Data and Method References

tags: Proof of Concept

Data Sources

Carbon Stocks

• Soil Organic Carbon Stock Estimates with Uncertainty across Latin America: https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1615
  • Type: Snapshot
• Soil organic carbon stock (0–30 cm) in kg/m2 time-series 2001–2015 based on the land cover changes
  • Type: time-series
  • "This only shows estimated SOC loss (in kg/m2) as a result of change in land use / land cover"
• A global map of mangrove forest soil carbon at 30 m spatial resolution. Environmental Research Letters 13: 055002. doi.org/10.1088/1748-9326/aabe1c
  • Type: Snapshot
• Global maps of twenty-first century forest carbon fluxes. Nature Climate Change. https://doi.org/10.1038/s41558-020-00976-6
• https://openlandmap.org
• Above and Below Ground Biomass Carbon Density -
  • Dataset Provider - UNEP-WCMC (UN Environment Programme World Conservation Monitoring Centre)
  • Type: Snapshot
• Global Aboveground and Belowground Biomass Carbon Density Maps -
  • Dataset Provider - NASA ORNL DAAC at Oak Ridge National Laboratory
  • Type: Snapshot
• Pantropical National Level Carbon Stock Dataset -
  • Dataset Provider - WHRC
  • Type: Snapshot
• Maps of soil organic carbon stocks in Brazil
• The global potential for increased storage of carbon on land
  • consistent maps for directing additional carbon storage under current and future climate, as well as a framework for determining how that storage could be gained through restoration, improved management, or maintenance of woody biomass and soil organic matter. Our estimates provide an upper bound on how improved land stewardship can mitigate the climate crisis.

DataSet

• This dataset represents above- and below-ground terrestrial carbon storage (tonnes (t) of C per hectare (ha)) for circa 2010.
  • The dataset was constructed by combining the most reliable publicly available datasets and overlying them with the ESA CCI landcover map for the year 2010 [ESA, 2017], assigning to each grid cell the corresponding above-ground biomass value from the biomass map that was most appropriate for the grid cell’s landcover type.

Methods

• ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery: https://zenodo.org/record/6813783#.Y2v0ii8w1QJ
  • "In this paper, we present ReforesTree, a benchmark dataset of forest carbon stock in six agro-forestry carbon offsetting sites in Ecuador. Furthermore, we show that a deep learning-based end-to-end model using individual tree detection from low cost RGB-only drone imagery is accurately estimating forest carbon stock within official carbon offsetting certification standards. Additionally, our baseline CNN model outperforms state-of-the-art satellite-based forest biomass and carbon stock estimates for this type of small-scale, tropical agro-forestry sites."
  • Main author is the founder of Tanso.io: Carbon Accounting and Management for Industrial Companies
• A Review: Forest Aboveground Biomass (AGB) Estimation Using Satellite
  Remote Sensing
  • some approaches to estimates biomass and carbon stock such
    as utilizing remote sensing data and/ or in-situ data.