Land Use and Land Cover Mapping of Davangere using Google Earth Engine
Geetha M1, Ashagowda Karegowda2, H S Sudhira3
1Geetha M, Asst. Professor, Dept. of MCA, BIET, Davangere, India.
2Dr. Asha Gowda, Karegowda, Associate Prof. Dept. of MCA,SIT, Tumkur, India
3Dr.H.S.Sudhira, Director, Gubbi Labs ,Gubbi, India.
Manuscript received on 15 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 474-477 | Volume-8 Issue-3 September 2019 | Retrieval Number: A1109058119/19©BEIESP | DOI: 10.35940/ijrte.A1109.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Ever since the advent of modern geo information systems, tracking environmental changes due to natural and/or manmade causes with the aid of remote sensing applications has been an indispensable tool in numerous fields of geography, most of the earth science disciplines, defence, intelligence, commerce, economics and administrative planning. One among these applications is the construction of land use and land cover maps through image classification process. Land Use / Land Cover (LULC) information is a crucial input in designing efficient strategies for managing natural resources and monitoring environmental changes from time to time. The present study aims to know the extent of land cover and its usage in Davangere region of Karnataka, India. In this study, satellite image of Davangere during October-November 2018 was used for LULC supervised classification with the help of remote sensing tools like QGIS and Google Earth Engine. Six LULC classes were decided to locate on the map and the accuracy assessment was done using theoretical error matrix and Kappa coefficient. The key findings include LULC under Water bodies (8%), Built up Area (15.1%), Vegetation (9%), Horticulture (20.8%), Agriculture (39.3%) and Others (7%) with overall accuracy of 94.8% and Kappa coefficient of 0.866 indicating almost accurate goodness of classification.
Keywords: Remote Sensing, Land Use, Land Cover, QGIS, Classification, Accuracy Assessment
Scope of the Article: Classification