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

Abstract

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.

Seasonal Separation of African Savanna Components Using Worldview-2 Imagery: A Comparison of Pixel- and Object-Based Approaches and Selected Classification Algorithms

“…However the latter approach had a significantly higher accuracy for almost every classifier with the highest overall accuracy score of 93%. This is consistent with several other studies indicating superiority of using OBIA in a range of environments [5,31,64]. In particular, Whiteside et al (2011) [5] concluded that OBIA outperforms pixel-based classification for medium and high resolution satellite imagery in Australian savannas.…”

“…In particular, Whiteside et al (2011) [5] concluded that OBIA outperforms pixel-based classification for medium and high resolution satellite imagery in Australian savannas. Moreover, study of Gibbes et al (2010) [64] indicates that OBIA has a great potential in discriminating African woodland savanna from shrubby dominated and grassland patches using IKONOS high resolution imagery. Our study goes further, demonstrating that object-based classification of imagery characterized by a small pixel size and 8 spectral bands provides tools for fine level differentiation of spectrally similar vegetation components in the African savanna ecosystem.…”

A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables

“…The advantages of Geographic Object-Based Image Analysis (GEOBIA) over more traditional pixel-based analyses have been discussed in a number of studies [1][2][3][4]. Whilst a large body of the literature has utilized commercial packages, such as eCognition [5], there is growing interest in open source alternatives [6].…”

The Role of Methodology and Spatiotemporal Scale in Understanding Environmental Change in Peri-Urban Ouagadougou, Burkina Faso

“…The 12 m crown size was chosen to differentiate between large trees and small trees and bushes, as trees with a crown diameter larger than 12 m are thought to represent keystone species in African savannahs [67]. The different types of man-made constructions above represent various stages in the urban sprawl of Ouagadougou.…”