RS AND GIS-BASED FOREST FIRE RISK ZONE MAPPING IN DA HINGGAN MOUNTAINS

被引:0
|
作者
YIN Hai-wei1
2. Institute of Applied Ecology
3. Graduate School for International Development and Cooperation
机构
基金
中国国家自然科学基金;
关键词
forest fire risk zone; RS; GIS; Da Hinggan Mountains;
D O I
暂无
中图分类号
S762 [林火];
学科分类号
0838 ;
摘要
The Da Hinggan Mountains is one of the most important forest areas in China, but forest fire there is also of high frequency. So it is completely necessary to map forest fire risk zones in order to effectively manage and protect the forest resources. Two forest farms of Tuqiang Forest Bureau (53°34′-52°15′N,124°05′-122°18′E) were chosen as typical areas in this study. Remote sensing (RS) and Geographic Information System (GIS) play a vital role and can be used effectively to obtain and combine different forest-fire-causing factors for demarcating the forest fire risk zone map. Forest fire risk zones were described by assigning subjective weights to the classes of all the coverage layers according to their sensitivity to fire, using the ARC/INFO GIS software. Four classes of forest fire risk ranging from low to extremely high were generated automatically in ARC/INFO. The results showed that about 60.33% of the study area were predicted to be upper moderate risk zones, indicating that the forest fire management task in this area is super onerous. The RS and GIS-based forest fire risk model of the study area was found to be highly compatible with the actual fire-affected sites in 1987. Therefore the forest fire risk zone map can be used for guidance of forest fire management, and as basis for fire prevention strategies.
引用
收藏
页码:60 / 66
页数:7
相关论文
共 50 条
  • [41] DEVELOPING A MULTI-VARIABLE FOREST FIRE RISK MODEL AND FIRE RISK ZONE MAPPING
    Norovsuren, Bayanmunkh
    Mart, Zaya
    Natsagdorj, Enkhjargal
    AltanchimegYouth, Tsolmon
    GEOSPATIAL WEEK 2023, VOL. 48-1, 2023, : 1485 - 1490
  • [42] GIS-based multi-criteria decision analysis for forest fire susceptibility mapping: a case study in Harenna forest, southwestern Ethiopia
    Suryabhagavan, K. V.
    Alemu, Misrak
    Balakrishnan, M.
    TROPICAL ECOLOGY, 2016, 57 (01) : 33 - 43
  • [43] Performance assessment of GIS-based spatial clustering methods in forest fire data
    Baykal, Tugba Memisoglu
    NATURAL HAZARDS, 2025,
  • [44] Mapping the probability of Forest fire in the Mediterranean region of Türkiye using the GIS-based fuzzy-AHP method
    Ucar, Zennure
    Guney, Coskun Okan
    Akay, Abdullah E.
    Bilici, Ebru
    Erkan, Nesat
    HUMAN AND ECOLOGICAL RISK ASSESSMENT, 2025, 31 (1-2): : 234 - 259
  • [45] Spatial mapping of artesian zone at Iraqi southern desert using a GIS-based random forest machine learning model
    Al-Abadi A.M.
    Shahid S.
    Modeling Earth Systems and Environment, 2016, 2 (2)
  • [46] Forest fire risk zone mapping for Irati National Forest, State of Parana, Brazil
    Tetto, Alexandre França
    Batista, Antonio Carlos
    Soares, Ronaldo Viana
    Scientia Forestalis/Forest Sciences, 2012, 40 (94): : 259 - 265
  • [47] Forest fire risk zone mapping for Irati National Forest, State of Parana, Brazil
    Tetto, Alexandre Franca
    Batista, Antonio Carlos
    Soares, Ronaldo Viana
    SCIENTIA FORESTALIS, 2012, 40 (94): : 259 - 265
  • [48] A GIS-based approach for comparative analysis of potential fire risk assessment
    Sun, Ying
    Hu, Lieqiu
    Liu, Hulping
    GEOINFORMATICS 2007: GEOSPATIAL INFORMATION TECHNOLOGY AND APPLICATIONS, PTS 1 AND 2, 2007, 6754
  • [49] Tree Planting: How Fast Can It Accelerate Post-fire Forest Restoration? - A Case Study in Northern Da Hinggan Mountains, China
    Li Xiuzhen
    He Hong S
    Wang Xugao
    Xie Fuju
    Hu Yuanman
    Li Yuehui
    CHINESE GEOGRAPHICAL SCIENCE, 2010, 20 (06) : 481 - 490
  • [50] Tree planting: How fast can it accelerate post-fire forest restoration? — A case study in Northern Da Hinggan Mountains, China
    Xiuzhen Li
    Hong S He
    Xugao Wang
    Fuju Xie
    Yuanman Hu
    Yuehui Li
    Chinese Geographical Science, 2010, 20 : 481 - 490