Land use/Land Cover and Change Detection in Chikodi Taluk, Belagavi District, Karnataka using Object based Image Classification
Santosh C1, Krishnaiah C2, Praveen G. Deshbhandari3
1Santosh C*, Department of Marine Geology, Mangalore University, Mangalagangothri, Mangalore, India.
2Krishnaiah C, Department of Marine Geology, Mangalore University, Mangalagangothri, Mangalore, India.
3Praveen G. Deshbhandari, Department of Applied Geology, Kuvempu University, Jnanasahydri, Shivamogga, India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 1522-1527 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1290109119/2019©BEIESP | DOI: 10.35940/ijeat.A1290.109119
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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: The study examines land use land cover and change detection in Chikodi taluk, Belagavi district, Karnataka. Land use land cover plays an important role in the study of global change. Due to fast urbanization there is variation in natural resources such as water body, agriculture, wasteland land etc. These environment problems are related to land use land cover changes. And for the sustainable development it is mandatory to know the interaction of human activities with the environment and to monitor the change detection. In present study for image classification Object Based Image Analysis (OBIA) method was adapted using multi-resolution segmentation for the year 1992, 1999 and 2019 imagery and classified into four different classes such as agriculture, built-up, wasteland and water-body. Random points (200) were generated in ArcGIS environment and converted points into KML layer in order to open in Google Earth. For the accuracy assessment confusion matrix was generated and result shows that overall accuracy of land use land cover for 2019 is 83% and Kappa coefficient is 0.74 which is acceptable. These outcomes of the result can provide critical input to decision making environmental management and planning the future.
Keywords: Chikodi, change detection, land use land cover, segmentation and OBIA.