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Dec 17 2011

TLS scanning at UMBC

We have been having an exciting time in New Jersey and Baltimore working with aTerrestrial Laser Scanner (TLS; Riegel VZ400) for generating high quality 3D reference datasets for validation of Ecosynth data.  We are in the lab today because of windy conditions, working on post-processing and data management of the large amounts of data collected in New Jersey and in the photo studio at UMBC.  I thought it would be a good time for a short update post.

These pictures are from our test setup of mobile scaffolding that we will use for gaining an elevated perspective on several open grown trees for TLS scanning.  The plan is to set up the scaffolding at each of the 4 orthogonal scan stations with the TLS mounted on the platform as shown.

The tower platform is about 2m above the ground and the TLS scanning head is about 3m off the ground.  The tower can be moved by 3-4 people to each of the scanning positions, after the TLS equipment has been taken down!

We have also configured the TLS for WLAN control, meaning that we can operate scanning and review data wirelessly.  This should be useful for when we attempt TLS scanning from the bucket crane.

Oct 25 2011

CAO Dreaming

Breakthrough technology enables 3D mapping of rainforests, tree by tree” - the latest news from the Carnegie Airborne Observatory (CAO)- but also old news: since about 2006, the CAO has been the most powerful 3D forest scanning system ever devised, and Greg Asner has continually improved it.

The CAO was the original inspiration behind Ecosynth.  In 2006/2007, I  was on sabbatical at the Department of Global Ecology at the Carnegie Institute of Washington at Stanford, and my office was right next to Greg’s.   Though he was mostly in Hawaii getting the CAO up and running, he and his team at Stanford completely sold me on the idea that the future of ecologically relevant remote sensing was multispectral 3D scanning (or better- hyperspectral- but one must start somewhere!). 

I coveted the CAO.   I wanted so much to use it to scan my research sites in China.  Our high-resolution ecological mapping efforts there had been so difficult and the 3D approach seemed to offer the chance to overcome so many of the challenges we faced. 

Yet it still seemed impossible to make it happen- gaining permission to fly a surveillance-grade remote sensing system over China?  It would take years and tremendous logistical and political obstacles to overcome.  So I changed my thinking…

What if we could fly over landscapes with a small hobbyist-grade remote controlled aircraft with a tiny LiDAR and a camera?  Alas, no, - LiDAR systems (high grade GPS + IMU) are way too heavy, and will be for a long time.

Then I saw Photosynth, and I thought- maybe that approach to generating 3D scans from multiple photographs might allow us to scan landscapes on demand without major logistical hassles?  The answer is yes, and the result, translated into reality by Jonathan Dandois, is Ecosynth.

Can Ecosynth achieve capabilities similar to CAO?  Our ultimate goal is to find out.   And make it cheap and accessible to all- as the first “personal” remote sensing system of the Anthropocene.

Oct 14 2011

Mikrokopter and Computer Vision/Photogrammetry used for Landslide Modeling

Researchers at the Universität Stuttgart, Institute for Geophysics in Stuttgart Germany, have used manually flown Mikrokopters and semi-automated photogrammetric software to generate high resolution photo mosaics and digital terrain models of a landslide area for tracking terrain displacement.  

An article published this spring in the journal Engineering Geology demonstrated the value of using remote controlled aircraft and off-the-shelf digital cameras for high resolution digtial terrain modeling.  The researchers used photogrammetry and computer vision software VMS to make 3D terrain models with aerial images and compared the results to aerial LIDAR and TLS terrain models.  A network of ~200 GPS measured ground control points were used to assist with image registration and model accuracy with good results.

The authors appear to agree with our sentiments that RC based aerial photography and 3D scanning has the benefits of low-cost and repeatability compared to traditional fixed wing or satellite based data collections.

Unlike our research, the authors of this study were interested in only the digital terrain model (DTM) and vegetation was considered noise to be removed for more accurate surface modelling.

Again...just one more reason for me to get cranking on that next paper!

Image source: http://commons.wikimedia.org/wiki/File:Super_sauze_landslide.JPG

Jun 28 2011

Automated terrestrial multispectral scanning

3D scanning just keeps getting better (but not cheaper!).

A post from Engadget: Topcon's IP-S2 Lite (~$300K) creates panoramic maps in 3D, spots every bump in the road (video) http://www.engadget.com/2011/06/28/topcons-ip-s2-lite-creates-panoramic-maps-in-3d-spots-every-bu/.

More from Topcon:




In China recently, we had the good fortune to collaborate in using a wonderful new ground-based (terrestrial) LiDAR scanner (TLS) from Riegl: The VZ-400, which fuzes LiDAR scans with images acquired from a digital camera (~$140K). Pictured at left- graduate students of the Chinese Academy of Forestry with us in the field- literally!

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.


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.