Preliminary study on dry and wet season changes of biomass on Chinese fir forest land based on UAV-Lidar
- Pages: 1-13(2023)
Published Online： 10 April 2023
DOI： 10.11834/jrs.20232498
扫 描 看 全 文
浏览全部资源
扫码关注微信
- About Journal
- Journal Online
- Special Source
摘要
伴随着全球变暖，极端降水格局变化显著，分布面积广且处于生长旺盛期的我国人工林将如何响应气候变化，其碳汇功能的趋势预测，以及相应的经营管理决策，迫切需要快速、准确监测森林生长的方法。本研究以17年生杉木中龄林为研究对象，采用无人机激光雷达（UAV-LiDAR）于2019年2月至2020年2进行了半年周期性监测。结合地面调查数据对比了3种基于UAV-LiDAR参数的单木地上生物量（AGB）估测方法，分别为树高冠幅回归法（HCD）、胸径-树高回归法（D-H）、胸径-树高冠幅回归法（D-HCD），并进一步估测杉木林的树高、胸径和AGB半年为周期的季节生长变化。结果表明，D-HCD法估测杉木单株AGB结果最优（
R
2
为0.77，RMSE为15.99 kg），即采用UAV-LiDAR提取的树高与冠径估测胸径，并将其与树高代入异速生长方程计算AGB。单株年AGB变化量（ΔAGB）的实测与估测值的
R
2
为0.64，RMSE为1.87 kg，相对误差
r
RMSE为29.74%；单木上推至样方尺度，单位面积ΔAGB估测相对误差有所降低，
r
RMSE从29.74%下降到17.10%。研究期间，春夏季的日均温比秋冬季高7°C，降雨量却超过秋冬季的3倍，降雨的季节分配严重不均，杉木生长也因此表现出十分明显的干湿季节差异，湿季和干季平均树高生长量分别为0.50 m和0.13 m，生物量增加量分别为5.12 kg和1.37 kg。随着径阶的增加，中龄杉木春夏季和年ΔAGB呈递增趋势，即个体越大，生长量越具有优势，而远小于平均水平的个体，生长则几乎停止。
Abstract
With global warming
the pattern of extreme precipitation has changed significantly. How China's plantation
which are widely distributed and in a rapid growth stage
will respond to the climate change
the prediction of their carbon sink functions
and the corresponding management decisions urgently need methods to monitor the growth quickly and accurately. In this study
the 17-year-old middle-aged Chinese fir plantation was monitored three times with a period of semi-annual using UAV LiDAR
from February 2019 to February 2020. Combined with the ground survey data
three methods for estimating the individual tree aboveground biomass (AGB) by UAV-LiDAR parameters were compared
namely
height and crown diameter regression (HCD)
diameter at breast height-tree height regression (D-H)
and diameter at breast height- tree height and crown diameter regression (D-HCD). Then
the seasonal growth changes of tree height
diameter at breast height
and AGB were further estimated in a six months interval. The results showed that the D-HCD method was the optimal method for estimating individual AGB of Chinese fir (
R
2
= 0.77
RMSE = 15.99 kg). The D-HCD method refers to the tree height and crown diameter extracted by UAV-LiDAR were used to estimate the diameter at breast height (DBH)
then the AGB were calculated by substituting the DBH and tree height into allometric equation. Annual and even seasonal growth changes of Chinese fir plantation in the fast-growing period can be accurately monitored by UAV-LiDAR. The average total accuracy of individual tree identification in 16 plots reached 0.927
the estimated RMSE of tree height was only 0.13 m
the
R
2
between the estimated and measured of annual individual AGB change (ΔAGB) was 0.64
the RMSE equaled to 1.87 kg
and the relative error
r
RMSE was 29.74%. Upscaling the individual to plot
the relative error of ΔAGB estimation reduced
r
RMSE decreased to 17.10%. During the study period
the average daily temperature in spring and summer was 7°C higher than that in autumn and winter
while
the rainfall was more than three times that in autumn and winter. The seasonal distribution of rainfall in this year was seriously uneven
the growth of Chinese fir also showed obvious differences in dry and wet seasons. The average growth of individual tree height in wet and dry seasons was 0.50 m and 0.13 m
and the biomass increment was 5.12 kg and 1.37 kg respectively. The individual ΔAGB increased with the DBH class during the annual and seasonal growth
that means
the larger the individual
the more advantageous the growth
especially the growth of the dominant trees in the dry season were significantly higher than that of other diameter classes
showing stronger drought tolerance. However
the growth of individuals whose DBH are much smaller than the average level almost stopped.
关键词
杉木地上生物量无人机激光雷达多时相干湿季节
Keywords
Chinese firAboveground BiomassUAV-LiDARMultitemporalDry and wet season
references
Andersen H E, Reutebuch S E, McGaughey R J, d'Oliveira M V N and Keller M. 2014. Monitoring selective logging in western Amazonia with repeat lidar flights. Remote Sensing of Environment, 151: 157-165. [DOI: 10.1016/j.rse.2013.08.049http://dx.doi.org/10.1016/j.rse.2013.08.049]
Asner G P, Mascaro J, Muller-Landau H C, Vieilledent G, Vaudry R, Rasamoelina M, Hall J S and Breugel M V. 2012. A universal airborne LiDAR approach for tropical forest carbon mapping. Oecologia, 168: 1147–1160. [DOI: 10.1007/s00442-011-2165-zhttp://dx.doi.org/10.1007/s00442-011-2165-z]
Cao L, Coops N C, Innes J L, Sheppard S R J, Fu L, Ruan H and She G. 2016. Estimation of forest biomass dynamics in subtropical forests using multi-temporal airborne LiDAR data. Remote Sensing of Environment, 178: 158-171. [DOI: 10.1016/j.rse.2016.03.012http://dx.doi.org/10.1016/j.rse.2016.03.012]
Chao L, Hong T, Li J, Chen C, Hong W and Wu C Z. 2015. Intraspecific competition law of Chinese fir plantations with different forest ages and DBH classes. Journal of Zhejiang A & F University, 32 (3): 353-360
巢林, 洪滔, 李键, 陈灿, 洪伟, 吴承祯. 2015. 不同林龄、径级杉木人工林种内竞争规律. 浙江农林大学学报, 32(3): 353-360 [DOI: 10.11833/j.issn.2095-0756.2015.03.004http://dx.doi.org/10.11833/j.issn.2095-0756.2015.03.004]
Dong S Y and Gao X J. 2014. Long climate change——IPCC's fifth evaluation report interpretation. Advances in Climate Change Research, 2014 (1): 56-59
董思言, 高学杰. 2014. 长期气候变化——IPCC第五次评估报告解读. 气候变化研究进展, 2014(1): 56-59 [DOI: 10.3969/j.issn.1673-1719.2014.01.012http://dx.doi.org/10.3969/j.issn.1673-1719.2014.01.012]
Dore M H. 2005. Climate change and changes in global precipitation patterns: what do we know?. Environment international, 31(8): 1167-1181. [DOI: 10.1016/j.envint.2005.03.004http://dx.doi.org/10.1016/j.envint.2005.03.004]
Du L, Zhou T, Zou Z, Zhao X, Huang K and Wu H. 2014. Mapping Forest Biomass Using Remote Sensing and National Forest Inventory in China. Forests， 5: 1267-83. [DOI: 10.3390/f5061267http://dx.doi.org/10.3390/f5061267]
Dubayah R O, Sheldon S L, Clark D B, Hofton M A, Blair J B, Hurtt G C and Chazdon R L. 2010. Estimation of tropical forest height and biomass dynamics using lidar remote sensing at La Selva, Costa Rica. Journal of Geophysical Research: Biogeosciences, 115(G2): n/a-n/a. [DOI: 10.1029/2009JG000933http://dx.doi.org/10.1029/2009JG000933]
Duncanson L, Rourke O and Dubayah R. 2015. Small Sample Sizes Yield Biased Allometric Equations in Temperate Forests. Scientific reports, 5: 17153. [DOI: 10.1038/srep17153http://dx.doi.org/10.1038/srep17153]
Guo Q H, Liu J, Tao S L, Xue B L, Li L, Xu G C, Li W K, Wu F F, Li Y M and Chen L H. 2014. Application status and Prospect of lidar in forest ecosystem monitoring and simulation. Chinese Science Bulletin, 2014(6): 20
郭庆华, 刘瑾, 陶胜利, 薛宝林, 李乐, 徐光彩, 李文楷, 吴芳芳, 李玉美, 陈琳海. 2014. 激光雷达在森林生态系统监测模拟中的应用现状与展望. 科学通报, 2014(6): 20 [DOI: 10.1360/972013-592http://dx.doi.org/10.1360/972013-592]
Jiang Q B, Zhao X H, Gao L S, Wang X M and Wang Y Q. 2012. Response of radial growth of Pinus tabulaeformis with different diameter classes to climate. Acta Ecologica Sinica, 32 (12): 3859-3865
姜庆彪, 赵秀海, 高露双, 王晓明, 王雨茜.2012. 不同径级油松径向生长对气候的响应. 生态学报, 32(12): 3859-3865 [DOI: 10.5846/stxb201109091323http://dx.doi.org/10.5846/stxb201109091323]
Kellner J R, Armston J, Birrer M, Cushman K C, Duncanson L, Eck C, Falleger C, Imbach B, Kral K and Krucek M. 2019. New Opportunities for Forest Remote Sensing Through Ultra-High-Density Drone Lidar. Surveys in Geophysics, 40(4): 959-977. [DOI: 10.1007/s10712-019-09529-9http://dx.doi.org/10.1007/s10712-019-09529-9]
Lefsky M A, Cohen W B, Parker G G and Harding D J. 2002. Harding Lidar Remote Sensing for Ecosystem Studies. BioScience, 52(1): 19–30. [DOI: 10.1641/0006-3568(2002)052[0019:LRSFES]2.0.CO;2]
Li W, Guo Q, Jakubowski M K and Kelly M. 2012. A New Method for Segmenting Individual Trees from the Lidar Point Cloud. Photogrammetric Engineering & Remote Sensing, 78(1): 75-84. [DOI: 10.14358/PERS.78.1.75http://dx.doi.org/10.14358/PERS.78.1.75]
Li W, Niu Y, Wang C, Gao S, Feng Q and Chen H Y. 2015. Estimation of Forest Aboveground Biomass in sample plot and single tree scale by airborne lidar data. National Remote Sensing Bulletin, 19 (004): 669-679
李旺, 牛铮, 王成, 高帅, 冯琦, 陈瀚阅. 2015. 机载lidar数据估算样地和单木尺度森林地上生物量. 遥感学报, 19(004): 669-679 [DOI: 10.11834/jrs.20154116http://dx.doi.org/10.11834/jrs.20154116]
Lin J, Chen D, Wu W and Liao X. 2022. Estimating aboveground biomass of urban forest trees with dual-source UAV acquired point clouds. Urban Forestry & Urban Greening, 69: 127521. [DOI: 10.1016/j.ufug.2022.127521http://dx.doi.org/10.1016/j.ufug.2022.127521]
Lin J, Wang M, Ma M and Lin Y. 2018. Aboveground Tree Biomass Estimation of Sparse Subalpine Coniferous Forest with UAV Oblique Photography. Remote Sensing, 10(11): 1849. [DOI: 10.3390/rs10111849http://dx.doi.org/10.3390/rs10111849]
Liu H, Zhang Z N and Cao L. 2018. Estimating forest stand characteristics in a coastal plain forest plantation based on vertical structure profile parameters derived from ALS data. National Remote Sensing Bulletin, 22(5): 872–888
刘浩, 张峥男, 曹林. 2018. 机载激光雷达森林垂直结构剖面参数的沿海平原人工林林分特征反演. 遥感学报, 22(5): 872–888 [DOI: 10.11834/jrs.20187465http://dx.doi.org/10.11834/jrs.20187465]
Liu L M，Zhao Y H and Gao L S. 2014. Response of radial growth of cunninghamia lanceolata plantation to climate. Journal of Northeast Forestry University, 000(005): 6-8
刘兰妹, 赵羿涵, 高露双. 2014. 杉木人工林径向生长对气候因子的响应. 东北林业大学学报, 000(005): 6-8 [DOI: 10.3969/j.issn.1000-5382.2014.05.002http://dx.doi.org/10.3969/j.issn.1000-5382.2014.05.002]
Liu X D, Zhou G Y, Chen X Z, Zhang D Q and Zhang Q M. 2013. The changes in the climate of the South Asian tropical forest and the response to climate change. Acta Ecologica Sinica, 34(10): 2755-2764
刘效东, 周国逸, 陈修治, 张德强, 张倩媚. 2013. 南亚热带森林演替过程中小气候的改变及对气候变化的响应. 生态学报, 34(10): 2755-2764 [DOI: 10.5846/stxb201307231934http://dx.doi.org/10.5846/stxb201307231934]
Lu D, Chen Q, Wang G, Liu L, Li G and Moran E. 2014. A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. International Journal of Digital Earth, 9(1): 63-105. [DOI: 10.1080/17538947.2014.990526http://dx.doi.org/10.1080/17538947.2014.990526]
Marinelli D, Paris C and Bruzzone L. 2018. A Novel Approach to 3-D Change Detection in Multitemporal LiDAR Data Acquired in Forest Areas. Ieee Transactions on Geoscience and Remote Sensing, 56(6): 3030-3046. [DOI: 10.1109/TGRS.2018.2789660http://dx.doi.org/10.1109/TGRS.2018.2789660]
Meng S W, Yang F T, Dai X Q and Wang H M. 2021. Radial growth dynamics of Chinese fir and its response to seasonal drought. Chinese Journal of Applied Ecology, 32(10): 3521-3530
孟盛旺, 杨风亭, 戴晓琴, 王辉民. 2021. 杉木径向生长动态及其对季节性干旱的响应. 应用生态学报, 32(10): 3521-3530 [DOI: 10.13287/j.1001-9332.202110.031http://dx.doi.org/10.13287/j.1001-9332.202110.031]
Økseter R, Bollandsås OM, Gobakken T and Næsset E. 2015. Modeling and predicting aboveground biomass change in young forest using multi-temporal airborne laser scanner data. Scandinavian Journal of Forest Research, 1-12. [DOI: 10.1080/02827581.2015.1024733http://dx.doi.org/10.1080/02827581.2015.1024733]
Pan Y, Birdsey R A, Fang J, Houghton R, Kauppi P E, Kurz W A, Phillips O L, Shvidenko A, Lewis S L and Canadell J G. 2011. A large and persistent carbon sink in the world's forests. Science, 333(6045): 988-993. [DOI: 10.1126/SCIENCE.1201609http://dx.doi.org/10.1126/SCIENCE.1201609]
Pang Y and Li Z Y. 2012. Inversion of biomass components of the temperate forest using airborne Lidar technology inXiaoxing'an Mountains, Northeastern of China. Chinese Journal of Plant Ecology, 36: 1095-1105
庞勇, 李增元. 2012. 基于机载激光雷达的小兴安岭温带森林组分生物量反演. 植物生态学报, 36: 1095-1105 [DOI: 10.3724/SP.J.1258.2012.01095http://dx.doi.org/10.3724/SP.J.1258.2012.01095]
Popescu SC. 2007. Estimating biomass of individual pine trees using airborne lidar. Biomass and Bioenergy, 31(9): 646-655. [DOI: 10.1016/j.biombioe.2007.06.022http://dx.doi.org/10.1016/j.biombioe.2007.06.022]
Qian N Z and Ye J Z. 1992. Biomass of Chinese fir mixed family plantation in Yangkou forest farm. Journal of Nanjing Forestry University (Natural Sciences), 16 (3): 6
钱能智, 叶镜中. 1992. 福建省洋口林场杉木混合家系人工林的生物量.南京林业大学学报: 自然科学版, 16(3): 6 [DOI: CNKI:SUN:NJLY.0.1992-03-004http://dx.doi.org/CNKI:SUN:NJLY.0.1992-03-004]
Réjou-Méchain M, Tymen B, Blanc L, Fauset S, Feldpausch T R, Monteagudo A, Phillips O L, Richard H and Chave J. 2015. Using repeated small-footprint LiDAR acquisitions to infer spatial and temporal variations of a high-biomass Neotropical forest. Remote Sensing of Environment, 169: 93-101. [DOI: 10.1016/j.rse.2015.08.001http://dx.doi.org/10.1016/j.rse.2015.08.001]
Rosenqvist Å, Milne A, Lucas R, Imhoff M and Dobson C. 2003. A review of remote sensing technology in support of the Kyoto Protocol. Environmental Science & Policy, 6(5): 441-455. [DOI: 10.1016/S1462-9011(03)00070-4http://dx.doi.org/10.1016/S1462-9011(03)00070-4]
Sandra E, Juilson J and Florian S. 2013. Quantifying Dynamics in Tropical Peat Swamp Forest Biomass with Multi-Temporal LiDAR Datasets. Remote Sensing, 5(5): 2368-2388. [DOI: 10.3390/rs5052368http://dx.doi.org/10.3390/rs5052368]
Wang Q, Pang Y, Chen D, Liang X and Lu J. 2021. Lidar biomass index: A novel solution for tree-level biomass estimation using 3D crown information. Forest Ecology and Management, 499: 119542. [DOI: 10.1016/j.foreco.2021.119542http://dx.doi.org/10.1016/j.foreco.2021.119542]
Xu Q, Man A, Fredrickson M, Hou Z, Pitkänen J, Wing B, Ramirez C, Li B and Greenberg J A. 2018. Quantification of uncertainty in aboveground biomass estimates derived from small-footprint airborne LiDAR. Remote Sensing of Environment, 216:514-528. [DOI: 10.1016/j.rse.2018.07.022http://dx.doi.org/10.1016/j.rse.2018.07.022]
Yu X T. 1986. Chiese fir. Fuzhou: Fujian Science and Technology Publishing House: 28-29
俞新妥. 1986. 杉木. 福州：福建科学技术出版社：28-29
Zeng W S. 2014. Comparison of three allometric equations for biomass modeling. Central South Forest Inventory and Planning, 33(1): 1-3
曾伟生. 2014. 3种异速生长方程对生物量建模的对比分析. 中南林业调查规划, 33(1): 1-3 [DOI: 10.3969/j.issn.1003-6075.2014.01.001http://dx.doi.org/10.3969/j.issn.1003-6075.2014.01.001]
Zhang C, Ju W, Chen J M, Li D, Wang X, Fan W, Li M and Zan M. 2014. Mapping forest stand age in China using remotely sensed forest height and observation data. Journal of Geophysical Research: Biogeosciences, 119(6): 1163-1179. [DOI: 10.1002/2013JG002515http://dx.doi.org/10.1002/2013JG002515]
Zhao K, Suarez J C, Garcia M, Hu T, Wang C and Londo A. 2018. Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux. Remote Sensing of Environment, 204: 883-897. [DOI: 10.1016/j.rse.2017.09.007http://dx.doi.org/10.1016/j.rse.2017.09.007]
Zhou G, Peng C, Li Y, Liu S, Zhang Q, Tang X, Liu J, Yan J, Zhang D and Chu G. 2013. A climate change-induced threat to the ecological resilience of a subtropical monsoon evergreen broad-leaved forest in Southern China. Global Change Biology, 19(4): 1197-1210. [DOI: 10.1111/gcb.12128http://dx.doi.org/10.1111/gcb.12128]
Zuo S D, Ren Y, Wang X K, Zhang X Q and Luo Y J. 2014. Biomass estimation factors and their determinants of cunninghamia lanceolata forests in China. Scientia Silvae Sinicae, 50(11), 1-12
左舒翟, 任引, 王效科, 张小全, 罗云建. 2014. 中国杉木林生物量估算参数及其影响因素. 林业科学, 50(11), 1-12 [DOI: 10.11707/j.1001-7488.20141101http://dx.doi.org/10.11707/j.1001-7488.20141101]
Zolkos S G, Goetz S J and Dubayah R. 2013. A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing. Remote Sensing of Environment, 128: 289–298. [DOI: 10.1016/j.rse.2012.10.017http://dx.doi.org/10.1016/j.rse.2012.10.017]
Alert me when the article has been cited
提交
相关作者
暂无数据
相关机构
First Institute of Oceanography, Ministry of Natural Resources
College of Geodesy and Geomatics, Shandong University of Science and Technology
College of Oceanography and Space Information, China University Petroleum (East China)
College of Information Science and Technology, Beijing Normal University
Institute of Forest Resource Information Technique, Chinese Academy of Forestry
选择翻译语言
- Postal code：100190
- Tel：010-58887052 Email：nrsb@aircas.ac.cn
- Technical support is provided by Beijing Founder electronics co., LTD 京ICP备20021838号-9
京公网安备11010802024621 - It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
- Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
0
Bulk Citation
Export to
TOP