Classification of Street Tree Species Using UAV Tilt Photogrammetry

As an important component of the urban ecosystem, street trees have made an outstanding contribution to alleviating urban environmental pollution. Accurately extracting tree characteristics and species information can facilitate the monitoring and management of street trees, as well as aiding landsc...

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Main Authors: Yutang Wang, Jia Wang, Shuping Chang, Lu Sun, Likun An, Yuhan Chen, Jiangqi Xu
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/13/2/216
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author Yutang Wang
Jia Wang
Shuping Chang
Lu Sun
Likun An
Yuhan Chen
Jiangqi Xu
author_facet Yutang Wang
Jia Wang
Shuping Chang
Lu Sun
Likun An
Yuhan Chen
Jiangqi Xu
author_sort Yutang Wang
collection DOAJ
description As an important component of the urban ecosystem, street trees have made an outstanding contribution to alleviating urban environmental pollution. Accurately extracting tree characteristics and species information can facilitate the monitoring and management of street trees, as well as aiding landscaping and studies of urban ecology. In this study, we selected the suburban areas of Beijing and Zhangjiakou and investigated six representative street tree species using unmanned aerial vehicle (UAV) tilt photogrammetry. We extracted five tree attributes and four combined attribute parameters and used four types of commonly-used machine learning classification algorithms as classifiers for tree species classification. The results show that random forest (RF), support vector machine (SVM), and back propagation (BP) neural network provide better classification results when using combined parameters for tree species classification, compared with those using individual tree attributes alone; however, the K-nearest neighbor (KNN) algorithm produced the opposite results. The best combination for classification is the BP neural network using combined attributes, with a classification precision of 89.1% and F-measure of 0.872, and we conclude that this approach best meets the requirements of street tree surveys. The results also demonstrate that optical UAV tilt photogrammetry combined with a machine learning classification algorithm is a low-cost, high-efficiency, and high-precision method for tree species classification.
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spelling doaj.art-383667bd817641fd8dcb7cda882c0b0f2023-12-03T12:40:28ZengMDPI AGRemote Sensing2072-42922021-01-0113221610.3390/rs13020216Classification of Street Tree Species Using UAV Tilt PhotogrammetryYutang Wang0Jia Wang1Shuping Chang2Lu Sun3Likun An4Yuhan Chen5Jiangqi Xu6The College of Forestry, Beijing Forestry University, Beijing 100083, ChinaThe College of Forestry, Beijing Forestry University, Beijing 100083, ChinaThe College of Forestry, Beijing Forestry University, Beijing 100083, ChinaThe College of Forestry, Beijing Forestry University, Beijing 100083, ChinaThe College of Forestry, Beijing Forestry University, Beijing 100083, ChinaThe College of Forestry, Beijing Forestry University, Beijing 100083, ChinaThe College of Forestry, Beijing Forestry University, Beijing 100083, ChinaAs an important component of the urban ecosystem, street trees have made an outstanding contribution to alleviating urban environmental pollution. Accurately extracting tree characteristics and species information can facilitate the monitoring and management of street trees, as well as aiding landscaping and studies of urban ecology. In this study, we selected the suburban areas of Beijing and Zhangjiakou and investigated six representative street tree species using unmanned aerial vehicle (UAV) tilt photogrammetry. We extracted five tree attributes and four combined attribute parameters and used four types of commonly-used machine learning classification algorithms as classifiers for tree species classification. The results show that random forest (RF), support vector machine (SVM), and back propagation (BP) neural network provide better classification results when using combined parameters for tree species classification, compared with those using individual tree attributes alone; however, the K-nearest neighbor (KNN) algorithm produced the opposite results. The best combination for classification is the BP neural network using combined attributes, with a classification precision of 89.1% and F-measure of 0.872, and we conclude that this approach best meets the requirements of street tree surveys. The results also demonstrate that optical UAV tilt photogrammetry combined with a machine learning classification algorithm is a low-cost, high-efficiency, and high-precision method for tree species classification.https://www.mdpi.com/2072-4292/13/2/216tree species classificationstreet treesUAVmachine learningtilt photogrammetry
spellingShingle Yutang Wang
Jia Wang
Shuping Chang
Lu Sun
Likun An
Yuhan Chen
Jiangqi Xu
Classification of Street Tree Species Using UAV Tilt Photogrammetry
Remote Sensing
tree species classification
street trees
UAV
machine learning
tilt photogrammetry
title Classification of Street Tree Species Using UAV Tilt Photogrammetry
title_full Classification of Street Tree Species Using UAV Tilt Photogrammetry
title_fullStr Classification of Street Tree Species Using UAV Tilt Photogrammetry
title_full_unstemmed Classification of Street Tree Species Using UAV Tilt Photogrammetry
title_short Classification of Street Tree Species Using UAV Tilt Photogrammetry
title_sort classification of street tree species using uav tilt photogrammetry
topic tree species classification
street trees
UAV
machine learning
tilt photogrammetry
url https://www.mdpi.com/2072-4292/13/2/216
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AT likunan classificationofstreettreespeciesusinguavtiltphotogrammetry
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