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纸质出版日期： 2021-06-07 ，
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董佳臣，倪文俭，张志玉，孙国清.2021.ICESat-2植被冠层高度和地表高程数据产品用于森林高度提取的效果评价.遥感学报，25（6）： 1294-1307
Dong J C，Ni W J，Zhang Z Y and Sun G Q. 2021. Performance of ICESat-2 ATL08 product on the estimation of forest height by referencing to small footprint LiDAR data. National Remote Sensing Bulletin， 25（6）：1294-1307
由美国宇航局（NASA）研制的新一代冰、云和陆地高程卫星（ICESat-2）于2018-09-15发射成功，其搭载的先进地形激光测高系统（ATLAS）采用微脉冲多波束光子计数激光雷达技术，可用于全球高程线采样数据的获取。目前公开发布了9种数据产品，其中包括植被冠层高度和地表高程数据产品（ATL08），为全球森林结构参数的估算提供了新的契机。本文以美国宾夕法尼亚州斯奈德县和印度尼西亚西加里曼丹吉打邦为研究区，在温带森林和热带雨林两种不同的生态系统立地条件下，对 ICESat-2的ATL08数据产品用于森林高度估算的效果进行评价。首先建立了地形高程数据产品（ATL03）与ATL08数据产品的关联规则，以获取分别记录在这两种产品中的光子空间分布信息和分类信息；进而以机载小光斑激光雷达数据为参考，对ATL08数据产品的光子分类可靠性及其用于森林高度估算的准确性进行了分析评价。结果表明：（1）在温带森林情况下，ATL08数据产品提供的平均冠层高度和最大冠层高度与参考数据的相关系数（
R
²）分别为0.54和0.61，相对误差分别为16.78%和10.71%，表明ATL08数据产品的光子分类结果能够用于刻画森林冠层结构和林下地形；（2）在热带雨林情况下，到达地面的光子数量相较于温带森林明显减少，地面光子类型识别的可靠性低，ATL08数据产品提供的平均冠层高度和最大冠层高度与参考数据的相关系数（
R
²）分别为0.21和0.19；（3）森林覆盖度的增大会导致ATL08计算的冠层高度误差增大，热带雨林平均冠层高度的误差随着坡度增大有增大趋势，在坡度为0°—10°、10°—20°和20°—30°共3组情况下，误差分别为5.7 m、6.6 m和9.3 m。因此，在高森林覆盖度情况下，现有的ATL08数据产品难以直接用于森林高度的提取。
The ICESat-2 satellite
which operates through the technology of multi-beam single-photon-counting
provides a new opportunity for the mapping of global forest structures. Although previous studies based on the airborne simulator of ICESat-2 have shown that it has great potential to estimate forest structure parameters using single-photon-counting data
the performance of ICESat-2 needs to be examined due to the major differences between airborne system and ICESat-2. The National Aeronautics and Space Administration has released nine types of ICESat-2 data products
one of which is the vegetation canopy height and surface elevation data (ATL08). Therefore
the purpose of this paper is to evaluate the performance of ATL08 on the estimation of forest height by referencing to small-footprint LiDAR data.
The basis for the accurate estimation of forest height using photon-counting LiDAR data is the correct classification of photons
that is
identification of noise photons
ground photons
and vegetation photons. The ATL08 only records photon classification and forest structure parameters
and the geographical coordinates of each photon are recorded in the Terrain Elevation (ATL03) product. Therefore
ATL03 and ATL08 need to be connected first according to the organization of two products. Taking the data of small-footprint LiDAR as reference
the ATL08 products were evaluated from two aspects. First
the classification results of photons are evaluated by referencing to the profiles from the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived using the small-footprint LiDAR data. Then
percentile height metrics of each 100-m-long segment in ATL08 product were evaluated by referencing to corresponding metrics calculated using the point cloud data of small-footprint LiDAR.
This study was carried out in two test sites: Snyder County in Pennsylvania
United States and Ketapang in West Kalimandin
Indonesia. These sites represented temperate forest and tropical rainforest
respectively. In the temperate forest
the classification accuracy of noise photons
ground photons
and canopy photons in ATL08 products were acceptable. The estimation accuracy of maximum canopy height was slightly better than that of mean canopy height with
R
²=0.61 against 0.54 and RMSE=10.71% against 16.78%. In the tropical rainforest with relatively low canopy cover
the ground photons in ATL08 could be correctly identified. However
in the forest with high canopy cover
the number of photons near the ground is inadequate. In addition
the identified ground photons in ATL08 were not enough to fit the terrain under the forest
which caused obvious errors in the estimation of forest height. The results of the quantitative analysis of the error of mean canopy height between canopy cover and terrain show that with the increase of canopy cover
the error of canopy height calculated by ATL08 will increase. In the tropical rainforest
the error of the mean canopy height will increase as the slope increases. When the slope is 0°—10°
10°—20°
and 20°—30°
the error is 5.7 m
6.6 m
and 9.3 m
respectively.
Ie can be concluded based on the results of this study that ATL08 product could be used to estimate the height of temperate forests. However
in dense tropical rainforests
due to the limited penetration capabilities of LiDAR and difficulties in the correct identification of ground photons
the existing ATL08 data will be difficult to use for forest height estimation.
ICESat-2ATLASATL08森林结构参数
ICESat-2ATLASATL08forest structure parameters
Abdalati W, Zwally H J, Bindschadler R, Csatho B, Farrell S L, Fricker H A, Harding D, Kwok R, Lefsky M, Markus T, Marshak A, Neumann T, Palm S, Schutz B, Smith B, Spinhirne J and Webb C. 2010. The ICESat-2 laser altimetry mission. Proceedings of the IEEE, 98(5): 735-751 [DOI: 10.1109/JPROC.2009.2034765http://dx.doi.org/10.1109/JPROC.2009.2034765]
Brown M E, Arias S D, Neumann T, Jasinski M F, Posey P, Babonis G, Glenn N F, Birkett C M, Escobar V M and Markus T. 2016. Applications for ICESat-2 data: from NASA’s early adopter program. IEEE Geoscience and Remote Sensing Magazine, 4(4): 24-37 [DOI: 10.1109/MGRS.2016.2560759http://dx.doi.org/10.1109/MGRS.2016.2560759]
Brunt K M, Neumann T A, Amundson J M, Kavanaugh J L, Moussavi M S, Walsh K M, Cook W B and Markus T. 2016. MABEL photon-counting laser altimetry data in Alaska for ICESat-2 simulations and development. The Cryosphere, 10(4): 1707-1719 [DOI: 10.5194/tc-10-1707-2016http://dx.doi.org/10.5194/tc-10-1707-2016]
Chen B W, Pang Y, Li Z Y, North P, Rosette J, Sun G Q, Suárez J, Bye I and Lu H. 2019. Potential of forest parameter estimation using metrics from photon counting LiDAR data in Howland research forest. Remote Sensing, 11(7): 856 [DOI: 10.3390/rs11070856http://dx.doi.org/10.3390/rs11070856]
Cook B D, Corp L A, Nelson R F, Middleton E M, Morton D C, McCorkel J T, Masek J G, Ranson K J, Ly V and Montesano P M. 2013. NASA Goddard's LiDAR, hyperspectral and thermal (G-LiHT) airborne imager. Remote Sensing, 5(8): 4045-4066 [DOI: 10.3390/rs5084045http://dx.doi.org/10.3390/rs5084045]
Farrell S L, Laxon S W, McAdoo D C, Yi D H and Zwally H J. 2009. Five years of Arctic sea ice freeboard measurements from the Ice, Cloud and land Elevation Satellite. Journal of Geophysical Research: Oceans, 114(C4): C04008 [DOI: 10.1029/2008JC005074http://dx.doi.org/10.1029/2008JC005074]
Gaumont-Guay D, Black T A, Barr A G, Griffis T J, Jassal R S, Krishnan P, Grant N and Nesic Z. 2014. Eight years of forest-floor CO2 exchange in a boreal black spruce forest: spatial integration and long-term temporal trends. Agricultural and Forest Meteorology, 184: 25-35 [DOI: 10.1016/j.agrformet.2013.08.010http://dx.doi.org/10.1016/j.agrformet.2013.08.010]
Huang W L, Sun G Q, Dubayah R, Cook B, Montesano P, Ni W J and Zhang Z Y. 2013. Mapping biomass change after forest disturbance: applying LiDAR footprint-derived models at key map scales. Remote Sensing of Environment, 134: 319-332 [DOI: 10.1016/j.rse.2013.03.017http://dx.doi.org/10.1016/j.rse.2013.03.017]
Khan S A, Kjær K H, Bevis M, Bamber J L, Wahr J, Kjeldsen K K, Bjørk A A, Korsgaard N J, Stearns L A, van den Broeke M R, Liu L, Larsen N K and Muresan I S. 2014. Sustained mass loss of the northeast Greenland ice sheet triggered by regional warming. Nature Climate Change, 4(4): 292-299 [DOI: 10.1038/nclimate2161http://dx.doi.org/10.1038/nclimate2161]
Li M, Guo Y, Yang G and Shu R. 2017. A noise filter method for the push-broom photon counting LiDAR and airborne cloud data verification. Science Technology and Engineering, 17(9): 53-58
李铭, 郭颖, 杨贵, 舒嵘. 2017. 一种推扫式光子计数激光雷达点云滤波算法及其验证. 科学技术与工程, 17(9): 53-58 [DOI: 10.3969/j.issn.1671-1815.2017.09.009http://dx.doi.org/10.3969/j.issn.1671-1815.2017.09.009]
Li Z Y, Liu Q W and Pang Y. 2016. Review on forest parameters inversion using LiDAR. Journal of Remote Sensing, 18(6): 1138-1150
李增元, 刘清旺, 庞勇. 2016. 激光雷达森林参数反演研究进展. 遥感学报, 20(5): 1138-1150 [DOI: 10.11834/jrs.20165130http://dx.doi.org/10.11834/jrs.20165130]
Lu D S, Chen Q, Wang G X, Liu L J, Li G Y and Moran E. 2016. 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]
Ma L X, Zheng G, Eitel J U H, Magney T S and Moskal L M. 2017. Retrieving forest canopy extinction coefficient from terrestrial and airborne LiDAR. Agricultural and Forest Meteorology, 236: 1-21 [DOI: 10.1016/j.agrformet.2017.01.004http://dx.doi.org/10.1016/j.agrformet.2017.01.004]
Magruder L A and Brunt K M. 2018. Performance analysis of airborne photon-counting LiDAR data in preparation for the ICESat-2 mission. IEEE Transactions on Geoscience and Remote Sensing, 56(5): 2911-2918 [DOI: 10.1109/TGRS.2017.2786659http://dx.doi.org/10.1109/TGRS.2017.2786659]
Manuri S, Andersen H E, McGaughey R J and Brack C. 2017. Assessing the influence of return density on estimation of LiDAR-based aboveground biomass in tropical peat swamp forests of Kalimantan, Indonesia. International Journal of Applied Earth Observation and Geoinformation, 56: 24-35 [DOI: 10.1016/j.jag.2016.11.002http://dx.doi.org/10.1016/j.jag.2016.11.002]
Markus T, Neumann T, Martino A, Abdalati W, Brunt K, Csatho B, Farrell S, Fricker H, Gardner A, Harding D, Jasinski M, Kwok R, Magruder L, Lubin D, Luthcke S, Morison J, Nelson R, Neuenschwander A, Palm S, Popescu S, Shum C K, Schutz B E, Smith B, Yang Y K and Zwally J. 2017. The ice, cloud, and land elevation satellite-2 (ICESat-2): science requirements, concept, and implementation. Remote Sensing of Environment, 190: 260-273 [DOI: 10.1016/j.rse.2016.12.029http://dx.doi.org/10.1016/j.rse.2016.12.029]
Melendy L, Hagen S C, Sullivan F B, Pearson T R H, Walker S M, Ellis P, Kustiyo, Sambodo A K, Roswintiarti O, Hanson M A, Klassen A W, Palace M W, Braswell B H and Delgado G M. 2018. Automated method for measuring the extent of selective logging damage with airborne LiDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 139: 228-240 [DOI: 10.1016/j.isprsjprs.2018.02.022http://dx.doi.org/10.1016/j.isprsjprs.2018.02.022]
Narine L L, Popescu S, Neuenschwander A, Zhou T, Srinivasan S and Harbeck K. 2019. Estimating aboveground biomass and forest canopy cover with simulated ICESat-2 data. Remote Sensing of Environment, 224: 1-11 [DOI: 10.1016/j.rse.2019.01.037http://dx.doi.org/10.1016/j.rse.2019.01.037]
Neuenschwander A and Pitts K. 2019. The ATL08 land and vegetation product for the ICESat-2 Mission. Remote Sensing of Environment, 221: 247-259 [DOI: 10.1016/j.rse.2018.11.005http://dx.doi.org/10.1016/j.rse.2018.11.005]
Neuenschwander A L and Magruder L A. 2016. The potential impact of vertical sampling uncertainty on ICESat-2/ATLAS terrain and canopy height retrievals for multiple ecosystems. Remote Sensing, 8(12): 1039 [DOI: 10.3390/rs8121039http://dx.doi.org/10.3390/rs8121039]
Nie S, Wang C, Xi X H, Luo S Z, Li G Y, Tian J Y and Wang H T. 2018. Estimating the vegetation canopy height using micro-pulse photon-counting LiDAR data. Optics Express, 26(10): A520-A540 [DOI: 10.1364/OE.26.00A520http://dx.doi.org/10.1364/OE.26.00A520]
Popescu S C, Zhou T, Nelson R, Neuenschwande A, Sheridan R, Narine L and Walsh K M. 2018. Photon counting LiDAR: an adaptive ground and canopy height retrieval algorithm for ICESat-2 data. Remote Sensing of Environment, 208: 154-170 [DOI: 10.1016/j.rse.2018.02.019http://dx.doi.org/10.1016/j.rse.2018.02.019]
Xia S B, Wang C, Xi X H, Luo S Z and Zeng H C. 2014. Point cloud filtering and tree height estimation using airborne experiment data of ICESat-2. Journal of Remote Sensing, 18(6): 1199-1207
夏少波, 王成, 习晓环, 骆社周, 曾鸿程. 2014. ICESat-2机载试验点云滤波及植被高度反演. 遥感学报, 18(6): 1199-1207 [DOI: 10.11834/jrs.20144029http://dx.doi.org/10.11834/jrs.20144029]
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