scholarly journals Deriving slope movements for an imminent landslide along the Jinsha river

2020 ◽  
Author(s):  
Wentao Yang ◽  
Lianyou Liu ◽  
Peijun Shi
Keyword(s):  
Remote Sensing ◽  
Correlation Method ◽  
Image Correlation ◽  
Optical Remote Sensing ◽  
Mountain Regions ◽  
Jinsha River ◽  
River Bank ◽  
Slope Movements ◽  
Tibet Autonomous Region ◽  
Landsat 7

Abstract. Landslides are major hazards that may pose serious threats to mountain communities. Even landslides in remote mountains could have non-negligible impacts on populous regions by blocking large rivers and forming megafloods. Usually, there are slope deformations before major landslides occur, and detecting precursors over large mountain regions is important for screening possible landslide disasters. In this work, we applied multi-temporal optical remote sensing images (Landsat 7 and Sentinel-2) and an image correlation method to detect sub-pixel slope deformations of a slope. Along the Jinsha river, this slope is located downstream the famous Baige landslide near the Mindu town, Tibet Autonomous Region. We used DEM derived aspect to restrain background noises in image correlation results. We found the slope remained stable from November 2015 to November 2018 and moved significantly from November 2018 to November 2019. We used more data to analyse slope movement in 2019 and found retrogressive slope movements with increasingly large deformations near the river bank. We also analysed spatial-temporal patterns of the slope deformation from October 2018 to February 2020 and found seasonal variations in slope deformations. Only the slope foot moved in dry seasons, whereas the entire slope activated in rainy seasons. Until 24 August 2019, the size of the slope with displacements larger than 3 m is similar to that of the Baige landslide. However, the river width at the foot of this slope is much narrower than the river width at the foot of the Baige landslide. We speculate it may continue to slide down and could threaten the Jinsha river. Further modelling works should be done to check if the imminent landslide could dam the Jinsha river and measures be taken to mitigate possible dammed breach flood disasters. This work illustrates the potential of using optical remote sensing to monitor slope deformations over large remote mountain regions.

scholarly journals Detecting precursors of an imminent landslide along the Jinsha River

2020 ◽  
Vol 20 (11) ◽  
pp. 3215-3224
Author(s):  
Wentao Yang ◽  
Lianyou Liu ◽  
Peijun Shi
Keyword(s):  
Remote Sensing ◽  
Correlation Method ◽  
Slope Movement ◽  
Image Correlation ◽  
Optical Remote Sensing ◽  
Jinsha River ◽  
Tibet Autonomous Region ◽  
Landsat 7 ◽  
Multi Temporal ◽  
The Right

Abstract. Landslides are major hazards that may pose serious threats to mountain communities. Even landslides in remote mountains could have non-negligible impacts on populous regions by blocking large rivers and forming dam-breached mega floods. Usually, there are slope deformations before major landslides occur, and detecting precursors such as slope movement before major landslides is important for preventing possible disasters. In this work, we applied multi-temporal optical remote sensing images (Landsat 7 and Sentinel-2) and an image correlation method to detect subpixel slope deformations of a slope near the town of Mindu in the Tibet Autonomous Region. This slope is located on the right bank of the Jinsha River, ∼80 km downstream from the famous Baige landslide. We used a DEM-derived aspect to restrain background noise in image correlation results. We found the slope remained stable from November 2015 to November 2018 and moved significantly from November 2018. We used more data to analyse slope movement in 2019 and found retrogressive slope movements with increasingly large deformations near the riverbank. We also analysed spatial–temporal patterns of the slope deformation from October 2018 to February 2020 and found seasonal variations in slope deformations. Only the foot of the slope moved in dry seasons, whereas the entire slope was activated in rainy seasons. Until 24 August 2019, the size of the slope with displacements larger than 3 m was similar to that of the Baige landslide. However, the river width at the foot of this slope is much narrower than the river width at the foot of the Baige landslide. We speculate it may continue to slide down and threaten the Jinsha River. Further modelling works should be carried out to check if the imminent landslide could dam the Jinsha River and measures should be taken to mitigate possible dam breach flood disasters. This work illustrates the potential of using optical remote sensing to monitor slope deformations over remote mountain regions.

scholarly journals Timely prediction potential of landslide early warning systems with multispectral remote sensing: a conceptual approach tested in the Sattelkar, Austria

2021 ◽  
Vol 21 (9) ◽  
pp. 2753-2772
Author(s):  
Doris Hermle ◽  
Markus Keuschnig ◽  
Ingo Hartmeyer ◽  
Robert Delleske ◽  
Michael Krautblatter
Keyword(s):  
Remote Sensing ◽  
Early Warning ◽  
Lead Time ◽  
Early Warning Systems ◽  
Image Correlation ◽  
Optical Data ◽  
Optical Remote Sensing ◽  
Warning Systems ◽  
Conceptual Approach ◽  
Landslide Early Warning

Abstract. While optical remote sensing has demonstrated its capabilities for landslide detection and monitoring, spatial and temporal demands for landslide early warning systems (LEWSs) had not been met until recently. We introduce a novel conceptual approach to structure and quantitatively assess lead time for LEWSs. We analysed “time to warning” as a sequence: (i) time to collect, (ii) time to process and (iii) time to evaluate relevant optical data. The difference between the time to warning and “forecasting window” (i.e. time from hazard becoming predictable until event) is the lead time for reactive measures. We tested digital image correlation (DIC) of best-suited spatiotemporal techniques, i.e. 3 m resolution PlanetScope daily imagery and 0.16 m resolution unmanned aerial system (UAS)-derived orthophotos to reveal fast ground displacement and acceleration of a deep-seated, complex alpine mass movement leading to massive debris flow events. The time to warning for the UAS/PlanetScope totals 31/21 h and is comprised of time to (i) collect – 12/14 h, (ii) process – 17/5 h and (iii) evaluate – 2/2 h, which is well below the forecasting window for recent benchmarks and facilitates a lead time for reactive measures. We show optical remote sensing data can support LEWSs with a sufficiently fast processing time, demonstrating the feasibility of optical sensors for LEWSs.

scholarly journals Challenging the timely prediction of landside early warning systems with multispectral remote sensing: a novel conceptual approach tested in the Sattelkar, Austria

2021 ◽  
Author(s):  
Doris Hermle ◽  
Markus Keuschnig ◽  
Ingo Hartmeyer ◽  
Robert Delleske ◽  
Michael Krautblatter
Keyword(s):  
Remote Sensing ◽  
Optical Sensors ◽  
Early Warning ◽  
Lead Time ◽  
Early Warning Systems ◽  
Image Correlation ◽  
Optical Data ◽  
Optical Remote Sensing ◽  
Warning Systems ◽  
Conceptual Approach

Abstract. While optical remote sensing has demonstrated its capabilities for landslide detection and monitoring, spatial and temporal demands for landslide early warning systems (LEWS) were not met until recently. We introduce a novel conceptual approach for comprehensive lead time assessment and optimisation for LEWS. We analysed time to warning as a sequence; (i) time to collect, (ii) to process and (iii) to evaluate relevant optical data. The difference between time to warning and forecasting window (i.e. time from hazard becoming predictable until event) is the lead time for reactive measures. We tested digital image correlation (DIC) of best–suited spatiotemporal techniques, i.e. 3 m resolution PlanetScope daily imagery, and 0.16 m resolution UAS derived orthophotos to reveal fast ground displacement and acceleration of a deep–seated, complex alpine mass movement leading to massive debris flow events. The time to warning for UAS and PlanetScope totals 31 h/21 h and is comprised of (i) time to collect 12/14 h, (ii) process 17/5 h and (iii) evaluate 2/2 h, which is well below the forecasting window for recent benchmarks and facilitates lead time for reactive measures. We show optical remote sensing data can support LEWS with a sufficiently fast processing time, demonstrating the feasibility of optical sensors for LEWS.

Multi-temporal analysis of optical remote sensing for time-series displacement of gravitational mass movements, Sattelkar, Obersulzbach Valley, Austria

2021 ◽  
Author(s):  
Doris Hermle ◽  
Michele Gaeta ◽  
Markus Keuschnig ◽  
Paolo Mazzanti ◽  
Michael Krautblatter
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Ground Motion ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Hot Spot ◽  
Gravitational Mass ◽  
Image Correlation ◽  
Mass Movements ◽  
Optical Remote Sensing

<p>Remote sensing for natural hazard assessment and applications offers data on even vast areas, often difficult and dangerous to access. Today, satellite data providers such as PlanetLabs Inc. and the European Copernicus program provide a sub-weekly acquisition frequency of high resolution multispectral imagery. The availability of this high temporal data density suggests that the detection of short-term changes is possible; however, limitations of this data regarding qualitative, spatiotemporal reliability for the early warning of gravitational mass movements have not been analysed and extensively tested.</p><p>This study analyses the effective detection and monitoring potential of PlanetScope Ortho Tiles (3.125 m, daily revisit rate) and Sentinel-2 (10 m, 5-day revisit) satellite imagery between 2018 and 2020. These results are compared to high accuracy UAS orthoimages (0.16 m, 5 acquisitions from 2018-2020). The analysis is conducted based on digital image correlation (DIC) using COSI-Corr (Caltech), a well-established software and the newly developed IRIS (NHAZCA). The mass wasting processes in a steep, glacially-eroded, high alpine cirque, Sattelkar (2’130-2’730 m asl), Austria, are investigated. It is surrounded by a headwall of granitic gneiss with a cirque infill characterised by massive volumes of glacial and periglacial debris including rockfall deposits. Since 2003 the dynamics of these processes have been increased, and between 2012-2015 rates up to 30 m/a were observed.</p><p>Similar results are returned by the two software tools regarding hot-spot detection and signal-to-noise ratio; nonetheless IRIS results in an overall better detection, including a more delimitable ground motion area, with its iterative reference and secondary image combination. This analysis is supported by field investigations as well as clearly demarcated DIC-results from UAS imagery. Here, COSI-Corr shows limitations in the form of decorrelation and ambiguous velocity vectors due to high ground motion and surface changes for very high resolution of this input data. In contrast, IRIS performs better returning more coherent displacement rates. The results of both DIC tools for satellite images are affected by spatial resolution, data quality and imprecise image co-registration.</p><p>Knowledge of data potential and applicability is of high importance for a reliable and precise detection of gravitational mass movements. UAS data provides trustworthy, relative ground motion rates for moderate velocities and thus the possibility to draw conclusions regarding landslide processes. In contrast satellite data returns results which cannot always be clearly delimited due to spatial resolution, precision, and accuracy. Nevertheless, iterative calculations by IRIS improve the validity of the results.</p>

Estimation of PM10 Distribution using Landsat 7 ETM+ Remote Sensing Data

2017 ◽  
Vol 6 (1) ◽  
pp. 2246-2252 ◽  
Author(s):  
Ajay Roy ◽  
◽  
Anjali Jivani ◽  
Bhuvan Parekh ◽  
◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Landsat 7

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

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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