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Ecological stability (ES) is recognized as a crucial factor for sustainable development at global and regional scales. However, the importance of this factor was not considered significant. Hence, the main aim of this study was to introduce a new approach that focuses on detecting ES over the Maharloo watershed in Iran. To achieve this goal, we extracted land use and land cover (LULC) data from the Google Earth Engine (GEE) platform by applying the random forest (RF) machine learning method, which obtained Kappa statistics of 0.85, 0.86, and 0.87 for the years 2002, 2013, and 2023, respectively. We identified both stable and unstable regions based on LULC changes and employed them using machine learning to forecast the ES. The most important predictors of ES were elevation, soil organic carbon index, precipitation, and salinity. The results of this research revealed that certain areas within the Maharloo watershed have experienced ecological instability in recent years, with gardens showing the highest percentage (60.65 %) of instability among all land-use categories. The performance and validation of our model suggest that the study results are reliable (AUC = 0.86). This study offers detailed maps of ES and trends, offering valuable insights for decision makers to support landscape conservation and restoration efforts. Overall, the findings contribute to a more comprehensive understanding of the ecological dynamics of the Maharloo watershed and provide valuable insights for sustainable development and conservation efforts in other regions.

Abstract In this study, a data-driven approach employed by utilizing the product called JRC-Global surface water mapping layers V1.4 on the Google Earth Engine (GEE) to map and monitor the effects of climate change on surface water resources. Key climatic variables affecting water bodies, including air temperature (AT), actual evapotranspiration (ETa), and total precipitation, were analyzed from 2000 to 2021 using the temperature-vegetation index (TVX) and Moderate Resolution Imaging Spectroradiometer (MODIS) products. The findings demonstrate a clear association between global warming and the shrinking of surface water resources in the LUB. According to the results, an increase in AT corresponded to a decrease in water surface area, highlighting the significant influence of AT and ETa on controlling the water surface in the LUB (partial rho of − 0.65 and − 0.68, respectively). Conversely, no significant relationship was found with precipitation and water surface area (partial rho of + 0.25). Notably, the results of the study indicate that over the past four decades, approximately 40% of the water bodies in the LUB remained permanent. This suggests a loss of around 30% of the permanent water resources, which have transitioned into seasonal water bodies, accounting for nearly 13% of the total. This research provides a comprehensive framework for monitoring surface water resource variations and assessing the impact of climate change on water resources. It aids in the development of sustainable water management strategies and plans, supporting the preservation and effective use of water resources.

Abstract The quantitative spatial analysis is a strong tool for the study of natural hazards and their interactions. Over the last decades, a range of techniques have been exceedingly used in spatial analysis, especially applying GIS and R software. In the present paper, the multi-hazard susceptibility maps compared in 2020 and 2021 using an array of data mining techniques, GIS tools, and Unmanned aerial vehicles. The produced maps imply the most effective morphometric parameters on collapsed pipes, gully heads, and landslides using the linear regression model. The multi-hazard maps prepared using seven classifiers of Boosted regression tree (BRT), Flexible discriminant analysis (FDA), Multivariate adaptive regression spline (MARS), Mixture discriminant analysis (MDA), Random forest (RF), Generalized linear model (GLM), and Support vector machine (SVM). The results of each model revealed that the greatest percentage of the study region was low susceptible to collapsed pipes, landslides, and gully heads, respectively. The results of the multi-hazard models represented that 52.22% and 48.18% of the study region were not susceptible to any hazards in 2020 and 2021, while 6.19% (2020) and 7.39% (2021) of the region were at the risk of all compound events. The validation results indicate the area under the receiver operating characteristic curve of all applied models was more than 0.70 for the landform susceptibility maps in 2020 and 2021. It was found where multiple events co-exist, what their potential interrelated effects are or how they interact jointly. It is the direction to take in the future to determine the combined effect of multi-hazards so that policymakers can have a better attitude toward sustainable management of environmental landscapes and support socio-economic development.

This study explored the innovative use of multiple remote sensing satellites and unmanned aerial vehicles to calculate soil losses in the Loess Plateau of Iran. This finding emphasized the importance of using advanced technologies to develop accurate and efficient soil erosion assessment techniques. Accordingly, this study developed an approach to compare sinkholes and gully heads in hilly regions on the Loess Plateau of northeast Iran using convolutional neural network (CNN or ConvNet). This method involved coupling data from UAV, Sentinel-2, and SPOT-6 satellite data. The soil erosion computed using UAV data showed AUC values of 0.9247 and 0.9189 for the gully head and the sinkhole, respectively. The use of SPOT-6 data in gully head and sinkhole computations showed AUC values of 0.9105 and 0.9123, respectively. The AUC values were 0.8978 and 0.9001 for the gully head and the sinkhole using Sentinel-2, respectively. Comparison of the results from the calculated UAV, SPOT-6, and Sentinel-2 data showed that the UAV had the highest accuracy for calculating sinkhole and gully head soil features, although Sentinel-2 and SPOT-6 showed good results. Overall, the combination of multiple remote sensing satellites and UAVs offers improved accuracy, timeliness, cost effectiveness, accessibility, and long-term monitoring capabilities, making it a powerful approach for calculating soil loss in the Loess Plateau of Iran.

The aim of this study was to analyze the effectiveness of watershed management activities in a representative sub-catchment (treated) and its comparison with a control on, and to identify the erosion sensitive areas in these sub-catchments areas of ​​58 hectares (control) and 83 hectares (treated) the using UAV photogrammetry. Based on a curve number and four other basic maps (elevation, slope, drainage direction, and accumulation density), three daily precipitation scenarios of 20, 40, and 60 mm were considered to model the LANDPLANER with two indices of Erosion Index and Topographic Threshold. The results were evaluated using the boxplot methods, foufold plot, and the area under the receiver operating characteristic curve. The Erosion index (IE) in the LANDPLANER model, considering the maximum daily precipitation of 60 mm, was 0.54821 for the control sub-catchment which was more than that of the treated one (0.15593). The modeling based on the topographic Threshold also showed that the numerical changes of the index between 0-2 in the trated sub-catchment were less than that of the control area(0-3). Therefore, it can be said that due to the appropriate protective operations that had taken place on the treated sub-catchment, this area is in a better condition than the control one, as the volume of soil erosion was less than that of the control sub-catchment. It is informative to know that all of the three precipitation scenarios predict less soil erosion in the treated sub-catchment. Therefore, simulation based on the rainfall and curve number scenarios with LANDPLANER physical model is recommended to evalute the effect of watershed management activities on the paired sub- catchments that are degraded due to soil erosion.

Piping erosion is an influential landform of intensified water erosion that interact with each other in the loess-derived soilsof the Province of Golestan (NE Iran). This study attempts to emphasize the use of high-precision data through field surveys and UAV images, and then to establish a threshold to study the factors that affect piping erosion. The present study was carried out using photogrammetric drones in an area of ​​approximately 2700 hectares in the loess lands in the east of the Province of Golestan and identified areas affected by piping erosion. A total of 833 pipes were recorded using the GPS and the orthophoto images from the UAV. Topographic, hydrological, and biological factors were treated as independent variables and piping erosion as dependent variables through statistical analyzes. The topographic factors indicated that the maximum density of piping occurred at altitudes of between 300–350 m, at maximum slope of more than 30%, at slope length less than 5m, and in concave tracts and profile curvatures. Regarding the hydrological factors, the maximum piping density occurred at the maximum numerical value of the topographic wetness index (more than 12), and at the closest distance to waterways (less than 100m). Biological factors indicated that the maximum density of pipes is in the rangelands and where this erosional facies is closest to the roads. Therefore, it can be suggested that an accurate identification of the piping erosion is facilated through ground observations aided by high-precision areal photography and understanding causative factors developing them.

Abstract The aim of the current study was to suggest a multi-hazard probability assessment in Fars Province, Shiraz City, and its four strategic watersheds. At first, we construct maps depicting the most effective factors on floods (12 factors), forest fires (10 factors), and landslides (10 factors), and used the Boruta algorithm to prioritize the impact of each respective factor on the occurrence of each hazard. Subsequently, flood, landslides, and forest fire susceptibility maps prepared using a Random Forest (RF) model in the R statistical software. Results indicate that 42.83% of the study area are not susceptible to any hazards, while 2.67% of the area is at risk of all three hazards. The results of the multi-hazard map in Shiraz City indicate that 25% of Shiraz city is very susceptible to flooding, while 16% is very susceptible to landslide occurrences. For four strategic watersheds, it is notable that in the Dorodzan Watershed, landslides and floods are the most important hazards; whereas, flood occurrences cover the largest area of the Maharlou Watershed. In contrast, the Tashk-Bakhtegan Watershed is so sensible to floods and landslides, respectively. Finally, in the Ghareaghaj Watershed, forest fire ranks as the strongest hazard, followed by floods. The validation results indicate an AUC of 0.834, 0.939, and 0.943 for the flood, landslide, and forest fire susceptibility maps, respectively. Also, other accuracy measures including, specificity, sensitivity, TSS, CCI, and Gini coefficient confirmed results of the AUC values. These results allow us to forecast the spatial behavior of such multi-hazard events, and researchers and stakeholders alike can apply them to evaluate hazards under various mitigation scenarios.

Understanding of distribution of plants and analyzing their spatial pattern reveal the interactions of plants and environment. Nebka as an indicator phenomenon of plant-sediment interaction, plays an important role in protecting of vegetation and sand stabilization. The aim of the present study was to evaluate the interaction of Nebka and plant based on a field study, measuring the morphometric parameters, and precise point positioning. Therefore, 322 Nebkas and 119 plants were determined in the northern region of Aq-Qala, Golestan province. In order to investigate and determine the spatial pattern and interactions of Nebkas and plants, and impact of soil parameters on their distribution pattern, univariate and bivariate functions including g and mark correlation functions were used. The pair correlation function showed that spatial pattern of plant at distance of less than 23 m was irregular. This means there is no competition between plants at distance of less than 23 m. Furthermore spatial pattern of Nebka was irregular at distance of less than 23 m. Bivariate g function at distances less than 50 m indicated positive correlation between plant and Nebka. Therefore, all plants and Nebkas have a positive impact on formation and distribution of each other. Results of the MCF function with 95% probability showed that soil properties such as EC, pH, OM, SAR, and skewness of Nebka sediments influence the Nebka’s pattern in the study area.

The spatial distribution analysis of trees in arid and semi-arid regions presents valuable information about their interactions and impacts on one another. This study was aimed to investigate how biometric characteristics of mount Atlas mastic (Pistacia atlantica Desf.) are influenced by their clustered spatial distribution in Zagros vegetation zone. For this purpose, a part of Wild Pistachio Research Site, Fars province, was selected and in addition to registering the position of all trees with > 2.5 cm diameter at breast height (DBH), DBH, height, and crown area of each tree were measured. Wild pistachio trees with DBH > 10 cm and < 10 cm were classified as adult and sapling, respectively. To achieve the aims of study, univariate and bivariate forms of mark variogram and mark-correlation function (γ(r), kmm(r), γm1m2(r) and km1m2(r), respectively) were applied. The observed pattern of wild pistachio trees showed significant spatial heterogeneity as compared to homogeneous Poisson process (α=0.05). This heterogeneity was caused by unfavorable environmental conditions of study area. The results of univariate and bivariate γ(r) and kmm(r) showed that DBH, height, and crown area of wild pistachio trees located at three meter distance from each other were less than overall average. Moreover, the results showed that investigated biometric characteristics of wild pistachio saplings that wild pistachio adults were located up to three meter distance from them were less than overall average. In general, it was concluded that aggregation of wild pistachio trees because of environmental heterogeneity negatively influenced their biometric characteristics and prevented their increase.

In the present study, gully erosion susceptibility was evaluated for the area of the Robat Turk Watershed in Iran. The assessment of gully erosion susceptibility was performed using four state-of-the-art data mining techniques: random forest (RF), credal decision trees (CDTree), kernel logistic regression (KLR), and best-first decision tree (BFTree). To the best of our knowledge, the KLR and CDTree algorithms have been rarely applied to gully erosion modeling. In the first step, from the 242 gully erosion locations that were identified, 70% (170 gullies) were selected as the training dataset, and the other 30% (72 gullies) were considered for the result validation process. In the next step, twelve gully erosion conditioning factors, including topographic, geomorphological, environmental, and hydrologic factors, were selected to estimate gully erosion susceptibility. The area under the ROC curve (AUC) was used to estimate the performance of the models. The results revealed that the RF model had the best performance (AUC = 0.893), followed by the KLR (AUC = 0.825), the CDTree (AUC = 0.808), and the BFTree (AUC = 0.789) models. Overall, the RF model performed significantly better than the others, which may support the application of this method to a transferable susceptibility model in other areas. Therefore, we suggest using the RF, KLR, and CDT models for gully erosion susceptibility mapping in other prone areas to assess their reproducibility.

Understanding the ecological processes underlying the spatial distribution of trees is enabled by simulation of their spatial structure within stands. Summary statistics enable modelling the spatial point patterns of trees and provide an efficient representation of the link between point patterns and ecological processes. In this study, five summary statistics, i.e. first-order (intensity function 𝜆(x)), second-order (pair correlation function g(r)), higher-order (T-function T(r)), nearest neighbor (nearest neighbor distribution function D(r)), and morphological (spherical contact distribution function Hs(r)) were used to model the spatial pattern of Christ thorn jujube trees (Ziziphus spina-christi (L.) Wild.) in the south of Fars Province. One real and two simulated homogeneous, 200 × 200 m2 sample plots were selected to investigate the performance of those functions. The results showed that 𝜆(x) significantly followed the homogeneous Poisson process and identified different spatial distributions of Christ thorn jujube trees in three plots. The results also indicated that g(r) was non-cumulative and sensitive to tree patterns in different scales. Although T(r) described the dispersion, randomness, and clustering of trees in the plots, its power to indicate fine structural patterns was not obvious due to low densities of the trees in the plots. The distances to nearest tree were quantified by D(r), which were located about 20 m from each other in all three plots. Finally, the amount of Hs(r) clearly showed the non-randomness patterns of trees in the plots. All in all, it was concluded that different summary statistics characterize different statistical properties of spatial point patterns across the study area.