Gully Erosion Susceptibility Mapping Using Multivariate Adaptive Regression Splines—Replications and Sample Size Scenarios

Environmental impacts of corn silage production: influence of wheat residues under contrasting tillage management types

The intensification of specific land management operations (tillage, herbicide, etc.) is increasing land degradation and contributing to ecosystem pollution. Mulches can be a sustainable tool to counter these processes. This is particularly relevant for rural areas in low-income countries where agriculture is a vital sector. In this research, the environmental impact of different rates of wheat residues (no residues, 25, 50, 75, and 100%) in corn silage cultivation was evaluated using the life cycle assessment (LCA) method under conventional tillage (CT) and no-tillage (NT) systems in a semi-arid region in Karaj, Iran. Results showed that in both tillage systems, marine aquatic ecotoxicity (ME) and global warming potential (GWP) had the highest levels of pollution among the environmental impact indicators. In CT systems, the minimum (17,730.70 kg 1,4-dichlorobenzene (DB) eq.) and maximum (33,683.97 kg 1,4-DB eq.) amounts of ME were related to 0 and 100% wheat residue rates, respectively. Also, in the CT system, 0 and 100% wheat residue rates resulted in minimum (176.72 kg CO₂ eq.) and maximum (324.95 kg CO₂ eq.) amounts of GWP, respectively. However, in the NT system, the 100% wheat residue rate showed the minimum amounts of ME (11,442.39 kg 1,4-DB eq.) and GWP (120.21 kg CO₂ eq.). Also, in the NT system, maximum amounts of ME (17,174 kg 1,4-DB eq.) and GWP (175.60 kg CO₂ eq.) were observed with a zero wheat residue rate. On-farm emissions and nitrogen fertilizers were the two factors with the highest contribution to the degradation related to environmental parameters at all rates of wheat residues. Moreover, in the CT system, the number of environmental pollutants increased with the addition of a higher wheat residue rate, while in the NT system, increasing residue rates decreased the amount of environmental pollutants. In conclusion, this LCA demonstrates that the NT system with the full retention of wheat residues (100%) is a more environmentally sustainable practice for corn silage production. Therefore, it may be considered one of the most adequate management strategies in this region and similar semi-arid conditions. Further long-term research and considering more environmental impact categories are required to assess the real potential of crop residues and tillage management for sustainable corn silage production.

Entropy-based Model for Gully Erosion - A combination of probabilistic and deterministic components

Gullies are a major threat to ecosystems, potentially leading to land degradation, groundwater depletion, crop loss, debris flow, and desertification. Gullies are also characterized by having a fast development and turning into primary sediment sources. Despite their impact, we have but scarce understanding of how gully erosion evolves and how to model it. In this paper, we propose a new gully erosion model that is based on the classical premise of net shear stress, i.e., hydraulic shear stress minus critical (resistant) shear stress, to calculate detachment rates. In order to calculate hydraulic shear stress, we developed a new equation derived from the principle of minimum cross-entropy; it was validated with laboratory measures from the literature with a Nash-Sutcliffe Efficiency of 0.95. Soil samples were analysed in the laboratory to assess critical shear stress and other soil properties. The novel gully erosion model was implemented in three gully impacted locations with catchment areas ranging from 10^-2 to 10⁺¹ ha. To assess channel geometry and eroded volumes, we used Unmanned Aerial Vehicle and Structure-from-Motion technique. The model successfully estimated long-term erosion rates, its efficiency was 0.77, and it is recommended for catchments up to 8 ha. Therefore, the new model provides planners and stakeholders with a tool to assess gully erosion, sediment yield and geometry in most areas.

CMIP5 climate projections and RUSLE-based soil erosion assessment in the central part of Iran

Soil erosion (SE) and climate change are closely related to environmental challenges that influence human wellbeing. However, the potential impacts of both processes in semi-arid areas are difficult to be predicted because of atmospheric variations and non-sustainable land use management. Thus, models can be employed to estimate the potential effects of different climatic scenarios on environmental and human interactions. In this research, we present a novel study where changes in soil erosion by water in the central part of Iran under current and future climate scenarios are analyzed using the Climate Model Intercomparison Project-5 (CMIP5) under three Representative Concentration Pathway-RCP 2.6, 4.5 and 8.5 scenarios. Results showed that the estimated annual rate of SE in the study area in 2005, 2010, 2015 and 2019 averaged approximately 12.8 t ha^-1 y^-1. The rangeland areas registered the highest soil erosion values, especially in RCP2.6 and RCP8.5 for 2070 with overall values of 4.25 t ha^-1 y^-1 and 4.1 t ha^-1 y^-1, respectively. They were followed by agriculture fields with 1.31 t ha^-1 y^-1 and 1.33 t ha^-1 y^-1. The lowest results were located in the residential areas with 0.61 t ha^-1 y^-1 and 0.63 t ha^-1 y^-1 in RCP2.6 and RCP8.5 for 2070, respectively. In contrast, RCP4.5 showed that the total soil erosion could experience a decrease in rangelands by - 0.24 t ha^-1 y^-1 (2050), and - 0.18 t ha^-1 y^-1 (2070) or a slight increase in the other land uses. We conclude that this study provides new insights for policymakers and stakeholders to develop appropriate strategies to achieve sustainable land resources planning in semi-arid areas that could be affected by future and unforeseen climate change scenarios.

Evaluation of multi-hazard map produced using MaxEnt machine learning technique

Natural hazards are diverse and uneven in time and space, therefore, understanding its complexity is key to save human lives and conserve natural ecosystems. Reducing the outputs obtained after each modelling analysis is key to present the results for stakeholders, land managers and policymakers. So, the main goal of this survey was to present a method to synthesize three natural hazards in one multi-hazard map and its evaluation for hazard management and land use planning. To test this methodology, we took as study area the Gorganrood Watershed, located in the Golestan Province (Iran). First, an inventory map of three different types of hazards including flood, landslides, and gullies was prepared using field surveys and different official reports. To generate the susceptibility maps, a total of 17 geo-environmental factors were selected as predictors using the MaxEnt (Maximum Entropy) machine learning technique. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic-ROC curves and calculating the area under the ROC curve-AUCROC. The MaxEnt model not only implemented superbly in the degree of fitting, but also obtained significant results in predictive performance. Variables importance of the three studied types of hazards showed that river density, distance from streams, and elevation were the most important factors for flood, respectively. Lithological units, elevation, and annual mean rainfall were relevant for detecting landslides. On the other hand, annual mean rainfall, elevation, and lithological units were used for gully erosion mapping in this study area. Finally, by combining the flood, landslides, and gully erosion susceptibility maps, an integrated multi-hazard map was created. The results demonstrated that 60% of the area is subjected to hazards, reaching a proportion of landslides up to 21.2% in the whole territory. We conclude that using this type of multi-hazard map may be a useful tool for local administrators to identify areas susceptible to hazards at large scales as we demonstrated in this research.

Susceptibility to Gully Erosion: Applying Random Forest (RF) and Frequency Ratio (FR) Approaches to a Small Catchment in Ethiopia

Soil erosion by gullies in Ethiopia is causing environmental and socioeconomic problems. A sound soil and water management plan requires accurately predicted gully erosion hotspot areas. Hence, this study develops a gully erosion susceptibility map (GESM) using frequency ratio (FR) and random forest (RF) algorithms. A total of 56 gullies were surveyed, and their extents were derived by digitizing Google Earth imagery. Literature review and a multicollinearity test resulted in 14 environmental variables for the final analysis. Model prediction potential was evaluated using the area under the curve (AUC) method. Results showed that the best prediction accuracy using the FR and RF models was obtained by using the top four most important gully predictor factors: drainage density, elevation, land use, and groundwater table. The notion that the groundwater table is one of the most important gully predictor factors in Ethiopia is a novel and significant quantifiable finding and is critical to the design of effective watershed management plans. Results from separate variable importance analyses showed land cover for Nitisols and drainage density for Vertisols as leading factors determining gully locations. Factors such as texture, stream power index, convergence index, slope length, and plan and profile curvatures were found to have little significance for gully formation in the studied catchment.

Assessing, mapping, and optimizing the locations of sediment control check dams construction

Check dams are considered to be one of the most effective measures for conservation of the soil and water resources. However, identifying the most suitable sites for the installation of check dams remain quite demanding. This research investigates and compares five machine learning algorithms (MLAs) - boosted regression trees (BRT), multivariate adaptive regression spline (MARS), mixture discriminant analysis (MDA), random forest (RF), and support vector machine (SVM) - for generating check-dam site-suitability maps (CDSSMs) and assessing them in Firuzkuh County, Iran. First, the locations of 475 existing check dams were monitored, registered, and divided into calibration (70%) and testing datasets (30%) for training and validation of the models. Fourteen check-dam conditioning factors (CDCFs) were selected and checked for multicollinearity. The relative importance of the CDCFs assessed using the elastic net (ENET) algorithm. Results demonstrated that distance from river (DFR) and drainage density (DD) to be the most significant factors for mapping the suitable sites for the erection of check dams. This research revealed that all of five MLAs had excellent accuracy for predicting the check-dam site-suitability with high AUC values: RF (0.966), SVM (0.878), MARS (0.878), MDA (0.844), and BRT (0.843). The most accurate model (RF) showed that 16.95%, 35.55%, 31.08%, and 16.42% of study area comes under low, moderate, high, and very high suitability classes. The outcome achieved by this research will be helpful to sustainability planners and managers in constructing check dams at suitable sites for better conservation of soil and water resources.

Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility Using ALOS/PALSAR Data

Piping erosion is one form of water erosion that leads to significant changes in the landscape and environmental degradation. In the present study, we evaluated piping erosion modeling in the Zarandieh watershed of Markazi province in Iran based on random forest (RF), support vector machine (SVM), and Bayesian generalized linear models (Bayesian GLM) machine learning algorithms. For this goal, due to the importance of various geo-environmental and soil properties in the evolution and creation of piping erosion, 18 variables were considered for modeling the piping erosion susceptibility in the Zarandieh watershed. A total of 152 points of piping erosion were recognized in the study area that were divided into training (70%) and validation (30%) for modeling. The area under curve (AUC) was used to assess the effeciency of the RF, SVM, and Bayesian GLM. Piping erosion susceptibility results indicated that all three RF, SVM, and Bayesian GLM models had high efficiency in the testing step, such as the AUC shown with values of 0.9 for RF, 0.88 for SVM, and 0.87 for Bayesian GLM. Altitude, pH, and bulk density were the variables that had the greatest influence on the piping erosion susceptibility in the Zarandieh watershed. This result indicates that geo-environmental and soil chemical variables are accountable for the expansion of piping erosion in the Zarandieh watershed.

Pollution Assessment Based on Element Concentration of Tree Leaves and Topsoil in Ayutthaya Province, Thailand

Atmospheric aerosol particles containing heavy metal contaminants deposit on the surface of plant leaves and the topsoil. Our aim was to reveal the pollution along an industrial–urban–rural gradient (IURG) in the central provinces of Thailand. Leaf samples from Ficus religiosa and Mimusops elengi were collected along with topsoil samples under the selected trees. Al, Ba, Ca, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, and Zn concentrations were determined by ICP-OES in soil and plant samples. Soils were not polluted according to the critical value; furthermore, the elemental composition did not differ among the sampling sites of the IURG. The rural site was also polluted due to heavy amounts of untreated wastewater of the adjacent Chao Phraya River. Bioaccumulation factors of Ba, Cu, and Mn was higher than 1, suggesting active accumulation of these elements in plant tissue. Our findings proved that the deposition of air pollutants and the resistance to air pollutants in the case of plant leaves were different and that humus materials of the soils had relevant role in bioaccumulation of Al, Ba, and Cu. At the same time, the geochemical background, the source of pollution, and the local plant species greatly influence the metal content of any given environmental compartment.

Automatic Gully Detection: Neural Networks and Computer Vision

Transition from manual (visual) interpretation to fully automated gully detection is an important task for quantitative assessment of modern gully erosion, especially when it comes to large mapping areas. Existing approaches to semi-automated gully detection are based on either object-oriented selection based on multispectral images or gully selection based on a probabilistic model obtained using digital elevation models (DEMs). These approaches cannot be used for the assessment of gully erosion on the territory of the European part of Russia most affected by gully erosion due to the lack of national large-scale DEM and limited resolution of open source multispectral satellite images. An approach based on the use of convolutional neural networks for automated gully detection on the RGB-synthesis of ultra-high resolution satellite images publicly available for the test region of the east of the Russian Plain with intensive basin erosion has been proposed and developed. The Keras library and U-Net architecture of convolutional neural networks were used for training. Preliminary results of application of the trained gully erosion convolutional neural network (GECNN) allow asserting that the algorithm performs well in detecting active gullies, well differentiates gullies from other linear forms of slope erosion — rills and balkas, but so far has errors in detecting complex gully systems. Also, GECNN does not identify a gully in 10% of cases and in another 10% of cases it identifies not a gully. To solve these problems, it is necessary to additionally train the neural network on the enlarged training data set.