Quantifying Forest Edge Area in the Northeastern USA 2016

Temperate forests are the most fragmented forest biome, yet current understanding of fragmentation effects on ecosystem processes, such as carbon cycling, is rooted in tropical forest research. In the associated manuscript, we review the effects of persistent fragmentation on temperate forest ecosystem processes and quantify the extent to which the US national forest inventory and land-cover maps represent forest edge area. We find a systematic underrepresentation of forest edges across all methods. Compared with very high resolution (1 m) maps, conventional 30 m resolution forest cover maps underestimate forest edge area by 16.4%, on average. Accounting for all forest edge area and edge effects on forest structure and growth results in a 14.8% median increase in aboveground forest carbon estimates with 23.8% and 74.2% increases in agriculturally and urban dominated counties, respectively. We conclude by proposing improvements to forest inventories, maps, and models to better represent the fragmented temperate forest landscape. We provide Google Earth Engine scripts (Gorelick et al. 2017; written in JavaScript) to calculate forest edge area and forest cover from commonly-used land cover maps, including the 2016 National Land Cover Database (NLCD), the 2016 Land Change Monitoring, Assessment, and Projection annual product (LCMAP), and the 2016 MODIS Land Cover IGBP annual product (Yang et al. 2018; Sulla-Menashe et al. 2019; Brown et al. 2020). We also include equivalent scripts to process a very-high resolution (1-m pixel size; VHR) land cover map of the Chesapeake Bay Watershed in 2014 (Pallai and Wesson 2017). We provide an R script to calculate forest edge proportion from the US national forest inventory (USDA FIA), following methods to identify inventory plots containing forest edge as described in Morreale et al. (2021) and using the R library rFIA to access the FIA data (Stanke et al. 2020). We also provide a data table containing the estimated forest area and forest edge proportion for each county within the separate from each of the land cover maps (NLCD, LCMAP, MODIS), the VHR land cover maps, and a separate data table with estimates of edge proportion from the FIA for each county within the study area. We include an R script that processes the intermediate outputs from Google Earth Engine and combines estimates of forest area and forest edge proportion from all sources into a single table.