Estimation of deracinated trees area in temperate forest with satellite images employing machine learning methods
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 o...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2021-08-01
|
| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-648.pdf |
| _version_ | 1819137606730907648 |
|---|---|
| author | Hideyuki Doi Tomoki Hirai |
| author_facet | Hideyuki Doi Tomoki Hirai |
| author_sort | Hideyuki Doi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T10:53:33Z |
| format | Article |
| id | doaj.art-e91372292c75452b8c5dbb7ebaca94a5 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-12-22T10:53:33Z |
| publishDate | 2021-08-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-e91372292c75452b8c5dbb7ebaca94a52022-12-21T18:28:41ZengPeerJ Inc.PeerJ Computer Science2376-59922021-08-017e64810.7717/peerj-cs.648Estimation of deracinated trees area in temperate forest with satellite images employing machine learning methodsHideyuki Doi0Tomoki Hirai1Graduate School of Information Science, University of Hyogo, Kobe, JapanGraduate School of Simulation Studies, University of Hyogo, Kobe, JapanClimate 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.https://peerj.com/articles/cs-648.pdfMachine learningCNNSVMRandom forestDisturbanceForest |
| spellingShingle | Hideyuki Doi Tomoki Hirai Estimation of deracinated trees area in temperate forest with satellite images employing machine learning methods PeerJ Computer Science Machine learning CNN SVM Random forest Disturbance Forest |
| title | Estimation of deracinated trees area in temperate forest with satellite images employing machine learning methods |
| title_full | Estimation of deracinated trees area in temperate forest with satellite images employing machine learning methods |
| title_fullStr | Estimation of deracinated trees area in temperate forest with satellite images employing machine learning methods |
| title_full_unstemmed | Estimation of deracinated trees area in temperate forest with satellite images employing machine learning methods |
| title_short | Estimation of deracinated trees area in temperate forest with satellite images employing machine learning methods |
| title_sort | estimation of deracinated trees area in temperate forest with satellite images employing machine learning methods |
| topic | Machine learning CNN SVM Random forest Disturbance Forest |
| url | https://peerj.com/articles/cs-648.pdf |
| work_keys_str_mv | AT hideyukidoi estimationofderacinatedtreesareaintemperateforestwithsatelliteimagesemployingmachinelearningmethods AT tomokihirai estimationofderacinatedtreesareaintemperateforestwithsatelliteimagesemployingmachinelearningmethods |