The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making.

   BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions.


   Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself.


   For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise.


   Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. 


   This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery.


   See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt


   See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

Abstract : The search for the most appropriate GIS data model to integrate, manipulate and analyse spatio-temporal data raises several research questions about the conceptualisation of geographic spaces. Although there is now a general consensus that many environmental phenomena require field and object conceptualisations to provide a comprehensive GIS representation, there is still a need for better integration of these dual representations of space within a formal spatio-temporal database. The research presented in this paper introduces a hybrid and formal dual data model for the representation of spatio-temporal data. The whole approach has been fully implemented in PostgreSQL and its spatial extension PostGIS, where the SQL language is extended by a series of data type constructions and manipulation functions to support hybrid queries. The potential of the approach is illustrated by an application to underwater geomorphological dynamics oriented towards the monitoring of the evolution of seabed changes. A series of performance and scalability experiments are also reported to demonstrate the computational performance of the model.Data Description : The data set used in our research is a set of bathymetric surveys recorded over three years from 2009 to 2011 as Digital Terrain Models (DTM) with 2m grid spacing. The first survey was carried out in February 2009 by the French hydrographic office, the second one was recorded on August-September 2010 and the third in July 2011, both by the “Institut Universitaire Européen de la Mer”.

The reference spatial database for 2019 contains 5142 plots. We use it to calculate a land use map from satellite images. It is organized according to a nested 3-level nomenclature. This is an update of the 2018 database. The sources and techniques used to build the database by land use groups are described below: For agricultural areas, we use a land use database based on farmers' declarations (for EU subsidies). This is the "Registre Parcellaire Graphique" (RPG) published in France by the French Institute for Geographical and Forestry Informations (IGN). The description of this data is available here: http://professionnels.ign.fr/doc/DC_DL_RPG-2-0.pdf. These vector data localize the crops. The release times imply that we use the RPG for last year (2018). It is therefore necessary to verify the good coherence of the data with the image at very high spatial resolution (VHSR) Pleiades. The RPG provides little information on arboriculture. For these classes we called on colleagues specialized in mango, lychee and citrus crops who are familiar with their area and can locate plots in the VHSR image. The plots of the "greenhouse or shade cultivation" class are derived from the "industrial building" layer of the IGN's "BD Topo" product. A random selection of 20% of the polygons in the layer height field allows to keep a diversity of greenhouse types. Each polygon was verified by photo-interpretation of the Pleiades image. If the greenhouse or shade was not visible in the image, the polygon was removed. The distinction between mowed and grazed grasslands was completed through collaboration with colleagues from the SELMET joint research unit (Emmanuel Tillard, Expédit Rivière, Colas Gabriel Tovmassian and Jeanne Averna). For natural areas , there is no regularly updated mapping, but the main classes can be recognized from the GIS layers of government departments that manage these areas (ONF and DEAL). Two specific classes have been added (identified by photo-interpretation): a class of shadows due to the island's steep relief (areas not visible because of the cast shade) and a class of vegetation located on steep slopes facing the morning sun called "rampart moor". The polygons for the distinction of savannahs have been improved thanks to the knowledge of Xavier Amelot (CNRS), Béatrice Moppert and Quentin Rivière (University of La Réunion). For wet land areas , the "marsh" and "water" classes were obtained by photo-interpretation of the 2019 Pleiades image. These classes are easily recognizable on this type of image. For urban areas we randomly selected polygons from the IGN BD Topo product. For the housing type building, 4 building height classes have previously been created (depending on the height of the layer field) in order to preserve a good diversity of the types of buildings present on the island. A random selection of polygons from each class was then made. The "built" layer was completed by a random selection of industrial buildings from the "industrial built" layer of the IGN's BD TOPO product. This selection was made in the "nature" field of the layer (i‧e. the following types: silo, industrial and livestock). The "photovoltaic panel" class was obtained by photo-interpretation of the polygons on 2019 Pleiades image. La base de données spatiale de référence pour 2019, est constituée de 5142 polygones. Nous l'utilisons pour calculer une carte d'occupation du sol à partir d'images satellites. Elle est organisée selon une nomenclature emboitée à 3 niveaux. Il s'agit d'une mise à jour de la base de données pour 2018. Voici une brève description des sources et techniques utilisées pour la constituer en fonction des groupes d’occupation du sol : Pour les espaces agricoles , nous disposons d’une base de données d’occupation du sol basée sur les déclarations que font des agriculteurs pour demander les subventions de l’Union Européenne. Il s’agit du Registre Parcellaire Graphique (RPG) diffusé en France par l’Institut français de l’information géographique et forestière (IGN). La description de cette donnée est disponible ici : http://professionnels.ign.fr/doc/DC_DL_RPG-2-0.pdf. Ces données vecteur sont précises et peuvent servir de modèle pour localiser les cultures. Les délais de diffusion impliquent que nous utilisons le RPG de l’année N -1. Il est donc nécessaire de vérifier la bonne cohérence des données par photo-interprétation de l’image THRS. Le RPG fournit peu d’informations sur l’arboriculture. Pour ces classes nous avons fait appel aux collègues techniciens spécialisés dans les cultures de mangues, litchis et agrumes qui connaissent bien leur secteur et peuvent localiser des parcelles sur l’image THRS. Les parcelles de la classe « culture sous serre ou ombrage » sont issues de la couche « bâti industriel » de la BD Topo de l’IGN. Une sélection aléatoire de 20% des polygones dans le champ hauteur de la couche de l’IGN permet de conserver une diversité des types de serre. Chacun des polygones...

The ORNL DAAC Spatial Data Access Tool (SDAT) is a suite of Web-based applications that enable users to visualize and download spatial data in user-selected spatial/temporal extents, file formats, and projections. SDAT incorporates Open Geospatial Consortium (OGC) standard Web services, including Web Coverage Service (WCS), Web Map Service (WMS), and Web Feature Service (WFS). The SDAT provides ORNL DAAC-archived data sets and additional relevant data products including agriculture, atmosphere, biosphere, climate indicators, human dimensions, land surface, oceans, terrestrial hydrosphere data types, and related model output data sets.

Geospatial Analytics Software Market Outlook

The global geospatial analytics software market size is projected to grow from USD 50.1 billion in 2023 to USD 114.5 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 9.5%. This remarkable growth is largely driven by the increasing adoption of geospatial technologies across various sectors, including urban planning, agriculture, transportation, and disaster management. The surge in the utilization of geospatial data for strategic decision-making, coupled with advancements in technology such as artificial intelligence (AI) and big data analytics, plays a pivotal role in propelling market growth.

One of the key growth factors of the geospatial analytics software market is the rapid digital transformation occurring globally. Governments and enterprises are increasingly recognizing the value of geospatial data in enhancing operational efficiency and strategic planning. The rise in smart city initiatives across the world has bolstered the demand for geospatial analytics, as cities leverage these technologies to optimize infrastructure, manage resources, and improve public services. Additionally, the integration of AI and machine learning with geospatial analytics has enhanced the accuracy and predictive capabilities of these systems, further driving their adoption.

Another significant driver is the growing need for disaster management and climate change adaptation. As the frequency and intensity of natural disasters increase due to climate change, there is a heightened demand for geospatial analytics to predict, monitor, and mitigate the impact of such events. Geospatial software aids in mapping hazard zones, planning evacuation routes, and assessing damage post-disaster. This capability is crucial for governments and organizations involved in disaster management and mitigation, thereby boosting the market growth.

The transportation and logistics sector is also a major contributor to the growth of the geospatial analytics software market. The advent of autonomous vehicles and the continuous evolution of logistics and supply chain management have heightened the need for precise geospatial data. Geospatial analytics enables real-time tracking, route optimization, and efficient fleet management, which are critical for the smooth operation of transportation systems. This trend is expected to continue, driving the demand for geospatial analytics solutions in transportation and logistics.

On a regional level, North America is anticipated to dominate the geospatial analytics software market, driven by technological advancements and substantial investments in geospatial technologies. The presence of major market players and the high adoption rate of advanced technologies in sectors such as defense, agriculture, and urban planning contribute to this dominance. However, the Asia Pacific region is expected to witness the highest growth rate, fueled by rapid urbanization, government initiatives for smart cities, and increasing investments in infrastructure development.

GIS Software plays a crucial role in the geospatial analytics software market, offering powerful tools for data visualization, spatial analysis, and geographic mapping. As organizations across various sectors increasingly rely on geospatial data for strategic decision-making, GIS Software provides the necessary infrastructure to manage, analyze, and interpret this data effectively. Its integration with other technologies such as AI and machine learning enhances its capabilities, enabling more accurate predictions and insights. This makes GIS Software an indispensable component for industries like urban planning, agriculture, and transportation, where spatial data is pivotal for optimizing operations and improving outcomes. The growing demand for GIS Software is a testament to its importance in driving the geospatial analytics market forward.

Component Analysis

The geospatial analytics software market is segmented into software and services when considering components. The software segment includes comprehensive solutions that integrate various geospatial data types and provide analytical tools for mapping, visualization, and data processing. This segment is expected to hold the largest market share due to the increasing adoption of these solutions in various industries for efficient data management and decision-making. The continuous advancements in software capabilities, such as the inclusion of AI and machine learning algorithms

The ORNL DAAC Spatial Data Access Tool (SDAT) is a suite of Web-based applications that enable users to visualize and download spatial data in user-selected spatial/temporal extents, file formats, and projections. SDAT incorporates Open Geospatial Consortium (OGC) standard Web services, including Web Coverage Service (WCS), Web Map Service (WMS), and Web Feature Service (WFS). The SDAT provides ORNL DAAC-archived data sets and additional relevant data products including agriculture, atmosphere, biosphere, climate indicators, human dimensions, land surface, oceans, terrestrial hydrosphere data types, and related model output data sets.

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.

The GDAL/OGR libraries are open-source, geo-spatial libraries that work with a wide range of raster and vector data sources. One of many impressive features of the GDAL/OGR libraries is the ViRTual (VRT) format. It is an XML format description of how to transform raster or vector data sources on the fly into a new dataset. The transformations include: mosaicking, re-projection, look-up table (raster), change data type (raster), and SQL SELECT command (vector). VRTs can be used by GDAL/OGR functions and utilities as if they were an original source, even allowing for chaining of functionality, for example: have a VRT mosaic hundreds of VRTs that use look-up tables to transform original GeoTiff files. We used the VRT format for the presentation of hydrologic model results, allowing for thousands of small VRT files representing all components of the monthly water balance to be transformations of a single land cover GeoTiff file.

Presentation at 2018 AWRA Spring Specialty Conference: Geographic Information Systems (GIS) and Water Resources X, Orlando, Florida, April 23-25, http://awra.org/meetings/Orlando2018/

This geospatial database gives the land use / land cover type of 2105 georeferenced points, distributed thoughout an area of 84 square kilometers located at the west of Vavatenina town, in the Analanjirofo Region, on the northeastern coast of Madagascar. The first attribute of the shapefile indicates wether the point's class was directly identified in the fields (GT = ground-truth) wether photointerpreted (PI). The second attribute of the shapefile indicates the Land use / land cover class among a list of 9 types: - Built up/road/bare areas, - Annual crops/pasture/short vegetation, - Clove dominated park, - Clove monoculture, - Diversified agroforest, - Diversified park, - Plantation of woody species, - Shrubby fallow, - Woody fallow. These 9 types are the basis of a more complete nomenclature which can be identified by satellite, thus being helpful for remote sensing analyses and mapping of the area. This database can be used for any ecology, agronomy, or social science studies needing spatial information on local land use.

The Spatial Data from the 2011 India Census contains gridded estimates of India population at a resolution of 1 kilometer along with two spatial renderings of urban areas, one based on the official tabulations of population and settlement type (statutory town, outgrowth, census town), and the second, remotely-sensed measures of built-up land derived from the Global Human Settlement Layer. This data set includes a constructed hybrid representation of the urban settlement continuum by cross-classifying the census and remotely-sensed data.

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. To map the vegetation and land cover of GRSM, 52 map classes were developed. Of these 52 map classes, 46 represent natural (including ruderal) vegetation types, most of which types are recognized in the USNVC. For the remaining 6 of the 52 map classes, 4 represent USNVC cultural types for agricultural and developed areas, and 2 represent non-USNVC types for nonvegetated open water and nonvegetated rock. Features were interpreted from viewing four-band digital aerial imagery using digital onscreen three-dimensional stereoscopic workflow systems in geographic information systems; digital aerial imagery was collected during September 23–October 30, 2015. The interpreted data were digitally and spatially referenced, thus making the spatial-database layers usable in a geographic information system. Polygon units were mapped to either a 0.5- or 0.25- hectare (ha) minimum mapping unit, depending on vegetation type. A geodatabase containing several feature-class layers and tables provides the locations and data of USNVC vegetation types (vegetation map layer), vegetation plots, verification sites, AA sites, project boundary extent, and aerial image centers and flight lines. Covering 210,875 ha, the feature-class layer and related tables for the vegetation map layer provide 34,084 polygons of detailed attribute data when special modifiers are not considered (average polygon size of 6.2 ha) and 36,589 polygons of detailed attribute data when special modifiers are considered (average polygon size of 5.8 ha). Each map polygon is assigned a map-class code and name and, when applicable, are linked to USNVC classification tables within the geodatabase. The vegetation map extent includes the administrative boundary for GRSM and the Foothills Parkway.

From the site: "This geology shapefile covers the entire Susquehanna River Basin (2006 boundary). It represents the merging of three state (NY, PA, MD) bedrock geology spatial datasets in order to provide basic rock types of the Susquehanna River Basin. The dominant formation lithology that appears in the attribute table were grouped into 19 general rock types, such as sandstone, shale, schist, limestone, dolomite, etc. These rock types are also categorized by hydrostratigraphic terrain. The hydrostratigraphic terrain helps to identify the way in which water flows over or through these rocks."

This reference database in vector format (ESRI shape format) is organized according to a multi-level nomenclature. It is used to train an image classification algorithm in order to produce land cover maps of the Antananarivo metropolitan area (see dataverse sheets describing these maps). In order to simplify the work we chose to update the database produced in 2017 and described in: Dupuy et all, 2020 (https://doi.org/10.1016/j.dib.2020.105952). All polygons were verified by photo-interpretation of the Pleiades image acquired on April 3, 2022. GPS points were collected to update some classes and take into account changes since 2017. Each GPS point was converted into a polygon by digitizing the boundaries of the corresponding parcel on a Pleiades image (pixel size of 0.5 m * 0.5 m). These polygons cover the entire study area in order to have a representative view of the existing crop types and urban structures. The final database contains 3113 polygons. Warning, since December 5, 2022 we publish a new version to limit the effects related to flooding (the wetland class was overestimated on the previous version of land cover map). Cette base de données de référence au format vecteur (ESRI shape format) est organisée selon une nomenclature à plusieurs niveaux. Elle est utilisée pour entrainer un algorithme de classification d’images en vue de produire des cartes d’occupation du sol sur l’agglomération d’Antananarivo (Cf. fiches du dataverse décrivant ces cartes). Afin de simplifier les travaux nous avons choisi de mettre à jour la base de données produites en 2017 et décrite dans : Dupuy et all, 2020 (https://doi.org/10.1016/j.dib.2020.105952). Tous les polygones ont été vérifiés par photo-interprétation de l’image Pléiades acquise le 3 avril 2022. Des points GPS ont été collectés pour mettre à jour certaines classes et prendre en compte les évolutions intervenues depuis 2017. Chaque point GPS a été converti en polygone en numérisant les limites de la parcelle correspondante sur une l’image Pléiades (taille de pixel de 0,5 m * 0,5 m). Ces polygones couvrent l’ensemble de la zone d’étude afin d'avoir une représentativité des types de cultures et des structures urbaines existantes. La base de données finale compte 3113 polygones. Attention, depuis le 5 décembre 2022 nous mettons en ligne une nouvelle version pour limiter les effets liés aux inondations (la classe marais était surestimée sur la carte d'occupation du sol).

The reference spatial database for 2017 is composed of 6256 plots. We use it to calculate a land use map from satellite images.It is organized according to a nomenclature offering 3 levels of precision. We randomly selected 20% of the plots in each class to build a validation database while the remaining 80% is used for learning (5002 polygons for learning and 1254 for validation). The following is a brief description of the sources and techniques used to develop it according to land use types : For agricultural areas , we have a land use database based on farmers' declarations to apply for EU subsidies. This is the Registre Parcellaire Graphique (RPG) published in France by the French Institute of Geography (IGN). The description of this data is available here: http://professionnels.ign.fr/doc/DC_DL_RPG-2-0.pdf . These vector data are accurate and can be used as a model to locate crops. The release times imply that we use the RPG for year N -1. It is therefore necessary to check the correct consistency of the data by photo-interpretation of the VHR image. The RPG provides limited information on orchards. For these classes we called on colleagues specialised in mango, lychee and citrus fruit cultivation technicians who are familiar with their sector and can locate plots in the VHR image. Field surveys were conducted using GPS for market gardening crops. The plots of the "greenhouse or shade cultivation" class are derived from the "industrial building" layer of the IGN's "BD Topo" product of IGN. A random selection of 20% of the polygons in the height field of the IGN layer allows to keep a diversity of greenhouse types. Each of the polygons was verified by photo-interpretation of the Pleiades image. If the greenhouse or shade was not visible in the image, the polygon was deleted. For natural areas, there is no regularly updated mapping, but the main classes can be recognized from the GIS layers of the State services that manage these areas (ONF and DEAL). Two specific classes have been added (identified by photo-interpretation) to address the problems of satellite images: a class of shadows due to the island's steep terrain (areas not visible because of the shadow cast) and a class of vegetation located on steep slopes facing the morning sun called "savannah on cliffs". For wet areas, the "marsh", "water" and "hillside retention" classes were obtained by photo-interpretation of the 2017 Pleiades image. These classes are easily recognizable on this type of image. For urban areas we randomly selected polygons from the IGN's BD Topo layer. For the housing type building, 4 building height classes have previously been created (depending on the height of the layer field) in order to preserve a good diversity of the types of buildings present on the island. A random selection of polygons from each class was then made. The "built" layer was completed by a random selection of industrial buildings from the "industrial built" layer of the IGN's TOPO database. This selection was made in the "nature" field of the layer (i‧e. the following types: silo, industrial and livestock). The "photovoltaic panel" class was obtained by photo-interpretation of the polygons on the 2017 Pleiades image. La base de données spatiale de référence terrain pour 2017, est constituée de 6256 parcelles. Nous l'utilisons pour calculer une carte d'occupation du sol à partir d'image satellites. Elle est organisée selon une nomenclature emboitée à 3 niveaux. Nous avons sélectionné de façon aléatoire 20% des parcelles de chaque classe pour constituer une base de donnée de validation alors que les 80% restant sont utilisés pour l’apprentissage (5002 polygones pour l’apprentissage et 1254 pour la validation). Voici une brève description des sources et techniques utilisées pour la constituer en fonction des groupes d’occupation du sol : Pour les espaces agricoles , nous disposons d’une base de données d’occupation du sol basée sur les déclarations que font des agriculteurs pour demander les subventions de l’Union Européenne. Il s’agit du Registre Parcellaire Graphique (RPG) diffusé en France par l’Institut français de l’information géographique et forestière (IGN). La description de cette donnée est disponible ici : http://professionnels.ign.fr/doc/DC_DL_RPG-2-0.pdf. Ces données vecteur sont précises et peuvent servir de modèle pour localiser les cultures. Les délais de diffusion impliquent que nous utilisons le RPG de l’année N -1. Il est donc nécessaire de vérifier la bonne cohérence des données par photo-interprétation de l’image THRS. Le RPG fournit peu d’information sur l’arboriculture. Pour ces classes nous avons fait appel aux collègues techniciens spécialisés dans les cultures de mangues, litchis et agrumes qui connaissent bien leur secteur et peuvent localiser des parcelles sur l’image THRS. Des relevés de terrain ont été réalisés à l’aide d’un GPS pour les cultures de type maraichage. Les parcelles de la classe « culture sous...

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. ArcGIS software was used as the GIS platform for the onscreen digital mapping. Because the 3D images were viewed directly in the GIS environment, vegetation could be mapped directly into ArcGIS. The polygon vector data were stored using an ArcGIS file geodatabase, which was projected in in Universal Transverse Mercator (UTM), Zone 15, by using the North American Datum of 1983 (NAD 83). The NPS VIP standard MMU of 0.5 ha was applied to mapping forest and cultural types. For shrub, herbaceous, and sparsely vegetated types, as well as non-vegetation features, a MMU of 0.25 ha was applied. This smaller MMU was applied because these vegetation types were comparatively rare across the park, the degree of vegetation diversity over small areas was higher, and the isolated patches across MISS were more prevalent. For woodlands, a MMU of 0.5 ha was applied to deciduous woodlands and a MMU of 0.25 ha was applied to conifer woodlands due to the individual circumstances surrounding these woodlands. Also, when vegetation types were found unique to their immediate surroundings (e.g., an herbaceous wetland within an upland forest), mapping below the MMU was allowed. All geospatial products for the MISS vegetation mapping project have been projected in UTM, Zone 15, by using the NAD 83.

The Referrals Spatial Database - Public records locations of referrals submitted to the Department under the Environment Protection and Biodiversity Conservation (EPBC Act) 1999. A proponent (those …Show full descriptionThe Referrals Spatial Database - Public records locations of referrals submitted to the Department under the Environment Protection and Biodiversity Conservation (EPBC Act) 1999. A proponent (those who are proposing a development) must supply the maximum extent (location) of any proposed activities that need to be assessed under the EPBC Act through an application process. Referral boundaries should not be misinterpreted as development footprints but where referrals have been received by the Department. It should be noted that not all referrals captured within the Referrals Spatial Database, are assessed and approved by the Minister for the Environment, as some are withdrawn before assessment can take place. For more detailed information on a referral a URL is provided to the EPBC Act Public notices pages. Status and detailed planning documentation is available on the EPBC Act Public notices (http://epbcnotices.environment.gov.au/referralslist/). Post September 2019, this dataset is updated using a spatial data capture tool embedded within the Referral form on the department’s website. Users are able to supply spatial data in multiple formats, review spatial data online and submitted with the completed referral form automatically. Nightly processes update this dataset that are then available for internal staff to use (usually within 24 hours). Prior to September 2019, a manual process was employed to update this dataset. In the first instance where a proponent provides GIS data, this is loaded as the polygons for a referral. Where this doesn't exist other means to digitize boundaries are employed to provide a relatively accurate reflection of the maximum extent for which the referral may impact (it is not a development footprint). This sometimes takes the form of heads up digitizing planning documents, sourcing from other state databases (such as PSMA Australia) features and coordinates supplied through the application forms. Any variations to boundaries after the initial referral (i.e. during the assessment, approval or post-approval stages) are processed on an ad hoc basis through a manual update to the dataset. The REFERRALS_PUBLIC_MV layer is a materialized view that joins the spatial polygon data with the business data (e.g. name, case id, type etc.) about a referral. This layer is available for use by the public and is available via a web service and spatial data download. The data for the web service is updated weekly, while the data download is updated quarterly.

The Agri-Environmental Spatial Data (AESD) product from the Census of Agriculture provides a large selection of farm-level variables from the Census of Agriculture and uses alternative data sources to improve the spatial distribution of the production activities. Therefore, the AESD database offers clients the possibility to better analyze the impact of agriculture activities on the environment and produce key indicators, or for any applications where accurate location of activities matters. Variables are offered using two types of physical boundaries: by Soil Landscape of Canada polygons and by Sub-sub-drainage areas (watersheds). The focus of the redistribution of the data is on the field crops and land use variables, but the database includes all census variables related to crops, livestock and management practices. This frame can also be used to extract Census of Agriculture data by custom geographic areas. Also, users interested in this version of the Census of Agriculture database using administrative types of regions can request it. In both cases, please contact Statistics Canada. This file was produced by Statistics Canada, Agriculture Division, Remote Sensing and Geospatial Analysis section, 2022, Ottawa.

The SCAR Spatial Data Model has been developed for Geoscience Standing Scientific Group (GSSG). It was presented to XXVII SCAR, 15-26 July 2002, in Shanghai, China.

The Spatial Data Model is one of nine projects of the Geographic Information Program 2000-2002. The goal of this project is 'To provide a SCAR standard spatial data model for use in SCAR and national GIS databases.'

Activities within this project include:

1. Continue developing the SCAR Feature Catalogue and the SCAR Spatial Data Model
2. Provide SCAR Feature Catalogue online
3. Creation and incorporation of symbology
4. Investigate metadata / data quality requirements
5. Ensure compliance to ISO TC211 and OGC standards

Source: http://www.geoscience.scar.org/geog/geog.htm#stds

Spatial data are increasingly being available in digital form, managed in a GIS and distributed on the web. More data are being exchanged between nations/institutions and used by a variety of disciplines. Exchange of data and its multiple use makes it necessary to provide a standard framework. The Feature Catalogue is one component of the Spatial Data Model, that will provide the platform for creating understandable and accessible data to users. Care has been taken to monitor the utility of relevant emerging ISO TC211 standards.

The Feature Catalogue provides a detailed description of the nature and the structure of GIS and map information. It follows ISO/DIS 19110, Geographic Information - Methodology for feature cataloguing. The Feature Catalogue can be used in its entirety, or in part. The Feature Catalogue is a dynamic document, that will evolve with use over time. Considerable effort has gone into ensuring that the Feature Catalogue is a unified and efficient tool that can be used with any GIS software and at any scale of geographic information.

The structure includes data quality information, terminology, database types and attribute options that will apply to any GIS. The Feature Catalogue is stored in a database to enable any component of the information to be easily viewed, printed, downloaded and updated via the Web.

A collection of text files containing configurations, and XSL rendering defaults for spatial data set, spatial feature types, spatial features, and feature cross-walks as part of the Spatial Identifier Reference Framework (SIRF) linked data platform
Lineage: The configuration files and rendering defaults were created by the SIRF project team and describe a variety of spatial data sources that were collated for the SIRF project including the UN gazetteer, Indonesian place name list. Feature type lists from Geoscence Australia, Badan Informasi Geospasial (BIG), UN FAO are also contained in the collection