Please wait a minute...
 
Remote Sensing for Natural Resources    2025, Vol. 37 Issue (5) : 224-232     DOI: 10.6046/zrzyyg.2024304
|
Estimation and spatial pattern analysis of forest above-ground biomass based on Sentinel-2 and GEDI data
WANG Lu1,2,3(), JI Yongjie1,2,3(), DONG Wenquan4, ZHANG Wangfei5
1. College of Soil and Water Conservation，Southwest Forestry University，Kunming 650224，China
2. Key Laboratory of Ecological Environment Evolution and Pollution Control in Mountainous and Rural Areas of Yunnan Province，Kunming 650224，China
3. Yunnan Zhanyi Karst Ecosystem Positioning Observation and Research Station，National Forestry and Grassland Administration，Kunming 650224，China
4. Royal Botanic Gardens Edinburgh，Edinburgh EH3 5LR，UK
5. College of Forestry，Southwest Forestry University，Kunming 650224，China
Download: PDF(3680 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Forest above-ground biomass （AGB） is recognized as an important indicator of forest productivity. Rapid and accurate estimation of forest AGB is crucial for sustainable forest management and carbon cycle research. Based on spaceborne light detection and ranging （LiDAR） data from the global ecosystem dynamic investigation （GEDI） and Sentinel-2 optical data，this study extracted GEDI L2B，Sentinel-2 remote sensing features，and topographic factors （elevation，aspect，and slope） in the study area. Among them，variables were determined through Pearson correlation analysis. Then，this study constructed the partial least squares regression （PLSR），gradient boosting regression tree （GBRT），and random forest （RF） models for forest AGB inversion. Consequently，this study estimated these models’ potential for forest AGB estimation and analyzed the spatial distribution differences of forest AGB. The results indicate that the estimation using multi-source data consistently outperformed that using single-source data. Among them，the RF model based on GEDI and Sentinel-2 data exhibited the best performance （R2=0.76，root mean square error （RMSE）=23.02 t/hm2），followed by the GBRT model，while the PLSR model performed the worst （R2=0.26）. In terms of spatial distribution，within the elevation range of 1 200~1 800 m，forest AGB density increased with elevation. Slope variation had little effect on forest AGB density，but a pronounced decrease in AGB density was observed on steep slopes. Aspect analysis showed that semi-shaded and sunny slopes exhibited high forest AGB density，while shaded and semi-sunny slopes presented similar values. Slope-aspect interaction analysis revealed that sunny and semi-sunny slopes displayed the highest total forest AGB on gentle and moderate slopes，respectively. In contrast，forest AGB significantly decreased across all orientations on flat and steep slopes，with a more significant decline observed on shaded and semi-shaded slopes. These findings provide a scientific basis for formulating forest protection and cultivation policies at the provincial level.
Keywords Sentinel-2      global ecosystem dynamics investigation （GEDI）      forest above-ground biomass （AGB）      random forest （RF） model     
ZTFLH:  S771.8  
  TP79  
Issue Date: 28 October 2025
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Lu WANG
Yongjie JI
Wenquan DONG
Wangfei ZHANG
Cite this article:   
Lu WANG,Yongjie JI,Wenquan DONG, et al. Estimation and spatial pattern analysis of forest above-ground biomass based on Sentinel-2 and GEDI data[J]. Remote Sensing for Natural Resources, 2025, 37(5): 224-232.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024304     OR     https://www.gtzyyg.com/EN/Y2025/V37/I5/224
Fig.1  Overview of the study area and sample sites
Fig.2  Distribution frequency of forest AGB values in sample plots
特征名称 物理含义 特征名称 物理含义
sensitivity 灵敏度 pgap_theta 森林冠层间隙率
modis_treecover 根据MODIS数据得出的植被百分比 modis_nonvegetated 根据MODIS数据得出的非植被百分比
pgap_theta_error 森林冠层间隙率的误差 landsat_treecover Landsat树冠覆盖率
rv 波形中植被分量的积分 height_lastbin 相对森林冠层间隙误差的地面高度
rh100 接收波形信号的起始点离地面的高度 fhd_normal 叶片高度多样性指数
rg 波形中地面分量的积分 digital_elevation_model 数字高程模型高于WGS84椭圆形的高度
pai 植被总面积指数 cover 树冠总覆盖率
shot number 激光点号 degrade_flag 指向或定位信息降级状态的标志
lat_lowestmode 最低模式中心的纬度 lon_lowestmode 最低模式中心的经度
quality_flag 标记以简化最有用数据的选择 leaf_off_flag 指示观察是否在落叶林条件下记录
beam 激光器强弱指示
Tab.1  Parameter information of GEDI L2B data
类型 特征名称
原始波段 B2，B3，B4，B5，B6，B7，B8，B11
植被指数 归一化植被指数、差值植被指数、比值植被指数、变换的归一化差异植被指数、绿色标准化差异植被指数、归一化差异指数、红边拐点指数、Sentinel-2红边位置指数
纹理特征 均值、方差、熵、对比度、同质性、相关性、非相似性、角二阶矩
缨帽变换特征 亮度、绿度、湿度
主成分分析 PCA1，PCA2，PCA3
Tab.2  Features information of Sentinel-2 data
数据源 模型 R2 RMSE/（t·hm-2）
Sentinel-2 RF 0.73 24.34
GBRT 0.48 33.72
PLSR 0.14 40.36
Sentinel-2+GEDI RF 0.76 23.02
GBRT 0.60 29.35
PLSR 0.26 37.56
Tab.3  Estimation accuracy of forest AGB model
Fig.3  Scatter plot and error line graphs of predicted and measured forest AGB values
Fig.4  Distribution of forest AGB in the study area
Fig.5  Distribution of forest AGB at different elevations，slopes and aspects
坡度 坡向 森林
AGB/104 t		 坡度 坡向 森林
AGB/104 t
平坡 平地 0.62 陡坡 平地 0.29
阴坡 3.97 阴坡 27.26
半阴坡 4.61 半阴坡 18.44
阳坡 5.16 阳坡 27.25
半阳坡 5.71 半阳坡 22.44
缓坡 平地 0.97 急坡 平地 0.11
阴坡 43.72 阴坡 8.73
半阴坡 35.79 半阴坡 5.85
阳坡 57.95 阳坡 7.47
半阳坡 58.08 半阳坡 5.24
斜坡 平地 0.76 险坡 平地 0.01
阴坡 59.37 阴坡 0.95
半阴坡 38.70 半阴坡 0.80
阳坡 64.98 阳坡 0.60
半阳坡 53.76 半阳坡 0.51
Tab.4  Forest AGB distribution under the interaction of slope and aspect
[1] Wang M J, Sun R, Xiao Z Q. Estimation of forest canopy height and aboveground biomass from spaceborne LiDAR and Landsat imageries in Maryland[J]. Remote Sensing, 2018, 10(2):344.
[2] 薛春泉, 徐期瑚, 林丽平, 等. 基于异速生长和理论生长方程的广东省木荷生物量动态预测[J]. 林业科学, 2019, 55(7):86-94.
[2] Xue C Q, Xu Q H, Lin L P, et al. Biomass dynamic predicting for Schima superba in Guangdong based on allometric and theoretical growth equation[J]. Scientia Silvae Sinicae, 2019, 55(7):86-94.
[3] 闻馨, 刘凯, 曹晶晶, 等. 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[J]. 热带地理, 2023, 43(1):1-11.
doi: 10.13284/j.cnki.rddl.003616
[3] Wen X, Liu K, Cao J J, et al. Estimation of mangrove aboveground biomass in China using forest canopy height through an allometric equation[J]. Tropical Geography, 2023, 43(1):1-11.
doi: 10.13284/j.cnki.rddl.003616
[4] Sun G Q, Ranson K J, Guo Z, et al. Forest biomass mapping from Lidar and Radar synergies[J]. Remote Sensing of Environment, 2011, 115(11):2906-2916.
[5] Foody G M, Boyd D S, Cutler M E J. Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions[J]. Remote Sensing of Environment, 2003, 85(4):463-474.
[6] 李旺, 牛铮, 王成, 等. 机载LiDAR数据估算样地和单木尺度森林地上生物量[J]. 遥感学报, 2015, 19(4):669-679.
[6] Li W, Niu Z, Wang C, et al. Forest above-ground biomass estimation at plot and tree levels using airborne LiDAR data[J]. Journal of Remote Sensing, 2015, 19(4):669-679.
[7] Luo S Z, Wang C, Xi X H, et al. Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation[J]. Ecological Indicators, 2017, 73:378-387.
[8] Dubayah R, Blair J B, Goetz S, et al. The global ecosystem dynami-cs investigation:High-resolution laser ranging of the earth’s forests and topography[J]. Science of Remote Sensing, 2020, 1:100002.
[9] 田国帅, 周小成, 郝优壮, 等. 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[J]. 生态学报, 2024, 44(16):7264-7277.
[9] Tian G S, Zhou X C, Hao Y Z, et al. Optimization model of forest aboveground biomass based on MGEDI canopy height:A case study in Fujian,China[J]. Acta Ecologica Sinica, 2024, 44(16):7264-7277.
[10] Guo Q Y, Du S H, Jiang J B, et al. Combining GEDI and sentinel data to estimate forest canopy mean height and aboveground biomass[J]. Ecological Informatics, 2023, 78:102348.
[11] Khati U, Lavalle M, Singh G. The role of time-series L-band SAR and GEDI in mapping sub-tropical above-ground biomass[J]. Frontiers in Earth Science, 2021, 9:752254.
[12] Silva C A, Duncanson L, Hancock S, et al. Fusing simulated GEDI,ICESat-2 and NISAR data for regional aboveground biomass mapping[J]. Remote Sensing of Environment, 2021, 253:112234.
[13] Xu L, Lai H Y, Yu J G, et al. Carbon storage estimation of Quercus aquifolioides based on GEDI spaceborne LiDAR data and Landsat9 images in Shangri-La[J]. Sustainability, 2023, 15(15):11525.
[14] 胥辉, 张会儒. 林木生物量模型研究[M]. 昆明: 云南科技出版社, 2002.
[14] Xu H, Zhang H R. Research on forest biomass models[M]. Kunming: Yunnan Science and Technology Press, 2002.
[15] 韩明辉, 邢艳秋, 李国元, 等. GEDI不同算法组数据反演森林最大冠层高度和生物量精度比较[J]. 中南林业科技大学学报, 2022, 42(10):72-82.
[15] Han M H, Xing Y Q, Li G Y, et al. Comparison of the accuracy of the maximum canopy height and biomass inversion of the data of different GEDI algorithm groups[J]. Journal of Central South University of Forestry & Technology, 2022, 42(10):72-82.
[16] Liu Y H, Zhong Y F, Ma A L, et al. Cross-resolution national-scale land-cover mapping based on noisy label learning:A case study of China[J]. International Journal of Applied Earth Observation and Geoinformation, 2023, 118:103265.
[17] 刘霜. 基于Sentinel-1/2的重庆市南川区森林生物量估算研究[D]. 成都: 成都理工大学, 2020.
[17] Liu S. Forest biomass estimation in Nanchuan district of Chongqing City using a combination of Sentinel-1 and Sentinel-2 data[D]. Chengdu: Chengdu University of Technology, 2020.
[18] Kauth R J, Thomas G S. The tasselled cap:A graphic description of the spectraltemporal development of agricultural crops as seen by Landsat[C]. Proceedings of the Symposium on Machine Processing of Remotely Sensed Data. West Lafayette: Purdue University, 1976,41-51.
[19] 姜丰, 朱家玲, 胡开永, 等. Pearson相关系数评价ORC系统蒸发器特性的应用研究[J]. 太阳能学报, 2019, 40(10):2732-2738.
[19] Jiang F, Zhu J L, Hu K Y, et al. Applied research to assess envaporator performances in orc system by Pearson correlation coefficient[J]. Acta Energiae Solaris Sinica, 2019, 40(10):2732-2738.
[20] 贺鹏, 张会儒, 雷相东, 等. 基于地统计学的森林地上生物量估计[J]. 林业科学, 2013, 49(5):101-109.
[20] He P, Zhang H R, Lei X D, et al. Estimation of forest above-ground biomass based on geostatistics[J]. Scientia Silvae Sinicae, 2013, 49(5):101-109.
[21] Amarsaikhan E, Erdenebaatar N, Amarsaikhan D, et al. Estimation and mapping of pasture biomass in Mongolia using machine learning methods[J]. Geocarto International, 2023, 38(1):2195824.
[22] Breiman L. Statistical modeling:The two cultures (with comments and a rejoinder by the author)[J]. Statistical Science, 2001, 16(3):199-231.
[23] 罗绍龙, 舒清态, 余金格, 等. 基于序贯高斯条件模拟的GEDI数据联合Landsat8反演森林地上生物量[J]. 林业科学研究, 2024, 37(3):49-60.
[23] Luo S L, Shu Q T, Yu J G, et al. Combined GEDI data and Landsat8 for inversion of forest aboveground biomass based on sequential Gaussian condition simulation[J]. Forest Research, 2024, 37(3):49-60.
[24] Lu D S, Chen Q, Wang G X, et al. A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems[J]. International Journal of Digital Earth, 2016, 9(1):63-105.
[25] Li Y C, Li C, Li M Y, et al. Influence of variable selection and forest type on forest aboveground biomass estimation using machine learning algorithms[J]. Forests, 2019, 10(12):1073.
[26] Zhang Y R, He B B, Chen R, et al. The potential of optical and SAR time-series data for the improvement of aboveground biomass carbon estimation in southwestern China’s evergreen coniferous forests[J]. GIScience & Remote Sensing, 2024, 61(1):2345438.
[27] Rana P, Popescu S, Tolvanen A, et al. Estimation of tropical forest aboveground biomass in Nepal using multiple remotely sensed data and deep learning[J]. International Journal of Remote Sensing, 2023, 44(17):5147-5171.
[28] McEwan R W, Lin Y C, Sun I F, et al. Topographic and biotic regu-lation of aboveground carbon storage in subtropical broad-leaved forests of Taiwan[J]. Forest Ecology and Management, 2011, 262(9):1817-1825.
[29] 刘兴良, 史作民, 杨冬生, 等. 山地植物群落生物多样性与生物生产力海拔梯度变化研究进展[J]. 世界林业研究, 2005, 18(4):27-34.
[29] Liu X L, Shi Z M, Yang D S, et al. Advances in study on changes of biodiversity and productivity along elevational gradient in mountainous plant community[J]. World Forestry Research, 2005, 18(4):27-34.
[30] Ferry B, Morneau F, Bontemps J D, et al. Higher treefall rates on slopes and waterlogged soils result in lower stand biomass and productivity in a tropical rain forest[J]. Journal of Ecology, 2010, 98(1):106-116.
[31] 窦佳慧, 梁宇, 怀保娟, 等. 不同地形条件下青藏高原森林生产力和碳收支动态[J]. 生态学杂志, 2024, 43(6):1521-1530.
[31] Dou J H, Liang Y, Huai B J, et al. Productivity and carbon budget dynamics of forests under different topographic conditions on Tibe-tan Plateau[J]. Chinese Journal of Ecology, 2024, 43(6):1521-1530.
[1] MA Min, ZUO Zhen, HAN Yandong, QIU Ye, QIAO Mudong. Origin of surface substrate for soil salinization and alkalization in the Songnen Plain[J]. Remote Sensing for Natural Resources, 2025, 37(2): 128-139.
[2] WEI Xin, REN Yu, CHEN Xidong, HU Qingfeng, LIU Hui, ZHOU Jing, SONG Dongwei, ZHANG Peipei, HUANG Zhiquan. Monitoring of dynamic changes in water bodies of Henan Province based on time-series Sentinel-2 data[J]. Remote Sensing for Natural Resources, 2024, 36(2): 268-278.
[3] ZHANG Wensong, ZHU Yuxin, QIU Yubao, WANG Yuhan, LIU Jinyu, YANG Kang. Remote sensing observation of surface meltwater on the Greenland Ice Sheet[J]. Remote Sensing for Natural Resources, 2024, 36(1): 110-117.
[4] LIU Meiyan, NIE Sheng, WANG Cheng, XI Xiaohuan, CHENG Feng, FENG Baokun. Forest stock volume inversion based on ICESat-2 and Sentinel-2A data[J]. Remote Sensing for Natural Resources, 2024, 36(1): 210-216.
[5] CHEN Jian, LI Hu, LIU Yufeng, CHANG Zhu, HAN Weijie, LIU Saisai. Crops identification based on Sentinel-2 data with multi-feature optimization[J]. Remote Sensing for Natural Resources, 2023, 35(4): 292-300.
[6] WU Weichao, YE Fawang. Cloud detection of Sentinel-2 images for multiple backgrounds[J]. Remote Sensing for Natural Resources, 2023, 35(3): 124-133.
[7] HOU Yingzhuo, JI Ling, XING Qianguo, SHENG Dezhi. Satellite remote sensing-assisted comparative monitoring of dynamic characteristics of macroalgae aquaculture in Weihai City, Shandong Province, China[J]. Remote Sensing for Natural Resources, 2023, 35(2): 34-41.
[8] TIAN Chen, ZHANG Jinlong, JIN Yirong, DONG Shiyuan, WANG Bin, ZHANG Naixiang. A remote sensing classification method for cyanobacteria using Bayesian optimization algorithm[J]. Remote Sensing for Natural Resources, 2023, 35(1): 49-56.
[9] LIAO Kuo, NIE Lei, YANG Zeyu, ZHANG Hongyan, WANG Yanjie, PENG Jida, DANG Haofei, LENG Wei. Classification of tea garden based on multi-source high-resolution satellite images using multi-dimensional convolutional neural network[J]. Remote Sensing for Natural Resources, 2022, 34(2): 152-161.
[10] LIU Chunting, FENG Quanlong, JIN Dingjian, SHI Tongguang, LIU Jiantao, ZHU Mingshui. Application of random forest and Sentinel-1/2 in the information extraction of impervious layers in Dongying City[J]. Remote Sensing for Natural Resources, 2021, 33(3): 253-261.
[11] ZHAO Yi, XU Jianhui, ZHONG Kaiwen, WANG Yunpeng, HU Hongda, WU Pinghao. Impervious surface extraction based on Sentinel-2A and Landsat8[J]. Remote Sensing for Land & Resources, 2021, 33(2): 40-47.
[12] WANG Dejun, JIANG Qigang, LI Yuanhua, GUAN Haitao, ZHAO Pengfei, XI Jing. Land use classification of farming areas based on time series Sentinel-2A/B data and random forest algorithm[J]. Remote Sensing for Land & Resources, 2020, 32(4): 236-243.
[13] CAI Yaotong, LIU Shutong, LIN Hui, ZHANG Meng. Extraction of paddy rice based on convolutional neural network using multi-source remote sensing data[J]. Remote Sensing for Land & Resources, 2020, 32(4): 97-104.
[14] Zhili LIU, Qibin ZHANG, Depeng YUE, Yuguang HAO, Kai SU. Extraction of urban built-up areas based on Sentinel-2Aand NPP-VIIRS nighttime light data[J]. Remote Sensing for Land & Resources, 2019, 31(4): 227-234.
[15] Dazhao WANG, Simeng WANG, Chang HUANG. Comparison of Sentinel-2 imagery with Landsat8 imagery for surface water extraction using four common water indexes[J]. Remote Sensing for Land & Resources, 2019, 31(3): 157-165.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech