Dataset Available Cold Storage

Remote sensing data and drone imagery obtained by UAS, Mississippi Sound, 2022-09-12 to 2022-09-16

Authors: Dash, Padmanava, Lee Hathcock, and Robert J. Moorhead
Published On: Mar 18 2024 21:42 UTC
DOI: https://doi.org/10.7266/9XVJNRF5
UDI: M2.x936.000:0014
Cold Storage Files

Number of Cold Storage Files:
38150

Cold Storage File Size:
95.54 GB

File Format:
tif, dat, txt

Project Information

Funded By:
Mississippi Based RESTORE Act Center of Excellence

Funding Cycle:
MBRACE 2

Research Group:
MBRACE Core Research Program (2020-2022)

Point of Contact

Padmanava Dash
Mississippi State University
pd175@msstate.edu

Data Collection Period
2022-09-12
to
2022-09-16
Theme keywords

unmanned aerial systems (UAS) data, brightness, imagery, reflectance, radiance, MicaSense RedEdge MX, drone imagery, remote sensing

ISO 19115-2 Metadata
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Abstract:

Unmanned Aerial Systems (UAS) imagery was collected during a 1-week mission to the Western Mississippi Sound to calibrate and validate remote sensing algorithms for estimating water quality parameters from UAS data. This dataset includes imagery, average radiance values, and calibration images. Similar data collected in 2021 are available in related dataset M2.x936.000:0013 (https://doi.org/10.7266/XEG6JGCJ).

Suggested Citation:

Dash, Padmanava, Lee Hathcock, and Robert J. Moorhead. 2024. Remote sensing data and drone imagery obtained by UAS, Mississippi Sound, 2022-09-12 to 2022-09-16. Distributed by: GRIIDC, Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/9XVJNRF5

Purpose:

To develop remote sensing algorithms.

Data Parameters and Units:

Brightness Values

Methods:

A DJI Inspire-2 Uncrewed Aerial Vehicle with a Micasense RedEdge MX sensor onboard was flown at 400 feet altitude to collect these data. Image processing followed the process provided by MicaSense at GitHub - micasense/imageprocessing: MicaSense RedEdge and Altum image processing tutorials . The steps included: 1. Remove dark pixel offset (imager noise) 2. Correct for vignetting effects 3. Correct for row gradient 4. Normalize by exposure and gain settings 5. Convert to radiance values 6. Further, convert to reflectance by using panel values 7. Remove lens distortion effects 8. Orthorectify image by using various GPS/IMU sources (hyperspectral IMU and UAS IMU values)

Instruments:

MicaSense RedEdge MX, DJI Inspire-2 UAV; 5.5mm lens, f2.8, ISO 100, resolution 300 x 300, 16 bits

Provenance and Historical References:

Dash, P., Beshah, W. T., Nur, A., Islam, M. S., Chowdhury, M. O. S., Moorhead, R. J., Moorhead, J., Panda, R. M., Wolfe, J. S., Turnage, G., McCraine, C., Hathcock, L., Chesser, G. D., Lowe, J. W., and Katkar, A. 2023. Water Quality Monitoring using Unmanned Aerial Systems Imagery and a Novel Autonomous Surface Vessel, In the Proceedings of Global OCEANS 2023 Conference and Exposition, Biloxi, MS, September 25-28, 2023. Dash, P., Sankar, M. S., Moorhead, R. J., Herman, J., Moorhead, J., Beshah, W., Chesser, D., Lowe, W., Simmerman, J., and Turnage, G. 2021. Evaluation of Water Quality Data Collected using a Novel Autonomous Surface Vessel, Global Oceans 2021 Conference and Exposition, San Diego, CA, September 20-23, 2021. DOI: 10.23919/OCEANS44145.2021.9705881

Individual Files: