Development of an observatory for spatial planning in South Africa: A best practice review

The National Development Plan (NDP) of South Africa describes a 2030 vision for the country. The NDP proposes an observatory as one of the measures to develop capabilities for effective spatial decision-making and implementation. This article presents results of a review of observatories with the aim to unpack the details for setting up the proposed observatory. A review of mainly South African observatories was conducted in order to clarify the focus of the observatory (i.e. its purpose and main operations) and how it should be set up (i.e. stakeholders to be involved and hosting options). The review draws on interviews, questionnaires and a workshop with stakeholders, experts and key players. A review of relevant scientific literature and observatory websites was also done. Results underline the importance of drawing on existing data collection, integration and analysis initiatives, as well as the coordinating role such an observatory will have to play.Partially funded by the Secretariat of the National Planning Commission of South Africa.http://www.sajg.org.za/index.php/sajgam2016Centre for Geoinformation ScienceGeography, Geoinformatics and Meteorolog

A technique for optimal selection of segmentation scale parameters for object-oriented classification of urban scenes

Multi-scale image segmentation produces high level object features at more than one level, compared to single scale segmentation. Objects generated from this type of segmentation hold additional attributes such as mean values per spectral band, distances to neighbouring objects, size, and texture, as well as shape characteristics. However, the accuracy of these high level features depends on the choice of segmentation scale parameters. Several studies have investigated techniques for scale parameter selection. These proposed approaches do not consider the different objects’ size variability found in complex scenes such as urban scene as they rely upon arbitrary object size measures, introducing instability errors when computing image variances. A technique to select optimal segmentation scale parameters based on image variance and spatial autocorrelation is presented in this paper. Optimal scales satisfy simultaneously the conditions of low object internal variance and high inter-segments spatial autocorrelation. Applied on three Cape Town urban scenes, the technique produced visually promising results that would improve object extraction over urban areas

Using Multi-criteria Evaluation and GIS for Flood Risk Analysis in Informal Settlements of Cape Town: The Case of Graveyard Pond

Rural-urban migrations have contributed to the steady increase in the population of Cape Town. Many of the migrants have settled in informal settlements because they cannot afford to rent or buy decent housing. Many of these settlements are however located on marginal and often poorly drained land. Consequently, most of these settelements are prone to flooding after prolonged rainfall. Current flood risk management techniques implemented by the authorities of the Cape Town City Council (CTCC) are not designed to support informal settlements. In fact, owing to a lack of information about the levels of flood risk within the individual settlements, either the CTCC has often been uninvolved or has implemented inappropriate remedies within such settlements. This study sought to investigate a methodology that the CTCC could use to improve flood risk assessment. Using a case study of an informal settlement in Cape Town, this study proposed a methodology of integration of community-based information into a Geographic Information System that can be used by the CTCC for risk assessment. In addition, this research demonstrated the use of a participatory multi-criteria evaluation (MCE) for risk assessment. A questionnaire was used to collect community-based information. The shack outlines of the informal settlement were digitised using CTCC aerial imagery. The questionnaires were captured using spreadsheets and linked to the corresponding shacks in the GIS. Risk weights were subsequently calculated using pairwise comparisons for each household, based on their responses to the questionnaires. The risk weights were then mapped in the GIS to show the spatial disparities in risk

Digital elevation model correction in urban areas using extreme gradient boosting, land cover and terrain parameters

The accuracy of digital elevation models (DEMs) in urban areas is influenced by numerous factors including land cover and terrain irregularities. Moreover, building artifacts in global DEMs cause artificial blocking of surface flow pathways. This compromises their quality and adequacy for hydrological and environmental modelling in urban landscapes where precise and accurate terrain information is needed. In this study, the extreme gradient boosting (XGBoost) ensemble algorithm is adopted for enhancing the accuracy of two medium-resolution 30m DEMs over Cape Town, South Africa: Copernicus GLO-30 and ALOS World 3D (AW3D). XGBoost is a scalable, portable and versatile gradient boosting library that can solve many environmental modelling problems. The training datasets are comprised of eleven predictor variables including elevation, urban footprints, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector roughness measure, forest cover and bare ground cover. The target variable (elevation error) was calculated with respect to highly accurate airborne LiDAR. After training and testing, the model was applied for correcting the DEMs at two implementation sites. The correction achieved significant accuracy gains which are competitive with other proposed methods. The root mean square error (RMSE) of Copernicus DEM improved by 46 to 53% while the RMSE of AW3D DEM improved by 72 to 73%. These results showcase the potential of gradient boosted trees for enhancing the quality of DEMs, and for improved hydrological modelling in urban catchments.Comment: 8 page

Use of agent based modelling to investigate the dynamics of slum growth

Informal settlements arise as a result of the urgent need for shelter by the urban poor. Urban planners and policy makers face challenges in effective management of slum settlements as they do not fully understand their dynamics and extents. Advances in Geomatics research have recently offered growing results in slum characteristics using various remote sensing and artificial intelligence approaches. The main objective of this research is to propose a conceptual model for the implementation of an empirically informed agent based prototype that can simulate future patterns and trends in land cover change over time specifically with reference to informal settlement proliferation in the city of Cape Town in South Africa. The study incorporates physical, environmental, social and economic factors specific to Cape Town in structuring behavioural rules for agents in a predictive environment. Input data is extracted from a time series study of remote sensing imagery, ancillary data and statistics. The resulting concept model for the prototype incorporates a static model, a dynamic and an interactive behaviour model that collectively form a combo for successful implementation of the physical agent based model. On implementation the model is expected to simulate city wide slum growth patterns and trends in Cape Town over time, highlighting likely areas of new settlement and revealing the eventualities of existing ones. Urban planners can use pattern information for proactive slum management and in preventing risk prone settlement especially in some areas of near coast Cape that are flood prone

Introduction to the AfricaGEO 2018 Conference Special Issue of the South African Journal of Geomatics

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Participatory approach to data collection for GIS for flood risk management in informal settlements of Cape Town

Also published in PositionIT, April/May 2012 under the title "GIS data collection for flood risk management"Inadequate flow of information between stakeholders can hamper development of sustainable flood risk management strategies. Using the case study of a flood prone informal settlement in Cape Town, this paper demonstrates a methodology for the collection and integration of community-based information into a Geographic Information System (GIS) that is useable by the Cape Town City Council (CTCC) for risk assessment. The study contributes to the body of Participatory GIS (PGIS) research. It demonstrates a practical approach to data collection towards development of sustainable flood risk management strategies in informal settlements

Vine Signal Extraction - an Application of remote sensing in precision Viticulture

This paper presents a study of precision agriculture in the wine industry. While precision viticulture mostly aims to maximise yields by delivering the right inputs to appropriate places on a farm in the correct doses and at the right time, the objective of this study was rather to assess vine biomass differences. The solution proposed in this paper uses aerial imagery as the primary source of data for vine analysis. The first objective to be achieved by the solution is to automatically identify vineyards blocks, vine rows, and individual vines within rows. This is made possible through a series of enhancements and hierarchical segmentations of the aerial images. The second objective is to determine the correlation of image data with the biophysical data (yield and pruning mass) of each vine. A multispectral aerial image is used to compute vegetation indices, which serve as indicators of biophysical measures. The results of the automatic detection are compared against a test field, to verify both vine location and vegetation index correlation with relevant vine parameters. The advantage of this technique is that it functions in environments where active cover crop growth between vines is evident and where variable vine canopy conditions are present within a vineyard block

A Comparison of Close-Range Photogrammetry to Terrestrial Laser Scanning for Heritage Documentation

This paper describes the photogrammetric and laser scan survey of an excavated section of the Laetoli hominid track-way in Tanzania. The survey was designed to allow for comparison to a prior detailed survey of the track-way carried out in 1995, and serves as a means to compare terrestrial laser scanning with close-range photogrammetry as survey methods for heritage documentation. Each hominid footprint in the track-way was photogrammetrically recorded using a rigorous multi-image controlled configuration. In a separate process a laser scanner was used to scan the entire track-way as well as the individual footprints. The data for the comparison and track-way / footprint shape assessment were a photogrammetrically generated point cloud and a 3D model (established in 1995 and 2011), as well as a laser scan point cloud acquired in the 2011 survey. The results showed a high agreement between the laser scan and the photogrammetric data captured in 2011. These two survey processes are entirely independent of each other, the results can be accepted as entirely objective and the excellent agreement between the data can serve as quality control, confirming that the footprint point clouds were captured with an external accuracy of approximately 0.3 to 0.4mm. Standard deviations which are internal precision measures, and typically optimistic, show an individual point accuracy of 0.1 to 0.2 mm. The accuracy for the full laser scan track-way survey was in the order of 1mm

Digital elevation model correction in urban areas using extreme gradient boosting, land cover and terrain parameters

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLVIII-4/W9-2024 GeoAdvances 2024 – 8th International Conference on GeoInformation Advances, 11–12 January 2024, Istanbul, Türkiye.LIDAR data for the City of Cape Town was provided by the Information and Knowledge Management Department, City of Cape Town.The accuracy of digital elevation models (DEMs) in urban areas is influenced by numerous factors including land cover and terrain irregularities. Moreover, building artefacts in global DEMs cause artificial blocking of surface flow pathways. This compromises their quality and adequacy for hydrological and environmental modelling in urban landscapes where precise and accurate terrain information is needed. In this study, the extreme gradient boosting (XGBoost) ensemble algorithm is adopted for enhancing the accuracy of two medium-resolution 30-metre DEMs over Cape Town, South Africa: Copernicus GLO-30 and ALOS World 3D (AW3D). XGBoost is a scalable, portable and versatile gradient boosting library that can solve many environmental modelling problems. The training datasets are comprised of eleven predictor variables including elevation, urban footprints, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector roughness measure, forest cover and bare ground cover. The target variable (elevation error) was calculated with respect to highly accurate airborne LiDAR. After training and testing, the model was applied for correcting the DEMs at two implementation sites. The corrections achieved significant accuracy gains which are competitive with other proposed methods. There was a 46 – 53% reduction in the root mean square error (RMSE) of Copernicus DEM, and a 72 - 73% reduction in the RMSE of AW3D DEM. These results showcase the potential of gradient-boosted decision trees for enhancing the quality of global DEMs, especially in urban areas.The Commonwealth Scholarship Commission UK, and the University of Cape Town Postgraduate Funding Office.http://www.isprs.org/publications/archives.aspxhj2024Geography, Geoinformatics and MeteorologySDG-11:Sustainable cities and communitie