My research is focused on developing a web-based forage species selection tool and estimating potential yield of key forage species grown in Oregon and Sichuan Province.

Our goal is to match appropriate species with each eco-region. Related to this class, the problem is how we can use the GIS spatial analyst tools to define and display a workable number of forage production eco-regions based on topography, climate, soil characteristics, land-use and land-cover, and agricultural systems.

Although there have been several important studies directed at defining ecoregions (Bailey, 1996; Thorson, 2003; Omernick, 2004), these have been based primarily on the Köppen Climate Classification System (1936) and broad groupings of species suitable for each zone. They are not helpful in quantifying potential annual forage yield or seasonal production profiles required for rational management of forage-livestock systems.

To provide useful guidance to Oregon and Sichuan Province farmers and ranchers, our agroecozone classification systems will use a hierarchical approach beginning with climate, with modifications due to physiography and land-use/land-cover, and soil characteristics.

Level I: Climate (Thermal Units and Precipitation)

Climate was chosen as the foundational level of the classification system due to the essential nature of temperature and precipitation in plant growth and development. Base spatial layers for climate factors will include extreme monthly cold and hot temperature, mean monthly maximum and minimum temperature, mean annual temperature, and mean annual, seasonal, and monthly precipitation. Climate-based indices will be developed to predict forage crop growth and development. These will include solar radiation and photosynthetically active radiation, accumulated thermal units (with various base temperatures), growing season length, and vernalization chilling days. For agricultural systems that include irrigation a soil water balance model will be applied.

Level II: Physiography, Land-use/Land-cover [Topography (DEM), MODIS Images]

The second level of the classification systems will involve physiography and land-use and land-cover. A DEM will be used to underlay the climate layers and identify terrain slope, with the following rankings: >60°, not useful; 60°— 50°, 30% can be useful for livestock; 50°— 40°50% can be useful; 40°— 30°, can be used as pasture; and >30°, useful as grassland (Zhang, Grassland management, 2010). Current land-use and land-cover will be characterized from current and historical MODIS satellite images, with particular focus on cropland, pastureland, and rangeland areas.

Level III: Soil Characteristics (pH, Drainage, Salinity)

Soil characteristics will be the final level of the hierarchy, since, to a large degree, these can be modified in agricultural systems. Spatial data layers will be obtained for soil type, pH, drainage classification, and salinity. Site specific data will be obtained for more detailed fertility information.

As I started to describe in class, my project will be dealing with output results from the model software ENVISION.  ENVISION is a GIS-based tool for scenario based community and regional planning, and environmental assessments.  It combines a spatially explicit polygon-based representation of a landscape (IDUs or Individual Decision Units in my case), a set of application-defined policies, landscape change models, and models of ecological, social, and economic services to simulate land use change and provide decision-makers, planners, and the public with information about resulting effects.

The ENVISION project I am involved with is centered on Tillamook County and its coastal communities. Through a series of stakeholder meetings (which have included a range of people such as private landowners and state land use commissioners) our group identified several land use policies to implement in the ENVISION model. The policies were then grouped into three types of management responses: the baseline (or status quo), ReAlign, and two types of Hold the Line (high vs. low management) scenarios. These policy scenarios have been combined with current physical parameters of the coastline such as dune height and beach width, and will be also linked with climate change conditions at low, medium, and high levels for the next 30, 50, and 100 years.

Since ENVISION is GIS-based already, I am having a tough time coming up with a problem that complements the project in ArcGIS.  ENVISION does a great job of visualizing the changes expected for each location along the coast via videos, graphs (see below), and can even include economic estimations.

Therefore, it may be best to explore the capabilities of software like R to analyze the output data. One idea would be to calculate the probability of occurrence for these different events and total number of occurrence.  I need to take a deeper look into how these events are calculated to begin with, and determine the inherent estimates of probability and uncertainty.  This type of analysis would help determine whether this type of exercise is beneficial for stakeholders and would help answer their own questions of trust in the results.

Another idea would be to focus on specific results from ENVISION and try to determine exactly how one policy is affecting the coastline and creating such disparate results. For instance, the graph below shows Numbers of Flooded and Eroded Structures in Pacific City under three types of scenarios. What is causing the large number of eroded/ flooded structures between 2035 and 2040? Why is there such a small difference between ReAlign and Hold the Line strategies if they are employing such different options? Some of these questions may be answered with a greater understanding of ENVISION, however, these are the types of questions that may be asked by stakeholders and it would be prudent to provide more quantitative answers that ArcGIS or R could glean.

 

My initial goal was to explore local food production over time near Corvallis, but I am getting ready to change topics because I cannot find enough information on farms in the area to discriminate crop types, either by visual assessment or ownership.  The federal data I could find on crop types did not list information more granular than the county level.  Land cover data categorizes farmland as “herbaceous” or “barren” and is not much help.  So I attempted visual assessment of orthographic imagery.  Here is the Willamette Valley around Corvallis:

If I zoom in on a parcel, this is the level of detail:

Clearly agricultural, but I couldn’t tell you what.  That was 2011, here is the same land in 2005:

Is that supposed to be grass?  What degree of certainty do I have?  Not enough for analysis.

Here is the adjacent parcel:

Clearly two different crop types, but is one hay and the other grass seed?  Don’t ask a city slicker like me.

The second strategy I tried was to determine ownership.  Certain farms produce specific types of crops, and other farms have a reputation for selling their food locally.  But I could not find the equivalent of the White and Yellow pages for GIS, or even a shapefile with polygons divided by tax lots.  Instead, I tried looking at the water rights.  Water rights data identifies the owner in a set of point data, and also displays a polygon associated with each right, showing the area of farmland using that right.  I selected only water rights related to agriculture, so municipal and industrial water rights would not show up in the layer.  Here is a closeup of water rights data layered on top of the orthographic data:

The water right for the parcel in the center on the right belongs to Donald G Hector for the purpose of irrigation.  An article in the Gazette-Times records the passing of Donald’s wife in 2004 from Alzheimer’s after being married to Donald for 53 years.  Businessprofiles.com lists the farm as currently inactive.  Other than that, I could not find much about Mr. Hector or his farm.

There is a more significant problem with using water rights data to determine farm ownership, which you might intuit from the picture above.  There are many parcels of land that are not associated with water rights.  In fact, only around 15% of Oregon’s crops are irrigated crops.  Once I zoom out, this becomes obvious:

The large blue area at the bottom left is the Greenberry Irrigation District, meaning a utility maintains the local irrigation infrastructure, and taxes farmers individually.

When I was interning at the WSDA, they had enough data to construct a map of the kind of information I want, but they could not publicize it because of privacy concerns, and I think that is the problem I am running into here.  I need some NSA style access.

Or a new spatial problem!

 

 

 

For my spatial problem I will examine the role of spatial autocorrelation and seasonality in developing a land use regression (LUR) model. In particular I am interested in optimizing the incorporation of spatial autocorrelation and seasonality for prediction of air pollution in the City of Eugene.

For those unfamiliar with a LUR, it essentially combines GIS variables that are predictive of air pollution concentrations along with actual air pollution measurements in order to predict air pollution at unmonitored locations using ordinary least squares (OLS) regression. The problem with a typical LUR model is that they don’t account for spatial autocorrelation. The value of accounting for spatial autocorrelation is due to the fact that spatially based data, such as air pollution, is typically spatially correlated.

This past quarter in my GEO580 course I developed a LUR that did account for spatial autocorrelation by modeling the covariance of air pollutant concentrations of adjacent zip code boundaries, using a spatial CAR model. For this class I wish to develop this idea even further by using multiple techniques, namely geographically weighted regression (GWR), a spatial CAR model, and OLS to compare the model results to actual air pollution measurements. This work will require me to use both ArcGIS spatial analyst toolbox and the R statistical software.

As mentioned above, I am interested in including seasonal trends in air pollutant variation in order to see if inclusion of seasonal variation is capable of improving model estimates. To do this I propose to incorporate seasonal ratios to annual ratios of air pollutant concentrations.

To keep this work focused I will use data on just one air pollutant, as opposed to last quarter wherein I developed a LUR for seven different pollutants. By focusing on just one pollutant I hope to keep the work efficient and effective toward achieving my goals in this class. Ideally, this work will help to inform my dissertation proposal work.