Geomatics, Volume 2, Issue 3 (September 2022) – 8 articles

Classification of airborne light detection and ranging (LiDAR) point cloud is still challenging due to the irregular point cloud distribution, relatively low point density, and the complex urban scenes being observed. The availability of multispectral LiDAR systems allows for acquiring data at different wavelengths with a variety of spectral information from land objects. In this research, a rule-based point classification method of three levels for multispectral airborne LiDAR data covering urban areas is presented. The first level includes ground filtering, which attempts to distinguish aboveground from ground points. The second level aims to divide the aboveground and ground points into buildings, trees, roads, or grass using three spectral indices, namely normalized difference feature indices (NDFIs). A multivariate Gaussian decomposition is then used to divide the NDFIs’ histograms into the aforementioned four classes. The third level aims to label more classes based on their spectral information such as power lines, types of trees, and swimming pools. Two data subsets were tested, which represent different complexity of urban scenes in Oshawa, Ontario, Canada. It is shown that the proposed method achieved an overall accuracy up to 93%, which is increased to over 98% by considering the spatial coherence of the point cloud. Full article

Scour around bridge piers is considered as one of the major factors which causes failure of bridges in the United States. An undetected scour can affect the stability of the bridge, eventually leading to the collapse of the bridge. The experimental investigation of scour around a pier using a non-contact measuring method is carried out in this research. A green laser-based non-contact ranging technique is performed on a prefabricated scour hole to study the factors influencing the ability to reconstruct the shape of a scour hole. The experiment was conducted in a 10 ft diameter pool and Leica scan station II was used for the scanning of the scour hole. The turbidity of the water was changed by adding Kaolinite powder to the water. The turbidity was varied from 1.2 NTU to 20.8 NTU by adding Kaolinite. The lab experiments involved changing the turbidity of water to simulate real world conditions. The results from the experimental study show that the turbidity of the water has a direct dependence on the efficiency of the green laser to map the underwater scour profile. The ability of the green laser to capture the fabricated scour hole and pool bed topography were decreased as the turbidity was increased even when the water depth of the pool was reduced. The results from the study show that the green laser is effective in underwater scanning and can be also used for bathymetry profiling and the detection of underwater objects. The method of underwater scanning using a green laser for detecting scour around bridge pier is safe, efficient, and economical. Full article

Reviewing hydrographic data for nautical charting is still a predominately manual process, performed by experienced analysts and based on directives developed over the years by the hydrographic office of interest. With the primary intent to increase the effectiveness of the review process, a set of automated procedures has been developed over the past few years, translating a significant portion of the NOAA Office of Coast Survey’s specifications for hydrographic data review into code (i.e., the HydrOffice applications called QC Tools and CA Tools). When applied to a large number of hydrographic surveys, it has been confirmed that such procedures improve both the quality and timeliness of the review process. Increased confidence in the reviewed data, especially by personnel in training, has also been observed. As such, the combined effect of applying these procedures is a novel holistic approach to hydrographic data review. Given the similarities of review procedures among hydrographic offices, the described approach has generated interest in the ocean mapping community. Full article

In this paper, a supervised land cover classification is presented based on the extracted information from polarimetric synthetic aperture radar (PolSAR) images. The analysis of the polarimetric scattering matrix is accomplished according to the Double Scatterer Model which interprets each PolSAR cell by a pair of elementary scattering mechanisms. Subsequently, by utilizing the contribution rate of the two fundamental scatterers, a novel data representation is accomplished, providing great informational content. The main component of the research is to highlight the robust new feature-tool and afterwards to present a classification scheme exploiting a fully connected artificial neural network (ANN). The PolSAR images used to verify the proposed method were acquired by RADARSAT-2 and the experimental results confirm the effectiveness of the presented methodology with an overall classification accuracy of 93%, which is considered satisfactory since only four feature-vectors are used. Full article

Coastal environments are globally recognized for their spectacular morphological characteristics as well as economic opportunities, such as fisheries and tourism industries. However, climate change, growth in tourism, and constant coastal urban sprawl in some places result in ever-increasing risk in the islands of the South Aegean Volcanic Arc (SAVA), necessitating thoughtful planning and decision making. GEOspatial INTelligence (GEOINT) can play a crucial role in the depiction and analysis of the natural and human surroundings, offering valuable information regarding the identification of vulnerable areas and the forecasting of urbanization rates. This work focuses on the delineation of the coastal zone boundaries, semi-automatization of Satellite-Derived Bathymetry (SDB), and urban mapping using a machine learning algorithm. The developed methodology has been implemented on the islands of Thira (Santorini island complex) and Milos. This study attempts to identify inaccuracies in existing open-source datasets, such as the European Settlement Map (ESM), as a result of the unique combination of the architectural style and bare-soil characteristics of the study areas. During the period 2016–2021, the average accuracy of the developed methodology for urban mapping in terms of the kappa index was 80.15% on Thira and 88.35% on Milos. The results showed that the average urbanization expansion on specified settlements was greater than 22% for both case studies. Ultimately, the findings of this study could contribute to the effective and holistic management of similar coastal regions in the context of climate change adaptation, mitigation strategies, and multi-hazard assessment. Full article

Mangroves grow in the tidal zone and have many benefits for the ecosystem and human life. Mangrove loss monitoring is important information to know the condition and status of mangrove forests. Along with the development of computer technology, machine learning and satellite imagery has widely used for mangrove mapping. The goal of this study is to monitor two decades (2000–2020) of mangrove loss using a random forest (RF) algorithm with Landsat-7 and Landsat-8 data in East Luwu, Indonesia. East Luwu has a high mangrove deforestation rate based on the previous study. More detailed mangrove loss monitoring in this area is needed to know the annual mangrove deforestation rate in this area. This study used an RF model to produce mangrove maps in the whole study area from 2000 to 2020. According to the large computing and storage capabilities of time-series satellite data, this study used Google Earth Engine (GEE) platform as the cloud computing process. A total of 2500 independent testing points were collected to calculate the evaluation assessment of produced mangrove maps. Based on the evaluation assessment, the average overall score of produced mangrove map is 0.966, while the average UA score of mangrove class is 0.936. In general, this study revealed the total area of mangroves in East Luwu from 2000 to 2020 has a decreased trend. The highest annual rate of mangrove loss happened from 2000 to 2005 with a loss rate of −14.11% (2477.39 Ha). The main factor of mangrove loss in this area is caused by the aquaculture ponds. In addition, we found an increase in mangrove areas from 2016 to 2020 by +1.04% (87.96 ha). Full article

This manuscript focuses on a quality assessment of DJI’s new sensors: the Zenmuse L1 and P1, which are LiDAR and photographic payload sensors, respectively, for UAVs/UASs. In particular, metric and statistical analyses aim to evaluate the data obtained from different 3D survey instruments. Furthermore, we compared these sensors with TLS data derived from a Trimble SX10 scanning station. The integration of LiDAR and photogrammetric data was then performed and tested inside a complex architectural context, the medieval Frinco Castle (AT-Italy). Point clouds obtained from aerial and terrestrial instruments were analysed and compared using specific tools to calculate variance/distance between points and cloud alignment (via the ICP algorithm), as well as to perform qualitative estimations (especially roughness analysis). The medieval castle proved crucial for the purpose of analysing different metric data of an extremely complex architecture and achieving more accurate results. The collected dataset and performed analyses are now essential information for the consolidation and restoration programme. Full article

Traditional interpolation methods, such as IDW, kriging, radial basis functions, and regularized splines, are commonly used to generate digital elevation models (DEM). All of these methods have strong statistical and analytical foundations (such as the assumption of randomly distributed data points from a gaussian correlated stochastic surface); however, when data are acquired non-homogeneously (e.g., along transects) all of them show over/under-smoothing of the interpolated surface depending on local point density. As a result, actual information is lost in high point density areas (caused by over-smoothing) or artifacts appear around uneven density areas (“pimple” or “transect” effects). In this paper, we introduce a simple but robust multigrid/multiresolution interpolation (MMI) method which adapts to the spatial resolution available, being an exact interpolator where data exist and a smoothing generalizer where data are missing, but always fulfilling the statistical requirement that surface height mathematical expectation at the proper working resolution equals the mean height of the data at that same scale. The MMI is efficient enough to use K-fold cross-validation to estimate local errors. We also introduce a fractal extrapolation that simulates the elevation in data-depleted areas (rendering a visually realistic surface and also realistic error estimations). In this work, MMI is applied to reconstruct a real DEM, thus testing its accuracy and local error estimation capabilities under different sampling strategies (random points and transects). It is also applied to compute the bathymetry of Gulf of San Jorge (Argentina) from multisource data of different origins and sampling qualities. The results show visually realistic surfaces with estimated local validation errors that are within the bounds of direct DEM comparison, in the case of the simulation, and within the $10\%$ of the bathymetric surface typical deviation in the real calculation. Full article