High-resolution land cover is one of the most useful datasets a planner can have in his/her GIS. Whether it's setting urban tree canopy goals or implementing a storm water utility based on impervious surface area, you need high-resolution land cover information. Unfortunately, the millions of dollars spent each year on high-resolution remotely sensed datasets, such as imagery and LiDAR, don't always result in corresponding high-resolution land cover information. There are two reasons for this: 1) traditional "pixel-based" approaches yield unacceptable low accuracies when applied to high-resolution data and 2) manual interpretation, while accurate, can be prohibitively expensive.
Beginning in 2005 we embarked on a series of collaborative projects with the US Forest Service that focused on assessing tree canopy in cities. That collaboration has now expanded to include more than 30 communities in the US and Canada. High-resolution land cover was a key input to the assessment process. The decision makers we collaborated with were demanding, they wanted not just high-resolution data, but accurate high-resolution data. Furthermore, developing the land cover dataset had to be cost effective. After all, there is no point in assessing the tree canopy if the assessment does not leave you with any funds to plant trees.
We came up with some automated, cost-effective methods of mapping land cover that leverage existing investments in remotely-sensed and GIS datasets from a variety of federal, state, and local sources. Although the primary driver for the land cover datasets was the urban tree canopy assessments we realized that the land cover datasets we were generating had a much wider range of uses. In collaboration with the Forest Service we put together this fact sheet titled, Turning Data Into Information. With all this money spent on data, it makes sense to budget a bit more to turn it into useful information.
Although doing this type of work is still somewhat of a niche field there are at least a few organizations that have this capability both in academia and private industry. When it comes to contracting the work out it's important to consider the overall accuracy of the end product. We are of the opinion that 90% should be the absolute minimum, with 95% the desired accuracy; particular if the dataset is going to be used as a basis to map change over time. I recommend asking for a sample of previous work prior to entering into a contract, and not just a screen capture, get the actual data for a reasonably sized area.
