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Refereed/Peer-reviewed The study and mapping of evidences of soil piping using non-destructive techniques is a key issue in quantitative geomorphology. This paper attempts to combine Aerial mapping systems (Unmanned Aerial Vehicles (UAV)), soil physical and chemical attributes, and near-surface geophysical survey tools (ground penetrating radar and electrical resistivity tomography) to provide a comprehensive understanding of geometric features of soil piping in arid and semi-arid regions. The UAV Mapping System was applied to prepare ortho-photos, topography, analytical hill shading, drainage density, and land use maps of two different piping sites (site 1: rangelands, and site 2: agricultural lands) in Sarakhs plain, Razavi Khorasan Province, north eastern Iran. The physical and chemical soil attributes were analyzed in six soil profiles to check the hypothesis that these soil attributes control the occurrence of piping-related features (i.e., sinkhole, blind gully, gully), and to test if there are any differences in soil properties between the two land use types. The near surface geophysical tools were used to determine the approximate size of soil pipes, and to simulate their internal structure. The results of the derived UAV’s independent variables confirmed that typical soil erosion features related to piping (i.e., sinkhole, blind gully, gully) developed exactly in adjustment with subsurface processes (i.e., drainage density). The quantitative algorithms of pedology revealed significant differences of Na+ and SAR for soil profiles with and without piping erosion, but these soil properties themselves are not enough to explain piping development at the two study sites. The volume of GPR line surveys was 1701.5 m3 in site 1 and 1203 m3 in site 2. The potential distribution of subsurface pipes revealed by applying GPR were more extensive than those deduced from soil surface observations: three-dimensional pipe number density (number of soil pipes per unit soil volume; # m−3) of potential pipes and collapsed cavities simulated with a mean pipe length and pipe depth of 143.4 cm and 88 cm in rangelands and 175.75 cm and 79.14 cm in agricultural lands, respectively. The maximum density of pipes or pipe roof collapses occurred within soil depth boundaries of 0–50 cm at site 1 and 30–100 cm at site 2. The ratio of surface erosion features to subsurface potential piping in rangeland and agricultural lands was ca. 26.1 % and 10.4 %, respectively. The electrical resistivity tomography (ERT) measurements indicated higher resistivity values in piping-prone areas, where pipes were initiated at the bedrock interface. Integrating all data obtained by different techniques in this study allowed to better understand the extent of piping erosion in both surface and subsurface soil horizons.

This investigation assessed the efficacy of 10 widely used machine learning algorithms (MLA) comprising the least absolute shrinkage and selection operator (LASSO), generalized linear model (GLM), stepwise generalized linear model (SGLM), elastic net (ENET), partial least square (PLS), ridge regression, support vector machine (SVM), classification and regression trees (CART), bagged CART, and random forest (RF) for gully erosion susceptibility mapping (GESM) in Iran. The location of 462 previously existing gully erosion sites were mapped through widespread field investigations, of which 70% (323) and 30% (139) of observations were arbitrarily divided for algorithm calibration and validation. Twelve controlling factors for gully erosion, namely, soil texture, annual mean rainfall, digital elevation model (DEM), drainage density, slope, lithology, topographic wetness index (TWI), distance from rivers, aspect, distance from roads, plan curvature, and profile curvature were ranked in terms of their importance using each MLA. The MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE (root mean square error), MAE (mean absolute error), and R-squared. Based on the comparisons among MLA, the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared, and was therefore selected as the best model. The variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least importance. According to the GESM generated using RF, most of the study area is predicted to have a low (53.72%) or moderate (29.65%) susceptibility to gully erosion, whereas only a small area is identified to have a high (12.56%) or very high (4.07%) susceptibility. The outcome generated by RF model is validated using the ROC (Receiver Operating Characteristics) curve approach, which returned an area under the curve (AUC) of 0.985, proving the excellent forecasting ability of the model. The GESM prepared using the RF algorithm can aid decision-makers in targeting remedial actions for minimizing the damage caused by gully erosion.

Landslides influence the capacity for safe and sustainable development of mountainous environments. This study explores the spatial distribution of and the interactions between landslides that are mapped using global positioning system (GPS) and extensive field surveys in Mazandaran Province, Iran. Point-pattern assessment is undertaken using several univariate summary statistical functions, including pair correlation, spherical-contact distribution, nearest-neighbor analysis, and O-ring analysis, as well as bivariate summary statistics, and a mark-correlation function. The maximum entropy method was applied to prioritize the factors controlling the incidence of landslides and the landslides susceptibility map. The validation processes were considered for separated 30% data applying the ROC curves, fourfold plot, and Cohen’s kappa index. The results show that pair correlation and O-ring analyses satisfactorily predicted landslides at scales from 1 to 150 ​m. At smaller scales, from 150 to 400 ​m, landslides were randomly distributed. The nearest-neighbor distribution function show that the highest distance to the nearest landslide occurred in the 355 ​m. The spherical-contact distribution revealed that the patterns were random up to a spatial scale of 80 ​m. The bivariate correlation functions revealed that landslides were positively linked to several linear features (including faults, roads, and rivers) at all spatial scales. The mark-correlation function showed that aggregated fields of landslides were positively correlated with measures of land use, lithology, drainage density, plan curvature, and aspect, when the numbers of landslides in the groups were greater than the overall average aggregation. The results of analysis of factor importance have showed that elevation (topography map scale: 1:25,000), distance to roads, and distance to rivers are the most important factors in the occurrence of landslides. The susceptibility model of landslides indicates an excellent accuracy, i.e., the AUC value of landslides was 0.860. The susceptibility map of landslides analyzed has shown that 35% of the area is low susceptible to landslides.

The instability of soil aggregates is one of the most important land degradation processes in different parts of Iran, particularly in arid and semi-arid regions with loess-derived soils. In this study, we modeled soil stability (measured as soil bulk density (BD) of the topsoil layer) using two algorithms: random forest (RF) and support vector regression (SVR). In this study, we used nine environmental variables, driven by unmanned aerial vehicles (UAV) and soil samples collected from a depth of 0–20 cm to map BD. The RF algorithm was applied to prioritize the factors affecting the BD. The Results were assessed using RMSE, MSE, and R2. The results of the variable importance analysis showed that distance to a river, distance to road, and altitude are the most important factors affecting soil stability. The prediction model for mapping soil BD has reasonable accuracy, i.e., the RMSE values of the training set of RF and SVR models were 0.08 (R2 = 0.95) and 0.16 (R2 = 0.21), respectively. We recognized that the methodology of this study could easily produce a soil property probability map (BD in this study) in the topsoil layer.

Rangelands, as one of the most valuable national assets, are characterized by plant communities which contain complex functional interactions. A major objective of plant ecology is simplification of complex spatial interactions, especially in monitoring activities. In this respect, vegetation indicators have an important role for monitoring the desertification trend in these crucial communities. Furthermore, identifying the connection between plant patterns and their underlying ecological mechanisms, such as competition and facilitation, is essential to predict how climate and human changes alter plant community organization. In this study, the conceptual model was introduced to summarize vegetation transition shifts in various dynamics states in rangeland. Firstly, detailed field studies were carried out to investigate the pattern of Halocnemum strobilaceum (Ha. St.) shrubs in Sufikam plain by using spatstat package in R software. Secondly, statistical results were introduced to the monitoring model for assessing vegetation transition shifts. The results of statistical analysis of spatial data in 2009 revealed that the Ha. St. shrubs had dispersed distribution up to scales of 17 m and these pattern was a significant departure from random labeling at the scales of 0–7 m. In 2017, the pattern of Ha. St. shrubs showed a greater tendency to clustered pattern at the scales of 0–18 m and after that they had a random pattern at the scales of 18–50 m. According to the new proposed model, the dispersed distribution in 2009 indicated a healthy state for Ha. St. shrubs, while a more aggregated pattern in 2017 reflected intraspecific competition for scarce resources. Thus, it was concluded that the model is useful for rangeland managers to get a correct understanding of eradication driving forces and destructive activities in these areas.