Supplementary material to "Effects of multi-temporal high-resolution remote sensing products on simulated hydrometeorological variables in a cultivated area (southwestern France)"

2017 ◽  
Author(s):  
Jordi Etchanchu ◽  
Vincent Rivalland ◽  
Simon Gascoin ◽  
Jérôme Cros ◽  
Aurore Brut ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Multi Temporal ◽  
Supplementary Material

Multi-scale analysis of landslide occurrence and evolution using optical and radar time series

2020 ◽  
Author(s):  
Sigrid Roessner ◽  
Robert Behling ◽  
Mahdi Motagh ◽  
Hans Ulrich-wetzel
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
High Resolution ◽  
Intense Rainfall ◽  
Large Area ◽  
Landslide Occurrence ◽  
Multi Scale ◽  
Multi Temporal ◽  
Hazard And Risk

<p>Landslides represent a worldwide natural hazard and often occur as cascading effects related to triggering events, such as earthquakes and hydrometeorological extremes. Recent examples are the Kaikoura earthquake in New Zealand (November 2016), the Gorkha earthquake in Nepal (April/May 2015), and the Typhoon Morakot in Taiwan (August 2009) as well as less intense rainfall events persisting over unusually long periods of time as observed for Central Asia (spring 2017) and Iran (spring 2019). Each of these events has caused thousands of landslides that account substantially to the primary disaster’s impact. Moreover, their initial failure usually represents the onset of long-term progressing slope destabilization leading to multiple reactivations and thus to long-term increased hazard and risk. Therefore, regular systematic high-resolution monitoring of landslide prone regions is of key importance for characterization, understanding and modelling of spatiotemporal landslide evolution in the context of different triggering and predisposing settings. Because of the large extent of the affected areas of up to several ten thousands km<sup>2</sup>, the use of multi-temporal and multi-scale remote sensing methods is of key importance for large area process analysis. In this context, new opportunities have opened up with the increasing availability of satellite remote sensing data of suitable spatial and temporal resolution (Sentinels, Planet) as well as the advances in UAV based very high resolution monitoring and mapping.</p><p>During the last decade, we have been pursuing extensive methodological developments in remote sensing based time series analysis including optical and radar observations with the goal of performing large area and at the same time detailed spatiotemporal analysis of landslide prone regions. These developments include automated post-failure landslide detection and mapping as well as assessment of the kinematics of pre- and post-failure slope evolution.  Our combined optical and radar remote sensing approaches aim at an improved understanding of spatiotemporal dynamics and complexities related to evolution of landslide prone slopes at different spatial and temporal scales.  In this context, we additionally integrate UAV-based observation for deriving volumetric changes also related to globally available DEM products, such as SRTM and ALOS.  </p><p>We present results for selected settings comprising large area co-seismic landslide occurrence related to the Kaikoura 2016 and the Nepal 2015 earthquakes. For the latter one we also analyzed annual pre- and post-seismic monsoon related landslide activity contributing to a better understanding of the interplay between these main triggering factors. Moreover, we report on ten years of large area systematic landslide monitoring in Southern Kyrgyzstan resulting in a multi-temporal regional landslide inventory of so far unprecedented spatiotemporal detail and completeness forming the basis for further analysis of the obtained landslide concentration patterns. We also present first results of our analysis of landslides triggered by intense rainfall and flood events in spring of 2019 in the North of Iran. We conclude that in all cases, the obtained results are crucial for improved landslide prediction and reduction of future landslide impact. Thus, our methodological developments represent an important contribution towards improved hazard and risk assessment as well as rapid mapping and early warning</p>

scholarly journals Bridge monitoring and deformation time-series analysis by high-resolution Multi-Temporal SAR Interferometry (MT-InSAR) 

2021 ◽  
Author(s):  
Valerio Gagliardi ◽  
Luca Bianchini Ciampoli ◽  
Amir Alani ◽  
Fabio Tosti ◽  
Andrea Benedetto
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
High Resolution ◽  
Health Monitoring ◽  
Interferometric Synthetic Aperture Radar ◽  
Space Agency ◽  
Multi Temporal ◽  
Italian Space Agency ◽  
Remote Sensing Techniques ◽  
Non Destructive

<p>Multi-temporal Interferometric Synthetic Aperture Radar (InSAR) is a space-borne monitoring technique capable of detecting cumulative surface displacements with millimeter accuracy in the Line of Sight (LOS) of the radar sensor [1-3]. Several developments in the processing methods and the increasing availability of SAR datasets from different satellite missions, have proven the viability of this technique in the near-real-time assessment of bridges and the health monitoring of transport infrastructures [2-4].</p><p>This research aims to demonstrate the potential of satellite-based remote sensing techniques as an innovative health-monitoring method for structural assessment of bridges and the prevention of damages by structural subsidence, using high-resolution SAR datasets integrated with complementary Ground-Based (GB) Non-Destructive Testing (NDT) techniques. To this purpose, high-resolution COSMO‐SkyMed (CSK) products provided by the Italian Space Agency (ASI) were acquired and processed.</p><p>In particular, a multi-temporal InSAR analysis was developed to identify and monitor the structural displacements of the Rochester Bridge, located in Rochester, Kent, UK. To this extent, a clustering operation is realised to collect the identified Persistent Scatterers (PSs) over the structural elements of the bridge (i.e., bridge piers and arcs). Furthermore, several sub-clusters with a comparable deformation trend were identified and located over the bridge elements. This operation paves the way for an automatisation of the process through a Machine Learning (ML) clustering algorithms to assign each PS data-point to specific groups, based on the structural element type and the trend of seasonal deformation time-series.</p><p>The outcomes of this study demonstrate how multi-temporal InSAR remote sensing techniques can be synergistically applied to complement non-destructive ground-based analyses, paving the way for future integrated methodologies in the monitoring of infrastructure assets.</p><p><strong>Acknowledgments: </strong>The authors want to acknowledge the Italian Space Agency (ASI) for providing the COSMO-SkyMed Products® (©ASI, 2017-2019),  in the framework of the ASI-Open Call Project “MoTIB, ID 742” accepted by ASI. In addition, the authors would like to acknowledge the Rochester Bridge Trust for facilitating and supporting this research. This research is supported by the Italian Ministry of Education, University and Research under the National Project “EXTRA TN”, PRIN 2017, Prot. 20179BP4SM.</p><p><strong>References</strong></p><p>[1] Alani A. M., Tosti F., Bianchini Ciampoli L., Gagliardi V., Benedetto A., Integration of GPR and InSAR methods for the health monitoring of masonry arch bridges. NDT&E International. (2020)</p><p>[2] Gagliardi V., Bianchini Ciampoli L., D'Amico F., Alani A. M., Tosti F., Battagliere M. L., Benedetto A., Bridge monitoring and assessment by high-resolution satellite remote sensing technologies, Proc. SPIE 11525, SPIE Future Sensing Technologies. 2020. doi: 10.1117/12.2579700</p><p>[3] Selvakumaran, S., Plank, S., Geiß, C., Rossi, C., Middleton, C. (2018). Remote monitoring to predict bridge scour failure using Interferometric Synthetic Aperture Radar (InSAR) stacking techniques, Int. J. .Appl. Earth Obs. and Geoinf. 73, 463-470.</p><p>[4] Qin X, Liao M., Zhang L., & Yang M., Structural Health and Stability Assessment of High-Speed Railways via Thermal Dilation Mapping with Time-Series InSAR Analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</p>

scholarly journals XÂY DỰNG KHUNG CƠ SỞ DỮ LIỆU SỐ VỀ HẢI DƯƠNG, MÔI TRƯỜNG VÙNG BIỂN NINH THUẬN - BÌNH THUẬN

2018 ◽  
Vol 17 (4) ◽  
pp. 445-458
Author(s):  
Ngo Manh Tien ◽  
Nguyen Huu Huan ◽  
Tran Van Chung ◽  
Tong Phuoc Hoang Son ◽  
Vo Trong Thach ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Coastal Waters ◽  
Remote Sensing Imagery ◽  
Digital Database ◽  
Multi Temporal

This paper presents the building and designing of the digital database frame about the oceanographic and environmental elements of the coastal waters of Ninh Thuan and Binh Thuan provinces, based on the integration of data from the observed, multi-temporal remote sensing as well as simulated sources. Especially, VNREDSat-1 the first remote sensing imagery of Vietnam with high resolution also got the attention in building the digital database frame. The building of the digital database frame is the first important step in order to achieve an overview of data’s structure, then to build the database’s structure. The database’s structure is built in the form of B-tree which optimize the operation when updating the data.

scholarly journals OBJECT MANIFOLD ALIGNMENT FOR MULTI-TEMPORAL HIGH RESOLUTION REMOTE SENSING IMAGES CLASSIFICATION

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 325-332
Author(s):  
G. Gao ◽  
M. Zhang ◽  
Y. Gu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Domain Adaptation ◽  
Spatial Relationship ◽  
Majority Voting ◽  
Alignment Method ◽  
Remote Sensing Images ◽  
High Resolution Satellite Images ◽  
Manifold Alignment ◽  
Multi Temporal

Multi-temporal remote sensing images classification is very useful for monitoring the land cover changes. Traditional approaches in this field mainly face to limited labelled samples and spectral drift of image information. With spatial resolution improvement, “pepper and salt” appears and classification results will be effected when the pixelwise classification algorithms are applied to high-resolution satellite images, in which the spatial relationship among the pixels is ignored. For classifying the multi-temporal high resolution images with limited labelled samples, spectral drift and “pepper and salt” problem, an object-based manifold alignment method is proposed. Firstly, multi-temporal multispectral images are cut to superpixels by simple linear iterative clustering (SLIC) respectively. Secondly, some features obtained from superpixels are formed as vector. Thirdly, a majority voting manifold alignment method aiming at solving high resolution problem is proposed and mapping the vector data to alignment space. At last, all the data in the alignment space are classified by using KNN method. Multi-temporal images from different areas or the same area are both considered in this paper. In the experiments, 2 groups of multi-temporal HR images collected by China GF1 and GF2 satellites are used for performance evaluation. Experimental results indicate that the proposed method not only has significantly outperforms than traditional domain adaptation methods in classification accuracy, but also effectively overcome the problem of “pepper and salt”.

A novel change detection approach based on visual saliency and random forest from multi-temporal high-resolution remote-sensing images

2018 ◽  
Vol 39 (22) ◽  
pp. 7998-8021 ◽  
Author(s):  
Wenqing Feng ◽  
Haigang Sui ◽  
Jihui Tu ◽  
Weiming Huang ◽  
Kaimin Sun
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Random Forest ◽  
Change Detection ◽  
Visual Saliency ◽  
Remote Sensing Images ◽  
Detection Approach ◽  
Multi Temporal

scholarly journals Present extent, features and regional distribution of Italian glaciers

2019 ◽  
pp. 159-175 ◽  
Author(s):  
Guglielmina Adele Diolaiuti ◽  
Roberto Sergio Azzoni ◽  
Carlo D'Agata ◽  
Davide Maragno ◽  
Davide Fugazza ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Regional Scale ◽  
Regional Distribution ◽  
Alpine Region ◽  
Glacier Area ◽  
Glacier Inventory ◽  
Mountain Glaciers ◽  
Multi Temporal ◽  
Comprehensive Study

Remote sensing investigations permit to map and describe at a regional scale and with a multi-temporal approach mountain glaciers. In this work, we present some results from the New Italian Glacier Inventory which we developed by analyzing high-resolution color orthophotos acquired in the timeframe 2005–2011. In particular, in this paper we focused on each Italian Alpine Region, describing in detail glacier extent and features of each mountain group. Although Italian glaciologists were the first to produce glacier inventories (developing a glacier database as early as the beginning of the 20th century), during the last three decades only regional and local glacier lists have been developed. Therefore, a comprehensive study describing the actual whole Italian glaciation has been lacking. The New Italian Glacier Inventory describes 903 glaciers covering altogether an area of 368.10 km2 ± 2%. We found that about 84% of the total number of ice bodies is composed of glaciers smaller than 0.5 km2 covering only 21% of the total area, indicating that the Italian glacier resource is spread into several small ice bodies with only few larger glaciers. A comparison between the total glacier area of the new inventory and the glacier coverage value from the CGI Inventory (1959–1962) suggests a reduction of the glacier extent of about 30%.

scholarly journals Detecting Damaged Building Regions Based on Semantic Scene Change from Multi-Temporal High-Resolution Remote Sensing Images

2017 ◽  
Vol 6 (5) ◽  
pp. 131 ◽  
Author(s):  
Jihui Tu ◽  
Deren Li ◽  
Wenqing Feng ◽  
Qinhu Han ◽  
Haigang Sui
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Scene Change ◽  
Remote Sensing Images ◽  
Multi Temporal ◽  
Semantic Scene

scholarly journals A Novel Change Detection Approach for Multi-Temporal High-Resolution Remote Sensing Images Based on Rotation Forest and Coarse-to-Fine Uncertainty Analyses

2018 ◽  
Vol 10 (7) ◽  
pp. 1015 ◽  
Author(s):  
Wenqing Feng ◽  
Haigang Sui ◽  
Jihui Tu ◽  
Weiming Huang ◽  
Chuan Xu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Remote Sensing Images ◽  
Rotation Forest ◽  
Detection Approach ◽  
Multi Temporal ◽  
Uncertainty Analyses ◽  
Coarse To Fine

Separate segmentation of multi-temporal high-resolution remote sensing images for object-based change detection in urban area

2017 ◽  
Vol 201 ◽  
pp. 243-255 ◽  
Author(s):  
Xueliang Zhang ◽  
Pengfeng Xiao ◽  
Xuezhi Feng ◽  
Min Yuan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Urban Area ◽  
Remote Sensing Images ◽  
Object Based ◽  
Multi Temporal
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