Temporal Characterization of Land Use Change and Land-scape Processes in Informal Settlements in the City of Cape Town, South Africa

IF 0.3 Q4 REMOTE SENSING South African Journal of Geomatics Pub Date : 2024-07-10 DOI:10.4314/sajg.v13i2.1
P. I. Okoye, Jörg Lalk
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Abstract

This study conducted a Land Use Change (LUC) analysis on informal settlements in Cape Town, South Africa, using bi-temporal steps, S1 (2010) and S2 (2016), to characterize land use (LU) conversions and landscape processes for informed policymaking. Utilizing the 2011 national land cover dataset and post-classification methods, two LU datasets and maps, D1 for S1 and D2 for S2, were derived. These classifications achieved an overall accuracy exceeding 95%, with Kappa coefficients above 0.9. The analysis employed change trajectories and conversion labels to evaluate LU changes and landscape dynamics, providing a thematic representation of LUC within informal settlements. Landscape reclamation processes, including abandonment, urban development, and RDP (Reconstruction and Development Programme) development, constituted approximately five percent of the total LU conversions, while degradation processes like persistence and intensification dominated, affecting approximately 93% of the area. Partial reclamation, notably through interspersed RDP (RDPi), accounted for about two percent of conversions. These findings highlight the importance of accurate and timely LUC data reporting in informal settlements to address socioeconomic challenges effectively and support policy decisions to enhance these communities' physical and socioeconomic infrastructure.
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南非开普敦市非正规住区土地利用变化和地貌过程的时间特征
本研究采用 S1(2010 年)和 S2(2016 年)两个时空步骤,对南非开普敦的非正规住区进行了土地利用变化(LUC)分析,以确定土地利用(LU)转换和景观过程的特征,为知情决策提供依据。利用 2011 年国家土地覆被数据集和后期分类方法,得出了两个土地利用数据集和地图,即 S1 的 D1 和 S2 的 D2。这些分类的总体准确率超过 95%,Kappa 系数超过 0.9。分析采用了变化轨迹和转换标签来评估土地利用变化和景观动态,提供了非正规居住区内土地利用变化的专题表述。景观开垦过程,包括废弃、城市开发和重建与发展计划(RDP)开发,约占土地利用总转换的 5%,而退化过程,如持续和强化,则占主导地位,影响了约 93% 的区域。部分开垦,特别是通过穿插的区域发展方案(RDPi),约占土地转换的 2%。这些发现凸显了准确、及时地报告非正规住区土地利用变化数据的重要性,以便有效地应对社会经济挑战,并为改善这些社区的物质和社会经济基础设施的决策提供支持。
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