Look for complete geospatial metadata in this layer's associated xml document available from the download link * Metric Name: Density – Large Trees * Tier: 2 * Data Vintage: 06/2020 * Unit Of Measure: Percent live trees per pixel * Metric Definition and Relevance: Large trees are important to forest managers for multiple reasons: they have a greater likelihood of survival from fire; they are an important source of seed stock; they provide vitally important wildlife habitat; and they contribute to other critical processes like carbon storage and nutrient cycling. Large trees are often the focus of management in order to protect existing ones and to foster recruitment of future ones., “Large trees” have been designated in two categories, 24”-30”and greater than 30” dbh. The data provided are an estimate of density of trees (in each dbh class) within a pixel. * Creation Method: To determine the cutoff for large trees, we developed an allometric equation to predict tree diameter as a function of height. We selected data for plots located in the Sierra Nevada region from the USDA Forest Inventory and Analysis program (FIA) for California (FIA DataMart 2023; California 2022 database; ver. 9.0.1). We included trees that met the following criteria: alive; crown class code of open-grown, dominant, or co-dominant; diameter at breast height (DBH, breast height = 4.5 ft) at least 1 inch; and height (HT) at least 5 feet. To minimize the impact of outliers, we trimmed the maximum tree height to the 0.995th percentile. These selection criteria yielded 82,444 trees. We used an information theoretic approach to select the best allometric model (Burnham and Anderson 2002). We evaluated three alternative functions: : linear, power, and saturating. The criteria for model selection were based on the Akaike Information Criterion (AIC). For this set of 3 potential models, we calculated the difference in AIC between every model and the model with the lowest AIC (ΔAIC). The best allometric model was a saturating function where: DIA = (187.2*HT)/(588.5+HT) The root mean square error on the DBH prediction was 6.02 in and the pseudoR2 = 0.71. Predicted diameters from heights are summarized here:. ~~~~ Block statistics were run on California Forest Observatory (CFO) canopy height pixels for the following ranges with a 3x3 window to calculate the sum for input cells within a 30m rectangular neighborhood. This assigned number of pixels per 30m (900m2) cell. Resultant values of 1 through 9 were converted to percent. All background values were calculated to equal 0, meaning 0% large tree existence. * 24in - 30in * greater than 30in References Burnham, K.P., and D.R. Anderson. 2002. Model selection and multimodel inference: a practical information-theoretic approach. 2nd ed. New York, Springer-Verlag. FIA DataMart. 2023. USDA Forest Inventory and Analysis DataMart. * Credits: California Forest Observatory (Salo Sciences), 2020