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Forest destruction is a main contributor to carbon emissions and loss of biodiversity, making it a matter of global importance. Due to the large global footprint and often inaccessibility of forested areas, remote sensing is one of the most valuable techniques for monitoring deforestation. Spectral imaging is typically favored for material classification of forested areas and identification of broad swaths of deforestation. However, spectral data can fall short in detecting more subtle destruction beneath the forest canopy. Radar remote sensing can help fill this gap, as it has the ability to penetrate through tree canopies such that pixels capture backscatter information from both the canopy and material beneath it. Synthetic aperture radar in particular can capture this information at fine spatial resolution, and techniques such as polarimetry and interferometry can be used to measure biomass and detect deforestation. In this study, we compare synthetic aperture radar data with multispectral data to improve characterization and identification of source signatures captured within a pixel, with specific consideration to detecting areas where thinning is happening beneath the forest canopy. We focus on identifying different types of forest thinning in the Valles Caldera, located in the Jemez Mountains of northern New Mexico. We apply anomalous change detection to a combination of data products derived from multispectral imagery and synthetic aperture radar to determine which combinations are most effective at identifying anomalous features of interest in thinning regions. We find that comparing phase change measured by synthetic aperture radar interferometry to differenced vegetation indices highlights anomalous relationships in the thinning region. When comparing multispectral reflectance to backscatter intensity measured by synthetic aperture radar, the most successful temporal comparisons contained synthetic aperture radar data during the thinning period. This suggests that synthetic aperture radar enhances detection of thinning practices via remote sensing, especially in regards to changes taking place beneath the tree canopy. These results were improved even further by segmenting the images according to vegetation coverage prior to applying anomalous change detection techniques.
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