Open AccessMonitoring the quality of life in urban area using TDVI- Case study of Kalaburagi city
Author(s) -
Abhilasha Kumari,
Bihar
Publication year - 2021
Publication title -
international journal of interdisciplinary and multidisciplinary research
Language(s) - English
Resource type - Journals
ISSN - 2456-4567
DOI - 10.54121/2021/09/1496
Subject(s) - normalized difference vegetation index , vegetation (pathology) , land cover , ground truth , remote sensing , enhanced vegetation index , vegetation cover , quality (philosophy) , recreation , index (typography) , cover (algebra) , urban area , environmental science , vegetation index , geography , land use , physical geography , computer science , ecology , machine learning , climate change , medicine , mechanical engineering , philosophy , epistemology , pathology , world wide web , engineering , biology
Many vegetation indices have been proposed over last decades made specialists search for the most suitable vegetation index for a given remote sensing application. Measuring the Quality of Place (QOP) is a hard task since it involves both physical and socio-economic dimensions. Being one of the major land use categories, urban vegetation plays a significant role in one‟s judgment for QOP in a neighborhood. Both quantity and quality of the community parks and recreation areas are major determinants of neighborhood attraction. For these reasons, detection of urban vegetation cover has been one of the important implication areas of urban image classification techniques. “Transformed Difference Vegetation Index (TDVI) developed by Bannari et al. (2002), is tested in a previous work where the index has performed better than NDVI and SAVI. In that work, a comparative study between TDVI, SAVI and NDVI for estimating vegetation cover in urban environment from the Indian Remote Sensing Satellite (IRS-1D) imagery has been conducted. The validation of the obtained results according to the ground truth showed that the TDVI is an excellent tool for vegetation cover monitoring in urban environment. It does not saturate like NDVI or SAVI, it shows an excellent linearity as a function of the rate of vegetation cover. This paper adds on the previous work by analyzing the performance of TDVI in urban image classification. Results indicate that, the performance of TDVI in urban image classification is better than NDVI and SAVI. The new index not only differentiates the urban vegetation cover better but also helps to minimize the error in classifying other unclassified pixels of urban categories.