Journal of Range and Watershed Managment

Abstract Soil moisture in the upper soil layers plays a vital role in water and soil resource management, directly influencing infiltration, runoff, agricultural productivity, and flood regulation. Its spatial variability is controlled by multiple factors, including climate conditions, topography, vegetation, and soil characteristics. Neglecting these variations often leads to significant errors in hydrological and agricultural modeling. This study investigates the relationship between geomorphometric indices and surface soil moisture across five sub-basins of the Simineh and Zarrineh rivers in northwest Iran, using both field observations and satellite data. Soil moisture measurements from 287 points (2015–2017) were compared with Soil Moisture Active Passive (SMAP) satellite estimates to generate high-resolution spatial maps. Several geomorphometric indices were derived, including the Topographic Wetness Index (TWI), Topographic Position Index (TPI), Wind Exposure Index (WEI), flow direction (Flow_D), flow accumulation, and Analytical Hillshading (AH). The Random Forest (RF) model was applied to determine the importance of geomorphometric attributes. Validation results revealed a strong correspondence between SMAP data and field observations, with July showing the highest correlation (r = 0.77, soil moisture = 0.18 cm³·cm⁻³) and May the lowest (r = 0.50). The RF model achieved robust performance (R² > 0.7, RMSE = 0.04%). Among the indices, WEI and TWI exhibited the greatest importance (>16%), followed by AH (13%), while Flow_D had the lowest influence (8.9%). These findings confirm the significant role of topographic and hydrological features in controlling soil moisture distribution. The integration of SMAP data with machine learning and geomorphometric indices provides a reliable framework for soil moisture monitoring, offering valuable insights for agricultural management, hydrological modeling, and environmental planning in similar watersheds.

Abstract Wild sheep (Ovis orientalis) are a critical component of wildlife biodiversity in Iran and are categorized as Vulnerable (VU) on the IUCN Red List. This species plays a crucial role in maintaining the integrity of rangeland ecosystems and contributes to ecological balance within their habitats. Variations in land characteristics (including climate, topography, soil, vegetation, hydrological factors, and land use) result in distinct habitat suitability classifications for this species. This study involved long-term observational research on wild sheep behavior over a decade, aiming to identify the most influential factors affecting habitat suitability and to generate a habitat suitability map using machine learning algorithms alongside the Analytical Hierarchy Process (AHP) in Khabr National Park. The findings indicate that the region has relatively high suitability for this species, with elevation, slope, vegetation cover, and proximity to water resources emerging as the most significant factors. Validation of the results using the kappa coefficient and the overall accuracy index confirms the high precision of the findings. This underscores the value of integrating machine learning models with AHP in habitat suitability assessments, aiding management in understanding the species’ ecological requirements and identifying priority conservation areas.

Abstract Rangelands are one of the essential components of natural ecosystems and are known as one of the main sources of forage supply, biodiversity conservation, soil and water resources. The aim of this study was to investigate the ecological potential of rangelands in Alborz province based on the Analytic Hierarchy Process (AHP) and pairwise comparisons using Geographic Information System (GIS). The digital map of ecological potential (EP) was prepared by weighting and integrating maps of slope, land unit, vegetation cover, land use, and precipitation. The results showed that each of the five criteria had equal importance coefficients (0.2) for determining the EP of rangelands. Overall, 21.77% of the province's area was unsuitable for rangeland use due to rocky outcrops. 15.41% of the province's area had first-degree potential and 37.28% had second-degree potential for rangeland use. Taleqan County had the highest first-degree potential for rangeland use with 56,321 hectares, and Karaj County had the highest second-degree potential with 96,507 hectares. Nazarabad County had no first-degree potential for rangeland use, and Taleqan County had no fourth-degree potential. Nazarabad County and Eshtehard County also had 48.15% and 63.92% potential for third- and fourth-degree rangeland use, respectively. Based on the weighting of different factors, slope was the most limiting factor for rangeland use, accounting for more than 50% of the limitation. In general, determining the EP of rangelands leads to identifying areas that are very suitable for optimal use of natural resources, and can increase rural income and employment both economically and socially.

Abstract Soil resource management is essential to maintain community production and the environment. Soil is usually used to produce agricultural products and livestock fodder. As a result, the mapping of high-resolution digital maps is crucial for the distribution of soil and soil properties and land management. The decision tree model is a widely used method for predicting soil class in digital soil mapping studies. This study aimed to provide a digital soil mapping in four levels of taxonomy using a decision tree with Boost-reinforced C5.0 algorithm using satellite data and digital Elevation Model and geological maps as environmental variables in 41,000 hectares of Abyek Area. This area was identified using randomized gridding of the geographic location of 128 soil profiles and then described, sampled, and classified. In this research, using the principal component analysis method on environmental variables, 20 environmental variables were selected as the representative of stacking factors for modeling. Multiresolution Valley Flatness Index is the most important environmental variable that was selected as input for the model. The results of the overall accuracy of the integrated model for predicting taxonomic levels of the Order, Suborder, great group, and subgroup were shown to be 89%, 85%, 58%, and 58%, respectively. The study also examined the effect of the boosting technique on the tree model, which showed that all taxonomic levels were better predicted by using the boost model than when no boosting was used and boosting resulted in an increase in overall accuracy and kappa coefficient It turned out.

Abstract Conventional soil mapping is related to High density sampling, affected by scale and expert knowledge So using of new data mining methods in digital soil properties mapping was the main aim of this study for resolving conventional soil survey problems. In this research, 62 surface soil samples based on regular grid and expert knowledge opinion were selected after that soil organic carbon(SOC), clay content and CaCO3 were determined in some part of Dryland Kuhin region with area of 372 ha. Data sets were divided to two 80%(calibration) and 20%(validation), respectively. From digital elevation model with 10-meter spatial resolution were derived 19 geomorphometric attribute in SAGA GIS software. Three geomorphometric covariate included TPI, TRI, DEM and landform map unit were chosen PCA and expert knowledge. RStudio and SoLIM Solution software were used for random forest (RF) and fuzzy logic modelling, respectively. The RF modelling results show that for SOC, clay and CaCO3 based on determination coefficient (R2) had 0.63,0.75,0.63 and RMSE 0.17,7.5,5.77 percentage and for SoLIM method revealed that R2 0.47,0.42,0.42 and RMSE 0.2,8.08,4.68 percentage, respectively. Generally, the RF model with creating nonlinear relationship among soil properties and environmental covariate can predicted digital map with appropriate precision for management and sustainable land utilization

Abstract In digital soil mapping, soil characteristic and classes could be extracted truly by numerical and quantitative modelling. Hence, derived rules could be fitted to similar regions for achieving ruled relations on areas without soil information which is called as extrapolation. In the present study, achieving digital soil class map of an area without adequate soil information by Random forest was tested by extrapolation at great group level. The results show overall accuracy 88% and kappa 0.77 of donor area which is able to fit over its similar region. Results of extrapolation show overall accuracy 81% and kappa 0.61 of recipient area which could show logical concordance to produce soil class map of recipient area without applying related soil samples of this area in short time and low cost. Since, extrapolation could be as an efficient way to predict soil classes of unknown areas.

Abstract Soil is known as a dynamic media so it easily degrade with inapplicable usage so with increasing in degradation of this limited source, the world’s food safety would be in danger. Thus, applicable and sustainable usage of agricultural lands are become an essential and inevitable agenda. Therefore, the aim of this study is to Digital soil mapping using decision tree for agricultural land suitability, In order to constitute management programs for sustainable use of agricultural lands. For this aim, samples were collected based on cLHS and after some laboratory experiments, modeling and digital soil mapping were created by Random Forest Model. Also, agricultural land suitability for dominant crops were investigated by parametric method. The results showed that the land evaluation for irrigated wheat with surface irrigation 75.27% of the total area S2 class and 24.73% of the land in the class S3, respectively. In assessing the suitability of land for Maize irrigation, 14.78% of the land in classes S1, S2 84.82 of class and 0.39% of the land in the class S3, respectively. Results for alfalfa irrigation land evaluation showed that 11.10 percent of the land in classes S1, 88.49% in the S2 class and 0.4% of the class S3, respectively

Abstract Data mining enables generalization of data of soil to remote areas and which is able to up/down scale of data in wide ranges of level that facilitate the decision-making process of executives. Cation Exchange Capacity (CEC) is one of the most important parameters in soil database and shows the ability of a soil to retention of minerals and pollutants. Due to low organic matter and specific mineralogy of soils in arid and semi-arid regions, measurement of CEC is time consuming and expensive. The objective of this study was to evaluate Coactive Neuro-Fuzzy Inference System (CANFIS) in prediction of CEC in soils of arid and semi-arid regions. A total of 85 soil samples from target area were selected among 440 soil sample database (available reference database) with a ratio of 1:5. Correlation test was conducted to assess the co-linearity of independent variables. Forward regression model was used to determine the most important and influential input parameters on the output results. The results indicated the reliability and high performance of the CANFIS approach in estimation of CEC using easily measurable characteristics, organic material, and satellite images.

Abstract Nowadays, the importance of soil conservation in agriculture and natural resources, with the goal of preventing its deterioration and degradation are necessary. Soil and land degradation as a direct cause of the threat to the global environment and human welfare is evident. In this study soil degradation mapping was carried out in East Qazvin. Soil map using geopedological method by integration of information layer of lithology, geomorphic and pedogenic was prepared in ArcGIS9.3 software. Data from soil maps with field studies was used as input in GLASOD model and the soil degradation map was prepared. In this study soil map units was used as basis of soil degradation status investigating in the region. Results showed that less than 25% of the study area has a low degree of degradation and in the present circumstances do not require specific management actions, but in other parts of the region, with various degrees of soil degradation was observed. Soil chemical properties degradation include decrease of soil organic matter, loss of soil nutrients and soil salinity are the most important aspects affecting on soil degradation of region. At total of 16,630 hectares of land, about 4028 ,5987, 5128 and 866 respectively low, middle, high and very high soil degradation class are located. Thus according to the results to prevent the spread of this process in this area, management actions is recommended.

Abstract This research was done in order to submit a model for salinity map made with TM satellite data and salinity values in a Buienzahra. The necessary processings such as principal component analysis and producing of different indices was done on the main bands. The 38 soil samples using random sampling (with 10×10 km dimension) from different horizons were designed and performed on the study area. The position of each node was registered with global positioning system (GPS), and the surface electric conductivity of samples was measured using EC meter instrument in soil saturation extract. Correlation between spectral values (main bands, produced indices) with electrical conductivity values were investigated for 80% of the samples. The regression analysis of ECe showed that there is a significant correlation between ECe with spectral data in all of main bands and with BI, NDMI, SI1, SI2, SI3 indices in 99% levels. The accuracy assessment of estimations using validation 20% samples was done. Results showed the produced ECe model could predict the soil salinity with ME and RMSE of 0.08 and 2.53 dS/m respectively. At finally, Salinity map with different salinity classes ( 0-2, 2-4, 4-16, 16-32, 32< dS m-1) was produced.

Abstract Today, extensive improvements in fields of soil mapping have increased the purity and accuracy of
soil maps. Usual mapping methods moreover depend on skills and experience of surveyor in
identifying and delineating the boundaries, also need the high cost and time consuming that face the
soil mapping with restrictions. In this study, aerial photographs with 1/40000 scale were used in order
to preparation of the initial interpretive map and determination of sample region. Then, the numbers of
24 profiles were described in determined units. After sampling and necessary physicochemical tests,
soil map of Kouhin (Qazvin) was prepared and accuracy of map was calculated in two methods in all
levels of taxonomy. The first method was formation of error matrix and calculation of kappa index and
second was comparison the geopedological map with described profiles and evaluation the results of
each. Then a part of a geopedological map that had overlapping with map prepared by usual method
was compared with this map. Results showed the overall accuracy of 67.5, 90.5 and 98.5 percent in
levels of family- subgroup and great group- suborder and order of soil for geopedological method
respectively.

Abstract Evaluation soil erosion is an important matter to protecting it from destroying in future due to the excessive use of the inherent capacity of soil and also improper management. Therefore, in this study land vulnerability related to water and wind erosion of Abyek for crops and pasture land has been evaluated, using Raizal model as one of the Microleis sub-models. Morphological and physical and chemical analysis data were obtained of studying and evaluating the 32 soil profiles. Agricultural - climatic and management information including temperature and precipitation data were collected from Bagh-e-Kosar climatological station for the last 17 years. To study the effect of climatic changes in the assessment of land area for the year 2080 AD (for 70 future year), the reports of International Panel on Climate Change (IPCC) allocated to West Asia have been used. Utilization types considered for evaluation, including wheat, corn, barley and alfalfa. The results obtained of Land vulnerability evaluation studies related to water and wind erosion using Raizal model part have been prepared as maps in the GIS environment. Information obtained from Land vulnerability models related to wind erosion for crops and pasture land, also implies vulnerability risk fora wide percent of the region lands under the current management, that the results evaluation of the proposed management methods express the improvement of destroying ability classes.

Abstract In recent years, alternative methods have been used for estimation of soil salinity. Therefore, at present research, 600 soil samples collected from Ardakan in central Iran. Then EM38 and terrain parameters such as wetness index, land index and curvature as readily measured properties and soil salinity (0-30 and 0-100) as predicted variables were measured. After that, the data set was divided into two subsets for calibration (80%) and testing (20%) of the models. For predicting of mentioned parameters, ANFIS, GA, ANNs and MLR were applied. In order to evaluate models, some evaluation parameters such as root mean square, average error, average standard error and coefficient of determination were used. Results showed that the ANFIS model gives better estimation than the other techniques for all characteristics whereas this model increased accuracy of predictions about 17 and 11% for EC30 and EC100 respectability. After ANFIS model, GA and ANN had better accuracy than multivariate regression.

Abstract The traditional fuzzy operators, such as t-norms, t-conorms and averaging operators have been used for soil suitability evaluation to aggregate criteria as an overall suitability index. However, such operators do not account for the degree of compensation common to human aggregation criteria, especially in the presence of conflicting criteria. Fuzzy integrals are powerful and flexible aggregation functions that combine the data provided by several information sources based on fuzzy measures. One of the most known fuzzy integrals is the Choquet Integral (CI) that is often used as a nonlinear aggregation tool that takes into account the interactions among the conflicting and interrelated criteria. CI has hardly ever been used in soil suitability evaluation. In this paper, we tested the CI performance as an aggregation operator to evaluate soil suitability for irrigated rice in Lorestan province, western Iran. To conduct standard fuzzy analysis, soil samples were taken from 29 farms under rice cultivation and for the purpose of validation, average dry matters of the three 1×1m quadrates were used to determine the grain yield of rice in each farm. Fuzzy membership values of seven evaluation criteria were combined using CI to obtain a single suitability index in each land unit. In order to validate the results, the soil suitability indices were tested and compared with the results obtained by Lukasiedwicz’s t-norm and t-conorm operator (CLO) by correlation with the measured rice yield. The results show R2 values were fairly high for both aggregated methods, but the soil suitability index obtained by the CI integral has significantly better agreement with rice yield (R2 = 0.83) than those obtained with the CLO (R2 = 0.75). These results call for further investigation of CI as an alternative, and perhaps a better, aggregation operator for soil suitability evaluation.

Abstract Investigation and preparing of wind erosion intensity maps are the main objectives of this research. In this research, the working unit map was prepared and then nine effective factors in wind erosion according to IRIFR.E.A model are determined at each of seven homogeneous units. According to the results, active sand dunes and landuse change showed the highest value with the rate of annual sedimentation more than 6000 ton/km2 while lands surrounding the villages showed the least value with an annual rate of sedimentation of 150-500 ton/km2. Also considering whole area which is about 16161 hectares, around 1978 hectares are classified in low class of desertification; 7430 hectares in medium class while 6753 hectares in high and very high classes.

Abstract Sustainable exploitation of land resources is directly affected by considering soil quality which finally will also conclude environmental protection. Therefore, assessing different soil quality aspects which are sensitive to various land management practices seems too important. In this study, some selected soil quality indicators have been compared in five land use systems including untouched rangelands, semi degraded rangelands due to grazing, rangelands that converted to rainfed agriculture, abandoned rainfed agriculture and an irrigated wheat farm in eastern Qazvin province, Iran. Samples were taken from the surface layer (A horizon) of soils in a completely randomized design with four replications. Statistical comparisons of the results revealed highest decrease in soil organic matter and total nitrogen owing to abandoned rainfed agriculture that showed 74% and 70% decline, respectively. Eventually the abandoned rainfed agriculture meets the sharpest slump in some soil properties such as cation exchange capacity (CEC), available phosphorous, total porosity and thickness of A horizon. Meantime the most increase in bulk density was also in recent land use. According to the results, the negative effects of inappropriate land use changes were led to soil productivity decline and will cause undesirable consequences in soil quality. So maintenance of soil quality is critical to environmental sustainability and this should be done on the basis of recognition all features that reduce its quality.