Joint processing of RS and WWLLN data for forest fire danger estimation: new concept

被引：1
|
作者：
Baranovskiy, Nikolay V. [1 ]
Krechetova, Svetlana Yu. [2 ]
Belikova, Marina Yu. [2 ]
Kocheeva, Nina A. [2 ]
Yankovich, Elena P. [1 ]
机构：
[1] Natl Res Tomsk Polytech Univ, Tomsk, Russia
[2] Gorno Altaisk State Univ, Gorno Altaisk 649000, Russia
来源：
REMOTE SENSING OF CLOUDS AND THE ATMOSPHERE XXI | 2016年 / 10001卷
关键词：
forest fire danger; prediction; lightning activity; remote sensing; WWLLN; GIS; INTERIOR ALASKA; WEATHER; FRAMEWORK; PATTERNS; SYSTEM;
D O I：
10.1117/12.2241853
中图分类号：
P4 [大气科学（气象学）];
学科分类号：
0706 ; 070601 ;
摘要：
The present article describes a new concept of lightning-caused forest fire danger using a probabilistic criterion. The assessment of forest fire danger is made on the basis of the algorithm that classifies the forest territory by vegetation conditions. Lightning activity is processed by the MODIS spectroradiometer according to the World Wide Lightning Location Network data and remote sensing data for the Timiryazevskiy forestry in the Tomsk Region. The cluster a nalysis of the WWLLN and MOD06_L2 product data are used in the present paper.
引用
收藏
页数：6
相关论文
共 50 条
  • [31] Application of artificial neural networks and logistic regression to the prediction of forest fire danger in Galicia using MODIS data
    Bisquert, Mar
    Caselles, Eduardo
    Manuel Sanchez, Juan
    Caselles, Vicente
    INTERNATIONAL JOURNAL OF WILDLAND FIRE, 2012, 21 (08) : 1025 - 1029
  • [32] Comparison of Canadian Forest Fire Danger Rating System and National Fire Danger Rating System fire indices derived from Weather Research and Forecasting (WRF) model data for the June 2005 Interior Alaska wildfires
    Molders, Nicole
    ATMOSPHERIC RESEARCH, 2010, 95 (2-3) : 290 - 306
  • [33] Joint Processing of Landsat and ALOS-PALSAR Data for Forest Mapping and Monitoring
    Lehmann, Eric A.
    Caccetta, Peter A.
    Zhou, Zheng-Shu
    McNeill, Stephen J.
    Wu, Xiaoliang
    Mitchell, Anthea L.
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2012, 50 (01): : 55 - 67
  • [34] The estimation of the powers of large forest fire using 'Resource-01' #3 data
    Butusov, O.B.
    Issledovanie Zemli iz Kosmosa, 2001, (02): : 76 - 85
  • [35] Forest fire risk estimation from time series analysis of NOAA NDVI data
    Gabban, A
    Libertà, G
    San-Miguel-Ayanz, J
    Barbosa, P
    REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY V, 2004, 5232 : 587 - 595
  • [36] A new fire-retardant sheathing concept for UTP data cables and fire-resistant cables
    Preston, J.
    Sawyer, D.
    Procter, M.
    Wire Journal International, 2002, 35 (09):
  • [37] Joint data and channel estimation using the Per-Branch Processing method
    Omidi, MJ
    Gulak, PG
    Pasupathy, S
    FIRST IEEE SIGNAL PROCESSING WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS, 1997, : 389 - 392
  • [38] Efficient Forest Fire Detection System: A Spatial Data Mining and Image Processing Based Approach
    Angayarkkani, K.
    Radhakrishnan, N.
    INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2009, 9 (03): : 100 - 107
  • [39] GIS-based spatial prediction of tropical forest fire danger using a new hybrid machine learning method
    Dieu Tien Bui
    Hung Van Le
    Nhat-Duc Hoang
    ECOLOGICAL INFORMATICS, 2018, 48 : 104 - 116
  • [40] A promising new approach to estimate drought indices for fire danger assessment using remotely sensed data
    Hausler, Melanie
    Nunes, Joao P.
    Silva, Joao M. N.
    Keizer, Jan Jacob
    Warneke, Thorsten
    Pereira, Jose M. C.
    AGRICULTURAL AND FOREST METEOROLOGY, 2019, 274 : 195 - 209
12345