scholarly journals Mapping Outburst Floods Using a Collaborative Learning Method Based on Temporally Dense Optical and SAR Data: A Case Study with the Baige Landslide Dam on the Jinsha River, Tibet

2021 ◽  
Vol 13 (11) ◽  
pp. 2205
Author(s):  
Zhongkang Yang ◽  
Jinbing Wei ◽  
Jianhui Deng ◽  
Yunjian Gao ◽  
Siyuan Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Landslide Dam ◽  
Flood Mapping ◽  
Outburst Flood ◽  
Backscattering Coefficient ◽  
Sar Images ◽  
Jinsha River ◽  
Outburst Floods ◽  
Sar Data ◽  
Sentinel 2

Outburst floods resulting from giant landslide dams can cause devastating damage to hundreds or thousands of kilometres of a river. Accurate and timely delineation of flood inundated areas is essential for disaster assessment and mitigation. There have been significant advances in flood mapping using remote sensing images in recent years, but little attention has been devoted to outburst flood mapping. The short-duration nature of these events and observation constraints from cloud cover have significantly challenged outburst flood mapping. This study used the outburst flood of the Baige landslide dam on the Jinsha River on 3 November 2018 as an example to propose a new flood mapping method that combines optical images from Sentinel-2, synthetic aperture radar (SAR) images from Sentinel-1 and a Digital Elevation Model (DEM). First, in the cloud-free region, a comparison of four spectral indexes calculated from time series of Sentinel-2 images indicated that the normalized difference vegetation index (NDVI) with the threshold of 0.15 provided the best separation flooded area. Subsequently, in the cloud-covered region, an analysis of dual-polarization RGB false color composites images and backscattering coefficient differences of Sentinel-1 SAR data were found an apparent response to ground roughness’s changes caused by the flood. We carried out the flood range prediction model based on the random forest algorithm. Training samples consisted of 13 feature vectors obtained from the Hue-Saturation-Value color space, backscattering coefficient differences/ratio, DEM data, and a label set from the flood range prepared from Sentinel-2 images. Finally, a field investigation and confusion matrix tested the prediction accuracy of the end-of-flood map. The overall accuracy and Kappa coefficient were 92.3%, 0.89 respectively. The full extent of the outburst floods was successfully obtained within five days of its occurrence. The multi-source data merging framework and the massive sample preparation method with SAR images proposed in this paper, provide a practical demonstration for similar machine learning applications using remote sensing.

scholarly journals COLORIZING SENTINEL-1 SAR IMAGES USING A VARIATIONAL AUTOENCODER CONDITIONED ON SENTINEL-2 IMAGERY

2018 ◽  
Vol XLII-2 ◽  
pp. 1045-1051 ◽  
Author(s):  
M. Schmitt ◽  
L. H. Hughes ◽  
M. Körner ◽  
X. X. Zhu
Keyword(s):  
Remote Sensing ◽  
Image Fusion ◽  
Single Channel ◽  
Image Interpretation ◽  
Sar Image ◽  
Sar Images ◽  
Variational Autoencoder ◽  
Future Work ◽  
Special Case ◽  
Sentinel 2

In this paper, we have shown an approach for the automatic colorization of SAR backscatter images, which are usually provided in the form of single-channel gray-scale imagery. Using a deep generative model proposed for the purpose of photograph colorization and a Lab-space-based SAR-optical image fusion formulation, we are able to predict artificial color SAR images, which disclose much more information to the human interpreter than the original SAR data. Future work will aim at further adaption of the employed procedure to our special case of multi-sensor remote sensing imagery. Furthermore, we will investigate if the low-level representations learned intrinsically by the deep network can be used for SAR image interpretation in an end-to-end manner.

scholarly journals COMBINED ANALYSIS OF RADARSAT-2 SAR AND SENTINEL-2 OPTICAL DATA FOR IMPROVED MONITORING OF TUBER INITIATION STAGE OF POTATO

2018 ◽  
Vol XLII-5 ◽  
pp. 275-279 ◽  
Author(s):  
D. Mandal ◽  
V. Kumar ◽  
Y. S. Rao ◽  
A. Bhattacharya ◽  
S. Bera ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Temporal Dynamics ◽  
Potato Production ◽  
Tuber Initiation ◽  
Optical Data ◽  
Growth Monitoring ◽  
Tuber Bulking ◽  
Potato Tuberization ◽  
Sar Data ◽  
Sentinel 2

<p><strong>Abstract.</strong> Tuber initiation and tuber bulking stages are critical part of various phenological phases for potato production. Tuber initiation covers the period from the formation of spherical rhizome ends, the flowering and the start of tuber bulking. In general, the tuberization spans from 3 to 5 weeks after emergence and ends with the row closer i.e. canopies in adjacent rows touch each other across the furrow. Hence, this rapid growth seeks critical agronomic management practices such as irrigation and fertilization. It majorly influences the growth of stems, foliar area, dry weight and number of tubers particularly at the phase of tuber initiation. During these phenological stages, potato crops are susceptible to the infestation of late blight diseases caused by <i>Phytophthora infestans</i> and largely affects the potato production. Thus identifying the crop risk using remote sensing approaches can provide an efficient potato growth monitoring framework. In the context of monitoring crop dynamics, quad-pol Synthetic Aperture Radar (SAR) data has proven to be effective due to its sensitivity towards dielectric and geometric properties. In addition to SAR data, optical remote sensing data derived vegetation information can provide an improved insight into crop growth when combined with SAR data. In this research, quad-pol RADARSAT-2 and Sentinel-2 optical data are analyzed to monitor potato tuberization phase over Bardhaman district in the state ofWest Bengal, which is one of the major potato growing regions in India. The proposed approach uses polarimetric parameters such as backscatter intensities, ratio (HH/VV, VH/VV, linear depolarization ratio), and co-pol correlation (<i>&amp;rho;<sub>HH–VV</sub></i>) along with the vegetation indices derived from the Sentinel-2 data for understanding the spatio-temporal dynamics. The initial results show a promising accuracy in monitoring the dynamics of potato tuberization. Integration of such earth observation (EO) data, in conjunction with in-situ field measurements, might significantly enhance the current capabilities for crop monitoring.</p>

An attempt to understand corn and winter wheat temporal behavior by means of Sentinel-1 and Sentinel-2 data and its relation with phonological stages.

2021 ◽  
Author(s):  
Marta Pasternak ◽  
Kamila Pawluszek-Filipiak
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Strong Correlation ◽  
Vegetation Index ◽  
Radar Data ◽  
Temporal Behavior ◽  
Spectral Indices ◽  
Chlorophyll Index ◽  
Sar Data ◽  
Sentinel 2

&lt;p&gt;Crops are of the fundamental food sources for humanity. Due to the population growth as well as climate change, monitoring of the crops is important to sustain agriculture and conserve natural resources. Development of the remote sensing techniques especially in terms of revisiting time opens new avenues to study crops temporal behaviors from space. Moreover, thanks to the Copernicus program, which guarantees optical as well as radar data to be freely available, there are opportunities to utilize them in an operative way. Additionally, utilization of spectral as well as radar data allows for the synergetic application of both datasets. However, to utilize this data in the operational crop monitoring, it is very important to understand the temporal variations of the remote sensing signal. Therefore, we make an attempt to understand spectral as well as radar remote sensing temporal behavior and its relation with phonological stages.&lt;/p&gt;&lt;p&gt;For the analysis, 14 cloud-free Sentinel-2 (S-2) acquisitions as well as 34 Sentinel-1 (S-1) acquisitions are utilized. S-2 data were collected with 2A-level while S-1 data was captured in the format of Single Look Complex (SLC) in the Interferometric Wide (IW) swath mode. SLC products consist of complex SAR data preserving phase information which allows studying polarimetric indicators. All remote sensing (spectral as well as SAR) data cover the time period from 04/05/2020 to 07/11/2020. During this time, also 14 field visits were carried out to capture information about phonological stages of corn and wheat according to the BBCH scale (Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie). Additionally, to better understand the temporal behavior of S-1/S-2 signal, weather information from the Institute of Meteorology and Water Management (IMGW) was captured.&lt;/p&gt;&lt;p&gt;Based on various spectral bands of S-2 data, 12 spectral indices were calculated e.g., GNDVI (Green Normalized Vegetation Index), IRECI (Inverted Red-Edge Chlorophyll Index), MCARI (Modified Chlorophyll Absorption in Reflectance Index), MSAVI (Modified Soil-Adjusted Vegetation Index), MTCI (MERIS Terrestrial Chlorophyll Index), NDVI (Normalized Difference Vegetation Index), PSSRa (Pigment Specific Simple Ratio) and others. After radiometric calibration and the Lee speckle filtering, backscattering coefficients (&amp;#963;&lt;sub&gt;VV&lt;/sub&gt;&lt;sup&gt;o&lt;/sup&gt; ,&amp;#963;&lt;sub&gt;VH&lt;/sub&gt;&lt;sup&gt;o&lt;/sup&gt;) of S-1 images were calculated as well as its backscattering ratio (&amp;#963;&lt;sub&gt;VH&lt;/sub&gt;&lt;sup&gt;o&lt;/sup&gt;/ &amp;#963;&lt;sub&gt;VV&lt;/sub&gt;&lt;sup&gt;o&lt;/sup&gt;).&amp;#160; All images were then converted from linear to decibel (dB). Additionally, 2 &amp;#215; 2 covariance matrix delivered from S-1 was extracted from the scattering matrix of each SLC image using PolSARpro version 6.0.2 software. After speckle filtration, total scattered power was derived which allows calculating the Shannon Entropy. This value measures the randomness of the scattering within a pixel.&lt;/p&gt;&lt;p&gt;Time series of many S-2 indices reveal the strong correlation between the development of phenology stages of corn and wheat and the increase of S2 delivered values of spectral indices. However, such a strong correlation cannot be observed within many of S-1 indices. Some of them very poorly indicate the correlation between the development of phenology stages of corn and wheat and increase of S-1 indices values. Additionally, it was observed that values of S1/S2 indices for the same phenology stage very between corn and winter wheat.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals Experimental Investigation of the Outburst Discharge of Landslide Dam Overtopping Failure

2021 ◽  
Vol 9 ◽  
Author(s):  
Mingjun Zhou ◽  
Zhenming Shi ◽  
Gordon G. D. Zhou ◽  
Kahlil Fredrick E. Cui ◽  
Ming Peng
Keyword(s):  
Landslide Dam ◽  
Longitudinal Direction ◽  
Peak Discharge ◽  
Outburst Flood ◽  
Outburst Floods ◽  
Flume Tests ◽  
Loose Deposits ◽  
Overtopping Failure ◽  
Solids Content ◽  
Inflow Discharge

Research on the factors and mechanisms that influence outburst floods are essential for estimating outflow hydrographs and the resulting inundation. In this study, large flume tests are conducted to investigate the effects of the upstream inflow and the presence of loose erodible deposits on the breaching flow and the subsequent outburst floods. Experimental results reveal that hydrographs of the breaching flow and outburst flood can be divided into three stages where each stage is separated by inflection points and peak discharges. It is found that the larger the inflow discharge, the larger the peak discharge of the outburst flood and the shorter the time needed to reach the peak and inflection discharges of the outburst flood. The breaching flow decreases along the longitudinal direction at rates that increase with the inflow discharge. The ratio between the length of the upstream dam shoulder and the dam width is inversely related to the ratio of the outburst discharge to inflow discharge. We also show that the presence of loose deposits at the dam toe can amplify the peak discharge of outburst flood by increasing the solids content of the water flow.

scholarly journals Rapid Flood Mapping and Evaluation with a Supervised Classifier and Change Detection in Shouguang Using Sentinel-1 SAR and Sentinel-2 Optical Data

2020 ◽  
Vol 12 (13) ◽  
pp. 2073 ◽  
Author(s):  
Minmin Huang ◽  
Shuanggen Jin
Keyword(s):  
Land Cover ◽  
Change Detection ◽  
Early Stage ◽  
Optical Data ◽  
Hazard Evaluation ◽  
Flood Mapping ◽  
Special Importance ◽  
Sar Images ◽  
Water Index ◽  
Sentinel 2

Rapid flood mapping is crucial in hazard evaluation and forecasting, especially in the early stage of hazards. Synthetic aperture radar (SAR) images are able to penetrate clouds and heavy rainfall, which is of special importance for flood mapping. However, change detection is a key part and the threshold selection is very complex in flood mapping with SAR. In this paper, a novel approach is proposed to rapidly map flood regions and estimate the flood degree, avoiding the critical step of thresholding. It converts the change detection of thresholds to land cover backscatter classifications. Sentinel-1 SAR images are used to get the land cover backscatter classifications with the help of Sentinel-2 optical images using a supervised classifier. A pixel-based change detection is used for change detection. Backscatter characteristics and variation rules of different ground objects are essential prior knowledge for flood analysis. SAR image classifications of pre-flood and flooding periods both take the same input to make sense of the change detection between them. This method avoids the inaccuracy caused by a single threshold. A case study in Shouguang is tested by this new method, which is compared with the flood map extracted by Otsu thresholding and normalized difference water index (NDWI) methods. The results show that our approach can identify the flood beneath vegetation well. Moreover, all required data and data processing are simple, so it can be popularized in rapid flooding mapping in early disaster relief.

scholarly journals Landslide-lake outburst floods accelerate downstream slope slippage

2021 ◽  
Author(s):  
Wentao Yang ◽  
Jian Fang ◽  
Jing Liu-Zeng
Keyword(s):  
River Channel ◽  
Disaster Mitigation ◽  
Jinsha River ◽  
Mountainous Regions ◽  
Deformation Response ◽  
Outburst Floods ◽  
Dynamic Channel ◽  
One Year ◽  
Landslide Lake ◽  
Sentinel 2

Abstract. The Jinsha River, carving a 2–4 km deep gorge, is one of the largest SE Asian rivers. Two successive landslide-lake outburst floods (LLFs) occurred after the 2018 Baige landslides along the river. Using Sentinel-2 images, we examined the LLFs' impacts on downstream river channel and adjacent hillslopes over a 100 km distance. The floods increased the width of the active river channel by 54 %. Subsequently, major landslides persisted for 15 months in at least nine locations for displacements > 2 m. Among them, three moving hillslopes, ~80 km downstream from the Baige landslides, slumped more than 10 m one year after the floods. Extensive undercuts by the floods probably removed hillslope buttresses and triggered deformation response, suggesting a strong and dynamic channel-hillslope coupling. Our findings indicate that infrequent catastrophic outburst flooding plays an important role in landscape evolution. Persistent post-flood hillslope movement should be considered in disaster mitigation in high-relief mountainous regions.

scholarly journals Landslide-lake outburst floods accelerate downstream hillslope slippage

2021 ◽  
Vol 9 (5) ◽  
pp. 1251-1262
Author(s):  
Wentao Yang ◽  
Jian Fang ◽  
Jing Liu-Zeng
Keyword(s):  
River Channel ◽  
Disaster Mitigation ◽  
River Channels ◽  
Jinsha River ◽  
Mountainous Regions ◽  
Deformation Response ◽  
Outburst Floods ◽  
Dynamic Channel ◽  
Landslide Lake ◽  
Sentinel 2

Abstract. The Jinsha River, which has carved a 2–4 km deep gorge, is one of the largest SE Asian rivers. Two successive landslide-lake outburst floods (LLFs) occurred after the 2018 Baige landslides along the river. Using Sentinel-2 images, we examined the LLF impacts on downstream river channels and adjacent hillslopes over a 100 km distance. The floods increased the width of the active river channel by 54 %. Subsequently, major landslides persisted for 15 months in at least nine locations for displacements >2 m. Among them, three moving hillslopes ∼80 km downstream from the Baige landslides slumped more than 10 m 1 year after the floods. Extensive undercuts by floods probably removed hillslope buttresses and triggered a deformation response, suggesting strong and dynamic channel–hillslope coupling. Our findings indicate that infrequent catastrophic outburst flooding plays an important role in landscape evolution. Persistent post-flood hillslope movement should be considered in disaster mitigation in high-relief mountainous regions.

scholarly journals Mapping of the risk of coastal erosion for two case studies: Pianosa island (Tuscany) and Piscinas (Sardinia)

2020 ◽  
pp. 713-722
Author(s):  
Nicola Ghirardi ◽  
Mariano Bresciani ◽  
Giulia Luciani ◽  
Gianfranco Fornaro ◽  
Virginia Zamparelli ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Case Studies ◽  
Satellite Data ◽  
Coastal Erosion ◽  
Dune System ◽  
Sar Images ◽  
Erosion Risk ◽  
Risk Maps ◽  
Sentinel 2

This study focuses on the use of remote sensing to generate coastal erosion risk maps for Pianosa Island (Tuscany) and Piscinas dune system (Sardinia). The method made use of both ancillary and satellite data (Sentinel-2), in addition to SAR images (COSMO SkyMed and Sentinel-1B). TOA radiance products were atmospherically corrected and processed using Sen2Coral and BOMBER in order to map different marine substrates and bathymetry. The coastal erosion risk maps have been generated based on these output and the results confirm that the coasts of these sites don’t have coastal erosion problems.

Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images

CATENA ◽  
2021 ◽  
Vol 205 ◽  
pp. 105442
Author(s):  
Xianglin He ◽  
Lin Yang ◽  
Anqi Li ◽  
Lei Zhang ◽  
Feixue Shen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Sentinel 2

scholarly journals Geomorphic effects of recurrent outburst superfloods in the Yigong River on the southeastern margin of Tibet

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kaiheng Hu ◽  
Chaohua Wu ◽  
Li Wei ◽  
Xiaopeng Zhang ◽  
Qiyuan Zhang ◽  
...  
Keyword(s):  
Landslide Dam ◽  
Upstream Migration ◽  
One Dimensional ◽  
Landform Evolution ◽  
Outburst Floods ◽  
Stepwise Method ◽  
Large Landslide ◽  
Southeastern Margin ◽  
Channel Slope ◽  
The One

AbstractLandslide dam outburst floods have a significant impact on landform evolution in high mountainous areas. Historic landslide dams on the Yigong River, southeastern Tibet, generated two outburst superfloods > 105 m3/s in 1902 and 2000 AD. One of the slackwater deposits, which was newly found immediately downstream of the historic dams, has been dated to 7 ka BP. The one-dimensional backwater stepwise method gives an estimate of 225,000 m3/s for the peak flow related to the paleo-stage indicator of 7 ka BP. The recurrence of at least three large landslide dam impoundments and super-outburst floods at the exit of Yigong Lake during the Holocene greatly changed the morphology of the Yigong River. More than 0.26 billion m3 of sediment has been aggraded in the dammed lake while the landslide sediment doubles the channel slope behind the dam. Repeated landslide damming may be a persistent source of outburst floods and impede the upstream migration of river knickpoints in the southeastern margin of Tibet.

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