Remotely-sensed data are commonly used to evaluate forest metrics, such as canopy cover, to assess change detection, and to inform land management planning. Often, canopy cover is measured only at the scale of the spatial data product used in the analysis, and there is a mismatch between the management question and the scale of the data. We compared four readily available remotely sensed landscape data products— Light detection and ranging (LiDAR), Landsat-8, Sentinel-2, and National Agriculture Imagery Program (NAIP) imagery —at different spatial grains and multiple extents to assess their consistency and efficacy for quantifying key landscape characteristics of forest canopy patches and sensitivity to change. We examined landscape-scale patterns of forest canopy cover across three landscapes in northern Arizona and assessed their performance using six landscape metrics. Changes in grain and extent affect canopy cover patch metrics and the inferences that can be made from each data product. Overall data products performed differently across landscape metrics. When performing analyses and choosing data layers, it is essential to match the scale of the data product to the management question and understand the limitations inherent in using canopy cover as a stand-alone metric.
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