Satellite derived urbanization and vegetation indices predict divergent seasonal distributions of Aedes aegypti and Aedes albopictus Larvae in an urban landscape of central India.

Abstract

Background: Urbanization and vegetation strongly shape the ecology of the dengue vectors Aedes aegypti and Ae. al­bopictus, yet their fine-scale seasonal distributions in rapidly growing Indian cities remain poorly characterised.
Methods: We integrated six satellite and terrain-derived environmental predictors with longitudinal larval data from 29 sites in Bhopal, central India. Seasonal larval distributions and environmental gradients were represented using inverse distance weighting (IDW) and analysed with exploratory Random Forest (RF) regression and SHAP. Environmental associations were further evaluated using partial least squares regression (PLSR) and LMG hierarchical partitioning, while leave-one-site-out (LOSO) validation assessed the complete IDW-RF spatial reconstruction workflow and Mo­ran’s I assessed residual spatial autocorrelation.
Results: Exploratory RF/SHAP identified species- and season-specific patterns. Aedes aegypti showed stronger associa­tions with urban-intensity variables, particularly population density and built volume, whereas NDVI variables had the highest relative importance for Ae. albopictus across both seasons; dry-season NDVI was also important for Ae. aegypti. LOSO-RF showed limited out-of-sample performance (R²= 0.089 to −0.496). PLSR leave-one-out cross-validation showed modest monsoon performance (Q²= 0.23–0.29) and weak dry-season performance. LMG supported broad spe­cies-level differences in relative predictor contribution, while residual spatial autocorrelation was not significant in three of four PLSR models. Seasonal mapping showed broader monsoon hotspots for Ae. aegypti and more localized distribu­tions for Ae. albopictus.
Conclusion: Integrating remote sensing, GIS, exploratory RF/SHAP and complementary multivariate methods provides a practical framework for spatially explicit characterisation of species-specific environmental associations that may in­form vector surveillance.