Canopy Height Layering Biomass Estimation Model (CHL-BEM) with Full-Waveform LiDAR
Forest biomass is an important descriptor for studying carbon storage, carbon cycles, and global change science. The full-waveform spaceborne Light Detection And Ranging (LiDAR) Geoscience Laser Altimeter System (GLAS) provides great possibilities for large-scale and long-term biomass estimation. To...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/11/12/1446 |
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| author | Jinyan Tian Le Wang Xiaojuan Li Dameng Yin Huili Gong Sheng Nie Chen Shi Ruofei Zhong Xiaomeng Liu Ronglong Xu |
| author_facet | Jinyan Tian Le Wang Xiaojuan Li Dameng Yin Huili Gong Sheng Nie Chen Shi Ruofei Zhong Xiaomeng Liu Ronglong Xu |
| author_sort | Jinyan Tian |
| collection | DOAJ |
| description | Forest biomass is an important descriptor for studying carbon storage, carbon cycles, and global change science. The full-waveform spaceborne Light Detection And Ranging (LiDAR) Geoscience Laser Altimeter System (GLAS) provides great possibilities for large-scale and long-term biomass estimation. To the best of our knowledge, most of the existing research has utilized average tree height (or height metrics) within a GLAS footprint as the key parameter for biomass estimation. However, the vertical distribution of tree height is usually not as homogeneous as we would expect within such a large footprint of more than 2000 m<sup>2</sup>, which would limit the biomass estimation accuracy vastly. Therefore, we aim to develop a novel canopy height layering biomass estimation model (CHL-BEM) with GLAS data in this study. First, all the trees with similar height were regarded as one canopy layer within each GLAS footprint. Second, the canopy height and canopy cover of each layer were derived from GLAS waveform parameters. These parameters were extracted using a waveform decomposition algorithm (refined Levenberg−Marquardt—RLM), which assumed that each decomposed vegetation signal corresponded to a particular canopy height layer. Third, the biomass estimation model (CHL-BEM) was established by using the canopy height and canopy cover of each height layer. Finally, the CHL-BEM was compared with two typical biomass estimation models of GLAS in the study site located in Ejina, China, where the dominant species was <i>Populus euphratica</i>. The results showed that the CHL-BEM presented good agreement with the field measurement biomass (<i>R<sup>2</sup></i> = 0.741, <i>RMSE</i> = 0.487, <i>%RMSE</i> = 24.192) and achieved a significantly higher accuracy than the other two models. As a whole, we expect our method to advance all the full-waveform LiDAR development and applications, e.g., the newly launched Global Ecosystem Dynamics Investigation (GEDI). |
| first_indexed | 2024-12-10T20:47:18Z |
| format | Article |
| id | doaj.art-8d3c80268fba4b919a053e095bf94e14 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-10T20:47:18Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8d3c80268fba4b919a053e095bf94e142022-12-22T01:34:11ZengMDPI AGRemote Sensing2072-42922019-06-011112144610.3390/rs11121446rs11121446Canopy Height Layering Biomass Estimation Model (CHL-BEM) with Full-Waveform LiDARJinyan Tian0Le Wang1Xiaojuan Li2Dameng Yin3Huili Gong4Sheng Nie5Chen Shi6Ruofei Zhong7Xiaomeng Liu8Ronglong Xu9Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, ChinaDepartment of Geography, The State University of New York at Buffalo, Buffalo, NY 14261, USABeijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, ChinaDepartment of Geography, The State University of New York at Buffalo, Buffalo, NY 14261, USABeijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, ChinaKey Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, ChinaBeijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, ChinaBeijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, ChinaBeijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, ChinaBeijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, ChinaForest biomass is an important descriptor for studying carbon storage, carbon cycles, and global change science. The full-waveform spaceborne Light Detection And Ranging (LiDAR) Geoscience Laser Altimeter System (GLAS) provides great possibilities for large-scale and long-term biomass estimation. To the best of our knowledge, most of the existing research has utilized average tree height (or height metrics) within a GLAS footprint as the key parameter for biomass estimation. However, the vertical distribution of tree height is usually not as homogeneous as we would expect within such a large footprint of more than 2000 m<sup>2</sup>, which would limit the biomass estimation accuracy vastly. Therefore, we aim to develop a novel canopy height layering biomass estimation model (CHL-BEM) with GLAS data in this study. First, all the trees with similar height were regarded as one canopy layer within each GLAS footprint. Second, the canopy height and canopy cover of each layer were derived from GLAS waveform parameters. These parameters were extracted using a waveform decomposition algorithm (refined Levenberg−Marquardt—RLM), which assumed that each decomposed vegetation signal corresponded to a particular canopy height layer. Third, the biomass estimation model (CHL-BEM) was established by using the canopy height and canopy cover of each height layer. Finally, the CHL-BEM was compared with two typical biomass estimation models of GLAS in the study site located in Ejina, China, where the dominant species was <i>Populus euphratica</i>. The results showed that the CHL-BEM presented good agreement with the field measurement biomass (<i>R<sup>2</sup></i> = 0.741, <i>RMSE</i> = 0.487, <i>%RMSE</i> = 24.192) and achieved a significantly higher accuracy than the other two models. As a whole, we expect our method to advance all the full-waveform LiDAR development and applications, e.g., the newly launched Global Ecosystem Dynamics Investigation (GEDI).https://www.mdpi.com/2072-4292/11/12/1446biomassfull-waveform LiDARGLASGEDIUAV LiDAR |
| spellingShingle | Jinyan Tian Le Wang Xiaojuan Li Dameng Yin Huili Gong Sheng Nie Chen Shi Ruofei Zhong Xiaomeng Liu Ronglong Xu Canopy Height Layering Biomass Estimation Model (CHL-BEM) with Full-Waveform LiDAR Remote Sensing biomass full-waveform LiDAR GLAS GEDI UAV LiDAR |
| title | Canopy Height Layering Biomass Estimation Model (CHL-BEM) with Full-Waveform LiDAR |
| title_full | Canopy Height Layering Biomass Estimation Model (CHL-BEM) with Full-Waveform LiDAR |
| title_fullStr | Canopy Height Layering Biomass Estimation Model (CHL-BEM) with Full-Waveform LiDAR |
| title_full_unstemmed | Canopy Height Layering Biomass Estimation Model (CHL-BEM) with Full-Waveform LiDAR |
| title_short | Canopy Height Layering Biomass Estimation Model (CHL-BEM) with Full-Waveform LiDAR |
| title_sort | canopy height layering biomass estimation model chl bem with full waveform lidar |
| topic | biomass full-waveform LiDAR GLAS GEDI UAV LiDAR |
| url | https://www.mdpi.com/2072-4292/11/12/1446 |
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