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.

森林在调节气候，吸收二氧化碳，水文循环，保护水，土壤和生物多样性方面发挥着至关重要的作用，并有助于减轻自然灾害。如今，借助各种遥感器，正在收集高分辨率的卫星图像，这有助于应对偏远地区森林制图的全球挑战。每种景观都会种植不同类型的树木，从而充实该国经济的一部分。本文使用可视化和机器学习（ML）过程对由先进的星载热发射和反射辐射计（ASTER）成像仪器组成的地形数据集上的林地进行分类，以利用箱线图和热图来了解累积数据。利用支持向量机（SVM）等不同的机器学习技术获得的准确性为95.4％，逻辑回归（LR）为94.5％，K最近邻（K-NN）为93.7％，决策树（DT）为89.5％ ，具有92.4％的随机梯度下降（SGD）和CN2规则归纳（RI）占85.3％，这在森林制图中得到了可观的结果，并用混淆矩阵和ROC进行了证实。我们还获得了所考虑的数据集的DT和规则。