Assessing industrial development influence on land use/cover drivers and change detection for West Bank East London, South Africa

South Africa’s nationwide socio-economic industrial development zone drive focuses on alleviating of the apartheid social ills legacy. To ensure sustainable industrial ecological development, land-cover monitoring is needed though limited attention has been accorded. This study, aimed at assessing the influence of East London Industrial Development Zone (ELIDZ) on land-use/land-cover (LULC) drivers and detecting LULC changes for 15 years over the West Bank East London. An integration of remote sensing with qualitative approaches was adopted to provide robust temporal and spatial LULC change analysis. Object-based classification was performed on the satellite images for 1998, 2007 and 2013. Normalised Difference Vegetation Index (NDVI) and Normalised Difference Built-up Index (NDBI) complemented and validated observed land cover changes. The study reveals that industrial development has been a key driver for land-use changes in West Bank. The classification indicated that vegetation (5.97%) and bare land (-9.06%) classes had the highest percentage increase and decrease respectively. Water (0.02%) and bare land (-0.6%) classes had the lowest annual rate of change. Built-up and bare land classes varied considerably. An overall land-cover classification mean accuracy assessment of 97.24% and a mean Kappa coefficient of 0.95 were attained for the entire study period. This study offers the value of integrated methods in monitoring land-cover change to enhance informed decision-making especially in rapidly changing landscapes for conservation purposes.This manuscript stems from the corresponding authors’ postgraduate study and who performed most of the experiments.The University of Pretoria and the United State Geological Survey (USCS).http://www.ripublication.comam2019Geography, Geoinformatics and Meteorolog

Efficacy of morphological approach in the classification of urban land covers.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Understanding the often-heterogeneous land use land cover (LULC) in urban areas is critical for among others environmental monitoring, spatial planning and enforcement. Recently, several earth observation satellites have been developed with enhanced spatial resolution that provide for precise and detailed representation of image objects. This has generated new demand for enhanced processing capabilities. Thus, the need for techniques that incorporate spatial and spectral information in the analysis of urban LULC has drawn increasing attention. Enhanced spatial resolution comes with challenges for most pixel based classifiers. This include salt and pepper effects that arise from incapability of pixel based techniques in considering spatial or contextual information related to the pixel of interest during image analysis. These challenges have often contributed to the inaccuracy of heterogeneous LULC classification. Object based techniques on the other hand have been proposed to provide effective framework for incorporating spatial information in their analysis. However, challenges such as over/under segmentation and difficulty or non-robust statistical estimation hamper most object techniques in achieving optimum performance. Thus, to achieve optimum LULC classification, the full exploitation of both spectral-spatial information is essential. Hence, this study investigated the efficacy of Mathematical Morphological (MM) techniques referred to as morphological profiles (MP) in LULC classification of a heterogeneous urban landscape. The first objective of the study evaluated two MP techniques i.e. concatenation of morphological profiles (CMP) and multi-morphological profiles (MMP) in the classification of a heterogeneous urban LULC. Findings from this study indicated that both CMP and MMP provided higher accuracies in classifying a heterogeneous urban landscape. However, in evaluating their capability in preserving geometrical characteristics such as shape, theme, edge and positional similarity of image structures, CMP provided higher accuracies than MMP. This was attributed to the use of Principal Component Analysis (PCA) in the construction of MMP that resulted in the distorted edges of some of the image objects. However, in comparing the techniques in terms of the capability to discriminate image objects, MMP provided higher classification accuracies compared to CMP. This can be attributed to the former’s capability to exploit both spectral and spatial information from very high spatial resolution imagery. Hence in the second objective, MMP was adopted due to its ability to deal with dimensionality problem associated with CMP and its superior object discrimination capability. The findings indicated that MMP significantly enhanced ML and SVM classification accuracies. Specifically, the use of MMP as a feature vector for SVM and ML classification increased LULC distinction of objects with similar spectral signatures in a heterogeneous urban landscape. This is due to its capability to provide an effective framework for synthesis of spectral and spatial information. Overall the study demonstrated that morphological techniques provides robust novel image analysis techniques which can effectively be used for operational classification of a heterogeneous urban LULC

Semi-automated workflow for natural disaster assessment : a case study of Banda Aceh, Indonesia

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesThe past decade has witnessed many natural disasters hitting highly populated areas causing billions of dollars in damage as well as many human casualties. During natural disasters, when attaining ground measurements are limited, remote sensing and geographical information systems (GIS) are useful tools for in-depth analysis of the affected area. This report will introduce a new semi-automatic workflow in which the road network will be used to break up the area into “blocks” and then zonal statistics will be applied to detect change based on the created blocks rather than the conventional methods of change detection; pixel by pixel and object oriented. This hybrid approach will take advantage of the simplicity and ease of applying pixel change detection methods on fixed objects or “blocks” to assess for damage. The change detection analysis results can then be used to map and quantify damage caused by natural disasters using pre and post Landsat imagery of the affected area. Multi-Criteria Analysis is performed on the damage map, proximity to roads, proximity to waterbodies and building size to find the most suitable locations for temporary housing sites. The image differencing of NDWI mean produced the highest overall accuracy of 71.70% among eleven bands/indices and the multi-criteria analysis successfully selected fourteen temporary housing center sites from a possible 114. When time is of essence with limited resources and GIS expertise on the field, local authorities can greatly benefit from a rapid generalized analysis that will provide a “bird-eye view” of the affected area to efficiently and effectively allocate emergency efforts within a short time frame