NDVI and SAVI Indices Analysis in Land Use Extraction and river route

Extended abstract 1- Introduction Land use reflects the interactive characteristics of humans and the environment and describes how human exploitation works for one or more targets on the ground. Land use is usually defined based on human use of the land, with an emphasis on the functional role of land in economic activities. Land use, which is associated with human activity, is changing over time. Land use information and land cover are important for activities such as mapping and land management. Over time, land cover patterns and, consequently, land-use change, and the human factor can play a major role in this process. Today, satellite-based measurements with geographic information systems are increasingly being used to identify and analyze land-use change and land cover. Therefore, accurate detection of changes in land surface properties, especially LULC changes have become a key issue for monitoring local, regional, and global resources and environments, Providing a basis for a better understanding of the interactions between humans and natural phenomena and the proper management and use of these terrestrial resources. About the problems of changes and transformations in the studied area, remote sensing can allow managers to categorize images and evaluate land-use changes, in addition to saving time and costs, which allows planners to make plans based on changes, more resources are lost, to be prevented. 2- Methodology The Gamasiab River originates from calcareous springs located 21 kilometers southeast of Nahavand in Hamadan province from the northern slopes of the Greene Highlands known as the Mirab Gamasiab. This river enters Kangavar, Harsin, and Bistoon Kermanshah from the east-west direction of Nahavand and then enters the Faraman area by going around Bistoon and continues its north-south direction after receiving other branches and water. The surface currents of the adjacent basins join the Gharasu. For this study, an approximately 80 kilometers interval from the Gamasiab River and its adjacent lands 5 kilometers from each side was selected. Three images of Landsat for TM, ETM+ and OLI sensors were selected for monitoring of the river adjacent lands and vegetation indices for the years 1987, 2000, and 2017, respectively. NDVI is the normalized difference vegetation index and is the most common vegetation index. SAVI Soil- Adjusted Vegetation Index by Huete (1988) has been developed to use soil optical properties on the canopy reflectance capability. This index has added a factor of L (soil texture correction factor) to the NDVI equation. Radiometric and atmospheric correction images are performed before applying spectral indices. In the process of atmospheric correction, the first step is to calculate the radius value, and from the radius value, the reflectance value is calculated. There are two advantages to using reflectance values compared to radiative values: first, the effect of the cosine angle of the different solar angles can be measured relative to the time difference between the data harvesting, and second, the different amounts of solar radiation outside the atmosphere caused by the differences. The band is spectral, corrected. Atmospheric correction is done to eliminate the effects of the transmission and absorption of electromagnetic waves in the visible and infrared range. In general, each of the terrestrial features has a special spectral sign (spectral signature). These spectral signatures depend on many factors, such as sensing properties, differences in radiation and reception angles, atmospheric and topographic conditions, and imaging time. Because of the factors mentioned above, digital numbers (DN) cannot represent the actual conditions of spectral reflection of the Earth. The purpose of radiometric correction is to remove or neutralize the above effects of the image. After the indexes are applied, the land units have to be separated so that they are threshold on them, which means that we separate the pure classes. So, values between -1 to 0 are considered as wet and water body, values between 0 to 0.3 as soil, and values between 0.3 to 1 as vegetation. 3- Results Due to the lack of user interference against the object-oriented and pixel-based classification algorithms, this process (applying spectral indices) uses spectral information of the bands used, as accurate as the radiometric resolution of the sensors used. The results showed that for the NDVI index in 1987 the amount of water land fields was 13% and this value decreased by 0.63% to 12.57% in the year 2000 and 18.71% in the whole study area in 2017. These figures for the SAVI index in the year 1987 amounted to 10.42%, for the year 2000 the value was 10.93%, and for the year 2017 amount was 16.54% of the total area studied. What is certain is the slight change in 2000 relative to 1987 for both indices. Both NDVI and SAVI show an increasing trend of vegetation cover (water land fields) in 2017. These figures show the unprecedented use of river water and groundwater in recent years. Excessive use of groundwater resources results in land-use changes and subsequently physical, chemical, and even biological changes in water resources and land surface. For soil class, it is clear that both NDVI and SAVI indices show slight changes from 1987 to 2000, and for 2017 both indices show decreases in soil class compared to 1987 and 2000. The results show the inefficiency of indices in river and water body extraction in the study area. Principal components analysis was used for river extraction. Consequently, by comparing the indices with the corresponding PCAs it can be said that the river is properly extracted using PCA which can lead to even better results for the wider rivers. 4- Discussion & Conclusions Identifying and discovering the land cover changes can help planners and planners identify effective factors in land-use change and land cover, and have useful planning to control them. High accuracy maps are required for this purpose. The use of spectral indices makes this possible with very high accuracy. The results of this study, in addition to prove the accuracy and efficiency of spectral indices for estimating land cover, showed that during the years of 1987, 2000 to 2017 the soil class reduce and, on the other hand, increased water land fields a general trend This illustrates the general trend of degradation in the region through the replacement dry-land fields than water land fields.