Author : A.Priyanga 1
Date of Publication :20th April 2018
Abstract: The appetite for up-to-date information about earth’s surface is ever increasing, as such information pro- vides a base for a large number of applications, including local, regional and global resources monitoring, land-cover and land-use change monitoring, and environmental studies. The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. Change detection is a technology ascertaining the changes of specific features within a certain time interval. It provides the spatial distribution of features and qualitative and quantitative information of features changes. The main aims are to determine whether vegetation composition, structure and condition are changing over time or in response to a management intervention. . Remote sensing refers to the detection and recording of values of emitted or reflected electromagnetic radiation with sensors in aircrafts or satellites. The remote sensing datasets, such as satellite imageries and aerial photographs, would be a useful tool for monitoring purposes. This paper gives with comparing the performance of various vegetation indices parameters on semi arid region Madurai, Tamilnadu. There are forty index parameters calculated for this purpose. These parameters are grouped into first generation indices and second generation indices. The classified vegetation indices are further grouped into two main categories: indices created from the combination of two spectral bands, notably the red and near infrared , and indices created from the combinations of three or four bands. Some of the popular indices calculated are NDVI, SAVI, TVI, EVI, and PVI computed for the Resourcesat (IRS- P6) satellite imagery and the results are compared. From the vegetation classified output maps, the change map is computed. The change map is a measure of extent to which vegetation has been degraded in the study area during the two dcades.. In this paper, a method is proposed for vegetation cover estimation from multi spectral satellite data based on vegetation index. The timely difference between the vegetation cover of Madurai region, Tamilnadu during different time periods have been computed using post classification change detection comparison. The results show the extent of vegetation degradation during the concerned time periods and it is evident that the vegetation cover has been constantly decreasing in the Madurai region during the past two decades
Reference :
-
- Arizona, Tucson. A, . “A review of vegetation indices”, Department of Soil and Water Science, University of Arizona, Tucson, AZ, USA.
- Opha Pauline Dube, “Remote Sensing, Climate Change and Land-Use Impacts in Semiarid Lands of Southern Africa”, Department of Environmental Science,University of Botswana, Private Bag UB 00704, Gaborone, Botswana.
- G.Fang.” Fusing Landsat and MODIS Data for Vegetation Monitoring”, USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA IEEE JOURNELS selected Topic in Remote Sensing. 2016
- Wen Wang, Hai-Xu Hu, Juan Hu, “Land Cover Change Detection Based on MODIS 250m Vegetation Index Time Series Data”, State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University Nanjing, 210098, China. 2015
- Nikolaos G. Silleos a , Thomas K. Alexandridis b , Ioannis Z. Gitas “Vegetation Indices: Advances Made in Biomass Estimation and Vegetation Monitoring in the Last 30 Years” Lab of Remote Sensing and GIS, Faculty of Agronomy , Aristotle University of Thessaloniki , G.R. 54124, 2014
- Abdolreza Ansari Amoli, Abbas Alimohammadi “Utilization of Maximum and Minimum based Multitemporal NDVI data for Agricultural Landcover Mapping in Gilan Province, North of Iran” Department of GIS K.N. University of Technology
- G.Csornai, Cs. Wirnhardt, Zs. Suba, P. Somogyi, G. Nádor,L. Martinovich, L. Tikász, A. Kocsis, Gy. Zelei, M. Lelkes(2009).” Crop Monitoring By Remote Sensing”Remote Sensing Centre,Institute of Geodesy, Cartography and Remote Sensing. 2012
- Andrew J. Elmore, John F. Mustard, Sara J. Manningand David B. Lobell“Quantifying Vegetation Change in Semiarid Environments: Precision and Accuracy of Spectral Mixture Analysis and the Normalized Difference Vegetation Index” Department of Geological Sciences, Brown University. 2011
- M. Marek , A.Sobieraj “Comparison of several vegetation indices calculated on the basis of a seasonal spot xs time series, and their suitability for land cover and agricultur crop identificatio Department of Photogrammetry and Remote Sensing University of Warmia and Mazury in Olsztyn.
- Bannari, A., Huete, A. R., Morin, D. and Zagolski, F. (1995) Effets de la couleur et de la brilliance du slur les indices de végétation. 17th Canadian Symposium on Remote Sensing, Saskatoon, Saskatchewan, Canada IEEE Journel paper 2011