This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

6 inch resolution raster image of New York City, classified by landcover type. High resolution land cover data set for New York City. This is the 6 inch version of the high-resolution land cover dataset for New York City. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at 3 square feet. The primary sources used to derive this land cover layer were the 2010 LiDAR and the 2008 4-band orthoimagery. Ancillary data sources included GIS data (city boundary, building footprints, water, parking lots, roads, railroads, railroad structures, ballfields) provided by New York City (all ancillary datasets except railroads); UVM Spatial Analysis Laboratory manually created railroad polygons from manual interpretation of 2008 4-band orthoimagery. The tree canopy class was considered current as of 2010; the remaining land-cover classes were considered current as of 2008. Object-Based Image Analysis (OBIA) techniques were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. More than 35,000 corrections were made to the classification. Overall accuracy was 96%. This dataset was developed as part of the Urban Tree Canopy (UTC) Assessment for New York City. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area. This project was funded by National Urban and Community Forestry Advisory Council (NUCFAC) and the National Science Fundation (NSF), although it is not specifically endorsed by either agency. The methods used were developed by the University of Vermont Spatial Analysis Laboratory, in collaboration with the New York City Urban Field Station, with funding from the USDA Forest Service.

A 6-in resolution 8-class land cover dataset derived from the 2017 Light Detection and Ranging (LiDAR) data capture. This dataset was developed as part of an updated urban tree canopy assessment and therefore represents a ''top-down" mapping perspective in which tree canopy overhanging features is assigned to the tree canopy class. The eight land cover classes mapped were: (1) Tree Canopy, (2) Grass\Shrubs, (3) Bare Soil, (4) Water, (5) Buildings, (6) Roads, (7) Other Impervious, and (8) Railroads. The primary sources used to derive this land cover layer were 2017 LiDAR (1-ft post spacing) and 2016 4-band orthoimagery (0.5-ft resolution). Object based image analysis was used to automate land-cover features using LiDAR point clouds and derivatives, orthoimagery, and vector GIS datasets -- City Boundary (2017, NYC DoITT) Buildings (2017, NYC DoITT) Hydrography (2014, NYC DoITT) LiDAR Hydro Breaklines (2017, NYC DoITT) Transportation Structures (2014, NYC DoITT) Roadbed (2014, NYC DoITT) Road Centerlines (2014, NYC DoITT) Railroads (2014, NYC DoITT) Green Roofs (date unknown, NYC Parks) Parking Lots (2014, NYC DoITT) Parks (2016, NYC Parks) Sidewalks (2014, NYC DoITT) Synthetic Turf (2018, NYC Parks) Wetlands (2014, NYC Parks) Shoreline (2014, NYC DoITT) Plazas (2014, NYC DoITT) Utility Poles (2014, ConEdison via NYCEM) Athletic Facilities (2017, NYC Parks)

For the purposes of classification, only vegetation > 8 ft were classed as Tree Canopy. Vegetation below 8 ft was classed as Grass/Shrub.

To learn more about this dataset, visit the interactive "Understanding the 2017 New York City LiDAR Capture" Story Map -- https://maps.nyc.gov/lidar/2017/ Please see the following link for additional documentation on this dataset -- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_LandCover.md

New Orleans Dataset for ArcGIS
This geodatabase contains citywide raster and vector data for New Orleans, Louisiana, USA in the North American Datum of 1983 (NAD 83) / Louisiana South State Plane Feet with EPSG code 3452. Unzip the archive and open in ArcGIS.

Data Sources

• U.S. Army Corps of Engineers 2012 Lidar Survey of New Orleans
• New Orleans Open Data

License
This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon.

Abstract

The Urban Green Raster Germany is a land cover classification for Germany that addresses in particular the urban vegetation areas. The raster dataset covers the terrestrial national territory of Germany and has a spatial resolution of 10 meters. The dataset is based on a fully automated classification of Sentinel-2 satellite data from a full 2018 vegetation period using reference data from the European LUCAS land use and land cover point dataset.
The dataset identifies eight land cover classes. These include Built-up, Built-up with significant green share, Coniferous wood, Deciduous wood, Herbaceous vegetation (low perennial vegetation), Water, Open soil, Arable land (low seasonal vegetation).
The land cover dataset provided here is offered as an integer raster in GeoTiff format. The assignment of the number coding to the corresponding land cover class is explained in the legend file.

Data acquisition

The data acquisition comprises two main processing steps: (1) Collection, processing, and automated classification of the multispectral Sentinel 2 satellite data with the “Land Cover DE method”, resulting in the raw land cover classification dataset, NDVI layer, and RF assignment frequency vector raster. (2) GIS-based postprocessing including discrimination of (densely) built-up and loosely built-up pixels according NDVI threshold, and creating water-body and arable-land masks from geo-topographical base-data (ATKIS Basic DLM) and reclassification of water and arable land pixels based on the assignment frequency.

Data collection

Satellite data were searched and downloaded from the Copernicus Open Access Hub (https://scihub.copernicus.eu/).

The LUCAS reference and validation points were loaded from the Eurostat platform (https://ec.europa.eu/eurostat/web/lucas/data/database).

The processing of the satellite data was performed at the DLR data center in Oberpfaffenhofen.

GIS-based post-processing of the automatic classification result was performed at IOER in Dresden.

Value of the data

The dataset can be used to quantify the amount of green areas within cities on a homogeneous data base [5].

Thus it is possible to compare cities of different sizes regarding their greenery and with respect to their ratio of green and built-up areas [6].

Built-up areas within cities can be discriminated regarding their built-up density (dense built-up vs. built-up with higher green share).

Data description

A Raster dataset in GeoTIFF format: The dataset is stored as an 8 bit integer raster with values ranging from 1 to 8 for the eight different land cover classes. The nomenclature of the coded values is as follows: 1 = Built-up, 2=open soil; 3=Coniferous wood, 4= Deciduous wood, 5=Arable land (low seasonal vegetation), 6=Herbaceous vegetation (low perennial vegetation), 7=Water, 8=Built-up with significant green share. Name of the file ugr2018_germany.tif. The dataset is zipped alongside with accompanying files: *.twf (geo-referencing world-file), *.ovr (Overlay file for quick data preview in GIS), *.clr (Color map file).

A text file with the integer value assignment of the land cover classes. Name of the file: Legend_LC-classes.txt.

Experimental design, materials and methods

The first essential step to create the dataset is the automatic classification of a satellite image mosaic of all available Sentinel-2 images from May to September 2018 with a maximum cloud cover of 60 percent. Points from the 2018 LUCAS (Land use and land cover survey) dataset from Eurostat [1] were used as reference and validation data. Using Random Forest (RF) classifier [2], seven land use classes (Deciduous wood, Coniferous wood, Herbaceous vegetation (low perennial vegetation), Built-up, Open soil, Water, Arable land (low seasonal vegetation)) were first derived, which is methodologically in line with the procedure used to create the dataset "Land Cover DE - Sentinel-2 - Germany, 2015" [3]. The overall accuracy of the data is 93 % [4].

Two downstream post-processing steps served to further qualify the product. The first step included the selective verification of pixels of the classes arable land and water. These are often misidentified by the classifier due to radiometric similarities with other land covers; in particular, radiometric signatures of water surfaces often resemble shadows or asphalt surfaces. Due to the heterogeneous inner-city structures, pixels are also frequently misclassified as cropland.

To mitigate these errors, all pixels classified as water and arable land were matched with another data source. This consisted of binary land cover masks for these two land cover classes originating from the Monitor of Settlement and Open Space Development (IOER Monitor). For all water and cropland pixels that were outside of their respective masks, the frequencies of class assignments from the RF classifier were checked. If the assignment frequency to water or arable land was at least twice that to the subsequent class, the classification was preserved. Otherwise, the classification strength was considered too weak and the pixel was recoded to the land cover with the second largest assignment frequency.

Furthermore, an additional land cover class "Built-up with significant vegetation share" was introduced. For this purpose, all pixels of the Built-up class were intersected with the NDVI of the satellite image mosaic and assigned to the new category if an NDVI threshold was exceeded in the pixel. The associated NDVI threshold was previously determined using highest resolution reference data of urban green structures in the cities of Dresden, Leipzig and Potsdam, which were first used to determine the true green fractions within the 10m Sentinel pixels, and based on this to determine an NDVI value that could be used as an indicator of a significant green fraction within the built-up pixel. However, due to the wide dispersion of green fraction values within the built-up areas, it is not possible to establish a universally valid green percentage value for the land cover class of Built-up with significant vegetation share. Thus, the class essentially serves to the visual differentiability of densely and loosely (i.e., vegetation-dominated) built-up areas.

Acknowledgments

This work was supported by the Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR) [10.06.03.18.101].The provided data has been developed and created in the framework of the research project “Wie grün sind bundesdeutsche Städte?- Fernerkundliche Erfassung und stadträumlich-funktionale Differenzierung der Grünausstattung von Städten in Deutschland (Erfassung der urbanen Grünausstattung)“ (How green are German cities?- Remote sensing and urban-functional differentiation of the green infrastructure of cities in Germany (Urban Green Infrastructure Inventory)). Further persons involved in the project were: Fabian Dosch (funding administrator at BBSR), Stefan Fina (research partner, group leader at ILS Dortmund), Annett Frick, Kathrin Wagner (research partners at LUP Potsdam).

References

[1] Eurostat (2021): Land cover / land use statistics database LUCAS. URL: https://ec.europa.eu/eurostat/web/lucas/data/database

[2] L. Breiman (2001). Random forests, Mach. Learn., 45, pp. 5-32

[3] M. Weigand, M. Wurm (2020). Land Cover DE - Sentinel-2—Germany, 2015 [Data set]. German Aerospace Center (DLR). doi: 10.15489/1CCMLAP3MN39

[4] M. Weigand, J. Staab, M. Wurm, H. Taubenböck, (2020). Spatial and semantic effects of LUCAS samples on fully automated land use/land cover classification in high-resolution Sentinel-2 data. Int J Appl Earth Obs, 88, 102065. doi: https://doi.org/10.1016/j.jag.2020.102065

[5] L. Eichler., T. Krüger, G. Meinel, G. (2020). Wie grün sind deutsche Städte? Indikatorgestützte fernerkundliche Erfassung des Stadtgrüns. AGIT Symposium 2020, 6, 306–315. doi: 10.14627/537698030

[6] H. Taubenböck, M. Reiter, F. Dosch, T. Leichtle, M. Weigand, M. Wurm (2021). Which city is the greenest? A multi-dimensional deconstruction of city rankings. Comput Environ Urban Syst, 89, 101687. doi: 10.1016/j.compenvurbsys.2021.101687

The Raster Based GIS Coverage of Mexican Population is a gridded coverage (1 x 1 km) of Mexican population. The data were converted from vector into raster. The population figures were derived based on available point data (the population of known localities - 30,000 in all). Cell values were derived using a weighted moving average function (Burrough, 1986), and then calculated based on known population by state. The result from this conversion is a coverage whose population data is based on square grid cells rather than a series of vectors. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI).

Basic Global Dataset for GRASS GIS
This geospatial dataset contains global raster and vector data. The top level directory global-dataset is a GRASS GIS location for the World Geodetic System 1984 (WGS84) with EPSG code 4326. Inside the location there is the PERMANENT mapset, a license file, and readme file.

Instructions
Install GRASS GIS, unzip this archive, and move the location into your GRASS GIS database directory. If you are new to GRASS GIS read the first time users guide.

Data Source

• Natural Earth

License
This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon.

Governor's Island Dataset for GRASS GIS This geospatial dataset contains raster and vector data for Governor's Island, New York City, USA. The top level directory governors_island_for_grass is a GRASS GIS location for NAD_1983_StatePlane_New_York_Long_Island_FIPS_3104_Feet in US Surveyor's Feet with EPSG code 2263. Inside the location there is the PERMANENT mapset, a license file, data record, readme file, workspace, color table, category rules, and scripts for data processing. This dataset was created for the course GIS for Designers. Instructions Install GRASS GIS, unzip this archive, and move the location into your GRASS GIS database directory. If you are new to GRASS GIS read the first time users guide. Data Sources https://orthos.dhses.ny.gov/ https://data.cityofnewyork.us/ Maps Orthophotographs from 2012, 2014, 2016, 2018, and 2020 Digital elevation model from 2017 Digital surface models from 2014 and 2017 Landcover from 2014 License This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon.

scripts.zip

arcgisTools.atbx: terrainDerivatives: make terrain derivatives from digital terrain model (Band 1 = TPI (50 m radius circle), Band 2 = square root of slope, Band 3 = TPI (annulus), Band 4 = hillshade, Band 5 = multidirectional hillshades, Band 6 = slopeshade). rasterizeFeatures: convert vector polygons to raster masks (1 = feature, 0 = background).

makeChips.R: R function to break terrain derivatives and chips into image chips of a defined size. makeTerrainDerivatives.R: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool). merge_logs.R: R script to merge training logs into a single file. predictToExtents.ipynb: Python notebook to use trained model to predict to new data. trainExperiments.ipynb: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. assessmentExperiments.ipynb: Python code to generate assessment metrics using PyTorch and the torchmetrics library. graphs_results.R: R code to make graphs with ggplot2 to summarize results. makeChipsList.R: R code to generate lists of chips in a directory. makeMasks.R: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool).

terraceDL.zip

dems: LiDAR DTM data partitioned into training, testing, and validation datasets based on HUC8 watershed boundaries. Original DTM data were provided by the Iowa BMP mapping project: https://www.gis.iastate.edu/BMPs. extents: extents of the training, testing, and validation areas as defined by HUC 8 watershed boundaries. vectors: vector features representing agricultural terraces and partitioned into separate training, testing, and validation datasets. Original digitized features were provided by the Iowa BMP Mapping Project: https://www.gis.iastate.edu/BMPs.

High resolution land cover dataset for City of Boston, MA. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The primary sources used to derive this land cover layer were 2013 LiDAR data, 2014 Orthoimagery, and 2016 NAIP imagery. Ancillary data sources included GIS data provided by City of Boston, MA or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2500 and all observable errors were corrected.

High resolution land cover dataset for City of Boston, MA. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The primary sources used to derive this land cover layer were 2013 LiDAR data, 2014 Orthoimagery, and 2016 NAIP imagery. Ancillary data sources included GIS data provided by City of Boston, MA or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2500 and all observable errors were corrected.

Credits: University of Vermont Spatial Analysis Laboratory in collaboration with the City of Boston, Trust for Public Lands, and City of Cambridge.

(Link to Metadata) High resolution land cover dataset for Vermont. Eight land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, (7) other paved surfaces,and (8) railroads. The primary sources used to derive this land cover layer were 2013-2017 LiDAR data and 2016 NAIP imagery. Ancillary data sources included GIS data provided by the State of Vermont or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected.

Wind Farms - follows on from the 'Dave' Data Download case study. View and symbolise OS raster and height data and Wind Farm location data. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2014-04-10 and migrated to Edinburgh DataShare on 2017-02-22.

This archive contains an ArcGIS Pro project with a geodatabase of raster and vector data for the Hills region of Governor's Island, New York City, USA. The SRS is NAD83 / New York Long Island (ftUS) with the EPSG code 2263.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. We converted the photointerpreted data into a GIS-usable format employing three fundamental processes; (1) orthorectify, (2) digitize, and (3) database enhancement. All digital map automation was projected in Universal Transverse Mercator (UTM) projection, Zone 12, using North American Datum of 1983 (NAD83). To produce a polygon vector coverage for use in GIS, we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format using ArcInfo (Version 8.0.2, Environmental Systems Research Institute, Redlands, California). In ArcTools, we used the ArcScan utility to trace the polygon data and produce ArcInfo vector-based coverages. We digitally assigned map attribute codes (both map class codes and physiognomic modifier codes) to the polygons, and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the 78 individual coverages into a seamless map coverage of GNP and immediate environs. We synchronized polygons and attributes along the boundary between the GNP and WLNP map coverages. Although GNP and WLNP are two separate map coverages, they are seamless in the sense they edge tie perfectly in both polygon location and map attribute.

Natural Earth is a public domain map dataset available at 1:10m, 1:50m, and 1:110 million scales. Featuring tightly integrated vector and raster data, with Natural Earth you can make a variety of visually pleasing, well-crafted maps with cartography or GIS software.

Governor's Island Dataset for ArcGIS This archive contains an ArcGIS Pro project with a geodatabase of raster and vector data for Governor's Island, New York City, USA. The SRS is NAD83 / New York Long Island (ftUS) with the EPSG code 2263.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

This dataset is a complete state-wide digital land use map of Queensland. The dataset is a product of the Queensland Land Use Mapping Program (QLUMP) and was produced by the Queensland Government. It presents the most current mapping of land use features for Queensland, including the land use mapping products from 1999, 2006 and 2009, in a single feature layer. This dataset was last updated July 2012. See additional information also.

Purpose

Indicates the current primary use or management objective of the land.

Dataset History

Source DataQueensland Government - Land use mapping (1999); Landsat TM and ETM imagery; Spot5 imagery; High resolution ortho photography through the Spatial Imagery Subscription Plan (SISP); Queensland Digital Cadastral Database (DCDB) (2009), Queensland Valuation and Sales Database (QVAS) (2009); Queensland Nature Refuges (2009); Queensland Estates (2009); Queensland Herbarium's Regional Ecosystem, Water Body and Wetlands datasets (2009); Statewide Landcover & Trees Study (SLATS) Queensland Dams and Waterbodies (2009) and land cover change data; scanned aerial photography (1999-2009).Additional verbal & written information on land uses & their locations was obtained from regional Queensland Government officers, Local Government Authorities, land owners & managers, private industry as well as from field observations & checking.Data captureA range of existing digital datasets containing land use information was collated from the Queensland Government spatial data inventory and prepared for use in a GIS using ArcGIS and ERDAS Imagine software.Processing steps To compile the 1999 baseline mapping, datasets containing baseline land cover (supplied by SLATS), Protected Areas, State Forest and Timber Reserves, plantations, coastal wetlands, reserves (from DCDB) and logged forests were interpreted in a spatial model to produce a preliminary land use raster image.The model incorporated a decision matrix which assigned each pixel a specific land use class according to a set of pre-determined rules.Individual catchments were clipped from the model output and enhanced with additional land use information interpreted primarily from Landsat TM and ETM imagery as well as scanned and hardcopy aerial photography (where available). The DCDB and other datasets containing land use information were used to help identify property and land use type boundaries. This process produced a draft land use raster.Verification of the draft land use dataset, particularly those with significant areas of intensive land uses, was undertaken by comparing mapped land use classes with observed land use classes in the field where possible. The final raster image was converted to a vector coverage in ARC/Info and GIS editing performed.The existing 1999 baseline (or later where available) land use dataset (vector) formed the basis for the 2006 and 2009 land use mapping. The 2006 & 2009 datasets were then updated primarily by interpretation of SPOT5 imagery, high-res orthophotography, scanned aerial photography and inclusion of expert local knowledge. This was performed in an ESRI ArcSDE geodatabase replication infrastructure, across some nine regional offices. The DCDB, QVAS, Estates, Queensland Herbarium wetlands and SLATS land cover change and waterbody datasets were used to assist in identification and delineation of property and land use type boundaries. Digitised areas of uniform land use type were assigned to land use classes according to ALUMC Version 7 (May 2010).This "current" land use mapping product presents a complete state-wide land use map of Queensland, after collating the most current land use datasets within a single mapping layer.An independent validation was undertaken to assess thematic (attribute) accuracy under the ALUM classification. Please refer to the orignal source data for the validation results.

Dataset Citation

Queensland Department of Science, Information Technology, Innovation and the Arts (2013) Bioregional_Assessment_Programme_Land use mapping - Queensland current. Bioregional Assessment Source Dataset. Viewed 21 December 2017, http://data.bioregionalassessments.gov.au/dataset/740d257f-b622-49c2-9745-be283239add3.

New Orleans Dataset for GRASS GIS
This geospatial dataset contains raster and vector data for New Orleans, Louisiana, USA. The top level directory new-orleans-dataset is a GRASS GIS location for the North American Datum of 1983 (NAD 83) / Louisiana South State Plane Feet with EPSG code 3452. Inside the location there are the PERMANENT mapset with citywide data, a vieux_carre mapset with data for the French Quarter, Python scripts for data processing, data records, a color table, a license file, and readme file.

Instructions
Install GRASS GIS, unzip this archive, and move the location into your GRASS GIS database directory. If you are new to GRASS GIS read the first time users guide.

Data Sources

• U.S. Army Corps of Engineers 2012 Lidar Survey of New Orleans
• New Orleans Open Data

License
This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon. The scripts are licensed under the GNU General Public License 3.0 by Brendan Harmon. The graphics are licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0) by Brendan Harmon.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. We converted the photointerpreted data into a GIS-usable format employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM) projection, Zone 16, using North American Datum of 1983 (NAD83). To produce a polygon vector layer for use in ArcGIS, we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format using ArcGIS (Version 9.2, © 2006 Environmental Systems Research Institute, Redlands, California). In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map attribute codes (both map class codes and physiognomic modifier codes) to the polygons, and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer of INDU and immediate environs. At this stage, the map layer has only map attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map class names, physiognomic definitions, link to NVC association and alliance codes), we produced a feature class table along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature class layers produced from this project, including vegetation sample plots, accuracy assessment sites, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

High resolution land cover dataset for Philadelphia. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at ten square feet. The primary sources used to derive this land cover layer were 2008 Orthophotography and 2008 LiDAR LAS data. Ancillary data sources included GIS data (building footprints, road polygons, and hydrography) provided by City of Philadelphia. This land cover dataset is considered current as of 2008. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control. More than 30700 corrections were made to the classification.

This data is hosted at, and may be downloaded or accessed from PASDA, the Pennsylvania Spatial Data Access Geospatial Data Clearinghouse http://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=138