Estimation of Forest Canopy Height Based on Large-Footprint Airborne LiDAR Data

doi:10.13466/j.cnki.lyzygl.2020.03.021

Abstract

Abstract:

Using the echo waveform data collected by the large-footprint airborne forestry LiDAR designed by Satellite Forestry Application Center of National Forestry and Grassland Administration,a series of parameters like background noise,signal starting position,ground echo position were processed based on Matlab 2014a,so as to estimate forest canopy height.Ten echo waveforms near the sample plot were selected to estimate the height of forest canopy.And the accuracy of the forest canopy height estimated by echo waveform data was verified.The results showed that echo waveform collected by airborne large-footprint LiDAR had different degrees of ability to estimate seven kinds of forest canopy height.Estimation of the weighted basal area average height and the average wood of dominant tree's average height was the best,with the relative errors of 4.36% and 8.29%,RMSE of 1.40 m and 1.55 m.For the average height H and the average height D of the dominant tree,the estimation ability was the worst,with the relative errors of 19.81% and 22.00%,and RMSE of 2.99 m and 3.34 m.

Key words: forestry, airborne, large-footprint LiDAR, forest canopy height, estimation

CLC Number:

S771

SUN Zhongqiu, WU Fayun, GAO Xianlian, GAO Jinping, HU Yang. Estimation of Forest Canopy Height Based on Large-Footprint Airborne LiDAR Data[J]. FOREST RESOURCES WANAGEMENT, 2020, (3): 111-117.

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References 25

[1]	Ni Xiliang, Zhou Yuke, Cao Chunxiang, et al. 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
[2]	Nelson R. Model effects on GLAS-based regional estimates of forest biomass and carbon[J]. International Journal of Remote Sensing, 2010,31(5):1359-1372. doi: 10.1080/01431160903380557
[3]	Feldpausch T R, Lloyd J, Lewis S L, et al. Tree height integrated into pantropical forest biomass estimates[J]. Biogeosciences, 2012,9(3):2567-2622.
[4]	娄雪婷, 曾源, 吴炳方. 森林地上生物量遥感估测研究进展[J]. 国土资源遥感, 2011,23(1):1-8. doi: 10.6046/gtzyyg.2011.01.01
[5]	SUN Guoqing, Ranson K J, Zhang Zhongjun. Forest Vertical Parameters from Lidar and Multi-angle Imaging Spectrometer Data[J]. Journal of Remote Sensing, 2006,10(4):523-530. doi: 10.11834/jrs.20060477
[6]	邢艳秋, 王立海. 基于森林生物量相容性模型长白山天然林生物量估测[J]. 森林工程, 2008,24(2):1-4.
[7]	汤旭光. 基于激光雷达与多光谱遥感数据的森林地上生物量反演研究[D]. 北京:中国科学院大学, 2013.
[8]	Sandberg G, Ulander L M H, Fransson J E S, et al. L- and P-band backscatter intensity for biomass retrieval in hemiboreal forest[J]. Remote Sensing of Environment, 2011,115(11):2874-2886. doi: 10.1016/j.rse.2010.03.018
[9]	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
[10]	Leeuwen M V, Nieuwenhuis M. Retrieval of forest structural parameters using LiDAR remote sensing[J]. European Journal of Forest Research, 2010,129(4):749-770. doi: 10.1007/s10342-010-0381-4
[11]	邢艳秋, 王蕊, 李增元. 基于星载雷达全波形数据估测森林结构参数研究综述[J]. 世界林业研究, 2013,26(6):27-32.
[12]	Pang Yong, Li Zengyuan, Ju Hongbo, et al. LiCHy:The CAF's LiDAR,CCD and Hyperspectral Integrated Airborne Observation System[J]. Remote Sensing, 2016,8(5):398. doi: 10.3390/rs8050398
[13]	胡凯龙, 刘清旺, 庞勇, 等. 基于机载激光雷达校正的ICESat/GLAS数据森林冠层高度估测[J]. 农业工程学报, 2017,33(16):88-95.
[14]	Lefsky M A, Keller M, Pang Yong, et al. Revised method for forest canopy height estimation from Geoscience Laser Altimeter System waveforms[J]. Journal of Applied Remote Sensing, 2007,1(1):6656-6659.
[15]	Sun G, Ranson K J, Kimes D S, et al. Forest vertical structure from GLAS:An evaluation using LVIS and SRTM data[J]. Remote Sensing of Environment, 2008,112(1):107-117. doi: 10.1016/j.rse.2006.09.036
[16]	Xing Yanqiu, Wang Lihai. Estimation of forest canopy height and above-ground biomass using ICESat full waveform data:a case study in Changbai Mountain,China[J]. Proc Spie, 2009,7471:74710J.
[17]	Xing Yanqiu, De Gier A, Zhang Junjie, et al. An improved method for estimating forest canopy height using ICESat-GLAS full waveform data over sloping terrain:a case study in Changbai mountains,China[J]. International Journal of Applied Earth Observations & Geoinformation, 2010,12(5):385-392.
[18]	Allouis T, Durrieu S, Couteron P. A New Method for Incorporating Hillslope Effects to Improve Canopy-Height Estimates From Large-Footprint LIDAR Waveforms[J]. IEEE Geoscience & Remote Sensing Letters, 2012,9(4):730-734.
[19]	Hayashi M, Saigusa N, Oguma H, et al. Forest canopy height estimation using ICESat/GLAS data and error factor analysis in Hokkaido,Japan[J]. Isprs Journal of Photogrammetry & Remote Sensing, 2013,81(7):12-18.
[20]	Iqbal I A, Dash J, Ullah S, et al. A novel approach to estimate canopy height using ICESat/GLAS data:A case study in the New Forest National Park,UK[J]. International Journal of Applied Earth Observation & Geoinformation, 2013,23(1):109-118. doi: 10.1016/j.jag.2012.12.009
[21]	湖南省统计局. 湖南统计年鉴[D]. 北京: 北京统计出版社, 2014
[22]	Hu Yang, Wu Fayun, Sun Zhongqiu, et al. The Laser Vegetation Detecting Sensor:A Full Waveform,Large-Footprint,Airborne Laser Altimeter for Monitoring Forest Resources[J]. Sensors, 2019,19(7):1699. doi: 10.3390/s19071699
[23]	Hu Yang, Xu Xuelei, Wu Fayun, et al. Estimating Forest Stock Volume in Hunan Province,China,by Integrating In Situ Plot Data,Sentinel-2 Images,and Linear and Machine Learning Regression Models[J]. Remote Sensing, 2020,12(1):186. doi: 10.3390/rs12010186
[24]	庞勇, 于信芳, 李增元, 等. 星载激光雷达波形长度提取与林业应用潜力分析[J]. 林业科学, 2006,42(7):137-140. doi: 10.11707/j.1001-7488.20060724
[25]	庞勇, 李增元, Sun Guoqing, 等. 地形对大光斑激光雷达森林回波影响研究[J]. 林业科学研究, 2007,20(4):464-468.

主要指标	编号
主要指标	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011
起始阈值	46.47	46.26	44.49	59.57	44.33	47.48	43.99	44.32	44.68	44.24
结束阈值	25.58	25.74	25.62	24.59	26.86	28.41	25.88	27.88	24.51	24.00
冠层位置	201	201	201	201	201	201	201	201	201	201
地面位置	281	279	275	274	263	261	264	254	274	273
冠层高度	12	11.7	11.1	10.95	9.3	9	9.45	7.95	10.95	10.8

类型	样地计算值/m	估测均值/m	绝对误差/m	相对误差/%	RMSE/m
胸高断面积加权平均高	10.79	10.32	0.47	4.36	1.40
优势树种平均木平均高	9.53	10.32	0.79	8.29	1.55
优势树种算术平均高	9.50	10.32	0.82	8.63	1.57
算术平均高	9.36	10.32	0.96	10.26	1.66
树冠面积加权平均高	11.80	10.32	1.48	12.54	2.04
优势木平均高D	12.87	10.32	2.55	19.81	2.99
优势木平均高H	13.23	10.32	2.91	22.00	3.34