Description
SE Computations
The SE computations (by way of variance) generally follow the approach described in the McRoberts 2010 and McRoberts 2006 papers.
The polygon aggregate variance is computed as the covariance of the constituent pixel predictions + the covariance of the constituent pixel residuals.
In this instance, since we are producing an aggregate estimate, each pixel prediction is treated a single estimate of the aggregate estimate (average of all pixels), and pixel residuals are computed as the difference between a pixel prediction and the aggregate prediction.
Variance is converted to SE by dividing the square-root of the aggregate estimate variance by the square-root of the number of constituent pixels.
Data
The Warren, Washington, Essex LiDAR region was leveraged for this analysis. Note that this area is relatively homogeneous with respect to landcover, so we might expect these error estimates to be optimistic relative to other regions.
The LINMOD ensemble predictions were used in this analysis, but were masked to only include LCMAP tree cover and wetland classes. Pixels falling in any other class were set to NA and excluded from the analysis.
2019 tax parcels were used as aggregation units. These aggregation units make sense from an application standpoint, though perhaps more arbitrary units of aggregation would be more suitable.
Results
Summary of Error Components
Average Contributions:
- Prediction Covariance: 51.54%
- Residual Covariance: 48.46%
Distribution of % Error (capped at 200%)
Parcel Errors By Parcel Size
SE By Parcel Size
% Error (capped at 200%) By Parcel Size
Summarized Parcel Errors for Size Groupings
Groups are exclusive, where each point along the x-axis represents the center of a 10 acre summary group. So where the x-axis says ‘55’ we are summarizing all parcels larger than 50 acres in size and smaller than or equal to 60 acres in size.
Parcel Maps
Percent errors capped at 200% for figure clarity
