This UAV_Land_Movement_README.txt file was generated on 2021-08-18 by L. Sebastian Bryson.


GENERAL INFORMATION

1. Title of Dataset: 
Multi-Temporal UAV Images and GeoDatabase Used to Estimate Temporal and Spatial Soil Moisture Content

2. Description of Dataset:
We used small unmanned aerial vehicle (UAV) with optical digital camera to detect a land movement and to extract soil parameters. Using multi-temporal images in Garrard County, Kentucky, we detected land movement on three pairs of images that were captured one month apart. The multi-temporal images and the result of the movement analysis are available in folders. In addition, vertical displacement analysis is carried out using Differential Interferometry technique (DinSAR) to a pair of Synthetic Aperture Radar (SAR) images. Soil moisture data was estimated using linear regression machine learning model, and the python code and table used as training points are available in this page. Our results indicate that using UAV equipped with an optical digital camera, we can estimate land surface movement, and extract soil parameters such as soil moisture data using the technique presented in this research (https://doi.org/10.13023/etd.2021.369).

3. Author Information
	A. Principal Investigator Contact Information
		Name: L. Sebastian Bryson
		Institution: University of Kentucky
		Address: 161 Raymond Building, Lexington, KY 40506-0281
		Email: sebastian.bryson@uky.edu
		ORCID ID: https://orcid.org/0000-0003-2350-2241

	B. Associate or Co-investigator Contact Information
		Name: Batmyagmar Dashbold
		Institution: University of Kentucky
		Address: 161 Raymond Building, Lexington, KY 40506-0281
		Email: mega.dashbold@uky.edu
		ORCID ID: https://orcid.org/0000-0002-1197-1668

4. Date of data collection (single date, range, approximate date): 
2020 to 2021

5. Geographic location of data collection: 
Garrard County, Kentucky

6. Information about funding sources that supported the collection of the data: 
Not applicable




SHARING/ACCESS INFORMATION

1. Licenses/restrictions placed on the data:
This dataset is distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided that the dataset creators and publication source are credited and that changes (if any) are clearly indicated.  

2. Links to publications that cite or use the data:
Dashbold, B., 2021. Landslide Site Assessment and Characterization Using Remote Sensing Techniques, UKnowledge Theses and Dissertations--Civil Engineering, https://doi.org/10.13023/etd.2021.369.

3. Links to other publicly accessible locations of the data: 
Not applicable

4. Links/relationships to ancillary data sets: 
Land cover data - 2016 National Land Cover Database (NLCD) product suite
https://www.mrlc.gov/

NASA Global Land Data Assimilation System (GLDAS) soil moisture data were accessed and acquired via the NASA Giovanni EarthDATA tool
https://giovanni.gsfc.nasa.gov/giovanni/

5. Was data derived from another source? No

6. Recommended citation for this dataset:
Bryson, L.S., Dashbold, M., 2021. Multi-Temporal UAV Images and GeoDatabase used to Estimate Temporal and Spatial Soil Moisture Content: UKnowledge Civil Engineering Research Data, https://doi.org/10.13023/wzxy-w419.


DATA & FILE OVERVIEW

1. File List: 
UAV and GLDAS Satellite Data
Python Code for Machine Learning
Co-Registered tiffs
Cosi-Corr tiffs
ArcGIS Input Data files
SAR Data Input Files

2. Relationship between files, if important: 
None.

3. Additional related data collected that was not included in the current data package: 
Not applicable

4. Are there multiple versions of the dataset? No

METHODOLOGICAL INFORMATION: Not applicable