Southwest Water Resources

Utilizing 2020 Landsat 8 OLI imagery to monitor stock ponds over the Kaibab National Forest, Arizona: MNDWI and AWEI were calculated for inputs in a K-means clustering algorithm, the green, purple, and blue pixels from left to right represent the MNDWI, AWEI, and the K-means clustering algorithm's ability to automatically detect and delineate water. This product will aid the community in making more timely and low-cost land and livestock management decisions.

Keywords: Stock Ponds, Water-Extent Monitoring, Remote Sensing, Landsat 8 OLI

Monitoring Surface Water Extents of Remote Stock Ponds in the Southwestern United States Using Earth Observing Systems for Enhanced Water Resources Management

Due to increasingly frequent and severe drought conditions in the southwestern US, land managers and livestock producers need to monitor stock ponds with increasing regularity. The ability to assess stock pond water levels with Earth observing satellite systems would enhance monitoring efforts of partners at the US Forest Service, Arizona Department of Game and Fish, and the Diablo Trust. This study employed Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), and Sentinel-2 Multispectral Instrument (MSI) to monitor surface water extent for hundreds of critical stock ponds in Arizona. Using methods adapted from previously developed image processing workflows, this project conducted a time-series analysis to capture seasonal and interannual variations in surface water area between 2013 to 2021. In addition, end users can monitor the surface water extent of stock ponds through the developed Google Earth Engine software tool called Surface Water Identification and Forecasting Tool (SWIFT). SWIFT incorporates the Automated Water Extraction Index, Modified Normalized Difference Water Index, and Tasseled Cap-Wetness Index for optical imagery and the incidence angle, VV and VH polarization bands for Sentinel-1 imagery to detect small water bodies in the study area with an overall accuracy range of 88-93%. These tools will empower our partners to monitor the extents of water in their stock ponds remotely, enabling them to develop data-informed and sustainable management solutions for decades to come.

Idaho - Pocatello
Summer 2021
US Forest Service, Kaibab National Forest, Range Program
Arizona Department of Game and Fish
Diablo Trust
USDA, US Forest Service, Rocky Mountain Research Center
NASA Earth Observations
Landsat 8 OLI
Sentinel-2 MSI
Sentinel-1 SAR
Rainey Aberle (Project Lead)
Rebecca Bernat
Michael Corley
Lukman Fashina
Kyle Paulekas
Seungbum Kim (NASA Jet Propulsion Laboratory Radar Remote Sensing)
Keith Weber (Idaho State University GIS Training and Research Center)