包玉龙, 张继权, 刘兴鹏, 等. 2013. 基于HJ-1B卫星数据的草原火烧迹地提取及灾前可燃物特征分析. 灾害学, 28(1): 32-35. (Bao Y L, Zhang J Q, Liu X P, et al. 2013. Analysis on grass fire traces extracted and pre-disaster characteristics of combustibles based on HJ-1B satellite data. Journal of Catastrophology, 28(1): 32-35. [in Chinese]) 车明亮, 陈报章, 王瑛, 等. 2014. 全球植被动力学模型研究综述. 应用生态学报, 25(1): 263-271. (Che M L, Chen B Z, Wang Y, et al. 2014. Review of dynamic global vegetation models (DGBMs). Chinese Journal of Applied Ecology, 25(1): 263-271. [in Chinese]) 李明泽, 康祥瑞, 范文义. 2017. 呼中林区火烧迹地遥感提取及林火烈度的空间分析. 林业科学, 53(3): 163-174. (Li M Z, Kang X R, Fan W Y. 2017. Burned area extraction in Huzhong forests based on remote sensing and the spatial analysis of the burned severity. Scientia Silvae Sinicae, 53(3): 163-174. [in Chinese]) 彭光雄, 沈蔚, 胡德勇, 等. 2008. 基于烟羽掩膜的森林火点MODIS探测方法研究. 红外与毫米波学报, 27(3): 185-189. (Peng G X, Shen W, Hu D Y, et al. 2008. Method to identify forest fire based on smoke plumes mask by using MODIS data. Journal of Infrared Millim Waves, 27(3): 0185-0189. [in Chinese]) 田晓瑞, 代玄, 王明玉, 等. 2016.多气候情景下中国森林火灾风险评估. 应用生态学报, 27(3): 769-776. (Tian X R, Dai X, Wang M Y, et al. 2016. Forest fire risk assessment for China under different climate scenarios. Chinese Journal of Applied Ecology, 27(3): 769-776. [in Chinese]) 王乾坤, 于信芳, 舒清态. 2017. 基于时间序列遥感数据的森林火烧迹地提取. 自然灾害学报, 26(1): 0001-0010. (Wang Q K, Yu X F, Shu Q T. 2017. Forest burned scars area extraction using time series remote sensing data. Journal of Natural Disasters, 26(1): 1-10. [in Chinese]) 肖潇, 冯险峰, 孙庆龄. 2016. 利用MODIS影像提取火烧迹地方法研究. 地球信息科学学报, 18(11): 1529-1536. (Xiao X, Feng X F, Sun Q L. 2016. Burned area detection in the ecosystem transition zone using MODIS data. Journal of Geo-Information Science, 18(11): 1529-1536. [in Chinese]) 杨伟, 张树文, 姜晓丽. 2015. 基于MODIS时序数据的黑龙江流域火烧迹地提取. 生态学报, 35(17): 5866-5873. (Yang W, Zhang S W, Jiang X L. 2015. Burned area mapping for Heilongjiang basin based on MODIS time series data. Acta Ecological Sinica, 35(17): 5866-5873 [in Chinese]) 尤慧, 刘荣高, 祝善友, 等. 2013.加拿大北方森林火烧迹地遥感分析. 地球信息科学学报, 15(4): 597-603. (You H, Liu R G, Zhu S Y, et al. 2013. Burned area detection in the Canadian boreal forest using MODIS imagery. Journal of Geo-Information Science, 15(4): 597-603. [in Chinese]) 祖笑峰, 覃先林, 尹凌宇, 等. 2015. 基于高分一号影像光谱指数识别火烧迹地的决策树方法. 林业资源管理, (4): 73-78. (Zu X F, Qin X L, Yin L Y, et al. 2015. Decision tree method for burned area identification based on the spectral index of GF-1 WFV image. Forest Resources Management, (4): 73-78. [in Chinese]) Achard F, Eva H D, Mollicone D, et al. 2008. The effect of climate anomalies and human ignition factor on wildfires in Russian boreal forests. Philosophical Transactions of the Royal Society B Biological Sciences, 363(1501): 2331-2339. Amiro B D, Barr A G, Barr J G,et al. 2010. Ecosystem carbon dioxide fluxes after disturbance in forests of North America. Journal of Geophysical Research Biogeosciences, 115(G00K02): 4869-4890. Andela N, van der Werf G R, Kaiser J W, et al. 2016. Biomass burning fuel consumption dynamics in the (sub)tropics assessed from satellite. Biogeosciences Discussions, 13(12): 1-30. Antonio L M, Maria T L, Mihai A T, et al. 2014. Forest fire severity assessment using ALS data in a Mediterranean environment. Remote Sensing, 6(5):4240-4265. Barbosa P M, Stroppiana D, Grégoire J M,et al. 1999. An assessment of vegetation fire in Africa (1981-1991): burned areas, burned biomass, and atmospheric emissions. Global Biogeochemical Cycles, 13(4): 933-950. Boschetti L, Eva H D, Brivio P A, et al. 2004. Lessons to be learned from the comparison of three satellite-derived biomass burning products. Geophysical Research Letters, 4805(21):177-178. Bowman D M J S, Balch J K, Artaxo P, et al. 2009. Fire in the earth system. Science, 324(5926): 481-484. Cardoso M F, Hurtt G C, Iii B M,et al. 2005. Field work and statistical analyses for enhanced interpretation of satellite fire data. Remote Sensing of Environment, 96(2): 212-227. Carmona C, Belward A, Malingreau J P, et al. 2005. Characterizing interannual variations in global fire calendar using data from earth observing satellites. Global Change Biology, 11(9): 1537-1555. Chuvieco E, Congalton R G. 1988. Mapping and inventory of forest fires from digital processing of TM data. Geocarto International, 3(3): 41-53. Chuvieco E, Englefield P, Trishchenko A P, et al. 2008. Generation of long time series of burn area maps of the boreal forest from NOAA-AVHRR composite data. Remote Sensing of Environment, 112(5): 2381-2396. Chuvieco E, Yue C, Heil A,et al. 2016. A new global burned area product for climate assessment of fire impacts. Global Ecology and Biogeography, 25(5): 619-629. Diagne M, Drame M, Ferrao C,et al. 2010. Multisource data integration for fire risk management: the local test of a global approach. IEEE Geoscience & Remote Sensing Letters, 7(1): 93-97. Dwyer E, Pinnock S, Gregoire J M, et al. 2000. Global spatial and temporal distribution of vegetation fire as determined from satellite observations. International Journal of Remote Sensing, 21(6): 1289-1302. Eckmann T C, Roberts D A, Still C J. 2008. Using multiple endmember spectral mixture analysis to retrieve subpixel fire properties from MODIS. Remote Sensing of Environment, 112(10): 3773-3783. Ellicott E, Vermote E, Giglio L,et al. 2009. Estimating biomass consumed from fire using MODIS FRE. Geophysical Research Letters, 36(13): 88-97. Gang C, Margaret R M, David M R, et al. 2015. Mapping burn severity in a disease-impacted forest landscape using Landsat and MASTER imagery. International Journal of Applied Earth Observation and Geoinformation, 40: 91-99. Gerard F, Plummer S, Wadsworth R,et al. 2003. Forest fire scar detection in the boreal forest with multitemporal SPOT-VEGETATION data. IEEE Transactions on Geoscience & Remote Sensing, 41(11): 2575-2585. Giglio L, Loboda T, Roy D P, et al. 2009b. An active-fire based burned area mapping algorithm for the MODIS sensor. Remote Sensing of Environment, 113(2): 408-420. Giglio L, Randerson J T, van der Werf G R, et al. 2009a. Assessing variability and long-term trends in burned area by merging multiple satellite fire products. Biogeoscience,7(3): 1171-1186. Giglio L, Randerson J T, 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. Giglio L, van der Werf G R, Randerson J T,et al 2006. Global estimation of burned area using MODIS active fire observations. Chemical Physics, 6(4): 957-974. Guido V D W, Randerson J, Giglio L, et al. 2017. Global fire emissions during 1997-2016.Earth System Science Data, 9(2):697-720. Huijnen V, Wooster M J, Kaiser J W, et al. 2016. Fire carbon emissions over maritime southeast Asia in 2015 largest since 1997. Scientific Reports, 6: 26886. Jing C, Chen W J, Liu J, et al. 2000. Annual carbon balance of Canada's forests during 1895-1996. Global Biogeochemical Cycles, 14(3): 839-850. Kaiser J W, Heil A, Andreae M O, et al. 2012. Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power. Biogeosciences, 9(1): 527-554. Kant Y, Punia M, Nautiyal N. 2008. Identifying biomass burned patches of agriculture residue using satellite remote sensing data. Current Science, 94(9): 1185-1190. Kushida K. 2010. Detection of active wildland fires using multitemporal MODIS images. IEEE Geoscience & Remote Sensing Letters, 7(2): 301-305. Luigi B, Roy D P. 2009. Strategies for the fusion of satellite fire radiative power with burned area data for fire radiative energy derivation. Journal of Geophysical Research Atmospheres, 114(D20): 215-216. Marcos F, Xavier U, Joan T, et al. 2016. The role of forest fire severity on vegetation recovery after 18 years. Global and Planetary Change, 145:11-16. Marlier M E, DeFries R S, Voulgarakis A, et al. 2013. El Niño and health risks from landscape fire emissions in southeast Asia. Nature Climate Change, 3(2): 131-136. Milne A K. 1986. The use of remote sensing in mapping and monitoring vegetational change associated with bushfire events in Eastern Australia. Geocarto International, 1(1): 25-32. Moreira F, Viedma O, Arianoutsou M, et al. 2011. Landscape—wildfire interactions in southern Europe: implications for landscape management. Journal of Environmental Management, 92(10): 2389-2402. Mouillot F, Schultz M G, Yue C, et al. 2014.Ten years of global burned area products from spaceborne remote sensing—a review: analysis of user needs and recommendations for future developments. International Journal of Applied Earth Observation and Geoinformation, 26(1):64-79. Núñez-Casillas L, García Lázaro J R, Moreno-Ruiz J A,et al. 2013. A comparative analysis of burned area datasets in Canadian boreal forest in 2000. Scientific World Journal.doi:10.1155/2013/289056. Padilla M, Stehman S V, Chuvieco E. 2014a. Validation of the 2008 MODIS-MCD45 global burned area product using stratified random sampling. Remote Sensing of Environment, 144(1): 187-196. Padilla M, Stehman S V, Litago J, et al. 2014b. Assessing the temporal stability of the accuracy of a time series of burned area products. Remote Sensing, 6(3): 2050-2068. Padilla M, Stehman S V, Ramo R,et al. 2015. Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation. Remote Sensing of Environment, 160:114-121. Pausas J G, Keeley J E. 2009. A burning story: the role of fire in the history of life. BioScience, 59(7): 593-601. Pereira J M C. 1999.A comparative evaluation of NOAA/AVHRR vegetation index for burned surface detection and mapping. IEEE Transactions on Geoscience and Remote Sensing, 37(1): 217-226. Plummer S, Arino O, Simon M, et al. 2006. Establishing aearth observation product service for the terrestrial carbon community: the globcarboninitiative. Mitigation and Adaptation Strategies for Global Change, 11(1): 97-111. Reid J S, Hyer E J, Prins E M, et al. 2009. Global monitoring and forecasting of biomass-burning smoke: description of and lessons from the fire locating and modeling of burning emissions (FLAMBE) program. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2(3): 144-162. Riano D, Ruiz J, Isidoro D, et al. 2007. Global spatial patterns and temporal trends of burned area between 1981 and 2000 using NOAA-NASA Pathfinder. Global Change Biology, 13(1): 40-50. Robbins A M J, Eckelmann C M, Quiñones M. 2015. Forest fires in the Insular Caribbean. Ambio, 37(7/8): 528. Roy D P, Boschetti L, Justice C O,et al. 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. Roy D P, Boschetti L. 2009. Southern Africa validation of the MODIS, L3JRC, and GlobCarbon burned-area products. IEEE Transactions on Geoscience & Remote Sensing, 47(4): 1032-1044. Ruiz J A M, Riaño D, Arbelo M, et al. 2012. Burned area mapping time series in Canada (1984-1999) from NOAA-AVHRR LTDR: a comparison with other remote sensing products and fire perimeters. Remote Sensing of Environment, 117(2): 407-414. Ruiz J, Lázaro J, Cano I, et al. 2014. Burned area mapping in the North American boreal forest using Terra-MODIS LTDR (2001-2011): acomparison with the MCD45A1, MCD64A1 and BA GEOLAND-2 products. Remote Sensing, 6(1): 815-840. Sa A C L, Pereira J M C, Gardner R H. 2007. Analysis of the relationship between spatial pattern and spectral detectability of areas burned in southern Africa using satellite data. International Journal of Remote Sensing, 28(16): 3583-3601. Sandra L, Matthias S, Elizabeth C A, et al. 2017. Spatial evaluation of Indonesia's 2015 fire-affected area and estimated carbon emissions using Sentinel-1. Global Change Biology, 24(2):644-654. Schroeder W, Ruminski M, Csiszar I, et al. 2008. Validation analyses of an operational fire monitoring product: the hazard mapping system. International Journal of Remote Sensing, 29(20): 6059-6066. Sean A P, Carol M, Cara R N, et al. 2014. Previous fires moderate burn severity of subsequent wildland fires in two large western US wilderness areas. Ecosystems, 17(1): 29-42. Sofiev M, Vankevich R, Lotjonen M, et al. 2009. An operational system for the assimilation of the satellite information on wild-land fires for the needs of air quality modelling and forecasting. Atmospheric Chemistry and Physics Discussions, 9(2): 315-326. Sparks A M, Kolden C A, Talhelm A F, et al. 2016. Spectral indices accurately quantify changes in seedling physiology following fire: towards mechanistic assessments of post-fire carbon cycling. Remote Sensing, 8(7): 572. Spessa A C, Field R D, Pappenberger F, et al. 2015. Seasonal forecasting of fire over Kalimantan, Indonesia. Natural Hazards and Earth System Science, 15(3): 429 - 442. Tansey K, Grégoire J M, Defourny P, et al. 2008. A new, global, multi‐annual (2000-2007) burnt area product at 1km resolution. Geophysical Research Letters, 35(1): 1-6. Tansey K, Grégoire J M, Stroppiana D, et al. 2004. Vegetation burning in the year 2000: global burned area estimates from SPOT VEGETATION data. Journal of Geophysical Research Atmospheres, 109(D14): 449-464. Thonicke K, Venevsky S, Sitch S, et al. 2001. The role of fire disturbance for global vegetation dynamics: coupling fire into a Dynamic Global Vegetation Model. Global Ecology & Biogeography, 10(6): 661-677. van der Werf G R, Randerson J T, Giglio L, et al. 2006.Interannual variability in global biomass burning emissions from 1997 to 2004. Atmospheric Chemistry & Physics, 6(11): 3423-3441. van der Werf G R, Randerson J T, Giglio L, et al. 2010. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997-2009). Atmospheric Chemistry & Physics, 10(6): 16153-16230. Vasilakos C, Kalabokidis K, Hatzopoulos J,et al. 2009. Identifying wildland fire ignition factors through sensitivity analysis of a neural network. Natural Hazards, 50(1): 125-143. Vogeler J C, Yang Z Q, Cohen W B. 2016. Mapping post-fire habitat characteristics through the fusion of remote sensing tools. Remote Sensing of Environment, 173: 294-303. Yi Y, Philippe C, Frederic C, et al. 2016. Variability of fire carbon emissions in equatorial Asia and its nonlinear sensitivity to El Niño. Geophysical Research Letters, 43(19): 10472-10479. |