This layer presents detectable thermal activity from MODIS satellites for the last 7 days. MODIS Global Fires is a product of NASA’s Earth Observing System Data and Information System (EOSDIS), part of NASA's Earth Science Data. EOSDIS integrates remote sensing and GIS technologies to deliver global MODIS hotspot/fire locations to natural resource managers and other stakeholders around the World.

Area Covered: World

What can I do with this layer?
The MODIS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.


It takes approximately 2 – 4 hours after satellite overpass for MODIS Rapid Response to process the data, and for the Fire Information for Resource Management System (FIRMS) to update the website. Occasionally, hardware errors can result in processing delays beyond the 2-4 hour range. Additional information on the MODIS system status can be found at MODIS Rapid Response.

Attribute Information

• Latitude and Longitude: The center point location of the 1km (approx.) pixel flagged as containing one or more fires/hotspots (fire size is not 1km, but variable). Stored by Point Geometry. See What does a hotspot/fire detection mean on the ground?
• Brightness: The brightness temperature measured (in Kelvin) using the MODIS channels 21/22 and channel 31.
• Scan and Track: The actual spatial resolution of the scanned pixel. Although the algorithm works at 1km resolution, the MODIS pixels get bigger toward the edge of the scan. See What does scan and track mean?
• Date and Time: Acquisition date of the hotspot/active fire pixel and time of satellite overpass in UTC (client presentation in local time). Stored by Acquisition Date.
  
• Acquisition Date: Derived Date/Time field combining Date and Time attributes.
• Satellite: Whether the detection was picked up by the Terra or Aqua satellite.
• Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel.
• Version: Version refers to the processing collection and source of data. The number before the decimal refers to the collection (e.g. MODIS Collection 6). The number after the decimal indicates the source of Level 1B data; data processed in near-real time by MODIS Rapid Response will have the source code “CollectionNumber.0”. Data sourced from MODAPS (with a 2-month lag) and processed by FIRMS using the standard MOD14/MYD14 Thermal Anomalies algorithm will have a source code “CollectionNumber.x”. For example, data with the version listed as 5.0 is collection 5, processed by MRR, data with the version listed as 5.1 is collection 5 data processed by FIRMS using Level 1B data from MODAPS.
• Bright.T31: Channel 31 brightness temperature (in Kelvins) of the hotspot/active fire pixel.
• FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).
• DayNight: The standard processing algorithm uses the solar zenith angle (SZA) to threshold the day/night value; if the SZA exceeds 85 degrees it is assigned a night value. SZA values less than 85 degrees are assigned a day time value. For the NRT algorithm the day/night flag is assigned by ascending (day) vs descending (night) observation. It is expected that the NRT assignment of the day/night flag will be amended to be consistent with the standard processing.
• Hours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.

This map is provided for informational purposes and is not monitored 24/7 for accuracy and

Follow these instructions to use the Google Spreadsheet in your own activity. Begin by copying the Google Spreadsheet into your own Google Drive account. Prefill the username column for your students/participants. This will help keep the students from overwriting their peers' work.Change the editing permissions for the spreadsheet and share it with your students/participants.Demonstrate what data goes into each column from the Wikipedia page. Be sure to demonstrate how to find the latitude and longitude from Wikipedia. For the images, make sure the students copy the url that ends in the appropriate file type (jpg, png, etc).Be prepared for lots of mistakes. This is a great learning opportunity to talk about data quality. When the students are done completing the spreadsheet, check the spreadsheet for obvious errors. Pay special attention to the sign of the longitude. All of those values should be negative. Download the spreadsheet as a CSV.Log into your AGO Org account.Click on the Content tab -> Add item -> From my computerUpload the CSV and save it as a layer feature. Be sure to include a few tags (Mesoamerica, pyramid, Aztec, Maya would be good ones).Once the layer has been uploaded and converted into a feature layer, click the Settings button and check Delete Protection and save. From the feature layer Overview tab, change the share settings to share with your students. I usually set up a group (something like Mesoamerica), add the students to the group, then share the feature layer with that group.From here explore the data. Symbolize the data by culture to see if there are spatial patterns to their distribution. Symbolize the data by height to see if some cultures built taller pyramids or if taller pyramids were confined to certain regions. Students can also set up the pop-ups to use the image URL in the data.From here, students can save their maps, add additional data from ArcGIS Online, create story maps, etc. If you are looking for more great data, from your ArcGIS Online map, choose Add -> Add Layer from Web and paste the following into the URL. https://services1.arcgis.com/TQSFiGYN0xveoERF/arcgis/rest/services/MesoAmerican_civs/FeatureServerImage thumbnail is from Wikipedia.

Stream segments with Strahler stream order values assigned. For more information about Strahler stream order, see: http://en.wikipedia.org/wiki/Strahler_Stream_Order

Stream order was assigned using an automated process. Data has not been verified and is subject to change. Be sure to check the values before using the layer.

Stream order values are maintained as tabular data and displayed as linear events on the Stream Routes with Kittle Numbers and Mile Measures layer. In the attribute table, designated segments extend from the FROM_MEAS (mile) to the TO_MEAS (mile) and have a total length = [LENGTH_MI] on a route with total length = [ROUTE_MI].

Soil and landscape mapping was collated from Western Australia, South Australia, Victoria, and New South Wales, in combination with latest Digital Soil Mapping products for Australia (Soil and Landscape Grid of Australia) as the basis for a new sandy soils map. A staged map compilation process was undertaken to combine all these available datasets into one uniform map that retains integrity of legacy contextual mapping information.

The key steps undertaken in the mapping of sandy soils include: 1. Define an agricultural region area of interest for this study; 2. Collate available soil-landscape mapping datasets across Australia (including state and national); 3. Assemble and edit existing mapping to form a new sandy soil map for agricultural regions of the study area; 4. Review and revise this mapping in response to feedback from NCST members including state/territory experts.

Maps were revised and updated with input from members of the Digital Soil Assessment Working Group and members of the National Committee on Soil and Terrain. While efforts were made to include these suggestions, it was not possible to refine the map indefinitely, and therefore editing ceased on the 23^rd of February 2021. Due to the variations in scale, mapping techniques, representation, and attribution across Australia, the use of these maps for such purposes as mapping sandy soils across southern Australia proved difficult.

From the new sandy soils map we were able to identify agricultural areas of sandy soils: (Western Australia - 10.611Mha; South Australia - 2.479Mha; New South Wales - 1.867Mha; Victoria - 0.864Mha and Tasmania - 0.215Mha). Nationally there were 16.039Mha of sandy soil identified which is considerably higher than the 11Mha from previous estimates.

This research is funded by the CRC for High Performance Soils and supported by the Cooperative Research Centres program, an Australian Government initiative.

Additional funding and in-kind support are provided by: Murdoch University, PIRSA, Federation University Australia, West Midlands Group and AORA. Contributions from Richard Bell, Amanda Schapel and David Davenport have been critical in shaping the logic and key considerations in mapping sandy soils and benefits of amelioration. James Hall is also thanked for providing insights into sandy soils for South Australia and the formation of the new Arenosol soil order for Australia.

We would also like to acknowledge the contributions of the Digital Soil Assessment Working Group and members of the National Committee on Soil and Terrain that provided valuable feedback on the approach used to map sandy soils.

Administrative and structural details on data files:

• A shapefile (Sandy_soil_map_aglands.shp) is provided for use in a Geographic Information System (GIS). This should be useable in commercial (e.g. ArcGIS) and open source software packages (e.g. QGIS). The shapefile data coordinate system is WGS1984 geographic.
• A RTF file is also provided which includes information on the data fields and content of the shapefile for users. Note that abbreviations for the Australian Soil Classification Order and Suborder fields (as 2021) were used.

Associated publication:

Robinson N, Pope R, Liddicoat C, Holmes K, Griffin E, Kidd D, Jenkins B, Rees D, Searle R. (2021) Sandy Soils: Organic and clay amendments to improve the productivity of sandy soils. Detailed plan for mapping and grouping of sands. Soil CRC Project 3.3.003. Cooperative Research Centre for High Performance Soils.

GeoJunxion uses a combination of methods to make this service very fast and efficient. The map service comes with on-demand tile rendering, often with smart-tiling, and custom styling. With smart tiling, all populated areas are pre-rendered to provide super-fast response to map requests.

KEY FEATURES

• 3 databases: GeoJunxion Maps, OSM Maps, Aerial/Satellite Imagery. • 4 custom map styles: GeoJunxion MapStyle, OSM Generic/Default, OSM Bright, OSM Bright with house numbers • Map tiles are delivered following the Slippy Maps convention.

TYPICAL USE CASES

The OSM Map Tile Server will help to display business locations on a map within a company website, it will also show moving objects on a map within a track & trace application. And furthermore it will also Provide an overview to a company’s assets on a map, as well as include geospatial analysis results within a GIS solution

BENEFITS

OSM Map Tile Server enables you to view online maps within websites or alternatively to view those maps hosted on premise through GIS software

DELIVERY FORMATS API

COVERAGE GeoJunxion, OSM: World Aerial/Satellite Imagery: The Netherlands, Flanders (Belgium)

The GeoJunxion Tile Server is the easiest way to receive map tiles to use within your own organization, application and with your preferred map viewer. The GeoJunxion Tile Server installation is Quick & Easy.

Security: On your own server or in the cloud Smart: Intelligent Map Tiling Quick & Easy: Seamless set-up of map tiles Legal: GeoJuxnion as an European contract party Helpdesk: Support from GeoJunxion with SLA LBS: Additional APIs available

On your own server or in the cloud: With the GeoJunxion Tile Server you can host your own map tiles in your own secure environment. You control your own data and connections. Alternatively, GeoJunxion can host the map tiles in the cloud for you.

OSM for Professional use: GeoJunxion offers enhanced services on top of OpenStreetMap for Professional use. The GeoJunxion Tile Server is part of the OSM for Professionals product portfolio: GeoJunxion will your contract party GeoJunxion can offer support on OSM services based on an agreed SLAControlled QA/QC reports on OpenStreetMap

Slippy Map

The provided map tiles can be used in a modern slippy map web map application which let you zoom and pan around. With a slippy map, basically, the map slips around when you drag the mouse. More info regarding this kind of map, can be found here: https://wiki.openstreetmap.org/wiki/Slippy_Map. Slippy Map - OpenStreetMap Wiki

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name