Using high-resolution topography for advancing the understanding of mass and energy transfer across landscapes

textChannel networks are a critical means of mass and energy transfer across the landscape. High resolution topographic imagery derived from lidar scans, provides new opportunities in the observation and analysis of these processes, especially as the resolution of these data is proportionate to channel and hillslope process scales. Channel feature extraction algorithms supply a method with which to analyze hydrologic and geomorphic processes; automatic, open-source frameworks such as GeoNet aim to provide a reliable platform for this task to the academic, public service, and professional communities. In this thesis, the GeoNet algorithm is tested across different types of landscapes and data resolutions. Innovative analysis methods are also assessed within the framework in order to advance the method in parallel with advancing understanding of channel processes and improving technologies. The goal of this research is to assess GeoNet performance across landscape type in terms of relief, vegetation, and anthropogenic influence and make recommendations for future development of the algorithm and the GeoNet user community. An alternate spectral analysis-based filtering method is tested, as well as curvature-based filtering of erroneously identified channel heads. Results from alternate filtering testing indicate that the nonlinear Perona-Malik filter is superior to a spectral-based filtering approach. The use of a contour curvature threshold is not wholly successful at removing spuriously identified channel heads in natural landscapes. This research also introduces the analysis of urban landscapes into the GeoNet repertoire, and examines the effects of data resolution on feature extraction therein. An urban dataset from the Austin, TX area is tested and the optimal settings for GeoNet are identified; field work at the site is used to validate the results. The recommendations resulting from this work aim to improve in the functionality and versatility of GeoNet, and enhance accessibility for the user-community.Civil, Architectural, and Environmental Engineerin

Remote sensing of tidal networks and their relation to vegetation

The study of the morphology of tidal networks and their relation to salt marsh vegetation is currently an active area of research, and a number of theories have been developed which require validation using extensive observations. Conventional methods of measuring networks and associated vegetation can be cumbersome and subjective. Recent advances in remote sensing techniques mean that these can now often reduce measurement effort whilst at the same time increasing measurement scale. The status of remote sensing of tidal networks and their relation to vegetation is reviewed. The measurement of network planforms and their associated variables is possible to sufficient resolution using digital aerial photography and airborne scanning laser altimetry (LiDAR), with LiDAR also being able to measure channel depths. A multi-level knowledge-based technique is described to extract networks from LiDAR in a semi-automated fashion. This allows objective and detailed geomorphological information on networks to be obtained over large areas of the inter-tidal zone. It is illustrated using LIDAR data of the River Ems, Germany, the Venice lagoon, and Carnforth Marsh, Morecambe Bay, UK. Examples of geomorphological variables of networks extracted from LiDAR data are given. Associated marsh vegetation can be classified into its component species using airborne hyperspectral and satellite multispectral data. Other potential applications of remote sensing for network studies include determining spatial relationships between networks and vegetation, measuring marsh platform vegetation roughness, in-channel velocities and sediment processes, studying salt pans, and for marsh restoration schemes

Indications of dynamic effects on scaling relationships between channel sinuosity and vegetation patch size across a salt marsh platform

Salt marshes are important coastal areas that consist of a vegetated intertidal marsh platform and a drainage network of tidal channels. How salt marshes and their drainage networks develop is not fully understood, but it has been shown that the biogeomorphic interactions and feedbacks between vegetation development and channel formation play an important role. We examined the relationships among tidal channel sinuosity, marsh roughness, vegetation type (pioneer, Elymus athericus or Phragmites australis), and patch size at different spatial scales using a high-resolution vegetation map (derived from aerial photography) and lower-resolution satellite imagery processed with linear spectral mixture analysis. The patch-size distribution in all vegetation types corresponded to a power law, suggesting the presence of self-organizational processes. While small vegetation patches are more dominant in pioneer vegetation, they were present in all vegetation types. The largest patch size is restricted to E. athericus. We observed an inverse logarithmic relationship between channel sinuosity and vegetation patch size in all vegetation types. The fact that this relationship is observed in both pioneer and later successional stages suggests that after the establishment of a drainage network in the dynamic pioneer stages of salt marsh development, the later stages of salt marsh succession largely inherit the meandering pattern of the early successional stages. Our study confirms recent evidence that no significant changes in the specific features of tidal channel networks (e.g., channel width, drainage density, and efficiency) take place during the later stages of salt marsh development

Realistic reconstruction and rendering of detailed 3D scenarios from multiple data sources

During the last years, we have witnessed significant improvements in digital terrain modeling, mainly through photogrammetric techniques based on satellite and aerial photography, as well as laser scanning. These techniques allow the creation of Digital Elevation Models (DEM) and Digital Surface Models (DSM) that can be streamed over the network and explored through virtual globe applications like Google Earth or NASA WorldWind. The resolution of these 3D scenes has improved noticeably in the last years, reaching in some urban areas resolutions up to 1m or less for DEM and buildings, and less than 10 cm per pixel in the associated aerial imagery. However, in rural, forest or mountainous areas, the typical resolution for elevation datasets ranges between 5 and 30 meters, and typical resolution of corresponding aerial photographs ranges between 25 cm to 1 m. This current level of detail is only sufficient for aerial points of view, but as the viewpoint approaches the surface the terrain loses its realistic appearance. One approach to augment the detail on top of currently available datasets is adding synthetic details in a plausible manner, i.e. including elements that match the features perceived in the aerial view. By combining the real dataset with the instancing of models on the terrain and other procedural detail techniques, the effective resolution can potentially become arbitrary. There are several applications that do not need an exact reproduction of the real elements but would greatly benefit from plausibly enhanced terrain models: videogames and entertainment applications, visual impact assessment (e.g. how a new ski resort would look), virtual tourism, simulations, etc. In this thesis we propose new methods and tools to help the reconstruction and synthesis of high-resolution terrain scenes from currently available data sources, in order to achieve realistically looking ground-level views. In particular, we decided to focus on rural scenarios, mountains and forest areas. Our main goal is the combination of plausible synthetic elements and procedural detail with publicly available real data to create detailed 3D scenes from existing locations. Our research has focused on the following contributions: - An efficient pipeline for aerial imagery segmentation - Plausible terrain enhancement from high-resolution examples - Super-resolution of DEM by transferring details from the aerial photograph - Synthesis of arbitrary tree picture variations from a reduced set of photographs - Reconstruction of 3D tree models from a single image - A compact and efficient tree representation for real-time rendering of forest landscapesDurant els darrers anys, hem presenciat avenços significatius en el modelat digital de terrenys, principalment gràcies a tècniques fotogramètriques, basades en fotografia aèria o satèl·lit, i a escàners làser. Aquestes tècniques permeten crear Models Digitals d'Elevacions (DEM) i Models Digitals de Superfícies (DSM) que es poden retransmetre per la xarxa i ser explorats mitjançant aplicacions de globus virtuals com ara Google Earth o NASA WorldWind. La resolució d'aquestes escenes 3D ha millorat considerablement durant els darrers anys, arribant a algunes àrees urbanes a resolucions d'un metre o menys per al DEM i edificis, i fins a menys de 10 cm per píxel a les fotografies aèries associades. No obstant, en entorns rurals, boscos i zones muntanyoses, la resolució típica per a dades d'elevació es troba entre 5 i 30 metres, i per a les corresponents fotografies aèries varia entre 25 cm i 1m. Aquest nivell de detall només és suficient per a punts de vista aeris, però a mesura que ens apropem a la superfície el terreny perd tot el realisme. Una manera d'augmentar el detall dels conjunts de dades actuals és afegint a l'escena detalls sintètics de manera plausible, és a dir, incloure elements que encaixin amb les característiques que es perceben a la vista aèria. Així, combinant les dades reals amb instàncies de models sobre el terreny i altres tècniques de detall procedural, la resolució efectiva del model pot arribar a ser arbitrària. Hi ha diverses aplicacions per a les quals no cal una reproducció exacta dels elements reals, però que es beneficiarien de models de terreny augmentats de manera plausible: videojocs i aplicacions d'entreteniment, avaluació de l'impacte visual (per exemple, com es veuria una nova estació d'esquí), turisme virtual, simulacions, etc. En aquesta tesi, proposem nous mètodes i eines per ajudar a la reconstrucció i síntesi de terrenys en alta resolució partint de conjunts de dades disponibles públicament, per tal d'aconseguir vistes a nivell de terra realistes. En particular, hem decidit centrar-nos en escenes rurals, muntanyes i àrees boscoses. El nostre principal objectiu és la combinació d'elements sintètics plausibles i detall procedural amb dades reals disponibles públicament per tal de generar escenes 3D d'ubicacions existents. La nostra recerca s'ha centrat en les següents contribucions: - Un pipeline eficient per a segmentació d'imatges aèries - Millora plausible de models de terreny a partir d'exemples d’alta resolució - Super-resolució de models d'elevacions transferint-hi detalls de la fotografia aèria - Síntesis d'un nombre arbitrari de variacions d’imatges d’arbres a partir d'un conjunt reduït de fotografies - Reconstrucció de models 3D d'arbres a partir d'una única fotografia - Una representació compacta i eficient d'arbres per a navegació en temps real d'escenesPostprint (published version

From Images to Hydrologic Networks - Understanding the Arctic Landscape with Graphs

Remote sensing-based Earth Observation plays an important role in assessing environmental changes throughout our planet. As an image-heavy domain, the evaluation of the data strongly focuses on statistical and pixel-based spatial analysis methods. However, considering the complexity of our Earth system, there are some environmental structures and dependencies that are not possible to accurately describe with these traditional image analysis approaches. One example for such a limitation is the representation of (spatial) networks and their characteristics. In this study, we thus propose a computer vision approach that enables the representation of semantic information gained from images as graphs. As an example, we investigate digital terrain models of Arctic permafrost landscapes with its very characteristic polygonal patterned ground. These regular patterns, which are clearly visible in high-resolution image and elevation data, are formed by subsurface ice bodies that are very vulnerable to rising temperatures in a warming Arctic. Observing these networks’ topologies and metrics in space and time with graph analysis thus allows insights into the landscape’s complex geomorphology, hydrology, and ecology and therefore helps to quantify how they interact with climate change. We show that results extracted with this analytical and highly automated approach are in line with those gathered from other manual studies or from manual validation. Thus, with this approach, we introduce a method that, for the first time, enables upscaling of such terrain and network analysis to potentially pan-Arctic scales where collecting in-situ field data is strongly limited

OBJECT-BASED CLASSIFICATION OF EARTHQUAKE DAMAGE FROM HIGH-RESOLUTION OPTICAL IMAGERY USING MACHINE LEARNING

Object-based approaches to the segmentation and supervised classification of remotely-sensed images yield more promising results compared to traditional pixel-based approaches. However, the development of an object-based approach presents challenges in terms of algorithm selection and parameter tuning. Subjective methods and trial and error are often used, but time consuming and yield less than optimal results. Objective methods are warranted, especially for rapid deployment in time sensitive applications such as earthquake induced damage assessment. Our research takes a systematic approach to evaluating object-based image segmentation and machine learning algorithms for the classification of earthquake damage in remotely-sensed imagery using Trimble’s eCognition software. We tested a variety of algorithms and parameters on post-event aerial imagery of the 2011 earthquake in Christchurch, New Zealand. Parameters and methods are adjusted and results compared against manually selected test cases representing different classifications used. In doing so, we can evaluate the effectiveness of the segmentation and classification of buildings, earthquake damage, vegetation, vehicles and paved areas, and compare different levels of multi-step image segmentations. Specific methods and parameters explored include classification hierarchies, object selection strategies, and multilevel segmentation strategies. This systematic approach to object-based image classification is used to develop a classifier that is then compared against current pixel-based classification methods for post-event imagery of earthquake damage. Our results show a measurable improvement against established pixel-based methods as well as object-based methods for classifying earthquake damage in high resolution, post-event imagery

TINITALY/01: a new Triangular Irregular Network of Italy

A new Digital Elevation Model (DEM) of the natural landforms of Italy is presented. A methodology is discussed to build a DEM over wide areas where elevation data from non-homogeneous (in density and accuracy) input sources are available. The input elevation data include contour lines and spot heights derived from the Italian Regional topographic maps, satellite-based global positioning system points, ground based and radar altimetry data. Owing to the great heterogeneity of the input data density, the DEM format that better preserves the original accuracy is a Triangular Irregular Network (TIN). A Delaunay-based TIN structure is improved by using the DEST algorithm that enhances input data by evaluating inferred break-lines. Accordingly to this approach, biased distributions in slopes and elevations are absent. To prevent discontinuities at the boundary between regions characterized by data with different resolution a cubic Hermite blending weight S-shaped function is adopted. The TIN of Italy consists of 1.39×109 triangles. The average triangle area ranges from 12 to about 13000 m2 accordingly to different morphologies and different sources. About 50% of the model has a local average triangle area <500 m2. The vertical accuracy of the obtained DEM is evaluated by more than 200000 sparse control points. The overall Root Mean Square Error (RMSE) is less than 3.5 m. The obtained national-scale DEM constitutes an useful support to carry out accurate geomorphological and geological investigations over large areas. The problem of choosing the best step size in deriving a grid from a TIN is then discussed and a method to quantify the loss of vertical information is presented as a function of the grid step. Some examples of DEM application are outlined. Under request, an high resolution stereo image database of the whole Italian territory (derived from the presented DEM) is available to browse via internet

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

Remote Sensing in Applications of Geoinformation

Remote sensing, especially from satellites, is a source of invaluable data which can be used to generate synoptic information for virtually all parts of the Earth, including the atmosphere, land, and ocean. In the last few decades, such data have evolved as a basis for accurate information about the Earth, leading to a wealth of geoscientific analysis focusing on diverse applications. Geoinformation systems based on remote sensing are increasingly becoming an integral part of the current information and communication society. The integration of remote sensing and geoinformation essentially involves combining data provided from both, in a consistent and sensible manner. This process has been accelerated by technologically advanced tools and methods for remote sensing data access and integration, paving the way for scientific advances in a broadening range of remote sensing exploitations in applications of geoinformation. This volume hosts original research focusing on the exploitation of remote sensing in applications of geoinformation. The emphasis is on a wide range of applications, such as the mapping of soil nutrients, detection of plastic litter in oceans, urban microclimate, seafloor morphology, urban forest ecosystems, real estate appraisal, inundation mapping, and solar potential analysis