The increasing population, thus financial interests, in coastal areas have increased the need to monitor coastal elevation and shoreline change. Though several resources exist to obtain this information, they often lack the required temporal resolution for short-term monitoring (e.g., every hour). To address this issue, this study implements a low-cost ZED 2i stereo camera system and close-range photogrammetry to collect images for generating 3D point clouds, digital surface models (DSMs) of beach elevation, and georectified imagery at a localized scale and high temporal resolution. The main contributions of this study are (i) intrinsic camera calibration, (ii) georectification and registration of acquired imagery and point cloud, (iii) generation of the DSM of the beach elevation, and (iv) a comparison of derived products against those from uncrewed aircraft system structure-from-motion photogrammetry. Preliminary results show that despite its limitations, the ZED 2i can provide the desired mapping products at localized and high temporal scales. The system achieved a mean reprojection error of 0.20 px, a point cloud registration of 27 cm, a vertical error of 37.56 cm relative to ground truth, and georectification root mean square errors of 2.67 cm and 2.81 cm for x and y.

Observations of sea-level rises in the Houston-Galveston region, Texas, USA, have revealed a notable growth since the 1990s, which may be primarily attributed to land subsidence. This study aimed to estimate subsidence near the area by integrating state-of-the-art space geodetic techniques such as interferometric synthetic aperture radar (InSAR) and global navigation satellite systems (GNSS). Specifically, Sentinel-1 synthetic aperture radar (SAR) images captured between 2015 and 2022 were processed using two multi-temporal InSAR (MT-InSAR) methods, i.e., persistent scatter interferogram (PSI) and small baseline subset (SBAS). By combining InSAR results from both ascending and descending orbit acquisitions, land subsidence estimates were obtained. InSAR subsidence results were validated with favorable agreement between trend values derived from PSI/SBAS and GNSS measurements. For two of the high subsidence locations selected between Houston and Galveston, Texas, the extraction of large quantities of oil/gas, in close spatial proximity to coastal subsiding locations, is considered to be the main cause of observed land subsidence.

Automatically detection the wet/dry shoreline would facilitates several applications in geological, and societal tasks such as biodiversity or beach management in coastal areas. High resolution remote imagery from UAVs simplifies detecting tiny wet/dry lines. Recently, deep learning models have shown much success compared to conventional image processing techniques for line/edge detection in terms of accuracy and automation. In this paper, an end-to-end deep learning model originated from Holistically-Nested Edge Detection (HED) model has been proposed to automatically detect the wet/dry shoreline in Fish Pass area, Texas, USA. The results shown 81% ODS (Optimal Dataset Scale), 100% OIS (per-Image best threshold), and 78.1% AP (Average Precision) score.

This study used persistent scatterer (PS) interferometric synthetic aperture radar (InSAR) techniques to estimate land deformation near Houston area, Texas, USA, between 2017 and 2021. In order to improve the spatial resolution of the subsidence map, two spatial interpolation techniques, i.e., empirical orthogonal function (EOF) and Kriging, were used. The interpolated results were compared against that obtained from the co-located global navigation satellite systems (GNSS) observation stations. It was concluded that the time series and trend of PS agreed well with GNSS, and that interpolated maps of EOF and Kriging showed similar spatial patterns and good agreement in scatterplots against co-located GNSS stations. Differences in results between GNSS and InSAR may stem from: 1) the spatial variability of subsidence, and 2) difference in observation directions.

A double difference (DD) method was employed to estimate vertical land motion (VLM) at 26 tide gauge (TG) sites with record lengths of at least ten years across the Texas Gulf Coast, USA, between 1993 and 2020. In the method, the first difference was conducted by coupling nearby correlated TG stations to remove sea-level variability for both TG and satellite radar altimetry (SRA) data. Upon completion of the first difference, a second difference was performed by subtracting between TG and SRA data. Results obtained from the DD method were compared against that of: 1) a single difference (SD) method through subtraction between measurements from TG and SRA, and 2) a global navigation satellite system (GNSS) precise point positioning (PPP) method. Results showed that the DD method improved the performance of VLM estimation with an uncertainty below 1.0 mm/yr at most TG stations. Meanwhile, the estimated VLM trends acquired from the DD method correlated better to that of the ground-truth GNSS PPP solutions than the SD method. The DD method possesses great potential to discover VLM knowledge, particularly along coastal regions where other techniques such as GNSS and interferometric synthetic aperture radar (InSAR) are of impaired estimation capability.

Full-waveform analysis (FWA) through modeling and decomposition of the digitized echo waveform, measured by a full-waveform (FW) laser scanning system, is typically employed to derive the waveform attributes, including the number of echoes, amplitude and width of each detected echo in the backscattered waveform signal. It has already been shown that such attributes in the feature vector of each measured point can enhance the performance of semantic point cloud segmentation. In this experiment, however, rather than modeling the waveform, the feature vector of each measured target is populated with the raw samples of the waveform related to the target. Random forest classification is used to classify a 3D scene consisting of both natural and man-made targets. The results show that the raw samples of the digitized waveform are discriminative enough to be used as independent features for a multi-class classification task, where, in this experiment, the overall accuracy of 90% was achieved for classifying the$3D$scene.