Latest articles in this journal
Geomatics, Volume 3, pp 174-187; https://doi.org/10.3390/geomatics3010009
In ship detection by means of Polarimetric SAR imagery, a very promising feature is the characterization of the pixels of the ship based on the elementary scattering mechanisms that can be extracted using different decomposition algorithms. Elementary scattering mechanisms provide information regarding the physical, electrical and geometrical properties of the scatterers in each Polarimetric SAR pixel. In this work, the newly established algorithm of the Double Scatterer Model is applied to interpret each pixel of the Polarimetric SAR image with the contributions of two elementary scattering mechanisms, namely, primary and secondary. The main idea is to construct a binary image while preserving the rich information content in order to proceed in simple and fast image processing for target detection. The present algorithm is applied to datasets with different inherent characteristics acquired by Radarsat-2 and ALOS-PALSAR. The results presented by this new perspective on ship monitoring are remarkable.
Geomatics, Volume 3, pp 156-173; https://doi.org/10.3390/geomatics3010008
Night-time light (NTL) data have been widely used as a remote proxy for the economic performance of regions. The use of these data is more advantageous than the traditional census approach is due to its timeliness, low cost, and comparability between regions and countries. Several recent studies have explored monthly NTL composites produced by the Visible Infrared Imaging Radiometer Suite (VIIRS) and revealed a dimming of the light in some countries during the national lockdowns due to the COVID-19 pandemic. Here, we explicitly tested the extent to which the observed decrease in the amount of NTL is associated with the economic recession at the subnational level. Specifically, we explore how the association between Gross Domestic Product (GDP) and the amount of NTL is modulated by the pandemic and whether NTL data can still serve as a sufficiently reliable proxy for the economic performance of regions even during stressful pandemic periods. For this reason, we use the states of the US and quarterly periods within 2014–2021 as a case study. We start with building a linear mixed effects model linking the state-level quarterly GDPs with the corresponding pre-processed NTL data, additionally controlling only for a long-term trends and seasonal fluctuations. We intentionally do not include other socio-economic predictors, such as population density and structure, in the model, aiming to observe the ‘pure’ explanatory potential of NTL. As it is built only for the pre-COVID-19 period, this model demonstrates a rather good performance, with R2 = 0.60, while its extension across the whole period (2014–2021) leads to a considerable worsening of this (R2 = 0.42), suggesting that not accounting for the COVID-19 phenomenon substantially weakens the ‘natural’ GDP–NTL association. At the same time, the model’s enrichment with COVID-19 dummies restores the model fit to R2 = 0.62. As a plausible application, we estimated the state-level economic losses by comparing actual GDPs in the pandemic period with the corresponding predictions generated by the pre-COVID-19 model. The states’ vulnerability to the crisis varied from ~8 to ~18% (measured as a fraction of the pre-pandemic GDP level in the 4th quarter of 2019), with the largest losses being observed in states with a relatively low pre-pandemic GDP per capita, a low number of remote jobs, and a higher minority ratio.
Geomatics, Volume 3, pp 137-155; https://doi.org/10.3390/geomatics3010007
Deep learning approaches are gaining popularity in image feature analysis and in attaining state-of-the-art performances in scene classification of remote sensing imagery. This article presents a comprehensive review of the developments of various computer vision methods in remote sensing. There is currently an increase of remote sensing datasets with diverse scene semantics; this renders computer vision methods challenging to characterize the scene images for accurate scene classification effectively. This paper presents technology breakthroughs in deep learning and discusses their artificial intelligence open-source software implementation framework capabilities. Further, this paper discusses the open gaps/opportunities that need to be addressed by remote sensing communities.
Geomatics, Volume 3, pp 115-136; https://doi.org/10.3390/geomatics3010006
Wheat is an important staple crop in the global food chain. The production of wheat in many regions is constrained by the lack of use of advanced technologies for wheat monitoring. Unmanned Aerial Vehicles (UAVs) is an important platform in remote sensing for providing near real-time farm-scale information. This information aids in making recommendations for monitoring and improving crop management to ensure food security. This study appraised global scientific research trends on wheat and UAV studies between 2005 and 2021, using a bibliometric method. The 398 published documents were mined from Web of Science, Scopus, and Dimensions. Results showed that an annual growth rate of 23.94% indicates an increase of global research based on wheat and UAVs for the surveyed period. The results revealed that China and USA were ranked as the top most productive countries, and thus their dominance in UAVs extensive usage and research developments for wheat monitoring during the study period. Additionally, results showed a low countries research collaboration prevalent trend, with only China and Australia managing multiple country publications. Thus, most of the wheat- and UAV-related studies were based on intra-country publications. Moreover, the results showed top publishing journals, top cited documents, Zipf’s law authors keywords co-occurrence network, thematic evolution, and spatial distribution map with the lack of research outputs from Southern Hemisphere. The findings of also show that “UAV” is fundamental in all keywords with the largest significant appearance in the field. This connotes that UAV efficiency was important for most studies that were monitoring wheat and provided vital information on spatiotemporal changes and variability for crop management. Findings from this study may be useful in policy-making decisions related to the adoption and subsidizing of UAV operations for different crop management strategies designed to enhance crop yield and the direction of future studies.
Geomatics, Volume 3, pp 93-114; https://doi.org/10.3390/geomatics3010005
Landform classification is crucial for a host of applications that include geomorphological, soil mapping, radiative and gravity-controlled processes. Due to the complexity and rapid developments in the field of landform delineation, this study provides a scoping review to identify trends in the field. The review is premised on the PRISMA standard and is aimed to respond to the research questions pertaining to the global distribution of landform studies, methods used, datasets, analysis units and validation techniques. The articles were screened based on relevance and subject matter of which a total of 59 articles were selected for a full review. The parameters relating to where studies were conducted, datasets, methods of analysis, units of analysis, scale and validation approaches were collated and summarized. The study found that studies were predominantly conducted in Europe, South and East Asia and North America. Not many studies were found that were conducted in South America and the African region. The review revealed that locally sourced, very high-resolution digital elevation model ( DEM) products were becoming more readily available and employed for landform classification research. Of the globally available DEM sources, the SRTM still remains the most commonly used dataset in the field. Most landform delineation studies are based on expert knowledge. While object-based analysis is gaining momentum recently, pixel-based analysis is common and is also growing. Whereas validation techniques appeared to be mainly based on expert knowledge, most studies did not report on validation techniques. These results suggest that a systematic review of landform delineation may be necessary. Other aspects that may require investigation include a comparison of different DEMs for landform delineation, exploring more object-based studies, probing the value of quantitative validation approaches and data-driven analysis methods.
Geomatics, Volume 3, pp 70-92; https://doi.org/10.3390/geomatics3010004
Fruit-tree crops generate food and income for local households and contribute to South Africa’s gross domestic product. Timely and accurate phenotyping of fruit-tree crops is essential for innovating and achieving precision agriculture in the horticulture industry. Traditional methods for fruit-tree crop classification are time-consuming, costly, and often impossible to use for mapping heterogeneous horticulture systems. The application of remote sensing in smallholder agricultural landscapes is more promising. However, intercropping systems coupled with the presence of dispersed small agricultural fields that are characterized by common and uncommon crop types result in imbalanced samples, which may limit conventionally applied classification methods for phenotyping. This study assessed the influence of balanced and imbalanced multi-class distribution and data-sampling techniques on fruit-tree crop detection accuracy. Seven data samples were used as input to adaptive boosting (AdaBoost), gradient boosting (GB), random forest (RF), support vector machine (SVM), and eXtreme gradient boost (XGBoost) machine learning algorithms. A pixel-based approach was applied using Sentinel-2 (S2). The SVM algorithm produced the highest classification accuracy of 71%, compared with AdaBoost (67%), RF (65%), XGBoost (63%), and GB (62%), respectively. Individually, the majority of the crop types were classified with an F1 score of between 60% and 100%. In addition, the study assessed the effect of size and ratio of class imbalance in the training datasets on algorithms’ sensitiveness and stability. The results show that the highest classification accuracy of 71% could be achieved from an imbalanced training dataset containing only 60% of the original dataset. The results also showed that S2 data could be successfully used to map fruit-tree crops and provide valuable information for subtropical crop management and precision agriculture in heterogeneous horticultural landscapes.
Geomatics, Volume 3, pp 68-69; https://doi.org/10.3390/geomatics3010003
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Geomatics, Volume 3, pp 47-67; https://doi.org/10.3390/geomatics3010002
We demonstrate the application of geomatics tools (remote sensing and geographic information systems) for spatial data analysis to determine potential locations for wind and solar photovoltaic (PV) energy plants in the Central North region of Namibia. In accordance with sustainable development goal 7 (affordable and clean energy) and goal 13 (climate action), the Namibian government has committed to reducing reliance on fossil fuels. In support of this, suitable locations for renewable energy plants need to be identified. Using multi-criteria decision-making and the analytical hierarchy process, sites were selected considering topographical, economic, climatic, and environmental factors. It was found that the highest potential for solar PV energy plants is in the northwest, southwest, and southern regions of the study area, whereas only the northwest region is highly suitable for wind power plants. These results were substantiated by comparison with global suitability maps, with some differences due to the datasets used. The findings can be used as a guide by governments, commercial investors, and other stakeholders to determine prospective sites for the development of renewable energy in Central North Namibia.
Geomatics, Volume 3, pp 1-46; https://doi.org/10.3390/geomatics3010001
A variety of positioning systems have emerged for indoor localization which are based on several system strategies, location methods, and technologies while using different signals, such as radio frequency (RF) signals. Demands regarding positioning in terms of performance, robustness, availability and positioning accuracies are increasing. The overall goal of indoor positioning is to provide GNSS-like functionality in places where GNSS signals are not available. Analysis of the state-of-the-art indicates that although a lot of work is being done to combine both the outdoor and indoor positioning systems, there are still many problems and challenges to be solved. Most people moving on the city streets and interiors of public facilities have a smartphone, and most professionals working in public facilities or construction sites are equipped with tablets or smartphone devices. If users already have the necessary equipment, they should be provided with further functionalities that will help them in day-to-day life and work. In this review study, user requirements and the state-of-the-art in system development for smartphone localization are discussed. In particular, localization with current and upcoming ‘signals-of-opportunity’ (SoP) for use in mobile devices is the main focus of this paper.
Geomatics, Volume 2, pp 540-553; https://doi.org/10.3390/geomatics2040029
In the world of seafloor mapping, the ability to explore and experiment with a dataset in its raw and processed forms is critical. Kluster is an open-source multibeam data processing software package written in Python that enables this exploration. Kluster provides a suite of multibeam processing features, including analysis, visualization, gridding, and data cleaning. We demonstrated these features using a recently acquired dataset from a Kongsberg EM712 multibeam echosounder aboard NOAA Ship Fairweather. This test dataset served to illustrate the fundamental analysis abilities of the software, as well as its utility as a troubleshooting tool both in the field and during post-processing. Kluster has the capability to perform the Sonar Acceptance Test in full, including common experiments like the patch test, extinction test, and accuracy test, which are generally performed on new systems. When questions arise regarding the integration or parameter settings of a system, this software allows the user to quickly and clearly visualize much of the raw data and its associated metadata, which is a vital step in any investigative effort. With its emphasis on accessibility and ease of use, Kluster is an excellent tool for users who are inexperienced with multibeam sonar data processing.