<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Angelis, Antonella</style></author><author><style face="normal" font="default" size="100%">Bajocco, Sofia</style></author><author><style face="normal" font="default" size="100%">Ricotta, Carlo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Phenological variability drives the distribution of wildfires in Sardinia</style></title><secondary-title><style face="normal" font="default" size="100%">Landscape Ecology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">Fire selectivity</style></keyword><keyword><style  face="normal" font="default" size="100%">Image segmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">MODIS</style></keyword><keyword><style  face="normal" font="default" size="100%">NDVI proﬁles</style></keyword><keyword><style  face="normal" font="default" size="100%">Residuals</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012///</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.springerlink.com/index/10.1007/s10980-012-9808-2</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">1535 - 1545</style></pages><isbn><style face="normal" font="default" size="100%">1098001298082</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Fuel characteristics play an important role in driving ﬁre ignition and propagation; at the landscape scale fuel availability and ﬂammability are closely related to vegetation phenology. In this view, the NDVI proﬁles obtained from high temporal resolution satellites, like MODIS, are an effective tool for monitoring the coarse-scale vegetation seasonal timing. The aim of this paper is twofold: our ﬁrst objective consists in classifying by means of multitemporal NDVI proﬁles the coarse-scale vegetation of Sardinia into ‘phenological clusters’ in which ﬁre incidence is higher (preferred) or lower (avoided) than expected from a random null model. If ﬁres would burn unselectively, then ﬁres would occur randomly across the landscape such that the number of ﬁres in a given phenological cluster would be nearly proportional to the relative area of that land cover type in the analyzed landscape. Actually, certain vegetation types are more ﬁre-prone than others. That is, they are burnt more frequently than others. In this framework, our second objective consists in investigating the temporal parameters of the remotely sensed NDVI proﬁles that best characterize the observed phenology–ﬁre selectivity relationship. The results obtained show a good association between the NDVI temporal proﬁles and the spatio-temporal wildﬁre distribution in Sardinia, emphasizing the role of bioclimatic timing in driving ﬁre regime characteristics.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Retuerto, Ruben</style></author><author><style face="normal" font="default" size="100%">Carballeira, Alejo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Use of direct gradient analysis to study the climate-vegetation relationships in Galicia, Spain</style></title><secondary-title><style face="normal" font="default" size="100%">Plant Ecology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">climatic factors</style></keyword><keyword><style  face="normal" font="default" size="100%">climatic position</style></keyword><keyword><style  face="normal" font="default" size="100%">Clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">frequential analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">indicator value</style></keyword><keyword><style  face="normal" font="default" size="100%">phytoclimatology</style></keyword><keyword><style  face="normal" font="default" size="100%">Principal component analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1992</style></year></dates><volume><style face="normal" font="default" size="100%">99-100</style></volume><pages><style face="normal" font="default" size="100%">183-194</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper reports a bioclimatic analysis of plant species in Galicia, NW Spain. A set of floristic data obtained from 150 plots located at euclimatopes (sites with monitored climate) was analysed using di- rect gradient analysis and clustering with respect to the 8 climatic variables thought to play a major role in regulating the distribution of the species considered in the study area. Principal component analysis (PCA) and hierarchical clustering were based on a matrix of species by climatic variables. Indicator taxa for the variables were identified on the basis of their Indicator values (Brisse &amp; Grandjouan 1978) and grouped by cluster analysis. The groups produced were compatible with the results of principal com- ponent analyis and the frequential analysis of the species, which identified their phytoclimatic nature. The groups were then characterized by determining their climatic positions and indicator values with respect to the chief climatic variables. The first three PCA axes, which were associated with Oceanity, Mean minimum temperatures and the temperature range in the coldest month, together accounted for 97.2 ~o of the variance of the data.</style></abstract></record></records></xml>