scholarly journals STUDY OF THE RELIEF AND COASTLINE DYNAMICS OF LARGE COASTAL ACCUMULATIVE FORMS ACCORDING TO REMOTE SENSING DATA

2021 ◽  
Vol 26 (3) ◽  
pp. 58-70
Author(s):  
Viacheslav V. Krylenko ◽  
◽  
Marina V. Krylenko ◽  
Alexander A. Aleynikov ◽  
◽  
...  
Keyword(s):  
Aerial Photography ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
New Methods ◽  
New Information ◽  
Relief Structure ◽  
Shoreline Dynamics ◽  
New Research ◽  
Modern Technologies ◽  
Sentinel 2

The study of the relief of large coastal accumulative forms, based on modern technologies, is rele-vant for solving many applied problems. Coastal and underwater bars, shoals, banks are characteristic elements of large coastal accumulative forms’ geosystems. Previously existing methods of relief re-searches, especially underwater, were labor-intensive and expensive. Accordingly, the development and implementation of new methods of geographical research are necessary. The Dolgaya Spit, includ-ing its underwater shoal and the Elenin Bank, is one of the largest accumulative forms of the Sea of Azov. The purpose of our work was to obtain new information on the relief structure and the shoreline dynamics of the Dolgaya Spit based on the use of new research methods. Digital models of surface and underwater relief were built on the basis of processing Sentinel-2 satellite images and data from unmanned aerial photography. The subsequent analysis allowed identify regularities that reflect the current and previous hydro-lithodynamic conditions that determined the transformation of the Dolgaya Spit during its evolution. The fulfilled studies confirmed the possibility of successful use of modern remote methods for studying the relief of coastal accumulative forms.

Mapping of permafrost on Vega Island, Antarctic Peninsula, using satellite images and aerial photography

2002 ◽  
Vol 34 ◽  
pp. 184-188 ◽  
Author(s):  
Evgeniy Ermolin ◽  
Hernán De Angelis ◽  
Pedro Skvarca
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Antarctic Peninsula ◽  
Aerial Photography ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Future Extension ◽  
Low Altitude ◽  
Permafrost Mapping ◽  
Cryogenic Processes

AbstractThe work presented deals with detailed mapping of permafrost in Devil Bay, Vega Island, northeastern Antarctic Peninsula. Mapping of landforms and permafrost features within a periglacial plain was performed using high resolution visible satellite images and aerial photography. Two maps of permafrost were produced: one based on analysis and interpretation of visible satellite imagery and another on low-altitude aerial photography, both yielding similar results. The principles of morphogenesis were applied to map production, distinguishing both the syncryogenic and epicryogenic formations and each constitutive cryofacies. The interpretation of remote-sensing data allowed areas of occurrence of particular cryogenic processes to be defined. Remote sensing was found to be useful for permafrost mapping at both medium and large scales, and applicable for future extension to other regions in Antarctica.

scholarly journals ESTIMATION OF THE INFORMATION CAPACITY OF SENTINEL-2 REMOTE SENSING DATA FOR MONITORING SOIL POLLUTION WITH PETROLEUM PRODUCTS IN THE POLAR ARCTIC

2021 ◽  
Vol 6 ◽  
pp. 149-154
Author(s):  
Kirill A. Kozhanov ◽  
Ekaterina N. Kulik
Keyword(s):  
Remote Sensing ◽  
Comparative Analysis ◽  
Data Processing ◽  
Soil Pollution ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Petroleum Products ◽  
Satellite Systems ◽  
Sensing Data ◽  
Sentinel 2

The article uses the methods of comparative analysis of the data of resource satellite systems to evaluate the informative capacity of Sentinel-2B satellite images. Remote sensing data processing and analysis methods are used to determine the suitability of Sentinel-2B data for assessing the state of open soils, including those contaminated with petroleum products.

scholarly journals SPATIAL RESLUTION SENSITIVITY ANALYSIS OF CLASSIFCIATION OF SENTINEL-2 IMAGES BY PRE-TRAINED DEEP MODELS FROM BIG EARTH NET DATABASE

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 87-92
Author(s):  
F. Naali ◽  
T. Alipour-Fard ◽  
H. Arefi
Keyword(s):  
Spatial Resolution ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Vegetation Types ◽  
Land Use Classification ◽  
Learning Models ◽  
New Methods ◽  
Training Samples ◽  
Sentinel 2 ◽  
Impacts Of Climate Change

Abstract. Classifying and monitoring different vegetation types is important for forest management, food resources, and assessing the potential impacts of climate change. In this regard, several methods have been developed to study them using remote sensing data, and with the advent of neural networks, new methods are being proposed, especially in the field of automatic land use classification. In this research, multispectral Sentinel-2 satellite image has been used due to having spectral information and different spatial resolution for classifying plant species. Deep learning models have the ability to learn and recognize different features of images, but require a large number of training samples, so we used pre-trained ResNet networks with depths of 50, 101 and 152 layers, that trained with BigEarthNet dataset. The main purpose of this study is to evaluate the sensitivity of ResNet networks to spatial resolution. Results show that ResNet 101 was more stable than other networks, and the Resent 50 with an overall accuracy of 76.2 has the highest accuracy at a resolution of 20 meters.

scholarly journals Mangrove Ecosystem Mapping Using Sentinel-1 and Sentinel-2 Satellite Images and Random Forest Algorithm in Google Earth Engine

2021 ◽  
Vol 13 (13) ◽  
pp. 2565
Author(s):  
Arsalan Ghorbanian ◽  
Soheil Zaghian ◽  
Reza Mohammadi Asiyabi ◽  
Meisam Amani ◽  
Ali Mohammadzadeh ◽  
...  
Keyword(s):  
Random Forest ◽  
Natural Resources ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Mangrove Ecosystem ◽  
Google Earth ◽  
High Rate ◽  
Aerial Roots ◽  
Google Earth Engine ◽  
Sentinel 2

Mangroves are among the most productive ecosystems in existence, with many ecological benefits. Therefore, generating accurate thematic maps from mangrove ecosystems is crucial for protecting, conserving, and reforestation planning for these valuable natural resources. In this paper, Sentinel-1 and Sentinel-2 satellite images were used in synergy to produce a detailed mangrove ecosystem map of the Hara protected area, Qeshm, Iran, at 10 m spatial resolution within the Google Earth Engine (GEE) cloud computing platform. In this regard, 86 Sentinel-1 and 41 Sentinel-2 data, acquired in 2019, were employed to generate seasonal optical and synthetic aperture radar (SAR) features. Afterward, seasonal features were inserted into a pixel-based random forest (RF) classifier, resulting in an accurate mangrove ecosystem map with average overall accuracy (OA) and Kappa coefficient (KC) of 93.23% and 0.92, respectively, wherein all classes (except aerial roots) achieved high producer and user accuracies of over 90%. Furthermore, comprehensive quantitative and qualitative assessments were performed to investigate the robustness of the proposed approach, and the accurate and stable results achieved through cross-validation and consistency checks confirmed its robustness and applicability. It was revealed that seasonal features and the integration of multi-source remote sensing data contributed towards obtaining a more reliable mangrove ecosystem map. The proposed approach relies on a straightforward yet effective workflow for mangrove ecosystem mapping, with a high rate of automation that can be easily implemented for frequent and precise mapping in other parts of the world. Overall, the proposed workflow can further improve the conservation and sustainable management of these valuable natural resources.

scholarly journals Detection of Liquefaction Phenomena from the 2017 Pohang (Korea) Earthquake Using Remote Sensing Data

2019 ◽  
Vol 11 (18) ◽  
pp. 2184 ◽  
Author(s):  
Baik ◽  
Son ◽  
Kim
Keyword(s):  
Satellite Images ◽  
Remote Sensing Data ◽  
Short Wave ◽  
Landsat 8 ◽  
Content Increase ◽  
Infrared Band ◽  
Water Index ◽  
Before And After ◽  
Optical Satellite Images ◽  
Sentinel 2

On 15 November 2017, liquefaction phenomena were observed around the epicenter after a 5.4 magnitude earthquake occurred in Pohang in southeast Korea. In this study, we attempted to detect areas of sudden water content increase by using SAR (synthetic aperture radar) and optical satellite images. We analyzed coherence changes using Sentinel-1 SAR coseismic image pairs and analyzed NDWI (normalized difference water index) changes using Landsat 8 and Sentinel-2 optical satellite images from before and after the earthquake. Coherence analysis showed no liquefaction-induced surface changes. The NDWI time series analysis models using Landsat 8 and Sentinel-2 optical images confirmed liquefaction phenomena close to the epicenter but could not detect liquefaction phenomena far from the epicenter. We proposed and evaluated the TDLI (temporal difference liquefaction index), which uses only one SWIR (short-wave infrared) band at 2200 nm, which is sensitive to soil moisture content. The Sentinel-2 TDLI was most consistent with field observations where sand blow from liquefaction was confirmed. We found that Sentinel-2, with its relatively shorter revisit period compared to that of Landsat 8 (5 days vs. 16 days), was more effective for detecting traces of short-lived liquefaction phenomena on the surface. The Sentinel-2 TDLI could help facilitate rapid investigations and responses to liquefaction damage.

scholarly journals POSSIBILITIES OF USING SATELLITE IMAGES AND GIS TECHNOLOGIES FOR MONITORING INUNDATION OF PIPELINE SYSTEMS

2020 ◽  
Vol 4 (1) ◽  
pp. 21-28
Author(s):  
Vyacheslav A. Melkiy ◽  
Daniil V. Dolgopolov ◽  
Alexey A. Verkhoturov
Keyword(s):  
Satellite Images ◽  
Spectral Characteristics ◽  
Remote Sensing Data ◽  
Space Systems ◽  
Water Index ◽  
Geotechnical Monitoring ◽  
Pipeline Systems ◽  
Normalized Difference Water Index ◽  
Automated Interpretation ◽  
Sentinel 2

The purpose of this research is the study of possibilities of practical use of multi-zone satellite images for implementation of geotechnical monitoring of pipeline transport facilities during floodings. Modern methods and approaches are required for monitoring extended objects and analyzing large amount of remote sensing data. Such methods can be applied for studying of spectral characteristics of the Earth's surface obtained using space systems, collected in databases using geoinformation technologies (GIS). Use of special indexes and technologies for automated interpretation of multi-zone satellite images allows obtaining and analyzing information about state of pipeline systems at time of flooding. Research showed that Sentinel-2 satellite data makes it possible for fairly correctly determine of flood situation by image indexed with using of Normalized Difference Water Index (NDWI) and highlight areas and objects flooded of water.

James Rennie (1787–1867), author, naturalist and lecturer

2008 ◽  
Vol 35 (1) ◽  
pp. 128-142 ◽  
Author(s):  
FREDERICK G. PAGE
Keyword(s):  
Personal Character ◽  
New Information ◽  
New Research ◽  
Unknown Period ◽  
Unpublished Works

New research is presented on the life of James Rennie (1787–1867) before his emigration to Australia in 1840. Though fragmentary and incomplete the results show Rennie as a naturalist of considerable standing and of literary and scientific skill. This new information illustrates an intriguingly marginal life in science of the period. On his personal character caution is exercised, although a thread of dogmatism, determination and self assurance, bordering on arrogance, can be traced from his student days until his departure from Britain. Rennie's early unpublished essays clearly point to his potential as a scientific writer. Rennie's final 27 years in Australia are not covered in any detail because of the lack of documentation about this relatively unknown period of his life outside Britain. A bibliography of his published and unpublished works is given as an appendix, together with notes and new insights into attribution.

The research study in the convergence of the results of processing radar images Sentinel-1A with field observations of agricultural sites

2019 ◽  
Vol 950 (8) ◽  
pp. 52-58
Author(s):  
D.V. Mozer ◽  
Е.L. Levin ◽  
A.K. Satbergenova
Keyword(s):  
Satellite Remote Sensing ◽  
Satellite Images ◽  
Research Study ◽  
High Accuracy ◽  
Agricultural Fields ◽  
Field Observations ◽  
Radar Images ◽  
The Earth ◽  
Radar Satellite ◽  
Modern Technologies

The manuscript discusses how to monitor the condition of seedlings on agricultural fields planted with winter wheat, fodder maize and areas of fir forest located in the Freudenstadt district of Baden-Wuerttemberg in Germany. To solve the range of agricultural problems , they often use modern technologies such as satellite remote sensing of the Earth. The paper displays the monitoring results of the Sentinel-1A radar satellites scenes, as well as visual spectrum imagery of field observations are presented when leaving directly to terrain segments. The processing deployed data chain, consisting of 11 Sentinel-1A scenes acquired in the timefrane from March to November 2018. Specifically, the SNAP Sentinel Toolboxes software was used to process the radar satellite images Sentinel-1А, the. Based on the the research outcomes the Committee of Agriculture of the Freudenstadt district is able to predict the yield amount with high accuracy due to good data convergence. According to the study, the following three important problems can be resolved by means of Sentinel-1A imagery

scholarly journals Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2313
Author(s):  
Elena Prudnikova ◽  
Igor Savin
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Reflectance ◽  
Remote Sensing Data ◽  
Soil Surface ◽  
Optical Remote Sensing ◽  
Test Field ◽  
Sensing Data ◽  
Sentinel 2

Optical remote sensing only provides information about the very thin surface layer of soil. Rainfall splash alters soil surface properties and its spectral reflectance. We analyzed the impact of rainfall on the success of soil organic matter (SOM) content (% by mass) detection and mapping based on optical remote sensing data. The subject of the study was the arable soils of a test field located in the Tula region (Russia), their spectral reflectance, and Sentinel-2 data. Our research demonstrated that rainfall negatively affects the accuracy of SOM predictions based on Sentinel-2 data. Depending on the average precipitation per day, the R2cv of models varied from 0.67 to 0.72, RMSEcv from 0.64 to 1.1% and RPIQ from 1.4 to 2.3. The incorporation of information on the soil surface state in the model resulted in an increase in accuracy of SOM content detection based on Sentinel-2 data: the R2cv of the models increased up to 0.78 to 0.84, the RMSEcv decreased to 0.61 to 0.71%, and the RPIQ increased to 2.1 to 2.4. Further studies are necessary to identify how the SOM content and composition of the soil surface change under the influence of rainfall for other soils, and to determine the relationships between rainfall-induced SOM changes and soil surface spectral reflectance.

Sign in / Sign up

Export Citation Format

Share Document