U.S. Geological Survey (USGS) and Virginia Institute of Marine Science (VIMS) scientists conducted field data collection efforts during June 11th - 16th, 2020, using a combination of remote sensing technologies to map riverbank and wetland topography and vegetation at five sites in the Chesapeake Bay Region of Virginia. The five sites are located along the James, Severn, and York Rivers. The work was initiated to evaluate the utility of different remote sensing technologies in mapping river bluff and wetland topography and vegetation for change detection and sediment transport modeling. The USGS team collected Global Navigation Satellite System (GNSS), total station, and ground based lidar (GBL) data while the VIMS team collected aerial imagery using an Unmanned Aerial System (UAS). This data release contains shapefiles of the processed GNSS and total station data, point clouds in the form of lidar data exchange (las) files from the ground lidar data and aerial imagery produce ...

This digital database is the product of collaboration between the U.S. Geological Survey, the Alfred Wegener Institute for Polar and Marine Research Potsdam, Foothill College GeoSpatial Technology Certificate Program, and the Geophysical Institute at the University of Alaska. The primary goal for creating this digital database is to enhance current estimates of organic carbon stored in deep permafrost, in particular Late Pleistocene syngenetic ice-rich loess permafrost deposits, called Yedoma. This deposit is vulnerable to thermokarst and erosion due to natural and anthropogenic disturbances. The original paper maps were issued by the Department of Natural Resources of the Russian Federation or its predecessor the Department of Geology of the Soviet Union and have their foundation in decades of geological field and remote sensing work and mapping at scales 1:50,000 to 1:500,000 by Russian geologists and cartographers in the respective regions. Eleven paper maps were scanned and digitized to record the geology unit boundaries, genetic type and clast size of each geologic unit, and borehole and outcrop locations. We also calculated area in km2, perimeter in km for each polygon. These attributes were used in support of (Grosse and others, 2013) which focused on extracting geologic units interpreted as Yedoma, based on lithology, ground ice conditions, geochronology, geomorphologic, and spatial association. Grosse, G., Robinson, J.E., Bryant, R., Taylor, M.D., Harper, W., DeMasi, A., Kyker-Snowman, E., Veremeeva, A., Schirrmeister, L., and Harden, J., 2013, Distribution of late Pleistocene ice-rich syngenetic permafrost of the Yedoma Suite in east and central Siberia, Russia: U.S. Geological Survey Open File Report 2013-1078, 37p. http://pubs.usgs.gov/of/2013/1078/ http://pubs.usgs.gov/of/2013/1078/pdf/ofr20131078.pdf

This map shows the population in households where a smartphone is the only computing device the household owns. Map is multi-scale, containing data for states, counties, and tracts. Pop-ups display total households that own a smartphone, households that own a smartphone and no other computing device, households that have a cellular data plan for internet, and households that have a cellular data plan and no other type of internet subscription. Cellular data plans might be cheaper and more accessible than a Broadband data plan, however it is less likely to be able to sustain remote learning, work from home, and telehealth.Data come from Census Bureau's American Community Survey Summary Tables: B28001 (used for symbology & pop-up) & B28002 (used for pop-up only). This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

Amur honeysuckle bush (Lonicera maackii) and Morrow's honeysuckle (Lonicera morrowii) are two of the most aggressively invasive species to become established throughout areas along the Blue River in metropolitan Kansas City, Missouri. These two large, spreading shrubs (locally referred to as bush honeysuckle in the Kansas City metropolitan area) colonize the understory, crowd out native plants, and may be allelopathic, producing a chemical that restricts growth of native species. Removal efforts have been underway for more than a decade by local conservation groups such as Bridging The Gap and Heartland Conservation Alliance, who are concerned with the loss of native species diversity associated with the spread of bush honeysuckle. Bush honeysuckle produces leaves early in the spring before almost all other vegetation and retains leaves late in the fall after almost all other species have lost their leaves. Appropriately timed imagery can be used during early spring and late fall to map the extent of bush honeysuckle. Using multispectral imagery collected in February 2016 and true color aerial imagery collected in March 2016, a coverage map of bush honeysuckle in the study area was made to investigate the extent of bush honeysuckle in a study area along the middle reach of the Blue River in the Kansas City metropolitan area in Jackson County, Missouri. The coverage map was further classified into unlikely, low-, and high-density bush honeysuckle density at a 30-foot cell size. The unlikely density class correctly predicted the absence and approximate density of bush honeysuckle for 86 percent of the field-verification points, the low-density class predicted the presence and approximate density with 73-percent confidence, and the high-density class was predicted with 67-percent confidence. This data was used to support the project work described in: Ellis, J.T., 2018, Remote sensing of bush honeysuckle in the Middle Blue River Basin, Kansas City, Missouri, 2016–17: U.S. Geological Survey Scientific Investigations Map XXXX, 1 sheet., https://doi.org/xxxx.

Providing accurate maps of coral reefs where the spatial scale and labels of the mapped features correspond to map units appropriate for examining biological and geomorphic structures and processes is a major challenge for remote sensing. The objective of this work is to assess the accuracy and relevance of the process used to derive geomorphic zone and benthic community zone maps for three western Pacific coral reefs produced from multi-scale, object-based image analysis (OBIA) of high-spatial-resolution multi-spectral images, guided by field survey data. Three Quickbird-2 multi-spectral data sets from reefs in Australia, Palau and Fiji and georeferenced field photographs were used in a multi-scale segmentation and object-based image classification to map geomorphic zones and benthic community zones. A per-pixel approach was also tested for mapping benthic community zones. Validation of the maps and comparison to past approaches indicated the multi-scale OBIA process enabled field data, operator field experience and a conceptual hierarchical model of the coral reef environment to be linked to provide output maps at geomorphic zone and benthic community scales on coral reefs. The OBIA mapping accuracies were comparable with previously published work using other methods; however, the classes mapped were matched to a predetermined set of features on the reef.

The main two products of this data set are (1) an Asia 30-second land cover data set and (2) an Asia 30-second ground truth data set of land cover classes. The purpose to distribute Asia land cover data set is to provide land cover information for global change studies and other global/continental applications. The purpose to distribute ground truth data of land cover classes is to build a better Asian ground truth database by improving coverage and adding new reliable ground truth data of Asia for future application.

The Land Cover Working Group (LCWG) of the Asian Association on Remote Sensing (AARS) prepared the land cover data set. The AARS land cover classification system was developed through discussion with members of the LCWG/AARS and is described in the Methods section of the CD-ROM. A table showing corresponding classes between the LCWG/AARS classification system and IGBP-DIS classification system is provided.

Ground truth data were collected mainly from existing thematic maps by the cooperation of the working group members. The maps used are listed in the documentation. Some of ground truth data were collected by field survey in Central Asia such as Kazakhstan, Uzbekistan and Turkmenistan. Three field trips were performed with the cooperation of WG member of Kazakhstan. Ground truth data of 31 land cover classes were collected from 19 types of information sources (thematic maps or field surveys).

Global land 1-km AVHRR data set was used as the source of satellite data. 10-days composite data of AVHRR NDVI, channel 4 and channel 5 were used for this project. NDVI data from April 1, 1992 to March 31, 1993 and NDVI and land surface temperature (Ts) data from April 1, 1992 to October 31, 1992 were used in the land cover analysis. (See Methods section for discussion of the theoretical support for using the ratio of land surface temperature (Ts) and NDVI in land cover analysis.) The Global Land One-kilometer Base Elevation (GLOBE), Version 1.0, (30 arc-second grid digital elevation data) and the Digital Chart of the World (DCW) data (1:1,000,000 scale vector base map of the world with 17 attibute layers including seashore lines and national boundaries) were used. The following data were prepared for the classification: Ts/NDVI (seven monthly data from April to October 1992); maximum NDVI (the maximum monthly data from April 1992 to March 1993); minimum NDVI (the minimum monthly data from April 1992 to March 1993); and digital elevation data. All these data are registered together in 30-second grid in latitude/longitude.

The land cover classification was done by the following steps: (1) clustering of monthly Ts/NDVI from April to October; (2) finding classification rules for decision tree method; (3) classification by decision tree method; and (4) post-classification modification.

Contribution to add and improve ground truth data is appreciated. It can be sent to Dr. Ryutaro Tateishi (see Data Center Contact information). Contributions should be the following way: (1) digital data or paper map; (2) ground truth data of land cover described either by class code defined in the data set land.htm or by contributor's own land cover class name with its definition; (3) ground truth region covering at least as large as 2.5 minute by 2.5 minute (approximately equivalent to 5 km by 5 km at the equator) in latitude/longitude with a homogeneous land cover type; and (4) four values of latitude/longitude of the north, south, east, and west end of ground truth regions. If you send information of ground truth, it will be included in the Asia ground truth data base as the next product.

Providing accurate maps of coral reefs where the spatial scale and labels of the mapped features correspond to map units appropriate for examining biological and geomorphic structures and processes is a major challenge for remote sensing. The objective of this work is to assess the accuracy and relevance of the process used to derive geomorphic zone and benthic community zone maps for three western Pacific coral reefs produced from multi-scale, object-based image analysis (OBIA) of high-spatial-resolution multi-spectral images, guided by field survey data. Three Quickbird-2 multi-spectral data sets from reefs in Australia, Palau and Fiji and georeferenced field photographs were used in a multi-scale segmentation and object-based image classification to map geomorphic zones and benthic community zones. A per-pixel approach was also tested for mapping benthic community zones. Validation of the maps and comparison to past approaches indicated the multi-scale OBIA process enabled field data, operator field experience and a conceptual hierarchical model of the coral reef environment to be linked to provide output maps at geomorphic zone and benthic community scales on coral reefs. The OBIA mapping accuracies were comparable with previously published work using other methods; however, the classes mapped were matched to a predetermined set of features on the reef.

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

NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was launched in September, 2018. The satellite carries a single instrument, ATLAS (Advanced Topographic Laser Altimeter System), a green wavelength, photon-counting lidar, enabling global measurement and monitoring of elevation with a primary focus on the cryosphere. Although bathymetric mapping was not one of the design goals for ATLAS, pre-launch work by our research team showed the potential to map bathymetry with ICESat-2, using data from MABEL (Multiple Altimeter Beam Experimental Lidar), NASA’s high-altitude airborne ATLAS emulator, and adapting the laser-radar equation for ATLAS specific parameters. However, many of the sensor variables were only approximations, which limited a full assessment of the bathymetric mapping capabilities of ICESat-2 during pre-launch studies. Following the successful launch, preliminary analyses of the geolocated photon returns have been conducted for a number of coastal sites, revealing several salient examples of seafloor detection in water depths of up to ~40 m. The geolocated seafloor photon returns cannot be taken as bathymetric measurements, however, since the algorithm used to generate them is not designed to account for the refraction that occurs at the air–water interface or the corresponding change in the speed of light in the water column. This paper presents the first early on-orbit validation of ICESat-2 bathymetry and quantification of the bathymetric mapping performance of ATLAS using data acquired over St. Thomas, U.S. Virgin Islands. A refraction correction, developed and tested in this work, is applied, after which the ICESat-2 bathymetry is compared against high-accuracy airborne topo-bathymetric lidar reference data collected by the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). The results show agreement to within 0.43—0.60 m root mean square error (RMSE) over 1 m grid resolution for these early on-orbit data. Refraction-corrected bottom return photons are then inspected for four coastal locations around the globe in relation to Visible Infrared Imaging Radiometer Suite (VIIRS) Kd(490) data to empirically determine the maximum depth mapping capability of ATLAS as a function of water clarity. It is demonstrated that ATLAS has a maximum depth mapping capability of nearly 1 Secchi in depth for water depths up to 38 m and Kd(490) in the range of 0.05–0.12 m−1. Collectively, these results indicate the great potential for bathymetric mapping with ICESat-2, offering a promising new tool to assist in filling the global void in nearshore bathymetry.

The U.S. Geological Survey, in cooperation with the National Park Service, Yellowstone Center for Resources, as part of work for the Yellowstone Volcano Observatory, has compiled a shapefile map of thermal areas and thermal water bodies in Yellowstone National Park. A thermal area is a continuous, or nearly continuous, geologic unit that contains one or more thermal features (e.g., hot springs, mud pots, or fumaroles); hydrothermally altered rocks and/or hydrothermal mineral deposits; heated ground and/or geothermal gas emissions; and is generally barren of vegetation or has stressed / dying vegetation. There are more than 10,000 thermal features in Yellowstone, most of which are clustered together into about 120 distinct thermal areas (e.g., Upper Geyser Basin, Crater Hills Thermal Area, or Roadside Springs). A thermal water body is a body of water, usually a lake, pond, or wetland area, that is thermally emissive because it receives heated water from a nearby thermal area, nearshore thermal springs, or from underwater vents. The shapefile released here is based on a thermal area polygon shapefile that was initially provided by the Spatial Analysis Center at the Yellowstone Center for Resources in Yellowstone National Park. The thermal area polygons were initially based on field mapping (by R. Hutchinson and others, unpublished data, 1997) and digitizing boundaries over high-spatial-resolution (1 m/pixel) visible color orthophotos from the National Agriculture Imagery Program (NAIP) acquired in 2006. Updates to this map are based on more recent field mapping and remote sensing data analysis, including nighttime thermal infrared data (e.g., ASTER and Landsat 8/9), high-spatial-resolution visible data from commercial satellites (e.g., WorldView-3), and NAIP imagery from 2015, 2017, 2019, and 2022. The downloadable shapefile contains a map of these thermal areas and thermal water bodies with information (if available) about their chemistry and thermal activity. The names of the thermal areas are either derived from the USGS Geographic Names Information System (GNIS) or are locally used names, as indicated in the attribute table. Thermal area mapping in Yellowstone is a work in progress, partly because there are still remote areas that have not yet been explored in detail, and partly because changes occur frequently. Thermal areas expand and contract, develop and decay, and migrate – over time scales that range from weeks to years. Thus, this map will be periodically assessed and updated. A note about safety: Thermal areas can be dangerous, with scalding water, mud, or gases that are sometimes hidden beneath unstable ground. Unstable ground sometimes looks solid, but stepping onto unstable ground can result in breaking through a thin crust and being exposed to scalding water, mud, or gases, which can cause severe burns. Since the establishment of the National Park, more than 20 people have died from burns suffered after they entered or fell into a hot spring. For the safety of park visitors and the protection of delicate thermal formations, it is prohibited to enter a thermal area in the back country, and one must stay on the trails or boardwalks when entering front country thermal areas (unless working in a thermal area on an official permit).

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.

The Border Rivers Gwydir and Namoi Regional Vegetation Map is a subset of the statewide vegetation mapping and classification program undertaken by the NSW Office of Environment and Heritage (OEH Regional Scale State Vegetation Map) and covers the two former Catchment Management Authority Regions.

The primary thematic data layer in this dataset is a map of regional scale Plant Community Types (PCT's). The map was developed from a process using vegetation surveys, remote sensing derivations, visual interpretation and spatial distribution models.

The full dataset comprises the following data layers as delivered in an ArcGIS 9.3 File Geo-database:

PLANT COMMUNITY TYPE: The primary map of Plant Community Types developed from an ensemble of visual interpretation of high resolution imagery and spatial distribution models.

WOODY EXTENT LAYER: A map of woody vegetation derived from classification of 5m SPOT-5 imagery.

KEITH CLASS: A map based on aerial photo interpretation and spatial distribution models.

MAP SOURCE: A map of the various sources of information used including spatial models, visual interpretation and existing map products.

SURVEY DENSITY ALL: A map of the density of all survey sites used.

SURVEY DENSITY FULL FLORISTICS: A map of the density of only full floristic survey sites used.

MODELLING CONFIDENCE: A map of the confidence outcomes achieved.

While much of the aerial photo interpretation employed was undertaken at around 1:8000, PCT attribution is generally at a much coarser scale. The Map Source layer (as described above) can be used as a guide to how vegetation attribution was derived. We recommend that the highest resolution appropriate for this product be 1:15000.

Validation Summary:

PCT Map: Based on 100% of the survey data (modelling and hand mapping), the final mapped product has an accuracy in the range 68%-70% for prediction of the three most likely PCTs. Be aware that these accuracies are highly variable across each PCT. Some PCT's utilised more site data than others. Keith Class reached a 76% accuracy using the independent test data. Modelled PCT and modelled top 3 PCT overall accuracies were 53% and 68% respectively. Woody Extent received a 92% overall accuracy.

Accompanying documents:
BRGNamoi Technical Notes.pdf - Technical Report
BRGN_PCT_KC_LUT.xls - A look-up table listing the relationship between PCT, Keith Class and Keith Formation classifications.\ BRGNv2_Spatial_Layer_Descriptors.txt
BRGN_V2.mxd
Border Rivers Gwydir / Namoi Regional Native Vegetation Mapping
Technical Notes Version 1.0. Reference: NSW Office of Environment and Heritage, 2015. BRG-Namoi Regional Native Vegetation Mapping. Technical Notes, NSW Office of Environment and Heritage, Sydney, Australia.

The download package contains a "quick view" map composite of the study area only. The quick view maps are of PCT, Keith Class, Keith Form, Map Source and Modelling Confidence. They also show the broad-scale line work. For more detailed line work and woody percent per polygon, please refer to the full dataset.

For access queries regarding the full dataset, please contact: data.broker@environment.nsw.gov.au

BRG_Namoi_v2_0_E_4204.
\ VIS_ID 4204

Purpose

This dataset was developed as part of the OEH State Vegetation Map to provide government and community with regional-scale information about native vegetation.

Dataset History

A summary of the product's lineage is below. Please refer to the Technical Notes v1.0 for a detailed description of the methodologies and source datasets.

The PCT map was derived primarily using a spatial modeling approach augmented with high resolution aerial imagery (50cm ADS40) for visual interpretation and automated line-work derivation.
\ In summary the process for PCT attribution involved the following:

1. Vegetation Survey and Classification: Existing floristic plot data comprised 9054 existing sites after data cleaning. A large number of gaps in existing survey coverage were evident and required further survey information. Stratification based on archive broad vegetation type mapping (Regional Vegetation Types; Eco Logical Australia 2008b) and gap analysis was undertaken to select locations for additional plot data collection. A total of 6013 additional rapid data points were collected. To allocate survey sites to PCTs, full floristic plots were analysed using a UPGMA clustering approach in Primer with significant groups identified using SIMPROF and species contributions for each resulting group calculated using SIMPER. The existing plot data were allocated across 258 PCTs.

2. Pattern Derivation: A multi-resolution segmentation algorithm was used to create image objects with low internal variation. Image objects represent patches of vegetation that can later be classified based on attributes such as crown cover, spectral response, or soil type. The segmentation parameters and scale was derived iteratively based on visual inspection. Vegetation patterns from existing stereoscopic aerial photo interpretation and those recognised in high spatial resolution imagery (ADS40) were used as a reference point. Segmentation was performed using ADS40, SPOT 5 and SRTM derived topographic indices. this process provided the line work for subsequent PCT attribution.

3. Visual attribution of Landscape Class: The purpose of attributing Landscape classes to polygons is to predetermine broad vegetation types for modelling purposes using remote sensing. These classes reduce the PCT options for any one polygon making the modeling more effective in its attribution with commensurate less computing effort/time. A landscape class was attributed to every polygon in the study area. Landscape classes were aided by reference to existing mapping. Corrections were made based on ADS40 with on-screen attribution. Every polygon was visually checked by an expert interpreter.

4. Modelling Envelopes:As a further constraint to modelling outcomes, spatial envelopes were used to constrain PCTs to a certain geographic range, reducing the amount of types competing within the model at any particular location. The constraints used were applied at different stages in the mapping process. The Keith Class (Keith 2004) models were constrained to particular IBRA (Interim Bioregionalisation of Australia v7; Commonwealth of Australia 2012) subregions, selected based on review of the literature and expert opinion. The type models were constrained to particular ranges of a topographic position index, again based on literature review and expert opinion. Not all types were constrained by topographic envelopes, as some were considered to be less correlated with particular topographic positions.

5. Spatial Distribution Modelling of Keith Classes and Plant Community Types. Modelling of Keith Class and PCT used a combination (ensemble) of Generalised Dissimilarity Model (GDM), Boosted Regression Trees (BRT), and a simple Nearest Neighbour model.A suite of candidate environmental predictor variables, including climate, geology, soil, geophysical data, and terrain indices, were compiled for use in the GDM and BRT models. A comprehensive list of these predictor variables can be found in the Technical Notes v1.0.

6. Uplifted API and Expert Editing: Vegetation communities from the Gwydir Wetlands and Floodplain Vegetation Map 2008 (Bowen & Simpson 2010) were spatially translated into the current line-work via a majority extent per polygon algorithm. The vegetation community mapping resulting from the aforementioned procedures was extensively edited on screen to correct attribution where there may have been for example existing API, missed vegetation, ecological anomalies, incorrect assignments, modelling noise and inclusion of late site data. The extent of each attribution source is delineated by the Map Source data layer provided in this dataset.

For further details on methodology and validation please refer to the Border Rivers Gwydir / Namoi Regional Native Vegetation Mapping
Technical Notes Version 1.0. Reference: NSW Office of Environment and Heritage, 2015. BRG-Namoi Regional Native Vegetation Mapping. Technical Notes, NSW Office of Environment and Heritage, Sydney, Australia.

Dataset Citation

NSW Office of Environment and Heritage (2015) Border Rivers Gwydir / Namoi Regional Native Vegetation Map Version 2.0. VIS_ID 4204. Bioregional Assessment Source Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/b3ca03dc-ed6e-4fdd-82ca-e9406a6ad74a.

This version (v1.0) is released for interim review for 3 months from release date. A subsequent official release will be published after review and subsequent alterations.

Please note, Central West / Lachlan Regional Native Vegetation Map (NSW Formation and Class) Version 1.0. VIS_ID 4214 web service and zipped dataset will be archived and will no longer be available on line after 31st March 2025.

The primary thematic data layer in this dataset is a map of regional scale Plant Community Types (PCT's). The map was developed from a process using vegetation surveys, remote sensing derivations, visual interpretation and spatial distribution models.

The download package includes a merged and simplified version of the full dataset.

The full dataset comprises the following data layers as delivered in an ArcGIS 9.3 File Geo-database:

MAP SHEET DATA: (CWLpct_v1p0.gdb\PCTv1) provided as 100k map sheets comprising detailed vegetation line-work with the following attributions per polygon:

FIELDS: !CWLpct_v1! - Plant Community Type (PCT) Codes !CommName! - Plant Community Type Common Names !KeithClass! - The mapped PCT's associated Keith Class !KeithForm! - The maplped PCT's assocated Keith Formation !Cover! - Percent woody cover per polygon (0 - 1.0) derived from the NSW Woody Vegetation Extent 2011 dataset. !EnvPCT1! - Most likely modelled PCT !EnvPCT2! - Second most likely modelled PCT !EnvPCT3! - Third most likely modelled PCT !confcat! - Categorical modelling confidence levels of EnvPCT1 (High > 60%, Medium 30%-60%, Low < 30%, None). None = non-modelling attribution (either manual expert API or pre-existing mapping) !cwlscv1up! - Expert Manual API code of structural class. Look up table below. !hectares! - Size of polygon in hectares

While much of the aerial photo interpretation employed was undertaken at around 1:8000, PCT attribution is generally at a much coarser scale. We recommend that the highest resolution appropriate for this product be 1:15000.

VALIDATION SUMMARY: Pending Technical Report.

ACCOMPANYING DATASETS:

KEITH CLASS QUICK-VIEW DISSOLVE: CWLpct_v1p0.gdb\KCv1\CWL_KC This is a dissolved (internal attribute polygon boundaries removed) and CWL-wide merged version of the Keith Class as found in the complete linework sheets of 100K Map sheet Data.

CWl BOUNDARY: CWLpct_v1p0.gdb\Boundaries\CWL_Boundary: A polygon boundary of the Central-West Lachlan v1 Regional Vegetation Map

CWL 100K SHEET BOUNDARIES: CWLpct_v1p0.gdb\Boundaries\CWL_100k Polygon boundaries of the 100k sheet extents within the CWL v1 extent.

SURVEY SITES: CWLpct_v1p0.gdb\Surveys\CWL_Sites_PCT_20150528 Point locations of on-ground rapid or full floristic vegetation surveys. Pertinent Fields: !SURVEYID! - code identifying for which project the sites were commissioned. !SITENUMBER! - Unique site code identifier. !PCT_1_0528! - Attributed PCT Code classification !PCT_1_0528! - Alternative PCT Code classification

LOOK UP TABLE: PCT Code, Name, Keith class & Keith Formation look-up: CWLpct_v1p0.gdb\CWL_LUT

MXD: CWLpct_v1p0.mxd

ACCESS QUERIES: For access queries regarding the full dataset, please contact: data.broker@environment.nsw.gov.au

VIS_ID 4214

This version (v1.0) is released for interim review for 3 months from release date. A subsequent official release will be published after review and subsequent alterations.\r \r Please note, Central West / Lachlan Regional Native Vegetation Map (NSW Formation and Class) Version 1.0. VIS_ID 4214 web service and zipped dataset will be archived and will no longer be available on line after 31st March 2025.\r \r The primary thematic data layer in this dataset is a map of regional scale Plant Community Types (PCT's). The map was developed from a process using vegetation surveys, remote sensing derivations, visual interpretation and spatial distribution models.\r \r The download package includes a merged and simplified version of the full dataset.\r \r The full dataset comprises the following data layers as delivered in an ArcGIS 9.3 File Geo-database:\r \r MAP SHEET DATA:\r (CWLpct_v1p0.gdb\PCTv1) provided as 100k map sheets comprising detailed vegetation line-work with the following attributions per polygon:\r \r FIELDS: !CWLpct_v1! - Plant Community Type (PCT) Codes\r !CommName! - Plant Community Type Common Names\r !KeithClass! - The mapped PCT's associated Keith Class\r !KeithForm! - The maplped PCT's assocated Keith Formation\r !Cover! - Percent woody cover per polygon (0 - 1.0) derived from the NSW Woody Vegetation Extent 2011 dataset.\r !EnvPCT1! - Most likely modelled PCT\r !EnvPCT2! - Second most likely modelled PCT\r !EnvPCT3! - Third most likely modelled PCT\r !confcat! - Categorical modelling confidence levels of EnvPCT1 (High > 60%, Medium 30%-60%, Low < 30%, None). None = non-modelling attribution (either manual expert API or pre-existing mapping)\r !cwlscv1up! - Expert Manual API code of structural class. Look up table below.\r !hectares! - Size of polygon in hectares\r \r While much of the aerial photo interpretation employed was undertaken at around 1:8000, PCT attribution is generally at a much coarser scale. We recommend that the highest resolution appropriate for this product be 1:15000.\r \r \r VALIDATION SUMMARY: Pending Technical Report.\r \r ACCOMPANYING DATASETS:\r \r KEITH CLASS QUICK-VIEW DISSOLVE:\r CWLpct_v1p0.gdb\KCv1\CWL_KC\r This is a dissolved (internal attribute polygon boundaries removed) and CWL-wide merged version of the Keith Class as found in the complete linework sheets of 100K Map sheet Data.\r \r CWl BOUNDARY:\r CWLpct_v1p0.gdb\Boundaries\CWL_Boundary:\r A polygon boundary of the Central-West Lachlan v1 Regional Vegetation Map\r \r \r CWL 100K SHEET BOUNDARIES:\r CWLpct_v1p0.gdb\Boundaries\CWL_100k\r Polygon boundaries of the 100k sheet extents within the CWL v1 extent.\r \r \r SURVEY SITES:\r CWLpct_v1p0.gdb\Surveys\CWL_Sites_PCT_20150528\r Point locations of on-ground rapid or full floristic vegetation surveys.\r Pertinent Fields: !SURVEYID! - code identifying for which project the sites were commissioned.\r !SITENUMBER! - Unique site code identifier.\r !PCT_1_0528! - Attributed PCT Code classification\r !PCT_1_0528! - Alternative PCT Code classification\r \r LOOK UP TABLE: PCT Code, Name, Keith class & Keith Formation look-up:\r CWLpct_v1p0.gdb\CWL_LUT\r \r MXD:\r CWLpct_v1p0.mxd\r \r ACCESS QUERIES:\r For access queries regarding the full dataset, please contact: data.broker@environment.nsw.gov.au\r \r VIS_ID 4214