Speeding up the clock in remote sensing: identifying the ‘black spots’ in exposure dynamics by capitalizing on the full spectrum of joint high spatial and temporal resolution

Abstract

Increasing human and monetary losses resulting from extreme natural events over the past decades have been documented in corresponding disaster data compilations such as the open-access EM-DAT, SHELDUS, and DesInventar or the private domain NatCatSERVICE (Munich Re) and Sigma (Swiss Re) databases. One of the most imminent questions in disaster risk research is therefore to identify the main root causes of those increasing impacts as a first step toward being able to address those aspects in mitigation planning. Statistical evidence about increase in frequency and intensity of hazardous extreme events depends on the quality and quantity of data available for probabilistic trend evaluation, and varies across regions and in particular for different types of events. The question is now what can remote sensing contribute to mapping exposure and more specifically elements at risk and their relevant characteristics that are needed for estimating potential losses and to initiate adequate mitigation strategies? It is clear that in a ‘static’ data environment characterized by temporal snapshots at regular intervals (i.e., as opposed to temporally continuous acquisition), as it has been common in high spatial resolution remote sensing, physical objects and structures can be well detected and characterized. Analyzing functional parameters or even relationships requires linking ancillary data to the remote sensing-based physical base Information. This article, however, focuses foremost on recent developments regarding remote sensing as a sole data source and just briefly concludes with a short outlook on associated integrative multisource data analysis.