Object-Based Feature Extraction of Google Earth Imagery for Mapping Termite Mounds in Amazon's Savannas

Abstract

This study investigates the potential of object-based feature extraction from Google Earth Imagery for mapping termite mounds. Termite mounds are often hotspots of plant growth (i.e. primary productivity). Accurate and timely information about termite mounds is crucial for land management decision-making and ecosystem monitoring. To address this issue, the effectiveness of object-based feature extraction that use automated image segmentation to extract meaningful ground features from imagery was tested. The study used very high resolution multispectral Google Earth images to produce termite mounds maps in Bahia, Brazil. The results from the study indicated that an object-based approach provides a better means for ground feature extraction than a pixel based method because it provides an effective way to incorporate spatial information and expert knowledge into the feature extraction process. Also the results suggest that Google earth imagery has considerable potential in mapping termite mounds.

Object-Based Image Analysis of Slum Settlements: A Case Study from Dar es Salaam, Tanzania

“…For this research model, the ERDAS Imagine Objective module was used to carry out feature extraction methods for slum settlements within the study area. A method similar to previous feature extraction models was used, with minor changes implemented (Sim & Lee, 2014). Figure 3 displays the step-by-step procedure used, to receive the final product, which were building footprints of slums.…”

“…If any object observed was measured at less than the specified minimum probability, it was simply removed from the dataset (Figure 7). After this, a size filter was applied, which removed any objects that had fewer than 1,000 pixels (Sim & Lee, 2014). See Figure 8.…”

“…Some of the feature extraction processes used included raster pixel processor, raster object creator, raster object operator, raster to vector conversion, vector object operator, vector object processor, and vector cleanup properties. This method proved effective with a 90% accuracy rate between the mapped termite mounds and the actual number of termite mounds (Sim & Lee, 2014). It is crucial that researchers, city planners, and other community stakeholders understand the importance of OBIA; this method employs powerful techniques to measure population, housing, and sanitation in slum areas.…”