This Book focuses on the Image Analysis of Remote Sensing Data Integrating Spectral, Temporal and Spatial Features of objects in the area of satellite image processing. We have used the multi-spectral remote sensing data to find the spectral signature of different objects of the different regions for the land cover classification, how the use of land changes according to time and also performed the temporal analysis to analyze the impact of climate over the surface. Some band combinations of remote sensed data are effective in the land cover classification. Spatial distributions of land cover types such as roads; urban area, agriculture land, and water resources can easily be interpreted.
This study investigated the possibilities and constraints for an integrated use of agriculture and other species growth model and earth observation techniques. The assimilation of information derived from earth observation sensors into agriculture growth models enables regional applications and may also help to improve the profound knowledge of the different involved processes and interactions. Both techniques can contribute to improved use of resources, reduced agriculture production risks, minimized environmental degradation, and increased farm income.
Up to now, agriculture growth modeling and remote sensing techniques mostly have been used separately for the assessment of agricultural applications. Agriculture growth models have made valuable contributions to, e.g., yield forecasting or to management decision support systems. Likewise, remote sensing techniques were successfully utilized in classification of agricultural areas or in the quantification of vegetation characteristics at various spatial and temporal scales. Multispectral remote sensing approaches for the quantification biophysical variables are rarely realized.