Soil erosion determines landforms, soil formation and distribution, soil fertility, and land degradation processes. In arid and semiarid ecosystems, soil erosion is a key process to understand, foresee, and prevent desertification. Addressing soil erosion throughout watersheds scales requires basic information to develop soil erosion control strategies and to reduce land degradation.
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.
The Soil Organic Carbon Mapping cookbook provides a step-by-step guidance for developing 1 km grids for soil carbon stocks. It includes the preparation of local soil data, the compilation and pre-processing of ancillary spatial data sets, upscaling methodologies, and uncertainty assessments.
Quantifying recharge from agricultural areas is important to sustain long-term groundwater use, make intelligent groundwater allocation decisions, and develop on-farm water management strategies. The scarcity of data in many arid regions, especially in the Middle East, has necessitated the use of combined mathematical models and field observations to estimate groundwater recharge.
Monitoring exchangeable sodium percentage (ESP) and sodium adsorption ratio (SAR) variability in soils is both time-consuming and expensive. However, in order to estimate the amounts of amendments and land management, it is essential to know ESP and SAR variations and values in sodic or saline and sodic soils.
One of the most prominent consequences of rapid urbanization has recently been the disintegrated distribution of municipal services which predisposes inequality in citizens' benefiting from these services.
Desertification is one of the main environmental and also social and economic problems facing Iran. Seventeen out of 31 Iranian provinces, which are home to approximately 70% of the total population, are affected by desertification.
As a result of the growing impacts on global environments, it has become important for land use planners to extract, detect, monitor and predict land use/cover changes (LUCCs). The monitoring of LUCCs within a certain time period and predicting future trends of temporal and spatial changes are absolutely necessary.
This study was carried out to assess the land suitability for rainfed faba bean (Vicia faba L.) cultivation in Gonbad-Kavous region (Golestan province, north of Iran) using geographic information system (GIS) and analytical hierarchy process (AHP), the most common methods for evaluation of land use suitability.
The present study compares the effectiveness of two common preclassification change detection (CD) methods that use two-dimensional data space of spectral-textural (S-T) change information. The methods are principal component analysis (PCA) and change vector analysis (CVA) in the Gorgan Township area, Golestn Province, Iran.