浏览全部资源
扫码关注微信
纸质出版日期： 2022-03-07 ，
扫 描 看 全 文
林文科，陆亚刚，蒋先蝶，李桂英，李登秋，陆灯盛.2022.协同多源遥感数据的北亚热带森林蓄积量贝叶斯分层估测.遥感学报，26（3）： 468-479
Lin W K，Lu Y G，Jiang X D，Li G Y，Li D Q and Lu D S. 2022. Modeling forest growing stock volume in a north subtropical region using the hierarchical Bayesian approach based on multi-source data. National Remote Sensing Bulletin， 26（3）：468-479
精确估算森林蓄积量是国家实现2060年前碳中和目标的迫切需求，而基于遥感的森林蓄积量定量反演是当前遥感应用领域面临的重要挑战和研究热点。光学遥感数据由于无法获取森林高度信息并存在信号饱和问题，反演森林蓄积量的精度较低，而机载Lidar数据能获取高度信息，但成本高、观测范围有限。本研究利用Sentinel-2多光谱、资源三号立体成像和机载Lidar数据，定量评价多源遥感数据在北亚热带估测森林蓄积量的能力，并探索小样本情况下贝叶斯模型在森林蓄积量估算中的优势，旨在解决单一数据集估测森林蓄积量精度低和样本数量不足的问题。研究结果表明，协同Sentinel-2和林分冠层高度模型（CHM）数据的贝叶斯分层建模能够显著提高森林蓄积量估测精度，其相对均方根误差（rRMSE）为27.6%，其估测误差比Sentinel-2数据减少了13.6 m
3
/ha，比单独CHM数据减少了7.4 m
3
/ha。多源数据可以有效改善低值高估和高值低估问题，相比单一数据集，多源数据在低值区估测误差减少了约1/4，在高值区减少了约1/3。分层贝叶斯模型有效缓解了因精细分层对样本数量的需求，减少了森林类型和地形差异对森林蓄积量估测精度的影响。为亚热带森林类型和地形复杂的地区，开展森林蓄积量估测制图提供关键技术手段。
Accurate estimation of Forest Growing Stock Volume (FGSV) is needed to achieve the goal of carbon neutral. Quantitative inversion of FGSV using remote sensing technologies is still a research challenge. Optical remote sensing technology is one of the most important means for FGSV estimation
but cannot provide sufficiently accurate estimates due to lack of canopy structure features and data saturation problem. Although airborne Lidar can overcome the shortcoming of optical sensor data
its high cost in data collection and limited observation area constrain its extensive application. This research employs integration of Sentinel-2
ZY-3 stereo
and airborne Lidar data to explore the performance of FGSV estimation in north subtropical regions
and examines the advantages of using the hierarchical Bayesian approach to develop FGSV estimation models under the condition of small population of sample plots. The objective is to solve low modeling accuracy caused by the single sensor data and insufficient number of sample plots. The results indicate that the hierarchical Bayesian approach based on combination of Sentinel-2 and Canopy Height Model (CHM) data (subtraction of Lidar-derived digital elevation model data from ZY-3 stereo-derived digital surface model data) provides the best estimation results with relative Root Mean Square Error (rRMSE) of 27.6%. The Root Mean Square Error (RMSE) using this approach reduced by 13.6 m
3
/ha comparing with the RMSE based on Sentinel-2 data alone
and reduced by 7.4 m
3
/ha based on CHM data alone. The research shows that use of multi-source data can effectively improve the problems of overestimation when FGSV is small and of underestimation when FGSV is relatively high
that is
use of multi-source data can reduce the overestimation by one forth and the underestimation by one third comparing with use of single data source alone. Comparing with traditional modeling approaches such as linear regression and random forest
the hierarchical Bayesian approach can effectively reduce the requirement of number of samples due to use of stratification strategy and reduce the impacts of forest types and terrain differences on FGSV estimation accuracy. This research provides new insights of using integration of different data sources to develop FGSV estimation models to achieve accurate estimates
and provides key technology for FGSV mapping in subtropical regions.
遥感森林蓄积量贝叶斯分层模型Sentinel-2资源三号机载Lidar多源数据
remote sensingforest growing stock volumehierarchical Bayesian approachSentinel-2ZY-3airborne Lidarmulti-source data
Babcock C, Finley A O, Andersen H E, Pattison R, Cook B D, Morton D C, Alonzo M, Nelson R, Gregoire T, Ene L, Gobakken T and Næsset E. 2018. Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations. Remote Sensing of Environment, 212: 212-230 [DOI: 10.1016/j.rse.2018.04.044http://dx.doi.org/10.1016/j.rse.2018.04.044]
Barrett F, McRoberts R E, Tomppo E, Cienciala E and Waser L T. 2016. A questionnaire-based review of the operational use of remotely sensed data by national forest inventories. Remote Sensing of Environment, 174: 279-289 [DOI: 10.1016/j.rse.2015.08.029http://dx.doi.org/10.1016/j.rse.2015.08.029]
Bates D, Mächler M, Bolker B and Walker S. 2015. Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1): 1-48 [DOI: 10.18637/jss.v067.i01http://dx.doi.org/10.18637/jss.v067.i01]
Brodu N. 2017. Super-resolving multiresolution images with band-independent geometry of multispectral pixels. IEEE Transactions on Geoscience and Remote Sensing, 55(8): 4610-4617 [DOI: 10.1109/TGRS.2017.2694881http://dx.doi.org/10.1109/TGRS.2017.2694881]
Bürkner P C. 2017. brms: Bayesian Regression Models using ‘Stan’. R package version 1.9.0, URL[EB/OL]. https://CRAN.R-project.org/package=brmshttps://CRAN.R-project.org/package=brms
Cao L, Xu T, Shen X and She G H. 2016. Mapping biomass by integrating Landsat OLI and airborne Lidar transect data in subtropical forests. Journal of Remote Sensing, 20(4): 665-678
曹林, 徐婷, 申鑫, 佘光辉. 2016. 集成Landsat OLI和机载Lidar条带数据的亚热带森林生物量制图. 遥感学报, 20(4): 665-678 [DOI: 10.11834/jrs.20165210http://dx.doi.org/10.11834/jrs.20165210]
Chrysafis I, Mallinis G, Siachalou S and Patias P. 2017. Assessing the relationships between growing stock volume and Sentinel-2 imagery in a Mediterranean forest ecosystem. Remote Sensing Letters, 8(6): 508-517 [DOI: 10.1080/2150704X.2017.1295479http://dx.doi.org/10.1080/2150704X.2017.1295479]
Dos Reis A A, Franklin S E, De Mello J M and Junior F W A. 2019. Volume estimation in a Eucalyptus plantation using multi-source remote sensing and digital terrain data: a case study in Minas Gerais State, Brazil. International Journal of Remote Sensing, 40(7): 2683-2702 [DOI: 10.1080/01431161.2018.1530808http://dx.doi.org/10.1080/01431161.2018.1530808]
Feng Y Y, Lu D S, Chen Q, Keller M, Moran E, Dos-Santos M N, Bolfe E L and Batistella M. 2017. Examining effective use of data sources and modeling algorithms for improving biomass estimation in a moist tropical forest of the Brazilian Amazon. International Journal of Digital Earth, 10(10): 996-1016 [DOI: 10.1080/17538947.2017.1301581http://dx.doi.org/10.1080/17538947.2017.1301581]
Gao Y K, Lu D S, Li G Y, Wang G X, Chen Q, Liu L J and Li D Q. 2018. Comparative analysis of modeling algorithms for forest aboveground biomass estimation in a subtropical region. Remote Sensing, 10(4): 627 [DOI: 10.3390/rs10040627http://dx.doi.org/10.3390/rs10040627]
Gao Y N and Zhang W C. 2008. Comparison test and research progress of topographic correction on remotely sensed data. Geographical Research, 27(2): 467-477
高永年, 张万昌. 2008. 遥感影像地形校正研究进展及其比较实验. 地理研究, 27(2): 467-477 [DOI: 10.3321/j.issn:1000-0585.2008.02.024http://dx.doi.org/10.3321/j.issn:1000-0585.2008.02.024]
Jayathunga S, Owari T and Tsuyuki S. 2018. The use of fixed–wing UAV photogrammetry with LiDAR DTM to estimate merchantable volume and carbon stock in living biomass over a mixed conifer–broadleaf forest. International Journal of Applied Earth Observation and Geoinformation, 73: 767-777 [DOI: 10.1016/j.jag.2018.08.017http://dx.doi.org/10.1016/j.jag.2018.08.017]
Junttila V, Kauranne T, Finley A O and Bradford J B. 2015. Linear models for airborne-laser-scanning-based operational forest inventory with small field sample size and highly correlated LiDAR data. IEEE Transactions on Geoscience and Remote Sensing, 53(10): 5600-5612 [DOI: 10.1109/TGRS.2015.2425916http://dx.doi.org/10.1109/TGRS.2015.2425916]
Junttila V and Laine M. 2017. Bayesian principal component regression model with spatial effects for forest inventory variables under small field sample size. Remote Sensing of Environment, 192: 45-57 [DOI: 10.1016/j.rse.2017.01.035http://dx.doi.org/10.1016/j.rse.2017.01.035]
Laurin G V, Balling J, Corona P, Mattioli W, Papale D, Puletti N, Rizzo M, Truckenbrodt J and Urban M. 2018. Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data. Journal of Applied Remote Sensing, 12(1): 016008 [DOI: 10.1117/1.JRS.12.016008http://dx.doi.org/10.1117/1.JRS.12.016008]
Lee J S and Phua M H. 2010. Estimation of stand volume of conifer forest: A Bayesian approach based on satellite-based estimate and forest register data. Forest Science and Technology, 6(1): 7-17 [DOI: 10.1080/21580103.2010.9656352http://dx.doi.org/10.1080/21580103.2010.9656352]
Lewandowski D, Kurowicka D and Joe H. 2009. Generating random correlation matrices based on vines and extended onion method. Journal of Multivariate Analysis, 100(9): 1989-2001 [DOI: 10.1016/j.jmva.2009.04.008http://dx.doi.org/10.1016/j.jmva.2009.04.008]
Li D R, Wang C W, Hu Y M and Liu S G. 2012. General review on remote sensing-based biomass estimation. Geomatics and Information Science of Wuhan University, 37(6): 631-635
李德仁, 王长委, 胡月明, 刘曙光. 2012. 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), 37(6): 631-635 [DOI: 10.13203/j.whugis2012.06.001http://dx.doi.org/10.13203/j.whugis2012.06.001]
Li G Y, Xie Z L, Jiang X D, Lu D S and Chen E X. 2019. Integration of ZiYuan-3 multispectral and stereo data for modeling aboveground biomass of Larch plantations in North China. Remote Sensing, 11(19): 2328 [DOI: 10.3390/rs11192328http://dx.doi.org/10.3390/rs11192328]
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]
Lu D. 2005. Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon. International Journal of Remote Sensing, 26(12): 2509-2525 [DOI: 10.1080/01431160500142145http://dx.doi.org/10.1080/01431160500142145]
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]
Luo H M, Chen E X, Li Z Y and Cao C X. 2011. Forest above ground biomass estimation methodology based on polarization coherence tomography. Journal of Remote Sensing, 15(6): 1138-1155
罗环敏, 陈尔学, 李增元, 曹春香. 2011. 森林地上生物量的极化相干层析估计方法. 遥感学报, 15(6): 1138-1155 [DOI: 10.11834/jrs.20110391http://dx.doi.org/10.11834/jrs.20110391]
Meng S L, Pang Y, Zhang Z J, Li Z Y, Wang X Q and Li S M. 2017. Estimation of aboveground biomass in a temperate forest using texture information from WorldView-2. Journal of Remote Sensing, 21(5): 812-824
蒙诗栎, 庞勇, 张钟军, 李增元, 王雪琼, 李世明. 2017. WorldView-2纹理的森林地上生物量反演. 遥感学报, 21(5): 812-824 [DOI: 10.11834/jrs.20176083http://dx.doi.org/10.11834/jrs.20176083]
Mura M, Bottalico F, Giannetti F, Bertani R, Giannini R, Mancini M, Orlandini S, Travaglini D and Chirici G. 2018. Exploiting the capabilities of the Sentinel-2 multi spectral instrument for predicting growing stock volume in forest ecosystems. International Journal of Applied Earth Observation and Geoinformation, 66: 126-134 [DOI: 10.1016/j.jag.2017.11.013http://dx.doi.org/10.1016/j.jag.2017.11.013]
Nuthammachot N, Askar A, Stratoulias D and Wicaksono P. 2022. Combined use of Sentinel-1 and Sentinel-2 data for improving above-ground biomass estimation. Geocarto International, 37(2): 366-376 [DOI: 10.1080/10106049.2020.1726507http://dx.doi.org/10.1080/10106049.2020.1726507]
Rahlf J, Breidenbach J, Solberg S, Næsset E and Astrup R. 2014. Comparison of four types of 3D data for timber volume estimation. Remote Sensing of Environment, 155: 325-333 [DOI: 10.1016/j.rse.2014.08.036http://dx.doi.org/10.1016/j.rse.2014.08.036]
Simard M, Pinto N, Fisher J B and Baccini A. 2011. Mapping forest canopy height globally with spaceborne Lidar. Journal of Geophysical Research: Biogeosciences, 116(G4): G04021 [DOI: 10.1029/2011JG001708http://dx.doi.org/10.1029/2011JG001708]
Ver Planck N R, Finley A O, Kershaw Jr J A, Weiskittel A R and Kress M C. 2018. Hierarchical Bayesian models for small area estimation of forest variables using LiDAR. Remote Sensing of Environment, 204: 287-295 [DOI: 10.1016/j.rse.2017.10.024http://dx.doi.org/10.1016/j.rse.2017.10.024]
Xie Z L, Chen Y L, Lu D S, Li G Y and Chen E X. 2019. Classification of land cover, forest, and tree species classes with ZiYuan-3 multispectral and stereo data. Remote Sensing, 11(2): 164 [DOI: 10.3390/rs11020164http://dx.doi.org/10.3390/rs11020164]
Zhao P P, Lu D S, Wang G X, Wu C P, Huang Y J and Yu S Q. 2016. Examining spectral reflectance saturation in Landsat imagery and corresponding solutions to improve forest aboveground biomass estimation. Remote Sensing, 8(6): 469 [DOI: 10.3390/rs8060469http://dx.doi.org/10.3390/rs8060469]
相关作者
相关机构
京公网安备11010802024621