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Article

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

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Department of Geography, University of the Aegean, 81100 Mytilene, Greece
2
Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, Greece
3
Department of Forest Engineering, Resources and Management, Oregon State University, Corvallis, OR 97331, USA
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(11), 420; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7110420
Received: 11 September 2018 / Revised: 10 October 2018 / Accepted: 27 October 2018 / Published: 30 October 2018
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. View Full-Text
Keywords: wildfire; vegetation regrowth; artificial neural network; time series; MODIS EVI; dynamic time warping; multidimensional scaling; optimal fuzzy clustering wildfire; vegetation regrowth; artificial neural network; time series; MODIS EVI; dynamic time warping; multidimensional scaling; optimal fuzzy clustering
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MDPI and ACS Style

Vasilakos, C.; Tsekouras, G.E.; Palaiologou, P.; Kalabokidis, K. Neural-Network Time-Series Analysis of MODIS EVI for Post-Fire Vegetation Regrowth. ISPRS Int. J. Geo-Inf. 2018, 7, 420. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7110420

AMA Style

Vasilakos C, Tsekouras GE, Palaiologou P, Kalabokidis K. Neural-Network Time-Series Analysis of MODIS EVI for Post-Fire Vegetation Regrowth. ISPRS International Journal of Geo-Information. 2018; 7(11):420. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7110420

Chicago/Turabian Style

Vasilakos, Christos, George E. Tsekouras, Palaiologos Palaiologou, and Kostas Kalabokidis. 2018. "Neural-Network Time-Series Analysis of MODIS EVI for Post-Fire Vegetation Regrowth" ISPRS International Journal of Geo-Information 7, no. 11: 420. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7110420

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