Mapping illegal dumping using a high resolution remote sensing image case study: Soweto township in South Africa

Abstract

Although a vast number of illegal dumping investigations have been conducted in the City of Johannesburg by City of Johannesburg Municipality, Government, Corporates as well as NGOs previously, there has been a limited attempt to integrate available datasets from the different methods of illegal dumping monitoring (satellite, spatial data collection and ground-based observations) and GIS modelling. Most South African municipal administrations have had to acknowledge their incapability to cope with the difficulty of illegal dumping monitoring. Illegal dumping challenges often emanate from the incapacity of municipality administrations to meet the required assemblage and removal of wastes. Vacant or unoccupied land is the target of illegal dumping in most areas. This study compares modelled, satellite and collected data using GIS methods to determine the most accurate estimate of detecting illegal dumping. A comparison between Random Forest (RF) and Support Vector Machine (SVM) in mapping illegal dumping and to quantity the significance of Worldview-2 band in detecting and mapping illegal dumping was pursued. Two results were generated: multispectral imagery sorting production using machine-learning RF and SVM algorithms in a comparable land and definition of the significance of unrelated WorldView bands on sorting production. Precision of the derivative thematic maps was evaluated by calculating mix-up milieus of the classifiers’ land use/ land cover maps with separate autonomous justification data sets. A complete classification accurateness of 84.07 % with a kappa value of 0.8116, and 85.16% with a kappa value of 0.8238 was attained using RF and SVM, respectively. An assessment of diverse WorldView-2 bands using the two classifiers indicated that the blend of the red-edge band had a vital consequence on the overall classification accurateness in mapping of illegal dumping.

Keywords: Illegal dumping, remote sensing, monitoring, vegetation, spatial datasets, image processing, image classification.