Improve information on drivers of forest change by using national and global open-source datasets and methods
•
0 gostou•136 visualizações
Presented by Yitebitu Moges, Niki De Sy, at "Scoping Workshop: Towards the Enhanced Transparency Framework for REDD+ MRV", ILRI, Addis Ababa, Ethiopia, 30 July 2021
Recomendados
Recomendados
Mais conteúdo relacionado
Mais procurados
Mais procurados (19)
Semelhante a Improve information on drivers of forest change by using national and global open-source datasets and methods
Semelhante a Improve information on drivers of forest change by using national and global open-source datasets and methods (20)
Mais de CIFOR-ICRAF
Mais de CIFOR-ICRAF (20)
Último
Último (20)
Improve information on drivers of forest change by using national and global open-source datasets and methods
- 1. Break Out 2 Improve information on drivers of forest change by using national and global open- source datasets and methods Facilitators: Yitebitu Moges, Niki De Sy Scoping Workshop: Towards the Enhanced Transparency Framework for REDD+ MRV Friday, 30 July, 2021 ILRI campus, Addis Ababa, Ethiopia
- 2. Overview • General background & preliminary plan for research activities • Discussion on: • Main opportunities and challenges • Reflection on data and methods • Land use classes • SEPAL • Available data sources • Regions (subnational/national/ forest types) • Collaboration and way forward – synergies with existing initiatives & institutes
- 3. Background • PhD Robert Masolele • Pantropical case study on spatial and temporal deep learning methods for deriving land-use following deforestation using Landsat time series
- 4. Methods • Reference dataset - visual interpretations based on FAO FRA remote sensing survey data 1990-2005 (De Sy et al, 2019) • Dense Landsat time-series • AI/deep learning models using spatial and temporal information to predict land use activities driving deforestation • Open source platform in SEPAL and GEE • Land use: small- and large-scale cropland, pasture, mining and tree crops
- 5. Way forward • Regional model performs better than pantropical model • Adapt deep learning model and approach to Ethiopian context • Land use classes • Method • Forest loss 2010 – 2014 (Hansen et al, 2013) • Land use following deforestation 2016 • Planet data (high res), Landsat & Sentinel 2 • Other open-source data for calibration and validation • SEPAL Follow-up land use classes Agriculture Large-scale croplands Small-scale cropland Pasture/free grazing Coffee crops Mining Infrastructure Roads Buildings and dams Plantation forest Other land with tree cover
- 6. Discussion • Main opportunities and challenges • Reflection on data and methods • Land use classes • SEPAL • Available data sources • Regions (subnational/national/ forest types) • Collaboration and way forward – synergies with existing initiatives & institutes