Modelling habitat suitability for alpine rock ptarmigan (<i>Lagopus muta helvetica</i>) combining object-based classification of IKONOS imagery and Habitat Suitability Index modelling

作     者：Zohmann, Margit Pennerstorfer, Josef Nopp-Mayr, Ursula 

作者机构：Univ Nat Resources & Life Sci Dept Integrat Biol & Biodivers Res Inst Wildlife Biol & Game Management A-1180 Vienna Austria Univ Nat Resources & Life Sci Dept Forest & Soil Sci Inst Forest Entomol Forest Pathol & Forest Protec A-1190 Vienna Austria 

出 版 物：《ECOLOGICAL MODELLING》 (生态模拟)

年 卷 期：2013年第254卷

页      面：22-32页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 09[农学] 0713[理学-生态学] 

基　　金：Salzburg and Vorarlberg University of Natural Resources and Life Sciences, Vienna 

主　　题：Object-based image analysis Image classification IKONOS Habitat suitability modelling Alpine rock ptarmigan Habitat monitoring tool 

摘      要：The maintenance and restoration of high-quality habitats of wildlife species in alpine ecosystems are key issues in conservation biology. The rock ptarmigan (Lagopus muta helvetica), which prefers open habitats above the treeline, is listed in Annex II of the EU Bird Directive. Large areas identified as potentially important for conservation and restricted financial resources for the implementation of conservation activities necessitate the development of tools supporting habitat monitoring and management. We developed a knowledge-based Habitat Suitability Index (HSI) model for rock ptarmigan and combined it with results of an object-based image analysis of very high resolution (VHR) satellite images (IKONOS) to create a rock ptarmigan habitat suitability map in the eastern Alps of Austria. The mechanistic habitat model contained 9 habitat variables, decisive for summer habitat use of rock ptarmigan (i.e., slope, patchiness, and land cover classes for: rocks, rocks intermixed with vegetation, scree, dwarf shrubs, dwarf pine, alpine/subnival grassland, and forest). We used Definiens Professional 5.0 (c) software for the object-based image analyses, applying multi-resolution segmentation methods. We generated a classification hierarchy comprising the same variables used in the HSI model, each augmented by areas in shadow, and took into account clouds, water bodies and human infrastructures. We assessed classification accuracies, applying an Error Matrix based on ITA Mask. We reached an overall classification accuracy of 0.75 and a kappa statistic value of 0.70, the latter indicating good to very good agreement. The classification results indicated that the object-oriented image classification approach using VHR data was appropriately used to create an adequate thematic map for further habitat modelling. We calculated the habitat suitability maps using MapModels. Model output was validated with ptarmigan presence-absence data, using signs (droppings) as indicators of