Vermont was recently accepted into AmericaView. The VermontView consortium is lead by the University of Vermont' s Spatial Analysis Lab and includes the following partners: VCGI, USGS, NRCS, USDA Forest Service, VACC, ANR, and VAPDA.
Sunday, March 29, 2009
Vermont joins AmericaView
Vermont was recently accepted into AmericaView. The VermontView consortium is lead by the University of Vermont' s Spatial Analysis Lab and includes the following partners: VCGI, USGS, NRCS, USDA Forest Service, VACC, ANR, and VAPDA.
Saturday, March 28, 2009
Should NLCD 2001 be used to estimate tree canopy in urban areas?
The simple answer is - no. We generated land cover data for three areas: Baltimore County, MD, Burlington, VT, and Des Moines, IA. Object-based image analysis techniques were employed to derive land cover from high resolution imagery and LiDAR. We then compared the estimates from our land cover data (>95% overall accuracy) to that from the NLCD 2001 tree canopy layer. The differences were striking, with NLCD underestimating tree canopy by 27% to 48%.
This isn't to say that NLCD is a bad product, it's not. NLCD tree canopy estimates are derived from 30m Landsat data. Urban areas are highly heterogeneous, with much of the tree canopy existing either individually or in small clumps. It's unreasonable to expect a sensor, such as Landsat, with only 7 bands (6 of which are used to derived the NLCD tree canopy layer) to detect such features. The graphic below shows that NLCD is clearly biased towards large clumps of trees, just as one would expect when performing land cover mapping using 30m pixels.
Accurate tree canopy estimates in urban area require the use of scale appropriate remotely sensed data. Landsat, for all its greatness and longevity, is not that dataset.
This isn't to say that NLCD is a bad product, it's not. NLCD tree canopy estimates are derived from 30m Landsat data. Urban areas are highly heterogeneous, with much of the tree canopy existing either individually or in small clumps. It's unreasonable to expect a sensor, such as Landsat, with only 7 bands (6 of which are used to derived the NLCD tree canopy layer) to detect such features. The graphic below shows that NLCD is clearly biased towards large clumps of trees, just as one would expect when performing land cover mapping using 30m pixels.
Accurate tree canopy estimates in urban area require the use of scale appropriate remotely sensed data. Landsat, for all its greatness and longevity, is not that dataset.
Thursday, March 12, 2009
ElderSmile
Our article on the ElderSmile program is in the April edition of the American Journal of Public Health.
Societal changes, including the aging of the US population and the lack of routine dental service coverage under Medicare, have left many seniors unable to afford any dental care whatsoever, let alone the most advanced treatments. In 2004, the Columbia University College of Dental Medicine and its partners instituted the ElderSmile program in the largely impoverished communities of Harlem and Washington Heights/Inwood in New York City. The long-term goal of this program is to improve access to and delivery of oral health care for seniors; the short-term goal is to establish and operate a network of prevention centers surrounding a limited number of treatment centers. Preliminary results indicate substantial unmet dental needs in this largely Hispanic and Black elderly population.
Friday, March 6, 2009
The Elements of Image Interpretation
There are generally five accepted elements of image interpretation: color, texture, pattern, shape, size, and location. From my readings, it appears that these principals were first documented the 1940s. Makes perfect sense as World War II and the subsequent Cold War resulted in the rapid development of military remote sensing capabilities. Coupled with this increase in capabilities was the need to thoroughly understand what principals should guide a photointerpreter. If remote sensing is considered to be both an art and a science, the trade craft employed by the military photointerpreters during these early days was certainly an art form.
I would like to put forth the argument that the advent of digital image processing techniques caused some sectors of the remote sensing community to focus too much on science of remote sensing, and less on the art form. In short, the trade craft was lost. No where was this more evident than the widespread use of pixel-based classifiers, such as the unsupervised and supervised routines that were commonly employed to extract land cover information from digital imagery.
When one considers the elements of image interpretation, it is clear that techniques that rely solely on the spectral (color) values associated with individual pixels violate the very principals of image interpretation. Perhaps much of this was due to the limitations of the technology at the time. After all, it was not until Definiens introduced their object-oriented software a decade ago that the shift towards object-based image analysis (OBIA) techniques that better replicated the human cognitive process, began. Nevertheless, when examining the literature over the past decades in which pixel-based classifiers where used, rarely do I see a disclaimer along the lines of "these techniques violate the very principals of image interpretation, but they are the best tools we have."
OBIA requires one to understand the elements of image interpretation. Perhaps the "art" is back in remote sensing.
I would like to put forth the argument that the advent of digital image processing techniques caused some sectors of the remote sensing community to focus too much on science of remote sensing, and less on the art form. In short, the trade craft was lost. No where was this more evident than the widespread use of pixel-based classifiers, such as the unsupervised and supervised routines that were commonly employed to extract land cover information from digital imagery.When one considers the elements of image interpretation, it is clear that techniques that rely solely on the spectral (color) values associated with individual pixels violate the very principals of image interpretation. Perhaps much of this was due to the limitations of the technology at the time. After all, it was not until Definiens introduced their object-oriented software a decade ago that the shift towards object-based image analysis (OBIA) techniques that better replicated the human cognitive process, began. Nevertheless, when examining the literature over the past decades in which pixel-based classifiers where used, rarely do I see a disclaimer along the lines of "these techniques violate the very principals of image interpretation, but they are the best tools we have."
OBIA requires one to understand the elements of image interpretation. Perhaps the "art" is back in remote sensing.
Thursday, March 5, 2009
Rock River LiDAR, First Looks
I received the initial delivery of the LiDAR acquired in 2008 for the Rock River watershed (St. Albans area) from this USGS yesterday. Here are some screen shots of the point cloud displayed in QT Modeler. Once all of the deliverables are in, the good folks at VCGI will distribute the data. We will be using the LiDAR in combination with the 2008 NAIP to derive high resolution land cover. LiDAR will play a key role in helping map and model critical source areas.
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