Drone-based composite risk mapping reveals vegetation–shade interaction and housing typology as key determinants of Aedes habitat risk

Persistent dengue transmission in tropical cities reflects a complex interplay between environmental microclimates and urban housing structure that supports Aedes mosquito breeding. This study applies drone-based microhabitat risk mapping integrated with a biologically defined Composite Risk Index (...

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Bibliographic Details
Main Authors: Mahfodz, Zulfadli, Naba, Agus, Isawasan, Pradeep, Osman, Mohd Azuraidi, Che Dom, Nazri
Format: Article
Language:en
Published: Nature Research 2026
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Online Access:http://psasir.upm.edu.my/id/eprint/123382/1/123382.pdf
http://psasir.upm.edu.my/id/eprint/123382/
https://www.nature.com/articles/s41598-026-39951-0?error=cookies_not_supported&code=89e866dc-283d-4f73-8c68-f5fdfa9b2a1e
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Summary:Persistent dengue transmission in tropical cities reflects a complex interplay between environmental microclimates and urban housing structure that supports Aedes mosquito breeding. This study applies drone-based microhabitat risk mapping integrated with a biologically defined Composite Risk Index (CRI) to quantify fine-scale environmental drivers of Aedes habitat risk across distinct residential typologies in Sect. 24, Shah Alam, Malaysia. High-resolution RGB imagery obtained using a DJI Phantom 4 Pro was processed to derive the Brightness Index (BI) as a proxy for shade intensity and the Excess Green Index (ExG) as an indicator of vegetation density. These indices were integrated a priori into a CRI to operationalise known ecological conditions favourable for Aedes. Spatial analysis revealed a consistent risk gradient, with terrace housing exhibiting higher Composite Risk Index (CRI) values than flat complexes (low-density terrace (Teres D) > dense terrace (Teres B) > medium-rise (Flat H) > high-rise (Flat B)), demonstrating that housing typology modulates the spatial expression of microhabitat risk rather than vegetation presence alone. Model calibration showed high predictive agreement (R² = 0.91), with the top 20% of CRI-ranked pixels capturing 65% of observed breeding-prone zones, indicating strong spatial discriminative performance. These findings highlight that vegetation–shade coupling, expressed through housing morphology, governs Aedes habitat persistence and that drone-based microclimate mapping provides a precision surveillance tool for spatially targeted dengue control.