Algorithm-Based Spatio-Temporal Study on Identification of Pure Bamboo Vegetation Using LULC Classification

Abstract

Bamboo is a natural air purifier that helps to keep the surroundings clean. Bamboo forests, an essential source of socioeconomic life for rural communities and an integral part of the ecosystem, are undergoing substantial changes. In the mapping and identification of natural resources, space technology has been beneficial. The objective of classification is to divide a large subject into fewer, more manageable fractions. For land use and land cover, four supervised learning methods, namely Naive Bayes, random forest, support vector machine, and decision tree, are used. Their overall accuracies will be compared to obtain the best algorithm. Land cover mapping and monitoring were carried out to preserve current natural resources and better understand the causative factors of land use in the study region, i.e., East Garo Hills, a district of Meghalaya, for the 2018 data. The application performance was measured in terms of Accuracy 97.23%, Recall 89.23%, F1 measure 97.23%, and Throughput 96.34%, which were improved and competed with future-level applications.