Semi-automated geomorphological mapping applied to landslide hazard analysis

Computer-assisted three-dimensional (3D) mapping using stereo and multi-image (“softcopy”) photogrammetry is shown to enhance the visual interpretation of geomorphology in steep terrain with the direct benefit of greater locational accuracy than traditional manual mapping. This would benefit multi-parameter correlations between terrain attributes and landslide distribution in both direct and indirect forms of landslide hazard assessment. Case studies involve synthetic models of a landslide, and field studies of a rock slope and steep undeveloped hillsides with both recently formed and partly degraded, old landslide scars. Diagnostic 3D morphology was generated semi-automatically both using a terrain-following cursor under stereo-viewing and from high resolution digital elevation models created using area-based image correlation, further processed with curvature algorithms. Laboratory-based studies quantify limitations of area-based image correlation for measurement of 3D points on planar surfaces with varying camera orientations. The accuracy of point measurement is shown to be non-linear with limiting conditions created by both narrow and wide camera angles and moderate obliquity of the target plane. Analysis of the results with the planar surface highlighted problems with the controlling parameters of the area-based image correlation process when used for generating DEMs from images obtained with a low-cost digital camera. Although the specific cause of the phase-wrapped image artefacts identified was not found, the procedure would form a suitable method for testing image correlation software, as these artefacts may not be obvious in DEMs of non-planar surfaces.Modelling of synthetic landslides shows that Fast Fourier Transforms are an efficient method for removing noise, as produced by errors in measurement of individual DEM points, enabling diagnostic morphological terrain elements to be extracted. Component landforms within landslides are complex entities and conversion of the automatically-defined morphology into geomorphology was only achieved with manual interpretation; however, this interpretation was facilitated by softcopy-driven stereo viewing of the morphological entities across the hillsides.In the final case study of a large landslide within a man-made slope, landslide displacements were measured using a photogrammetric model consisting of 79 images captured with a helicopter-borne, hand-held, small format digital camera. Displacement vectors and a thematic geomorphological map were superimposed over an animated, 3D photo-textured model to aid non-stereo visualisation and communication of results

Characterising vegetation structure using MODIS multi-angular data

Bidirectional Reflectance Distribution Functions (BRDF) seek to represent variations in surface reflectance resulting from changes in a satellite sensor’s view and solar illumination angles. NASA’s Moderate Resolution Image Spectroradiometer (MODIS) is a wide field of view Earth orbiting sensor that generates observations over a large range of view angles. Based on MODIS observations, a BRDF product and several sub-products have been developed by MODIS science teams, i.e. the MCD43 product suite. With the aim of using BRDF information from the MCD43 product to assist with the characterisation of the vertical structure of vegetation, a simple geometric optical model has been developed within this thesis for interpreting the MCD43 BRDF product in terms of an alternative set of parameters. The model developed within the thesis, and its application to single species cropped fields, a transect between Melbourne – Darwin and a semi-arid area in central Australia. The thesis identified that reflectance variations associated with enlargement of pixels’ ground instantaneous field of view is the principal source of variation in the MODIS BRDF product; rather than directional scatter effects that the product is intended to measure. Variations in pixels’ ground instantaneous field of view is a well known effect associated with wide field of view sensors such as MODIS, but is not explicitly considered in the MODIS BRDF algorithm. The presence of this artefact within the MODIS BRDF product has implications for the validity, use and interpretation of all land surface products based directly or indirectly on MODIS BRDF modelling

Predicting glacier accumulation area distributions

A mass balance model based on energy balance at the terrain surface was developed and used to predict glacier accumulation areas in the Jotunheimen, Norway. Spatially distributed melt modelling used local climate and energy balance surfaces to drive predictions, derived from regional climate and topographic data. Predictions had a temporal resolution of 1 month and a spatial resolution of 100 m, which were able to simulate observed glacier accumulation area distributions. Data were stored and manipulated within a GIS and spatial trends and patterns within the data were explored. These trends guided the design of a suite of geomorphologically and climatologically significant variables which were used to simulate the observed spatial organisation of climatic variables, specifically temperature, precipitation and wind speed and direction. DEM quality was found as a critical factor in minimising error propagation. A new method of removing spatially and spectrally organised DEM error is presented using a fast Fourier transformation. This was successfully employed to remove error within the DEM minimising error propagation into model predictions. With no parameter fitting the modeled spatial distribution of snowcover showed good agreement with observed distributions. Topographic maps and a Landsat ETM+ image are used to validate the predictions and identify areas of over or under prediction. Topographically constrained glaciers are most effectively simulated, where aspect, gradient and altitude impose dominant controls on accumulation. Reflections on the causes of over or under prediction are presented and future research directions to address these are outlined. Sensitivity of snow accumulation to climatic and radiative variables was assessed. Results showed the mass balance of accumulation areas is most sensitive to air temperature and cloud cover parameterisations. The model was applied to reconstruct snow accumulation at the last glacial maximum and under IPCC warming scenarios to assess the sensitivity of melt to changing environmental conditions, which showed pronounced sensitivity to summer temperatures Low data requirements: regional climate and elevation data identify the model as a powerful tool for predicting the onset, duration and rate of melt for any geographical area

Integrated Land Use Change Analysis For Soil Ersion Study In Ulu Kinta Catchment [S623. B354 2006 f rb].

Ulu Kinta catchment has experienced rapid changes in land use and land cover from 1991 to 2004. These changes have resulted in increased upland erosion and higher concentrations of suspended sediment within the catchment. The goal of this research was to investigate the application of integrated satellite remote sensing and Geographic Information Systems (GIS) techniques to assess land cover changes and the estimation of soil erosion in the water catchment. Kawasan tadahan Ulu Kinta telah mengalami perubahan yang ketara di dalam penggunaan tanah dan liputan tanah dari tahun 1991 hingga 2004. Perubahan ini telah meningkatkan hakisan tanah dan meninggikan kepekatan bahan asing yang terampai di dalam kawasan tadahan. Tujuan kajian ini adalah untuk mengkaji penggunaan bersama teknik penderiaan jauh satelit dan Sistem Maklumat Geografi (GIS) untuk menilai perubahan litupan bumi dan anggaran penghakisan tanah untuk kawasan tadahan air

Harmonization of remote sensing land surface products : correction of clear-sky bias and characterization of directional effects

Tese de doutoramento, Ciências Geofísicas e da Geoinformação (Deteção Remota), Universidade de Lisboa, Faculdade de Ciências, 2018Land surface temperature (LST) is the mean radiative skin temperature of an area of land resulting from the mean energy balance at the surface. LST is an important climatological variable and a diagnostic parameter of land surface conditions, since it is the primary variable determining the upward thermal radiation and one of the main controllers of sensible and latent heat fluxes between the surface and the atmosphere. The reliable and long-term estimation of LST is therefore highly relevant for a wide range of applications, including, amongst others: (i) land surface model validation and monitoring; (ii) data assimilation; (iii) hydrological applications; and (iv) climate monitoring. Remote sensing constitutes the most effective method to observe LST over large areas and on a regular basis. Satellite LST products generally rely on measurements in the thermal infrared (IR) atmospheric window, i.e., within the 8-13 micrometer range. Beside the relatively weak atmospheric attenuation under clear sky conditions, this band includes the peak of the Earth’s spectral radiance, considering surface temperature of the order of 300K (leading to maximum emission at approximately 9.6 micrometer, according to Wien’s Displacement Law). The estimation of LST from remote sensing instruments operating in the IR is being routinely performed for nearly 3 decades. Nevertheless, there is still a long list of open issues, some of them to be addressed in this PhD thesis. First, the viewing position of the different remote sensing platforms may lead to variability of the retrieved surface temperature that depends on the surface heterogeneity of the pixel – dominant land cover, orography. This effect introduces significant discrepancies among LST estimations from different sensors, overlapping in space and time, that are not related to uncertainties in the methodologies or input data used. Furthermore, these directional effects deviate LST products from an ideally defined LST, which should correspond to the ensemble directional radiometric temperature of all surface elements within the FOV. In this thesis, a geometric model is presented that allows the upscaling of in situ measurements to the any viewing configuration. This model allowed generating a synthetic database of directional LST that was used consistently to evaluate different parametric models of directional LST. Ultimately, a methodology is proposed that allows the operational use of such parametric models to correct angular effects on the retrieved LST. Second, the use of infrared data limits the retrieval of LST to clear sky conditions, since clouds “close” the atmospheric window. This effect introduces a clear-sky bias in IR LST datasets that is difficult to quantify since it varies in space and time. In addition, the cloud clearing requirement severely limits the space-time sampling of IR measurements. Passive microwave (MW) measurements are much less affected by clouds than IR observations. LST estimates can in principle be derived from MW measurements, regardless of the cloud conditions. However, retrieving LST from MW and matching those estimations with IR-derived values is challenging and there have been only a few attempts so far. In this thesis, a methodology is presented to retrieve LST from passive MW observations. The MW LST dataset is examined comprehensively against in situ measurements and multiple IR LST products. Finally, the MW LST data is used to assess the spatial-temporal patterns of the clear-sky bias at global scale.Fundação para a Ciência e a Tecnologia, SFRH/BD/9646

Soil erosion in the Alps : causes and risk assessment

The issue of soil erosion in the Alps has long been neglected due to the low economic value of the agricultural land. However, soil stability is a key parameter which affects ecosystem services like slope stability, water budgets (drinking water reservoirs as well as flood prevention), vegetation productivity, ecosystem biodiversity and nutrient production. In alpine regions, spatial estimates on soil erosion are difficult to derive because the highly heterogeneous biogeophysical structure impedes measurement of soil erosion and the applicability of soil erosion models. However, remote sensing and geographic information system (GIS) methods allow for spatial estimation of soil erosion by direct detection of erosion features and supply of input data for soil erosion models. Thus, the main objective of this work is to address the problem of soil erosion risk assessment in the Alps on catchment scale with remote sensing and GIS tools. Regarding soil erosion processes the focus is on soil erosion by water (here sheet erosion) and gravity (here landslides). For these two processes we address i) the monitoring and mapping of the erosion features and related causal factors ii) soil erosion risk assessment with special emphasis on iii) the validation of existing models for alpine areas. All investigations were accomplished in the Urseren Valley (Central Swiss Alps) where the valley slopes are dramatically affected by sheet erosion and landslides. For landslides, a natural susceptibility of the catchment has been indicated by bivariate and multivariate statistical analysis. Geology, slope and stream density are the most significant static landslide causal factors. Static factors are here defined as factors that do not change their attributes during the considered time span of the study (45 years), e.g. geology, stream network. The occurrence of landslides might be significantly increased by the combined effects of global climate and land use change. Thus, our hypothesis is that more recent changes in land use and climate affected the spatial and temporal occurrence of landslides. The increase of the landslide area of 92% within 45 years in the study site confirmed our hypothesis. In order to identify the cause for the trend in landslide occurrence time-series of landslide causal factors were analysed. The analysis revealed increasing trends in the frequency and intensity of extreme rainfall events and stocking of pasture animals. These developments presumably enhanced landslide hazard. Moreover, changes in land-cover and land use were shown to have affected landslide occurrence. For instance, abandoned areas and areas with recently emerging shrub vegetation show very low landslide densities. Detailed spatial analysis of the land use with GIS and interviews with farmers confirmed the strong influence of the land use management practises on slope stability. The definite identification and quantification of the impact of these non-stationary landslide causal factors (dynamic factors) on the landslide trend was not possible due to the simultaneous change of several factors. The consideration of dynamic factors in statistical landslide susceptibility assessments is still unsolved. The latter may lead to erroneous model predictions, especially in times of dramatic environmental change. Thus, we evaluated the effect of dynamic landslide causal factors on the validity of landslide susceptibility maps for spatial and temporal predictions. For this purpose, a logistic regression model based on data of the year 2000 was set up. The resulting landslide susceptibility map was valid for spatial predictions. However, the model failed to predict the landslides that occurred in a subsequent event. In order to handle this weakness of statistic landslide modelling a multitemporal approach was developed. It is based on establishing logistic regression models for two points in time (here 1959 and 2000). Both models could correctly classify >70% of the independent spatial validation dataset. By subtracting the 1959 susceptibility map from the 2000 susceptibility map a deviation susceptibility map was obtained. Our interpretation was that these susceptibility deviations indicate the effect of dynamic causal factors on the landslide probability. The deviation map explained 85% of new independent landslides occurring after 2000. Thus, we believe it to be a suitable tool to add a time element to a susceptibility map pointing to areas with changing susceptibility due to recently changing environmental conditions or human interactions. In contrast to landslides that are a direct threat to buildings and infrastructure, sheet erosion attracts less attention because it is often an unseen process. Nonetheless, sheet erosion may account for a major proportion of soil loss. Soil loss by sheet erosion is related to high spatial variability, however, in contrast to arable fields for alpine grasslands erosion damages are long lasting and visible over longer time periods. A crucial erosion triggering parameter that can be derived from satellite imagery is fractional vegetation cover (FVC). Measurements of the radiogenic isotope Cs-137, which is a common tracer for soil erosion, confirm the importance of FVC for soil erosion yield in alpine areas. Linear spectral unmixing (LSU), mixture tuned matched filtering (MTMF) and the spectral index NDVI are applied for estimating fractional abundance of vegetation and bare soil. To account for the small scale heterogeneity of the alpine landscape very high resolved multispectral QuickBird imagery is used. The performance of LSU and MTMF for estimating percent vegetation cover is good (r²=0.85, r²=0.71 respectively). A poorer performance is achieved for bare soil (r²=0.28, r²=0.39 respectively) because compared to vegetation, bare soil has a less characteristic spectral signature in the wavelength domain detected by the QuickBird sensor. Apart from monitoring erosion controlling factors, quantification of soil erosion by applying soil erosion risk models is done. The performance of the two established models Universal Soil Loss Equation (USLE) and Pan-European Soil Erosion Risk Assessment (PESERA) for their suitability to model erosion for mountain environments is tested. Cs-137 is used to verify the resulting erosion rates from USLE and PESERA. PESERA yields no correlation to measured Cs-137 long term erosion rates and shows lower sensitivity to FVC. Thus, USLE is used to model the entire study site. The LSU-derived FVC map is used to adapt the C factor of the USLE. Compared to the low erosion rates computed with the former available low resolution dataset (1:25000) the satellite supported USLE map shows “hotspots” of soil erosion of up to 16 t ha-1 a-1. In general, Cs-137 in combination with the USLE is a very suitable method to assess soil erosion for larger areas, as both give estimates on long-term soil erosion. Especially for inaccessible alpine areas, GIS and remote sensing proved to be powerful tools that can be used for repetitive measurements of erosion features and causal factors. In times of global change it is of crucial importance to account for temporal developments. However, the evaluation of the applied soil erosion risk models revealed that the implementation of temporal aspects, such as varying climate, land use and vegetation cover is still insufficient. Thus, the proposed validation strategies (spatial, temporal and via Cs-137) are essential. Further case studies in alpine regions are needed to test the methods elaborated for the Urseren Valley. However, the presented approaches are promising with respect to improve the monitoring and identification of soil erosion risk areas in alpine regions

Semi-Automated DIRSIG scene modeling from 3D lidar and passive imagery

The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model is an established, first-principles based scene simulation tool that produces synthetic multispectral and hyperspectral images from the visible to long wave infrared (0.4 to 20 microns). Over the last few years, significant enhancements such as spectral polarimetric and active Light Detection and Ranging (lidar) models have also been incorporated into the software, providing an extremely powerful tool for multi-sensor algorithm testing and sensor evaluation. However, the extensive time required to create large-scale scenes has limited DIRSIG’s ability to generate scenes ”on demand.” To date, scene generation has been a laborious, time-intensive process, as the terrain model, CAD objects and background maps have to be created and attributed manually. To shorten the time required for this process, this research developed an approach to reduce the man-in-the-loop requirements for several aspects of synthetic scene construction. Through a fusion of 3D lidar data with passive imagery, we were able to semi-automate several of the required tasks in the DIRSIG scene creation process. Additionally, many of the remaining tasks realized a shortened implementation time through this application of multi-modal imagery. Lidar data is exploited to identify ground and object features as well as to define initial tree location and building parameter estimates. These estimates are then refined by analyzing high-resolution frame array imagery using the concepts of projective geometry in lieu of the more common Euclidean approach found in most traditional photogrammetric references. Spectral imagery is also used to assign material characteristics to the modeled geometric objects. This is achieved through a modified atmospheric compensation applied to raw hyperspectral imagery. These techniques have been successfully applied to imagery collected over the RIT campus and the greater Rochester area. The data used include multiple-return point information provided by an Optech lidar linescanning sensor, multispectral frame array imagery from the Wildfire Airborne Sensor Program (WASP) and WASP-lite sensors, and hyperspectral data from the Modular Imaging Spectrometer Instrument (MISI) and the COMPact Airborne Spectral Sensor (COMPASS). Information from these image sources was fused and processed using the semi-automated approach to provide the DIRSIG input files used to define a synthetic scene. When compared to the standard manual process for creating these files, we achieved approximately a tenfold increase in speed, as well as a significant increase in geometric accuracy

Utility of Spatial Filtering Techniques in the Remote Sensing of Soil Erosion in the Sefid-Rud Reservoir Catchment in Iran

The objective of this study is to investigate the applicability of Landsat Thematic Mapper digital images assisted by computer analysis to the study of soil erosion. The study aims to identify the sources of sediment and areas of dissected land in the catchment basin of the Sefid Rud reservoir in northern Iran. First, histogram equalization is deliberately applied to the original band 3 to reduce the noise and unv/anted edges and lines in the dark tail of the histogram, mainly vegetation, and the light tail, the non-eroded areas, and also to improve the visual appearance of edges and lines on the processed image. The next step is high pass filtering, unlike the conventional edge detection technique in which the first step is low pass filtering. In this instance, the result of low pass filtering was that faint edges, evidence of the gullies, were removed and highly eroded areas appeared as non eroded areas. Therefore low pass filtering was replaced with high pass filtering, which highlighted faint edges and lines. The next step is detecting the edges and lines. When using the edge and line detecting technique for detecting dissected lands one needs to take into account that a gully might appear as two or three edges if its width is more than one pixel or as one line if it is just one pixel or less than one pixel in width on the Thematic Mapper image. Therefore an algorithm should be chosen which has the ability to detect both edges and lines. The existina edge and line detecting filters such as the Sobel , the Robert, compass, the Laplacian convolution masks and the directional line detecting technique were evaluated. The Sobel and the Robert operators were found to be powerful edge detecting techniques, but the Laplacian convolution mask was found to be the best for detecting the badland and gullied areas because it has the ability to detect faint edges as well as coarse edges. Not only does it detect both edges and lines, but it also gives stronger weight to the lines than the edges. Only edges and lines in gullied areas were of interest for detecting the dissected lands, but all other artificial and natural lines and edges were also detected. The result of applying the Laplacian function appears on the screen as black, white and gray pixels. The black pixels are non-eroded land, white pixels are eroded and gray pixels are transitional between eroded and non eroded. To change the transitional pixels to either eroded or non eroded and also for printing the image as hardcopy the thresholding function of IAX was applied to the edge detected image. In order to mask out the noise within the vegetated areas caused by edges of plots of different crops the vegetation index (VI) was added to the detected image. In the derived image black pixels are evidence of gullies and white pixels are non dissected lands. In this image it is possible to find out the relative proportion of dissected and non dissected land globally and / or within the regions of interest. Although it is possible to measure the proportions of dissected and non dissected land and they are also visually distinguishable, they have not been categorised so far. To provide a map with categories of dissection, the first step is to smooth the image. To obtain the smooth image a low pass filter was used. Two ways were tested for producing the map of dissected lands from the smoothed image. In the first method one of the strongest edge detecting techniques, the Sobel operator was used on the smoothed image of dissected lands. In the result boundaries were detected and eroded and non eroded areas outlined. In the second method for categorising the smoothed image, the density slicing function of IAX was used to split the dissected land into different levels of severity. We concluded that the second method gives a better result. It was found in previous work that among erosion features gullies are recognizable on Thematic Mapper data. Detection of gullies and gullied areas by means of classification, whether supervised or unsupervised, was not successful in this study area. We came to the conclusion that the application of a Laplacian mask on the enhanced band 3 image could detect dissected lands. When aerial photographs and Thematic Mapper data are compared, the advantage of aerial photographs was that gullies actively cutting headwards were detectable, but on the Thematic Mapper data distiguishing between active and non active gullies was impossible. Aerial photographs are a very good tool to detect all kinds of erosion features (sheet, rill, and gully), but in my study area applying this new method (DLDT) on Thematic Mapper data can provide as much detail of soil erosion as is included in previous soil erosion maps made from aerial photographs. The Sobel and the Robert operators were found to be very strong edge detectors, but the ability of the Laplacian convolution mask for detecting gullies was greater. (Abstract shortened by ProQuest.)

Nature and origin of secondary mineral coatings on volcanic rocks of the Black Mountain, Stonewall Mountain, and Kane Springs Wash volcanic centers, southern, Nevada

The following subject areas are covered: (1) genetic, spectral, and LANDSAT Thematic Mapper imagery relationship between desert varnish and tertiary volcanic host rocks, southern Nevada; (2) reconnaissance geologic mapping of the Kane Springs Wash Volcanic Center, Lincoln County, Nevada, using multispectral thermal infrared imagery; (3) interregional comparisons of desert varnish; and (4) airborne scanner (GERIS) imagery of the Kane Springs Wash Volcanic Center, Lincoln County, Nevada