Abstract
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.
Highlights
United States federal land management agencies are directed to manage national forests to promote resilient landscapes, defined as within their natural range of variability [1]
General patterns of patch characteristics were observed across spatial extents and between the four data products (Landsat-8 30 m, Sentinel-2 10 m, National Agriculture Imagery Program (NAIP) 1 m, Light detection and ranging (LiDAR) 1 m) that match our understanding of expected changes of patches with scale and grain
We report the differences in data products and how the patterns change across spatial extents
Summary
United States federal land management agencies are directed to manage national forests to promote resilient landscapes, defined as within their natural range of variability [1]. Forests are subject to a variety of disturbances, including large wildfires, insects, pathogens, and post-fire flooding While these disturbances were always components of the historical ecosystem, they are strongly influenced by human activities and climate change and uncharacteristic disturbances have become more frequent, higher in intensity, and cover larger areas than historically [4,5]. Public lands have seen both leadership direction and political pressure to include increased participation in planning and decision making from multiple agencies and stakeholders [9]. As a result, both land managers and stakeholders are interested in understanding landscape-level processes at scales appropriate to planning and implementation and quantifying metrics that are indicative of resilient and restored ecosystems that capture the natural range of variability
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