scholarly journals Combined usage of the optical and radar remote sensing data in territory monitoring tasks

2019 ◽  
pp. 334-341
Author(s):  
V N Kopenkov
Keyword(s):  
Remote Sensing ◽  
Information Technology ◽  
Satellite Images ◽  
Integrated Approach ◽  
Radar Data ◽  
Optical Data ◽  
Joint Analysis ◽  
Data Types ◽  
Joint Processing ◽  
Radar Satellite

At the present time, a lot of problems in a sphere of fundamental sciences as well as technical and applied tasks can be solved only with the use of satellite images, since their usage reduces material, financial and time costs significantly in comparison with traditional methods. One of the modern integrated approach remote sensing processing is to join the measurements obtained from the various sources, such as optical and radar sensors, allowing to achieve a gain in comparison with independent processing due to the extension of the information volume and the opportunities of data acquisition (weather conditions, spectral ranges, etc.). However, methods of digital processing and interpretation of radar data, as well as qualitative and proven methods and algorithms for joint processing of optical and radar satellite images, has not sufficiently been well developed yet. Therefore, the development of new methods and information technology of joint analysis and interpretation of optical and radar data which are a major issue of the current paper, are certainly relevant. The paper presents an information technology for joint processing of optical and radar satellite imagery, based on training the processing procedure based on the reference values of data from sensors of the one type (optical data), followed by applying to both data types: optical and SAR data.

scholarly journals Utilization of BEAM and NEST open source toolboxes in education and research

2010 ◽  
Vol 5 ◽  
pp. 37-48
Author(s):  
Markéta Potůčková ◽  
Eva Štefanová
Keyword(s):  
Remote Sensing ◽  
Open Source ◽  
Satellite Images ◽  
European Space Agency ◽  
Radar Data ◽  
Optical Data ◽  
Basic Principles ◽  
Data Formats ◽  
Space Agency ◽  
Sar Imagery

European Space Agency (ESA) provides several open source toolboxes for visualization, processing and analyzing satellite images acquired both in optical and microwave domains. Basic ERS & Envisat (A)ATSR and MERIS Toolbox (BEAM) was originally developed for easier handling ENVISAT optical data. Today this toolbox supports several raster data formats and datasets collected with other EO instruments such as MODIS, AVHRR, CHRIS/Proba. The NEXT ESA SAR Toolbox (NEST) has been created for processing radar data acquired from different satellites such as ERS 1&2, ENVISAT, RADARSAT or TerraSAR X. Both toolboxes are suitable for the education of the basic principles of data processing (geometric and radiometric corrections, classification, filtering of radar data) but also for research. Possibilities for utilization of these toolboxes in remote sensing courses based on two examples of practical exercises are described. Use of the NEST toolbox is demonstrated on a research project dealing with snow cover detection from SAR imagery.

scholarly journals New technology for mineral deposits prediction to identify prospective areas in the Zhezkazgan ore region

2021 ◽  
Vol 15 (2) ◽  
pp. 134-142
Author(s):  
Boris Zeylik ◽  
Yalkunzhan Arshamov ◽  
Refat Baratov ◽  
Alma Bekbotayeva
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
New Technology ◽  
Methodological Approach ◽  
Cost Savings ◽  
Remote Sensing Data ◽  
Ring Structure ◽  
Mineral Deposits ◽  
Erdas Imagine ◽  
Radar Satellite

Purpose. Exploration and predicting the prospective areas in the Zhezkazgan ore region to set up detailed prospecting and evaluation works using new integrated technologies of prediction constructions in the mineral deposits geology. Methods. An integrated methodological approach is used, including methods for deciphering the Earth’s remote sensing (ERS) data, the use of geophysical data and methods of analogy and actualism. All constructions are made in accordance with the principles of shock-explosive tectonics (SET). Prediction constructions are started with the selection of remote sensing data for the studied region and interpretation based on the processing of radar satellite images obtained from the Radarsat-1 satellite. The radar satellite images are processed in the Erdas Imagine software package. Findings. New local prospective areas have been identified, within which it is expected to discover the deposits. Their reserves are to replenish the depleted ore base in the Zhezkazgan region. Area of the gravity maximum 1 (the Near), considered to be the most promising, is located in close proximity to the city of Zhezkazgan; area of the gravity maximum 2 (the Middle); area of the gravity maximum 3 (the Distant-Tabylga); area of the gravity maximum 6 (the Central). A prospective area has been also revealed, overlaid by a loose sediment cover and located inside the Terekty ring structure, as well as the area of a thick stratum of pyritized grey sandstones, which is adjacent to the Sh-2 well drilled to the south of the Zhezkazgan field. Originality. The use of a new prediction technology, in contrast to the known ones, is conditioned by the widespread use of the latest remote information from satellite images, which increases the accuracy of identifying the prospective areas of fields. Practical implications. The new technology for predicting mineral deposits makes it possible to significantly reduce the areas exposed to priority prospecting, which provides significant cost savings.

scholarly journals Detection of Archaeological Looting from Space: Methods, Achievements and Challenges

2019 ◽  
Vol 11 (20) ◽  
pp. 2389 ◽  
Author(s):  
Deodato Tapete ◽  
Francesca Cigna
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
High Resolution ◽  
Satellite Data ◽  
Satellite Images ◽  
Google Earth ◽  
Optical Data ◽  
Mena Region ◽  
Processing Methods ◽  
Optical Imagery

Illegal excavations in archaeological heritage sites (namely “looting”) are a global phenomenon. Satellite images are nowadays massively used by archaeologists to systematically document sites affected by looting. In parallel, remote sensing scientists are increasingly developing processing methods with a certain degree of automation to quantify looting using satellite imagery. To capture the state-of-the-art of this growing field of remote sensing, in this work 47 peer-reviewed research publications and grey literature are reviewed, accounting for: (i) the type of satellite data used, i.e., optical and synthetic aperture radar (SAR); (ii) properties of looting features utilized as proxies for damage assessment (e.g., shape, morphology, spectral signature); (iii) image processing workflows; and (iv) rationale for validation. Several scholars studied looting even prior to the conflicts recently affecting the Middle East and North Africa (MENA) region. Regardless of the method used for looting feature identification (either visual/manual, or with the aid of image processing), they preferred very high resolution (VHR) optical imagery, mainly black-and-white panchromatic, or pansharpened multispectral, whereas SAR is being used more recently by specialist image analysts only. Yet the full potential of VHR and high resolution (HR) multispectral information in optical imagery is to be exploited, with limited research studies testing spectral indices. To fill this gap, a range of looted sites across the MENA region are presented in this work, i.e., Lisht, Dashur, and Abusir el Malik (Egypt), and Tell Qarqur, Tell Jifar, Sergiopolis, Apamea, Dura Europos, and Tell Hizareen (Syria). The aim is to highlight: (i) the complementarity of HR multispectral data and VHR SAR with VHR optical imagery, (ii) usefulness of spectral profiles in the visible and near-infrared bands, and (iii) applicability of methods for multi-temporal change detection. Satellite data used for the demonstration include: HR multispectral imagery from the Copernicus Sentinel-2 constellation, VHR X-band SAR data from the COSMO-SkyMed mission, VHR panchromatic and multispectral WorldView-2 imagery, and further VHR optical data acquired by GeoEye-1, IKONOS-2, QuickBird-2, and WorldView-3, available through Google Earth. Commonalities between the different image processing methods are examined, alongside a critical discussion about automation in looting assessment, current lack of common practices in image processing, achievements in managing the uncertainty in looting feature interpretation, and current needs for more dissemination and user uptake. Directions toward sharing and harmonization of methodologies are outlined, and some proposals are made with regard to the aspects that the community working with satellite images should consider, in order to define best practices of satellite-based looting assessment.

scholarly journals Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data

2021 ◽  
Vol 13 (2) ◽  
pp. 243
Author(s):  
Amal Chakhar ◽  
David Hernández-López ◽  
Rocío Ballesteros ◽  
Miguel A. Moreno
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Dynamics ◽  
Remote Sensing Data ◽  
Radar Data ◽  
Optical Data ◽  
Support Vector ◽  
Sensing Data ◽  
Crop Classification

The availability of an unprecedented amount of open remote sensing data, such as Sentinel-1 and -2 data within the Copernicus program, has boosted the idea of combining the use of optical and radar data to improve the accuracy of agricultural applications such as crop classification. Sentinel-1’s Synthetic Aperture Radar (SAR) provides co- and cross-polarized backscatter, which offers the opportunity to monitor agricultural crops using radar at high spatial and temporal resolution. In this study, we assessed the potential of integrating Sentinel-1 information (VV and VH backscatter and their ratio VH/VV with Sentinel-2A data (NDVI) to perform crop classification and to define which are the most important input data that provide the most accurate classification results. Further, we examined the temporal dynamics of remote sensing data for cereal, horticultural, and industrial crops, perennials, deciduous trees, and legumes. To select the best SAR input feature, we tried two approaches, one based on classification with only SAR features and one based on integrating SAR with optical data. In total, nine scenarios were tested. Furthermore, we evaluated the performance of 22 nonparametric classifiers on which most of these algorithms had not been tested before with SAR data. The results revealed that the best performing scenario was the one integrating VH and VV with normalized difference vegetation index (NDVI) and cubic support vector machine (SVM) (the kernel function of the classifier is cubic) as the classifier with the highest accuracy among all those tested.

scholarly journals Evaluation of Floods and Landslides Triggered by a Meteorological Catastrophe (Ordu, Turkey, August 2018) Using Optical and Radar Data

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Sultan Kocaman ◽  
Beste Tavus ◽  
Hakan A. Nefeslioglu ◽  
Gizem Karakas ◽  
Candan Gokceoglu
Keyword(s):  
Remote Sensing ◽  
Landslide Susceptibility ◽  
Radar Data ◽  
Regional Level ◽  
Disaster Mitigation ◽  
Machine Learning Techniques ◽  
Optical Data ◽  
Economic Losses ◽  
Airborne Imaging ◽  
Sentinel 2

This study explores the potential of photogrammetric datasets and remote sensing methods for the assessment of a meteorological catastrophe that occurred in Ordu, Turkey in August 2018. During the event, flash floods and several landslides caused losses of lives, evacuation of people from their homes, collapses of infrastructure and large constructions, destruction of agricultural fields, and many other economic losses. The meteorological conditions before and during the flood were analyzed here and compared with long-term records. The flood extent and the landslide susceptibility were investigated by using multisensor and multitemporal data. Sentinel-1 SAR (Synthetic Aperture Radar), Sentinel-2 optical data, and aerial photogrammetric datasets were employed for the investigations using machine learning techniques. The changes were assessed both at a local and regional level and evaluated together with available damage reports. The analysis of the rainfall data recorded during the two weeks before the floods and landslides in heavily affected regions shows that the rainfall continued for consecutive hours with an amount of up to 68 mm/hour. The regional level classification results obtained from Sentinel-1 and Sentinel-2 data by using the random forest (RF) method exhibit 97% accuracy for the flood class. The landslide susceptibility prediction performance from aerial photogrammetric datasets was 92% represented by the Area Under Curve (AUC) value provided by the RF method. The results presented here show that considering the occurrence frequency and immense damages after such events, the use of novel remote sensing technologies and spatial analysis methods is unavoidable for disaster mitigation efforts and for the monitoring of environmental effects. Although the increasing number of earth observation satellites complemented with airborne imaging sensors is capable of ensuring data collection requirement with diverse spectral, spatial, and temporal resolutions, further studies are required to automate the data processing, efficient information extraction, and data fusion and also to increase the accuracy of the results.

scholarly journals Mapping Forest Vertical Structure in Jeju Island from Optical and Radar Satellite Images Using Artificial Neural Network

2020 ◽  
Vol 12 (5) ◽  
pp. 797
Author(s):  
Yong-Suk Lee ◽  
Sunmin Lee ◽  
Won-Kyung Baek ◽  
Hyung-Sup Jung ◽  
Sung-Hwan Park ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Test Data ◽  
Aboveground Biomass ◽  
Satellite Images ◽  
Vertical Structure ◽  
Canopy Height ◽  
Jeju Island ◽  
Field Surveys ◽  
Radar Satellite ◽  
Forest Aboveground Biomass

Recently, due to the acceleration of global warming, an accurate understanding and management of forest carbon stocks, such as forest aboveground biomass, has become very important. The vertical structure of the forest, which is the internal structure of the forest, was mainly investigated by field surveys that are labor intensive. Recently, remote sensing techniques have been actively used to explore large and inaccessible areas. In addition, machine learning techniques that could classify and analyze large amounts of data are being used in various fields. Thus, this study aims to analyze the forest vertical structure (number of tree layers) to estimate forest aboveground biomass in Jeju Island from optical and radar satellite images using artificial neural networks (ANN). For this purpose, the eight input neurons of the forest related layers, based on remote sensing data, were prepared: normalized difference vegetation index (NDVI), normalized difference water index (NDWI), NDVI texture, NDWI texture, average canopy height, standard deviation canopy height and two types of coherence maps were created using the Kompsat-3 optical image, L-band ALOS PALSAR-1 radar images, digital surface model (DSM), and digital terrain model (DTM). The forest vertical structure data, based on field surveys, was divided into the training/validation and test data and the hyper-parameters of ANN were trained using the training/validation data. The forest vertical classification result from ANN was evaluated by comparison to the test data. It showed about a 65.7% overall accuracy based on the error matrix. This result shows that the forest vertical structure map can be effectively generated from optical and radar satellite images and existing DEM and DTM using the ANN approach, especially for national scale mapping.

The main issues of the Cyrene necropolis and the use of remote sensing for monitoring in the case of the eastern necropolis

2016 ◽  
Vol 47 ◽  
pp. 7-30 ◽  
Author(s):  
Fawzi Al Raeid ◽  
Eugenio Di Valerio ◽  
Maria Giorgia Di Antonio ◽  
Oliva Menozzi ◽  
Mazen A. S. Abdalgader El Mziene ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Radar Data ◽  
Google Earth ◽  
Differential Gps ◽  
Old Maps ◽  
Ancient Site ◽  
The Moment ◽  
The Mediterranean Basin ◽  
Robotic Total Station

AbstractCyrene offers one of the largest and most spectacular necropoleis of the Mediterranean basin and, owing especially to its vastness, it is particularly difficult to control and protect. It reveals an extraordinary patrimony of rock-cut architecture, monumentally built around the ancient site, and also represents a zone at major risk of damage and destruction as a result of continuous looting, uncontrolled urbanisation and vandalism. Recent studies by Jim and Dorothy Thorn have presented a preliminary view and gazetteer of the architectonic monumentality, especially of the rocky chamber tombs, showing the need for survey projects in the area. A joint team of Libyan and Italian archaeologists, geologists, topographers and technicians of the local Department of Antiquities and Chieti University started in 1999 a project of surveying and mapping the southern and eastern parts of the necropolis, giving birth to a GIS using differential GPS and Robotic total station for the positioning and recording of the tombs, as well as multispectral HD satellite images, previously orthorectified and georeferred, combined with old maps and RADAR data for a highly detailed topographic base, up to DEM and DTM. From 2004, the survey and the GIS have been extended to the western and northern parts of the necropolis, at the moment counting more than 2,000 mapped and recorded tombs. An important step in the last two years has been the use of remote-sensing and photo-interpretation analysis in order to map the damages of urbanisation and modern construction in the areas of the necropolis. Using images covering the development of the situation every two to three months, mainly shots coming from Google Earth archives, in combination with further satellite images specifically bought for a more complete view of the last two to three years, it has been possible to start drawing a map of the areas under threat from building works and urbanisation. The aim of this paper is to present the main problems of this huge necropolis, which is at the moment one of the most threatened areas of Cyrene, every day at risk from the destruction of its monumental buildings.

scholarly journals Measuring the Morphology and Dynamics of the Snake River by Remote Sensing

2011 ◽  
Vol 34 ◽  
pp. 53-67
Author(s):  
Carl Legleiter ◽  
Brandon Overstreet
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Field Measurements ◽  
Optical Data ◽  
Snake River ◽  
Central Component ◽  
Data Sets ◽  
Data Set ◽  
Channel Changes ◽  
Multispectral Satellite Images

The Snake River is a central component of Grand Teton National Park, and this dynamic fluvial system plays a key role in shaping the landscape and maintaining a diversity of habitat conditions. The river’s inherent variability and propensity for change complicate effective characterization of this important resource, however; conventional, ground-based methods are not adequate for this purpose. Remote sensing provides an appealing alternative that could facilitate resource management while providing novel insight on factors influencing channel form and behavior. This study evaluates the potential for using optical data to measure the morphology and dynamics of a large, complex river such as the Snake. More specifically, we assessed the feasibility of estimating flow depth from multispectral satellite images acquired in September 2011. Our initial results indicate that reliable maps of river bathymetry can be produced from such data. We are also examining channel changes associated with a prolonged period of high flow during the 2011 snowmelt runoff season by comparing these satellite images with digital aerial photography from August 2010. An extensive field data set on flow velocities provides some hydraulic context for the observed morphodynamics. More sophisticated hyperspectral and LiDAR data sets are scheduled for collection in 2012, along with additional field measurements.

scholarly journals Synchronous Response Analysis of Features for Remote Sensing Crop Classification Based on Optical and SAR Time-Series Data

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4227 ◽  
Author(s):  
Yingwei Sun ◽  
Jiancheng Luo ◽  
Tianjun Wu ◽  
Ya’nan Zhou ◽  
Hao Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series Data ◽  
Agricultural Research ◽  
Optical Data ◽  
Series Data ◽  
Response Analysis ◽  
Growth Monitoring ◽  
Data Types ◽  
Crop Classification ◽  
Crop Planting

Accurate crop classification is the basis of agricultural research, and remote sensing is the only effective measuring technique to classify crops over large areas. Optical remote sensing is effective in regions with good illumination; however, it usually fails to meet requirements for highly accurate crop classification in cloud-covered areas and rainy regions. Synthetic aperture radar (SAR) can achieve active data acquisition by transmitting signals; thus, it has strong resistance to cloud and rain interference. In this study, we designed an improved crop planting structure mapping framework for cloudy and rainy regions by combining optical data and SAR data, and we revealed the synchronous-response relationship of these two data types. First, we extracted geo-parcels from optical images with high spatial resolution. Second, we built a recurrent neural network (RNN)-based classifier suitable for remote sensing images on the geo-parcel scale. Third, we classified crops based on the two datasets and established the network. Fourth, we analyzed the synchronous response relationships of crops based on the results of the two classification schemes. This work is the basis for the application of remote sensing data for the fine mapping and growth monitoring of crop planting structures in cloudy and rainy areas in the future.

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