Abstract
The emergence of high-resolution satellite data, such as WorldView-2, has opened the opportunity for urban land cover mapping at fine resolution. However, it is not straightforward to map detailed urban land cover and to detect urban deprived areas, such as informal settlements, in complex urban environments based merely on high-resolution spectral features. Thus, approaches integrating hierarchical segmentation and rule-based classification strategies can play a crucial role in producing high quality urban land cover maps. This study aims to evaluate the potential of WorldView-2 high-resolution multispectral and panchromatic imagery for detailed urban land cover classification in Kigali, Rwanda, a complex urban area characterized by a subtropical highland climate. A multi-stage object-based classification was performed using support vector machines (SVM) and a rule-based approach to derive 12 land cover classes with the input of WorldView-2 spectral bands, spectral indices, gray level co-occurrence matrix (GLCM) texture measures and a digital terrain model (DTM). In the initial classification, confusion existed among the informal settlements, the high- and low-density built-up areas, as well as between the upland and lowland agriculture. To improve the classification accuracy, a framework based on a geometric ruleset and two newly defined indices (urban density and greenness density indices) were developed. The novel framework resulted in an overall classification accuracy at 85.36% with a kappa coefficient at 0.82. The confusion between high- and low-density built-up areas significantly decreased, while informal settlements were successfully extracted with the producer and user’s accuracies at 77% and 90% respectively. It was revealed that the integration of an object-based SVM classification of WorldView-2 feature sets and DTM with the geometric ruleset and urban density and greenness indices resulted in better class separability, thus higher classification accuracies in complex urban environments.
Highlights
Cities are considered important areas for economic opportunities and an engine for a country’s development [1,2]
High-resolution WorldView-2 data was evaluated for detailed urban land cover mapping in Kigali, an urbanization hotspot in Sub-Sahara Africa, using hierarchical object-based and rule-based classification strategies
The results showed that an object-based support vector machines (SVM) classification coupled with an integrated rule-based approach and two newly defined indices yielded a very good overall classification accuracy (85.36%, kappa coefficient: 0.8228)
Summary
Cities are considered important areas for economic opportunities and an engine for a country’s development [1,2]. The authorities in charge of urban planning often lack detailed information on spatial patterns of land use development [9]. Reliable data and information on both trajectories of land cover patterns, and the extension of deprived zones, such as slums, informal settlements and environmentally sensitive zones, are paramount for responding to the pressing urban land administration and management questions. This information is further needed for assessing the environmental impact of urbanization and for forecasting the supply and demand of urban ecosystem services
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