Estimation of deracinated trees area in temperate forest with satellite images employing machine learning methods.

ABSTRACT: Climate change can increase the number of uprooted trees. Although there have been an increasing number of machine learning applications for satellite image analysis, the estimation of deracinated tree area by satellite image is not well developed. Therefore, we estimated the deracinated tree area of forests via machine-learning classification using Landsat 8 satellite images. We employed support vector machines (SVMs), random forests (RF), and convolutional neural networks (CNNs) as potential machine learning methods, and tested their performance in estimating the deracinated tree area. We collected satellite images of upright trees, deracinated trees, soil, and others (e.g., waterbodies and cities), and trained them with the training data. We compared the accuracy represent

SUBMITTER: Doi H 

PROVIDER: S-EPMC8384040 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature