联合ICESat-2和GEDI星载激光雷达数据的浙江省森林地上生物量估算
Forest aboveground biomass estimation in Zhejiang Province combining ICESat-2 and GEDI spaceborne LiDAR data
- 2022年 页码：1-17
DOI： 10.11834/jrs.20222120
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摘要
本研究重点对比分析ICESat-2和GEDI两种星载激光雷达数据用于森林地上生物量估算的适用性，首先在古田山典型研究区采用线性逐步回归方法分别构建基于ICESat-2的条带和基于GEDI的光斑尺度森林地上生物量估算模型，再结合MODIS光学遥感数据和ASTER GDEM地形数据，采用随机森林法在浙江省分森林类型构建逐像元尺度的森林地上生物量外推模型，根据40个森林地上生物量地面调查样点的精度验证探讨大区域尺度森林地上生物量的最优估算方法，实现了2020年浙江省森林地上生物量成图。研究结果表明：（1）在古田山典型研究区基于ICESat-2的条带尺度森林地上生物量估算模型精度（R,2,=0.7057，RMSE=0.3571 ln（t/ha））高于基于GEDI的光斑尺度模型（R,2,=0.5186，RMSE=0.2805 ln（t/ha））；（2）外推到浙江省的森林地上生物量估算结果表明基于ICESat-2的精度（R,2,=0.59，RMSE=31.2525 t/ha）明显优于GEDI（R,2,=0.4113，RMSE=39.2652 t/ha），且研究区的高程对两种数据的估算差异影响最大，仅采用高程在600m以下的GEDI光斑数据来构建外推模型，发现估算精度有显著提高（R,2,=0.5387，RMSE=25.4017 t/ha）；（3）将ICESat-2条带与高程在600m以下的GEDI光斑联合起来构建外推模型，是利用两种星载激光雷达数据进行浙江省森林地上生物量估算的最优方法（R,2,=0.678，RMSE=27.3592 t/ha）。该研究融合地面、机载与星载遥感数据，以浙江省为例开展森林地上生物量的区域尺度估算研究，可为森林碳储量的动态监测与固碳能力评估提供方法借鉴与应用示范。
Abstract
Objective : Forest aboveground biomass (AGB) plays an important role in the study of carbon cycle and global change. Spaceborne LiDAR can provide information about forest vertical structures that is advantageous in AGB estimation, among which ICESat-2 and GEDI are the latest available spaceborne data. In this study, we investigated the applicability of ICESat-2 and GEDI for forest AGB estimation at regional scale, and analyzed the effect of data fusion of ICESat-2 and GEDI to find an optimal method to map the spatial distribution of forest AGB accurately in Zhejiang Province.Method First, we built footprint-level forest AGB estimation models by stepwise regression in the typical study area of Gutian Mountain based on ICEsat-2 and GEDI spaceborne LiDAR data, respectively. Then, combined with MODIS data and ASTER GDEM terrain information, forest AGB estimation models with spatial continuity at 250m pixel scale for different forest types were constructed by Random Forest algorithm throughout Zhejiang Province. Estimation results were validated using 40 forest AGB field plots. Finally, by comparing validation results of AGB estimation based on ICESat-2 or GEDI solely and the combination of the two spaceborne LiDAR data, the optimal method of forest AGB scaling was selected and the spatial distribution of forest AGB of the year 2020 was mapped in Zhejiang Province.Result The accuracy of segment-level forest AGB estimation based on ICESat-2 (R,2,=0.7057, RMSE=0.3571 ln(t/ha)) outmatches footprint-level forest AGB estimation based on GEDI (R,2,=0.5186, RMSE=0.2805 ln(t/ha)) in the typical study area of Gutian Mountain. Validation accuracy of forest AGB estimation result based on ICEsat-2 (R,2,=0.59, RMSE=31.2525 t/ha) is superior to GEDI (R,2,=0.4113, RMSE=39.2652 t/ha) in Zhejiang Province. The difference of forest AGB estimation performance between ICESat-2 and GEDI is mainly related to elevation, validation accuracy based on GEDI is higher when filtering footprints that are acquired in high elevation areas with an elevation threshold of 600m (R,2,=0.5387, RMSE=25.4017 t/ha). Combining ICESat-2 and GEDI data (elevation ≤ 600m) to build scaling model is the optimal method to estimate forest AGB in Zhejiang Province (R,2,=0.678, RMSE=27.3592 t/ha).Conclusion We have obtained a reliable estimation of forest AGB in Zhejiang Province based on ICESat-2 and GEDI data, which is a significant practice of regional scale forest AGB estimation. Our study can provide an effective method for forest carbon dynamic and sequestration potential monitoring using spaceborne LiDAR data.
关键词
森林地上生物量ICESat-2GEDI线性逐步回归随机森林尺度外推
Keywords
forest aboveground biomassICESat-2GEDIstepwise regressionRandom Forestscaling
references
Alrutz M, Gomez-Diaz J A, Schneidewind U, Kromer T and Kreft H. 2021. Forest structural parameters and aboveground biomass in old-growth and secondary forests along an elevational gradient in Mexico. Botanical Sciences 100(1): 67-85 [DOI: 10.17129/botsci.2855http://dx.doi.org/10.17129/botsci.2855]
Campbell M J, Dennison P E, Kerr K L, Brewer S C and Anderegg W R L. 2021. Scaled biomass estimation in woodland ecosystems: Testing the individual and combined capacities of satellite multispectral and lidar data. Remote Sensing of Environment, 262: 112511 [DOI: 10.1016/j.rse.2021.112511http://dx.doi.org/10.1016/j.rse.2021.112511]
Chen Z J and Pu X A. 2016. Comparison of multiple linear regression analysis and stepwise regression analysis. Journal of Mudanjiang College of Education, 5: 131-133
陈正江，蒲西安. 2016. 多元线性回归分析与逐步回归分析的比较研究. 牡丹江教育学院学报，5：131-133 [DOI: 10.3969/j.issn.1009-2323.2016.05.063http://dx.doi.org/10.3969/j.issn.1009-2323.2016.05.063]
Dorado-Ronda I, Pascual A, Godinho S, Silva C A, Botequim B, Rodríguez-Gonzálvez P, González-Ferreiro E and Guerra-Hernández J. 2021. Assessing the accuracy of GEDI data for canopy height and aboveground biomass estimates in Mediterranean forests. Remote Sensing, 13:2279 [DOI: 10.3390/rs13122279http://dx.doi.org/10.3390/rs13122279]
Du Z L, Su T, Ge J M and Wang X. 2021. Towards the carbon neutrality: the role of carbon sink and its spatial spillover effects. Economic Research Journal, 12: 187-202
杜之利，苏彤，葛佳敏，王霞. 2021. 碳中和背景下的森林碳汇及其空间溢出效应. 经济研究，12：187-202
Dubayah R, Blair J B, Goetz S, Fatoyinbo L, Hansen M, Healey S, Hofton M, Hurtt G, Kellner J, Luthcke S, Armston J, Tang H, Duncanson L, Hancock S, Jantz P, Marselis S, Patterson P L, Qi W L and Silva C. 2020. The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth's forests and topography. Science of Remote Sensing, 1: 100002 [DOI: 10.1016/j.srs.2020.100002http://dx.doi.org/10.1016/j.srs.2020.100002]
Duduman G, Barnoaiea I, Avacaritei D, Barbu C O, Cosofret V C, Danila I C, Duduman M L, Maciuca A and Dragoi M. 2021. Aboveground biomass of living trees depends on topographic conditions and tree diversity in temperate Montane forests from the Slătioara-Rarău Area (Romania). Forests, 12(11): 1507 [DOI: 10.3390/f12111507http://dx.doi.org/10.3390/f12111507]
Fang K N, Wu J B, Zhu J P, Xie B C. 2011. A review of technologies on Random Forests. Statistics & Information Forum, 26(3): 32-38
方匡南，吴见彬，朱建平，，谢邦昌. 2011. 随机森林方法研究综述. 统计与信息论坛，26(3)：32-38 [DOI: 10.3969/j.issn.1007-3116.2011.03.006http://dx.doi.org/10.3969/j.issn.1007-3116.2011.03.006]
Fu T and Huang Q F. 2010. Development of forest biomass estimations using airborne laser scanner data. Forestry Prospect and Design, 4: 86-89
付甜，黄庆丰. 2010. 基于机载激光雷达数据的森林生物量估测研究进展. 林业勘查设计，4：86-89
Fu W J, Fu Z J, Ge H L, Ji B Y, Jiang P K, Li Y F, Wu J S and Zhao K L. 2015. Spatial variation of biomass carbon density in a subtropical region of southeastern China. Forests, 6(12): 1966-1981 [DOI: 10.3390/f6061966http://dx.doi.org/10.3390/f6061966]
Huang J P. 2021. Study on canopy height estimation based on ICESat-2/ATLAS photon counting LiDAR data. Northeast Forestry University.
黄佳鹏. 2021. 基于ICESat-2/ATLAS光子计数LiDAR数据反演森林冠层高度研究. 东北林业大学. [DOI: 10.27009/d.cnki.gdblu.2021.000073http://dx.doi.org/10.27009/d.cnki.gdblu.2021.000073]
Lang N, Kalischek N, Armston J, Schindler K, Dubayah R and Wegner J D. 2022. Global canopy height regression and uncertainty estimation from GEDI LiDAR waveforms with deep ensembles. Remote Sensing of Environment, 268: 112760 [DOI: 10.1016/j.rse.2021.112760http://dx.doi.org/10.1016/j.rse.2021.112760]
Li M, Lu Y Y, Lu T F, Xu H and Ou G L. 2022. Forest aboveground biomass estimation and saturation point analysis using optical remote sensing in Nujiang River Basin of Yunnan Province. Journal of Southwest Forestry University, 42(3): 1-12
李猛，陆彦羽，卢腾飞，胥辉，欧光龙. 云南怒江流域森林地上生物量光学遥感估测及饱和点分析. 西南林业大学学报，42(3)：1-12 [DOI: 10.11929/j.swfu.202102014http://dx.doi.org/10.11929/j.swfu.202102014]
Li W, Niu Z, Wang C, Gao S, Feng Q and Chen H Y. 2015. Forest above-ground biomass estimation at plot and tree levels using airborne LiDAR data. Journal of Remote Sensing, 19(4): 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(4)：669-679 [DOI: 10.11834 /jrs.20154116http://dx.doi.org/10.11834/jrs.20154116]
Liu Q, Yang L, Liu Q H and Li J. 2015. Review of forest above ground biomass inversion methods based on remote sensing technology. Journal of Remote Sensing, 19(1): 62-74
刘茜，杨乐，柳钦火，李静. 2015. 森林地上生物量遥感反演方法综述. 遥感学报, 19(1)，62-74 [DOI: 10.11834 / jrs.20154108http://dx.doi.org/10.11834/jrs.20154108］
Luthcke S B, Thomas T C, Pennington T A, Rebold T W, Nicholas J B, Rowlands D D, Gardner A S and Bae S. 2020. ICESat‐2 pointing calibration and geolocation performance. Earth and Space Science. [DOI: 10.1029/2020EA001494http://dx.doi.org/10.1029/2020EA001494]
Magruder L, Brunt K, Neumann T, Klotz B and Alonzo M. 2020. Passive ground-based optical techniques for monitoring the on-orbit ICESat-2 altimeter geolocation and footprint diameter. Advancing Earth And Space Science. [DOI: 10.1029/2020EA001414http://dx.doi.org/10.1029/2020EA001414]
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, Schutz B E, Smith B, Yang Y 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]
Nandy S, Srinet R and Padalia H. 2021. Mapping forest height and aboveground biomass by integrating ICESat-2, Sentinel-1 and Sentinel-2 data using random forest algorithm in northwest himalayan foothills of India. Geophysical Research Letters, 48(14). [DOI: 10.1029/2021GL093799http://dx.doi.org/10.1029/2021GL093799]
Narine L L, Popescu S C and Malambo L. 2020. Using ICESat-2 to estimate and map forest aboveground biomass: A first example. Remote Sensing, 12: 1824 [DOI: 10.3390/rs12111824http://dx.doi.org/10.3390/rs12111824]
Neuenschwander A L and Magruder L A. 2019. Canopy and terrain height retrievals with ICESat-2: A first look. Remote Sensing, 11: 1721 [DOI: 10.3390/rs11141721http://dx.doi.org/10.3390/rs11141721]
Neumann, Thomas A. , Martino, Anthony J. , Markus, & Thorsten, et al. (2019). The Ice, Cloud, and Land Elevation Satellite-2 mission: A global geolocated photon product derived from the Advanced Topographic Laser Altimeter System. Remote Sensing of Environment, 233: 111325 [DOI: 10.1016/j.rse.2019.111325http://dx.doi.org/10.1016/j.rse.2019.111325]
Pang Y, Li Z Y, Chen B W and Liang X J. 2019. Status and development of spaceborne Lidar applications in forestry. Aerospace Shanghai, 36(3): 20-28.
庞勇，李增元，陈博伟，梁晓军. 2019. 星载激光雷达森林探测进展及趋势. 上海航天, 36(3)：20-28 [DOI: 10.19328/j.cnki.1006-1630.2019.03.003http://dx.doi.org/10.19328/j.cnki.1006-1630.2019.03.003]
Pang Y, Meng S L and Li Z Y. 2017. Temperate Forest aboveground biomass estimation using fourier-based textural ordination (FOTO) indices from high resolution aerial optical image. Scientia Silvae Sinicae, 53(3): 94-103
庞勇，蒙诗栎，李增元. 2017. 机载高分辨率遥感影像的傅氏纹理因子估测温带森林地上生物量. 林业科学，53(3)：94-103 [DOI: 10.11707/j.1001-7488.20170311http://dx.doi.org/10.11707/j.1001-7488.20170311]
Qi W L, Saarela S, Armston J, Ståhl G and Dubayah R. 2019. Forest biomass estimation over three distinct forest types using TanDEM-X InSAR data and simulated GEDI lidar data. Remote Sensing of Environment, 232: 111283 [DOI: 10.1016/j.rse.2019.111283http://dx.doi.org/10.1016/j.rse.2019.111283]
Qin G W and Tian M H. 2022. The development opportunities and implementation path of forestry carbon sequestration under the target of carbon peak and carbon neutrality. Administration Reform, 1:45-54
秦国伟，田明华. 2022. "双碳"目标下林业碳汇的发展机遇及实施路径. 行政管理改革，1：45-54 [DOI: 10.14150/j.cnki.1674-7453.2022.01.008http://dx.doi.org/10.14150/j.cnki.1674-7453.2022.01.008]
Queinnec M, White J C and Coops N C. 2021. Comparing airborne and spaceborne photon-counting lidar canopy structural estimates across different boreal forest types. Remote Sensing of Environment, 262: 112510 [DOI: 10.1016/j.rse.2021.112510http://dx.doi.org/10.1016/j.rse.2021.112510]
Rosenfield M F and Souza A F. 2014. Forest biomass variation in Southernmost Brazil: the impact of Araucaria trees. REV BIOL TROP, 62(1): 359-372 [DOI: 10.15517/rbt.v62i1.7923http://dx.doi.org/10.15517/rbt.v62i1.7923]
Shen A H, Wu C F, Jiang B, Deng J S, Yuan W G, Wang K, He S, Zhu En Y, Lin Y and Wu C P. 2018. Spatiotemporal variations of aboveground biomass under different terrain conditions. Forests, 9(12): 778 [DOI: 10.3390/f9120778http://dx.doi.org/10.3390/f9120778]
Silva C A, Duncanson L, Hancock S, Neuenschwander A, Thomas N, Hofton M, Fatoyinbo L, Simard M, Marshak C Z, Armston J, Lutchke S and Dubayah R. 2021. Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping. Remote Sensing of Environment, 253: 112234 [DOI: 10.1016/j.rse.2020.112234http://dx.doi.org/10.1016/j.rse.2020.112234]
Wu B F, Qian J K, Zeng Y, Zhang L, Yan C Z, Wang Z M, Li A N, Ma R H, Yu X F, Huang J L, Bao A M, Chen J S, Liu C L, Yi K P, Cheng W M, Song Y, Qi C M, Gao X M and Xue H H. 2017. Land Cover Atlas of the People’s Republic of China (1:1,000,000). Beijing: Sinomaps Press.
吴炳方，钱金凯，曾源， 张磊，颜长珍，王宗明，李爱农，马荣华，于信芳，黄进良，包安明，陈劲松，刘成林，伊坤朋，程维明，宋杨，祁彩梅，高晓梅，薛海红. 2017. 中华人民共和国土地覆被地图集(1：1000000). 北京：中国地图出版社.
Wu B F, Zeng Y and Zhao D. 2019. Remote sensing methods of China’s ecological parameters and changing patterns. Beijing: Science Press
吴炳方，曾源，赵旦. 2019. 中国生态参数遥感监测方法及其变化格局. 北京：科学出版社
Wu C F. 2016. Regional biomass estimation and application based on remote sensing. Zhejiang University.
吴超凡. 2016. 区域森林生物量遥感估测与应用研究. 浙江大学.
Xiong J. 2019. Forest tree species classification and aboveground biomass estimation using airborne LiDAR and hyperspectral data. University of Chinese Academy of Sciences.
熊杰. 2019. 基于机载激光雷达和高光谱数据的森林树种分类及地上生物量估算研究. 中国科学院大学.
Yi H Y, Zeng Y, Zhao Y J, Zheng Z J, Xiong J and Zhao D. 2020. Forest species diversity mapping based on clustering algorithm. Chinese Journal of Plant Ecology, 44(6): 598-615
衣海燕，曾源，赵玉金，郑朝菊，熊杰，赵旦. 2020. 利用聚类算法监测森林乔木物种多样性. 植物生态学报，44(6)：598-615 [DOI: 10.17521/cjpe.2019.0347http://dx.doi.org/10.17521/cjpe.2019.0347]
Yan Y J, Fan S H and Guan F Y. 2018. Research progress in TLS technology in forest investigation. World Forestry Research, 31(4): 42-47
晏颖杰，范少辉，官凤英. 2018. 地基激光雷达技术在森林调查中的应用研究进展. 世界林业研究，31(4)：42-47 [DOI: 10.13348 /j. cnki.sjlyyj.2018.0055.yhttp://dx.doi.org/10.13348/j.cnki.sjlyyj.2018.0055.y]
Yu M J, Hu Z H, Yu J P, Ding B Y and Fang T. 2001. Forest vegetation types in Gutianshan Natural Reserve in Zhejiang. Journal of Zhejiang University (Agric. & Life Sci. ), 27(4): 375-380
于明坚，胡正华，余建平，丁炳扬，方腾. 2001. 浙江古田山自然保护区森林植被类型. 浙江大学学报：农业与生命科学版, 27(4)：375-380 [DOI: 10.3321/j.issn:1008-9209.2001.04.007http://dx.doi.org/10.3321/j.issn:1008-9209.2001.04.007]
Yue C Y, Zheng Y C, Xing Y Q, Pang Y, Li S M, Cai L T and He H Y. 2020. Technical and application development study of space-borne LiDAR in forestry remote sensing. Infrared and Laser Engineering, 49(11): 10
岳春宇，郑永超，邢艳秋，庞勇，李世明，蔡龙涛，何红艳. 2020. 星载激光遥感林业应用发展研究. 红外与激光工程，49(11)：10 [DOI: 10.3788/IRLA20200235http://dx.doi.org/10.3788/IRLA20200235]
Zhang Z P. 1993. Preference of linear regression models and log-linear regression models: the use of several simple tests. Statistical Research, 1:57-58
张忠平. 1993. 线性回归模型与对数线性回归模型的优选——几种简便检验方法的运用. 统计研究，1：57-58 [DOI: 10.19343http://dx.doi.org/10.19343
／j.cnki.11-1302/c.1993.01.013]
Zheng Z J. 2017. Forest aboveground biomass estimation using LiDAR and scaling model in the Three Gorges region of China. Chinese University of Chinese Academy of Sciences.
郑朝菊. 2017. 基于激光雷达和外推模型的森林地上生物量估算研究. 中国科学院大学.
Zhou G Y, Yin G C, Tang X L, Wen D Z, Liu C P, Kuang Y W and Wang W T. 2018. Forest ecosystem carbon stocks in China: Biomass equation. Beijing: Science Press
周国逸，尹光彩，唐旭利，温达志，刘昌平，旷远文，王万同. 2018. 中国森林生态系统碳储量—生物量方程. 北京：科学出版社
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