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[Forest fire division by using MODIS data based on the temporal-spatial variation law].

Cheng He1, Cheng He2, Yin-xi Gong3

  • 1Forest Fire Research Center, Nanjing Forest Police College, Nanjing 210023, China. hech_eng@163.com

Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|December 28, 2013
PubMed
Summary
This summary is machine-generated.

Satellite remote sensing effectively monitors forest fires using MODIS data. Analysis of 11 years of data reveals spring and autumn as peak fire seasons, with coniferous forests in the Da Hinggan Ling region most affected.

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Area of Science:

  • Environmental Science
  • Remote Sensing
  • Forestry

Background:

  • Forest fires pose significant ecological threats globally.
  • Accurate monitoring and analysis of fire-prone areas are crucial for effective management.

Purpose of the Study:

  • To evaluate the utility of MODIS (Moderate Resolution Imaging Spectroradiometer) data for forest fire monitoring.
  • To conduct a temporal and spatial analysis of forest fire occurrences over an 11-year period (2000-2010).
  • To identify key factors influencing forest fire distribution and risk.

Main Methods:

  • Comparison of fire activity data from MODIS with burned area data.
  • Extraction and utilization of a specific waveband (8-9) from MOD14A1 data for fire monitoring.
  • Time-series and spatial analysis of forest fire data from 2000 to 2010.
  • Correlation analysis with climate, topography, and vegetation type.

Main Results:

  • The 8-9 waveband from MOD14A1 data showed high accuracy (goodness of fit = 0.83) in fire monitoring, aligning with field investigations.
  • Forest fires occurred most frequently in spring and autumn; summer fires were rare unless associated with drought.
  • Coniferous forests (53.68%) and temperate mixed forests (44%) were the most affected vegetation types.
  • The Da Hinggan Ling region (64.7%) and Xiao Hinggan Ling region (23.49%) were primary combustion zones.
  • Fires predominantly occurred on gentle slopes (< or =5 percent) at altitudes between 200 and 500 meters.

Conclusions:

  • Satellite remote sensing, particularly using specific MODIS wavebands, is a reliable tool for forest fire monitoring.
  • Understanding the temporal and spatial patterns of forest fires is essential for risk assessment.
  • Identifying relationships between fire activity, climate, topography, and vegetation can improve fire management strategies and risk prediction.