Background and objectives:The changes in desert areas depend on climate condition and water balance of upstream watershed. satellite image can help us in distinguishing the trend of areas of Playa wetland.And with achieving these trend, both the status of the non-conventional water resources will be identified and this information can be used in wind erosion management.Materials and methods:In the present study, the changes in Gavkhuni Wetland was evaluated using MODIS satellite images from 2000 to 2016. For this purpose, after performing the required modifications on the satellite images, they were classified and their changes in studies time intervals were detected. Since the changes of desert areas depend on the humidity variations, the TVDI، MTVDI، VTCI indices were calculated to enhance the satellite images. The indices along with the bands of MODIS images were used in classification. The classification was done in August and March (maximum changes in desert areas and wet age) during 16 years. Due to the large number of used images, coding in MATLAB software was used to facilitate calculation of these parameters.Results and conclusion:The results indicated that on August and March, the desert areas faced the descending precipitation, which led to reducing water right. In the studied intervals, in 78.98% of the study areas, no changes were observed and the maximum changes (15%) was for a wet edge. Evaluating the validity of the maps revealed that the Kappa coefficient and total validation were respectively 95% and 96%. تفاصيل المقالة

Background and objective: There are several methods of measuring desert pavement roughness. Among these methods, one can name laser and sonic rangefinder, 3D photography, and close-range photogrammetry. Remote sensing techniques need less and cheaper equipment than laser and sonic methods. In short-range photogrammetry, the quantitative amount of terrains can be obtained by processing the images of a digital camera using special methods of photography and camera calibration.Materials and methods:This method can be introduced as an accurate and cost-effective measuring method to provide a digital model of complications and a three-dimensional model of objects. The present study aimed to evaluate the possibility of using close-range photogrammetry in measuring desert pavement roughness. In this research, first, the calibration parameter of the camera was calculated by taking photos of standard patterns. Then, the meshed samples of desert pavement were photographed and the photos were three-dimensionally simulated.Results and conclusion:The results showed that since in this method the selected points have more effective height and uniform dispersion, the measurement of the average height of roughness is more accurate. It means that measuring the roughness of the soil surface is done with high accuracy in a short time. تفاصيل المقالة

Assessing and monitoring rangelands in arid regions are important and essential tasks in order to manage the desired regions. Nowadays, satellite images are used as an approximately economical and fast way to study the vegetation in a variety of scales. This research aims to estimate the coverage percent using the digital data given by ETM+ Landsat satellite. In late May and early June 2018, the vegetation was measured in Ardakan plain, Yazd province, Iran. Information was obtained by 320 plots in 40 transects and also, the satellite images in terms of sampling time were downloaded and processed in USGS website. 16 indices involving NDVI, NIR, MSI, SS, IR1, MIRV1, NVI, TVI, RAI, SAVI, LWC, PD322, PD321, PD312, PD311 and IR2 were estimated. Through estimating the indices and extracting the values in order to conduct index-based predictions, six data mining models of Artificial Neural Network (ANN), the K Nearest Neighbor (KNN), Gaussian Process (GP), Linear Regression (LR), Support Vector Machine (SVM) and Decision Tree (DT M5) have been applied. Model assessment results indicated high vegetation estimate efficiency based on the indices but the model KNN with Root Mean Square Error (RMSE= 2.520) and Coefficient of determination (R2= 0.94) and (RMSE= 2.872 and R2= 0.96) had the highest accuracy in the training and data sets, respectively. As well, to determine the weight and importance of parameters, and to estimate the coverage percent, the weighing process were conducted based on support vector machine. Weighing results indicated that the KNN model and the Simple Subtraction (SS) index had higher weight and importance in terms of vegetation percent. تفاصيل المقالة