National Wetland Inventory (NWI) data for Minnesota provide information on the location, extent, and type of Minnesota wetlands. Natural resource managers use NWI data to improve the management, protection, and restoration of wetlands. Wetlands provide many ecological benefits including habitat for fish and wildlife, reducing floods, recharging, improving water quality, and supporting recreation.

These data were updated through a decade-long, multi-agency collaborative effort under leadership of the Minnesota Department of Natural Resources (MNDNR). Major funding was provided by the Environmental and Natural Resources Trust Fund.

This is the first statewide update of the NWI for Minnesota since the original inventory in the mid-1980s. The work was completed in phases by dividing the state into five project areas. Those project areas have all been edgematched into a final seamless statewide dataset.

Ducks Unlimited (Ann Arbor, MI) and St. Mary’s University Geospatial Services (Winona, MN) conducted the wetland mapping and classification under contract to the MNDNR. The Remote Sensing and Geospatial Analysis Laboratory at the University of Minnesota provided support for methods development and field validation. The DNR Resource Assessment Office provided additional support for data processing, field checking, and quality control review.

The updated NWI data delineate and classify wetlands according to the system developed by Cowardin et al. (1979), which is consistent with the original NWI. The updated data also contain a simplified plant community classification (SPCC) and a simplified hydrogeomorphic (HGM) classification. Quality assurance of the data included visual inspection, automated checks for attribute validity and topologic consistency, as well as a formal accuracy assessment based on an independent field verified data set. Further details on the methods employed can be found in the technical procedures document for this project located on the project website (http://www.dnr.state.mn.us/eco/wetlands/nwi_proj.html ).

DOWNLOAD NOTE: NWI data are only provided in either ESRI File Geodatabase or OGC GeoPackage formats. A Shapefile is not available because the size of the NWI dataset exceeds the limit for that format. If you are unable to use the File Geodatabase or GeoPackage, you can view data through Wetland Finder, an interactive mapping application on the DNR’s website (https://arcgis.dnr.state.mn.us/ewr/wetlandfinder ).

SYMBOLOGY NOTE: The ESRI File Geodatabase download includes four layer files that symbolize the data using four different wetland classification systems. The symbology layer files for the Cowardin class and the simplified HGM class are grouped into a smaller number of classes than the full elaborated classifications. Detail is available in the Minnesota Wetland Inventory User Guide and Summary Statistics report (https://files.dnr.state.mn.us/eco/wetlands/nwi-user-guide.pdf ). The layer files for these data have been set up to restrict drawing of the data when zoomed out beyond 1:250,000 scale. This is, in part, to prevent problems with slow performance with this large dataset.

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sgs&identifier=802 Webpage with information and links to data files for download

The National Wetland Inventory (NWI) data for Minnesota are being updated through a multi-agency collaborative effort under leadership of the Minnesota Department of Natural Resources (MNDNR). The update is being conducted in geographic phases with data released for each region as it is finalized. This metadata record covers the first three geographic regions: northeast, east-central, and southern Minnesota. Major funding was provided by the Environmental and Natural Resources Trust Fund. The updated NWI classify wetlands according to the system developed by Cowardin et al. (1979). The data also contains a simplified plant community classification (SPCC) and a simplified hydrogeomorphic (HGM) classification. Quality assurance of the data included visual inspection, automated checks for attribute validity and topologic consistency, as well as a formal accuracy assessment based on an independent field verified data set. Further details on the methods employed can be found in the technical procedures document for this project located on the project website (http://www.dnr.state.mn.us/eco/wetlands/nwi_proj.html ). The updated NWI data are primarily based on spring aerial imagery acquired in 2011 and LiDAR elevation data as well as other modern ancillary data. These data are intended to replace the original 1980s NWI data. NWI data support effective wetland management, protection, and restoration. The data provide a baseline for assessing the effectiveness of wetland policies and management actions. These data are used at all levels of government, as well as by private industry and non-profit organizations for wetland regulation and management, land use and conservation planning, environmental impact assessment, and natural resource inventories.

EAST-CENTRAL: Operational support for wetland mapping and classification was provided by Ducks Unlimited (DU) and support for methods development and field validation were provided by the Remote Sensing and Geospatial Analysis Laboratory at the University of Minnesota. The DNR Resource Assessment Office provided additional support data processing, field checking, and quality control review. The east-central project area consists of 13 counties including: Anoka, Carver, Chisago, Dakota, Goodhue, Hennepin, Isanti, Ramsey, Rice, Scott, Sherburne, Washington, and Wright Counties. The updated wetland inventory area included complete coverage for all USGS quarter quadrangles that intersect any of these counties (about 7,150 square mile).The NWI classification process for east-central Minnesota consisted of three basic steps: 1) creation of image segments (polygons), 2) RandomForest classification of the segments, and 3) photo-interpretation/editing of the classified image segments.

NORTHEAST: Operational support for wetland mapping and classification was provided by Ducks Unlimited (DU) and support for methods development and field validation were provided by the Remote Sensing and Geospatial Analysis Laboratory at the University of Minnesota. The DNR Resource Assessment Office provided additional support data processing, field checking, and quality control review. The project area consists of 5 counties in northeast Minnesota including: Cook, Koochiching, Lake, St. Louis, and a portion of Carlton Counties. The project encompasses 1,097 USGS quarter quads covering an area of 14,330 square miles (17% of the state). The NWI classification process for northeast Minnesota consisted of three basic steps: 1) creation of image segments (polygons), 2) RandomForest classification of the segments, and 3) photo-interpretation of the classified image segments. Please note that a portion of Koochiching County was completed as a separate pilot project. Those data are not yet included in the greater northeast regional product and will be incorporated after their validation is complete.

SOUTHERN: Operational support for wetland mapping and classification was provided by Geospatial Services of St. Mary's University of Minnesota (SMUMN) and support for methods development and field validation were provided by the Remote Sensing and Geospatial Analysis Laboratory at the University of Minnesota. The MNDNR Resource Assessment Office provided additional support data processing, field checking, and quality control review. Major funding was provided by the Environmental and Natural Resources Trust Fund. The project area consists of 36 counties in southern Minnesota including: Big Stone, Blue Earth, Brown, Chippewa, Cottonwood, Dodge, Faribault, Fillmore, Freeborn, Houston, Jackson, Kandiyohi, Lac qui Parle, Le Sueur, Lincoln, Lyon, Martin, McLeod, Meeker, Mower, Murray, Nicollet, Nobles, Olmstead, Pipestone, Redwood, Renville, Rock, Sibley, Steele, Swift, Wabasha, Waseca, Watonwan, Winona, and Yellow Medicine Counties. The project encompasses 1,787 USGS quarter quads covering an area of 23,900 square miles (28% of the state). The NWI classification process for southern Minnesota relied on visual image interpretation and other geospatial techniques to identify and classify wetlands using remote sensing data.

NOTE: The layer files for this data have been set up to restrict drawing of the data when zoomed out beyond 1:100,000 scale for the east-central region and when zoomed out beyond 1:250,000 scale for the northeast and southern regions. This is, in part, to prevent problems with slow performance with this large dataset. However, the data have also been compressed to speed the drawing performance and this results in a terminal system instability in ArcMap version 10.2 when the east-central data are viewed zoomed out beyond about 1:100,000 scale (1:60,000 scale for the Cowardin symbolized layer). This does not affect the more recent versions of ArcMap such as ArcMap 10.2.2. It also does not affect uncompressed versions of the data.

The MYD17A2H Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP minus the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.

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

Improvments/Changes from Previous Version * 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). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR.

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sgs&identifier=809 Webpage with information and links to data files for download

The MOD17A2HGF Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.

The MOD17A2HGF will be generated at the end of each year when the entire yearly 8-day MOD15A2H is available. Hence, the gap-filled MOD17A2HGF is the improved MOD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD17A2HGF in near-real time because it will be generated only at the end of a given year.

Known Issues * Operational and uncertainty issues are provided under Section 2 in the User Guide. * 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). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR.

This dataset contains raw GIS data sourced from the BAG (Basisregistratie Adressen en Gebouwen; Registry of Addresses and Buildings). It provides comprehensive information on buildings, including advanced height data and administrative details. It also contains geographic divisions within The Hague. Additionally, the dataset incorporates energy label data, offering insights into the energy efficiency and performance of these buildings. This combined dataset serves as the backbone of a Master's thesis in Industrial Ecology, analysing residential and office cooling and its environmental impacts in The Hague, Netherlands. The codebase of this analysis can be found in this Github repository: https://github.com/simonvanlierde/msc-thesis-ie

The dataset includes a background research spreadsheet containing supporting calculations. It also presents geopackages with results from the cooling demand model (CDM) for various scenarios: Status quo (SQ), 2030, and 2050 scenarios (Low, Medium, and High)

Background research data

The background_research_data.xlsx spreadsheet contains comprehensive background research calculations supporting the shaping of input parameters used in the model. It contains several sheets:

• Cooling Technologies: Details the various cooling technologies examined in the study, summarizing their characteristics and the market penetration mixes used in the analysis.
• LCA Results of Ventilation Systems: Provides an overview of the ecoinvent processes serving as proxies for the life-cycle impacts of cooling equipment, along with calculations of the weight of cooling systems and contribution tables from the LCA-based assessment.
• Material Scarcity: A detailed examination of the critical raw material content in the material footprint of ecoinvent processes, representing cooling equipment.
• Heat Plans per Neighbourhood: Forecasts of future heating solutions for each neighbourhood in The Hague.
• Building Stock: Analysis of the projected growth trends in residential and office building stocks in The Hague. AC Market: Market analysis covering air conditioner sales in the Netherlands from 2002 to 2022.
• Climate Change: Computations of climate-related parameters based on KNMI climate scenarios.
• Electricity Mix Analysis: Analysis of future projections for the Dutch electricity grid and calculations of life-cycle carbon intensities of the grid.

Input data

Geographic divisions

• The outline of The Hague municipality through the Municipal boundaries (Gemeenten) layer, sourced from the Administrative boundaries (Bestuurlijke Gemeenten) dataset on the PDOK WFS service.
• District (Wijken) and Neighbourhood (Buurten) layers were downloaded from the PDOK WFS service (from the CBS Wijken en Buurten 2022 data package) and clipped to the outline of The Hague.
• The 4-digit postcodes layer was downloaded from PDOK WFS service (CBS Postcode4 statistieken 2020) and clipped to The Hague's outline. The postcodes within The Hague were subsequently stored in a csv file.
• The census block layer was downloaded from the PDOK WFS service (from the CBS Vierkantstatistieken 100m 2021 data package) and also clipped to the outline of The Hague.
• These layers have been combined in the GeographicDivisions_TheHague GeoPackage.

BAG data

• BAG data was acquired through the download of a BAG GeoPackage from the BAG ATOM download page.
• In the resulting GeoPackage, the Residences (Verblijfsobject) and Building (Pand) layers were clipped to match The Hague's outline.
• The resulting residence data can be found in the BAG_buildings_TheHague GeoPackage.

3D BAG

• Due to limitations imposed by the PDOK WFS service, which restricts the number of downloadable buildings to 10,000, it was necessary to acquire 145 individual GeoPackages for tiles covering The Hague from the 3D BAG website.
• These GeoPackages were merged using the ogr2ogr append function from the GDAL library in bash.
• Roof elevation data was extracted from the LoD 1.2 2D layer from the resulting GeoPackage.
• Ground elevation data was obtained from the Pand layer.
• Both of these layers were clipped to match The Hague's outline.
• Roof and ground elevation data from the LoD 1.2 2D and Pand layers were joined to the Pand layer in the BAG dataset using the BAG ID of each building.
• The resulting data can be found in the BAG_buildings_TheHague GeoPackage.

Energy labels

• Energy labels were downloaded from the Energy label registry (EP-online) and stored in energy_labels_TheNetherlands.csv.

UHI effect data

• A bitmap with the UHI effect intensity in The Hague was retrieved from the from the Dutch Natural Capital Atlas (Atlas Natuurlijk Kapitaal) and stored in UHI_effect_TheHague.tiff.

Output data

• The residence-level data joined to the building layer is contained in the BAG_buildings_with_residence_data_full GeoPackage.
• The results for each building, according to different scenarios, are compiled in the buildings_with_CDM_results_[scenario]_full GeoPackages. The scenarios are abbreviated as follows:
  • SQ: Status Quo, covering the 2018-2022 reference period.
  • 2030: An average scenario projected for the year 2030.
  • 2050_L: A low-impact, best-case scenario for 2050.
  • 2050_M: A medium-impact, moderate scenario for 2050.
  • 2050_H: A high-impact, worst-case scenario for 2050.

Click to downloadClick for metadataService URL: https://gis.dnr.wa.gov/site2/rest/services/Public_Forest_Practices/WADNR_PUBLIC_FP_Water_Type/MapServer/4For large areas, like Washington State, download as a file geodatabase. Large data sets like this one, for the State of Washington, may exceed the limits for downloading as shape files, excel files, or KML files. For areas less than a county, you may use the map to zoom to your area and download as shape file, excel or KML, if that format is desired.The DNR Forest Practices Wetlands Geographic Information System (GIS) Layer is based on the National Wetlands Inventory (NWI). In cooperation with the Washington State Department of Ecology, DNR Forest Practices developed a systematic reclassification of the original USFWS wetlands codes into WAC 222-16-035 types. The reclassification was done in 1995 according to the Forest Practice Rules in place at the time. The WAC's for defining wetlands are 222-16-035 and 222-16-050.The DNR Forest Practices Wetlands Geographic Information System (GIS) Layer is based on the National Wetlands Inventory (NWI). In cooperation with the Washington State Department of Ecology, DNR Forest Practices developed a systematic reclassification of the original USFWS wetlands codes into WAC 222-16-035 types. The reclassification was done in 1995 according to the Forest Practice Rules in place at the time. The WAC's for defining wetlands are 222-16-035 and 222-16-050.It is intended that these data be only a first step in determining whether or not wetland issues have been or need to be addressed in an area. The DNR Forest Practices Division and the Department of Ecology strongly supports the additional use of hydric soils (from the GIS soils layer) to add weight to the call of 'wetland'. Reports from the Department of Ecology indicate that these data may substantially underestimate the extent of forested wetlands. Various studies show the NWI data is 25-80% accurate in forested areas. Most of these data were collected from stereopaired aerial photos at a scale of 1:58,000. The stated accuracy is that of a 1:24,000 map, or plus or minus 40 feet. In addition, some parts of the state have data that are 30 years old and only a small percentage have been field checked. Thus, for regulatory purposes, the user should not rely solely on these data. On-the-ground checking must accompany any regulatory call based on these data.The reclassification is based on the USFWS FWS_CODE. The FWS_CODE is a concatenation of three subcomponents: Wetland system, class, and water regime. Forest Practices further divided the components into system, subsystem, class, subclass, water regime, special modifiers, xclass, subxclass, and xsystem. The last three items (xsomething) are for wetland areas which do not easily lend themselves to one class alone. The resulting classification system uses two fields: WLND_CLASS and WLND_TYPE. WLND_CLASS indicates whether the polygon is a forested wetland (F), open water (O), or a vegetated wetland (W). WLND_TYPE, indicates whether the wetland is a type A (1), type B (2), or a generic wetland (3) that doesn't fit the categories for A or B type wetlands. WLND_TYPE = 0 (zero) is used where WLND_CLASS = O (letter "O").

The wetland polygon is classified as F, forested wetland; O, open water; or W, vegetated wetland depending on the following FWS_CODE categories: F O W --------------------------------------------------- Forested Open Vegetated Wetland Water Wetland --------------------------------------------PFO* POW PUB5 E2FO PRB* PML2 PUB1-4 PEM* PAB* L2US5 PUS1-4 L2EM2 PFL* PSS* L1RB* PML1 L1UB*
L1AB* L1OW L2RB* L2UB* L2AB* L2RS* L2US1-4 L2OW

• indicates inclusion of the subcategory (ie. PEM* includes PEM1F, PEM1FB, etc.).

DNR FOREST PRACTICES WETLANDS DATASET ON FPARS Internet Mapping Website: The FPARS Resource Map and Water Type Map display Forested, Type A, Type B, and "other" wetlands. Open water polygons are not displayed on the FPARS Resource Map and Water Type Map in an attempt to minimize clutter. The following code combinations are found in the DNR Forest Practices wetlands dataset:

WLND_CLASS WLND_TYPE wetland polygon classification F 3 Forested wetland as defined in WAC 222-16-035 O 0 *NWI open water (not displayed on FPARS Resource or Water Type Maps) W 1 Type A Wetland as defined in WAC 222-16-035 W 2 Type B Wetland as defined in WAC 222-16-035 W 3 other wetland

• NWI open water polygons are indicated by WLND_CLASS = O and WLND_TYPE = 0. Open water is used in the USFWS and WAC 222-16-035 classification system. These open water polygons are not included in the FPARS Resource Map and Water Type Map views of this dataset in an attempt to minimize clutter on the FPARS maps.

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire 8-Day (MYD14A2) Version 6.1 data are generated at 1 kilometer (km) spatial resolution as a Level 3 product. The MYD14A2 gridded composite contains maximum value of individual fire pixel classes detected during the eight days of acquisition.

The Science Dataset (SDS) layers include the fire mask and pixel quality indicators.

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

Improvments/Changes from Previous Version * 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).

The Terra and Aqua combined MCD64A1 Version 6.1 Burned Area data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned-area and quality information. The MCD64A1 burned-area mapping approach employs 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance imagery coupled with 1 kilometer (km) MODIS active fire observations. The algorithm uses a burn sensitive Vegetation Index (VI) to create dynamic thresholds that are applied to the composite data. The VI is derived from MODIS shortwave infrared atmospherically corrected surface reflectance bands 5 and 7 with a measure of temporal texture. The algorithm identifies the date of burn for the 500 m grid cells within each individual MODIS tile. The date is encoded in a single data layer as the ordinal day of the calendar year on which the burn occurred with values assigned to unburned land pixels and additional special values reserved for missing data and water grid cells.

The data layers provided in the MCD64A1 product include Burn Date, Burn Data Uncertainty, Quality Assurance, along with First Day and Last Day of reliable change detection of the year.

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).

The MYD17A2HGF Version 6.1 Gross Primary Productivity (GPP) Gap-Filled product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.

The MYD17A2HGF will be generated at the end of each year when the entire yearly 8-day MYD15A2H is available. Hence, the gap-filled MYD17A2HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A2HGF in near-real time because it will be generated only at the end of a given year.

Known Issues * Operational and uncertainty issues are provided under Section 2 in the User Guide.
* For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

Improvments/Changes from Previous Version * 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). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR.

The Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire MOD14 Version 6.1 product is produced daily in 5-minute temporal satellite increments (swaths) at 1 kilometer (km) spatial resolution. The MOD14 product is used to generate all of the higher level fire products, but can also be used to identify fires and other thermal anomalies, such as volcanoes. Each swath of data is approximately 2,030 kilometers along track (long), and 2,300 kilometers across track (wide).

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).

This wetlands location information has been temporarily removed.This is to ensure we present information in a way that is consistent with the National Policy Statement for Freshwater Management 2020, which describes how regional councils should undertake mapping of wetlands.The temporary removal of this map has no bearing on our rules and Good Management Practices regarding wetlands – which must still be followed. Please visit the EnvironmentCanterbury website for more information.

If you need information or data about wetlands on your property, please contact Customer Services on 0800 324 636 or email ecinfo@ecan.govt.nz.Description:This geodatabase collates wetland location and extent information for Canterbury Region. Information comes from a variety of sources and has varying levels of detail or precision on features such as wetland location, extent, type, condition and ecological significance.The Canterbury Wetlands geodatabase distinguishes between ‘ground survey-detailed’, ‘ground survey–rapid’ and ‘aerial survey’ wetlands. At present, most of the mapped wetlands are in the ‘aerial survey’ category. Survey methodologies are described below.Initial database coverage of Canterbury Region was completed in March 2019. Updates and edits to the database are ongoing.Background:Canterbury has a wide variety of wetland types, reflecting diverse geography, hydrology and climate. The formal definition of wetlands in the Resource Management Act (1991) is “permanently or intermittently wet areas, shallow water or land/water margins that support a natural ecosystem of plants and animals that are adapted to living in wet conditions”. This definition is very broad and could be interpreted to include all waterbodies – springs, streams, rivers, lakes, estuaries, the marine environment. However, the ‘wetland’ habitats shown in this geodatabase share a number of environmental and ecological features that distinguish them from adjoining aquatic (and terrestrial) ecosystems. These are: Temporary or permanent shallow standing water and/or waterlogged soils; Temporary or permanent anaerobic conditions in the soil; Dominance by emergent aquatic plants.The Canterbury Wetlands geodatabase generally excludes streams, rivers, lakes and the Coastal Marine Area (CMA) as these waterbodies have been depicted in other datasets, although wetland habitats (as defined above) located on the margins of rivers, streams, lakes and the CMA are included. Nevertheless, wetlands, springs, streams, rivers, lakes, estuaries and the coastal marine environment are all waterbodies which merge into one another and are ecologically connected. We therefore recommend that the Canterbury Wetlands geodatabase is used in conjunction with other spatial information relating to the region’s waterbodies.Methodology:“Ground survey-detailed” wetlands have been field inspected and mapped/described in some detail. These ground surveys have mostly been carried out by Environment Canterbury staff, or wetland ground survey information provided by other agencies such as the Department of Conservation and the New Zealand Defence Force. Wetlands that have been ecologically surveyed and mapped as part of Resource Consent Applications may also be included in this category.For vegetated wetland survey areas, wetland boundaries are delineated where more than 50% of the dominant plant species from all vegetation strata are ‘Obligate’, ‘Facultative Wetland’ or ‘Facultative’ (i.e. the plant community is considered hydrophytic – Clarkson 2013). Descriptions of wetland hydrosystems, wetland class and vegetation types listed in the attributes tables for ground surveyed wetlands follow Johnson and Gerbeaux (2004), while assessment scores for wetland condition and threat follow Clarkson et al. (2003 & 2014). An assessment of ecological significance against Canterbury Regional Policy Statement criteria is also provided for “ground survey-detailed” wetlands.“Ground survey-rapid” wetlands have also been field inspected with the presence and extent of wetland habitats confirmed. Again, wetland boundaries are delineated where more than 50% of the dominant plant species from all vegetation strata are ‘Obligate’, ‘Facultative Wetland’ or ‘Facultative’ (Clarkson 2013).“Aerial survey” wetlands have been mapped by delineating the outline of known and likely/potential wetland habitats from the latest high-resolution aerial imagery available at the time of mapping. Characteristic vegetation types, colours, patterns, presence of visible water were used to identify wetlands on aerial photos, with hydrological and topographical information also considered. Note that precision limitations and uncertainties mean that there will be errors or omissions in this part of the dataset. We also did not attempt to map wetland habitats smaller than 50m and/or narrower than 5m by the ‘aerial survey’ method. For this reason, ‘aerial survey’ wetlands in this geodatabase should be used to indicate likely presence of wetlands but should not relied on to show precise extent of wetlands depicted, or location of all wetland habitats.Where available, existing ecological information for ‘aerial survey’ wetlands is noted in the attributes and the source referenced. However, for most ‘aerial survey’ wetlands, apart from date of base imagery and data capture, no further information is provided in the attributes.Currency:Current at time of survey date for ‘ground survey – detailed’ and ‘ground survey – rapid’ sites; current to date of aerial imagery for ‘aerial survey’ sites.Data owner:Environment CanterburyData interpretation and limitations:The Canterbury Wetlands geodatabase is not a ‘schedule’ in a plan and has not been tested through a statutory planning process. The GIS layer is not comprehensive, nor does it attempt to systematically apply the Canterbury Land and Water Regional Plan definition of ‘wetland’. Nor does it attempt to apply the definition of ‘natural wetland’ in the National Policy Statement-Freshwater Management (NPS-FM) or National Environmental Standards-Freshwater (NES-F). The database is not ‘locked in’ and can potentially be changed or updated with more information.Attributes of the Canterbury Wetlands geodatabaseNote: the attribute tables will only be complete for ‘ground surveyed – detailed’ wetlands.AREA NAMEName of wetland or survey area.HYDROSYSTEMWetland ecosystems are differentiated by landform and hydrological setting, and by water salinity, chemistry and temperature. Estuarine, riverine, lacustrine and palustrine hydrosystems have been recorded for ground-surveyed wetlands in Canterbury.SUBSYSTEMHydrosystems can be further described according to the water regime. Periodicity of inundation is the main feature e.g. permanent, seasonal, tidal, non-tidal, ephemeral.WETLAND CLASSMain wetland classes for Canterbury are swamp, marsh, fen, bog, seepage, shallow water, ephemeral wetland and saltmarsh.WETLAND FORMLandforms that wetlands occupy, and forms they create or contain.VEGETATION TYPEDominant vegetation type(s) within wetland. A general description of the growth form (or structure) and composition of the vegetation. For example: raupō reedland, saltmarsh herbfield, willow forest.SURVEY TYPE‘Ground survey-detailed’, ‘Ground survey-rapid’ or ‘Aerial survey’.SURVEY DATEDate of wetland ground survey or delineation by aerial survey.IMAGERY DATEDate of imagery used when delineating wetlands by aerial survey method.ECOLOGICAL SIGNIFICANCEAssessment of ecological significance for ground surveyed wetlands against Canterbury Regional Policy Statement (CRPS) criteria and associated guidelines (Wildland Consultants, 2013). Three level score:High: Meets one or more significance criteria; Regionally, Nationally or Internationally SignificantModerate: Meets one or more significance criteria; Locally SignificantLow: Does not meet any CRPS ecological significance criteriaWetland IDUnique wetland polygon identification numberDocument ID 1Hyperlink to wetland condition, threat and significance assessment (only for ‘ground survey – detailed’ wetlands).Document ID 2Hyperlink to historic wetland condition, threat and significance assessment (only for repeat ground survey-detailed wetlands). ReferencesClarkson BR, Sorrell BK, Reeves PN, Champion PD, Partridge TR, Clarkson BD. 2003. Handbook for monitoring wetland condition. Coordinated monitoring of New Zealand wetlands. A Ministry for the Environment SMF funded project. Ministry for the Environment, Wellington. 74pp.Clarkson BR. 2013. A vegetation tool for wetland delineation in New Zealand. Landcare Research contract report prepared for Meridian Energy Limited. 62pp.Clarkson BR, Fitzgerald NB, Overton JM 2014. A methodology for monitoring Bay of Plenty wetlands. Landcare Research contract report LC1779.Fraser S, Singleton P, Clarkson BR. 2018. Hydric soils – field identification guide. Landcare Research contract report LC3233.Johnson P. Gerbeaux G. 2004. Wetland Types in New Zealand. Department of Conservation. Wellington. 184pp.Wildlands Consultants. 2013. Guidelines for the application of ecological significance criteria for indigenous vegetation and habitats of indigenous fauna in Canterbury Region. Contract Report No. 2289i.

This geodatabase can be found internally as GIS.DBO.WETLANDS_NZTM_Canterbury_Wetlands_Updated2019

The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire Daily (MOD14A1) Version 6.1 data are generated every eight days at 1 kilometer (km) spatial resolution as a Level 3 product. MOD14A1 contains eight consecutive days of fire data conveniently packaged into a single file.

The Science Dataset (SDS) layers include the fire mask, pixel quality indicators, maximum fire radiative power (MaxFRP), and the position of the fire pixel within the scan. Each layer consists of daily per pixel information for each of the eight days of data acquisition.

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).

The MOD17A3HGF Version 6.1 product provides information about annual Gross and Net Primary Production (GPP and NPP) at 500 meter (m) pixel resolution. Annual Terra Moderate Resolution Imaging Spectroradiometer (MODIS) GPP and NPP is derived from the sum of all 8-day GPP Net Photosynthesis (PSN) products (MOD17A2H) from the given year. The PSN value is the difference of the GPP and the Maintenance Respiration (MR).

The MOD17A3HGF will be generated at the end of each year when the entire yearly 8-day MOD15A2H is available. Hence, the gap-filled MOD17A3HGF is the improved MOD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MOD17A3HGF in near-real time because it will be generated only at the end of a given year.

Known Issues * Operational and uncertainty issues are provided under Section 2 in the User Guide. * 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). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR.

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sgs&identifier=814 Webpage with information and links to data files for download

The MYD17A3HGF Version 6.1 product provides information about annual Gross and Net Primary Production (GPP and NPP) at 500 meter (m) pixel resolution. Annual Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) GPP and NPP is derived from the sum of all 8-day GPP and Net Photosynthesis (PSN) products (MYD17A2H) from the given year. The PSN value is the difference of the GPP and the Maintenance Respiration (MR).

The MYD17A3HGF will be generated at the end of each year when the entire yearly 8-day MYD15A2H is available. Hence, the gap-filled MYD17A3HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A3HGF in near-real time because it will be generated only at the end of a given year.

Known Issues * Operational and uncertainty issues are provided under Section 2 in the User Guide. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

Improvments/Changes from Previous Version * 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). * The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR.

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire (MYD14) Version 6.1 product is produced daily in 5-minute temporal satellite increments (swaths). The MYD14 product is used to generate all of the higher level fire products, but can also be used to identify fires and other thermal anomalies, such as volcanoes. Each swath of data is approximately 2,030 kilometers along track (long), and 2,300 kilometers across track (wide).

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

Improvments/Changes from Previous Version * 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).

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sgs&identifier=811 Webpage with information and links to data files for download