Topeka Pedestrian Priority Area Heat Map

This story map explains how to use heat mapping within smart mapping to show density within your maps in ArcGIS Online. You can easily select the heat map style to show where your data is spatially clustered. Go beyond the defaults to show density for an attribute, telling the story of an area that is statistically significant. Add the points layer back into the map with transparency as a reference to the heat map. This story map walks you through examples, which can help get you started with smart mapping heat maps. For more information, visit the Help Pages.

About the App This app hosts data from Heat Resilience Solutions for Boston (the Heat Plan). It features maps that include daytime and nighttime air temperature, urban heat island index, and extreme heat duration. About the DataA citywide urban canopy model was developed to produce modeled air temperature maps for the City of Boston Heat Resilience Study in 2021. Sasaki Associates served as the lead consultant working with the City of Boston. The technical methodology for the urban canopy model was produced by Klimaat Consulting & Innovation Inc. A weeklong analysis period during July 18th-24th, 2019 was selected to produce heat characteristics maps for the study (one of the hottest weeks in Boston that year). The data array represents the modelled, average hourly urban meteorological condition at 100 meter spatial resolution. This dataset was processed into urban heat indices and delivered as georeferenced image layers. The data layers have been resampled to 10 meter resolution for visualization purposes. For the detailed methodology of the urban canopy model, visit the Heat Resilience Study project website.

The heat map below depicts the Top 50 Cities where ACCA patrons come from, with the Top 20 Cities labeled on the map in a graph indicating the number of tickets issued to patrons from that city and the percentage of total tickets issued that city represents.Numbers reflect percent of the entire 336,613 tickets issued in 2024. Total Attendance does not include Paramount School of the Arts Enrollment (1,240) or Christkindlmarket attendance (275,000) in 2024.Total Cities: 1,589Total Tickets: 336,613This heat map was developed internally by the Aurora Civic Center Authority and provided to the City of Aurora Data Analytics Division.

The following dataset includes "Active Benchmarks," which are provided to facilitate the identification of City-managed standard benchmarks. Standard benchmarks are for public and private use in establishing a point in space. Note: The benchmarks are referenced to the Chicago City Datum = 0.00, (CCD = 579.88 feet above mean tide New York). The City of Chicago Department of Water Management’s (DWM) Topographic Benchmark is the source of the benchmark information contained in this online database. The information contained in the index card system was compiled by scanning the original cards, then transcribing some of this information to prepare a table and map. Over time, the DWM will contract services to field verify the data and update the index card system and this online database.This dataset was last updated September 2011. Coordinates are estimated. To view map, go to https://data.cityofchicago.org/Buildings/Elevation-Benchmarks-Map/kmt9-pg57 or for PDF map, go to http://cityofchicago.org/content/dam/city/depts/water/supp_info/Benchmarks/BMMap.pdf. Please read the Terms of Use: http://www.cityofchicago.org/city/en/narr/foia/data_disclaimer.html.

This is a heatmap (a graphical representation of data where the individual values contained in a matrix are represented as colors) of 2013 deer hunt kills within the California Department of Fish & Wildlife (CDFW) North Central Region (Region 2). The data was compiled from 2013 CDFW Automated Licensing Data System (ALDS) tables. Text descriptions from hunters were approximated and placed with geographic coordinates. The resulting point data was converted to a heatmap using Kernel Density Tool in ArcGIS 10.1

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.Consumption Best Practices:

As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cacheable relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment.When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cacheable.Source: NASA FIRMS - Active Fire Data - for WorldScale/Resolution: 1kmUpdate Frequency: 1/2 Hour (every 30 minutes) using the Aggregated Live Feed MethodologyArea Covered: WorldWhat 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.Additional InformationMODIS stands for MODerate resolution Imaging Spectroradiometer. The MODIS instrument is on board NASA’s Earth Observing System (EOS) Terra (EOS AM) and Aqua (EOS PM) satellites. The orbit of the Terra satellite goes from north to south across the equator in the morning and Aqua passes south to north over the equator in the afternoon resulting in global coverage every 1 to 2 days. The EOS satellites have a ±55 degree scanning pattern and orbit at 705 km with a 2,330 km swath width.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 InformationLatitude 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.RevisionsJune 22, 2022: Added 'HOURS_OLD' field to enhance Filtering data. Added 'Last 7 days' Layer to extend data to match time range of VIIRS offering. Added Field level descriptions.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

The map simulates a chain of coffee stores in Manhattan. The heat map layer helps explain the public's impression that "there's one on every corner."A simple map of store locations is useful to a point. When the map's symbols start to "stack up" on one another, a heat map can often help visualize the local area better than the collective "dots on the map" can.The data in this map was created for demonstration purposes only.

Parcels downloaded from LINZ weekly, includes historic parcels and temporal information. Contains primary, secondary and tertiary parcels.Polygons within this layer have a nominal accuracy of 0.1-1m in urban areas and 1-100m in rural areas. For more detailed information about parcel accuracies please refer to the Survey Boundary Marks layer which contains accuracies for each parcel node.

This layer presents detectable thermal activity from VIIRS satellites for the last 7 days. VIIRS Thermal Hotspots and Fire Activity is a product of NASA’s Land, Atmosphere Near real-time Capability for EOS (LANCE) Earth Observation Data, part of NASA's Earth Science Data.Consumption Best Practices:

As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cacheable relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment.When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cacheable.Source: NASA LANCE - VNP14IMG_NRT active fire detection - WorldScale/Resolution: 375-meterUpdate Frequency: Hourly using the aggregated live feed methodologyArea Covered: WorldWhat can I do with this layer?This layer represents the most frequently updated and most detailed global remotely sensed wildfire information. Detection attributes include time, location, and intensity. It can be used to track the location of fires from the recent past, a few hours up to seven days behind real time. This layer also shows the location of wildfire over the past 7 days as a time-enabled service so that the progress of fires over that timeframe can be reproduced as an animation.The VIIRS 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.Fire points in this service are generally available within 3 1/4 hours after detection by a VIIRS device. LANCE estimates availability at around 3 hours after detection, and esri livefeeds updates this feature layer every 15 minutes from LANCE.Even though these data display as point features, each point in fact represents a pixel that is >= 375 m high and wide. A point feature means somewhere in this pixel at least one "hot" spot was detected which may be a fire.VIIRS is a scanning radiometer device aboard the Suomi NPP, NOAA-20, and NOAA-21 satellites that collects imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans in several visible and infrared bands. The VIIRS Thermal Hotspots and Fire Activity layer is a livefeed from a subset of the overall VIIRS imagery, in particular from NASA's VNP14IMG_NRT active fire detection product. The downloads are automatically downloaded from LANCE, NASA's near real time data and imagery site, every 15 minutes.The 375-m data complements the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Hotspots and Fire Activity layer; they both show good agreement in hotspot detection but the improved spatial resolution of the 375 m data provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters.Attribute informationLatitude and Longitude: The center point location of the 375 m (approximately) pixel flagged as containing one or more fires/hotspots.Satellite: Whether the detection was picked up by the Suomi NPP satellite (N) or NOAA-20 satellite (1) or NOAA-21 satellite (2). For best results, use the virtual field WhichSatellite, redefined by an arcade expression, that gives the complete satellite name.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel. This value is based on a collection of intermediate algorithm quantities used in the detection process. It is intended to help users gauge the quality of individual hotspot/fire pixels. Confidence values are set to low, nominal and high. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Nominal confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.Please note: Low confidence nighttime pixels occur only over the geographic area extending from 11 deg E to 110 deg W and 7 deg N to 55 deg S. This area describes the region of influence of the South Atlantic Magnetic Anomaly which can cause spurious brightness temperatures in the mid-infrared channel I4 leading to potential false positive alarms. These have been removed from the NRT data distributed by FIRMS.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: D = Daytime fire, N = Nighttime fireHours 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.Additional information can be found on the NASA FIRMS site FAQ.Note about near real time data:Near real time data is not checked thoroughly before it's posted on LANCE or downloaded and posted to the Living Atlas. NASA's goal is to get vital fire information to its customers within three hours of observation time. However, the data is screened by a confidence algorithm which seeks to help users gauge the quality of individual hotspot/fire points. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Medium confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.RevisionsMarch 7, 2024: Updated to include source data from NOAA-21 Satellite.September 15, 2022: Updated to include 'Hours_Old' field. Time series has been disabled by default, but still available.July 5, 2022: Terms of Use updated to Esri Master License Agreement, no longer stating that a subscription is required!This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

Heat map density display that highlights the hot spot locations of hurricane force wind events. This heat maps is only for the OPC season from June 1, 2020, through May 31, 2021. This season was marked by an unusually high number of barrier and tip jet events near the south and southeast coasts of Greenland.

Heat map density display highlighting the hot spot locations of hurricane force wind events across OPC's area of responsibility in the North Atlantic from June 1, 2020, through May 31, 2021. This OPC cold season contained a high occurrence of tip jet and barrier jet events along the south and southeastern coast of Greenland.

Under the Natural Capital and Ecosystem Assessment (NCEA) Pilot, Natural England and the Botanical Society of Britain and Ireland (BSBI) have been working in partnership to use BSBI's vast database of plant records to inform the evidence base for tree-planting activities. Poorly targeted tree planting risks damaging wildlife and carbon-rich habitats, therefore using these data we aim to ensure that areas of high conservation value are preserved in the landscape. The summarised botanical value map provides an easily interpretable output which categorises monads (1 x 1 km grid squares) as being of Low, Moderate or High botanical value according to the presence of Rare, Scarce and Threatened (RST) plant species and/or the proportion of Priority Habitat Positive Indicator (PHPI) species that were recorded within the 1 x 1 km grid square between 1970 and 2022. The PHPI species are a combination of BSBI axiophytes, positive indicators for common standards monitoring and ancient woodland indicators. The dataset includes an overall botanical value, as well as values based on only the presence of RST plant species, and a value for each broad habitat type based on the PHPI species records. By viewing the different attributes, you can gain insights into how valuable a monad is for different habitat types and for plant species of conservation concern, as well as an indication of how well a particular monad has been surveyed. The categories of 'No indicators, poor survey coverage' and 'No indicators, good survey coverage' indicate where no indicator species have been recorded and survey coverage either is above or below a threshold of 3 'recorder days'. A 'recorder day' is defined as being when 40 or more species have been recorded on a single visit and 3 recorder days is assumed sufficient to achieve good survey coverage within a 1 x 1 km grid square. This map is not intended to be used to carry out detailed assessments of individual site suitability for tree planting, for which the RST plant species heatmap at 100 x 100 m resolution and the PHPI heatmaps at 1 x 1 km resolution have been developed by BSBI and Natural England. However, the summarised botanical value map can provide useful insights at a strategic landscape scale, to highlight monads of high value for vascular plants and inform spatial planning and prioritisation, and other land management decision-making. These should be used alongside other environmental datasets and local knowledge to ensure decisions are supported by the appropriate evidence. Please get in contact if you have any queries about the data or appropriate uses at botanicalheatmaps@naturalengland.org.uk.Datasets used:BSBI botanical heatmap data - BSBIOS Grids - OSONS Country boundaries - ONSCommon Standards Monitoring guidance - JNCC 2004BSBI's Axiophyte list - Walker 2018Ancient Woodland Indicators - Glaves et al. 2009Plantatt - Hill et al. 2004Further information can be found in the technical report at:Botanical Heatmaps and the Botanical Value Map: Technical Report (NERR110)Full metadata can be viewed on data.gov.uk.

This dataset contains tree canopy cover layers as derived and calculated via a land cover classification for the City of Mesa using 2024 MAG Imagery. The land cover classification utilized a Support Vector Machine Classifier and was calculated for various areas including city boundary, census tracts, census blocks, council district, etc.This dataset also contains the point locations and attributes of trees maintained by the City of Mesa. The point dataset was obtained by WCA from WCA in September 2024. The attributes of interest to this study included unique TreeID, Exact DBH, DBH Range, Height Range, Botanical Name, Common Name, Latitude, and Longitude. Updates to the tree layer were made by joining the results from the September 2024 i-Tree report. An i-Tree Eco Analysis was run in September 2024 using i-Tree Eco v6.0.38 and the results were joined based on unique tree ID to the Mesa’s tree inventory. Attributes added were: Structural Value ($), Carbon Storage (lb), Carbon Storage ($), Gross Carbon Sequestration (lb/yr), Gross Carbon Sequestration ($/yr), Avoided Runoff (cubicFT/yr), Avoided Runoff ($/yr), Pollution Removal (oz/yr), Pollution Removal ($/yr), Total Annual Benefits ($/yr), Height (ft), Canopy Cover (sqft), Tree Condition, Leaf Area (sqft), Leaf Biomass (lb), Leaf Area Index Basal Area (sqft), Cond, i-Tree_ID_BotName, i-Tree_ID_ComName and i-Tree_ID Genus. The exact definitions, meanings, calculations, etc. for the i-Tree Values can be found on i-Tree’s website via the i-Tree Eco User Manual. For certain layers the individual i-Tree values were aggregated by census tract, census block, zip code, etc. These results can be seen in the polygon layers with the following attribute values: CanopyCoverPer_Final, COUNT_Tree_ID, SUM_Replacement_Value_, SUM_Carbon_Storage_lb_, SUM_Carbon_Storage_, SUM_Gross_Carbon_Sequestration_lb_, SUM_Gross_Carbon_Sequestration_y, SUM_Avoided_Runoff_ftÂ_yr_, SUM_Avoided_Runoff_yr_, SUM_Pollution_Removal_oz_yr_, SUM_Pollution_Removal_yr_, and SUM_Total_Annual_Benefits_yr_

The Kernel Density tool calculates the density of features in a neighborhood around those features.Kernel Density calculates the density of point features around each output raster cell. Conceptually, a smoothly curved surface is fitted over each point. The surface value is highest at the location of the point and diminishes with increasing distance from the point, reaching zero at the Search radius distance from the point. Only a circular neighborhood is possible. The volume under the surface equals the Population field value for the point, or 1 if NONE is specified. The density at each output raster cell is calculated by adding the values of all the kernel surfaces where they overlay the raster cell center. This layer is included in a storymap about the Panama City crayfish, a species listed as Threatened under the Endangered Species Act in 2022. Storymap link: https://fws.maps.arcgis.com/home/item.html?id=a791906fe3f8433eabadda5898184372

Date of Image:3/23/2023Date of Next Image:UnknownSummary:NASA used a Sentinel-1 SAR backscatter change approach developed in GEE (Handwerger et al., 2022) to detect areas with high landslide density. This approach detects potential landslides by calculating the change in the backscatter coefficient before and after the triggering event using the log ratio approach. False positives such as backscatter change due to flooding, agriculture, and more, are removed by using threshold-based masks made from the topographic slope from the 1 arcsec (∼30 m) resolution NASADEM (NASA JPL, 2020). Using stacks of SAR data reduces noise, and furthermore, the pre-event stack provides backscatter data that is more representative of the pre-event ground surface properties. Finally, to detect landslide areas, the backscatter change raster was thresholded using the 99th percentile to highlight strong signals, and the heatmap was calculated.Pre-event time frame: 2022-01-01 to 2023-03-10Post-event time frame: 2023-03-11 - 2023-03-23This map should be used as a guidance to identify areas likely affected by landslides. This is a rapid response product. We have not done any form of manual corrections to remove false positives.Suggested Use:The red and yellow areas indicate potential zones of dense landsliding. The major red zone to the South is not validated, since there is extensive cloud cover, but the smaller red blob in the North-East is where media reports have highlighted landslides from some available optical imagery. Disclaimer: not verified in field and optical imagery has clouds preventing verification.Satellite/Sensor:Copernicus Sentinel-1 Synthetic Aperture Radar (SAR)Resolution:30 metersCredits:NASA GSFC Landslides Team, Copernicus Sentinel-1 dataHandwerger AL, Huang M-H, Jones SY, Amatya P, Kerner HR, Kirschbaum DB. 2022. Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine. Nat. Hazards Earth Syst. Sci. Copernicus Publications, 22(3): 753–773. https://doi.org/10.5194/nhess-22-753-2022.Esri REST Endpoint:See URL section on right side of pageWMS Endpoint:https://maps.disasters.nasa.gov/ags04/services/cyclone_freddy_2023/landslide_density_sentinel1_20230323/MapServer/WMSServerData Download:https://maps.disasters.nasa.gov/download/gis_products/event_specific/2023/cyclone_freddy/landslides/

The emergence of urban heat as a climate-induced health stressor is receiving increasing attention among researchers, practitioners, and climate educators. However, the measurement of urban heat poses several challenges with current methods leveraging either ground based, in situ observations, or satellite-derived surface temperatures estimated from land use emissivity. While both techniques contain inherent advantages and biases to predicting temperatures, their integration may offer an opportunity to improve the spatial resolution and global application of urban heat measurements. Using a combination of ground-based measurements, machine learning techniques, and spatial analysis, we addressed three research questions: (1) How much do ambient temperatures vary across time and space in a metropolitan region? (2) To what extent can the integration of ground-based measurements and satellite imagery help to predict temperatures? (3) What landscape features consistently amplify and temper heat? We applied our analysis to the city of Richmond, Virginia, using geocomputational machine learning processes on data collected on days when maximum air temperatures were above the 90th percentile of historic averages. Our results suggest that the urban microclimate was highly variable—with differences of up to 10 C between coolest and warmest locations at the same time—and that these air temperatures were primarily dependent on underlying landscape features. Additionally, we found that integrating satellite data with ground-based measures provided highly accurate and precise descriptions of temperatures in all three study regions. These results suggest that accurately identifying areas of extreme urban heat hazards for any region is possible through integrating ground-based temperature and satellite data.

Date of Image:9/28/2024, 10/3/2024, 10/5/2024, 10/10/2024, 10/15/2024, 10/17/2024Date of Next Image:UnknownSummary:NASA used a Sentinel-1 SAR backscatter change approach developed in GEE (Handwerger et al., 2022) to detect areas with high landslide density. This approach detects potential landslides by calculating the change in the backscatter coefficient before and after the triggering event using the log ratio approach. False positives such as backscatter change due to flooding, agriculture, and more, are removed by using threshold-based masks made from the topographic slope from the 1 arcsec (∼30 m) resolution NASADEM (NASA JPL, 2020). Using stacks of SAR data reduces noise, and furthermore, the pre-event stack provides backscatter data that is more representative of the pre-event ground surface properties. Finally, to detect landslide areas, the backscatter change raster was thresholded using the 99th percentile to highlight strong signals, and the heatmap was calculated.Pre-event time frame: 2024-01-10 to 2024-09-26Post-event time frame: 2024-09-28 to 2024-10-17This map should be used as a guidance to identify areas likely affected by landslides. This is a rapid response product. We have not done any form of manual corrections to remove false positives.Suggested Use:The red and yellow areas indicate potential zones of dense landsliding.Disclaimer: not verified in field and optical imagery has clouds preventing verification.Satellite/Sensor:Copernicus Sentinel-1 Synthetic Aperture Radar (SAR)Resolution:10 metersCredits:NASA GSFC Landslides Team, Copernicus Sentinel-1 dataHandwerger AL, Huang M-H, Jones SY, Amatya P, Kerner HR, Kirschbaum DB. 2022. Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine. Nat. Hazards Earth Syst. Sci. Copernicus Publications, 22(3): 753–773. https://doi.org/10.5194/nhess-22-753-2022.Esri REST Endpoint:See URL section on right side of pageWMS Endpoint:https://maps.disasters.nasa.gov/ags03/services/hurricane_helene_2024/Landslide_Proxy_Heat_Map_S1_on_Sept_28_2024_for_Hurricane_Helene/MapServer/WMSServerData Download:N/A

This heat map shows the density of hurricane force low centers across the North Pacific Ocean as analyzed on the Ocean Prediction Center's surface analyses.

Heat map density display highlighting the hot spot locations of hurricane force wind events across OPC's area of responsibility in the North Atlantic ocean from June 1, 2005, through May 31, 2021. Common low tracks emerge northeast along the Gulf Stream, north into the Labrador Sea, and along both the southwest and southeast coasts of Greenland in barrier and tip jet events.