Mapping and Quantifying Carbon On UT Campus

1. Pre-field preparation:

• Sampling frame of 30 plots 

• Plots were naturally divided into strata (Coniferous, broadleaf, and Mixed) based on data attributes 

• Sampling type: stratified sampling with equal allocation 

• Sample size of 6 plots 

• Instruments: Locus Map, measuring tapes, DBH tape, metal pin, and field forms. 

2. Field measurement 

• Attached the measurement tape to a central point and indicated a circular plot of 12.62 m radius.  

• Used a second measurement tape to make a ‘wedge’ to make it easier to systematically measure all trees in the plot.  

• For each plot, the DBH of all trees (minimal DBH 1 cm) was taken, and it was indicated if the tree is a broadleaf or conifer.  

• Adhered to the DBH measurement protocol by Ravindranath and Ostwald (2007). 

• (See gallery for pictures.)

3. Post Field: 

• Data entry and cleaning of field measurements

• Calculated Above Ground Biomass (AGB) for each of the measured plots using the allometric equation by Lambert et al. (2005) for softwood (conifer) and hardwood (broadleaf) trees.

• Used secondary data of existing field measurements to increase the sample size and help in extrapolation and estimation of carbon storage.

4. Mapping:

• Mapping Approach: Remote sensing

• Use vegetation indices from Sentinel-2 imagery to extrapolate the plot-level carbon storage data for the whole forest area.

• Use regression analysis to establish the relation between these variables carbon and different vegetation indices (NDVI, GNDVI, SAVI, IRECI)

• Vegetation indices were the explanatory variable.

• Used both a linear and quadratic regression. The best vegetation was SAVI (highest R²).

• Masked forest land cover and created a carbon map.

Tools used: Copernicus Sentinel Browser, ESA SNAP tool, R, Excel and ArcGIS Pro