation, leading to the recognition of land degradation as one of the foremost challenges society faces. This recognition stems from its profound adverse impacts on natural ecosystem functioning, biodiversity, soil productivity, and food availability. Consequently, understanding the spatial distribution of land degradation across all scales becomes imperative. This study employed land cover change and soil organic carbon (SOC) stock assessments to analyse land degradation within the eThekwini Municipality beyond the baseline period (2000–2015). Utilizing remote sensing and machine learning techniques, this research examined land degradation within the eThekwini Municipality over the period spanning 2000 to 2022. Landsat 7 (Enhanced Thematic Mapper Plus – ETM+), Landsat 8 (Operational Land Imager 1 - OLI1), and Landsat 9 (Operational Land Imager 2 - OLI2) images were employed to extract variables for both land cover change and SOC stock prediction through XGBoost, LightGBM, Random Forest (RF), and Support Vector Machine (SVM) models. Among these models, LightGBM demonstrates superior performance, achieving an overall accuracy of 80.646 in land cover predictions and 77.869 in SOC stock predictions. Analysis of land cover change within the eThekwini Municipality unveiled a shift from forests and shrubland landscapes to cropland and built-up areas. This shift results in the municipality encountering losses in SOC stock between 2015 and 2022. The model predicted that most SOC stock losses occur at the 20–50 cm depth (9.27%), in comparison to the 7.21% loss at the 0–20 cm depth. These findings underscore the pivotal role of remote sensing and machine learning in aiding policymakers to assess land degradation and implement pertinent measures to enhance the landscape.

Does spatial resolution matter in the estimation of average annual soil loss by using RUSLE?-a study of the Urmodi River Watershed (Maharashtra), India.

The study investigates the influence of multispectral satellite data's spatial resolution on land degradation in the Urmodi River Watershed in which Kaas Plateau, a UNESCO World Heritage site, is located. Specifically, the research focuses on soil erosion and its risk zonation. The study employs Landsat 8 (30-m resolution) and Sentinel-2 (10-m resolution) data to assess soil erosion risk. The Revised Universal Soil Loss Equation (RUSLE) is used to quantify the average annual soil erosion output denoted by (A), by using its factors such as rainfall (R), soil erodibility (K), slope-length (LS), cover management (C), and support practices (P). R-factor was computed from MERRA-2 rainfalldata, K-factor was derived from field soil sample-based analysis, LS factor was from Cartosat Digital Elevation Model-based data. The C factor was derived from NDVI of Landsat 8 and Sentinel-2, and the P factor was prepared from LULC derived from Landsat 8, and Sentinel-2 was incorporated in the final integration. The soil erosion hazard map ranged from slight to extremely severe. Remote sensing (RS)-based parameters like Land Use Land Cover (LULC) are derived from the Landsat 8 and Sentine-2 satellite data and used to compute the difference in the final outcome of the integration. The study found similarities in average annual soil loss (A) in plain areas, but differences in final soil erosion risk zone (A) were influenced by LULC map variations due to different cell sizes, P factor, and slope gradient. Notable differences were observed in soil erosion risk categories, particularly in high to very severe zones, with a cumulative difference of 73.85 km2. In addition to this, a scatterplot between the final outputs was computed and found the moderate (R2 = 42.08%) correlation between Landsat 8 and Sentinel-2 imagery-based final average annual soil erosion (A) of RUSLE. The study area encompasses various landforms ranging from the plateau to pediplain, and in such situation, the water-led soil erosion categories vary depending on terrain condition along with its biophysical factors and, hence, need to analyze the need of such factors on the average annual soil erosion quantification. Different spatial resolution has an effect on the final output, and hence, there is a need to track this change at various spatial resolutions. This analysis highlights the significant impact of spatial resolution on land degradation assessment, providing precise identification of surface features and enhancing soil erosion risk zoning accuracy.

A Geospatial Decision Support System for Supporting the Assessment of Land Degradation in Europe

Nowadays, Land Degradation Neutrality (LDN) is on the political agenda as one of the main objectives in order to respond to the increasing degradation processes affecting soils and territories. Nevertheless, proper implementation of environmental policies is very difficult due to a lack of the operational, reliable and easily usable tools necessary to support political decisions when identifying problems, defining the causes of degradation and helping to find possible solutions. It is within this framework that this paper attempts to demonstrate a new valuable web-based operational LDN tool as a component of an already running Spatial Decision Support System (S-DSS) developed on a Geospatial Cyberinfrastructure (GCI). The tool could be offered to EU administrative units (e.g., municipalities) so that they may better evaluate the state and the impact of land degradation in their territories. The S-DSS supports the acquisition, management and processing of both static and dynamic data, together with data visualization and on-the-fly computing, in order to perform modelling, all of which is potentially accessible via the Web. The land degradation data utilized to develop the LDN tool refer to the SDG 15.3.1 indicator and were obtained from a platform named Trends.Earth, designed to monitor land change by using earth observations, and post-processed to correct some of the major artefacts relating to urban areas. The tool is designed to support land planning and management by producing data, statistics, reports and maps for any EU area of interest. The tool will be demonstrated through a short selection of practical case studies, where data, tables and stats are provided to challenge land degradation at different spatial extents. Currently, there are WEBGIS systems to visualize land degradation maps but—to our knowledge—this is the first S-DSS tool enabling customized LDN reporting at any NUTS (nomenclature of territorial units for statistics) level for the entire EU territory.

Assessment of potential soil erosion in Mongolia based on the RUSLE model and RCP 8.5 scenario

AbstractDue to its relatively high altitude and a continental climate, environmental issues related to soil erosion and land degradation more seriously affect the ecosystem and crop production in Mongolia. As detailed soil erosion and land degradation assessments have only been performed in some regions of Mongolia, the purpose of this study is to evaluate the soil vulnerability on a national scale and predict future soil erosion under climate change prior to establishing an appropriate management plan. The revised universal soil loss equation model was chosen for this study, which reflects geographic and climatic characteristics in a vast area as the Mongolian territory. We evaluated soil erosion using historical data from 1993 and 2013 and generated predictions in the near (2041–2060) and far future (2061–2080) periods under the representative concentration pathway 8.5 scenario. The soil erosion rates were divided into six classes: very low, low, medium, high, very high and extreme. The total of 46,039 km2 of the Mongolian territory was classified as very high and extreme risk, which is predicted to increase up to 48,961 and 51,769 km2, in the near and far future periods, respectively. At the national scale, most of the soil erosion appeared in bare area, as it covers more than 60% of the total area. Meanwhile, the highest soil erosion risk was expected in high mountain ranges and ecologically vulnerable regions of the Gobi Desert and steppe area. This study identifies three land cover types with high priority for management and areas that should be considered first in the sustainable national land use and soil management plans considering regional characteristics.

Assessing the accuracy of spectral indices obtained from Sentinel images using field research to estimate land degradation.

Wind erosion resulting from soil degradation is a significant problem in Iran's Baluchistan region. This study evaluated the accuracy of remote sensing models in assessing degradation severity through field studies. Sentinel-2 Multispectral Imager's (MSI) Level-1C satellite data was used to map Rutak's degradation severity in Saravan. The relationship between surface albedo and spectral indices (NDVI, SAVI, MSAVI, BSI, TGSI) was assessed. Linear regression establishes correlations between the albedo and each index, producing a degradation severity map categorized into five classes based on albedo and spectral indices. Accuracy was tested with 100 ground control points and field observations. The Mann-Whitney U-Test compares remote sensing models with field data. Results showed no significant difference (P > 0.05) between NDVI, SAVI, and MSAVI models with field data, while BSI and TGSI models exhibited significant differences (P ≤ 0.001). The best model, BSI-NDVI, achieves a regression coefficient of 0.86. This study demonstrates the advantage of remote sensing technology for mapping and monitoring degraded areas, providing valuable insights into land degradation assessment in Baluchistan. By accurately identifying severity levels, informed interventions can be implemented to mitigate wind erosion and combat soil degradation in the region.

Assessing Land Degradation and Restoration in Eastern China Grasslands from 1985 to 2018 Using Multitemporal Landsat Data

Assessing Land Degradation and Restoration in Eastern China Grasslands from 1985 to 2018 Using Multitemporal Landsat Data

Monitoring and mapping of desertification process using geospatial data and GIS technologies in Mirzachul area

Desertification reduces the land’s ability to withstand changes in climate, including the availability of water and other resources. Remote sensing technology has the potential to monitor and assess land degradation over time. The aim of this study is to use remote sensing images to assess desertification in Uzbekistan and compare the results with formal land productivity monitoring. The Mirzachul area was selected as a case study for monitoring desertification. Landsat images from 1994 to 2024 and the Soil Map of Uzbekistan were used as secondary data to determine the types of soil present in the case study area. The analysis focused on NDVI, SAVI, and WDVI. The results showed a significant difference in sandy bare soil and steppe trends in 1994, with approximately 4.5 million hectares of sandy bare soil and 250,000 hectares of steppe. However, by 2024, the area of sandy bare soil had decreased sharply by about 50% to 1.5 million hectares, while the area of steppe had increased to 2 million hectares.