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Alessandro Baccini

Researcher at Woods Hole Research Center

Publications -  63
Citations -  11305

Alessandro Baccini is an academic researcher from Woods Hole Research Center. The author has contributed to research in topics: Greenhouse gas & Deforestation. The author has an hindex of 35, co-authored 61 publications receiving 9109 citations. Previous affiliations of Alessandro Baccini include Boston University.

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Global land cover mapping from MODIS: algorithms and early results

TL;DR: This product provides maps of global land cover at 1-km spatial resolution using several classification systems, principally that of the IGBP, and a supervised classification methodology is used that exploits a global database of training sites interpreted from high-resolution imagery in association with ancillary data.
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Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps

TL;DR: In this paper, the authors provided the most detailed estimate of the carbon density of vegetation and associated carbon dioxide emissions from deforestation for ecosystems across the tropics across the world, including tropical rainforests.
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Mapping forest canopy height globally with spaceborne lidar

TL;DR: In this article, a wall-to-wall, global map of canopy height at 1-km spatial resolution, using 2005 data from the Geoscience Laser Altimeter System (GLAS) aboard ICESat (Ice, Cloud, and land Elevation Satellite).
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Tropical forests are a net carbon source based on aboveground measurements of gain and loss

TL;DR: 12 years of MODIS satellite data are used to quantify net annual changes in the aboveground carbon density of tropical woody live vegetation, providing direct, measurement-based evidence that the world’s tropical forests are a net carbon source.
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Some challenges in global land cover mapping : An assessment of agreement and accuracy in existing 1 km datasets

TL;DR: There is a strong relationship between class accuracy, spatial agreement among the datasets, and the heterogeneity of landscapes and suggestions for future mapping projects include careful definition of mixed unit classes, and improvement in mapping heterogeneous landscapes.