| Citation: |
YU Lei, XU Jiajia, NI Chen, et al. Individual tree parameter extraction and biomass estimation based on the quantitative structure model[J]. Journal of South China Agricultural University, 2025, 46(3): 379-389. DOI: 10.7671/j.issn.1001-411X.202406034
|
This article takes the deciduous broad-leaved forest in the Tianma National Nature Reserve in Anhui Province as the research subject, exploring the application potential of the quantitative structure model in biomass estimation of individual tree in complex environment using unmanned aerial vehicle laser scanning data.
Through ground surveys and the use of unmanned aerial vehicles, plot data and LiDAR point cloud data were collected, with the ground data serving as the reference measurements. The comparative shortest-path algorithm was used for point cloud segmentation. Subsequently, tree parameters (such as diameter at breast height, trunk volume, branch volume, number of branches, canopy base height, canopy area, canopy volume, and crown width) were extracted from the segmented individual tree point clouds using the quantitative structure model. Pearson correlation coefficient and variance inflation factor were then employed for variable selection of the parameters. Finally, an individual tree biomass estimation model was constructed based on the three machine learning algorithms.
Among the biomass models, the one based on random forest (RF) achieved the best training performance (R2 = 0.880 0, RMSE = 192.81 kg, rRMSE = 29.88%). The performance of the multilayer perceptron (MLP) model (R2 = 0.820 0, RMSE = 233.62 kg, rRMSE = 36.65%) was quite similar to that of the support vector machine (SVM) model (R2 = 0.810 0, RMSE = 243.67 kg, rRMSE = 37.77%).
This article confirms that the tree parameters extracted from the quantitative structure model can be used to construct a high-precision biomass estimation model for deciduous broadleaf species, providing a new method for resource surveys in complex forest environment.
| [1] |
BROWN S, SCHROEDER P, KERN J. Spatial distribution of biomass in forests of the eastern USA[J]. Forest Ecology and Management, 1999, 123(1): 81-90. doi: 10.1016/S0378-1127(99)00017-1
|
| [2] |
刘立斌, 周运超, 程安云, 等. 利用皆伐法估算黔中喀斯特森林地上生物量[J]. 生态学报, 2020, 40(13): 4455-4461.
|
| [3] |
孟凡栋, 王常顺, 朱小雪, 等. 西藏高原金露梅灌丛草甸物种丰富度和生物量取样方法探讨[J]. 生态学杂志, 2016, 35(12): 3435-3442.
|
| [4] |
万五星, 王效科, 李东义, 等. 暖温带森林生态系统林下灌木生物量相对生长模型[J]. 生态学报, 2014, 34(23): 6985-6992.
|
| [5] |
WHITE J C, COOPS N C, WULDER M A, et al. Remote sensing technologies for enhancing forest inventories: A review[J]. Canadian Journal of Remote Sensing, 2016, 42(5): 619-641. doi: 10.1080/07038992.2016.1207484
|
| [6] |
WULDER M A, WHITE J C, NELSON R F, et al. Lidar sampling for large-area forest characterization: A review[J]. Remote Sensing of Environment, 2012, 121: 196-209. doi: 10.1016/j.rse.2012.02.001
|
| [7] |
LU J, WANG H, QIN S, et al. Estimation of aboveground biomass of Robinia pseudoacacia forest in the Yellow River Delta based on UAV and backpack LiDAR point clouds[J]. International Journal of Applied Earth Observation and Geoinformation, 2020, 86: 102014. doi: 10.1016/j.jag.2019.102014
|
| [8] |
罗谨璇, 田义超, 张强, 等. 利用无人机激光雷达估算红树林地上生物量[J]. 海洋学报, 2023, 45(8): 108-119.
|
| [9] |
GARCIA M, RIANO D, CHUVIECO E, et al. Estimating biomass carbon stocks for a Mediterranean forest in central spain using LiDAR height and intensity data[J]. Remote Sensing of Environment, 2010, 114(4): 816-830. doi: 10.1016/j.rse.2009.11.021
|
| [10] |
CORTE A P D, SOUZA D V, REX F E, et al. Forest inventory with high-density UAV-LiDAR: Machine learning approaches for predicting individual tree attributes[J]. Computers and Electronics in Agriculture, 2020, 179: 105815. doi: 10.1016/j.compag.2020.105815
|
| [11] |
KNAPP N, FISCHER R, CAZCARRA-BES V, et al. Structure metrics to generalize biomass estimation from LiDAR across forest types from different continents[J]. Remote Sensing of Environment, 2020, 237: 111597. doi: 10.1016/j.rse.2019.111597
|
| [12] |
刘浩然, 范伟伟, 徐永胜, 等. 基于无人机激光雷达点云的单木生物量估测[J]. 中南林业科技大学学报, 2021, 41(8): 92-99.
|
| [13] |
HALL S A, BURKE I C, BOX D O, et al. Estimating stand structure using discrete-return LiDAR: An example from low density, fire prone ponderosa pine forests[J]. Forest Ecology and Management, 2005, 208(1/2/3): 189-209. doi: 10.1016/j.foreco.2004.12.001
|
| [14] |
HOSOI F, NAKAI Y, OMASA K. 3-D voxel-based solid modeling of a broad-leaved tree for accurate volume estimation using portable scanning LiDAR[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 82: 41-48. doi: 10.1016/j.isprsjprs.2013.04.011
|
| [15] |
DEMOL M, CALDERS K, VERBEECK H, et al. Forest above-ground volume assessments with terrestrial laser scanning: A ground-truth validation experiment in temperate, managed forests[J]. Annals of Botany, 2021, 128(6): 805-819. doi: 10.1093/aob/mcab110
|
| [16] |
HAUGLIN M, ASTRUP R, GOBAKKEN T, et al. Estimating single-tree branch biomass of Norway spruce with terrestrial laser scanning using voxel-based and crown dimension features[J]. Scandinavian Journal of Forest Research, 2013, 28(5): 456-469. doi: 10.1080/02827581.2013.777772
|
| [17] |
BREDE B, TERRYN L, BARBIER N, et al. Non-destructive estimation of individual tree biomass: Allometric models, terrestrial and UAV laser scanning[J]. Remote Sensing of Environment, 2022, 280: 113180. doi: 10.1016/j.rse.2022.113180
|
| [18] |
DALLA CORTE A P, DE VASCONCELLOS B N, REX F E, et al. Applying high-resolution UAV-LiDAR and quantitative structure modelling for estimating tree attributes in a crop-livestock-forest system[J]. Land, 2022, 11(4): 507. doi: 10.3390/land11040507
|
| [19] |
YE N, VAN LEEUWEN L, NYKTAS P. Analysing the potential of UAV point cloud as input in quantitative structure modelling for assessment of woody biomass of single trees[J]. International Journal of Applied Earth Observation and Geoinformation, 2019, 81: 47-57. doi: 10.1016/j.jag.2019.05.010
|
| [20] |
赵丽娟, 项文化. 常绿阔叶林石栎和青冈种群生活史特征与空间分布格局[J]. 西北植物学报, 2014, 34(6): 1259-1268. doi: 10.7606/j.issn.1000-4025.2014.06.1259
|
| [21] |
ZHANG W, QI J, WAN P, et al. An easy-to-use airborne LiDAR data filtering method based on cloth simulation[J]. Remote Sensing, 2016, 8(6): 501. doi: 10.3390/rs8060501
|
| [22] |
TAO S, WU F, GUO Q, et al. Segmenting tree crowns from terrestrial and mobile LiDAR data by exploring ecological theories[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 110: 66-76. doi: 10.1016/j.isprsjprs.2015.10.007
|
| [23] |
DELAGRANGE S, JAUVIN C, ROCHON P. PypeTree: A tool for reconstructing tree perennial tissues from point clouds[J]. Sensors, 2014, 14(3): 4271-4289. doi: 10.3390/s140304271
|
| [24] |
RAUMONEN P, KAASALAINEN M, AKERBLOM M, et al. Fast automatic precision tree models from terrestrial laser scanner data[J]. Remote Sensing, 2013, 5(2): 491-520. doi: 10.3390/rs5020491
|
| [25] |
HACKENBERG J, SPIECKER H, CALDERS K, et al. SimpleTree: An efficient open source tool to build tree models from TLS clouds[J]. Forests, 2015, 6(11): 4245-4294. doi: 10.3390/f6114245
|
| [26] |
国家市场监督管理总局, 国家标准化管理委员会. 主要树种立木生物量模型与碳计量参数: GB/T 43648—2024[S]. 北京: 中国标准出版社, 2024.
|
| [27] |
MAXWELL A E, WARNER T A, FANG F. Implementation of machine-learning classification in remote sensing: An applied review[J]. International Journal of Remote Sensing, 2018.
|
| [28] |
ZHANG Y, MA J, LIANG S, et al. An evaluation of eight machine learning regression algorithms for forest aboveground biomass estimation from multiple satellite data products[J]. Remote Sensing, 2020, 12(24): 4015. doi: 10.3390/rs12244015
|
| [29] |
FEKRY R, YAO W, CAO L, et al. Ground-based/UAV-LiDAR data fusion for quantitative structure modeling and tree parameter retrieval in subtropical planted forest[J]. Forest Ecosystems, 2022, 9: 100065. doi: 10.1016/j.fecs.2022.100065
|
| [30] |
KRŮČEK M, KRÁL K, CUSHMAN K C, et al. Supervised segmentation of ultra-high-density drone lidar for large-area mapping of individual trees[J]. Remote Sensing, 2020, 12(19): 3260. doi: 10.3390/rs12193260
|
| [31] |
SCHNEIDER F D, KÜKENBRINK D, SCHAEPMAN M E, et al. Quantifying 3D structure and occlusion in dense tropical and temperate forests using close-range LiDAR[J]. Agricultural and Forest Meteorology, 2019, 268: 249-257. doi: 10.1016/j.agrformet.2019.01.033
|
| [32] |
COOPS N C, TOMPALSKI P, GOODBODY T R H, et al. Modelling LiDAR-derived estimates of forest attributes over space and time: A review of approaches and future trends[J]. Remote Sensing of Environment, 2021, 260: 112477. doi: 10.1016/j.rse.2021.112477
|
| [33] |
陈中超, 刘清旺, 李春干, 等. 基于无人机激光雷达的人工林碳储量线性与非线性估测模型比较[J]. 北京林业大学学报, 2021, 43(12): 9-16. doi: 10.12171/j.1000-1522.20200417
|
| [34] |
ABD RAHMAN M Z, ABU BAKAR M A, RAZAK K A, et al. Non-destructive, laser-based individual tree aboveground biomass estimation in a tropical rainforest[J]. Forests, 2017, 8(3): 86. doi: 10.3390/f8030086
|
| [35] |
唐依人, 贾炜玮, 王帆, 等. 基于TLS辅助的长白落叶松一级枝条生物量模型构建[J]. 南京林业大学学报(自然科学版), 2023, 47(2): 130-140.
|
| [36] |
CHEN S, FENG Z, CHEN P, et al. Nondestructive estimation of the above-ground biomass of multiple tree species in boreal forests of china using terrestrial laser scanning[J]. Forests, 2019, 10(11): 936. doi: 10.3390/f10110936
|
| [37] |
刘鲁霞, 庞勇, 李增元. 基于地基激光雷达的亚热带森林单木胸径与树高提取[J]. 林业科学, 2016, 52(2): 26-37.
|
| [38] |
CALDERS K, ADAMS J, ARMSTON J, et al. Terrestrial laser scanning in forest ecology: Expanding the horizon[J]. Remote Sensing of Environment, 2020, 251: 112102. doi: 10.1016/j.rse.2020.112102
|
| [39] |
QI Y, COOPS N, DANIELS L, et al. Comparing tree attributes derived from quantitative structure models based on drone and mobile laser scanning point clouds across varying canopy cover conditions[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 192: 49-65. doi: 10.1016/j.isprsjprs.2022.07.021
|
| [40] |
ÅKERBLOM M, RAUMONEN P, MÄKIPÄÄ R, et al. Automatic tree species recognition with quantitative structure models[J]. Remote Sensing of Environment, 2017, 191: 1-12. doi: 10.1016/j.rse.2016.12.002
|
| [41] |
TERRYN L, CALDERS K, DISNEY M, et al. Tree species classification using structural features derived from terrestrial laser scanning[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 168: 170-181. doi: 10.1016/j.isprsjprs.2020.08.009
|
| [42] |
HUI Z, CAI Z, XU P, et al. Tree species classification using optimized features derived from light detection and ranging point clouds based on fractal geometry and quantitative structure model[J]. Forests, 2023, 14(6): 1265. doi: 10.3390/f14061265
|
| 1. |
潘婷婷，康喜龙，黄霞，孟闯，潘志明，焦新安. 非洲猪瘟诊断靶标的研究进展. 微生物学通报. 2025(02): 510-521 .
| |
| 2. |
苗雅倩，杨艳歌，赵健淞，魏莹，王秀娟，袁飞，王正亮，张峰. 配方乳粉中6种致病菌双重酶促等温扩增快速检测方法的建立. 微生物学报. 2025(03): 1319-1336 .
| |
| 3. |
华冰洁. 非洲猪瘟的研究进展. 中国动物保健. 2024(02): 7-8+28 .
| |
| 4. |
潘建良. 非洲猪瘟防控期间养猪场生物安全管理探讨. 今日畜牧兽医. 2023(02): 22-23+26 .
| |
| 5. |
王帅，杨艳歌，吴占文，李红娜，李涛，孙冬梅，袁飞. 重组酶聚合酶扩增、重组酶介导等温扩增及酶促重组等温扩增技术在食源性致病菌快速检测中的研究进展. 食品科学. 2023(09): 297-305 .
| |
| 6. |
林志伟，王帅，王迎春，吴占文，李涛，李红娜，杨艳歌，袁飞. 婴儿配方乳粉中食源性致病菌双重ERA快速检测方法的建立. 食品科学. 2023(18): 347-354 .
| |
| 7. |
杨艳歌，王帅，李红娜，李涛，吴占文，孙冬梅，袁飞. 沙门氏菌ERA可视化快速检测方法的建立. 中国食品学报. 2023(10): 261-272 .
| |
| 8. |
杨艳歌，吴占文，李涛，王帅，李红娜，孙冬梅，袁飞. GI和GII诺如病毒ERA的可视化快速检测. 华南理工大学学报(自然科学版). 2023(12): 140-151 .
| |
| 9. |
王翠，许宗丽，黄溢泓，覃艳然，梁竞臻，周师师，刘针伶，黄小武，马小蓉，李志源，严斯刚. 非洲猪瘟病原学检测技术研究进展. 中国动物检疫. 2022(02): 74-81 .
| |
| 10. |
张帅，刘媛，赵云环，刘莹，左玉柱，范京惠. 非洲猪瘟病毒研究进展. 中国兽医学报. 2022(12): 2569-2577 .
| |
| 11. |
魏晓通，周宇航，刘扬，荣馨锐，张士新，徐亚维. 酶促恒温扩增技术快速鉴定食品中牡蛎成分. 食品科技. 2022(11): 255-261 .
| |
| 12. |
倪俊芬，陈前岭，仲向前. 我国对非洲猪瘟病毒的研究进展综述. 中国猪业. 2022(06): 70-74 .
| |
| 13. |
王良辉. 精准清洗消毒是防控非洲猪瘟的重要措施. 兽医导刊. 2021(23): 79-80 .
|
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: