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Forest and Grassland Resources Research >

2023 , Vol. 0 >Issue 3: 90 - 97

DOI: https://doi.org/10.13466/j.cnki.lyzygl.2023.03.012

Scientific Research

Experiments on Estimating Planted Forest Inventory Attributes Based on UAV-LiDAR Data

  • Mei ZHOU ,
  • Chungan LI ,
  • Chengling YANG ,
  • Zhen LI
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  • 1. School of Computer,Electronic and Information in Guangxi University,Nanning 530004,China
    2. Forestry College of Guangxi University,Nanning 530004,China
    3. Guangxi Forest Inventory and Planning Institute,Nanning 530011,China

Received date: 2023-04-28

  Revised date: 2023-05-19

  Online published: 2023-08-09

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Abstract

To explore advanced,reliable,and feasible technical schemes for small-scale forest resource inventory and monitoring,unmanned aerial vehicle-based LiDAR (UAV-LiDAR) was tested for estimating and mapping forest inventory attributes.Thirteen UAV-LiDAR-derived metrics,which depict the three-dimensional structural aspects of the forest canopy and have clear forest mensuration and ecology significance,were used to construct 86 multiplicative power formulations consisting of 2~5 predictors for forest inventory attribute estimation by using a rule-based exhaustive combination.All the formulations were calibrated and validated using the sample plot data,and six optimal models were achieved.The results indicated that the coefficients of determination (R2) of the mean stand height,basal area,and volume estimation for the pine and eucalyptus planted forests were 0.616~0.853,the relative root mean squared errors (rRMSE) were 10.85%~18.79%,and the mean predictive errors (MPE) were 3.80%~9.72%.With its ability to accurately estimate and map forest attributes,UAV-LiDAR provides an innovative technological tool for small-scale forest resource inventory,and effectively overcomes many of the problems of conventional field measurements.However,there are still numerous technical issues that need to be further investigated in the application of UAV-LiDAR to forest resource inventory to improve accuracy and reduce inventory costs.

Key words： forest resources; stand factor; estimation; model; remote sensing

Cite this article

Mei ZHOU , Chungan LI , Chengling YANG , Zhen LI . Experiments on Estimating Planted Forest Inventory Attributes Based on UAV-LiDAR Data[J]. Forest and Grassland Resources Research, 2023 , 0(3) : 90 -97 . DOI: 10.13466/j.cnki.lyzygl.2023.03.012

References

[1] N?sset E T. Gobakken J, Holmgren H, et al. Laser scanning of forest resources:The Nordic experience[J]. Scandinavian Journal of Forest Research, 2004, 19(6):482-499.
[2] White J C, Tompalski P, Vastaranta M, et al. A model development and application guide for generating an enhanced forest inventory using airborne laser scanning data and an area-based approach[R]. Victoria: Canadian Wood Fibre Centre, 2017.
[3] 李春干, 李振. 机载激光雷达大区域亚热带森林参数估测的普适性模型式[J]. 林业科学, 2021, 57(10):23-35.
[4] 代华兵, 李春干, 庞勇, 等. 基于天空地一体化森林资源调查的小班因子设置与信息获取方法[J]. 林业资源管理, 2021(2):180-188.
[5] 李春干, 代华兵. 中国森林资源调查:历史、现状与趋势[J]. 世界林业研究, 2021, 34(6):72-80.
[6] Sankey T, Donager J, McVay J, et al. UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA[J]. Remote Sensing of Environment, 2017, 195:30-43.
[7] Liu Kun, Shen Xin, Cao Lin, et al. Estimating forest structural attri-butes using UAV-LiDAR data in Ginkgo plantations[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 146:465-482.
[8] Cao Lin, Liu Kun, Shen Xin, et al. Estimation of forest structural parameters using UAV-LiDAR data and a process-based model in Ginkgo planted forests[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(11):4175-4190.
[9] D'Oliveira M V N, Broadbent E N, Oliveira L C, et al. Aboveground biomass estimation in Amazonian Tropical Forests:A comparison of aircraft- and gatorEye UAV-borne LiDAR data in the chico mendes extractive reserve in Acre,Brazil[J]. Remote Sensing, 2020, 12:1754.
[10] Corte A P D, de Vasconcello 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, 11:507.
[11] Xu Dandan, Wang Haobin, Xu Weixin, et al. LiDAR applications to estimate forest biomass at individual tree scale:Opportunities,challenges and future perspectives[J]. Forests, 2021, 12(5):550.
[12] 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.
[13] Cao Lin, Liu Kai, Shen Xin, et al. Estimation of forest structural parameters using UAV-LiDAR data and a process-based model in Ginkgo planted forests[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(11):4175-4189.
[14] Peng Xi, Zhao Anjiu, Chen Yongfu, et al. Comparison of modeling algorithms for forest canopy structures based on UAV-LiDAR:A case study in tropical china[J]. Forests, 2020, 11:1324.
[15] Neuville R Bates J S, Jonard F. Estimating forest structure from UAV-mounted LiDAR point cloud using machine learning[J]. Remote Sensing, 2021, 13:352.
[16] N?set E. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data[J]. Remote Sensing of Environment, 2002, 80(1):88-99.
[17] 周梅, 王新华, 李春干, 等. 不同样地面积对人工林林分参数的影响[J]. 西部林业科学, 2018, 47(1):110-116.
[18] Li Chungan, Lin Xin, Dai Huabing, et al. Effects of plot size on airborne LiDAR-derived metrics and predicted model performances of subtropical planted forest attributes[J]. Forests, 2022, 13:2124.
[19] Li Chungan, Chen Zhongchao, Zhou Xiangbei, et al. Generalized models for subtropical forest inventory attribute estimations using a rule-based exhaustive combination approach with airborne LiDAR-derived metrics[J]. Giscience & Rremote Sensing, 2023, 60(1):2194601.
[20] Ferster C J, Coops N C, Trofymow J A. Aboveground large tree mass estimation in a coastal forest in British Columbia using plot-level metrics and individual tree detection from lidar[J]. Canadian Journal of Remote Sensing, 2009, 35(3):270-275.
[21] 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):189-209.
[22] 曾伟生, 唐守正. 立木生物量方程的优度评价和精度分析[J]. 林业科学, 2011, 47(11):106-113.
[23] 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.
[24] Gobakken T, N?sset E. Assessing effects of laser point density,ground sampling intensity,and field sample plot size on biophysical stand properties derived from airborne laser scanner data[J]. Canadian Journal of Forest Research, 2008, 38:1095-1109.
[25] 余铸, 李春干, 苏凯. 等. 基于垂直结构分类的机载激光雷达森林参数估测[J/OL]. 桂林理工大学学报.(2022-05-06)[2023-05-16]. http://kns.cnki.net/kcms/detail/45.1375.n.20220429.1213.002.html.
[26] 曾伟生, 孙乡楠, 王六如, 等. 基于机载激光雷达数据的森林蓄积量模型研建[J]. 林业科学, 2021, 57(2):31-38.
[27] Liu Hao, Cao Lin, She Guanghui, et al. Extrapolation assessment for forest structural parameters in planted Forests of southern China by UAV-LiDAR samples and multispectral satellite imagery[J]. Remote Sensing, 2022, 14(11):2677.
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