Description

Scale as linked to D2.2 framework

• National
• City
• Neighborhood

Data source [ link to data framework]

• several tools for modelling including:
  • i-Tree tools (USDA Forest Service, 2019).
  • Classification of above-ground vegetation polygons using a LiDAR dataset

Data needs

• Requires data on extent of vegetation cover & characteristics of vegetation (e.g., type, age and height), land use, air quality data, and meteorological and other local information for modelling. These can be obtained from forest inventory analysis (FIA), a national land cover database (NLCD) or databases for housing density mapping. Users may need permission to gain access to national databases unless the data are open (freely available).

Expected Outputs

• Annual measurements
• Quantitative

Calculation methods

• There are several tools for modelling carbon in trees including the U.S. Forest Service Forest Inventory and Analysis Database, such as the suite of i-Tree tools (USDA Forest Service, 2019). The i-Tree Eco model inputs a database of city trees with information on location, size and species to a geographic information system platform. Alternatively, an estimate of C storage or sequestration in above-ground vegetation can be manually determined 32 using a similar approach to the i-Tree Eco application. First, each above-ground vegetation polygon in a digital cartographic dataset can be classified per light detection and ranging (LiDAR) data as, e.g., herbaceous vegetation (grasses and non-woody plants), shrub (woody bushes and trees with mean height typically <2>5 m in height) after Davies, Edmonson, Heinemeyer, Leake, & Gaston (2011). Davies et al. (2011) recommend surveying to ground-truth map data and classification estimates. Species-specific allometric equations are available from the scientific literature to estimate above-ground dry weight biomass of the classified vegetation, and carbon storage calculated using conversion factors also available from the scientific literature. Where there are multiple equations for a given species, the equations can be combined to obtain a general result. Total above ground tree biomass can be converted to C storage using conversion factors based on tree type. The dry-weight of above-ground biomass of each class of vegetation along with the mean C content can also be determined via laboratory analysis.

Links and references

• Davies, Z.G., Edmonson, J.L., Heinemeyer, A., Leake, J.R., & Gaston, K.J. (2011). Mapping an urban ecosystem service: quantifying above-ground carbon storage at a city-wide scale. Journal of Applied Ecology, 48, 21125-1134.
• Fong, W.K., Sotos, M., Doust, M., Schultz, S., Marques, A., & Deng-Beck, C. (2015). Global Protocol for Community-Scale Greenhouse Gas Emission Inventories. Washington, D.C.: World Resources Institute. Retrieved from https://www.wri.org/publication/global-protocol-community scale-greenhouse-gas-emission-inventories
• Intergovernmental Panel on Climate Change (IPCC). (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Prepared by the National Greenhouse Gas Inventories Programme, Eggleston, S., Buendia, L., Miwa, K., Ngara, T., & Tanabe, K. (Eds.). Hayama, Japan: Institute for Global Environmental Strategies (IGES). Retrieved from https://www.ipcc-nggip.iges.or.jp/public/2006gl/.
• Intergovernmental Panel on Climate Change (IPCC). (2014). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Core Writing Team, R.K. Pachauri and L.A. Meyer (Eds.). Geneva, Switzerland: IPCC.
• United States Department of Agriculture (USDA) Forest Service. (2019). i-Tree Eco Manual. Northern Research Station, USDA Forest Service. Retrieved from https://www.itreetools.org/resources/manuals/ Ecov6_ManualsGuides/Ecov6_UsersManual.pdf