Remote sensing has become an integral part of using GIS in biological research, and this week’s paper of the week is a nice example of how just how detailed the information you can get from remote sensing can be. This paper is Lin et al. (2015) from PLoS One and is titled ‘Classification of Tree Species in Overstorey Canopy of Subtropical Forest Using QuickBird Images‘. In it, the authors set out to see if they could take high spatial resolution satellite imagery and use them to identify individual trees to the species level in a sub-tropical forest in Taiwan.
This may sound like an unlikely thing to be able to do, but it’s based on the idea that the leaves of different species of trees will reflect different wave lengths of light in slightly different ways, and while it takes some fairly fancy processing, it turn out that it’s possible. In fact, with the right images and the right processing, Lin et al. were able to separate out 40 different species of trees with a pretty high level of accuracy.
While this is impressive stuff in its own right, this isn’t the only reason I selected it as my paper of the week. I also selected it because of the potential that this type of processing offers to biologists. Through the processing and analysis of high spatial resolution images, it should possible to pull out almost any type of information that a biologist might ever need to know.
One example of this is something I’ve been pondering for some time now. This is how to assess the productivity of trees in an oak woodland as part of a study of breeding success in hole-nesting birds. The research question here is whether breeding success is related to the number of caterpillars found in the territory which surrounds each nest box. Now, measuring the number of caterpillars found around 300 nest boxes is just not feasible, but given that the number of caterpillars should be related to the productivity of the trees on which they are feeding, so this could be used as a proxy, and using the type of processing of high spatial resolution satellite imagery done by Lin et al. (2015) potentially provides a way to extract this information automatically. I haven’t yet had the chance to see whether this is, indeed, the case, but I’m looking forward to giving it a go (or more likely finding an eager student and persuading them into taking the project on!)
So, I think the take home point from this week’s paper is that it’s impressive just how much information can be extracted from high spatial resolution satellite images, and in many cases, all that’s needed is a bit of imagination, followed up by some intense work as you work out exactly how to pull out just the information you require to help you answer your research questions.
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Dr Colin D. MacLeod,
Founder, GIS In Ecology
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GIS In Ecology 