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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.)

Determining a Threshold to Delimit the Amazonian Forests from the Tree Canopy Cover 2000 GFC Data

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Cruz Gasparini, Kaio Allan [1] ; Leite Silva Junior, Celso Henrique [1] ; Shimabukuro, Yosio Edemir [1] ; Arai, Egidio [1] ; Oliveira Cruz e Aragao, Luiz Eduardo [1] ; Silva, Carlos Alberto [2] ; Marshall, Peter L. [3]
Total Authors: 7
[1] Inst Nacl Pesquisas Espaciais, Div Sensoriamento Remoto, Sao Jose Dos Campos, SP - Brazil
[2] Univ Maryland, Dept Geog Sci, College Pk, MD 20740 - USA
[3] Univ British Columbia, Dept Forest Resources Management, 2424 Main Mall, Vancouver, BC V6T 1Z4 - Canada
Total Affiliations: 3
Document type: Letter
Source: SENSORS; v. 19, n. 22 NOV 2019.
Web of Science Citations: 0

Open global forest cover data can be a critical component for Reducing Emissions from Deforestation and Forest Degradation (REDD+) policies. In this work, we determine the best threshold, compatible with the official Brazilian dataset, for establishing a forest mask cover within the Amazon basin for the year 2000 using the Tree Canopy Cover 2000 GFC product. We compared forest cover maps produced using several thresholds (10%, 30%, 50%, 80%, 85%, 90%, and 95%) with a forest cover map for the same year from the Brazilian Amazon Deforestation Monitoring Project (PRODES) data, produced by the National Institute for Space Research (INPE). We also compared the forest cover classifications indicated by each of these maps to 2550 independently assessed Landsat pixels for the year 2000, providing an accuracy assessment for each of these map products. We found that thresholds of 80% and 85% best matched with the PRODES data. Consequently, we recommend using an 80% threshold for the Tree Canopy Cover 2000 data for assessing forest cover in the Amazon basin. (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