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Forest data visualization and land mapping using support vector machines and decision trees
, , Chatterjee J.M., Hemanth D.J.
Published in Springer Science and Business Media Deutschland GmbH
2020
Volume: 13
   
Issue: 4
Pages: 1119 - 1137
Abstract
Forests play a vital role in the regulation of climate, absorption of carbon dioxide, hydrological cycle, conservation of water, soil and biodiversity and help mitigate natural disasters. With the help of various remote sensors, high-resolution satellite images are being collected nowadays, which helps in tackling the global challenges of forest mapping in remote areas. Each landscape will grow different types of trees and in turn substantiate a part of the country’s economy. This paper uses visualization and machine learning (ML) processes to classify the forest land on the terrain dataset composed of the advanced spaceborne thermal emission and reflection radiometer (ASTER) imaging instrument to get the insight of the cumulated data by using Box Plot and Heat Map. The accuracy obtained by utilizing different machine learning techniques like Support Vector Machine (SVM) gives 95.4%, Logistic Regression (LR) gives 94.5%, K-Nearest Neighbor (K-NN) gives 93.7%, Decision Tree (DT) with 89.5%, Stochastic Gradient Descendent (SGD) with 92.4% and CN2 Rule Induction (RI) gives 85.3% are allied which gives appreciable results in forest mapping substantiated the same with confusion matrix and ROC. We also obtained the DT and rules for the considered dataset. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
About the journal
JournalData powered by TypesetEarth Science Informatics
PublisherData powered by TypesetSpringer Science and Business Media Deutschland GmbH
ISSN18650473
Open AccessNo