{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T05:25:49Z","timestamp":1781155549369,"version":"3.54.1"},"reference-count":65,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:00:00Z","timestamp":1731888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","award":["401661\/2023-7"],"award-info":[{"award-number":["401661\/2023-7"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u20137% wider.<\/jats:p>","DOI":"10.3390\/rs16224295","type":"journal-article","created":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T06:06:54Z","timestamp":1731996414000},"page":"4295","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Incorporating Forest Mapping-Related Uncertainty into the Error Propagation of Wall-to-Wall Biomass Maps: A General Approach for Large and Small Areas"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7980-8859","authenticated-orcid":false,"given":"Hassan C.","family":"David","sequence":"first","affiliation":[{"name":"Department of Forest Science, Federal University of Paran\u00e1, Pref. Loth\u00e1rio Meissner Avenue 900, Curitiba 80210-170, PR, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8789-5833","authenticated-orcid":false,"given":"Alexander C.","family":"Vibrans","sequence":"additional","affiliation":[{"name":"Department of Forest Resources, Regional University of Blumenau, S\u00e3o Paulo Street 3250, Blumenau 89030-000, SC, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5318-2627","authenticated-orcid":false,"given":"Rorai P.","family":"Martins-Neto","sequence":"additional","affiliation":[{"name":"Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague (CULS), Kam\u00fdck\u00e1 129, 165 00 Prague, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8529-5554","authenticated-orcid":false,"given":"Ana Paula","family":"Dalla Corte","sequence":"additional","affiliation":[{"name":"Department of Forest Science, Federal University of Paran\u00e1, Pref. Loth\u00e1rio Meissner Avenue 900, Curitiba 80210-170, PR, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sylvio","family":"P\u00e9llico Netto","sequence":"additional","affiliation":[{"name":"Department of Forest Science, Federal University of Paran\u00e1, Pref. Loth\u00e1rio Meissner Avenue 900, Curitiba 80210-170, PR, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,18]]},"reference":[{"key":"ref_1","first-page":"179","article-title":"Harmonizing National Forest Inventories","volume":"107","author":"McRoberts","year":"2009","journal-title":"J. For."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.foreco.2017.08.044","article-title":"Carbon Stock Classification for Tropical Forests in Brazil: Understanding the Effect of Stand and Climate Variables","volume":"404","author":"David","year":"2017","journal-title":"For. Ecol. 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