Spatial scale dependence of error in fractional component cover maps

Authors: RIGGE, MATTHEW - Us Geological Survey (USGS); BUNDE, BRETTE - Us Geological Survey (USGS); McCord, Sarah; Harrison, Georgia; ASSAL, TIMOTHY - Bureau Of Land Management; SMITH, JAMES - Nature Conservancy

Submitted to: Rangeland Ecology and Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/10/2025
Publication Date: 2/14/2025
Citation: Rigge, M., Bunde, B., McCord, S.E., Harrison, G.R., Assal, T.J., Smith, J.L. 2025. Spatial scale dependence of error in fractional component cover maps. Rangeland Ecology and Management. 99:77-87. https://doi.org/10.1016/j.rama.2025.01.004.
DOI: https://doi.org/10.1016/j.rama.2025.01.004

Interpretive Summary: We evaluate the spatial scale-dependence of error in RCMAP products. Data users of fractional vegetation cover data such as Rangeland Condition Monitoring Assessment and Projection (RCMAP) often seek guidance on the appropriate spatial scale of application. While some qualitative guidance such as “the bigger the better” has been offered, no quantitative advice has been given. We assess these patterns by comparing RCMAP predictions to accurate high-resolution satellite imagery derived, spatially continuous training data. We evaluate the relationships between the high-resolution training and RCMAP predictions at scales of 30-1500 m. Our results demonstrate a clear accuracy improvement at broader windows, across all components, and that most of these improvements level off at ~200-600 m scales. Our results provide empirical data on the scale dependence of error, which RCMAP users may consider when applying these data within the requirements and tolerances of their question. While the general principle remains that remotely sensed products such as RCMAP are intended to address landscape-scale questions, this analysis indicates that applying products at finer spatial scales and grouping even a handful of pixels resulted in lowered error compared to pixel-level comparisons.

Technical Abstract: Geospatial products such as fractional vegetation cover maps often report overall, pixel-wise accuracy, but decision-making with these products often occurs at coarser scales. As such, data users often desire guidance on the appropriate spatial scale to apply these data. We worked toward establishing this guidance by assessing RCMAP (Rangeland Condition Monitoring Assessment and Projection) accuracy relative to a series of high-resolution predictions of component cover. We scale the 2-m and RCMAP predictions to various focal window sizes scales ranging from 30 to 1 500 m using focal averaging. We also evaluated variation in scaling effects on error at ecoregion and pasture (mean area of 1 050 ha) scales. Our results demonstrate increased accuracy at broader windows, across all components, and most increases in accuracy level off at ~200–600 m scales. At the scale with highest accuracy, cross-component average correlation (r) increased by 6.5%, and root mean square error (RMSE) was reduced 46.4% relative to 30-m scale data. Scaling-related improvements to accuracy were greatest in components such as shrub and tree with more spatially heterogeneous cover and in ecoregions with more spatially heterogenous cover. When components were aggregated at the pasture scale, r increased 10% and RMSE decreased 34.3% on average relative to the 30-m scale. Our results provide empirical data on the scale dependence of error, which fractional cover data users may consider alongside their needs when using these data. Although the general principle remains that remotely sensed products are intended to address landscape-scale questions, our analysis indicates that applying data at finer than landscape spatial scales and grouping even a handful of pixels resulted in lowered error compared to pixel-level comparisons. Our results quantify the trade-offs between data granularity and error related to scale for fractional vegetation cover.