Massive OceanColor Data Processing and Analysis System: TuPiX-OC

The amount and heterogeneity of data in biomedical research, notably in transnational research, requires new methods for the collection, presentation and analysis of information. Important data from laboratory experiments as well as patient trials are available as images. Thus, the integration and processing of image data represent a crucial component of information systems in biomedical research. The Charite Medical School in Berlin has established a new information service center for kidney diseases and transplantation (Open European Nephrology Science Centre - OpEN.SC) together with the German Research Agency (DFG). The aims of this project are (i) to improve the availability of raw data, (ii) to establish an infrastructure for clinical trials, (iii) to monitor the occurrence of rare disease patterns and (iv) to establish a quality assurance system. Major diagnostic procedures in medicine are based on the processing and analysis of image data. In diagnostic pathology, the availability of automated slide scanners provide the opportunity to digitize entire microscopic slides. The processing, presentation and analysis of these image data are called virtual microscopy. The integration of this new technology into the OpEN.SC system and the link to other heterogeneous data of individual patients represent a major technological challenge. Thus, new ways in communication between clinical and scientific partners have to be established and will be promoted by the project. The technological basis of the repository are web services for a scalable and adaptable system. HL7 and DICOM are considered the main medical standards of communication.

Structures of macromolecules in their native state provide unique unambiguous insights into their functions. Cryo-electron tomography combined with subtomogram averaging demonstrated the power to solve such structures in situ at resolutions in the range of 3 Angstrom for some macromolecules. In order to be applicable to the structural determination of the majority of macromolecules observable in cells in limited amounts, processing of tomographic data has to be performed in a high-throughput manner. Here we present TomoBEAR—a modular configurable workflow engine for streamlined processing of cryo-electron tomographic data for subtomogram averaging. TomoBEAR combines commonly used cryo-EM packages with reasonable presets to provide a transparent (“white box”) approach for data management and processing. We demonstrate applications of TomoBEAR to two data sets of purified macromolecular targets, to an ion channel RyR1 in a membrane, and the tomograms of plasma FIB-milled lamellae and demonstrate the ability to produce high-resolution structures. TomoBEAR speeds up data processing, minimizes human interventions, and will help accelerate the adoption of in situ structural biology by cryo-ET. The source code and the documentation are freely available.

Spot panchromatic satellite image had been employed to study and know the difference Between ground and satellite data( DN ,its values varies from 0-255) where it is necessary to convert these DN values to absolute radiance values through special equations ,later it converted to spectral reflectance values .In this study a monitoring of the environmental effect resulted from throwing the sewage drainages pollutants (industrial and home) into the Tigris river water in Mosul, was achieved, which have an effect mostly on physical characters specially color and turbidity which lead to the variation in Spectral Reflectance of the river water ,and it could be detected by using many remote sensing techniques. The contaminated areas within the water of the river which represents the difference in the reflectance values were isolated and signed, as well as the field estimations, which had been done by using spectrometer device, which gave an acceptable agreement with satellite data considering time difference between these estimations. satellite imagery analysis program ERDAS version 8.4 was used to determine the values of Spectral Reflectance in the satellite image. A geographic information systems through the ARC INFO has been used to draw photo map of the study area determined it specific sites of measuring the Reflectance, which represent areas that are near the sources of pollution and the other various regions along the river.

In flooding, dry land capable of residential, agricultural, and other economic activities is submerged by overflowing water. This causes loss of lives, properties, and destruction of infrastructure. This study applies remote sensing and GIS techniques to produce a flood vulnerability map of the Akure South metropolis. For this study, satellite image data (Landsat 8), location map of Akure South metropolis, SRTM DEM, rainfall data, soil data, and GPS coordinates; acquired during the field survey were integrated to map areas vulnerable to flooding. Using Pairwise Comparison, the various weights of factors constituting flood in the area were acquired. A weighted linear combination and analytical hierarchical process were used to produce the flood hazard and flood vulnerability map. ArcGIS Pro 2.7.3 software was used in spatial and attribute data acquisition, processing, and information presentation. The flood vulnerability results indicated that the very high vulnerability zone occupied 13.9% of the study area, while high vulnerability zone occupied 25.5%. Moderate vulnerability zone occupied 36.8% while low vulnerability zone occupied 23.8% of the study area. The study shows that, remote sensing and GIS can be effectively implemented to analyse and provide results on flood vulnerability required for prompt and effective decision-making on floods.

This research was conducted to investigate abrasion-prone areas using the Vertical Electrical Sounding (VES) method at Pasar Bawah Manna Beach, South Bengkulu Regency. Data collection using a track length of 200 with the MAE X612-EM geoelectric tool that runs along the abrasion-prone area. Data processing using IP2WIN software and satellite images. This study aims to determine the subsurface of abrasion-prone areas and rock characteristics of abrasion. The results of this study indicate that the research location has seawater ntrusion that seeps into the aquifer with a resistivity value of (0.763 - 8.01 Ωm) and the rock formation of the research area is dominated by alluvium rocks including gravel (130 - 356. 2 Ωm), clay (14.65 Ωm), silt (16.5 -61.41 Ωm), sand (258 - 313 Ωm), sandstone (97.6 Ωm), passive gravel (1496 Ωm), and limestone (2560 Ωm). Based on satellite image data, abrasion-prone is identified from several points in the study area that have significant shoreline changes at sounding point 1 where the point from 2008 to 2023 shows that abrasion has occurred quite significantly over the past 15 years, where at Sounding point 1 abrasion occurs along ± 23 meters / year. The findings of this research are expected to provide knowledge about disaster mitigation in sustainable tourism governance.

The article explores the current trends, challenges, and prospects of automated mapping in land management, focusing on the increasing demand for high-precision, up-to-date cadastral information. Traditional cartographic methods, based on manual data processing and analog technologies, are labor-intensive and time-consuming, often leading to inconsistencies and inaccuracies. Automated mapping is an important direction in the development of modern geographic information systems and technologies in land management. The introduction of automated methods allows to significantly increase the productivity, accuracy and reliability of cartographic work, as well as to ensure the prompt updating of cartographic information. The study emphasizes the advantages of automated mapping through the integration of modern geoinformation technologies, including remote sensing (RS), global navigation satellite systems (GNSS), geographic information systems (GIS), and unmanned aerial vehicles (UAVs). These technologies significantly enhance the efficiency, accuracy, and reliability of spatial data collection, processing, and visualization. The article reviews recent scientific studies on the application of automated methods for processing remote sensing data, including aerial and satellite imagery, in the creation and updating of cartographic materials. Special attention is given to the use of UAVs for high-precision orthophoto mapping and 3D terrain modeling. The research also highlights the importance of ensuring data compatibility and consistency between various sources, such as cadastral registers, GPS measurements, and geoinformation resources. Furthermore, the study addresses the regulatory and legal aspects of automated mapping in land management, analyzing key legislative acts and government regulations in Ukraine that define the standards for cadastral mapping and land use planning. It discusses the necessity of implementing automated data validation and quality control procedures to improve the credibility of cartographic information. The article concludes by identifying key benefits of automated mapping, including increased accuracy, reduced costs, faster data processing, and improved decision-making in land management. It provides recommendations for the integration of advanced geoinformation technologies in land administration, ensuring the timely and reliable update of cadastral data, optimizing land resource management, and supporting sustainable land use planning.

This work is devoted to the study and analysis of the Eastern Anti-Atlass drainage networks, its characteristics, distribution and arrangement in relation with geological and geomorphological nature of this area. Its a multi-source approach based on several types of data, including Landsat 8 satellite imagery and 30 meters resolution Digital Elevation Model (DEM) generated from Terra-Aster images, allowing the identification and mapping of geological structures and the automatic extraction of the drainage network. Data processing in GIS allowed to extract several density thresholds of drainage network in order to identify the most representative one to the climate and surface of the study area. The analyzes were carried out on the distribution of the drainage network and faults density, the different drainage patterns and their rose diagrams of directions.

Since 2012 red tides have been increasing in frequency and intensity along the Pacific coast of Costa Rica with substantial impact in eco-tourism, mariculture, and the environment. In 2022, only in Papagayo Peninsula, a coastal eco-tourism hotspot, a total economic loss of $1 million was estimated due to severe red tides happening in the Gulf of Papagayo. Additionally, corals, fish, and marine marketable products died, and shellfish consumption was banned, negatively affecting sales and livelihoods of mariculture producers and fishermen. These red tide events happening in 2022, triggered the MAReS project (Monitoring Algae blooms using Remote Sensing), a two-year pilot project funded by the Dutch government’s Partners for Water program.The consortium that launched the MAReS project was formed through an interdisciplinary approach, driven by the goal of mitigating the impacts of red tides on ecosystems and the productive activities dependent on marine and coastal resources. MAReS aims to develop a near-real-time geospatial monitoring service to support timely management and mitigation of red tides in the Costa Rican Pacific, especially in protected areas. This service uses optical satellite data such as the sea watercolor, sea water temperature, and chlorophyll-a, which is calibrated and validated with local measurements, then processed into maps and early red tide alerts, providing key information on the intensity and location of red tides. Combining satellite data with in-situ measurements enhances the accuracy of detection and alert systems. Local measurements include optical monitoring equipment, laboratory water analysis of chlorophyll-a, phytoplankton and citizen observations of watercolor.MAReS seeks to improve information on the location and intensity of red tides so that: (a) governments can warn against unsafe areas for shellfish collection and consumption to prevent poisoning; (b) environmental organizations can protect marine species; (c) eco-tourism operators know where to conduct activities like diving or sport fishing; (d) the fishing industry can identify optimal areas, avoiding unnecessary fuel costs; (e) mariculture operations can relocate or harvest early; and (f) insurers can offer index-based risk insurance.The main MAReS results are: over 10,000 km² of the sea monitored with remote sensing technology for potential red tides occurrence in the Pacific Ocean, Costa Rica; over 100 km² of the sea sampled for potential red tides occurrence in the Papagayo Gulf,; installation and operation of an optical monitoring station to monitor the occurrence of red tides in Culebra Bay; in situ monitoring of physic- chemical and biological parameters, phytoplankton characterization and business model development based on annual subscriptions. MAReS provides accurate and timely information on the location and intensity of red tides and changes in water quality, enabling governments, eco-tourism operators, and the fishing industry to make informed decisions and mitigate the negative impacts of this phenomenon. Through interdisciplinary and transdisciplinary efforts, the MAReS project represents a unique and innovative approach. From an interdisciplinary perspective, this project is invaluable as it engages communities dependent on marine resources. Understanding the occurrence and detection of red tides is essential for early warning mechanisms for these communities, productive activities and environmental preservation.

Modelling species distributions with remote sensing data: bridging disciplinary perspectives

(1988). Processing and analysis of commercial satellite image data of the nuclear accident near Chernobyl U.S.S.R. (U.S. geological survey bulletin 1785) Geocarto International: Vol. 3, No. 3, pp. 71-71.

The fusion of unmanned aerial system (UAS) and satellite imagery has emerged as a pivotal strategy in advancing precision agriculture. This review explores the significance of integrating high-resolution UAS and satellite imagery via pixel-based, feature-based, and decision-based fusion methods. The study investigates optimization techniques, spectral synergy, temporal strategies, and challenges in data fusion, presenting transformative insights such as enhanced biomass estimation through UAS-satellite synergy, improved nitrogen stress detection in maize, and refined crop type mapping using multi-temporal fusion. The combined spectral information from UAS and satellite sources confirms instrumental in crop monitoring and biomass estimation. Temporal optimization strategies consider factors such as crop phenology, spatial resolution, and budget constraints, offering effective and continuous monitoring solutions. The review systematically addresses challenges in spatial and temporal resolutions, radiometric calibration, data synchronization, and processing techniques, providing practical solutions. Integrated UAS and satellite data impact precision agriculture, contributing to improved resolution, monitoring capabilities, resource allocation, and crop performance evaluation. A comparative analysis underscores the superiority of combined data, particularly for specific crops and scenarios. Researchers exhibit a preference for pixel-based fusion methods, aligning fusion goals with specific needs. The findings contribute to the evolving landscape of precision agriculture, suggesting avenues for future research and reinforcing the field’s dynamism and relevance. Future works should delve into advanced fusion methodologies, incorporating machine learning algorithms, and conduct cross-crop application studies to broaden applicability and tailor insights for specific crops.

The complex action of environmental factors often triggers the biomass formation in forest plantations, which is crucial for carbon balance and environmental monitoring, especially in the context of climate change. In this article, we present data on the aboveground biomass accumulation for black locust and common pine (Pinus sylvestris and Robinia pseudo­acacia) as the two most common forest-forming species in the steppe zone. For this purpose, we propose a reliable approach to monitoring of aboveground forest biomass with combining Sentinel-2 multispectral imaging techniques (with L-band) and biometric processing data from coniferous and deciduous stands obtained from field surveys. We represent the results of field surveys with established indicators of aboveground biomass of forest plantations in the field experiment, which averaged 159.9 ± 9.0 t/ha in the studied region. The biometric indexes obtained from the field experiments were used to develop models for predicting biomass using the remote method. Based on the processing of satellite image data, forest vegetation indices were analysed, among which the NDVI (normalized difference vegetation index) was the best predictor to assess biomass. The multiple regression method was found to be the best for predicting and mapping the aboveground biomass in P. sylvestris and R. pseudoacacia within the studied area (RMSE – 23.46 t/ha). Based on the results obtained, we created a map of the aboveground biomass distribution in black locust and common pine stands within the studied region. We established reliable correlations between biometric parameters (mean diameter at breast height, mean height) and aboveground biomass of stands with indicators of spectral bands in satellite images. This enables us to use the constructed models to estimate the overall productivity of coniferous and deciduous forest stands for large areas.

In this paper, we develop an alternative navigation system for Unmanned Aerial Vehicle (UAV) in Global Positioning Systems (GPS)-denied environment. We use two image inputs, one is acquired with an on-board camera placed on the UAV (which is the large-area image) and the other is from satellite images (which is small known image) with GPS information. We use a convolutional neural network (CNN) architecture based on Oxford’s Visual Geometry Group network (VGG-16) and utilize normalized variant mutual information between these two images to obtain position of the UAV. Satellite images are labelled and given to the UAV. When GPS information is lost, our algorithm starts to function and images from UAV camera are searched whether satellite image is seen by cameras on UAV image or not. If the UAV is in that area, our algorithm finds the GPS information from satellite image data.

Image is widely used in modern life which produces huge amounts of data and that required to store them with minimized space. Image compression is the mechanism by which a digital image is effectively compressed to minimize the number of bits used to display an image. The images take huge space in the computers or servers, hence a long time during transmission. This paper provides an enhanced compression method to reduce the store size and the cost of transmission using a fixed predictive scheme. Predictive coding eliminates spatial redundancy between successive neighboring pixels since only a few information outcomes have to be encoded. This paper suggested an enhanced method to improve image compression of Satellite Images without missing data by using a static structure (predictions) that compresses image data without loss based on the fixed prediction technique. The Satellite Images data was used to test the enhanced method by using six Satellite Images. The achieved results were very promising conducting a compression ratio up to 79.35%. The SNR/PSNR was tested to check the quality of the compressed images with a value =38.30.

Artificial intelligence-based anomaly detection of the Assen iron deposit in South Africa using remote sensing data from the Landsat-8 Operational Land Imager