Scale considerations for integrating forest inventory plot data and satellite image data for regional forest mapping

The integration of satellite image data with forest inventory plot data is a popular approach for mapping forest vegetation over large regions. Several methodological choices regarding spatial scale, mostly related to spatial resolution or grain, can profoundly influence forest maps developed from plot and imagery data. Yet often the consequences of scaling choices are not explicitly addressed. Our objective was to quantify the effects of several scale-related methods on map accuracy for multiple forest attributes, using a variety of diagnostics that address different map characteristics, to help guide map developers and users. We conducted nearest-neighbor imputation over a large region in the Pacific Northwest, USA, to investigate effects of imputation grain (single pixel or kernel); inclusion of heterogeneous plots; accuracy assessment grain and extent; and value of k (k = 1 and k = 5), where k is the number of nearest-neighbor plots. Spatial predictors were from rasters describing climate and topography and a time-series of Landsat imagery. Reference data were from regional forest inventory plots measured over two decades. All analyses were conducted at a spatial resolution of 30 m x 30 m. Effects of imputation grain and heterogeneous plots on map accuracy were small. Excluding heterogeneous plots slightly improved map accuracy and did not lessen the systematic agreement (AC(sys)) between our maps and observed plot data. Accuracy assessment grain strongly influenced map accuracy: maps assessed with a multi-pixel block were much more accurate than when assessed with a single pixel for almost all map diagnostics, but this was an artifact of methods rather than reflecting real differences among maps. Unsystematic agreement (AC(UNS)) between our maps and plots, or random error, improved notably with increasing accuracy assessment extent for all scaling methods, indicating that reliability of most map applications can be improved through coarsening the map grain. Value of k strongly influenced map diagnostics. The k = 5 maps were better than k = 1 maps for local-scale accuracy, but at the cost of reduced AC(sys), and loss of variability and poor areal representation of forest conditions over the study region. The k = 1 maps produced notably better predictions of the least abundant forest conditions (early-successional, late-successional, and broadleaf). None of the scaling methods were optimal for all map diagnostics. Nevertheless, given a variety of diagnostics associated with a range of scaling options, map developers and map users can make informed choices about methods and resulting maps that best meet their particular objectives, and we present some general guidelines in this regard. Most of our findings are applicable to mapping with Landsat data in other forested regions with similar forest inventory data, and to other methods for spatial prediction. Published by Elsevier Inc.