Estimationof carbon emissions from biomass burning based on parameters retrieved

被引：0
作者：
Wu Q. [1 ,2 ]
Chen F. [1 ,3 ]
Wang C. [1 ]
Li B. [1 ]
Wu W. [1 ,2 ]
Liu S. [4 ,5 ]
Xu F. [4 ,5 ]
机构：
[1] Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing
[2] University of Chinese Academy of Sciences, Beijing
[3] The State Key Laboratory of Remote Sensing Science, Chinese Academy of Sciences, Beijing
[4] National Disaster Reduction Center of China, MCA, Beijing
[5] Satellite Application Center for Disaster Reduction, MCA, Beijing
来源：
Yaogan Xuebao/Journal of Remote Sensing | 2016年 / 20卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Biomass burning; Burned area; Burning efficiency; Carbon emission; Fuel loading;
D O I：
10.11834/jrs.20154291
中图分类号：
学科分类号：
摘要：
Biomass burning is a widespread practice. During burning, fire combusts organic matter and emits a large amount of carbonaceous gases into the atmosphere. Biomass burning not only changes the structure and process of the ecosystem but also affects the carbon cycle of the entire system. To elucidate the impact of wildfire on global carbon cycle, large-scale carbon emissions from biomass burning have been estimated using satellite remote sensing. Many remote-sensing-based models have been developed to estimate biomass burning emissions at different scales. The most widely used model contains four key parameters: burned area, fuel load, burning efficiency, and carbon fraction. The first three parameters can be retrieved from satellite data. This paper discusses methodologies for the retrieval of these three key input parameters and describes the advantages and disadvantages of each methodology. Methods for the estimation of burned area can be categorized into three types: reflectance-, emission-, and backscatter feature-based methods. Fuel load mapping can be classified as direct and indirect. Indirect fuel load mapping classifies satellite data to determine the fuel type and then assigns fuel load value to each pixel depending on the fuel type in the fuel models. This method strongly relies on fuel model and is mostly not suitable for large-scale areas. Direct fuel load mapping estimates fuel load value on the basis of the relationship among fuel load, relative factor of fuel load, and satellite data. Burning efficiency or combustion completeness is usually estimated through direct and indirect retrieval methods. The direct retrieval method is difficult to be use data large scale, whereas the indirect retrieval method maps the burn severity first and then adjusts the preset fixed burning efficiency on the basis of burn severity. Finally, suggestions are provided to improve the accuracy of remote sensing in estimating carbon emissions from biomass burning. Many studies have been conducted to retrieve carbon emission-related parameters through remote sensing. However, the adaptability and uncertainty of these estimations for large-scale areas remain unclear, and the estimation accuracy of global carbon emission does not satisfy the demand of research on carbon cycle. © 2016, Science Press. All right reserved.
引用
收藏
页码：11 / 26
页数：15
相关论文
共 117 条
  • [1] Anderson H.E., Aids to determining fuel models for estimating fire behavior, The Bark Beetles, Fuels, and Fire Bibliography, (1982)
  • [2] Andersen H.E., McGaughey R.J., Reutebuch S.E., Estimating forest canopy fuel parameters using LiDAR data, Remote Sensing of Environment, 94, 4, pp. 441-449, (2005)
  • [3] Arroyo L.A., Pascual C., Manzanera J.A., Fire models and methods to map fuel types: The role of remote sensing, Forest Ecology and Management, 256, 6, pp. 1239-1252, (2008)
  • [4] Bastarrika A., Chuvieco E., Martin M.P., Mapping burned areas from Landsat TM/ETM+ data with a two-phase algorithm: balancing omission and commission errors, Remote Sensing of Environment, 115, 4, pp. 1003-1012, (2011)
  • [5] Boschetti L., A multitemporal algorithm for burned area detection in Mexican woodland and shrubland environment with SPOT-VEGETATION data, Proceedings of the 2003 IEEE International Geoscience and Remote Sensing Symposium, pp. 1293-1295, (2003)
  • [6] Brandis K., Jacobson C., Estimation of vegetative fuel loads using Landsat TM imagery in New South Wales, Australia, International Journal of Wildland Fire, 12, 2, pp. 185-194, (2003)
  • [7] Cahoon D.R., Stocks B.J., Levine J.S., Cofer W.R., Pierson J.M., Satellite analysis of the severe 1987 forestfires in Northern China and southeastern Siberia, Journal of Geophysical Research: Atmospheres, 99, D9, pp. 18627-18638, (1994)
  • [8] Cao X., Chen J., Matsushita B., Imura H., Wang L., An automatic method for burn scar mapping using support vector machines, International Journal of Remote Sensing, 30, 3, pp. 577-594, (2009)
  • [9] Chen F., Weber K.T., Gokhale B., Herbaceous biomass estimation from SPOT 5 imagery in semiarid rangelands of Idaho, Giscience & Remote Sensing, 48, 2, pp. 195-209, (2011)
  • [10] Chen F., Weber K.T., Anderson J., Gokhal B., Assessing the susceptibility of semiarid rangelands to wildfires using Terra MODIS and Landsat Thematic Mapper data, International Journal of Wildland Fire, 20, 5, pp. 690-701, (2011)
12345678910