Lab 8: Vegetation Metrics Modeliing

Goals and Background

The purpose of the lab is to provide us practice extracting variations of forest metrics from LiDAR data. During this lab I will utilize land cover data to decipher tree species distribution and extract the various metrics for each tree species. During the conclusion of the lab I will make a recommendation to the U.S. Forest Service about the carbon sink potential of the forest in the study area.

Methods

All of the following operations were preformed in ArcMap 10.4.1 except for the DTM creation which was completed in LP 360 for Windows.

Canopy Height Modeling

I created a two Digital Terrain Model (DTM) from the Eau Claire County LiDAR data I was provided. The first DTM was created from the first returns of the data to produce an accurate model of the vegetation height. The second DTM was created from the last returns of the data to produce an accurate model of the ground surface.

I utilized Raster Calculator to subtract the ground DTM from the vegetation DTM which produced a raster image with the various vegetation heights. The produced raster does not contain an attribute table so I used the Copy Raster tool to change the pixel type to 32_BIT_SIGNED to calculate the height information.

Above Ground Biomass (AGB) Modeling

Estimating the AGB requires the use of the canopy height model created in the previous step. Additionally I needed land use/land cover data. The data for the study area was provided to me by my professor. The first step was to select various tree species from the the land use/land cover. I reclassed the data into the following 5 separate classes:

1. Hardwood (Northern Hardwoods + Central Hardwoods + Swamp Hardwoods + Bottomland Hardwoods)
2. Red Maple
3. Pine
4. Oak
5. Aspen (Aspen/Paper Birch + Aspen Forested Wetlands)

All other species were eliminated from the study area.

Using an algorithm developed by Tabacchi et al. (2011) I was able to utilize model builder in ArcMap to calculate the AGB for each species in the study area. The equasion used was AGB= a+b*(dbh)^2*H. The parameters are as follows:

• AGB= Above ground biomass
• a=gain
• b=offset
• dbh= Diameter at breast height
• H=height

All of the parameters I used were provided from in a chart derived from Jenkins et. al. 2003 (Fig. 1), except the height. The height parameter was calculated from the canopy height model I created above.

(Fig. 1) Chart of algorithm parameters from Jenkins et. al. (2003).

The next step was to create a feature class of each of the 5 species groups. I again used model builder to separate the classes and apply the algorithm to each class to calculate the AGB (Fig. 2). I used a conditional statement (Con) to select each tree species and then using the raster calculator I applied the algorithm to height model (Fig. 3). The singled out tree species was applied as a mask to the calculation. I repeated this process for the remaining 4 species groups. When all of the feature classes were created I used Mosaic to New Raster tool to merge all 5 species back to one complete raster of the study area.

(Fig. 2) Display in model builder of the work flow calculating the AGB for pine in the study area. 

(Fig. 3) Raster calculator displaying the equation used to calculate the AGB. 

Calculation of additional metrics

During this section of the lab I calculated the following:

• Stem Biomass
• Branch Biomass
• Foliage Biomass 

To calculate each variation of the biomass I utilized an algorithm from Jenkins et al (2003) (Fig. 4). The DBH was calculated from the previous chart (Fig. 1) and the remaining parameter were taken from (Fig. 5) depending on the species type.

(Fig. 4) Equation to calculate stem, branch, and foliage biomass.

(Fig. 5) Parameters chart used in the calculation of stem, branch, and foliage biomass.

I completed the calculation in Excel to determine the coefficient which I multiplied against the original biomass calculation to determine each subset biomass. I used model builder again to apply the algorithm to each species class created in AGB portion of the lab (Fig. 6). Once all of the feature classes were created I created a new model which combined each species and each individual biomass in to one complete raster (Fig. 7).

(Fig. 6) Model displayed in model builder used to calculate stem, branch, and foliage biomass.

(Fig. 7) Model displayed in model builder combining each species and the type of biomass in to one complete raster.

Results

(Fig. 8) Canopy Height map broken in to 5 different categories. -12-0 (non vegetation), .1-1.6 (above ground not vegetation), 1.7-6.6 (low vegetation), 6.7-19.7 (medium vegetation), 19.8-328 (high vegetation).

(Fig. 9) Above ground biomass map for the study area.

(Fig. 10) Tree density per acre by height class.

(Fig. 11) Stem above ground biomass map.

(Fig. 12) Branch above ground biomass map.

(Fig. 13) Foliage above ground biomass map.

Discussion

The three maps displaying the stem, branch, and foliage are derived from the total biomass by a simple multiplication of the values. The scale at which these maps are displayed here does not show the variation between the types of biomass. There seems to be a slight correlation between tree height canopy height and AGB especially where the trees are the tallest in the study area.

Acknowledgements

I would like to thank Peter Sawall for his assistance throughout various steps of this lab. Check out Peter's blog.

Sources

Jenkins, J. C., Chojnacky, D. C., Heath, L. S., & Birdsey, R. A. (2003). National-scale biomass estimators for United States tree species. Forest Science, 49(1), 12-35.

Jenkins, Jennifer C.; Chojnacky, David C.; Heath, Linda S.; Birdsey, Richard A. 2004.     Comprehensive database of diameter-based biomass regressions for North American tree species.  Gen. Tech. Rep. NE-319. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northeastern Research Station. 45 p.