The Longest Journey is an experimental Master essay which consists of 27 images with accompanying texts. Part of the images are digital photographs and part of them are images created through a special process involving different software tools – mainly Adobe Photoshop and Google Earth. The texts comment on how the images themselves were created and how one might understand what they are and how they function as aesthetic objects and as potential catalysts for thought. / <p>The essay was published as part of a Master of Fine Art Degree exhibition with the same title. For an English translation of it and photographic documentation of the exhibition, please contact: markus.bowie@gmail.com</p>

The thesis presents the main points of standardization of remote sensing (RS) usage for Forensic Ecotechnique: forest and trees (FEft). Based on the study FEft expertise database, the types of expertise, which remote sensing data have already been used and the types where the use of remote sensing increases the exactness of solutions were analyzed. RS data are essential in a problematic situation when an examined object does not exist or has been changed at the time of the assignment. This situation may be solved by obtaining information about the investigated object via retrospective image data, as described in the model example. There are chosen categories of image data relevant to the needs of experts in this thesis. These categories of RS data and their products are described with an indication of their possible use in the expertise processing, including freely available or paid resources and archives of image data.

The influence of soil water repellency (WR) on vegetation recovery after a fire is poorly understood. This dissertation presents strategies to broaden opportunities for enhanced post-fire rangeland restoration and monitoring of burned piñon and juniper (P-J) woodlands by: 1) mapping the extent and severity of critical and subcritical WR, 2) determining the influence of WR on soil ecohydrologic properties and revegetation success, and 3) evaluating the suitability of a wetting agent composed of alkylpolyglycoside-ethylene oxide/propylene oxide block copolymers as a post-fire restoration tool for ameliorating the effects of soil WR and increasing seedling establishment. Results indicate that: • Post-fire patterns of soil WR were highly correlated to pre-fire P-J woodland canopy structure. Critical soil WR levels occurred under burned tree canopies while sub-critical WR extended out to approximately two times the canopy radius. At sites where critical soil WR was present, infiltration rate, soil moisture, and vegetation cover were significantly less than at non-hydrophobic sites. These parameters were also reduced in soils with subcritical WR relative to non-hydrophobic soils (albeit to a lesser extent). Aerial photography coupled with feature extraction software and geographic information systems (GIS) proved to be an effective tool for mapping P-J cover and density, and for scaling-up field surveys of soil WR to the fire boundary scale. • Soil WR impairs seed germination and seedling establishment by decreasing soil moisture availability by reducing infiltration, decreasing soil moisture storage capacity, and disconnecting soil surface layers from underlying moisture reserves. Consequently, soil WR appears to be acting as a temporal ecological threshold by impairing establishment of desired species within the first few years after a fire. • Wetting agents can significantly improve ecohydrologic properties required for plant growth by overcoming soil WR; thus, increasing the amount and duration of available water for seed germination and seedling establishment. Success of this technology appears to be the result of the wetting agent increasing soil moisture amount and availability by 1) improving soil infiltration and water holding capacity; and 2) allowing seedling roots to connect to underling soil moisture reserves.

From the Proceedings of the 1974 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - April 19-20, 1974, Flagstaff, Arizona / Population pressures on the land resources of Arizona have led to the sale and development of areas subject to flooding and because of the inadequacy of land use controls, the area is open to various land speculation schemes and unplanned subdivision growth. A floodplain delineation project was conducted for the planning department of Cochise County, Arizona, in which imagery acquired by earth resources technology satellite (ERT-1) and by high-altitude aircraft was employed. Parameters of the analysis included soils and geomorphology, vegetation, hydrologic calculations, and historical data. Floodplain soils lack developed b horizons, as compared to older, more mature soils not subject to flooding. General soil maps can only be used as guidelines, but a detailed soil survey can add significantly to the accuracy of image interpretations. Erosion-affected soil tones in areas adjacent to active channels proved beneficial in that the heightened contrast served to enhance resolution of vegetation-type boundaries. Hydrologic calculations were done based on valley cross-sections surveyed at two-to-three mile intervals. The historic data input into the system of floodplain delineation is dependent on the location of high-water marks and on obtaining a record of the amount of rainfall which resulted in the high-water mark.

Abstract<p>Since 1999, Very High spatial Resolution satellite data (Ikonos-2, QuickBird and OrbView-3) represent the surface of the Earth with more detail. However, information extraction by multispectral pixel-based classification proves to have become more complex owing to the internal variability increase in the land-cover units and to the weakness of spectral resolution. <p>Therefore, one possibility is to consider the internal spectral variability of land-cover classes as a valuable source of spatial information that can be used as an additional clue in characterizing and identifying land cover. Moreover, the spatial resolution gap that existed between satellite images and aerial photographs has strongly decreased, and the features used in visual interpretation transposed to digital analysis (texture, morphology and context) can be used as additional information on top of spectral features for the land cover classification.<p>The difficulty of this approach is often to transpose the visual features to digital analysis.<p>To overcome this problem region-based classification could be used. Segmentation, before classification, produces regions that are more homogeneous in themselves than with nearby regions and represent discrete objects or areas in the image. Each region becomes then a unit analysis, which makes it possible to avoid much of the structural clutter and allows to measure and use a number of features on top of spectral features. These features can be the surface, the perimeter, the compactness, the degree and kind of texture. Segmentation is one of the only methods which ensures to measure the morphological features (surface, perimeter.) and the textural features on non-arbitrary neighbourhood. In the pixel-based methods, texture is calculated with mobile windows that smooth the boundaries between discrete land cover regions and create between-class texture. This between-class texture could cause an edge-effect in the classification.<p><p>In this context, our research focuses on the potential of land cover region-based classification of VHR satellite data through the study of the object extraction capacity of segmentation processes, and through the study of the relevance of region features for classifying the land-cover classes in different kinds of Belgian landscapes; always keeping in mind the parallel with the visual interpretation which remains the reference.<p><p>Firstly, the results of the assessment of four segmentation algorithms belonging to the two main segmentation categories (contour- and region-based segmentation methods) show that the contour detection methods are sensitive to local variability, which is precisely the problem that we want to overcome. Then, a pre-processing like a filter may be used, at the risk of losing a part of the information. The “region-growing” segmentation that uses the local variability in the segmentation process appears to be the best compromise for the segmentation of different kinds of landscape.<p>Secondly, the features calculated thanks to segmentation seem to be relevant to identify some land-cover classes in urban/sub-urban and rural areas. These relevant features are of the same type as the features selected visually, which shows that the region-based classification gets close to the visual interpretation. <p>The research shows the real usefulness of region-based classification in order to classify the land cover with VHR satellite data. Even in some cases where the features calculated thanks to the segmentation prove to be useless, the region-based classification has other advantages. Working with regions instead of pixels allows to avoid the salt-and-pepper effect and makes the GIS integration easier.<p>The research also highlights some problems that are independent from the region-based classification and are recursive in VHR satellite data, like shadows and the spatial resolution weakness for identifying some land-cover classes.<p><p>Résumé<p>Depuis 1999, les données satellitaires à très haute résolution spatiale (IKONOS-2, QuickBird and OrbView-3) représentent la surface de la terre avec plus de détail. Cependant, l’extraction d’information par une classification multispectrale par pixel devient plus complexe en raison de l’augmentation de la variabilité spectrale dans les unités d’occupation du sol et du manque de résolution spectrale de ces données. Cependant, une possibilité est de considérer cette variabilité spectrale comme une information spatiale utile pouvant être utilisée comme une information complémentaire dans la caractérisation de l’occupation du sol. De plus, de part la diminution de la différence de résolution spatiale qui existait entre les photographies aériennes et les images satellitaires, les caractéristiques (attributs) utilisées en interprétation visuelle transposées à l’analyse digitale (texture, morphologie and contexte) peuvent être utilisées comme information complémentaire en plus de l’information spectrale pour la classification de l’occupation du sol.<p><p>La difficulté de cette approche est la transposition des caractéristiques visuelles à l’analyse digitale. Pour résoudre ce problème la classification par région pourrait être utilisée. La segmentation, avant la classification, produit des régions qui sont plus homogène en elles-mêmes qu’avec les régions voisines et qui représentent des objets ou des aires dans l’image. Chaque région devient alors une unité d’analyse qui permet l’élimination de l’effet « poivre et sel » et permet de mesurer et d’utiliser de nombreuses caractéristiques en plus des caractéristiques spectrales. Ces caractéristiques peuvent être la surface, le périmètre, la compacité, la texture. La segmentation est une des seules méthodes qui permet le calcul des caractéristiques morphologiques (surface, périmètre, …) et des caractéristiques texturales sur un voisinage non-arbitraire. Avec les méthodes de classification par pixel, la texture est calculée avec des fenêtres mobiles qui lissent les limites entre les régions d’occupation du sol et créent une texture interclasse. Cette texture interclasse peut alors causer un effet de bord dans le résultat de la classification.<p><p>Dans ce contexte, la recherche s’est focalisée sur l’étude du potentiel de la classification par région de l’occupation du sol avec des images satellitaires à très haute résolution spatiale. Ce potentiel a été étudié par l’intermédiaire de l’étude des capacités d’extraction d’objet de la segmentation et par l’intermédiaire de l’étude de la pertinence des caractéristiques des régions pour la classification de l’occupation du sol dans différents paysages belges tant urbains que ruraux. / Doctorat en sciences agronomiques et ingénierie biologique / info:eu-repo/semantics/nonPublished