Estimation of Chlorophyll Content at Stand and Individual Tree Level by UAV Hyperspectral Combined with LiDAR
Chlorophyll plays a significant role in evaluating vegetation health and forest carbon sink. In this study, a total of 36 characteristic variables from hyperspectral image and lidar point cloud data acquired through an unmanned aerial vehicle (UAV) platform were used to evaluate the accuracy of stat...
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MDPI AG
2025-05-01
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| author | Zhuonan Meng Ying Yu Xiguang Yang Tao Yang |
| author_facet | Zhuonan Meng Ying Yu Xiguang Yang Tao Yang |
| author_sort | Zhuonan Meng |
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| description | Chlorophyll plays a significant role in evaluating vegetation health and forest carbon sink. In this study, a total of 36 characteristic variables from hyperspectral image and lidar point cloud data acquired through an unmanned aerial vehicle (UAV) platform were used to evaluate the accuracy of statistical models including multiple stepwise regression, BP neural network, BP neural network optimized by firefly algorithm, random forest, and the mixed data-driven mechanistic model PROSPECT model in estimating chlorophyll content for three different forest types in Maoershan Forest Farm of Northeast Forestry University in Heilongjiang Province, namely coniferous forest, broad-leaved forest, and coniferous–broad-leaved mixed forest. The accuracy of the models was evaluated by the coefficient of determination (R<sup>2</sup>) and root mean square error (RMSE). The results show that random forest (R<sup>2</sup> = 0.59–0.64, RMSE = 3.79–5.83 µg·cm<sup>−2</sup>) among all statistical models is superior to other models. The accuracy of the mechanism model was the highest (R<sup>2</sup> = 0.97, RMSE = 3.40 µg·cm<sup>−2</sup>). There were significant differences in chlorophyll content among different forest types. It ranged from 25.25 to 31.60 µg·cm<sup>−2</sup> for broad-leaved forests, which was higher than that of coniferous and broad-leaved mixed forests (13.52–23.93 µg·cm<sup>−2</sup>) and coniferous forests (6.40–13.71 µg·cm<sup>−2</sup>). In the horizontal direction, the chlorophyll content near the center of the canopy was lower than that at the edge of the canopy. In the vertical direction, there was no significant difference in chlorophyll content between different canopies of <i>Pinus sylvestris</i> var. <i>mongolica</i>, while there was a significant difference in chlorophyll content between the upper, middle, and lower canopies of <i>Juglans mandshurica</i>. For different tree species, the variation in chlorophyll with crown height was different. |
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| publishDate | 2025-05-01 |
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| spelling | doaj-art-229fc2c3838145ed88cf3eefcf4b8b642025-08-20T02:34:02ZengMDPI AGRemote Sensing2072-42922025-05-011710169910.3390/rs17101699Estimation of Chlorophyll Content at Stand and Individual Tree Level by UAV Hyperspectral Combined with LiDARZhuonan Meng0Ying Yu1Xiguang Yang2Tao Yang3School of Forestry, Northeast Forestry University, Harbin 150040, ChinaSchool of Forestry, Northeast Forestry University, Harbin 150040, ChinaSchool of Forestry, Northeast Forestry University, Harbin 150040, ChinaSchool of Forestry, Northeast Forestry University, Harbin 150040, ChinaChlorophyll plays a significant role in evaluating vegetation health and forest carbon sink. In this study, a total of 36 characteristic variables from hyperspectral image and lidar point cloud data acquired through an unmanned aerial vehicle (UAV) platform were used to evaluate the accuracy of statistical models including multiple stepwise regression, BP neural network, BP neural network optimized by firefly algorithm, random forest, and the mixed data-driven mechanistic model PROSPECT model in estimating chlorophyll content for three different forest types in Maoershan Forest Farm of Northeast Forestry University in Heilongjiang Province, namely coniferous forest, broad-leaved forest, and coniferous–broad-leaved mixed forest. The accuracy of the models was evaluated by the coefficient of determination (R<sup>2</sup>) and root mean square error (RMSE). The results show that random forest (R<sup>2</sup> = 0.59–0.64, RMSE = 3.79–5.83 µg·cm<sup>−2</sup>) among all statistical models is superior to other models. The accuracy of the mechanism model was the highest (R<sup>2</sup> = 0.97, RMSE = 3.40 µg·cm<sup>−2</sup>). There were significant differences in chlorophyll content among different forest types. It ranged from 25.25 to 31.60 µg·cm<sup>−2</sup> for broad-leaved forests, which was higher than that of coniferous and broad-leaved mixed forests (13.52–23.93 µg·cm<sup>−2</sup>) and coniferous forests (6.40–13.71 µg·cm<sup>−2</sup>). In the horizontal direction, the chlorophyll content near the center of the canopy was lower than that at the edge of the canopy. In the vertical direction, there was no significant difference in chlorophyll content between different canopies of <i>Pinus sylvestris</i> var. <i>mongolica</i>, while there was a significant difference in chlorophyll content between the upper, middle, and lower canopies of <i>Juglans mandshurica</i>. For different tree species, the variation in chlorophyll with crown height was different.https://www.mdpi.com/2072-4292/17/10/1699UAVhyperspectralLiDARforest standchlorophyllmechanism models |
| spellingShingle | Zhuonan Meng Ying Yu Xiguang Yang Tao Yang Estimation of Chlorophyll Content at Stand and Individual Tree Level by UAV Hyperspectral Combined with LiDAR Remote Sensing UAV hyperspectral LiDAR forest stand chlorophyll mechanism models |
| title | Estimation of Chlorophyll Content at Stand and Individual Tree Level by UAV Hyperspectral Combined with LiDAR |
| title_full | Estimation of Chlorophyll Content at Stand and Individual Tree Level by UAV Hyperspectral Combined with LiDAR |
| title_fullStr | Estimation of Chlorophyll Content at Stand and Individual Tree Level by UAV Hyperspectral Combined with LiDAR |
| title_full_unstemmed | Estimation of Chlorophyll Content at Stand and Individual Tree Level by UAV Hyperspectral Combined with LiDAR |
| title_short | Estimation of Chlorophyll Content at Stand and Individual Tree Level by UAV Hyperspectral Combined with LiDAR |
| title_sort | estimation of chlorophyll content at stand and individual tree level by uav hyperspectral combined with lidar |
| topic | UAV hyperspectral LiDAR forest stand chlorophyll mechanism models |
| url | https://www.mdpi.com/2072-4292/17/10/1699 |
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