Description

• Title:
  Spatial and temporal monitoring of wildfires in Golestan province using remote sensing data
• Creator:
  Asadi Oskouei, Ebrahim;
  Shobairi, Seyed Omid Reza;
  Sadeghi, Hadis;
  Shokouhi, Mojtaba;
  Fatahi, Ebrahim;
  Khazanedari, Leili;
  Lingxiao, Sun;
  Haiyan, Zhang;
  Chunlan, Li;
  Jing, He;
  Ayombekov, Qirghizbek
• Description:
  Wildfires are one of the most significant factors of ecosystem change. Knowing the wildfire regime (frequency, intensity, and distribution pattern) is essential in wildfire management. This research aims to analyze the spatiotemporal pattern of wildfires in Golestan in 2001-2021 using MODIS data, burned area product (MCD64A1). For this purpose, the annual and monthly frequency, as well as the trend of wildfires based on types of forest, pasture, and crop cover, were statistically analyzed. The local Moran pattern analysis method and kernel density function were used to analyze the spatial dynamics of wildfire. The results showed that 18,462 wildfires occurred in Golestan, the highest of which was in 2010, with 2,517 wildfires (13.8%). The lowest number of wildfires, with only 57 events (0.5%), was in 2001. Based on the local Moran model results and the kernel density function, the wildfires' extent and intensity were greater in the plains and foothills to the south and southeast of Golestan. The lowest extent and intensity of the wildfire corresponded to the eastern parts of the province. The frequency of wildfires was higher in the hot period of the year (spring and summer). However, the period of occurrence of wildfire and the peak of wildfire changes in different uses. The wildfire zones in June were wider and more intense than in other months. The frequency and spatial extent of wildfires in agricultural lands from May to July, pasture lands in July, August, and September, and forest lands in November and December were more than in other months. Weather conditions play a significant role in the occurrence of wildfire in the forest lands of Golestan. The results of this research help understand wildfire risk areas and provide a scientific basis for predicting and controlling wildfires and reducing carbon emissions related to them.
• Object availability:
  free access
• Rights: https://creativecommons.org/licenses/by-nd/4.0 ;
  Prawa autorskie (c) 2024 Seyed Omid Reza Shobairi, Ebrahim Asadi Oskouei, Hadis Sadeghi, Mojtaba Shokouhi; Ebrahim Fatahi, Leili Khazanedari; Sun Lingxiao, Zhang Haiyan, Li Chunlan, He Jing, Qirghizbek Ayombekov
• Date:
  2024.03.17
• Type:
  info:eu-repo/semantics/article;
  info:eu-repo/semantics/publishedVersion
• Language:
  English
• Source:
  Ecological Questions; Vol. 35 No. 3 (2024); 1-24;
  Ecological Questions; Tom 35 Nr 3 (2024); 1-24;
  1644-7298
• Format:
  application/pdf
• Publisher:
  Uniwersytet Mikołaja Kopernika w Toruniu
• Relation: https://apcz.umk.pl/EQ/article/view/48647/38012
• Subject:
  wildfire;
  Kernel density function;
  Moran's index;
  Modis;
  Iran
• Identifier: https://apcz.umk.pl/EQ/article/view/48647
• Data provider:
  Akademicka Platforma Czasopism
• Can I use it?:
  seek permission
• Type:
  text