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What is Ecosynth?

Ecosynth is a suite of tools used to map and measure vegetation in 3 dimensions (3D) using off-the-shelf digital cameras and open-source computer vision software, from the ground or using low altitude hobbyist aircraft.

The goals of the Ecosynth project are to:

  • Generate 3D and spectral imaging data useful for ecological research and monitoring work
  • Create low-cost portable systems built on publicly available hardware and open-source software.
  • Be deployable by individual users, including researchers and citizen scientists, as-needed across sample areas.

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How Ecosynth Works

The Ecosynth process generates 3D scans of vegetation from sets of digital photographs acquired from the ground or at low altitude (<130 m) from light-weight aerial platforms or the ground.

Computer vision algorithms generate 3D "point clouds" by building geometry from matching features identified in multiple overlapping photographs. 3D point clouds are then georeferenced and used to make ecological measurements.

Ecosynth Available as AWS Image

We now have a publicly available AWS image with Ecosynther preloaded onto it that you can mirror and run your datasets through. Instructions for getting started can be found on the blog here: Ecosynth Blog

Applications of Hobbyist Air Craft

Ecosynth utilizes several platforms of hobbyist level aircraft to complete 3D scans of forests and other landscapes. However, these platforms can be used for many other applications. Ecosynth's air craft can carry pay loads of up to 1kg, fly up to 8km or 30 minutes of flight time and range in price from $800-$2300 plus start up costs. This include tools, spare parts and ground equipment. This being said, small air craft can be used to carry almost any type of instrument so long as it is within the pay load and flight time limit. Check out our Platforms page to see what's right for you.


The Ecosynth Wiki content is licensed under a Creative Commons Attribution 4.0 International License.

Funding and Support

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This material is based upon work supported by the National Science Foundation under Grant DBI 1147089 awarded March 1, 2012. Initial and continuing support provided by the USDA Forest Service joint venture agreement 06-JV-11242300-135. Graduate student support by NSF IGERT 054969 to UMBC CUERE, undergraduate student support from the Baltimore Ecosystem Study DEB-0423476. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.