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.
To address the need for high-quality long-term records of land cover and land cover change, the data set being developed uses a long time series of Landsat imagery to create a global record of annual land cover and land cover change. Specifically, the Global Land Cover Estimation (GLanCE) data record covers all land areas outside of Antarctica, includes the period from 2001-2019 at annual time steps, and is generated at 30 m spatial resolution. The science data sets (SDSs) included in the GLanCE data record provide annual information related to land cover, land cover change, and associated dynamics in surface ecological conditions. The core mapping algorithm used in this project is the Continuous Change Detection and Classification (CCDC) algorithm. CCDC assumes that noise is ephemeral, that land cover change is persistent, and uses all available Landsat observations to map land cover and identify the timing of land cover change at each pixel.
The GlanCE product includes 10 Science Data Sets (SDSs), all of which are described and defined in detail in Section 2. Broadly speaking, the GLAnCE SDSs are designed to characterize three broad landscape attributes:
- Land Cover and Land Cover Change. Four SDSs provide information related to: (1) the land cover class; (2) the estimated quality associated with the land cover class1; (3) the previous land cover class for those pixels where change occured; and (4) the approximate day of year (DOY) of change.
- Magnitude, Seasonality, and Changes in Greenness. Four SDSs are included that characterize annual greenness at each pixel via the Enhanced Vegetation Index (EVI2; Huete et al., 2002): (1) median; (2) amplitude; (3) rate of change (if present); and (4) magnitude of change in EVI2 median for those pixels where change occurred.
- Leaf Type and Phenology. Two SDSs are included that indicate the inferred leaf type and phenology for pixels classified as tree cover.