{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T01:00:49Z","timestamp":1772240449719,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T00:00:00Z","timestamp":1661126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Centre national d\u2019\u00e9tudes spatiales (CNES)","award":["ANR-17-EGAS-0002-01"],"award-info":[{"award-number":["ANR-17-EGAS-0002-01"]}]},{"name":"Centre national d\u2019\u00e9tudes spatiales (CNES)","award":["696356"],"award-info":[{"award-number":["696356"]}]},{"name":"European Union\u2019s Horizon 2020","award":["ANR-17-EGAS-0002-01"],"award-info":[{"award-number":["ANR-17-EGAS-0002-01"]}]},{"name":"European Union\u2019s Horizon 2020","award":["696356"],"award-info":[{"award-number":["696356"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remotely sensed maps of forest carbon stocks have enormous potential for supporting greenhouse gas (GHG) inventory and monitoring in tropical countries. However, most countries have not used maps as the reference data for GHG inventory due to the lack of confidence in the accuracy of maps and of data to perform local validation. Here, we use the first national forest inventory (NFI) data of the Democratic Republic of Congo to perform an independent assessment of the country\u2019s latest national spaceborne carbon stocks map. We compared plot-to-plot variations and areal estimates of forest aboveground biomass (AGB) derived from NFI data and from the map across jurisdictional and ecological domains. Across all plots, map predictions were nearly unbiased and captured c. 60% of the variation in NFI plots AGB. Map performance was not uniform along the AGB gradient, and saturated around c. 290 Mg ha\u22121, increasingly underestimating forest AGB above this threshold. Splitting NFI plots by land cover types, we found map predictions unbiased in the dominant terra firme Humid forest class, while plot-to-plot variations were poorly captured (R2 of c. 0.33, or c. 0.20 after excluding disturbed plots). In contrast, map predictions underestimated AGB by c. 33% in the small AGB woodland savanna class but captured a much greater share of plot-to-plot AGB variation (R2 of c. 0.41, or 0.58 after excluding disturbed plots). Areal estimates from the map and NFI data depicted a similar trend with a slightly smaller (but statistically indiscernible) mean AGB from the map across the entire study area (i.e., 252.7 vs. 280.6 Mg ha\u22121), owing to the underestimation of mean AGB in the woodland savanna domain (31.8 vs. 57.3 Mg ha\u22121), which was broadly consistent with the results obtained at the provincial level. This study provides insights and outlooks for country-wide AGB mapping efforts in the tropics and the computation of emission factors in Democratic Republic of Congo for carbon monitoring initiatives.<\/jats:p>","DOI":"10.3390\/rs14164126","type":"journal-article","created":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T23:49:56Z","timestamp":1661212196000},"page":"4126","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Assessing the Predictive Power of Democratic Republic of Congo\u2019s National Spaceborne Biomass Map over Independent Test Samples"],"prefix":"10.3390","volume":"14","author":[{"given":"Augustin","family":"Lamulamu","sequence":"first","affiliation":[{"name":"D\u00e9partement de Gestion des Ressources Naturelles, Facult\u00e9 des Sciences Agronomiques, Universit\u00e9 de Kinshasa, Kinshasa 01031, Democratic Republic of the Congo"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pierre","family":"Ploton","sequence":"additional","affiliation":[{"name":"AMAP, Univ Montpellier, IRD, CNRS, INRAE, CIRAD, 34394 Montpellier, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luca","family":"Birigazzi","sequence":"additional","affiliation":[{"name":"Forestry Consultant, Via Unione Sovietica 105, 58100 Grosseto, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Environment and Sustainability, University of California, Los Angeles, CA 90095, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sassan","family":"Saatchi","sequence":"additional","affiliation":[{"name":"Institute of Environment and Sustainability, University of California, Los Angeles, CA 90095, USA"},{"name":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91125, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean-Paul","family":"Kibambe Lubamba","sequence":"additional","affiliation":[{"name":"D\u00e9partement de Gestion des Ressources Naturelles, Facult\u00e9 des Sciences Agronomiques, Universit\u00e9 de Kinshasa, Kinshasa 01031, Democratic Republic of the Congo"},{"name":"Wildlife Conservation Society, Kinshasa 14537, Democratic Republic of the Congo"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1038\/s41586-018-0300-2","article-title":"The Tropical Forest Carbon Cycle and Climate Change","volume":"559","author":"Mitchard","year":"2018","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"eaat2993","DOI":"10.1126\/sciadv.aat2993","article-title":"Congo Basin Forest Loss Dominated by Increasing Smallholder Clearing","volume":"4","author":"Tyukavina","year":"2018","journal-title":"Sci. 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