<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Digital change detection is the process that helps in shaping the changes associated with land use land cover (LULC) properties with reference to geo-registered multi-temporal remote sensing data. In this study different methods of analyzing satellite images are presented, with the aim to identify changes in land cover in a certain period of time (1980-2016). The methods represented in this study are vegetation indices, image differencing and supervised classification. These methods gave different results in terms of land cover area. Urban expansion has brought serious losses of agriculture land, vegetation and water bodies. The present study demonstrates changes in land trajectories of Varanasi district, India using Landsat MSS (1980), TM (1990 and 2010), ETM+ (2000) and Landsat-8 OLI data (2016). The LULC classes in the study area are divided into eight categories using supervised classification method. Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) are also calculated to estimate the changes in LULC classes during these time periods. Major changes are seen from 2000 to 2016 for the built-up, agriculture land, water bodies and wasteland.</p></abstract>

Digital change detection is the process that helps in shaping the changes associated with land use land cover (LULC) properties with reference to geo-registered multi-temporal remote sensing data. In this study different methods of analyzing satellite images are presented, with the aim to identify changes in land cover in a certain period of time (1980-2016). The methods represented in this study are vegetation indices, image differencing and supervised classification. These methods gave different results in terms of land cover area. Urban expansion has brought serious losses of agriculture land, vegetation and water bodies. The present study demonstrates changes in land trajectories of Varanasi district, India using Landsat MSS (1980), TM (1990 and 2010), ETM+ (2000) and Landsat-8 OLI data (2016). The LULC classes in the study area are divided into eight categories using supervised classification method. Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) are also calculated to estimate the changes in LULC classes during these time periods. Major changes are seen from 2000 to 2016 for the built-up, agriculture land, water bodies and wasteland.

Application of Earth Observation Data for Estimation of Changes in Land Trajectories in Varanasi District, India

Department of Geography, Institute of Science

Journal of Landscape Ecology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Digital change detection is the process that helps in shaping the changes associated with land use land cover (LULC) properties with reference to...

Application of Earth Observation Data for Estimation of Changes in Land Trajectories in Varanasi District, India

Data publikacji: 21 cze 2018

Zakres stron: 5 - 18

Otrzymano: 08 maj 2017

Przyjęty: 14 lip 2017

DOI: https://doi.org/10.1515/jlecol-2017-0017

Słowa kluczowe
Land use Land cover, Change detection, Landsat, remote sensing, supervised classification, NDVI, SAVI

© 2018 Sunita Singh et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Application of Earth Observation Data for Estimation of Changes in Land Trajectories in Varanasi District, India

Data publikacji: 21 cze 2018

Zakres stron: 5 - 18

Otrzymano: 08 maj 2017

Przyjęty: 14 lip 2017

DOI: https://doi.org/10.1515/jlecol-2017-0017

Słowa kluczoweLand use Land cover, Change detection, Landsat, remote sensing, supervised classification, NDVI, SAVI

© 2018 Sunita Singh et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
Land use Land cover, Change detection, Landsat, remote sensing, supervised classification, NDVI, SAVI