Breakfast
Ideas
With the ever increasing intensity of the human footprint on Earth, protected areas stand as the last safeguards of biodiversity in our planet. There are 261,377 official protected areas covering only 15% of the land today. Can you imagine the planet 100, 200, or more than 300 years from now?, how will these management systems perform?; and also, how can we bridge the gap between technology and decision-making to strengthen their mission?. Research in our lab investigates interactions between natural protected areas and people as working socioecological systems, within the context of biodiversity management and conservation. We also investigate and propose methodological frameworks for effective monitoring of protected areas with the use of geospatial data, remote sensing and field data.
Revisiting people and parks: science, monitoring technology and capacity building
Dynamics of Socio-Environmental Systems:
We are interested in addressing the question of how management systems are affected by gradual and sudden socioeconomic change (driven by political instability, climate change, diseases, natural disasters, armed conflicts, etc). We propose to investigate the resilience capacity of management systems by studying: 1) the impact of socioeconomic change on the relationship between demographics, land use, land tenure, economic conditions, and ecosystem loss dynamics, 2) the impact of socioeconomic change on the relationship between ecosystem loss dynamics and species diversity/carbon density maintenance and 3) the relationship between community social, cultural and financial coping strategies and socio-ecological resilience.
The Parktrends Project (PT)
This is an engaged scholarship project that aims to build a network of platforms for engaged ecosystem monitoring in multiple managed areas in America and abroad. PT project is intended to be co-created by land managers and scientists, for the mapping of local plant community composition, ecological functions, and disturbance trends in and around their reserves. With PT we try to convey highly complex information in simple, easy-to-interpret terms.
Methodologically, we use a combination of multiple optical and radar imagery sources, remotely sensed indices for ecological function, as well as terrain information, and process these using machine learning classification techniques. For mapping disturbances or shifts in community traits, we apply spatiotemporal data clustering techniques to quantify emerging trends from sub-annual data obtained from dense time series of Landsat, Sentinel and Planet Labs satellite imagery. For communicating results to the public, PT intends to use the concept of the 'one pager' for summarizing and communicating information to managers and the public in general.
@ Department of Natural Resources Management. Texas Tech University