scholarly journals Tropical Peatland Identification using L-Band Full Polarimetric Synthetic Aperture Radar (SAR) Imagery (Study Case: Siak Regency, Riau Province)

2019 ◽  
Vol 26 (2) ◽  
pp. 63
Author(s):  
Desti Ayunda ◽  
Ketut Wikantika ◽  
Dandy A. Novresiandi ◽  
Agung B. Harto ◽  
Riantini Virtriana ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Vegetation Index ◽  
Synthetic Aperture ◽  
Optical Remote Sensing ◽  
Error Matrix ◽  
Tropical Peatland ◽  
Average Value ◽  
Sar Imagery ◽  
Aperture Radar

From previous research reported that tropical peatland is one of terrestrial carbon storage in Earth, and has contribution to climate change. Synthetic Aperture Radar (SAR) is one of remote sensing technology which is more efcient than optical remote sensing. Its ability to penetrate cloud makes it useful to monitor tropical environment. This research is conducted in a tropical peatland in Siak Regency, Riau Province. This research was conducted to identify tropical peatland in Siak Regency using polarimetric decomposition, unsupervised classifcation ISODATA, and Radar Vegetation Index (RVI) from SAR data that had been geometrically and radiometrically corrected. Polarimetric decomposition Freeman-Durden was performed to analyze radar backscattering mechanism in tropical peatland, which shows that volume and surface scattering was dominant because of the presence of vegetation and open area. Unsupervised classifcation ISODATA was then performed to extract “shrub class”. By assessing its accuracy, the class that represents shrub class in reference map was selected as the selected “shrub class”. RVI then was calculated using a certain formula. Spatial analysis was then conducted to acquire certain information that average value of RVI in tropical peatland tend to be higher than in non-tropical peatland. By integrating selected “shrub class” and RVI, peat classes were extracted. The best peat class was selected by comparing with peatland referenced map which is acquired from the Indonesian Agency for Agricultural Resources and Development (IAARD) using error matrix. In this research, the best peat class yielded 73.5 percent of Producer’s Accuracy (PA), 81.6 percent of User’s Accuracy (UA), 66.1 percent of Overall Accuracy (OA), and 0.1079 of Kappa coefcient (Ks).

scholarly journals Complementing Optical Remote Sensing with Synthetic Aperture Radar Observations of Hail Damage Swaths to Agricultural Crops in the Central United States

2020 ◽  
Vol 59 (4) ◽  
pp. 665-685 ◽  
Author(s):  
Jordan R. Bell ◽  
Esayas Gebremichael ◽  
Andrew L. Molthan ◽  
Lori A. Schultz ◽  
Franz J. Meyer ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Synthetic Aperture Radar ◽  
Vegetation Index ◽  
Growing Season ◽  
Synthetic Aperture ◽  
Optical Remote Sensing ◽  
Hail Damage ◽  
Sar Imagery ◽  
Moisture Conditions ◽  
Aperture Radar

AbstractThe normalized difference vegetation index (NDVI) has been frequently used to map hail damage to vegetation, especially in agricultural areas, but observations can be blocked by cloud cover during the growing season. Here, the European Space Agency’s Sentinel-1A/1B C-band synthetic aperture radar (SAR) imagery in co- and cross polarization is used to identify changes in backscatter of corn and soybeans damaged by hail during intense thunderstorm events in the early and late growing season. Following a June event, hail-damaged areas produced a lower mean backscatter when compared with surrounding, unaffected pixels [vertical–vertical (VV): −1.1 dB; vertical–horizontal (VH): −1.5 dB]. Later, another event in August produced an increase in co- and cross-polarized backscatter (VV: 0.7 dB; VH: 1.7 dB) that is hypothesized to result from the combined effects of crop growth, change in structure of damaged crops, and soil moisture conditions. Hail damage regions inferred from changes in backscatter were further assessed through coherence change detections to support changes in the structure of crops damaged within the hail swath. While studies using NDVI have routinely concluded a decrease in NDVI is associated with damage, the cause of change with respect to the damaged areas in SAR backscatter values is more complex. Influences of environmental variables, such as vegetation structure, vegetation maturity, and soil moisture conditions, need to be considered when interpreting SAR backscatter and will vary throughout the growing season.

scholarly journals Fault-Based Geological Lineaments Extraction Using Remote Sensing and GIS—A Review

Geosciences ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 183
Author(s):  
Hemayatullah Ahmadi ◽  
Emrah Pekkan
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Thermal Emission ◽  
Active Faults ◽  
Remote Sensing Data ◽  
Shuttle Radar Topography Mission ◽  
Synthetic Aperture ◽  
Optical Remote Sensing ◽  
Remotely Sensed Data ◽  
Aperture Radar

Geological lineaments are the earth’s linear features indicating significant tectonic units in the crust associated with the formation of minerals, active faults, groundwater controls, earthquakes, and geomorphology. This study aims to provide a systematic review of the state-of-the-art remote sensing techniques and data sets employed for geological lineament analysis. The critical challenges of this approach and the diverse data verification and validation techniques will be presented. Thus, this review spanned academic articles published since 1975, including expert reports and theses. Landsat series, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Sentinel 2 are the prevalent optical remote sensing data widely used for lineament detection. Moreover, Shuttle Radar Topography Mission (SRTM) derived Digital Elevation Model (DEM), Synthetic-aperture radar (SAR), Interferometric synthetic aperture radar (InSAR), and Sentinel 1 are the typical radar remotely sensed data which are widely used for the detection of geological lineaments. The geological lineaments acquired via GIS techniques are not consistent even though a variety of manual, semi-automated, and automated techniques are applied. Therefore, a single method may not provide an accurate lineament distribution and may include artifacts requiring integration of multiple algorithms, e.g., manual and automated algorithms.

DETECTION AND MAPPING OF FLOATING OIL EMULSIONS WITH SYNTHETIC APERTURE RADAR (SAR) IMAGERY

2014 ◽  
Vol 2014 (1) ◽  
pp. 300657 ◽  
Author(s):  
Oscar Garcia-Pineda ◽  
Ian MacDonald ◽  
Chuanmin Hu ◽  
Jan Svejkovsky ◽  
Mark Hess ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Ocean Surface ◽  
Synthetic Aperture ◽  
Water Detection ◽  
L Band ◽  
Sar Imagery ◽  
Remote Sensing Techniques ◽  
Oil Emulsions ◽  
Aperture Radar

Detection of floating oil on the ocean surface, and particularly thick layers, is crucial for emergency response to accidental oil discharges. While detection of oil presence on the ocean surface is relatively easy under most conditions with a variety of remote sensing techniques, estimation of the thickness of oil layers is technically challenging. In this paper we use Synthetic Aperture Radar (SAR) imagery collected during the DeepWater Horizon (DWH) oil spill and the Texture Classifier Neural Network Algorithm (TCNNA) to identify SAR image signatures that may correspond to regions of very thick emulsified oil. These locations were generally consistent with sea level observations and optical and thermal remote sensing instruments. Oil emulsions form after crude oil is discharged in the ocean and is subjected to weathering and coagulation processes that increase thicknesses of floating oil layers. The method of detection identifies regions of increased radar backscattering within larger regions of oil-covered water. Detection is dependent on SAR incident angles and the type of SAR beam mode configuration. L-band SAR was found to have the largest window of incidence angles (16 – 38o off-nadir) that could be used to detect oil emulsions. C-band SAR showed a narrower window (18 – 32o off-nadir) than L-band, while X-band SAR had the narrowest window (20 – 31o off-nadir). The results suggest that in case of future spills in the ocean, SAR data may be used to find locations of thick oil to help make management decisions.

scholarly journals Urban Sensing with Spaceborne Interferometric Synthetic Aperture Radar

2021 ◽  
pp. 345-365
Author(s):  
Hongyu Liang ◽  
Wenbin Xu ◽  
Xiaoli Ding ◽  
Lei Zhang ◽  
Songbo Wu
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Optical Remote Sensing ◽  
Microwave Radar ◽  
Urban Remote Sensing ◽  
Multi Temporal ◽  
Urban Sensing ◽  
Interferometric Sar ◽  
Aperture Radar

AbstractSynthetic aperture radar (SAR) and interferometric SAR (InSAR) are state-of-the-art radar remote sensing technologies and are very useful for urban remote sensing. The technologies have some very special characteristics compared to optical remote sensing and are especially advantageous in cloudy regions due to the ability of the microwave radar signals used by the current SAR sensors to penetrate clouds. This chapter introduces the basic concepts of SAR, differential InSAR, and multi-temporal InSAR, and their typical applications in urban remote sensing. Examples of applying the various InSAR techniques in generating DEMs and monitoring ground and infrastructure deformation are given. The capabilities and limitations of InSAR techniques in urban remote sensing are briefly discussed.

scholarly journals Mapping Land Cover Based on Time Series Synthetic Aperture Radar (SAR) Data in Klaten, Indonesia

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Vidya Nahdhiyatul Fikriyah
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Synthetic Aperture Radar ◽  
Agricultural Land ◽  
Unsupervised Classification ◽  
Synthetic Aperture ◽  
Optical Remote Sensing ◽  
Tropical Regions ◽  
Aperture Radar

<p><em>Information </em><em>on </em><em>the existing land cover is important for land management and planning because it can represent the intensity, location, and pattern of human activities. However, mapping land cover in tropical regions is not easy when using optical remote sensing due to the scarcity of cloud-free images. Therefore, the objective of this study is to map the land cover in Klaten Regency using a time-series Sentinel-1 data. Sentinel-1 data is one of remote sensing image</em><em>s</em><em> with Synthetic Aperture Radar (SAR) system which is well known by its capabilit</em><em>y</em><em> of cloud penetration and all-weather observation. A time-series Sentinel-1 data of both polarisations, VV and VH were automatically classified using an unsupervised classification technique, ISODATA. The results show that the land cover classifications obtained overall accuracies of 79</em><em>.</em><em>26% and 73</em><em>.</em><em>79</em><em>% </em><em>for VV and VH respectively. It is also found that Klaten is still dominated by the vegetated land (agriculture and non-agricultural land).</em><em> T</em><em>hese results suggest the opportunity of mapping land cover using SAR multi temporal data. </em></p><p><strong><em> </em></strong></p><p><strong><em> Keywords</em></strong><em>: </em><em>Land cover; Synthetic Aperture Radar; Time series; Sentinel-1; Klaten</em><em></em></p>

scholarly journals Earth Environmental Monitoring Using Multi-Temporal Synthetic Aperture Radar: A Critical Review of Selected Applications

2021 ◽  
Vol 13 (4) ◽  
pp. 604
Author(s):  
Donato Amitrano ◽  
Gerardo Di Martino ◽  
Raffaella Guida ◽  
Pasquale Iervolino ◽  
Antonio Iodice ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Microwave Remote Sensing ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Special Focus ◽  
Arid Environments ◽  
Synthetic Aperture Radar Data ◽  
Multi Temporal ◽  
Aperture Radar

Microwave remote sensing has widely demonstrated its potential in the continuous monitoring of our rapidly changing planet. This review provides an overview of state-of-the-art methodologies for multi-temporal synthetic aperture radar change detection and its applications to biosphere and hydrosphere monitoring, with special focus on topics like forestry, water resources management in semi-arid environments and floods. The analyzed literature is categorized on the base of the approach adopted and the data exploited and discussed in light of the downstream remote sensing market. The purpose is to highlight the main issues and limitations preventing the diffusion of synthetic aperture radar data in both industrial and multidisciplinary research contexts and the possible solutions for boosting their usage among end-users.

Deformation information extraction method based on differential synthetic aperture radar interferometry

2020 ◽  
Vol 39 (4) ◽  
pp. 5311-5318
Author(s):  
Zhengquan Hu ◽  
Yu Liu ◽  
Xiaowei Niu ◽  
Guoping Lei
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Adaptive Filtering ◽  
Extraction Method ◽  
Deformation Monitoring ◽  
Radar Interferometry ◽  
Synthetic Aperture ◽  
Filtering Method ◽  
Synthetic Aperture Radar Interferometry ◽  
Aperture Radar

As aerospace technology, computer technology, network communication technology and information technology become more and more perfect, a variety of sensors for measurement and remote sensing are constantly emerging, and the ability to acquire remote sensing data is also continuously enhanced. Synthetic Aperture Radar Interferometry (InSAR) technology greatly expands the function and application field of imaging radar. Differential InSAR (DInSAR) developed based on InSAR technology has the advantages of high precision and all-weather compared with traditional measurement methods. However, DInSAR-based deformation monitoring is susceptible to spatiotemporal coherence, orbital errors, atmospheric delays, and elevation errors. Since phase noise is the main error of InSAR, to determine the appropriate filtering parameters, an iterative adaptive filtering method for interferogram is proposed. For the limitation of conventional DInSAR, to improve the accuracy of deformation monitoring as much as possible, this paper proposes a deformation modeling based on ridge estimation and regularization as a constraint condition, and introduces a variance component estimation to optimize the deformation results. The simulation experiment of the iterative adaptive filtering method and the deformation modeling proposed in this paper shows that the deformation information extraction method based on differential synthetic aperture radar has high precision and feasibility.

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