scholarly journals Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite Images

Information ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 321
Author(s):  
Vasilis Kopsachilis ◽  
Lucia Siciliani ◽  
Marco Polignano ◽  
Pol Kolokoussis ◽  
Michail Vaitis ◽  
...  

Scientists in the marine domain process satellite images in order to extract information that can be used for monitoring, understanding, and forecasting of marine phenomena, such as turbidity, algal blooms and oil spills. The growing need for effective retrieval of related information has motivated the adoption of semantically aware strategies on satellite images with different spatio-temporal and spectral characteristics. A big issue of these approaches is the lack of coincidence between the information that can be extracted from the visual data and the interpretation that the same data have for a user in a given situation. In this work, we bridge this semantic gap by connecting the quantitative elements of the Earth Observation satellite images with the qualitative information, modelling this knowledge in a marine phenomena ontology and developing a question answering mechanism based on natural language that enables the retrieval of the most appropriate data for each user’s needs. The main objective of the presented methodology is to realize the content-based search of Earth Observation images related to the marine application domain on an application-specific basis that can answer queries such as “Find oil spills that occurred this year in the Adriatic Sea”.


2016 ◽  
Vol 17 (1-2) ◽  
pp. 22-30
Author(s):  
S. G. Chornyy ◽  
D. A. Abramov

For rational use of soils it is necessary to possess exact information on soil properties. The traditional methods of monitoring of soils and (or) their separate properties based on local, one-time supervision don’t give an adequate assessment of a current state of a soil cover it should be noted. Transition to spatio-temporal methods with use of modern geoinformation and space technologies is necessary. Remote satellite methods of soil monitoring gain fast distribution, owing to the efficiency, a certain objectivism and relative low cost now, and also because of unique opportunities of one-time coverage by the images received from big height, enough territories, big on the area. For the development of remote monitoring chernozems southern used materials of multispectral scanning multispectral camera ETM + ( «Enhanced Thematic Mapper Plus»), which is on board the satellite «Landsat-7» (data of 2006, 2010, 2012) and OLI («Operational Land Imager»), which is on board the satellite «Landsat-8»(data 2015). The information obtained from them is unified from the point of view of preservation of geometry, calibration, a covering, spectral characteristics, quality of the image and availability of data, despite various carriers of devices ETM+ and OLI. The composite image which has been received from three cloudless satellite images of spring of 2012 (three terms of shooting – 21.04, 30.04, 05.05) has allowed to make the correlation analysis of extent of influence of maintenance of organic matter in a layer of soil of 0–10 cm at a brightness with various spectral channels of the camera ETM+. Such analysis has shown that the closest connection exists between the content of soil organic matter and brightness of the second (green), the third (red) and the fourth (the neighbor infrared) spectral channels. From them three, the greatest value of correlation has dependence between the content of soil organic matter (humus) and brightness of the red spectral channel (r=-0,32). For the purpose of spatio-temporal interpretation of the equation of multiple regressions, 20 agro landscapes in different parts of the Right-bank steppe of Ukraine (The Mykolayiv district and Zhovtnevy district of the Mykolayiv oblast) have been selected. For each agro landscapes was defined content of soil organic matter in the soil using Landsat 7 satellite images taken in 2006 and in 2010 and Landsat images 8 for 2015. The made estimates of maintenance of soil organic matter have shown on processes of fast loss of humus in all layers of soil. Annual losses of soil organic matter in a layer of 0–10 cm from 2006 for 2015 have made 0,16 % in a year, in a layer of 0–50 cm of about 0,13 % in a year, and in a layer of 0–100 cm at 0,10 % in a year. The irrational structure of sown areas and distribution of wind and water erosion processes is the reason of this sad process.



Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 340
Author(s):  
Wenjuan Ouyang ◽  
Zhe Li ◽  
Jixiang Yang ◽  
Lunhui Lu ◽  
Jinsong Guo

The resting stages of phytoplankton are usually regarded as the seed bank and source of harmful algal blooms because of the recruitment of phytoplankton from sediment to the water column under suitable environmental conditions. Information about resting stages of phytoplankton is abundant in shallow lakes and littoral sea; yet, studies on river–reservoir systems are rare. The river–reservoir continuum shows a unique structuring of longitudinal gradients of hydrological and hydrodynamic conditions. We hypothesized that the seed bank and algal blooms in reservoirs are influenced by the hydrodynamic conditions of each reservoir. We used Illumina Miseq sequencing to examine the spatio-temporal variation in the phytoplankton community in the sediment as reservoir drawdown and in surface water during algal blooms in Pengxi River, a tributary of China’s Three Gorges Reservoir. The results show that the cyanobacteria community in sediment is significantly influenced by temperature, total carbon, maximum flow velocity, and total phosphorous, the eukaryotic phytoplankton community in sediment is significantly influenced by total phosphorous, temperature, total carbon, maximum flow velocity, and total nitrogen. Additionally, the dominant species in sediment is significantly different from that in surface water during algal blooms. Our results suggest that the dominant species in surface water during algal blooms is more influenced by the environmental factors and hydrodynamic conditions in the water column than the seeds in the sediment. These findings are fundamental for further research on the influence of hydrodynamic conditions on algal blooms in artificially regulated river-reservoir systems.



Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1432
Author(s):  
Xwégnon Ghislain Agoua ◽  
Robin Girard ◽  
Georges Kariniotakis

The efficient integration of photovoltaic (PV) production in energy systems is conditioned by the capacity to anticipate its variability, that is, the capacity to provide accurate forecasts. From the classical forecasting methods in the state of the art dealing with a single power plant, the focus has moved in recent years to spatio-temporal approaches, where geographically dispersed data are used as input to improve forecasts of a site for the horizons up to 6 h ahead. These spatio-temporal approaches provide different performances according to the data sources available but the question of the impact of each source on the actual forecasting performance is still not evaluated. In this paper, we propose a flexible spatio-temporal model to generate PV production forecasts for horizons up to 6 h ahead and we use this model to evaluate the effect of different spatial and temporal data sources on the accuracy of the forecasts. The sources considered are measurements from neighboring PV plants, local meteorological stations, Numerical Weather Predictions, and satellite images. The evaluation of the performance is carried out using a real-world test case featuring a high number of 136 PV plants. The forecasting error has been evaluated for each data source using the Mean Absolute Error and Root Mean Square Error. The results show that neighboring PV plants help to achieve around 10% reduction in forecasting error for the first three hours, followed by satellite images which help to gain an additional 3% all over the horizons up to 6 h ahead. The NWP data show no improvement for horizons up to 6 h but is essential for greater horizons.







2013 ◽  
Vol 726-731 ◽  
pp. 4682-4685 ◽  
Author(s):  
Jie Ying Xiao ◽  
Na Ji ◽  
Xing Li

There are a great number of index methods used to extract impervious surface from satellite images. However, these indices are not robust enough to detect steel framed roof due to the diversity of impervious materials. The extraction of steel framed roof information by remote sensing technology is becoming increasingly important because of its environmental and socio-economic significance. A new index, Normalized Difference Steel framed roof Index (NDSI) is proposed to extract steel framed roof surface information from TM images. The NDSI was created based on its spectral characteristics of TM image and the steel framed roof information can be extracted fast by NDSI threshold method. Additionally, Shijiazhuang city, which has experienced rapid urbanization, was chosen as the study area. And the classification results show that the new index NDSI can effectively extract steel framed roof information with higher accuracy.



2021 ◽  
Vol 3 (1) ◽  
pp. 5
Author(s):  
Federico Filipponi

Earth observation provides timely and spatially explicit information about crop phenology and vegetation dynamics that can support decision making and sustainable agricultural land management. Vegetation spectral indices calculated from optical multispectral satellite sensors have been largely used to monitor vegetation status. In addition, techniques to retrieve biophysical parameters from satellite acquisitions, such as the Leaf Area Index (LAI), have allowed to assimilate Earth observation time series in numerical modeling for the analysis of several land surface processes related to agroecosystem dynamics. More recently, biophysical processors used to estimate biophysical parameters from satellite acquisitions have been calibrated for retrieval from sensors with different high spatial resolution and spectral characteristics. Virtual constellations of satellite sensors allow the generation of denser LAI time series, contributing to improve vegetation phenology estimation accuracy and, consequently, enhancing agroecosystems monitoring capacity. This research study compares LAI estimates over croplands using different biophysical processors from Sentinel-2 MSI and Landsat-8 OLI satellite sensors. The results are used to demonstrate the capacity of virtual satellite constellation to strengthen LAI time series to derive important cropland use information over large areas.



Author(s):  
O. O. Kryvoshein ◽  
O. A. Kryvobok ◽  
T. I. Adamenko

The article studies one of the most important issues of agricultural production maintenance – development of a system of crops area estimation in Ukraine. The objective of this paper is to describe the similar system that uses high resolution satellite data and operational agrometeorological data from the network of the Hydrometeorological Centre of Ukraine as input information. The system is based on step-by-step solving of the following tasks: obtaining geoinformation data for individual agricultural crops; development of methods for multispectral satellite images classification; development of software applications to automate the process of these images classification with subsequent classification of crop areas. The research uses the following algorithms (or classifiers) to classify the agricultural land: SVM (support vector machine), RF ("random forest") and NN (neural networks). The choice of the most accurate of them formed the basis of the general method of classification. The values of spectral characteristics of red and infrared channels of a complete set of cloudless satellite images during the growing period were used as input data (features). As a result, in 2018 some test calculations were conducted to estimate the area of agricultural crops in Kyiv Region. The results of evaluation of accuracy of the satellite-based agricultural crops area estimation using the statistical data showed that the lowest accuracy is typical for winter wheat and corn. The accuracy of soybeans and spring barley classification is quite low for most of the tested fields. Sunflower and rapeseed crops showed the highest accuracy. In order to improve the accuracy of classification, it is necessary to introduce more classification features (in a temporary aspect) by processing more satellite images during the growing period, and to increase the number of test samples through systematic sampling of ground data across the regions in Ukraine. We suggest using the scheme of main agricultural crops area estimation satellite-based system by the Hydrometeorological Centre of Ukraine.



Agriculture data is a main source of country’s economic growth. It is important to provide agriculture related information to all the people who are involved in agriculture activities as and when required. This meaningful information is used by people who supply services to agriculture domain and to take some correct decision related to agriculture to apply for their field. The solutions to this problem are given by the efficient interaction of computer with human. Chatbot system provides ability to extract the exact answer to the queries posed by farmers. The proposed system is called as Agriculture Chatbot system or even it is called as Question-Answering system for agriculture domain, where farmer is asking the agriculture related question which fetches the precise answers for the asked questions by farmers in natural language and processes the query using RNN (Recurrent Neural Network) deep learning algorithm to extract correct answer.



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