Abstract
Inner Mongolia, as a fragile ecological zone in northern China, is prone to severe fires due to natural forces and intensive human disturbances. The development of a fire risk assessment system at the finer spatial scale is not sufficient in this region. In this study, we obtained the data of burned areas and fire hotspots numbers for Inner Mongolia from the Terra/Aqua Moderate-resolution Imaging Spectroradiometer data (MCD45A1 and MOD14A1/MYD14A1, 2002~2016). These fire maps were used to determine the fire spatial and temporal variability, as well as the interactions with environmental controls (climatic, vegetation, topography, and anthropic characteristics) derived in geographic information system (GIS) layers. Based on this, the fire-causing variables were selected as the dependent variables for model building, whereas data on burned area and number of fire hotspots were used for model validation. The fire risk assessment map was then generated in a 500 × 500 m grid cell using an analytic hierarchy process approach and a GIS technique. This work could be easily used for the ultimate aim of supporting fire management.
Highlights
Fires are the important part of terrestrial ecosystems and play a significant role in affecting the patterns and successional status of ecosystems [1]
The main objectives of this study are to (a) reveal the spatial–temporal distribution of fire hotspots numbers and burned areas based on Moderate-resolution Imaging Spectroradiometer (MODIS) products in Inner Mongolia from 2002 to 2016; (b) analyze the relationship between fire hotspots and environmental variables; and (c) generate a fire risk map with the analytic hierarchy process (AHP) method and achieve the risk regionalization
We presented a pixel-scale fire risk assessment scheme and achieved fire risk regionalization for Inner Mongolia during 2002~2016 based on the AHP method and MODIS burned area and active fire products
Summary
Fires are the important part of terrestrial ecosystems and play a significant role in affecting the patterns and successional status of ecosystems [1]. Oliveira [31] compared the performance of the multiple regression and random forest methods to model the spatial patterns of fire occurrence in Mediterranean Europe In these methods, the AHP, as a knowledge-based method, can quickly achieve fire risk mapping with a high accuracy when considering the importance of the indicators [25]. The main objectives of this study are to (a) reveal the spatial–temporal distribution of fire hotspots numbers and burned areas based on MODIS products in Inner Mongolia from 2002 to 2016; (b) analyze the relationship between fire hotspots and environmental variables; and (c) generate a fire risk map with the AHP method and achieve the risk regionalization
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