Forest Stress Detection Using Feature Engineering and Selection Approach Optimized for Satellite Imagery

Y. Salii, V. Kuzin, N. Kussul and A. Lavreniuk, "Forest Stress Detection Using Feature Engineering and Selection Approach Optimized for Satellite Imagery," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 19, pp. 2461-2473, 2026, doi: 10.1109/JSTARS.2025.3644488.

Abstract: Accurate detection of forest stress from satellite data depends heavily on selecting informative spectral features. Traditional approaches rely on a limited set of predefined vegetation indices, which may not generalize across environmental conditions. In this study, we introduce enhanced maximum informativeness maximum independence (E-MIMI), an efficient and interpretable feature selection strategy that identifies optimal combinations of spectral features from generalized vegetation index classes rather than fixed indices. The method combines a genetic algorithm with a caching mechanism and informativeness-based scoring to reduce computation time while maintaining high accuracy. Applied to Sentinel-2 imagery from two ecologically distinct regions, E-MIMI consistently selected index combinations involving red-edge and shortwave infrared bands—spectral domains known to reflect canopy water content and chlorophyll degradation. E-MIMI demonstrates exceptional computational efficiency, completing feature selection up to 80 times faster and using over 1000 times less memory than other traditional methods on large feature spaces. Despite this, E-MIMI achieves comparable levels of segmentation performance with a test intersection over union (IoU) of 0.61–0.63, while other methods reach an IoU of 0.60–0.64. Obtained models show a substantial improvement over previous studies in the same region (0.515–0.549 IoU). The model also generalized well to an independent dataset from Chornobyl, confirming its robustness. By integrating computer vision techniques with biophysically grounded features, our approach supports scalable, ecologically meaningful forest stress monitoring and offers a practical foundation for broader environmental applications requiring interpretable and computationally efficient feature selection.