Monitoring population and land use change in tropical forest protected areas

Abstract:

Monitoring human-environment interactions in tropical forest protected areas requires linking interdisciplinary datasets collected across a range of spatial and temporal scales. Recent assessments have shown that forest degradation and loss outside of protected areas is strongly associated with declines in biodiversity within protected areas. Biodiversity monitoring efforts must therefore develop approaches that consider change in the broader landscape, using biophysical and socioeconomic datasets that not only cover the extent of a protected area, but also the region surrounding it. The Tropical Ecology Assessment and Monitoring (TEAM) Network has developed an approach for linking remotely sensed imagery from Landsat and MODIS sensors with in-situ ecological data and socioeconomic datasets to better understand the effects of landscape change on biodiversity. The TEAM Network is a global system for monitoring biodiversity, land use/cover change (LUCC), and climate in sixteen tropical forest sites evenly distributed across global biophysical gradients (rainfall and seasonality) and gradients of expected climate change and land use change.

TEAM adopts the Zone of Interaction (ZOI) concept to delineate the spatial extent around protected areas for linking broader-scale trends in LUCC to plot-based monitoring data. This talk reports on a cross-site comparison examining LUCC and biodiversity change across the TEAM network. The analysis indicates a gradient of forest loss in the tropics dependent on landscape-level human factors, such as population and road density. The highest losses of forest cover are associated with changing patterns of land use and agricultural development, particularly plantation forestry in Southeast Asia.

While the spatial and temporal resolution of remote sensing-derived datasets continues to increase, a key challenge for monitoring efforts is linking this data to spatially explicit socioeconomic datasets for use in statistical modeling. We will discuss best practices for handling major global datasets (i.e. GRUMP, gROADS, WorldPop, Landscan) in the context of ongoing LUCC and biodiversity monitoring programs.