田丽等： Automatic detection of forest fire disturbance based on dynamic modelling from MODIS time-series observations

作者:Tian, L (Tian, Li)[ 1 ] ; Wang, JD (Wang, Jindi)[ 1 ] ; Zhou, HM (Zhou, Hongmin)[ 1 ] ; Wang, J (Wang, Jian)[ 1 ]

INTERNATIONAL JOURNAL OF REMOTE SENSING

卷: 39  期: 12  页: 3801-3815

DOI: 10.1080/01431161.2018.1437294

出版年: 2018

文献类型:Article

摘要

Forest disturbances provide an important reference and a basis for studying the carbon cycle, biodiversity, and eco-social development. Remote sensing is a promising data source for monitoring forest ecosystem dynamics and detecting disturbance areas. This research used a seasonal trend method to model Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series from 2007 to 2011 recursively with a fixed-size temporal sliding window and a step length of 1 (i.e. 16days). Model parameter variations were monitored to detect changes in the structure of the time-series data. Significant changes in the time-series structure were captured as disturbance signals. The method was applied to the 2009 Minto Flats fire in Alaska, USA, and the forest-disturbance detection results obtained using the proposed method essentially agreed with the Monitoring Trends of Burned Severity data set. This result indicates that the proposed method can reliably reveal the occurrence of forest fire disturbances. Moreover, because the model parameter variations reflect the disturbance signal, and the modelling and detection process requires only MODIS NDVI time-series data without any other ancillary ground information, the disturbance area can be detected effectively and automatically.

通讯作者地址: Wang, JD (通讯作者)

Beijing Normal Univ, Beijing Engn Res Ctr Global Land Remote Sensing P, Inst Remote Sensing Sci & Engn, State Key Lab Remote Sensing Sci,Fac Geog Sci, Beijing, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, Beijing Engn Res Ctr Global Land Remote Sensing P, Inst Remote Sensing Sci & Engn, State Key Lab Remote Sensing Sci,Fac Geog Sci, Beijing, Peoples R China