Description and assessment of regional sea-level trends and variability from altimetry and tide gauges at the northern Australian coast

This paper aims at providing a descriptive view of the lowfrequency sea level changes around the northern Australian coastline. Twenty years of sea level observations from multi-mission satellite altimetry and tide gauges are used to characterise sea level trends and inter-annual variability over the study region. The results show that the interannual sea level fingerprint in the northern Australian coastline is closely related to El Niño Southern Oscillation (ENSO) and Madden-Julian Oscillation (MJO) events, with the greatest influence on the Gulf Carpentaria, Arafura Sea, and the Timor Sea. The basin average of 14 tide-gauge time series is in strong agreement with the basin average of the altimeter data, with a root mean square difference of 18 mm and correlation coefficient of 0.95. The rate of sea level rise (6.3 ± 1.4 mm/yr) estimated from tide gauges is slightly higher than that (6.1 ± 1.3 mm/yr) from altimetry in the time interval 1993-2013, which can vary with the length of the time interval. Here we provide new insights into examining the significance of sea level trends by applying the non-parametric Mann-Kendall test. This test is applied to assess if the trends are significant (upward or downward). Apart from a positive rate of sea level rise, trends are not statistically significant in this region due to the effects of natural variability. The findings suggest that altimetric trends are not significant along the coasts and some parts of the Gulf Carpentaria (14°S-8°S), where geophysical corrections (e.g., ocean tides) cannot be estimated accurately and altimeter measurements are contaminated by reflections from the land

Spectral Analysis of Satellite Altimeters and Tide Gauges Data around the Northern Australian Coast

The north of Australia is known for its complex tidal system, where the highest astronomical tides (HATs) reach 12 m. This paper investigates the tidal behaviour in this region by developing spectral climatology for tide gauge and altimetry data. Power spectral density analysis is applied to detect the magnitude of ocean tides in 20 years of sea-level data from multimission satellite altimeters and tide gauges. The spectra of altimetry sea level anomaly (SLA) time series have their strongest peaks centred at approximately 2.11, 5.88, and 7.99 cycles per year (cpy), corresponding to the diurnal and semidiurnal tidal constituents K1, M2, and O1, respectively. Closer to the coastline, the spectra peak at high-frequency overtide and shallow-water constituents such as M4, MK4, and MK3. There have been many large, high-frequency spectral peaks near the coastline, indicating the difficulty of predicting tidal signals by coastal altimetry. Similar to altimetry observations, there are dominant semidiurnal and diurnal tidal peaks in tide gauge SLA time series accompanying a number of overtides. The semidiurnal and diurnal peaks are mostly higher on the northwest coast of Australia compared with the north and northeast coast. The results from both altimetry and tide gauges indicate that tidal range increases with increasing continental shelf

Review of Automatic Processing of Topography and Surface Feature Identification LiDAR Data Using Machine Learning Techniques

Machine Learning (ML) applications on Light Detection And Ranging (LiDAR) data have provided promising results and thus this topic has been widely addressed in the literature during the last few years. This paper reviews the essential and the more recent completed studies in the topography and surface feature identification domain. Four areas, with respect to the suggested approaches, have been analyzed and discussed: the input data, the concepts of point cloud structure for applying ML, the ML techniques used, and the applications of ML on LiDAR data. Then, an overview is provided to underline the advantages and the disadvantages of this research axis. Despite the training data labelling problem, the calculation cost, and the undesirable shortcutting due to data downsampling, most of the proposed methods use supervised ML concepts to classify the downsampled LiDAR data. Furthermore, despite the occasional highly accurate results, in most cases the results still require filtering. In fact, a considerable number of adopted approaches use the same data structure concepts employed in image processing to profit from available informatics tools. Knowing that the LiDAR point clouds represent rich 3D data, more effort is needed to develop specialized processing tools

Random forest machine learning technique for automatic vegetation detection and modelling in LiDAR data

Machine learning techniques have gained a distinguished position in the automatic processing of Light Detection and Ranging (LiDAR) data area. They represent the actual research topic in the remote sensing domain. Indeed, this paper presents one method of supervised machine learning, which is called Random Forest. This algorithm is discussed, and their primary applications in automatic vegetation extraction and modelling in the LiDAR data area are presented here

3D LoD2 and LoD3 Modeling of Buildings with Ornamental Towers and Turrets Based on LiDAR Data

This paper presents an innovative approach to the automatic modeling of buildings composed of rotational surfaces, based exclusively on airborne LiDAR point clouds. The proposed approach starts by detecting the gravity center of the building's footprint. A thin point slice parallel to one coordinate axis around the gravity center was considered, and a vertical cross-section was rotated around a vertical axis passing through the gravity center, to generate the 3D building model. The constructed model was visualized with a matrix composed of three matrices, where the same dimensions represented the X, Y, and Z Euclidean coordinates. Five tower point clouds were used to evaluate the performance of the proposed algorithm. Then, to estimate the accuracy, the point cloud was superimposed onto the constructed model, and the deviation of points describing the building model was calculated, in addition to the standard deviation. The obtained standard deviation values, which express the accuracy, were determined in the range of 0.21 m to 1.41 m. These values indicate that the accuracy of the suggested method is consistent with approaches suggested previously in the literature. In the future, the obtained model could be enhanced with the use of points that have considerable deviations. The applied matrix not only facilitates the modeling of buildings with various levels of architectural complexity, but it also allows for local enhancement of the constructed models

Comparative Approach of Unmanned Aerial Vehicle Restrictions in Controlled Airspaces

Recent public discourse regarding unmanned aerial vehicle (UAV) usage and regulation is focused around public privacy and safety. Most authorities have employed key guidelines and licensing procedures for piloting UAVs, however there is marginal consensus amongst regulators and a limited view towards unified procedures. This paper aims to analyze the key challenges that affect the use of UAVs and to determine if the current rules address those challenges. For this purpose: privacy, safety, security, public nuisance and trespass are tested. A set of criteria are developed to perform a comparative analysis against the existing UAV regulations to determine how they are meeting the specified criteria. Within this framework, five countries are selected: Australia, Canada, European Union (EU), United Kingdom (UK) and the United States of America (USA), with usage data and length of time between regulatory reviews ensuring any analysis is realized on updated protocols. The regulations of each country are then compared against the developed criteria. The findings show there are shortfalls with the majority of regulations failing to meet some criteria and the results confirm that key issues fail to be addressed. Finally, recommendations are suggested for filling the gaps in the regulations

A Framework for Burnt Area Mapping and Evacuation Problem Using Aerial Imagery Analysis

The study aims to develop a holistic framework for maximum area coverage of a disaster region during a bushfire event. The monitoring and detection of bushfires are essential to assess the extent of damage, its direction of spread, and action to be taken for its containment. Bushfires limit human’s access to gather data to understand the ground situation. Therefore, the application of Unmanned Aerial Vehicles (UAVs) could be a suitable and technically advanced approach to grasp the dynamics of fires and take measures to mitigate them. The study proposes an optimization model for a maximal area coverage of the fire-affected region. The advanced Artificial Bee Colony (ABC) algorithm will be applied to the swarm of drones to capture images and gather data vital for enhancing disaster response. The captured images will facilitate the development of burnt area maps, locating access points to the region, estimating damages, and preventing the further spread of fire. The proposed algorithm showed optimum responses for exploration, exploitation, and estimation of the maximum height of the drones for the coverage of wildfires and it outperformed the benchmarking algorithm. The results showed that area coverage of the affected region was directly proportional to drone height. At a maximum drone height of 121 m, the area coverage was improved by 30%. These results further led to a proposed framework for bushfire relief and rescue missions. The framework is grounded on the ABC algorithm and requires the coordination of the State Emergency Services (SES) for quick and efficient disaster response

Assessment and Prediction of Sea Level Trend in the South Pacific Region

Sea level rise is an important and topical issue in the South Pacific region and needs an urgent assessment of trends for informed decision making. This paper presents mean sea level trend assessment using harmonic analysis and a hybrid deep learning (DL) model based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) technique, Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Neighbourhood Component Analysis (NCA) to build a highly accurate sea level forecasting model for three small islands (Fiji, Marshall Island and Papua New Guinea (PNG)) in the South Pacific. For a 20-year period, the estimated mean sea level rise per year from the harmonic computation is obtained: 112 mm for PNG, 98 mm for Marshall Island and 52 mm for Fiji. The DL procedure uses climate and environment-based remote sensing satellite (MODIS, GLDAS-2.0, MODIS TERRA, MERRA-2) predictor variables with tide gauge base mean-sea level (MSL) data for model training and development for forecasting. The developed CEEMDAN-CNN-GRU as the objective model is benchmarked by comparison to the hybrid model without data decomposition, CNN-GRU and standalone models, Decision Trees (DT) and Support Vector Regression (SVR). All model performances are evaluated using reliable statistical metrics. The CEEMDAN-CNN-GRU shows superior accuracy when compared with other standalone and hybrid models. It shows an accuracy of >96% for the correlation coefficient and an error of < 1% for all study sites

Automatic Filtering of Lidar Building Point Cloud in Case of Trees Associated to Building Roof

This paper suggests a new algorithm for automatic building point cloud filtering based on the Z coordinate histogram. This operation aims to select the roof class points from the building point cloud, and the suggested algorithm considers the general case where high trees are associated with the building roof. The Z coordinate histogram is analyzed in order to divide the building point cloud into three zones: the surrounding terrain and low vegetation, the facades, and the tree crowns and/or the roof points. This operation allows the elimination of the first two classes which represent an obstacle toward distinguishing between the roof and the tree points. The analysis of the normal vectors, in addition to the change of curvature factor of the roof class leads to recognizing the high tree crown points. The suggested approach was tested on five datasets with different point densities and urban typology. Regarding the results’ accuracy quantification, the average values of the correctness, the completeness, and the quality indices are used. Their values are, respectively, equal to 97.9%, 97.6%, and 95.6%. These results confirm the high efficacy of the suggested approach

Coastal altimetry for sea level changes in Northern Australian coastal oceans

Sea level rise is undoubtedly one of the most threatening consequences of climate change. The impact of sea level rise will be strongly felt in northern Australian coastal regions, where a rapid rising of sea levels is causing an increase in the frequency and severity of storm surge events. This dissertation investigates two main objectives: (1) the short-term local sea level variability associated with tropical cyclones, and (2) the long-term regional sea level variability and trends for the northern coasts of Australia. This study uses 21 years of sea level observations from multiple satellite altimetry missions (e.g., TOPEX/Poseidon, Jason-1, and Jason-2) and 14 tide gauges. First, it focuses on the analysis of the Non-Tidal Sea Level component of Sea Level Anomalies (SLAs), which theoretically only contains the storm surge level, and is constructed by removing the mean sea surface and ocean tides from sea level observations. The SLAs are analysed using the Power Spectral Density method to explore the tidal features in the study area. This concludes that the pointwise response method provides better ocean tidal corrections than the global tidal models, which were available at the time of this study, due to the complexity of the study area. Then, a multivariate regression (MR) model is used to predict sea level variations using both altimetry and tide gauge data. The modelled solution provides sea level predictions at the times of interest, which can be used to monitor extreme sea level events. To overcome the drawback that the MR model cannot model the non-linear variations, a new method has been developed to investigate non-linear components of sea level through the rebuilding the model using a state-of-the-art Multivariate Adaptive Regression Spline (MARS). The comparison results show that MARS can, in general, explain 62% of sea level variance while MR only accounts for 45% of the variance, suggesting an improved sea level prediction from MARS. Comparison results also indicate that the cyclone-induced surge peaks predicted by the MARS model agree well with those observed at independent validating tide gauges. Finally, to achieve the second objective, sea level observations from both datasets are used to characterise sea level trends and interannual variability over the study region. The results show that the interannual sea level fingerprint in the northern Australian coastline is closely related to El Niño Southern Oscillation (ENSO) and Madden-Julian Oscillation (MJO) events. The rate of sea level rise (6.3 ± 0.4 mm/yr) estimated from tide gauges is slightly higher than (6.1 ± 0.3 mm/yr) from altimetry in the period of 1993-2013, which varies with the length of the time interval. This study also provides a novel framework for examining the significance of sea level trends by applying the non-parametric Mann-Kendall test, which is of significance in interpreting sea level trends. Recommendations for further research are to improve the altimetry sea level measurement close to the coastline using re-tracking techniques and to investigate the potential capability of monitoring coastal sea level from new satellite altimetry data, such as Jason-CS, Jason-3, CryoSat and Saral/AltiKa. An improved understanding of sea level rise and storm surges will be helpful in evaluating the coastal flooding scenario in the high flooding risk regions