浏览全部资源
扫码关注微信
纸质出版日期： 2020-05-07 ，
扫 描 看 全 文
蒲东川,张兆明,龙腾飞,牛雪峰,何国金,王桂周,孙嘉悦,唐朝,魏明月.2020.分层随机抽样下全球30 m火烧迹地产品验证.遥感学报,24(5): 550-558
Pu D C,Zhang Z M,Long T F,Niu X F,He G J,Wang G Z,Sun J Y,Tang C and Wei M Y. 2020. GABAM2010 accuracy assessment using stratified random sampling. Journal of Remote Sensing(Chinese), 24(5): 550-558[DOI：10.11834/jrs.20209171]
火烧迹地是全球及区域碳循环和气候变化等研究所需的重要参数之一，卫星遥感技术为快速获取大区域火烧迹地空间分布信息提供了有效手段。中国科学院基于Landsat系列卫星数据研发了首个30 m空间分辨率全球火烧迹地产品GABAM (Global Annual Burned Area Map)。遥感数据产品的精度验证对产品使用具有重要意义，迄今尚未有研究机构对GABAM产品精度进行独立评价和分析。为系统评价GABAM产品精度，利用2010年全球30 m空间分辨率火烧迹地产品(GABAM2010)开展精度验证研究工作，在全球和几个陆地生物群落中估算了产品精度，并探索了全球遥感专题产品精度验证的技术框架。基于分层随机抽样选择80个非重叠的泰森多边形区域TSA (Thiessen Scene Areas)，采用误差矩阵和6个精度指标对GABAM2010产品做全面精度评价和分析，以满足火烧迹地产品用户的使用要求。结果表明：在全球范围内，GABAM2010产品的错分率和漏分率分别为24.32%和31.60%，总体精度为97.85%；由于数据质量(如条带、云)等的影响，火烧迹地的范围会被低估，对于较容易发生火灾的生物群落，如热带亚热带草原区域，GABAM2010产品精度较高；在生物群落内部，高密度火烧迹地区域的精度高于低密度火烧迹地区域。
Burned area is one of the key parameters for global and regional carbon cycle and climate change research. Satellite remote sensing technologies provide an effective way for obtaining the large-scale spatial distributions of burned areas. In 2018
the Chinese Academy of Sciences released the first GABAM (Global Annual Burned Area Map) based on Landsat series satellite data. The accuracy assessment of remote sensing data products is of great significance for product users. To date
the accuracy of GABAM products is yet to be independently evaluated. In this study
an accuracy validation study was performed on the 2010 global 30 m spatial resolution burned area product (GABAM2010) for the systematic evaluation of GABAM product accuracy. The GABAM2010 product was validated
and accuracy measures were estimated at the global scale and in several terrestrial biomes
and the technical framework for the accuracy assessment of global remote sensing thematic products was explored.
Global accuracy was estimated by stratified random sampling and estimation in a weighted manner on the basis of the error matrix of each sampling unit. This method enables the verification of a large number of products
such as the global burned area product. Stratified random sampling was used in selecting 80 non-overlapping TSA (Thiessen Scene Areas)
and reference fire perimeters were determined from multitemporal Landsat TM images for each sampled TSA. Error matrices and six accuracy measures were used in satisfying the criteria specified by the end-users of burned area products.
The global validation result showed that the overall accuracy of GABAM2010 was 97.85%
and the commission and omission errors were 24.32% and 31.60%
respectively. The extent of a burned area is often underestimated due to the impact of data quality
such as strips
and clouds. The GABAM2010 products showed high precision in biomes
including tropical and subtropical grasslands
which are highly prone to fire. The high-density burned areas within biomes had higher accuracy than low-density ones.
A statistically rigorous accuracy verification method
stratified random sampling was used in verifying the GABAM 2010 product. The method is applicable for the accuracy verification of similar remote sensing products
and the results of the evaluation performed on the GABAM 2010 product illustrated the specific applications of sampling design and accuracy analysis. Compared with simple estimatior
combined ratio estimator with stratified random sampling increased the reliability of the accuracy measures. Simple estimatior is ineffective in resolving some problems arising from the small proportion of an area burned and small number of sampling areas
which result in unreliable accuracy estimates. By contrast
stratification ratio estimation uses global ecological and fire behavior data and can obtain reliable accuracy estimate results.
遥感全球火烧迹地生物群落精度验证误差矩阵分层联合比率估计
remote sensingglobal annual burned area mapbiomesaccuracy assessmenterror matrixcombined ratio estimator
Alonso-Canas I and Chuvieco E . 2015. Global burned area mapping from ENVISAT-MERIS and MODIS active fire data. Remote Sensing of Environment, 163: 140-152 [DOI: 10.1016/j.rse.2015.03.011http://dx.doi.org/10.1016/j.rse.2015.03.011 ]
Barbosa P M, Pereira J M C and Grégoire J M . 1998. Compositing criteria for burned area assessment using multitemporal low resolution satellite data. Remote Sensing of Environment, 65(1): 38-49 [DOI: 10.1016/S0034-4257(98)00016-9http://dx.doi.org/10.1016/S0034-4257(98)00016-9 ]
Carmona‐Moreno C, Belward A, Malingreau J P, Hartley A, Garcia‐Alegre M, Antonovskiy M, Buchshtaber V and Pivovarov V . 2005. Characterizing interannual variations in global fire calendar using data from Earth observing satellites. Global Change Biology, 11(9): 1537-1555 [DOI: 10.1111/j.1365-2486.2005.01003.xhttp://dx.doi.org/10.1111/j.1365-2486.2005.01003.x ]
Chuvieco E, Opazo S, Sione W, Del Valle H, Anaya J, Di Bella C, Cruz I, Manzo L, López G, Mari N, González-Alonso F, Morelli F, Setzer A, Csiszar I, Kanpandegi J A, Bastarrika A and Libonati R . 2008. Global burned‐land estimation in Latin America using MODIS composite data. Ecological Applications, 18(1): 64-79 [DOI: 10.1890/06-2148.1http://dx.doi.org/10.1890/06-2148.1 ]
Chuvieco E, Lizundia-Loiola J, Pettinari M L, Ramo R, Padilla M, Tansey K, Mouillot F, Laurent P, Storm T, Heil A and Plummer s . 2018. Generation and analysis of a new global burned area product based on MODIS. Earth System Science Data, 10(4): 2015-2031 [DOI: 10.5194/essd-10-2015-2018http://dx.doi.org/10.5194/essd-10-2015-2018 ]
GCOS . 2011. Systematic Observation Requirements for Satellite-Based Data Products for Climate 2011 Update. Supplemental Details to the Satellite-Based Component of the “Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC ( 2010 Update)”. GCOS – 154. World Meteorological Organization
Giglio L, Boschetti L, Roy D P, Humber M L and Justice C O . 2018. The collection 6 MODIS burned area mapping algorithm and product. Remote Sensing of Environment, 217: 72-85 [DOI: 10.1016/j.rse.2018.08.005http://dx.doi.org/10.1016/j.rse.2018.08.005 ]
Giglio L, Loboda T, Roy D P, Quayle B and Justice C O . 2009. An active-fire based burned area mapping algorithm for the MODIS sensor. Remote Sensing of Environment, 113(2): 408-420 [DOI: 10.1016/j.rse.2008.10.006http://dx.doi.org/10.1016/j.rse.2008.10.006 ]
Giglio L, Randerson J T and Van Der Werf G R . 2013. Analysis of daily, monthly, and annual burned area using the fourth‐generation global fire emissions database (GFED4). Journal of Geophysical Research: Biogeosciences, 118(1): 317-328 [DOI: 10.1002/jgrg.20042http://dx.doi.org/10.1002/jgrg.20042 ]
Liu J X, Heiskanen J, Maeda E E and Pellikka P K E . 2018. Burned area detection based on Landsat time series in savannas of southern Burkina Faso. International Journal of Applied Earth Observation and Geoinformation, 64: 210-220 [DOI: 10.1016/j.jag.2017.09.011http://dx.doi.org/10.1016/j.jag.2017.09.011 ]
Long T F, Zhang Z M, He G J, Jiao W L, Tang C, Wu B F, Zhang X M, Wang G Z and Yin R Y . 2019. 30 m resolution global annual burned area mapping based on Landsat images and Google earth engine. Remote Sensing, 11(5): 489 [DOI: 10.3390/rs11050489http://dx.doi.org/10.3390/rs11050489 ]
Lozano F J, Suárez-Seoane S and De Luis E . 2007. Assessment of several spectral indices derived from multi-temporal Landsat data for fire occurrence probability modelling. Remote Sensing of Environment, 107(4): 533-544 [DOI: 10.1016/j.rse.2006.10.001http://dx.doi.org/10.1016/j.rse.2006.10.001 ]
Olson D M, Dinerstein E, Wikramanayake E D, Burgess N D, Powell G V N, Underwood E C, D'amico J A, Itoua I, Strand H E, Morrison J C, Loucks C J, Allnutt T F, Ricketts T H, Kura Y, Lamoreux J F, Wettengel W W, Hedao P and Kassem K R . 2001. Terrestrial Ecoregions of the World: a New Map of Life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience, 51(11): 933-938 [DOI: 10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2http://dx.doi.org/10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2
Padilla M, Stehman S V and Chuvieco E . 2014a. Validation of the 2008 MODIS-MCD45 global burned area product using stratified random sampling. Remote Sensing of Environment, 144: 187-196 [DOI: 10.1016/j.rse.2014.01.008http://dx.doi.org/10.1016/j.rse.2014.01.008 ]
Padilla M, Stehman S V, Litago J and Chuvieco E . 2014b. Assessing the temporal stability of the accuracy of a time series of burned area products. Remote Sensing, 6(3): 2050-2068 [DOI: 10.3390/rs6032050http://dx.doi.org/10.3390/rs6032050 ]
Padilla M, Stehman S V, Ramo R, Corti D, Hantson S, Oliva P, Alonso-Canas I, Bradley A V, Tansey K, Mota B, Pereira J M and Chuvieco E . 2015. Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation. Remote Sensing of Environment, 160: 114-121 [DOI: 10.1016/j.rse.2015.01.005http://dx.doi.org/10.1016/j.rse.2015.01.005 ]
Plummer S, Arino O, Simon M and Steffen W . 2006. Establishing a earth observation product service for the terrestrial carbon community: the GLOBCARBON initiative. Mitigation and Adaptation Strategies for Global Change, 11(1): 97-111 [DOI: 10.1007/s11027-006-1012-8http://dx.doi.org/10.1007/s11027-006-1012-8 ]
Pontius Jr R G and Millones M . 2011. Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15): 4407-4429 [DOI: 10.1080/01431161.2011.552923http://dx.doi.org/10.1080/01431161.2011.552923 ]
Roy D P and Boschetti L . 2009. Southern Africa validation of the MODIS, L3JRC, and GlobCarbon burned-area products. IEEE Transactions on Geoscience and Remote Sensing, 47(4): 1032-1044 [DOI: 10.1109/tgrs.2008.2009000http://dx.doi.org/10.1109/tgrs.2008.2009000 ]
Roy D P, Boschetti L, Justice C O and Ju J . 2008. The collection 5 MODIS burned area product — Global evaluation by comparison with the MODIS active fire product. Remote Sensing of Environment, 112(9): 3690-3707 [DOI: 10.1016/j.rse.2008.05.013http://dx.doi.org/10.1016/j.rse.2008.05.013 ]
Roy D P, Jin Y, Lewis P E and Justice C O . 2005. Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data. Remote Sensing of Environment, 97(2): 137-162 [DOI: 10.1016/j.rse.2005.04.007http://dx.doi.org/10.1016/j.rse.2005.04.007 ]
Simon M, Plummer S, Fierens F, Hoelzemann J J and Arino O . 2004. Burnt area detection at global scale using ATSR‐2: the GLOBSCAR products and their qualification. Journal of Geophysical Research: Atmospheres, 109 (D 14: D14S02 [DOI: 10.1029/2003jd003622http://dx.doi.org/10.1029/2003jd003622 ]
Stehman S V, Arora M K, Kasetkasem T and Varshney P K . 2007. Estimation of fuzzy error matrix accuracy measures under stratified random sampling. Photogrammetric Engineering and Remote Sensing, 73(2): 165-173 [DOI: 10.14358/PERS.73.2.165http://dx.doi.org/10.14358/PERS.73.2.165 ]
Tansey K, Grégoire J M, Defourny P, Leigh R, Pekel J F, Van Bogaert E and Bartholomé E . 2008. A new, global, multi-annual (2000-2007) burnt area product at 1 km resolution. Geophysical Research Letters, 35(1): L01401 [DOI: 10.1029/2007gl031567http://dx.doi.org/10.1029/2007gl031567 ]
Tansey K, Grégoire J M, Stroppiana D, Sousa A, Silva J, Pereira J M C, Boschetti L, Maggi M, Brivio P A, Fraser R, Flasse S, Ershov D, Binaghi E, Graetz D and Peduzzi P . 2004. Vegetation burning in the year 2000: global burned area estimates from SPOT VEGETATION data. Journal of Geophysical Research: Atmospheres, 109 (D 14: D14S03 [DOI: 10.1029/2003jd003598http://dx.doi.org/10.1029/2003jd003598 ]
Van Der Werf G R, Randerson J T, Collatz G J, Giglio L, Kasibhatla P S, Arellano Jr A F, Olsen S C and Kasischke E S . 2004. Continental-scale partitioning of fire emissions during the 1997 to 2001 El Niño/La Niña period. Science, 303(5654): 73-76 [DOI: 10.1126/science.1090753http://dx.doi.org/10.1126/science.1090753 ]
Wu Q C, Chen F, Wang C L, Li B, Wu W, Liu S C and Xu F . 2016. Estimationof carbon emissions from biomass burning based on parameters retrieved. Journal of Remote Sensing, 20(1): 11-26
吴沁淳, 陈方, 王长林, 李斌, 吴薇, 刘三超, 徐丰 . 2016. 自然火灾碳排放估算模型参数的遥感反演进展. 遥感学报, 20(1): 11-26 [DOI: 10.11834/jrs.20154291http://dx.doi.org/10.11834/jrs.20154291 ]
Wu X D, Xiao Q, Wen J G, Liu Q, Peng J J and Li X W . 2014. Advances in uncertainty analysis for the validation of remote sensing products: take leaf area index for example. Journal of Remote Sensing, 18(5): 1011-1023
吴小丹, 肖青, 闻建光, 刘强, 彭菁菁, 李小文 . 2014. 遥感数据产品真实性检验不确定性分析研究进展. 遥感学报, 18(5): 1011-1023 [DOI: 10.11834/jrs.20143332http://dx.doi.org/10.11834/jrs.20143332 ]
相关作者
相关机构
京公网安备11010802024621