Forest Vertical Structure Mapping Using Two-Seasonal Optic Images and LiDAR DSM Acquired from UAV Platform through Random Forest, XGBoost, and Support Vector Machine Approaches

Research on the forest structure classification is essential, as it plays an important role in assessing the vitality and diversity of vegetation. However, classifying forest structure involves in situ surveying, which requires considerable time and money, and cannot be conducted directly in some in...

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Autores principales:	Jin-Woo Yu, Young-Woong Yoon, Won-Kyung Baek, Hyung-Sup Jung
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	forest vertical structure multiseason machine learning classification Science Q
Acceso en línea:	https://doaj.org/article/b91cb541b46c42dc90934cb0731d52c1
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spelling	oai:doaj.org-article:b91cb541b46c42dc90934cb0731d52c12021-11-11T18:52:53ZForest Vertical Structure Mapping Using Two-Seasonal Optic Images and LiDAR DSM Acquired from UAV Platform through Random Forest, XGBoost, and Support Vector Machine Approaches10.3390/rs132142822072-4292https://doaj.org/article/b91cb541b46c42dc90934cb0731d52c12021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4282https://doaj.org/toc/2072-4292Research on the forest structure classification is essential, as it plays an important role in assessing the vitality and diversity of vegetation. However, classifying forest structure involves in situ surveying, which requires considerable time and money, and cannot be conducted directly in some instances; also, the update cycle of the classification data is very late. To overcome these drawbacks, feasibility studies on mapping the forest vertical structure from aerial images using machine learning techniques were conducted. In this study, we investigated (1) the performance improvement of the forest structure classification, using a high-resolution LiDAR-derived digital surface model (DSM) acquired from an unmanned aerial vehicle (UAV) platform and (2) the performance comparison of results obtained from the single-seasonal and two-seasonal data, using random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM). For the performance comparison, the UAV optic and LiDAR data were divided into three cases: (1) only used autumn data, (2) only used winter data, and (3) used both autumn and winter data. From the results, the best model was XGBoost, and the F1 scores achieved using this method were approximately 0.92 in the autumn and winter cases. A remarkable improvement was achieved when both two-seasonal images were used. The F1 score improved by 35.3% from 0.68 to 0.92. This implies that (1) the seasonal variation in the forest vertical structure can be more important than the spatial resolution, and (2) the classification performance achieved from the two-seasonal UAV optic images and LiDAR-derived DSMs can reach 0.9 with the application of an optimal machine learning approach.Jin-Woo YuYoung-Woong YoonWon-Kyung BaekHyung-Sup JungMDPI AGarticleforest vertical structuremultiseasonmachine learningclassificationScienceQENRemote Sensing, Vol 13, Iss 4282, p 4282 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	forest vertical structure multiseason machine learning classification Science Q
spellingShingle	forest vertical structure multiseason machine learning classification Science Q Jin-Woo Yu Young-Woong Yoon Won-Kyung Baek Hyung-Sup Jung Forest Vertical Structure Mapping Using Two-Seasonal Optic Images and LiDAR DSM Acquired from UAV Platform through Random Forest, XGBoost, and Support Vector Machine Approaches
description	Research on the forest structure classification is essential, as it plays an important role in assessing the vitality and diversity of vegetation. However, classifying forest structure involves in situ surveying, which requires considerable time and money, and cannot be conducted directly in some instances; also, the update cycle of the classification data is very late. To overcome these drawbacks, feasibility studies on mapping the forest vertical structure from aerial images using machine learning techniques were conducted. In this study, we investigated (1) the performance improvement of the forest structure classification, using a high-resolution LiDAR-derived digital surface model (DSM) acquired from an unmanned aerial vehicle (UAV) platform and (2) the performance comparison of results obtained from the single-seasonal and two-seasonal data, using random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM). For the performance comparison, the UAV optic and LiDAR data were divided into three cases: (1) only used autumn data, (2) only used winter data, and (3) used both autumn and winter data. From the results, the best model was XGBoost, and the F1 scores achieved using this method were approximately 0.92 in the autumn and winter cases. A remarkable improvement was achieved when both two-seasonal images were used. The F1 score improved by 35.3% from 0.68 to 0.92. This implies that (1) the seasonal variation in the forest vertical structure can be more important than the spatial resolution, and (2) the classification performance achieved from the two-seasonal UAV optic images and LiDAR-derived DSMs can reach 0.9 with the application of an optimal machine learning approach.
format	article
author	Jin-Woo Yu Young-Woong Yoon Won-Kyung Baek Hyung-Sup Jung
author_facet	Jin-Woo Yu Young-Woong Yoon Won-Kyung Baek Hyung-Sup Jung
author_sort	Jin-Woo Yu
title	Forest Vertical Structure Mapping Using Two-Seasonal Optic Images and LiDAR DSM Acquired from UAV Platform through Random Forest, XGBoost, and Support Vector Machine Approaches
title_short	Forest Vertical Structure Mapping Using Two-Seasonal Optic Images and LiDAR DSM Acquired from UAV Platform through Random Forest, XGBoost, and Support Vector Machine Approaches
title_full	Forest Vertical Structure Mapping Using Two-Seasonal Optic Images and LiDAR DSM Acquired from UAV Platform through Random Forest, XGBoost, and Support Vector Machine Approaches
title_fullStr	Forest Vertical Structure Mapping Using Two-Seasonal Optic Images and LiDAR DSM Acquired from UAV Platform through Random Forest, XGBoost, and Support Vector Machine Approaches
title_full_unstemmed	Forest Vertical Structure Mapping Using Two-Seasonal Optic Images and LiDAR DSM Acquired from UAV Platform through Random Forest, XGBoost, and Support Vector Machine Approaches
title_sort	forest vertical structure mapping using two-seasonal optic images and lidar dsm acquired from uav platform through random forest, xgboost, and support vector machine approaches
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b91cb541b46c42dc90934cb0731d52c1
work_keys_str_mv	AT jinwooyu forestverticalstructuremappingusingtwoseasonalopticimagesandlidardsmacquiredfromuavplatformthroughrandomforestxgboostandsupportvectormachineapproaches AT youngwoongyoon forestverticalstructuremappingusingtwoseasonalopticimagesandlidardsmacquiredfromuavplatformthroughrandomforestxgboostandsupportvectormachineapproaches AT wonkyungbaek forestverticalstructuremappingusingtwoseasonalopticimagesandlidardsmacquiredfromuavplatformthroughrandomforestxgboostandsupportvectormachineapproaches AT hyungsupjung forestverticalstructuremappingusingtwoseasonalopticimagesandlidardsmacquiredfromuavplatformthroughrandomforestxgboostandsupportvectormachineapproaches
_version_	1718431722790453248

Forest Vertical Structure Mapping Using Two-Seasonal Optic Images and LiDAR DSM Acquired from UAV Platform through Random Forest, XGBoost, and Support Vector Machine Approaches

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