**THIS NEWER 2016 DIGITAL MAP REPLACES THE OLDER 2014 VERSION OF THE GRI GATE Geomorphological-GIS data. The Unpublished Digital Pre-Hurricane Sandy Geomorphological-GIS Map of the Staten Island Unit, Gateway National Recreation Area, New York is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (stis_geomorphology.gdb), a 10.1 ArcMap (.MXD) map document (stis_geomorphology.mxd), individual 10.1 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (gate_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.TXT) and FAQ (.HTML) formats, and a GIS readme file (gate_gis_readme.pdf). Please read the gate_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O’Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. Google Earth software is available for free at: http://www.google.com/earth/index.html. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Rutgers University Institute of Marine and Coastal Sciences. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (stis_pre-sandy_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/gate/stis_pre-sandy_metadata_faq.html). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:6,000 and United States National Map Accuracy Standards features are within (horizontally) 5.08 meters or 16.67 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone 18N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Gateway National Recreation Area.

[Metadata] This map represents the distribution of seven moisture zones for the main Hawaiian Islands. The maps were produced as part of a species range modeling effort for the Hawaiian flora. Details on methodology and related products can be found in: Price, J. P., J. D. Jacobi, S. M. Gon, III, D. Matsuwaki, L. Mehrhoff, W. L. Wagner, M. Lucas, and B. Rowe. 2012, Mapping plant species ranges in the Hawaiian Islands-Developing a methodology and associated GIS layers. U.S. Geological Survey Open File Report OFR 2012-1192, Reston, VA. For more information, please see complete metadata at https://files.hawaii.gov/dbedt/op/gis/data/moisture_zones.html or contact the Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii, PO Box 2359, Honolulu, HI 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis/.

Crowther_Nature_Files.zip This description pertains to the original download. Details on revised (newer) versions of the datasets are listed below. When more than one version of a file exists in Figshare, the original DOI will take users to the latest version, though each version technically has its own DOI. -- Two global maps (raster files) of tree density. These maps highlight how the number of trees varies across the world. One map was generated using biome-level models of tree density, and applied at the biome scale. The other map was generated using ecoregion-level models of tree density, and applied at the ecoregion scale. For this reason, transitions between biomes or between ecoregions may be unrealistically harsh, but large-scale estimates are robust (see Crowther et al 2015 and Glick et al 2016). At the outset, this study was intended to generate reliable estimates at broad spatial scales, which inherently comes at the cost of fine-scale precision. For this reason, country-scale (or larger) estimates are generally more robust than individual pixel-level estimates. Additionally, due to data limitations, estimates for Mangroves and Tropical coniferous forest (as identified by WWF and TNC) were generated using models constructed from Topical moist broadleaf forest data and Temperate coniferous forest data, respectively. Because we used ecological analogy, the estimates for these two biomes should be considered less reliable than those of other biomes . These two maps initially appeared in Crowther et al (2015), with the biome map being featured more prominently. Explicit publication of the data is associated with Glick et al (2016). As they are produced, updated versions of these datasets, as well as alternative formats, will be made available under Additional Versions (see below).

Methods: We collected over 420,000 ground-sources estimates of tree density from around the world. We then constructed linear regression models using vegetative, climatic, topographic, and anthropogenic variables to produce forest tree density estimates for all locations globally. All modeling was done in R. Mapping was done using R and ArcGIS 10.1.

Viewing Instructions: Load the files into an appropriate geographic information system (GIS). For the original download (ArcGIS geodatabase files), load the files into ArcGIS to view or export the data to other formats. Because these datasets are large and have a unique coordinate system that is not read by many GIS, we suggest loading them into an ArcGIS dataframe whose coordinate system matches that of the data (see File Format). For GeoTiff files (see Additional Versions), load them into any compatible GIS or image management program.

Comments: The original download provides a zipped folder that contains (1) an ArcGIS File Geodatabase (.gdb) containing one raster file for each of the two global models of tree density – one based on biomes and one based on ecoregions; (2) a layer file (.lyr) for each of the global models with the symbology used for each respective model in Crowther et al (2015); and an ArcGIS Map Document (.mxd) that contains the layers and symbology for each map in the paper. The data is delivered in the Goode homolosine interrupted projected coordinate system that was used to compute biome, ecoregion, and global estimates of the number and density of trees presented in Crowther et al (2015). To obtain maps like those presented in the official publication, raster files will need to be reprojected to the Eckert III projected coordinate system. Details on subsequent revisions and alternative file formats are list below under Additional Versions.----------

Additional Versions: Crowther_Nature_Files_Revision_01.zip contains tree density predictions for small islands that are not included in the data available in the original dataset. These predictions were not taken into consideration in production of maps and figures presented in Crowther et al (2015), with the exception of the values presented in Supplemental Table 2. The file structure follows that of the original data and includes both biome- and ecoregion-level models.

Crowther_Nature_Files_Revision_01_WGS84_GeoTiff.zip contains Revision_01 of the biome-level model, but stored in WGS84 and GeoTiff format. This file was produced by reprojecting the original Goode homolosine files to WGS84 using nearest neighbor resampling in ArcMap. All areal computations presented in the manuscript were computed using the Goode homolosine projection. This means that comparable computations made with projected versions of this WGS84 data are likely to differ (substantially at greater latitudes) as a product of the resampling. Included in this .zip file are the primary .tif and its visualization support files.

References:

Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., Smith, J. R., Hintler, G., Duguid, M. C., Amatulli, G., Tuanmu, M. N., Jetz, W., Salas, C., Stam, C., Piotto, D., Tavani, R., Green, S., Bruce, G., Williams, S. J., Wiser, S. K., Huber, M. O., Hengeveld, G. M., Nabuurs, G. J., Tikhonova, E., Borchardt, P., Li, C. F., Powrie, L. W., Fischer, M., Hemp, A., Homeier, J., Cho, P., Vibrans, A. C., Umunay, P. M., Piao, S. L., Rowe, C. W., Ashton, M. S., Crane, P. R., and Bradford, M. A. 2015. Mapping tree density at a global scale. Nature, 525(7568): 201-205. DOI: http://doi.org/10.1038/nature14967Glick, H. B., Bettigole, C. B., Maynard, D. S., Covey, K. R., Smith, J. R., and Crowther, T. W. 2016. Spatially explicit models of global tree density. Scientific Data, 3(160069), doi:10.1038/sdata.2016.69.

THE BOUNDARIES OF THE CITY OF UNALASKA, ALASKABeginning at the intersection of the west boundary of T71S, R117W, Seward Meridian (S.M.) and the mean high tide line of the Bering Sea; thence south to the protracted NE corner of T72S, R118W, S.M.; thence west to the NW corner of T72S, R118W, S.M.; thence south to the SW corner of T72S, R118W, S.M.; thence east to the NW corner of Section 1, T73S, R119W, S.M.; thence south to the SW corner of Section 13, T73S, R119W, S.M.; thence east to the SE corner of Section 13, T73S, R119W, S.M.; thence south to the SW corner of T73S, R118W, S.M.; thence east to the SE corner of the W ½ of Section 31, T73S, R117W, S.M.; thence in a northeasterly direction to the SE corner of the W ½ of Section 35, T72S, R116W, S.M.; thence north to the intersection of the east border of the W ½ of Section 23, T71S, R116W, S.M.; thence continuing north a distance of 3 nautical miles; thence west to a point 3 nautical miles north of the intersection of the west boundary of T71S, R117W, S.M.; thence south 3 nautical miles to the point of beginning.Containing 115.84 square miles of land, more or less, and 98.56 square miles of water, more or less for a total combined area of 214.4 square miles, more or less, all in the Third Judicial District, State of Alaska.

A challenge in agricultural drought risk assessment is the lack of standardization for selecting indicators and aggregation methods, leading to inconsistent and less reliable outcomes. This issue is particularly evident in Vietnam, where diverse agricultural practices and regional climates add complexity to the assessment process. This study proposes a methodological framework specifically designed for Vietnam’s agricultural sector. It recommends the use of the Standardized Precipitation Index Vegetation Health Index and Soil Moisture (SM) for assessing drought hazards, while socioeconomic indicators such as agricultural land, population, Gross Domestic Product total income, agriculture-based income, literacy rate, and poverty rate are suggested for evaluating exposure and vulnerability. The research assesses drought risk across mainland Vietnam from 2015 to 2022, employing both equal proportion and Principal Component Analysis (PCA) to determine indicator weightings. The study highlights the advantages of Geographic Information System (GIS) and Remote Sensing data in evaluating drought risk across Vietnam. The result of spatiotemporal analysis shows that the drought hazard index varies significantly on a monthly basis, while exposure and vulnerability indices remain relatively stable over the years. During the examined period, 2015 and 2016 were identified as the years with the highest drought risk, followed by 2019 and 2020. The Mekong Delta, Central Highlands, and Northwest regions consistently exhibited high drought risk, reflecting their agricultural practices and socioeconomic vulnerabilities. This dynamic analysis provides critical insights for policymakers and stakeholders to proactively manage drought impacts in Vietnam’s agricultural sector.

This data package, LAGOS-NE-GIS v1.0, is 1 of 5 data packages associated with the LAGOS-NE database-- the LAke multi-scaled GeOSpatial and temporal database. Three of the data packages each contain different types of data for 51,101 lakes and reservoirs larger than 4 ha in 17 lake-rich U.S. states to support research on thousands of lakes. These three package are: (1) LAGOS-NE-LOCUS v1.01: lake location and physical characteristics for all lakes. (2) LAGOS-NE-GEO v1.05: ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes. These geospatial data were created by processing national-scale and publicly-accessible datasets to quantify numerous metrics at multiple spatial resolutions. And, (3) LAGOS-NE-LIMNO v1.087.1: in-situ measurements of lake water quality from the past three decades for approximately 2,600-12,000 lakes, depending on the variable. This module was created by harmonizing 87 water quality datasets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. The other two data packages contain supporting data for the LAGOS-NE database: (4) LAGOS-NE-GIS v1.0: the GIS data layers for lakes, wetlands, and streams, as well as the spatial resolutions that were used to create the LAGOS-NE-GEO module. (5) LAGOS-NE-RAWDATA: the original 87 datasets of lake water quality prior to processing, the R code that converts the original data formats into LAGOS-NE data format, and the log file from this procedure to create LAGOS-NE. This latter data package supports the reproducibility of LAGOS-NE-LIMNO. The LAGOS-NE GIS v1.0 module includes GIS datasets for: lake polygons and their hydrologic classification; wetland polygons and their classification; streams as a line coverage and their classification by stream order; the zones used for this study (state and county; hydrologic units [at the 4, 8 and 12 scales]); and, lake watersheds (IWS). We also include boundaries of U.S. states and Canadian provinces for mapping.

      Citation for the full documentation of this database:


      Soranno, P.A., E.G. Bissell, K.S. Cheruvelil, S.T. Christel, S.M.
      Collins, C.E. Fergus, C.T. Filstrup, J.F. Lapierre, N.R. Lottig, S.K.
      Oliver, C.E. Scott, N.J. Smith, S. Stopyak, S. Yuan, M.T. Bremigan,
      J.A. Downing, C. Gries, E.N. Henry, N.K. Skaff, E.H. Stanley, C.A.
      Stow, P.-N. Tan, T. Wagner, K.E. Webster. 2015. Building a
      multi-scaled geospatial temporal ecology database from disparate data
      sources: Fostering open science and data reuse. GigaScience 4:28
      doi:10.1186/s13742-015-0067-4


      Citation for the data paper for this database:


      Soranno, P.A., L.C. Bacon, M. Beauchene, K.E. Bednar, E.G. Bissell,
      C.K. Boudreau, M.G. Boyer, M.T. Bremigan, S.R. Carpenter, J.W. Carr,
      K.S. Cheruvelil, S.T. Christel, M. Claucherty, S.M.Collins, J.D.
      Conroy, J.A. Downing, J. Dukett, C.E. Fergus, C.T. Filstrup, C. Funk,
      M.J. Gonzalez, L.T. Green, C. Gries, J.D. Halfman, S.K. Hamilton, P.C.
      Hanson, E.N. Henry, E.M. Herron, C. Hockings, J.R. Jackson, K.
      Jacobson-Hedin, L.L. Janus, W.W. Jones, J.R. Jones, C.M. Keson, K.B.S.
      King, S.A. Kishbaugh, J.F. Lapierre, B. Lathrop, J.A. Latimore, Y.
      Lee, N.R. Lottig, J.A. Lynch, L.J. Matthews, W.H. McDowell, K.E.B.
      Moore, B.P. Neff, S.J. Nelson, S.K. Oliver, M.L. Pace, D.C. Pierson,
      A.C. Poisson, A.I. Pollard, D.M. Post, P.O. Reyes, D.O. Rosenberry,
      K.M. Roy, L.G. Rudstam, O. Sarnelle, N.J. Schuldt, C.E. Scott, N.K.
      Skaff, N.J. Smith, N.R. Spinelli, J.J. Stachelek, E.H. Stanley, J.L.
      Stoddard, S.B. Stopyak, C.A. Stow, J.M. Tallant, P.-N. Tan, A.P.
      Thorpe, M.J. Vanni, T. Wagner, G. Watkins, K.C. Weathers, K.E.
      Webster, J.D. White, M.K. Wilmes, S. Yuan. In Review. LAGOS-NE: A
      multi-scaled geospatial and temporal database of lake ecological
      context and water quality for thousands of U.S. lakes. In Review at
      GigaScience. Submitted April 2017.

TESTING: NOT FOR OFFICIAL USE AT THIS TIME! This surface hydrology data is utilized as tool for development in the U.S. Drought Montior

Intellectual Merit: The Principal Investigators will reconstruct the Late Quaternary-Holocene behavior of Jakobshavns Isbrae (JAKIB) in western Greenland, one of the largest ice streams draining the modern Greenland Ice Sheet (GIS). The period from the Last Glacial Maximum (LGM) to the present will be studied because it involves the most recent large scale change in the mass-balance of the ice sheet, it is the period that will be best preserved in continental shelf sediments, and it is the period for which the highest resolution proxy records of paleo-climate from the Greenland ice cores are available. Given the scale of this ice stream and the size of its associated drainage basin, the investigation will provide information on the Late-Quaternary-Holocene behavior and stability of a major area of the GIS. This research will allow assessment of the links between deglaciation and internal and external environmental controls, such as the influence of inflowing Atlantic Water, and will facilitate modeling of the likely future behavior of the GIS. The Principal Investigators will participate in a research cruise of the British Research Vessel, Sir James Clark Ross, to West Greenland in the late summer of 2007 and collaborate with British colleagues on the post-cruise, interdisciplinary program of laboratory work and modeling. Well-dated, high-resolution sediment records from a transect of sites extending from the shelf edge and along the shelf trough, to sites within Disko Bugt both proximal and distal to the modern ice margin will be acquired on the basis of geophysical data. The hypotheses to test using these cores are: Hypothesis 1: Glacier ice extent and interactions between the West Greenland Current and the GIS are recorded in the foraminiferal faunas, mineralogical variations, and Sm and Nd isotopic compositions of the sediments. Hypothesis 2: The West Greenland Current has changed in strength, flowpath and watermass composition from the LGM through the Holocene. These variations have played and continue to play a key role in ice-sheet behavior. Broader Impacts: The underlying rationale for this research is to determine if recent (last ~ 100 yr) observed changes to the mass balance of the GIS reflect natural variability in ice sheet dynamics, or if they relate to anthropogenically-induced climate warming. Key to resolving this is an understanding of ice sheet behavior since the LGM and including periods in the past near Greenland that were as warm or, even warmer than today, such as the middle Holocene optimum. This research will make new discoveries concerning the timing and extent of the Greenland Ice Sheet at the LGM on its western margin, and the behavior of the JAKIB ice stream during deglaciation, new information that will inform paleoceanographers and climate and sea-level modelers. This project is an international effort. It will support a U.S. PhD student, and two undergraduate students. It will involve international student exchanges between the University of Colorado and the United Kingdom. The data acquired will be lodged in the NOAA Paleoclimate Database. CORES: 2008-070CC - 68.228N -57.618W JR175-VC20 - 68.201N -57.756W JR175-VC35 - 67.701N -59.342W

This data set is a geographic information systems (GIS) version of part of the results of Canada-Alberta Mineral Development Agreement project 'Geological mapping, prospecting and sampling of the Southern Alberta Rift.' The data represent elemental analyses of 415 stream sediment samples collected from the study area in 1992. Analytes include: Ag, Cd, Cu, Pb, and Zn by atomic absorption. Ag, As, Au, Ba, Br, Ce, Co, Cr, Cs, Eu, Fe, Hf, Ir, La, Lu, Mo, Na, Ni, Rb, Sb, Sc, Se, Sm, Sn, Ta, Tb, Te, Th, U, W, Yb, Zn, and Zr by neutron activation. and Hg by cold vapour extraction/atomic absorption. Results show that some of the stream sediments contain higher concentrations of mineral elements than normal.

TESTING: NOT FOR OFFICIAL USE AT THIS TIME! This surface hydrology data is utilized as tool for development in the U.S. Drought Montior

The Geology of the Northern Jetty Peninsula GIS dataset contains the shapefiles and tables of the basement geology of the Northern Jetty Peninsula in East Antarctica. This dataset is derived from the map product ‘Geology of Northern Jetty Peninsula, Mac.Robertson Land, Antarctica'.

Northern Jetty Peninsula, incorporating Else Platform (~140 km2) and Kamenistaja Platform (~15 km2), represents a mostly ice-free low-lying region located on the western flanks of the Lambert Graben. The region is underlain by granulite-facies Proterozoic gneisses and unmetamorphosed Permian sediments.

Collected through SM GOIS Data Download Page: https://gis-data-downloads.s3-us-west-1.amazonaws.com/SAN_MATEO_COUNTY_ACTIVE_PARCELS.zip

TESTING: NOT FOR OFFICIAL USE AT THIS TIME! This surface hydrology data is utilized as tool for development in the U.S. Drought Montior

[From The Landmap Project: Introduction, "http://www.landmap.ac.uk/background/intro.html"]

 A joint project to provide orthorectified satellite image mosaics of Landsat,
 SPOT and ERS radar data and a high resolution Digital Elevation Model for the
 whole of the UK. These data will be in a form which can easily be merged with
 other data, such as road networks, so that any user can quickly produce a
 precise map of their area of interest.

 Predominately aimed at the UK academic and educational sectors these data and
 software are held online at the Manchester University super computer facility
 where users can either process the data remotely or download it to their local
 network.

 Please follow the links to the left for more information about the project or
 how to obtain data or access to the radar processing system at MIMAS. Please
 also refer to the MIMAS spatial-side website,
 "http://www.mimas.ac.uk/spatial/", for related remote sensing materials.

This datset represents 2300 sites across the county that are regularly inspected and/or treated for mosquito activity. Fields include: name, habitat, address, city, and postal code. The name field consists of a 2 letter regional code plus a treatment site number, as follows: BH - Treatment sites in Brookhaven Township BN - Sites in the Town of Babylon EH - Sites in the Town of East Hampton FI - Sites on Fire Island FS - Sites on Fisher's Island HN - Sites in the Town of Huntington IS - Sites in the Town of Islip RH - Sites in the Town of Riverhead SD - Sites in the Town of Southold SI- Sites on Shelter Island SM - Sites in the Town of Smithtown SN - Sites in the Town of Southampton

Here we provide four ArcGIS map packages with georeferenced files on the spatial distribution of demersal and pelagic fishes in the wider Weddell Sea (Antarctica), which were created in the context of the development of a marine protected area (MPA) in the Weddell Sea. Antarctic toothfish: The map of Dissostichus mawsoni occurrence probability is based on catch per unit effort (CPUE) data from the database of the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) (data request: 03-08-2016) and on bathymetric data from the International Bathymetric Chart of the Southern Ocean (IBCSO). We fitted a four-parameter Weibull model to the simulated CPUE data per depth interval by means of the R package 'fitdistrplus'. The highest D. mawsoni occurrence probability was shown at depths between 1500 and 2000 m and only approximately 20 % of the Antarctic toothfish population occurred deeper than 2000 m. Antarctic silverfish: The map of interpolated abundances of Pleuragramma antarctica was based on pelagic trawl survey data, which were collected during "Polarstern" cruises ANT-I/2, ANT-III/3 and in the context of the Lazarev Sea Krill Survey (LAKRIS) ("Polarstern" cruises ANT-XXI/4, ANT-XXIII/6, ANT-XXIV/2). The first mentioned data were provided by V. Siegel (retired; formerly Thünen Institute), the LAKRIS data by H. Flores (AWI). Those data were complemented by benthic trawl survey data, which were collected during seven "Polarstern" cruises between 1996 and 2011 (ANT-XIII/3, ANT-XV/3, ANT-XVII/3, ANT-XIX/5, ANT-XXI/2, ANT-XXIII/8, ANT-XXVII/3) and were provided by R. Knust (AWI) as well as by data on counts of fish species from trawl and dredge samples by Drescher et. (2012), Ekau et al. (2012a, b), Hureau et al. (2012), Kock et al. (2012) and Wöhrmann et al. (2012). An inverse distance weighted interpolation was performed for a 10 nautical mile radius around each record. Areas with highest numbers of P. antarctica (> 36 individuals/1000 m²) occurred offshore Riiser -Larsen Ice Shelf and on the southern Weddell Sea continental shelf offshore Filchner Ice Shelf. Demersal fish: The map of predicted habitat suitability for demersal fish is based on data, which were collected during seven "Polarstern" cruises between 1996 and 2011 (ANT-XIII/3, ANT-XV/3, ANT-XVII/3, ANT-XIX/5, ANT-XXI/2, ANT-XXIII/8, ANT-XXVII/3) and were provided by R. Knust (AWI). The habitat suitability model was developed by the use of the modelling package "biomod2". Most suitable habitat conditions for demersal fish in the wider Weddell Sea occurred on the continental shelf between approx. 5° and 30°W, on the shelf west and east of the tip of the Antarctic Peninsula as well as around the South Shetland and South Orkney Islands. Nesting sites of demersal fish: The map on observation of nesting sites of demersal fish is based on data, which were collected during "Polarstern" cruises ANT-XXVII/3, ANT-XXIX/9 and ANT-XXXI/2 and were obtained by T. Lundälv (retired; formerly University of Gothenburg), D. Gerdes (retired; formerly AWI) and E. Riginella (University of Padova), respectively. Those data were complemented by a literature research. Most nesting sites were observed west of 25°W, north of the tip of the Antarctic Peninsula and along the west coast of the Antarctic Peninsula. More information is given in the working paper WG-EMM-16/03 submitted to the CCAMLR Working Group on Ecosystem Monitoring and Management CCAMLR (available at https://www.ccamlr.org/en/wg-emm-16). Revised versions of the spatial analysis are described in working paper WG-SAM-17/30 and WS-SM-18/13 submitted to the CCAMLR Working Group on Statistics, Assessments and Modelling and the CCAMLR Workshop on Spatial Management, respectively (available at https://www.ccamlr.org/en/wg-sam-17; https://www.ccamlr.org/en/ws-sm-18).

Highlighted SANDAG EMP Projects and Conserved Lands for use in the EMP Story Map at http://www.keepsandiegomoving.com/EMP/EMP-intro.aspx

Vectorised interpretations of possible archaeological features identified in airborne remote sensing (airborne laser scanning and aerial photographs dating back to 1917).Used in the following article:Stott, D., Kristiansen, S.M., Lichtenberger, A. and Raja, R., 2018. Mapping an ancient city with a century of remotely sensed data. Proceedings of the National Academy of Sciences, 115(24), pp.E5450-E5458.

The Ivotuk study site is located in northwestern Alaska, on the North Slope of the Brooks Mountains Range. The study site was chosen because four tundra vegetation types, moist acidic tundra (MAT), moist nonacidic tundra (MNT), mossy tussock tundra, and shrub tundra exist within a 2 km2 area. This project was funded by the NSF Arctic System Sciences, Land-Atmosphere-Ice Interactions Program (OPP-9908829) as part of the Arctic Transitions in the Land- Atmosphere System (ATLAS) effort. The four 100 x 100 m grids were mapped as raster maps, using aerial photographs and ground verification. Both vegetation types (22 units) and microsites (13 units) were mapped on the grids. The GIS data and raster maps will be uploaded when they become available. References Riedel, S. M., H. E. Epstein, D. A. Walker, D. L. Richardson, M. P. Calef, E. J. Edwards, and A. Moody. 2005 Spatial and temporal heterogeneity of vegetation properties among four tundra plant communities at Ivotuk, Alaska, U.S.A. Arctic, Antarctic and Alpine Research 37:25-33.

Water quality management remains a critical challenge in arid and semi-arid regions, where limited freshwater resources are increasingly stressed by anthropogenic activities and natural constraints. This study provides a summer 2024 assessment of surface water quality in Egypt’s Fayoum Governorate, emphasizing spatial variability, dominant pollution drivers, and sectoral suitability. Ten sites across agricultural drains and wastewater discharge points were analyzed for 17 physicochemical parameters. The Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) was applied to evaluate drinking, irrigation, industrial, and ecological uses, while spatial patterns were mapped using Inverse Distance Weighting (IDW) in a Geographic Information System. The results reveal critical exceedances in salinity (TDS up to 3,420 mg/L; EC up to 6,840 μS/cm), nutrient enrichment (PO43− up to 10.85 mg/L; NH3–N up to 10.78 mg/L), and turbidity (105 nephelometric turbidity units), mainly from untreated sewage, agricultural return flows, and limited dilution. WQI classification for drinking water showed 30% good, 50% fair, and 20% poor (0) and corrosion (RSI >8.5) in more than half the samples. Ecologically, 50% of sites recorded poor WQI (