Search Results (21)

Infrastructure facilities that serve the city as a whole and should be considered as a whole should be built in an orderly and planned manner, just as cities are. Infrastructure facilities become obsolete over time. Aging infrastructure facilities may become unserviceable over time. When the need for maintenance and repair arises, it is mandatory to renew or replace infrastructure facilities. In this case, necessary maintenance/repair and renovation works should be completed as soon as possible. These infrastructure facilities may not be transferred to maps in the digital environment and may often be managed with person-oriented information, not institutional. There is a problem for decision makers, namely, that the construction, maintenance, repair and governance of infrastructure facilities cannot be carried out systematically, on time and effectively. The only way to provide such a service is through the combined use of today’s informatics, Geographical Information System (GIS) and Global Navigation Satellite System (GNSS) technologies, unlike the classical methods of the past. The aim of the study is to effectively manage the scarce resource of drinking water and its facilities, which are an important component of infrastructure facilities, with a method that uses current mapping technologies and informatics facilities. Especially after Infrastructure for Spatial Information (INSPIRE) and the transformation of Land Administration Domain Model (LADM) to the International Organization for Standardization (ISO) standard, Turkish National Geographic Information System (TNGIS) studies and many academic studies carried out in Türkiye have been modelled with Unified Modelling Language (UML) diagrams in accordance with LADM. Similarly, within the scope of this study, UML diagrams were prepared, and then a GIS database was established. Thanks to field workers, chiefs, engineers and others working on water pipelines, all necessary data, classic, as-built and digital, were gathered. These were collected in different ways in order to conduct spatial and non-spatial analysis in the study area of Trabzon. The most important result from the study is that the entire drinking water infrastructure of Trabzon has been transferred to the system in a structure that allows spatial queries, ensuring that damage detection on water components, maintenance and repair processes are carried out in the shortest time and at the lowest cost. The investigation and application of a sensor-integrated GIS-aided system, making it possible to control and monitor the use of lost and illegal water to be controlled as well as inform consumers who will be affected by possible maintenance and repair, is recommended. Full article

Human actions have led to consistent and profound alterations in land use, which in turn have had a notable effect on the services provided by ecosystems. In this research, the Google Earth Engine (GEE) was initially employed to perform a supervised classification of Landsat satellite images from 2000 to 2020, which allowed us to obtain land-use data for Putian City, China. Next, the geo-informatic Tupu model and the revised valuation model were used to explore the spatial attributes and ecological effects of land-use changes (LUCs). Subsequently, EEH (eco-economic harmony), ESTD (ecosystem services tradeoffs and synergies degree index), and ESDA (exploratory spatial data analysis) methods were employed to further analyze the coordination level, trade-offs, synergies, and spatial patterns of ecological-economic system development. The findings revealed that: (1) The land-use composition in Putian City was predominantly cultivated land and forest land, with other types of land intermixed. Concurrently, there was an ongoing trend of expansion in urban areas. (2) ESV in Putian City exhibited an upward trend, increasing from 15.4 billion CNY to 23.1 billion CNY from 2000 to 2020. (3) ESV exhibited an imbalance in spatial distribution, with high-high agglomeration areas concentrated in the central part of Putian City and the coastal region of Hanjiang District, while low-low agglomeration areas were prevalent in Xianyou County in the southwest, Xiuyu District along the coast, and Licheng District in the urban center. (4) Synergistic relationships among ESs predominated, though the trade-off relationship showed a tendency to expand. (5) The ecological environment and economic progress in Putian City collectively faced a region of potential risk. The findings of this study are intended to serve as a guide for improving the distribution of land resources and for developing strategies that ensure the sustainable development of the region’s socio-economic framework. Full article

With the development of agricultural and rural modernization, the informatization of rural roads has been an inevitable requirement for promoting rural revitalization. To date, however, the vast majority of road extraction methods mainly focus on urban areas and rely on very high-resolution satellite or aerial images, whose costs are not yet affordable for large-scale rural areas. Therefore, a deep learning (DL)-based super-resolution mapping (SRM) method has been considered to relieve this dilemma by using freely available Sentinel-2 imagery. However, few DL-based SRM methods are suitable due to these methods only relying on the spectral features derived from remote sensing images, which is insufficient for the complex rural road extraction task. To solve this problem, this paper proposes a spatial relationship-informed super-resolution mapping network (SRSNet) for extracting roads in rural areas which aims to generate 2.5 m fine-scale rural road maps from 10 m Sentinel-2 images. Based on the common sense that rural roads often lead to rural settlements, the method adopts a feature enhancement module to enhance the capture of road features by incorporating the relative position relation between roads and rural settlements into the model. Experimental results show that the SRSNet can effectively extract road information, with significantly better results for elongated rural roads. The intersection over union (IoU) of the mapping results is 68.9%, which is 4.7% higher than that of the method without fusing settlement features. The extracted roads show more details in the areas with strong spatial relationships between the settlements and roads. Full article

With the increasing trend towards informatization and intelligence in modern warfare, high-intensity and continuous reconnaissance activities are becoming increasingly common in battlefield environments via airborne, vehicle, UAV, satellite and other platforms. Visible and infrared images are preferred due to their high resolution, strong contrast, rich texture details and color features, and strong information expression ability. However, the quality of imaging is easily affected by environmental factors, making it crucial to quickly and accurately filter useful information from massive image data. To this end, super-resolution image preprocessing can improve the detection performance of UAV, and reduce false detection and missed detection of targets. Additionally, super-resolution reconstruction results in high-quality images that can be used to expand UAV datasets and enhance the UAV characteristics, thereby enabling the enhancement of small targets. In response to the challenge of “low-slow small” UAV targets at long distances, we propose a multi-scale fusion super-resolution reconstruction (MFSRCNN) algorithm based on the fast super-resolution reconstruction (FSRCNN) algorithm and multi-scale fusion. Our experiments confirm the feasibility of the algorithm in reconstructing detailed information of the UAV target. On average, the MFSRCNN reconstruction time is 0.028 s, with the average confidence before and after reconstruction being 80.73% and 86.59%, respectively, resulting in an average increase of 6.72%. Full article

Flood is one of the most damaging natural hazards, and timely detection of it is very important to save human lives and assess the level of damage. The occurrence of floods in cloudy weather conditions makes the use of radar-based sensors for real-time flood mapping inevitable. In the present study, the ETCI 2021 flood event detection competition dataset, organized by the NASA Advanced Concepts and Implementation Team in collaboration with the IEEE GRSS Geoscience Informatics Technical Committee, has been used. Moreover, we have utilized the U-Net and X-Net architecture as a segmentation model to map flooded regions. This study aimed to identify the optimum polarization of the Sentinel-1 satellite for flood detection. By examining and comparing the obtained results, it was observed that the VV polarization offered better results in both models. Furthermore, U-Net had a better performance than X-Net in both polarizations. Full article

A recently proposed, game-changing transformative energetics concept based on predictive synthesis and preprocessing at the nanoscale is considered as a pathway towards the development of the next generation of high-end nanoenergetic materials for future multimode solid propulsion systems and deep-space-capable small satellites. As a new door for the further performance enhancement of transformative energetic materials, we propose the predictive ion-assisted pulse-plasma-driven assembling of the various carbon-based allotropes, used as catalytic nanoadditives, by the 2D-ordered linear-chained carbon-based multicavity nanomatrices serving as functionalizing nanocarriers of multiple heteroatom clusters. The vacant functional nanocavities of the nanomatrices available for heteroatom doping, including various catalytic nanoagents, promote heat transfer enhancement within the reaction zones. We propose the innovative concept of fine-tuning the vibrational signatures, functionalities and nanoarchitectures of the mentioned nanocarriers by using the surface acoustic waves-assisted micro/nanomanipulation by the pulse-plasma growth zone combined with the data-driven carbon nanomaterials genome approach, which is a deep materials informatics-based toolkit belonging to the fourth scientific paradigm. For the predictive manipulation by the micro- and mesoscale, and the spatial distribution of the induction and energy release domains in the reaction zones, we propose the activation of the functionalizing nanocarriers, assembled by the heteroatom clusters, through the earlier proposed plasma-acoustic coupling-based technique, as well as by the Teslaphoresis force field, thus inducing the directed self-assembly of the mentioned nanocarbon-based additives and nanocarriers. Full article

Correct use of multi-temporal Interferometric Synthetic Aperture Radar (InSAR) datasets to complement geodetic surveying for geo-hazard applications requires rigorous assessment of their precision and accuracy. Published inter-comparisons are mostly limited to ground displacement estimates obtained from different algorithms belonging to the same family of InSAR approaches, either Persistent Scatterer Interferometry (PSI) or Small BAseline Subset (SBAS); and accuracy assessments are mainly focused on vertical displacements or based on few Global Navigation Satellite System (GNSS) or geodetic leveling points. To fill this demonstration gap, two years of Sentinel-1 SAR ascending and descending mode data are processed with both PSI and SBAS consolidated algorithms to extract vertical and horizontal displacement velocity datasets, whose accuracy is then assessed against a wealth of contextual geodetic data. These include permanent GNSS records, static GNSS benchmark repositioning, and geodetic leveling monitoring data that the National Institute of Statistics, Geography, and Informatics (INEGI) of Mexico collected in 2014−2016 in the Aguascalientes Valley, where structurally-controlled land subsidence exhibits fast vertical rates (up to −150 mm/year) and a non-negligible east-west component (up to ±30 mm/year). Despite the temporal constraint of the data selected, the PSI-SBAS inter-comparison reveals standard deviation of 6 mm/year and 4 mm/year for the vertical and east-west rate differences, respectively, thus reassuring about the similarity between the two types of InSAR outputs. Accuracy assessment shows that the standard deviations in vertical velocity differences are 9−10 mm/year against GNSS benchmarks, and 8 mm/year against leveling data. Relative errors are below 20% for any locations subsiding faster than −15 mm/year. Differences in east-west velocity estimates against GNSS are on average −0.1 mm/year for PSI and +0.2 mm/year for SBAS, with standard deviations of 8 mm/year. When discrepancies are found between InSAR and geodetic data, these mostly occur at benchmarks located in proximity to the main normal faults, thus falling within the same SBAS ground pixel or closer to the same PSI target, regardless of whether they are in the footwall or hanging wall of the fault. Establishing new benchmarks at higher distances from the fault traces or exploiting higher resolution SAR scenes and/or InSAR datasets may improve the detection of the benchmarks and thus consolidate the statistics of the InSAR accuracy assessments. Full article

Spatio-temporal distribution of irrigation water components was evaluated at the canal command area in Indus Basin Irrigation System (IBIS) by using a remote sensing-based geo-informatics approach. Satellite-derived MODIS product-based Surface Energy Balance Algorithm for Land (SEBAL) was used for the estimation of the actual evapotranspiration (ETa). The ground data-based advection aridity method (AA) was used to calibrate and validate the model. Statistical analysis of the SEBAL based ETa and AA shows the mean values of 87.1 mm and 47.9 mm during Kharif season (May–November) and 100 mm and 77 mm during the Rabi Season (December–April). Mean NSEs of 0.72 and 0.85 and RMSEs 34.9 and 5.76 during the Kharif and the Rabi seasons were observed for ETa and AA, respectively. Rainfall data were calibrated with the point observatory data of the metrological stations. The average annual ETa was found 899 mm for defined four cropping years (2011–2012 to 2014–2015) with the minimum average value of 63.3 mm in January and the maximum average value of 110.6 mm in August. Average of the sum of net canal water use (NCWU) and rainfall during the study period of four years was 548 mm (36% of ETa). Seasonal analysis revealed 39% and 61% of groundwater extraction proportion during Rabi and Kharif seasons, dependent upon the occurrence of rainfall and crop phenology. Overall, the results provide insight into the interrelationships between key water resources management components and the variation of these through time, offering information to improve the strategic planning and management of available water resources in this region. Full article

Precision viticulture (PV) aims to optimize vineyard management, reducing the use of resources, the environmental impact and maximizing the yield and quality of the production. New technologies as UAVs, satellites, proximal sensors and variable rate machines (VRT) are being developed and used more and more frequently in recent years thanks also to informatics systems able to read, analyze and process a huge number of data in order to give the winegrowers a decision support system (DSS) for making better decisions at the right place and time. This review presents a brief state of the art of precision viticulture technologies, focusing on monitoring tools, i.e., remote/proximal sensing, variable rate machines, robotics, DSS and the wireless sensor network. Full article

Natural resource management requires reliable and timely information available at local, regional, national, and global scales. Geo-informatics, by remote sensing, global navigation satellite systems, geographical information systems, and related technologies, provides information for natural resource management, environmental protection, and support related to sustainable development. Geo-informatics has proven to be a powerful technology for studying and monitoring natural resources as well as in generating predictive models, making it an important decision-making tool. The manuscripts included in this Special Issue focus on disciplines that advance the field of resource management in geomatics. The manuscripts showcased here provide different examples of challenges in resource management. Full article

The rapid development of industrialization and urbanization has resulted in a large amount of carbon dioxide (CO₂) emissions, which are closely related to the long-term stability of urban surface temperature and the sustainable development of cities in the future. However, there is still a lack of research on the temporal and spatial changes of CO₂ emissions in long-term series and their relationship with land surface temperature. In this study, Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) data, Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) composite data, energy consumption statistics data and nighttime land surface temperature are selected to realize the spatial informatization of long-term series CO₂ emissions in the Yangtze River Delta region, which reveals the spatial and temporal dynamic characteristics of CO₂ emissions, spatial autocorrelation distribution patterns and their impacts on nighttime land surface temperature. According to the results, CO₂ emissions in the Yangtze River Delta region show an obvious upward trend from 2000 to 2017, with an average annual growth rate of 6.26%, but the growth rate is gradually slowing down. In terms of spatial distribution, the CO₂ emissions in that region have significant regional differences. Shanghai, Suzhou and their neighboring cities are the main distribution areas with high CO₂ emissions and obvious patch distribution patterns. From the perspective of spatial trend, the areas whose CO₂ emissions are of significant growth, relatively significant growth and extremely significant growth account for 8.78%, 4.84% and 0.58%, respectively, with a spatial pattern of increase in the east and no big change in the west. From the perspective of spatial autocorrelation, the global spatial autocorrelation index of CO₂ emissions in the Yangtze River Delta region in the past 18 years has been greater than 0.66 (p < 0.01), which displays significant positive spatial autocorrelation characteristics, and the spatial agglomeration degree of CO₂ emissions continues to increase from 2000 to 2010. From 2000 to 2017, the nighttime land surface temperature in that region showed a warming trend, and the areas where CO₂ emissions are positively correlated with nighttime land surface temperature account for 88.98%. The increased CO₂ emissions lead to, to a large extent, the rise of nighttime land surface temperature. The research results have important theoretical and practical significance for the Yangtze River Delta region to formulate a regional emission reduction strategy. Full article

The detection, quantification, diagnosis, and identification of plant diseases is particularly crucial for precision agriculture. Recently, traditional visual assessment technology has not been able to meet the needs of precision agricultural informatization development, and hyperspectral technology, as a typical type of non-invasive technology, has received increasing attention. On the basis of simply describing the types of pathogens and host–pathogen interaction processes, this review expounds the great advantages of hyperspectral technologies in plant disease detection. Then, in the process of describing the hyperspectral disease analysis steps, the articles, algorithms, and methods from disease detection to qualitative and quantitative evaluation are mainly summarizing. Additionally, according to the discussion of the current major problems in plant disease detection with hyperspectral technologies, we propose that different pathogens’ identification, biotic and abiotic stresses discrimination, plant disease early warning, and satellite-based hyperspectral technology are the primary challenges and pave the way for a targeted response. Full article

Satellite observation is one of the main methods used to monitor the global distribution and variation of atmospheric carbon dioxide (CO₂). Several CO₂ monitoring satellites have been successfully launched, including Japan’s Greenhouse Gases Observing SATellite (GOSAT), the USA’s Orbiting Carbon Observatory-2 (OCO-2), and China’s Carbon Dioxide Observation Satellite Mission (TanSat). Satellite observation targeting the ground-based Fourier transform spectrometer (FTS) station is the most effective technique for validating satellite CO₂ measurement precision. In this study, the coincident observations from TanSat and ground-based FTS were performed numerous times in Beijing under a clear sky. The column-averaged dry-air mole fraction of carbon dioxide (XCO₂) obtained from TanSat was retrieved by the Department for Eco-Environmental Informatics (DEEI) of China’s State Key Laboratory of Resources and Environmental Information System based on a full physical model. The comparison and validation of the TanSat target mode observations revealed that the average of the XCO₂ bias between TanSat retrievals and ground-based FTS measurements was 2.62 ppm, with a standard deviation (SD) of the mean difference of 1.41 ppm, which met the accuracy standard of 1% required by the mission tasks. With bias correction, the mean absolute error (MAE) improved to 1.11 ppm and the SD of the mean difference fell to 1.35 ppm. We compared simultaneous observations from GOSAT and OCO-2 Level 2 (L2) bias-corrected products within a ±1° latitude and longitude box centered at the ground-based FTS station in Beijing. The results indicated that measurements from GOSAT and OCO-2 were 1.8 ppm and 1.76 ppm higher than the FTS measurements on 20 June 2018, on which the daily observation bias of the TanSat XOC₂ results was 1.87 ppm. These validation efforts have proven that TanSat can measure XCO₂ effectively. In addition, the DEEI-retrieved XCO₂ results agreed well with measurements from GOSAT, OCO-2, and the Beijing ground-based FTS. Full article

This study aims to identify the vulnerable landscape areas using landslide frequency ratio and land-use change associated soil erosion hazard by employing geo-informatics techniques and the revised universal soil loss equation (RUSLE) model. Required datasets were collected from multiple sources, such as multi-temporal Landsat images, soil data, rainfall data, land-use land-cover (LULC) maps, topographic maps, and details of the past landslide incidents. Landsat satellite images from 2000, 2010, and 2019 were used to assess the land-use change. Geospatial input data on rainfall, soil type, terrain characteristics, and land cover were employed for soil erosion hazard classification and mapping. Landscape vulnerability was examined on the basis of land-use change, erosion hazard class, and landslide frequency ratio. Then the erodible hazard areas were identified and prioritized at the scale of river distribution zones. The image analysis of Sabaragamuwa Province in Sri Lanka from 2000 to 2019 indicates a significant increase in cropping areas (17.96%) and urban areas (3.07%), whereas less dense forest and dense forest coverage are significantly reduced (14.18% and 6.46%, respectively). The average annual soil erosion rate increased from 14.56 to 15.53 t/ha/year from year 2000 to 2019. The highest landslide frequency ratios are found in the less dense forest area and cropping area, and were identified as more prone to future landslides. The river distribution zones Athtanagalu Oya (A-2), Kalani River-south (A-3), and Kalani River- north (A-9), were identified as immediate priority areas for soil conservation. Full article

Currently, it is possible to access a large amount of satellite weather information from monitoring and forecasting severe storms. However, there are no methods of employing satellite images that can improve real-time early warning systems in different regions of Mexico. The auto-estimator is the most commonly used technique that was developed for specific locations in the United States of America (32°–49° latitude) for the type of convective storms. However, the estimation of precipitation intensities for meteorological conditions in tropic latitudes, using the auto-estimator technique, needs to be re-adjusted and calibrated. It is necessary to improve this type of technique that allows decision-makers to have hydro-informatic tools capable of improving early warning systems in tropical regions (15°–25° Mexican tropic latitude). The main objective of the work is to estimate rainfall from satellite imagery in the infrared (IR) spectrum from the Geostationary Operational Environmental Satellite (GOES), validating these estimates with a network of surface rain gauges. Using the GOES-13 IR images every 15 min and using the auto-estimator, a downscaling of six hurricanes was performed from which surface precipitation events were measured. The two main difficulties were to match the satellite images taken every 15 min with the surface data measured every 10 min and to develop a program in C+ that would allow the systematic analysis of the images. The results of this work allow us to get a new adjustment of coefficients in a new equation of the auto-estimator, valid for rain produced by hurricanes, something that has not been done until now. Although no universal relationship has been found for hurricane rainfall, it is evident that the original formula of the auto-estimator technique needs to be modified according to geographical latitude. Full article