Natural disasters are generally brutal and may affect large areas, which then need to be rapidly mapped to assess the impacts of such events on ecosystems and to prevent related risks. Ground investigations may be complex, whereas remote-sensing techniques enable a fast regional-scale assessment of damage and offer a cost-effective option for large and inaccessible areas. Here, an efficient, quasi-automatic object-based method for change mapping using high-spatial-resolution (HR) (5–10 m) satellite imagery is proposed. Our contribution comprises two main novelties with respect to similar works in forestry. First, an automatic feature selection process optimizes the image segmentation and classification steps via an original calibration-like procedure. Second, an automatic bitemporal classification enables the separation of damaged and intact areas thanks to a new descriptor based on the level of fragmentation of the obtained regions. The mean shift algorithm is used in both the segmentation and classification processes. The method was assessed in a maritime pine forest using bitemporal HR Formosat-2 multispectral images acquired pre- and post-Windstorm Klaus, which occurred in January 2009 in southwestern France. The binary overall classification accuracy reached 87.8% and outperformed a pixel-based K-means classification with no feature selection. A thematic analysis of the results highlights the correlation between the ages of trees and their sensitivity to wind.