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Remote Sens. 2015, 7(6), 7712-7731; doi:10.3390/rs70607712

A Remote Sensing and GIS Approach to Study the Long-Term Vegetation Recovery of a Fire-Affected Pine Forest in Southern Greece

Department of Environmental and Natural Resources Management, University of Patras, G. Seferi 2, 30100 Agrinio, Greece
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Academic Editors: Heiko Balzter and Prasad S. Thenkabail
Received: 9 December 2014 / Revised: 28 May 2015 / Accepted: 2 June 2015 / Published: 10 June 2015
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Abstract

Management strategies and silvicultural treatments of fire-prone ecosystems often rely on knowledge of the regeneration potential and long-term recovery ability of vegetation types. Remote sensing and GIS applications are valuable tools providing cost-efficient information on vegetation recovery patterns and their associated environmental factors. In this study we used an ordinal classification scheme to describe the land cover changes induced by a wildfire that occurred in 1983 in Pinus brutia woodlands on Karpathos Aegean Island, south-eastern Greece. As a proxy variable that indicates ecosystem recovery, we also estimated the difference between the NDVI and NBR indices a few months (1984) and almost 30 years after the fire (2012). Environmental explanatory variables were selected using a digital elevation model and various thematic maps. To identify the most influential environmental factors contributing to woodland recovery, binary logistic regression and linear regression techniques were applied. The analyses showed that although a large proportion of the P. brutia woodland has recovered 26 years after the fire event, a considerable amount of woodland had turned into scrub vegetation. Altitude, slope inclination, solar radiation, and pre-fire woodland physiognomy were identified as dominant factors influencing the vegetation’s recovery probability. Additionally, altitude and inclination are the variables that explain changes in the satellite remote sensing vegetation indices reflecting the recovery potential. Pinus brutia showed a good post-fire recovery potential, especially in parts of the study area with increased moisture availability. View Full-Text
Keywords: LANDSAT; vegetation indices; NDVI; logistic regression; Mediterranean; regeneration; wildland fires LANDSAT; vegetation indices; NDVI; logistic regression; Mediterranean; regeneration; wildland fires
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Nioti, F.; Xystrakis, F.; Koutsias, N.; Dimopoulos, P. A Remote Sensing and GIS Approach to Study the Long-Term Vegetation Recovery of a Fire-Affected Pine Forest in Southern Greece. Remote Sens. 2015, 7, 7712-7731.

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