A set of three estimates of land-cover types and annual transformations of land use are provided on a global 0.5 x0.5 degree lat/lon grid at annual time steps. The longest of the three estimates spans 1770-2010. The dataset presented here takes into account land-cover change due to four major land-use/management activities: (1) cropland expansion and abandonment, (2) pastureland expansion and abandonment, (3) urbanization, and (4) secondary forest regrowth due to wood harvest. Due to uncertainties associated with estimating historical agricultural (crops and pastures) land use, the study uses three widely accepted global reconstruction of cropland and pastureland in combination with common wood harvest and urban land data set to provide three distinct estimates of historical land-cover change and underlying land-use conversions. Hence, these distinct historical reconstructions offer a wide range of plausible regional estimates of uncertainty and extent to which different ecosystem have undergone changes. The three estimates use a consistent methodology, and start with a common land-cover map during pre-industrial conditions (year 1765), taking different courses as determined by the land-use/management datasets (cropland, pastureland, urbanization and wood harvest) to attain forest area distributions close to satellite estimates of forests for contemporary period. The satellite based estimates of forest area are based on MODIS sensor. All data uses the WGS84 spatial coordinate system for mapping.

Summary: | Unit 6 |Storymap metadata page: URL forthcoming Possible K-12 Next Generation Science standards addressed:Grade level(s) 6-8: Standard MS-LS2-1 - Ecosystems: Interactions, Energy, and Dynamics - Analyze and interpret data to provide evidence for the effects of resource availability on organisms and populations of organisms in an ecosystemGrade level(s) 6-8: Standard MS-LS2-4 - Ecosystems: Interactions, Energy, and Dynamics - Construct an argument supported by empirical evidence that changes to physical or biological components of an ecosystem affect populationsGrade level(s) 6-8: Standard MS-LS4-4 - Biological Evolution: Unity and Diversity - Construct an explanation based on evidence that describes how genetic variations of traits in a population increase some individuals’ probability of surviving and reproducing in a specific environmenGrade level(s) 6-8: Standard MS-LS4-6 - Biological Evolution: Unity and Diversity - Use mathematical representations to support explanations of how natural selection may lead to increases and decreases of specific traits in populations over timeGrade level(s) 6-8: Standard MS-ESS3-3 - Earth and Human Activity - Apply scientific principles to design a method for monitoring and minimizing a human impact on the environment.Grade level(s) 6-8: Standard MS-ESS3-4 - Earth and Human Activity - Construct an argument supported by evidence for how increases in human population and per-capita consumption of natural resources impact Earth’s systemsGrade level(s) 9-12: Standard HS-LS2-1 - Ecosystems: Interactions, Energy, and Dynamics - Use mathematical and/or computational representations to support explanations of factors that affect carrying capacity of ecosystems at different scalesGrade level(s) 9-12: Standard HS-LS2-2 - Ecosystems: Interactions, Energy, and Dynamics - Use mathematical representations to support and revise explanations based on evidence about factors affecting biodiversity and populations in ecosystems of different scalesGrade level(s) 9-12: Standard HS-LS2-7 - Ecosystems: Interactions, Energy, and Dynamics - Design, evaluate, and refine a solution for reducing the impacts of human activities on the environment and biodiversityGrade level(s) 9-12: Standard HS-LS3-3 - Heredity: Inheritance and Variation of Traits - Apply concepts of statistics and probability to explain the variation and distribution of expressed traits in a populationGrade level(s) 9-12: Standard HS-LS4-4 - Biological Evolution: Unity and Diversity - Construct an explanation based on evidence for how natural selection leads to adaptation of populationsGrade level(s) 9-12: Standard HS-ESS3-1 - Earth and Human Activity - Construct an explanation based on evidence for how the availability of natural resources, occurrence of natural hazards, and changes in climate have influenced human activityGrade level(s) 9-12: Standard HS-ESS3-3 - Earth and Human Activity - Create a computational simulation to illustrate the relationships among the management of natural resources, the sustainability of human populations, and biodiversityGrade level(s) 9-12: Standard HS-ESS3-3 - Earth and Human Activity - Create a computational simulation to illustrate the relationships among the management of natural resources, the sustainability of human populations, and biodiversityGrade level(s) 9-12: Standard HS-ESS3-4 - Earth and Human Activity - Evaluate or refine a technological solution that reduces impacts of human activities on natural systems.Grade level(s) 9-12: Standard HS-ESS3-6 - Earth and Human Activity - Use a computational representation to illustrate the relationships among Earth systems and how those relationships are being modified due to human activityMost frequently used words:populationcitiesurbancityhousingApproximate Flesch-Kincaid reading grade level: 9.6. The FK reading grade level should be considered carefully against the grade level(s) in the NGSS content standards above.

The Global One-Eighth Degree Urban Land Extent Projection and Base Year Grids by SSP Scenarios, 2000-2100 consists of global SSP-consistent spatial urban land fraction data for the base year 2000 and projections at ten-year intervals for 2010-2100 at a resolution of one-eighth degree (7.5 arc-minutes). Spatial urban land projections are key inputs for the analysis of land use, energy use, and emissions, as well as for the assessment of climate change vulnerability, impacts and adaptation. This data set presents a set of global, spatially explicit urban land scenarios that are consistent with the Shared Socioeconomic Pathways (SSPs) to produce an empirically-grounded set of urban land spatial distributions over the 21st century. A data-science approach is used exploiting 15 diverse data sets, including a newly available 40-year global time series of fine-spatial-resolution remote sensing observations from the Landsat satellite series. The SSPs are developed to support future climate and global change research, the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6), along with Special Reports.

These data describe the effective urban planning documents and the procedures in progress for the municipalities of Tarn in their last known state. This description is voluntarily limited to achieve a specific objective: — show, by means of summary maps, the geographical distribution of the enforceable documents and the progress of the procedures for the PLU(i) and the communal maps relevant to the management of urban and rural planning policies.

Only urban planning procedures under development or revision are included.

On the other hand, old urban planning procedures (i.e. those that have resulted in planning documents that are no longer enforceable) and procedures cancelled before their completion are not kept in these data.

This feature layer is intended for analysis purposes. For display, please use the map image layer: Land Use Designations (Tacoma).The Future Land Use Map illustrates the City’s intended future land use pattern through the geographic distribution of residential and commercial areas, the designation of mixed-use and manufacturing/industrial centers, as well as shoreline and residential designations. This land use distribution was a result of analysis of the urban form policies, existing land use and zoning, development trends, anticipated land use needs and desirable growth and development goals. The land use designations are established in the One Tacoma Comprehensive Plan and provide a basis for applying zoning districts and for making land use decisions. Contact: City of Tacoma Planning & Development Services

Characterization of climate uncertainties due to different land cover maps in regional climate models is essential for adaptation strategies. The spatiotemporal heterogeneity in surface characteristics is considered to play a key role in terrestrial surface processes. Here, we quantified the sensitivity of model results to changes in land cover input data (GlobCover 2009, GLC 2000, CCI, and ECOCLIMAP) in the regional climate model (RCM) COSMO-CLM (v5.0_clm16). We investigated land cover changes due to the retrieval year, number, fraction and spatial distribution of land cover classes by performing convection-permitting simulations driven by ERA5 reanalysis data over Germany from 2002 to 2011. The role of the surface parameters on the surface turbulent fluxes and temperature is examined, which is related to the land cover classes. The bias of the annual temperature cycle of all the simulations compared with observations is larger than the differences between simulations. The latter is well within the uncertainty of the observations. The land cover class fractional differences are small among the land cover maps. However, some land cover types, such as croplands and urban areas, have greatly changed over the years. These distribution changes can be seen in the temperature differences. Simulations based on the CCI retrieved in 2000 and 2015 revealed no accreditable difference in the climate variables as the land cover changes that occurred between these years are marginal, and thus, the influence is small over Germany. Increasing the land cover types as in ECOCLIMAP leads to higher temperature variability. The largest differences among the simulations occur in maximum temperature and from spring to autumn, which is the main vegetation period. The temperature differences seen among the simulations relate to changes in the leaf area index, plant coverage, roughness length, latent and sensible heat fluxes due to differences in land cover types. The vegetation fraction was the main parameter affecting the seasonal evolution of the latent heat fluxes based on linear regression analysis, followed by roughness length and leaf area index. If the same natural vegetation (e.g. forest) or pasture grid cells changed into urban types in another land cover map, daily maximum temperatures increased accordingly. Similarly, differences in climate extreme indices are strongest for any land cover type change to urban areas. The uncertainties in regional temperature due to different land cover datasets were overall lower than the uncertainties associated with climate projections. Although the impact and their implications are different on different spatial and temporal scales as shown for urban area differences in the land cover maps. For future development, more attention should be given to land cover classification in complex areas, including more land cover types or single vegetation species and regional representative classification sample selection. Including more sophisticated urban and vegetation modules with synchronized input data in RCMs would improve the underestimation of the urban and vegetation effect on local climate.

The USGS’s FORE-SCE model was used to produce unprecedented landscape projections for the Upper Missouri River Basin of the northern Great Plains of the United States. The projections are characterized by 1) high spatial resolution (30-meter cells), 2) high thematic resolution (29 land use and land cover classes), 3) broad spatial extent (covering much of the Great Plains), 4) use of real land ownership boundaries to ensure realistic representation of landscape patterns, and 5) representation of both anthropogenic land use and natural vegetation change. A variety of scenarios were modeled from 2014 to 2100, with decadal timesteps (i.e., 2014, 2020, 2030, etc.). Modeled land use and natural vegetation classes were responsive to projected future changes in environmental conditions, including changes in groundwater and water access. Eleven primary land-use scenarios were modeled, from four different scenario families. The land-use scenarios focused on socioeconomic impacts on anthropogenic land use (demographics, energy use, agricultural economics, and other socioeconomic considerations). The following provides a brief summary of the 11 major land-use scenarios. 1) Business-as-usual - Based on an extrapolation of recent land-cover trends as derived from remote-sensing data. Overall trends were provided by 2001 to 2011 change in the National Land Cover Database, while change in crop types were extrapolated from 2008 to 2014 change in the Cropland Data Layer. Overall the scenario is marked by expansion of high-value traditional crops (corn, soybeans) and higher growth in urban development than other scenarios. 2) Billion Ton Update scenario ($40 farmgate price) - This scenario is based on US Department of Energy biofuel scenarios from the Billion Ton Update (BTU). The $40 scenario represents likely agricultural conditions under an assumed farmgate price of $40 per dry ton of biomass (for the production of biofuel). This is the least aggressive BTU scenario for placing "perennial grass" (for biofuel feedstock) on the landscape. 3) Billion Ton Update scenario ($60 farmgate price) - This scenario is based on US Department of Energy biofuel scenarios from the Billion Ton Update. The $60 scenario represents likely agricultural conditions under an assumed farmgate price of $60 per dry ton of biomass (for the production of biofuel). At the higher farmgate price, the perennial grass class expands substantially compared to the $40 scenario. 4) Billion Ton Update scenario ($80 farmgate price) - This scenario is based on US Department of Energy biofuel scenarios from the Billion Ton Update. The $80 scenario represents likely agricultural conditions under an assumed farmgate price of $80 per dry ton of biomass (for the production of biofuel). With the high farmgate price, this scenario shows the highest expansion of perennial grass among the 11 modeled scenarios. 5) GCAM Reference scenario - Based on global-scale scenarios from the GCAM model, the "reference" scenario provides a likely landscape under a world without specific carbon or climate mitigation efforts. As such, it's another form of a "business-as-usual" scenario. 6) GCAM 4.5 scenario - Based on global-scale scenarios from the GCAM model, the GCAM 4.5 model represents a mid-level mitigation scenario, where carbon payments and other mitigation efforts result in a net radiative forcing of ~4.5 W/m2 by 2100. Agriculture becomes even more concentrated in the Great Plains and Midwestern US, resulting in substantial increases in cropland (including perennial grass used as feedstock for cellulosic biofuel production). Forested lands expand with carbon payments encouraging afforestation efforts. 7) GCAM 2.6 scenario - Based on global-scale scenarios from the GCAM model, the GCAM 2.6 model represents a very aggressive mitigation scenario, where carbon payments and other mitigation efforts result in a net radiative forcing of only ~2.6 W/m2 by 2100. Agriculture becomes even more concentrated in the Great Plains and Midwestern US, resulting in substantial increases in cropland (including perennial grass used as feedstock for cellulosic biofuel production). Forested lands expand with carbon payments encouraging afforestation efforts. 8) SRES A1B scenario - A scenario consistent with the Intergovernmental Panel on Climate Change (IPCC's) Special Report on Emissions Scenarios (SRES) A1B storyline. In the A1B scenario, economic activity is prioritized over environmental conservation. Agriculture expands substantially, including use of perennial grasses for biofuel production. 9) SRES A2 scenario - A scenario consistent with the IPCC's SRES A2 storyline. In the A2 scenario, global population levels reach 15 billion by 2100. Economic activity is prioritized over environmental conservation. This scenario has very high expansion of traditional cropland, given the very high demand for foodstuffs and other agricultural commodities. 10) SRES B1 scenario - A scenario consistent with the IPCC's SRES B1 storyline. In the B1 scenario, environmental conservation is valued, as is regional cooperation. Much less agricultural expansion occurs as compared to the A1B or A2 scenarios. 11) SRES B2 scenario - A scenario consistent with the IPCC's SRES B2 storyline. In the B2 scenario, environmental conservation is highly valued. Of the eleven modeled scenarios, the B2 scenarios has the smallest overall agricultural footprint (traditional cropland, hay/pasture, perennial grasses). For each of the eleven land-use scenarios, three alternative climate / vegetation scenarios were modeled, resulting in 33 unique scenario combinations. The alternative vegetation scenarios represent the potential changes in quantity and distribution of the major vegetation classes that were modeled (grassland, shrubland, deciduous forest, mixed forest, and evergreen forest), as a response to potential future climate conditions. The three alternative vegetation scenarios correspond to climate conditions consistent with 1) The Intergovernmental Panel on Climate Change (IPCC's) Representative Concentration Pathway (RCP) 8.5 scenario (a scenario of high climate change), 2) the RCP 4.5 scenario (a mid-level climate change scenario), and 3) a mid-point climate that averages RCP4.5 and RCP8.5 conditions Data are provided here for each of the 33 possible scenario combinations. Each scenario file is provided as a zip file containing 1) starting 2014 land cover for the region, and 2) decadal timesteps of modeled land-cover from 2020 through 2100. The "attributes" section of the metadata provides a key for identifying file names associated with each of the 33 scenario combinations.

Future land use is intended to illustrate the general location and distribution of the various categories of land uses anticipated by the Comprehensive Plan policies over the life of this plan. It is not intended to provide the basis for rezones and other legislative and quasi-judicial decisions, for which the decision makers must look to the Comprehensive Plan policies and various implementing regulations.This map may be amended annually as part of the regular comprehensive plan update process.See the data in action in this web app.

The Rural Access Index (RAI) is a measure of access, developed by the World Bank in 2006. It was adopted as Sustainable Development Goal (SDG) indicator 9.1.1 in 2015, to measure the accessibility of rural populations. It is currently the only indicator for the SDGs that directly measures rural access.The RAI measures the proportion of the rural population that lives within 2 km of an all-season road. An all-season road is one that is motorable all year, but may be temporarily unavailable during inclement weather (Roberts, Shyam, & Rastogi, 2006). This dataset implements and expands on the most recent official methodology put forward by the World Bank, ReCAP's 2019 RAI Supplemental Guidelines. This is, to date, the only publicly available application of this method at a global scale.MethodologyReCAP's methodology provided new insight on what makes a road all-season and how this data should be handled: instead of removing unpaved roads from the network, the ones that are classified as unpaved are to be intersected with topographic and climatic conditions and, whenever there’s an overlap with excess precipitation and slope, a multiplying factor ranging from 0% to 100% is applied to the population that would access to that road. This present dataset developed by SDSN's SDG Transformation Centre proposes that authorities ability to maintain and remediate road conditions also be taken into account.Data sourcesThe indicator relies on four major items of geospatial data: land cover (rural or urban), population distribution, road network extent and the “all-season” status of those roads.Land cover data (urban/rural distinction)Since the indicator measures the acess rural populations, it's necessary to define what is and what isn't rural. This dataset uses the DegUrba Methodology, proposed by the United Nations Expert Group on Statistical Methodology for Delineating Cities and Rural Areas (United Nations Expert Group, 2019). This approach has been developed by the European Commission Global Human Settlement Layer (GHSL-SMOD) project, and is designed to instil some consistency into the definitions based on population density on a 1-km grid, but adjusted for local situations.Population distributionThe source for population distribution data is WorldPop. This uses national census data, projections and other ancillary data from countries to produce aggregated, 100 m2 population data. Road extentTwo widely recognized road datasets are used: the real-time updated crowd-sourced OpenStreetMap (OSM) or the GLOBIO’s 2018 GRIP database, which draws data from official national sources. The reasons for picking the latter are mostly related to its ability to provide information on the surface (pavement) of these roads, to the detriment of the timeliness of the data, which is restrained to the year 2018. Additionally, data from Microsoft Bing's recent Road Detection project is used to ensure completeness. This dataset is completely derived from machine learning methods applied over satellite imagery, and detected 1,165 km of roads missing from OSM.Roads’ all-season statusThe World Bank's original 2006 methodology defines the term all-season as “… a road that is motorable all year round by the prevailing means of rural transport, allowing for occasional interruptions of short duration”. ReCAP's 2019 methodology makes a case for passability equating to the all-season status of a road, along with the assumption that typically the wet season is when roads become impassable, especially so in steep roads that are more exposed to landslides.This dataset follows the ReCAP methodology by creating an passability index. The proposed use of passability factors relies on the following three aspects:• Surface type. Many rural roads in LICs (and even in large high-income countries including the USA and Australia) are unpaved. As mentioned before, unpaved roads deteriorate rapidly and in a different way to paved roads. They are very susceptible to water ingress to the surface, which softens the materials and makes them very vulnerable to the action of traffic. So, when a road surface becomes saturated and is subject to traffic, the deterioration is accelerated. • Climate. Precipitation has a significant effect on the condition of a road, especially on unpaved roads, which predominate in LICs and provide much of the extended connectivity to rural and poor areas. As mentioned above, the rainfall on a road is a significant factor in its deterioration, but the extent depends on the type of rainfall in terms of duration and intensity, and how well the roadside drainage copes with this. While ReCAP suggested the use of general climate zones, we argue that better spatial and temporal resolutions can be acquired through the Copernicus Programme precipitation data, which is made available freely at ~30km pixel size for each month of the year.• Terrain. The gradient and altitude of roads also has an effect on their accessibility. Steep roads become impassable more easily due to the potential for scour during heavy rainfall, and also due to slipperiness as a result of the road surface materials used. Here this is drawn from slope calculated from SRTM Digital Terrain data.• Road maintenance. The ability of local authorities to remediate damaged caused by precipitation and landslides is proposed as a correcting factor to the previous ones. Ideally this would be measured by the % of GDP invested in road construction and maintenance, but this isn't available for all countries. For this reason, GDP per capita is adopted as a proxy instead. The data range is normalized in such a way that a road maxed out in terms of precipitation and slope (accessibility score of 0.25) in a country at the top of the GDP per capita range is brought back at to the higher end of the accessibility score (0.95), while the accessibility score of a road meeting the same passability conditions in a country which GDP per capita is towards the lower end is kept unchanged.Data processingThe roads from the three aforementioned datasets (Bing, GRIP and OSM) are merged together to them is applied a 2km buffer. The populations falling exclusively on unpaved road buffers are multiplied by the resulting passability index, which is defined as the normalized sum of the aforementioned components, ranging from 0.25 to. 0.9, with 0.95 meaning 95% probability that the road is all-season. The index applied to the population data, so, when calculated, the RAI includes the probability that the roads which people are using in each area will be all-season or not. For example, an unpaved road in a flat area with low rainfall would have an accessibility factor of 0.95, as this road is designed to be accessible all year round and the environmental effects on its impassability are minimal.The code for generating this dataset is available on Github at: https://github.com/sdsna/rai

The Global Grid of Probabilities of Urban Expansion to 2030 presents spatially explicit probabilistic forecasts of global urban land cover change from 2000 to 2030 at a 2.5 arc-minute resolution. For each grid cell that is non-urban in 2000, a Monte-Carlo model assigned a probability of becoming urban by the year 2030. The authors first extracted urban extent circa 2000 from the NASA MODIS Land Cover Type Product Version 5, which provides a conservative estimate of global urban land cover. The authors then used population densities from the Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) to create the population density driver map. They estimated the amount of new urban land in each United Nations region by 2030 in a Monte-Carlo fashion based on present empirical distribution of regional urban population densities and probability density functions of projected regional population and GDP values for 2030. To facilitate integration with other data products, CIESIN reprojected the data from Goode's Homolosine to Geographic WGS84 projection.

The USGS Upper Midwest Environmental Sciences Center developed a Monarch Butterfly Relevant Land Cover data set covering the conterminous United States of America. This data set was used primarily to assist in forecasting the number of milkweed stems on the landscape. Milkweed are required by monarch butterflies for reproduction and one possible cause for the decline in monarch butterfly numbers is thought to be the loss of milkweed. We used the Cropland Data Layer 2015 as well as additional spatially explicit information to develop the monarch relevant land cover data set. Additional sources of information included 2014 United States Department of Agriculture Conservation Reserve Program enrollment locations; railroad, power line, and road rights of way; marginal versus productive farmland as determined by the 2012 United States Department of Agriculture National Commodity Cropland Productivity Index; and a characterization of urban versus outside urban environs. Due to the inherent sensitivity of the Conservation Reserve Program and Transmission line data sets, we created this non-sensitive version of the Monarch Butterfly Relevant Land Cover data set for distribution with those two data sets not used in the development.

The first basin-wide map of large stands of invasive Phragmites australis (common reed) in the coastal zone was created through a collaboration between the U.S. Geological Survey and Michigan Tech Research Institute (Bourgeau-Chavez et al 2013). This data set represents a revised version of that map and was created using multi-temporal PALSAR data and Landsat images from 2016-2017. In addition to Phragmites distribution, the data sets shows several land cover types including urban, agriculture, forest, shrub, emergent wetland, forested wetland, and some based on the dominant plant species (e.g., Schoenoplectus, Typha). The classified map was validated using over 400 field visits.This map covers the eastern portion of Lake Erie.

Long term global archives of high-moderate spatial resolution, multi-spectral satellite imagery are now readily accessible, but are not being fully utilised by management agencies due to the lack of appropriate methods to consistently produce accurate and timely management ready information. This work developed an object-based remote sensing approach to map land cover and seagrass distribution in an Australian coastal environment for a 38 year Landsat image time-series archive (1972-2010). Landsat Multi-Spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) imagery were used without in situ field data input (but still using field knowledge) to produce land and seagrass cover maps every year data were available, resulting in over 60 map products over the 38 year archive. Land cover was mapped annually using vegetation, bare ground, urban and agricultural classes. Seagrass distribution was also mapped annually, and in some years monthly, via horizontal projected foliage cover classes, sand and deep water. Land cover products were validated using aerial photography and seagrass maps were validated with field survey data, producing several measures of accuracy. An average overall accuracy of 65% and 80% was reported for seagrass and land cover products respectively, which is consistent with other studies in the area. This study is the first to show moderate spatial resolution, long term annual changes in land cover and seagrass in an Australian environment, created without the use of in situ data; and only one of a few similar studies globally. The land cover products identify several long term trends; such as significant increases in South East Queensland's urban density and extent, vegetation clearing in rural and rural-residential areas, and inter-annual variation in dry vegetation types in western South East Queensland. The seagrass cover products show that there has been a minimal overall change in seagrass extent, but that seagrass cover level distribution is extremely dynamic; evidenced by large scale migrations of higher seagrass cover levels and several sudden and significant changes in cover level. These mapping products will allow management agencies to build a baseline assessment of their resources, understand past changes and help inform implementation and planning of management policy to address potential future changes.

Based on the future population forecast data, urban expansion driving factor data (road network density, residential area, night light, GDP) and so on, the future urban expansion model is used to simulate and predict the urban expansion pattern and land use distribution of Xining City in 2050. The data set contains four data results corresponding to the urban pattern of Xining in 2050 under different scenarios. They are maintaining the status quo (BAU), urban compact development (infill), continuing the existing pattern and protecting cultivated land (protect), compact development and protecting cultivated land (infill).

The LandScan data set is a worldwide population database compiled on a 30" X 30" latitude/longitude grid. Census counts (at sub-national level) were apportioned to each grid cell based on likelihood coefficients, which are based on proximity to roads, slope, land cover, nighttime lights, and other data sets. LandScan has been developed as part of the Oak Ridge National Laboratory (ORNL) Global Population Project for estimating ambient populations at risk. The LandScan files are available via the internet in ESRI grid format by continent and for the world. You can access the data files after user registration through the data links. For an overview of the methods used to develop LandScan, please read the documentation and FAQs.

[Summary provided by Oak Ridge National Laboratory]

These data describe the planning procedures in their latest known state, specifying their situation in terms of progress and effectiveness. On average, a planning procedure lasts three years. This description is voluntarily limited to achieve a specific objective: show, through summary maps, the geographical distribution and progress of PLU procedures relevant to the management of urban and rural planning policies. These include planning procedures in preparation, revision or repeal. In order to allow an exhaustive summary of the progress of the procedures, the procedures of the past years which have led to urban planning documents which are now enforceable are kept in these data (a planning document is associated with them in the file N_DOCUMENT_URBA_ddd). On the other hand, old urban planning procedures (i.e. those that have resulted in planning documents that are no longer enforceable) and procedures cancelled before their completion are not kept in these data.

The U.S. Geological Survey (USGS), in association with the Multi-Resolution Land Characteristics (MRLC) Consortium, produces the National Land Cover Database (NLCD) for the United States. The MRLC, a consortium of federal agencies who coordinate and generate consistent and relevant land cover information at the national scale for a wide variety of environmental, land management, and modeling applications, have been providing the scientific community with detailed land cover products for more than 30 years. Over that time, NLCD has been one of the most widely used geospatial datasets in the U.S., serving as a basis for understanding the Nation’s landscapes in thousands of studies and applications, trusted by scientists, land managers, students, city planners, and many more as a definitive source of U.S. land cover. NLCD land cover suite is created through the classification of Landsat imagery and uses partner data from the MRLC Consortium to help refine many of the land cover classes. The classification system used by NLCD is modified from the Anderson Land Cover Classification System. The NLCD Class Legend and Description is maintained at https://www.mrlc.gov/data/legends/national-land-cover-database-class-legend-and-description. The land cover theme includes two separate products. The first is a standard land cover product suite that provides 16 land cover classes for the conterminous United States and Alaska only land cover types and is available at https://www.mrlc.gov/data. The second product suite, NLCD Land Cover Science Products, provides additional discrimination and land cover classes differentiating grass and shrub and regenerating forest regime from grass and shrub and rangeland setting and is available at https://www.mrlc.gov/nlcd-2021-science-research-products. The latest release of NLCD land cover spans the timeframe from 2001 to 2021 in 2 to 3-year intervals. These new products use a streamlined compositing process for assembling and preprocessing Landsat imagery and geospatial ancillary datasets; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a theme-based post-classification protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and a scripted operational system. Unmasked Impervious - To produce the unmasked impervious layer a multilayered perceptron neural network (MLP) was deployed across CONUS. The MLP was trained to perform the regression task of predicting the 1-100 impervious fractional cover. To sample data to train the network, we broke CONUS into a grid comprised of 256x256 pixel regions of interest (ROIs) and sampled from that grid all ROIs with at least 40% impervious cover according to NLCD 2019 impervious fractional cover, which gave us samples from large impervious areas. From those ROIs, we then sampled 66 million training and 16 million validation data points with an even distribution across each impervious intensity (1-100). Those training points were then randomly split into 4 subsets, each corresponding to one of the following respective years: 2011, 2013, 2016, 2019. We used those points to query surface reflectance values from leaf-on composite and leaf-off synthetic imagery (see metadata for NLCD 2021 land cover), elevation data, and spatial urban intensity probabilities. The spatial urban intensity probabilities were generated by an ensemble of U-net models that were trained to predict the 4 urban intensity classes as defined by the NLCD product legend (open space, low intensity, medium intensity, high intensity). Two U-net models were trained using all ROIs in the CONUS 256x256 pixel grid. Inputs to these models included leaf-on composite and leaf-off synthetic imagery, and elevation data. To create the final training and validation datasets we randomly split the CONUS grid into to 2 equal sets: A and B. Using the ROIs from set A we queried the input features from the years 2011 and 2016 and from the ROIs in set B we queried input features from the years 2013 and 2019. These U-net models do not act as the final impervious predictors but instead as spatial feature generators. The spatial features learned by these convolutional neural networks were then fed into the pixel-based MLP, as spatial probabilities of urban intensity, to boost its predicting power. The U-nets were trained using categorical focal Jaccard loss and monitored with the Jaccard Index metric (IOU). The impervious fractional cover regression model (MLP) was trained using mean squared error as a loss function and monitored with mean absolute error as the metric. Initial impervious footprint - To generate an initial impervious footprint, three U-net models were trained on the multiclass-classification task of predicting “urban” and “roads”. The model was trained with 120,000 training and 40,000 validation 256X256 pixel Landsat image chips covering the entire extent of CONUS. The model inputs are consistent with what was used to generate the urban intensity U-net models; the only difference was the target mask the models were trained to predict. These models mapped all NLCD impervious footprint pixels to two classes (“urban” and “roads”); this was used to generate the impervious extent. Impervious Change Pixels - The initial 2021 impervious change pixels were created by comparing the 2021 urban footprint with the 2019 published urban descriptor and extracting the difference. These change pixels were manually edited for omission and commission errors. Ancillary data were then added to the change pixels to create the final 2021 impervious change pixels. These ancillary data consisted of solar installations, wind turbines, and roads. The solar installations dataset is an edited version of the Solar Photovoltaic Generating Units dataset produced by Kruitwagen et al (2021) (https://doi.org/10.5281/zenodo.5005867). The U.S. Wind Turbine Database from Hoen et al (2021) (https://doi.org/10.5066/F7TX3DN0) was used without edits. NavStreets road datasets were used in previous versions of NLCD but an updated version was not available to the USGS. New subdivision roads from the 2021 urban footprint and a small number of manually drawn roads were added to the 2021 impervious change pixels. 2021 impervious extent - The final impervious change pixels were added to that 2019 impervious descriptor file to create the new 2021 impervious descriptor file. This file maps the extent of all impervious for the 2021 NLCD. 2021 impervious product - The percent imperviousness values (1-100%) for the impervious change pixels were extracted from the unmasked impervious layer. Values for previously published urban remained the same except for areas that were 40% or more greater in value, in the unmasked impervious layer. 2021 impervious descriptor - The final impervious change pixels were mapped to the class legend for the NLCD 2019 published impervious descriptor. These pixels were then added to the NLCD 2019 impervious descriptor file to create the new 2021 impervious descriptor file.

Abstract The article proposes the adaptation and application of the German methodology for the construction of climatic maps for the city of Belo Horizonte. Land use data, geographic aspects and wind information were analyzed, producing different layers of thematic maps. Their combination allowed a definition of eight classes of climatopes, making an analytical urban climatic map. It was verified that the edges of the city at south, southeast and northeast, which the green areas are concentrated, have greater dynamic potential and lower thermal load, considered an advantage for nocturnal cooling. However, the densely built-up areas located in the city center have low nocturnal cooling capacity, due to thermal load storage and the lowest dynamic potential that favor the heating of the surfaces. Concerning to the distribution of climatope classes, it was observed that about half of the city area presents a negative thermal load and a good dynamic potential as an atmospheric response. This result indicates that the negative impact of the urban elements on the surface thermal load can be considered still low. The results are also the basis for urban planning recommendations in order to preserve and expand urban areas that can contribute to the improvement of the local climate of Belo Horizonte.

This map shows a simplified distribution of land cover types across Canada interpreted from satellite data obtained in 1995. Land cover has an important role in the water cycle as it significantly affects evapotranspiration and the amount of water leaving a watershed. Evapotranspiration is the sum of evaporation and transpiration, both of which are related to types of land cover. Plants with larger surface area and deeper roots have greater transpiration.

Soil_Samples_BACI

   Available only by request on a case by case basis. Contact rthe author, David Nowak, at dnowak@fs.fed.us



   Tags

   Biophysical Resources, Land, Social Institutions, Health, BES, Soil, Lead, Sample, UFORE




   Summary


   Samples were taken to relate soil data to vegetation data obtained for the Urban Forestry Effects Model (UFORE).


   Description


   The data is soil concentrations and characteristics of the following: land use, bulk density, sand, silt, clay, pH, organic matter, nitrogen, Al, P, S, Ti, Cr, Mn, Fe, Co, Ni, Cu Zn, Mo, Pb, Cd, Na, Mg, K, Ca, and V. Soils were sampled in 125 plots located within the City of Baltimore in the summer of 2000. The plots were randomly stratified by Anderson Land Cover Classification System Level II, which included commercial, industrial, institutional, transportation right-of-ways, high and medium density residential (there were no low density residential areas identified within the city boundaries), golf course, park, urban open, forest, and wetland land-use types. The number of plots situated in each land-use type was weighted to their proportion of spatial area within the City. The resultant number of plots sampled for soil by land-use type was: commercial (n = 2); industrial (n = 3); institutional (n = 10); transportation right-of-ways (n = 7); high density residential (n = 19); medium density residential (n = 33); golf course (n = 3); riparian (n=2); park (n = 10); urban open (n = 10); and forest (n = 26) land-use types, respectively. The distribution of plots represents the proportion of area covered by impervious surfaces.


   Credits


   Rich Pouyat, USDA Forest Service


   Use limitations


   Not for profit use only


   Extent



   West -76.711030  East -76.530612 

   North 39.371355  South 39.200686 




   Scale Range

   There is no scale range for this item.



   The data is soil concentrations and characteristics of the following: land use, bulk density, sand, silt, clay, pH, organic matter, nitrogen, Al, P, S, Ti, Cr, Mn, Fe, Co, Ni, Cu Zn, Mo, Pb, Cd, Na, Mg, K, Ca, and V. Soils were sampled in 125 plots located within the City of Baltimore in the summer of 2000. The plots were randomly stratified by Anderson Land Cover Classification System Level II, which included commercial, industrial, institutional, transportation right-of-ways, high and medium density residential (there were no low density residential areas identified within the city boundaries), golf course, park, urban open, forest, and wetland land-use types. The number of plots situated in each land-use type was weighted to their proportion of spatial area within the City. The resultant number of plots sampled for soil by land-use type was: commercial (n = 2); industrial (n = 3); institutional (n = 10); transportation right-of-ways (n = 7); high density residential (n = 19); medium density residential (n = 33); golf course (n = 3); riparian (n=2); park (n = 10); urban open (n = 10); and forest (n = 26) land-use types, respectively. The distribution of plots represents the proportion of area covered by impervious surfaces.