Land deformation monitoring using optical remote sensing and PS-InSAR technique nearby Gangotri glacier in higher Himalayas

The topography and landforms of Guizhou Province in China are complicated, and the climatic conditions of heavy precipitation make landslide disasters in Guizhou Province occur frequently. To avoid damage to people’s lives and economic property caused by disasters, a reliable early landslide identification method and landslide monitoring method are urgently needed. Traditional landslide identification and monitoring methods have limitations. InSAR technology has unique advantages in large-scale landslide identification and monitoring, but landslide identification results based on a single deformation value are one-sided. Therefore, this paper uses Sentinel-1A radar satellite image data and uses InSAR technology and optical remote sensing technology to carry out large-scale surface deformation monitoring and identification of dangerous deformation areas in Liupanshui City, Tongren City, Guiyang City and other regions in Guizhou Province. The potential landslide identification methods based on the time series normalized difference vegetation index and landslide development environment elements are combined to investigate hidden landslide hazards in the study area. In this paper, time series InSAR technology is used to monitor three key landslides in Jichang Town, Yujiaying and Fana, to grasp the movement status of the landslide in time. The method of landslide identification and monitoring in this paper is of great significance for disaster prevention and management in Guizhou Province.

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A DLM-LSTM Framework for North-South Land Deformation Trend Analysis from Low-Cost GPS Sensor Time Series

Journal of Sensors ◽

10.1155/2018/3054295 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Fangling Pu ◽

Zhaozhuo Xu ◽

Hongyu Chen ◽

Xin Xu ◽

Nengcheng Chen

Keyword(s):

Remote Sensing ◽

Time Series ◽

Trend Analysis ◽

Low Cost ◽

Periodic Variation ◽

Deformation Monitoring ◽

High Temporal Resolution ◽

Land Deformation ◽

Lstm Network ◽

Remote Sensing Methods

Landslides endanger regular industrial production and human safety. Displacement trend analysis gives us an explicit way to observe and forecast landslides. Although satellite-borne remote sensing methods such as synthetic aperture radar have gradually replaced manual measurement in detecting deformation trends, they fail to observe displacement in a north-south direction. Wireless low-cost GPS sensors have been developed to assist remote sensing methods in north-south deformation monitoring because of their high temporal resolution and wide usage. In our paper, a DLM-LSTM framework is developed to extract and predict north-south land deformation trends from meter accuracy GPS receivers. A dynamic linear model is introduced to model the relation between measurement and the state vector, including the trend, periodic variation, and autoregressive factors in a discontinuous low-cost latitude time series. The deformation trend with submeter-level accuracy is extracted by a Kalman filter and smoother. With validated input as in previous work, the power of an LSTM network is also shown in its ability to predict deformation trends in submeter-level accuracy. A submeter-level deformation trend is detected from wireless low-cost GPS sensors with meter-level navigation error. The framework will have broad application prospects in geological disaster monitoring.

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356. Development of an Semi-OMER Beam Configuration for Identification of Gas Leak Time and Location With Use of an Optical Remote Sensing and Computed Tomographic Technique

10.3320/1.2762758 ◽

1999 ◽

Author(s):

D.Y. Park ◽

M.G. Yost ◽

R. Hashimonay ◽

C.F. Wu

Keyword(s):

Remote Sensing ◽

Optical Remote Sensing ◽

Computed Tomographic ◽

Gas Leak ◽

Tomographic Technique

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Arbitrary Oriented Ship Detection in Optical Remote Sensing Images via Partially Supervised Learning

2020 39th Chinese Control Conference (CCC) ◽

10.23919/ccc50068.2020.9188661 ◽

2020 ◽

Author(s):

Linhao Li ◽

Zhiqiang Zhou ◽

Lingjuan Miao ◽

Junfu Liu ◽

Xiaowu Xiao

Keyword(s):

Remote Sensing ◽

Supervised Learning ◽

Optical Remote Sensing ◽

Remote Sensing Images ◽

Ship Detection ◽

Partially Supervised Learning

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Characterization of the auroral ionosphere using optical remote-sensing techniques

10.2514/6.1980-1383 ◽

1980 ◽

Author(s):

R. SEARS

Keyword(s):

Remote Sensing ◽

Optical Remote Sensing ◽

Auroral Ionosphere ◽

Remote Sensing Techniques

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Optical remote sensing of the ocean

10.2514/6.1984-380 ◽

1984 ◽

Author(s):

W. HOVIS

Keyword(s):

Remote Sensing ◽

Optical Remote Sensing

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Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

Engineering and Technology Journal ◽

10.30684/etj.v38i4a.399 ◽

2020 ◽

Vol 38 (4A) ◽

pp. 510-514

Author(s):

Tay H. Shihab ◽

Amjed N. Al-Hameedawi ◽

Ammar M. Hamza

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Artificial Neural Network ◽

Random Forest ◽

Satellite Image ◽

Landsat 8 ◽

Optical Remote Sensing ◽

Data Set ◽

Artificial Neural ◽

Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019. They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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