Estimating fuel moisture in grasslands using UAV-mounted infrared and visible light sensors: MicaSense remote sensing data

Metadata:
Identification_Information:
Citation:
Citation_Information:
Originator: Barber, Nastassia R.
Originator: Alvarado, Ernesto
Originator: Moskal, L. Monika
Originator: Kane, Van R.
Originator: Cronan, James B.
Publication_Date: 2021
Title:
Estimating fuel moisture in grasslands using UAV-mounted infrared and visible light sensors: MicaSense remote sensing data
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2021-0071
Description:
Abstract:
This data publication consists of the complete set of raw images, orthomosaics created from these images, and ground control points (GCPs) from two unmanned aerial vehicle (UAV) flights over the Mazama Meadows site near Olympia, Washington on 09/22/2020. There are single-band images in five wavelengths - red, green, blue, red edge, and near infrared collected using a MicaSense RedEdge camera from the flights at 9:34am and 2:56pm. The drone flew at 3 meters per second at a height of 16 meters and the images were recorded once per second.
Purpose:
Computational fluid dynamics (CFD) fire behavior models have the potential to substantially improve decision support systems for wildland fire management in the United States, but satisfactory fuel inputs and training datasets are impediments to model development. These data were collected to help address the need for continuous fuels maps of fuel moisture across burn areas. Together with the short-wave infrared (SWIR) data (https://doi.org/10.2737/RDS-2021-0072) and the field moisture data (https://doi.org/10.2737/RDS-2021-0070), these data were used in a study to investigate creating a spatially-explicit moisture input for fire models. A regression model was developed using the imagery data to predict the moisture collected in the field.
Supplemental_Information:
Original publication date was 09/23/2021. Minor metadata updates were made on 10/05/2021 and 10/20/2021.
Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 20200922
Currentness_Reference:
Ground condition
Status:
Progress: Complete
Maintenance_and_Update_Frequency: As needed
Spatial_Domain:
Description_of_Geographic_Extent:
Mazama Meadows, a grass field near Olympia, Washington
Bounding_Coordinates:
West_Bounding_Coordinate: -122.98565
East_Bounding_Coordinate: -122.98453
North_Bounding_Coordinate: 46.81906
South_Bounding_Coordinate: 46.81848
Bounding_Altitudes:
Altitude_Minimum: 180.000
Altitude_Maximum: 230.000
Altitude_Distance_Units: feet
Keywords:
Theme:
Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: biota
Theme_Keyword: environment
Theme:
Theme_Keyword_Thesaurus: None
Theme_Keyword: UAV
Theme_Keyword: unmanned aerial vehicle
Theme_Keyword: wildfire
Theme_Keyword: infrared
Theme_Keyword: remote sensing
Theme_Keyword: MicaSense
Theme_Keyword: wildfire modeling
Theme_Keyword: fuel moisture
Theme_Keyword: fire fuels
Theme:
Theme_Keyword_Thesaurus: National Research & Development Taxonomy
Theme_Keyword: Fire
Theme_Keyword: Fire detection
Theme_Keyword: Fire ecology
Theme_Keyword: Fire suppression, pre-suppression
Theme_Keyword: Inventory, Monitoring, & Analysis
Theme_Keyword: Assessments
Theme_Keyword: Biometrics
Theme_Keyword: Techniques
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: Washington
Access_Constraints: None
Use_Constraints:
These data were collected using funding from the U.S. Government and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the following citation:

Barber, Nastassia R.; Alvarado, Ernesto; Moskal, L. Monika; Kane, Van R.; Cronan, James B. 2021. Estimating fuel moisture in grasslands using UAV-mounted infrared and visible light sensors: MicaSense remote sensing data. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2021-0071
Point_of_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Nastassia Barber
Contact_Organization: University of Washington
Contact_Position: Research Assistant
Contact_Address:
Address_Type: mailing and physical
Address: 5605 Corson Ave S
Address: Apt 18
City: Seattle
State_or_Province: Washington
Postal_Code: 98108
Country: USA
Contact_Voice_Telephone: 770-815-9335
Contact_Electronic_Mail_Address: nastassiabot@gmail.com
Data_Set_Credit:
This project was funded by the USDA Forest Service, Pacific Northwest Research Station, Fire and Environmental Research Applications Team.


Author Information:

Barber, Nastassia R.
University of Washington
https://orcid.org/0000-0002-3382-3455

Alvarado, Ernesto
University of Washington
https://orcid.org/0000-0002-9606-9963

Moskal, L. Monika
University of Washington
https://orcid.org/0000-0003-1563-6506

Kane, Van R.
University of Washington
https://orcid.org/0000-0002-0792-4850

Cronan, James B.
USDA Forest Service, Pacific Northwest Research Station
Cross_Reference:
Citation_Information:
Originator: Barber, Nastassia R.
Originator: Alvarado, Ernesto
Originator: Kane, Van R.
Originator: Mell, William E.
Originator: Moskal, L. Monika
Publication_Date: 2021
Title:
Estimating fuel moisture in grasslands using UAV-mounted infrared and visible light sensors
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Sensors
Issue_Identification: 21(19): 6350
Online_Linkage: https://doi.org/10.3390/s21196350
Cross_Reference:
Citation_Information:
Originator: Barber, Nastassia R.
Originator: Alvarado, Ernesto
Originator: Moskal, L. Monika
Originator: Kane, Van R.
Originator: Cronan, James B.
Publication_Date: 2021
Title:
Estimating fuel moisture in grasslands using UAV-mounted infrared and visible light sensors: Field data
Geospatial_Data_Presentation_Form: tabular and vector digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2021-0070
Cross_Reference:
Citation_Information:
Originator: Barber, Nastassia R.
Originator: Alvarado, Ernesto
Originator: Moskal, L. Monika
Originator: Kane, Van R.
Originator: Cronan, James B.
Publication_Date: 2021
Title:
Estimating fuel moisture in grasslands using UAV-mounted infrared and visible light sensors: Tau remote sensing data
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2021-0072
Analytical_Tool:
Analytical_Tool_Description:
Agrisoft Metashape was used to produce orthorectified images of the MicaSense imagery.
Tool_Access_Information:
Online_Linkage: https://www.agisoft.com/
Tool_Access_Instructions:
Software requires purchasing a license.
Analytical_Tool:
Analytical_Tool_Description:
ArcGIS Pro Version 2.8
Tool_Access_Information:
Online_Linkage: https://pro.arcgis.com/en/pro-app/latest/get-started/get-started.htm
Tool_Access_Instructions:
Software requires purchasing a license.
Analytical_Tool:
Analytical_Tool_Description:
UGCS flight control software which was used for flight planning and UAV control
Tool_Access_Information:
Online_Linkage: https://www.ugcs.com
Tool_Access_Instructions:
Software requires purchasing a license.
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Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report:
Images were examined for quality and any that were very blurry or overexposed were removed. The images were calibrated using the reflectance panel that comes with the MicaSense.
Logical_Consistency_Report:
The plot reflectance statistics data were performed using two slightly different methods which were compared and found them to be similar.
Completeness_Report:
There are a few gaps in the imagery of the overall study area, but none in the actual plots used for the study.
Lineage:
Methodology:
Methodology_Type: Field
Methodology_Description:
The overall location of the test site was delineated with wooden aerial placards marked with the cardinal direction. Objects to aid in image alignment were scattered around the area (pool noodles and bi-colored metal objects). Eighteen GCPs made of steel sheets with black “x’s” drawn on them were also scattered throughout the study area. Throughout the rest of this process, the locations for these GCPs were being recorded using the global positioning system (GPS) unit.


GPS Coordinates:

High accuracy GPS coordinates were collected for each GCP to improve image stitching in structure for motion software. GPS coordinates were collected with a Javad (Moscow, Russia) Triumph-2 global navigation satellite system (GNSS) capable of receiving the L1/L2 bands for multiple satellite constellations including GPS and GLONASS. For each GCP the Javad was mounted on a leveled tripod 1.5 meters above the center of the target and recorded points for at least 15 minutes. GCPs were located in open areas at least 1500 meters from obstructions such as buildings or trees that could diminish satellite reception. Staff operating the GNSS recorded start and stop time for point recording and GCP number. See \Supplements\GNSS_Post_Processing_Protocol.pdf for details on GCP post-processing procedures.


MicaSense:

Check the MicaSense and ensure that it has an empty, properly formatted SD card. An SD card with a write speed of at least 100 megabytes per second (MB/s) should be used, ideally 32+ gigabytes (GB), or images will be skipped in recording. The status of the SD card can be viewed in the MicaSense web app, which is accessible by connecting to the camera’s wifi network and navigating to the IP address listed on the camera.

The MicaSense was initiated from the web app. The capture rate should be set to once per second (the fastest it goes). In this dataset, all flights have one photo/second, and the afternoon flight had to be restarted partway through due to a flight planning error. Therefore the data represents the time for two separate flights all together.

This study used an M600 drone and UGCS flight control software. The drone setup and flight safety procedures are standard AFSL (Autonomous Flight Systems Lab) procedures. At the start of the flight, the drone was flown over the calibrated reflectance panel. The flight control settings are listed below for both flights.
Flight pattern: double-grid
Side-lap: 80%
Overlap: 85%
Height: 52 feet
Speed: 3 meters per second

The raw MicaSense images collected for this project can be found in the \Supplements\raw_images, organized in directories created by MicaSense. The ground control points (GCPs) used to orthorectify the imagery are found in \Supplements\gcps. Post-processing procedures for the GCPs are found in \Supplements\GNSS_Post_Processing_Protocol.pdf.
Methodology_Citation:
Citation_Information:
Originator: Barber, Nastassia R.
Originator: Alvarado, Ernesto
Originator: Kane, Van R.
Originator: Mell, William E.
Originator: Moskal, L. Monika
Publication_Date: 2021
Title:
Estimating fuel moisture in grasslands using UAV-mounted infrared and visible light sensors
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Sensors
Issue_Identification: 21(19): 6350
Online_Linkage: https://doi.org/10.3390/s21196350
Source_Information:
Source_Citation:
Citation_Information:
Originator: Cronan, James B.
Publication_Date: 2021
Title:
Ground Control Points
Geospatial_Data_Presentation_Form: vector digital data
Type_of_Source_Media: Personal communication
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 20200922
Source_Currentness_Reference:
Ground Condition
Source_Citation_Abbreviation:
GCPs
Source_Contribution:
Ground control points (GCPs)
Process_Step:
Process_Description:
Stitched together MicaSense images in Agisoft using default settings (make sure to import it as a multi-camera system).

Aligned photos with high accuracy, generic preselection, and reference preselection, key point limit of 80,000 and tie point limit of 4,000.

Created a high-quality mesh with medium face count and constructed orthomosaics with the mesh as the surface and mosaic blending mode.

Calibrated reflectance in the MicaSense images:
First, the reflectance panel was located in an image and put in a separate folder called “Calibration images”. Then, applied a mask and removed the rest of the image except the panel.

The calibration values, obtained from the manufacturer, are provided below.
For RedEdge-MX with serial RX01 or lower, all RedEdge-M and RedEdge 3 cameras, and Altum cameras with serial AL04 or lower:
blue 0.97 (97.32%)
green 0.98 (97.76%)
red 0.98 (98.37%)
re 0.99 (98.54%)
nir 0.99 (98.56%)

Set the raster transform to each band individually, set them to the same scale and a black and white gradient, and then exported them as individual band rasters.

Used GCP coordinates to georeference the visible light orthomosaic, and field notes to identify the correct GCPs.

Imported the other MicaSense orthos and aligned them using georeferencing (affine transform) with 9 points.

Drew plots by hand, as close to right inside the PVC as possible, didn’t force them to be squares since occasionally there was a little warping in the image/the plot was slightly askew.

Added a value for plot number to the attribute table and numbered the plots based on field notes.

Remove values in plots above a certain threshold using “set null” function.
Source_Used_Citation_Abbreviation:
GCPs
Process_Date: Unknown
Cloud_Cover: 0
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Spatial_Data_Organization_Information:
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:
Raster_Object_Type: Pixel
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Spatial_Reference_Information:
Horizontal_Coordinate_System_Definition:
Planar:
Map_Projection:
Map_Projection_Name: WGS 1984 Web Mercator (auxiliary sphere)
Geodetic_Model:
Horizontal_Datum_Name: World Geodetic System of 1984
Ellipsoid_Name: World Geodetic System of 1984
Semi-major_Axis: 6378133.0000
Denominator_of_Flattening_Ratio: 298.257223563
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Entity_and_Attribute_Information:
Detailed_Description:
Entity_Type:
Entity_Type_Label: GCP_locations.shp and GCP_locations.gpkg
Entity_Type_Definition:
Ground control points and base station locations.
Entity_Type_Definition_Source:
United States Forest Service, Fire and Environmental Research Applications (FERA) Team
Attribute:
Attribute_Label: FID
Attribute_Definition:
Unique identifier for record.
Attribute_Definition_Source:
Environmental Ssytems Research Institute
Attribute:
Attribute_Label: Shape
Attribute_Definition:
Shapefile attribute contain spatial information.
Attribute_Definition_Source:
Environmental Systems Research Institute
Attribute:
Attribute_Label: ID
Attribute_Definition:
Unique identifier for the point
Attribute_Definition_Source:
JAVAD GNSS
Attribute:
Attribute_Label: Caption
Attribute_Definition:
Label for the point
Attribute_Definition_Source:
JAVAD GNSS
Attribute:
Attribute_Label: Comment
Attribute_Definition:
Any comments associated with the respective point.
Attribute_Definition_Source:
JAVAD GNSS
Attribute:
Attribute_Label: GUID
Attribute_Definition:
Globally unique identifier for the point
Attribute_Definition_Source:
JAVAD GNSS
Overview_Description:
Entity_and_Attribute_Overview:
Below you will find a list and description of the files included in this data publication.

DATA FILES
These data files are georeferenced TIFF files containing orthomosaics created from the specified MicaSense:

\Data\MS_blue_922AM.tif: MicaSense 475 +/- 16 nanometer (nm) band, AM (morning) flight
\Data\MS_blue_922PM.tif: MicaSense 475 +/- 16 nm band, PM (afternoon) flight
\Data\MS_green_922AM.tif: MicaSense 560 +/- 13.5 nm band, AM flight
\Data\MS_green_922PM.tif: MicaSense 560 +/- 13.5 nm band, PM flight
\Data\MS_NIR_922AM.tif: MicaSense 842 +/- 28 nm band, AM flight
\Data\MS_NIR_922PM.tif: MicaSense 842 +/- 28 nm band, PM flight
\Data\MS_red_922AM.tif: MicaSense 668 +/- 7 nm band, AM flight
\Data\MS_red_922PM.tif: MicaSense 668 +/- 7 nm band, PM flight
\Data\MS_rededge_922AM.tif: MicaSense 717 +/- 6 nm band, AM flight
\Data\MS_rededge_922PM.tif: MicaSense 717 +/- 6 nm band, PM flight


SUPPLEMENTAL FILES

\Supplements\GNSS_Post_Processing_Protocol.pdf: Portable Document Format file containing post-processing instructions "Javad Justin software (V2.122.160.11) Post-Processing of forest plots".

\Supplements\gcps\GCP_locations.shp: Shapefile (and associated files) containing the locations of the ground control points and base station location, obtained by GPS and used to locate the *.tif files. Variables for this file are defined in the Entity and Attributes detailed section. (These locations are also available as a geopackage: \Supplements\gcps\GCP_locations.gpkg.)

\Supplements\gcps\GCP_locations.gpkg: Geopackage containing the locations of the ground control points and base station location, obtained by GPS and used to locate the *.tif files. Variables for this file are defined in the Entity and Attributes detailed section. (These locations are also available as a shapefile: \Supplements\gcps\GCP_locations.shp.)

\Supplements\raw_images\AM\IMG_XXXX_Y.tif: TIF files (1545) containing AM raw images from the MicaSense, in the original file structure from the camera. (XXXX is the index of the image in the order they were taken; Y represents each of the five bands where 1=blue, 2=green, 3=red, 4=NIR, and 5=rededge)

\Supplements\raw_images\PM\IMG_XXXX_Y.tif: TIF files (4243) containing PM raw images from the MicaSense, in the original file structure from the camera. (XXXX is the index of the image in the order they were taken; Y represents each of the five bands where 1=blue, 2=green, 3=red, 4=NIR, and 5=rededge)
Entity_and_Attribute_Detail_Citation:
Barber, Nastassia R.; Alvarado, Ernesto; Kane, Van R.; Mell, William E.; Moskal, L. Monika. 2021. Estimating fuel moisture in grasslands using UAV-mounted infrared and visible light sensors. Sensors 21(19): 6350. https://doi.org/10.3390/s21196350
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Distribution_Information:
Distributor:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service, Research and Development
Contact_Position: Research Data Archivist
Contact_Address:
Address_Type: mailing and physical
Address: 240 West Prospect Road
City: Fort Collins
State_or_Province: CO
Postal_Code: 80526
Country: USA
Contact_Voice_Telephone: see Contact Instructions
Contact Instructions: This contact information was current as of October 2021. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Resource_Description: RDS-2021-0071
Distribution_Liability:
Metadata documents have been reviewed for accuracy and completeness. Unless otherwise stated, all data and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. However, neither the author, the Archive, nor any part of the federal government can assure the reliability or suitability of these data for a particular purpose. The act of distribution shall not constitute any such warranty, and no responsibility is assumed for a user's application of these data or related materials.

The metadata, data, or related materials may be updated without notification. If a user believes errors are present in the metadata, data or related materials, please use the information in (1) Identification Information: Point of Contact, (2) Metadata Reference: Metadata Contact, or (3) Distribution Information: Distributor to notify the author or the Archive of the issues.
Standard_Order_Process:
Digital_Form:
Digital_Transfer_Information:
Format_Name: TIFF
Format_Version_Number: see Format Specification
Format_Specification:
Georeferenced raster Tagged Image Format file (and associated files)
File_Decompression_Technique: Files zipped with 7-Zip 19.0
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: https://doi.org/10.2737/RDS-2021-0071
Digital_Form:
Digital_Transfer_Information:
Format_Name: SHP
Format_Version_Number: see Format Specification
Format_Specification:
Shapefile (and associated files)
File_Decompression_Technique: Files zipped with 7-Zip 19.0
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: https://doi.org/10.2737/RDS-2021-0071
Digital_Form:
Digital_Transfer_Information:
Format_Name: GPKG
Format_Version_Number: see Format Specification
Format_Specification:
Open Geospatial Consortium (OGC) GeoPackage 1.3
File_Decompression_Technique: Files zipped with 7-Zip 19.0
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: https://doi.org/10.2737/RDS-2021-0071
Digital_Form:
Digital_Transfer_Information:
Format_Name: PDF
Format_Version_Number: see Format Specification
Format_Specification:
Portable Document Format file
File_Decompression_Technique: Files zipped with 7-Zip 19.0
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: https://doi.org/10.2737/RDS-2021-0071
Fees: None
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Metadata_Reference_Information:
Metadata_Date: 20211020
Metadata_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Nastassia Barber
Contact_Organization: University of Washington
Contact_Position: Research Assistant
Contact_Address:
Address_Type: mailing and physical
Address: 5605 Corson Ave S
Address: Apt 18
City: Seattle
State_or_Province: Washington
Postal_Code: 98108
Country: USA
Contact_Voice_Telephone: 770-815-9335
Contact_Electronic_Mail_Address: nastassiabot@gmail.com
Metadata_Standard_Name: FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001.1-1999
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