An evaluation of the role played by remote sensing technology following the World Trade Center attack

Remote sensing technology has been widely recognized for contributing to emergency response efforts after the World Trade Center attack on September 11th, 2001. The need to coordinate activities in the midst of a dense, yet relatively small area, made the combination of imagery and mapped data strategically useful. This paper reviews the role played by aerial photography, satellite imagery, and LIDAR data at Ground Zero. It examines how emergency managers utilized these datasets, and identifies significant problems that were encountered. It goes on to explore additional ways in which imagery could have been used, while presenting recommendations for more effective use in future disasters and Homeland Security applications. To plan adequately for future events, it was important to capture knowledge from individuals who responded to the World Trade Center attack. In recognition, interviews with key emergency management and geographic information system (GIS) personnel provide the basis of this paper. Successful techniques should not be forgotten, or serious problems dismissed. Although widely used after September 11th, it is important to recognize that with better planning, remote sensing and GIS could have played an even greater role. Together with a data acquisition timeline, an expanded discussion of these issues is available in the MCEER/NSF report “Emergency Response in the Wake of the World Trade Center Attack; The Remote Sensing Perspective” (Huyck and Adams, 2002) Keywords World Trade Center (WTC) - terrorism - emergency response - emergency management - ground zero - remote sensing - emergency operations - disasters - geographic information systems (GIS) - satellite imagery - synthetic aperture radar (SAR) - light detection and ranging imagery (LIDAR)

Applying Earth Observation Technologies to Economic Consequence Modeling:A Case Study of COVID‑19 in Los Angeles County,California

Earth observation(EO) technologies,such as very high-resolution optical satellite data available from Maxar,can enhance economic consequence modeling of disasters by capturing the fine-grained and real-time behavioral responses of businesses and the public.We investigated this unique approach to economic consequence modeling to determine whether crowd-sourced interpretations of EO data can be used to illuminate key economic behavioral responses that could be used for computable general equilibrium modeling of supply chain repercussions and resilience effects.We applied our methodology to the COVID-19 pandemic experience in Los Angeles County,California as a case study.We also proposed a dynamic adjustment approach to account for the changing character of EO through longer-term disasters in the economic modeling context.We found that despite limitations,EO data can increase sectoral and temporal resolution,which leads to significant differences from other data sources in terms of direct and total impact results.The findings from this analytical approach have important implications for economic consequence modeling of disasters,as well as providing useful information to policymakers and emergency managers,whose goal is to reduce disaster costs and to improve economic resilience.