Title |
Identifying areas of deforestation risk for REDD+ using a species modeling tool
|
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Published in |
Carbon Balance and Management, November 2014
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DOI | 10.1186/s13021-014-0010-5 |
Pubmed ID | |
Authors |
Naikoa Aguilar-Amuchastegui, Juan Carlos Riveros, Jessica L Forrest |
Abstract |
To implement the REDD+ mechanism (Reducing Emissions for Deforestation and Forest Degradation, countries need to prioritize areas to combat future deforestation CO2 emissions, identify the drivers of deforestation around which to develop mitigation actions, and quantify and value carbon for financial mechanisms. Each comes with its own methodological challenges, and existing approaches and tools to do so can be costly to implement or require considerable technical knowledge and skill. Here, we present an approach utilizing a machine learning technique known as Maximum Entropy Modeling (Maxent) to identify areas at high deforestation risk in the study area in Madre de Dios, Peru under a business-as-usual scenario in which historic deforestation rates continue. We link deforestation risk area to carbon density values to estimate future carbon emissions. We quantified area deforested and carbon emissions between 2000 and 2009 as the basis of the scenario. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Nepal | 1 | 11% |
Indonesia | 1 | 11% |
United States | 1 | 11% |
Unknown | 6 | 67% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 8 | 89% |
Scientists | 1 | 11% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Japan | 1 | 1% |
United States | 1 | 1% |
Peru | 1 | 1% |
Thailand | 1 | 1% |
Unknown | 69 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 18 | 25% |
Student > Master | 14 | 19% |
Student > Bachelor | 7 | 10% |
Student > Ph. D. Student | 4 | 5% |
Student > Doctoral Student | 3 | 4% |
Other | 10 | 14% |
Unknown | 17 | 23% |
Readers by discipline | Count | As % |
---|---|---|
Environmental Science | 25 | 34% |
Agricultural and Biological Sciences | 14 | 19% |
Computer Science | 3 | 4% |
Earth and Planetary Sciences | 3 | 4% |
Engineering | 2 | 3% |
Other | 6 | 8% |
Unknown | 20 | 27% |