Earthquake science and engineering are experience-driven fields in which lessons are learned after each significant earthquake. Remote sensing represents a suite of technologies that can play a significant role in documenting the effects of earthquakes and lead to important developments in our understanding of earthquakes. This paper describes current remote sensing technologies and the experience to date in using them in earthquake studies. The most promising activities that may benefit from remote sensing data products are identified, as well as the challenges that may impede the widespread use of remote sensing in earthquake studies. A comprehensive review of the use of remote sensing to document the effects of the 2003 Bam, Iran earthquake is presented, and recommendations for future developments in remote sensing in the context of earthquake science and engineering are provided.

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An exploratory study was conducted on the role of coastal ecosystems in protecting communities from the December 2004 Indian Ocean tsunami, focusing on mangrove forests on the Andaman coast of Thailand and how well villages were undertaking environmental conservation. Remote sensing analysis identified predisaster mangrove change and postdisaster structural damage and landscape changes. Field data from five sites (20 villages), gathered via the VIEWS™ data collection system, validated and supplemented this analysis. Key informants at several of these villages were also interviewed. A preliminary comparison of villages that otherwise faced similar tsunami exposure suggests that the presence of healthy mangroves did afford substantial protection. Village performance in mangrove conservation and management efforts, and thus the presence of healthy forests, is influenced by both social capital and the design of external aid delivery programs.

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This paper presents a methodology for quantifying the number of buildings that collapsed following the Bam earthquake. The approach is object rather than pixel-oriented, commencing with the inventory of buildings as objects in high-resolution QuickBird satellite imagery captured before the event. The number of collapsed structures is computed based on the unique statistical characteristics of these objects/buildings within the “after” scene. A total of 18,872 structures were identified within Bam, of which the results suggest that 34% collapsed—a total of 6,473. Preliminary assessments indicate an overall accuracy for the damage classification of 70.5%.