title: [Forest fire division by using MODIS data based on the temporal-spatial variation law].
abstract: Forest fires are harmful to the ecological environment, which have induced global attention. In the present paper fire activities extracted from MODIS and burned areas were compared, and it was found that the wave band of 8-9 extracted from MOD14A1 was useful for fire monitoring, and the data accorded with field investigation with goodness of fit reaching up to 0. 83. Through combining this wave band and the relative data to make the time and space analysis of the forest fires for 11 years, from 2000 to 2010, the study showed that the fire occurred most frequently in the spring, the autumn took the second place, and in the summer there was almost no fire occurrence unless drought. Through the analysis of the research area, the burned areas of the coniferous forest and temperate mixed forest were 53.68% and 44%, respectively, while the grassland was only 2.32%. Da Hinggan Ling region was the main combustion area, the burned areas were 64.7% and that for Xiao Hinggan Ling was about 23.49%, while those for other areas were less than 5%. The majority of forest land of burned areas has a gentle slope (< or =5 percent), and is in the middle altitude between 200 and 500 m. So, using satellite remote sensing to analyze the time series of burned areas in forests would make the relationship between the fire activities, climate change, topography and vegetation type clear and it is also helpful to predicting the risk level of the fire areas.
authors.affiliation: Forest Fire Research Center, Nanjing Forest Police College, Nanjing 210023, China. hech_eng@163.com
authors.forename: Cheng
authors.initials: C
authors.lastname: He
authors.affiliation: Forest Fire Research Center, Nanjing Forest Police College, Nanjing 210023, China.
authors.forename: Cheng
authors.initials: C
authors.lastname: He
authors.affiliation: The First Institute of Photogrammetry and Romote Sensing, SBSM, Xi'an 710054, China.
authors.forename: Yin-xi
authors.initials: YX
authors.lastname: Gong
authors.affiliation: Forest Fire Research Center, Nanjing Forest Police College, Nanjing 210023, China.
authors.forename: Si-yu
authors.initials: SY
authors.lastname: Zhang
authors.affiliation: Institute of GIS, RS&GPS, Beijing Forestry University, Beijing 100083, China.
authors.forename: Teng-fei
authors.initials: TF
authors.lastname: He
authors.affiliation: Institute of GIS, RS&GPS, Beijing Forestry University, Beijing 100083, China.
authors.forename: Feng
authors.initials: F
authors.lastname: Chen
authors.affiliation: Institute of GIS, RS&GPS, Beijing Forestry University, Beijing 100083, China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Sun
authors.affiliation: Institute of GIS, RS&GPS, Beijing Forestry University, Beijing 100083, China.
authors.forename: Zhong-ke
authors.initials: ZK
authors.lastname: Feng
mesh_terms: D055864:Droughts; D004784:Environmental Monitoring; D005390:Fires; D065928:Forests; D063809:Satellite Imagery; D012621:Seasons; D062211:Spatio-Temporal Analysis