The general capability of Synthetic Aperture Radar (SAR) for monitoring forest ecosystems is well documented. However, the majority of SAR studies of forest dynamics use only imagery acquired by one SAR system and are thus limited to the lifecycle of a particular satellite. The synergistic analysis of SAR data from one of the earliest spaceborne SAR missions, the SEASAT mission, with the Japanese JERS-1 satellite-borne SAR is presented. Biophysical parameters frequently retrieved from SAR are tree biomass using backscatter and tree height from the interferometric phase. One potential application that has not been thoroughly examined is mapping of incremental tree growth from SAR backscatter changes. Tree growth measures biomass changes over time, and is correlated to the amount of carbon sequestered by a the trees. This paper examines the retrieval of tree growth from multitemporal spaceborne L-band SAR. A SEASAT SAR image from 1978 and a JERS-1 SAR image from 1997 over Thetford forest, UK, are used to retrieve tree growth of Corsican Pine stands. Incremental growth was estimated from the changes in backscatter coefficient, and compared to the expected tree growth from general yield class models used by the UK Forestry Commission. The accuracy of the retrieval algorithm depends on the minimum forest stand size included in the analysis. For managed forest plantations, multitemporal L-band SAR has some potential for detecting incremental biomass to support sustainable forest management.
This article presents a set of performance metrics, whose purpose is to provide a quantitative measure of the ability of oil spill dispersion models to simulate real-world oil spills. The metrics are described in detail and are applied to the output from an existing oil spill model for two specific case studies. The metrics in question make use of both satellite imagery and coastal impact reports as the basis of the validation. Specifically, we recommend the 2-D measure of effectiveness as a means of quantifying model performance based on the extent of overlap between the observations and the model output. Additionally, we show that it is advantageous to supplement the 2-D measure of effectiveness with a newly proposed set of skill scores, based on the geometric area and centroid of a given oil spill. We also demonstrate how the metrics can be used to assess the sensitivity of a model to its input parameters and the impact this has on the accuracy of the resultant forecast. Finally, we offer a real-world interpretation for each metric introduced and suggest ways that they can be used to assist in cleanup operations of actual oil spills.
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