Source: NASA LANCE - VNP14IMG_NRT active fire detection - World
Scale/Resolution: 375-meter
Update Frequency: Hourly using the aggregated live feed methodology
Area Covered: World

What 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 and NOAA-20 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 information

• Latitude 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). 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 fire
• 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.

Introduction

This is the supporting dataset for our work on the detection and analysis of frontal waves using VIIRS Day-Night Band satellite imagery. The Dataset we used for the model training is provided. The dataset includes image files and text files that indicate labels. Dataset S1, obj.zip is the dataset we used for training, and dataset S2 is for validation. We also provide the trained weights file of YOLOv3. The event list and all frontal wave images we found using the ML model are also provided.

Dataset S1: uploaded as “obj.zip”

The zip file should be unzipped. The folder contains PNG images and text files we used for the training. Each text file contains label information of the corresponding image file. The format is
“class_id, x_center_norm, y_center_norm, width_norm, height_norm”.
The class_id is the index number of the class and is always 0 here. The x_center_norm and y_center_norm indicate the coordinate of the label in the image in normalized values. The width_norm and height_norm indicate the width and height of the label in normalized values, respectively. This is the format supported by YOLOv3.

Dataset S2: uploaded as “test.zip”

The format is the same as Dataset S1, but the dataset is for validation.

Weight S1: uploaded as “yolov3_custom.weights”

The weight of the network of YOLOv3 trained in this study. The file format is supported by YOLOv3.

Text S1: uploaded as “eventlist.csv”

The event list of frontal waves in CSV format. The event list is the result of the event survey on the moonless images of Suomi NPP VIIRS/DNB from January 2012 to June 2023 using the trained YOLOv3 model. The first column indicates the event ID. The second and third columns show the intensity and geolocation data file names, respectively. The data files can be downloaded at https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/5200/VNP02DNB/ and https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/5200/VNP03DNB. The fourth column indicates the name of the frontal wave image. The corresponding image files can be found in Dataset S3. The fifth column shows the observation time. The format is “YYYY-MM-DD hh:mm:ss,” where YYYY, MM, DD, hh, mm, and ss mean year, month, day, hour, minute, and second. The sixth and seventh columns present the latitude and longitude of the center of the frontal wave. Columns eight to eleven list 'X_CENTER_NORM,' 'Y_CENTER_NORM,' 'WIDTH_NORM,' and 'HEIGHT_NORM,' delineating the rectangular position of the wave event in normalized values within the image from the top left corner (0,0) to bottom right (1,1).
Dataset S3.

Dataset S3: uploaded as “image.zip”

The zip file should be unzipped. The folder contains PNG image files of the frontal waves detected in this study. A white rectangle(s) within each image marks the frontal wave's location.

This dataset and code are related to artificial light emissions in the arctic area. They are a supplement to the report "Capabilities and limitations of advanced optical satellite missions for snow, vegetation, and artificial light source applications in Arctic areas". Dataset: The Radiance Light Trends app was used to identify artificial light sources on the Yamal Peninsula in Russia. In order to determine whether a location was lit, a threshold of 5 nW/cm² sr (displayed in yellow in the Radiance Light Trends app) was defined. Visible band daytime imagery from Google Maps and Bing Maps was then used to identify what type of human activity was responsible for the light. The positions of the 78 lit areas and their light source classification are provided in a csv table and kmz file. The classes are defined as: industry, industry / flare, community, ship/ airport, road, water and unknown. This data publication includes the artificial light sources on the Yamal Penninsula (Western Siberia) in .csv and .kmz formats. Code: The data publication includes the python code "Arctic light pollution clustering script", which identifies areas with bright light emissions in the arctic. The script requires the monthly composite images from the Day/Night Band of the Visible Infrared Imaging Radiometer Suite produced by the Earth Observation Group as an input. These data are currently available here: https://eogdata.mines.edu/download_dnb_composites.html