The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLIII-B3-2022
30 May 2022
 | 30 May 2022


A. C. Blanco

Keywords: damage assessment, LISA, Local Moran’s I, change detection, building damage

Abstract. Damages to built and natural environments are essentially changes that needs to be detected and quantified. This is particularly true for the change detection approach. While the use of vegetation indices is effective for such assessment in natural or vegetated areas, the use of built-up indices does not yield useful results. This is because there is usually no significant reduction in materials. However, typhoon-damaged buildings are usually characterized by a change in form, shape, color, and texture. In this study, we examined the use of local spatial autocorrelation (LSA) to evaluate the level of damage to buildings. In particular, the local Moran’s I was used, and an index called the normalized difference spatial autocorrelation (NDSA) was developed. Similar with other indices, the values are within the range of −1 to 1. NDSA using local Getis-Ord Gi and local Geary’s C were also generated. With the observation that LSA generally decreases due to damages (especially to manmade structures), positive NDSA identifies the damage areas. The magnitude of the values corresponded to the level or degree of damage sustained as interpreted from very high-resolution satellite image. It was noted that the manual tagging of damages had missed buildings which are clearly damaged or destroyed based on the visual comparison of pre- and post-typhoon satellite images. This illustrates the value of the NDSA not only to assess damage on its own but also for guiding manual tagging from image and prioritizing post-disaster needs assessment and recovery operation.