Neural-Network Time-Series Analysis of MODIS EVI for Post-Fire Vegetation Regrowth

ISPRS International Journal of Geo-Information

GeorgeE. Tsekouras¹  Palaiologos Palaiologou²  Christos Vasilakos³  Kostas Kalabokidis³ 

[1] Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, Greece;Department of Forest Engineering, Resources and Management, Oregon State University, Corvallis, OR 97331, USA;Department of Geography, University of the Aegean, 81100 Mytilene, Greece;

关键词: wildfire;    vegetation regrowth;    artificial neural network;    time series;    MODIS EVI;    dynamic time warping;    multidimensional scaling;    optimal fuzzy clustering;   

DOI  :  10.3390/ijgi7110420

来源: DOAJ

【 摘 要 】

The time-series analysis of multi-temporal satellite data is widely used for vegetation regrowth after a wildfire event. Comparisons between pre- and post-fire conditions are the main method used to monitor ecosystem recovery. In the present study, we estimated wildfire disturbance by comparing actual post-fire time series of Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and simulated MODIS EVI based on an artificial neural network assuming no wildfire occurrence. Then, we calculated the similarity of these responses for all sampling sites by applying a dynamic time warping technique. Finally, we applied multidimensional scaling to the warping distances and an optimal fuzzy clustering to identify unique patterns in vegetation recovery. According to the results, artificial neural networks performed adequately, while dynamic time warping and the proposed multidimensional scaling along with the optimal fuzzy clustering provided consistent results regarding vegetation response. For the first two years after the wildfire, medium-high- to high-severity burnt sites were dominated by oaks at elevations greater than 200 m, and presented a clustered (predominant) response of revegetation compared to other sites.

【 授权许可】

Unknown