Geomatics, Volume 1, Issue 2 (June 2021) – 8 articles

Very short-term (0~3 h) radar-based quantitative precipitation forecasting (QPF), also known as nowcasting, plays an essential role in flash flood warning, water resource management, and other hydrological applications. A novel nowcasting method combining radar data and a model wind field was developed and validated with two hurricane precipitation events. Compared with several existing nowcasting approaches, this work attempts to enhance the prediction capabilities from two major aspects. First, instead of using a radar reflectivity field, this work proposes the use of the rainfall rate field estimated from polarimetric radar variables in the motion field derivation. Second, the derived motion field is further corrected by the Rapid Refresh (RAP) model field. With the corrected motion field, the future rainfall rate field is predicted through a linear extrapolation method. The proposed method was validated using two hurricanes: Harvey and Irma. The proposed work shows an enhanced performance according to statistical scores. Compared with the model only and centroid-tracking only approaches, the average probability of detection (POD) increases about 25% and 50%; the average critical success index (CSI) increases about 20% and 37%; and the average false alarm rate (FAR) decreases about 14% and 16%, respectively. Full article

Recently, light detection and ranging (LiDAR)-based mobile mapping systems (MMS) have been utilized for extracting lane markings using deep learning frameworks. However, huge datasets are required for training neural networks. Furthermore, with accurate lane markings being detected utilizing LiDAR data, an algorithm for automatically reporting their intensity information is beneficial for identifying worn-out or missing lane markings. In this paper, a transfer learning approach based on fine-tuning of a pretrained U-net model for lane marking extraction and a strategy for generating intensity profiles using the extracted results are presented. Starting from a pretrained model, a new model can be trained better and faster to make predictions on a target domain dataset with only a few training examples. An original U-net model trained on two-lane highways (source domain dataset) was fine-tuned to make accurate predictions on datasets with one-lane highway patterns (target domain dataset). Specifically, encoder- and decoder-trained U-net models are presented wherein, during retraining of the former, only weights in the encoder path of U-net were allowed to change with decoder weights frozen and vice versa for the latter. On the test data (target domain), the encoder-trained model (F1-score: 86.9%) outperformed the decoder-trained (F1-score: 82.1%). Additionally, on an independent dataset, the encoder-trained one (F1-score: 90.1%) performed better than the decoder-trained one (F1-score: 83.2%). Lastly, on the basis of lane marking results obtained from the encoder-trained U-net, intensity profiles were generated. Such profiles can be used to identify lane marking gaps and investigate their cause through RGB imagery visualization. Full article

The rapid rise of ultra-low-cost dual-frequency GNSS chipsets and micro-electronic-mechanical-system (MEMS) inertial sensors makes it possible to develop low-cost navigation systems, which meet the requirements for many applications, including self-driving cars. This study proposes the use of a dual-frequency u-blox F9P GNSS receiver with xsens MTi670 industrial-grade MEMS IMU to develop an ultra-low-cost tightly coupled (TC) triple-constellation GNSS PPP/INS integrated system for precise land vehicular applications. The performance of the proposed system is assessed through comparison with three different TC GNSS PPP/INS integrated systems. The first system uses the Trimble R9s geodetic-grade receiver with the tactical-grade Stim300 IMU, the second system uses the u-blox F9P receiver with the Stim300 IMU, while the third system uses the Trimble R9s receiver with the xsens MTi670 IMU. An improved robust adaptive Kalman filter is adopted and used in this study due to its ability to reduce the effect of measurement outliers and dynamic model errors on the obtained positioning and attitude accuracy. Real-time precise ephemeris and clock products from the Centre National d’Etudes Spatials (CNES) are used to mitigate the effects of orbital and satellite clock errors. Three land vehicular field trials were carried out to assess the performance of the proposed system under both open-sky and challenging environments. It is shown that the tracking capability of the GNSS receiver is the dominant factor that limits the positioning accuracy, while the IMU grade represents the dominant factor for the attitude accuracy. The proposed TC triple-constellation GNSS PPP/INS integrated system achieves sub-meter-level positioning accuracy in both of the north and up directions, while it achieves meter-level positioning accuracy in the east direction. Sub-meter-level positioning accuracy is achieved when the Stim300 IMU is used with the u-blox F9P GNSS receiver. In contrast, decimeter-level positioning accuracy is consistently achieved through TC GNSS PPP/INS integration when a geodetic-grade GNSS receiver is used, regardless of whether a tactical- or an industrial-grade IMU is used. The root mean square (RMS) errors of the proposed system’s attitude are about 0.878°, 0.804°, and 2.905° for the pitch, roll, and azimuth angles, respectively. The RMS errors of the attitude are significantly improved to reach about 0.034°, 0.038°, and 0.280° for the pitch, roll, and azimuth angles, respectively, when a tactical-grade IMU is used, regardless of whether a geodetic- or low-cost GNSS receiver is used. Full article

From a global perspective, dams are constructed to trap water flowing from a higher concentration to a lower concentration into a basin for several purposes to aid humanity. The continuous monitoring of dams is prudent for measuring the rate of sedimentation and siltation, and to ensure that it functions to its full capacity. The Tagwai dam is used for irrigation and domestic activities. It was observed that there is a shortage in its storage capacity and supplies due to sedimentation, and coupled with this is the fact that the majority of the communities are not connected to the tap water system; if not, the problem would have been evidently pronounced. However, to determine the present volume of water and provide possible ways of increasing the reservoir’s storage capacity, the leapfrogging approach was used to improve the basin. The data were collected using a single beam echosounder and Hi-Target V30 differential global positioning system (DGPS). The sounder was used to acquire bathymetric data, while the DGPS was used to delineate the shoreline. The data were interpolated using the ordinary Kriging technique. After that, the leapfrogging method was grouped into four scenarios: Scenario A, B, C, and D. In each stage, the volume was computed using Simpson’s 3/8 integrated model. Scenario A is the present stage of the reservoir. Consequently, the results show that, while scenario B and C presented an appreciable increase in volume at the instant, scenario D illustrated a tremendous improvement in the storage capacity, and it is a win-win situation. The decision on which leapfrogging approach to employ depends on the government’s willingness to enhance the reservoir’s capacity and the resources available, such as human and financial capital to execute the project. Full article

The advent of Geographical Information Systems (GIS) has changed the way people think and interact with the world. The main objectives of this paper are: (i) to provide an overview of 10 years (2010–2020) regarding the creation/development of GIS open-source applications; and (ii) to evaluate the GIS open-source plugins for environmental science. In the first objective, we evaluate the publications regarding the development of GIS open-source geospatial software in the last 10 years, considering desktop, web GIS and mobile applications, so that we can analyze the impact of this type of application for different research areas. In the second objective, we analyze the development of GIS open-source applications in the field of environmental sciences (with more focus on QGIS plugins) in the last 10 years and discuss the applicability and usability of these GIS solutions in different environmental domains. A bibliometric analysis was performed using Web of Science database and VOSViewer software. We concluded that, in general, the development of GIS open-source applications has increased in the last 10 years, especially GIS mobile applications, since the big data and Internet of Things (IoT) era, which was expected given the new advanced technologies available in every area, especially in GIS. Full article

The truss upgrade for the Calgary Municipal Building posed a unique challenge for live tracking of the structure’s reaction to the pre-loadings, welding operations, and the removal of the preloads. The authors, therefore, devised a method for a special case of deflection monitoring, with the pre-condition of having a displacement-free location available where cameras could be installed. The dust and other construction material would appear above the specimen, and the light over the specimen was variable. The proposed approach of this research was to use a correlation-based object recognition for retro-reflective targets. The technique maintained an accuracy of 0.08 mm in deflection monitoring with a camera at 15-m away from the targets over a period of eight months data acquisition. The conclusion was that this digital image correlation (DIC) technique can provide deflections in the perpendicular plane to the line of sight of the cameras and can be used under harsh conditions for the targets (e.g., dust and physical damage), with a limited light source. The effect of external environmental parameters, such as daily temperature, solar radiation, and air pressure on the observed deflections, were analyzed and the close relationship between temperature and variations in deflection were observed. Full article

This study examined the use of hyperspectral profiles for identifying three selected weed species in the alpine region of New South Wales, Australia. The targeted weeds included Orange Hawkweed, Mouse-ear Hawkweed and Ox-eye daisy, which have caused a great concern to regional biodiversity and health of the environment in Kosciuszko National Park. Field surveys using a spectroradiometer were undertaken to measure the hyperspectral profiles of leaves and flowers of the selected weeds and companion native plants. Random Forest (RF) classification was then applied to distinguish which spectral bands would differentiate the weeds from the native plants. Our results showed that an accuracy of 95% was achieved if the spectral profiles of the distinct flowers of the weeds were considered, and an accuracy of 80% was achieved if only the profiles of the leaves were considered. Emulation of the spectral profiles of two multispectral sensors (Sentinel-2 and Parrot Sequoia) was then conducted to investigate whether classification accuracy could potentially be achieved using wider spectral bands. Full article

Mobile location-based services (MLBS) are attracting attention for their potential public and personal use for a variety of applications such as location-based advertisement, smart shopping, smart cities, health applications, emergency response, and even gaming. Many of these applications rely on Inertial Navigation Systems (INS) due to the degraded GNSS services indoors. INS-based MLBS using smartphones is hindered by the quality of the MEMS sensors provided in smartphones which suffer from high noise and errors resulting in high drift in the navigation solution rapidly. Pedestrian dead reckoning (PDR) is an INS-based navigation technique that exploits human motion to reduce navigation solution errors, but the errors cannot be eliminated without aid from other techniques. The purpose of this study is to enhance and extend the short-term reliability of PDR systems for smartphones as a standalone system through an enhanced step detection algorithm, a periodic attitude correction technique, and a novel PCA-based motion direction estimation technique. Testing shows that the developed system (S-PDR) provides a reliable short-term navigation solution with a final positioning error that is up to 6 m after 3 min runtime. These results were compared to a PDR solution using an Xsens IMU which is known to be a high grade MEMS IMU and was found to be worse than S-PDR. The findings show that S-PDR can be used to aid GNSS in challenging environments and can be a viable option for short-term indoor navigation until aiding is provided by alternative means. Furthermore, the extended reliable solution of S-PDR can help reduce the operational complexity of aiding navigation systems such as RF-based indoor navigation and magnetic map matching as it reduces the frequency by which these aiding techniques are required and applied. Full article