Integration of geospatial technologies with RUSLE for analysis of land use/cover change impact on soil erosion: case study in Rib watershed, north-western highland Ethiopia

Modeling the effect of land use and climate change on water resources and soil erosion in a tropical West African catch-ment (Dano, Burkina Faso) using SHETRAN

Soil erosion is a critical global challenge that degrades land and water resources, leading to reduced soil fertility, pollution of water bodies, and sedimentation in hydraulic structures and reservoirs. In Ethiopia, where agriculture forms the backbone of the economy, unplanned LULC changes have intensified soil erosion, posing a significant threat to food security and sustainable development. In the Holota watershed of Ethiopia, rapid population growth and urbanization have accelerated unplanned land use and land cover (LULC) changes, significantly affecting soil erosion patterns. This study aims to assess the spatiotemporal changes in LULC and their impact on soil erosion from 2000 to 2050. Using Landsat imagery from 2000, 2010, and 2020, supervised classification with the maximum likelihood algorithm was applied in Google Earth Engine (GEE) to map five LULC classes: forest, cropland, built-up areas, shrubland, and grassland. The future LULC for 2050 was predicted using the CA–Markov chain model. Soil erosion for 2020 and 2050 LULC maps was estimated using the Revised Universal Soil Loss Equation (RUSLE). Results indicate that annual soil loss in the watershed was 13.3 t ha − 1 yr − 1 in 2020, increasing to 15.9 t ha − 1 yr − 1 by 2050. Cropland, built-up areas, and grassland are expected to be the major contributors to future soil erosion, while forest and shrubland are likely to play a mitigating role. The novelty of this research lies in its integration of cutting-edge remote sensing technologies, such as GEE and the CA-Markov model, to predict the combined impact of LULC changes on soil erosion in a data-scarce region, providing actionable insights for conservation planning in Ethiopian highlands. These findings offer essential guidance for conservation planners to implement sustainable land management practices aimed at reducing soil erosion, including promoting forest restoration, adopting contour farming, and enforcing land use regulations to limit the expansion of cropland and built-up areas in erosion-prone zones.

Land use and land cover (LULC) changes, which are the major causes of soil erosion in watersheds are important processes of land degradation in the world. Erosion models are useful to investigate temporal changes in the past and estimate the future soil losses that might threat the sustainability of crop production. In this study, temporal changes (between 1990 and 2018) of soil loss under different LULC were investigated in a small watershed located in Almus, Turkey. Universal soil loss equation (USLE) was used to model soil loss in the watershed at a fine spatial resolution using geographic information techniques. Rainfall (R), soil erodibility (K), slope length and steepness (LS) and supporting and conservation practices (P) factors of USLE model were kept constant, while cover and management factor (C) was determined by based on LULC types in 1990 and 2018. Spatial and temporal changes in erosion risk were mapped. The maps obtained indicated that LULC change has have both reducing and increasing effects on erosion risk. The average soil loss in 1990 decreased from 0.312 to 0.308 t ha-1 year-1 in 2018. Coverage of low-risk class areas in the basin increased by 20.19 km2 between 1990 and 2018. The increase in mixed forest cover caused a significant decrease in erosion risk. The results demonstrated that LULC is the driving factor for increasing or decreasing in soil erosion at the watershed. Conversion of forests to agricultural lands disturbed the surface cover and increased the erosion risk. The results revealed the significant impacts of LULC changes on soil erosion potential in the Almus Lake Watershed.

Soil erosion causes land degradation which negatively impacts not only natural resources but also livelihoods of people due to low agricultural productivity. Cambodia is prone to soil erosion due to poor agricultural practices. In this research we use Battambang province as a case study to quantify impact of land use and land cover change (LULC) on soil erosion. This study assessed the impact from LULC changes to soil erosion. LULC change maps were analyzed based on Landsat satellite imagery of 1998, 2008, and 2018, computed in QGIS 6.2.9, while the soil erosion loss was estimated by the integration of remote sensing, GIS tools, and Revised Universal Soil Loss Equation (RUSLE) model. The results showed that the area of agricultural land of Battambang province significantly increased from 44.50% in 1998 to 61.11% in 2008 and 68.40% in 2018. The forest cover significantly decreased from 29.82% in 1998 to 6.18% in 2018. Various soil erosion factors were estimated using LULC and slope. Based on that, the mean soil loss was 2.92 t/ha.yr in 1998, 4.20 t/ha.yr in 2008, and 4.98 t/ha.yr in 2018. Whereas the total annual soil loss was 3.49 million tons in 1998, 5.03 million tons in 2008, and 5.93 million tons in 2018. The annual soil loss at the agricultural land dramatically increased from 190,9347.9 tons (54%) in 1998 to 3,543,659 tons (70.43%) in 2008 and to 4,267,439 tons (71.91%) in 2018 due to agricultural land expansion and agricultural practices. These losses were directly correlated with LULC, especially agricultural land expansion and forest cover decline. Our results highlight the need to develop appropriate land use and crop management practices to decrease land degradation and soil erosion. These data are useful to bring about public awareness of land degradation and alert local citizens, researchers, policy makers, and actors towards land rehabilitation to bring the area of land back to a state which is safe for increasing biodiversity and agricultural productivity. Measures to reduce or prevent soil erosion and the use of conservation agriculture practices, along with water and soil conservation, management, agroforestry practices, vegetation cover restoration, the creation of slope terraces, and the use of direct sowing mulch-based cropping systems should be considered.

Understanding how land use and land cover (LULC) changes affect soil erosion is essential for effective management of watershed areas. This study used Geographic Information Systems (GISs) and the Revised Universal Soil Loss Equation (RUSLE) model to analyze the impact of LULC changes on soil erosion in the Dondor Watershed. Remote sensing data, including Landsat and Sentinel-2 satellite images, alongside field surveys, topographic data, rainfall, and soil data were used. The results showed agricultural land as the primary LULC type, increasing from 43.49% in 2002 to 59.10% in 2023. Forest and built-up areas also expanded, while grassland decreased. Soil erosion estimates revealed that more than 85% of the watershed experienced very slight erosion though the average annual soil loss increased from 4.98 t ha⁻1 year⁻1 in 2002 to 7.96 t ha⁻1 year⁻1 in 2023. Agriculture and built-up areas were identified as the primary contributors to erosion. This study underscores the importance of monitoring LULC dynamics for responsible land management and conservation efforts in the watershed.

Land Use and Land Cover (LULC) are the basic units of human activities and also serve as the significant factors to assess the climate change studies and environmental protection. Therefore, it is of significance to accurately and timely obtain the LULC maps on the earth’s surface, in particular for those regions where dramatic LULC changes are undergoing. Since 2017 April, a new state-level new area, Xiong’an New Area, was established in China, which would inevitably lead to some LULC changes in this region. In order to better understand what kinds of LULC change in this region with more details and further evaluate the potential impacts that the LULC changes will bring, this study makes full use of the 10-m multi-temporal Sentinel-2 images on the cloud-computing Google Earth Engine (GEE) and the powerful classification capability of Random Forest (RF) models, to generate the continuous LULC maps in Xiong’an New Area from 2017 to 2020. The derived LULC map for each year in Xiong’an New Area achieves high accuracy, with OA and Kappa values less than 0.9. Based on the obtained LULC maps, this study analyzed the spatial and temporal changes of LULC types in the last four years. It revealed that the estimated dry farmland has decreased from 2017 to 2018 and then kept almost stable, and the majority of it has converted to non-cropland, especially to the tree seedlings and landscape dual-use regions. The estimated impervious areas that were mainly composed of buildings and transport infrastructures first increased due to the newly constructed Xiong’an Station with some high-speed roads and then decreased from 2019 to 2020, mainly due to the relocation of some small villages. Accordingly, some building groups and aggregations were going to show in this region, and are expected to further developed in the future. Other LULC types such as lakes and forests have not changed dramatically, indicating that the environmental protection in Xiong’an New Area was, to date, satisfactory. The obtained 10-m and 4-year LULC maps in this study can provide some valuable information on the monitoring and understanding of what kinds of changes were and will be undergoing in Xiong’an New Area, and can also be used for evaluating the potential impacts and challenges such as environmental protection. Additionally, the utilization of GEE and the multi-temporal 10-m Sentinel-2 images can help to achieve LULC mapping in this region in an accurate and timely manner, which can be used in future studies.

Quantification of soil erosion and sediment yield using the RUSLE model in Boyo watershed, central Rift Valley Basin of Ethiopia

Land use changes impact on selected chemical denudation element and components of water cycle in small mountain catchment using SWAT model

This study evaluates the land use and land cover (LULC) dynamics that play an indispensable role in the degradation and deterioration of soil and water quality affecting the natural resources throughout the Kulfo watershed in Ethiopia. Directed image classification is initiated for satellite images to study the watershed. The image classification is categorized into ten different LULC classes with validation of ground control points. A Revised Universal Soil Loss Equation (RUSLE) model was used to generate the average soil loss of the watershed. The model involves the Rainfall Erosivity factor (R), Soil Erodibility factor (K), Length and Slope factor (LS), Cover Management factor (C), and Support Practice factor (P). The dynamics of LULC change and rainfall erosivity over the past 30 years have been interpreted using maps from 1990, 2005, and 2020 using the C-factor and R-factor. The remaining factors, like K-factor, LS-factor, and P-factor, were kept constant over the period. The results reveal that the average annual soil loss rate (A) of the watershed is estimated to be 138.8 t ha-1, 161.2 t ha-1, and 173.25 t ha-1 per year, for the selected period intervals. During the past three decades, the soil loss rate in the watershed has increased by 34.4 t ha-1 per year. The watershed and sustainable soil and water conservation practices need special attention to mitigate the severity of soil erosion risks to avoid disaster.

Land use and land cover (LULC) are fundamental units of human activities. Therefore, it is of significance to accurately and in a timely manner obtain the LULC maps where dramatic LULC changes are undergoing. Since 2017 April, a new state-level area, Xiong’an New Area, was established in China. In order to better characterize the LULC changes in Xiong’an New Area, this study makes full use of the multi-temporal 10-m Sentinel-2 images, the cloud-computing Google Earth Engine (GEE) platform, and the powerful classification capability of random forest (RF) models to generate the continuous LULC maps from 2017 to 2020. To do so, a novel multiple RF-based classification framework is adopted by outputting the classification probability based on each monthly composite and aggregating the multiple probability maps to generate the final classification map. Based on the obtained LULC maps, this study analyzes the spatio-temporal changes of LULC types in the last four years and the different change patterns in three counties. Experimental results indicate that the derived LULC maps achieve high accuracy for each year, with the overall accuracy and Kappa values no less than 0.95. It is also found that the changed areas account for nearly 36%, and the dry farmland, impervious surface, and other land-cover types have changed dramatically and present varying change patterns in three counties, which might be caused by the latest planning of Xiong’an New Area. The obtained 10-m four-year LULC maps in this study are supposed to provide some valuable information on the monitoring and understanding of what kinds of LULC changes have taken place in Xiong’an New Area.

The change detection and land use and land cover (LULC) maps are more important powerful forces behind numerous ecological systems and fallow land. The current research focuses on demarcating the spatiotemporal LULC changes, NDVI and change detections maps. These effects directly affect the ecosystem, land resources, cropping pattern and agriculture. LULC assessment and surveillance are essential for long-term planning and sustainable use of natural resources. However, we have developed the soft computing machine learning algorithm for mapping land use and land cover based on the Google earth engine (GEE) platform and change detection mapping done by SAGA GIS software. It is significantly used for ecological safety and planning under various climate variations. To accurately describe the land use and land cover classes with changes are identified in the area. This area exclusively uses the multitemporal Landsat-5 (30 m) and Sentinel-2 (10 m) imageries in LULC mapping. The GEE is a cloud-computing platform with the prevailing classification ability of random forest (RF) models to make five-year interval LULC maps for 2010, 2015 and 2020. To unique multiple RF models established as a classifier in the algorithm created by JavaScript and GEE. SAGA GIS has provided the best platform for detecting changes in land use and land cover classes. NDVI maps are created based on the cloud-based platform. These maps value ranges between −0.68 to −0.15, 0.76 to −0.29 and 0.66 to −0.11 in 2010, 2015 and 2020. Experimental outcomes indicate five classes such as water bodies, built up, barren, cropland and fallow land during 2010, 2015 and 2020. The overall accuracy of User and Producer for 2010, 2015 and 2019 years in between 86.23%, 88.34%, 85.53% and 92.51%, 94.34% and 91.54%, respectively. We have observed that (2010, 2015 − 2020) agriculture and built-up land increased by 1040.76 ha, 1246.32 ha, 1500.93 ha and 34.96 ha, 37.08 ha, 42.58 ha, respectively. Other side degraded land, fallow land, waterbodies areas (953.19 ha, 679.23 ha, 937.24 ha and 1385.73 ha, 1513.53 ha, 991.08 ha and 32.85 ha, 21.33 ha, 25.66 ha) are increased during the year of 2010, 2015 and 2020, respectively. While results have been done by GEE cloud platform and remote sensing data, this developed algorithm easily classified the land use maps from Landsat-5 and Sentinel-2 TM imagery in the machine learning approach. The determined 30-m and 10-m three-year LULC maps are made-up to deliver vital data on the changes, monitoring and understanding of which types of LULC classes and changes have occupied a place in the Rahuri area.

Land use and land cover (LULC) analysis is crucial for understanding societal development and assessing changes during the Anthropocene era. Conventional LULC mapping faces challenges in capturing changes under cloud cover and limited ground truth data. To enhance the accuracy and comprehensiveness of the descriptions of LULC changes, this investigation employed a combination of advanced techniques. Specifically, multitemporal 30 m resolution Landsat-8 satellite imagery was utilized, in addition to the cloud computing capabilities of the Google Earth Engine (GEE) platform. Additionally, the study incorporated the random forest (RF) algorithm. This study aimed to generate continuous LULC maps for 2014 and 2020 for the Shrirampur area of Maharashtra, India. A novel multiple composite RF approach based on LULC classification was utilized to generate the final LULC classification maps utilizing the RF-50 and RF-100 tree models. Both RF models utilized seven input bands (B1 to B7) as the dataset for LULC classification. By incorporating these bands, the models were able to influence the spectral information captured by each band to classify the LULC categories accurately. The inclusion of multiple bands enhanced the discrimination capabilities of the classifiers, increasing the comprehensiveness of the assessment of the LULC classes. The analysis indicated that RF-100 exhibited higher training and validation/testing accuracy for 2014 and 2020 (0.99 and 0.79/0.80, respectively). The study further revealed that agricultural land, built-up land, and water bodies have changed adequately and have undergone substantial variation among the LULC classes in the study area. Overall, this research provides novel insights into the application of machine learning (ML) models for LULC mapping and emphasizes the importance of selecting the optimal tree combination for enhancing the accuracy and reliability of LULC maps based on the GEE and different RF tree models. The present investigation further enabled the interpretation of pixel-level LULC interactions while improving image classification accuracy and suggested the best models for the classification of LULC maps through the identification of changes in LULC classes.

In this study, the impact of change in land use and land cover (LULC) on runoff estimation in the Kidangoor watershed was assessed using the SCS-CN technique. Recent flood-like natural disasters in Kerala are thought to be driven by changes in rainfall patterns and LULC. The accurate calculation of runoff from watersheds is urgently needed. In ArcGIS 10.5, the supervised classification approach is used to classify satellite images from 2000, 2011, 2013, and 2017. Similarly, the Inverse Distance Weighted (IDW) technique is used to produce spatial distribution maps of rainfall for each antecedent moisture condition (AMC). The runoff maps were generated by superimposing the distributed rainfall, LULC, and Hydrological Soil Group (HSG) maps. It was observed that the built-up area expanded by 168% between 2000 and 2017, whereas other classes decreased by 10–23%. However, compared to 2000, both with and without a change in LULC, runoff generation increased by just 31%, and 27% in 2017. The SCS-CN technique for runoff estimation indicates that the change in LULC in the Kidangoor watershed is insignificant. Thus, this study will help land use planners and decision-makers in limiting the potential damage from flooding when it comes to flood management techniques.

Spatial and temporal evaluation of the land use and land cover (LULC) changes, its dynamism and overall consequences are considered the fundamental variables in global climate change. These immense changes influence ecosystem, life, and livelihoods. Over the last few decades, industrial expansion in Bangladesh has a major effect on LULC across the suburban areas of the capital city Dhaka, especially the surrounding areas of Ashulia industrial hub. While providing new approaches to improve the frontiers of antecedent revolutions, particularly those of LULC changes monitoring and mapping, this study tried to evaluate further land development and planning in the study area between 2014 and 2020. Remote sensing imageries and relevant multiple secondary information were consecutively used as datasets. The interactive supervised classification tool using a maximum likelihood process was applied in LULC changes evaluations, detections, and as well providing fruitful explanations. Therefore, evaluated LULC maps’ overall accuracies were between 84% and96%, and kappa coefficient between 0.83 and 0.92. The results revealed that the urbanization and built-up area were the prime LULC type (77.36% in 2020) in the study area and were exceedingly increasing land cover type over water bodies, bare land and vegetation. Overall, in between 2014 and 2020, the LULC types as of water bodies, barelands, and vegetation have decreased by 212 hectares, 435 hectares, and 470 hectares, respectively. Moreover, overall downward trends of LULC changes were identified in all the land use types except built-up area. Hence, imbalanced land conversions and lack of proper planning together were creating the region highly vulnerable to several disasters as well as imbalanced ecosystem. Th study findings can help the decision makers and planners apart from future research.

Agricultural expansion and urban development without proper soil erosion control measures have become major environmental problems in Cambodia. Due to a high population growth rate and increased economic activities, land use and land cover (LULC) changes will cause environmental disturbances, particularly soil erosion. This research aimed to estimate total amounts of soil loss using the Revised Universal Soil Loss Equation (RUSLE) model within a Geographic Information System (GIS) environment. LULC maps of Japan International Cooperation Agency (JICA) 2002 and Mekong River Commission (MRC) 2015 were used to evaluate the impact of LULC on soil erosion loss in Stung Sangkae catchment. LULC dynamics for the study periods in Stung Sangkae catchment showed that the catchment experienced a rapid conversion of forests to paddy rice fields and other croplands. The results indicated that the average soil loss from the catchment was 3.1 and 7.6 t/ha/y for the 2002 and 2015 periods, respectively. The estimated total soil loss in the 2002 and 2015 periods was 1.9 million t/y and 4.5 million t/y, respectively. The soil erosion was accelerated by steep slopes combined with the high velocity and erosivity of stormwater runoff. The spatial distribution of soil loss showed that the highest value (14.3 to 62.9 t/ha/y) was recorded in the central, southwestern and upland parts of the catchment. It is recommended that priority should be given to erosion hot spot areas, and appropriate soil and water conservation practices should be adopted to restore degraded lands.