Regional Forest Canopy Height Estimation Using Multi-source Remote Sensing Data

HU Kailong; LIU Qingwang; CUI Ximin; PANG Yong; MU Xiyun

doi:10.13203/j.whugis20160066

Volume 43 Issue 2

Feb. 2018

Turn off MathJax

Article Contents

Abstract

References

Geomatics and Information Science of Wuhan University > 2018 > 43(2): 289-296, 303. > DOI: 10.13203/j.whugis20160066

HU Kailong, LIU Qingwang, CUI Ximin, PANG Yong, MU Xiyun. Regional Forest Canopy Height Estimation Using Multi-source Remote Sensing Data[J]. Geomatics and Information Science of Wuhan University, 2018, 43(2): 289-296, 303. DOI: 10.13203/j.whugis20160066

Citation:

PDF (1291 KB)

Regional Forest Canopy Height Estimation Using Multi-source Remote Sensing Data

1.
College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China
2.
Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
3.
Institute of Forestry Ecology, Chifeng Academy of Forestry, Chifeng 024000, China

Funds:

he National High Technology Research and Development Program of China 2013AA12A302

the National Basic Research Program 13CB733404

More Information

Author Bio:
HU Kailong, PhD candidate, specializes in forest remote sensing and LiDAR. E-mail: hklong_gis@163.com
Corresponding author:
LIU Qingwang, PhD, assistant researcher. E-mail: liuqw@caf.ac.cn
Received Date: May 10, 2016
Published Date: February 04, 2018

Graphical Abstract

Abstract

Abstract

In this paper, a method which based on multi-source remote sensing data was designed to estimate forest canopy height. An improved topographic correction model which was used for calculating based-GLAS canopy height was put forward with airborne LiDAR data, since surface topography directly impacts vegetation LiDAR waveforms and vegetation height retrieval. Then, with corrected GLAS footprints as input samples, forest canopy height in different ecological zones was estimated by support vector regression (SVR) model combining with MODIS imagery. Lastly, the accuracy of estimation results was verified using field survey data and airborne LiDAR canopy height. The result show: slope levels in the study area directly impacted based-GLAS canopy height estimation accuracy. The improved topographic correction model could relieve the influence of topography on based-GLAS canopy height estimation, and the RMSE of estimation result ranged from 3.25m to 3.48m. In different ecological zones, SVR model estimation accuracy was stable and the RMSE ranged from 6.41m to 7.56m. Compared with arithmetic mean height, Lorey's height was closest to the estimation result and the average estimated accuracy of 80.3% in different ecological zones. The average estimated accuracy verified with airborne LiDAR metric height was 79.5% and consistence with Lorey's height.
- canopy height,
- slope,
- multi-source remote sensing data,
- topographic correction,
- support vector regression,
- ecological zones

FullText(HTML)

References (27)

References

[1]	Morton D, Victoria D, Keller M. Tree Height and Tropical Forest Biomass Estimation[J]. Biogeosciences, 2013, 10(1):8385-8399 http://ntrs.nasa.gov/search.jsp?R=20160001382&qs=N%3D4294927712
[2]	Kohyama T, Suzuki E, Partomihardjo T, et al. Tree Species Differentiation in Growth, Recruitment and Allometry in Relation to Maximum Height in a Bornean Mixed Dipterocarp Forest[J]. Journal of Ecology, 2003, 91(5):797-806 doi: 10.1046/j.1365-2745.2003.00810.x
[3]	Ni X, Zhou Y, Cao C. Mapping Forest Canopy Height over Continental China Using Multi-Source Remote Sensing Data[J]. Remote Sensing, 2015, 7(7):8436-8452 doi: 10.3390/rs70708436
[4]	李德仁, 王长委, 胡月明, 等.遥感技术估算森林生物量的研究进展[J].武汉大学学报·信息科学版, 2012, 37(6):631-635 http://ch.whu.edu.cn/CN/abstract/abstract236.shtml Li Deren, Wang Changwei, Hu Yueming, et al. General Review on Remote Sensing-Based Biomass Estimation[J]. Geomatics & Information Science of Wuhan University, 2012, 37(6):631-635 http://ch.whu.edu.cn/CN/abstract/abstract236.shtml
[5]	Chopping M, Nolin A, Moisen G G, et al. Forest Canopy Height from the Multiangle Imaging SpectroRadiometer (MISR) Assessed with High Resolution Discrete Return Lidar[J]. Remote Sensing of Environment, 2009, 113(10):2172-2185 doi: 10.1016/j.rse.2009.05.017
[6]	吴红波. 基于星载大光斑LiDAR数据反演森林冠层高度及应用研究[D]. 哈尔滨: 东北林业大学, 2011 Wu Hongbo. Study on Retrieval and Application of Forest Canopy Height Based on Spaceborne Large-footprint LiDAR Data[D]. Haerbin: Northeast Forestry University, 2011
[7]	Baltsavias E P. Airborne Laser Scanning:Basic Relations and Formulas[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 1999, 54(S 2/3):199-214 https://www.sciencedirect.com/science/article/pii/S0924271699000155
[8]	Nelson R, Krabill W, Maclean G. Determining Forest Canopy Characteristics Using Airborne Laser Data[J]. Remote Sensing of Environment, 1984, 15(3):201-212 doi: 10.1016/0034-4257(84)90031-2
[9]	Clark M L, Clark D B, Roberts D A. Small-Footprint LiDAR Estimation of Sub-canopy Elevation and Tree Height in a Tropical Rain Forest Landscape[J]. Remote Sensing of Environment, 2004, 91(1):68-89 doi: 10.1016/j.rse.2004.02.008
[10]	段祝庚, 赵旦, 曾源, 等.基于遥感的区域尺度森林地上生物量估算研究[J].武汉大学学报·信息科学版, 2015, 40(10):1400-1408 http://ch.whu.edu.cn/CN/abstract/abstract3355.shtml Duan Zhugeng, Zhao Dan, Zeng Yuan, et al. Estimation of the Forest Aboveground Biomass at Regional Scale Based on Remote Sensing[J]. Geomatics & Information Science of Wuhan University, 2015, 40(10):1400-1408 http://ch.whu.edu.cn/CN/abstract/abstract3355.shtml
[11]	Pang Y, Lefsky M, Andersen H E, et al. Validation of the ICEsat Vegetation Product Using Crown-area-weighted Mean Height Derived Using Crown Delineation with Discrete Return Lidar Data[J]. Canadian Journal of Remote Sensing, 2008, 34(sup2):471-484 doi: 10.5589/m08-074
[12]	Lefsky M, Harding D, Keller M. Estimates of Forest Canopy Height and Aboveground Biomass Using ICESat[J]. Geophysical Research Letters, 2005, 32, L22S02 doi: 10.1029/2004GL021684
[13]	Lefsky. M A. A Global Forest Canopy Height Map from the Moderate Resolution Imaging Spectroradiometer and the Geoscience Laser Altimeter System[J]. Geophysical Research Letters, 2010, 37(15):1-10 http://www.cabdirect.org/abstracts/20103278680.html
[14]	Simard M, Pinto N, Fisher J B, et al. Mapping Forest Canopy Height Globally with Spaceborne LiDAR[J]. Journal of Geophysical Research, 2011, 116(G4):2156-2202 doi: 10.1029/2011JG001708
[15]	Gleason C J, Im J. Forest Biomass Estimation from Airborne LiDAR Data Using Machine Learning Approaches[J]. Remote Sensing of Environment, 2012, 125:80-91 doi: 10.1016/j.rse.2012.07.006
[16]	Lucht W, Prentice I C, Myneni R B, et al. Climatic Control of the High-latitude Vegetation Greening Trend and Pinatubo Effect[J]. Science, 2002, 296(5573):1687-1689 doi: 10.1126/science.1071828
[17]	Zeng B, Yang T B. Impacts of Climate Warming on Vegetation in Qaidam Area from 1990 to 2003[J]. Environmental Monitoring & Assessment, 2008, 144(1-3):403-417 doi: 10.1007/s10661-007-0003-x
[18]	李海奎, 法蕾.基于分级的全国主要树种树高-胸径曲线模型[J].林业科学, 2011, 47(10):83-90 doi: 10.11707/j.1001-7488.20111013 Li Haikui, Fa Lei. Height-Diameter Model for Major Tree Species in China Using the Classified Height Method[J]. Scientia Silvae Sinicae, 2011, 47(10):83-90 doi: 10.11707/j.1001-7488.20111013
[19]	FAO. Global Ecological Zones for FAO Forest Reporting[R]. Global Forest Resources Assessment, Rome, 2010
[20]	鄂栋臣, 徐莹, 张小红. ICESat卫星及其在南极Dome A地区的应用[J].武汉大学学报·信息科学版, 2007, 32(12):1139-1142 http://ch.whu.edu.cn/CN/abstract/abstract2044.shtml E Dongchen, Xu Ying, Zhang Xiaohong. ICESat's Performance and Its Application in Dome A Area in Antarctica[J]. Geomatics & Information Science of Wuhan University, 2007, 32(12):1139-1142 http://ch.whu.edu.cn/CN/abstract/abstract2044.shtml
[21]	郭颖. 森林地上生物量的非参数化遥感估测方法优化[D]. 北京: 中国林业科学研究院, 2011 Guo Ying. Optimum Non-parametric Method for Forest Above Ground Biomass Estimation Based on Remote Sensing Data[D]. Beijing: Chinese Academy of Forestry, 2011
[22]	Park T, Kennedy R, Choi S, et al. Application of Physically-Based Slope Correction for Maximum Forest Canopy Height Estimation Using Waveform Lidar across Different Footprint Sizes and Locations:Tests on LVIS and GLAS[J]. Remote Sensing, 2014, 6(7):6566-6586 doi: 10.3390/rs6076566
[23]	Lee S, Nimeister W, Yang W. Physically Based Vertical Vegetation Structure Retrieval from ICESat Data:Validation Using LVIS in White Mountain National Forest, New Hampshire, USA[J]. Remote Sensing of Environment, 2011, 115(11):2776-2785 doi: 10.1016/j.rse.2010.08.026
[24]	Selkowitz D J, Green G, Peterson B, et al. A Multi-sensor Lidar, Multi-spectral and Multi-angular Approach for Mapping Canopy Height in Boreal Forest Regions[J]. Remote Sensing of Environment, 2012, 121(2):458-471 https://www.sciencedirect.com/science/article/pii/S0034425712001137#!
[25]	Yang W, Nimeister W, Lee S. Assessment of the Impacts of Surface Topography, Off-nadir Pointing and Vegetation Structure on Vegetation Lidar Waveforms Using an Extended Geometric Optical and Radiative Transfer Model[J]. Remote Sensing of Environment, 2011, 115(11):2810-2822 doi: 10.1016/j.rse.2010.02.021
[26]	Mountrakis G, Im J, Ogole C. Support Vector Machines in Remote Sensing:A Review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66(3):247-259 doi: 10.1016/j.isprsjprs.2010.11.001
[27]	邢艳秋, 王立海.基于ICESat-GLAS完整波形的坡地森林冠层高度反演研究:以吉林长白山林区为例[J].武汉大学学报·信息科学版, 2009, 34(6):696-700 http://ch.whu.edu.cn/CN/abstract/abstract1334.shtml Xing Yanqiu, Wang Lihai. ICESat-GLAS Full Waveform-based Study on Forest Canopy Height Retrieval in Sloped Area:A Case Study of Forests in Changbai Mountains, Jilin[J]. Geomatics & Information Science of Wuhan University, 2009, 34(6):696-700 http://ch.whu.edu.cn/CN/abstract/abstract1334.shtml

[1]	PANG Qipei, WU Yunlong, XU Jingtian, SHI Xuguo, ZHANG Yi. Deep Structural Characteristics and Dynamic Processes of the Ms 6.2 Jishishan Earthquake and Its Adjacent Areas[J]. Geomatics and Information Science of Wuhan University, 2025, 50(2): 356-367. DOI: 10.13203/j.whugis20240085
[2]	YANG Jiuyuan, WEN Yangmao, XU Caijun. Seismogenic Fault Structure of the 2023 Ms 6.2 Jishishan （Gansu,China） Earthquake Revealed by InSAR Observations[J]. Geomatics and Information Science of Wuhan University, 2025, 50(2): 313-321. DOI: 10.13203/j.whugis20230501
[3]	WU Yunlong, LI Hao, ZHANG Fan, PANG Qipei, ZHANG Yi, YAN Jianguo, CHU Risheng. Analysis of Deep Tectonic Characteristics and Seismogenic Environment of the Ms 6.8 Earthquake in Dingri，Xizang，China[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20250052
[4]	YANG Jiuyuan, XU Caijun. Ramp-flat Seismogenic Structure of the 2020 Yutian (Xinjiang， China) MW 6.3 Earthquake Revealed by InSAR Observations[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20240482
[5]	LI Zhenhong, HAN Bingquan, LIU Zhenjiang, ZHANG Miaomiao, YU Chen, CHEN Bo, LIU Haihui, DU Jing, ZHANG Shuangcheng, ZHU Wu, ZHANG Qin, PENG Jianbing. Source Parameters and Slip Distributions of the 2016 and 2022 Menyuan, Qinghai Earthquakes Constrained by InSAR Observations[J]. Geomatics and Information Science of Wuhan University, 2022, 47(6): 887-897. DOI: 10.13203/j.whugis20220037
[6]	LIU Yang, XU Caijun, WEN Yangmao, LI Zhicai. InSAR Inversion and Boundary Element Analysis of the Zadoi Mw 5.9 Earthquake Fault Slip[J]. Geomatics and Information Science of Wuhan University, 2020, 45(11): 1678-1686. DOI: 10.13203/j.whugis20190368
[7]	WANG Leyang, LI Haiyan, CHEN Hanqing. Source Parameters and Slip Distribution Inversion of 2013 Lushan Ms 7.0 Earthquake[J]. Geomatics and Information Science of Wuhan University, 2019, 44(3): 347-354. DOI: 10.13203/j.whugis20160485
[8]	XU Caijun, HE Ping, WEN Yangmao, ZHANG Lei. Coseismic Deformation and Slip Distribution for 2011 Tohoku-Oki Mw 9.0 Earthquake:Constrained by GPS and InSAR[J]. Geomatics and Information Science of Wuhan University, 2012, 37(12): 1387-1391.
[9]	Lin Aiwen. The Research on Fault Structure in the Wuhan Area[J]. Geomatics and Information Science of Wuhan University, 1992, 17(3): 69-76.
[10]	Guo Qingsheng. Structural Principle and Realization by Computer for Areal Regular Pattern[J]. Geomatics and Information Science of Wuhan University, 1992, 17(1): 28-33.

Cited By

Cited by

Periodical cited type(19)

1.	李雨森，李为乐，许强，许善淼，王运生. 2023年积石山Ms6.2级地震InSAR同震形变探测与断层滑动分布反演. 成都理工大学学报(自然科学版). 2024(01): 22-32+75 .
2.	王乐洋，孙龙翔，许光煜. 利用GPS数据反演震源参数的单纯形组合加权距离灰狼优化算法. 武汉大学学报(信息科学版). 2024(07): 1140-1154 .
3.	王乐洋，席灿. 贝叶斯框架下利用GPS数据反演震源参数的一种改进MCMC算法. 地球物理学报. 2024(09): 3367-3385 .
4.	程燕，蒋亚楠，侯中健，曾锐，罗袆沅. 2016年和2022年青海门源强震活动的InSAR形变观测与区域强震危险性分析. 地质力学学报. 2024(06): 965-977 .
5.	于仪，李雪，孙振，刘珠妹，张朝阳. 2022年青海门源地震震源机制与同震滑动分布研究. 大地测量与地球动力学. 2023(01): 46-51 .
6.	刘洋，李航昊，熊露雲，温扬茂，杨九元. 联合地震位错模型和ESISTEM方法提取地震同震三维形变场. 武汉大学学报(信息科学版). 2023(03): 349-358+395 .
7.	钟储汉. 基于InSAR技术的输气管道工程穿越煤矿采空区形变特征分析研究. 石油工程建设. 2023(03): 10-16 .
8.	李媛，杨周胜，庞亚瑾，梁洪宝，刘峡. 2022年门源M_S6.9地震前断层活动及应力状态的数值模拟. 地震地质. 2023(06): 1286-1308 .
9.	颜丙囤，季灵运，蒋锋云，殷海涛，陈其峰，连凯旋. InSAR数据约束的2022年1月8日青海门源M_S6.9地震发震构造研究. 地震工程学报. 2022(02): 450-457 .
10.	郭东美，何慧优. 应用全张量重力梯度组合识别并提取中国南海断裂. 武汉大学学报(信息科学版). 2022(05): 738-746 .
11.	李振洪，韩炳权，刘振江，张苗苗，余琛，陈博，刘海辉，杜静，张双成，朱武，张勤，彭建兵. InSAR数据约束下2016年和2022年青海门源地震震源参数及其滑动分布. 武汉大学学报(信息科学版). 2022(06): 887-897 .
12.	何秀凤，高壮，肖儒雅，罗海滨，贾东振，章浙涛. InSAR与北斗/GNSS综合方法监测地表形变研究现状与展望. 测绘学报. 2022(07): 1338-1355 .
13.	梁斌，魏冠军. 利用InSAR技术观测台湾花莲地震断层滑动与运动机理分析. 测绘通报. 2022(09): 68-73 .
14.	金鑫田，王世杰，姜鑫，张兰军. 2022年青海门源M_W6.9地震同震形变及断层滑动分布反演. 地球物理学进展. 2022(06): 2267-2274 .
15.	付阿龙，安张辉，范莹莹，侯泽宇. 2022年1月8日青海门源县M_S 6.9地震地电场响应特征. 地震地磁观测与研究. 2022(06): 30-40 .
16.	钟储汉，王强，王霞迎，张双成，牛玉芬. 基于InSAR技术的东营市地面沉降监测及多诱发因素分析. 大地测量与地球动力学. 2021(07): 727-731 .
17.	温少妍，李成龙，李金. 2020年1月19日新疆伽师M_S6.4地震InSAR同震形变场特征及发震构造初步探讨. 内陆地震. 2020(01): 1-9 .
18.	徐小波，连达军，白俊武. 基于CRInSAR与PSInSAR技术监测断裂带震间形变研究. 苏州科技大学学报(自然科学版). 2020(04): 51-58 .
19.	万秀红，屠泓为，姚生海，殷翔，蔡丽雯. InSAR技术在青海地区地震中的应用研究. 高原地震. 2019(04): 14-20 .

Other cited types(14)

Get Citation

PDF

XML

Article views (2568) PDF downloads (405) Cited by(33)

Regional Forest Canopy Height Estimation Using Multi-source Remote Sensing Data

Abstract

References

Related Articles

Cited by

Periodical cited type(19)

Other cited types(14)

Catalog

Related

Regional Forest Canopy Height Estimation Using Multi-source Remote Sensing Data

Abstract

References

Related Articles

Cited by

Periodical cited type(19)

Other cited types(14)

Catalog

Related

Export File

Citation

Format

Content