Special Issue "Mass Movement and Soil Erosion Monitoring Using Remote Sensing"

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 (30 April 2019).

Special Issue Editors

Dr. Jagannath Aryal
E-Mail Website
Guest Editor
Discipline of Geography and Spatial Sciences, School of Technology Environments and Design College of Sciences and Engineering, University of Tasmania, Australia
Tel. +61362262848
Interests: GIS, Surveying, Spatial Sciences, GEOBIA, Earth Observation, Vegetation, Bush-fire, Environmental Modelling, Change Detection
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Mass movements (e.g., cliff collapses, soil creeps, mudflows, landslides, etc.) and soil erosion (e.g., coastal erosion, etc.) are severe forms of natural disasters that occur in mountainous terrain as well as in coastal areas and are subject to specific geological, geomorphological and climatological conditions, as well as human activities. It is a challenging task to accurately define the position, type, and activity of mass movements for the purpose of creating inventory records and continuous monitoring. High and moderate resolution remote sensing data, such as Quickbird, Worldview 3, LiDAR, SPOT 5, Google Earth Engine, etc., with the aid of Geographic Information System tools, offer state-of-the-art investigations in identifying mass movements and soil erosion and modeling surface processes for hazard monitoring. Advanced state-of-the-art image processing techniques, using pixel-based and object based image analysis (OBIA) based on data mining, machine learning, deep-learning and ensemble models, can be used to identify and monitor these mass movements and soil erosion features. Issues of scale (spatial and spectral) and selection of morphological attributes for scientific analysis of mass movements require new developments in image processing and spatial/temporal GIS analysis.

The topics of interest include, but not limited to:

  • Mass movement detection and monitoring
  • Soil erosion monitoring
  • Multi-temporal high resolution satellite images
  • Laser scanning technologies for feature identification
  • High resolution aerial photographs
  • Deep-leaning in image classification
  • New machine learning techniques in mass movement detection
  • New pixel based image analysis
  • New object based image analysis

Prof. Biswajeet Pradhan
Dr. Jagannath Aryal
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (12 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Other

Open AccessArticle
Landslide Detection Using Multi-Scale Image Segmentation and Different Machine Learning Models in the Higher Himalayas
Remote Sens. 2019, 11(21), 2575; https://doi.org/10.3390/rs11212575 - 02 Nov 2019
Cited by 2
Abstract
Landslides represent a severe hazard in many areas of the world. Accurate landslide maps are needed to document the occurrence and extent of landslides and to investigate their distribution, types, and the pattern of slope failures. Landslide maps are also crucial for determining [...] Read more.
Landslides represent a severe hazard in many areas of the world. Accurate landslide maps are needed to document the occurrence and extent of landslides and to investigate their distribution, types, and the pattern of slope failures. Landslide maps are also crucial for determining landslide susceptibility and risk. Satellite data have been widely used for such investigations—next to data from airborne or unmanned aerial vehicle (UAV)-borne campaigns and Digital Elevation Models (DEMs). We have developed a methodology that incorporates object-based image analysis (OBIA) with three machine learning (ML) methods, namely, the multilayer perceptron neural network (MLP-NN) and random forest (RF), for landslide detection. We identified the optimal scale parameters (SP) and used them for multi-scale segmentation and further analysis. We evaluated the resulting objects using the object pureness index (OPI), object matching index (OMI), and object fitness index (OFI) measures. We then applied two different methods to optimize the landslide detection task: (a) an ensemble method of stacking that combines the different ML methods for improving the performance, and (b) Dempster–Shafer theory (DST), to combine the multi-scale segmentation and classification results. Through the combination of three ML methods and the multi-scale approach, the framework enhanced landslide detection when it was tested for detecting earthquake-triggered landslides in Rasuwa district, Nepal. PlanetScope optical satellite images and a DEM were used, along with the derived landslide conditioning factors. Different accuracy assessment measures were used to compare the results against a field-based landslide inventory. All ML methods yielded the highest overall accuracies ranging from 83.3% to 87.2% when using objects with the optimal SP compared to other SPs. However, applying DST to combine the multi-scale results of each ML method significantly increased the overall accuracies to almost 90%. Overall, the integration of OBIA with ML methods resulted in appropriate landslide detections, but using the optimal SP and ML method is crucial for success. Full article
(This article belongs to the Special Issue Mass Movement and Soil Erosion Monitoring Using Remote Sensing)
Show Figures

Graphical abstract

Open AccessArticle
UAV-Based Slope Failure Detection Using Deep-Learning Convolutional Neural Networks
Remote Sens. 2019, 11(17), 2046; https://doi.org/10.3390/rs11172046 - 30 Aug 2019
Cited by 2
Abstract
Slope failures occur when parts of a slope collapse abruptly under the influence of gravity, often triggered by a rainfall event or earthquake. The resulting slope failures often cause problems in mountainous or hilly regions, and the detection of slope failure is therefore [...] Read more.
Slope failures occur when parts of a slope collapse abruptly under the influence of gravity, often triggered by a rainfall event or earthquake. The resulting slope failures often cause problems in mountainous or hilly regions, and the detection of slope failure is therefore an important topic for research. Most of the methods currently used for mapping and modelling slope failures rely on classification algorithms or feature extraction, but the spatial complexity of slope failures, the uncertainties inherent in expert knowledge, and problems in transferability, all combine to inhibit slope failure detection. In an attempt to overcome some of these problems we have analyzed the potential of deep learning convolutional neural networks (CNNs) for slope failure detection, in an area along a road section in the northern Himalayas, India. We used optical data from unmanned aerial vehicles (UAVs) over two separate study areas. Different CNN designs were used to produce eight different slope failure distribution maps, which were then compared with manually extracted slope failure polygons using different accuracy assessment metrics such as the precision, F-score, and mean intersection-over-union (mIOU). A slope failure inventory data set was produced for each of the study areas using a frequency-area distribution (FAD). The CNN approach that was found to perform best (precision accuracy assessment of almost 90% precision, F-score 85%, mIOU 74%) was one that used a window size of 64 × 64 pixels for the sample patches, and included slope data as an additional input layer. The additional information from the slope data helped to discriminate between slope failure areas and roads, which had similar spectral characteristics in the optical imagery. We concluded that the effectiveness of CNNs for slope failure detection was strongly dependent on their design (i.e., the window size selected for the sample patch, the data used, and the training strategies), but that CNNs are currently only designed by trial and error. While CNNs can be powerful tools, such trial and error strategies make it difficult to explain why a particular pooling or layer numbering works better than any other. Full article
(This article belongs to the Special Issue Mass Movement and Soil Erosion Monitoring Using Remote Sensing)
Show Figures

Graphical abstract

Open AccessArticle
Integrated Use of Satellite Remote Sensing, Artificial Neural Networks, Field Spectroscopy, and GIS in Estimating Crucial Soil Parameters in Terms of Soil Erosion
Remote Sens. 2019, 11(9), 1106; https://doi.org/10.3390/rs11091106 - 09 May 2019
Abstract
Soil erosion is one of the main causes of soil degradation among others (salinization, compaction, reduction of organic matter, and non-point source pollution) and is a serious threat in the Mediterranean region. A number of soil properties, such as soil organic matter (SOM), [...] Read more.
Soil erosion is one of the main causes of soil degradation among others (salinization, compaction, reduction of organic matter, and non-point source pollution) and is a serious threat in the Mediterranean region. A number of soil properties, such as soil organic matter (SOM), soil structure, particle size, permeability, and Calcium Carbonate equivalent (CaCO3), can be the key properties for the evaluation of soil erosion. In this work, several innovative methods (satellite remote sensing, field spectroscopy, soil chemical analysis, and GIS) were investigated for their potential in monitoring SOM, CaCO3, and soil erodibility (K-factor) of the Akrotiri cape in Crete, Greece. Laboratory analysis and soil spectral reflectance in the VIS-NIR (using either Landsat 8, Sentinel-2, or field spectroscopy data) range combined with machine learning and geostatistics permitted the spatial mapping of SOM, CaCO3, and K-factor. Synergistic use of geospatial modeling based on the aforementioned soil properties and the Revised Universal Soil Loss Equation (RUSLE) erosion assessment model enabled the estimation of soil loss risk. Finally, ordinary least square regression (OLSR) and geographical weighted regression (GWR) methodologies were employed in order to assess the potential contribution of different approaches in estimating soil erosion rates. The derived maps captured successfully the SOM, the CaCO3, and the K-factor spatial distribution in the GIS environment. The results may contribute to the design of erosion best management measures and wise land use planning in the study region. Full article
(This article belongs to the Special Issue Mass Movement and Soil Erosion Monitoring Using Remote Sensing)
Show Figures

Graphical abstract

Open AccessArticle
A Collaborative Change Detection Approach on Multi-Sensor Spatial Imagery for Desert Wetland Monitoring after a Flash Flood in Southern Morocco
Remote Sens. 2019, 11(9), 1042; https://doi.org/10.3390/rs11091042 - 01 May 2019
Cited by 5
Abstract
This study aims to present a technique that combines multi-sensor spatial data to monitor wetland areas after a flash-flood event in a Saharan arid region. To extract the most efficient information, seven satellite images (radar and optical) taken before and after the event [...] Read more.
This study aims to present a technique that combines multi-sensor spatial data to monitor wetland areas after a flash-flood event in a Saharan arid region. To extract the most efficient information, seven satellite images (radar and optical) taken before and after the event were used. To achieve the objectives, this study used Sentinel-1 data to discriminate water body and soil roughness, and optical data to monitor the soil moisture after the event. The proposed method combines two approaches: one based on spectral processing, and the other based on categorical processing. The first step was to extract four spectral indices and utilize change vector analysis on multispectral diachronic images from three MSI Sentinel-2 images and two Landsat-8 OLI images acquired before and after the event. The second step was performed using pattern classification techniques, namely, linear classifiers based on support vector machines (SVM) with Gaussian kernels. The results of these two approaches were fused to generate a collaborative wetland change map. The application of co-registration and supervised classification based on textural and intensity information from Radar Sentinel-1 images taken before and after the event completes this work. The results obtained demonstrate the importance of the complementarity of multi-sensor images and a multi-approach methodology to better monitor changes to a wetland area after a flash-flood disaster. Full article
(This article belongs to the Special Issue Mass Movement and Soil Erosion Monitoring Using Remote Sensing)
Show Figures

Graphical abstract

Open AccessArticle
Assessment of Landslide Susceptibility Using Statistical- and Artificial Intelligence-Based FR–RF Integrated Model and Multiresolution DEMs
Remote Sens. 2019, 11(9), 999; https://doi.org/10.3390/rs11090999 - 26 Apr 2019
Cited by 9
Abstract
Landslide is one of the most important geomorphological hazards that cause significant ecological and economic losses and results in billions of dollars in financial losses and thousands of casualties per year. The occurrence of landslide in northern Iran (Alborz Mountain Belt) is often [...] Read more.
Landslide is one of the most important geomorphological hazards that cause significant ecological and economic losses and results in billions of dollars in financial losses and thousands of casualties per year. The occurrence of landslide in northern Iran (Alborz Mountain Belt) is often due to the geological and climatic conditions and tectonic and human activities. To reduce or control the damage caused by landslides, landslide susceptibility mapping (LSM) and landslide risk assessment are necessary. In this study, the efficiency and integration of frequency ratio (FR) and random forest (RF) in statistical- and artificial intelligence-based models and different digital elevation models (DEMs) with various spatial resolutions were assessed in the field of LSM. The experiment was performed in Sangtarashan watershed, Mazandran Province, Iran. The study area, which extends to 1072.28 km2, is severely affected by landslides, which cause severe economic and ecological losses. An inventory of 129 landslides that occurred in the study area was prepared using various resources, such as historical landslide records, the interpretation of aerial photos and Google Earth images, and extensive field surveys. The inventory was split into training and test sets, which include 70 and 30% of the landslide locations, respectively. Subsequently, 15 topographic, hydrologic, geologic, and environmental landslide conditioning factors were selected as predictor variables of landslide occurrence on the basis of literature review, field works and multicollinearity analysis. Phased array type L-band synthetic aperture radar (PALSAR), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), and SRTM (Shuttle Radar Topography Mission) DEMs were used to extract topographic and hydrologic attributes. The RF model showed that land use/land cover (16.95), normalised difference vegetation index (16.44), distance to road (15.32) and elevation (13.6) were the most important controlling variables. Assessment of model performance by calculating the area under the receiving operating characteristic curve parameter showed that FR–RF integrated model (0.917) achieved higher predictive accuracy than the individual FR (0.865) and RF (0.840) models. Comparison of PALSAR, ASTER, and SRTM DEMs with 12.5, 30 and 90 m spatial resolution, respectively, with the FR–RF integrated model showed that the prediction accuracy of FR–RF–PALSAR (0.917) was higher than FR–RF–ASTER (0.865) and FR–RF–SRTM (0.863). The results of this study could be used by local planners and decision makers for planning development projects and landslide hazard mitigation measures. Full article
(This article belongs to the Special Issue Mass Movement and Soil Erosion Monitoring Using Remote Sensing)
Show Figures

Graphical abstract

Open AccessArticle
Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm
Remote Sens. 2019, 11(8), 931; https://doi.org/10.3390/rs11080931 - 17 Apr 2019
Cited by 12
Abstract
We used a novel hybrid functional machine learning algorithm to predict the spatial distribution of landslides in the Sarkhoon watershed, Iran. We developed a new ensemble model which is a combination of a functional algorithm, stochastic gradient descent (SGD) and an AdaBoost (AB) [...] Read more.
We used a novel hybrid functional machine learning algorithm to predict the spatial distribution of landslides in the Sarkhoon watershed, Iran. We developed a new ensemble model which is a combination of a functional algorithm, stochastic gradient descent (SGD) and an AdaBoost (AB) Meta classifier namely ABSGD model to predict the landslides. The model incorporates 20 landslide conditioning factors, which we ranked using the least-square support vector machine (LSSVM) technique. For the modeling, we considered 98 landslide locations, of which 70% (79) were used for training and 30% (19) for validation processes. Model validation was performed using sensitivity, specificity, accuracy, the root mean square error (RMSE) and the area under the receiver operatic characteristic (AUC) curve. We also used soft computing benchmark models, including SGD, logistic regression (LR), logistic model tree (LMT) and functional tree (FT) algorithms for model validation and comparison. The selected conditioning factors were significant in landslide occurrence but distance to road was found to be the most important factor. The ABSGD model (AUC= 0.860) outperformed the LR (0.797), SGD (0.776), LMT (0.740) and FT (0.734) models. Our results confirm that the combined use of a functional algorithm and a Meta classifier prevents over-fitting, reduces noise and enhances the power prediction of the individual SGD algorithm for the spatial prediction of landslides. Full article
(This article belongs to the Special Issue Mass Movement and Soil Erosion Monitoring Using Remote Sensing)
Show Figures

Graphical abstract

Open AccessArticle
Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection
Remote Sens. 2019, 11(2), 196; https://doi.org/10.3390/rs11020196 - 20 Jan 2019
Cited by 28
Abstract
There is a growing demand for detailed and accurate landslide maps and inventories around the globe, but particularly in hazard-prone regions such as the Himalayas. Most standard mapping methods require expert knowledge, supervision and fieldwork. In this study, we use optical data from [...] Read more.
There is a growing demand for detailed and accurate landslide maps and inventories around the globe, but particularly in hazard-prone regions such as the Himalayas. Most standard mapping methods require expert knowledge, supervision and fieldwork. In this study, we use optical data from the Rapid Eye satellite and topographic factors to analyze the potential of machine learning methods, i.e., artificial neural network (ANN), support vector machines (SVM) and random forest (RF), and different deep-learning convolution neural networks (CNNs) for landslide detection. We use two training zones and one test zone to independently evaluate the performance of different methods in the highly landslide-prone Rasuwa district in Nepal. Twenty different maps are created using ANN, SVM and RF and different CNN instantiations and are compared against the results of extensive fieldwork through a mean intersection-over-union (mIOU) and other common metrics. This accuracy assessment yields the best result of 78.26% mIOU for a small window size CNN, which uses spectral information only. The additional information from a 5 m digital elevation model helps to discriminate between human settlements and landslides but does not improve the overall classification accuracy. CNNs do not automatically outperform ANN, SVM and RF, although this is sometimes claimed. Rather, the performance of CNNs strongly depends on their design, i.e., layer depth, input window sizes and training strategies. Here, we conclude that the CNN method is still in its infancy as most researchers will either use predefined parameters in solutions like Google TensorFlow or will apply different settings in a trial-and-error manner. Nevertheless, deep-learning can improve landslide mapping in the future if the effects of the different designs are better understood, enough training samples exist, and the effects of augmentation strategies to artificially increase the number of existing samples are better understood. Full article
(This article belongs to the Special Issue Mass Movement and Soil Erosion Monitoring Using Remote Sensing)
Show Figures

Graphical abstract

Open AccessArticle
Ice-Gouging Topography of the Exposed Aral Sea Bed
Remote Sens. 2019, 11(2), 113; https://doi.org/10.3390/rs11020113 - 09 Jan 2019
Cited by 1
Abstract
Ice gouging, or scouring, i.e., ice impact on the seabed, is a well-studied phenomenon in high-latitude seas. In the mid-latitudes, it remains one of the major geomorphic processes in freezing seas and large lakes. Research efforts concerning its patterns, drivers and intensity are [...] Read more.
Ice gouging, or scouring, i.e., ice impact on the seabed, is a well-studied phenomenon in high-latitude seas. In the mid-latitudes, it remains one of the major geomorphic processes in freezing seas and large lakes. Research efforts concerning its patterns, drivers and intensity are scarce, and include aerial and geophysical studies of ice scours in the Northern Caspian Sea. This study aims to explain the origin of the recently discovered linear landforms on the exposed former Aral Sea bottom using remotely sensed data. We suggest that they are relict ice gouges, analogous to the modern ice scours of the Northern Caspian, Kara and other seas and lakes, previously studied by side scan sonar (SSS) surveys. Their average dimensions, from 3 to 90 m in width and from hundreds to thousands of meters in length, and spatial distribution were derived from satellite imagery interpretation and structure from motion-processing of UAV (unmanned aerial vehicle) images. Ice scouring features are virtually omnipresent at certain seabed sections, evidencing high ice gouging intensity in mid-latitude climates. Their greatest density is observed in the central part of the former East Aral Sea. The majority of contemporary ice gouges appeared during the rapid Aral Sea level fall between 1980 and the mid-1990s. Since then, the lake has almost completely drained, providing a unique opportunity for direct studies of exposed ice gouges using both in situ and remote-sensing techniques. These data could add to our current understanding of the scales and drivers of ice impact on the bottom of shallow seas and lakes. Full article
(This article belongs to the Special Issue Mass Movement and Soil Erosion Monitoring Using Remote Sensing)
Show Figures

Figure 1

Open AccessArticle
A Random Forest-Based Approach to Map Soil Erosion Risk Distribution in Hickory Plantations in Western Zhejiang Province, China
Remote Sens. 2018, 10(12), 1899; https://doi.org/10.3390/rs10121899 - 28 Nov 2018
Cited by 3
Abstract
Increasing agroforestry areas with improper management has produced serious environmental problems, such as soil erosion. It is necessary to rapidly predict the spatial distribution of such erosion risks in a large area, but there is a lack of approaches that are suitable for [...] Read more.
Increasing agroforestry areas with improper management has produced serious environmental problems, such as soil erosion. It is necessary to rapidly predict the spatial distribution of such erosion risks in a large area, but there is a lack of approaches that are suitable for mountainous regions. The objective of this research was to develop an approach that can effectively employ remotely-sensed and ancillary data, to map soil erosion risks in an agroforestry ecosystem in a mountainous region. This research employed field survey data, soil-type maps, digital elevation model data, weather station data, and Landsat imagery, for extraction of potential variables. It used the random forest approach to identify eight key variables—slope, slope of slope, normalized difference greenness index at leaf-on season, soil organic matter, fractional vegetation at leaf-on season, fractional soil at leaf-off season, precipitation in June, and percent of soil clay—for mapping soil erosion risk distribution in hickory plantations in Western Zhejiang Province, China. The results showed that an overall accuracy of 89.8% was obtained for three levels of soil erosion risk. Approximately one-fourth of hickory plantations were at high-risk, requiring the owners or decision makers to take proper measures to reduce the soil erosion problem. This research provides a new approach to predict soil erosion risk, based on the primary variables that can be extracted directly from remotely-sensed data and ancillary data. This proposed approach will be valuable for other agroforestry and plantations, such as Torreya grandis, eucalyptus, and the rubber tree, that are playing important roles in improving economic conditions for the local farmers but face soil erosion problems. Full article
(This article belongs to the Special Issue Mass Movement and Soil Erosion Monitoring Using Remote Sensing)
Show Figures

Graphical abstract

Open AccessArticle
Quantitative Assessment for Detection and Monitoring of Coastline Dynamics with Temporal RADARSAT Images
Remote Sens. 2018, 10(11), 1705; https://doi.org/10.3390/rs10111705 - 29 Oct 2018
Cited by 2
Abstract
This study aims to detect coastline changes using temporal synthetic aperture radar (SAR) images for the state of Kelantan, Malaysia. Two active images, namely, RADARSAT-1 captured in 2003 and RADARSAT-2 captured in 2014, were used to monitor such changes. We applied noise removal [...] Read more.
This study aims to detect coastline changes using temporal synthetic aperture radar (SAR) images for the state of Kelantan, Malaysia. Two active images, namely, RADARSAT-1 captured in 2003 and RADARSAT-2 captured in 2014, were used to monitor such changes. We applied noise removal and edge detection filtering on RADARSAT images for preprocessing to remove salt and pepper distortion. Different segmentation analyses were also applied to the filtered images. Firstly, multiresolution segmentation, maximum spectral difference and chessboard segmentation were performed to separate land pixels from ocean ones. Next, the Taguchi method was used to optimise segmentation parameters. Subsequently, a support vector machine algorithm was applied on the optimised segments to classify shorelines with an accuracy of 98% for both temporal images. Results were validated using a thematic map from the Department of Survey and Mapping of Malaysia. The change detection showed an average difference in the shoreline of 12.5 m between 2003 and 2014. The methods developed in this study demonstrate the ability of active SAR sensors to map and detect shoreline changes, especially during low or high tides in tropical regions where passive sensor imagery is often masked by clouds. Full article
(This article belongs to the Special Issue Mass Movement and Soil Erosion Monitoring Using Remote Sensing)
Show Figures

Graphical abstract

Open AccessArticle
Derivation of Three-Dimensional Displacement Vectors from Multi-Temporal Long-Range Terrestrial Laser Scanning at the Reissenschuh Landslide (Tyrol, Austria)
Remote Sens. 2018, 10(11), 1688; https://doi.org/10.3390/rs10111688 - 26 Oct 2018
Cited by 3
Abstract
Deep-seated gravitational slope deformations (DSGSDs) endanger settlements and infrastructure in mountain areas all over the world. To prevent disastrous events, their activity needs to be continuously monitored. In this paper, the movement of the Reissenschuh DSGSD in the Schmirn valley (Tyrol, Austria) is [...] Read more.
Deep-seated gravitational slope deformations (DSGSDs) endanger settlements and infrastructure in mountain areas all over the world. To prevent disastrous events, their activity needs to be continuously monitored. In this paper, the movement of the Reissenschuh DSGSD in the Schmirn valley (Tyrol, Austria) is quantified based on point clouds acquired with a Riegl VZ®-6000 long-range laser scanner in 2016 and 2017. Geomorphological features (e.g., block edges, terrain ridges, scarps) travelling on top of the landslide are extracted from the acquired point clouds using morphometric attributes based on locally computed eigenvectors and -values. The corresponding representations of the extracted features in the multi-temporal data are exploited to derive 3D displacement vectors based on a workflow exploiting the iterative closest point (ICP) algorithm. The subsequent analysis reveals spatial patterns of landslide movement with mean displacements in the order of 0.62 ma 1 , corresponding well with measurements at characteristic points using a differential global navigation satellite system (DGNSS). The results are also compared to those derived from a modified version of the well-known image correlation (IMCORR) method using shaded reliefs of the derived digital terrain models. The applied extended ICP algorithm outperforms the raster-based method particularly in areas with predominantly vertical movement. Full article
(This article belongs to the Special Issue Mass Movement and Soil Erosion Monitoring Using Remote Sensing)
Show Figures

Graphical abstract

Other

Jump to: Research

Open AccessFeature PaperEditor’s ChoiceTechnical Note
Quantitative Assessment of Desertification in an Arid Oasis Using Remote Sensing Data and Spectral Index Techniques
Remote Sens. 2018, 10(12), 1862; https://doi.org/10.3390/rs10121862 - 22 Nov 2018
Cited by 4
Abstract
Desertification is an environmental problem worldwide. Remote sensing data and technique offer substantial information for mapping and assessment of desertification. Desertification is one of the most serious forms of environmental threat in Morocco, especially in the oases in the south-eastern part of the [...] Read more.
Desertification is an environmental problem worldwide. Remote sensing data and technique offer substantial information for mapping and assessment of desertification. Desertification is one of the most serious forms of environmental threat in Morocco, especially in the oases in the south-eastern part of the country. This study aims to map the degree of desertification in middle Draa Valley in 2017 using a Sentinel-2 MSI (multispectral instrument) image. Firstly, three indices, namely, tasselled cap brightness (TCB), greenness (TCG) and wetness (TCW) were extracted using the tasselled cap transformation method. Secondly, other indices, such as normalized difference vegetation index (NDVI) and albedo, were retrieved. Thirdly, a linear regression analysis was performed on NDVI–albedo, TCG–TCB and TCW–TCB combinations. Results showed a higher correlation between TCW and TCB (r = −0.812) than with that of the NDVI–albedo (r = −0.50). On the basis of this analysis, a desertification degree index was developed using the TCW–TCB feature space classification. A map of desertification grades was elaborated and divided into five classes, namely, nondesertification, low, moderate, severe and extreme levels. Results indicated that only 6.20% of the study area falls under the nondesertification grade, whereas 26.92% and 32.85% fall under the severe and extreme grades, respectively. The employed method was useful for the quantitative assessment of desertification with an overall accuracy of 93.07%. This method is simple, robust, powerful, and easy to use for the management and protection of the fragile arid and semiarid lands. Full article
(This article belongs to the Special Issue Mass Movement and Soil Erosion Monitoring Using Remote Sensing)
Show Figures

Graphical abstract

Back to TopTop