- A multi-sensor satellite imagery approach to monitor on-farm reservoirs , REMOTE SENSING OF ENVIRONMENT (2022)
- Can we detect more ephemeral floods with higher density harmonized Landsat Sentinel 2 data compared to Landsat 8 alone? , ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2022)
- Effects of Climate and Anthropogenic Drivers on Surface Water Area in the Southeastern United States , WATER RESOURCES RESEARCH (2022)
- Forest water use is increasingly decoupled from water availability even during severe drought , LANDSCAPE ECOLOGY (2022)
- Leveraging the NEON Airborne Observation Platform for socio-environmental systems research , ECOSPHERE (2021)
- Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets , REMOTE SENSING (2021)
- On-farm reservoir monitoring using Landsat inundation datasets , AGRICULTURAL WATER MANAGEMENT (2021)
- Regional matters: On the usefulness of regional land-cover datasets in times of global change , REMOTE SENSING IN ECOLOGY AND CONSERVATION (2021)
- Understanding the Importance of Dynamic Landscape Connectivity , LAND (2020)
- Spatiotemporal patterns and effects of climate and land use on surface water extent dynamics in a dryland region with three decades of Landsat satellite data , Science of The Total Environment (2019)
Spatiotemporal quantification of surface water and flooding is essential for research on hydrological cycles. Satellite remote sensing is the only means of monitoring these dynamics across vast areas and over time. Several regional to global surface water data sets have been developed using optical time-series, either from MODIS-type sensors with coarse spatial resolution but daily frequency, or based on the entire Landsat archive. Despite its high spatial resolution, the 16-day repeat frequency of Landsat means that short-lived hazardous flooding and the maximum extent of large floods are likely missed. Meanwhile, spatially coarser MODIS-type sensors may miss small water bodies and floods entirely. In addition, two limitations when mapping inundation with optical data have been detecting water under vegetation and cloud obscuration, which often coincides with floods. Both issues can be overcome by fusing multiple optical with synthetic aperture radar (SAR) data, taking advantage of complementary observation properties including SARÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s ability to penetrate through clouds. Thus, combining observations and spectral properties of the newly available Sentinel 1 SAR (S1) and Sentinel 2 (S2) series of satellites with Landsat 8 (L8) holds promise for global surface water and flood mapping with improved spatial and temporal resolution and accuracy. To accurately capture maximum extent of all floods in near real time, our key objectives are to (1) map flooding dynamics globally, using machine learning applied to time-series of multi-sensor optical (L8, S2) and radar (S1) time series data, (2) assess the accuracy of the mapped flood extent, and (3) test the ability of our algorithms to map (a) ephemeral floods in a dynamic dryland river system (b) a complex delta including inundated vegetation in Western Canada (leveraging field validation data on extent of inundated vegetation collected during NASAÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s Arctic Boreal Vulnerability Experiment), (c) extreme flooding in North Carolina (during hurricanes in 2016, 2018 and 2019), and (d) small water bodies (< 5ha) in irrigated areas (i.e. Arkansas, the U.S. state with the 3rd largest irrigated area, where hundreds of small reservoirs have been constructed since 2015). We will use NASAÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s 30m Harmonized L8/S2 (HLS) Products that seamlessly combine L8 and S2 observations, and S1 as input to machine learning-based mapping of surface water and flooding. As training data, we will use the freely available USGS Spatial Procedures for Automated Removal of Cloud and Shadow dataset, which contains ÃƒÂ¢Ã¢â€šÂ¬Ã…â€œwaterÃƒÂ¢Ã¢â€šÂ¬Ã‚Â and ÃƒÂ¢Ã¢â€šÂ¬Ã…â€œfloodedÃƒÂ¢Ã¢â€šÂ¬Ã‚Â masks. We will further augment flood labels via active learning, by evaluating initial model results and adding labels on misclassified areas. To assess the accuracy of our flood maps we will use a stratified sampling design, with flooding and water as the rare classes used as strata to improve precision of the accuracy estimates. We will assess whether the increased temporal frequency resulting from multiple/fused data streams will result in improved detections of small and short-lived flooding events, and maximum extent of large floods compared to the use of L8, S2 or S1 alone over a dynamic dryland basin (i.e., AustraliaÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s Murray-Darling Basin), and over small farm dams of Arkansas. To test the improved capacity of flood mapping when adding SAR to HLS during cloudy conditions we will focus on 3 hazardous floods in North Carolina. We will assess the ability of C-band S1 combined with optical image time series to detect water under vegetation in CanadaÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s Peace-Athabasca Delta, where detailed validation data will be available. This proposal is significant to this NASA solicitation as it will enable improved quantification of flood extent dynamics and water quantity. The algorithms and maps produced can be used for better mapping of floods during hazardous conditions and assessment of how changes in land cover and land use and climate impact surface water and flood dynamics.
Fresh water stored by on-farm reservoirs (OFRs) is a fundamental component of surface hydrology and is critical for meeting global irrigation needs. Farmers use OFRs to store water during the wet season for crop irrigation during the dry season. There are more than 2.6 million OFRs in the US alone, and many of these OFRs were constructed during the last 40 years. Despite their importance for irrigating crops, OFRs can contribute to downstream water stress by decreasing stream discharge and peak flow in the watersheds where they are built, thereby exacerbating water stress intensified by climate change and population growth. However, modeling the impact of OFRs on surface hydrology remains a challenge because they are so abundant and have frequent fluctuations in surface area and water volume. Prior to the recent availability of satellite data, widespread monitoring of OFRsÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ surface area and water volume across space and time was impossible due to temporal latency of satellite observations. The goal of this project, therefore, is to harness a multi-sensor satellite imagery approach to reduce observation latency and improve surface hydrology modeling, with the aim of supporting more efficient management of OFRs and mitigation of their downstream impacts. Our objectives are: 1) Develop a multi-sensor imagery approach to reduce latency and obtain sub-weekly OFRs surface area and volume change; and 2) Input sub-weekly OFRs volume change into the Soil Water and Assessment Tool (SWAT) model to estimate OFRsÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ impact on surface hydrology. Specifically for Objective 1, a novel method based on the Kalman filter will be used to harmonize data from multiple optical sensors and to provide sub-weekly OFRs surface area change, which will be converted to volume change using area-elevation equations. Then for Objective 2, we will carry out hydrological simulations in SWAT to quantify OFRsÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ impact on simulated daily and monthly stream discharge, simulating stream discharge with and without the OFRs. We will perform yearly simulations, based on satellite imagery availability, to measure OFRsÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ impact during low and peak flows in each watershed of our study region, which will account for both intra- as well as inter-annual variability in flows. This project will monitor OFRsÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ surface area and volume change to enable better assessment and management of water quantity, and further the use of Earth system science to inform decisions and provide benefits to society regarding preservation of surface water resources, both of which are overarching science goals that guide NASAÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s Earth Science Division program.
Industrial agriculture disproportionately affects minority, low-income, and Tribal communities, propagating environmental injustice. Concentrated Animal Feeding Operations (CAFOs) apply massive amounts of untreated waste annually to nearby farmlands. Environmental health impacts of CAFOs are documented; however, studies almost exclusively rely on known CAFO locations from public records, which are incomplete. This is because only CAFOs discharging into US waters need a permit; poultry CAFOs generate dry waste and operate without permits. North Carolina (NC) communities have observed a dramatic but poorly documented expansion of poultry CAFOs since the 2007 swine CAFO moratorium but the locations of these facilities are essentially unknown and their environmental impacts therefore undocumented. Researchers have attempted to manually scan satellite/aerial data for CAFOs or automate detection but no complete dataset exists. We will use heuristics from the literature on poultry CAFO barn types (number, size, orientation) on a recently developed poultry CAFO layer derived using deep learning of Earth Observations to detect poultry CAFOs in eastern NC (where CAFOs are heavily clustered). We will use these locations to assess impacts on surface water contamination using public water quality data and examine how CAFO density correlates with census-block level race/income and EPAâ€™s environmental justice indices on other stressors/contaminants. This study will generate the first full record of non-permitted poultry CAFOs in eastern NC for accurate assessment of their environmental impacts and how these impacts intersect with sociodemographic/environmental vulnerabilities. These data will support R01-scale NIH applications to scale up our approach and assess impacts on broader environmental health outcomes.
On-farm water reservoirs ÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“ artificial water impoundment to retain water from rainfall and run-off ÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“ are essential to global food production, as they enable growers to store water during the rainfall season to support water use during the dry season, particularly for crop irrigation. Understanding their volume spatial temporal variability is essential to describe their impact to terrestrial hydrological system. However, the wide monitoring of inter- and intra-annual volume variability of these waterbodies remains a challenging task ÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“ since they have a changing nature, a high occurrence number and difficult accessibility in private properties. Therefore, I propose a remote sensing multi-sensor (optical + radar), multi-temporal approach to deliver an assessment of the spatial distribution, volume change, seasonality and hydrological impact of on-farm reservoirs in Eastern Arkansas, United States. I chose Eastern Arkansas due to its importance for irrigated food production; it ranks as the third most irrigated region in the United States and has seen a rapid increase of on-farm reservoirs occurrence since the 1980s. In this study, I will harness the power of openly available satellite imagery (i.e. Landsat-8, Sentinel-1 and Sentinel-2) and the high-resolution Planet CubeSats combined with digital elevation models, and long-term surface water area datasets (e.g. Dynamic Surface Water Extent) to derive volume-area and volume-elevation relationships. I will apply the widely used Soil and Water Assessment Tool (SWAT) to model the hydrological impact of on-farm reservoirs at the watershed scale. This study will provide methods and algorithms to guide water authorities and policy makers when implementing water preservation policies in Eastern Arkansas. The findings will advance understanding of the on-farms reservoirs spatial temporal variability in volume, which is pivotal for irrigation planning purposes. The developed methods will be openly available to be applied to other important agricultural regions of the world.