The Key Map Grid Index dataset contains rectangular features representing index pages within Montgomery County, Texas. Each index page is proportioned to fit a letter-sized map and is assigned a unique identifier for reference purposes. This dataset facilitates the organization and retrieval of key map grids, with 24 key map grids fitting within a single index page. The index pages are numbered sequentially, and the key map grids within each index page are lettered accordingly, excluding the letters "I" and "O" to avoid confusion with numbers. The Key Map Grid Index was created by the Houston Map Company, which covers multiple counties in the Houston metropolitan area including Harris, Fort Bend, Galveston, Brazoria, Liberty, Waller, and Montgomery Counties. More information can be found on the Houston Map Company's website at www.keymaps.com.Data Fields Included:Index Page ID: Unique identifier assigned to each index pageBoundary Polygon: Rectangle representing the proportionate index page

This map service provides access to the USGS 7.5 Minute Quadrangle Index for all tiles that cover the Commonwealth of Kentucky.

(Link to Metadata) USNGVT is a U.S. National Grid Index (1000m x 1000m) covering Vermont. It is a polygon feature class originally constructed by the Center for Interdisciplinary Geospatial Information Technologies at Delta State University with support from the US Geological Survey under the Cooperative Agreement 07ERAG0083. VCGI merged UTM zone 18 and 19 into a single layer, the projected to VCS NAD83. Further information about the US National Grid is available from http://www.fgdc.gov/usng and a viewing of these layers as applied to local geography may be seen at the National Map, http://www.nationalmap.gov. This dataset includes the USNG grid for parts of Vermont, New Hampshire, Massachusetts and Connecticut that lie in UTM zone 18.

This Kentucky-specific quadrangle index grid was developed for the KyTopo Map Series. The 60,000' x 40,000' grid tiles are landscape oriented, fit on a standard Arch-D sized sheet, and have newly generated contours based on a KyFromAbove LiDAR-derived DEM. The 60k x 40k grid is a superset of the Kentucky Single Zone based 5k grid that is utilized for organizing and distributing most all of the Commonwealth's raster data holdings. Quadrangle names were developed utilizing a USGS methodology that focuses on the most prominent map features. Clicking on a grid tile shows the names, contour interval, contour index interval, and provides links to download currently available versions of that map.

This map page grid was designed and implemented by the Los Angeles County Fire Department, Information Management Division, and Geographic Information Systems Section. Wholly based on the United States National Grid (USNG), it aims to be the Los Angeles County regional grid because of its ease of use and extensive coverage.

Each block measures 2,000 meters by 2,000 meters and are comprised of four 1,000 meter/1 kilometer USNG blocks. i.e. 11SLT4771, 11SLT4871, 11SLT4770 and 11SLT4870; left to right and up to down. For ease of use each 2,000 meter block is designated by a page number from 1 to 9,750, and it is inferred that the 1,000 meter divisions of each page are designated A, B, C or D; left to right and up to down. Therefore, each 1,000 meter block within this regional grid has a unique descriptor of 4 numerals and 1 letter.

The region covered by the grid includes Los Angeles County completely and 2 of the islands in the Channel Islands archipelago that fall into the Los Angeles County jurisdiction. It also covers close to 100% of Orange County, 50% of Ventura County, and the Los Angeles County adjacent portions of Kern County, San Bernardino County and Riverside County, and a portion of Marine Corps Base Camp Pendleton in Northwest San Diego County.

PAGE = Grid Number

Reference Date: 2016

Contact Information:

Los Angeles County Fire Department Geographic Information Systems Section LACoFDGIS@fire.lacounty.gov

This Kentucky-specific quadrangle index grid was developed for the KyTopo Map Series. The 60,000' x 40,000' grid tiles are landscape oriented, fit on a standard Arch-D sized sheet, and have contours based on a LiDAR-derived DEM. This Kentucky-specific quadrangle index grid was developed for the KyTopo Map Series. The 60,000' x 40,000' grid tiles are landscape oriented, fit on a standard Arch-D sized sheet, and have newly generated contours based on a KyFromAbove LiDAR-derived DEM. The 60k x 40k grid is a superset of the Kentucky Single Zone based 5k grid that is utilized for organizing and distributing most all of the Commonwealth's raster data holdings. Quadrangle names were developed utilizing a USGS methodology that focuses on the most prominent map features. Clicking on a grid tile shows the names, contour interval, contour index interval, and provides links to download currently available versions of that map.Data Download: https://ky.box.com/v/kymartian-KyTopo-QuadTiles

A polygon feature class of Miami-Dade County, Digital Map Library (DML) index layer. This layer identifies the areas, which is divided into square mile that we have Geographic Information System (GIS) data for as well as, the type of data.

The MCD15A3H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is a 4-day composite data set with 500 meter pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA’s Terra and Aqua satellites from within the 4-day period.

LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nm) absorbed by the green elements of a vegetation canopy.

Known Issues

• For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

Improvements/Changes from Previous Versions

• The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.
• A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).

Overview

Project Goals and Benefits

The simulation data provides insight into the behavior of the boundary layer by analyzing key parameters along a flat plate. Using numerical methods, the study solves the Navier-Stokes equations—five fully coupled, time-dependent 3D partial differential equations—while applying Prandtl's theoretical framework to simplify the problem. This provides insights into the steady-state evolution of boundary layer dynamics, which is crucial for optimizing aerodynamic designs.

The primary focus is on the evolution of boundary layer parameters in steady, incompressible flow over a flat plate with a zero-pressure gradient, in accordance with boundary layer theory. Specifically, the simulation investigates:

• Velocity Profile
• Boundary Layer Thickness
• Displacement Thickness
• Wall Friction

For a detailed overview of the numerical scheme used and the assumptions underlying the model, please refer to the GitHub repository (see related works).

Data Structure

The generated simulation data is stored in a structured text format in the file simulation-data.csv. The file is divided into two main sections:

First Section

This section contains general information about the flow along the streamwise coordinate (in the x-direction). For each x-value (ranging from 0 to N), the following parameters are recorded:

'Um': The mean streamwise velocity.
'Vm': The mean normal velocity.
'd': The displacement thickness.
'd99': The boundary layer thickness at which the velocity reaches 99% of the free-stream velocity.
'tw': The wall shear stress.

This section is formatted as follows:

x Um Vm d d99 tw
0 0.929966 0.000000 0.138634 0.529339 0.005000
1 0.928717 0.045704 0.141137 0.532872 0.004728
...

Second Section

This section contains detailed data for each point in the grid, including both the x- and y-coordinates. For each x-coordinate, the following is recorded:

'y': The vertical grid point.
'u': The streamwise velocity at the grid point (x,y).
'v': The normal velocity at the grid point (x,y).
'tau_xy': The shear stress at the grid point (x,y).

This section is formatted as follows:

x 0
y u v tau_xy
0 0.000000 0.000000 0.005000
1 0.016665 0.000000 0.004999
...
#
x 1
y u v tau_xy
0 0.000000 0.000000 0.004728
1 0.015761 0.000030 0.004729
...
#
x 2
y u v tau_xy
0 0.000000 0.000000 0.004632
1 0.015440 -0.000019 0.004631

Extracting the Data

To extract the simulation data, the following pseudo code can be used:

(1) Open the file raw_simulation_data.csv for reading.

(2) Initialize variables to store the extracted data:
'x_values': A list to store the grid index x-coordinates (from the first section).
'Um_values': A list to store the streamwise velocity values (Um) from the first section.
'Vm_values': A list to store the normal velocity values (Vm) from the first section.
'd_values': A list to store the displacement thickness values from the first section.
'd99_values': A list to store the boundary layer thickness at 99% velocity values from the first section.
'tw_values': A list to store the wall shear stress values from the first section.
'flow_data': A dictionary to store detailed flow data, indexed by grid x and y:
flow_data[x] = {y: [u, v, tau_xy]}

(3) While reading the file:
a. Read each line.
b. If the line starts with "x Um Vm d d99 tw":
This marks the start of the first section. Skip this line and continue to the next.
c. If the line contains a data row like "x Um Vm d d99 tw":
Parse the values and append them to the corresponding lists: 'x_values', 'Um_values', 'Vm_values', 'd_values', 'd99_values', and 'tw_values'.
d. If the line contains "x

(4) Important Transformation:
When processing the raw simulation data, the grid points (x and y) represent discrete indices. To convert these into physical coordinates, use the following formulas:

xtrue = x / N * L
ytrue = y / M * H

N and M are the grid sizes, and L and H are the physical dimensions of the domain, with the converted values representing the actual coordinates in the flow domain.

(5) Close the file.

The extracted data is now organized as follows:
* 'x_values', 'Um_values', 'Vm_values', 'd_values', 'd99_values', and 'tw_values' contain the general information about the flow along the streamwise coordiante.

* 'flow_data' is a dictionary where each grid index x is a key, and the corresponding values for u, v, and τxy at each grid index y are stored.

This structure provides easy access to both the overall flow characteristics and the detailed distribution of velocity and shear stress, with the transformation to physical coordinates prepared for further analysis or visualization.

Technical Details

Technical Requirements

Tools: Any text editor or IDE (e.g., VS Code, PyCharm)

General Information

Authors

Kofler Joshua
- [GitHub]
- [ORCID]

FAQ or Troubleshooting

For any issues or questions, please refer to the GitHub repository (see related works) and check the issues section or open a new issue.

License

This data is licensed under the [CC-BY-4.0].

The MYD15A2H Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD15A2H Version 6.1 data product.

The MYD15A2H Version 6 Moderate Resolution Imaging Spectroradiometer (MODIS) combined Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product is an 8-day composite dataset with 500 meter (m) pixel size. The algorithm chooses the “best” pixel available from all the acquisitions of the Aqua sensor from within the 8-day period.

LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nanometers (nm)) absorbed by the green elements of a vegetation canopy.

Science Datasets (SDS) in the Level 4 (L4) MYD15A2H product include LAI, FPAR, two quality layers, and standard deviation for LAI and FPAR. Two low resolution browse images, LAI and FPAR, are also available for each MYD15A2H granule.

Known Issues * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

Improvements/Changes from Previous Versions * The Version 6 product uses the daily L2G-lite surface reflectance as input as opposed to MODAGAGG used in Version 5. * Products are generated at native resolution of 500 m rather than the 1000 m of the Version 5. * Version 6 uses an improved multi-year land cover product.

For data privacy reasons, houses within a residential environment are summed up to a "virtual" micro-geographic segment (so-called micro-cell), which on average comprises eight, but at least five households. Houses in which at least five households live become a distinct micro-cell, while houses with less than five households are combined with similar houses on the same street. Combined houses are as close as possible in spatial terms. Structural indicators are aggregated on the micro cell level and subsequently computed household level averages are computed (microm 2016, p.8). If such data exist, the calculated data is made consistent with official data sources (microm 2014, p. 2). Additionally, due to the cooperation with SOEP, it is possible to validate the small scale regional data of microm (microm 2016, p. 8). The dataset is based on the variable group microm-Basis which is comprised of four categories: number of households, number of business enterprises, number of houses (including those purely used for business), and number of residential houses (excluding those purely used for business) (cf. microm 2016, p. 26). The number of houses on the street segment level is the basis for all aggregations to other regional levels. Based on business registers, the number of enterprises in each house is determined.

The MOD15A2H Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MOD15A2H Version 6.1 data product.

The MOD15A2H Version 6 Moderate Resolution Imaging Spectroradiometer (MODIS) combined Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product is an 8-day composite dataset with 500 meter (m) pixel size. The algorithm chooses the “best” pixel available from all the acquisitions of the Terra sensor from within the 8-day period.

LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation, 400-700 nanometers (nm), absorbed by the green elements of a vegetation canopy.

Science Datasets (SDSs) in the Level 4 (L4) MOD15A2H product include LAI, FPAR, two quality layers, and standard deviation for LAI and FPAR. Two low resolution browse images, LAI and FPAR, are also available for each MOD15A2H granule.

Known Issues * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

Improvements/Changes from Previous Versions * The Version 6 product uses the daily L2G-lite surface reflectance as input as opposed to MODAGAGG used in Version 5. * Products are generated at native resolution of 500 m rather than the 1000 m of the Version 5. * Version 6 uses an improved multi-year land cover product.

This data layer contains the index grid for the National Agriculture Imagery Program (NAIP) 2015 imagery. The NAIP program is administered by the U.S. Department of Agriculture Farm Service Agency and has been established to support two main FSA strategic goals centered on agricultural production.Owner: U.S. Department of Agriculture Farm Service Agency.This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Feature Service Layer Link:https://mdgeodata.md.gov/imap/rest/services/Imagery/MD_NAIPImageryGrid/FeatureServer/0

This parcels polygons feature class represents current city parcels within the City of Los Angeles. It shares topology with the Landbase parcel lines feature class. The Mapping and Land Records Division of the Bureau of Engineering, Department of Public Works provides the most current geographic information of the public right of way, ownership and land record information. The legal boundaries are determined on the ground by license surveyors in the State of California, and by recorded documents from the Los Angeles County Recorder's office and the City Clerk's office of the City of Los Angeles. Parcel and ownership information are available on NavigateLA, a website hosted by the Bureau of Engineering, Department of Public Works.Associated information about the landbase parcels is entered into attributes. Principal attributes include:PIN and PIND: represents the unique auto-generated parcel identifier and key to related features and tables. This field is related to the LA_LEGAL, LA_APN and LA_HSE_NBR tables. PIN contains spaces and PIND replaces those spaces with a dash (-).LA_LEGAL - Table attributes containing legal description. Principal attributes include the following:TRACT: The subdivision tract number as recorded by the County of Los AngelesMAP_REF: Identifies the subdivision map book reference as recorded by the County of Los Angeles.LOT: The subdivision lot number as recorded by the County of Los Angeles.ENG_DIST: The four engineering Districts (W=Westla, C=Central, V= Valley and H=Harbor).CNCL_DIST: Council Districts 1-15 of the City of Los Angeles. OUTLA means parcel is outside the City.LA_APN- Table attributes containing County of Los Angeles Assessors information. Principal attributes include the following:BPP: The Book, Page and Parcel from the Los Angeles County Assessors office. SITUS*: Address for the property.LA_HSE_NBR - Table attributes containing housenumber information. Principal attributes include the following:HSE_ID: Unique id of each housenumber record.HSE_NBR: housenumber numerical valueSTR_*: Official housenumber addressFor a complete list of attribute values, please refer to Landbase_parcel_polygons_data_dictionary.Landbase parcels polygons data layer was created in geographical information systems (GIS) software to display the location of the right of way. The parcels polygons layer delineates the right of way from Landbase parcels lots. The parcels polygons layer is a feature class in the LACityLandbaseData.gdb Geodatabase dataset. The layer consists of spatial data as a polygon feature class and attribute data for the features. The area inside a polygon feature is a parcel lot. The area outside of the parcel polygon feature is the right of way. Several polygon features are adjacent, sharing one line between two polygons. For each parcel, there is a unique identifier in the PIND and PIN fields. The only difference is PIND has a dash and PIN does not. The types of edits include new subdivisions and lot cuts. Associated legal information about the landbase parcels lots is entered into attributes. The landbase parcels layer is vital to other City of LA Departments, by supporting property and land record operations and identifying legal information for City of Los Angeles. The landbase parcels polygons are inherited from a database originally created by the City's Survey and Mapping Division. Parcel information should only be added to the Landbase Parcels layer if documentation exists, such as a Deed or a Plan approved by the City Council. When seeking the definitive description of real property, consult the recorded Deed or Plan.List of Fields:ID: A unique numeric identifier of the polygon. The ID value is the last part of the PIN field value.ASSETID: User-defined feature autonumber.MAPSHEET: The alpha-numeric mapsheet number, which refers to a valid B-map or A-map number on the Cadastral grid index map. Values: • B, A, -5A - Any of these alpha-numeric combinations are used, whereas the underlined spaces are the numbers. An A-map is the smallest grid in the index map and is used when there is a large amount of spatial information in the map display. There are more parcel lines and annotation than can fit in the B-map, and thus, an A-map is used. There are 4 A-maps in a B-map. In areas where parcel lines and annotation can fit comfortably in an index map, a B-map is used. The B-maps are at a scale of 100 feet, and A-maps are at a scale of 50 feet.OBJECTID: Internal feature number.BPPMAP_REFTRACTBLOCKMODLOTARBCNCL_DIST: LA City Council District. Values: • (numbers 1-15) - Current City Council Member for that District can be found on the mapping website http://navigatela.lacity.org/navigatela, click Council Districts layer name, under Boundaries layer group.SHAPE: Feature geometry.BOOKPAGEPARCELPIND: The value is a combination of MAPSHEET and ID fields, creating a unique value for each parcel. The D in the field name PIND, means "dash", and there is a dash between the MAPSHEET and ID field values. This is a key attribute of the LANDBASE data layer. This field is related to the APN and HSE_NBR tables.ENG_DIST: LA City Engineering District. The boundaries are displayed in the Engineering Districts index map. Values: • H - Harbor Engineering District. • C - Central Engineering District. • V - Valley Engineering District. • W - West LA Engineering District.PIN: The value is a combination of MAPSHEET and ID fields, creating a unique value for each parcel. There are spaces between the MAPSHEET and ID field values. This is a key attribute of the LANDBASE data layer. This field is related to the APN and HSE_NBR tables.

The vector grid system provides a spatial and statistical infrastructure that allows the integration of environmental and socio-economic data. Its exploitation allows the crossing of different spatial data within the same grid units. Project results obtained using this grid system can be more easily linked. This grid system forms the geographic and statistical infrastructure of the Southern Quebec Land Accounts of the Institute of Statistics of Quebec (ISQ). It forms the geospatial and statistical context for the development of ecosystem accounting in Quebec. **In order to improve the vector grid system and the Land Accounts of Southern Quebec and to better anticipate the future needs of users, we would like to be informed of their use (field of application, objectives of use, territory, association with other products, etc.). You can write to us at maxime.keith@stat.gouv.qc.ca **. This grid system allows the spatial integration of various data relating, for example, to human populations, the economy or the characteristics of land. The ISQ wishes to encourage the use of this system in projects that require the integration of several data sources, the analysis of this data at different spatial scales and the monitoring of this data over time. The fixed geographic references of the grids simplify the compilation of statistics according to different territorial divisions and facilitate the monitoring of changes over time. In particular, the grid system promotes the consistency of data at the provincial level. The spatial intersection of the grid and the spatial data layer to be integrated makes it possible to transfer the information underlying the layer within each cell of the grid. In the case of the Southern Quebec Land Accounts, the spatial intersection of the grid and each of the three land cover layers (1990s, 2000s and 2010s) made it possible to report the dominant coverage within each grid cell. The set of matrix files of Southern Quebec Land Accounts is the result of this intersection. **Characteristics: ** The product includes two vector grids: one formed of cells of 1 km² (or 1,000 m on a side), which covers all of Quebec, and another of 2,500 m² cells (or 50 m on a side, or a quarter of a hectare), which fits perfectly into the first and covers Quebec territory located south of the 52nd parallel. Note that the nomenclature of this system, designed according to a Cartesian plan, was developed so that it was possible to integrate cells with finer resolutions (up to 5 meters on a side). In its 2024 update, the 50 m grid system is divided into 331 parts with a side of 50 km in order to limit the number of cells per part of the grid to millions and thus facilitate geospatial processing. This grid includes a total of approximately 350 million cells or 875,000 km2. It is backwards compatible with the 50m grid broadcast by the ISQ in 2018 (spatial structure and unique identifiers are identical, only the fragmentation is different). **Attribute information for 50 m cells: ** * ID_m50: unique code of the cell; * CO_MUN_2022: geographic code of the municipality of January 2022; * CERQ_NV2: code of the natural region of the ecological reference framework of Quebec; * CL_COUV_T50: unique code of the cell; * CL_COUV_T00, CL_COUV_T01: codes for coverage classes Terrestrial maps from the years 1990, 2000 and 2010. Note: the 2000s are covered by two land cover maps: CL_COUV_T01A and CL_COUV_T01b. The first inventories land cover prior to reassessment using the 2010s map, while the second shows land cover after this reassessment process. **Complementary entity classes: ** * Index_grille50m: index of the parts of the grid; * Decoupage_mun_01_2022: division of municipalities; * Decoupage_MRC_01_2022: division of geographical MRCs; * Decoupage_RA_01_2022: division of administrative regions. Source: System on administrative divisions [SDA] of the Ministry of Natural Resources and Forests [MRNF], January 2022, allows statistical compilations to be carried out according to administrative divisions hierarchically superior to municipalities. * Decoupage_CERQ_NV2_2018: division of level 2 of the CERQ, natural regions. Source: Ministry of the Environment, the Fight against Climate Change, Wildlife and Parks [MELCCFP]. Geospatial processes delivered with the grid (only with the FGDB data set) : * ArcGIS ModelBuilder allowing the spatial intersection and the selection of the dominant value of the geographic layer to populate the grid; * ModelBuilder allowing the statistical compilation of results according to various divisions. Additional information on the grid in the report Southern Quebec Land Accounts published in October 2018 (p. 46). View the results of the Southern Quebec Land Accounts on the interactive map of the Institut de la Statistique du Québec.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Social distancing is a public health measure intended to reduce infectious disease transmission, by maintaining physical distance between individuals or households. In the context of the COVID-19 pandemic, populations in many countries around the world have been advised to maintain social distance (also referred to as physical distance), with distances of 6 feet or 2 metres commonly advised. Feasibility of social distancing is dependent on the availability of space and the number of people, which varies geographically. In locations where social distancing is difficult, a focus on alternative measures to reduce disease transmission may be needed. To help identify locations where social distancing is difficult, we have developed an ease of social distancing index. By index, we mean a composite measure, intended to highlight variations in ease of social distancing in urban settings, calculated based on the space available around buildings and estimated population density. Index values were calculated for small spatial units (vector polygons), typically bounded by roads, rivers or other features. This dataset provides index values for small spatial units within urban areas in South Sudan. Measures of population density were calculated from high-resolution gridded population datasets from WorldPop, and the space available around buildings was calculated using building footprint polygons derived from satellite imagery (Ecopia.AI and Maxar Technologies. 2020). These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the Bill and Melinda Gates Foundation and the United Kingdom’s Department for International Development. Project partners included the United Nations Population Fund (UNFPA), Center for International Earth Science Information Network (CIESIN) in the Earth Institute at Columbia University, and the Flowminder Foundation.

The dataset collection consists of maps of Sweden, sourced from the Swedish website 'Lantmäteriet' (The Land Survey). The maps are provided in Shape and MapInfo *.tab formats, and use the SWEREF 99 TM (EPSG:3006) coordinate system. The contents of these maps have been selected separately for each scale, ensuring high-quality display. The content includes coastlines, islands, streams, national boundaries, county boundaries, municipality boundaries, the Arctic Circle and grid/degree grids. Please note that the maps on a scale of 1:5 million, 1:10 million and 1:20 million are not updated. For a more detailed description of the content, refer to the product descriptions available in both Swedish and English. The data size for the map of Sweden in a scale of 1:5 million is approximately 0.53 MB, in a scale of 1:10 million is about 0.34 MB, and in a scale of 1:20 million is approximately 0.21 MB. Data in Shape format is delivered in 4 files per layer (geometry file, attribute file in Dbase format, index file, and projection file), while data in MapInfo (tab) format is also delivered in 4 files per layer (main file/table definitions, attribute file, geometry file, and index file for graphical features). These data sets can be retrieved as open data from the website of Lantmäteriet. This dataset is licensed under CC0 (Creative Commons Zero, https://creativecommons.org/public-domain/cc0/).

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/).

The MUSES LAI product at 500m spatial resolution and 8-day temporal resolution is provided on a Sinusoidal grid and spans from 2000 to 2023 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous.

This dataset is the MUSES LAI product in 2023. Please click here to download the MUSES LAI product in 2022.

Dataset Characteristics:

• Spatial Coverage: Global
• Temporal Coverage: 2023
• Spatial Resolution: 500m
• Temporal Resolution: 8 days
• Projection: Sinusoidal
• Data Format: HDF
• Scale: 0.01
• Valid Range: 0 – 1000

Citation (Please cite this paper whenever these data are used):

1. Xiao Zhiqiang, et al. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223.
2. Xiao Zhiqiang, et al. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318.
3. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225.
4. Xiao Zhiqiang, et al. (2017). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230.

If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

These data are a copy of MODIS data from the NASA Level-1 and Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC). The copy is potentially only a subset. Below is the description from https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MYD13A2 Global MODIS vegetation indices are designed to provide consistent spatial and temporal comparisons of vegetation conditions. Blue, red, and near-infrared reflectances, centered at 469-nanometers, 645-nanometers, and 858-nanometers, respectively, are used to determine the MODIS daily vegetation indices. The MODIS Normalized Difference Vegetation Index (NDVI) complements NOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI products providing continuity for time series applications over this rich historical archive. MODIS also includes a new Enhanced Vegetation Index (EVI) product that minimizes canopy background variations and maintains sensitivity over dense vegetation conditions. The EVI also uses the blue band to remove residual atmosphere contamination caused by smoke and sub-pixel thin cloud clouds. The MODIS NDVI and EVI products are computed from atmospherically-corrected bi-directional surface reflectances that have been masked for water, clouds, heavy aerosols, and cloud shadows. Global MYD13A2 data are provided every 16 days at 1-kilometer spatial resolution as a gridded level-3 product in the Sinusoidal projection. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes and global and regional climate. These data also may be used for characterizing land surface biophysical properties and processes, including primary production and land cover conversion. Collection-5 MODIS/Aqua Vegetation Indices products are Validated at Stage 2, meaning that accuracy has been assessed over a widely distributed set of locations and time periods via several ground-truth and validation efforts. Although there may be later improved versions, these data are ready for use in scientific publications. Shortname: MYD13A2 , Platform: Aqua , Instrument: MODIS , Processing Level: Level-3 , Spatial Resolution: 1 km , Temporal Resolution: 16 day , ArchiveSets: 6, 61 , Collection: MODIS Collection 6 (ArchiveSet 6) , PGE Number: PGE35 , File Naming Convention: MYD13A2.AYYYYDDD.hHHvVV.CCC.YYYYDDDHHMMSS.hdf YYYYDDD = Year and Day of Year of acquisition hHH = Horizontal tile number (0-35) vVV = Vertical tile number (0-17) CCC = Collection number YYYYDDDHHMMSS = Production Date and Time , Citation: Kamel Didan - University of Arizona, Alfredo Huete - University of Technology Sydney and MODAPS SIPS - NASA. (2015). MYD13A2 MODIS/Aqua Vegetation Indices 16-Day L3 Global 1km SIN Grid. NASA LP DAAC. http://doi.org/10.5067/MODIS/MYD13A2.006 , Keywords: Climate Change, Canopy Characteristics, Biomass, Vegetation Index, Plant Phenology, Length of Growing Season

The lack of spatial industrial output value data limited the risk and disaster assessment of industrial economy responding to global change. Therefore, we developed a new method to spatialize industrial output value coupling DMSP/OLS (Defense meteorological satellite program/operational linescan system) nighttime light data, MODIS (Moderate-resolution imaging spectroradiometer) annual vegetation data, industrial land distribution data and urbanization rate. A grid data set of 1 km industrial output value of China was created using this method. The main steps creating the data set were as follows: (1) data preprocessing and selecting stable lighting data; (2) constructing an Enhanced Vegetation Index (EVI) adjusted nighttime light index (EANTLI); (3) obtaining optimum light index by industrial land distribution data; (4) constructing spatial distribution model of industrial output value; (5) verifying data accuracy. We randomly selected 105 cities nationwide to assess the accuracy of the data set. The results show that the relative errors of whole samples ranged from 0% to 39.6%,the relative errors of most samples were less than 15%, and the average accuracy of the data set was as high as 81.40%. The dataset solved the problem that the industrial output value and service output value are difficult to be distinguished in value spatialization. The dataset broke the limits of administrative boundaries so as to directly reflect the spatial and temporal disparities and distribution features of industrial output value. The advances of the dataset could contribute to the identification of China’s key industrial distribution areas and discern the change trend of industry.