Metadata templates in ArcGIS

Metadata. Few people bother creating it and no one enjoys doing it, but it's a crucial step if you want to make geospatial data useful to others. Metadata standards are cumbersome, with many fields containing repetitive information. In this video I created for the GIS Practicum course I teach, I show how to create a metadata template in ArcCatalog and apply that template to an existing dataset. Thanks to Keith Pelletier who pointed out that you can safely use the find an replace tools in Microsoft Word 2007 to make changes to your metadata in XML format.

The only element of image interpretation successfully automated?

In reviewing the list of workshops for ASPRS/MAPPS 2009 Specialty Conference I noticed that the eminent Dr. Charles Olson is providing a workshop of the fundamentals of image interpretation. From previous posts you can tell that I am a strong believer in the elements of image interpretation. Olson does a fantastic job of discussing how the remote sensing community sacrificed accuracy by focusing on automating only one element of image interpretation (tone) using statistical algorithms in this 2008 paper. I was nevertheless surprised to see that the workshop description cites tone as the only element successfully automated.

I will be the last to argue that the remote sensing community has completely come around and is now successfully using the all of the elements of image interpretation to automatically extract features. One only has to review a recent syllabus from an upper level digital image processing course (I won't post links to the offenders) to see that pixel-based unsupervised and supervised methods, which ignore the elements of image interpretation and all the advances in neuroscience over the past several decades, are still widely taught as the de facto methods of automated feature extraction. (I even found one syllabus that grouped supervised and unsupervised classifiers under the heading of "pattern recognition," when nothing could be farther from the truth). That being said, I think Olson is entirely incorrect when he states that tone is the only element successfully automated. A team from The University of Queensland Brisbane has a very nice article in the latest issue of PE&RS, demonstrating how mapping banana plantations was automated using tone, texture, and context. Microsoft Research has some other interesting examples that clearly show that many of the elements of image interpretation can be incorporated into an automated process.

Now if you put me on a desert island and asked me to bring only one reference to guide my image interpretation it would still be Olson's 196o classic, Elements of Photographic Interpretation Common to Several Sensors. Nothing beats a human when it comes to accuracy, but tone is no longer the only element to be successfully automated.

Sweat the small stuff

Several years ago we performed our first urban tree canopy (UTC) assessment in Baltimore City, which lead to Baltimore City establishing one of the first UTC goals in the nation (40%). The land cover data used to determine Baltimore's existing tree canopy percentage of 20% came from the Strategic Urban Forest Assessment (SUFA) dataset. SUFA relied on pixel-based classifiers to extract land cover information from 2001 IKONOS satellite imagery.

A few months ago, with funding from the Baltimore Ecosystem Study (BES) and in collaboration with the US Forest Service and Tree Baltimore, we decide to repeat the land cover mapping to see if we could improve upon the SUFA dataset. We took a data fusion approach, integrating LiDAR, color infrared NAIP imagery, and vector datasets within an object-based image analysis (OBIA) environment. While we knew that SUFA underestimated tree canopy, due largely to the fact that the pixel-based approach failed to detect individual trees and smaller forest patches, it was rather surprising to find that it underestimated tree canopy by a full 7 percentage points. Now one might argue that tree canopy increased from 2001-2007, but after reviewing the data we discovered that it was more likely that tree canopy decreased during that time period due to development. Remember that book Dont' Sweat the Small Stuff? Turns out that when it comes to tree canopy in urban areas, the small stuff adds up and the source data and methods can have a big impact on the final percentages. As Baltimore seeks to increase its tree canopy though individual tree plantings, the assessment of whether these plantings are impacting overall tree canopy will hinge on accurate land cover mapping. A data fusion approach using OBIA technology delivers far superior results.

Some examples of the comparison between 2001 SUFA and the new 2007 UTC assessment data are below. You can download the 2007 land cover data here.

2007 color infrared NAIP

2001 SUFA 3-class land cover
(Tree canopy is dark green)

2007 UTC assessment 7-class land cover
(Tree canopy is dark green)