Pattern Classification Technique to Assess Land Use/Cover Changes in Granite Quarry Area of Dharmapuri and Krishnagiri Districts of Tamil Nadu
P.Nithya1, G. Arulselvi2

1P.Nithya*, Research Scholar, Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar, India.
2Dr. G. Arulselvi, Asst. Professor/Research Supervisor, Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar, India.
Manuscript received on September 12, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 2947-2956 | Volume-8 Issue-12, October 2019. | Retrieval Number: K19290981119/2019©BEIESP | DOI: 10.35940/ijitee.K1929.1081219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: The rapid phase of urbanization and infrastructure development in Krishnagiri Distirct has been observed recently. The unique characteristics of the granite deposits in Krishnagiri and Dharmapuri Districts of Tamilnadu resulted in making the country a global producer of the granite rocks. This led to intensified quarrying activities between Dharmapuri and Krishnagiri. However, this surface mining method, has a potential to impact the environment in a negative way causing loss in vegetation, depletion of natural resources, increases the Temperature, loss of scenic beauty and contamination of surface water resources. To assess the land cover changes caused by granite quarrying activities, remotely sensed data in the form of Landsat images between 2000 and 2017 were used. Pattern classification was used to create maps. Accuracy assessment using Google EarthTM as a reference data yielded an overall accuracy of 88%. The post classification change detection method was used to value the land cover changes within the granite quarries. Granite quarries increased by 2562.78 ha while formation of quarry lakes increased to 5.3ha over the 18-year period. Vegetation cover decreased by 1521ha in area while 18.3ha bare land was lost during the same period. This study demonstrated the utility of remote sensing to detect changes in land cover within granite quarries.
Keywords: Land Use Land Cover (LULC), Geographic Information System (GIS), Remote Sensing (RS), Images Classification, Phuentsholing.
Scope of the Article: Classification