For this installment of GFW User Profiles, we spoke with Wenman Liu, Ph.D. Candidate at Georgia Institute of Technology. How did you find out about GFW? I’ve been working on forest issues for a while. I was always searching the internet for forest-related data and had a pretty hard time finding stuff, so I actually found GFW through a Google search! It must have been the early days when the website first launched because I check pretty frequently. How did you become interested in forests? Before coming to the US, I was stationed in Southeast Asia for about 4 years working on ecosystem services projects for the Stockholm Environmental Institute. I was working with biofuel assessments, so I wasn’t really focused on forests, but I did field work in villages across the region. A consistent environmental threat I noticed was expansion of biofuels at the cost of forests – I would walk around and see trees chopped down everywhere. It was painful to witness. The Mekong region has some of the highest levels of biodiversity in the world. But there are always changing business trends affecting this unique environment; 20 years ago aquaculture was in fashion and people chopped down mangroves to grow shrimp, until they found the shrimp ponds were polluting. Then it was biofuels. Now palm oil is affecting forests at a large scale, and someday the trend will switch to something else.
How did that experience affect you? I felt a clear calling to respond somehow, so when I came to Georgia Tech for my Ph.D., I started to really focus on forests. I use the global tree cover loss data from University of Maryland, which is a macro scale data set, so it’s easy to become desensitized by all these pixels and lose attachment to the issues. But seeing actual forest conversion is pretty emotional, and that’s strong motivation for my work. How does GFW fit into the work you do now? My dissertation focuses on the way humans interact with forests and how communities have an effect on nature. I map coordinates for settlements all around the world and draw an area around the village that could be reasonably affected by the community. Then I add GFW tree cover loss and gain data within the vicinity, add field survey data, and link to biophysical, socioeconomic and site-specific institutional data (i.e. land rights, collective action). Ultimately I try to analyze how these factors jointly affect forest loss or gain, and how resilient the forest is to this change.
Why such a strong focus on society? It’s important to link to survey and site data about human intervention rather than just biophysical and socioeconomic ones all the time. People’s actions, how we organize ourselves, how governments create policies to manage forests, and how forests respond to human intervention – they all function together under the local circumstance. Recommendations for improving forest management differ according to each settlement and how their forest responds to change. So I like working with GFW tools because not only do you have data on forests, but you integrate it with data about people, land use, and conservation. Did you find anything surprising in the data? Well, when GFW first launched I immediately checked China, but was surprised to see that the huge reforestation projects in northern China hadn’t been picked up by the University of Maryland tree cover gain data. But this was probably just due to the parameters of how the data was collected. I’ll have to wait until the trees are tall enough to be picked up by the next round of gain data. But that’s also what I like about GFW – it’s not just a static map like we’re used to. With more current data, researchers like me can do more advanced analysis. You can learn more by following Wenmen on Twitter.