Analysis of Rainfall through Space - Time during 1999-2014 in Salem District, South India

The present study deals about the Rainfall assessment using various recent techniques with the help of remote sensing and GIS in Salem District, South India. The rainfall assessment carried over a period of 16 years from 1999 to 2014, which are clearly analyzed by using mean annual rainfall, mean seasonal rainfall, mean annual rainfall variability, mean seasonal rainfall variability, mean annual precipitation ratio and mean seasonal precipitation ratio methods. The methodology adopted based on literature study and which has given an accurate results. Therefore, the output shows that the study area has received 1 %, 19%, 41% and 39% of precipitation in winter, summer, southwest and northeast season respectively and the average annual rainfall is relatively more in N and NE and it is gradually decreases the eastern, western and southern parts of the study area. The rainfall variability indicates more than 100% in winter season except Nangavalli which is indicate the not dependable rainfall and other three season's rainfall variability less than 100% except Veeraganoor station, which is indicate the dependable rainfall available during these period. The precipitation ratio is less abnormality during SW and NE season and high abnormality during summer and winter season. Finally, rainfall variation assessment depicts that the rainfall conditions in Salem district is normal and fluctuation depends upon time and space

Lithological Discrimination of Anorthosite using ASTER data in Oddanchatram Area, Dindigul district, Tamil Nadu, India

The present study applies with hyperspectral remote sensing techniques to map the lithology of the Oddanchatram anorthosite. The hyperspectral data were subjected to Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Minimum Noise Fraction (MNF), Pixel Purity Index (PPI) and n-Dimensional Visualization for better lithology mapping. The proposed study area has various typical rock types. The PCA, ICA and MNF have been proposed best band combination for effectiveness of lithological mapping such as PCA (R: G: B=2:1:3), MNF (R: G: B=4:3:2) and ICA (R: G: B=3:1:2). The derived lithological map has compared with published geological map from Geological Survey of India and validated with field investigation. Therefore, ASTER data based lithological mapping are fast, cost-effective and more accurate

Estimation and Extrapolation of Tree Parameters Using Spectral Correlation between UAV and Pléiades Data

The latest technological advances in space-borne imagery have significantly enhanced the acquisition of high-quality data. With the availability of very high-resolution satellites, such as Pléiades, it is now possible to estimate tree parameters at the individual level with high fidelity. Despite innovative advantages on high-precision satellites, data acquisition is not yet available to the public at a reasonable cost. Unmanned aerial vehicles (UAVs) have the practical advantage of data acquisition at a higher spatial resolution than that of satellites. This study is divided into two main parts: (1) we describe the estimation of basic tree attributes, such as tree height, crown diameter, diameter at breast height (DBH), and stem volume derived from UAV data based on structure from motion (SfM) algorithms; and (2) we consider the extrapolation of the UAV data to a larger area, using correlation between satellite and UAV observations as an economically viable approach. Results have shown that UAVs can be used to predict tree characteristics with high accuracy (i.e., crown projection, stem volume, cross-sectional area (CSA), and height). We observed a significant relation between extracted data from UAV and ground data with R2 = 0.71 for stem volume, R2 = 0.87 for height, and R2 = 0.60 for CSA. In addition, our results showed a high linear relation between spectral data from the UAV and the satellite (R2 = 0.94). Overall, the accuracy of the results between UAV and Pléiades was reasonable and showed that the used methods are feasible for extrapolation of extracted data from UAV to larger areas