{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T12:03:42Z","timestamp":1773921822834,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2013,3,25]],"date-time":"2013-03-25T00:00:00Z","timestamp":1364169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The quantification of forest above-ground biomass (AGB) is important for such broader applications as decision making, forest management, carbon (C) stock change assessment and scientific applications, such as C cycle modeling. However, there is a great uncertainty related to the estimation of forest AGB, especially in the tropics. The main goal of this study was to test a combination of field data and Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) backscatter intensity data to reduce the uncertainty in the estimation of forest AGB in the Miombo savanna woodlands of Mozambique (East Africa). A machine learning algorithm, based on bagging stochastic gradient boosting (BagSGB), was used to model forest AGB as a function of ALOS PALSAR Fine Beam Dual (FBD) backscatter intensity metrics. The application of this method resulted in a coefficient of correlation (R) between observed and predicted (10-fold cross-validation) forest AGB values of 0.95 and a root mean square error of 5.03 Mg\u00b7ha\u22121. However, as a consequence of using bootstrap samples in combination with a cross validation procedure, some bias may have been introduced, and the reported cross validation statistics could be overoptimistic. Therefore and as a consequence of the BagSGB model, a measure of prediction variability (coefficient of variation) on a pixel-by-pixel basis was also produced, with values ranging from 10 to 119% (mean = 25%) across the study area. It provides additional and complementary information regarding the spatial distribution of the error resulting from the application of the fitted model to new observations.<\/jats:p>","DOI":"10.3390\/rs5041524","type":"journal-article","created":{"date-parts":[[2013,3,26]],"date-time":"2013-03-26T04:34:12Z","timestamp":1364272452000},"page":"1524-1548","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":89,"title":["Estimating the Above-Ground Biomass in Miombo Savanna Woodlands (Mozambique, East Africa) Using L-Band Synthetic Aperture Radar Data"],"prefix":"10.3390","volume":"5","author":[{"given":"Jo\u00e3o","family":"Carreiras","sequence":"first","affiliation":[{"name":"Tropical Research Institute (IICT), Travessa do Conde da Ribeira, 9, Lisboa 1300-142, Portugal"}]},{"given":"Joana","family":"Melo","sequence":"additional","affiliation":[{"name":"Tropical Research Institute (IICT), Travessa do Conde da Ribeira, 9, Lisboa 1300-142, Portugal"}]},{"given":"Maria","family":"Vasconcelos","sequence":"additional","affiliation":[{"name":"Tropical Research Institute (IICT), Travessa do Conde da Ribeira, 9, Lisboa 1300-142, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2013,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1126\/science.1201609","article-title":"A Large and persistent carbon sink in the world\u2019s forests","volume":"333","author":"Pan","year":"2011","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Le Quere, C., Raupach, M., Canadell, J., Marland, G., Bopp, L., Ciais, P., Conway, T., Doney, S., Feely, R., and Foster, P. 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