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Apr 27 2011

The Great Tree Survey

 

This month's issue of National Geographic on page 30, has a rather interesting
article with many similarities to our current research endeavors. While this
article's research platform is a LiDAR scanning system, the method to gain a
concept of carbon storage within a canopied system, by way of 3D visualization
is fairly similar. The author notes the large scale tree planting programs currently
 underway in China and their effect on the reduction of CO2 emissions. Long running
political debates regarding carbon trading are summarized in this online abridged
 version of the full article, but the focus is following Greg Asner's team
of ecologists and their "CAO" program.  The teams involvement in the REDD
(Reduce Emissions from Deforestation and Degradation) have boosted confidence in
providing a cost-effective and high precision method in estimating carbon stores
 within the Amazon. Results found that older, more developed forests stored as much
 as three times the amount of carbon than those composed primarily of secondary growth.
One abstract application of their airborne observatory noted using this 3D imaging
 platform to map termite mounds in savannahs, I suppose this would have some application
 for unmanned vehicles, because hexacopters are resistant to termites and fire ants….

Links:

http://ngm.nationalgeographic.com/2011/05/big-idea/tree-map-text 

http://cao.ciw.edu/

Apr 27 2011

Indeas About Automating the Coordinate Transform Algorithm

We had a few ideas about automating the coordinate transform alrogithm to try to match inputted arbitrary coordinates to those from the GPS without manually selecting matching values. One idea revolved around matching the geometry of the camera and GPS tracks. Since the two have the same geometry, our idea was to interpolate the camera and GPS tracks, then select a fixed amount of points from both tracks. Since the geometry should be the same, the selected points should match up, and we can then run the coordinate transform program with those points.

Apr 22 2011

MAV (Micro Air Vehicle) enters Fukushima

Yet another use for a small drone!  The main thing I Iiked about this article is the use of a new term (new to me at least): “MAV” = Micro Air Vehicle or Micro Aerial Vehicle, to describe small drones similar to those we are using for Ecosynth.  

Should we start using this term?  Does it apply to the Mikrokopter Hexakopter and/or Ardupilot drones?

Link: http://www.engadget.com/2011/04/21/t-hawk-uav-enters-fukushima-danger-zone-returns-with-video/

Apr 20 2011

Flagging and Final projects in Environmental Mapping (485)

     After an afternoon with Mariah, we both realized we were over-thinking the compass with a lack of confidence, after believing we were working off the wrong  bearing (and briefly losing some equipment), begging Jonathan for help we went back out in the field only to find we weren’t far off in the first place. So today we’ll be making some effective plotting from 2 to at least 4 pm. Following the laying out of flags, each plot will have the DBH and species identified, and following the physical classification this should be digitized. So as of yet, 23 plots to go, and we’ll see how today goes.
    The 485 class was not so lucky this past weekend, getting rained out on Saturday for our soil profile collections, this ended up pushing our planned field days for our individual group projects back as well. Some of these projects have some immediate impact on the ecosynth project in part, Chris Leeney, Lauren Colburn, Nathan Rolls and I have chosen our final projects in furthering accuracy of Chris’ groundsynth work. Nathan is interested in tree growth and density characteristics from streambeds, and in this case Herbert Run makes for a good study area. Laurens work concerns the degree to which Groundsynthing can recognize trees (what is the minimum recognizable DBH). My work will be in depicting the covered area in 5x5 grid cells that are manually drawn, including high levels of detail that can be noticed in Groundsynthing to further spatial referencing in small studied areas, and for recognition of erroneous data ( the maps will include any tree above 2m height, fallen trees, stumps, etc.) Hopefully the undergraduates field day doesn’t get pushed back by seasonal weather patterns once again.

Apr 15 2011

Finishing ground measurements of the Knoll

With the weather picking up and tree species becoming recognizable once again, we have the opportunity to finish surveying the Knoll. Using a standardized procedure Mariah and I will be going to each of the 25 by 25 cells ( 23 total) and taking a number of measurements. Identification of the species within, finding their diameters at breast height and taking crown height measurements using our trusty laser hypsometer of the 5 largest trees within the plot will be the general order to follow. Because our data will be correlated with over- canopy imagery, collecting the crown heights of smaller trees that will only be covered by the canopies of larger trees are not totally necessary. Our study area has come under significant change since last summer. Renovation and management of the stream nearest to the parking garage was done from January till just last week (major construction ceased last week at least) removing around 250 square meters of vegetation along the run. Next Wednesday afternoon we will begin the survey and should be complete within two or three weeks.

Apr 14 2011

Computer vision beats Kinect?

“Just when you thought Kinect had the body tracking problem all sewn up, another approach promises to be cheaper and implementable using nothing but software and standard video cameras. The good news is that the software is open source, download-able and ready to go.”

http://www.i-programmer.info/news/105-artificial-intelligence/2310-predator-better-than-kinect.html

Apr 12 2011

Better Optimization in Python!

After experimenting with a few scipy optimization functions for our coordinate transformation program, we have observed that the Powell function works best. This function finds a reasonable solution given arbitrary starting parameters, and also converges more effectively to the desired values. Originally we were using the leastsq function, but found that many of the rotation parameters would not stay within reasonable approximations when the starting parameters were not close to the actual values. The fmin_powell function appears to solve this problem, as even parameter values of all 1's still gives successful rotation, translation, and scaling values.

Apr 10 2011

Visualizing point clouds in your browser

Check out 3DTubeMe.com to see some of the latest in web based 3D visualizations.  I was directed to a post on Slashdot about the website by a professor and am totally thrilled about what this could mean for visualizing or own 3D point cloud data.  Currently you need to login and add this as an app through Facebook to upload and view, but the website authors say they are going to get rid of this feature soon.  I uploaded a small set of photos for processing, but was notified that my camera was not in their database and to wait to hear back about the processing of my cloud.  Maybe we could get this WebGL working to visualize our own point clouds? 

That’s all for now, back to the grind!

Apr 07 2011

Open Source Terrain Processing

I am very excited by the current prospects of incorporating free, open-source terrain processing algorithms into our workflow.  While we are ultimately interested in studying the trees in our 3D scans, it is necessary to automatically derive a digital terrain model (DTM) that represents the ground below the canopy for the purpose of estimating tree height.

A recent paper in the open-source journal Remote Sensing, describes several freely available algorithms for terrain processing.  I am in the process of converting the entire ArcGIS workflow we used in our first paper into an automated Python workflow, and am excited about the prospect of incorporating other open-source algorithms into the mix.  Currently, by working with Numpy in Python, my processing code takes a input Ecosynth point cloud and applies two levels of ‘global’ and ‘local’ statistical filtering to remove outlier and noise elevation points in about a minute for 500,000 points.  This had previously taken hours with ArcGIS, but by formatting the data into arrays, Numpy effortlessly screams through all the points in no time. 

I am going to focus on two pieces of software.  One is the Multiscale Curvature Classification algorithm (MCC-LIDAR) by Evans and Hudak, at sourceforge here, that was mentioned in the recent paper in Remote Sensing.  The other is the libLAS module for Python, included with OSGeo, that can be used to read and write to the industry standard LAS data format for working with LiDAR data. Fun, fun!  This of course if going on in the meantime while I try to get my proposal finished.

Refs: 

Dandois, J.P.; Ellis, E.C. Remote Sensing of Vegetation Structure Using Computer Vision. Remote Sens. 2010, 2, 1157-1176.

Tinkham, W.T.; Huang, H.; Smith, A.M.S.; Shrestha, R.; Falkowski, M.J.; Hudak, A.T.; Link, T.E.; Glenn, N.F.; Marks, D.G. A Comparison of Two Open Source LiDAR Surface Classification Algorithms. Remote Sens. 2011, 3, 638-649.

Apr 07 2011

Drone photos: Fukushima Dai-ichi Aerials

Pictures speak for themselves: “On March 24, 2011, a small unmanned drone flew over and photographed the crippled Fukushima Dai-ichi nuclear plant, giving a bird's eye view of the damage.”   http://photos.oregonlive.com/photo-essay/2011/03/fukushima_dai-ichi_aerials.html