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Special Issue "Remote Sensing of Landslides"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 May 2017)

Special Issue Editors

Guest Editor
Prof. Zhong Lu

Huffington Department of Earth Sciences, Southern Methodist University, PO Box 750395, Dallas, TX 75275, USA
Website | E-Mail
Phone: 214-768-0101
Interests: technique developments of interferometric synthetic aperture radar (InSAR) and multi-temporal InSAR processing, and their applications to natural hazard monitoring and natural resource management
Guest Editor
Dr. Chaoying Zhao

College of Geological Engineering and Geomatics, Chang’an University, No.126, Yanta road, Xi’an, 710054, China
Website | E-Mail
Phone: +86-29-82339251
Interests: synthetic aperture radar (SAR), interferometric SAR (InSAR), geo-hazards, geodetic measurement, image processing

Special Issue Information

Dear Colleagues,

Triggered by earthquakes, rainfall, and anthropogenic activities, such as irrigation and constructions, landslide represents a widespread and problematic geo-hazard worldwide. In recent years, multiple remote sensing techniques, including synthetic aperture radar (SAR), optical, as well light detection and ranging (LiDAR) measurements from spaceborne, airborne, and ground-based platforms, have been widely applied for the monitoring of landslide processes. Current techniques include landslide classification, detection, digital elevation model reconstruction, surface deformation monitoring, and volume/mechanism inversion. In addition, landslide susceptibility mapping, hazard assessment, and risk evaluation can be further analyzed using a synergic fusion of multiple remote sensing data and other observations affecting landslides. This Special Issue aims to gather current innovative remote sensing methods, analysis and inversion techniques, and applications on landslide studies.

Prof. Dr. Zhong Lu
Dr. Chaoying Zhao
Guest Editors

Manuscript Submission Information

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Keywords

  • Landslide detection with remote sensing data (SAR, optical, LiDAR, and others)
  • Landslide inventory mapping techniques and results
  • Landslide time-series deformation monitoring and validation
  • Landslide modeling and volume estimation
  • Landslide trigger factor analysis and mechanism inversion
  • Landslide susceptibility zonation and hazard assessment
  • Landslide risk evaluation with the aid of GIS
  • Multiple remote sensing data assimilation and synergy

Published Papers (12 papers)

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Research

Open AccessArticle Measures of Spatial Autocorrelation Changes in Multitemporal SAR Images for Event Landslides Detection
Remote Sens. 2017, 9(6), 554; doi:10.3390/rs9060554
Received: 28 February 2017 / Revised: 4 May 2017 / Accepted: 29 May 2017 / Published: 2 June 2017
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Abstract
Landslides cause damages and affect victims worldwide, but landslide information is lacking. Even large events may not leave records when they happen in remote areas or simply do not impact with vulnerable elements. This paper proposes a procedure to measure spatial autocorrelation changes
[...] Read more.
Landslides cause damages and affect victims worldwide, but landslide information is lacking. Even large events may not leave records when they happen in remote areas or simply do not impact with vulnerable elements. This paper proposes a procedure to measure spatial autocorrelation changes induced by event landslides in a multi-temporal series of synthetic aperture radar (SAR) intensity Sentinel-1 images. The procedure first measures pixel-based changes between consecutive couples of SAR intensity images using the Log-Ratio index, then it follows the temporal evolution of the spatial autocorrelation inside the Log-Ratio layers using the Moran’s I index and the semivariance. When an event occurs, the Moran’s I index and the semivariance increase compared to the values measured before and after the event. The spatial autocorrelation growth is due to the local homogenization of the soil response caused by the event landslide. The emerging clusters of autocorrelated pixels generated by the event are localized by a process of optimal segmentation of the log-ratio layers. The procedure was used to intercept an event that occurred in August 2015 in Myanmar, Tozang area, when strong rainfall precipitations triggered a number of landslides. A prognostic use of the method promises to increase the availability of information about the number of events at the regional scale, and to facilitate the production of inventory maps, yielding useful results to study the phenomenon for model tuning, landslide forecast model validation, and the relationship between triggering factors and number of occurred events. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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Open AccessArticle Physically Based Susceptibility Assessment of Rainfall-Induced Shallow Landslides Using a Fuzzy Point Estimate Method
Remote Sens. 2017, 9(5), 487; doi:10.3390/rs9050487
Received: 3 March 2017 / Revised: 12 May 2017 / Accepted: 13 May 2017 / Published: 16 May 2017
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Abstract
The physically based model has been widely used in rainfall-induced shallow landslide susceptibility analysis because of its capacity to reproduce the physical processes governing landslide occurrence and a higher predictive capability. However, one of the difficulties in applying the physically based model is
[...] Read more.
The physically based model has been widely used in rainfall-induced shallow landslide susceptibility analysis because of its capacity to reproduce the physical processes governing landslide occurrence and a higher predictive capability. However, one of the difficulties in applying the physically based model is that uncertainties arising from spatial variability, measurement errors, and incomplete information apply to the input parameters and analysis procedure. Uncertainties have been recognized as an important cause of mismatch between predicted and observed distributions of landslide occurrence. Therefore, probabilistic analysis has been used to quantify the uncertainties. However, some uncertainties, because of incomplete information, cannot be managed satisfactorily using a probabilistic approach. Fuzzy set theory is applicable in this case. In this study, in order to handle uncertainty propagation through a physical model, fuzzy set theory, coupled with the vertex method and the point estimate method, was adopted for regional landslide susceptibility assessment. The proposed approach was used to evaluate susceptibility to rainfall-induced shallow landslides for a regional study area, and the analysis results were compared with landslide inventory to evaluate the performance of the proposed approach. The AUC values arising from the landslide susceptibility analyses using the proposed approach and probabilistic analysis were 0.734 and 0.736, respectively. However, when the COV values of the input parameters were reduced, the AUC values of the proposed approach and the probabilistic analysis were reduced to 0.722 and 0.688, respectively. It means that the performance of the fuzzy approach is similar to that of probabilistic analysis but is more robust against variation of input parameters. Thus, at catchment scale, the fuzzy approach can respond appropriately to the uncertainties inherent in physically based landslide susceptibility analysis, and is especially advantageous when the amount of quality data is very limited. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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Open AccessArticle A Recognition and Geological Model of a Deep-Seated Ancient Landslide at a Reservoir under Construction
Remote Sens. 2017, 9(4), 383; doi:10.3390/rs9040383
Received: 17 January 2017 / Revised: 15 April 2017 / Accepted: 17 April 2017 / Published: 19 April 2017
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Abstract
Forty-six ancient Tibetan star-shaped towers and a village are located on a giant slope, which would be partially flooded by a nearby reservoir currently under construction. Ground survey, boreholes, and geophysical investigations have been carried out, with results indicating that the slope consists
[...] Read more.
Forty-six ancient Tibetan star-shaped towers and a village are located on a giant slope, which would be partially flooded by a nearby reservoir currently under construction. Ground survey, boreholes, and geophysical investigations have been carried out, with results indicating that the slope consists of loose deposit with a mean thickness of approximately 80 m in addition to an overlying bedrock of micaceous schist and phyllite. Ground survey and Interferometric Synthetic Aperture Radar (InSAR) indicated that the slope is experiencing some local deformations, with the appearance of cracks and occurrence of two small landslides. Through using borehole logs with the knowledge of the regional geological background, it can be inferred that the loose deposit is a result of an ancient deep-seated translational landslide. This landslide was initiated along the weak layer of the bedding plane during the last glaciation in the late Pleistocene (Q3) period, which was due to deep incision of the Dadu River at that time. Although it has not shown a major reaction since the ancient Tibetan star-shaped towers have been built (between 200 and 1600 AD), and preliminary studies based on geological and geomorphological analyses incorporated with InSAR technology indicated that the landslide is deformable. Furthermore, these studies highlighted that the rate of deformation is gradually reducing from the head to the toe area of the landslide, with the deformation also exhibiting relationships with seasonal rainstorms. The state of the toe area is very important for stabilizing a landslide and minimizing damage. It can be expected that the coming impoundment of the reservoir will increase pore pressure of the rupture zone at the toe area, which will then reduce resistance and accelerate the deformation. Future measures for protection of the slope should be focused on toe erosion and some bank protection measures (i.e., rock armor) should be adopted in this area. Meanwhile, some long-term monitoring measures should be installed to gain a deep understanding on the stability of this important slope. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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Open AccessArticle Recent Landslide Movement in Tsaoling, Taiwan Tracked by TerraSAR-X/TanDEM-X DEM Time Series
Remote Sens. 2017, 9(4), 353; doi:10.3390/rs9040353
Received: 14 February 2017 / Revised: 24 March 2017 / Accepted: 5 April 2017 / Published: 7 April 2017
Cited by 1 | PDF Full-text (35965 KB) | HTML Full-text | XML Full-text
Abstract
The Tsaoling Landslide in Taiwan has captured attentions of researchers worldwide due to its frequent catastrophic failure and distinctive features. Thanks to the launch of TerraSAR-X/TanDEM-X (TSX/TDX) constellation, retrieval of global DEM with high spatial resolution and accuracy becomes possible, which is extremely
[...] Read more.
The Tsaoling Landslide in Taiwan has captured attentions of researchers worldwide due to its frequent catastrophic failure and distinctive features. Thanks to the launch of TerraSAR-X/TanDEM-X (TSX/TDX) constellation, retrieval of global DEM with high spatial resolution and accuracy becomes possible, which is extremely useful for the study of natural hazards (e.g., landslides) globally. We attempt here for the first time to track recent landslide movements in Tsaoling Taiwan by analyzing DEM time series reconstructed from TSX/TDX image pairs. Quality improvement of InSAR derived DEM through an iterated differential operation is addressed during the data processing. Five cliffs and the Chingshui River are selected to determine the spatial pattern of morphologic changes of the landslide. The results show that: (a) A large amount of collapses occurred on dip slopes in the period from 2011 to 2014 and on surrounding debris deposits during the rainy seasons; (b) The average recession rate of the Chunqui Cliff decreased from 24.4 m/yr to 19.6 m/yr compared with the result between 1999 and 2009; (c) The Tsaoling Landslide has lost 6.90 ×106 m³ of soil from November of 2011 to April of 2014, which shows a positive correlation of 0.853 with rainfall; (d) The Chingshui River is undergoing a gradual bed erosion with a volumes of 1.84 ×106 m³. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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Open AccessArticle Retrieving 3-D Large Displacements of Mining Areas from a Single Amplitude Pair of SAR Using Offset Tracking
Remote Sens. 2017, 9(4), 338; doi:10.3390/rs9040338
Received: 7 February 2017 / Revised: 29 March 2017 / Accepted: 31 March 2017 / Published: 2 April 2017
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Abstract
Due to the side-looking imaging geometry of the current synthetic aperture radar (SAR) sensors, only ground deformation along the radar’s line-of-sight (LOS) and azimuth directions can be potentially obtained from a single amplitude pair (SAP) of SAR using offset tracking (OT) procedures. This
[...] Read more.
Due to the side-looking imaging geometry of the current synthetic aperture radar (SAR) sensors, only ground deformation along the radar’s line-of-sight (LOS) and azimuth directions can be potentially obtained from a single amplitude pair (SAP) of SAR using offset tracking (OT) procedures. This significantly hinders the accurate assessment of mining-related hazards and better understanding of the mining subsidence mechanism. In this paper, we propose a method for completely retrieving three-dimensional (3-D) mining-induced displacements with OT-derived observations of LOS deformation from a single amplitude pair of SAR (referred to as OT-SAP hereinafter). The OT-SAP method first constructs two extra constraints at each pixel of the mining area based on the proportional relationship between the horizontal motion of the mining area and the gradients of the vertical subsidence in the east and north directions. The full 3-D mining-induced displacements are then solved by coupling the two constructed extra constraints with the OT-derived observations of the LOS deformation. The Daliuta coal mining area in China was selected to test the proposed OT-SAP method. The results show that the maximum 3-D displacements of this mining area were about 4.3 m, 1.1 m, and 1.3 m in the vertical, east, and north directions, respectively, from 21 November 2012 to 6 February 2013. The accuracies of the retrieved displacements in the vertical and horizontal directions are about 0.201 m and 0.214 m, respectively, which are much smaller than the mining-induced displacements in this mining area and can satisfy the basic requirements of mining deformation monitoring. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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Open AccessArticle Object-Oriented Landslide Mapping Using ZY-3 Satellite Imagery, Random Forest and Mathematical Morphology, for the Three-Gorges Reservoir, China
Remote Sens. 2017, 9(4), 333; doi:10.3390/rs9040333
Received: 9 January 2017 / Revised: 27 March 2017 / Accepted: 29 March 2017 / Published: 31 March 2017
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Abstract
Landslide mapping (LM) has recently become an important research topic in remote sensing and geohazards. The area near the Three Gorges Reservoir (TGR) along the Yangtze River in China is one of the most landslide-prone regions in the world, and the area has
[...] Read more.
Landslide mapping (LM) has recently become an important research topic in remote sensing and geohazards. The area near the Three Gorges Reservoir (TGR) along the Yangtze River in China is one of the most landslide-prone regions in the world, and the area has suffered widespread and significant landslide events in recent years. In our study, an object-oriented landslide mapping (OOLM) framework was proposed for reliable and accurate LM from ‘ZY-3’ high spatial resolution (HSR) satellite images. The framework was based on random forests (RF) and mathematical morphology (MM). RF was first applied as an object feature information reduction tool to identify the significant features for describing landslides, and it was then combined with MM to map the landslides. Three object-feature domains were extracted from the ‘ZY-3’ HSR data: layer information, texture, and geometric features. A total group of 124 features and 24 landslides were used as inputs to determine the landslide boundaries and evaluate the landslide classification accuracy. The results showed that: (1) the feature selection (FS) method had a positive influence on effective landslide mapping; (2) by dividing the data into two sets, training sets which consisted of 20% of the landslide objects (OLS) and non-landslide objects (ONLS), and test sets which consisted of the remaining 80% of the OLS and ONLS, the selected feature subsets were combined for training to obtain an overall classification accuracy of 93.3% ± 0.12% of the test sets; (3) four MM operations based on closing and opening were used to improve the performance of the RF classification. Seven accuracy evaluation indices were used to compare the accuracies of these landslide mapping methods. Finally, the landslide inventory maps were obtained. Based on its efficiency and accuracy, the proposed approach can be employed for rapid response to natural hazards in the Three Gorges area. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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Open AccessArticle Loess Landslide Inventory Map Based on GF-1 Satellite Imagery
Remote Sens. 2017, 9(4), 314; doi:10.3390/rs9040314
Received: 4 January 2017 / Revised: 20 March 2017 / Accepted: 24 March 2017 / Published: 28 March 2017
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Abstract
Rainfall-induced landslides are a major threat in the hilly and gully regions of the Loess Plateau. Landslide mapping via field investigations is challenging and impractical in this complex region because of its numerous gullies. In this paper, an algorithm based on an object-oriented
[...] Read more.
Rainfall-induced landslides are a major threat in the hilly and gully regions of the Loess Plateau. Landslide mapping via field investigations is challenging and impractical in this complex region because of its numerous gullies. In this paper, an algorithm based on an object-oriented method (OOA) has been developed to recognize loess landslides by combining spectral, textural, and morphometric information with auxiliary topographic parameters based on high-resolution multispectral satellite data (GF-1, 2 m) and a high-precision DEM (5 m). The quality percentage (QP) values were all greater than 0.80, and the kappa indices were all higher than 0.85, indicating good landslide detection with the proposed approach. We quantitatively analyze the spectral, textural, morphometric, and topographic properties of loess landslides. The normalized difference vegetation index (NDVI) is useful for discriminating landslides from vegetation cover and water areas. Morphometric parameters, such as elongation and roundness, can potentially improve the recognition capacity and facilitate the identification of roads. The combination of spectral properties in near-infrared regions, the textural variance from a grey level co-occurrence matrix (GLCM), and topographic elevation data can be used to effectively discriminate terraces and buildings. Furthermore, loess flows are separated from landslides based on topographic position data. This approach shows great potential for quickly producing accurate results for loess landslides that are induced by extreme rainfall events in the hilly and gully regions of the Loess Plateau, which will help decision makers improve landslide risk assessment, reduce the risk from landslide hazards and facilitate the application of more reliable disaster management strategies. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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Open AccessArticle Application of Bivariate and Multivariate Statistical Techniques in Landslide Susceptibility Modeling in Chittagong City Corporation, Bangladesh
Remote Sens. 2017, 9(4), 304; doi:10.3390/rs9040304
Received: 12 January 2017 / Revised: 9 March 2017 / Accepted: 15 March 2017 / Published: 23 March 2017
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Abstract
The communities living on the dangerous hillslopes in Chittagong City Corporation (CCC) in Bangladesh recurrently experience landslide hazards during the monsoon season. The frequency and intensity of landslides are increasing over time because of heavy rainfall occurring over a few days. Furthermore, rapid
[...] Read more.
The communities living on the dangerous hillslopes in Chittagong City Corporation (CCC) in Bangladesh recurrently experience landslide hazards during the monsoon season. The frequency and intensity of landslides are increasing over time because of heavy rainfall occurring over a few days. Furthermore, rapid urbanization through hill-cutting is another factor, which is believed to have a significant impact on the occurrence of landslides. This study aims to develop landslide susceptibility maps (LSMs) through the use of Dempster-Shafer weights of evidence (WoE) and the multiple regression (MR) method. Three different combinations with principal component analysis (PCA) and fuzzy membership techniques were used and tested. Twelve factor maps (i.e., slope, hill-cutting, geology, geomorphology, NDVI, soil moisture, precipitation and distance from existing buildings, stream, road and drainage network, and faults-lineaments) were prepared based on their association with historical landslide events. A landslide inventory map was prepared through field surveys for model simulation and validation purposes. The performance of the predicted LSMs was validated using the area under the relative operating characteristic (ROC) curve method. The overall success rates were 87.3%, 90.9%, 91.3%, and 93.9%, respectively for the WoE, MR with all the layers, MR with PCA layers, and MR with fuzzy probability layers. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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Open AccessArticle Basin Scale Assessment of Landslides Geomorphological Setting by Advanced InSAR Analysis
Remote Sens. 2017, 9(3), 267; doi:10.3390/rs9030267
Received: 23 December 2016 / Revised: 8 March 2017 / Accepted: 9 March 2017 / Published: 15 March 2017
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Abstract
An extensive investigation of more than 90 landslides affecting a small river basin in Central Italy was performed by combining field surveys and remote sensing techniques. We thus defined the geomorphological setting of slope instability processes. Basic information, such as landslides mapping and
[...] Read more.
An extensive investigation of more than 90 landslides affecting a small river basin in Central Italy was performed by combining field surveys and remote sensing techniques. We thus defined the geomorphological setting of slope instability processes. Basic information, such as landslides mapping and landslides type definition, have been acquired thanks to geomorphological field investigations and multi-temporal aerial photos interpretation, while satellite SAR archive data (acquired by ERS and Envisat from 1992 to 2010) have been analyzed by means of A-DInSAR (Advanced Differential Interferometric Synthetic Aperture Radar) techniques to evaluate landslides past displacements patterns. Multi-temporal assessment of landslides state of activity has been performed basing on geomorphological evidence criteria and past ground displacement measurements obtained by A-DInSAR. This step has been performed by means of an activity matrix derived from information achieved thanks to double orbital geometry. Thanks to this approach we also achieved more detailed knowledge about the landslides kinematics in time and space. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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Open AccessArticle Freeze/Thaw-Induced Deformation Monitoring and Assessment of the Slope in Permafrost Based on Terrestrial Laser Scanner and GNSS
Remote Sens. 2017, 9(3), 198; doi:10.3390/rs9030198
Received: 26 December 2016 / Revised: 16 February 2017 / Accepted: 21 February 2017 / Published: 24 February 2017
Cited by 1 | PDF Full-text (10262 KB) | HTML Full-text | XML Full-text
Abstract
Most previous studies of the Qinghai-Tibet engineering corridor (QTEC) have focused on the impacts of climate change on thaw-induced slope failures, whereas few have considered freeze-induced slope failures. Terrestrial laser scanning was used in combination with global navigation satellite systems to monitor three-dimensional
[...] Read more.
Most previous studies of the Qinghai-Tibet engineering corridor (QTEC) have focused on the impacts of climate change on thaw-induced slope failures, whereas few have considered freeze-induced slope failures. Terrestrial laser scanning was used in combination with global navigation satellite systems to monitor three-dimensional surface changes between 2014 and 2015 on the slope of permafrost in the QTEC, which experienced two thawing periods and a freezing period. Soil temperature and moisture sensors were also deployed at 11 depths to reveal the hydrological–thermal dynamics of the active layer. We analyzed scanned surface changes in the slope based on comparisons of multi-temporal point cloud data to determine how the hydrological–thermal process affected active layer deformation during freeze–thaw cycles, thereby comprehensively quantifying the surface deformation. During the two thawing periods, the major structure of the slope exhibited subsidence trends, whereas the major structure of the slope had an uplift trend in the freezing period. The seasonal subsidence trend was caused by thaw settlement and the seasonal uplift trend was probably due to frost heaving. This occurred mainly because the active layer and the upper permafrost underwent a phase transition due to heat transfer. The ground movements occurred approximately in the soil temperature conduction direction between the top of the soil and the permafrost table. The elevation deformation range was mainly −0.20 m to 0.20 m. Surface volume increases with heaving after freezing could have compensated for the loss of thawing twice and still led to the upward swelling of the slope. Thus, this type of slope in permafrost is dominated by frost heave. Deformation characteristics of the slope will support enhanced decision making regarding the implementation of remote sensing and hydrological–thermal measurement technologies to monitor changes in the slopes in permafrost adjacent to engineering corridors, thereby improving the understanding and assessment of hazards. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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Open AccessArticle Monitoring the Rapid-Moving Reactivation of Earth Flows by Means of GB-InSAR: The April 2013 Capriglio Landslide (Northern Appennines, Italy)
Remote Sens. 2017, 9(2), 165; doi:10.3390/rs9020165
Received: 23 November 2016 / Revised: 31 January 2017 / Accepted: 13 February 2017 / Published: 17 February 2017
Cited by 1 | PDF Full-text (15296 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents the main results of the GB-InSAR (ground based interferometric synthetic aperture radar) monitoring of the Capriglio landslide (Northern Apennines, Emilia Romagna Region, Italy), activated on 6 April 2013. The landslide, triggered by prolonged rainfall, is constituted by two main adjacent
[...] Read more.
This paper presents the main results of the GB-InSAR (ground based interferometric synthetic aperture radar) monitoring of the Capriglio landslide (Northern Apennines, Emilia Romagna Region, Italy), activated on 6 April 2013. The landslide, triggered by prolonged rainfall, is constituted by two main adjacent enlarging bodies with a roto-translational kinematics. They activated in sequence and subsequently joined into a large earth flow, channelizing downstream of the Bardea Creek, for a total length of about 3600 m. The displacement rate of this combined mass was quite high, so that the landslide toe evolved with velocities of several tens of meters per day (with peaks of 70–80 m/day) in the first month, and of several meters per day (with peaks of 13–14 m/day) from early May to mid-July 2013. In the crown area, the landslide completely destroyed a 450 m sector of provincial roadway S.P. 101, and its retrogression tendency exposed the villages of Capriglio and Pianestolla, located in the upper watershed area of the Bardea Creek, to great danger. Furthermore, the advancing toe seriously threatened the Antria bridge, representing the “Massese” provincial roadway S.P. 665R transect over the Bardea Creek, the only strategic roadway left able to connect the above-mentioned villages. With the final aim of supporting local authorities in the hazard assessment and risk management during the emergency phase, on 4 May 2013 aerial optical surveys were conducted to accurately map the landslide extension and evolution. Moreover, a GB-InSAR monitoring campaign was started in order to assess displacements of the whole landslide area. The versatility and flexibility of the GB-InSAR sensors allowed acquiring data with two different configurations, designed and set up to continuously retrieve information on the landslide movement rates (both in its upper slow-moving sectors and in its fast-moving toe). The first acquisition mode revealed that the Capriglio and Pianestolla villages were affected by minor displacements (at an order of magnitude of a few millimeters per month). The second acquisition mode allowed to acquire data every 28 seconds, reaching very high temporal resolution values by applying the GB-InSAR technique. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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Open AccessArticle Potential and Limitation of SPOT-5 Ortho-Image Correlation to Investigate the Cinematics of Landslides: The Example of “Mare à Poule d’Eau” (Réunion, France)
Remote Sens. 2017, 9(2), 106; doi:10.3390/rs9020106
Received: 6 November 2016 / Revised: 19 January 2017 / Accepted: 20 January 2017 / Published: 26 January 2017
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Abstract
Over the last 10 years, the accessibility of high spatial resolution remote sensing images has strongly increased. These images are available in ortho-rectified format which do not necessitate any further geometrical processing to be analyzed. In parallel, image correlation software has become more
[...] Read more.
Over the last 10 years, the accessibility of high spatial resolution remote sensing images has strongly increased. These images are available in ortho-rectified format which do not necessitate any further geometrical processing to be analyzed. In parallel, image correlation software has become more efficient and friendly. In this paper, image correlation methods are tested to evaluate their potential and limitations to measure the surface displacements in a complex case of a landslide located in a tropical environment. This studied landslide, called “Mare à Poule d’Eau”, is located in the Salazie erosion watershed in Réunion Island (France). This landslide is monitored daily by a DGPS station which registers the south-north displacements. Two pairs of ortho-rectified SPOT-5 images at 2.5 m resolution provided by Kalideos (http://kalideos.cnes.fr) were selected. The first pair frames the period between 2002 and 2005 during which the landslide activity was low. The second pair of images (2006–2008) frames a period of time during which the landslide was more active. Fifty-nine Image Control Points (ICP) were selected on the images by the SIFT method (Scale Invariant Feature Transform) and visually controlled. The shifts of these points used as external control are estimated for the two time periods. Two image correlator softwares are used: MicMac and Cosi-Corr. The results obtained by the two correlators are similar. For the 2002–2005 period, the shift measured by correlators in the landslide is similar to the shift outside the landslide. This means that the displacement cannot be detected and estimated during periods of low activity of the landslide. The shift of the landslide for the 2006–2008 period is out of noise and reaches 8.5 m. The displacement can be estimated by applying a correction factor extracted from the ICP located in the stable areas. The potential and limits of the image correlation in such complex environments is discussed. A strategy is proposed to evaluate the quality of the results and to extract the displacement signal from the shift measurements. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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