Yale Center for Earth Observation (YCEO) Surface Urban Heat Islands, Version 4, 2003-2018

Abstract and Limitations of Use: The Global Human Built-up And Settlement Extent (HBASE) Dataset from Landsat is a global map of HBASE derived from the Global Land Survey (GLS) Landsat dataset for the target year 2010. The HBASE dataset consists of two layers: 1) the HBASE mask; and 2) the pixel-wise probability of HBASE. These layers are co-registered to the same spatial extent at a common 30m spatial resolution. The spatial extent covers the entire globe except Antarctica and some small islands. This dataset is one of the first global, 30m datasets of urban extent to be derived from the GLS data for 2010 and is a companion dataset to the Global Man-made Impervious Surface (GMIS) dataset. The HBASE mask was created for post-processing of the GMIS dataset, but can also be utilized by users needing a binary map. The dataset is expected to have a rather broad spectrum of users, from those wishing to examine/study the fine details of urban land cover over the globe at full 30m resolution to global modelers trying to understand the climate/environmental impacts of man-made surfaces at continental to global scales. For example, the data are applicable to local modeling studies of urban impacts on the energy, water, and carbon cycles, as well as analyses at the individual country level.   Intended Use: To provide high spatial resolution estimates of global urban extent derived from global 30m Landsat satellite data for the target year 2010 and a companion dataset to the Global Man-made Impervious Surface (GMIS) dataset. Because this is a historical dataset, this item does not follow a routine update frequency. Variables:Human Built-up and Settlement Extents (HBASE)  Probability of HBASE Class Legend:Color Legend Label Description       255: NoData/Clouds/Shadow The HBASE dataset consists of two layers: 1) the HBASE mask; and 2) the pixel-wise probability of HBASE. These layers are co-registered to the same spatial extent at a common 30m spatial resolution. It identifies pixel values as a HBASE class or not.   202: Road   201: HBASE   200: Not HBASE Citation: Global Human Built-up And Settlement Extent (HBASE) Dataset From Landsat. Wang, P., C. Huang, E. C. Brown de Colstoun, J. C. Tilton, and B. Tan. (2017). NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/H4DN434S  Publication References:  Global Man-made Impervious Surface (GMIS) Dataset From Landsat. Brown de Colstoun, E. C., C. Huang, P. Wang, J. C. Tilton, B. Tan, J. Phillips, S. Niemczura, P.-Y. Ling, and R. E. Wolfe. (2017). NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/H4P55KKF Documentation for the Global Human Built-up And Settlement Extent (HBASE) Dataset From Landsat. Center for International Earth Science Information Network - CIESIN - Columbia University. (2017). NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/H48W3BCM    Contact: For inquiries about this service, please contact ciesin.info@ciesin.columbia.edu or post/view questions on the Earthdata Forum.

Urban land area in square kilometers, based on a combination of population counts (persons), settlement points, and the presence of nighttime lights. Areas are defined as urban where contiguous lighted cells from the nighttime lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons.

Abstract and Limitations of Use: The Global Man-made Impervious Surface (GMIS) Dataset From Landsat consists of global estimates of fractional impervious cover derived from the Global Land Survey (GLS) Landsat dataset for the target year 2010. The GMIS dataset consists of two components: 1) global percent of impervious cover; and 2) per-pixel associated uncertainty for the global impervious cover. These layers are co-registered to the same spatial extent at a common 30m spatial resolution. The spatial extent covers the entire globe except Antarctica and some small islands. This dataset is one of the first global, 30m datasets of man-made impervious cover to be derived from the GLS data for 2010 and is a companion dataset to the Global Human Built-up And Settlement Extent (HBASE) dataset. The dataset is expected to have a rather broad spectrum of users, from those wishing to examine/study the fine details of urban land cover over the globe at full 30m resolution to global modelers trying to understand the climate/environmental impacts of man-made surfaces at continental to global scales. For example, the data are applicable to local modeling studies of urban impacts on the energy, water, and carbon cycles, as well as analyses at the individual country level.   Intended Use: To provide high spatial resolution estimates of global man-made imperviousness for the target year 2010, derived from global 30m Landsat satellite data and a companion dataset to the Global Human Built-up And Settlement Extent (HBASE) dataset. Because this is a historical dataset, this item does not follow a routine update frequency.  Variables:Human Built-up and Settlement Extents (HBASE)  Probability of HBASE Class  Legend:  Color Legend Label Description   255: NoData/Clouds/Shadow The GMIS dataset consists of two components: 1) global percent of impervious cover; and 2) per-pixel associated uncertainty for the global impervious cover.    200: Not HBASE   90 - 100   80 - 90   80 - 90   70 - 80   60 - 70   50 - 60   40 - 50   30 - 40   20 - 30   10 - 20   0 - 10 Citation: Global Man-made Impervious Surface (GMIS) Dataset From Landsat. Brown de Colstoun, E. C., C. Huang, P. Wang, J. C. Tilton, B. Tan, J. Phillips, S. Niemczura, P.-Y. Ling, and R. E. Wolfe. (2017). NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/H4P55KKF  Publication References: Documentation for the Global Man-made Impervious Surface (GMIS) Dataset From Landsat. Center for International Earth Science Information Network - CIESIN - Columbia University. (2017). NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/H4JD4TVQ Global Human Built-up And Settlement Extent (HBASE) Dataset From Landsat. Wang, P., C. Huang, E. C. Brown de Colstoun, J. C. Tilton, and B. Tan. (2017). NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/H4DN434S Contact: For inquiries about this service, please contact ciesin.info@ciesin.columbia.edu or post/view questions on the Earthdata Forum.

The goal of the updated map is to show the heat differences across the region and is not intended to show the maximum temperature that any specific area can reach. An updated timestamp (2022 vs. 2016) and finer grained resolution (10m vs. 30m) improves the utility of the data for planning purposes. The LST dataset from 2016 can be accessed via the Minnesota Geospatial Commons. High resolution (10 meter) land surface temperature (LST) from September 1, 2022 is mapped for the seven-county metropolitan region of the Twin Cities. The goal of the map is to show the heat differences across the region and is not intended to show the maximum temperature that any specific area can reach. The raster dataset was computed at 30 meters using satellite imagery from Landsat 9 and downscaled to 10 meters using Copernicus Sentinel-2. These datasets were integrated using techniques modified from Ermida et al. 2020 and Onačillová et al. 2022). Open water was removed using ancillary data from OpenStreetMap and 2020 Generalized Land Use for the Twin Cities (Metropolitan Council).  First, Landsat 9 imagery taken at 11:59 am CDT on September 01, 2022 was processed into 30-meter resolution LST (based on Ermida et al. 2020). At this time, the air temperature was 88° F at the Minneapolis-St. Paul International Airport (NOAA). A model predicting LST based on spectral indices of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI) was created and applied to 10-meter Sentenel-2 imagery. Sentinel-2 imagery was also taken on September 1, 2022, and this resulted in a 10-meter downscaled LST image (based on Onačillová et al. 2022). To account for anomalies in NDVI on the primary image date of September 1 (e.g., recently harvested agricultural fields), maximum NDVI occurring between July 1, 2022 and September 1, 2022 was used for both Landsat and Sentinel image processing. Water bodies were removed for all processing steps (OpenStreetMap 2023, Metropolitan Council 2021).  This dataset is an update to the 2016 LST data for the Twin Cities Region (Metropolitan Council).  The code to create and processes this dataset is available at: https://github.com/Metropolitan-Council/extreme.heat  Sources:  Ermida, S.L., Soares, P., Mantas, V., Göttsche, F.-M., Trigo, I.F., 2020. Google Earth Engine open-source code for Land Surface Temperature estimation from the Landsat series. Remote Sensing, 12 (9), 1471; https://doi.org/10.3390/rs12091471.  Metropolitan Council. 2021. Generalized Land Use 2020. Minnesota Geospatial Commons. https://gisdata.mn.gov/dataset/us-mn-state-metc-plan-generl-lnduse2020  Metropolitan Council. 2017. Land Surface Temperature for Climate Vulnerability Analysis. Minnesota Geospatial Commons. https://gisdata.mn.gov/dataset/us-mn-state-metc-env-cva-lst2016  NOAA, National Oceanic and Atmospheric Administration, National Centers for Environmental Information, station USW00014922. September 1, 2022.  Onačillová, K., Gallay, M., Paluba, D., Péliová, A., Tokarčík, O., Laubertová, D. 2022. Combining Landsat 8 and Sentinel 2 data in Google Earth Engine to derive higher resolution land surface temperature maps in urban environment. Remote Sensing, 14 (16), 4076. https://doi.org/10.3390/rs14164076.  OpenStreetMap contributors. 2023. Retrieved from https://planet.openstreetmap.org on April 12, 2023.

Global Human Footprint Index is the relative human influence in each terrestrial biome expressed as a percentage. The purpose is to provide an updated map of anthropogenic impacts on the environment in geographic projection which can be used in wildlife conservation planning, natural resource management, and research on human-environment interactions. Dataset Summary The Global Human Footprint Index Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1-kilometer grid cells, created from nine global data layers covering human population pressure (population density), human land use and infrastructure (built-up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). A value of zero represents the least influenced- the “most wild” part of the biome with value of 100 representing the most influenced (least wild) part of the biome. The dataset is produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN). Recommended Citation Wildlife Conservation Society - WCS, and Center for International Earth Science Information Network - CIESIN - Columbia University. 2005. Last of the Wild Project, Version 2, 2005 (LWP-2): Global Human Footprint Dataset (Geographic). Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4M61H5F. Accessed DAY MONTH YEAR.

Abstract: The Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3 data set contains land areas with urban, quasi-urban, rural, and total populations (counts) within the LECZ for 234 countries and other recognized territories for the years 1990, 2000, and 2015. This data set updates initial estimates for the LECZ population by drawing on a newer collection of input data, and provides a range of estimates for at-risk population and land area. Constructing accurate estimates requires high-quality and methodologically consistent input data, and the LECZv3 evaluates multiple data sources for population totals, digital elevation model, and spatially-delimited urban classifications. Users can find the paper "Estimating Population and Urban Areas at Risk of Coastal Hazards, 1990-2015: How data choices matter" (MacManus, et al. 2021) in order to evaluate selected inputs for modeling Low Elevation Coastal Zones. According to the paper, the following are considered core data sets for the purposes of LECZv3 estimates: Multi-Error-Removed Improved-Terrain Digital Elevation Model (MERIT-DEM), Global Human Settlement (GHSL) Population Grid R2019 and Degree of Urbanization Settlement Model Grid R2019a v2, and the Gridded Population of the World, Version 4 (GPWv4), Revision 11. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) and the City University of New York (CUNY) Institute for Demographic Research (CIDR). Purpose: To provide estimates of urban and rural populations and land areas for the years 1990, 2000, 2015 for 234 countries and statistical areas with contiguous coastal elevations of less than or equal to 5m above sea level, 5-10m above sea level, and national totals using multiple updated data sources for comparative analysis. Citation: Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3. Center for International Earth Science Information Network - CIESIN - Columbia University, and CUNY Institute for Demographic Research - CIDR - City University of New York. (2021). NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/H4TM782G Legend: Color Service Pixel Value Legend Label Description: 5 0 - 5m LECZ LECZs are derived from Multi-Error-Removed Improved Terrain Digital Elevation Model (MERIT-DEM) are provided as a spatial layer in a 9 arc second resolution raster (GeoTIFF). This data categorically demarcates the two low elevation coastal zones (0-5m and 5-10m LECZs) and a third category representing the non-coastal areas at any elevation (outside of LECZs) coded as 31. This raster data is also provided as a web map and image service. It is in the WGS84 coordinate system. 10 >5 - 10m LECZ 31 >10m - Outside of LECZ Resolution: nine arc-seconds (~300m) Publication References: Low Elevation Coastal Zone (LECZ) Urban-Rural Population Estimates, Global Rural-Urban Mapping Project (GRUMP), Alpha Version. McGranahan, G., D. Balk, and B. Anderson. (2007). NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/H4TM782G Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 2. Center for International Earth Science Information Network - CIESIN - Columbia University. (2013). NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/H4MW2F2J Sea Level Rise Impacts on Ramsar Wetlands of International Importance. Center for International Earth Science Information Network - CIESIN - Columbia University. (2013). NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/H4CC0XMD Documentation for the Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3. Center For International Earth Science Information Network - CIESIN - Columbia University. (2021). NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/z0r0-gc08