Technical Note: Use of remote sensing for landslide studies  in Europe

Abstract. Within the framework of FP7, an EU-funded SafeLand project, a questionnaire was prepared to collect information about the use of remote sensing for landslide study and to evaluate its actual application in landslide detection, mapping and monitoring. The questionnaire was designed using a Google form and was disseminated among end-users and researchers involved in landslide studies in Europe. In total, 49 answers from 17 different European countries were collected. The outcomes showed that landslide detection and mapping is mainly performed with aerial photos, often associated with optical and radar imagery. Concerning landslide monitoring, satellite radars prevail over the other types of data. Remote sensing is mainly used for detection/mapping and monitoring of slides, flows and lateral spreads with a preferably large scale of analysis (1:5000–1:25 000). All the compilers integrate remote sensing data with other thematic data, mainly geological maps, landslide inventory maps and DTMs and derived maps. According to the research and working experience of the compilers, remote sensing is generally considered to have a medium effectiveness/reliability for landslide studies. The results of the questionnaire can contribute to an overall sketch of the use of remote sensing in current landslide studies and show that remote sensing can be considered a powerful and well-established instrument for landslide mapping, monitoring and hazard analysis.

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LESS: LargE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes

Remote Sensing of Environment ◽

10.1016/j.rse.2018.11.036 ◽

2019 ◽

Vol 221 ◽

pp. 695-706 ◽

Cited By ~ 12

Author(s):

Jianbo Qi ◽

Donghui Xie ◽

Tiangang Yin ◽

Guangjian Yan ◽

Jean-Philippe Gastellu-Etchegorry ◽

...

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Remote Sensing Data ◽

Image Simulation ◽

Simulation Framework ◽

Sensing Data

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An efficient organization method for large-scale and long time-series remote sensing data in a cloud computing environment

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2021.3110900 ◽

2021 ◽

pp. 1-1

Author(s):

Jining Yan ◽

Yuanxing Liu ◽

Lizhe Wang ◽

Zhipeng Wang ◽

Xiaohui Huang ◽

...

Keyword(s):

Remote Sensing ◽

Time Series ◽

Cloud Computing ◽

Large Scale ◽

Remote Sensing Data ◽

Computing Environment ◽

Cloud Computing Environment ◽

Efficient Organization ◽

Sensing Data ◽

Long Time

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Remote Sensing Approaches and Related Techniques to Map and Study Landslides

Landslides - Investigation and Monitoring ◽

10.5772/intechopen.93681 ◽

2020 ◽

Author(s):

Ram L. Ray ◽

Maurizio Lazzari ◽

Tolulope Olutimehin

Keyword(s):

Remote Sensing ◽

Hazard Assessment ◽

Remote Sensing Data ◽

Remotely Sensed ◽

Landslide Monitoring ◽

In Situ Observation ◽

Remotely Sensed Data ◽

Sensing Data ◽

Stability Model

Landslide is one of the costliest and fatal geological hazards, threatening and influencing the socioeconomic conditions in many countries globally. Remote sensing approaches are widely used in landslide studies. Landslide threats can also be investigated through slope stability model, susceptibility mapping, hazard assessment, risk analysis, and other methods. Although it is possible to conduct landslide studies using in-situ observation, it is time-consuming, expensive, and sometimes challenging to collect data at inaccessible terrains. Remote sensing data can be used in landslide monitoring, mapping, hazard prediction and assessment, and other investigations. The primary goal of this chapter is to review the existing remote sensing approaches and techniques used to study landslides and explore the possibilities of potential remote sensing tools that can effectively be used in landslide studies in the future. This chapter also provides critical and comprehensive reviews of landslide studies focus¬ing on the role played by remote sensing data and approaches in landslide hazard assessment. Further, the reviews discuss the application of remotely sensed products for landslide detection, mapping, prediction, and evaluation around the world. This systematic review may contribute to better understanding the extensive use of remotely sensed data and spatial analysis techniques to conduct landslide studies at a range of scales.

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Automatic calibration of a large-scale sediment model using suspended sediment concentration, water quality, and remote sensing data

RBRH ◽

10.1590/2318-0331.241920180127 ◽

2019 ◽

Vol 24 ◽

Author(s):

Hugo de Oliveira Fagundes ◽

Fernando Mainardi Fan ◽

Rodrigo Cauduro Dias de Paiva

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Suspended Sediment ◽

Large Scale ◽

Suspended Sediment Concentration ◽

Remote Sensing Data ◽

Sediment Concentration ◽

Spatial Discretization ◽

Calibration And Validation ◽

Sediment Model

ABSTRACT Calibration and validation are two important steps in the application of sediment models requiring observed data. This study aims to investigate the potential use of suspended sediment concentration (SSC), water quality and remote sensing data to calibrate and validate a large-scale sediment model. Observed data from across 108 stations located in the Doce River basin was used for the period between 1997-2010. Ten calibration and validation experiments using the MOCOM-UA optimization algorithm coupled with the MGB-SED model were carried out, which, over the same period of time, resulted in 37 calibration and 111 validation tests. The experiments were performed by modifying metrics, spatial discretization, observed data and parameters of the MOCOM-UA algorithm. Results generally demonstrated that the values of correlation presented slight variations and were superior in the calibration step. Additionally, increasing spatial discretization or establishing a background concentration for the model allowed for improved results. In a station with high quantity of SSC data, calibration improved the ENS coefficient from -0.44 to 0.44. The experiments showed that the spectral surface reflectance, total suspended solids and turbidity data have the potential to enhance the performance of sediment models.

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Review of Satellite Interferometry for Landslide Detection in Italy

Remote Sensing ◽

10.3390/rs12081351 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1351 ◽

Cited By ~ 8

Author(s):

Lorenzo Solari ◽

Matteo Del Soldato ◽

Federico Raspini ◽

Anna Barra ◽

Silvia Bianchini ◽

...

Keyword(s):

Remote Sensing ◽

Management Strategies ◽

Back Analysis ◽

Remote Sensing Data ◽

Economic Losses ◽

Interferometric Synthetic Aperture Radar ◽

Monitoring Tools ◽

Average Production ◽

Landslide Mapping ◽

Remote Sensing Techniques

Landslides recurrently impact the Italian territory, producing huge economic losses and casualties. Because of this, there is a large demand for monitoring tools to support landslide management strategies. Among the variety of remote sensing techniques, Interferometric Synthetic Aperture Radar (InSAR) has become one of the most widely applied for landslide studies. This work reviews a variety of InSAR-related applications for landslide studies in Italy. More than 250 papers were analyzed in this review. The first application dates back to 1999. The average production of InSAR-related papers for landslide studies is around 12 per year, with a peak of 37 papers in 2015. Almost 70% of the papers are written by authors in academia. InSAR is used (i) for landslide back analysis (3% of the papers); (ii) for landslide characterization (40% of the papers); (iii) as input for landslide models (7% of the papers); (iv) to update landslide inventories (15% of the papers); (v) for landslide mapping (32% of the papers), and (vi) for monitoring (3% of the papers). Sixty-eight percent of the authors validated the satellite results with ground information or other remote sensing data. Although well-known limitations exist, this bibliographic overview confirms that InSAR is a consolidated tool for many landslide-related applications.

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