10:00 – 10:06 AM – Land Deformation in West Central Florida: Insights From Time Series InSAR
West Central Florida is particularly susceptible to land deformation due to its underlying geology and environmental conditions, making it prone to sinkholes. This vulnerability is exacerbated by increasing groundwater extraction driven by population growth, agricultural expansion, and industrial activities such as mining. During freeze events and dry months, groundwater usage spikes, further destabilizing the subsurface. Additionally, factors like excessive rainfall and well drilling can exacerbate sinkhole formation. In this study, we employed time series Interferometric Synthetic Aperture Radar (InSAR) to analyze land deformation patterns across West Central Florida. Our results reveal that mining areas exhibit the highest rates of land deformation, followed by wetlands and croplands. The increased deformation in mining regions can be attributed to substantial groundwater withdrawal and the consequent alteration of subsurface structures. In wetlands, land subsidence is influenced by both natural hydrological processes and anthropogenic activities. Croplands, meanwhile, are affected by irrigation practices that impact groundwater levels. These findings underscore the need for integrated land and water management strategies to mitigate the risks associated with land deformation and sinkhole formation. By understanding the spatial and temporal dynamics of land deformation through InSAR, stakeholders can better anticipate and address the challenges posed by groundwater extraction and land use practices in West Central Florida.
Juniad Lone, University of Florida
10:06 – 10:12 AM – Improved Satellite Derived Bathymetry Leveraging High Revisit Rate SmallSat Constellations
Bathymetric datasets generated from multispectral satellite imagery, referred to as satellite derived bathymetry (SDB) are gaining widespread use for hydrographic survey planning, coral reef habitat mapping, and a range of other science uses. The primary limiting factor in SDB is water clarity: when the water is too turbid for the seafloor to register a detectable contribution to the received signal at the imaging sensor, bathymetry retrieval is impossible. Importantly, water clarity is highly temporally variable: it can change drastically on time scales ranging from minutes to tide cycles, to seasons, to years (or longer). Drivers of water clarity include wind speed, wave height, local hydrodynamics, substrate type, storms, and runoff, among other factors. For this reason, recent constellations of SmallSats, such as Planet Labs’ SuperDove, that provide very short revisit cycles (e.g., daily imagery) may greatly facilitate successful bathymetry retrieval by providing many more opportunities to acquire imagery at a time of suitable water clarity. We tested how SDB created using SuperDove imagery compares to SDB created from Sentinel-2 imagery that has a longer, five-day revisit cycle, and to independent bathymetric reference data. Preliminary results show that SuperDove’s daily revisit cycle increases the probability of obtaining imagery with higher water clarity and is significant in creating higher accuracy SDB than satellites with a longer revisit cycle. These results suggest that SmallSat constellations may become increasingly useful for SDB.
Ruth McCullough, Oregon State University
10:12 – 10:18 AM – Addressing Spatial Variability of Uncertainties Across DEMs of Difference Derived From Historic Topographic Maps and Modern Lidar Datasets
Comparing modern and historic landscapes allows researchers to detect and quantify changes in the landscape that have occurred over time. To accurately determine the magnitude of changes in a DEM of Difference (DoD), positional uncertainties within the input data must be understood and quantified to establish confidence in the results. The quantification of uncertainties in historical topographic datasets poses challenges to researchers due to a lack of available documentation and a lack of understanding of historical map production processes. Issues with quantifying uncertainties of modern elevation datasets like lidar can also occur. This study investigates uncertainties in historical topographic maps and modern publicly available lidar data by examining the positional uncertainties in two DEMs, one derived from a 1940 topographic map and the other from a 2016 lidar survey. Key uncertainties addressed include map accuracy (vertical and horizontal), slope, vertical datums, and georeferencing. An error budget model was formulated to quantify these uncertainties and was applied to a DoD operation. The resulting spatial uncertainty surface distinguishes between changes in the DoD due to uncertainty and real changes that have occurred. This study provides a step-by-step method allowing researchers to integrate historical and modern elevation datasets in change detection analyses that can detect and quantify real changes in the landscape.
Briar Pierce, University of Florida
10:18 – 10:24 AM – Detecting Low-Level Methane Emissions Using Remote Sensing
The oil & gas (O&G) industry is a significant source of methane (CH4) emissions, a potent greenhouse gas. Among these sources, abandoned O&G wells present a persistent challenge, with approximately 3 million wells across the U.S. Many of these wells are improperly sealed or uncapped, emitting methane continuously, although often at low and intermittent levels. The spatial variability and uncertainty of their locations underscore the urgent need for efficient detection methods. In this study, we unitized a small unmanned aerial system (sUAS) equipped with a methane-sensitive remote sensor to detect methane emissions and identify high-probability regions for well location. The research focused on developing statistical methods to address noisy sensor data, which is common in environmental monitoring due to fluctuating methane levels caused by environmental factors and sensor sensitivity. The method was successfully tested in a field with documented abandoned wells, demonstrating its potential for locating abandoned wells, even in areas with sparse emissions.
Hoyt Thomas, University of Alaska Anchorage
10:24 – 10:30 AM – Development of a Ground Filtering Tool using Adaptive Spatial Techniques
David Abiola developed an adaptive ground filtering tool to address challenges in lidar and point cloud data, such as distinguishing ground points from vegetation, managing varying terrain slopes, and reducing noise in large datasets. Accurate ground filtering is crucial for generating reliable DTMs used in applications like flood risk assessment and urban planning. The tool uses a grid-based analysis to segment lidar data, vertical difference filtering to refine ground points, and spatial filtering with Random Sample Consensus (RANSAC) to detect and adjust for ground points, adapting to different terrain types. Tested on GM lidar data from Leominster, MA, the tool demonstrated high accuracy and robustness, particularly in forested and urban environments, improving terrain representation and supporting reliable downstream analyses.
David Abiola, Oregon State University