MF-RADAR
Multi-frequency RADAR imaging for the analysis of tropical forest structure in the Amazon
Total Cost €
0EC-Contrib. €
0Partnership
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Project "MF-RADAR" data sheet
The following table provides information about the project.
| Coordinator |
UNIVERSITY OF LEICESTER
Organization address contact info |
| Coordinator Country | United Kingdom [UK] |
| Project website | https://www2.le.ac.uk/departments/geography/research/projects/mf-radar/mf-radar |
| Total cost | 183˙454 € |
| EC max contribution | 183˙454 € (100%) |
| Programme |
1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility) |
| Code Call | H2020-MSCA-IF-2014 |
| Funding Scheme | MSCA-IF-EF-ST |
| Starting year | 2016 |
| Duration (year-month-day) | from 2016-01-01 to 2017-12-31 |
Partnership
Take a look of project's partnership.
| # | ||||
|---|---|---|---|---|
| 1 | UNIVERSITY OF LEICESTER | UK (LEICESTER) | coordinator | 183˙454.00 |
Map
Project objective
The tropical rainforest of the Amazon basin is a global biodiversity hotspot and stores significant amounts of carbon, stabilising the regional and global climate. Deforestation, forest degradation and climate change impacts are posing a threat to its future. This Marie Curie fellowship will develop a systematic integration of geomorphometry methods with satellite remote sensing techniques from Synthetic Aperture Radar to study the floristic-structural associations in the tropical forest of the Amazon, map disturbances and degradation, reduce greenhouse gas emissions and preserve floristic diversity. Its research objectives (RO) areto identify geomorphometric variables related to tree species abundance and richness in Tapajos, Brazil, and structural forest variables from multi-frequency radar satellites (RO1); to analyse tree species, radar data and geomorphometry witha machine-learning (maximum-entropy) approach to produce species probability maps (RO2); to determine the explanatory power of the integrated radar/geomorphometry approach for biomass mapping (RO3); and to estimate the aboveground carbon stocks (RO4). The technical and complementary training objectives (TO) are to learn advanced radar processing skills for forest structure estimation (TO1); to learn effective data integration techniques for multi-frequency radar data and geomorphometric parameters (TO2); to learn how to communicate scientific research to the wider public (TO3); and to acquire complementary and leadership skills (TO4). The fellow will undertake a world-class programme of research and training in Earth Observation research methods, several international secondments, participate in postgraduate training modules and specific researcher development courses in complementary skills. She will transfer her expertise in tropical forest structure and biodiversityof the Amazon to Europe and develop her academic career to reach and enforce a senior academic position.
Publications
| year | authors and title | journal | last update |
|---|---|---|---|
| 2016 |
Polyanna da Conceição Bispo, João Roberto dos Santos, Márcio de Morisson Valeriano, Paulo MaurÃcio Lima de Alencastro Graça, Heiko Balzter, Helena França, Pitágoras da Conceição Bispo Predictive Models of Primary Tropical Forest Structure from Geomorphometric Variables Based on SRTM in the Tapajós Region, Brazilian Amazon published pages: e0152009, ISSN: 1932-6203, DOI: 10.1371/journal.pone.0152009 |
PLOS ONE 11/4 | 2019-06-18 |
| 2017 |
Polyanna da Conceição Bispo, Heiko Balzter, Yadvinder Malhi, J. W. Ferry Slik, João Roberto dos Santos, Camilo Daleles Rennó, Fernando D. EspÃrito-Santo, Luiz E. O. C. Aragão, Arimatéa C. Ximenes, Pitágoras da Conceição Bispo Drivers of metacommunity structure diverge for common and rare Amazonian tree species published pages: e0188300, ISSN: 1932-6203, DOI: 10.1371/journal.pone.0188300 |
PLOS ONE 12/11 | 2019-06-18 |
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