Geospatial Approach for the Analysis of Forest Cover Change Detection using Machine Learning

  • R. Sanjeeva Reddy Department of Computer Science, Sri Venkateswara University, Tirupati, Andhra Pradesh, 517502, India
  • G. Anjan Babu Department of Computer Science, Sri Venkateswara University, Tirupati, Andhra Pradesh, 517502, India
  • A. Rama Mohan Reddy Department of Computer Science & Engineering, Sri Venkateswara University, Tirupati, Andhra Pradesh, 517502, India


Spatial data classification is famous over recent years in order to extract knowledge and insights into the data. It occurs because vast experimentation was used with various classifiers, and significant improvement was examined in accuracy and performance. This study aimed to analyze forest cover change detection using machine learning. Supervised and unsupervised learning methods were used to analyze spatial data. A Vector machine was used to support the supervised learning, and a neural network method was used to support unsupervised learning. The Normalized Difference Vegetation Index (NDVI) was used to identify the bands and extract pixel information relevant to the vegetation. The supervised method shows better results because of its robust performance and better analysis of spatial data classification using vegetation index. The proposed system experimentation was implemented by analyzing the results obtained from Support Vector Machine (SVM) and NN (Neural Network) methods. It is demonstrated in the results that the use of NDVI mainly enhances the performance and increases the classifier's accuracy to a greater extent.

Keywords: Spatial data; Normalized Difference Vegetation Index; NDVI;Vegetation index, Support Vector Machine; Neural Network; Forest Cover Change

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How to Cite
REDDY, R. Sanjeeva; BABU, G. Anjan; REDDY, A. Rama Mohan. Geospatial Approach for the Analysis of Forest Cover Change Detection using Machine Learning. Geosfera Indonesia, [S.l.], v. 5, n. 3, p. 335-351, dec. 2020. ISSN 2614-8528. Available at: <>. Date accessed: 14 may 2021. doi:
Original Research Articles