Remote sensing support for post fire forest management

Corona, Piermaria; Lamonaca, Andrea; Chirici, Gherardo

doi:10.3832/ifor0305-0010006

Cited by 26 publications

(19 citation statements)

References 22 publications

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“…This is in general accordance with other studies examining ecosystem resilience [58] or long-term post-fire regeneration [17]. Our combined approach based on remote sensing techniques [31] and geographic information systems does not require large capital investment. Through simple techniques, sites that require special focus and extra effort regarding ecosystems recovery can be identified.…”

Section: Discussionsupporting

confidence: 89%

“…Given the discrepancies between the land cover legends of the available pre-and post-fire land cover maps and the limitations arising from the use of satellite data [31], we developed a classification scheme that minimizes misclassification errors. The classification scheme adopted consists of four land cover classes: (a) dense P. brutia woodland (cover > 25%); (b) open P. brutia woodland (cover < 25%); (c) scrubland (with non-significant presence of individual P. brutia trees (scattered trees) from the cover-abundance point of view; and (d) phrygana (garrigues) representing sparsely vegetated areas where trees and tall shrubs were absent.…”

Section: Pre-fire and Post-fire Land Cover Mapping And Changesmentioning

confidence: 99%

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

et al. 2015

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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.

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Section: Discussionsupporting

confidence: 89%

Section: Pre-fire and Post-fire Land Cover Mapping And Changesmentioning

confidence: 99%

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

et al. 2015

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“…The choice of remote sensing platform for fire recovery monitoring depends on both the severity and size of the fire. As noted by Corona et al [2008] burned areas larger than 50 ha can be mapped by medium to low resolution sensors such as WiFS-IRS, NOAA AVHRR, MODIS, or Spot-Vegetation. However, most of these records lack the long archive of Landsat which hinders monitoring burn recovery across several decades.…”

Section: Introductionmentioning

confidence: 99%

Monitoring forest regrowth following large scale fire using satellite data-A case study of Yellowstone National Park, USA-

Franks

Masek

Turner

2013

European Journal of Remote Sensing

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Monitoring forest regrowth following major fires is important for understanding controls on forest regeneration and succession and detecting change in postfire plant communities. In this study we examined the extent to which forest regrowth following the 1988 Yellowstone National Park fires can be characterized by optical remote sensing data, and the spatial patterns associated with regrowth. Using a near-annual time series of Landsat satellite imagery, several satellite-based metrics were compared with field-based data of burn extent and post-fire stand structure and function. While there was little correlation between lodgepole pine density and our satellite metrics, single year regressions produced results over 80% and trend analysis of multiyear sites were able to explain 60 to 70% of the variability found between the ground collected data and the satellite metrics.

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“…In order to target preventive and mitigation measures in forest and shrublands, however, field-based indicators of land degradation should be collected to validate ESAI assessments , eventually combined with remotely sensed monitoring of forest conditions (Corona et al 2008). LD monitoring would be particularly crucial in areas experiencing rapid changes in climate quality (e.g., reduced precipitation rates over a relatively short time or recurrent drought episodes, intense rainfall events, continuous increase in summer temperatures causing vegetation stress, etc.)…”

Section: Discussionmentioning

confidence: 99%