A Spatio-Temporal Evaluation of Water Resilience and Urban Metabolism in Informal Settlements using Remote Sensing

Ramdan Pano Anwar; Kollányi László

doi:10.22487/peweka.v5i1.115

Penulis

Ramdan Pano Anwar Department of Landscape Planning and Regional Development, Institute of Landscape Architecture, Urban Planning and Garden Art Hungarian University of Agriculture and Life Sciences, Hungary
Kollányi László Department of Landscape Planning and Regional Development, Institute of Landscape Architecture, Urban Planning and Garden Art Hungarian University of Agriculture and Life Sciences, Hungary

DOI:

https://doi.org/10.22487/peweka.v5i1.115

Kata Kunci:

Urban Metabolism, Informal Settlements, Remote Sensing, Landscape Resilience, NDVI/NDBI/LST/MSI

Abstrak

Informal settlements face layered challenges of environmental degradation, governance fragmentation, and infrastructure inequity. This study aims to evaluate the spatial-temporal dynamics of environmental and hydrological stress within these settlements to inform targeted upgrading strategies. Utilizing Google Earth Engine and QGIS, multi-temporal and multi-spectral satellite imagery from Landsat 8/9 and Sentinel-2 (2015–2024) was processed to map ecological stress and urban expansion in Makassar, Indonesia. The methodological approach includes the calculation and zonal statistics extraction of key indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Land Surface Temperature (LST), and Moisture Stress Index (MSI). The findings reveal a severe ecological gradient characterized by a continuous downward trend in vegetation (NDVI < 0.2) and a corresponding escalation of surface impermeability in informal zones. This urban densification directly driven thermal intensification, with LST peaking above 34°C, and extreme moisture stress exceeding critical resilience thresholds (MSI > 0.9). Ultimately, this integrated geospatial diagnostic empowers the SLAIS Framework to transition landscape architecture into an operable, evidence-based discipline capable of targeting interventions where metabolic pathology is greatest.

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