The LP DAAC is pleased to announce the expansion of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Land Cover Mapping and Estimation (GLanCE) data product with the addition of data for Oceania.
The purpose of GLanCE is to provide 30 meter (m) global land cover and land cover change data for the period 2001 to 2019. The GLanCE datasets are designed to be useful for a wide range of applications, including ecosystem, climate, and hydrologic modeling; monitoring the response of terrestrial ecosystems to climate change; carbon accounting; and land management. The GLanCE data product is derived from the global results1 of the Continuous Change Detection and Classification algorithm (CCDC) algorithm run on the Landsat archive on Google Earth Engine2.
The GLanCE data product now contains continental coverage over North America, South America, Europe, and Oceania. Coverage for additional continents will be delivered in the coming months. The LP DAAC will announce those data as they become available.
A citation containing the Digital Object Identifier (DOI) for the GLanCE dataset is provided below.
Global Land Cover Mapping and Estimation Yearly 30 m V001 – GLanCE30.001
Arevalo, P., Stanimirova, R., Bullock, E., Zhang, Y., Tarrio, K., Turlej, K., Hu, K., McAvoy, K., Pasquarella, V., Woodcock, C., Olofsson, P., Zhu, Z., Gorelick, N., Loveland, T., Barber, C., Friedl, M. (2022). Global Land Cover Mapping and Estimation (GLanCE) Yearly 30 m, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MEaSUREs/GLanCE/GLanCE30.001. Accessed YYYY-MM-DD.
The data are available for download through NASA’s Earthdata Search.
Have questions? Please contact LP DAAC User Services.
1Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R., 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2017.06.031
2Gorelick, N., Yang, Z., Arévalo, P., Bullock, E.L., Insfrán, K.P., Healey, S.P., 2023. A global time series dataset to facilitate forest greenhouse gas reporting. Environ. Res. Lett. 18, 084001. https://doi.org/10.1088/1748-9326/ace2da