Incorporating Forest Mapping-Related Uncertainty into the Error Propagation of Wall-to-Wall Biomass Maps: A General Approach for Large and Small Areas

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2024-11, Vol.16 (22), p.4295
• Main Authors: David, Hassan C., Vibrans, Alexander C., Martins-Neto, Rorai P., Dalla Corte, Ana Paula, Péllico Netto, Sylvio
• Format: Article
• Language: English
• Subjects: Accuracy; Biomass; Carbon; Classification; Clusters; Confidence intervals; Earth resources technology satellites; Emission standards; Error analysis; Estimates; Estimators; forest cover map; Forest reserves; Forests; Landsat; Mapping; model-assisted estimation; national forest inventory; National forests; Nuclear factor I; Pixels; Plot (Narrative); proportion forest; Rainforests; Remote sensing; Spatial discrimination; Spatial resolution; Statistical analysis; Trees; Uncertainty; uncertainty analysis
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16224295

The sources of uncertainty in wall-to-wall AGB maps propagate from the tree to pixel, but uncertainty due to forest cover mapping is rarely incorporated into the error propagation process. This study aimed to (1) elaborate an analytical procedure to incorporate forest-mapping-related uncertainty into the error propagation from plot and pixel predictions; (2) develop a stratified estimator with a model-assisted estimator for small and large areas; and (3) estimate the effect of ignoring the mapping uncertainty on the confidence intervals (CIs) for totals. Data consist of a subset of the Brazilian national forest inventory (NFI) database, comprising 75 counties that, once aggregated, served as strata for the stratified estimator. On-ground data were gathered from 152 clusters (plots) and remotely sensed data from Landsat-8 scenes. Four major contributions are highlighted. First, we describe how to incorporate forest-mapping-related uncertainty into the CIs of any forest attribute and spatial resolution. Second, stratified estimators perform better than non-stratified estimators for forest area estimation when the response variable is forest/non-forest. Comparing our stratified estimators, this study indicated greater precision for the stratified estimator than for the regression estimator. Third, using the ratio estimator, we found evidence that the simple field plot information provided by the NFI clusters is sufficient to estimate the proportion forest for large regions as accurately as remote-sensing-based methods, albeit with less precision. Fourth, ignoring forest-mapping-related uncertainty erroneously narrows the CI width as the estimate of proportion forest area decreases. At the small-area level, forest-mapping-related uncertainty led to CIs for total AGB as much as 63% wider in extreme cases. At the large-area level, the CI was 5–7% wider.