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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Multi-scale approach to estimating aboveground biomass in the Brazilian Amazon using Landsat and LiDAR data

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Dos Santos, Erone Ghizoni [1] ; Shimabukuro, Yosio Edemir [1] ; De Moura, Yhasmin Mendes [2] ; Goncalves, Fabio Guimaraes [3] ; Jorge, Anderson [1] ; Gasparini, Kaio Alan [1] ; Arai, Egidio [1] ; Duarte, Valdete [1] ; Ometto, Jean Pierre [4]
Total Authors: 9
[1] Natl Inst Space Res INPE, Remote Sensing Dept, Sao Jose Dos Campos - Brazil
[2] Univ Leicester, Ctr Landscape & Climate Res, Leicester, Leics - England
[3] Canopy Remote Sensing Solut, Florianopolis, SC - Brazil
[4] Natl Inst Space Res INPE, Earth Syst Sci Ctr CCST, Sao Jose Dos Campos - Brazil
Total Affiliations: 4
Document type: Journal article
Source: International Journal of Remote Sensing; v. 40, n. 22 JUN 2019.
Web of Science Citations: 0

Forest degradation from either natural or anthropogenic drivers involves processes that change the capacity of the ecosystem to provide services. In Brazil, estimates of carbon emissions do not currently take into account emissions from forest degradation caused by fire or by selective logging. Here, we present a methodology to estimate aboveground biomass in forest degradedareas, that can be accounted to estimate carbon emissions. We explored a multi-scale and temporal approach involving Airborne Laser Scanning (ALS) and orbital images from Landsat 8 Operational Land Imager (OLI) sensor to estimate the aboveground biomass. Cross-validation results showed that 49% of the variation in biomass could be explained using this approach, with an estimation error 58 Mg ha(-1) (49.08%). Due to the difficulty in measuring biomass in tropical forests, the proposed methodology can be an alternative in future works to estimate aboveground biomass in order to improve the estimates of carbon emissions by the governmental organizations. (AU)

FAPESP's process: 16/19806-3 - Mapping and monitoring forest degradation using remote sensing data with medium and moderate spatial resolution
Grantee:Yosio Edemir Shimabukuro
Support type: Regular Research Grants