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

Veksttyper på jordbruksareal fra satellitt

JordbrukSat er et nasjonalt vektorkart som viser den geografiske fordelingen av ulike veksttyper på jordbruksareal, samt jordbruksareal som er nedbygd. Kartet framstilles fra satellittbilder og offentlig kartdata med bruk av maskinlæring og deler jordbruksarealet i seks klasser.Veksttyper på jordbruksareal fra satellittpublishedVersio

Mer og samlet areal gir mer areal i drift

I 2021 var det cirka 122 000 landbrukseiendommer i Norge som hadde mer enn 10 dekar maskinelt høstbart jordbruksareal. De fleste av disse, nesten hundre tusen, hadde så og si alt jordbruksareal i drift. Det betyr imidlertid også at mer enn 20 000 landbrukseiendommer har noe eller mesteparten av eiendommens jordbruksareal tilsynelatende ute av drift. Vi har delt landbrukseiendommer inn i fire grupper basert på mengde areal som kan være ute av drift, og vurdert om det er forskjell mellom gruppene med tanke på størrelse og fordeling av jordstykker.Mer og samlet areal gir mer areal i driftpublishedVersio

The Application of Digital Terrain Analysis for Digital Soil Mapping : Examples from Vestfold County, South-Eastern Norway

Digital terrain modeling has revolutionized the way topography is characterized and analyzed. Its applicability has widened to almost anything where topography has a role to play. On the other hand, digital soil mapping has become the pedological paradigm of the time as it is making tremendous improvements in the ways soil information is obtained, stored, retrieved and manipulated. This research was conducted in Vestfold County of south-eastern Norway to use digital terrain analysis aided by statistical modeling and remote sensing image classification algorithms to make digital soil maps. A digital elevation model of 25 meter resolution and digitized soil map of part of the study area accompanied by data on some analytical properties of soils were used as original data for the terrain and soil respectively. Fifteen terrain attributes were derived from the digital elevation model through digital terrain analysis. There were thirteen WRB soil classes in the surveyed area of the study site. Besides, five most important topsoil properties (the soils content of Clay, Organic carbon, Keldjahl’s Nitrogen, KNHO3- and pH) for limited number of soil profiles were also used. The relationship between soil properties and the terrain attributes were analyzed using multiple linear regression in SPSS. The significant regression models were then fed into ARCGIS to predict the spatial distribution of the soil properties. The performance of this prediction was evaluated by comparing it with validation-based ordinary kriging interpolation of the soil properties, which was conducted in ARCGIS. The prediction of soil classes using digital terrain analysis was conducted using two conceptually different approaches. First, soil classes were considered as discrete objects and analysis of variance was used to check if there was significant difference among them in their terrain attribute values. Then, in analogy with satellite image channels, the terrain attributes were used as channels and object-oriented supervised classification algorithm was applied in eCgnition by collecting training areas from the reference soil map. To know the relative performance of this object-oriented approach, ordinary pixel-based supervised classification was conducted in ARCGIS using the same training areas. Second, the spatial variation of soil classes was conceptualized as gradual and fuzzy logic approach was employed for the prediction. Here, the relationship between the soil classes and the terrain attributes was first modeled using multinomial logistic regression in SPSS to identify the most influential terrain attributes and to construct logit models for each soil class. The logit models were used to derive probability prediction models which were then used in ARCGIS to predict the probability of existence of each of the soil classes as fuzzy variables. The reliability of this approach was evaluated qualitatively using expert knowledge, empirical soil map of the area and theoretical background of the soil classes, and quantitatively through correlation study of the probability values. The result from the spatial prediction of topsoil properties using terrain attribute showed that the approach predicted topsoil clay content, KHNO3 content and extractible nitrogen content with better accuracy compared to the validation-based ordinary kriging. Besides, it showed that about 60% of each of their spatial variation can be attributed to terrain. On the other hand, insignificant correlation was found between the terrain attributes and organic carbon content and pH of the soils of the area. All of the terrain attributes, with the exception of plan curvature, were found significantly influential in the spatial distribution of soils both by the ANOVA and the logistic regression analysis. Elevation, flow length, duration of daily direct solar radiation, slope, aspect and topographic wetness index were found to be the most significant terrain attributes. The crisp approach to the prediction of soil classes showed that the object-oriented approach performed better than the pixel-based terrain classification approach. The overall accuracy for the object-oriented approach was 30% while it was only 14% for the pixel-based. However, the accuracies of some soil classes reached up to 75% in the first approach. Higher accuracies were obtained for soil classes with higher spatial coverage in the area. The probability prediction for each soil class using logit models was found to be reliable when evaluated against the empirical soil maps except for those soil classes which are not greatly influenced by topography but by other factors such as human activity. In general, the study revealed that digital terrain analysis has a great potential in digital mapping of soils and their properties. Fuzzy probability mapping and object-oriented approach were found to be reliable to a considerable extent in the prediction of soil classes and deserve further research and application

Spatial Big Data tools and methods within NIBIO

Rapporten utforsker og diskuterer potensialet for økt bruk av Stordata (engelsk: big data) teknologi og metode innenfor instituttets arbeidsområder. I dag benyttes Stordata-tilnærminger til å løse forvaltningsstøtteoppgaver, samt til forskningsformål, særlig i sentrene for presisjonslandbruk og presisjonsjordbruk. Potensialet for økt bruk av Stordata innenfor instituttet er stort. For å realisere potensialet er det behov for god samordning mellom organisasjonsenhetene og utvikling av strategisk kompetanse på fagområdet

Measurement of Surface Displacement and Deformation of Mass Movements Using Least Squares Matching of Repeat High Resolution Satellite and Aerial Images

Displacement and deformation are fundamental measures of Earth surface mass movements such as glacier flow, rockglacier creep and rockslides. Ground-based methods of monitoring such mass movements can be costly, time consuming and limited in spatial and temporal coverage. Remote sensing techniques, here matching of repeat optical images, are increasingly used to obtain displacement and deformation fields. Strain rates are usually computed in a post-processing step based on the gradients of the measured velocity field. This study explores the potential of automatically and directly computing velocity, rotation and strain rates on Earth surface mass movements simultaneously from the matching positions and the parameters of the geometric transformation models using the least squares matching (LSM) approach. The procedures are exemplified using bi-temporal high resolution satellite and aerial images of glacier flow, rockglacier creep and land sliding. The results show that LSM matches the images and computes longitudinal strain rates, transverse strain rates and shear strain rates reliably with mean absolute deviations in the order of 10−4 (one level of significance below the measured values) as evaluated on stable grounds. The LSM also improves the accuracy of displacement estimation of the pixel-precision normalized cross-correlation by over 90% under ideal (simulated) circumstances and by about 25% for real multi-temporal images of mass movements

Measurement of Surface Displacement and Deformation of Mass Movements Using Least Squares Matching of Repeat High Resolution Satellite and Aerial Images

Displacement and deformation are fundamental measures of Earth surface mass movements such as glacier flow, rockglacier creep and rockslides. Ground-based methods of monitoring such mass movements can be costly, time consuming and limited in spatial and temporal coverage. Remote sensing techniques, here matching of repeat optical images, are increasingly used to obtain displacement and deformation fields. Strain rates are usually computed in a post-processing step based on the gradients of the measured velocity field. This study explores the potential of automatically and directly computing velocity, rotation and strain rates on Earth surface mass movements simultaneously from the matching positions and the parameters of the geometric transformation models using the least squares matching (LSM) approach. The procedures are exemplified using bi-temporal high resolution satellite and aerial images of glacier flow, rockglacier creep and land sliding. The results show that LSM matches the images and computes longitudinal strain rates, transverse strain rates and shear strain rates reliably with mean absolute deviations in the order of 10−4 (one level of significance below the measured values) as evaluated on stable grounds. The LSM also improves the accuracy of displacement estimation of the pixel-precision normalized cross-correlation by over 90% under ideal (simulated) circumstances and by about 25% for real multi-temporal images of mass movements

Gjengroing i reiselivets landskap

Reiselivet i Norge har de siste tiåra vært oppmerksomme på landskapsendringene som skjer i Norge. I følge reiselivsnæringa truer gjengroing av kulturlandskapet viktige segmenter innen det norske reiselivet. Samtidig legges det årlig ned et sted mellom 1500 - 2000 gardsbruk i Norge.publishedVersio