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!
Hi Erik,
Interesting problem. Some resources that may be helpful to you include the Oregon Imagery Explorer where you can stream the data as a map service: http://oregonexplorer.info/imagery/StreamImagery and Oregon Farm Explorer: http://oregonexplorer.info/farm/home_farm.
Good luck! Kuuipo
Looks like you’ve made some progress since you completed the blog post. I have a few questions/ideas about where to get more information:
1. Oregon Spatial Data Library (http://spatialdata.oregonexplorer.info/geoportal/catalog/main/home.page) is a great source for orthoimagery as well as land use/land cover shapefiles and information.
2. Not sure if you’ve seen this on the highway, but the Oregon Women for Agriculture groups hangs up signs describing the crop being grown on field fences alongside highways. I haven’t been able to find a list of where and what, but you may want to contact them to ask for a master list. That way, you can ID a plot of land with a specific crop, and use that to either visually or with coding identify other locations with similar looking crops. http://owaonline.org/about/educate/
3. Another resource that may be helpful in conjunction with the crop signs is a soil map for the area. If specific types of soil are related to specific kinds of crop, there may be an easier way to go through the lots than the tedious visual approach. http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm
Hope that helps!
This seems like a very interesting and difficult problem to solve. I’m trying to think of ways where you can get your data that aren’t subject so privacy/property concerns.
But generally, I’m wondering if you have identified a maximum spatial extent for ‘near Corvallis’ and a temporal scale for ‘over time’.
Would it be interesting enough to start an analysis on geographic areas and % of land around Corvallis that has been in agricultural production through the years? I’ve seen other projects that did similar work on areas and % of wetlands around Corvallis, so I think it would be possible to do that with agricultural land too.
I would find a way to determine what kind of crop is growing in a particular field by using NDVI or something similar. That way you can get spatial and temporal trends and wouldn’t have to get a history of agricultural production from each farm owner.
1. Are the images of high enough resolution to identify crops or distinguish between a heterogeneous and monocrop field? You could answer this question by looking at a subset of images with known data about what was grown in the field at the time of the picture. You could then do a maximum likelihood classification analysis on crop/field type with data derived from image analysis software to determine if there is a strong enough signature to identify your parcel.
2. A second approach could involve taking a spatially random sample of parcels within the region to decrease the amount of data you have to work with. By narrowing your analysis to a selection of farms, you could spend more energy in collecting data about the farms through records or by conducting interviews with owners in regards to ownership, crops produced, and distance crops travel.
This is a very interesting spatial question, best of luck!
A few solutions to possibly consider:
NASS CDL provides classified crop types for the US on a yearly basis. Resolution is course and while not a perfect product supplementing detailed vector layers with extracted crop types would yield reasonable results.
Another option is to use traditional remote sensing methods to classify aerial and satellite imagery.