This polygon was constructed from the GPS vertex positions using the NAD27 system and later re projected to WGS84. The original metadata reads: We are using the following GPS coordinates for the 480 m x 800 m rectangle (38.4-ha) encompassing the original 26.6-ha Gigante Fertilization Plot. The coordinates are based on the GPS location determined for position 210, 430 on the plot. Data were collected on 25 January 1998, using Pamela Philips’ rover while the base station at Tupper was simultaneously collecting data. Pamela Philips later differentially corrected the rover data against the base station data to provide us with this location. Easting and Northing are in UTMs. Pamela notes: UTM Zone 17, NAD 27.Since 210,430 is more-or-less near the centroid of the 38.4-ha rectangle, this will be used as the geo-referenced position of the Gigante Fertilization Project plot: 9 deg. 6 min. 30.71139 sec. N, 79 deg., 50 min., 36.89953 sec. W.

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. Following the vegetation data analysis, the vegetation cover-type map was edited and refined to develop a preliminary association-level vegetation map. Using ArcView 3.2, polygon boundaries were revised onscreen based on the plot data, field observations, classification analyses, aerial photography signatures, and topographic maps. Each polygon was assigned the name of a preliminary vegetation association based on the five information sources listed above. A mirror stereoscope type F-71 and a Bausch and Lomb zoom stereoscope were used to interpret the aerial photography signatures. The field-collected “true” or “reference” GPS coordinates for the remaining 41 points were compared to the coordinates obtained from the mosaic viewed in ArcMap.

The points in this file have been remotely and field validated by NEON specialists and have been determined to meet NEON criteria. To download published data collected as these locations visit https://data.neonscience.org/home.The sampling locations were created by stratifying vegetation types from the National Land Cover Database (NLCD). Points were distributed in each vegetation type using a spatially balanced system implemented in GIS called the Reversed Randomized Quadrant Recursive Raster (RRQRR) technique. Subsequently, the points went through a remote sensing and ground-truth procedures to validate the vegetation type and NEON criteria. For more information please contact the Permitting Department at NEON, contact information is available at www.neonscience.org. This layer is current as of July 2, 2024.

TIME PERIOD COVERED These data were collected between May-July 2004 by field crews working for California Department of Fish and Game. GEOGRAPHIC EXTENT OF THE RECORDS Vegetation plots occur in two study areas on public lands in Yuba and Tehama Counties in the Sierra Nevada foothills. NUMBER OF RECORDS There are 183 records with habitat attributes measured from 0.05 - 0.10 hectare sampling plots. BASE DATA STRUCTURE The file is a flat Excel table which gives vegetation attributes for each habitat plot. Each habitat plot is represented by two key fields called "SAMPLE_ID" and "PLOT_NUM" which relate these habitat records to bird and herpetile survey data collected in the same year from the same sample points. WHAT EACH RECORD REPRESENTS Each record in the table represents the average values for habitat attributes from plots that can be linked with bird count data from the same points. Average values for each sample represent the mean of average values from 3 habitat plots measured at each point.

The geodatabase was created by Molly Cox, a contract worker with SWCA Environmental Consultants, Inc. in winter 2012. All monitoring data is stored within the FEAT/FIREMON Integrated (FFI) database. Using the FFI GIS module (toolbar), monitoring plot location data are mapped as points in ArcGIS, using Datum, UTM Zone, UTME Coordinate, UTMN Coordinate information recorded into the FFI database. Summary attribute information for each plot point was added by joining an Excel spreadsheet table, created by Molly Cox.

CDFW BIOS GIS Dataset, Contact: Armand Gonzales, Description: These data are the characteristics of the individual snags (standing dead trees) found at 15 sample points with three 0.05-ha circular plot habitat samples taken in 2005 at sample points at Spears and Didion Ranches, Placer County, California. Twelve of the forty-five 0.05-ha circular plots contained snags. To be counted, snags had to be > 4" dbh and > 9.8 ft tall and within the 12.6 m radius plot.

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. ecologists used field data (plot data, observation points, photographs, and field notes) and digital aerial imagery (NAIP 2005) to map draft vegetation polygons for BEOL within an ESRI personal geodatabase. In most cases, the map units are equivalent to vegetation associations, although one is represented at the alliance level. Table relationships were used to create a drop-down list of plant associations and map unit categories in the attribute table to ensure consistent data entry. A CNHP GIS Specialist then cleaned the layer topology, removing overlaps, gaps, slivers, and any data inconsistencies. FGDC compliant metadata was created for the vegetation layers and the layers were exported from the geodatabase as ESRI shapefiles. The layers are all in the coordinate system UTM Zone 13, North American Datum 1983.

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.

Random sample points were generated in ArcGIS. Points were buffered 40 meters from the park boundary and 80 meters from another point. The minimum mapping unit used in delineating vegetation polygons was 0.5 hectare. All random points were selected within the park boundary to avoid any private land issues. Randomly selected site locations were loaded onto a Garmin GPS unit for field navigation. All accuracy assessment field work was completed on June 26, 2012. Field staff was provided with a GPS unit, dichotomous key for mapping vegetation map classes and vegetation class definitions. Plot shape and size varied according to the extent of the vegetation class patch containing the sample point. Circular 0.25 hectare (28 m radius) plots were used for larger patches while circular 0.1 hectare (18 m radius) plots were used for small patches approaching the minimum mapping unit. A circular plot size of 0.5 hectare (40 m radius) was used to capture information for a single large homogenous patch. In all cases, plot size exceeded the minimum patch size for PIPE.

Minimum Mapping Unit = 0.5 hectare Minimum Patch Size=.007 hectares Total Size = 55 Polygons Average Polygon Size = 5.39 acres (2.18 hectares) Overall Thematic Accuracy = 97.9% Project Completion Date: 12/2013

Abstract

Spatial datasets containing groundwater hydrochemistry points with attribution for the generation of maps and piper plots for the Cooper GBA Region reports. This dataset contains hydrochemistry data for the artesian GAB aquifers, the Rolling Downs aquitard, the Winton-Mackunda partial aquifer and the Cenozoic aquifers in the Cooper GBA region.

Attribution

Geological and Bioregional Assessment Program

History

Generated by Geoscience Australia in ESRI ArcGIS for mapping and plotting uses in the Cooper GBA Reports. This dataset contains hydrochemisrty data for the artesian GAB aquifers, the Rolling Downs aquitard, the Winton-Mackunda partial aquifer and the Cenozoic aquifers in the Cooper GBA region.

This dataset contains 6 ha plots established in 2004 by Joe Wright to map trees around 5 ARTS towers at which seed traps were established. The plots were recensused in 2014. All trees above 30 cm dbh are censused. The plots are named by the towers they are around. The plots extend 100 m W,E and S of the tower, and 200 m to the north. (Directions are magnetic north at the time of plot establishment.)These plots were digitized from GPS coordinates received from Patrick Jansen who compiled them from Andres Hernandez.

New-ID: NBI16

Agro-ecological zones datasets is made up of AEZBLL08, AEZBLL09, AEZBLL10.

The Africa Agro-ecological Zones Dataset documentation

Files: AEZBLL08.E00 Code: 100025-011 AEZBLL09.E00 100025-012 AEZBLL10.E00 100025-013

Vector Members The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename.

The Africa agro-ecological zones dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. The daset was developed by United Nations Environment Program (UNEP), Kenya. The base maps that were used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the Global Navigation and Planning Charts (various 1976-1982) and the National Geographic Atlas of the World (1975). basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. This edit step required appending the country boundaries from Administrative Unit map and then producing the computer plot.

Contact: UNEP/GRID-Nairobi, P O Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy ESRI, 380 New York Street, Redlands, CA 92373, USA

The AEZBLL08 data covers North-West of African continent The AEZBLL09 data covers North-East of African continent The AEZBLL10 data covers South of African continent

References:

ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP

FAO/UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris

Defence Mapping Agency. Global Navigation and Planning Charts for Africa (various dates:1976-1982). Scale 1:5000000. Washington DC.

G.M. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:8500000. National Geographic Society, Washington DC.

FAO. Statistical Data on Existing Animal Units by Agro-ecological Zones for Africa (1983). Prepared by Todor Boyadgiev of the Soil Resources, Management and Conservation Services Division.

FAO. Statistical Data on Existing and Potential Populations by Agro-ecological Zones for Africa (1983). Prepared by Marina Zanetti of the Soil Resources, Management and Conservation Services Division. FAO. Report on the Agro-ecological Zones Project. Vol.I (1978), Methodology & Result for Africa. World Soil Resources No.48.

Source : UNESCO/FAO Soil Map of the World, scale 1:5000000 Publication Date : Dec 1984 Projection : Miller Type : Polygon Format : Arc/Info Export non-compressed Related Datasets : All UNEP/FAO/ESRI Datasets, Landuse (100013/05, New-ID: 05 FAO Irrigable Soils Datasets and Water balance (100050/53)

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. After fieldwork was completed, ecologists used field data (plot data, observation points, photographs, and field notes) and digital aerial imagery (NAIP 2005) to map draft vegetation polygons for SAND within an ESRI personal geodatabase. Vegetation polygons were drawn based on aerial photo signatures and plot and observation point data. Photographs, field notes, and plot data were used to refine visual analysis of aerial photo signatures in order to draw final polygons. Table relationships were used to create a drop-down list of plant associations and map unit categories in the attribute table to ensure consistent data entry. A CNHP GIS Specialist then cleaned the layer topology, removing overlaps, gaps, slivers, and any data inconsistencies. FGDC compliant metadata was created for the vegetation layers and the layers were exported from the geodatabase as ESRI shapefiles. The layers are all in the coordinate system UTM Zone 13, North American Datum 1983.

The points in this file have been remotely and field validated by NEON specialists and have been determined to meet NEON criteria. To download published data collected as these locations visit http://data.neonscience.org/home.The sampling locations were created by stratifying vegetation types from the National Land Cover Database (NLCD). Points were distributed in each vegetation type using a spatially balanced system implemented in GIS called the Reversed Randomized Quadrant Recursive Raster (RRQRR) technique. Subsequently, the points went through a remote sensing and ground-truth procedures to validate the vegetation type and NEON criteria. For more information please contact the Permitting Department at NEON, contact information is available at www.neonscience.org. This layer is current as of January 22, 2019.

This data was pulled from the BLM's MLRS database for each state using the case code (380910,380913,380911). The data was joined with the calculated centroid for each section in the states presented. Some sections did not have the proper designation or a point for plotting and in those instances the developer made every attempt to make a point in the logical place.For each state the first division of the PLSS for each state was obtained from either: a local government agency, the BLM Navigator, or from the USGS. Data was pulled in November of 2021. A snapshot of mining claims listings in each state from the BLM’s MLRS online database (Date Specified on Mining Claims Map) For each state, the projection of the PLSS layer is the projection that was used to create the claim points. From the PLSS first division for each state, the centroid was calculated using the calculate geometry function in ArcMap. A SectionID field was added to generate unique values. These unique values consist of the Meridian, Township, Range, and Section identifiers formatted to match the MTRS field when pulling the mining claims listings. Fields where concatenated together to generate the Section ID. Mining claims with a status of Active, Pending, Submitted, and Filed claims were queried from the Bureau of Land Management’s MLRS online database using the PUB MC Serial Number Index under the Public Mining Claims Reports. The claims data was joined with the SectionID data to assign an easting and a northing, based on the MTRS description for the given claim from the MLRS database. A “claim point listings” feature class was generated using the coordinates from the centroid of the section it is listed to be within. Some plans or notices did not plot. plans or notices that did not plot were visually inspected by and modifications were made if possible, to display the plans or notices. The reason for plans or notices not plotting was due to protracted blocks and the absence of a first division polygon. The section numbers for protracted blocks are greater than 36, so in areas where claims were present on protracted blocks, the section numbers were reassigned the section number of which the general public would refer to it as (1-36 only). For any states where the first division was not available for a Township, section centroid points were made with the INFERRED PLSS description assigned to the points. Understand that assumptions were made during this process. Polygons were not made for missing sections.

The points in this file have been remotely and field validated by NEON specialists and have been determined to meet NEON criteria. To download published data collected as these locations visit http://data.neonscience.org/home.The sampling locations were created by stratifying vegetation types from the National Land Cover Database (NLCD). Points were distributed in each vegetation type using a spatially balanced system implemented in GIS called the Reversed Randomized Quadrant Recursive Raster (RRQRR) technique. Subsequently, the points went through a remote sensing and ground-truth procedures to validate the vegetation type and NEON criteria. For more information please contact the Permitting Department at NEON, contact information is available at www.neonscience.org. This layer is current as of January 22, 2019.

This data was pulled from the BLM's MLRS database for each state using the case code (380910,380913,380911). The data was joined with the calculated centroid for each section in the states presented. Some sections did not have the proper designation or a point for plotting and in those instances the developer made every attempt to make a point in the logical place.For each state the first division of the PLSS for each state was obtained from either: a local government agency, the BLM Navigator, or from the USGS. Data was pulled in November of 2021. A snapshot of mining claims listings in each state from the BLM’s MLRS online database (Date Specified on Mining Claims Map) For each state, the projection of the PLSS layer is the projection that was used to create the claim points. From the PLSS first division for each state, the centroid was calculated using the calculate geometry function in ArcMap. A SectionID field was added to generate unique values. These unique values consist of the Meridian, Township, Range, and Section identifiers formatted to match the MTRS field when pulling the mining claims listings. Fields where concatenated together to generate the Section ID. Mining claims with a status of Active, Pending, Submitted, and Filed claims were queried from the Bureau of Land Management’s MLRS online database using the PUB MC Serial Number Index under the Public Mining Claims Reports. The claims data was joined with the SectionID data to assign an easting and a northing, based on the MTRS description for the given claim from the MLRS database. A “claim point listings” feature class was generated using the coordinates from the centroid of the section it is listed to be within. Some plans or notices did not plot. plans or notices that did not plot were visually inspected by and modifications were made if possible, to display the plans or notices. The reason for plans or notices not plotting was due to protracted blocks and the absence of a first division polygon. The section numbers for protracted blocks are greater than 36, so in areas where claims were present on protracted blocks, the section numbers were reassigned the section number of which the general public would refer to it as (1-36 only). For any states where the first division was not available for a Township, section centroid points were made with the INFERRED PLSS description assigned to the points. Understand that assumptions were made during this process. Polygons were not made for missing sections.

These are results from a network of 163 tree census plots and inventories in Panama. At plots, every individual stem in a rectangular area of specified size is given a unique number and identified to species. Available here are counts of the number of individual trees of each species in each plot, in two categories: all individuals 10 mm in stem diameter or larger, and all individuals 100 mm in stem diameter or larger. In inventories, all species present are tallied, but no individuals are counted, so data provided here is simply a list of species observed at each location.Precise location of every site, elevation, and estimated rainfall (for many sites) are also included. Data from these numerous plots and inventories are collected following the same methods as, and species identity harmonized with, the 50-ha long-term tree census at Barro Colorado Island.For all plots, full census information (diameter and identity of every individual in multiple censuses) is available at a Smithsonian archive.Field DescriptionLong Plot Name: Plot Site nameLatitudeLongitudeElev = elevation in metersDry Season = dry season intensity: maximum cumulative moisture deficit each year (mm), averaged across yearsPPT = Average annual precipitation (mm)PlotSize = Size of plot in hectares for count of trees 100 mm dbh (0 for inventories)Shapefile for download has additional columns with location name and descriptions.

This data was pulled from the BLM's MLRS database for each state using the case code (380910,380913,380911). The data was joined with the calculated centroid for each section in the states presented. Some sections did not have the proper designation or a point for plotting and in those instances the developer made every attempt to make a point in the logical place.For each state the first division of the PLSS for each state was obtained from either: a local government agency, the BLM Navigator, or from the USGS. Data was pulled in November of 2021. A snapshot of mining claims listings in each state from the BLM’s MLRS online database (Date Specified on Mining Claims Map) For each state, the projection of the PLSS layer is the projection that was used to create the claim points. From the PLSS first division for each state, the centroid was calculated using the calculate geometry function in ArcMap. A SectionID field was added to generate unique values. These unique values consist of the Meridian, Township, Range, and Section identifiers formatted to match the MTRS field when pulling the mining claims listings. Fields where concatenated together to generate the Section ID. Mining claims with a status of Active, Pending, Submitted, and Filed claims were queried from the Bureau of Land Management’s MLRS online database using the PUB MC Serial Number Index under the Public Mining Claims Reports. The claims data was joined with the SectionID data to assign an easting and a northing, based on the MTRS description for the given claim from the MLRS database. A “claim point listings” feature class was generated using the coordinates from the centroid of the section it is listed to be within. Some plans or notices did not plot. plans or notices that did not plot were visually inspected by and modifications were made if possible, to display the plans or notices. The reason for plans or notices not plotting was due to protracted blocks and the absence of a first division polygon. The section numbers for protracted blocks are greater than 36, so in areas where claims were present on protracted blocks, the section numbers were reassigned the section number of which the general public would refer to it as (1-36 only). For any states where the first division was not available for a Township, section centroid points were made with the INFERRED PLSS description assigned to the points. Understand that assumptions were made during this process. Polygons were not made for missing sections.

A parcel is a tract or plot of land surveyed and defined by legal ownership. Data were compiled from plats and deeds recorded at the Clerk of the Court and from historic tax maps. Source material was digitized or the coordinates were entered into the database via ARC/INFO Coordinate Geometry (COGO). Digital data from engineering companies has also been incorporated for newer subdivisions. A MCPI number is used to identify each parcel, which is a unique ID number further explained below.