Spatial scale dependence of error in fractional component cover maps
Fractional vegetation cover products often report pixel‑level accuracy, yet most decisions occur at broader spatial scales. This study evaluates RCMAP accuracy across scales by comparing it with 2‑m predictions aggregated from 30 to 1 500 m. Accuracy consistently improved with larger focal windows, leveling off around 200–600 m, where correlation increased by 6.5% and RMSE dropped by 46%. Improvements were strongest for heterogeneous components like shrubs and trees and varied across ecoregions. At pasture scales (~1 050 ha), accuracy increased further. These results quantify how scale affects error and provide practical guidance for fractional cover data users seeking to balance spatial detail with reliability.
Subject Tags
- Conservation Technology
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.
Citation
Rigge, M., Bunde, B., McCord, S. E., Harrison, G., Assal, T. J., & Smith, J. L. (2025). Spatial scale dependence of error in fractional component cover maps. Rangeland Ecology & Management, 99, 77-87. https://doi.org/10.1016/j.rama.2025.01.004
TNC Authors
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James L. Smith
Landfire Project Manager, Florida
The Nature Conservancy
Email: jim_smith@tnc.org