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Remote Sens. 2011, 3(11), 2384-2402;

Tracking Environmental Compliance and Remediation Trajectories Using Image-Based Anomaly Detection Methodologies

Department of Geography, Ohio University, Athens, OH 45701, USA
Received: 1 September 2011 / Revised: 1 November 2011 / Accepted: 1 November 2011 / Published: 7 November 2011
(This article belongs to the Special Issue Remote Sensing in Support of Environmental Policy)
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Recent interest in use of satellite remote sensing for environmental compliance and remediation assessment has been heightened by growing policy requirements and the need to provide more rapid and efficient monitoring and enforcement mechanisms. However, remote sensing solutions are attractive only to the extent that they can deliver environmentally relevant information in a meaningful and time-sensitive manner. Unfortunately, the extent to which satellite-based remote sensing satisfies the demands for compliance and remediation assessment under the conditions of an actual environmental accident or calamity has not been well documented. In this study a remote sensing solution to the problem of site remediation and environmental compliance assessment was introduced based on the use of the RDX anomaly detection algorithm and vegetation indices developed from the Tasseled Cap Transform. Results of this analysis illustrate how the use of standard vegetation transforms, integrated into an anomaly detection strategy, enable the time-sequenced tracking of site remediation progress. Based on these results credible evidence can be produced to support compliance evaluation and remediation assessment following major environmental disasters. View Full-Text
Keywords: environmental compliance; remediation; anomaly detection; vegetation transform environmental compliance; remediation; anomaly detection; vegetation transform

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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Lein, J.K. Tracking Environmental Compliance and Remediation Trajectories Using Image-Based Anomaly Detection Methodologies. Remote Sens. 2011, 3, 2384-2402.

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