GsAL Research Assistant Receives Award for “Excellence in Academic Research”

On Monday April 24th, 2017 the Graduate Student Senate (GSS) at USF hosted Graduate Student Academic Showcase to highlight graduate students’ research and other work. This is a unique graduate student event that aims to highlight and showcase the diverse array of academic and scholarly works for graduate students from all of the academic schools and colleges at USF.

During this event, GsAL research assistant, Radhika Bhargava, received the Excellence in Academic Research award for her involvement in a project designed to build the capacity to monitor land use and land cover changes in the Lower Mekong region. The project is based on a geospatial needs assessment conducted by SERVIR-Mekong in 2015 for the Lower Mekong countries which included individual country consultations. The assessment revealed that many countries were dependent on land cover and land use maps for land resource planning, quantifying ecosystem services including resilience to climate change, biodiversity conservation, and other critical social issues. Many of the Lower Mekong countries have developed national-scale land cover maps derived in part from remote sensing products and geospatial technologies. However, updates are infrequent and classification systems and accuracy assessment do not always meet the needs of key user groups. In addition, data products stop at political boundaries and are often not accessible. Many of the Lower Mekong countries rely on global land cover products to fill the gaps of their national efforts, compromising consistency between data and policies. These gaps in national efforts can be filled by a flexible regional land cover monitoring system that is co-developed by regional partners with the specific intention of meeting national trans-boundary needs, for example including consistent forest definitions in trans-boundary watersheds. During this assessment, regional stakeholders identified a need for a land cover monitoring system that will produce frequent, high quality land cover maps using a consistent regional classification scheme that is compatible with national requirements.

This system is dependent on a sustainable source of field data that insures data quality and improves potential impact. Based on this need, collaborative workshops were held to create a robust regional reference data system that integrates results from field data, national inventories and high resolution imagery to meet the end user needs. The results presented here highlight the value of collaboratively developed systems that use data convergence to improve land cover mapping results for multiple end users.