Lab 7: Vegetation Metrics Modeling

Goals and Introduction:

This lab demonstrated vegetation metrics modeling in the diverse forest of Eau Claire County. The goal of the lab was to extract various vegetation metrics for the different species of trees found in the project area. This was then used to make hypothetical recommendations to the U.S Forest Service about the carbon sink potential of the forest in the project area. This lab made use of LiDAR point cloud data and the Wiscland 2 land use and land use classification raster.

Methods:

Part 1: Canopy height modeling

Section 1: Canopy surface and ground surface creation

This section consisted of generating a 3 foot cell size canopy surface as well as a ground (DTM) surface model which were used in the next section for creation of a canopy height raster. The LiDAR was brought into LP360 and the using the Export LiDAR Data function the models were created. For the DTM single returns were used and for the canopy surface model first returns were used.

Section 2: Canopy height derivation

The canopy height raster was calculated using ArcMap's raster calculator. The canopy surface was subtracted from the ground. A map was created classifying the canopy height into three classes by equal interval. The resultant raster was then copied with the Copy Raster tool in order to resample the file to 32 Bit Signed. The Build Raster Attribute Table tool was then run to add an attribute table for the raster file.

Figure 1

Part 2: Above ground biomass modeling

Section 1: Above ground biomass estimation

This section created a model for the estimation of the above ground biomass (AGB) of five different tree species in the project area. Using the Wiscland2 class level 3 forest species information for a mask, and the canopy height DTM as the main input, a raster calculator tool was run for each of the five species in a model. In the raster calculator, the empirical model developed by Tabacchi et al. (2011) was used to model the AGB. The empirical model is shown below. The values used for different tree species are shown in Table 1. The model used is shown in Figure 2.

AGB = a + b *(dbh)^2 * H

where:

a is gain
b is offset
H is LiDAR derived tree height (raster data created in Part 1)

Table 1

Figure 2

Part 3: Calculation of additional metrics

In this section, three other metrics were calculated for the five different species of tree these were stem biomass, branch biomass, foliage biomass. These utilized the equation shown below as well as the coefficients that were contained within Jenkins et al. 2004, Jenkins et al. 2003, and Ter Mikaelian & Korzukhin 1997. The model is shown in Figure 3.

PB = a * (dbh^2 * H)^b

Where: