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author:

Chen, Xu (Chen, Xu.) [1] | Chen, Wei (Chen, Wei.) [2] | Xu, Min (Xu, Min.) [3]

Indexed by:

EI Scopus SCIE

Abstract:

The frequency of forest fires is increasing under global climate change, and forest fires can cause devastating disturbances to forest systems and varying degrees of recovery of forest ecosystems after a disaster. Due to the different intensity of forest fires and forest systems, and in particular the fact that forest ecological recovery is influenced by many topographical and climatic factors, the process of postfire vegetation recovery is unclear and must be studied in depth. In this study, the Greater Hinggan Mountain Range was taken as the study area. Based on the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat time-series images acquired from 2000 to 2018, this study used the spatiotemporal data fusion method to construct reflectance images of vegetation with a relatively consistent growth period to study the vegetation restoration after forest fires. The vegetation restoration was characterized by disturbance index (DI) values, which eliminated phenological influence. Six types of topography and climatic factors (elevation, aspect, slope; temperature, precipitation, and wind speed) were coupled with DI. Through single-factor analysis of variance and multiple comparison statistical methods, it was found that there was a significant relationship between the six factors and DI, which indicated those factors had a significant impact on the restoration of forest vegetation in burned areas. The results will be useful as a reference for future monitoring and management of forest resources.

Keyword:

disturbance index MODIS time series vegetation index vegetation restoration wildfires

Community:

  • [ 1 ] [Chen, Xu]Fuzhou Univ, Coll Environm & Resources, Fuzhou 350108, Peoples R China
  • [ 2 ] [Chen, Wei]Tianjin Univ, Sch Earth Syst Sci, Inst Surface Earth Syst Sci, Tianjin 300072, Peoples R China
  • [ 3 ] [Chen, Wei]Haihe Lab Sustainable Chem Transformat, Tianjin 300192, Peoples R China
  • [ 4 ] [Chen, Wei]Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
  • [ 5 ] [Xu, Min]Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

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Source :

REMOTE SENSING

ISSN: 2072-4292

Year: 2022

Issue: 13

Volume: 14

5 . 0

JCR@2022

4 . 2 0 0

JCR@2023

ESI Discipline: GEOSCIENCES;

ESI HC Threshold:51

JCR Journal Grade:1

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 6

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