Assessment of urban extent and imperviousness of Cape Town using TerraSAR-X and Landsat images

Klein, Doris; Esch, Thomas; Himmler, Vitus; Thiel, Michael; Dech, Stefan

doi:10.1109/igarss.2009.5417959

Cited by 2 publications

(2 citation statements)

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“…Considering the rapid changes in LULC changes in many urban areas worldwide, ground-based field surveys are not available to keep pace with changes in urban features due to the high cost, labour intensity, and low sampling frequency. Subsequently, remote sensing has become a practical tool for providing updated geospatial information about features in urban environments [8]. It offers consistent, synoptic, cost-and time-efficient data sets to monitor urban features with great thematic detail.…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have shown that better urban LULC classifications are achieved by integrating high-resolution imagery and machine learning algorithms [23,24]. For example, Klein et al [8] performed a support vector machine (SVM) semi-automated classification of the urban areas of Cape Town using TerraSAR-X data. They achieved an overall accuracy of 82.3%.…”

Section: Introductionmentioning

confidence: 99%

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Fine-Scale Classification of Urban Land Use and Land Cover with PlanetScope Imagery and Machine Learning Strategies in the City of Cape Town, South Africa

2022

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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.

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Section: Introductionmentioning

confidence: 99%