Potential and climate effects of large-scale rooftop photovoltaic energy deployment in northwest China's capital cities.

China's pursuit of photovoltaic (PV) power, particularly rooftop installations, addresses energy and ecological challenges, aiming to reduce basic energy consumption by 50% by 2030. The northwest region, with its solar potential, is a focal point for distributed PV growth, which has already exceeded 50% of the energy mix by 2021. This study assesses the rooftop PV potential in five northwestern capitals, finding favorable conditions such as ample space, dense populations, and high sunlight exposure. Cities like Yinchuan and Xining are highlighted for their high projected annual PV yields. Although there is a negative correlation between surface temperature and vegetation coverage, the Weather Research and Forecasting (WRF) numerical simulation found that large-scale deployment of rooftop photovoltaics will not have a significant impact on the vegetation ecology of the area. The research underscores the significant role of rooftop PV in achieving China's energy and climate goals in its northwestern urban centers.

Integrated Building Modelling Using Geomatics and GPR Techniques for Cultural Heritage Preservation: A Case Study of the Charles V Pavilion in Seville (Spain).

This paper highlights the fundamental role of integrating different geomatics and geophysical imaging technologies in understanding and preserving cultural heritage, with a focus on the Pavilion of Charles V in Seville (Spain). Using a terrestrial laser scanner, global navigation satellite system, and ground-penetrating radar, we constructed a building information modelling (BIM) system to derive comprehensive decision-making models to preserve this historical asset. These models enable the generation of virtual reconstructions, encompassing not only the building but also its subsurface, distributable as augmented reality or virtual reality online. By leveraging these technologies, the research investigates complex details of the pavilion, capturing its current structure and revealing insights into past soil compositions and potential subsurface structures. This detailed analysis empowers stakeholders to make informed decisions about conservation and management. Furthermore, transparent data sharing fosters collaboration, advancing collective understanding and practices in heritage preservation.

Advancing coastal resilience: Coastal vulnerability assessment using cutting-edge space geodetic and optical imaging techniques.

Space geodetic and optical imaging techniques are employed to assess the coastal vulnerability index (CVI) in order to adapt to the contemporary approach to coastal classification. Satellite altimeter, Satellite Pour l'Observation de la Terre (SPOT) and integrated multi-satellite retrievals for GPM (IMERG) were among the satellite data used. The variables were deemed adequate based on the spatiotemporal estimates, then were quantified through expert-weighted scores and integrated into a single index over the Terengganu coastal area. The CVI findings performed better with eight variables, showing higher overall accuracy (70.83%), Kappa coefficient (59.02%), and area under curve (AUC) (0.8) than the conventional six variables. Vulnerability rankings are distributed relatively evenly across the Terengganu coast, with the moderate (2) ranking being the most predominant at 27.2% of the total area. Space geodetic and optical imagery techniques prove highly beneficial to CVI assessment, offering a viable alternative to traditional methods, especially for broader-scale coastal management.

A Multimodal Research Approach to Assessing the Karst Structural Conditions of the Ceiling of a Cave with Palaeolithic Cave Art Paintings: Polychrome Hall at Altamira Cave (Spain).

Integrating geomatics remote sensing technologies, including 3D terrestrial laser scanning, unmanned aerial vehicles, and ground penetrating radar enables the generation of comprehensive 2D, 2.5D, and 3D documentation for caves and their surroundings. This study focuses on the Altamira Cave's karst system in Spain, resulting in a thorough 3D mapping encompassing both cave interior and exterior topography along with significant discontinuities and karst features in the vicinity. Crucially, GPR mapping confirms that primary vertical discontinuities extend from the near-surface (Upper Layer) to the base of the Polychrome layer housing prehistoric paintings. This discovery signifies direct interconnections helping with fluid exchange between the cave's interior and exterior, a groundbreaking revelation. Such fluid movement has profound implications for site conservation. The utilization of various GPR antennas corroborates the initial hypothesis regarding fluid exchanges and provides concrete proof of their occurrence. This study underscores the indispensability of integrated 3D mapping and GPR techniques for monitoring fluid dynamics within the cave. These tools are vital for safeguarding Altamira, a site of exceptional significance due to its invaluable prehistoric cave paintings.

Integrating spatially-and temporally-heterogeneous data on river network dynamics using graph theory.

The study of non-perennial streams requires extensive experimental data on the temporal evolution of surface flow presence across different nodes of channel networks. However, the consistency and homogeneity of available datasets is threatened by the empirical burden required to map stream network expansions and contractions. Here, we developed a data-driven, graph-theory framework aimed at representing the hierarchical structuring of channel network dynamics (i.e., the order of node activation/deactivation during network expansion/retraction) through a directed acyclic graph. The method enables the estimation of the configuration of the active portion of the network based on a limited number of observed nodes, and can be utilized to combine datasets with different temporal resolutions and spatial coverage. A proof-of-concept application to a seasonally-dry catchment in central Italy demonstrated the ability of the approach to reduce the empirical effort required for monitoring network dynamics and efficiently extrapolate experimental observations in space and time.

Regional thermal environment changes: Integration of satellite data and land use/land cover.

Land surface temperature (LST) is subject to location and environmental influences, which makes quantification difficult in terms of timeliness. Based on 10-d geostationary satellite LST TCI products, we quantitatively evaluated the thermal environment differentiation of various ground objects in North, South, and Northwest China from 2017 to 2021. We found that the thermal condition index (TCI) in Northwest China decreased, whereas it increased in North and South China. In contrast, Moran's I index increased in Northwest and South China, with strong spatial agglomeration. The TCI for artificial surfaces decreased from North (0.633) to Northwest (0.554) and South China (0.384). The bare land TCI was always the lowest among the land use/land cover (LULC) types in each region. Our results reflect the LULC thermal environment of China against the background of new urbanization and provide theoretical support for scientific planning to improve the ecological environment.

Reconstruction of a real-world car-to-pedestrian collision using geomatics techniques and numerical simulations.

The aim of this study is to provide an improved method for traffic accident reconstruction based on geomatics techniques and numerical simulations. A combination of various techniques was used. First, an unmanned aerial vehicle (UAV), laser scanner and structured-light scanner were used to obtain information on the accident scene, vehicle and victim. The collected traces provided detailed initial impact conditions for subsequent numerical simulations. Then, multi-body system (MBS) simulations were conducted to reconstruct the kinematics of the car-to-pedestrian collision. Finally, a finite element (FE) simulation using the THUMS model was performed to predict injuries. A real-life vehicle-pedestrian collision was used to verify the feasibility and effectiveness of this method. The reconstruction result revealed that the kinematic and injury predictions of the numerical simulations effectively conformed to the surveillance video and investigation of the actual accident. UAV photogrammetry was demonstrated to be more efficient in accident data collection than hand sketch and measurement, and 3D laser scanning enabled an easier and more accurate modeling process of vehicle. The present study shows the feasibility of this method for use in traffic accident reconstruction.

Mapping potential desertification-prone areas in North-Eastern Algeria using logistic regression model, GIS, and remote sensing techniques.

Desertification is an environmental threat that affects many countries in the world, and it poses specially an ecological issue to Algeria. This study aimed to assess areas sensitive to desertification in North-Eastern Algeria (Tebessa province) using a logistic regression model (LRM), and geomatics-based approaches. Topsoil Grain Size Index (TGSI), Normalized Difference Vegetation Index (NDVI), Aridity index (AI), and Anthropic pressure on the steppe environment (APSE) were selected as desertification indicators for representing land surface conditions from soil, vegetation, climate, and anthropic disruptors. Results indicate that both AI and TGSI are the most crucial indices conditioning desertification risk. Other indices; NDVI and ASPE were appeared as secondary important indices. Herein, although vegetation generally is a key factor for reading desertification, this result shows that vegetation changes in this study are less important than other desertification conditioning parameters. Area under curve value equal 0.94 indicates a satisfactory accuracy for the proposed model. In total, desertification risk changes increasingly along a North-to-South gradient of the whole research area. Besides, slight, moderate, high, and very high classes occupied 0.87%, 21.08%, 19.33% and 58.72% of the total land area, respectively. LRM is recommended as an accurate and easily applied tool to monitor desertification, especially in scarce data environment in developing countries. Additionally, the results obtained in this paper represent a basic scientific tool for implementing current and future policies to control desertification at areas with high risk.

Generating High-Granularity COVID-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning.

The pandemic of COVID-19 has posed unprecedented threats to healthcare systems worldwide. Great efforts were spent to fight the emergency, with the widespread use of cutting-edge technologies, especially big data analytics and AI. In this context, the present study proposes a novel combination of geographical filtering and machine learning (ML) for the development and optimization of a COVID-19 early alert system based on Emergency Medical Services (EMS) data, for the anticipated identification of outbreaks with very high granularity, up to single municipalities. The model, implemented for the region of Lombardy, Italy, showed robust performance, with an overall 80% accuracy in identifying the active spread of the disease. The further post-processing of the output was implemented to classify the territory into five risk classes, resulting in effectively anticipating the demand for interventions by EMS. This model shows state-of-art potentiality for future applications in the early detection of the burden of the impact of COVID-19, or other similar epidemics, on the healthcare system.

Geospatial Correlation Analysis between Air Pollution Indicators and Estimated Speed of COVID-19 Diffusion in the Lombardy Region (Italy).

BACKGROUND: the Lombardy region in Italy was the first area in Europe to record an outbreak of COVID-19 and one of the most affected worldwide. As this territory is strongly polluted, it was hypothesized that pollution had a role in facilitating the diffusion of the epidemic, but results are uncertain. AIM: the paper explores the effect of air pollutants in the first spread of COVID-19 in Lombardy, with a novel geomatics approach addressing the possible confounding factors, the reliability of data, the measurement of diffusion speed, and the biasing effect of the lockdown measures. METHODS AND RESULTS: all municipalities were assigned to one of five possible territorial classes (TC) according to land-use and socio-economic status, and they were grouped into districts of 100,000 residents. For each district, the speed of COVID-19 diffusion was estimated from the ambulance dispatches and related to indicators of mean concentration of air pollutants over 1, 6, and 12 months, grouping districts in the same TC. Significant exponential correlations were found for ammonia (NH3) in both prevalently agricultural (R2 = 0.565) and mildly urbanized (R2 = 0.688) areas. CONCLUSIONS: this is the first study relating COVID-19 estimated speed of diffusion with indicators of exposure to NH3. As NH3 could induce oxidative stress, its role in creating a pre-existing fragility that could have facilitated SARS-CoV-2 replication and worsening of patient conditions could be speculated.

A web-based support system for biometeorological research.

Data are the fundamental building blocks to conduct scientific studies that seek to understand natural phenomena in space and time. The notion of data processing is ubiquitous and nearly operates in any project that requires gaining insight from the data. The increasing availability of information sources, data formats and download services offered to the users, makes it difficult to reuse or exploit the potential of those new resources in multiple scientific fields. In this paper, we present a spatial extract-transform-load (spatial-ETL) approach for downloading atmospheric datasets in order to produce new biometeorological indices and expose them publicly for reuse in research studies. The technologies and processes involved in our work are clearly defined in a context where the GDAL library and the Python programming language are key elements for the development and implementation of the geoprocessing tools. Since the National Oceanic and Atmospheric Administration (NOAA) is the source of information, the ETL process is executed each time this service publishes an updated atmospheric prediction model, thus obtaining different forecasts for spatial and temporal analyses. As a result, we present a web application intended for downloading these newly created datasets after processing, and visualising interactive web maps with the outcomes resulting from a number of geoprocessing tasks. We also elaborate on all functions and technologies used for the design of those processes, with emphasis on the optimisation of the resources as implemented in cloud services.

An Agave Counting Methodology Based on Mathematical Morphology and Images Acquired through Unmanned Aerial Vehicles.

Blue agave is an important commercial crop in Mexico, and it is the main source of the traditional mexican beverage known as tequila. The variety of blue agave crop known as Tequilana Weber is a crucial element for tequila agribusiness and the agricultural economy in Mexico. The number of agave plants in the field is one of the main parameters for estimating production of tequila. In this manuscript, we describe a mathematical morphology-based algorithm that addresses the agave automatic counting task. The proposed methodology was applied to a set of real images collected using an Unmanned Aerial Vehicle equipped with a digital Red-Green-Blue (RGB) camera. The number of plants automatically identified in the collected images was compared to the number of plants counted by hand. Accuracy of the proposed algorithm depended on the size heterogeneity of plants in the field and illumination. Accuracy ranged from 0.8309 to 0.9806, and performance of the proposed algorithm was satisfactory.

Integrated Geophysics and Geomatics Surveys in the Valley of the Kings.

Recent results within the framework of the collaborative project The Complete Geophysical Survey of the Valley of the Kings (VOK) (Luxor, Egypt) are reported in this article. In October 2018, a team of geomatics and geophysics researchers coordinated by the Polytechnic University of Turin worked side by side in the VOK. Topographic measurements in support of geophysical surveys and the achievement of a very large-scale 3D map of the Eastern VOK were the two main objectives of the geomatics campaign. Innovative 3D metric technologies and methods, based on terrestrial laser scanning (both static and mobile) and close-range photogrammetry were employed by the Geomatics team. The geophysical campaign focused on the acquisition of Electrical Resistivity Tomography (ERT), Ground Penetrating Radar (GPR) and high spatial density Geomagnetic (GM) data. ERT new data around KV62, both inverted in 2D sections and added to the previous ones to perform a new global 3D inversion, confirm the previous results showing both conductive and resistive anomalies that have to be explained. GPR timeslices showed some interesting features in the area in front of the KV2 entrance where GM gradient map also presents localized anomalies. In the area SSW of the KV2 the GM gradient maps evidenced also a large semicircular anomaly which, up to now, has no explanation. The potentialities of using magnetic techniques as a complement to other non-invasive techniques in the search for structures of archeological significance have been explored. The application of modern and innovative methods of 3D metric survey enabled to achieve a complete 3D mapping of what is currently visible in the valley. The integration of 2D/3D mapping data concerning visible elements and hypothetical anomalies, together with the recovering in the same global reference system of underground documentation pertaining to the Theban Mapping Project, prefigure the enhancement of multi-temporal site representation. This strategy enables the fruition development of the already discovered archaeological heritage, using modern criteria of valorization and conservation.

An Improvement in the Identification of the Centres of Checkerboard Targets in Point Clouds Using Terrestrial Laser Scanning.

Measurement using terrestrial laser scanning is performed at several stations to measure an entire object. In order to obtain a complete and uniform point cloud, it is necessary to register each and every scan in one local or global coordinate system. One registration method is based on reference points-in this case, checkerboard targets. The aim of this research was to analyse the accuracy of checkerboard target identification and propose an algorithm to improve the accuracy of target centre identification, particularly for low-resolution and low-quality point clouds. The proposed solution is based on the geometric determination of the target centre. This work presents an outline of a new approach, designed by the author, to discuss the influence of the point cloud parameters on the process of checkerboard centre identification and to propose an improvement in target centre identification. The validation of the proposed solutions reveals that the difference between the typical automatic target identification and the proposed method amounts to a maximum of 6 mm for scans of different qualities. The proposed method may serve as an alternative to, or supplement for, checkerboard identification, particularly when the quality of these scans is not sufficient for automatic algorithms.

Deformation Analysis of a Composite Bridge during Proof Loading Using Point Cloud Processing.

Remote sensing in structural diagnostics has recently been gaining attention. These techniques allow the creation of three-dimensional projections of the measured objects, and are relatively easy to use. One of the most popular branches of remote sensing is terrestrial laser scanning. Laser scanners are fast and efficient, gathering up to one million points per second. However, the weakness of terrestrial laser scanning is the troublesome processing of point clouds. Currently, many studies deal with the subject of point cloud processing in various areas, but it seems that there are not many clear procedures that we can use in practice, which indicates that point cloud processing is one of the biggest challenges of this issue. To tackle that challenge we propose a general framework for studying the structural deformations of bridges. We performed an advanced object shape analysis of a composite foot-bridge, which is subject to spatial deformations during the proof loading process. The added value of this work is the comprehensive procedure for bridge evaluation, and adaptation of the spheres translation method procedure for use in bridge engineering. The aforementioned method is accurate for the study of structural element deformation under monotonic load. The study also includes a comparative analysis between results from the spheres translation method, a total station, and a deflectometer. The results are characterized by a high degree of convergence and reveal the highly complex state of deformation more clearly than can be concluded from other measurement methods, proving that laser scanning is a good method for examining bridge structures with several competitive advantages over mainstream measurement methods.

A Multi-Disciplinary Approach to Remote Sensing through Low-Cost UAVs.

The use of Unmanned Aerial Vehicles (UAVs) based on remote sensing has generated low cost monitoring, since the data can be acquired quickly and easily. This paper reports the experience related to agave crop analysis with a low cost UAV. The data were processed by traditional photogrammetric flow and data extraction techniques were applied to extract new layers and separate the agave plants from weeds and other elements of the environment. Our proposal combines elements of photogrammetry, computer vision, data mining, geomatics and computer science. This fusion leads to very interesting results in agave control. This paper aims to demonstrate the potential of UAV monitoring in agave crops and the importance of information processing with reliable data flow.