UW RSGAL Geospatial Canopy Cover Assessment Workshop
Learn the OBIA Object Based Image Analysis technique for accurately analyzing, quantifying and reporting canopy cover with free software and remotely sensed imagery

The main purpose of this project is to provide guided analytical training to urban foresters, land managers, and city planners in an innovative technique to quantify tree canopy cover using high-resolution aerial imagery, calculate forest change metrics, and select sampling sites for ground-based tree inventories.





Workshop Lead: Dr. L. Monika Moskal is an AssociateProfessor of Remote Sensing at the University of Washington (UW), College of the Environment, School of Environmental and Forest Sciences & Precision Forestry Cooperative, where she directs the Remote Sensing and Geospatial Analysis Laboratory (RSGAL)

Workshop Instructor: Dr. Diane Styers was a Post-doctoral Research Associate who joined RSGAL in January 2010 till July 2011. Currently, Dr. Styers is an Assistant Professor of Remote Sensing in the Department of Geosciences and Natural Resources at Western Carolina University, located in Cullowhee, NC.

Workshop Graduate Research Assistant: Justin Kirsch is a MS student who joined us in 2010 from Evergreen State College where he worked under Dr. Dylan Fischer participating in monitoring of the Evergreen Ecological Observation Network.

Workshop Volunteers: Matt Dressler (workshop tester); Meghan Halabisky, Alexandra Kazakova, David Stephens, Dr. Jeffery Richardson and Dr. Guang Zheng.
  • Trial workshops: Feb 15 & 28
  • Workshop @ UW: March 3, 2011 -- this workshop is now full
  • Online version will be available by June 15, 2011
  • NEW: LiDAR workshop
  • DATA

Remote sensing technologies can provide a means to explore a variety of continuous environmental variables over large areas.  Remote assessments are reasonably simple and can be conducted quickly, inexpensively, and without access or disturbance issues encountered in ground-based data collections.  These assessments provide a means to measure and monitor complex urban environments, and their dynamic ecologies. 

For instance, canopy cover surveys and forest pattern metrics are useful to help a city quantify current tree cover status (Hunsinger & Moskal, 2005), determine the locations and drivers of canopy loss or gain (Turner & Gardner 1991), and monitor these trends in over time (Moskal et al. 2004).  These data can then be used to establish tree protection requirements for new developments, assist with urban forest health management, and determine target areas for planting projects.  Remote sensing techniques can be applied to the analysis of many other environmental and human dynamics within urban systems to aid in sustainable planning and management of these areas.

Traditional remote sensing techniques are not generally appropriate for assessing complex scenes like that of urban areas.  Though historically used for earth observation, the spatial resolution of Landsat imagery (30 meters) limits the ability to map small features found within urban areas.  In such cases, aerial imagery is generally preferred. 

A relatively new method, object-based image analysis (GOBIA), sometimes referred to as feature extraction or object-based remote sensing, allows for use of additional variables such as texture, shape, and context to segment and classify image features (Hay & Castila 2008).  This can both improve accuracy results and allow us to map very small urban features, such as mature individual trees or small clusters of shrubs (Moskal et al. 2011). 

Figure 1. Aerial photography depicting land conversion from 1990-2007, DuPont, WA.

Although historical aerial photography has been available for over 60 years, due to improvements in image processing tools, it is just now begin to rapidly evolve as a management tool (Morgan et al. 2010). Land cover and land use classes, such as forest canopy and impervious areas (Figure 2) can be automatically classified and extracted from high resolution aerial and satellite imagery using new Object Based Image Analysis (OBIA) techniques (Hey and Castilia 2008).
Figure 3. Comparing 2006 NLCD LULC to the 2009 OBIA-based LULC: The red in the 2006 NLCD data represent impervious surfaces, blue is water; note that no green (canopy) is summarized in this classification. The OBIA-based classification shows fine features such as canopy (green), buildings (red), impervious (gray) and water (blue).

University of Washington

College of the Environment, School of Environmental and Forest Sciences  
Phone: 206.221.6391
Bloedel 334, Box 352100
email: lmmoskal@uw.edu
Seattle, WA 98195-2100