结合修正后的全球生态系统动态调查冠层高度的森林地上生物量模型优化——以福建省为例
作者:
基金项目:

福建省科技厅高校产学合作项目(2022N5008);福建省科技厅对外合作项目(2022I0007)


Optimization model of forest aboveground biomass based on MGEDI canopy height: a case study in Fujian, China
Author:
  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 文章评论
    摘要:

    森林地上生物量(Above Ground Biomass,AGB)是衡量森林生态系统碳存储、能量流动和生物多样性的关键指标,对于气候变化研究和森林资源管理至关重要。福建省地处多云多雨的亚热带,地形和森林类型复杂,森林地上生物量估算难度大。为提升森林地上生物量估算效果,将最新星载激光雷达数据全球生态系统动态调查(GEDI)、Landsat以及Sentinel系列卫星等多源遥感数据进行集成和综合利用,通过Landsat影像计算的林龄对GEDI_V27冠层高度产品进行优化,结合优化后的MGEDI_V27冠层高度产品,建立传统遥感特征结合冠层高度的极端梯度提升模型(XGBoost)生物量反演模型,实现了福建省森林地上生物量的有效估算与制图。研究结果表明:(1)通过林龄优化后的GEDI冠层高度精度评价结果为R2=0.67,RMSE=2.24m; (2)通过递归特征消除算法对三种森林类型进行特征优选,得到10个遥感特征,其中,三种森林类型最重要的遥感特征均为森林冠层高度,并且对比评价了在包含传统遥感特征因子的情况下有无冠层高度对于模型精度的影响,结果表明,在冠层高度因子参加特征构建时,森林AGB回归分析的精度明显提高,证实了冠层高度在生物量估算中具有显著的重要性; (3) 研究得到的福建省森林AGB范围为0.001-363.331Mg/hm2,整体精度评价结果为R2=0.75,RMSE=17.34Mg/hm2,2020年全省AGB总量为8.22亿Mg,平均值为101.24Mg/hm2。通过优化GEDI中的森林冠层高度,并且结合传统遥感特征,可以实现对福建省森林地上生物量的精确估算和监测,研究成果有助于区域森林碳汇的评估。

    Abstract:

    Above Ground Biomass (AGB) is a key indicator of forest ecosystem carbon storage, energy flow changes and biodiversity, and is crucial for climate change research and forest resource management. Fujian Province, as the largest collective forest area in southern China, has abundant forest resources, accurately estimating forest aboveground biomass can lay the foundation for estimating carbon storage and provide decision-making support for achieving the dual carbon goals. Fujian Province is located in a cloudy and rainy subtropical zone with complex terrain and forest types, making it difficult to estimate forest aboveground biomass, estimating forest aboveground biomass using traditional methods is difficult to meet accuracy requirements. In order to improve the accuracy of aboveground forest biomass, this study integrated and comprehensively utilized multi-source remote sensing data such as the latest spaceborne lidar data GEDI, Landsat and Sentinel series satellites. Above all, the GEDI_V27 canopy height product was optimized based on the forest age calculated from Landsat. Then combined with the optimized MGEDI_V27 canopy height product, by establishing an XGBoost biomass inversion model that combined traditional remote sensing features with canopy height, we effectively improved model accuracy,estimated and mapped the aboveground biomass of forests in Fujian Province. The research results showed that: (1) The GEDI canopy height accuracy evaluation result optimized by forest age was R2=0.67, RMSE=2.24m; (2) The recursive feature elimination algorithm was used to optimize the features of the three forest types, and 10 remote sensing features were obtained. Among them, the most important remote sensing features of the three forest types were forest canopy height, and a comparative evaluation was performed on the features including traditional remote sensing features. The results showed that when the canopy height factor was included in the feature construction, the accuracy of the forest AGB regression analysis was significantly improved, confirming that canopy height played a significant role in biomass estimation; (3) The studied forest AGB range in Fujian Province was 0.001--363.331Mg/hm2, the overall accuracy evaluation result was R2=0.75, RMSE=17.34 Mg/hm2, and the total AGB amount in the province in 2020 was 822 million Mg. The average value was 101.24Mg/hm2, reflecting the good ecological quality of Fujian Province. By optimizing the forest canopy height in GEDI and combining it with traditional remote sensing features, the accuracy of forest aboveground biomass modeling can be significantly improved, and it is possible to accurately estimate and monitor forest biomass in Fujian Province. The research results are helpful for the high-precision estimation of aboveground biomass in regional forests, and have certain guiding significance for the assessment of carbon sinks.

    参考文献
    [1] Ar'evalo P, Baccini A, Woodcock C E, Olofsson P, Walker W S. Continuous mapping of aboveground biomass using Landsat time series. Remote Sensing of Environment, 2023, 288: 113483.
    [2] Moradi F, Darvishsefat A A, Pourrahmati M R, Deljouei A, Borz S A. Estimating aboveground biomass in dense Hyrcanian forests by the use of sentinel-2 data. Forests, 2022, 13(1): 104.
    [3] 吴培强, 任广波, 张程飞, 王浩, 刘善伟, 马毅. 无人机多光谱和LiDAR的红树林精细识别与生物量估算. 遥感学报, 2022, 26(6): 1169-1181.
    [4] Tamiminia H, Salehi B, Mahdianpari M, Beier C M, Johnson L. Evaluating pixel-based and object-based approaches for forest above-ground biomass estimation using a combination of optical, sar, and an extreme gradient boosting model. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022,: 485-492.
    [5] Zhang X, Liu L Y, Chen X D, Gao Y, Xie S, Mi J. GLC_FCS30: global land-cover product with fine classification system at 30? m using time-series Landsat imagery. Earth System Science Data, 2021, 13(6): 2753-2776.
    [6] Zolkos S G, Goetz S J, Dubayah R. A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing. Remote Sensing of Environment, 2013, 128: 289-298.
    [7] Main-Knorn M, Cohen W B, Kennedy R E, Grodzki W, Pflugmacher D, Griffiths P, Hostert P. Monitoring coniferous forest biomass change using a Landsat trajectory-based approach. Remote Sensing of Environment, 2013, 139: 277-290.
    [8] 柳钦火, 仲波, 吴纪桃, 肖志强, 王桥. 环境遥感定量反演与同化: 科学出版社, 2011.
    [9] 苟睿坤, 陈佳琦, 段高辉, 杨瑞, 卜元坤, 赵君, 赵鹏祥. 基于GF-2的油松人工林地上生物量反演. 应用生态学报, 2019, 30(12): 4031-4040.
    [10] 菅永峰, 韩泽民, 黄光体, 王熊, 李源, 周靖靖, 佃袁勇. 基于高分辨率遥感影像的北亚热带森林生物量反演. 生态学报, 2021, 41(6): 2161-2169.
    [11] 蒋馥根, 孙华, 李成杰, 马开森, 陈松, 龙江平, 任蓝翔. 联合GF-6和Sentinel-2红边波段的森林地上生物量反演. 生态学报, 2021, 41(20): 8222-8236.
    [12] Pham T D, Yoshino K, Le N N, Bui D T. Estimating aboveground biomass of a mangrove plantation on the Northern coast of Vietnam using machine learning techniques with an integration of ALOS-2 PALSAR-2 and Sentinel-2A data. International Journal of Remote Sensing, 2018, 39(22): 7761-7788.
    [13] Jan A, Henrik P, Lars U. Biomass growth from multi-temporal TanDEM-X interferometric synthetic aperture radar observations of a boreal forest site. Remote Sensing, 2018, 10(4): 603.
    [14] Kumar S, Garg R D, Govil H, Kushwaha S P S. PolSAR-decomposition-based extended water cloud modeling for forest aboveground biomass estimation. Remote sensing, 2019, 11(19): 2287.
    [15] Su H, Shen W, Wang J, Ali A, Li M. Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests. Forest Ecosystems, 2020, 7(04):851-870.
    [16] Lu D, Chen Q, Wang G X, Liu L J, Li G Y, Moran E. A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. International Journal of Digital Earth, 2016, 9(1): 63-105.
    [17] 闻馨, 刘凯, 曹晶晶, 朱远辉, 王子予. 基于森林冠层高度和异速生长方程的中国红树林地上生物量估算. 热带地理, 2023, 43(1): 1-11.
    [18] Du C Y, Fan W Y, Ma Y, Jin H I, Zhen Z. The effect of synergistic approaches of features and ensemble learning algorith on aboveground biomass estimation of natural secondary forests based on ALS and landsat 8. Sensors, 2021, 21(17): 5974.
    [19] 巨一琳, 姬永杰, 黄继茂, 张王菲. 联合LiDAR和多光谱数据森林地上生物量反演研究. 南京林业大学学报: 自然科学版, 2022, 46(1): 58-68.
    [20] Nandy S, Srinet R, Padalia H. 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, 2021, 48(14): e2021GL093799.
    [21] Silva C A, Duncanson L, Hancock S, Neuenschwander A, Thomas N, Hofton M, Fatoyinbo L, Simard M, Marshak C Z, Armston J, Lutchke S, Dubayah R. Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping. Remote Sensing of Environment, 2021, 253: 112234.
    [22] Qi W L, Saarela S, Armston J, Ståhl G, Dubayah R. Forest biomass estimation over three distinct forest types using TanDEM-X InSAR data and simulated GEDI lidar data. Remote Sensing of Environment, 2019, 232: 111283.
    [23] Yue J B, Yang G J, Li C C, Li Z H, Wang Y J, Feng H K, Xu B. Estimation of winter wheat above-ground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models. Remote Sensing, 2017, 9(7): 708.
    [24] Quegan S, Le Toan T, Chave J, Dall J, Exbrayat J, Minh D H T, Lomas M, D'Alessandro M M, Paillou P, Papathanassiou K, Rocca F, Saatchi S, Scipal K, Shugart H, Smallman T L, Soja M J, Tebaldini S, Ulander L, Villard L, Williams M. The European Space Agency BIOMASS mission: Measuring forest above-ground biomass from space. Remote Sensing of Environment, 2019, 227: 44-60.
    [25] Xiang W H, Zhou J, Ouyang S, Zhang S L, Lei P F, Li J X, Deng X W, Fang X, Forrester D I. Species-specific and general allometric equations for estimati孮??嵴??略戠慢祩慯桭?剳???汯慭楰牯???????潦攠瑳穵?却???慩瑣潡祬椠湦扯潲?????慩湮猠敓湯?????敮愠汃敨祩?卡???潵晲瑯潰湥?????畵牲瑮瑡?????敆汯汲湥敳牴?????畡瑲档捨欬攠′匰???爠洱猳琵漨渵????吶愳渭朹?????畲渾捛愲渶獝漠湋?????慹渠捒漠捅欬?卙???愠湚琠穑?倠???慥牮猠敗氠楂献?卄??健慣瑴瑩敮牧猠潴湲?偮????兮椠?坯??即楴氠癤慩????呢桡敮??氠潡扮慤氠??捣潯獶祥獲瑹攠浵??祮湧愠浹楥捡獲??渠癌敡獮瑤楳条慴琠楴潩湭???楥杲桩?牳攺猠漱氮甠瑌楡潮湤?汲慥獮敤牲?牴慥湭杰楯湲条?漠晳?瑧桭敥??慡牴瑩桯?猠?晬潧牯敲獩瑴獨?慳渮搠?瑥潭灯潴来爠慓灥桮祳??卧挠楯敦渠捅敮?潩晲?剮敭浥潮瑴攬?匲攰渱猰椬渠朱??㈨?有〩??????????日?
    [27] Guyon I, Weston J, Barnhill S, Vapnik V. Gene Selection for Cancer Classification using Support Vector Machines. Machine Learning, 2002, 46(1): 389-422.
    [28] 苏华, 张明慧, 李静, 陈修治, 汪小钦. 基于光学与SAR因子的森林生物量多元回归估算——以福建省为例. 遥感技术与应用, 2019, 34(4): 847-856.
    [29] 宋涵玥, 舒清态, 席磊, 邱霜, 魏治越, 杨泽至. 基于星载ICESat-2/ATLAS数据的森林地上生物量估测.农业工程学报,2022,38(10):191-199.
    [30] 张宁, 王金牛, 石凝, 王丽华, 朱牛, 田炳辉, 张林, 盖艾鸿. 岷江源区两种优势针叶树当年生小枝性状与生物量分配随海拔的分异规律. 生态学报, 2023, 43(23): 9814-9826.
    [31] 兰思仁. 福建省森林景观类型及地理分布概述. 林业资源管理, 2002(1): 55-59.
    [32] Adugna T, Xu W B, Fan J L. Comparison of random forest and support vector machine classifiers for regional land cover mapping using coarse resolution FY-3C images. Remote Sensing, 2022, 14(3): 574.
    [33] Decuyper M, Chávez R O, Lohbeck M, Lastra J A, Tsendbazar N, Hackländer J, Herold M, Vågen T G. Continuous monitoring of forest change dynamics with satellite time series. Remote Sensing of Environment, 2022, 269: 112829.
    [34] Liu A B, Cheng X, Chen Z Q. Performance evaluation of GEDI and ICESat-2 laser altimeter data for terrain and canopy height retrievals. Remote Sensing of Environment, 2021, 264: 112571.
    [35] Li Y C, Li M Y, Li C, Liu Z Z. Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms. Scientific Reports, 2020, 10: 9952.
    [36] Luo M, Wang Y F, Xie Y H, Zhou L, Qiao J J, Qiu S Y, Sun Y J. Combination of feature selection and CatBoost for prediction: the first application to the estimation of aboveground biomass. Forests, 2021, 12(2): 216.
    [37] 谭雨欣, 田义超, 黄卓梅, 张强, 陶进, 刘虹秀, 杨永伟, 张亚丽, 林俊良, 邓静雯. 北部湾茅尾海无瓣海桑红树林地上生物量反演——基于XGBoost机器学习算法. 生态学报, 2023, 43(11): 4674-4688.
    引证文献
引用本文

田国帅,周小成,郝优壮,谭芳林,王永荣,吴善群,林华章.结合修正后的全球生态系统动态调查冠层高度的森林地上生物量模型优化——以福建省为例.生态学报,2024,44(16):7264~7277

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数: