International Soil and Water Conservation Research最新文献

Benggang is a typical fragmented erosional landscape in southern and southeastern China, posing significant risk to the local residents and economic development. Therefore, an efficient and accurate fine-grained segmentation method is crucial for monitoring the Benggang areas. In this paper, we propose a deep learning-based automatic segmentation method for Benggang by integrating high-resolution Digital Orthophoto Map (DOM) and Digital Surface Model (DSM) data. The DSM data is used to extract slope maps, aiming to capture primary morphological features. The proposed method consists of a dual-stream convolutional encoder-decoder network in which multiple cascaded convolutional layers and a skip connection scheme are used to extract morphological and visual features of the Benggang areas. The rich discriminative information in the DOM and slope data is fused by a channel exchanging mechanism that dynamically exchanges the most discriminative features from either the DOM or DSM stream according to their importance at the channel level. Evaluation experiments were conducted on a challenging dataset collected from Guangdong Province, China, and the results show that the proposed channel exchanging network based deep fusion method achieves 84.62% IoU in Benggang segmentation, outperforming several existing unimodal or multimodal baselines. The proposed multimodal segmentation method greatly improves the efficiency of large-scale discovery of Benggang, and thus is important for the management and restoration of Benggang in southern and southeastern China, as well as the monitoring of other similar erosional landscapes.

Conservation practices are encouraged to improve soil health and sustain agronomic crop production. Mehlich-3 is often used as a multi-nutrient extractant to determine soil fertility status. A study investigated the impacts of the conservation practices of gypsum, cover crops, and crop rotation on 28 Mehlich-3 extractable elements, of which 11 were considered plant nutrients, from soil at three midwestern US locations. Soil was collected from 0 to 15 and 15–30 cm depths 5 years after implementing the conservation practices. Treatments consisted of (1) with and without cereal rye (Secale cereale L.) winter cover, (2) continuous soybean [Glycine max (L.) Merr.] vs. soybean-corn (Zea mays L.) rotation, and (3) annual gypsum application (0, 1.1, and 2.2 Mg ha⁻¹). Differences were observed by site, depth, and conservation practice depending on the element evaluated. Minimal interactive effects were observed among treatments. The most consistent effect was observed for crop rotation across sites. Gypsum only affected the site with the greatest clay content, where more Ca and S were retained, and Mg and Mn displaced. Cover crop only affected elements at this high clay site, where different elements were positively or negatively affected. Results suggest that not one practice fits all, and optimum conservation practices must be tailored for the site.

Efforts made to restore the degraded landscape of the Tigray region, Northern Ethiopia, over the last three decades have been relatively successful. However, an armed conflict that broke out in the region in November 2020 has significantly destroyed the restored vegetation, either directly associated with conflict (environment, pollution, fire) or indirectly (agricultural abandonment). This study aimed at assessing spatio-temporal changes in vegetation cover in a 50 km radius zone centered on Mekelle city, Tigray. Vegetation cover dynamics was evaluated using Landsat Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) datasets for the years 2000, 2020, and 2022 and analysed using ENVI 5.3 and ArcGIS 10.8.1 software. These data were analysed using the Modified Normalized Difference Vegetation Index (MNDVI), Optimized Soil Adjusted Vegetation Index (OSAVI), and Moisture Adjusted Vegetation Index (MAVI). Based on the MNDVI, results show that vegetation cover increased in the period 2000–2020 by 179 km² or 2% of the area, whereas in the period 2020–2022, there was a decrease in vegetation cover by 403 km² or 5% of the area. This was accompanied by a decrease in vegetation density. These vegetation changes in 2020–2022 are attributed to the impact of armed conflict on the land surface which can include farmlands and village abandonment, spread of weeds and scrub vegetation, or failure to harvest crops. Monitoring vegetation change using Landsat data can help understand the environmental impacts of armed conflict in rural agricultural landscapes, including potential food security risks.

Environmental crises, land degradation, declining factor productivity, and farm profitability questioned the sustainability of linear economy-based existing agricultural production model. Hence, there is a dire need to design and develop circular economy-based production systems to meet the twin objectives of environmental sustainability and food security. Therefore, the productive capacity, natural resource conserving ability, and biomass recycling potential of four intensified maize-based systems viz. maize (Zea mays) + sweet potato (Ipomoea batatas)-wheat, maize + colocasia (Colocasia esculenta)-wheat, maize + turmeric (Curcuma longa), and maize + ginger (Zingiber officinale) were tested consecutively for three years (2020, 2021 and 22) in a fixed plot manner at Dehradun region of the Indian Himalaya against the existing maize-wheat systems. The result showed that the maize + sweet potato-wheat system significantly reduced runoff loss (166.3 mm) over the maize-wheat system. The highest through fall (68.12 %) and the lowest stem flow (23.54 %) were recorded with sole maize. On the contrary, the maize + sweet potato system has the highest stem flow (36.15 %) and the lowest through fall. Similarly, the maize + sweet potato system had 5.6 times lesser soil erosion and 0.77 t ha⁻¹ higher maize productivity over the maize-wheat system. Furthermore, the maize + sweet potato system recorded significantly higher soil moisture (19.3%), infiltration rate (0.95 cm h⁻¹), and organic carbon (0.78%) over the rest of the systems. The maize + sweet potato system also recycled the highest nitrogen (299.2 kg ha⁻¹), phosphorus, (31.0 kg ha⁻¹), and potassium (276.2 kg ha⁻¹) into the soil system. Hence, it can be inferred that concurrent cultivation of sweet potato, with maize, is a soil-supportive, resource-conserving, and productive production model and can be recommended for achieving the circular economy targets in the Indian Himalayas.

This review article presents a comprehensive overview of the current status of the Landsat program and its applications in soil erosion modelling and assessment within arid environments. Literature for the period between 1972 and 2022 was retrieved using directed search strategies and keywords. A total of 170 journal articles were gathered and analyzed. The literature analysis reveals that 27 (16%) of the publications fall within the period from 2007 to 2011, marking the highest occurrence within a five-year interval. The scrutinized literature was classified into ten distinct periods, or “pentades,” to accommodate the evolving applications of the Landsat program in response to advancements in remotely sensed data quality. This review article underscores the substantial contribution of Landsat data to the monitoring and assessment of soil erosion attributed to the action of water. Numerous studies have been conducted to model soil erosion using the Revised Universal Soil Loss Equation (RUSLE) model, facilitated by Geographic Information Systems (GIS) and remote sensing technologies. Nonetheless, the integration of Landsat data does present some challenges. Notably, the limitations of coarse resolution and data loss, particularly the scan line issues affecting Landsat 7, have hindered the full potential of the affected satellite datasets. As a solution, a multi-source approach that amalgamates diverse datasets is advocated to bridge data gaps and address disparities in spatial and temporal resolutions. To conclude, the Landsat mission has indisputably emerged as an indispensable instrument for facilitating the assessment and monitoring of soil erosion in resource-constrained communities. To advance this field, there is need to bolster storage infrastructure to manage large datasets, ensuring continuity for these sensor outputs, presenting a promising path for future research.

Tillage methods play a crucial role in controlling rainwater partitioning and soil erosion. This study utilized rainfall simulation experiments to investigate the impact of four tillage methods (manual digging (MD), manual hoeing (MH), traditional ploughing (TP), and ridged ploughing (RP)) on runoff and soil erosion at the plot scale. The smooth slope (SS) was used as a benchmark. Rainfall intensities of 30, 60, 90, and 120 mm h⁻¹ were considered. The study revealed that tillage altered rainwater distribution into depression storage, infiltration, and runoff. Tillage reduces runoff and increases infiltration. The four tillage methods (30–73%) increased the proportion of rainwater converted to infiltration to varying degrees compared to the SS (22–53%). Microrelief features influenced the role of tillage methods in soil erosion. Surface roughness and depression storage accounted for 79% of the variation in sediment yield. The four tillage methods reduced runoff by 2.1–64.7% and sediment yield by 2.5–77.2%. Moreover, increased rainfall intensity weakens the ability of tillage to control soil erosion. When rainfall intensity increased to 120 mm h⁻¹, there was no significant difference in runoff yield among RP, TP, MH, and SS. Therefore, assessing the effectiveness of tillage in reducing soil erosion should consider changes in rainfall intensity. Additionally, the cover management (C) factor of the RUSLE was used to assess the effects of different tillage methods on soil loss. Overall, the C factor values for tilled slopes are in the order MH > TP > RP > MD with a range of 0.23–0.97. As the surface roughness increases, the C factor tends to decrease, and the two are exponential functions (R² = 0.86). These studies contribute to our understanding of how different tillage methods impact runoff and soil erosion in sloped farmland and provide guidance for selecting appropriate local manual tillage methods.

Check dams are widely used throughout the world to tackle soil and water loss. However, the frequency of extreme rainfall events has increased owing to global climate change and the main structure of check dam is gradually aging, which lead to an increase in the failure risk of check dams. Thus, it is necessary to carry out the study on failure risk diagnosis and assessment of check dams. In the study, machine learning algorithms (ML), including random forests (RF), support vector machine (SVM), and logistic regression (LR), were used to integrate the environmental and engineering factors and then assess the risk of check dam failure due to the “7.26” rainstorm on July 26, 2017, in the Chabagou watershed, located in the hilly-gully region of the Loess Plateau, China. To verify the generalizability of the model in this study, these models were used for the Wangmaogou catchment north of the Loess Plateau. The accuracy assessment by the receiver operating characteristic (ROC) curve indicated that the RF model with an area under the ROC curve (AUC) greater than 0.89 was the most precise model and had a higher generalization ability. In addition, the model dataset was relatively small and easy to obtain, which make the risk modeling of check dam failure in the study has the potential for application in other regions. In the RF model, each factor selected was confirmed to be important, and the importance values for engineering factors were generally higher than those for the environmental factors. The risk map of check dam failure in the RF model indicated that 56.34% of check dams in the study area had very high and high risks of dam failure under high-intensity rainfall in 2017. Based on the importance of factors and the risk map of check dam failure, the prevention and control measures for reducing the risk of check dam failure and promoting the construction of check dam are proposed. These proposals provide a scientific basis for the reinforcement of check dams and the future layout of check dams in the Chinese Loess Plateau.

Check dams have been widely constructed in the Chinese Loess Plateau and has played an important role in controlling soil loss during last 70 years. However, the large-scale and automatic mapping of the check dams and the resulting silted fields are lacking. In this study, we present a novel methodological framework to extract silted fields and to estimate the location of the check dams at a pixel level in the Wuding River catchment by remote sensing and ensemble learning models. The random under-sampling method and 23 features were used to train and validate three ensemble learning models, namely Random Forest, Extreme Gradient Boosting and EasyEnsemble, based on a large number of samples. The established optimal model was then applied to the whole study area to map check dams and silted fields. Our results indicate that the imbalance ratio of the samples has a significant impact on the performance of the models. Validation of the results on the testing set show that the F1-score of silted fields of three models is higher than 0.75 at the pixel level. Finally, we produced a map of silted fields and check dams at 10 m-spatial resolution by the optimal model with an accuracy of ca. 90% at the object level. The proposed framework can be used for the large-scale and high-precision mapping of check dams and silted fields, which is of great significance for the monitoring and management of the dynamics of check dams and the quantitative evaluation of their eco-environmental benefits.

Soil salinization and water scarcity are main restrictive factors for irrigated agriculture development in arid regions. Knowing dynamics of soil water and salt content is an important antecedent in remediating salinized soils and optimizing irrigation management. Previous studies mostly used remote sensing technologies to individually monitor water or salt content dynamics in agricultural areas. Their ability to asses different levels of crop water and salt management has been less explored. Therefore, how to extract effective diagnostic features from remote sensing images derived spectral information is crucial for accurately estimating soil water and salt content. In this study, Linear spectral unmixing method (LSU) was used to obtain the contribution of soil water and salt to each band spectrum (abundance), and endmember spectra from Sentinel-2 images. Calculating spectral indices and selecting optimal spectal combination were individually based on soil water and salt endmember spectra. The estimation models were constructed using six machine learning algorithms: BP Neural Network (BPNN), Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), Gradient Boost Regression Tree (GBRT), and eXtreme Gradient Boosting tree (XGBoost). The results showed that the spectral indices calculated from endmember spectra were able to effectively characterize the response of crop spectral properties to soil water and salt, which circumvent spectral ambiguity induced by water-salt mixing. NDRE spectral index was a reliable indicator for estimating water and salt content, with determination coefficients (R²) being 0.55 and 0.57, respectively. Compared to other models, LSU-XGBoost model achieved the best performance. This model properly reflected the process of soil water-salt dynamics in farmland during crop growth period. This study provided new methods and ideas for soil water-salt estimation in dry irrigated agricultural areas, and provided decision support for governance of salinized land and optimal management of irrigation.

Gully erosion is one of the main natural hazards, especially in arid and semi-arid regions, destroying ecosystem service and human well-being. Thus, gully erosion susceptibility maps (GESM) are urgently needed for identifying priority areas on which appropriate measurements should be considered. Here, we proposed four new hybrid Machine learning models, namely weight of evidence -Multilayer Perceptron (MLP- WoE), weight of evidence –K Nearest neighbours (KNN- WoE), weight of evidence - Logistic regression (LR- WoE), and weight of evidence - Random Forest (RF- WoE), for mapping gully erosion exploring the opportunities of GIS tools and Remote sensing techniques in the El Ouaar watershed located in the Souss plain in Morocco. Inputs of the developed models are composed of the dependent (i.e., gully erosion points) and a set of independent variables. In this study, a total of 314 gully erosion points were randomly split into 70% for the training stage (220 gullies) and 30% for the validation stage (94 gullies) sets were identified in the study area. 12 conditioning variables including elevation, slope, plane curvature, rainfall, distance to road, distance to stream, distance to fault, TWI, lithology, NDVI, and LU/LC were used based on their importance for gully erosion susceptibility mapping. We evaluate the performance of the above models based on the following statistical metrics: Accuracy, precision, and Area under curve (AUC) values of receiver operating characteristics (ROC). The results indicate the RF- WoE model showed good accuracy with (AUC = 0.8), followed by KNN-WoE (AUC = 0.796), then MLP-WoE (AUC = 0.729) and LR-WoE (AUC = 0.655), respectively. Gully erosion susceptibility maps provide information and valuable tool for decision-makers and planners to identify areas where urgent and appropriate interventions should be applied.