Quantitative, Near Real-Time Mapping of Bushfires Through Integration of Optical and SAR Remote Sensing Techniques

2021 ◽  
Author(s):  
Linlin Ge ◽  
Yufei Wang ◽  
Qi Zhang ◽  
Zheyuan Du ◽  
Chang Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Remote Sensing Techniques ◽  
Sar Remote Sensing

scholarly journals Multi-Spectral Ship Detection Using Optical, Hyperspectral, and Microwave SAR Remote Sensing Data in Coastal Regions

2018 ◽  
Vol 10 (11) ◽  
pp. 4064 ◽  
Author(s):  
Kyung-Ae Park ◽  
Jae-Jin Park ◽  
Jae-Cheol Jang ◽  
Ji-Hyun Lee ◽  
Sangwoo Oh ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Satellite Images ◽  
Coastal Region ◽  
Probability Of Detection ◽  
Constant False Alarm Rate ◽  
Coastal Regions ◽  
Ship Detection ◽  
Radar Images ◽  
Sar Remote Sensing

The necessity of efficient monitoring of ships in coastal regions has been increasing over time. Multi-satellite observations make it possible to effectively monitor vessels. This study presents the results of ship detection methodology, applied to optical, hyperspectral, and microwave satellite images in the seas around the Korean Peninsula. Spectral matching algorithms are used to detect ships using hyperspectral images with hundreds of spectral channels and investigate the similarity between the spectra and in-situ measurements. In the case of SAR (Synthetic Aperture Radar) images, the Constant False Alarm Rate (CFAR) algorithm is used to discriminate the vessels from the backscattering coefficients of Sentinel-1B SAR and ALOS-2 PALSAR2 images. Validation results exhibited that the locations of the satellite-detected vessels showed good agreement with real-time location data within the Sentinel-1B coverage in the Korean coastal region. This study presented the probability of detection values of optical and SAR-based ship detection and discussed potential causes of the errors. This study also suggested a possibility for real-time operational use of vessel detection from multi-satellite images based on optical, hyperspectral, and SAR remote sensing, particularly in the inaccessible coastal regions off North Korea, for comprehensive coastal management and sustainability.

scholarly journals Evaluation of Grass Quality under Different Soil Management Scenarios Using Remote Sensing Techniques

2019 ◽  
Vol 11 (15) ◽  
pp. 1835 ◽  
Author(s):  
Mohammad Sadegh Askari ◽  
Timothy McCarthy ◽  
Aidan Magee ◽  
Darren J. Murphy
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Quality Indicators ◽  
Prediction Models ◽  
Unmanned Aircraft ◽  
Multispectral Imagery ◽  
European Climate ◽  
Grass Biomass ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Hyperspectral and multispectral imagery have been demonstrated to have a considerable potential for near real-time monitoring and mapping of grass quality indicators. The objective of this study was to evaluate the efficiency of remote sensing techniques for quantification of aboveground grass biomass (BM) and crude protein (CP) in a temperate European climate such as Ireland. The experiment was conducted on 64 plots and 53 paddocks with varying quantities of nitrogen applied. Hyperspectral imagery (HSI) and multispectral imagery (MSI) were analyzed to develop the prediction models. The MSI data used in this study were captured using an unmanned aircraft vehicle (UAV) and the satellite Sentinel-2, while the HSI data were obtained using a handheld hyperspectral camera. The prediction models were developed using partial least squares regression (PLSR) and stepwise multi-linear regression (MLR). Eventually, the spatial distribution of grass biomass over plots and paddocks was mapped to assess the within-field variability of grass quality metrics. An excellent accuracy was achieved for the prediction of BM and CP using HSI (RPD > 2.5 and R2 > 0.8), and a good accuracy was obtained via MSI-UAV (2 < RPD < 2.5 and R2 > 0.7) for the grass quality indicators. The accuracy of the models calculated using MSI-Sentinel-2 was reasonable for BM prediction and insufficient for CP estimation. The red-edge range of the wavelengths showed the maximum impact on the predictability of grass BM, and the NIR range had the greatest influence on the estimation of grass CP. Both the PLSR and MLR techniques were found to be sufficiently robust for spectral modelling of aboveground BM and CP. The PLSR yielded a slightly better model than MLR. This study suggested that remote sensing techniques can be used as a rapid and reliable approach for near real-time quantitative assessment of fresh grass quality under a temperate European climate.

Smart Technologies for Emergency Response and Disaster Management

2019 ◽  
pp. 939-979
Author(s):  
Kavitha T ◽  
Saraswathi S
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Urban Areas ◽  
Earth Observation ◽  
Extreme Temperatures ◽  
Sensing Technology ◽  
Sensor Information ◽  
Remote Sensing Techniques ◽  
Smart Technologies ◽  
Timely Warning

Disasters are the convergence of hazards that strikes a vulnerable community which is insufficient to withstand with its adverse effects and impact. Completely avoiding natural or anthropogenic disaster is not possible but its impact can be minimized by generating timely warning. The real-time earth observation is very important for generating such early warning. The earth observation is improving through the advancement in remote sensing technologies. Sensing technology provides real time monitoring and risk assessment. It helps in fast communication of an event occurrence. Disaster detection in urban areas is greatly improved by using remote sensing techniques. This chapter discus various devices used for real time earth monitoring of disaster events like Flood, Tsunami, Tornadoes, Droughts, Extreme Temperatures, Avalanches and Landslide. These devices gather information by continuous monitoring in their deployed location. The sensor information thus gathered must be communicated and processed to extract the disaster information.

Smart Technologies for Emergency Response and Disaster Management

2018 ◽  
pp. 1-40
Author(s):  
Kavitha T ◽  
Saraswathi S
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Urban Areas ◽  
Earth Observation ◽  
Extreme Temperatures ◽  
Sensing Technology ◽  
Sensor Information ◽  
Remote Sensing Techniques ◽  
Smart Technologies ◽  
Timely Warning

Disasters are the convergence of hazards that strikes a vulnerable community which is insufficient to withstand with its adverse effects and impact. Completely avoiding natural or anthropogenic disaster is not possible but its impact can be minimized by generating timely warning. The real-time earth observation is very important for generating such early warning. The earth observation is improving through the advancement in remote sensing technologies. Sensing technology provides real time monitoring and risk assessment. It helps in fast communication of an event occurrence. Disaster detection in urban areas is greatly improved by using remote sensing techniques. This chapter discus various devices used for real time earth monitoring of disaster events like Flood, Tsunami, Tornadoes, Droughts, Extreme Temperatures, Avalanches and Landslide. These devices gather information by continuous monitoring in their deployed location. The sensor information thus gathered must be communicated and processed to extract the disaster information.

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