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OALib Journal期刊
ISSN: 2333-9721
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Habitat Mapping in Rugged Terrain using IKONOS Satellite Images
崎岖地形动植物栖息地生态环境遥感制图与应用

Keywords: Habitat mapping,IKONOS,structural classes,segmentation
动植物栖息地生态环境制图
,IKONOS,结构分类,图像分割

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Abstract:

Ecological mapping in the tropics is difficult due to the heterogeneity of the vegetation,the nature of the terrain which is often highly dissected,and general problem of determining ecological boundaries which may be indistinct,even to a field observer.There are no studies in the literature discussing the successful mapping of vegetation or habitats over large areas.In the last 20 years,two habitat surveys in the form of vegetation maps have been completed by Hong Kong government departments and private consultants,with inadequate accuracy and poor results.Since these previous projects used only medium spatial resolution sensors:Landsat and Satellite pour l'Observation de la Terre(SPOT),it may be possible to produce more accurate ecological maps using the new generation of Very High Resolution(VHR)satellite sensor images.Traditionally,habitat mapping has used Aerial Photographic Interpretation(API).However,45 air photos are required to cover the study area,Shing Mun and Tai Mo Shan country parks in Hong Kong,compared with a single IKONOS scene.Additional advantages of IKONOS include spatial,spectral and temporal consistency.Therefore,if a suitable methodology for automatic habitat mapping can be developed,reduced costs and less processing time would be required.This study attempts to develop a methodology for detailed ecological mapping based on a suite of integrated image processing techniques,and with stated accuracy levels,for IKONOS images-"Multi-scale object-oriented segmentation with decision tree classification"(MOOSC).The results show that 95% overall accuracy was achieved using API and 94% was achieved using MOOSC method when the results were referenced to GPS field data.These findings support the applicability and feasibility of MOOSC method,and it was only one third of the cost comparing with API.

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