فهرست مطالب yaghoub niazi

The SVM algorithm is an applied method that has been considered in recent years to study landslides. The main purpose of this study is to evaluate the mapping power of the GIS-based SVM model with kernel functions analysis for spatial prediction of landslides at the Ilam dam watershed.

According to review sources, 14 underlying factors including elevation, slope, aspect, plan curvature, profile curvature, LS factor, TWI, SPI, Lithologic units, land cover, NDVI, road distance, distance to the drainage channel, distance to fault were selected as factors affecting the occurrence of landslides in the study area and the mentioned layers were prepared in the GIS. In the present study, the non-linear two-class SVM method was used, the two-class SVM requires both datasets representing the occurrence of landslides and non-occurrence of landslides. The landslide inventory was randomly divided into a training dataset of 75% for building the models and the remaining 25% for the validation of the models.

Results and conclusion:

The validation results showed that the area of the prediction-rate curve under the curve (AUC) for landslide susceptibility maps produced by the SVM linear function, SVM polynomial function, SVM radial basic function, and SVM sigmoid function are 0.946, 0.931, 0.912, and 0.871 respectively. To assess the influences of factors on the landslide susceptibility map were used the Cohen’s kappa index of the model. The result shows that the most effective factors are the distance to roads, distance to drainages, and plan curvature in this area.

Droughts are long-term phenomena that affect vast areas, causing significant economic damages andlosses in human lives. Droughts are the most costly natural disaster in the world, and affect more people than any other natural disaster. Therefore, it is important to develop early warning systems to mitigate the effects of drought. The easiest way to monitor drought is to use drought indices that calculate drought severity, duration and actual range for each drought type. Several drought indices have been developed based on different variables and parametersto assess drought types. Soil moisture is a significant hydrological variable related to flood and drought and plays an important role in the process of converting precipitation into runoff andstorage of groundwater. Due to the difficulty, cost and time required for the field measurements of soil moisture, this parameter has not been widely used in drought indexes. Recent developments of global databases, based on satellite estimates, as well as rapid progress in hardware and software for modeling complex processes governing the water balance at the ground surface, have led to many efforts to deploy this new tool to reduce the limitations in this field. In this research, a new drought index based on soil moisture, derived from the land surface models of Global Land Data Assimilation System (GLDAS-SMDI) has been provided to monitor the evolution of drought severity.Thisindex is based on the fact that soil moisture is a determinant factor in most of complex environmental processes and has an important role in the occurrence of drought. The central Iran is located between 27N-37N latitudes and 48E-61E longitudes with an area of about 837,184 km2. There are 50 synoptic stations within the area. In the present study, soil moisture derived from Global Land Data Assimilation System using the GLDAS-SMDI index was used to prepare the spatial distribution map of drought in central Iran over the period of 2001-2004. The accuracy of the GLDAS-SMDI index based on satellite data was carried out using the evaluation criteria of R and RMSE compared with drought spatial distribution map derived from the SPI index based on monthly precipitationdata of 50 synoptic stations. In this study, the drought spatial distribution index of Soil Moisture based on the Global Land Data Assimilation System (GLDAS-SMDI) and SPI was obtained based on the monthly precipitation data from 50 synoptic stations over the period of 2001-2004. The results of the statistical criteria of the moisture drought spatial distribution mapcompatibility assessment based on GLDAS data with corresponding pixels on the drought spatial distribution map based on the precipitation data of thesynoptic stations showed that the drought severity map has had a high precision and good conformity with the land data (R=0.65, RMSE=0.22) based on GLDAS data.The highest correlation coefficient (0.74) was in 2004 and the lowest (0.45) in 2003.
The lowest and the highest mean errors in 2004 and 2001were 0.19 and 0.26, respectively,.The highest droughtseverity based on the GLDAS-SMDI index occurred in the Central Iran region at Iranshahr, Kahnuj, Bam, Baft and Birjandstationsduring the studied period. Droughts are hydro-meteorological anomalies characterized by prolonged shortage in regional water supply and can cause temporary difficulties (even failures) in water reservoirs. Today, most of the severe droughts are breaking out in terms of frequency, magnitude and duration due to constantly increasing water consumption, causing serious social, economic and environmental problems worldwide. Therefore, in order to deal with frequent droughts, great efforts have been made to estimate a more accurate assessment for better decision-making in order to prevent and mitigate drought losses. The most successful efforts among these methods might be the development and the use of various objective indices. In this research, the monthlymoisture data of the Global Land Data Assimilation System was evaluated to estimate the drought severity index based on soil moisture. The evaluation was performed using the coefficient of determination (R2) and Root Mean Square Error (RMSE). This analysis has demonstrated that the GLDAS products have very good compatibility with the land data over the selected area of Central Iran on monthly timescales and a 0.25° spatial scale. As a result, it can be said that the GLDAS data has a good potential for useful application of hydrological simulation and the calculation of water balance sheet, in the regions with low observations and low quality station. Therefore, it can be concluded that the soil moisture output of Global Land Data Assimilation System can be used for rapid and low cost estimation of drought severity based on soil moisture, which is a major factor in many complex environmental processes and has an important role in the occurrence ofdrought. In order to increase the spatial accuracy of drought intensity maps, it is recommended that the satellite data be combined with the values ​​of ground stations.

  Precipitation data have been widely used in many earth science applications ranging from crop yield estimates, tropical infectious diseases, drought and flood monitoring. However, in many tropical regions and parts of the mid-latitudes, rainfall estimates still remain a major challenge due to sparse rain gauges. To better develop applications for these regions, it is necessary to have rainfall data with adequate spatial and temporal resolutions. Precipitation data plays the key role in drought monitoring. Rain gauges are the mainly measuring methods for precipitation but they are concentrated in developed countries and are spare in developing countries and remote areas in the world. Researchers have shown that remote sensing techniques using space-borne sensors provide an excellent complement to continuous monitoring of rain events both spatially and temporally. Microwave and Visible/Infrared are the main forms of remote sensing technologies; both have varied advantages in terms of imaging accuracy and spatial-temporal resolutions. So, the fine spatial-temporal precipitation products need the coalescence of both. Tropical Precipitation Measuring Mission (TRMM) carrying sensors on precipitation provides the opportunity for fine spatial-temporal precipitation products. In this research for Central Iran, the precipitation data of TRMM satellite was evaluated and used to estimate the severity of a drought based on precipitation. Central Iran is located between 27N-37N latitudes and 48E-61E longitudes and has an area of about 837,184 km2. There are 50 synoptic stations within the area. The data set used includes monthly precipitation depth from both synoptic stations and TRMM data (3B43 V.7, in ASCII format). A five year (2001–2005) period were chosen for the analysis. The accuracy of precipitation data that are used from synoptic stations and TRMM satellite are provided by the source provider. Firstly, the evaluation of TRMM satellite data was measured using coefficient of determination (R2), mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) in 95% confidence levels. Then, remote sensing data of TRMM are used to provide the required data of precipitation drought index in central Iran for mapping the spatial distribution of drought. Finally, accuracy of the PDI drought index based on satellite data carried out using the evaluation criteria compared with drought spatial distribution map of the PDI based on ground-based precipitation measurements data and soil moisture values of 50 synoptic stations. In this study for the first time, a comparison between monthly rainfall values estimated by satellite products and rain gauge observations was implemented over the Central Iran. The validation of TRMM 3B43 data were performed at monthly, season and annual scales. The average of monthly rainfall, seasonal and annual for all selected synoptic stations and TRMM data were compared during the period 2001-2005. TRMM data at all time steps except August, estimates the average of monthly rainfall more than observed data. The correlation coefficient between the average of monthly rainfall, seasonal and annual rain gauge and TRMM has shown that this ratio is variable between 0.45 to 0.94 for all time steps, the average of this ratio is equal to 0.76. Highest and lowest values of R2 at monthly time step obtained for April (0.92) and June (0.45) respectively. In this time step, lowest and Highest values of statistical error criteria obtained for June and January, respectively. The seasonal time step, the highest and lowest correlation is related to the spring and summer with determination coefficient (R2) of 0.94 and 0.64, respectively. In this time step, lowest and Highest values of statistical error criteria obtained for summer and winter, respectively. Generally, TRMM data performs best in summer, but worst in winter, which is likely to be associated with the effects of snow/ice-covered surfaces and shortcomings of precipitation retrieval algorithms. The correlation coefficient for the annual time step is equal to 0.83. The results of statistical criteria showed that TRMM rainfall data in all time steps overestimated for all months except of August. The lowest to the highest of statistical error criteria obtained for monthly, Seasonal and annually rainfall data, respectively. In the next step, spatial distribution of drought based on measured data from ground stations and TRMM data over the period 2001-2005 obtained from using Precipitation Drought Index (PDI) method in study area. The results of the statistical criteria of conformity assessment PDI spatial distribution map based on TRMM data with corresponding pixels spatial distribution map based on the synoptic stations precipitation data showed that the drought severity map based on TRMM data had a high precision and good conformity with ground data (R2=0.89, ME=0.08, MAE=0.14, RMSE=0.19). Also, the results of the evaluation criteria showed that PDI index in accordance with soil moisture values had the significant correlation (0.71) and the lowest estimation error (2.33). In this research, for estimation of drought severity index based on precipitation, the monthly precipitation data of TRMM satellite (3B43) was evaluated. The evaluation was measured using coefficient of determination (R2), mean error (ME), mean absolute error (MAE) and root mean square error (RMSE). This analysis has demonstrated that the TRMM rainfall products show very good agreement with gauge data over the selected area of Central Iran on monthly timescales and 0.25° space scales. In conclusion, it can be said that the satellite-based rainfall, e.g. TRMM data, have good potential for useful application to hydrological simulation and water balance calculations at monthly or seasonal time steps, which is a useful merit for regions where rain gauge observations are sparse or of bad quality. However, several shortcomings, such as the TRMM overestimates the rainfall in some years and areas and underestimates in other years and areas, and failed to detect the extreme rainfall, reduced the accuracy of stream flow simulation at short time steps and other applications including drought monitoring and flood forecasting. The above mentioned conclusions indicate that it is necessary to further develop algorithms of satellite-based rainfall estimation in terms of both the accuracy and spatiotemporal resolutions of rainfall estimates

Monitoring and evolution of drought is the first step in any drought management system. In this study, evaluation of a new indexa new method is provided to monitor the severity of drought with Remote Sensing Combined Drought Index (RCDI). The index is based on the fact that drought is a natural phenomenon caused by a combination of various factors such as a shortage in the amount of precipitation, less than the average long-term rainfall, temperature higher than normal and the properties of the soil moisture. The new index is a statistical index comparing the present hydrometeorological conditions with the long-term average characteristics in the same interest period within the year. Three data sources used in the RCDI index includes rainfall, temperature and vegetation data. In the present study, remote sensing data of TRMM and MODIS are used to provide the required data of RCDI index in central Iran for mapping the spatial distribution of drought over the period 2001-2004. Accuracy of the RCDI index based on satellite data carried out using the evaluation criteria of R and RMSE compared with soil moisture values based on monthly data of 50 synoptic stations in 95% confidence levels. The results of the evaluation criteria showed that drought severity index calculated by the RCDI index in accordance with soil moisture values had the significant correlation (0.61) and the lowest estimation error (1.98). Thus, a RCDI index could well use in drought early warning systems.

Change detection is a basic requirement in management and evaluation of natural resources. Landuse change map which is the result of land change processes can be obtained from multi-temporal images.Various techniques have been presented for landuse/lancover change detection. In this study, images of landsat TM) 1988 and landsat (ETM+) 2001 were analyzed using 4 change detection techniques in 80470 hectares in Daresher region, Ilam Province. Change detection techniques considered were standardized and non-standardized principal component analysis (PCA) differencing, applying Canonical component analysis (CCA) differencing and Tasselled Cap (KT) differencing that all are in transformation group. Since these methods require determining threshold, therefor, statistical methods for determining the threshold level was used being achieved from the change threshold. In this study, threshold level was set at ±1 standard deviation from the mean. After determing optimal threshold, areas having decreasing change increasing change and no change were determined. Based on ground data and field work, aerial photo of 1:20000 and Google Earth, accuracy assessment of change detection techniques was carried out using overall accuracy and Kappa coefficient. According to the results, PC1difference image of CCA transform with overall accuracy of 98 and Kappa coefficient of 0.97 showed the largest accuracy among applied change detetion techniques in the Daresher region.