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Remote Sens. 2010, 2(12), 2748-2772; doi:10.3390/rs2122748

Application of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis

Department of Geography & Land Use and Environmental Change Institute (LUECI), University of Florida, 3141 Turlington Hall, P. O. Box 117315, Gainesville, FL 32611, USA
Author to whom correspondence should be addressed.
Received: 16 October 2010 / Revised: 7 December 2010 / Accepted: 8 December 2010 / Published: 10 December 2010
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Savanna ecosystems are an important component of dryland regions and yet are exceedingly difficult to study using satellite imagery. Savannas are composed are varying amounts of trees, shrubs and grasses and typically traditional classification schemes or vegetation indices cannot differentiate across class type. This research utilizes object based classification (OBC) for a region in Namibia, using IKONOS imagery, to help differentiate tree canopies and therefore woodland savanna, from shrub or grasslands. The methodology involved the identification and isolation of tree canopies within the imagery and the creation of tree polygon layers had an overall accuracy of 84%. In addition, the results were scaled up to a corresponding Landsat image of the same region, and the OBC results compared to corresponding pixel values of NDVI. The results were not compelling, indicating once more the problems of these traditional image analysis techniques for savanna ecosystems. Overall, the use of the OBC holds great promise for this ecosystem and could be utilized more frequently in studies of vegetation structure. View Full-Text
Keywords: savannas; vegetation structure; tree canopies; object based; IKONOS savannas; vegetation structure; tree canopies; object based; IKONOS

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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Gibbes, C.; Adhikari, S.; Rostant, L.; Southworth, J.; Qiu, Y. Application of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis. Remote Sens. 2010, 2, 2748-2772.

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