Abstract
For most sub-Saharan African (SSA) cities, in order to control the historically unplanned urban growth and stimulate sustainable future urban development, there is a need for accurate identification of the past and present urban land use (ULU). However, studies addressing ULU classification in SSA cities are lacking. In this study, we developed an integrated approach of remote sensing and Geographical Information System (GIS) techniques to classify ULU in the developing SSA city of Lusaka. First, we defined six ULU classes (i.e., unplanned high density residential; unplanned low density residential; planned medium-high density residential; planned low density residential; commercial and industrial; public institutions and service areas). ULU parcels, created using road networks as homogenous units separating ULU classes, were used to classify ULU. We utilised the combined detail of cadastral and land use data plus high-resolution Google Earth imagery to infer ULU and classify the parcels. For residential ULU, we also created density thresholds for accurate separation of the classes. We then used the classified ULU parcels for post-classification sorting of built-up pixels extracted from three Landsat TM/ETM+ imageries (1990, 2000, and 2010) into respective ULU classes. Three ULU maps were produced with overall accuracy values of 84.09% to 85.86%. The maps provide information that is relevant to urban planners and policy makers for sustainable future urban planning of Lusaka City. The study also provides an insight for ULU classification in SSA cities with complex urban landscapes similar to Lusaka.
Highlights
Urban land use (ULU) classification, i.e., discriminating the built-up-area into different ULU types remains a challenge in remote sensing urban studies due to spectral confusion among ULU classes within the urban environment [1]
The ULU maps produced in this study provide detailed information on the spatial-temporal patterns of ULU in the study area of Lusaka
We presented an approach for classifying ULU in the developing sub-Saharan African (SSA) city of Lusaka
Summary
Urban land use (ULU) classification, i.e., discriminating the built-up-area into different ULU types (e.g., residential, industrial, commercial, public, etc.) remains a challenge in remote sensing urban studies due to spectral confusion among ULU classes within the urban environment [1]. Inconsistent recommendations are regularly made on the choice of classification techniques, despite many studies using different types of imagery data with varying spatial resolutions [5]. To overcome this problem, several studies have attempted to develop different approaches that integrate remote sensing data with additional data or analysis. The chaotic spatial structures and resulting spectral mix-up among different ULUs in SSA cities presents additional challenges to applying advanced methods, such as those that incorporate structure information [2,15,16,17] and texture features [6,18]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
