<?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%">Amici, Valerio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dealing with vagueness in complex forest landscapes: A soft classification approach through a niche-based distribution model</style></title><secondary-title><style face="normal" font="default" size="100%">Ecological Informatics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">classification uncertainty</style></keyword><keyword><style  face="normal" font="default" size="100%">Forecasting forests</style></keyword><keyword><style  face="normal" font="default" size="100%">Forest cover map</style></keyword><keyword><style  face="normal" font="default" size="100%">Fuzzy set</style></keyword><keyword><style  face="normal" font="default" size="100%">Maxent</style></keyword><keyword><style  face="normal" font="default" size="100%">Remote sensing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011///</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://linkinghub.elsevier.com/retrieve/pii/S1574954111000550</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">371 - 383</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The increasing interest in biodiversity conservation has led to the development of new approaches to facilitate ecologically based conservation policies and management plans. In this context, the development of effective methods for the classiﬁcation of forest types constitutes a crucial issue as forests represent the most widespread vegetation structure and play a key role in ecosystem functioning. In this study a maximum entropy approach (Maxent) to forest type classiﬁcation in a complex Mediterranean area, has been investigated. Maxent, a niche-based model of species/habitat distribution, allowed researchers to estimate the potential distribution of four forest types: Holm oak, Mixed oak, Mixed broadleaved and Riparian forests. The Maxent model's internal tests have proved a powerful tool for estimating the model's accuracy and analyzing the effects of the most important variables in the produced models. Moreover the comparison with a spectral response-based fuzzy classiﬁcation, showed a higher accuracy in the Maxent outputs, demonstrating how the use of environmental variables, combined with spectral information in the classiﬁcation of natural or seminatural land cover classes, improves map accuracies. The modeling approach followed by this study, taking into account the uncertainty proper of the natural ecosystems and the use of environmental variables in land cover classiﬁcation, can represent a useful approach to making more efﬁcient and effective ﬁeld inventories and to developing effective conservation policies.</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><notes><style face="normal" font="default" size="100%">The following values have no corresponding Zotero field:&lt;br/&gt;publisher: Elsevier B.V.</style></notes></record></records></xml>