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Research Highlights - High-Resolution Panama

Carnegie scientists integrated field data with satellite imagery and airborne Light Detection and Ranging (LiDAR) data from the CAO to quantify aboveground carbon stocks throughout the Republic of Panama. The results are the first maps that report carbon stocks locally in areas as small as a hectare and yet cover millions of hectares in a short time. The system has the lowest demonstrated uncertainty of any carbon-counting approach yet—a carbon estimation uncertainty of about 10% in each hectare as compared to field-based estimates.

See the Youtube video showing how it worked: Carbon Mapping in 1 Minute

The new approach will greatly boost conservation and efforts to mitigate climate change through carbon sequestration. It will also inform our understanding of how carbon storage can be used to assess other fundamental ecosystem characteristics such as hydrology, habitat quality, and biodiversity. The approach provides much-needed technical support for carbon-based economic activities such as the United Nations Reducing Emissions from Deforestation and Forest Degradation (REDD) program in developing countries.

Panama has complex landscapes, with variable topography, and diverse ecosystems (ranging from grasslands and mangroves to shrublands and dense forests). As a result, Panama was an ideal laboratory to develop and test a method for quantifying aboveground carbon.

Traditional carbon monitoring has relied upon on-the-ground sampling of field plots, but field-based approaches represent just small areas of land and are time-consuming. There has been growing interest in using satellite imagery to cover larger areas, but it is low resolution both spatially and in terms of the structural information about the vegetation.

This work was carried out in close collaboration with the Smithsonian Tropical Research Institute (STRI), McGill University, and UC-Berkeley.

The journal paper can be accessed on the CAO Publications page.

Deforestation mapping with CLASlite

Panama's carbon stocks in high fidelity

Geospatial modeling of uncertainty

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