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* Metric Name: CenCal - Index of Large Tree Presence  

* Tier: 2  

* Data Vintage: 06/2020  

* Unit Of Measure: Floating point; values represent the percent of nine 10-meter pixels within a 30 meter pixel that contains at least one tree 30” dbh or greater.  

* Metric Definition and Relevance: Large trees are important to forest managers as they have a greater likelihood of survival from fire, provide sources of seed stock, wildlife habitat, and 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 future ones. In consultation with National Forests and within the California Wildlife Habitat Relationships (CWHR) model, “large trees” in the Central California region have been designated as greater than 30” dbh (diameter at breast height). The data provided are an estimate of the frequency of trees within a 30 meter pixel expressed as a percentage.  

* Creation Method: To determine the presence and proportion of large tree in a 30 meter pixel, we used the 10 meter data from the California Forest Observatory (CFO) that contains the height (in meters) of the largest tree in that 10 meter pixel. After converting meters to feet, we then developed an allometric equation to predict tree diameter as a function of tree height in feet. We selected data for plots located in the Central California 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 7,089 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:

DBH (in ) = 0.752*HT(ft)0.772

The root mean square error on the DBH prediction was 6.67 in and the pseudoR2
= 0.70. Predicted diameters from heights are summarized here:.

Block statistics were run to compute the frequency of California Forest
Observatory (CFO) 10-meter pixels within a 3x3 window of the number of pixels
containing at least one tree larger than the minimum diameter sized tree.

For the Central California region, one metric has been developed:

  1. 1) frequency within pixel of greater than 30” dbh in trees

  

* Credits: California Forest Observatory (Salo Sciences), 2020