High-resolution forest fire weather index computations using satellite remote sensing

被引:6
|
作者
Han, KS
Viau, A
Anctil, F
机构
[1] Univ Laval, Dept Sci Geomat, Quebec City, PQ G1K 7P4, Canada
[2] Univ Laval, Inst Environm Rural & Forestier, Quebec City, PQ G1K 7P4, Canada
[3] Univ Laval, Dept Genie Civil, Quebec City, PQ G1K 7P4, Canada
关键词
D O I
10.1139/X03-014
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
Wildfires are important in regions dominated by forest, such as found in large parts of Canada. The principal objective of this study was to provide homogeneously distributed indices for the Canadian Fire Weather Index (FWI) System. The FWI was calculated using four sets of input variables: meteorological station measurements (OBS); weather forecast model output (SIM); meteorological station measurements and remote sensing estimations combined (SAT1); and weather forecast model output and remote sensing estimations combined (SAT2). Remote sensing parameterization of air temperature and relative humidity was performed. The air temperature and relative humidity reproduced showed good agreement with ground-based measurements (R-2 = 0.77 and SE = 1.48degreesC; R-2 = 0.73 and SE = 5%, respectively). For the FWI regionalized using this requirement, category SAT1 showed the best fit. Category SAT2 produced more precise results (0.09 to 2.19% of the normalized root mean square error) versus SIM.
引用
收藏
页码:1134 / 1143
页数:10
相关论文
共 50 条
  • [41] Estimation of Forest Carbon Stock in South Korea Using Machine Learning with High-Resolution Remote Sensing Data
    Shin, Jaewon
    Jeong, Sujong
    Chang, Dongyeong
    [J]. ATMOSPHERE-KOREA, 2023, 33 (01): : 61 - 72
  • [42] Estimation of Aboveground Biomass at Species Level in Tropical Rain Forest Using High Resolution Remote Sensing Satellite Data
    Ali, Hamzah Mohd
    Rasib, Abd Wahid
    Amran, Nur Fatihah
    Omar, Hamdan
    Kassim, Abdul Rahman
    Faidi, Azahari
    Rahman, Muhammad Zulkarnain Abdul
    Shin, Alvin Lau Meng
    Yusof, Abdul Razak Mohd
    Dollah, Rozilawati
    Ahmad, Asmala
    [J]. PROCEEDINGS OF INNOVATIVE RESEARCH AND INDUSTRIAL DIALOGUE 2018 (IRID'18), 2019, : 222 - 223
  • [43] High spatial resolution remote sensing of forest trees
    McGraw, JB
    Warner, TA
    Key, TL
    Lamar, WR
    [J]. TRENDS IN ECOLOGY & EVOLUTION, 1998, 13 (08) : 300 - 301
  • [44] High-resolution satellite remote sensing: a new frontier for biodiversity exploration in Indian Himalayan forests
    Gairola, Sanjay
    Proches, Serban
    Rocchini, Duccio
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2013, 34 (06) : 2006 - 2022
  • [45] High-resolution topography as an indicator of malaria risk: A remote sensing approach with satellite radar imagery
    Gingras, C.A.
    Bomblies, A.
    [J]. Biological Engineering Transactions, 2013, 6 (02): : 117 - 140
  • [46] Recognition and extraction of high-resolution satellite remote sensing image buildings based on deep learning
    Zeng, Yifu
    Guo, Yi
    Li, Jiayi
    [J]. NEURAL COMPUTING & APPLICATIONS, 2022, 34 (04): : 2691 - 2706
  • [47] Recognition and extraction of high-resolution satellite remote sensing image buildings based on deep learning
    Yifu Zeng
    Yi Guo
    Jiayi Li
    [J]. Neural Computing and Applications, 2022, 34 : 2691 - 2706
  • [48] Monitoring forest biodiversity and the impact of climate on forest environment using high-resolution satellite images
    Bochenek, Zbigniew
    Ziolkowski, Dariusz
    Bartold, Maciej
    Orlowska, Karolina
    Ochtyra, Adrian
    [J]. EUROPEAN JOURNAL OF REMOTE SENSING, 2018, 51 (01) : 166 - 181
  • [49] Using satellite remote sensing data to estimate the high-resolution distribution of ground-level PM2.5
    Lin, Changqing
    Li, Ying
    Yuan, Zibing
    Lau, Alexis K. H.
    Li, Chengcai
    Fung, Jimmy C. H.
    [J]. REMOTE SENSING OF ENVIRONMENT, 2015, 156 : 117 - 128
  • [50] Super-Resolution Reconstruction of Remote Sensing Data Based on Multiple Satellite Sources for Forest Fire Smoke Segmentation
    Liang, Haotian
    Zheng, Change
    Liu, Xiaodong
    Tian, Ye
    Zhang, Jianzhong
    Cui, Wenbin
    [J]. REMOTE SENSING, 2023, 15 (17)