Open AccessEstimation of deracinated trees area in temperate forest with satellite images employing machine learning methods
Author(s) -
Hideyuki Doi,
Tsuneo Hirai
Publication year - 2021
Publication title -
peerj. computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.806
H-Index - 24
ISSN - 2376-5992
DOI - 10.7717/peerj-cs.648
Subject(s) - random forest , support vector machine , artificial intelligence , tree (set theory) , computer science , convolutional neural network , satellite , remote sensing , machine learning , pattern recognition (psychology) , artificial neural network , decision tree , satellite imagery , contextual image classification , image (mathematics) , mathematics , geography , mathematical analysis , engineering , aerospace engineering
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 represented by the correct classification rate of these methods, to determine the deracinated tree area. It was found that the SVM and RF performed better than the CNN for two-class classification (deracinated and upright trees), and the correct classification rates of all methods were up to 93%. We found that the CNN and RF performed significantly higher for the four- and two-class classification compared to the other methods, respectively. We conclude that the CNN is useful for estimating deracinated tree areas using Landsat 8 satellite images.