Day 5

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Paper title Improved activity data for REDD+ using Sentinel-1 and Sentinel-2 in Uganda
Authors
  1. Petra Miletich JOANNEUM RESEARCH Forschungsgesellschaft mbH Speaker
  2. Laura Costa Ortega CLOUDFLIGHT
  3. Janik Deutscher JOANNEUM RESEARCH Forschungsgesellschaft mbH
  4. Klaus Granica JOANNEUM RESEARCH Forschungsgesellschaft mbH
  5. Ana Gregorac JOANNEUM RESEARCH Forschungsgesellschaft mbH
  6. Karlheinz Gutjahr JOANNEUM RESEARCH Forschungsgesellschaft mbH
  7. Hannah Meyer JOANNEUM RESEARCH Forschungsgesellschaft mbH
  8. Werner Mücke CLOUDFLIGHT
  9. Mathias Schardt JOANNEUM RESEARCH Forschungsgesellschaft mbH
Form of presentation Poster
Topics
  • A3. Biosphere
    • A3.12 Forest Monitoring
Abstract text The United Nations Framework Convention on Climate Change (UNFCCC) has initiated the REDD+ programme to reduce deforestation and forest degradation. In order to support the REDD+ activities, four different types of remote sensing products are targeted by the research community, i.e. a) forest and forest change maps which are addressing deforestation areas; b) Land Use maps addressing carbon sinks or carbon sources through land cover or land-use changes; c) forest degradation maps addressing carbon sources within forests and d) biomass maps estimating carbon stock directly without emission factors.

In the project REACTIFI, we develop a prototype for a Copernicus satellite data-based forest inventory service for test areas in Uganda. This service shall provide Central and East African REDD+ stakeholders with data to support regional and national Monitoring, Reporting and Verification (MRV) processes. Uganda’s REDD+ forest monitoring is one of the most advanced in Africa. The existing activity data is based on Landsat and Sentinel-2 imagery. The existing forest monitoring system still does not account for intra-annual forest degradation, does not provide the wall-to-wall information necessary for forest management, and does not cover all relevant LULC types, such as agroforestry systems. Our research focuses on the development of innovative methods for improved mapping of forest degradation & agroforestry systems, methods for improved activity data (LULC), and for detecting different forest changes (deforestation, selective logging, afforestation).

Here we present the first project results: new methods for forest change detection based on Sentinel-1 and Sentinel-2 data and LULC products for improved activity data in Uganda. Regarding the near-real-time forest change detection two methods are improved and applied in the test regions using different sensors:

- Sentinel-2 data: We apply a near-real-time change detection method that combines a structural time series model with the Kalman filter. Forest changes are detected based on the cumulative sum control chart (CUSUM) that are used to decide if new observations deviate from model-based forecasts (Puhm et.al, 2020). This approach is more robust to phenology outliers than simple least squares fitting approaches. Workflow improvements focus on weighting the model innovations by a cloud probability estimate, which reduces model update errors.

- Sentinel-1 data: We introduce a near real-time mapping approach based on a moving time window, Sentinel-1 backscatter changes (for VV and VH polarisation) and the coefficient of variation. We use sets of ten ensuing Sentinel-1 scenes to calculate the coefficient of variation and backscatter change. Changes within the forest layers are derived by applying empirical thresholds from known forest disturbance areas. This method allows for a timely detection of forest changes.

LULC products are improved providing more detailed mapping of the thematic classes using the higher spatial resolution of Sentinel-2 (10m) instead of Landsat-8 (30m).

- Improved LULC maps for the following classes: Tropical High Forest, Bushland, Natural Grassland, Wetland, Cultivated and Managed Areas, Build-up Areas, Open Water and Impediments. The annual Land Cover Land Use products are derived using yearly Sentinel-2 time series and the Random Forest classifier. To cover the phenological differences of different land cover and land use types, time features over seasonal periods are generated, covering dry or wet seasons. From these LULC products, we derive several layers like a new forest map with an MMU of 1ha and a detailed Tree Cover Map with an MMU of 0.1ha, which also includes smaller tree-covered patches belonging to agroforestry areas.

- First demo-products for mapping potential agroforestry areas are generated covering smaller tree patches in the context of agricultural areas. Due to their varying percentage of tree cover, they are difficult to assess with Earth Observation (EO) data, especially with Landsat spatial resolution (30m). A higher spatial resolution and temporal density of the COPERNICUS Sentinel systems show high potential for increasing mapping accuracies of agroforestry systems.

Currently, an airborne LiDAR campaign is being planned in Uganda. The plan is to combine the improved activity data with LiDAR 3D models and the national terrestrial forest inventory data. This will allow a much more accurate estimation of biomass values, carbon stocks and land use change specific carbon emission values for REDD+ reporting. The LULC change maps can then be combined with calculated emission factors for an improved wall-to-wall carbon accounting for REDD+ MRV.

A fully tailored and scalable web service, built on open source technologies, visualizes all data and results generated. The web services will allow stakeholders to explore remote sensing data and derived products for their areas of interest, with the main goal to support their regional and national MRV processes. This way, stakeholders can access information on forest status and carbon loss caused by deforestation and forest degradation as well as additional auxiliary datasets such as forest and land cover layers and combine them with their own datasets or compute statistical analysis on the fly. This will enable users to act more quickly and accurately than they can with their current systems.

The poster will present first product examples from forest disturbance mapping and LULC products and explain the workflows in more detail.

References

Puhm, M., Deutscher, J., Hirschmugl, M., Wimmer, A., Schmitt, U., & Schardt, M. (2020). A near real-time method for forest change detection based on a structural time series model and the Kalman filter. Remote Sensing, 12(19), 3135.