Journal Description
Geomatics
Geomatics
is an international, and interdisciplinary, peer-reviewed, open access journal of geomatic science, published quarterly online by MDPI.
- Open Access—free to download, share, and reuse content. Authors receive recognition for their contribution when the paper is reused.
- Rapid Publication: First decisions in 15 days; acceptance to publication in 3 days (median values for MDPI journals in the second half of 2020).
- Recognition of Reviewers: APC discount vouchers, optional signed peer review, and reviewer names published annually in the journal.
- Geomatics is a companion journal of Remote Sensing.
Latest Articles
Cloud Optimized Raster Encoding (CORE): A Web-Native Streamable Format for Large Environmental Time Series
Geomatics 2021, 1(3), 369-382; https://doi.org/10.3390/geomatics1030021 - 18 Aug 2021
Abstract
The Environmental Data Portal EnviDat aims to fuse data publication repository functionalities with next-generation web-based environmental geospatial information systems (web-EGIS) and Earth Observation (EO) data cube functionalities. User requirements related to mapping and visualization represent a major challenge for current environmental data portals.
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The Environmental Data Portal EnviDat aims to fuse data publication repository functionalities with next-generation web-based environmental geospatial information systems (web-EGIS) and Earth Observation (EO) data cube functionalities. User requirements related to mapping and visualization represent a major challenge for current environmental data portals. The new Cloud Optimized Raster Encoding (CORE) format enables an efficient storage and management of gridded data by applying video encoding algorithms. Inspired by the cloud optimized GeoTIFF (COG) format, the design of CORE is based on the same principles that enable efficient workflows on the cloud, addressing web-EGIS visualization challenges for large environmental time series in geosciences. CORE is a web-native streamable format that can compactly contain raster imagery as a data hypercube. It enables simultaneous exchange, preservation, and fast visualization of time series raster data in environmental repositories. The CORE format specifications are open source and can be used by other platforms to manage and visualize large environmental time series.
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(This article belongs to the Special Issue GIS Open Source Software Applied to Geosciences)
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Open AccessArticle
The Results of Applying Different Methodologies to 10 Years of Quantitative Precipitation Estimation in Catalonia Using Weather Radar
Geomatics 2021, 1(3), 347-368; https://doi.org/10.3390/geomatics1030020 - 18 Jul 2021
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The single polarization C-Band weather radar network of the Meteorological Service of Catalonia covers the entire region (32,000 km2), which allows it to apply a series of corrections that improve preliminary estimations of the rainfall field (hourly and daily). In addition,
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The single polarization C-Band weather radar network of the Meteorological Service of Catalonia covers the entire region (32,000 km2), which allows it to apply a series of corrections that improve preliminary estimations of the rainfall field (hourly and daily). In addition, an automatic re-processing using automatic weather stations helps to incorporate ground-based information. The last process of the quantitative precipitation estimation (QPE) is running the end-product again eight days later, when the data have been reviewed and corrected in the case of detecting anomalies in the radar or gauge data. These corrections are applied operationally, with the fields generated and stored automatically. The QPE fields are generated in the GeoTIFF format, allowing easy use with multiple applications and simplifying processes such as quality control. In this way, the analysis of a 10 year period of GeoTIFF QPE daily data compared with ground rainfall values is introduced. The results help to understand different points regarding the functioning of the network such as the dependance on the type of precipitation and the seasonality. In addition, the description of a heavy rainfall episode (22 October 2019) shows the variations and improvements in the different products. The main conclusions refer to how using GeoTIFF combined with point data (rain gauges), it is possible to ensure simple but effective quality control of an operational radar network.
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Open AccessArticle
Effectiveness of Protected Areas in the Pan-Tropics and International Aid for Conservation
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Geomatics 2021, 1(3), 335-346; https://doi.org/10.3390/geomatics1030019 - 04 Jul 2021
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Evaluation of the effectiveness of protected areas is critical for forest conservation policies and priorities. We used 30 m resolution forest cover change data from 1990 to 2010 for ~4000 protected areas to evaluate their effectiveness. Our results show that protected areas in
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Evaluation of the effectiveness of protected areas is critical for forest conservation policies and priorities. We used 30 m resolution forest cover change data from 1990 to 2010 for ~4000 protected areas to evaluate their effectiveness. Our results show that protected areas in the tropics avoided 83,500 ± 21,200 km2 of deforestation during the 2000s. Brazil’s protected areas have the largest amount of avoided deforestation at 50,000 km2. We also show the amount of international aid received by tropical countries compared to the effectiveness of protected areas. Thirty-four tropical countries received USD 42 billion during the 1990s and USD 62 billion during the 2000s in international aid for biodiversity conservation. The effectiveness of international aid was highest in Latin America, with 4.3 m2/USD, led by Brazil, while tropical Asian countries showed the lowest average effect of international aid, reaching only 0.17 m2/USD.
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Open AccessArticle
Solving the Multilateration Problem without Iteration
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Geomatics 2021, 1(3), 324-334; https://doi.org/10.3390/geomatics1030018 - 29 Jun 2021
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The process of positioning, using only distances from control stations, is called trilateration (or multilateration if the problem is over-determined). The observation equation is Pythagoras’s formula, in terms of the summed squares of coordinate differences and, thus, is nonlinear. There is one observation
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The process of positioning, using only distances from control stations, is called trilateration (or multilateration if the problem is over-determined). The observation equation is Pythagoras’s formula, in terms of the summed squares of coordinate differences and, thus, is nonlinear. There is one observation equation for each control station, at a minimum, which produces a system of simultaneous equations to solve. Over-determined nonlinear systems of simultaneous equations are typically solved using iterative least squares after forming the system as a truncated Taylor’s series, omitting the nonlinear terms. This paper provides a linearization of the observation equation that is not a truncated infinite series—it is exact—and, thus, is solved exactly, with full rigor, without iteration and, thus, without the need of first providing approximate coordinates to seed the iteration. However, there is a cost of requiring an additional observation beyond that required by the non-linear approach. The examples and terminology come from terrestrial land surveying, but the method is fully general: it works for, say, radio beacon positioning, as well. The approach can use slope distances directly, which avoids the possible errors introduced by atmospheric refraction into the zenith-angle observations needed to provide horizontal distances. The formulas are derived for two- and three-dimensional cases and illustrated with an example using total-station and global navigation satellite system (GNSS) data.
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Open AccessArticle
A Short-Term Quantitative Precipitation Forecasting Approach Using Radar Data and a RAP Model
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Geomatics 2021, 1(2), 310-323; https://doi.org/10.3390/geomatics1020017 - 13 Jun 2021
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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
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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.
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Open AccessArticle
Transfer Learning for LiDAR-Based Lane Marking Detection and Intensity Profile Generation
Geomatics 2021, 1(2), 287-309; https://doi.org/10.3390/geomatics1020016 - 04 Jun 2021
Abstract
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
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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.
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(This article belongs to the Special Issue Spatial-Temporal Monitoring of Environmental and Ecological Processes Using LiDAR)
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Ultra-Low-Cost Tightly Coupled Triple-Constellation GNSS PPP/MEMS-Based INS Integration for Land Vehicular Applications
Geomatics 2021, 1(2), 258-286; https://doi.org/10.3390/geomatics1020015 - 27 May 2021
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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
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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.
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Open AccessArticle
Bathymetric Survey for Enhancing the Volumetric Capacity of Tagwai Dam in Nigeria via Leapfrogging Approach
Geomatics 2021, 1(2), 246-257; https://doi.org/10.3390/geomatics1020014 - 02 May 2021
Abstract
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,
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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.
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(This article belongs to the Special Issue GIS Open Source Software Applied to Geosciences)
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GIS Open-Source Plugins Development: A 10-Year Bibliometric Analysis on Scientific Literature
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Geomatics 2021, 1(2), 206-245; https://doi.org/10.3390/geomatics1020013 - 28 Apr 2021
Cited by 2
Abstract
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)
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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.
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(This article belongs to the Special Issue GIS Open Source Software Applied to Geosciences)
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Open AccessArticle
Target Based 2D Digital Image Correlation Deflection Monitoring to Analyze the Environmental Effect on Variations of Deflection on Structures
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Geomatics 2021, 1(2), 192-205; https://doi.org/10.3390/geomatics1020012 - 07 Apr 2021
Cited by 1
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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,
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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.
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Open AccessFeature PaperArticle
Identifying Invasive Weed Species in Alpine Vegetation Communities Based on Spectral Profiles
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Geomatics 2021, 1(2), 177-191; https://doi.org/10.3390/geomatics1020011 - 01 Apr 2021
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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
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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.
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Open AccessFeature PaperArticle
S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices
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Geomatics 2021, 1(2), 148-176; https://doi.org/10.3390/geomatics1020010 - 25 Mar 2021
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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
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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.
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Open AccessArticle
Mapping Urban and Peri-Urban Land Cover in Zimbabwe: Challenges and Opportunities
Geomatics 2021, 1(1), 114-147; https://doi.org/10.3390/geomatics1010009 - 03 Mar 2021
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Accurate and current land cover information is required to develop strategies for sustainable development and to improve the quality of life in urban areas. This study presents an approach that combines multi-seasonal Sentinel-1 (S1) and Sentinel-2 (S2) data, and a random forest (RF)
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Accurate and current land cover information is required to develop strategies for sustainable development and to improve the quality of life in urban areas. This study presents an approach that combines multi-seasonal Sentinel-1 (S1) and Sentinel-2 (S2) data, and a random forest (RF) classifier in order to map land cover in four major urban centers in Zimbabwe. The specific objective of this study was to assess the potential of multi-seasonal (rainy, post-rainy, and dry season) S1, rainy season S2, post-rainy season, dry season S2, multi-seasonal S2, and multi-seasonal composite S1 and S2 data for mapping land cover in urban areas. The study results show that the combination of multi-seasonal S1 and S2 data improve land cover mapping in urban and peri-urban areas relative to only multi-seasonal S1, mono-seasonal S2, and multi-seasonal S2 data. The overall accuracy scores for the multi-seasonal S1 and S2 land cover maps are above 85% for all urban centers. Our results indicate that rainy and post-rainy S2 spectral bands, as well as dry-season S1 VV and VH bands (ascending orbit) are the most important features for land cover mapping. In particular, S1 data proved useful in separating built-up areas from cropland, which is usually problematic when only optical imagery is used in the study area. While there are notable improvements in land cover mapping, some challenges related to the S1 data analysis still remain. Nonetheless, our land cover mapping approach shows a potential to map land cover in other urban areas in Zimbabwe or in Sub-Sahara Africa. This is important given the urgent need for reliable geospatial information, which is required to implement the United Nations Sustainable Development Goals (UN SDGs) and United Nations New Urban Agenda (NUA) programmes.
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Open AccessArticle
Geospatial Analysis of Rain Fields and Associated Environmental Conditions for Cyclones Eline and Hudah
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Geomatics 2021, 1(1), 92-113; https://doi.org/10.3390/geomatics1010008 - 24 Feb 2021
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Tropical cyclones (TCs) that landfall over Madagascar and Mozambique can cause flooding that endangers lives. To better understand how environmental conditions affect the rain fields of these TCs, this study utilized spatial metrics to analyze two storms taking similar paths two months apart.
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Tropical cyclones (TCs) that landfall over Madagascar and Mozambique can cause flooding that endangers lives. To better understand how environmental conditions affect the rain fields of these TCs, this study utilized spatial metrics to analyze two storms taking similar paths two months apart. Using a geographic information system, rain rates of 1 mm/h were extracted from a satellite-based dataset and contoured to define the rain field edge. Average extent of rainfall was measured for each quadrant and asymmetry was calculated along with rain field area, dispersion, closure, and solidity. Environmental conditions and storm intensity were analyzed every six hours. Results indicate that although both TCs intensified prior to first interaction with land, stronger vertical wind shear experienced by Eline was associated with higher asymmetry and dispersion. Additionally, rain fields were less solid although the center was mostly enclosed by rain. Storm shape was altered as both storms tracked over Madagascar, with Hudah recovering more quickly. Moisture increased for both storms and shear decreased for Eline, allowing it to become more centered and solid, and grow larger. Relationships between intensity, land interaction, and rain field shape support the results of previous research and demonstrate the global utility of these metrics.
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Open AccessArticle
Application of Multimodel Superensemble Technique on the TIGGE Suite of Operational Models
Geomatics 2021, 1(1), 81-91; https://doi.org/10.3390/geomatics1010007 - 19 Feb 2021
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One widely recognized portal which provides numerical weather prediction forecasts is “The Observing System Research and Predictability Experiment” (THORPEX) Interactive Grand Global Ensemble (TIGGE), an initiative of WMO project. This data portal provides forecasts from 1 to 16 days (2 weeks in advance)
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One widely recognized portal which provides numerical weather prediction forecasts is “The Observing System Research and Predictability Experiment” (THORPEX) Interactive Grand Global Ensemble (TIGGE), an initiative of WMO project. This data portal provides forecasts from 1 to 16 days (2 weeks in advance) for many variables such as rainfall, winds, geopotential height, temperature, and relative humidity. These weather forecasting centers have delivered near-real-time (with a delay of 48 hours) ensemble prediction system data to three TIGGE data archives since October 2006. This study is based on six years (2008–2013) of daily rainfall data by utilizing output from six centers, namely the European Centre for Medium-Range Weather Forecasts, the National Centre for Environmental Prediction, the Center for Weather Forecast and Climatic Studies, the China Meteorological Agency, the Canadian Meteorological Centre, and the United Kingdom Meteorological Office, and make consensus forecasts of up to 10 days lead time by utilizing the multimodal multilinear regression technique. The prediction is made over the Indian subcontinent, including the Indian Ocean. TRMM3B42 daily rainfall is used as the benchmark to construct the multimodel superensemble (SE) rainfall forecasts. Based on statistical ability ratings, the SE offers a better near-real-time forecast than any single model. On the one hand, the model from the European Centre for Medium-Range Weather Forecasting and the UK Met Office does this more reliably over the Indian domain. In a case of Indian monsoon onset, 05 June 2014, SE carries the lowest RMSE of 8.5 mm and highest correlation of 0.49 among six member models. Overall, the performance of SE remains better than any individual member model from day 1 to day 10.
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Open AccessArticle
Analyzing GNSS Measurements to Detect and Predict Bridge Movements Using the Kalman Filter (KF) and Neural Network (NN) Techniques
Geomatics 2021, 1(1), 65-80; https://doi.org/10.3390/geomatics1010006 - 07 Feb 2021
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In this study, we present a data processing framework to apply measurements of the Global Navigation Satellite System (GNSS) technique for analyzing and predicting the movements of civil structures such as bridges. The proposed approach reduces the noise level of GNSS measurements using
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In this study, we present a data processing framework to apply measurements of the Global Navigation Satellite System (GNSS) technique for analyzing and predicting the movements of civil structures such as bridges. The proposed approach reduces the noise level of GNSS measurements using the Kalman Filter (KF) approach and enables the estimation of static, semi-static, and dynamic components of the bridge’s movements using a series of analyses such as the temporal filtering and the Least Squares Harmonic Estimation (LS-HE). The numerical results indicate that by using a RTK-GNSS system the semi-static component is extracted with a Standard Deviation (STD) of 0.032, 0.048, and 0.06 m in the North, East, and Up (NEU) directions, while that of the dynamic component is 0.004, 0.003, and 0.01 m, respectively. Comparing the dominant frequencies of the bridge movements from LS-HE with those of the permanent stations provides information about the bridge’s stability. To predict its deflection, the Neural Network (NN) technique is tested to simulate the time-varying components, which are then compared with the safety limits, known by its design, to assess the structural health under usual load.
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Open AccessArticle
Forest Fire Spreading Using Free and Open-Source GIS Technologies
Geomatics 2021, 1(1), 50-64; https://doi.org/10.3390/geomatics1010005 - 25 Jan 2021
Cited by 1
Abstract
Forest fires are one of the most dangerous events, causing serious land and environmental degradation. Indeed, besides the loss of a huge quantity of plant species, the effects of fires can go far beyond: desertification, increased risk of landslides, soil erosion, death of
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Forest fires are one of the most dangerous events, causing serious land and environmental degradation. Indeed, besides the loss of a huge quantity of plant species, the effects of fires can go far beyond: desertification, increased risk of landslides, soil erosion, death of animals, etc. For these reasons, mathematical models able to predict fire spreading are needed in order to organize and optimize the extinguishing interventions during fire emergencies. This work presents a new system to simulate and predict the movement of the fire front based on free and open source Geographic Information System (GIS) technologies and the Rothermel surface fire spread model, with the adjustments made by Albini. We describe the mathematical models used, provide an overview of the GIS design and implementation, and present the results of some simulations at Etna volcano (Sicily, Italy), characterized by high geomorphological heterogeneity, and where the native flora and fauna may be preserved and perpetuated. The results consist of raster maps representing the progress times of the fire front starting from an ignition point and as a function of the topography and wind directions. The reliability of results is strictly affected by the correct positioning of the fire ignition point, by the accuracy of the topography that describes the morphology of the territory, and by the setting of the meteorological conditions at the moment of the ignition and propagation of the fire.
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(This article belongs to the Special Issue GIS Open Source Software Applied to Geosciences)
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Open AccessArticle
Train Fast While Reducing False Positives: Improving Animal Classification Performance Using Convolutional Neural Networks
Geomatics 2021, 1(1), 34-49; https://doi.org/10.3390/geomatics1010004 - 15 Jan 2021
Cited by 1
Abstract
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The combination of unmanned aerial vehicles (UAV) with deep learning models has the capacity to replace manned aircrafts for wildlife surveys. However, the scarcity of animals in the wild often leads to highly unbalanced, large datasets for which even a good detection method
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The combination of unmanned aerial vehicles (UAV) with deep learning models has the capacity to replace manned aircrafts for wildlife surveys. However, the scarcity of animals in the wild often leads to highly unbalanced, large datasets for which even a good detection method can return a large amount of false detections. Our objectives in this paper were to design a training method that would reduce training time, decrease the number of false positives and alleviate the fine-tuning effort of an image classifier in a context of animal surveys. We acquired two highly unbalanced datasets of deer images with a UAV and trained a Resnet-18 classifier using hard-negative mining and a series of recent techniques. Our method achieved sub-decimal false positive rates on two test sets (1 false positive per 19,162 and 213,312 negatives respectively), while training on small but relevant fractions of the data. The resulting training times were therefore significantly shorter than they would have been using the whole datasets. This high level of efficiency was achieved with little tuning effort and using simple techniques. We believe this parsimonious approach to dealing with highly unbalanced, large datasets could be particularly useful to projects with either limited resources or extremely large datasets.
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Open AccessArticle
Assessing the Potential of Artificial Intelligence (Artificial Neural Networks) in Predicting the Spatiotemporal Pattern of Wildfire-Generated PM2.5 Concentration
Geomatics 2021, 1(1), 18-33; https://doi.org/10.3390/geomatics1010003 - 11 Jan 2021
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To evaluate the health effects of wildfire smoke, it is crucial to identify reliable models, at fine spatiotemporal resolution, of exposure to wildfire-generated PM2.5. To this end, satellite-drived aerosol optical depth (AOD) measurements are widely used in exposure models, providing long
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To evaluate the health effects of wildfire smoke, it is crucial to identify reliable models, at fine spatiotemporal resolution, of exposure to wildfire-generated PM2.5. To this end, satellite-drived aerosol optical depth (AOD) measurements are widely used in exposure models, providing long and short-term PM2.5 predictions. Multiple regression models, specifically land use regression (LUR), incorporating AOD images have shown good potential for estimating long-term PM2.5 exposure, but less so for short-term predictions. In this study, we developed artificial neural networks (ANNs) and, in particular, multilayer perceptron (MLP) by integrating ground-based PM2.5 measurements with AOD images and meteorological and spatial variables. Moreover, we used spatial- and temporal-ANNs to investigate and compare the ANNs’ ability to predict different PM2.5 concentration levels caused by abrupt spatial and temporal changes in fire smoke. The study herein analyzes and compares the viability of previously established neural network approaches in predicting short-term PM2.5 exposure during the 2014–2017 wildfire seasons in the province of Alberta, Canada. The performance of ANNs is also compared to classical models, including simple correlation (PM2.5 vs. AOD) and multiple linear regression (MLR) including meteorological and land-use predictors (MET_AOD_LUR). Our study shows that ANN achieved a 15% to 113% R2 increase compared to competing models.
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Open AccessArticle
Monitoring and Mapping of Shallow Landslides in a Tropical Environment Using Persistent Scatterer Interferometry: A Case Study from the Western Ghats, India
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Geomatics 2021, 1(1), 3-17; https://doi.org/10.3390/geomatics1010002 - 29 Dec 2020
Abstract
Persistent Scatterer Interferometry (PSI) techniques are now well established and accepted for monitoring ground displacements. The presence of shallow-seated landslides, ubiquitous phenomena in the tropics, offers an opportunity to monitor and map these hazards using PSI at the regional scale. Thus, the Western
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Persistent Scatterer Interferometry (PSI) techniques are now well established and accepted for monitoring ground displacements. The presence of shallow-seated landslides, ubiquitous phenomena in the tropics, offers an opportunity to monitor and map these hazards using PSI at the regional scale. Thus, the Western Ghats of India, experiencing a tropical climate and in a topographically complex region of the world, provides an ideal study site to test the efficacy of landslide detection with PSI. The biggest challenge in using the PSI technique in tropical regions is the additional noise in data due to vegetation. In this study, we filtered these noises by utilizing the 95-percentile of the highest coherence data, which also reduced the redundancy of the PSI points. The study examined 12 landslides that occurred within one of the three temporal categories grouped as Group 1, Group 2, and Group 3, categorized in relation to PSI monitoring periods, which was also further classified into east- and west-facing landslides. The Synthetic Aperture Radar (SAR) data is in descending mode, and, therefore, the east-facing landslides are characterized by positive deformation velocity values, whereas the west-facing landslides have negative deformation values. Further, the landslide-prone areas, delineated using the conventional factor of safety (FS), were refined and mapped using PSI velocity values. The combination of PSI with the conventional FS approach helped to identify exclusive zones prone to landslides. The main aim of such an attempt is to identify critical areas in the unstable category in the map prepared using FS and prioritizing the mitigation measures, and to develop a road map for any developmental activities. The approach also helps to increase confidence in the susceptibility mapping and reduce false alarms.
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(This article belongs to the Special Issue Ground-Based, UAV, Airborne and Satellite SAR for Geosciences)
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