Landscape Associations with Flood Response to Extreme Rainfall: A Remote-Sensing, Multi-Event Analysis Across Sumatra, Indonesia

Kuswaji Dwi Priyono (Department of Geography, Faculty of Geography, Universitas Muhammadiyah Surakarta, Indonesia)
Siti Azizah Susilawati (Department of Geography Education, Faculty of Teacher Education and Educational Sciences, Muhammadiyah University of Surakarta, Surakarta, Indonesia)
Dewi Novita Sari (Department of Geographic Information Science, Faculty of Geography, Universitas Muhammadiyah Surakarta, Surakarta, Indonesia)
M. Iqbal Sunariya (Department of Geography, Faculty of Geography, Universitas Muhammadiyah Surakarta, Surakarta, Indonesia)
Yuli Priyana (Department of Geography, Faculty of Geography, Universitas Muhammadiyah Surakarta, Surakarta, Indonesia)
Andhika Argya Pragata (Department of Geography, Faculty of Geography, Universitas Muhammadiyah Surakarta, Surakarta, Indonesia)
Arif Rohman (Faculty of Environment, University of Leeds, Leeds, United Kingdom)
Muhammad Yusuf (Interdisciplinary Research Center for Industrial Nuclear Energy (IRC-INE), King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia)
Mohd Hairy Ibrahim (Faculty of Humanity, Universiti Sultan Idris, Tanjung Malim, Malaysia)
Farha Sattar (Faculty of Arts and Society, Charles Darwin University, Darwin City NT 0800, Australia)
Muhammad Nawaz (Department of Geography, Faculty of Art and Social Sciences, National University of Singapore, Singapore)

Forum Geografi
Vol. 41 No. 1 (2027): April 2027

Tropical flooding emerges from interactions among rainfall forcing, drainage structure, land-surface condition, and evolving land cover, yet these components are often analyzed in separate remote-sensing workflows. This study develops a multi-event, sub-basin-scale association framework linking extreme-rainfall coverage, vegetation cover, built-up cover, and Sentinel-1-derived flood response across HydroBASINS Level 9 units in Sumatra, Indonesia. Nine annual area-mean daily rainfall maxima from 2017–2025 were selected using CHIRPS. Vegetation fraction was defined as the proportion of valid Sentinel-2 pixels with NDVI ≥ 0.40, built-up fraction as the proportion with NDBI ≥ 0.00, flood fraction as the proportion of Sentinel-1 pixels with Random-Forest flood probability ≥ 0.50, and extreme fraction as the proportion of CHIRPS pixels whose 7-day accumulated rainfall exceeded the local monthly 95th percentile. The full panel contained 14,409 polygon-event observations, of which 11,818 were complete across the four core parameters. Fractional logit models with event fixed effects showed a negative association between vegetation fraction and flood fraction (β = −0.073, p = 0.004) and a positive association for built-up fraction (β = 0.082, p < 0.001), whereas extreme-rainfall coverage showed no direct association after event-level heterogeneity was absorbed. The corresponding landscape effects were modest in magnitude and should be interpreted as associations rather than causal effects. Cross-scale analysis at HydroBASINS Level 8 preserved the direction of the vegetation and built-up effects. The contribution therefore lies not in a new satellite sensor or classifier, but in combining multi-sensor fractions, hydrologically aligned sub-basin units, and repeated-event statistical inference. Because the Sentinel-1 flood product is pseudo-label based and was not independently validated for all nine events, flood_fraction is treated as a SAR-derived flood indicator rather than a fully validated inundation product, and no operational warning threshold is inferred.