Urban land use and land cover (LULC) change can be efficiently monitored with high-resolution satellite products for a variety of purposes, including sustainable planning. These, together with machine learning strategies, have great potential to detect even subtle changes with satisfactory accuracy. In this study, we used PlaneScope Imagery and machine learning strategies (Random Forests, Support Vector Machines, Naïve Bayes and K-Nearest Neighbour) to classify and detect LULC changes over the City of Cape Town between 2016 and 2021. Our results showed that K-Nearest Neighbour outperformed other classifiers by achieving the highest overall classification of accuracy (96.54% with 0.95 kappa), followed by Random Forests (94.8% with 0.92 kappa), Naïve Bayes (93.71% with 0.91 kappa) and Support Vector Machines classifiers with relatively low accuracy values (92.28% with 0.88 kappa). However, the performance of all classifiers was acceptable, exceeding the overall accuracy of more than 90%. Furthermore, the results of change detection from 2016 to 2021 showed that the high-resolution PlanetScope imagery could be used to track changes in LULC over a desired period accurately.

The rapid growth of cities, owing to rural-urban migration and high birth rate has resulted in encroachment of the land use and land cover (LULC) in the Cape Flats, situated north of Cape Town, Western Cape, South Africa. PlanetScope imagery and K-Nearest Neighbour (KNN) classifier are used to map and monitor the following LULC classes within the Cape Flats; waterbodies, trees, built-up, and vegetation, we further do a time series map between 2016 and 2021. The results showed that LULC changes between 2016 to 2021 are as follows: a decrease of 2.1% in vegetation, 0.4% in water bodies, and 11% in trees. Built-up areas, on the other hand, showed a significant increase of 13.6% over five years. The LULC changes in the Cape Flats were mainly triggered by an increase in built-up areas due to household construction to accommodate the increased population resulting from rural-to-urban migration and high birth rate. Classification accuracy from 2016 and 2021 was as follows; overall accuracy of 98.31% and 0.97 kappa coefficient in 2016, while overall accuracy 96.54% and kappa coefficient 0.95 in 2021. A combination of machine learning and high-resolution imagery showcased that high classification results can be achieved in monitoring subtle LULC changes. We recommended that all relevant stakeholders, including government officials and municipalities, formulate and adopt policies to protect against LULC degradation.

Residents of informal settlements are often faced with lack of essential services. These include services such as water, sanitation and electricity. Authorities responsible for providing these services often use Geographical Information Systems (GIS) for analysis prior to proposing developments in these settlements. However, the information used in the GIS analysis is often based on the physical characteristics of the informal settlement being analysed. In contrast, there has been a growing call for the use of information collected from the actual residents of the settlements for analysis prior to the upgrading of the settlements. The work done in this study is conducted in partnership with a local NGO that uses baseline information in informal settlements to create strategies to lower incidences of crime. This paper specifically focuses on introducing GIS spatial analysis and mapping in relation to informal settlements upgrading based on information gathered from the residents of the informal settlement. Moreover the final produced maps will be interpreted together with VPUU, which represents residents of Monwabisi Park community, simply because they have indigenous knowledge of the area. This research outlines a methodology of GIS analysis for Informal settlements upgrading using GIS, making use of five different analysis methods: Buffer analysis, thematic mapping, Thiessen polygons, distance mapping and Multi Criteria Evaluation. The case study area is Monwabisi Park in Cape Town. The study has revealed that the use of indigenous knowledge in GIS analysis for upgrading could be very valuable in making scientific and alternate decisions during informal settlement upgrading process. Key words: Informal settlements, GIS participation, Buffer Analysis, Distance Matrix, Thiessen Polygons, Thematic Mapping, Multi-Criteria Evaluation (MCE)