Remote sensing

Much of Interior Alaska contains permafrost, which is a permanently frozen layer found within or at the surface of the Earth. Historically, this permafrost has experienced relative stability, with limited thaw during warmer summer months and fire events. However, largely due to the impact of a warming climate, among other factors, permafrost that would typically experience limited thawing during the summer season has recently been thawing at an unprecedented rate. Trapped by this layer of permafrost is a large quantity of carbon (C), which could be released into the atmosphere as greenhouse gases such as carbon dioxide (CO2) and methane (CH4). Due to the remoteness of the Arctic, there is a lack of yearly recorded permafrost thaw depth and snow depth values across much of the region. As such, the focus of this research was to establish a framework to identify how permafrost thaw depth and snow depth can be predicted across both a 1 km2 local scale and a 100 km2 regional scale in Interior Alaska by a combination of 1 m2 field data, airborne and spaceborne remote sensing products, and object-based machine learning techniques from 2014 – 2022. Machine learning techniques Random Forest, Support Vector Machine, k-Nearest Neighbor, Multiple Linear Regression, and Ensemble Analysis were applied to predict the permafrost thaw depth and snow depth. Results indicated that this methodology was able to successfully upscale both the 1 m2 field permafrost thaw depth and snow depth data to a 1 km2 local scale before successfully further upscaling the estimated results to a 100 km2 regional scale, while also linking the estimated values with ecotypes. The best results were produced by Ensemble Analysis, which tended to have the highest Pearson’s Correlation Coefficient, alongside the lowest Mean Absolute Error and Root Mean Square Error. Both Random Forest and k-Nearest Neighbor also provided encouraging results. The presence or absence of a thick canopy cover was strongly connected with thaw depth and snow depth estimates. Image resolution was an important factor when upscaling field data to the local scale, however it was overall less critical for further upscaling to the regional scale.

The term "collapse" has become a widely used term that oversimplifies the intricate histories of human-environment interactions. It has contributed to the belief that civilizations in the Americas and the tropics could not endure over time. However, the Manteño civilization of the Ecuadorian coast challenges this notion. Flourishing for a thousand years (ca. 650–1700 CE), the Manteños inhabited the neotropics at the gates of one of the world's most influential climatic forces, the El Niño-Southern Oscillation (ENSO). To thrive, the Manteños needed to navigate the extremes of ENSO during the Medieval Climate Anomaly (MCA, ca. 950–1250 CE) and the Little Ice Age (LIA, ca. 1400–1700 CE) while capitalizing on ENSO's milder phases. This research uses change detection analysis of Normalized Difference Vegetation Index (NDVI) on Landsat satellite imagery under various ENSO conditions from 1986 to 2020 in southern Manabí, where the 16th-century Manteño territory of Salangome was situated. The findings indicate that the cloud forests found in the highest elevations of the Chongón-Colonche Mountains provide the most resilient environment in the region to adapt to a changing climate. Further investigations of the cloud forest of the Bola de Oro Mountain using Uncrewed Aerial Vehicles (UAV) equipped with LiDAR, ground-truthing, and excavation uncovered a landscape shaped by the Manteños.

The objective of this thesis is to study the proper placement and denoising of Total Field Magnetometers (TFM) installed on an Autonomous Underwater Vehicle (AUV), in support of a long-term goal to perform geophysical navigation based on total field magnetic sensing. This new form of navigation works by using the magnetic field of the Earth as a source of reference to find the desired heading. The primary tools used in this experiment are a REMUS 100 AUV, a QuSpin scalar magnetometer, and a TwinLeaf vector magnetometer. The Earth’s magnetic field was measured over periods of several hours to determine the range of values it provides under natural conditions. Digital filters were created to digitally reduce fluctuations caused by sources of external interference and sources of internal interference. To mitigate the issue of platform based interference, two methods were examined. These methods involved the use of the Tolles-Lawson model and Wavelet Multiresolution Analysis. The Tolles-Lawson model is used to determine the compensation coefficients from a calibration mission to mitigate the effects from the permanently detected magnetic field, the induced magnetic field, eddy currents. and the geomagnetic field. Wavelet multiresolution analysis follows the same basic steps as Fourier transformations and is used to analyze time series with power sources in motion over a frequency spectrum. Several acquisitions were run with the QuSpin in various locations around and along REMUS, and it was concluded that placing the sensor at the very front of the vessel which is approximately 1.8 [m] from the DC motor, with assistance from wavelet analysis was acceptable for the project.

Significant reduction in space, weight, power, and cost (SWAP-C) of imaging hardware has induced a paradigm shift in remote sensing where unmanned platforms have become the mainstay. However, mitigating the degraded visual environment (DVE) remains an issue. DVEs can cause a loss of contrast and image detail due to particle scattering and distortion due to turbulence-induced effects. The problem is especially challenging when imaging from unmanned platforms such as autonomous underwater vehicles (AUV) and unmanned ariel vehicles (UAV).
While single-frame image restoration techniques have been studied extensively in recent years, single image capture is not adequate to address the effects of DVEs due to under-sampling, low dynamic range, and chromatic aberration. Significant development has been made to employ multi-frame image fusion techniques to take advantage of spatial and temporal information to aid in the recovery of corrupted image detail and high-frequency content and increasing dynamic range.

Salt marshes are highly dynamic ecosystems that rely on multiple environmental and physical drivers that determine species distribution and soil property distribution. However, climate change and human interference are threatening the delicate ecosystem. One of the easiest ways to monitor marsh dynamics is through remote sensing. Traditional methods may not handle the large, non-parametric datasets well and often do not spatially determine areas of uncertainty. This dissertation research developed a framework to map marsh species and predict ground soil properties using multiple remote sensing data sources by integrating modern Object-based Image Analysis (OBIA), machine learning, data fusion, and band indices techniques. It also sought to determine areas of uncertainty in the final outputs and differences between different spectral resolutions. Five machine learning classifiers were examined including Support Vector Machine (SVM) and Random Forest (RF) to map marsh species. Overall results illustrated that RF and SVM typically performed best, especially when using hyperspectral data combined with DEM information. Seven regressors were assessed to map three different soil properties. Again, RF and SVM performed the best no matter the dataset used, or soil property mapped. Soil salinity had r as high as 0.93, soil moisture had r as high as 0.91, and soil organic an r as high as 0.74 when using hyperspectral data.

Sinkholes are common karst features in Florida, having the highest rate of sinkhole occurrence in the US, which results in hundreds of millions estimated costs in damage per year and occasional life losses. While most sinkhole incidents reported in Florida relate to surface subsidence and collapse processes, other sinkhole formation mechanisms (like sagging) have received little attention as a relevant subsidence process. This is important since extensive areas of karst bedrock are overlain by variable thicknesses of non-soluble formations that may affect both the kinematics and damaging potential of these sinkholes in Florida. This research presents an automated GIS-based method to easily delineate surface depressional features in Martin County that result in surface depressional features and are related to cover sagging sinkholes. A total of 3,091 depressional features in Martin County were mapped using GIS methods and constrained with already existing direct drill cores. Results show a consistent statistically significant negative correlation between several morphometric features (i.e., area, perimeter, or depth) from these depressional features and depth to the limestone, suggesting that depressions are linked to sinkholes developed in deep-seated karst. While further subsurface imaging is needed to confirm this correlation, previous studies confirm these results and suggest that cover sagging, or cover suffusion sinkholes may represent a very large group of sinkholes traditionally unaccounted for in current sinkhole assessment maps in Florida. The methodology presented in this study can be easily extrapolated to other areas to further expand current sinkhole hazard and distribution maps.

Global population is increasing at an alarming rate with rapid urbanization of the earth’s land surface. Currently, more than half of the world’s population lives in urban areas and this number is projected to increase to 66% by 2050. Urban expansion in coastal zones is more complex due to the rapid urbanization and higher population growth. In the United States (US), more than 39% of the total population now lives in coastal counties. Although urbanization offers some advantages such as economic development, unplanned urbanization can adversely affect our environment. Additionally, coastal communities in the US are frequently impacted by disasters. Climate change such as sea level rise could intensify these coastal disasters and impact more lives and properties. Therefore, using Geographic Information Systems (GIS) and remote sensing, this study examines these pressing environmental challenges with the coastal US as the Study area. We first quantified the historical spatiotemporal patterns and major explanatory factors of urban expansion in the Miami Metropolitan Area during 1992 - 2016 at different spatial scales. Additionally, different urban expansion dynamics such as expansion rate, pattern, types, intensity, and landscape metrics were analyzed. Multi-level spatiotemporal analyses suggest that urban growth varied both spatially and temporally across the study area. We then measured the community resilience to coastal disasters by constructing a composite index. Additionally, spatial relationships between resilience components and disaster impacts were investigated. Results suggest that northeastern coastal communities in the US are more resilient to disasters compared to the southeastern communities. Furthermore, community resilience varies across the space and resilience components used in this study can explain disaster damages. Finally, this research also simulates and predicts three future urban growth scenarios including business as usual, planned growth, and sustainable growth in the study area. Then current and future exposures to flooding were estimated by considering different sea level rise scenarios. Results suggest that future urban areas will be developed significantly in the flood risk areas if development is not restricted in the high-risk flooding zone. Findings from this study could be useful for area-specific disaster management policy guidelines and formation of land use policy and planning.

Lidar has the ability to supplant or compliment many current measurement technologies in ocean optics. Lidar measures Inherent Optical Properties over long distances without impacting the orientation and assemblages of particles it measures, unlike many systems today which require pumps and flow cells. As an active sensing technology, it has the benefit of being independent of time of day and weather. Techniques to interpret oceanographic lidar lags behind atmospheric lidar inversion techniques to measure optical properties due to the complexity and variability of the ocean. Unlike in the atmosphere, two unknowns in the lidar equation backscattering at 180o (𝛽𝜋) and attenuation (c) do not necessarily covary. A lidar system developed at the Harbor Branch Oceanographic Institute is used as a test bed to validate a Monte-Carlo model to investigate the inversion of optical properties from lidar signals. Controlled tank experiments and field measurements are used to generate lidar waveforms and provide optical situations to model. The Metron EODES backscatter model is used to model waveforms. A chlorophyll based forward optical model provides a set of 1500 unique optical situations which are modeled to test inversion techniques and lidar geometries. Due to issues with the lidar system and model the goal of validating the model as well as a more mature inversion experiment were not completed. However, the results are valuable to show the complexity and promise of lidar systems.

Traditional sand analysis is labor and cost-intensive, entailing specialized equipment and operators trained in geological analysis. Even a small step to automate part of the traditional geological methods could substantially improve the speed of such research while removing chances of human error. Digital image analysis techniques and computer vision have been well developed and applied in various fields but rarely explored for sand analysis. This research explores capabilities of remote sensing digital image analysis techniques, such as object-based image analysis (OBIA), machine learning, digital image analysis, and photogrammetry to automate or semi-automate the traditional sand analysis procedure. Here presented is a framework combining OBIA and machine learning classification of microscope imagery for use with unconsolidated terrigenous beach sand samples. Five machine learning classifiers (RF, DT, SVM, k-NN, and ANN) are used to model mineral composition from images of ten terrigenous beach sand samples. Digital image analysis and photogrammetric techniques are applied and evaluated for use to characterize sand grain size and grain circularity (given as a digital proxy for traditional grain sphericity). A new segmentation process is also introduced, where pixel-level SLICO superpixel segmentation is followed by spectral difference segmentation and further levels of superpixel segmentation at the object-level. Previous methods of multi-resolution and superpixel segmentation at the object level do not provide the level of detail necessary to yield optimal sand grain-sized segments. In this proposed framework, the DT and RF classifiers provide the best estimations of mineral content of all classifiers tested compared to traditional compositional analysis. Average grain size approximated from photogrammetric procedures is comparable to traditional sieving methods, having an RMSE below 0.05%. The framework proposed here reduces the number of trained personnel needed to perform sand-related research. It requires minimal sand sample preparation and minimizes user-error that is typically introduced during traditional sand analysis.

This research investigates land use change and the area of occurrence of an introduced primate species, Chlorocebus sabaeus, from 1940 until the present. Research into the importation and subsequent release of these monkeys has revealed that they were released from a failed tourist attraction in 1947. The attraction was located southeast of the Hollywood International Airport in Fort Lauderdale, Florida. Remote sensing techniques were utilized to examine land use change over time, create a land classification map, and create a canopy model. These data were used to better understand the area of occurrence of an introduced primate species by examining anthropogenic changes through time and measuring changes in available forest habitat. Corridors, and their transformation through the decades, were evaluated to better understand potential dispersal routes and connectivity to natural areas for colonization.