جستجوی مقالات مرتبط با کلیدواژه « auxiliary variables » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

Due to the ability of land use/cover changes monitoring and predicting to understand the performance and health of ecosystems, this purposed method can provide possibility of sustainable land use management and planning, especially in the rapid change areas without master/land use plan. The present study has aimed to introduce Google Earth Engine to evaluate the pattern of land changes during 2006- 2021 and predict the pattern of future changes by using an integrated model based on Cellular automata and Markov chain using Google Earth Engine system. Three Landsat images (2006, 2014 and 2021) were classified using the support vector machine classifier method, and were simulated using the integrated model of cellular automata and Markov chain. In order to evaluate the accuracy of the predicted map of 2021, the classified map of the same year was applied. The accuracy of classified and simulated maps was Kno=0.812, Klocation=0.816, Kstandard=0.786 respectively. Evaluation of the land use/cover changes shows that between 2006 and 2035, the buildup areas will reach from 4839.01 hectares to 7199.76 hectares with increasing of 2360.75 hectares. These results indicate the necessity of land use planning principles. Simulation models can reduce the risks of long-term decision-making in land use management and Google Earth Engine can reduce the time and cost for classification and satellite image processing.

Soil saturated hydraulic conductivity (Ks) is among the most important soil hydraulic-physical properties that required for soil-water modeling. Due to high cost and time- consuming nature of Ks measurement, estimating Ks from basic, inexpensive and easily measured physical and chemical soil properties is becoming increasingly important. In the last two decades, the development of estimation methods called pedotransfer functions that use cheap auxiliary variables has been a sharpening focus of soil research. This study was conducted (i) to develop different pedotransfer functions and (ii) to evaluate and compare statistical regression and neural network based pedotransfer functions for estimating Ks in a sub- catchment of Zayanderood River, located in Chaharmahal-va-Backtiari province. The data set was divided in to subsets for modeling (n=86) and validation (n=25). Root-mean-square error (RMSE), mean error (ME) and percentage of relative improvement (RI) were used as the validation indices. The artificial neural network-based models provided more reliable estimation than the statistical regression-based pedotransfer functions.