Estimating Tree Volume Distributions in Subtropical Forests Using Airborne LiDAR Data
Accurate and reliable information on tree volume distributions, which describe tree frequencies in volume classes, plays a key role in guiding timber harvest, managing carbon budgets, and supplying ecosystem services. Airborne Light Detection and Ranging (LiDAR) has the capability of offering reliab...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-01-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/11/1/97 |
| _version_ | 1819110881568489472 |
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| author | Lin Cao Zhengnan Zhang Ting Yun Guibin Wang Honghua Ruan Guanghui She |
| author_facet | Lin Cao Zhengnan Zhang Ting Yun Guibin Wang Honghua Ruan Guanghui She |
| author_sort | Lin Cao |
| collection | DOAJ |
| description | Accurate and reliable information on tree volume distributions, which describe tree frequencies in volume classes, plays a key role in guiding timber harvest, managing carbon budgets, and supplying ecosystem services. Airborne Light Detection and Ranging (LiDAR) has the capability of offering reliable estimates of the distributions of structure attributes in forests. In this study, we predicted individual tree volume distributions over a subtropical forest of southeast China using airborne LiDAR data and field measurements. We first estimated the plot-level total volume by LiDAR-derived standard and canopy metrics. Then the performances of three Weibull parameter prediction methods, i.e., parameter prediction method (PPM), percentile-based parameter recover method (PPRM), and moment-based parameter recover method (MPRM) were assessed to estimate the Weibull scale and shape parameters. Stem density for each plot was calculated by dividing the estimated plot total volume using mean tree volume (i.e., mean value of distributions) derived from the LiDAR-estimated Weibull parameters. Finally, the individual tree volume distributions were generated by the predicted scale and shape parameters, and then scaled by the predicted stem density. The results demonstrated that, compared with the general models, the forest type-specific (i.e., coniferous forests, broadleaved forests, and mixed forests) models had relatively higher accuracies for estimating total volume and stem density, as well as predicting Weibull parameters, percentiles, and raw moments. The relationship between the predicted and reference volume distributions showed a relatively high agreement when the predicted frequencies were scaled to the LiDAR-predicted stem density (mean Reynolds error index eR = 31.47–54.07, mean Packalén error index eP = 0.14–0.21). In addition, the predicted individual tree volume distributions predicted by PPRM of (average mean eR = 37.75) performed the best, followed by MPRM (average mean eR = 40.43) and PPM (average mean eR = 41.22). This study demonstrated that the LiDAR can potentially offer improved estimates of the distributions of tree volume in subtropical forests. |
| first_indexed | 2024-12-22T03:48:46Z |
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| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-22T03:48:46Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-62bc83194cd9407bb45411544d956d682022-12-21T18:40:04ZengMDPI AGRemote Sensing2072-42922019-01-011119710.3390/rs11010097rs11010097Estimating Tree Volume Distributions in Subtropical Forests Using Airborne LiDAR DataLin Cao0Zhengnan Zhang1Ting Yun2Guibin Wang3Honghua Ruan4Guanghui She5Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, ChinaCo-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, ChinaSchool of Information Science & Technology, Nanjing Forestry University, Nanjing 210037, ChinaCo-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, ChinaCo-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, ChinaCo-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, ChinaAccurate and reliable information on tree volume distributions, which describe tree frequencies in volume classes, plays a key role in guiding timber harvest, managing carbon budgets, and supplying ecosystem services. Airborne Light Detection and Ranging (LiDAR) has the capability of offering reliable estimates of the distributions of structure attributes in forests. In this study, we predicted individual tree volume distributions over a subtropical forest of southeast China using airborne LiDAR data and field measurements. We first estimated the plot-level total volume by LiDAR-derived standard and canopy metrics. Then the performances of three Weibull parameter prediction methods, i.e., parameter prediction method (PPM), percentile-based parameter recover method (PPRM), and moment-based parameter recover method (MPRM) were assessed to estimate the Weibull scale and shape parameters. Stem density for each plot was calculated by dividing the estimated plot total volume using mean tree volume (i.e., mean value of distributions) derived from the LiDAR-estimated Weibull parameters. Finally, the individual tree volume distributions were generated by the predicted scale and shape parameters, and then scaled by the predicted stem density. The results demonstrated that, compared with the general models, the forest type-specific (i.e., coniferous forests, broadleaved forests, and mixed forests) models had relatively higher accuracies for estimating total volume and stem density, as well as predicting Weibull parameters, percentiles, and raw moments. The relationship between the predicted and reference volume distributions showed a relatively high agreement when the predicted frequencies were scaled to the LiDAR-predicted stem density (mean Reynolds error index eR = 31.47–54.07, mean Packalén error index eP = 0.14–0.21). In addition, the predicted individual tree volume distributions predicted by PPRM of (average mean eR = 37.75) performed the best, followed by MPRM (average mean eR = 40.43) and PPM (average mean eR = 41.22). This study demonstrated that the LiDAR can potentially offer improved estimates of the distributions of tree volume in subtropical forests.http://www.mdpi.com/2072-4292/11/1/97volume distributionLiDARWeibullsubtropical forestsforest structure |
| spellingShingle | Lin Cao Zhengnan Zhang Ting Yun Guibin Wang Honghua Ruan Guanghui She Estimating Tree Volume Distributions in Subtropical Forests Using Airborne LiDAR Data Remote Sensing volume distribution LiDAR Weibull subtropical forests forest structure |
| title | Estimating Tree Volume Distributions in Subtropical Forests Using Airborne LiDAR Data |
| title_full | Estimating Tree Volume Distributions in Subtropical Forests Using Airborne LiDAR Data |
| title_fullStr | Estimating Tree Volume Distributions in Subtropical Forests Using Airborne LiDAR Data |
| title_full_unstemmed | Estimating Tree Volume Distributions in Subtropical Forests Using Airborne LiDAR Data |
| title_short | Estimating Tree Volume Distributions in Subtropical Forests Using Airborne LiDAR Data |
| title_sort | estimating tree volume distributions in subtropical forests using airborne lidar data |
| topic | volume distribution LiDAR Weibull subtropical forests forest structure |
| url | http://www.mdpi.com/2072-4292/11/1/97 |
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