Using pre-failure and post-failure remote sensing data to constrain the three-dimensional numerical model of a large rock slope failure

Landslides ◽  
2020 ◽  
Author(s):  
Davide Donati ◽  
Doug Stead ◽  
Marc-André Brideau ◽  
Monica Ghirotti
Keyword(s):  
Remote Sensing ◽  
Numerical Model ◽  
Slope Failure ◽  
Rock Slope ◽  
Three Dimensional ◽  
Remote Sensing Data ◽  
Sensing Data

Tsunami Assessment and Evacuation Analysis Using Remote Sensing for Tianya District of Sanya City, China

2019 ◽  
Vol 13 (05n06) ◽  
pp. 1941003
Author(s):  
Jingming Hou ◽  
Zhiyuan Ren ◽  
Peitao Wang ◽  
Juncheng Wang ◽  
Yi Gao
Keyword(s):  
Remote Sensing ◽  
Numerical Model ◽  
Infrared Imaging ◽  
Remote Sensing Data ◽  
Tsunami Inundation ◽  
Sensing Data ◽  
Inundation Area ◽  
Nighttime Lights ◽  
Elevation Data ◽  
Landsat Satellite

Tsunami is one of the world’s most dangerous marine disaster. In this paper, freely available remote sensing data are applied to study the hazard, vulnerability, and evacuation in the event that a tsunami strikes the district of Tianya in the city of Sanya. Tsunami inundation is calculated using a tsunami numerical model, and the tsunami vulnerability and evacuation in the inundation area are analyzed. Aster Global Digital Elevation Model elevation data are applied to provide input data for the tsunami numerical model and thus obtain tsunami inundation areas, while they are also used to study the surface slope for evacuation. Landsat satellite imagery is used to analyze land–water borders and land cover in both hazard assessment and evacuation analysis. Visible Infrared Imaging Radiometer Suite nighttime lights data provide information of the socioeconomic activity for vulnerability analysis. The analysis results show that the remote sensing data is suitable for tsunami assessment and evacuation analysis of China’s county-level region. We can get a general understanding about tsunami vulnerability and evacuation situation. One kind of remote sensing data can accomplish several tasks, avoiding the error caused by different source data. Remote sensing can play an important role in tsunami assessment.

Modelling the surface circulation and thermal structure of a tropical reservoir using three-dimensional hydrodynamic lake model and remote-sensing data

2013 ◽  
Vol 28 (4) ◽  
pp. 516-525 ◽  
Author(s):  
Marcelo Pedroso Curtarelli ◽  
Enner Alcântara ◽  
Camilo Daleles Rennó ◽  
Arcilan Trevenzoli Assireu ◽  
Marie Paule Bonnet ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Thermal Structure ◽  
Three Dimensional ◽  
Remote Sensing Data ◽  
Tropical Reservoir ◽  
Sensing Data ◽  
Surface Circulation ◽  
Lake Model

scholarly journals Indirect Validation of Ocean Remote Sensing Data via Numerical Model: An Example of Wave Heights from Altimeter

2020 ◽  
Vol 12 (16) ◽  
pp. 2627 ◽  
Author(s):  
Haoyu Jiang
Keyword(s):  
Remote Sensing ◽  
Numerical Model ◽  
Numerical Models ◽  
Remote Sensing Data ◽  
Validation Method ◽  
Wave Hindcast ◽  
Sensing Data ◽  
Data Pair ◽  
Wave Heights ◽  
Ocean Remote Sensing

Using numerical model outputs as a bridge, an indirect validation method for remote sensing data was developed to increase the number of effective collocations between remote sensing data to be validated and reference data. The underlying idea for this method is that the local spatial-temporal variability of specific parameters provided by numerical models can compensate for the representativeness error induced by differences of spatial-temporal locations of the collocated data pair. Using this method, the spatial-temporal window for collocation can be enlarged for a given error tolerance. To test the effectiveness of this indirect validation approach, significant wave height (SWH) data from Envisat were indirectly compared against buoy and Jason-2 SWHs, using the SWH gradient information from a numerical wave hindcast as a bridge. The results indicated that this simple indirect validation method is superior to “direct” validation.

scholarly journals A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset

2020 ◽  
Vol 12 (23) ◽  
pp. 3888
Author(s):  
Mingyuan Peng ◽  
Lifu Zhang ◽  
Xuejian Sun ◽  
Yi Cen ◽  
Xiaoyang Zhao
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Temporal Resolution ◽  
Three Dimensional ◽  
Remote Sensing Data ◽  
High Temporal Resolution ◽  
Fusion Method ◽  
Sensing Data ◽  
Resolution Data

With the growing development of remote sensors, huge volumes of remote sensing data are being utilized in related applications, bringing new challenges to the efficiency and capability of processing huge datasets. Spatiotemporal remote sensing data fusion can restore high spatial and high temporal resolution remote sensing data from multiple remote sensing datasets. However, the current methods require long computing times and are of low efficiency, especially the newly proposed deep learning-based methods. Here, we propose a fast three-dimensional convolutional neural network-based spatiotemporal fusion method (STF3DCNN) using a spatial-temporal-spectral dataset. This method is able to fuse low-spatial high-temporal resolution data (HTLS) and high-spatial low-temporal resolution data (HSLT) in a four-dimensional spatial-temporal-spectral dataset with increasing efficiency, while simultaneously ensuring accuracy. The method was tested using three datasets, and discussions of the network parameters were conducted. In addition, this method was compared with commonly used spatiotemporal fusion methods to verify our conclusion.

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