GFW User Profile: Rudo Kemper
For this installment of GFW User Profiles, we spoke with Rudo Kemper, GIS & Web Development Coordinator.
What organization do you work with? I work with GIS and remote sensing at the Amazon Conservation Team, primarily for the country of Suriname. Much of my work deals with mapping forest change remotely with satellite data, working on participatory and ethnographic mapping projects with local communities, and coordinating between our offices in Washington and Suriname. How did you find out about Global Forest Watch? I started using Terra-i data while I was still a student. By the time I began working with Amazon Conservation Team (ACT), others at the organization were already using Global Forest Watch, so we most likely found out about it through the wider remote sensing and conservation community. How does GFW fit into your work? ACT is in the beginning stages of a long-term project to use remote sensing to monitor gold mining activity across Suriname and the Guianas. We need a lot of forest change data for mapping environmental pressures like industrial and small-scale gold mining, as well as land use by forest communities. Beforehand, we would have a general idea of where mining sites in the jungle might be, and could circle an area on the map. But now with GFW, we have a very clear picture. We can confirm gold mining operations on the map, discover unknown operations, or reject places we thought mining was occurring. What do you do with the GFW data? We primarily download the University of Maryland (UMD) tree cover loss data and Terra-i clearance alerts to map forest change. By comparing Terra-i monthly alerts to UMD annual data, we can see areas of loss that each data set is not picking up (Figure 1). In the case of Suriname, the maps created with GFW data are used to illustrate how serious of a threat gold mining is to the forest. The maps also serve as a base for our participatory mapping work with forest communities in the country. So it’s been a great combination of remotely-sensed data coupled with our work on the ground. For instance, some communities we work with have shifting cultivation plots or roads that don’t appear yet in the GFW data.
Was there anything unexpected in the data? Because Suriname is located in a region that is often covered by clouds, it can be difficult to find satellite imagery showing trees, so we actually sometimes use the Tree Cover Extent data from UMD to create base maps for Suriname. This wasn’t so much surprising in terms of data, but was an unexpected use. Do you have any insight for other GFW users? The GFW website is great for exploring data sets and geographic areas on the map before going through the whole process of downloading data. We also have used GFW to visually compare with other maps. To read a full report of Rudo’s work at ACT, click here: Monitoring gold mining extraction in Suriname using Global Forest Watch data sources.