scholarly journals Assessment of the environmental situation in Krasnoyarsk using remote sensing data

2021 ◽  
Author(s):  
K.V. Krasnoshchekov ◽  
O.E. Yakubailik
Keyword(s):  
Remote Sensing ◽  
Monitoring System ◽  
Monitoring Network ◽  
Remote Sensing Data ◽  
Air Monitoring ◽  
Coefficient Of Determination ◽  
Scientific Center ◽  
Sensing Data ◽  
Environmental Situation ◽  
Analytical System

The data on ground concentrations of aerosols and small gas components (particulate matter PM2.5 and sulfur dioxide NO2) were compared with remote sensing data obtained over the territory of Krasnoyarsk from June to August 2020. We use the air monitoring system of the Krasnoyarsk Scientific Center of the Siberian Branch of the Russian Academy of Sciences (KSC SB RAS) to determine the concentration of PM2.5. NO2 concentrations were taken according to the data of the State departmental information and analytical system of the Ministry of Ecology of the region. It is shown that the remote sensing data of the MODIS MAIAC algorithm with a spatial resolution of 1 km can be used to determine the concentration of PM2.5 as an addition to the data obtained by the ground-based air monitoring system of the KSC SB RAS. The MAIAC data were calculated using two different models and are given to the measurement system used in the KSC SB RAS monitoring network. A high coefficient of determination between satellite and ground monitoring data was obtained. Determination coefficients were also obtained for NO2, showing how applicable the remote sensing data are for assessing the environmental situation in Krasnoyarsk.

scholarly journals Application of remote sensing data to assess environmental situation in Krasnoyarsk

2021 ◽  
Vol 333 ◽  
pp. 02004
Author(s):  
Konstantin Krasnoshchekov ◽  
Oleg Yakubailik
Keyword(s):  
Remote Sensing ◽  
Monitoring System ◽  
Remote Sensing Data ◽  
The State ◽  
Air Monitoring ◽  
Coefficient Of Determination ◽  
Monitoring Networks ◽  
Scientific Center ◽  
Sensing Data ◽  
Analytical System

The work compared the data on ground-based concentrations of suspended particles (PM2.5 (particulate matter with diameters less than 2.5 microns) and NO2 (sulfur dioxide)) with remote sensing data obtained over the territory of Krasnoyarsk in the summer period of 2019 and 2020. We use the air monitoring system of the Krasnoyarsk Scientific Center of the Siberian Branch of the Russian Academy of Sciences (KSC SB RAS) to determine the concentration of PM 2.5. NO2 concentrations were taken from the data of the State departmental information and analytical system of data on the state of the Ministry of Ecology of the region. It is shown that the data of the MAIAC product, which has a spatial resolution of 1 km, can be used to determine the PM2.5 concentration as a supplement to the data obtained by the ground-based air monitoring system of the KSC SB RAS. A high coefficient of determination between satellite and ground monitoring data was obtained. Joint processing of data from ground-based monitoring networks with remote sensing data will contribute to improving the assessment of the ecological situation in Krasnoyarsk.

scholarly journals OPEN: A New Estimation of Global Ocean Heat Content for Upper 2000 Meters from Remote Sensing Data

2020 ◽  
Vol 12 (14) ◽  
pp. 2294
Author(s):  
Hua Su ◽  
Haojie Zhang ◽  
Xupu Geng ◽  
Tian Qin ◽  
Wenfang Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Network Architecture ◽  
Climate Changes ◽  
Heat Content ◽  
Remote Sensing Data ◽  
Ocean Heat Content ◽  
Global Ocean ◽  
Coefficient Of Determination ◽  
Sensing Data

Retrieving information concerning the interior of the ocean using satellite remote sensing data has a major impact on studies of ocean dynamic and climate changes; however, the lack of information within the ocean limits such studies about the global ocean. In this paper, an artificial neural network, combined with satellite data and gridded Argo product, is used to estimate the ocean heat content (OHC) anomalies over four different depths down to 2000 m covering the near-global ocean, excluding the polar regions. Our method allows for the temporal hindcast of the OHC to other periods beyond the 2005–2018 training period. By applying an ensemble technique, the hindcasting uncertainty could also be estimated by using different 9-year periods for training and then calculating the standard deviation across six ensemble members. This new OHC product is called the Ocean Projection and Extension neural Network (OPEN) product. The accuracy of the product is accessed using the coefficient of determination (R2) and the relative root-mean-square error (RRMSE). The feature combinations and network architecture are optimized via a series of experiments. Overall, intercomparison with several routinely analyzed OHC products shows that the OPEN OHC has an R2 larger than 0.95 and an RRMSE of <0.20 and presents notably accurate trends and variabilities. The OPEN product can therefore provide a valuable complement for studies of global climate changes.

Remote sensing data and field survey activities for monitoring the evolution of forest systems after coppicing and soil erosion: A case study in Sardinia (Italy)

2020 ◽  
Author(s):  
Filippo Giadrossich ◽  
Antonio Ganga ◽  
Sergio Campus ◽  
Ilenia Murgia ◽  
Irene Piredda ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Vegetation Indices ◽  
Monitoring Network ◽  
Remote Sensing Data ◽  
Forest Stands ◽  
Sensing Data ◽  
Long Term Monitoring ◽  
Term Monitoring

&lt;p&gt;The practice of coppicing is debated in the literature for the risk factors associated with soil erosion. Although erosion experiments provide useful data for estimating the susceptibility to soil erosion, there are many open questions that cannot be solved in isolated experiments, but which can be assessed by activating a long-term monitoring process. In this way, it is possible to correctly frame the spatial and temporal scale of soil erosion in coppice forests.&amp;#160;&lt;/p&gt;&lt;p&gt;The aim of the work is to evaluate the effectiveness of the use of remote sensing data in combination with field data, for monitoring the evolution of forest stands interested by coppicing in relation to soil erosion.&amp;#160;&lt;/p&gt;&lt;p&gt;We have installed a long-term monitoring network for erosion estimation, while Sentinel-2C satellite data were used for the period 2016-2018. Starting from this dataset, a selection of vegetation indices was calculated and compared to the morphological and topographical parameters of the study area, as well as the above-ground data collected during field activities. Using the Canonical Correspondences Analysis (CCA) the relationships between the matrix of vegetation indices, topographic and vegetational parameters and the respective performances of this protocol have been explored in order to describe the evolution of the forest stands in the study area associated to soil losses.&lt;/p&gt;

scholarly journals Analysis and Distribution of the Rainfall Monitoring Network in a Brazilian Pantanal Region

2017 ◽  
Vol 32 (2) ◽  
pp. 199-205 ◽  
Author(s):  
Marcia Ferreira Cristaldo ◽  
Celso Correia de Souza ◽  
Leandro de Jesus ◽  
Carlos Roberto Padovani ◽  
Paulo Tarso Sanches de Oliveira ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Monitoring Network ◽  
Remote Sensing Data ◽  
Monitoring Systems ◽  
Sensing Data ◽  
The World ◽  
Data Gap ◽  
Flood Dynamics ◽  
Brazilian Pantanal ◽  
Current Monitoring

Abstract To better understand drought and flood dynamics in the Pantanal is crucial an adequate hydrometeorological monitoring network. However, few studies have investigated whether the current monitoring systems are suitable in this region. Here, we analyzed the hydrometeorological monitoring network of the Aquidauana region, composed of pluviometric, meteorological and fluviatile gauging stations. We obtained data of all hydrometeorological gauges available in this region to compare with the World Meteorological Organization (WMO) recommendation. We found that although the number of stations in operation is satisfactory when compared with that established by the WMO, the network is not satisfactory in the operating stations because of lack of maintenance, thus creating a need for additional stations. This fact was also observed when analyzing the meteorological network. Using remote sensing data may be possible to fill these data gap. However, to improve the knowledge on hydrological processes in this region is still necessary to install additional ground-based stations.

scholarly journals An Improved Traffic Congestion Monitoring System Based on Federated Learning

Information ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 365
Author(s):  
Chenming Xu ◽  
Yunlong Mao
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Monitoring System ◽  
Traffic Congestion ◽  
Data Privacy ◽  
Remote Sensing Data ◽  
Transportation System ◽  
Remote Sensing Images ◽  
Training Process ◽  
Sensing Data

This study introduces a software-based traffic congestion monitoring system. The transportation system controls the traffic between cities all over the world. Traffic congestion happens not only in cities, but also on highways and other places. The current transportation system is not satisfactory in the area without monitoring. In order to improve the limitations of the current traffic system in obtaining road data and expand its visual range, the system uses remote sensing data as the data source for judging congestion. Since some remote sensing data needs to be kept confidential, this is a problem to be solved to effectively protect the safety of remote sensing data during the deep learning training process. Compared with the general deep learning training method, this study provides a federated learning method to identify vehicle targets in remote sensing images to solve the problem of data privacy in the training process of remote sensing data. The experiment takes the remote sensing image data sets of Los Angeles Road and Washington Road as samples for training, and the training results can achieve an accuracy of about 85%, and the estimated processing time of each image can be as low as 0.047 s. In the final experimental results, the system can automatically identify the vehicle targets in the remote sensing images to achieve the purpose of detecting congestion.

scholarly journals Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery

2018 ◽  
Vol 10 (9) ◽  
pp. 1489 ◽  
Author(s):  
Feng Gao ◽  
Martha Anderson ◽  
Craig Daughtry ◽  
David Johnson
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Crop Yields ◽  
Remote Sensing Data ◽  
Coefficient Of Determination ◽  
Spatial And Temporal Variability ◽  
Spatiotemporal Resolution ◽  
Sensing Data ◽  
Temporal Sampling ◽  
Medium Resolution

The utility of remote sensing data in crop yield modeling has typically been evaluated at the regional or state level using coarse resolution (>250 m) data. The use of medium resolution data (10–100 m) for yield estimation at field scales has been limited due to the low temporal sampling frequency characteristics of these sensors. Temporal sampling at a medium resolution can be significantly improved, however, when multiple remote sensing data sources are used in combination. Furthermore, data fusion approaches have been developed to blend data from different spatial and temporal resolutions. This paper investigates the impacts of improved temporal sampling afforded by multi-source datasets on our ability to explain spatial and temporal variability in crop yields in central Iowa (part of the U.S. Corn Belt). Several metrics derived from vegetation index (VI) time-series were evaluated using Landsat-MODIS fused data from 2001 to 2015 and Landsat-Sentinel2-MODIS fused data from 2016 and 2017. The fused data explained the yield variability better, with a higher coefficient of determination (R2) and a smaller relative mean absolute error than using a single data source alone. In this study area, the best period for the yield prediction for corn and soybean was during the middle of the growing season from day 192 to 236 (early July to late August, 1–3 months before harvest). These findings emphasize the importance of high temporal and spatial resolution remote sensing data in agricultural applications.

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