Principal Investigator (PI): Mark Friedl, Boston University

Land cover and land use maps provide the single most important basis for characterizing the ecological state and biophysical properties of the Earth’s land areas. Because such maps synthesize a rich array of information related to both the ecological condition of land areas and their exploitation by humans, they are widely used for model-based investigations that require information related to land surface biophysical properties (e.g., terrestrial carbon models, water balance models, weather and climate models, etc.), and are core inputs to models used by natural resource scientists and land managers.

As the Earth’s global population has grown over the last several decades, rates of land cover change have increased dramatically, with enormous impacts on ecosystem services (e.g., biodiversity, water supply, carbon sequestration/emissions, loss and expansion of agricultural land, etc.). Hence, accurate information related to changes in land use and land cover is essential for both managing natural resources and for understanding the ecological, biophysical, and resource footprint of society.

While a number of global land cover products have been developed, nearly all of these data sets are based on coarse spatial resolution remote sensing (i.e., 300–500 meters), which is much coarser than the scale at which most land cover, land use, and land cover change occurs. Further, no current products provide accurate and comprehensive information related to global land cover change, which is critical for many applications. Hence, currently available products do not meet the needs of the large, diverse, and growing community of basic and applied scientists who require better quality, higher resolution, and more timely information related to global land cover, land use, and land cover change.

To address this need, we are creating a Global Land Cover, land Use, and land cover Change (GLCUC) data record based on Landsat imagery that will provide high-quality representation of contemporary GLCUC, along with retrospective characterization of GLCUC at annual time steps from 2001 to 2020. The GLCUC data record that we propose leverages decades of combined experience by the proposal team developing algorithms and data sets in support of local-to-global scale mapping of land cover, land use, and land cover change.

To create this data record, we are partnering with the USGS, who have prime responsibility for processing and distribution of Landsat data and extensive experience creating operational and validated land cover maps for the United States. As part of our validation effort we have recruited a team of international experts who will help us compile validation data and quantify product uncertainties. Finally, to ensure that the user community is able to easily access the GLCUC data record, we will partner with the Land Processes Distributed Active Archive Center (LP DAAC) to archive and distribute results from this effort.

Once complete, the data sets developed through this project will provide annual high-quality maps of 21st century global land cover, land use, and land cover change at 30-m spatial resolution, along with detailed characterization of product accuracy.