基于幂律分布的森林燃烧生物量卫星遥感估测方法

doi:10.11707/j.1001-7488.20171010

摘要/Abstract

摘要： [目的]利用长时间序列卫星遥感数据产品按森林类型建立大区域燃烧生物量估测模型，并按年生成不同森林类型的燃烧生物量，为我国年林火碳排放估测提供一种新的技术手段。[方法]采用覆盖我国陆地区域的2001-2014年MODIS火产品数据（MOD14A2），按3种森林类型分析该数据产品中的火灾辐射率（FRP）分布特性，并按森林类型构建基于幂律分布的燃烧生物量估测模型，对我国2001-2014年各年林火消耗的森林生物总量进行估测；利用对数形式的概率分布函数线性回归拟合方法求解模型幂参数m；选取每年10场左右的典型森林火灾建立回归方程，修正每年的火灾持续时间d，并以年为单位估测我国不同森林类型因燃烧消耗掉的生物量；同时，利用林火排放物计算模型结合MODIS火烧迹地数据集（MCD45A1），对估测的燃烧生物量进行对比分析。[结果]阔叶林、针叶林和灌木林的FRP数据均呈幂律分布规律，在2001-2014年14年中，林火导致全国的阔叶林年消耗总生物量在0.94~1.37 Mt之间、针叶林在0.80~1.92 Mt之间、灌木林在0.37~0.53 Mt之间。通过与林火排放物计算模型对比分析发现，这2种方法的估测结果在某些年份差异显著，甚至林火排放物计算模型估测的某些年份森林燃烧生物量超过本文研究方法估测的14年总结果。相比国家统计局公布的森林火灾发生次数和森林过火面积，本文研究方法估测的结果和年际变化更符合我国森林火灾发生规律。[结论]基于长时间序列的MODIS火产品数据表明，我国阔叶林、针叶林和灌木林燃烧释放的能量具有幂律分布特性；基于该分布特性，构建按森林类型估测全国森林因燃烧消耗的年森林生物总量模型，并估测出逐年森林因燃烧消耗的森林生物总量，通过与林火排放物计算模型估测的全国同年林火消耗掉的森林生物总量进行对比，该方法比林火排放物计算模型的估测结果更准确。

关键词: 卫星遥感数据, 森林火灾, 燃烧生物量估测, FRP

Abstract: [Objective] Exploring the burned biomass estimation method by using the long time series of satellite remote sensing data products according to forest types at large scale, and getting the burned biomass estimation result by annual for different forest types, were the major objective of the current work.[Method] The MODIS satellite active fire detection products(MOD14A2)covering the land of P. R. China in 2001-2014 have been selected as the datasets, the feature of power law distribution of FRP(fire radiative power)has been analyzed on three forest types; meanwhile, the burned forest biomass estimation models have been developed on the forest types. To get the scaling parameter m, the linear regression fit to probability distribution function in log scales has been applied, and about 10 times forest fire every year have been selected to modify the annual fire duration d. The national burned forest biomass has been estimated on an annual basis. At the same time, the study result calculated from MODIS burned dataset(MCD45A1)were compared with those derived from forest fire emissions calculation model to validate the efficacy of the current estimation method.[Result] The value of FRP of broadleaf forest, coniferous forest and shrub forest follow the power law distribution. In the 14 years, the annual burned biomass of broadleaf forest was in 0.94-1.37 Mt, with a burned biomass in 0.80-1.92 Mt for coniferous forest, shrub forest's burns in 0.37-0.53 Mt. The result of the two methods were significantly different in some years, even certain values derived from forest fire emissions model were far beyond the total burned biomass of the observed 14 years calculated from the method developed in the current work. The result and inter annual variation were more consistent with the statistics of fire numbers and burned area issued by the National Bureau of Statistics.[Conclusion] The value of FRP of the broadleaf forest, coniferous forest and shrub forest follow the power-law distribution characteristics, the national burned forest biomass estimation models have been developed based on the feature by forest type in the 14 years. The method based on power law distribution is more accurate than the result of fire emissions model estimation method.

Key words: satellite remote sensing data, forest fires, biomass burning estimation, FRP

中图分类号:

S757

祖笑锋, 覃先林, 李增元, 孙桂芬, 刘树超. 基于幂律分布的森林燃烧生物量卫星遥感估测方法[J]. 林业科学, 2017, 53(10): 90-99.

Zu Xiaofeng, Qin Xianlin, Li Zengyuan, Sun Guifen, Liu Shuchao. Method for Burned Forest Biomass Estimation Using Satellite Remote Sensing Based on Power Law Distribution[J]. Scientia Silvae Sinicae, 2017, 53(10): 90-99.

参考文献

胡海清, 张喆, 吴学伟. 2007. 基于遥感的塔河林业局森林可燃物类型划分. 东北林业大学学报,35(7):20-21.
(Hu H Q, Zhang Z,Wu X W.2007. Type classification of forest fuel in Tahe Forestry Bureau based on remote sensing. Journal of Northeast Forestry University, 35(7):20-21.[in Chinese])
田晓瑞, 舒立福, 王明玉. 2003. 1991-2000年中国森林火灾直接释放碳量估算. 火灾科学,12(1):6-10.
(Tian R X, Shu L F, Wang M Y. 2003.Direct carbon emissions from Chinese forest fires, 1991-2000. Fire Safety Science, 12(1):6-10.[in Chinese])
田晓瑞, 舒立福, 王明玉,等. 2006. 卫星遥感数据在林火排放模型中的应用. 安全与环境学报,6(4):104-108.
(Tian R X, Shu L F, Wang M Y, et al. 2006.An emission model for the use of satellite data to the forest fire evaluation. Journal of Safety and Environment, 6(4):104-108.[in Chinese])
田晓瑞, 殷丽, 刘景双,等. 2009. 2005-2007年大兴安岭林火释放碳量. 应用生态学报, 20(12):2877-2883.
(Tian X R, Yin L, Liu J S, et al. 2009. Carbon emission from forest fires in Daxing' anling region in 2005-2007. Chinese Journal of Applied Ecology, 20(12):2877-2883.[in Chinese])
王效科, 冯宗炜, 欧阳志云. 2001. 中国森林生态系统的植物碳储量和碳密度研究. 应用生态学报,12(1):13-16.
(Wang X K, Feng Z W, Ouyang Z Y. 2001.Vegetation carbon storage and density of forest ecosystems in China. Acta Ecologica Sinica, 12(1):13-16.[in Chinese])
杨国福, 江洪, 余树全, 等. 2009. 浙江省1991-2006森林火灾直接碳释放量的估算. 应用生态学报, 20(5):1038-1043.
(Yang G F, Jiang H, Yu S Q,et al.2009.Black carbon emission from forest fires of Zhejiang Province during 1991-2006. Acta Ecologica Sinica, 20(5):1038-1043.[in Chinese])
魏书精, 罗碧珍, 孙龙, 等. 2014. 黑龙江省温带森林火灾碳排放的计量估算. 生态学报,34(11):3048-3063.
(Wei S J, Luo B Z, Sun L, et al. 2014. Estimates of carbon emissions caused by forest fires in the temperate climate of Heilongjiang Province,China,from 1953 to 2012. Acta Ecologica Sinica, 34(11):3048-3063.[in Chinese])
Andreae M O, Merlet P. 2001. Emission of trace gases and aerosols from biomass burning. Global Biogeochemical Cycles, 15(4):955-966.
Carvalho J A, Higuchi N, Araújo T M,et al. 1998. Combustion completeness in a rainforest clearing experiment in Manaus, Brazil. Journal of Geophysical Research, 103(13):195-213.
Clauset A, Shalizi C R, Newman M E J. 2009. Power law distributions in empirical data. SIAM Rev, 51(4):661-703.
Fearnside P M, de Alencastro Graça P M L, Leal Filho N,et al. 1999. Tropical forest burning in Brazilian Amazonia:measurement of biomass loading, burning efficiency and charcoal formation at Altamira, Pará. Forest Ecology and Management, 123(1):65-79.
Heward H, Smith A M S, Roy D P,et al. 2013. Is burn severity related to fire intensity? Observations from landscape scale remote sensing. International Journal of Wildland Fire, 22(7):910-918.
Kaufman Y J, Remer L, Ottmar R, et al.1996. Relationship between remotely sensed fire intensity and rate of emission of smoke:SCAR-C experiment//Levine J. Global biomass burning. MIT Press:Cambridge, MA, 685-696.
Kumar S S, Roy D P, Boschetti L, et al. 2011. Exploiting the power law distribution properties of satellite fire radiative power retrievals:a method to estimate fire radiative energy and biomass burned from sparse satellite observations. Journal of Geophysical Research:Atmospheres,116(D19):1-18.
Levine J S, Cofer Ⅲ W R. 2000. Boreal forest fire emissions and the chemistry of the atmosphere//Fire, climate change, and carbon cycling in the boreal forest. Springer New York,31-48.
Newman M E. 2005. Power laws, Pareto distributions and Zipf's law. Contemporary Physics, 46(5):323-351.
Robinson J M. 1989. On uncertainty in the computation of global emissions from biomass burning. Climatic Change, 14(3):243-261.
Roberts G, Wooster M J, Perry G L W,et al. 2005. Retrieval of biomass combustion rates and totals from fire radiative power observations:application to southern Africa using geostationary SEVIRI imagery. Journal of Geophysical Research:Atmospheres,110(D21):1-19.
Roberts G J, Wooster M J. 2008. Fire detection and fire characterization over Africa using Meteosat SEVIRI. IEEE Transactions on Geoscience and Remote Sensing, 46(4):1200-1218.
Seiler W, Crutzen P J. 1980. Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning.Climatic Change,2(3):207-247.
Streets D G, Bond T C, Carmichael G R,et al. 2003. An inventory of gaseous and primary aerosol emissions in Asia in the year 2000. Journal of Geophysical Research:Atmospheres, 108(D21):8809-8809.
Wooster M, Zhang Y. 2004. Boreal forest fires burn less intensely in Russia than in North America. Geophysical Research Letters, 31(20):L20505.
Wooster M J, Zhukov B, Oertel D. 2003. Fire radiative energy for quantitative study of biomass burning:derivation from the BIRD experimental satellite and comparison to MODIS fire products. Remote Sensing of Environment, 86(1):83-107.
Wooster M J, Roberts G, Perry G L W, et al. 2005. Retrieval of biomass combustion rates and totals from fire radiative power observations:FRP derivation and calibration relationships between biomass consumption and fire radiative energy release. Journal of Geophysical Research:Atmospheres,110(D24):1-24.

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