Comparison of forest canopy height profiles in a mountainous region of Taiwan derived from airborne lidar and unmanned aerial vehicle imagery
Tree height is essential for assessing carbon budgets and biodiversity. One of the most commonly used assessment methods is a field survey. However, this approach is extremely challenging for obtaining highly accurate estimates in forests with tall and dense canopies. In this study, we utilized airb...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2019-11-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/15481603.2019.1627044 |
| _version_ | 1797679096976637952 |
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| author | Chih-Hsin Chung Chao-Huan Wang Han-Ching Hsieh Cho-Ying Huang |
| author_facet | Chih-Hsin Chung Chao-Huan Wang Han-Ching Hsieh Cho-Ying Huang |
| author_sort | Chih-Hsin Chung |
| collection | DOAJ |
| description | Tree height is essential for assessing carbon budgets and biodiversity. One of the most commonly used assessment methods is a field survey. However, this approach is extremely challenging for obtaining highly accurate estimates in forests with tall and dense canopies. In this study, we utilized airborne remotely sensed mean canopy height (MCH) spatial coverage acquired by high-cost airborne light detection and ranging (lidar) and low-cost unmanned aerial vehicle (UAV) sensors to quantify the tree height for a 590-ha complex tropical forest in the mountainous region of central Taiwan. The performances of the acquisition techniques were evaluated by comparing the statistical relationships of MCH from lidar and MCH from UAV with the concurrently obtained field mean tree height measurement (MTH) at the plot (25 × 20 m) scale. In addition, we further analyzed the forest structural variables that may influence lidar and UAV MCHs by using a general linear model. The results showed that both MCHs derived from lidar and UAV accurately estimated MTH. MCH from UAV had a superior performance to that of a small model offset, and the slope of the model fit line was close to one, which was possibly due to the finer spatial resolution of the UAV imagery. MCH from lidar may be utilized to delineate the entire vertical profile of a forest stand, but MCH from UAV can only detect the upper half of the canopies. This is a result of instrument and data differences. General linear model statistics revealed that the maximum stand height and mean tree age may be the major forest stand structure determinants affecting MCH estimates, which might indicate that the airborne estimations of mean canopy height are mainly governed by large trees within a forest stand. |
| first_indexed | 2024-03-11T23:09:29Z |
| format | Article |
| id | doaj.art-787cb77fcd5b40fd84908e92da563e2c |
| institution | Directory Open Access Journal |
| issn | 1548-1603 1943-7226 |
| language | English |
| last_indexed | 2024-03-11T23:09:29Z |
| publishDate | 2019-11-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj.art-787cb77fcd5b40fd84908e92da563e2c2023-09-21T12:34:15ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262019-11-015681289130410.1080/15481603.2019.16270441627044Comparison of forest canopy height profiles in a mountainous region of Taiwan derived from airborne lidar and unmanned aerial vehicle imageryChih-Hsin Chung0Chao-Huan Wang1Han-Ching Hsieh2Cho-Ying Huang3National Ilan UniversityNational Ilan UniversityTaiwan Forestry Research InstituteNational Taiwan UniversityTree height is essential for assessing carbon budgets and biodiversity. One of the most commonly used assessment methods is a field survey. However, this approach is extremely challenging for obtaining highly accurate estimates in forests with tall and dense canopies. In this study, we utilized airborne remotely sensed mean canopy height (MCH) spatial coverage acquired by high-cost airborne light detection and ranging (lidar) and low-cost unmanned aerial vehicle (UAV) sensors to quantify the tree height for a 590-ha complex tropical forest in the mountainous region of central Taiwan. The performances of the acquisition techniques were evaluated by comparing the statistical relationships of MCH from lidar and MCH from UAV with the concurrently obtained field mean tree height measurement (MTH) at the plot (25 × 20 m) scale. In addition, we further analyzed the forest structural variables that may influence lidar and UAV MCHs by using a general linear model. The results showed that both MCHs derived from lidar and UAV accurately estimated MTH. MCH from UAV had a superior performance to that of a small model offset, and the slope of the model fit line was close to one, which was possibly due to the finer spatial resolution of the UAV imagery. MCH from lidar may be utilized to delineate the entire vertical profile of a forest stand, but MCH from UAV can only detect the upper half of the canopies. This is a result of instrument and data differences. General linear model statistics revealed that the maximum stand height and mean tree age may be the major forest stand structure determinants affecting MCH estimates, which might indicate that the airborne estimations of mean canopy height are mainly governed by large trees within a forest stand.http://dx.doi.org/10.1080/15481603.2019.1627044canopy heightforest structuregeneral linear modelrelative variable importance (rvi)tree height |
| spellingShingle | Chih-Hsin Chung Chao-Huan Wang Han-Ching Hsieh Cho-Ying Huang Comparison of forest canopy height profiles in a mountainous region of Taiwan derived from airborne lidar and unmanned aerial vehicle imagery GIScience & Remote Sensing canopy height forest structure general linear model relative variable importance (rvi) tree height |
| title | Comparison of forest canopy height profiles in a mountainous region of Taiwan derived from airborne lidar and unmanned aerial vehicle imagery |
| title_full | Comparison of forest canopy height profiles in a mountainous region of Taiwan derived from airborne lidar and unmanned aerial vehicle imagery |
| title_fullStr | Comparison of forest canopy height profiles in a mountainous region of Taiwan derived from airborne lidar and unmanned aerial vehicle imagery |
| title_full_unstemmed | Comparison of forest canopy height profiles in a mountainous region of Taiwan derived from airborne lidar and unmanned aerial vehicle imagery |
| title_short | Comparison of forest canopy height profiles in a mountainous region of Taiwan derived from airborne lidar and unmanned aerial vehicle imagery |
| title_sort | comparison of forest canopy height profiles in a mountainous region of taiwan derived from airborne lidar and unmanned aerial vehicle imagery |
| topic | canopy height forest structure general linear model relative variable importance (rvi) tree height |
| url | http://dx.doi.org/10.1080/15481603.2019.1627044 |
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