scholarly journals Earthquake-induced landslides monitoring and survey by means of InSAR

2021 ◽  
Author(s):  
Tayeb Smail ◽  
Mohamed Abed ◽  
Ahmed Mebarki ◽  
Milan Lazecky
Keyword(s):  
Time Series ◽  
Pilot Study ◽  
Proper Time ◽  
Line Of Sight ◽  
Sar Images ◽  
Ground Deformations ◽  
Site Investigations ◽  
Optical Images ◽  
Interferometric Sar ◽  
Sentinel 2

Abstract. This study uses interferometric SAR techniques to identify landslides and lands prone to landslides, detect fringes and changes in areas struck by earthquakes. The pilot study investigates the Mila region (Algeria) which suffered significant landslides and structural damages (earthquake: Mw 5, 2020-08-07): the study checks ground deformations and tracks earthquake-induced landslides. DInSAR analysis shows normal interferograms, with atmospheric contribution, and slight fringes. However, it identifies many landslides, the most important (2.5 m displacement) being located in Kherba neighborhood, causing severe damages to dwellings. In addition, SAR images and optical images (Sentinel-2) confirm site investigations. Although in Grarem City, optical images could not detect any disorder, the DInSAR analysis detected some coherence decays and small fringes (3.94 km2 area). These unnoticed ground disorders were confirmed during fields inspection. Such results have key importance since they can serve as an alert to monitor the zone at the proper time. Furthermore, Displacement time series analysis of many interferograms (April 2015 to September 2020) using LiCSBAS were performed to investigate the pre-event conditions and precursors of the slopes instabilities., LiCSBAS detects a line-of-sight subsidence velocity of −110 mm/y in the back hillside of Kherba, and high displacement velocity at specific points in Grarem region.

scholarly journals Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

2019 ◽  
Vol 11 (7) ◽  
pp. 820 ◽  
Author(s):  
Haifeng Tian ◽  
Ni Huang ◽  
Zheng Niu ◽  
Yuchu Qin ◽  
Jie Pei ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Time Windows ◽  
Vegetation Indices ◽  
Optical Images ◽  
Landsat 7 ◽  
Multi Temporal ◽  
Winter Crop ◽  
Winter Crops ◽  
Sentinel 2

Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows—a period of low NDVI values and a period of high NDVI values—for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km2, with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses.

scholarly journals Sentinel-2 Cloud Removal Considering Ground Changes by Fusing Multitemporal SAR and Optical Images

2021 ◽  
Vol 13 (19) ◽  
pp. 3998
Author(s):  
Jianhao Gao ◽  
Yang Yi ◽  
Tang Wei ◽  
Haoguan Zhang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Sar Images ◽  
The Real ◽  
Removal Process ◽  
Optical Images ◽  
Cloud Removal ◽  
Real Time Information ◽  
Time Information ◽  
Sentinel 2

Publicly available optical remote sensing images from platforms such as Sentinel-2 satellites contribute much to the Earth observation and research tasks. However, information loss caused by clouds largely decreases the availability of usable optical images so reconstructing the missing information is important. Existing reconstruction methods can hardly reflect the real-time information because they mainly make use of multitemporal optical images as reference. To capture the real-time information in the cloud removal process, Synthetic Aperture Radar (SAR) images can serve as the reference images due to the cloud penetrability of SAR imaging. Nevertheless, large datasets are necessary because existing SAR-based cloud removal methods depend on network training. In this paper, we integrate the merits of multitemporal optical images and SAR images to the cloud removal process, the results of which can reflect the ground information change, in a simple convolution neural network. Although the proposed method is based on deep neural network, it can directly operate on the target image without training datasets. We conduct several simulation and real data experiments of cloud removal in Sentinel-2 images with multitemporal Sentinel-1 SAR images and Sentinel-2 optical images. Experiment results show that the proposed method outperforms those state-of-the-art multitemporal-based methods and overcomes the constraint of datasets of those SAR-based methods.

scholarly journals Mapping Paddy Fields in Japan by Using a Sentinel-1 SAR Time Series Supplemented by Sentinel-2 Images on Google Earth Engine

2020 ◽  
Vol 12 (10) ◽  
pp. 1622 ◽  
Author(s):  
Shimpei Inoue ◽  
Akihiko Ito ◽  
Chinatsu Yonezawa
Keyword(s):  
Time Series ◽  
Paddy Field ◽  
Water Resource Management ◽  
Mapping Method ◽  
Google Earth ◽  
Paddy Fields ◽  
Optical Images ◽  
Sar Data ◽  
Decision Tree Method ◽  
Sentinel 2

Paddy fields play very important environmental roles in food security, water resource management, biodiversity conservation, and climate change. Therefore, reliable broad-scale paddy field maps are essential for understanding these issues related to rice and paddy fields. Here, we propose a novel paddy field mapping method that uses Sentinel-1 synthetic aperture radar (SAR) time series that are robust for cloud cover, supplemented by Sentinel-2 optical images that are more reliable than SAR data for extracting irrigated paddy fields. Paddy fields were provisionally specified by using the Sentinel-1 SAR data and a conventional decision tree method. Then, an additional mask using water and vegetation indexes based on Sentinel-2 optical images was overlaid to remove non-paddy field areas. We used the proposed method to develop a paddy field map for Japan in 2018 with a 30 m spatial resolution. The producer’s accuracy of this map (92.4%) for non-paddy reference agricultural fields was much higher than that of a map developed by the conventional method (57.0%) using only Sentinel-1 data. Our proposed method also reproduced paddy field areas at the prefecture scale better than existing paddy field maps developed by a remote sensing approach.

scholarly journals BUILDING DETECTION FROM SAR IMAGES USING UNET DEEP LEARNING METHOD

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 215-218
Author(s):  
R. A. Emek ◽  
N. Demir
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Optical Image ◽  
Building Detection ◽  
Multispectral Images ◽  
Learning Method ◽  
Sar Images ◽  
Optical Images ◽  
Image Patches ◽  
Sentinel 2

Abstract. SAR images are different from the optical images in terms of image properties with the values of scattering instead of reflectance. This makes SAR images difficult to apply the traditional object detection methodologies. In recent years, deep learning models are frequently used in segmentation and object detection purposes. In this study, we have investigated the potential of U-Net models for building detection from SAR and optical image fusion. The datasets used are Sentinel 1 SAR and Sentinel-2 multispectral images, provided from ‘SpaceNet 6 Multi Sensor All-Weather Mapping’ challenge. These images cover an area of 120 km2 in Rotterdam, the Netherlands. As training datasets 20 pieces of 900 by 900 pixel sized HV polarized and optical image patches have been used together. The calculated loss value is 0.4 and the accuracy is 81%.

scholarly journals MULTI-SENSOR APPROACH TO LEAF AREA INDEX ESTIMATION USING STATISTICAL MACHINE LEARNING MODELS: A CASE ON MANGROVE FORESTS

2021 ◽  
Vol V-3-2021 ◽  
pp. 109-115
Author(s):  
K. P. Martinez ◽  
D. F. M. Burgos ◽  
A. C. Blanco ◽  
S. G. Salmo III
Keyword(s):  
Machine Learning ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Machine Learning Algorithms ◽  
Optical Parameters ◽  
Mangrove Forests ◽  
Sar Images ◽  
Area Index ◽  
Optical Images ◽  
Sentinel 2

Abstract. Leaf Area Index (LAI) is a quantity that characterizes canopy foliage content. As leaf surfaces are the primary sites of energy, mass exchange, and fundamental production of terrestrial ecosystem, many important processes are directly proportional to LAI. With this, LAI can be considered as an important parameter of plant growth. Multispectral optical images have been widely utilized for mangrove-related studies, such as LAI estimation. In Sentinel-2, for example, LAI can be estimated using a biophysical processor in SNAP or using various machine learning algorithms. However, multispectral optical images have disadvantages due to its weather-dependence and limited canopy penetration. In this study, a multi-sensor approach was implemented by using free multi-spectral optical images (Sentinel-2 ) and synthetic aperture radar (SAR) images (Sentinel-1) to perform Leaf Area Index (LAI) estimation. The use of SAR images can compensate for the above-mentioned disadvantages and it then can pave the way for regular mapping and assessment of LAI, despite any weather conditions and cloud cover. In this study, generation of LAI models that explores linear, non-linear and decision trees modelling algorithms to incorporate Sentinel-1 derivatives and Sentinel-2 LAI were executed. The Random Forest model have exhibited the most robust model having the lowest RMSE of 0.2845. This result poses a concrete relationship of a biophysical entity derived from optical parameters to RADAR derivatives to which opens the opportunity of integrating both systems to compensate each disadvantages and produce a more efficient quantification of LAI.

scholarly journals Estimation of Equivalent Number of Looks in Time-Series Pol(In)SAR Data

2020 ◽  
Vol 12 (17) ◽  
pp. 2715
Author(s):  
Peng Shen ◽  
Changcheng Wang ◽  
Haiqiang Fu ◽  
Jianjun Zhu ◽  
Jun Hu
Keyword(s):  
Time Series ◽  
Speckle Noise ◽  
Statistical Characteristics ◽  
Data Types ◽  
Sar Images ◽  
Practical Applications ◽  
Equivalent Number ◽  
Essential Parameter ◽  
Sar Data ◽  
Interferometric Sar

As an essential parameter in synthetic aperture radar (SAR) images, the equivalent number of looks (ENL) not only indicates the speckle noise level in multi-look SAR data but also can be used for evaluating the region homogeneity level. Currently, time-series polarimetric (interferometric) SAR (TSPol(In)SAR) data are increasingly abundant, but traditional equivalent number of looks (ENL) estimators only use polarimetric information from a mono-temporal observation and do not consider the temporal characteristics or interferometric coherence of ground targets. Therefore, this paper puts forward four novel ENL estimators to overcome the restrictions of inadequate observation information. Firstly, based on the traditional trace moment estimator for polarimetric SAR data (TM-PolSAR), we extend it to both PolInSAR and TSPolInSAR data and then propose both TM-PolInSAR and TM-TSPolInSAR estimators, respectively. Secondly, for both TSPolSAR and single-reference TSPolInSAR data, we estimate the ENL by stacking the trace moments (STM) of multitemporal coherency matrices, called STM-TSPolSAR and STM-TSPolInSAR estimators, respectively. Therefore, these proposed ENL estimators can effectively deal with most of the requirements of TSPol(In)SAR data types in practical applications, mainly including statistical distribution modeling and region homogeneity evaluation. The simulation and real experiments detailedly compare the proposed four ENL estimators to the classical TM-PolSAR estimator and quantitatively analyze the estimation performance. The proposed estimators have obtained the ENL with less bias and standard deviation than the traditional estimator, especially in case of small spatial samples coherency matrices. Additionally, these STM-TSPolSAR, STM-TSPolInSAR, and TM-TSPolInSAR estimators have provided more effective statistical characteristics with the increase of the time-series size. It has been demonstrated that the proposed STM-TSPolSAR estimator considers the time-varying polarimetric characteristics of the crop and detects many edges that the traditional estimator cannot discover, which means a superior capability of region homogeneity evaluation.

scholarly journals Separability of Mowing and Ploughing Events on Short Temporal Baseline Sentinel-1 Coherence Time Series

2020 ◽  
Vol 12 (22) ◽  
pp. 3784
Author(s):  
Kaupo Voormansik ◽  
Karlis Zalite ◽  
Indrek Sünter ◽  
Tanel Tamm ◽  
Kalev Koppel ◽  
...  
Keyword(s):  
Time Series ◽  
Management Practices ◽  
Coherence Time ◽  
Sar Interferometry ◽  
Large Dataset ◽  
Wide Area Monitoring ◽  
Detection Algorithms ◽  
Optical Images ◽  
Area Monitoring ◽  
Sentinel 2

Short temporal baseline regular Synthetic Aperture Radar (SAR) interferometry is a tool well suited for wide area monitoring of agricultural activities, urgently needed in European Union Common Agricultural Policy (CAP) enforcement. In this study, we demonstrate and describe in detail, how mowing and ploughing events can be identified from Sentinel-1 6-day interferometric coherence time series. The study is based on a large dataset of 386 dual polarimetric Sentinel-1 VV/VH SAR and 351 Sentinel-2 optical images, and nearly 2000 documented mowing and ploughing events on more than 1000 parcels (average 10.6 ha, smallest 0.6 ha, largest 108.5 ha). Statistical analysis revealed that mowing and ploughing cause coherence to increase when compared to values before an event. In the case of mowing, the coherence increased from 0.18 to 0.35, while Sentinel-2 NDVI (indicating the amount of green chlorophyll containing biomass) at the same time decreased from 0.75 to 0.5. For mowing, there was virtually no difference between the polarisations. After ploughing, VV-coherence grew up to 0.65 and VH-coherence to 0.45, while NDVI was around 0.2 at the same time. Before ploughing, both coherence and NDVI values were very variable, determined by the agricultural management practices of the parcel. Results presented here can be used for planning further studies and developing mowing and ploughing detection algorithms based on Sentinel-1 data. Besides CAP enforcement, the results are also useful for food security and land use change detection applications.

scholarly journals Exploitation of the full potential of PSI data for subsidence monitoring

2015 ◽  
Vol 372 ◽  
pp. 311-314 ◽  
Author(s):  
M. Crosetto ◽  
N. Devanthéry ◽  
M. Cuevas-González ◽  
O. Monserrat ◽  
B. Crippa
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Full Potential ◽  
Persistent Scatterer Interferometry ◽  
Sar Images ◽  
Remote Sensing Technique ◽  
Persistent Scatterer ◽  
Land Deformation ◽  
Interferometric Sar ◽  
Subsidence Monitoring

Abstract. Persistent Scatterer Interferometry (PSI) is a remote sensing technique used to measure and monitor land deformation from a stack of interferometric SAR images. The main products that can be derived using the PSI technique are the deformation maps and the time series of deformation. In this paper, an approach to apply the PSI technique to a stack of Sentinel-1 images is described. Moreover, the problems encountered during the processing are detailed and an explanation of how they were dealt with is provided. Finally, Sentinel-1 deformation maps and time series obtained over the metropolitan area of Mexico DF are shown.

Combination of high frequency (Sentinel-2) and high resolution (Pléiades) satellite images for the monitoring of clayey landslide reactivations, application to the Harmalière landslide (French Alps).

2020 ◽  
Author(s):  
Denis Jongmans ◽  
Sylvain Fiolleau ◽  
Gregory Bièvre ◽  
Guillaume Chambon ◽  
Pascal Lacroix
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
High Resolution ◽  
High Frequency ◽  
Satellite Images ◽  
Cross Correlation ◽  
Temperate Climate ◽  
Optical Images ◽  
Slow Moving ◽  
Sentinel 2

<p>Many regions of the world are exposed to landslides in clay deposits, which poses major problems for land management and population safety. In recent years, optical satellite imaging has emerged as a major and inexpensive tool for understanding and monitoring the kinematics of slow moving landslides, such as earthflows/earthslides, through easy access of data and reliable calibration.</p><p>The Sentinel-2 optical satellites provide a global coverage of land surfaces with a 5-day revisit time at the Equator. We studied the ability of these freely available optical images to detect landslide reactivations in a zone of 25 km<sup>2</sup> around the Harmalière landslide in the Trièves area (western Alps, France). This area is characterized by the presence of a thick lacustrine clay layer that is affected by numerous landslides. Using a 9-month time-series of displacement derived from Sentinel-2 data, Lacroix et al. 2018 recently evidenced a precursor displacement of a major reactivation of the Harmalière landslide that occurred in June 2016.</p><p>In this study, we attempted to detect following reactivations using the medium resolution high frequency satellite images (Sentinel 2) coupled with high resolution images (Pléiades) over a longer period (2016- 2019). We used an inversion strategy of redundant cross-correlation images to produce a robust time-series of displacement from Sentinel 2 data (Bontemps et al. 2018). By applying this technique, we were able to identify a reactivation of the same order of magnitude as the previous one, which affected the headscarp in January 2017. The reactivation signal is validated by the cross-correlation of Pléiades images taken at 2 years interval. We quantified this reactivation in time and space. We have also identified an area of 30x10<sup>3</sup> m2 located at the foot of the landslide, which was simultaneously accelerated by 10 m/month during this event. This information contributes to better understand the dynamics of the landslide that evolves from a solid to fluid behavior from the headscarp to the toe. However, a smaller slide that occurred in January 2018 at the headscarp was not detected by this method despite its significant size (10x10<sup>3</sup> m<sup>2</sup>). We attribute this non-detection to a major reshaping of the surface following reactivation.</p><p>This study identified the possibilities and limitations of the proposed treatment method to detect and monitor landslides on a low-slope area located in clayey soils in a temperate climate.</p><p> </p><p>Bontemps, N., Lacroix, P. & Doin, M.-P. (2018) Inversion of deformation fields time-series from optical images, and application to the long term kinematics of slow-moving landslides in Peru. Remote Sensing of Environment, <strong>210</strong>, 144–158. doi:10.1016/j.rse.2018.02.023</p><p>Lacroix, P., Bièvre, G., Pathier, E., Kniess, U. & Jongmans, D. (2018) Use of Sentinel-2 images for the detection of precursory motions before landslide failures. Remote Sensing of Environment, <strong>215</strong>, 507–516. doi:10.1016/j.rse.2018.03.042</p>

scholarly journals Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain

2019 ◽  
Vol 11 (15) ◽  
pp. 1836 ◽  
Author(s):  
Hassan Bazzi ◽  
Nicolas Baghdadi ◽  
Dino Ienco ◽  
Mohammad El Hajj ◽  
Mehrez Zribi ◽  
...  
Keyword(s):  
Time Series ◽  
Water Resource Management ◽  
Vegetation Index ◽  
Scale Up ◽  
Principal Component ◽  
Radar Data ◽  
Temporal Series ◽  
Optical Images ◽  
Combined Use ◽  
Sentinel 2

Mapping irrigated plots is essential for better water resource management. Today, the free and open access Sentinel-1 (S1) and Sentinel-2 (S2) data with high revisit time offers a powerful tool for irrigation mapping at plot scale. Up to date, few studies have used S1 and S2 data to provide approaches for mapping irrigated plots. This study proposes a method to map irrigated plots using S1 SAR (synthetic aperture radar) time series. First, a dense temporal series of S1 backscattering coefficients were obtained at plot scale in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations over a study site located in Catalonia, Spain. In order to remove the ambiguity between rainfall and irrigation events, the S1 signal obtained at plot scale was used conjointly to S1 signal obtained at a grid scale (10 km × 10 km). Later, two mathematical transformations, including the principal component analysis (PCA) and the wavelet transformation (WT), were applied to the several SAR temporal series obtained in both VV and VH polarization. Irrigated areas were then classified using the principal component (PC) dimensions and the WT coefficients in two different random forest (RF) classifiers. Another classification approach using one dimensional convolutional neural network (CNN) was also performed on the obtained S1 temporal series. The results derived from the RF classifiers with S1 data show high overall accuracy using the PC values (90.7%) and the WT coefficients (89.1%). By applying the CNN approach on SAR data, a significant overall accuracy of 94.1% was obtained. The potential of optical images to map irrigated areas by the mean of a normalized differential vegetation index (NDVI) temporal series was also tested in this study in both the RF and the CNN approaches. The overall accuracy obtained using the NDVI in RF classifier reached 89.5% while that in the CNN reached 91.6%. The combined use of optical and radar data slightly enhanced the classification in the RF classifier but did not significantly change the accuracy obtained in the CNN approach using S1 data.

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