Green infrastructure directly impacts our daily life and promotes the mitigation of climate change at large. Urban woodlands are one of the green infrastructures that need regular monitoring. Existing urban tree inventories and monitoring schemes are based on spatial sampling assessment techniques. Urban tree health monitoring using remote sensing techniques such as LiDAR is used for inventory but needs a regular revisit. However, radar remote sensing has the potential to investigate the estimation of tree height, an important parameter towards tree health monitoring. Here we use Digital Elevation Model (DEM) differential interference based on Synthetic Aperture Radar (SAR) satellite data. We use Sentinel-1 (C-band) data to estimate the three heights in urban setting. In addition, we use exiting LiDAR data to estimate the tree height and ground-based smartphone Augmented Reality (AR) based height estimation for comparison and validation purposes. The result can be integrated with the available forest database and contribute towards regular monitoring of green infrastructure. As a case study to demonstrate the methodology, we investigate sample trees in Ealing, one of the boroughs of London with good coverage of urban trees and woodlands.
Structural health monitoring (SHM) is crucial in preserving the civil infrastructure asset and ensuring safety of the operations. Amongst the available SHM techniques, the ground-based synthetic aperture radar (GB-SAR) is one of the most reliable. However, a gap in knowledge with the use of this system exists when multiple targets are in the same acquisition range. The present study investigates into this aspect and proposes a two-stage procedure based on i) controlling the signal propagation characteristics during the data collection and ii) implementing advanced signal processing techniques to aid the interpretation of the measured signal. To this effect, three scenarios of interest are implemented in the laboratory environment, i.e., i) absence of targets, ii) presence of one target, and iii) presence of two targets in the centerline of the radar. The data collection is aided by augmented reality (AR), which allows to visualise the radar footprint and precisely control the acquisition according to the set scenarios. The collected data are processed using the empirical mode decomposition (EMD) and the Hilbert-Huang transform (HHT) techniques. The proposed methodology is shown to be effective in both the data control and processing stages. Results have proven that the signal response from multiple targets differs from that observed in the other investigated scenarios, hence showing potential for enhancing multi-target detection in structures with GB-SAR.
Tree trunk monitoring is crucial for both industrial and environmental applications, as well as for ecological preservation. To date, Ground Penetrating Radar (GPR) has shown promising results in mapping the inner structures of tree trunks, which could lead to novel forest management practices and support preventing the collapse of trees in urban areas. This is crucial to avoid harming the lives and properties of citizens. Nevertheless, it has been observed that the irregular geometry of the tree trunk surface can cause mismatches in associating the GPR traces to their correct position along the bark and, hence, distortion in the processed GPR images. This can escalate into an incorrect location of internal targets of interest, e.g., holes and decays within the tree trunk. To this end, this research aims to correct the effect of GPR data distortion along complex tree trunk surface geometries, by locating the GPR A-scans (traces) in their actual position. First, Smartphone Light Detection and Ranging (LiDAR) technology, a multi-angle measuring ruler, and a high-resolution camera are employed to extract the irregular contours of a sample tree bark. GPR data are subsequently collected along the measured surface and correlated with the respective profile’s traces. The methods’ accuracy is investigated by way of comparison between the different employed edge detection techniques through laboratory measurements on an oak tree trunk sample. The results show that the Smartphone LiDAR technology improves the accuracy of the reconstructed images of tree trunks by providing high-precision and affordable coordinate information. This integrated technique has great potential for enhancing the prediction of the location of decays in tree trunks and creating more accurate GPRimage- based models. The achieved results pave the way for novel forest management practices and contribute to preserving ecological systems.
Structural Health Monitoring (SHM) is critical to ensuring the safety of structures such as bridges, tunnels, and dams. Despite some sensors being highly accurate, it is not always feasible to interrupt the serviceability of the structure for data collection. Within this framework, remote sensing methods such as the Ground-Based Interferometric Synthetic Aperture Radar (GB-SAR) have shown their capability in remotely collecting data of multiple targets simultaneously with a high sampling rate. However, detecting targets in their exact position is currently an area that requires further investigation for this technology. The present research focuses on developing a field investigation methodology to limit uncertainties and raise awareness about the significance of data collected by GB-SAR aided by augmented reality (AR). To this effect, head-mounted and mobile AR can support the use of techniques, such as GB-SAR, which work on fundamental geometrical principles, by providing guidance markers in real time for positional and reference information. The integration of both technologies can allow to pre-visualise the optimal position for data collection by aiding to match the structural targets under investigation within the area of interest. The proposed methodology is here implemented in clinical laboratory conditions to investigate the sensor’ sensitivity against testing parameters, such as the radar position and the distance to targets. The proposed methodology will contribute to collecting data with a higher accuracy and a lower uncertainty compared to other non-destructive methods utilised in the field. This study demonstrates the potential of using AR to enhance remote sensing methods for SHM and it builds up the foundation for future development into a more comprehensive SHM approach.
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