This map shows the population density of Mexico in relation to freshwater sources and water bodies.

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Mexico: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

This map layer includes Sargassum density images downloaded from the University of South Florida Optical Oceanography Labratory. The images are downloaded and reprojected to display on the HABSOS web map application.

The Global Human Footprint dataset of the Last of the Wild Project, version 2, 2005 (LWPv2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1 km grid cells, created from nine global data layers covering human population pressure (population density), human land use and infraestructure (built-up areas, nighttime lights, land use/land cover) and human access (coastlines, roads, navigable rivers).The Human Footprint Index (HF) map, expresses as a percentage the relative human influence in each terrestrial biome. HF values from 0 to 100. A value of zero represents the least influence -the "most wild" part of the biome with value of 100 representing the most influence (least wild) part of the biome.

This map shows the population density of Mexico in relation to freshwater sources and water bodies.

In 2022, the population density in the United States remained nearly unchanged at around 36.43 inhabitants per square kilometer. Nevertheless, 2022 still represents a peak in the population density in the United States. Population density refers to the average number of residents per square kilometer of land across a given country or region. It is calculated by dividing the total midyear population by the total land area.Find more key insights for the population density in countries like Mexico.

Ground data from the National Forest and Soil Inventory of Mexico (INFyS) were used to calibrate a maximum entropy (MaxEnt) algorithm to generate forest biomass (AGB), its associated uncertainty, and forest probability maps. The input predictor layers for the MaxEnt algorithm were extracted from the moderate resolution imaging spectrometer (MODIS) vegetation index (VI) products, ALOS PALSAR L-band dual-polarization backscatter coefficient images, and the Shuttle Radar Topography Mission (SRTM) digital elevation model. A Jackknife analysis of the model accuracy indicated that the ALOS PALSAR layers have the highest relative contribution (50.9%) to the estimation of AGB, followed by MODIS-VI (32.9%) and SRTM (16.2%). The forest cover mask derived from the forest probability map showed higher accuracy (κ = 0.83) than alternative masks derived from ALOS PALSAR (κ = 0.72–0.78) or MODIS vegetation continuous fields (VCF) with a 10% tree cover threshold (κ = 0.66). The use of different forest cover masks yielded differences of about 30 million ha in forest cover extent and 0.45 Gt C in total carbon stocks. The AGB map showed a root mean square error (RMSE) of 17.3 t C ha− 1 and R2 = 0.31 when validated at the 250 m pixel scale with inventory plots. The error and accuracy at municipality and state levels were RMSE = ± 4.4 t C ha− 1, R2 = 0.75 and RMSE = ± 2.1 t C ha− 1, R2 = 0.94 respectively. We estimate the total carbon stored in the aboveground live biomass of forests of Mexico to be 1.69 Gt C ± 1% (mean carbon density of 21.8 t C ha− 1), which agrees with the total carbon estimated by FAO for the FRA 2010 (1.68 Gt C). The new map, derived directly from the biomass estimates of the national inventory, proved to have similar accuracy as existing forest biomass maps of Mexico, but is more representative of the shape of the probability distribution function of AGB in the national forest inventory data. Our results suggest that the use of a non-parametric maximum entropy model trained with forest inventory plots, even at the sub-pixel size, can provide accurate spatial maps for national or regional REDD + applications and MRV systems.

This dataset contains oceanographic data collected in deep water areas south and southeast of the flower Garden Banks National Marine Sanctuary (FGBNMS), including Keathley Canyon and adjacent parts of the Sigsbee Escarpment, and areas on the West Florida Escarpment southwest of Tampa. Daily ROV dives were conducted with full shore-based science participation. Evening and nighttime mapping and CTD operations were also conducted and focused in depths less than 500 m. Most of the operations, including the transit, were conducted within the 200nm exclusive economic zone (EEZ) maritime boundary of the United States of America.

This digital elevation model (DEM) is a part of a series of DEMs produced for the National Oceanic and Atmospheric Administration Coastal Services Center's Sea Level Rise and Coastal Flooding Impacts Viewer. The DEM includes the 'best available' lidar data known to exist at the time of DEM creation that meets project specifications for those counties within the boundary of the Houston/Galveston TX Weather Forecast Office (WFO), as defined by the NOAA National Weather Service. The counties within this boundary are: Jackson, Matagorda, Brazoria (portion), Harris (portion), Galveston, and Chambers. For all the counties listed, except for Harris, the DEM is derived from LiDAR data sets collected for the Texas Water Development Board (TWDB) in 2006 with a point density of 1.4 m GSD. LiDAR data for Harris County was collected in October 2001 by the Harris County Flood Control District Tropical Storm Allison Recovery Project (TSARP) with a point density of 2.0 m GSD. Hydrographic breaklines used in the creation of the DEM were delineated using LiDAR intensity imagery generated from the data sets. The DEM is hydro flattened such that water elevations are less than or equal to 0 meters.The DEM is referenced vertically to the North American Vertical Datum of 1988 (NAVD88) with vertical units of meters and horizontally to the North American Datum of 1983 (NAD83). The resolution of the DEM is approximately 10 meters.