This map presents a tour of the City of Redlands, California using the detailed map of Redlands included in the community basemap. The City of Redlands is located in Southern California, about 65 miles east of Los Angeles. The map tour highlights some of the unique features in the history of Redlands as well as several of the places and events that make it a very livable community today.The map features a detailed basemap for the City of Redlands, California, including buildings, parcels, vegetation, land use, landmarks, streets, and more. The map features special detail for areas of high interest within the City, including local parks, landmarks, and the ESRI campus.The map references detailed GIS data provided by the City of Redlands, Department of Innovation and Technology, GIS Division. The map was authored using map templates available from ESRI, including:Topographic Map Template - Large ScalesCampus Basemap TemplateThe map was published as part of ESRI's Community Maps Program and is one of several detailed maps of cities and counties in the World Topographic Map.

Indoors Demo for Building L in Esri Redlands Campus.

Redlands Sample GoProHero8

This map presents a tour of the City of Redlands, California using the detailed map of Redlands included in the community basemap. The City of Redlands is located in Southern California, about 65 miles east of Los Angeles. The map tour highlights some of the unique features in the history of Redlands as well as several of the places and events that make it a very livable community today.The map features a detailed basemap for the City of Redlands, California, including buildings, parcels, vegetation, land use, landmarks, streets, and more. The map features special detail for areas of high interest within the City, including local parks, landmarks, and the ESRI campus.The map references detailed GIS data provided by the City of Redlands, Department of Innovation and Technology, GIS Division. The map was authored using map templates available from ESRI, including:Topographic Map Template - Large ScalesCampus Basemap TemplateThe map was published as part of ESRI's Community Maps Program and is one of several detailed maps of cities and counties in the World Topographic Map.

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

Aerial light detection and ranging (lidar) data were collected over the study site between April 12 – 14, 2012 as part of the Fauquier, Fairfax, Frederick (MD), and Jefferson County acquisition for FEMA Region 3 FY12 VA lidar (Dewberry 2012). Lidar points classified as ground and water were used to create a 3-m digital elevation model (DEM) clipped to the Difficult Run watershed with a 500-m buffer in ArcGIS 10.3.1 (ESRI, Redlands, CA).

New Group Layer

Tracklines and associated observations were mapped and analyzed using ArcMap (ESRI, Redlands, CA). GPS data were recorded in NAD27 map datum and projected to an USGS Albers Equal Area Conic map projection for presentation and subsequent density analyses. Concatenated GPS and observation data were then used to generate point and line coverages in ArcMap (ESRI, Redlands, CA). We designed a custom analytic tool using ArcMap Model Builder that allows for the construction and export of user-specified and effort-adjusted spatial binning of species observations along continuous trackines. For the purposes of this report, we calculated seabird density estimates and marine mammal counts along continuous 3.0-kilometer and 7.7-kilometer trackline segments (i.e., 3.0 kilometer and 7.7 kilometer bins). Therefore, marine bird densities (at 3-kilometer scale, for example) are based on a composite strip area ranging from 0.15 per kilometer squared (one observer on effort) to 0.30 per kilometer squared (two observers on effort). We made no effort to adjust densities such that they would be proportional to variations in the area of buffered transect strip bin (i.e., weighted offset variable). These data are associated with the following publication: Mason, J.W., McChesney, G.J., McIver, W.R., Carter, H.R., Takekawa, J.Y., Golightly, R.T., Ackerman, J.T., Orthmeyer, D.L., Perry, W.M., Yee, J.L. and Pierson, M.O. 2007. At-sea distribution and abundance of seabirds off southern California: a 20-Year comparison. Cooper Ornithological Society, Studies in Avian Biology Vol. 33. References- ESRI. ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research Institute.

To assess the current topography of tidal marsh at the study sites we conducted survey-grade global positioning system (GPS) surveys between 2009 and 2014 using a Leica RX1200 Real Time Kinematic (RTK) rover (±1 cm horizontal, ±2 cm vertical accuracy; Leica Geosystems Inc., Norcross, GA; Figure 4). At sites with RTK GPS network coverage (Padilla, Port Susan, Nisqually, Siletz, Bull Island, and Bandon), rover positions were received in real time from the Leica Smartnet system via a CDMA modem (www.lecia-geosystems.com). At sites without network coverage (Skokomish, Grays Harbor, and Willapa), rover positions were received in real time from a Leica GS10 antenna base station via radio link. At sites where we used the base station, we adjusted all elevation measurements using an OPUS correction (www.ngs.noaa.gov/OPUS). We used the WGS84 ellipsoid model for vertical and horizontal positioning and referenced positions to a local National Geodetic Survey (NGS) benchmark or a benchmark established by a surveyor (Figure 4). Average measured vertical errors at benchmarks were 1-9 cm throughout the study, comparable to the stated error of the GPS. To measure topographic variation at each site, we surveyed marsh surface elevation along transects perpendicular to the major tidal sediment source, with a survey point taken every 12.5 m; 50 m separated transect lines (Appendix Figs. A1 – I1). We used the Geoid09 model to calculate orthometric heights from ellipsoid measurements (m, NAVD88; North American Vertical Datum of 1988) and projected all points to NAD83 UTM zone 10 using Leica GeoOffice v7.0.1 (Leica Geosystems Inc, Norcross, GA).In ArcGIS 10.2.1 Spatial Analyst (ESRI 2013, Redlands, CA), we created a digital elevation model (DEM) for each site using each sites survey elevation data points. We processed the elevation point data with exponential ordinary kriging methods (5 x 5 m cell size) while adjusting model parameters to minimize the root-mean-square (RMS) error to create the best model fit for the DEM (Table 2). We used elevation models as the baseline conditions for subsequent analyses including tidal inundation patterns, SLR response modeling, and mapping of sites by specific elevation (flooding) zones.

This geodatabase includes spatial datasets that represent the Cambrian-Ordovician aquifer system in the States of Wisconsin, Michigan, Minnesota, Iowa, and Illinois. Included are: (1) a polygon dataset that represents the aquifer system extent, (2) raster datasets for the altitude of each aquifer subunit, (3) altitude, or if applicable, thickness contours used to generate the surface rasters, (4) georeferenced images of the figures that were digitized to create the altitude or thickness contours, (5) the line datasets representing the altitude contours that were digitized from the images, and (6) an ArcGIS Toolbox used to develop the surface raster datasets. The images and digitized contours are supplied for reference. The extent of the Cambrian-Ordovician aquifer system is from the linework of the Cambrian-Ordovician aquifer system extent maps in U.S. Geological Survey Professional Paper 1405-B (PP1405B), and a digital version of the aquifer extent presented in the Groundwater Atlas of the United States (the U.S. Geological Survey Hydrologic Atlas 730-J, (USGS HA 730-J) and 730-K (USGS HA 730-K)), available at http://water.usgs.gov/ogw/NatlAqCode-reflist.html . The Cambrian-Ordovician Aquifer System has four aquifer subunits, in order from the most surficial to the deepest: (A1) St. Peter Sandstone Aquifer, (A2) Prairie du Chien Aquifer, (A3) Ironton and Galesville Sandstone Aquifer, and (A4) Mt. Simon or Lamotte Sandstone Aquifer. The altitude contour line datasets for each subunit available were digitized from georeferenced figures of altitude contours in PP1405B, and the altitude values were interpolated into surface rasters within a GIS using tools that create hydrologically correct surfaces from contour data, derives the altitude from the thickness (depth from the land surface), and merges the subareas into a single surface. The primary tool was "Topo to Raster" used in ArcGIS, ArcMap, Esri 2014. (ArcGIS Desktop: Release 10.2 Redlands, CA: Environmental Systems Research Institute). The surface rasters were corrected for the areas where the altitude of the top of the aquifer exceeded the land surface, and where the bottom of an aquifer exceeded the altitude of the corrected top of the aquifer.

This geodatabase contains the spatial datasets that represent the Edwards-Trinity aquifer system in the States of Arkansas, Oklahoma, and Texas. Included are: (1) polygon extents; datasets that represent the aquifer system extent, the entire extent subdivided into subareas or subunits, and any polygon extents of special interest (no data available, areas underlying other aquifers, anomalies, for example), (2) raster datasets for the altitude of each aquifer subarea or subunit, (3) altitude, and/or if applicable, thickness contours used to generate the surface rasters, (4) georeferenced images of the figures that were digitized to create the altitude or thickness contours. The images and digitized contours are supplied for reference. The extent of the Edwards-Trinity aquifer system encompasses all subunits. It is delineated from the linework of the Edwards-Trinity aquifer system extent and outcrop maps of the U.S. Geological Survey Hydrologic Atlas 730-E (USGS HA 730-E) , available at http://water.usgs.gov/ogw/NatlAqCode-reflist.html. Included are the "no data available" extent polygons where there were no altitude data available for the bottom surface of the Edwards-Trinity aquifer system. These were digitized from USGS HA-730-E, figure 81, and U.S. Geological Survey Water-Resources Investigations Report 85-4116 (USGS WRIR 85-4116), plate 9, and U.S. Geological Survey Water-Resources Investigations Paper 91-4071 (USGS WRIR 91-4071), plate 1. The Edwards-Trinity aquifer system has three aquifer subunits, but for the purposes of this geodatabase only the ultimate top and bottom surface rasters are published. The altitudes for the top surface raster are from georeferenced images of altitude contours from USGS HA-730-E, figures 84, 98 and 114, and USGS WRIR 85-4116, plate 8. In the areas where the Edwards-Trinity top surface underlies the Pecos River alluvial aquifer (USGS HA 730-E, Pecos River Basin alluvial aquifer), and the High Plains aquifer (see USGS HA 730-E, High Plains aquifer), the altitude of the bottom those two aquifers is the top of the Edwards-Trinity aquifer system. The altitudes of the bottom surface raster are from georeferenced images of altitude contours from USGS HA-730-E figure 81, USGS WRIR 85-4116 plate 9, and USGS WRIR 91-4071 plate 1. The altitude contours were interpolated into surface rasters within a GIS using tools that create hydrologically correct surfaces from contour data, derives the altitude from the thickness (depth from the land surface) if necessary, and merges the subareas into a single surface. The primary tool was "Topo to Raster" used in ArcGIS, ArcMap, Esri 2014. ArcGIS Desktop: Release 10.2 Redlands, CA: Environmental Systems Research Institute.

Hydrologically conditioned digital elevation model (DEM) generated from lidar data clipped to the Difficult Run watershed with a 500-m buffer in ArcGIS 10.3.1 (ESRI, Redlands, CA). The DEM was hydrologically corrected by breaching through pits with no downslope neighboring cells to force surface flow to continuously move downslope using Whitebox Geospatial Analysis Tools (Lindsay and Dhun 2015, Lindsay 2016). Pits that were not properly breached were manually adjusted using elevation information from the DEM and aerial imagery to locate culverts under roadways.

Data files can be accessed using Excel, R and JMP.

To download:1. Click the Download button above.2. A side panel will appear showing download options. Under Shapefile, click the Download button.3. When the download completes, browse to the location of the downloaded .zip, copy it to the location where you manage your redistricting files, then right-click to extract the contents. You will then be able to use the file in GIS software.If, rather than downloading the data, you wish the reference online versions of these datasets directly to ensure you are always using the most up-to-date data, please contact the County of San Bernardino Innovation and Technology Departments at 909-884-4884 or by emailing OpenData@isd.sbcounty.gov for informations and instructions for doing so.This dataset should only be used for the purpose of establishing election divisions within a district. It will be removed once the redistricting process has concluded.

QuickCapture Project for taking pictures during the Picture Hunt - Redlands Edition.

Tracklines and associated observations were mapped and analyzed using ArcMap (ESRI, Redlands, CA). GPS data were recorded in NAD27 map datum and projected to an USGS Albers Equal Area Conic map projection for presentation and subsequent density analyses. Concatenated GPS and observation data were then used to generate point and line coverages in ArcMap (ESRI, Redlands, CA). We designed a custom analytic tool using ArcMap Model Builder that allows for the construction and export of user-specified and effort-adjusted spatial binning of species observations along continuous trackines. For the purposes of this report, we calculated seabird density estimates and marine mammal counts along continuous 3.0-kilometer and 7.7-kilometer trackline segments (i.e., 3.0 kilometer and 7.7 kilometer bins). Therefore, marine bird densities (at 3-kilometer scale, for example) are based on a composite strip area ranging from 0.15 per kilometer squared (one observer on effort) to 0.30 per kilometer sq ...

Predictive mapping of tree species assemblages in an African montane rainforest

https://doi.org/10.5061/dryad.rv15dv4fp

Predictive mapping of tree species assemblages in an African montane rainforest

Babaasa, Dennis, Mbarara University, https://orcid.org/0000-0002-4855-4534 Finn, John T., University of Massachusetts Schweik, Charles M., University of Massachusetts Fuller, Todd K., University of Massachusetts Sheil, Douglas, Wageningen University

dbabaasa@must.ac.ug

Research facility: Institute of Tropical Forest Conservation Published January …., 2024 on Dryad. https://doi.org/10.5061/dryad.rv15dv4fp

Cite this dataset: Babaasa, Dennis, Finn, John T., Schweik, Charles M., Fuller, Todd K., Sheil, Douglas (2024). Predictive mapping of tree species assemblages in an African montane rainforest. [Dataset] Dryad. https://doi.org/10.5061/dryad.rv15dv4fp

Abstract

In this study, Bwindi Impenetrable National Park, Uganda, was divided into five strata based on geological for...

The 2015 LU/LC data set is the sixth in a series of land use mapping efforts that was begun in 1986. Revisions and additions to the initial baseline layer were done in subsequent years from imagery captured in 1995/97, 2002, 2007, 2012 and 2015. This present 2015 update was created by comparing the 2012 LU/LC layer from NJDEP's Geographic Information Systems (GIS) database to 2015 color infrared (CIR) imagery and delineating and coding areas of change. Work for this data set was done by Aerial Information Systems, Inc., Redlands, CA, under direction of the New Jersey Department of Environmental Protection (NJDEP), Bureau of Geographic Information System (BGIS). LU/LC changes were captured by adding new line work and attribute data for the 2015 land use directly to the base data layer. All 2012 LU/LC polygons and attribute fields remain in this data set, so change analysis for the period 2012-2015 can be undertaken from this one layer. The classification system used was a modified Anderson et al., classification system. An impervious surface (IS) code was also assigned to each LU/LC polygon based on the percentage of impervious surface within each polygon as of 2015. Minimum mapping unit (MMU) is 1 acre. ADVISORY: This metadata file contains information for the 2015 Land Use/Land Cover (LU/LC) data sets, which were mapped by USGS Subbasin (HU8). There are additional reference documents listed in this file under Supplemental Information which should also be examined by users of these data sets. As stated in this metadata record's Use Constraints section, NJDEP makes no representations of any kind, including, but not limited to, the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the digital data layers furnished hereunder. NJDEP assumes no responsibility to maintain them in any manner or form. By downloading this data, user agrees to the data use constraints listed within this metadata record.

New Group Layer