Tree Canopy Assessment Reports

It's been great to see the growing interest from decision makers in our tree canopy assessment products.  Although our tree canopy assessments originally started out as "urban" tree canopy assessments, and were thus known by the acronym "UTC," more and more we are now doing the assessments for entire counties and thus use the more generic term "tree canopy assessment."  Of course "UTC" sounds better and is certainly great description for our urban work.  Our reports eventually end up on the Forest Service's UTC web site, but due to the time lag in the posting process and the general interest in the reports I thought I would post a collection here.  The report is only one of the outputs from an assessment, but arguably the most visible one.  We  learn a great deal from our collaborators and continually update the format to reflect many of the great suggestions we have received.  As always, don't hesitate to contact me if you have any questions about (urban) tree canopy assessments.
Cumberland, MD
Baltimore, MD
Frederick, MD
Howard County, MD
Montgomery County, MD
Montpelier, VT 
Philadelphia, PA
Rutland,VT
Virginia Beach, VA
Washington DC

OBIA Spectral Gradient Tutorial

One of the challenges in getting started with object-based image analysis (OBIA) is the complexity inherent in high-resolution remotely sensed data sets.  Inspired by one of Bruce Gorham's OBIA workshops we started developing some examples using pseudo data.  I recently posted a tutorial rule set to the eCognition Community site that I am calling "Spectral Gradient."  This post provides an overview of that rule set.  Please note that the rule set is designed to be a self contained tutorial in that the description of each process step is present within the rule set.  To access the documentation click on the appropriate parent process then scroll to the lower right of the Process Tree window.  The show/hide comment icon will appear, click on it and the documentation will be displayed in a sub-window.

The tutorial starts with this image. 

The objective is to classify "Red Circles."  Red Circles must meet the following criteria:
- Have a pixel that is at least 90% pure red
- Not contain any pixels that are pure white
- Have the geometric attributes consistent with a circle.

In the image above, the circle on the right along with the rounded rectangle and the triangle have pixels that are at least 90% pure red.  The circle in the top left does not.


A quadtree segmentation is used to crate image objects.  While it is not the most robust segmentation algorithm, it is fast.

Pure red objects are classified using the "ratio of red" feature.  This feature measure the amount of that the red band contributes to the total brightness of the object.  Setting a ratio of red threshold of 0.9 is equivalent to saying "classify objects that are 90% or more red."

Red objects are grown into other objects providing the brightness of those objects is less than or equal to 254 (e.g. not white).  An infinite loop is used, meaning that the grow region algorithm will run until the criteria can no longer be fulfilled. 

The merge region algorithm is first used to merge objects that are classified as Red then those objects that are unclassified.  This is a necessary prerequisite if geometric attributes are to be used to classify circles.

Circles have low asymmetry and roundness values, thus both criteria are used to evaluate Red objects as circles.

The end result is that we have successfully extracted the only circle containing "pure red" pixels.

I'm on Twitter

After seeing all of the good remote sensing info Tyler Erickson was putting out and not wanting to miss Mike Galvin's urban forestry tweets, I finally got my act together and joined Twitter.  You can find me @jarlathond.

ASPRS 2011 OBIA Workshop

My colleague Keith Pelletier and I are offering an object-based image analysis (OBIA) workshop at the 2011 ASPRS conference in Milwaukee.  If you are interested in attending the conference and the workshop there is still time to register.  Our workshop is #9 and it is an all-day workshop running on Monday the 2nd.  I will also be giving a presentation, Incorporating Contextual Information Into Object-Based Image Analysis Workflows, during the Using Multiple Data Sources session on Wednesday the 4th.  A description of the workshop is below.

This full-day, advanced workshop is designed to help participants harness the true power of object-based image analysis (OBIA). It is recommended that participants have a strong foundation in remote sensing and GIS, and at least some exposure to OBIA. This workshop is particularly well suited to individuals who are finding it difficult to extract information from the latest generation of high-resolution imaging and LiDAR sensors using OBIA techniques. Specific emphasis in this workshop will be paid to moving beyond the standard “segment and classify” approach that is typically employed in most OBIA projects, to an iterative workflow that better mimics the type of mapping carried out by human analysts by fully incorporating the spectral, geometric, and contextual information present in an image. Through a series of lectures, demonstrations, and hands-on exercises, participants will be exposed to the methods that will enable them to build effective and efficient OBIA routines.

The workshop will be divided into four parts. In the first part, the theoretical foundation for the effective application of OBIA technology will be laid out by drawing from the remote sensing, neurobiology, and cognitive sciences literature. This will be followed by a review of the current approaches to OBIA, with particular attention to some of the pitfalls that often prevent OBIA technology from being applied to its full potential. The second part will focus on effective approaches to and best practices for object-based feature extraction, including a thorough review of segmentation algorithms. The third part will cover more advanced topics, including: 1) image object fusion, 2) pattern recognition, 3) morphological routines, and 4) context-based classification. The workshop will conclude with recommendations on how to design and deploy enterprise OBIA systems capable of processing of datasets containing billions of pixels.

Demonstrations and exercises will make use of a broad range of remotely sensed (e.g. imagery and LiDAR) datasets and a particular focus in the exercises will be integrating remotely sensed and thematic datasets in an OBIA context. Participants are encouraged to bring their own computers to use during the hands-on exercises. OBIA software will be provided (requires Windows XP, Vista, or 7).

New LiDAR new article on NYC tree canopy mapping

Head on over to LiDAR News to check out the latest article published by my colleague Sean MacFaden on LiDAR-based Tree-canopy Mapping for New York City.

Streaming NAIP into ArcGIS

With all of the tree canopy mapping we do here in the SAL you can imagine that we are big fans of the National Agricultural Imagery Program (NAIP).  Each state is typically imaged on a 3 year cycle with the current standard being 1m resolution products.  Best part for us tree canopy mappers is that the images arer typically leaf on!  While you can download NAIP products from the NRCS Geospatial Data Gateway you can stream NAIP into ArcGIS by establishing a new ArcGIS Server connection.  Please note that this service only displays true color NAIP.  Four band NAIP has to be procured from the APFO.

Step 1
Double-click on Add ArcGIS Server in ArcCatalog.

Step  2
Click Next once until you get to the General window where you can choose the type of ArcGIS Server connection.  Make sure the radio button for Internet is selected and enter the following URL in a Server URL field.
http://gis.apfo.usda.gov/arcgis/services
Click Finish.

Step 3
Within ArcCatalog under GIS Servers navigate to arcgis on gis.apfo.usda.gov and go into the NAIP folder.  Select the state image service you wish and load it into ArcMap.

Fun with NAIP

My colleague, Keith Pelletier, found this in the 2010 National Agricultural Imagery Program (NAIP) for Lancaster County, PA.  I imagine the aliens are impressed.