遥感与地理信息系统技术相结合的林火预警方法的研究
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摘要
森林火灾是一种世界性的严重自然灾害。它分布广、发生频度高,破坏森林资源,干扰人民正常生活秩序,造成全球性环境污染,越来越受到各国政府的重视。如何采用遥感、地理信息系统技术等现代高新技术对森林火险等级进行预报和对森林火灾进行监测,成为当前国内外研究的热点。本文在查阅大量文献及系统分析的基础上,以遥感和地理信息系统等现代高新信息技术为主要手段,研究了以中等空间分辨率的遥感数据(MODIS)和地理信息系统技术相结合的林火预警应用方法。
本文在综述国内外相关研究的基础上,分别对MODIS数据的处理方法及其在林火预警中的应用方法进行了研究。主要开展了MODIS数据几何校正方法的对比研究,MODIS数据在全国森林可燃物分布与分类、植被状态的反演、植被燃烧信息的提取以及森林火险等级预报等方法的研究。通过开展本研究工作,获得了基于GIS平台的、以MODIS为主要信息源的卫星遥感数据在林火预警中的应用方法。即:
(1) 获得了MODIS L1B数据的几何校正和融合等处理方法
本文对比研究了利用GLT方法和GCP方法对MODIS L1B数据的几何校正的优缺点,并提出了对MODIS L1B数掘几何校正处理的建议。同时,获得了不同空间分辨率的MODIS L1B数据集间的融合方法,并利用程序实现了融合算法。
(2) 获得了基于MODIS数据的大尺度森林可燃物分类方法
本文在国内外前人已做的植被、土地覆盖/土地利用等分类研究的工作基础上,借鉴已有的分类成果,首次在国内提出了基于MODIS数据的大尺度的森林可燃物分类体系。在制定的分类体系指导下,采用非监督分类和监督分类相结合的综合分类方法,对我国的森林可燃物分类方法进行了研究与实践,获得了全国可燃物分布图;并探讨了中等空间分辨率的卫星数据在大尺度分类研究中的结果检验方法,即利用GIS技术,将分类结果与专题数据叠加进行精度验证。
(3) 获得了森林火险等级预报因子的处理方法
本文将输入森林火险预警模型中的数据是否经常随着时间的变化而将其分为动态数据和静态数据。通过实验,分别获得了对这两大类数据的处理方法。即:
① 对于静态数据,采用建立背景数据库后再数量化的方法参与模型中计算。
② 对于动态数据中的每日气象数据,在对比三种空间插值方法在气象数据栅格化结果的精度与效率的基础上,获得了利用反间距空间插值方法对气象数据各因子进行空间插值处理,从而实现了利用已有气象站数据预估其他没有气象观测站设置地区的气象数据的方法;对于植被长势的估测,利用MODIS数据获得的相对绿度指数作为其植被
Forest fire is a kind of worldwide natural calamity. It is extensively distributed with high occurrence frequency and destroys forest resources thus disturbing normal living order of people and leading to environmental deterioration. It has been paid more attention to by many governments. It's a study hotspot in worldwide how to using Modern high-tech information technologies, such as Remote Sensing (RS) and Geographic Information Systems (GIS), to forest fire danger rating predicting and forest fire monitoring. Based on the review of internationally and domestically published papers, Moderate spatial resolution RS data (MODERATE RESOLUTION IMAGING SPECTRO RADIOMETE, MODIS) and GIS Techniques have been used as main techniques in this application study on forest fire early warning and monitoring.Based on the review of internationally and domestically published papers on MODIS, research on how to process MODIS data and their application in forest fire danger prediction have been carried out. Main work includes comparison study of geometric correction method on MODIS data, Chinese national forest fuel distribution and classification using MODIS data, Chinese national vegetation status, Chinese vegetation burning information retrieval and forest fire danger rating. Application methods based on remote sensing data that use GIS as the platform and MODIS as the main information source have been developed in forest fire prediction. Summarization of this dissertation is as follows:(1) Approaches on geometric correction and fuse of MODIS LIB data have been obtainedThe advantages and disadvantages of using geometric correction to process MODIS LIB data by Georeference from Input Geometry (GLT) method and Geometry Control Point (GCP) method were discussed through the comparison study. A followed suggestion about Geometric Correction of MODIS LIB data has been given. Fuse method of different spatial resolution of MODIS LIB data has also been obtained and program of fuse implement has been developed.(2) Classification of Large Scale Forest Fuels Map Based on MODIS Data has been generated.Based on previous researches and achievements on the classification of vegetation, land
cover, and land utilization, large scale forest fuels classification system based on MODIS data has been bring forward in China for the first time. Under this classification system, China national forest fuels class has been studied by using an integrative unsupervised and supervised classification method. A national forest fuels distribution map has been produced. At the same times, the classification's precision and its verification by middle spatial resolution satellite data has been discussed. The precision of this classification was verified by overlaying the classifying results with background thematic data in GIS.(3) The processing method of different forest fire danger rating factors has been obtainedData used by forest fire danger rating model was classified into static data and dynamic data based on whether these data were stable against time. After lab test, the processing method of these two types of data was obtained as the followings:a) For the static data, after it was corrected by the background database, it was digitalized before being inputted into the rating model program.b) For the dynamic data, three kinds of dynamic data resource have been used in the forest fire danger rating system. The first one is the daily weather observation data. Based on the precision and efficiency of three kinds of spatial interpretation method, the best way is the use of Invert Distance Weight (IDW) spatial interpretation method. IDW can predict the weather condition of the region where there is no weather observation station by using weather observation station data from other regions. The second one is the growth of vegetation. Relative greenness parameter has been used to estimate the forest fuels' growth. The third one is the moisture content of forest fuel, which can be estimated by an integrative method using near infrared reflectance (NIR) channels and short wave infrared reflectance (SWIR) channels. The result shows that the best way to estimate moisture content of forest fuels is using the rate of channel 2 to channel 7 of MODIS according to comparing the results of channel 2 to channel 5, channel 6 and channel 7.(4) National Forest Fire Danger Rating Prediction method has been obtainedIn this paper, the dynamic and static data related with forest fire danger rating prediction have been quantified and used to calculate fire danger index. The fire danger index is the quantified index and grading criterion for Chinese national forest fire danger rating prediction. Consequently, it helps the realization of describing forest fire danger rating from quality to quantification. The method has been verified by the tests in North East of China. The results demonstrated the RS and GIS based method is useful in forest fire prediction.
(5) Method to retrieve the vegetation burning information using MODIS data has been obtainedBase on previous research achievement of fire monitoring using MODIS data, the information of related MODIS bands' data in daytime was analyzed and the accuracy of the result by using Surface Bright Temperature method and Bright Temperature-NDVI method was compared. A model was developed to identify forest fire by using Bright Temperature-NDVI method and integrating GIS technology. In experiment, the totally precision of identification was above 80%, which can satisfy the need of forest fire check-up. At the same time, it can also identify the vegetation type of fire.
本文在综述国内外相关研究的基础上,分别对MODIS数据的处理方法及其在林火预警中的应用方法进行了研究。主要开展了MODIS数据几何校正方法的对比研究,MODIS数据在全国森林可燃物分布与分类、植被状态的反演、植被燃烧信息的提取以及森林火险等级预报等方法的研究。通过开展本研究工作,获得了基于GIS平台的、以MODIS为主要信息源的卫星遥感数据在林火预警中的应用方法。即:
(1) 获得了MODIS L1B数据的几何校正和融合等处理方法
本文对比研究了利用GLT方法和GCP方法对MODIS L1B数据的几何校正的优缺点,并提出了对MODIS L1B数掘几何校正处理的建议。同时,获得了不同空间分辨率的MODIS L1B数据集间的融合方法,并利用程序实现了融合算法。
(2) 获得了基于MODIS数据的大尺度森林可燃物分类方法
本文在国内外前人已做的植被、土地覆盖/土地利用等分类研究的工作基础上,借鉴已有的分类成果,首次在国内提出了基于MODIS数据的大尺度的森林可燃物分类体系。在制定的分类体系指导下,采用非监督分类和监督分类相结合的综合分类方法,对我国的森林可燃物分类方法进行了研究与实践,获得了全国可燃物分布图;并探讨了中等空间分辨率的卫星数据在大尺度分类研究中的结果检验方法,即利用GIS技术,将分类结果与专题数据叠加进行精度验证。
(3) 获得了森林火险等级预报因子的处理方法
本文将输入森林火险预警模型中的数据是否经常随着时间的变化而将其分为动态数据和静态数据。通过实验,分别获得了对这两大类数据的处理方法。即:
① 对于静态数据,采用建立背景数据库后再数量化的方法参与模型中计算。
② 对于动态数据中的每日气象数据,在对比三种空间插值方法在气象数据栅格化结果的精度与效率的基础上,获得了利用反间距空间插值方法对气象数据各因子进行空间插值处理,从而实现了利用已有气象站数据预估其他没有气象观测站设置地区的气象数据的方法;对于植被长势的估测,利用MODIS数据获得的相对绿度指数作为其植被
Forest fire is a kind of worldwide natural calamity. It is extensively distributed with high occurrence frequency and destroys forest resources thus disturbing normal living order of people and leading to environmental deterioration. It has been paid more attention to by many governments. It's a study hotspot in worldwide how to using Modern high-tech information technologies, such as Remote Sensing (RS) and Geographic Information Systems (GIS), to forest fire danger rating predicting and forest fire monitoring. Based on the review of internationally and domestically published papers, Moderate spatial resolution RS data (MODERATE RESOLUTION IMAGING SPECTRO RADIOMETE, MODIS) and GIS Techniques have been used as main techniques in this application study on forest fire early warning and monitoring.Based on the review of internationally and domestically published papers on MODIS, research on how to process MODIS data and their application in forest fire danger prediction have been carried out. Main work includes comparison study of geometric correction method on MODIS data, Chinese national forest fuel distribution and classification using MODIS data, Chinese national vegetation status, Chinese vegetation burning information retrieval and forest fire danger rating. Application methods based on remote sensing data that use GIS as the platform and MODIS as the main information source have been developed in forest fire prediction. Summarization of this dissertation is as follows:(1) Approaches on geometric correction and fuse of MODIS LIB data have been obtainedThe advantages and disadvantages of using geometric correction to process MODIS LIB data by Georeference from Input Geometry (GLT) method and Geometry Control Point (GCP) method were discussed through the comparison study. A followed suggestion about Geometric Correction of MODIS LIB data has been given. Fuse method of different spatial resolution of MODIS LIB data has also been obtained and program of fuse implement has been developed.(2) Classification of Large Scale Forest Fuels Map Based on MODIS Data has been generated.Based on previous researches and achievements on the classification of vegetation, land
cover, and land utilization, large scale forest fuels classification system based on MODIS data has been bring forward in China for the first time. Under this classification system, China national forest fuels class has been studied by using an integrative unsupervised and supervised classification method. A national forest fuels distribution map has been produced. At the same times, the classification's precision and its verification by middle spatial resolution satellite data has been discussed. The precision of this classification was verified by overlaying the classifying results with background thematic data in GIS.(3) The processing method of different forest fire danger rating factors has been obtainedData used by forest fire danger rating model was classified into static data and dynamic data based on whether these data were stable against time. After lab test, the processing method of these two types of data was obtained as the followings:a) For the static data, after it was corrected by the background database, it was digitalized before being inputted into the rating model program.b) For the dynamic data, three kinds of dynamic data resource have been used in the forest fire danger rating system. The first one is the daily weather observation data. Based on the precision and efficiency of three kinds of spatial interpretation method, the best way is the use of Invert Distance Weight (IDW) spatial interpretation method. IDW can predict the weather condition of the region where there is no weather observation station by using weather observation station data from other regions. The second one is the growth of vegetation. Relative greenness parameter has been used to estimate the forest fuels' growth. The third one is the moisture content of forest fuel, which can be estimated by an integrative method using near infrared reflectance (NIR) channels and short wave infrared reflectance (SWIR) channels. The result shows that the best way to estimate moisture content of forest fuels is using the rate of channel 2 to channel 7 of MODIS according to comparing the results of channel 2 to channel 5, channel 6 and channel 7.(4) National Forest Fire Danger Rating Prediction method has been obtainedIn this paper, the dynamic and static data related with forest fire danger rating prediction have been quantified and used to calculate fire danger index. The fire danger index is the quantified index and grading criterion for Chinese national forest fire danger rating prediction. Consequently, it helps the realization of describing forest fire danger rating from quality to quantification. The method has been verified by the tests in North East of China. The results demonstrated the RS and GIS based method is useful in forest fire prediction.
(5) Method to retrieve the vegetation burning information using MODIS data has been obtainedBase on previous research achievement of fire monitoring using MODIS data, the information of related MODIS bands' data in daytime was analyzed and the accuracy of the result by using Surface Bright Temperature method and Bright Temperature-NDVI method was compared. A model was developed to identify forest fire by using Bright Temperature-NDVI method and integrating GIS technology. In experiment, the totally precision of identification was above 80%, which can satisfy the need of forest fire check-up. At the same time, it can also identify the vegetation type of fire.
引文
1.叶镜中 编著.森林生态学.北京:东北林业大学出版社,1991。
2.周生贤 编著.中国林业的历史性转变.北京:中国林业出版社,2002。
3.宋志杰 编著.林火原理和林火预报.气象出版社,1991。
4.郑焕能 等编著.中国东北林火.哈尔滨:东北林业大学出版社,2000。
5.舒立福,田晓瑞.世界森林火灾状况综述.世界林业研究,1998,11(6):41～46。
6.赵宪文 主编.森林火灾遥感监测评价.北京:中国林业出版社,1995。
7.毕忠镇.中国森林火灾现状、发生规律及建议.1999年减轻自然灾害学术研讨会论文汇编.北京:中国科协学会部,1999。
8.承继成,林珲,周成虎 等编著.数字地球导论.北京:科学出版社,2000。
9.李德仁,关泽群.空间信息系统的集成与实现.武汉测绘科技大学出版社,2000。
10.易浩若.信息技术推进森林防火现代化.台北:海峡两岸森林保育研讨会论文集.台湾中华林学会,1999。
11.舒立福,田晓瑞.3S集成技术在林火管理中的应用研究.火灾科学,1998,(1):46～51。
12.徐祥德,王馥棠,萧永生 等编著.农业气象防灾调控工程与技术系统.北京:气象出版社,2000。
13. Jack D. Cohen, John E. Deeming. The national fire danger rating system: Basic equations.USDA Forest Service. 2002.
14. Robert E. Burgan. 1988 Revisions to the 1978 national fire danger rating system. USDA Forest Service. 1988.
15. http://www.fs.fed.us/land/wfas/welcome.html.
16. Carlson J. D., Robert E. Burgan and David M. Engle et al. The Oklahoma Fire Danger Model: An operational tool for mesoscale fire danger rating in Oklahoma. International Journal of Wildland Fire, 2002, 11:183～191.
17. B. J. Stocks. Canadian Forest Fire Danger rating System Users' Guide. Canadia Forest Service Fire Danger Group. 1987.
18. http://cwfis.cfs.nrcan.gc.ca/en/currentJcc_fdr_e.php.
19.邸学颖 著.林火预测预报.哈尔滨:东北林业大学出版社,1993。
20.董武,张远洪,张玉贵等.卫星遥感在森林火灾监测与火险预报中的应用研究.森林防火,1994,4:31～33。
21.郭广猛.MODIS数据处理及其在林火预警中的理论与方法研究,中国科学院博士学 位论文,2004。
22.舒立福,张小罗等.林火研究综述(Ⅱ)—林火预测预报.世界林业研究,2003,16(4):34～37。
23. Robert E. Burgan. Use of Remotely Sensed Data for Fire Danger Estimation. Earsel Advances in Remote Sensing. Remote Sensing of Environment, 1996, 4(4): 1～8.
24. Robert E. Burgan, Roberta A. Hartford. Estimation of Vegetation Greenness and Site Moisture Using AVHRR Data. The 11th Conference on Fire and Forest Meteorology at Missoula, MT. April 16～19, 1991.
25. Robert E. Burgan, Roberta A. Hartford. Vegetation Condition and Fire Ocurrence: A Remote Sensing Connection. The Interior West Fire Council Meeting and Symposium, Coeurd Alene. November 1～3, 1994.
26. Robert E. Burgan, Robert W. Klaver and Jacqueline M. Klaver. Fuel Models and Fire Potential from Satellite and Surface Observations. International Journal of Wildland Fire, 1998, 8(3): 159～170.
27. By Fire Danger Working Team. Gaining A Baseic Understanding of the National Fire Danger Rating System. A Self-Study Reading Course. A Publication of the National Wildfire Coordinating Group, 2002.
28. Mark A. Finney. Calculation of Fire Spread Rates Across Random landscapes. International Journal of Wildland Fire, 2003, 12:167～174.
29. Patricia L. Andrews, Don O. Loftsgaarden and Larry S. Bradshaw. Evaluation of Fire Danger Rating Indexes Using Logistic Regression and Percentile Analysis. International Journal of Wildland Fire, 2003, 12:213～226.
30.王栋.北美林火管理的信息革命.森林防火,1996,1:6～8。
31.易浩若,纪平,覃先林.全国森林火险预报系统的研究与运行.林业科学,2004,40(3):203～207。
32. Weiqi Zhou, Yi Zhou, Shixin Wang, Qing Zhao. Early Warning For Grassland Fire Danger In North China Using Remote Sensing. IEEE, 2003, 3: 2505～2507.
33. Litao Wang, Yi Zhou, Shixin Wang, Shirong Chen. Monitoring for Grassland and Forest Fire Danger Using Remote Sensing Data. IEEE, 2004, 2: 2095～2098.
34. Yi Zhou, Shirong Chen, Weiqi Zhou, Litao Wang. Early Warning and Monitoring System For Forest and Grassland Fires by Remote Sensing Data. IEEE, 2004, 2: 4799～4802.
35. Susan L. Ustin, Dar A. Roberts. Estimating Canopy Water Content of Ctiaparral Shrubs Using Optical Methods. Remote Sensing of Environment, 1998, 65:280-291.
36. http://www.fs.fed.us/land/wfas/firepot/fpipap.htm
37. Dodi Sudiana, Hiroaki Kuze and Nobuo Takeuchi et al. Assessing Forest Fire Potential in Kalimantan Island, Indonesia, Using Satellite and Surface Weather Data. International Journal of Wildland Fire, 2003, 12:175～184.
38. http://www.ffp.csiro.au/nfm/fbm/fire_mts.html.
39. Cheney N. P. Models used for fire danger rating in Australia. In: Conference on Bushfire Modelling and Fire Danger Rating Systems Proceedings (Eds. N. P. Cheney and A. M. Gill). CSIRO. Div. Forestry, Canberra. 1991, 19～28.
40. Paltridge G. W., Barber J. Monitoring grassland dryness and fire potential in Australia with NOAA/AVHRR data. Remote Sensing of Environment, 1988, 25: 381～394.
41. N. P. Cheney. Predicting forest fire behaviour: the Australian experience. In: Integrating Research on Hazards in Fire Prone Environments (Eds T. C. Daniel and I. S. Ferguson). US MABP, Washington, 1991, 32～37.
42.叶兵.国内外森林防火技术及其发展趋势,中国林业科学院硕士论文,2000。
43.吕晋文,卢柏威.广东省森林火险天气预报系统简介.森林防火,1994,4:29～30。
44.潘莉卿,丁梅,轩春怡.北京城近郊火警与气象条件的关系.气象科技,2001,29(4):36～38。
45.常利智,任石厚.城市火险等级预报方法(洛阳市).河南气象,1998,2:10～11。
46.毛贤敏,刘桂芬,刘素沽.城乡火险预报模式探讨(辽宁省).应用气象学报,1996,7(1):76～81。
47.袁美英,许秀红,邹立尧等.黑龙江省草原火险及其预报,黑龙江气象,1997,3:36～38.
48.吴增福,田永飞,李正伟.镇江市火险天气指数预报服务系统.气象,2000,26(12):43～46.
49.李春云,成玉杰,张民等.哲盟火灾的气象条件分析及火险预报.中国农业气象,1997,18(3):30～32。
50.王振宇,金钟跃.不同天气形势下森林火险天气预报模型的建立.森林防火,1992,2:10～11。
51.李德甫,于国峰.草原林火与气象条件的关系及火险天气预报.吉林林业科技,1989,5:32～33。
52.王述洋.森林火灾重灾年(季)灾变原理和机制的研究.中国安全科学学报,2002,12(6):1～5。
53.中华人民共和国林业部.全国森林火险区划等级LY1063-92.北京:中国标准出版社,1992。
54.中华人民共和国林业部.全国森林火险天气等级LY/T 1172-93.北京:中国标准出版社,1993。
55.中华人民共和国林业部.全国森林火险天气等级LY/T 1172-95.北京:中国标准出版社,1995。
56.王栋.中国森林火险调查与区划.中国林业出版社,2000。
57.钱锦霞,张洪涛.试用三维地形模式计算茂密林区火险等级.山西气象,2001,3:15～17。
58.冯家沛,刘步宽.连云港市森林火灾发生特点及火险预报方法的研究.南京气象学院学报,1998,21(4):709～714。
59.温广玉,陈华豪.人工神经网络在林火发生预报中的应用.生物数学:学报,2001,16(2):225～228。
60.傅泽强,蔡运龙.基于灰色系统理论的森林火灾预测模型研究.林业科学,2002,38(5):95～100。
61.赵宪文.森林火灾预报的新视角.中国工程科学,2000,2(5):66～71。
62.徐丽华.地被可燃物与林型火险等级划分.辽宁林业科技,2001,6:3～6。
63.高歌,张洪涛.北京市火险气象等级预警数值模拟研究.应用气象学报,2002,13(5):609～620。
64.张子凡,李祖滨.惠州市森林火灾的气象分析和短期火险预报,1998,4:13～14。
65.郑海青,张春桂等.利用NOAA卫星遥感监测福建省森林火险.福建林学院学报,2003,23(2):114～118。
66.唐季林,张晓丽.应用RS及GIS进行林业局森林火险区划的研究.北京林业大学学报,1996,18(1):13～21。
67.编委会.森林火灾预测预报与监测巡护及火险区综合治理工程项目建设标准实用手册.中国知识出版社,2004。
68.王正非.三指标林火预报法.生态学杂志,1988,7(增):75～81。
69.王瑞军.韩家园子林业局林火行为预测预报系统的研究,东北林业大学硕士研究生论文,1990。
70. Weise D. R., and Biging G. S. A qualitative comparison of fire spread models incorporating wind and slope effects. Forest Science, 1997, 43(2): 170～180.
71.常健斌,顾凤岐,温广玉.大兴安岭林火气候的区划.东北林业大学学报,1995,23(3):98～102。
72. Dodi Sudiana, Hiroaki Kuze and Nobuo Takeuchi et al. Assessing Forest Potential in Kalimantan Island. Indonesia. Using Satellite and Surface Weather Data. International Journal of Wildland Fire, 2003, 12:175～184.
73. Maselli, F., Rodolfi A. and Conese C. Evaluation of forest fire risk by the analysis of environmental data and TM images. International Journal of Remote Sensing, 1996, 17(7):1417～1423.
74.刘玉洁,杨忠东.MODIS遥感信息处理原理与算法.学出版社,2001。
75. http://modis.gsfc.nasa.gov/data/dataproducts.html
76. Mash Nishihama, Robert Wolfe and David Solomon et al. MODIS Level 1A Earth Location: Algorithm Theoretical Basis Document Version 3. 0, 1997.
77. Hary Montgomery, Shiyue Qiu and Bob Barnes. Draft of the MODIS Level 1B Algorithm Theoretical Basis Document Version 2.0[ATBMOD-O1], 1997.
78.马建文,赵忠明,布和敖斯尔.遥感数据模型与处理方法.中国科学技术出版社,2001。
79.李怡.NOAA/AVHRR通道反照率和植被指数的大气影响订正试验.气候与环境研究,1998,3(2):180～186。
80. Vermote E., Tanre D. and Deuze J. L. et al. Second simulation of the satellite signal in the solar spectrum 6S: An overview. IEEE Transactions on Geoscience and Remote Sensing, 1994, 35:675～686.
81. Chavez, P. S. Jr. An improved dark-object subtraction technique for atmosphere scattering correction of multispectral data. Remote Sensing of Environment, 1988, 24:459～479.
82. D. Lu, P. Mausel, E. Brondizio, E. Moran. Assessment of atmospheric correction methods for Landsat TM data applicable to Amazon basin LBA research. International Journal of Remote Sensing, 1994, 13:2651～2671.
83. Chavez, P. S. Image-based atmospheric corrections-revisited and improved. Photogrammectric Engineering and Remote Sensing, 1996, 62:1025～1294.
84. E. F., Vermote, N. E1 Saleous, C. O., Justice et al. Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces: Background operational algorithm and validation. Journal of Geophysical Research, 1997, 102:17131～17141.
85. E. F. Vermote and A. Vermeulen. Atmospheric Correction Algorithm: Spectral Reflectances (MOD09) Version 4.0. MODIS Algorithm Technical Background Document, 1996.
86.朱庆,陈楚江.不规则三角网的快速建立及其动态更新.武汉测绘科技大学学报,1998,23(3):204～207。
87.居恩德.森林可燃物.森林防火,1985,(3):28～31。
88. Samran S., Woodard P. M. and Rothweell R. L. The effect of soil water on ground fuel availability. Forest Science, 1995, 41 (2): 255～267.
89. McCaw W. L., Neal J. E., and Smith R. H. Fuel accumulation following burning in young even-aged stands of karri (Eucalyptus diversicolor). Australian Forestry, 1996, 59(4): 171～177.
90. Grant C. D., Longeragan W. A. and Koch J.M. et al. Fuel characteristics, vegetation structure and fire behavior of 11-15 year-old rehabilitated bauxite mines in West Australia. Australian Forestry, 1997, 60(3): 147～157.
91. Pinol J., Filella I., Ogaya R., and Penuelas J. Groud-based speetroradiometrie estimation of live fine fuel moisture of Mediterranean plants. Agricultural and Forest Meteorology, 1998, 90: 173～186.
92.高国平,周志权,王忠友等.森林可燃物研究综述.辽宁林业科学,1998,(4):34～37。
93.覃先林,易浩若.森林可燃物研究现状及发展趋势.森林资源管理(增刊),2003,57～61。
94. http://www.fs.fed.us/land/wfas/nfdr_map.html.
95. http://nofc.cfs.nrean.gc.cedfire/cwifs/fbp.html.
96. Lenney M. E, Woodcock C. E., Colins J. B., and Hamdi H. The status of agricultural lands in Egypt: the use of multitemporal NDVI features derived from Landsat TM. Remote Sensing Environment, 1996, 56: 8～20.
97. Brian P. Oswald, John T. Francher, David L. Kulhavy and Hershel C. Reeves. Classifying Fuels with Aerial Photography in East Texas. International Journal of Wildland Fire, 1999, 9(2): 109～113.
98. Sader S. A., Ahl D., and Liou W. S. Accuracy of Landsat-TM and GIS Rule-based methods for forest wetland classification in Maine. Remote Sensing of Environment, 1995, 53: 133～144.
99.杜秀文.几种森林可燃物含水量与气象因子的关系.东北林业大学学报,1988,16(3):33～39。
100.顾凤香.死可燃物含水率的变化规律.林业科技,1995,(2):44～46。
101.胡海清.大兴安岭森林可燃物理化特性测定与分析.森林防火,1995,(1):27～31。
102.覃先林,张子辉,易浩若等.一种预测森林可燃物含水率的方法.火灾科学,2001,10(3):159～162。
103.张蓓,王世新,周艺等.利用MODIS数据进行植被水监测的应用研究.遥感信息,2004,1:19～22。
104.王瑞君,于建军,郑春艳.森林可燃物含水率预测及燃烧性等级划分.森林防火,1997,2:16～17。
105.张景群,窦彬生,叶宏谋等.八种林分类型易燃可燃物分布研究.陕西林业科技,1996,1:50～51,55。
106.张恩恕,胡传弟,孙瑞玉等.大兴安岭森林火险潜力等级系统.科学通报,1996,41:1234～1236。
107.张思玉,兰海涛.针叶幼林树冠火发生的内在机制.东北林业大学学报,1998,26(5):77～80。
108.张敏.全国可燃物类型区的划分,东北林业大学硕士研究生论文,1997。
109.郑焕能,胡海清.几种红松可燃物类型的研究.森林防火,1988,1:32～34。
110.郑焕能,胡海清.东北东部山地可燃物类型的研究.森林防火,1990,(3):8～10。
111.何忠秋,李长胜,张成钢等.森林可燃物载量模型研究.森林防火,1993,(3):11～13。
112.刘晓东,王军,王东升等.大兴安岭地区兴安落叶松林可燃物模型的研究.森林防火,1995,(3):8～12。
113.赵宪文,李崇贵等.基于“3S”的森林资源定量估测:原理、方法、应用及软件实现.北京:中国林业出版社,2001。
114. Kate Brandis and Carol Jacobson. Estimation of Vegetation Fuel Loads Using Landsat TM Imagery in New South Wales, Australia. International Journal of Wildland Fire, 2003, 12:185～194.
115. Ehrlich D., J. E. Estes and Singh A. Application of NOAA—AVHRR 1km data for environmental monitoring. International Journal of Remote Sensing, 1994, 15(1): 145～161.
116. Cihlar, J., Manak, D., and Iorio M. Evaluation of compositing algorithms for AVHRR data over land. IEEE. Transactions on Geosciences and Remote Sensing, 1994, 32:427～437.
117. Fleischman, C. G., and Walsh, S. J. Multitemporal AVHRR digital data: an approach for land cover mapping of heterogeneous landscapes. Geocarto International, 1991, (4): 5～20.
118. Loveland T. R., Merchant J. W. and Brown J. F. etc. Map Supplement: Seasonal Land Cover Regions of the United States. Annals of the Association of American Geographers, 1995, 82(2): 339～355.
119. Goward S. N., C. J. Tucker and Dye D. G.. North American Vegetation Patterns Observed with the NOAA-7 Advanced Very High Resolution Radiometer. Vegetation, 1985, 1: 27～39.
120. Kate Brandis and Carol Jacobson. Estimation of Vegetative Fuel Loads Using Landsat TM Imagery in New South Wales. Australia. International Journal of Wildland Fire, 2003, 12:185～194.
121. Tucker, C. J., Townshend, J. R. G. and Goff T. E. African land cover classification using satellite data. Science, 1985, 22(7):369～375.
122.盛永伟,陈维英,肖乾广等.利用气象卫星植被指数进行中国植被宏观分类.科学通报,1995,40(1):68～71。
123.朱启疆.应用气象卫星NOAA-AVHRR图像进行植被研究的多时相途径.环境遥感,1991,6(2):99～105。
124. Holben, B. Characteristics of Maximum Value Composite Images from Temporal AVHRR Data. International Journal of Remote Sensing, 1986, 7:1417～1434.
125. Loveland T. R., Belward A. S. The IGBP—DIS global lkm land cover data set Discover: first results. International Journal of Remote Sensing, 1997, 18(15): 3289～3295.
126. Townshend J., C. Justice, and W. Li. Global Land Cover Classification by Remote Sensing: Present Capabilities and Future Possibilities. Remote Sensing Environment, 1991, 35: 243～255.
127.曾志远 著.卫星遥感图像计算机分类与地学应用研究.科学出版社,2004。
128. Liang S. Land cover classification methods for multi year AVHRR data. International Journal of Remote Sensing, 2001, 22(8): 1479～1493.
129.李晓兵.NOAA/AVHRR数据在土地覆盖变化研究中的应用.地学前缘,1997,4(1):16。
130. Rogers D. J., HAY S. I., PACKER M. J., Wint G. R. W. Mapping land cover over large areas using multispectral data derived from the NOAA/AVHRR: a case study of Nigeria. International Journal of Remote Sensing, 1997, 18 (15): 3297～3303.
131. Townshend J., Justice C. O. and Kalb V. Characterization and classification of South American land cover types using satellite data. International Journal of Remote Sensing, 1987, 7: 1395～1416.
132. Gopal S., Woodcock C. E., and Strahler A. H. Fuzzy neural network classification of global land cover from a 1 degree AVHRR data set. Remote Sensing of Environment, 1999, 67, 230～243.
133. Townshend, J. G. R. Global data sets for land application from Advanced Very High Resolution Radiometer: an introduction. International Journal of Remote Sensing, 1994, 15: 3319～3332.
134. Campbell, J. B. A Geographical Analysis of Image Interpretation Methods. The Professional Geographer, 1978, 30(3): 264～269.
135. Lambin, E. F. and Ehrlich, D. Combining vegetation indices and surface temperature for land cover mapping at broad spatial scales. International Journal of Remote Sensing, 1995, 16, 573～579.
136. Wen C. G.., and Tateishi R. 30-second degree grid landcover classification of Asia. International Journal of Remote Sensing, 2001, 22 (18): 3845～3854.
137.香宝,刘纪远,张增祥.东亚土地覆盖环境背景数字地面模型研究.地理研究,2001,20(6):653～660。
138. Pan Y., Li X., and Gong R etc. An integrative classification of vegetation in China based on NOAA/AVHRR and vegetation—climate indices of the Holdridge life zone. International Journal of Remote Sensing, 2003, 24(5): 1009～1027.
139. Defries R., Hansen M., and Townshend J. Global discrimination of land cover types from matrices derived from AVHRR pathfinder Data. Remote Sensing of Environment, 1995, 54,209～222.
140.吴健平.区域土地利用/土地覆盖遥感调查.上海:华东师范大学出版社,1999。
141.聂娟.大尺度中国土地覆盖综合分类比较研究,北京师范大学硕士学位论文,2003。
142.倪健.植被—气候分类指标及其应用.生态学杂志,1998,17(2):33～44。
143. Cort J. W. and Scott M. R. Climatologically aided interpolation (CAI) of Terrestrial air temperature. International Journal of Climatology, 1995, (15): 221～229.
144. Robeson S. M. Influence of Spatial sampling and interpolation on estimates of air temperature change. Climate Research, 1994, 4:119～126.
145.徐建华.现代地理学中的数学方法.高等教育出版社,2002。
146.田庆久,闵祥军.植被指数研究进展.地球科学进展,1998,13(4):327～333。
147. Illera P., Fernandez A. and Delgado J. A. Temporal evolution of the NDVI as an indicator of forest fire danger. International Journal of Remote Sensing, 1996, 17:1093～1105.
148. Paltridge G. W. and Barber, J. Monitoring grassland dryness and fire potential in Australia with NOAA/AVHRR data. Remote Sensing of Environment, 1988, 25: 381～394.
149. Lopez S., Gonzalez, F., Llop R. and Cuevas J. M. An evaluation of the utility of NOAA AVHRR images for monitoring forest fire risk in Spain. International Journal of Remote Sensing, 1991, 12:1841～1851.
150. Fabio Maselli, Stefano Romanelli and Lorenzo Bottai, etc. Use of NOAA-AVHRR NDVI images for the estimation of dynamic fire risk in Mediterranean areas. Remote Sensing of Environment, 2003, 86:187～197.
151.覃先林,易浩若.MODIS数据在树种长势监测中的应用.遥感技术与应用,2003,18(3):123～128。
152.王正兴,刘闯,Huete Alfredo.植被指数研究进展:从AVHRR—NDVI到MODIS—EVI.生态学报,2003,23(5):979～987。
153. Arnon Karnieli, Yoram J. Kaufman, Lorraine Remer and Andrew Wald. AFRI—aerosol free vegetation index. Remote Sensing of Environment, 2001, 77(1): 10～21.
154.李小文,高峰等.二向性归一化植被指数:概念及应用,自然科学进展,2001,11(8):819～823.
155. Roujean J L. A bidirectional reflectance model of the earth's surface for the correction of remote sensing. Journal of Geophysic Research, 1992, 7: 20455～20468.
156.田年军,宋卫东.长白山森林可燃物含水率分布规律的研究.吉林林业科技,1997,3:5～11。
157.马丽华,李兆山.大兴安岭6种活森林可燃物含水率的测试与研究.吉林林学院学报,1998,14(1):21～23。
158.王金叶,车克钧.可燃物含水率与气象要素相关性研究.甘肃林业科技,1994, 2:21～23。
159.金森,李绪尧,李有祥.几种细小可燃物失水过程中含水率的变化规律.东北林业大学学报,2000,28(1):35～38。
160. Bowman W. D. The relationship between leaf water status, gas exchange, and spectral reflectance in cotton leaves. Remote Sensing of Environment, 1989, 30: 249～255.
161. Cohen W. B. Response of Vegetation Indexes to Changes in 3 Measures of Leaf Water-Stress. Photogrammetric Engineering and Remote Sensing, 1991, 57:195～202.
162. Pietro Ceccato, Ste'phane Flasse and Stefano Tarantola etc. Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sensing of Environment, 2001, 77: 22～33.
163. Pietro Ceccato, Nadine Gobron, Ste'phane Flasse, Jean-Marie Gre'goire. Designing a spectral index to estimate vegetation water content from remote sensing data. Part 1: Theoretical approach. Remote Sensing of Environment, 2002, 82:188～197.
164. Pietro Ceccato, Ste'phane Flasse, Jean-Marie Gre'goire. Designing a spectral index to estimate vegetation water content from remote sensing data. Part 2: Validation and applications. Remote Sensing of Environment, 2002, 82: 198～207.
165. Chuvieco E., Riano, Aguado I. and Cocero D. Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper Reflectance data: applications in fire danger assessment. International Journal of Remote Sensing, 2002, 23(11):2145～2162.
166.刘勇卫,贺雪鸿 译.遥感精解.武汉:中国测绘出版社,1993。
167. Carter G. A. (). Ratios of leaf reflectances in narrow wavebands indicator of plant stress. International Journal of Remote Sensing. 1994, 15:697～703.
168. Hunt E. R. and Rock B. N. Detection of changes in leaf water content using near and middle-infrared reflectances. Remote Sensing of environment, 1989, 22: 429～435.
169. Jackson R. D., Idso S. B., Reginato R. J., and Pinter P. J. Canopy temperature as a crop water stress indicator. Water Resource, 1981, 1:1133～1138.
170. Moran M. S., Clarke T. R., Inoue Y., and Vidal, A. Estimating Crop Water-Deficit Using the Relation between Surface-Air Temperature and Spectral Vegetation Index. Remote Sensing of Environment, 1994, 49: 246～263.
171. Vidal. Evaluating forest fire hazard with a Landsat TM derived water stress index. Agricultural and Forest Meteorology, 1995, 77: 207～224.
172. Norman J. M., Divakarla M., and Goel N. S. Algorithms for Extracting Information from Remote Thermal IR Observations of the Earths Surface. Remote Sensing of Environment, 1995, 51(1):157～168.
173. Vidal A., Pinglo F., and Durand H. etc. Evaluation of a temporal fire risk index in Mediterranean forest from NOAA thermal IR. Remote Sensing of Environment, 1994, 49: 296～303.
174. Chuvieco E., Riano D., Aguado I., and Cocero D. Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: applications in fire danger assessment. International Journal of Remote Sensing, 2002, 23(11):2145～2162.
175.易浩若.利用NOAA/AVHRR资料监测南方林区森林火灾的研究.国土资源遥感,1995,(1):24～30。
176.易浩若.专家系统用于遥感图像处理的方法研究.林业科学研究,1994,7(1):13～17。
177.易浩若,纪平.森林过火面积的遥感测算方法研究.遥感技术与应用,1998,13(2):10～14。
178.易浩若,纪平.林火卫星遥感监测及早期报警技术研究.遥感技术与应用,1996,11(1):40～46。
179.纪平,易浩若,白黎娜.人工神经网络识别NOAA数据异常高温点的研究.重大自然灾害遥感监测与评估研究进展.北京:科学技术出版社,1993。
180.陈立新,景东侠等.用极轨卫星监测森林火情的原理和方法.森林防火,1997,2:18～24。
181.覃先林.NOAA/AVHRR数据林火信息提取方法研究,中国林科院硕士学位论文,1999,5～12。
182. Pierre Carrega. Instantaneous and Automatic Identification of Weather Types for a forest Fire Decision Support System. International Journal of Remote Sensing Wildland Fire, 1997, 7(3): 265～274.
183. Lopei, S., Gonzalez-Alonso F., Llop, R., and Cuevas, J. M. An evaluation of the utility of NOAA-AVHRR images for monitoring forest fire risk in Spain. International Journal of Remote Sensing, 1991, 12: 1842～1851.
184. Kasischke E. S. and French H. F. N. Locating and estimating the area extent of wildfires in Alaskan boreal forests using multiple-season AVHRR NDVI composite data. Remote Sens. Environ, 1995, 51: 263～275.
185. Yoram J. Kaufman, etc. Algorithm for Remote Sensing of Tropospheric Aerosol from MODIS. Algorithm Technical Background Document, 1998, 10.
186. Kaufman, Y. J., Herring, D. D., Ranson, K. J. and Collatz, G. J. Earth Observing System AM1 mission to Earth. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36: 1045～1055.
187. Kaufman, Y. J., Justice, C. O. and Flynn, L. P., etc. Potential global fire monitoring from EOS-MODIS. Journal of Geophysical Research, 1998, 103: 32215～32238.
188. Louis Giglio, etc. An Enhanced Contextual Fire Detection Algorithm for MODIS. Remote Sensing of Environment, 2003, 87: 273-282.
189.Giglio, L., Kendall, J. D. and Justice, C.O. Evaluation of global fire detection algorithms using simulated AVHRR infrared data. International Journal of Remote Sensing, 1999, 20: 1947-1985.
190.Justice, C.O., Giglio, L. and Korontzi, S. etc. The MODIS fire products. Remote Sensing of Environment, 2002, 83: 244- 262.
191.Justice, C.O., Kendall, J. D., Dowty, P. R. and Scholes, R. J. Satellite remote sensing of fires during the SAFARI campaign using NOAA AVHRR data. Journal of Geophysical Research, 1996, 101: 23851- 23863.
192.Justice, C.O., Townshend, J. R. G., and Vermote, E. F. etc. An overview of MODIS land data processing and product status. Remote Sensing of Environment, 2002, 83: 244-262.
193.Morisette, J. T., Privette, J. L., Justice, C.O. A framework for the validation of MODIS land products. Remote Sensing of Environment. 2002, 83: 77-96.
194.Nishihama, M., Wolfe, R., Solomon, D., and Patt, F., etc. MODIS level 1A Earth location: Algorithm theoretical basis document version 3.0. SDST-092, MODIS Science Data Support Team, 1997.
195.Seielstad, C. A., Riddering, J. P., and Brown, S. R., etc. Testing the sensitivity of a MODIS-like daytime active fire detection model in Alaska using NOAA/AVHRR infrared data. Photogrammetric Engineering and Remote Sensing, 2002, 68: 831-838.
196.Flases,S.P. and Ceccato. A contextual algorithm for AVHRR fire detection. International Journal of Remote Sensing, 1996, 17(2): 419-424.
197.Andrew J., Harris, L. Towards Automated Fire Monitoring From Space: Semi-Automated Mapping of the January 1994 New South Wales Wildfires Using AVHRR Data. International Journal of WildLand Fire, 1996, 6(3): 107- 116.
198.Stroppiana, D., Pinnock, S., Gre'goire, J. M. The Global Fire Product: Daily fire occurrence from April 1992 to December 1993 derived from NOAA AVHRR data. International Journal of Remote Sensing, 2000,21: 1279- 1288.
199.Masuoka, E., Fleig, A., Wolfe, R. W., Patt, F. Key characteristics of the MODIS data products. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36. 1313- 1323.
200.Rauste, Y. and Herladez, E. Satellite-based forest fire detection for fire control in boreal forests. International Journal of Remote Sensing, 1997,18(12): 2641—2656.
201.Pozo, D., Olmo, F. J. and L.Alados-Arboledas. Fire Detection and Monitoring Using a multitemporal Technique on AVHRR Mid-Infrared and Thermal Channels. Remote
Sensing of Environment, 1997, 60, 111～120.
202. Franca, G. B. and Cracknell, A. P. A simple cloud masking approach using NOAA daytime data for tropical areas. International Journal of Remote Sensing, 1995, 16 (9): 1697～1705.
203. Fang, M. and Huang, W. Tracking the Indonesian forest fire using NOAA/AVHRR images. International Journal of Remote Sensing, 1998, 19(3):387～390.
204. Edward A. Ashton and Dan G. Baldwin. Algorithms for the Detection of Sub Pixel Targets in Multispectral Imagery. Photogrammetric Engineering & Remote Sensing, 1998, 64(7): 723～731.
205. Vincent G. Ambrosia, etc. An Integration of Remote Sensing, GIS, and Information Distribution for Wildfire Detection and Management, Photogrammetric Engineering Remote Sensing, 1998, 64(10): 977～985.
206. Ackerman, S. A., Strabala, K. I., and Menzel, W. P., etc. Discriminating clear sky from clouds with MODIS. Journal of Geophysical Research, 1998, 103: 32141～32157.
207. Dowty, P. R. A theoretical study of fire detection using AVHRR data. MS Thesis. Department of Environmental Sciences. University of Virginia. Charlottesville. Virginia. USA. 1993.
208. Dozier, J. A method for satellite identification of surface temperature fields of subpixel resolution. Remote Sensing of Environment, 1981, 11:221～229.
209. Flasse, S. P. and Ceccato, P. A contextual algorithm for AVHRR fire detection. International Journal of Remote Sensing, 1996, 17:419～424.
210. Matson, M. and Dozier, J. Identification of subresolution high temperature sources using a thermal IR sensor. Photogrammetric Engineering and Remote Sensing, 1981, 47: 1311～1318.
211.覃先林,易浩若.基于MODIS数据的林火识别方法研究.火灾科学,2004 13(2):83～89。
212. Zhan, X., Huang, C., and Townshend, J. R. G., etc. Land cover change detection with change vector in the red and near-infrared reflectance space. IGARSS'98. IEEE International Geoscience and Remote Sensing Symposium Proceedings. Seattle. Washington, 1998, Vol. Ⅱ: 859～861.
213. Zhan, X., DeFries, R. and Townshend J. R. G. etc. The 250m global land cover change product from the Moderate Resolution Imaging Speetroradiometer of NASA's Earth Observing System. International Journal of Remote Sensing, 2000, 21: 1433～1460.
214. Cihlar J., Hung L., and Xiao Q. Land cover classification with AVHRR mulitichannel composites in northern environments. Remote Sensing of Environment, 1996, 58: 36～51.
215. Holdaway M. R. Spatial modeling and interpolation of monthly temperature using Kriging. Climate Research, 1996, 24:1835～1845.
216. Robeson S. M. Spatial interpolation, network bias, and terrestrial air temperature variability. Public Climate, 1993a, 46(1): 1～15.
217. Robeson, S. M. and Willmott C. Spherical spatial interpolation and terrestrial air temperature variability Proceedings. Second International Conference on Integrating GIS and Environmental Modeling, Breckenridge, CO., 1993b.
218.林忠辉,莫兴国,李宏轩等.中国陆地区域气象要素的空间插值.地理学报,2002,57(1):47～56。
219. Shunlin Liang. Quantilative Remote Sensing of Land Surface. WILEY INTERSCIENCE: A JOHN WILEY & SONS. INC. PUBLICATION, 2004.
2.周生贤 编著.中国林业的历史性转变.北京:中国林业出版社,2002。
3.宋志杰 编著.林火原理和林火预报.气象出版社,1991。
4.郑焕能 等编著.中国东北林火.哈尔滨:东北林业大学出版社,2000。
5.舒立福,田晓瑞.世界森林火灾状况综述.世界林业研究,1998,11(6):41～46。
6.赵宪文 主编.森林火灾遥感监测评价.北京:中国林业出版社,1995。
7.毕忠镇.中国森林火灾现状、发生规律及建议.1999年减轻自然灾害学术研讨会论文汇编.北京:中国科协学会部,1999。
8.承继成,林珲,周成虎 等编著.数字地球导论.北京:科学出版社,2000。
9.李德仁,关泽群.空间信息系统的集成与实现.武汉测绘科技大学出版社,2000。
10.易浩若.信息技术推进森林防火现代化.台北:海峡两岸森林保育研讨会论文集.台湾中华林学会,1999。
11.舒立福,田晓瑞.3S集成技术在林火管理中的应用研究.火灾科学,1998,(1):46～51。
12.徐祥德,王馥棠,萧永生 等编著.农业气象防灾调控工程与技术系统.北京:气象出版社,2000。
13. Jack D. Cohen, John E. Deeming. The national fire danger rating system: Basic equations.USDA Forest Service. 2002.
14. Robert E. Burgan. 1988 Revisions to the 1978 national fire danger rating system. USDA Forest Service. 1988.
15. http://www.fs.fed.us/land/wfas/welcome.html.
16. Carlson J. D., Robert E. Burgan and David M. Engle et al. The Oklahoma Fire Danger Model: An operational tool for mesoscale fire danger rating in Oklahoma. International Journal of Wildland Fire, 2002, 11:183～191.
17. B. J. Stocks. Canadian Forest Fire Danger rating System Users' Guide. Canadia Forest Service Fire Danger Group. 1987.
18. http://cwfis.cfs.nrcan.gc.ca/en/currentJcc_fdr_e.php.
19.邸学颖 著.林火预测预报.哈尔滨:东北林业大学出版社,1993。
20.董武,张远洪,张玉贵等.卫星遥感在森林火灾监测与火险预报中的应用研究.森林防火,1994,4:31～33。
21.郭广猛.MODIS数据处理及其在林火预警中的理论与方法研究,中国科学院博士学 位论文,2004。
22.舒立福,张小罗等.林火研究综述(Ⅱ)—林火预测预报.世界林业研究,2003,16(4):34～37。
23. Robert E. Burgan. Use of Remotely Sensed Data for Fire Danger Estimation. Earsel Advances in Remote Sensing. Remote Sensing of Environment, 1996, 4(4): 1～8.
24. Robert E. Burgan, Roberta A. Hartford. Estimation of Vegetation Greenness and Site Moisture Using AVHRR Data. The 11th Conference on Fire and Forest Meteorology at Missoula, MT. April 16～19, 1991.
25. Robert E. Burgan, Roberta A. Hartford. Vegetation Condition and Fire Ocurrence: A Remote Sensing Connection. The Interior West Fire Council Meeting and Symposium, Coeurd Alene. November 1～3, 1994.
26. Robert E. Burgan, Robert W. Klaver and Jacqueline M. Klaver. Fuel Models and Fire Potential from Satellite and Surface Observations. International Journal of Wildland Fire, 1998, 8(3): 159～170.
27. By Fire Danger Working Team. Gaining A Baseic Understanding of the National Fire Danger Rating System. A Self-Study Reading Course. A Publication of the National Wildfire Coordinating Group, 2002.
28. Mark A. Finney. Calculation of Fire Spread Rates Across Random landscapes. International Journal of Wildland Fire, 2003, 12:167～174.
29. Patricia L. Andrews, Don O. Loftsgaarden and Larry S. Bradshaw. Evaluation of Fire Danger Rating Indexes Using Logistic Regression and Percentile Analysis. International Journal of Wildland Fire, 2003, 12:213～226.
30.王栋.北美林火管理的信息革命.森林防火,1996,1:6～8。
31.易浩若,纪平,覃先林.全国森林火险预报系统的研究与运行.林业科学,2004,40(3):203～207。
32. Weiqi Zhou, Yi Zhou, Shixin Wang, Qing Zhao. Early Warning For Grassland Fire Danger In North China Using Remote Sensing. IEEE, 2003, 3: 2505～2507.
33. Litao Wang, Yi Zhou, Shixin Wang, Shirong Chen. Monitoring for Grassland and Forest Fire Danger Using Remote Sensing Data. IEEE, 2004, 2: 2095～2098.
34. Yi Zhou, Shirong Chen, Weiqi Zhou, Litao Wang. Early Warning and Monitoring System For Forest and Grassland Fires by Remote Sensing Data. IEEE, 2004, 2: 4799～4802.
35. Susan L. Ustin, Dar A. Roberts. Estimating Canopy Water Content of Ctiaparral Shrubs Using Optical Methods. Remote Sensing of Environment, 1998, 65:280-291.
36. http://www.fs.fed.us/land/wfas/firepot/fpipap.htm
37. Dodi Sudiana, Hiroaki Kuze and Nobuo Takeuchi et al. Assessing Forest Fire Potential in Kalimantan Island, Indonesia, Using Satellite and Surface Weather Data. International Journal of Wildland Fire, 2003, 12:175～184.
38. http://www.ffp.csiro.au/nfm/fbm/fire_mts.html.
39. Cheney N. P. Models used for fire danger rating in Australia. In: Conference on Bushfire Modelling and Fire Danger Rating Systems Proceedings (Eds. N. P. Cheney and A. M. Gill). CSIRO. Div. Forestry, Canberra. 1991, 19～28.
40. Paltridge G. W., Barber J. Monitoring grassland dryness and fire potential in Australia with NOAA/AVHRR data. Remote Sensing of Environment, 1988, 25: 381～394.
41. N. P. Cheney. Predicting forest fire behaviour: the Australian experience. In: Integrating Research on Hazards in Fire Prone Environments (Eds T. C. Daniel and I. S. Ferguson). US MABP, Washington, 1991, 32～37.
42.叶兵.国内外森林防火技术及其发展趋势,中国林业科学院硕士论文,2000。
43.吕晋文,卢柏威.广东省森林火险天气预报系统简介.森林防火,1994,4:29～30。
44.潘莉卿,丁梅,轩春怡.北京城近郊火警与气象条件的关系.气象科技,2001,29(4):36～38。
45.常利智,任石厚.城市火险等级预报方法(洛阳市).河南气象,1998,2:10～11。
46.毛贤敏,刘桂芬,刘素沽.城乡火险预报模式探讨(辽宁省).应用气象学报,1996,7(1):76～81。
47.袁美英,许秀红,邹立尧等.黑龙江省草原火险及其预报,黑龙江气象,1997,3:36～38.
48.吴增福,田永飞,李正伟.镇江市火险天气指数预报服务系统.气象,2000,26(12):43～46.
49.李春云,成玉杰,张民等.哲盟火灾的气象条件分析及火险预报.中国农业气象,1997,18(3):30～32。
50.王振宇,金钟跃.不同天气形势下森林火险天气预报模型的建立.森林防火,1992,2:10～11。
51.李德甫,于国峰.草原林火与气象条件的关系及火险天气预报.吉林林业科技,1989,5:32～33。
52.王述洋.森林火灾重灾年(季)灾变原理和机制的研究.中国安全科学学报,2002,12(6):1～5。
53.中华人民共和国林业部.全国森林火险区划等级LY1063-92.北京:中国标准出版社,1992。
54.中华人民共和国林业部.全国森林火险天气等级LY/T 1172-93.北京:中国标准出版社,1993。
55.中华人民共和国林业部.全国森林火险天气等级LY/T 1172-95.北京:中国标准出版社,1995。
56.王栋.中国森林火险调查与区划.中国林业出版社,2000。
57.钱锦霞,张洪涛.试用三维地形模式计算茂密林区火险等级.山西气象,2001,3:15～17。
58.冯家沛,刘步宽.连云港市森林火灾发生特点及火险预报方法的研究.南京气象学院学报,1998,21(4):709～714。
59.温广玉,陈华豪.人工神经网络在林火发生预报中的应用.生物数学:学报,2001,16(2):225～228。
60.傅泽强,蔡运龙.基于灰色系统理论的森林火灾预测模型研究.林业科学,2002,38(5):95～100。
61.赵宪文.森林火灾预报的新视角.中国工程科学,2000,2(5):66～71。
62.徐丽华.地被可燃物与林型火险等级划分.辽宁林业科技,2001,6:3～6。
63.高歌,张洪涛.北京市火险气象等级预警数值模拟研究.应用气象学报,2002,13(5):609～620。
64.张子凡,李祖滨.惠州市森林火灾的气象分析和短期火险预报,1998,4:13～14。
65.郑海青,张春桂等.利用NOAA卫星遥感监测福建省森林火险.福建林学院学报,2003,23(2):114～118。
66.唐季林,张晓丽.应用RS及GIS进行林业局森林火险区划的研究.北京林业大学学报,1996,18(1):13～21。
67.编委会.森林火灾预测预报与监测巡护及火险区综合治理工程项目建设标准实用手册.中国知识出版社,2004。
68.王正非.三指标林火预报法.生态学杂志,1988,7(增):75～81。
69.王瑞军.韩家园子林业局林火行为预测预报系统的研究,东北林业大学硕士研究生论文,1990。
70. Weise D. R., and Biging G. S. A qualitative comparison of fire spread models incorporating wind and slope effects. Forest Science, 1997, 43(2): 170～180.
71.常健斌,顾凤岐,温广玉.大兴安岭林火气候的区划.东北林业大学学报,1995,23(3):98～102。
72. Dodi Sudiana, Hiroaki Kuze and Nobuo Takeuchi et al. Assessing Forest Potential in Kalimantan Island. Indonesia. Using Satellite and Surface Weather Data. International Journal of Wildland Fire, 2003, 12:175～184.
73. Maselli, F., Rodolfi A. and Conese C. Evaluation of forest fire risk by the analysis of environmental data and TM images. International Journal of Remote Sensing, 1996, 17(7):1417～1423.
74.刘玉洁,杨忠东.MODIS遥感信息处理原理与算法.学出版社,2001。
75. http://modis.gsfc.nasa.gov/data/dataproducts.html
76. Mash Nishihama, Robert Wolfe and David Solomon et al. MODIS Level 1A Earth Location: Algorithm Theoretical Basis Document Version 3. 0, 1997.
77. Hary Montgomery, Shiyue Qiu and Bob Barnes. Draft of the MODIS Level 1B Algorithm Theoretical Basis Document Version 2.0[ATBMOD-O1], 1997.
78.马建文,赵忠明,布和敖斯尔.遥感数据模型与处理方法.中国科学技术出版社,2001。
79.李怡.NOAA/AVHRR通道反照率和植被指数的大气影响订正试验.气候与环境研究,1998,3(2):180～186。
80. Vermote E., Tanre D. and Deuze J. L. et al. Second simulation of the satellite signal in the solar spectrum 6S: An overview. IEEE Transactions on Geoscience and Remote Sensing, 1994, 35:675～686.
81. Chavez, P. S. Jr. An improved dark-object subtraction technique for atmosphere scattering correction of multispectral data. Remote Sensing of Environment, 1988, 24:459～479.
82. D. Lu, P. Mausel, E. Brondizio, E. Moran. Assessment of atmospheric correction methods for Landsat TM data applicable to Amazon basin LBA research. International Journal of Remote Sensing, 1994, 13:2651～2671.
83. Chavez, P. S. Image-based atmospheric corrections-revisited and improved. Photogrammectric Engineering and Remote Sensing, 1996, 62:1025～1294.
84. E. F., Vermote, N. E1 Saleous, C. O., Justice et al. Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces: Background operational algorithm and validation. Journal of Geophysical Research, 1997, 102:17131～17141.
85. E. F. Vermote and A. Vermeulen. Atmospheric Correction Algorithm: Spectral Reflectances (MOD09) Version 4.0. MODIS Algorithm Technical Background Document, 1996.
86.朱庆,陈楚江.不规则三角网的快速建立及其动态更新.武汉测绘科技大学学报,1998,23(3):204～207。
87.居恩德.森林可燃物.森林防火,1985,(3):28～31。
88. Samran S., Woodard P. M. and Rothweell R. L. The effect of soil water on ground fuel availability. Forest Science, 1995, 41 (2): 255～267.
89. McCaw W. L., Neal J. E., and Smith R. H. Fuel accumulation following burning in young even-aged stands of karri (Eucalyptus diversicolor). Australian Forestry, 1996, 59(4): 171～177.
90. Grant C. D., Longeragan W. A. and Koch J.M. et al. Fuel characteristics, vegetation structure and fire behavior of 11-15 year-old rehabilitated bauxite mines in West Australia. Australian Forestry, 1997, 60(3): 147～157.
91. Pinol J., Filella I., Ogaya R., and Penuelas J. Groud-based speetroradiometrie estimation of live fine fuel moisture of Mediterranean plants. Agricultural and Forest Meteorology, 1998, 90: 173～186.
92.高国平,周志权,王忠友等.森林可燃物研究综述.辽宁林业科学,1998,(4):34～37。
93.覃先林,易浩若.森林可燃物研究现状及发展趋势.森林资源管理(增刊),2003,57～61。
94. http://www.fs.fed.us/land/wfas/nfdr_map.html.
95. http://nofc.cfs.nrean.gc.cedfire/cwifs/fbp.html.
96. Lenney M. E, Woodcock C. E., Colins J. B., and Hamdi H. The status of agricultural lands in Egypt: the use of multitemporal NDVI features derived from Landsat TM. Remote Sensing Environment, 1996, 56: 8～20.
97. Brian P. Oswald, John T. Francher, David L. Kulhavy and Hershel C. Reeves. Classifying Fuels with Aerial Photography in East Texas. International Journal of Wildland Fire, 1999, 9(2): 109～113.
98. Sader S. A., Ahl D., and Liou W. S. Accuracy of Landsat-TM and GIS Rule-based methods for forest wetland classification in Maine. Remote Sensing of Environment, 1995, 53: 133～144.
99.杜秀文.几种森林可燃物含水量与气象因子的关系.东北林业大学学报,1988,16(3):33～39。
100.顾凤香.死可燃物含水率的变化规律.林业科技,1995,(2):44～46。
101.胡海清.大兴安岭森林可燃物理化特性测定与分析.森林防火,1995,(1):27～31。
102.覃先林,张子辉,易浩若等.一种预测森林可燃物含水率的方法.火灾科学,2001,10(3):159～162。
103.张蓓,王世新,周艺等.利用MODIS数据进行植被水监测的应用研究.遥感信息,2004,1:19～22。
104.王瑞君,于建军,郑春艳.森林可燃物含水率预测及燃烧性等级划分.森林防火,1997,2:16～17。
105.张景群,窦彬生,叶宏谋等.八种林分类型易燃可燃物分布研究.陕西林业科技,1996,1:50～51,55。
106.张恩恕,胡传弟,孙瑞玉等.大兴安岭森林火险潜力等级系统.科学通报,1996,41:1234～1236。
107.张思玉,兰海涛.针叶幼林树冠火发生的内在机制.东北林业大学学报,1998,26(5):77～80。
108.张敏.全国可燃物类型区的划分,东北林业大学硕士研究生论文,1997。
109.郑焕能,胡海清.几种红松可燃物类型的研究.森林防火,1988,1:32～34。
110.郑焕能,胡海清.东北东部山地可燃物类型的研究.森林防火,1990,(3):8～10。
111.何忠秋,李长胜,张成钢等.森林可燃物载量模型研究.森林防火,1993,(3):11～13。
112.刘晓东,王军,王东升等.大兴安岭地区兴安落叶松林可燃物模型的研究.森林防火,1995,(3):8～12。
113.赵宪文,李崇贵等.基于“3S”的森林资源定量估测:原理、方法、应用及软件实现.北京:中国林业出版社,2001。
114. Kate Brandis and Carol Jacobson. Estimation of Vegetation Fuel Loads Using Landsat TM Imagery in New South Wales, Australia. International Journal of Wildland Fire, 2003, 12:185～194.
115. Ehrlich D., J. E. Estes and Singh A. Application of NOAA—AVHRR 1km data for environmental monitoring. International Journal of Remote Sensing, 1994, 15(1): 145～161.
116. Cihlar, J., Manak, D., and Iorio M. Evaluation of compositing algorithms for AVHRR data over land. IEEE. Transactions on Geosciences and Remote Sensing, 1994, 32:427～437.
117. Fleischman, C. G., and Walsh, S. J. Multitemporal AVHRR digital data: an approach for land cover mapping of heterogeneous landscapes. Geocarto International, 1991, (4): 5～20.
118. Loveland T. R., Merchant J. W. and Brown J. F. etc. Map Supplement: Seasonal Land Cover Regions of the United States. Annals of the Association of American Geographers, 1995, 82(2): 339～355.
119. Goward S. N., C. J. Tucker and Dye D. G.. North American Vegetation Patterns Observed with the NOAA-7 Advanced Very High Resolution Radiometer. Vegetation, 1985, 1: 27～39.
120. Kate Brandis and Carol Jacobson. Estimation of Vegetative Fuel Loads Using Landsat TM Imagery in New South Wales. Australia. International Journal of Wildland Fire, 2003, 12:185～194.
121. Tucker, C. J., Townshend, J. R. G. and Goff T. E. African land cover classification using satellite data. Science, 1985, 22(7):369～375.
122.盛永伟,陈维英,肖乾广等.利用气象卫星植被指数进行中国植被宏观分类.科学通报,1995,40(1):68～71。
123.朱启疆.应用气象卫星NOAA-AVHRR图像进行植被研究的多时相途径.环境遥感,1991,6(2):99～105。
124. Holben, B. Characteristics of Maximum Value Composite Images from Temporal AVHRR Data. International Journal of Remote Sensing, 1986, 7:1417～1434.
125. Loveland T. R., Belward A. S. The IGBP—DIS global lkm land cover data set Discover: first results. International Journal of Remote Sensing, 1997, 18(15): 3289～3295.
126. Townshend J., C. Justice, and W. Li. Global Land Cover Classification by Remote Sensing: Present Capabilities and Future Possibilities. Remote Sensing Environment, 1991, 35: 243～255.
127.曾志远 著.卫星遥感图像计算机分类与地学应用研究.科学出版社,2004。
128. Liang S. Land cover classification methods for multi year AVHRR data. International Journal of Remote Sensing, 2001, 22(8): 1479～1493.
129.李晓兵.NOAA/AVHRR数据在土地覆盖变化研究中的应用.地学前缘,1997,4(1):16。
130. Rogers D. J., HAY S. I., PACKER M. J., Wint G. R. W. Mapping land cover over large areas using multispectral data derived from the NOAA/AVHRR: a case study of Nigeria. International Journal of Remote Sensing, 1997, 18 (15): 3297～3303.
131. Townshend J., Justice C. O. and Kalb V. Characterization and classification of South American land cover types using satellite data. International Journal of Remote Sensing, 1987, 7: 1395～1416.
132. Gopal S., Woodcock C. E., and Strahler A. H. Fuzzy neural network classification of global land cover from a 1 degree AVHRR data set. Remote Sensing of Environment, 1999, 67, 230～243.
133. Townshend, J. G. R. Global data sets for land application from Advanced Very High Resolution Radiometer: an introduction. International Journal of Remote Sensing, 1994, 15: 3319～3332.
134. Campbell, J. B. A Geographical Analysis of Image Interpretation Methods. The Professional Geographer, 1978, 30(3): 264～269.
135. Lambin, E. F. and Ehrlich, D. Combining vegetation indices and surface temperature for land cover mapping at broad spatial scales. International Journal of Remote Sensing, 1995, 16, 573～579.
136. Wen C. G.., and Tateishi R. 30-second degree grid landcover classification of Asia. International Journal of Remote Sensing, 2001, 22 (18): 3845～3854.
137.香宝,刘纪远,张增祥.东亚土地覆盖环境背景数字地面模型研究.地理研究,2001,20(6):653～660。
138. Pan Y., Li X., and Gong R etc. An integrative classification of vegetation in China based on NOAA/AVHRR and vegetation—climate indices of the Holdridge life zone. International Journal of Remote Sensing, 2003, 24(5): 1009～1027.
139. Defries R., Hansen M., and Townshend J. Global discrimination of land cover types from matrices derived from AVHRR pathfinder Data. Remote Sensing of Environment, 1995, 54,209～222.
140.吴健平.区域土地利用/土地覆盖遥感调查.上海:华东师范大学出版社,1999。
141.聂娟.大尺度中国土地覆盖综合分类比较研究,北京师范大学硕士学位论文,2003。
142.倪健.植被—气候分类指标及其应用.生态学杂志,1998,17(2):33～44。
143. Cort J. W. and Scott M. R. Climatologically aided interpolation (CAI) of Terrestrial air temperature. International Journal of Climatology, 1995, (15): 221～229.
144. Robeson S. M. Influence of Spatial sampling and interpolation on estimates of air temperature change. Climate Research, 1994, 4:119～126.
145.徐建华.现代地理学中的数学方法.高等教育出版社,2002。
146.田庆久,闵祥军.植被指数研究进展.地球科学进展,1998,13(4):327～333。
147. Illera P., Fernandez A. and Delgado J. A. Temporal evolution of the NDVI as an indicator of forest fire danger. International Journal of Remote Sensing, 1996, 17:1093～1105.
148. Paltridge G. W. and Barber, J. Monitoring grassland dryness and fire potential in Australia with NOAA/AVHRR data. Remote Sensing of Environment, 1988, 25: 381～394.
149. Lopez S., Gonzalez, F., Llop R. and Cuevas J. M. An evaluation of the utility of NOAA AVHRR images for monitoring forest fire risk in Spain. International Journal of Remote Sensing, 1991, 12:1841～1851.
150. Fabio Maselli, Stefano Romanelli and Lorenzo Bottai, etc. Use of NOAA-AVHRR NDVI images for the estimation of dynamic fire risk in Mediterranean areas. Remote Sensing of Environment, 2003, 86:187～197.
151.覃先林,易浩若.MODIS数据在树种长势监测中的应用.遥感技术与应用,2003,18(3):123～128。
152.王正兴,刘闯,Huete Alfredo.植被指数研究进展:从AVHRR—NDVI到MODIS—EVI.生态学报,2003,23(5):979～987。
153. Arnon Karnieli, Yoram J. Kaufman, Lorraine Remer and Andrew Wald. AFRI—aerosol free vegetation index. Remote Sensing of Environment, 2001, 77(1): 10～21.
154.李小文,高峰等.二向性归一化植被指数:概念及应用,自然科学进展,2001,11(8):819～823.
155. Roujean J L. A bidirectional reflectance model of the earth's surface for the correction of remote sensing. Journal of Geophysic Research, 1992, 7: 20455～20468.
156.田年军,宋卫东.长白山森林可燃物含水率分布规律的研究.吉林林业科技,1997,3:5～11。
157.马丽华,李兆山.大兴安岭6种活森林可燃物含水率的测试与研究.吉林林学院学报,1998,14(1):21～23。
158.王金叶,车克钧.可燃物含水率与气象要素相关性研究.甘肃林业科技,1994, 2:21～23。
159.金森,李绪尧,李有祥.几种细小可燃物失水过程中含水率的变化规律.东北林业大学学报,2000,28(1):35～38。
160. Bowman W. D. The relationship between leaf water status, gas exchange, and spectral reflectance in cotton leaves. Remote Sensing of Environment, 1989, 30: 249～255.
161. Cohen W. B. Response of Vegetation Indexes to Changes in 3 Measures of Leaf Water-Stress. Photogrammetric Engineering and Remote Sensing, 1991, 57:195～202.
162. Pietro Ceccato, Ste'phane Flasse and Stefano Tarantola etc. Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sensing of Environment, 2001, 77: 22～33.
163. Pietro Ceccato, Nadine Gobron, Ste'phane Flasse, Jean-Marie Gre'goire. Designing a spectral index to estimate vegetation water content from remote sensing data. Part 1: Theoretical approach. Remote Sensing of Environment, 2002, 82:188～197.
164. Pietro Ceccato, Ste'phane Flasse, Jean-Marie Gre'goire. Designing a spectral index to estimate vegetation water content from remote sensing data. Part 2: Validation and applications. Remote Sensing of Environment, 2002, 82: 198～207.
165. Chuvieco E., Riano, Aguado I. and Cocero D. Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper Reflectance data: applications in fire danger assessment. International Journal of Remote Sensing, 2002, 23(11):2145～2162.
166.刘勇卫,贺雪鸿 译.遥感精解.武汉:中国测绘出版社,1993。
167. Carter G. A. (). Ratios of leaf reflectances in narrow wavebands indicator of plant stress. International Journal of Remote Sensing. 1994, 15:697～703.
168. Hunt E. R. and Rock B. N. Detection of changes in leaf water content using near and middle-infrared reflectances. Remote Sensing of environment, 1989, 22: 429～435.
169. Jackson R. D., Idso S. B., Reginato R. J., and Pinter P. J. Canopy temperature as a crop water stress indicator. Water Resource, 1981, 1:1133～1138.
170. Moran M. S., Clarke T. R., Inoue Y., and Vidal, A. Estimating Crop Water-Deficit Using the Relation between Surface-Air Temperature and Spectral Vegetation Index. Remote Sensing of Environment, 1994, 49: 246～263.
171. Vidal. Evaluating forest fire hazard with a Landsat TM derived water stress index. Agricultural and Forest Meteorology, 1995, 77: 207～224.
172. Norman J. M., Divakarla M., and Goel N. S. Algorithms for Extracting Information from Remote Thermal IR Observations of the Earths Surface. Remote Sensing of Environment, 1995, 51(1):157～168.
173. Vidal A., Pinglo F., and Durand H. etc. Evaluation of a temporal fire risk index in Mediterranean forest from NOAA thermal IR. Remote Sensing of Environment, 1994, 49: 296～303.
174. Chuvieco E., Riano D., Aguado I., and Cocero D. Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: applications in fire danger assessment. International Journal of Remote Sensing, 2002, 23(11):2145～2162.
175.易浩若.利用NOAA/AVHRR资料监测南方林区森林火灾的研究.国土资源遥感,1995,(1):24～30。
176.易浩若.专家系统用于遥感图像处理的方法研究.林业科学研究,1994,7(1):13～17。
177.易浩若,纪平.森林过火面积的遥感测算方法研究.遥感技术与应用,1998,13(2):10～14。
178.易浩若,纪平.林火卫星遥感监测及早期报警技术研究.遥感技术与应用,1996,11(1):40～46。
179.纪平,易浩若,白黎娜.人工神经网络识别NOAA数据异常高温点的研究.重大自然灾害遥感监测与评估研究进展.北京:科学技术出版社,1993。
180.陈立新,景东侠等.用极轨卫星监测森林火情的原理和方法.森林防火,1997,2:18～24。
181.覃先林.NOAA/AVHRR数据林火信息提取方法研究,中国林科院硕士学位论文,1999,5～12。
182. Pierre Carrega. Instantaneous and Automatic Identification of Weather Types for a forest Fire Decision Support System. International Journal of Remote Sensing Wildland Fire, 1997, 7(3): 265～274.
183. Lopei, S., Gonzalez-Alonso F., Llop, R., and Cuevas, J. M. An evaluation of the utility of NOAA-AVHRR images for monitoring forest fire risk in Spain. International Journal of Remote Sensing, 1991, 12: 1842～1851.
184. Kasischke E. S. and French H. F. N. Locating and estimating the area extent of wildfires in Alaskan boreal forests using multiple-season AVHRR NDVI composite data. Remote Sens. Environ, 1995, 51: 263～275.
185. Yoram J. Kaufman, etc. Algorithm for Remote Sensing of Tropospheric Aerosol from MODIS. Algorithm Technical Background Document, 1998, 10.
186. Kaufman, Y. J., Herring, D. D., Ranson, K. J. and Collatz, G. J. Earth Observing System AM1 mission to Earth. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36: 1045～1055.
187. Kaufman, Y. J., Justice, C. O. and Flynn, L. P., etc. Potential global fire monitoring from EOS-MODIS. Journal of Geophysical Research, 1998, 103: 32215～32238.
188. Louis Giglio, etc. An Enhanced Contextual Fire Detection Algorithm for MODIS. Remote Sensing of Environment, 2003, 87: 273-282.
189.Giglio, L., Kendall, J. D. and Justice, C.O. Evaluation of global fire detection algorithms using simulated AVHRR infrared data. International Journal of Remote Sensing, 1999, 20: 1947-1985.
190.Justice, C.O., Giglio, L. and Korontzi, S. etc. The MODIS fire products. Remote Sensing of Environment, 2002, 83: 244- 262.
191.Justice, C.O., Kendall, J. D., Dowty, P. R. and Scholes, R. J. Satellite remote sensing of fires during the SAFARI campaign using NOAA AVHRR data. Journal of Geophysical Research, 1996, 101: 23851- 23863.
192.Justice, C.O., Townshend, J. R. G., and Vermote, E. F. etc. An overview of MODIS land data processing and product status. Remote Sensing of Environment, 2002, 83: 244-262.
193.Morisette, J. T., Privette, J. L., Justice, C.O. A framework for the validation of MODIS land products. Remote Sensing of Environment. 2002, 83: 77-96.
194.Nishihama, M., Wolfe, R., Solomon, D., and Patt, F., etc. MODIS level 1A Earth location: Algorithm theoretical basis document version 3.0. SDST-092, MODIS Science Data Support Team, 1997.
195.Seielstad, C. A., Riddering, J. P., and Brown, S. R., etc. Testing the sensitivity of a MODIS-like daytime active fire detection model in Alaska using NOAA/AVHRR infrared data. Photogrammetric Engineering and Remote Sensing, 2002, 68: 831-838.
196.Flases,S.P. and Ceccato. A contextual algorithm for AVHRR fire detection. International Journal of Remote Sensing, 1996, 17(2): 419-424.
197.Andrew J., Harris, L. Towards Automated Fire Monitoring From Space: Semi-Automated Mapping of the January 1994 New South Wales Wildfires Using AVHRR Data. International Journal of WildLand Fire, 1996, 6(3): 107- 116.
198.Stroppiana, D., Pinnock, S., Gre'goire, J. M. The Global Fire Product: Daily fire occurrence from April 1992 to December 1993 derived from NOAA AVHRR data. International Journal of Remote Sensing, 2000,21: 1279- 1288.
199.Masuoka, E., Fleig, A., Wolfe, R. W., Patt, F. Key characteristics of the MODIS data products. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36. 1313- 1323.
200.Rauste, Y. and Herladez, E. Satellite-based forest fire detection for fire control in boreal forests. International Journal of Remote Sensing, 1997,18(12): 2641—2656.
201.Pozo, D., Olmo, F. J. and L.Alados-Arboledas. Fire Detection and Monitoring Using a multitemporal Technique on AVHRR Mid-Infrared and Thermal Channels. Remote
Sensing of Environment, 1997, 60, 111～120.
202. Franca, G. B. and Cracknell, A. P. A simple cloud masking approach using NOAA daytime data for tropical areas. International Journal of Remote Sensing, 1995, 16 (9): 1697～1705.
203. Fang, M. and Huang, W. Tracking the Indonesian forest fire using NOAA/AVHRR images. International Journal of Remote Sensing, 1998, 19(3):387～390.
204. Edward A. Ashton and Dan G. Baldwin. Algorithms for the Detection of Sub Pixel Targets in Multispectral Imagery. Photogrammetric Engineering & Remote Sensing, 1998, 64(7): 723～731.
205. Vincent G. Ambrosia, etc. An Integration of Remote Sensing, GIS, and Information Distribution for Wildfire Detection and Management, Photogrammetric Engineering Remote Sensing, 1998, 64(10): 977～985.
206. Ackerman, S. A., Strabala, K. I., and Menzel, W. P., etc. Discriminating clear sky from clouds with MODIS. Journal of Geophysical Research, 1998, 103: 32141～32157.
207. Dowty, P. R. A theoretical study of fire detection using AVHRR data. MS Thesis. Department of Environmental Sciences. University of Virginia. Charlottesville. Virginia. USA. 1993.
208. Dozier, J. A method for satellite identification of surface temperature fields of subpixel resolution. Remote Sensing of Environment, 1981, 11:221～229.
209. Flasse, S. P. and Ceccato, P. A contextual algorithm for AVHRR fire detection. International Journal of Remote Sensing, 1996, 17:419～424.
210. Matson, M. and Dozier, J. Identification of subresolution high temperature sources using a thermal IR sensor. Photogrammetric Engineering and Remote Sensing, 1981, 47: 1311～1318.
211.覃先林,易浩若.基于MODIS数据的林火识别方法研究.火灾科学,2004 13(2):83～89。
212. Zhan, X., Huang, C., and Townshend, J. R. G., etc. Land cover change detection with change vector in the red and near-infrared reflectance space. IGARSS'98. IEEE International Geoscience and Remote Sensing Symposium Proceedings. Seattle. Washington, 1998, Vol. Ⅱ: 859～861.
213. Zhan, X., DeFries, R. and Townshend J. R. G. etc. The 250m global land cover change product from the Moderate Resolution Imaging Speetroradiometer of NASA's Earth Observing System. International Journal of Remote Sensing, 2000, 21: 1433～1460.
214. Cihlar J., Hung L., and Xiao Q. Land cover classification with AVHRR mulitichannel composites in northern environments. Remote Sensing of Environment, 1996, 58: 36～51.
215. Holdaway M. R. Spatial modeling and interpolation of monthly temperature using Kriging. Climate Research, 1996, 24:1835～1845.
216. Robeson S. M. Spatial interpolation, network bias, and terrestrial air temperature variability. Public Climate, 1993a, 46(1): 1～15.
217. Robeson, S. M. and Willmott C. Spherical spatial interpolation and terrestrial air temperature variability Proceedings. Second International Conference on Integrating GIS and Environmental Modeling, Breckenridge, CO., 1993b.
218.林忠辉,莫兴国,李宏轩等.中国陆地区域气象要素的空间插值.地理学报,2002,57(1):47～56。
219. Shunlin Liang. Quantilative Remote Sensing of Land Surface. WILEY INTERSCIENCE: A JOHN WILEY & SONS. INC. PUBLICATION, 2004.