Grain and Extent Considerations Are Integral for Monitoring Landscape-Scale Desired Conditions in Fire-Adapted Forests

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.

Southwestern ponderosa pine forest patterns following wildland fires managed for resource benefit differ from reference landscapes

“…In a functioning forest, such as our reference landscapes, this disturbance is su cient to maintain low tree density and discontinuous canopy, but fails to do so once the age of dominant trees develop re resistance and high tree densities promote canopy transmission of re with catastrophic results (Reynolds et al 2013;Tarancón et al 2014;Bottero et al 2017;Ritter et al 2020). Sustained small-scale wild re will promote heterogeneity primarily at small scales, but not over large landscape scales, wherein the mosaic of burned areas becomes homogenous (Wasserman et al 2019). Our results showed this pattern of greater heterogeneity transitioning to greater homogeneity among both reference and wild re landscapes decreased with scale.…”

“…See Wasserman and others (2019) for a comparison of landscape metrics resulting from a gradient of data resolution. Higher resolution data, such as lidar or airborne imagery, may more accurately capture smaller patch sizes; however, expense and availability of these types of datasets for regional landscapes is limited (Buyantuyev and Wu 2007;Wasserman et al 2019). We suggest that methods used in our study have broad applicability for assessment and post-treatment monitoring of ponderosa forest structure in the Southwest (see also Botequilha Leitão and Ahern 2002; de Almeida et al 2020).…”

“…We suggest that methods used in our study have broad applicability for assessment and post-treatment monitoring of ponderosa forest structure in the Southwest (see also Botequilha Leitão and Ahern 2002; de Almeida et al 2020). However, further research is needed to understand metrics vary not only with extent, but also grain as interpretation of landscape structure and pattern can vary with grain size (Hargis et al 1998;Buyantuyev and Wu 2007;Corry and Lafortezza 2007;Rutchey and Godin 2009;Šímová and Gdulová 2012;Wasserman et al 2019;Wickham and Riitters 2019).…”

“…Speci cally, medium resolution (10-100m) multi-spectral satellite imagery such as the Landsat family of satellites (30 m) or the Sentinel-2 satellites (10-20 m), provide frequent image coverage that can be used to monitor forest cover and dynamics (Eidenshink et al 2007;Wulder et al 2012;Li and Roy 2017). The quanti cation of forest structure from inter-stand (15ha) to landscape scales (> 400ha) is sensitive to the grain of the data used to quantify landscape metrics (Wasserman et al 2019). Imagery from the Sentinel-2 satellites are medium-resolution (10-20m), multispectral (12 spectral band) datasets and provide wall-to-wall coverage every 5 days on average (Li and Roy 2017).…”

“…In the same way, we compared reference landscapes to managed wild res among intervals of landscape extents by subsampling both within reference landscapes and managed wild re boundaries and comparing the resulting distribution of landscape metrics. In this way, comparisons were made at each of those extents so as to avoid metric comparisons sensitive to extent (Wasserman et al 2019). The maximum extent for comparison was approximately 800 ha, which was determined by the maximum size of available reference landscapes within the study area.…”