فهرست مطالب fariba esfandayari darabad

One of the important conditions for optimal use of land is obtaining information about landuse patterns and their changes over time. Landuse is usually defined based on human use of land, emphasizing the role of land in economic activities. Today, remote sensing technology is considered as the main element in landuse monitoring. The aim of the current research is to extract landuse maps for the years 2000 and 2021 in FirozabadKhalkhal region and to investigate the changes made in the studied time period in the region using the images of ETM and OLI sensors of Landsat. Also, checking the capability of basic pixel and object-oriented methods for landuse classification is another purpose of this study. In the current research, the object-oriented technique nearest neighbor algorithm and the vector machine method supporting the pixel-based algorithm have been used for landuse classification. Then, to verify the accuracy of these two methods, the overall accuracy and Kappa were extracted. The results of this evaluation show the high accuracy of the object-oriented method in extracting land use classes. Based on the results of the detection of landuse changes in the studied time period, the highest amount of changes occurred is related to the use of good pasture to poor pasture with a value of 51.72 square kilometers, followed by forest to good pasture with a value of 30.11 and the lowest changes It is related to the use of pasture and water with the amount of 0.03 square kilometers. The reasons for these changes are the increase in population, indiscriminate grazing of livestock, incorrect and illegal use of different lands. The use of more parameters such as scale, shape, compactness, color, texture, smoothness criterion and pattern for landuse classification in the object oriented technique can be considered as an innovation of the present study.

Surface soil moisture is an important variable in nature's water cycle and can be affected by various factors, including temperature and soil characteristics. The use of ground sensors for measuring moisture can lead to spending time and expense and inappropriate distribution of samples on large scales. Therefore, Remote sensing observations can be an important tool in estimating soil moisture. The present study aims to use the TOTRAM model using Landsat 8 images and the SVR method using Sentile 1 images to estimate soil moisture.

In the present study, two TOTRAM methods based on pixel distribution in LST- VI space and the SVR method were used to extract soil moisture using the SAR technique and Sentinel 1 data. To implement the TOTRAM method, Landsat 8 images related to 4/29/1398 and 5/30/1398 are downloaded and after extracting NDVI and LST maps, The correlation between the dependent variable of moisture and independent temperature variables and vegetation variables has been investigated using Geographically weighted regression (GWR). To implement the SVR method after acquiring Sentinel 1 images related to 31/05/1398 and 27/04/1398, Soil Moisture Data Product FLDAS and 500 meters product of Modis Satellite (MCD12q1) were called to classify land cover in the Google Earth Engine system, and maps related to soil moisture were extracted. After extracting the moisture maps the distribution of moisture using the local Moran index has been investigated. By defining this index, positive values ​​for this index represent the cluster of distribution.

Examination of the soil moisture map obtained by the SVR method showed the concentration of moisture in areas with vegetation and water and the change in moisture status from July to August was visible. The humidity pattern has shown the reflection of the precipitation pattern so that maximum precipitation and humidity were observed in April and in summer both precipitation and humidity components decreased. Examination of the TOTRAM method and application of the GWR method has shown a complete correlation between NDVI LST and moisture. However, the correlation between LST and humidity with B (values) and standard error (SE) of 0.995 and zero corresponding to July and 0.981 and zero corresponding to August showed the highest correlation with vegetation variable with moisture dependence parameter, which this correlation In August, with increasing the coefficient of determination of R2 to 0.997 and a significant decrease of NDVI to the value of 0.415 in July, it has increased much more. Application of Moran local index with values ​​less than 0.05 for p-value and positive values ​​for z and near positive number 1 for Moran index showed the cluster distribution of moisture variable.

The results of TOTRAM and SVR methods showed the dependence of soil moisture status on conditions and cluster moisture distribution. According to the correlation coefficients of geographical regression, there is a greater correlation between temperature and humidity variables, especially in August, due to the decrease in vegetation density. The results of the SVR algorithm maps showed that in areas with the presence of vegetation, especially dense vegetation, we see an increase and with increasing temperature, we see a decrease in humidity. Also, the coordination of moisture patterns of the SVR algorithm and precipitation showed a direct relationship between moisture and precipitation. Considering that the SVR method uses parameters such as radar scattering intensity and land cover classification, as well as the use of Sentinel 1 radar images by this algorithm, more accurate results can be expected from this algorithm.