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Title: Vulnerable land ecosystems classification using spatial context and spectral indices
Authors: Ibarrola-Ulzurrun, E. 
Gonzalo-Martín, Consuelo
Marcello, Javier 
UNESCO Clasification: 220921 Espectroscopia
250616 Teledetección (Geología)
Keywords: Ecosystems management
Spectral-spatial classification
Teide National Park
Issue Date: 2017
Journal: Proceedings of SPIE - The International Society for Optical Engineering 
Conference: 17th SPIE Conference on Earth Resources and Environmental Remote Sensing/GIS Applications 
Earth Resources and Environmental Remote Sensing/GIS Applications VIII 2017 
Abstract: Natural habitats are exposed to growing pressure due to intensification of land use and tourism development. Thus, obtaining information on the vegetation is necessary for conservation and management projects. In this context, remote sensing is an important tool for monitoring and managing habitats, being classification a crucial stage. The majority of image classifications techniques are based upon the pixel-based approach. An alternative is the object-based (OBIA) approach, in which a previous segmentation step merges image pixels to create objects that are then classified. Besides, improved results may be gained by incorporating additional spatial information and specific spectral indices into the classification process. The main goal of this work was to implement and assess object-based classification techniques on very-high resolution imagery incorporating spectral indices and contextual spatial information in the classification models. The study area was Teide National Park in Canary Islands (Spain) using Worldview-2 orthoready imagery. In the classification model, two common indices were selected Normalized Difference Vegetation Index (NDVI) and Optimized Soil Adjusted Vegetation Index (OSAVI), as well as two specific Worldview-2 sensor indices, Worldview Vegetation Index and Worldview Soil Index. To include the contextual information, Grey Level Co-occurrence Matrices (GLCM) were used. The classification was performed training a Support Vector Machine with sufficient and representative number of vegetation samples (Spartocytisus supranubius, Pterocephalus lasiospermus, Descurainia bourgaeana and Pinus canariensis) as well as urban, road and bare soil classes. Confusion Matrices were computed to evaluate the results from each classification model obtaining the highest overall accuracy (90.07%) combining both Worldview indices with the GLCM-dissimilarity.
ISBN: 9781510613201
ISSN: 0277-786X
DOI: 10.1117/12.2278496
Source: Proceedings of SPIE - The International Society for Optical Engineering [ISSN 0277-786X], v. 10428, article number 104280V
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