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* Metric Name: NorCal - - 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 within each dbh class.  

* 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. In consultation with National Forests, “large trees” for the Northern California region have been designated in three categories, 24”-30”, 30”-40”, and >40” dbh. The data provided are an estimate of density of trees (in each dbh class) within a pixel. Trees larger than 24” dbh are considered “large” within the California Wildlife Habitat Relationships model and thus we have specifically identified areas that have these features. The data provided are an estimate of the frequency of trees (in each dbh class) 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 Northern 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 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 power function ( ΔAIC = 58.7) where:

DBH (in ) = 0.2071*HT(ft)1.0296

The root mean square error on the DBH prediction was 5.8 in and the pseudoR2 =
0.75. Predicted diameters from heights are summarized here:.

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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. The
resulting frequency within a 30m pixel (values 0 through 9) is expressed as a
percentage.

Three metrics have been developed for the Northern California region:

1) frequency within pixel of 24”-30” dbh in trees

2) frequency within pixel of 30”-40” dbh in trees

2) frequency within pixel of greater than 40” dbh in trees

  

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