Spring 2011

The Newsletter of the Minnesota GIS/LIS Consortium

Table of Contents

MN GIS/LIS Consortium

From the Chair
Conference Planning

Mn/DOT Road Closures Site
Economic Data
Gypsy Moth Response

Proximity Finder

Red River LiDAR

Minneapolis Predictive Crime Mapping
Anoka Co. Recreation
GIS - Core Govt. Service

Land Cover 2006
USGS Historic Topo Scanning

Higher Education
Smart Growth & Transit

Will Craig, UCGIS Fellow
Robert McMaster, UCGIS Education Award
Marv Bauer, Pecora Award


CropScape Web Application Takes the Pain Out of Crop Data Analysis
By Mike Dolbow, GIS & Graphics Supervisor, MN Department of Agriculture

As the GIS support lead for a state department of agriculture (MDA), sometimes I have to admit to the embarrassing fact that we don’t really create or maintain any GIS data on crops. You see, my agency performs a host of services to achieve our mission, including regulating pesticides and fertilizers, and inspecting dairy farms and food facilities. But we neither regulate nor promote what crops people put into the ground, or even what animals they raise.  Given that, we don’t have a solid mechanism to sample or identify crop plantings.

For a long time, we’ve had access to county-level aggregate data from USDA’s National Agricultural Statistics Service (NASS), who cooperate very closely with MDA.  But as GIS folks know, county-level data only goes so far if you’re working with a more complex area, like a watershed. County data is great for “quick glance” statewide maps, but that’s about it.

About five years ago, that all changed, when USDA expanded its coverage of the Cropland Data Layer into Minnesota. This layer, a remotely-sensed dataset with ground-truthing from Farm Service Agency data, provided a much more specific look at where crops were planted. It was easy to download from the NASS website and serve up as an ArcGIS layer file. At 30-meter resolution, it was (and still is) fairly coarse compared to something like the Metropolitan Council’s land use layer, but it was a huge upgrade from county-level data sets.

There was one problem:  with such a big raster file, analysis was tough.  If we wanted to do any kind of statistics or analysis on a specific crop or crops, we had to cut the data into smaller portions, using counties, watersheds, or other analytical units. 

Now, that’s all changed - with CropScape. Let’s let the GIS Cafe provide the introduction:

To provide easier access to geospatial satellite products, the U.S. Department of Agriculture's National Agricultural Statistics Service (NASS) today announced the launch of CropScape, a new cropland exploring service. CropScape provides data users access to a variety of new resources and information, including the 2010 cropland data layer (CDL) just released in conjunction with CropScape.

This new service offers advanced tools such as interactive visualization, web-based data dissemination and geospatial queries and automated data delivery to systems such as Google Earth.

What a relief!  Now when we have users who want to perform some quick analysis on this data, we can send them here.  They can define an area of interest (AOI) by state, county, NASS Agricultural Statistics District, or interactively using a rectangle, polygon, or circle.  After their AOI is defined, they can run statistics on that area, getting summaries of all the planted crops (subject to CDL location and year availability), and even an analysis of change between two years.  We no longer have to store local copies of the CDL - we can just store layer files that point to the service for folks who only want to view the data.

We’ll probably still have to download and store the data for analyzing a complex area like a watershed, but at least now we have a place we can send people to test out the concept.  We also have a place to send folks who aren’t ArcGIS users, which is a big bonus.  This is a great tool to have in the Agriculture GIS toolbox, and we’re very pleased that USDA decided to create it.

Editor’s Note:  Also see an earlier article about the Crop Data Layer datasets.