| dc.contributor.author |
Poirazidis, KS |
en |
| dc.contributor.author |
Zografou, K |
en |
| dc.contributor.author |
Kordopatis, P |
en |
| dc.contributor.author |
Kalivas, DP |
en |
| dc.contributor.author |
Arianoutsou, M |
en |
| dc.contributor.author |
Kazanis, D |
en |
| dc.contributor.author |
Korakaki, E |
en |
| dc.date.accessioned |
2014-06-06T06:51:35Z |
|
| dc.date.available |
2014-06-06T06:51:35Z |
|
| dc.date.issued |
2012 |
en |
| dc.identifier.issn |
12864560 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1007/s13595-012-0222-3 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/5578 |
|
| dc.subject |
AHP |
en |
| dc.subject |
Geostatistics |
en |
| dc.subject |
MCDA |
en |
| dc.subject |
Pinus halepensis |
en |
| dc.subject |
Redundancy analysis |
en |
| dc.subject |
Regeneration |
en |
| dc.subject.other |
accuracy assessment |
en |
| dc.subject.other |
Bayesian analysis |
en |
| dc.subject.other |
coniferous forest |
en |
| dc.subject.other |
correlation |
en |
| dc.subject.other |
forest fire |
en |
| dc.subject.other |
forest management |
en |
| dc.subject.other |
geostatistics |
en |
| dc.subject.other |
GIS |
en |
| dc.subject.other |
grass |
en |
| dc.subject.other |
regeneration |
en |
| dc.subject.other |
seedling |
en |
| dc.subject.other |
Elis |
en |
| dc.subject.other |
Greece |
en |
| dc.subject.other |
Peloponnese |
en |
| dc.subject.other |
Western Greece |
en |
| dc.subject.other |
Ilia |
en |
| dc.subject.other |
Pinus halepensis |
en |
| dc.title |
A GIS-based integrated approach predicts accurately post-fire Aleppo pine regeneration at regional scale |
en |
| heal.type |
journalArticle |
en |
| heal.identifier.primary |
10.1007/s13595-012-0222-3 |
en |
| heal.publicationDate |
2012 |
en |
| heal.abstract |
Context: This study investigates post-fire natural regeneration of Aleppo pine (Pinus halepensis) forests at Ilia region (Peloponnesus, Greece) following the catastrophic fire of 2007. Aims: The objective of this study is the prediction of P. halepensis post-fire regeneration at a regional scale through an integrated geographic information systems (GIS) model as a basis for post-fire management plans. Methods: The model was developed in three interconnected stages: (1) field data collection, (2) development of two prediction models (based on interpolation of field data and multicriteria evaluation (MCE) that combined factors known to affect regeneration), and (3) combination of applied models using Bayesian statistics. Results: Post-fire pine regeneration presented high variation among the studied plots. Redundancy analysis revealed the positive effect of fallen branches and a negative correlation with altitude. Both modeling approaches (geostatistical and MCE) predicted the post-fire pine regeneration with high accuracy. A very significant correlation (r00.834, p<0.01) was found between the combined final model and the actual number of counted seedlings, illustrating that less than 10 % of the studied area corresponds to sites of very low post-fire pine regeneration. Conclusion:s The combination of GIS models increased the prediction success of different levels of pine regeneration. Lowaltitude areas with low grass cover overlying tertiary deposits were proved the most suitable for pine regeneration, while stands developing on limestone proved least suitable. The proposed methodology providesmanagement authoritieswith a sound tool to quickly assess Aleppo pine post-fire regeneration potential. © INRA /Springer-Verlag France 2012. |
en |
| heal.journalName |
Annals of Forest Science |
en |
| dc.identifier.issue |
4 |
en |
| dc.identifier.volume |
69 |
en |
| dc.identifier.doi |
10.1007/s13595-012-0222-3 |
en |
| dc.identifier.spage |
519 |
en |
| dc.identifier.epage |
529 |
en |