Scientia Silvae Sinicae ›› 2022, Vol. 58 ›› Issue (2): 13-22.doi: 10.11707/j.1001-7488.20220202
• Frontier & Focus: Topic of forest carbon sequestration • Previous Articles Next Articles
Yonglei Shi1,2,Zhihui Wang2,*,Shiming Li3,Chunyi Li1,Peiqing Xiao2,Pan Zhang2,Xiaoge Chang1
Received:
2021-06-24
Online:
2022-02-25
Published:
2022-04-26
Contact:
Zhihui Wang
CLC Number:
Yonglei Shi,Zhihui Wang,Shiming Li,Chunyi Li,Peiqing Xiao,Pan Zhang,Xiaoge Chang. A Method of Estimation Aboveground Biomass of Sparse Tree-Shrub Using Optical Remote Sensing[J]. Scientia Silvae Sinicae, 2022, 58(2): 13-22.
Table 1
Canopy coverage-AGB models of tree-shrub based on three stratification schemes"
分层方案 Stratification schemes | 类型Types | 冠层覆盖度-乔灌木地上部分生物量模型 Canopy coverage-AGB model of tree-shrub | R2 |
不分层Non-stratification | 所有类型All types | lnAGB=1.36lnC-2.80 | 0.25 |
基于乔木和灌木分层 Stratification based on arbor and shrub | 乔木Arbor | lnAGB=1.10lnC-0.66 | 0.68 |
灌木Shrub | lnAGB=0.92lnC-2.51 | 0.60 | |
基于5个树种分层 Stratification based on five tree species | 樟子松Pinus sylvestris var. mongolica | lnAGB=1.04lnC-0.91 | 0.85 |
新疆杨Populus alba var. pyramidalis | lnAGB=1.22lnC-1.14 | 0.74 | |
旱柳Salix matsudana | lnAGB=1.08lnC-0.10 | 0.83 | |
沙蒿Artemisia ordosida | lnAGB=0.93lnC-2.66 | 0.64 | |
柠条锦鸡儿Caragana korshinskii | lnAGB= 0.88lnC-1.70 | 0.67 |
Table 2
Spectral index -AGB models of tree-shrub based on three stratification schemes"
光谱指数 Spectral index | 分层方案 Stratification schemes | 光谱指数-乔灌木地上部分生物量模型 Spectral index -AGB model of tree-shrub | R2 |
NDVI | 不分层 Non-stratification | AGB = 97.735VI - 18.003 | 0.51 |
有无草本植被样地分层 Stratification using plots with and without herbaceous vegetation | 无Non-existence: AGB = 111.54VI - 19.293 有Existence: AGB = 91.556VI - 20.825 | 0.65 0.39 | |
3个草本植被覆盖度等级样地分层 Stratification using plots with three herbaceous coverage levels | 0: AGB = 111.54VI - 19.293 0~30%: AGB = 121.40VI - 24.702 >30%: AGB = 102.32VI - 29.124 | 0.65 0.64 0.50 | |
RVI | 不分层 Non-stratification | AGB = 97.735VI - 18.003 | 0.51 |
有无草本植被样地分层 Stratification using plots with and without herbaceous vegetation | 无Non-existence: AGB = 21.533VI - 27.986 有Existence: AGB = 17.586VI - 26.256 | 0.66 0.48 | |
3个草本植被覆盖度等级样地分层 Stratification using plots with three herbaceous coverage levels | 0: AGB = 21.533VI - 27.986 0~30%: AGB = 24.120VI - 33.575 >30%: AGB = 18.921VI - 33.453 | 0.66 0.63 0.66 | |
TCG | 不分层 Non-stratification | AGB = 0.027 6VI + 19.053 | 0.55 |
有无草本植被样地分层 Stratification using plots with and without herbaceous vegetation | 无Non-existence: AGB = 0.030 8VI + 22.505 有Existence: AGB = 0.025 8VI + 14.303 | 0.66 0.47 | |
3个草本植被覆盖度等级样地分层 Stratification using plots with three herbaceous coverage levels | 0: AGB = 0.030 8VI + 22.505 0~30%: AGB = 0.035 6VI + 23.009 >30%: AGB = 0.030 8VI + 9.648 6 | 0.66 0.73 0.6 | |
NDMI | 不分层 Non-stratification | AGB = 149.93VI + 20.082 | 0.64 |
有无草本植被样地分层 Stratification using plots with and without herbaceous vegetation | 无Non-existence: AGB = 169.41VI + 22.313 有Existence: AGB = 120.34VI + 16.576 | 0.69 0.59 | |
3个草本植被覆盖度等级样地分层 Stratification using plots with three herbaceous coverage levels | 0: AGB = 169.41VI + 22.313 0~30%: AGB = 132.19VI + 21.200 >30%: AGB = 122.47VI + 13.608 | 0.69 0.69 0.61 | |
MSAVI | 不分层 Non-stratification | AGB = 83.160VI - 25.995 | 0.54 |
有无草本植被样地分层 Stratification using plots with and without herbaceous vegetation | 无Non-existence: AGB = 94.951VI - 28.409 有Existence: AGB = 79.089VI - 28.951 | 0.67 0.44 | |
3个草本植被覆盖度等级样地分层 Stratification using plots with three herbaceous coverage levels | 0: AGB = 94.951VI - 28.409 0~30%: AGB = 107.71VI - 36.386 >30%: AGB = 98.414VI - 43.915 | 0.67 0.48 0.51 | |
NIRv | 不分层 Non-stratification | AGB = 480.20VI - 22.667 | 0.70 |
有无草本植被样地分层 Stratification using plots with and without herbaceous vegetation | 无Non-existence: AGB = 516.61VI - 22.848 有Existence: AGB = 459.16VI - 25.291 | 0.81 0.59 | |
3个草本植被覆盖度等级样地分层 Stratification using plots with three herbaceous coverage levels | 0: AGB = 516.61VI - 22.848 0~30%: AGB = 652.07VI - 34.755 >30%: AGB = 410.11VI - 24.852 | 0.81 0.86 0.58 |
白壮壮, 崔建新. 近2000 a毛乌素沙地沙漠化及成因. 中国沙漠, 2019, 39 (2): 177- 185. | |
Bai Z Z , Cui J X . Desertification and its causes in Mu Us Desert in recent 2000 years. Journal of Desert Research, 2019, 39 (2): 177- 185. | |
陈尔学, 李增元, 庞勇, 等. 2007. 基于极化合成孔径雷达干涉测量的平均树高提取技术. 林业科学, 43(4): 66-70. | |
Chen E X, Li Z Y, Pang Y, et al. Polarimetric synthetic aperture radar interferometry based mean tree height extraction technique. Scientia Silvae Sinicae. 43(4): 66-70. [in Chinese] | |
陈晋, 马磊, 陈学泓, 等. 混合像元分解技术及其进展. 遥感学报, 2016, 20 (5): 1102- 1109. | |
Chen J , Ma L , Chen X H , et al. Hybrid pixel decomposition technology and its progress. Journal of Remote Sensing, 2016, 20 (5): 1102- 1109. | |
方精云, 朱江玲, 王少鹏, 等. 全球变暖、碳排放及不确定性. 中国科学: 地球科学, 2011, 41 (10): 1385- 1395. | |
Fang J Y , Zhu J L , Wang S P , et al. Global warming, carbon emissions and uncertainty. Scientia Sinica(Terrae), 2011, 41 (10): 1385- 1395. | |
冯宗炜, 王效科, 吴刚, 等. 中国森林生态系统的生物量和生产力. 北京: 科学出版社, 1999. | |
Feng Z W , Wang X K , Wu G , et al. Biomass and productivity of forest ecosystem in China. Beijing: Science Press, 1999. | |
李博伦, 遆超普, 颜晓元. Landsat 8陆地成像仪影像的缨帽变换推导. 测绘科学, 2016, 41 (4): 102- 107. | |
Li B L , Ti C P , Yan X Y . Study of derivation of tasseled cap transformation for Landsat 8 OLI images. Science of Surveying and Mapping, 2016, 41 (4): 102- 107. | |
李德仁, 王长委, 胡月明, 等. 遥感技术估算森林生物量的研究进展. 武汉大学学报(信息科学版), 2012, 37 (6): 631- 635. | |
Li D R , Wang C W , Hu Y M , et al. Research progress on estimation of forest biomass by remote sensing technology. Geomatics and Information Science of Wuhan University, 2012, 37 (6): 631- 635. | |
李海奎, 雷渊才. 中国森林植被生物量和碳储量评估. 北京: 中国森林出版社, 2010. | |
Li H K , Lei Y C . Estimation and evaluation of forest biomass carbon storage in China. Beijing: China Forest Press, 2010. | |
柳钦火, 曹彪, 曾也鲁, 等. 植被遥感辐射传输建模中的异质性研究进展. 遥感学报, 2016, 20 (5): 933- 945. | |
Liu Q H , Cao B , Zeng Y L , et al. Research progress on heterogeneity in remote sensing radiative transfer modeling of vegetation. Journal of Remote Sensing, 2016, 20 (5): 933- 945. | |
刘茜, 杨乐, 柳钦火, 等. 森林地上生物量遥感反演方法综述. 遥感学报, 2015, 19 (1): 32- 74. | |
Liu X , Yang L , Liu Q H , et al. Review of forest above ground biomass inversion methods based on remote sensing technology. Journal of Remote Sensing, 2015, 19 (1): 32- 74. | |
庞勇, 李增元. 基于机载激光雷达的小兴安岭温带森林组分生物量反演. 植物生态学报, 2012, 36 (10): 1095- 1105. | |
Pang Y , Li Z Y . Inversion of biomass components of the temperate forest using airborne lidar technology in Xiaoxing'an Mountains, Northeastern of China. Chinese Journal of Plant Ecology, 2012, 36 (10): 1095- 1105. | |
庞勇, 蒙诗栎, 李增元, 等. 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, 2017, 53 (3): 95- 104. | |
Pang Y , Meng S L , Li Z Y , et al. Temperate forest aboveground biomass estimation using fourier-based textural ordination (FOTO) indices from high resolution aerial optical image. Scientia Silvae Sinicae, 2017, 53 (3): 95- 104. | |
彭守璋, 赵传燕, 彭焕华, 等. 黑河下游柽柳种群地上生物量及耗水量的空间分布. 应用生态学报, 2010, 21 (8): 1940- 1946. | |
Peng S Z , Zhao C Y , Peng H H , et al. Spatial distribution of Tamarix ramosissima aboveground biomass and water consumption in the lower reaches of Heihe River, Northwest China. Chinese Journal of Applied Ecology, 2010, 21 (8): 1940- 1946. | |
张华, 赵传燕, 张勃, 等. 高分辨率遥感影像GeoEye-1在黑河下游柽柳生物量估算中的应用. 遥感技术与应用, 2012, 26 (6): 713- 718. | |
Zhang H , Zhao C Y , Zhang B , et al. The application of high resolution satellite imagery GeoEye-1 on the biomass estimation of tamarix ramosissima in lower reaches of Heihe river basin. Remote Sensing Technology and Application, 2012, 26 (6): 713- 718. | |
郑玉峰, 焦志荣, 于泽民. 毛乌素沙地气候分析及沙地面积变化. 环境与发展, 2015, 27 (5): 19- 21. | |
Zheng Y F , Jiao Z R , Yu Z M . Climate analysis and area change of sandy land in Maowusu. Environmental and Development, 2015, 27 (5): 19- 21. | |
Badgley G , Field C B , Berry J A . Canopy near-infrared reflectance and terrestrial photosynthesis. Science Advances, 2017, 3 (3): e1602244.
doi: 10.1126/sciadv.1602244 |
|
Cao L , Coops N C , Hermosilla T , et al. Using small-footprint discrete and full-waveform airborne LiDAR metrics to estimate total biomass and biomass components in subtropical forests. Remote Sensing, 2014, 6 (8): 7110- 7135.
doi: 10.3390/rs6087110 |
|
Gao B C . NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 1996, 58 (3): 257- 266.
doi: 10.1016/S0034-4257(96)00067-3 |
|
Gao L , Wang X F , Johnson B A , et al. Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: a review. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 159, 364- 377.
doi: 10.1016/j.isprsjprs.2019.11.018 |
|
Guo Z C , Wang T , Liu S L , et al. Biomass and vegetation coverage survey in the Mu Us sandy land-based on unmanned aerial vehicle RGB images. International Journal of Applied Earth Observation and Geoinformation, 2021, 94, 102239.
doi: 10.1016/j.jag.2020.102239 |
|
He L , Li A , Yin G , et al. Retrieval of grassland aboveground biomass through inversion of the PROSAIL model with MODIS imagery. Remote Sensing, 2019, 11 (13): 1597.
doi: 10.3390/rs11131597 |
|
Jiang X , Li G , Lu D S , et al. Stratification-based forest aboveground biomass estimation in a subtropical region using airborne Lidar data. Remote Sensing, 2020, 12 (7): 1101.
doi: 10.3390/rs12071101 |
|
Jos'e M , Jochem V , Leonor C , et al. Hybrid inversion of radiative transfer models based on high spatial resolution satellite reflectance data improves fractional vegetation cover retrieval in heterogeneous ecological systems after fire. Remote Sensing of Environment, 2021, 255, 112304.
doi: 10.1016/j.rse.2021.112304 |
|
Latifi H , Fassnacht F E , Hartig F , et al. Stratified aboveground forest biomass estimation by remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 2015, 38, 229- 241.
doi: 10.1016/j.jag.2015.01.016 |
|
Liu L Y . Opportunities of mapping forest carbon stock and its annual increment using Landsat time-series data. Geoinformatics and Geostatistics: An Overview, 2012, 4 (4): 1000151. | |
Liu L Y , Peng D L , Wang Z H , et al. Improving artificial forest biomass estimates using afforestation age information from time series Landsat stacks. Environmental Monitoring and Assessment, 2014, 186 (11): 7293- 7306.
doi: 10.1007/s10661-014-3927-y |
|
Ni W , Zhang Z , Sun G , et al. The penetration depth derived from the synthesis of ALOS/PALSAR InSAR data and ASTER GDEM for the mapping of forest biomass. Remote Sensing, 2014, 6 (8): 7303- 7319.
doi: 10.3390/rs6087303 |
|
Ozdemir I . Estimating stem volume by tree crown area and tree shadow area extracted from pan-sharpened Quickbird imagery in open Crimean juniper forests. International Journal of Remote Sensing, 2008, 29 (19): 5643- 5655.
doi: 10.1080/01431160802082155 |
|
Pearson R L, Miller L D. 1972. Remote mapping of standing crop biomass for estimation of the productivity of the short-grass prairie//Proceedings of the 8th international symposium on remote sensing of environment. Pawnee National Grasslands, Colorado, Ann Arbor, MI, USA, 2-6 October 1972; pp. 1357-1381. | |
Peng D L , Zhang H L , Liu L Y , et al. Estimating the aboveground biomass for planted forests based on stand age and environmental variables. Remote Sensing, 2019, 11 (19): 2270.
doi: 10.3390/rs11192270 |
|
Qi J , Huete A R , Moran M S , et al. Interpretation of vegetation indices derived from multi-temporal SPOT images. Remote Sensing of Environment, 1993, 44, 89- 101.
doi: 10.1016/0034-4257(93)90105-7 |
|
Rouse J W, Haas R H, Schell J A, et al. 1973. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation, Gooddard Space Flight Center 3d ERTS-1 Symp: 309-317. | |
Shoshany M , Karnibad L . Mapping shrubland biomass along Mediterranean climatic gradients: the synergy of rainfall-based and NDVI-based models. International Journal of Remote Sensing, 2011, 32 (24): 9497- 9508.
doi: 10.1080/01431161.2011.562255 |
|
Smith W , Dannenberg M , Yan D , et al. Remote sensing of dryland ecosystem structure and function: progress, challenges, and opportunities. Remote Sensing of Environment, 2019, 233, 111401.
doi: 10.1016/j.rse.2019.111401 |
|
Suganuma H , Abe Y , Taniguchi M , et al. Stand biomass estimation method by canopy coverage for application to remote sensing in an arid area of Western Australia. Forest Ecology and Management, 2006, 222 (1): 75- 87. | |
Wang Z H , Bastin G , Liu L Y , et al. Estimating woody above-ground biomass in an arid zone of central Australia using Landsat imagery. Journal of Applied Remote Sensing, 2015, 9 (1): 096036.
doi: 10.1117/1.JRS.9.096036 |
|
Wang Z H , Liu L Y , Peng D L , et al. Estimating woody aboveground biomass in an area of agroforestry using airborne light detection and ranging and compact airborne spectrographic imager hyperspectral data: individual tree analysis incorporating tree species information. Journal of Applied Remote Sensing, 2016, 10 (3): 036007.
doi: 10.1117/1.JRS.10.036007 |
|
Xiao J , Chevallier F , Gomez C , et al. Remote sensing of the terrestrial carbon cycle: a review of advances over 50 years. Remote Sensing of Environment, 2019, 233, 111383.
doi: 10.1016/j.rse.2019.111383 |
|
Yan G J , Li L Y , Coy A , et al. Improving the estimation of fractional vegetation cover from UAV RGB imagery by colour unmixing. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 158, 23- 34.
doi: 10.1016/j.isprsjprs.2019.09.017 |
|
Zandler H , Brenning A , Samimi C , et al. Quantifying dwarf shrub biomass in an arid environment: comparing empirical methods in a high dimensional setting. Remote Sensing of Environment, 2015, 158, 140- 155.
doi: 10.1016/j.rse.2014.11.007 |
|
Zhang Y , Peng C , Li W , et al. Multiple afforestation programs accelerate the greenness in the 'Three North' region of China from 1982 to 2013. Ecological Indicators, 2015, 61, 404- 412. |
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