Location: Southeast Watershed Research
Title: Storage estimates of irrigation ponds generated from topobathy surface models using UAS data in south GeorgiaAuthor
ALBRIGHT, ANDREA - Orise Fellow | |
Coffin, Alisa | |
Pisani, Oliva | |
Bosch, David |
Submitted to: US-International Association for Landscape Ecology
Publication Type: Abstract Only Publication Acceptance Date: 2/7/2023 Publication Date: 3/20/2023 Citation: Albright, A., Coffin, A.W., Pisani, O., Bosch, D.D. 2023. Storage estimates of irrigation ponds generated from topobathy surface models using UAS data in south Georgia. US-International Association for Landscape Ecology. Abstract. Interpretive Summary: Irrigation ponds are a ubiquitous feature in agricultural landscapes as they allow for storage of pumped groundwater until it is needed for crop irrigation. However, researchers modeling surface water changes and groundwater usage do not often have access to bathymetry estimates of these ponds. Thus, accurate estimates of surface water volume are not available for many small water bodies without completing an in situ bathymetry survey. Recently, a case study for measuring bathymetry of typical irrigation ponds was performed by the Southeast Watershed Research Laboratory (SEWRL) in south Georgia. In this case, measurements from two in situ surveys and one remote sensed survey are fused into a continuous topobathy surface model. The data sources include depth measurements taken from a boat using a Depthmate SM-5 Portable Sounder, precise shoreline locations surveyed using a Trimble Geo7X mapping grade GNSS, and high-resolution RGB imagery collected using an DJI Mavic 2 Pro Unoccupied Aerial System (UAS). All three data sets are used to derive volume and error estimates of the irrigation pond. Additionally, a model shoreline is extracted from the UAS imagery and compared with the in situ survey to investigate whether an in situ GPS survey is necessary for accurate bathymetry modeling. And furthermore, we discuss leveraging an existing bathymetry model to derive new surface water volume estimates from future UAS imagery without any in situ surveys. Technical Abstract: Irrigation ponds are a ubiquitous feature in agricultural landscapes as they allow for storage of pumped groundwater until it is needed for crop irrigation. However, researchers modeling surface water changes and groundwater usage do not often have access to bathymetry estimates of these ponds. Thus, accurate estimates of surface water volume are not available for many small water bodies without completing an in situ bathymetry survey. Recently, a case study for measuring bathymetry of typical irrigation ponds was performed by the Southeast Watershed Research Laboratory (SEWRL) in south Georgia. In this case, measurements from two in situ surveys and one remote sensed survey are fused into a continuous topobathy surface model. The data sources include depth measurements taken from a boat using a Depthmate SM-5 Portable Sounder, precise shoreline locations surveyed using a Trimble Geo7X mapping grade GNSS, and high-resolution RGB imagery collected using an DJI Mavic 2 Pro Unoccupied Aerial System (UAS). All three data sets are used to derive volume and error estimates of the irrigation pond. Additionally, a model shoreline is extracted from the UAS imagery and compared with the in situ survey to investigate whether an in situ GPS survey is necessary for accurate bathymetry modeling. And furthermore, we discuss leveraging an existing bathymetry model to derive new surface water volume estimates from future UAS imagery without any in situ surveys. |