Description of Research Question

My objective is to capture modes of variance that exist between and within a subset of variables that I expect to correlate most strongly with soil CO₂ efflux in the HJ Andrews (HJA) forest. By stratifying the forest, I plan to determine future sampling sites that will be used to explore the relationship between soil C inputs, soil C stocks and CO₂ outputs. Aboveground and belowground biomass are major sources of soil carbon and drivers of soil respiration, so biomass will be used as a proxy for soil CO₂ efflux for the purposes of this analysis.

Research Question: How is the spatial distribution of biomass in the HJA forest related to stand age, slope, aspect, elevation and geomorphon as a result of varying degrees of exposure to solar radiation, wind gusts, precipitation, humidity, etc.? What is the overall variance of the HJA forest along these vectors and is that variance spatially autocorrelated?

Description of Dataset

I will use LiDAR data from 2011 and 2014 at 0.3-0.5 m vertical resolution and at 1 m² horizontal resolution covering the Lookout Creek Watershed and the Upper Blue River Watershed to the northern extent of HJA. These LiDAR data include a high hit model and a bare earth model. I will also use NAIP imagery to approximate forest stand age, which is 1 m resolution and covers years between 2002 and 2018.

Hypotheses

I expect areas containing more biomass to positively correlate with south-facing slopes due to more exposure to solar radiation resulting in faster rates of vegetation growth. I expect older stands to positively correlate with greater biomass. I expect steeper slopes to correspond to less biomass due to more weathering and thinner soil horizons, supporting less growth. I expect higher elevations to correspond to lower biomass due to greater sustained winds, higher windspeeds and more snow accumulation. As geomopohon describes the geometric structure of the terrain, it is a collection of multiple factors that could positively or negatively correlate with biomass. For example, I expect a ridge to negatively correlate with biomass because of the combination of greater slopes and more exposure to winds, while I expect a valley to positively correlate with biomass due to more wind protection and thicker soil horizons with more organic matter and more water retention.

Approaches

With an end goal of identifying sampling sites, I’ll need to cluster or stratify the HJA forest. I’ll begin with a clustering analysis, then perform supervised and unsupervised classification, followed by sensitivity analysis comparing the results of the clustering analysis and both classifications. I will need to address spatial autocorrelation either as part of the clustering analysis or separately. I’ll need to plan sampling by accessibility, so I’ll examine an HJA roads layer as well.

Expected Outcome

I plan to produce maps (or use/improve on already available ones) at a spatial scale relevant to my study so I can identify potential sampling sites. I plan to map biomass across the HJA forest and produce a stratification of factors most closely related to soil CO₂ efflux. Depending on resolution of the data and the results of the stratification, I may need to constrain my analysis. I plan to produce a statistical summary of the strata relating and describing the covariates and how much variance is explained by the stratification.

Significance

Carbon sequestration is a highly relevant research area where many unknowns still exist. Given that soil is an enormous C reservoir, small changes in soil C stocks can have huge impacts on the rest of the C cycle. As CO₂ is a potent greenhouse gas, more release of C from soil can cause greater warming of our planet and can lead to a positive feedback loop where the warming cycle is amplified. It is in our best interest to have a good understanding of current soil C stocks and fluxes in forested systems so we can hypothesize how they might change under different or future conditions. By using biomass as a proxy for soil CO₂ efflux, I will identify locations that are likely to have greater CO₂ efflux and I will be able to make informed predictions about which drivers are most significantly correlated to CO₂ efflux. I will be able to test these analyses in the future using field sampling techniques.

My level of preparation/proficiency

I have limited experience with Arc-Info (GEOG 560) and I’ve used Modelbuilder a few times. I have no experience with GIS programming in Python or R, but am proficient with coding in R (3 statistics courses and my own data analysis) and am comfortable seeking answers to questions in the R environment. I have no experience image processing.