Matching of repeat remote sensing images for precise analysis of mass movements

Photogrammetry, together with radar interferometry, is the most popular of the remote sensing techniques used to monitor stability of high mountain slopes. By using two images of an area taken from different view angles, photogrammetry produces digital terrain models (DTM) and orthoprojected images. Repeat digital terrain models (DTM) are differenced to compute elevation changes. Repeat orthoimages are matched to compute the horizontal displacement and deformation of the masses. The success of the photogrammetric approach in the computation of horizontal displacement (and also the generation of DTM through parallax matching, although not covered in this work) greatly relies on the success of image matching techniques. The area-based image matching technique with the normalized cross-correlation (NCC) as its similarity measure is widely used in mass movement analysis. This method has some limitations that reduce its precision and reliability compared to its theoretical potential. The precision with which the matching position is located is limited to the pixel size unless some sub-pixel precision procedures are applied. The NCC is only reliable in cases where there is no significant deformation except shift in position. Identification of a matching entity that contains optimum signal-to-noise ratio (SNR) and minimum geometric distortion at each location has always been challenging. Deformation parameters such as strains can only be computed from the inter-template displacement gradient in a post-matching process. To find appropriate solutions for the mentioned limitations, the following investigations were made on three different types of mass movements; namely, glacier flow, rockglacier creep and land sliding. The effects of ground pixel size on the accuracy of the computed mass movement parameters such as displacement were investigated. Different sub-pixel precision algorithms were implemented and evaluated to identify the most precise and reliable algorithm. In one approach images are interpolated to higher spatial resolution prior to matching. In another approach the NCC correlation surface is interpolated to higher resolution so that the location of the correlation peak is more precise. In yet another approach the position of the NCC peak is computed by fitting 2D Gaussian and parabolic curves to the correlation peak turn by turn. The results show that the mean error in metric unit increases linearly with the ground pixel size being about half a pixel at each resolution. The proportion of undetected moving masse increases with ground pixel size depending on the displacement magnitudes. Proportion of mismatching templates increases with increasing ground pixel size depending on the noise content, i.e. temporal difference, of the image pairs. Of the sub-pixel precision algorithms, interpolating the image to higher resolution using bi-cubic convolution prior to matching performs best. For example, by increasing the spatial resolution (i.e. reducing the ground pixel size) of the matched images by 2 to 16 times using intensity interpolation, 40% to 80% of the performances of the same resolution original image can be achieved. A new spatially adaptive algorithm that defines the template sizes by optimizing the SNR, minimizing the geometric distortion and optimizing the similarity measure was also devised, implemented and evaluated on aerial and satellite images of mass movements. The algorithm can also exclude ambiguous and occluded entities from the matching. The evaluation of the algorithm was conducted on simulated deformation images and in relation to the image-wide fixed template sizes ranging from 11 to 101 pixels. The evaluation of the algorithm on the real mass movements is conducted by a novel technique of reconstructing the reference image from the deformed image and computing the global correlation coefficient and the corresponding SNR between the reference and the reconstructed image. The results show that the algorithm could reduce the error of displacement estimation by up to over 90% (in the simulated case) and improve the SNR of the matching by up to over 4 times compared to the globally fixed template sizes. The algorithm pushes terrain displacement measurement from repeat images one step forward towards full automation. The least squares image matching (LSM) matches images precisely by modeling both the geometric and radiometric deformation. The potential of the LSM is not fully utilized for mass movement analysis. Here, the procedures with which horizontal surface displacement, rotation and strain rates of glacier flow, rockglacier creep and land sliding are computed from the spatial transformation parameters of LSM automatically during the matching are implemented and evaluated. The results show that the approach computes longitudinal strain rates, transverse strain rates and shear strain rates reliably with mean absolute deviation in the order of 10-4 as evaluated on stable grounds. The LSM also improves the accuracy of displacement estimation of the NCC by about 90% in ideal (simulated) case and the SNR of the matching by about 25% in real multi-temporal images of mass movements. Additionally, advanced spatial transformation models such as projective and second degree polynomial are used for the first time for mass movement analysis in addition to the affine. They are also adapted spatially based on the minimization of the sum of square deviation between the matching templates. The spatially adaptive approach produces the best matching, closely followed by the second-order polynomial. Affine and projective models show similar results closely following the two approaches. In the case of the spatially adaptive approach, over 60% of the entities matched for the rockglacier and the landslide are best fit by the second-order polynomial model. In general, the NCC alone may be sufficient for low resolution images of moving masses with limited or no deformation. To gain better precision and reliability in such cases, the template sizes can be adapted spatially and the images can be interpolated to higher resolution (preferably not more detail than 1/16th of a pixel) prior to the matching. For highly deformed masses where higher resolution images are used, the LSM is recommended as it results in more accurate matching and deformation parameters. Improved accuracy and precision are obtained by selecting matchable areas using the spatially adaptive algorithm, identifying approximate matches using the NCC and optimizing the matches and measuring the deformation parameters using the LSM algorithm

Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives

Landslides represent major natural hazards, which cause every year significant loss of lives and damages to buildings, properties and lifelines. In the last decades, a significant increase in landslide frequency took place, in concomitance to climate change and the expansion of urbanized areas. Remote sensing techniques represent a powerful tool for landslide investigation: applications are traditionally divided into three main classes, although this subdivision has some limitations and borders are sometimes fuzzy. The first class comprehends techniques for landslide recognition, i.e., the mapping of past or active slope failures. The second regards landslide monitoring, which entails both ground deformation measurement and the analysis of any other changes along time (e.g., land use, vegetation cover). The third class groups methods for landslide hazard analysis and forecasting. The aim of this paper is to give an overview on the applications of remote-sensing techniques for the three categories of landslide investigations, focusing on the achievements of the last decade, being that previous studies have already been exhaustively reviewed in the existing literature. At the end of the paper, a new classification of remote-sensing techniques that may be pertinently adopted for investigating specific typologies of soil and rock slope failures is proposed

Characterizing slope instability kinematics by integrating multi-sensor satellite remote sensing observations

Over the past few decades, the occurrence and intensity of geological hazards, such as landslides, have substantially risen due to various factors, including global climate change, seismic events, rapid urbanization and other anthropogenic activities. Landslide disasters pose a significant risk in both urban and rural areas, resulting in fatalities, infrastructure damages, and economic losses. Nevertheless, conventional ground-based monitoring techniques are often costly, time-consuming, and require considerable resources. Moreover, some landslide incidents occur in remote or hazardous locations, making ground-based observation and field investigation challenging or even impossible. Fortunately, the advancements in spaceborne remote sensing technology have led to the availability of large-scale and high-quality imagery, which can be utilized for various landslide-related applications, including identification, monitoring, analysis, and prediction. This efficient and cost-effective technology allows for remote monitoring and assessment of landslide risks and can significantly contribute to disaster management and mitigation efforts. Consequently, spaceborne remote sensing techniques have become vital for geohazard management in many countries, benefiting society by providing reliable downstream services. However, substantial effort is required to ensure that such benefits are provided. For establishing long-term data archives and reliable analyses, it is essential to maintain consistent and continued use of multi-sensor spaceborne remote sensing techniques. This will enable a more thorough understanding of the physical mechanisms responsible for slope instabilities, leading to better decision-making and development of effective mitigation strategies. Ultimately, this can reduce the impact of landslide hazards on the general public. The present dissertation contributes to this effort from the following perspectives: 1. To obtain a comprehensive understanding of spaceborne remote sensing techniques for landslide monitoring, we integrated multi-sensor methods to monitor the entire life cycle of landslide dynamics. We aimed to comprehend the landslide evolution under complex cascading events by utilizing various spaceborne remote sensing techniques, e.g., the precursory deformation before catastrophic failure, co-failure procedures, and post-failure evolution of slope instability. 2. To address the discrepancies between spaceborne optical and radar imagery, we present a methodology that models four-dimensional (4D) post-failure landslide kinematics using a decaying mathematical model. This approach enables us to represent the stress relaxation for the landslide body dynamics after failure. By employing this methodology, we can overcome the weaknesses of the individual sensor in spaceborne optical and radar imaging. 3. We assessed the effectiveness of a newly designed small dihedral corner reflector for landslide monitoring. The reflector is compatible with both ascending and descending satellite orbits, while it is also suitable for applications with both high-resolution and medium-resolution satellite imagery. Furthermore, although its echoes are not as strong as those of conventional reflectors, the cost of the newly designed reflectors is reduced, with more manageable installation and maintenance. To overcome this limitation, we propose a specific selection strategy based on a probability model to identify the reflectors in satellite images

Detection and mapping of small-scale and slow-moving landslides from very high resolution optical satellite data

Small slope failures are often ignored because of their perceived less severe impact. Although they may have small velocity, small slope failures can cause damages to facilities such roads and pipelines. The main objective of this research is to utilise very high resolution Pleiades-1 data to extract surface features and identify surface deformations susceptible to small slope failures. An algorithm was developed using object-based image analysis (OBIA), Pleiades-1 imagery, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) and Real Time Kinematic-Global Positioning System (RTK-GPS) data. Using the OBIA algorithm four different object attribute parameters namely spectral, textural, spatial and topographic characteristics were applied in a rule-based classification, for semi-automated detection of small translational landslides. The developed OBIA algorithm was further modified by using Pleiades-1 imagery, Nearest Neighbors (k-NN) and Support Vector Machine (SVM) techniques in example-based classification for the detection of small landslides, with focus on the effects of the training samples size and type on the results of the classification. The horizontal displacement of the landslides was investigated based on sub-pixel image correlation method using Pleiades-1 images and Shuttle Radar Topographic Mission (SRTM). Kalman filtering method and RTK-GPS observations from TUSAGA-Aktif Global Navigation Satellite System (GNSS) Network in Turkey were utilised to formulate kinematic analysis model for the landslides. The developed algorithms were validated in Kutlugün test site in Northeastern Turkey. In the rule-based classification results, a total of 123 small landslides covering a total area of approximately 413.332 m2 were detected. The size of landslides detected varied between 0.747 and 7.469 m2. The detected landslides yielded user’s accuracy of 81.8%, producer’s accuracy of 80.6%, quality percentage of 82% and computed kappa index of 0.87. In the small landslides detection using the example-based classification, the SVM method had higher producer accuracy (85.9%), user accuracy (89.4%) and kappa index (0.82) compared to the k-NN algorithm that had producer accuracy (83.1%), user accuracy (86.0%) and kappa index (0.80). A total of 128 small landslides were detected using k-NN algorithm, while a total of 134 landslides were detected using SVM algorithm. The displacement results from RTK-GPS measurements varied from 2.77 mm to 24.87 mm in 6 months, while the velocities varied from 0.80 mm to 8.28 mm/6 month. The displacements from optical image correlation agreed well with RTK-GPS results and provided a more uniform movement pattern than could be derived solely using the RTK-GPS measurements. The landslide movements are dominantly toward the north direction. These trends agree with the results of previous study in the area. The main contributions of this research include – development of a comprehensive metrics to quantify the attribute parameters of small landslides, derivation of susceptibility and inventory maps for small landslides, and the design of an early warning system for small slope failures on highway infrastructures. The results of this research will add to the increasing applications of Pleiades-1 image in landslide investigations

Time series analysis of very slow landslides in the three Gorges region through small baseline SAR offset tracking

Sub-pixel offset tracking has been used in various applications, including measurements of glacier movement, earthquakes, landslides, etc., as a complementary method to time series InSAR. In this work, we explore the use of a small baseline subset (SBAS) Offset Tracking approach to monitor very slow landslides with centimetre-level annual displacement rate, and in challenging areas characterized by high humidity, dense vegetation cover, and steep slopes. This approach, herein referred to as SBAS Offset Tracking, is used to minimize temporal and spatial de -correlation in offset pairs, in order to achieve high density of reliable measurements. This approach is applied to a case study of the Tanjiahe landslide in the Three Gorges Region. Using the TerraSAR-X Staring Spotlight (TSX-ST) data, with sufficient density of observations, we estimate the precision of the SBAS offset tracking approach to be 2-3 cm on average. The results demonstrated accord well with corresponding GPS measurements

Quantifying spatial uncertainties in structure from motion snow depth mapping with drones in an alpine environment

Due to the heterogeneous nature of alpine snow distribution, advances in hydrological monitoring and forecasting for water resource management require an increase in the frequency, spatial resolution and coverage of field observations. Such detailed snow information is also needed to foster advances in our understanding of how snowpack affects local ecology and geomorphology. Although recent use of structure-from-motion multi-view stereo (SFM-MVS) 3D reconstruction techniques combined with aerial image collection using drones has shown promising potential to provide higher spatial and temporal resolution snow depth data for snowpack monitoring, there still remain challenges to produce high-quality data with this approach. These challenges, which include differentiating observations from noise and overcoming biases in the elevation data, are inherent in digital elevation model (DEM) differencing. A key issue to address these challenges is our ability to quantify measurement uncertainties in the SFM-MVS snow depths which can vary in space and time. The purpose of this thesis was to develop data-driven approaches for spatially quantifying, characterizing and reducing uncertainties in SFM-MVS snow depth mapping in alpine areas. Overall, this thesis provides a general framework for performing a detailed analysis of the spatial pattern of SFM-MVS snow depth uncertainties, as well as provides an approach for correction of snow depth errors due to changes in the sub-snow topography occurring between survey acquisition dates. It also contributes to the growing support of SFM-MVS combined with imagery acquired from drones as a suitable surveying technique for local scale snow distribution monitoring in alpine areas

Genesis, conservation and deformation of ice-rich mountain permafrost:: Driving factors, mapping and geodetic monitoring

This thesis analyses ice-rich mountain permafrost with regard to its genesis, distribution, deformation and interaction with other environmental factors. The processes influencing ground ice formation in ice-rich and ice-poor mountain permafrost are highlighted. Factors influencing the presence of ice-rich permafrost are identified and their individual or combined effect on frozen ground is determined. Based on these findings, a new permafrost distribution map of Switzerland was created, which specifies permafrost temperature and ice contents and considers rock glacier creep paths. The deformation of rock glaciers is investigated with newly developed monitoring systems and concepts. This enables a better understanding of the processes leading to rock glacier acceleration at different time scales

NOVI PRISTUP PRAĆENJA POMAKA KLIZIŠTA POMOĆU BESPILOTNIH FOTOGRAMETRIJSKIH SUSTAVA

Landslides represent great dangers that can cause fatalities and huge property damage. To prevent or reduce all possible consequences that landslides cause, it is necessary to know the kinematics of the surface and undersurface sliding masses. Geodetic surveying techniques can be used for landslide monitoring and creating a kinematic model of the landslide. One of the most used surveying techniques for landslide monitoring is the photogrammetric survey by Unmanned Aerial System. The results of the photogrammetric survey are dense point clouds, digital terrain models, and digital orthomosaic maps, where landslide displacements can be determined by comparing these results in two measurement epochs. This paper presents a new data processing method with a novel approach for calculating landslide displacements based on Unmanned Aerial System photogrammetric survey data. The main advantage of the new method is that it does not require the production of dense point clouds, digital terrain models, or digital orthomosaic maps to determine displacements. The applicability and accuracy of the new method were tested in a test field with simulated displacements of known values within the range of 20-40 cm in various directions. The new method successfully determined these displacements with a 3D accuracy of ±1.3 cm.Klizišta predstavljaju velike opasnosti koje mogu uzrokovati katastrofalne ljudske žrtve te nanijeti veliku materijalnu štetu. Da bi se spriječile ili umanjile sve moguće posljedice koje klizišta prouzročuju, važno je poznavati kinematiku kretanja površinskih i podzemnih kliznih masa klizišta. Geodetske tehnike izmjere mogu se koristiti za potrebe praćenja te za izradu kinematičkoga modela klizišta. U današnje vrijeme jedna od najčešće korištenih geodetskih tehnika za potrebe praćenja klizišta jest fotogrametrijsko snimanje pomoću bespilotnih zrakoplovnih sustava. Rezultati su takvih snimanja gusti oblaci točaka, digitalni modeli terena te digitalne ortomozaik karte, a na temelju usporedbe tih rezultata u dvjema mjernim epohama mogu se odrediti pomaci klizišta. Ovaj rad predstavlja novu metodu obrade podataka s novim pristupom za određivanje pomaka klizišta na temelju podataka fotogrametrijskoga snimanja bespilotnim zrakoplovnim sustavima. Glavna je prednost nove metode u tome što ne zahtijeva izradu gustih oblaka točaka, digitalnih modela terena ili digitalnih ortomozaik karata za određivanje pomaka. Primjenjivost i točnost nove metode ispitane su na testnome polju sa simuliranim pomacima poznatih vrijednosti čiji su se iznosi kretali u rasponu od 20 do 40 cm u različitim smjerovima. Nova metoda uspješno je odredila te pomake s 3D točnošću od ±1,3 cm

Measuring liquefaction-induced deformation from optical satellite imagery

textLiquefaction-induced deformations associated with lateral spreading represent a significant hazard that can cause substantial damage during earthquakes. The ability to accurately predict lateral-spreading displacement is hampered by a lack of field data from previous earthquakes. Remote sensing via optical image correlation can fill this gap and provide data regarding liquefaction-induced lateral spreading displacements. In this thesis, deformations from three earthquakes (2010 Darfield, February 2011 Christchurch, and 2011 Tohoku Earthquakes) are measured using optical image correlation applied to 0.5-m resolution satellite imagery. The resulting deformations from optical image correlation are compared to the geologic conditions, as well as field observations and measurements of liquefaction. Measurements from optical image correlation are found to have a precision within 0.40 m in all three cases, and results agree well with field measurements.Civil, Architectural, and Environmental Engineerin