Forests help offset a quarter of anthropogenic emissions of fossil-fuel, and hold 70-90% of the Earth’s above ground terrestrial biomass. Sustainable forest management is critical for the resilience of ecosystems and society, and central to the UN SDG 13 (Climate), SDG 2 (Zero Hunger) and SDG 15 (Life on Land). Effective global climate policies rely on the understanding of the Global Carbon Cycle, and therefore need accurate assessment of the global carbon budget which is determined by anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean and terrestrial biosphere.
The emissions from Land Use and Land Change (LUC) make up an important factor of the global carbon budget and are determined mainly by deforestation, especially from conversion of forests to croplands. However, currently, the estimations of LUC emissions are highly uncertain.
Understanding the causes of observed forest biomass dynamics is key to the prediction of future environmental changes, but in absence of reliable estimations, quantifying the links between LUC, physical processes and climate remains a challenge, especially with rapidly occurring LUC such as deforestation.
In this context, the availability of Sentinel-1A and -1B together with additional recently available L-band data creates a unique opportunity to gain insights on the carbon release and sink as a result of LUC in the Amazonas. Furthermore, new algorithm validation and verification approaches are supported by the availability of new observations.
This activity is looking to assess how Sentinel-1 IW imagery (SLC, dual polarization) can contribute to the estimation of the terrestrial carbon loss/gain ratio associated with Land Use and Land Cover changes determined by anthropogenic and natural factors in the tropical forest domain. Such factors include both forest degradation (e.g. from deforestation or impact of extreme events like severe droughts and fires) and restoration (e.g. from re-/afforestation or regrowth). The activity considers the 5-year dense Sentinel-1 time series and investigates the potential informational gain from complementing these time series with other data such as L-band SAR for better estimations of terrestrial carbon loss and gain from LUC.
There are three primary objectives of this activity:
- To develop, test and validate a Multi-temporal forest Change Detection (MCD) algorithm for Sentinel-1 IW SLC time series, with statistically derived confidence measures of the detected spatio-temporal changes.
- To quantify carbon loss and gain from LUC in the Amazonas based on the Multi-temporal Change Detection information product output by the MCD Algorithm.
- To perform a contextual scientific analysis and interpretation of the quantified carbon gain/loss from LUC, accounting for stressors such as severe droughts or fires.
Learn more about this Invitation To Tender on the EMITS page.
Featured image : Vôo de resgate na foz do rio Breu. Courtesy of VIHH