One of the most difficult issues when dealing with optical water remote-sensing is its acceptance as a useful application for environmental research. This problem is, on the one hand, concerned with the optical complexity and variability of the investigated natural media, and therefore the question arises as to the plausibility of the parameters derived from remote-sensing techniques. Detailed knowledge about the regional bio- and chemico-optical properties is required for such studies, however such information is seldom available for the sites of interest. On the other hand, the primary advantage of remote-sensing information, which is the provision of a spatial overview, may not be exploited fully by the disciplines that would benefit most from such information. It is often seen in a variety of disciplines that scientists have been primarily trained to look at discrete data sets, and therefore have no experience of incorporating information dealing with spatial heterogeneity. <br><br> In this thesis, the opportunity was made available to assess the potential of Ocean Colour data to provide spatial and seasonal information about the surface waters of Lake Baikal (Siberia). While discrete limnological field data is available, the spatial extension of Lake Baikal is enormous (ca. 600 km), while the field data are limited to selected sites and expedition time windows. Therefore, this remote-sensing investigation aimed to support a multi-disciplinary limnological investigation within the framework of the paleoclimate EU-project ‘High Resolution CONTINENTal Paleoclimate Record in Lake Baikal, Siberia (CONTINENT)’ using spatial and seasonal information from the SeaWiFS satellite (NASA). From this, the SeaWiFS study evolved to become the first efficient bio-optical satellite study of Lake Baikal. <br><br> During the course of three years, field work including spectral field measurements and water sampling, was carried out at Lake Baikal in Southern Siberia, and at the Mecklenburg and Brandenburg lake districts in Germany. The first step in processing the SeaWiFS satellite data involved adapting the SeaDAS (NASA) atmospheric-correction processing to match as close as possible the specific conditions of Lake Baikal. Next, various Chl-<i>a</i> algorithms were tested on the atmospherically-corrected optimized SeaWiFS data set (years 2001 to 2002), comparing the CONTINENT pigment ground-truth data with the Chl-<i>a</i> concentrations derived from the satellite data. This showed the high performance of the global Chl-<i>a</i> products OC2 and OC4 for the oligotrophic, transparent waters (bio-optical Case 1) of Lake Baikal. However, considerable Chl-<i>a</i> overestimation prevailed in bio-optical Case 2 areas for the case of discharge events. High-organic terrigenous input into Lake Baikal could be traced and information extracted using the SeaWiFS spectral data. Suspended Particulate Matter (SPM) was quantified by the regression of the SeaDAS attenuation coefficient as the optical parameter with SPM field data. <br><br> Finally, the Chl-<i>a</i> and terrigenous input maps derived from the remote sensing data were used to assist with analyzing the relationships between the various discrete data obtained during the CONTINENT field work. Hence, plausible spatial and seasonal information describing autochthonous and allochthonous material in Lake Baikal could be provided by satellite data.<br>Lake Baikal, with its bio-optical complexity and its different areas of Case 1 and Case 2 waters, is a very interesting case study for Ocean Colour analyses. Proposals for future Ocean Colour studies of Lake Baikal are discussed, including which bio-optical parameters for analytical models still need to be clarified by field investigations. / Die Gewässerfernerkundung entwickelte sich seit den 70ern vor allem aus der Ozeanographie und der Atmosphärenforschung, und wird inzwischen als anerkannte Methode genutzt, um global die Phytoplanktonverteilung in den Weltmeeren erfassen zu können, u.a. für CO₂-Haushaltsmodellierungen. Atmosphärenkorrigierte Multi- und Hyperspektralscannerdaten ermöglichen die Qualifizierung bio-optischer Gewässertypen und die Quantifizierung optisch sichtbarer Wasserinhaltsstoffe und bieten gerade auch für dynamische und heterogene Küsten- und Binnengewässer das große Potential des räumlichen Informationsgewinnes.<br>Im Rahmen des Paläoklimaprojektes CONTINENT wurde in dieser Arbeit das Oberflächenwasser des Baikalsees mit Gewässerfernerkungsmethoden analysiert. Wichtig für die Interpretation von Klima-Proxies sind v.a. auch Hinweise auf die Verteilung des autochthonen Materials im Baikalsee (Fernerkundungsparameter: Chlorophyll-<i>a</i>), ebenso wie Hinweise auf allochthone Einträge an den Bohrungsstellen (Fernerkundungsparameter ‚Terrigener Eintrag’). Auf den Geländekampagnen in den Sommern 2001, 2002, 2003 in Sibirien und in Deutschland wurden Feldspektrometermessungen mit gleichzeitiger Wasserprobenahme auf die optisch sichtbaren Wasserinhaltsstoffe Phytoplankton, Schwebstoff, und DOC durchgeführt. Dabei konnten Messtechniken für Geländespektrometer evaluiert, und grundlegende Aussagen über die spektrale Verteilung des In-Wasser Lichtfeldes im Baikalsee gemacht werden. <br><br> Die Ocean Colour Satellitendaten des NASA-Instrumentes SeaWiFS und die Möglichkeiten der komplexen NASA Software SeaDAS wurden genutzt. Für die Ableitung des am Baikalsee anzutreffenden organikreichen terrigenen Eintrages, wurde ein vorläufiger Algorithmus aus den Geländedaten generiert. Verschiedene Algorithmen für den Parameter ‚Chlorophyll-<i>a</i>’ wurden mit dem Geländedatensatz der Projektpartnerin S. Fietz (Institut für Gewässerökologie und Binnenfischerei, IGB) evaluiert. Als geeignetester etablierte sich der auf oligotrophe Gewässer optimierte NASA Chlorophyll Algorithmus ‚Ocean Colour (OC) 2’. Die Quantifizierungen und Ergebnisse werden diskutiert. <br><br> Als Endergebnis wird der Überblick über Sedimenteintrag und Phytoplanktondynamik im Baikalsee für den Zeitraum 2001-2002 zur Verfügung gestellt und die autochthonen versus allochthonen Einflüsse an den Projektlokationen werden beschrieben. Der Baikalsee erwies sich als bio-optisch ein sehr komplexes und interessantes Studienobjekt. Ein wichtiger Punkt, der in dieser Arbeit angesprochen wird, ist die Atmosphärenkorrektur, die wesentliche Einflüsse auf die Qualifizierungen und Quantifizierungen hat, aber als Standardprogramm nur für den pelagialen Wasserkörper in Meeresspiegelhöhe mit marinen, bzw. Küstenatmosphären konditioniert ist. Ein weiterer bedeutender Punkt, der in dieser Arbeit diskutiert wird, ist der relevante spektrale Einfluss des organikreichen terrigenen Eintrages auf die Gewässerfarbe und dadurch auf die Qualität der Chlorophyll-Ableitung. Somit boten sich die Möglichkeiten, das räumliche Ausmaß und die Dynamik rezenter terrigener Einträge zu erfassen. Auch die Entwicklung des Phytoplankton von Frühsommer bis Spätsommer im Baikalsee konnte mit den SeaWiFS Daten nachvollzogen werden. Die hier vorgestellte Studie stellte sich als die erste grundlegende optische Gewässerfernerkundungsstudie mit Satellitendaten am Baikalsee heraus, und konnte erfolgreich abgeschlossen werden.

Knowledge about land use and land cover represents an important information basis for various planning applications. In particular, urban and suburban regions are subject to a high dynamic development. The detection and identification of changes is therefore an important instrument to follow and accompany the developments by planning. Here, aerial photography and, increasingly, satellite images serve as an important basis for information. The recognition and mapping of changes is still a time-consuming and cost-intensive matter which is mostly realized by visual interpretation of aerial photography and to an increasing degree of high- and ultra-high-resolution satellite images. Within the scope of the present work a new, robust and largely automated process based on a statistical change analysis is developed and presented. Basis for the data are multitemporal high-resolution satellite image data. The generated suspect areas, respectively areas of change, are supposed to function as clues in order to facilitate the process of the visual interpretation of multitemporal image datasets with regard to change mapping, since only marked areas of change have to undergo further examination. Consequently, this process can be used as a tool to ease and accelerate the updating of planning bases in general and maps in particular so far realised by visual interpretation. However, the automation of the process is not only supposed to serve the purpose of saving time and cost but also to bring the interpretation process to a higher level of objectivity. In order to improve the quality of the whole process, for the preprocessing of the image data selected methods of image processing have been integrated. Through the use of additional geo-information reference data for the automated calculation of the areas of change, a further refinement of the results can be reached. The obtained results in the first time-cut (1997-1998) can be proved and verified by a different data-take (1997-2000). To reach a convenient use and a good distribution of the developed method, the process has been implemented by means of the widespread image processing software ERDAS IMAGINE. This allows to make the developed method available for other users, since it can easily be integrated into the working environment of ERDAS IMAGINE. / Das Wissen um die Landnutzung und Landbedeckung ist für planerische Anwendungsgebiete eine wichtige Informationsgrundlage. Gerade urbane und suburbane Regionen unterliegen einer hohen Entwicklungsdynamik. Das Erkennen und Aufzeigen von Veränderungen ist somit ein wichtiges Instrument um Entwicklungen zu verfolgen und planerisch zu begleiten. Luft- und zunehmend Satellitenbilder dienen hierfür als wichtige Informationsgrundlage. Das Erkennen und Kartieren von Veränderungen ist nach wie vor eine zeitaufwändige und kostenintensive Angelegenheit, die überwiegend durch visuelle Interpretation von Luft- und zunehmend auch mit hoch- und höchstauflösenden Satellitenbildern realisiert wird. In dieser Arbeit wird ein neues, robustes, weitgehend automatisiertes, auf einem statistischen Ansatz beruhendes Verfahren der Veränderungsanalyse entwickelt und vorgestellt. Die Datengrundlage bilden multitemporale, hoch auflösende Satellitenbilddaten. Die generierten Verdachts- bzw. Veränderungsflächen sollen als Anhaltspunkte fungieren, um den Prozess der visuellen Interpretation von multitemporalen Bilddatensätzen in Hinsicht auf eine Veränderungskartierung zu erleichtern, da nur als Veränderungsflächen markierte Areale einer weiteren Untersuchung unterzogen werden müssen. Das Verfahren kann somit als Werkzeug dienen, die durch visuelle Interpretation realisierte Aktualisierung von Planungsgrundlagen bzw. Kartenwerken zu erleichtern und zu beschleunigen. Die Automatisierung des Verfahrens soll jedoch nicht allein dem Zweck der Zeit- und Kostenersparnis dienen, sondern auch den Interpretationsprozess objektiver gestalten. Um die Qualität des Verfahrens zu erhöhen, werden ausgewählte Methoden der Bildverarbeitung für die Vorverarbeitung der Bilder in das Verfahren integriert. Durch das Einbinden zusätzlicher Geobasisdaten in die automatisierte Berechnung der Veränderungsflächen kann eine weitere Verbesserung der Ergebnisse erzielt werden. Die Ergebnisse, der im ersten Zeitschnitt (1997-1998) untersuchten Datensätze, werden mit Hilfe eines weiteren Zeitschnitts (1997-2000) überprüft und verifiziert. Um eine unkomplizierte Anwendung und Verbreitung der Methode zu erreichen, wurde das Verfahren mit Hilfe der weit verbreiteten Bildverarbeitungssoftware ERDAS IMAGINE realisiert. Dies ermöglicht, das Verfahren auch anderen Nutzern zur Verfügung zu stellen, da es problemlos in die Arbeitsumgebung des Bildverarbeitungssystems ERDAS IMAGINE integriert werden kann

A lack of quality data on high clouds has led to inadequate representations within global weather and climate models. Recent advances in spaceborne measurements of the Earth’s atmosphere have provided complementary information on the interior of these clouds. This study demonstrate how an array of space-borne measurements can be used and combined, by close co-located comparisons in space and time, to form a more complete representation of high cloud processes and properties. High clouds are found in the upper atmosphere, where sub-zero temperatures frequently result in the formation of cloud particles that are composed of ice. Weather and climate models characterise the bulk properties of these ice particles to describe the current state of the cloud-sky atmosphere. By directly comparing measurements with simulations undertaken at the same place and time, this study demonstrates how improvements can be made to the representation of cloud properties. The results from this study will assist in the design of future cloud missions to provide a better quality input. These improvements will also help improve weather predictions and lower the uncertainty in cloud feedback response to increasing atmospheric temperature. Most clouds are difficult to monitor by more than one instrument due to continuous changes in: large-scale and sub-cloud scale circulation features, microphysical properties and processes and characteristic chemical signatures. This study undertakes co-located comparisons of high cloud data with a cloud ice dataset reported from the Microwave Limb Sounder (MLS) instrument onboard the Aura satellite that forms part of the A-train constellation. Data from the MLS science team include vertical profiles of temperature, ice water content (IWC) and the mixing ratios of several trace gases. Their vertical resolutions are 3 to 6 km. Initial investigations explore the link between cloud-top properties and the longwave radiation budget, developing methods for estimating cloud top heights using; longwave radiative fluxes, and IWC profiles. Synergistic trios of direct and indirect high cloud measurements were used to validate detections from the MLS by direct comparisons with two different A-train instruments; the NASA Moderate-resolution Imaging Spectroradiometer (MODIS) and the Clouds and the Earth’s Radiant Energy System (CERES) onboard on the Aqua satellite. This finding focuses later studies on two high cloud scene types that are well detected by the MLS; deep convective plumes that form from moist ascent, and their adjacent outflows that emanate outwards several hundred kilometres. The second part of the thesis identifies and characterises two different high cloud scenes in the tropics. Direct observational data is used to refine calculations of the climate sensitivity to upper tropospheric humidity and high cloud in different conditions. The data reveals several discernible features of convective outflows are identified using a large sample of MLS data. The key finding, facilitated by the use of co-location, reveals that deep convective plumes exert a large longwave warming effect on the local climate of 52 ± 28Wm−2, with their adjacent outflows presenting a more modest warming of 33 ± 20Wm−2.

Η χωρική και χρονική κατανομή των αιωρούμενων σωματιδίων επηρεάζεται σημαντικά από τις τοπικές πηγές ρύπανσης αλλά και από την ατμοσφαιρική κυκλοφορία. Στην εργασία επιχειρήθηκε η μελέτη των επιπέδων του οπτικού βάθους των αιωρούμενων σωματιδίων στην Ελλάδα, με έμφαση στη συνεισφορά των τοπικών πηγών ρύπανσης. Συγκεκριμένα, στην παρούσα εργασία πραγματοποιήθηκε: 1. ανάλυση δορυφορικών δεδομένων και παρουσίαση των επιπέδων και των πιθανών τάσεων του οπτικού βάθους των αιωρούμενων σωματιδίων σε διάφορες περιοχές της Ελλάδας 2. σύγκριση των επιπέδων του οπτικού βάθους μεταξύ επιβαρημένων περιοχών, όπως μεγάλα αστικά κέντρα και περιοχές όπου λειτουργούν μεγάλες βιομηχανικές μονάδες, σε σχέση με περιοχές όπου η επίδραση των τοπικών πηγών ρύπανση είναι πολύ μικρή 3. εκτίμηση της συνεισφοράς των τοπικών πηγών ρύπανσης στα προαναφερόμενα αποτελέσματα 4. σύγκριση των δορυφορικών δεδομένων με επίγεια Η μελέτη των παραπάνω αντικειμένων έγινε χρησιμοποιώντας εκτιμήσεις των οπτικών ιδιοτήτων των αιωρούμενων σωματιδίων από το όργανο MODIS (Moderate Resolution Imaging Spectoradiometer) που βρίσκεται στους δορυφόρους Terra και Aqua. Τα δεδομένα καλύπτουν τη χρονική περίοδο 9 ετών (από Φεβρουάριο 2000 έως Σεπτέμβριο 2009) για το διαστημικό σκάφος Terra και 7 ετών (από Ιούλιο 2002 έως Σεπτέμβριο 2009) για το Aqua. Οι χρονικές στιγμές διέλευσης των δορυφόρων Terra και Aqua πάνω από την Ελλάδα είναι 9.35±0.50 UTC και 11.34±0.53 UTC αντίστοιχα. Η μέση τιμή του οπτικού βάθους (AOD550), για όλη την Ελλάδα είναι 0.20±0.07 και 0.19±0.06, από τους δορυφόρους Terra και Aqua αντίστοιχα. Ο συντελεστής γραμμικής συσχέτισης των δεδομένων AOD550 μεταξύ των δορυφορικών μετρήσεων MODIS/Terra και MODIS/Aqua είναι ίσος με 0.81. Στην χρονοσειρά των οπτικών βαθών παρατηρήθηκε μία εποχική διακύμανση με τις μέγιστες τιμές να εμφανίζονται κατά τους πρώτους ανοιξιάτικους μήνες και το καλοκαίρι. Η εποχικότητα αυτή αποδόθηκε στο αυξημένο σωματιδιακό φορτίο που παρατηρείται στην ελεύθερη τροπόσφαιρα τις συγκεκριμένες χρονικές περιόδους λόγω μεταφοράς ερημικής σκόνης από την Σαχάρα, αλλά και λόγω μεταφοράς αιωρούμενων από καύση βιομάζας που παρατηρούνται συχνά κατά τον Αύγουστο, όπως και στους επικρατούντες Βορειανατολικούς ανέμους κατά τους καλοκαιρινούς μήνες που μεταφέρουν σωματιδιακή ρύπανση από ρυπασμένες περιοχές όπως τα Ανατολικά Βαλκάνια. Γενικά, οι μεγαλύτερες τιμές AOD550 εμφανίζονται στις αστικές περιοχές όπου υπάρχουν έντονες ανθρωπογενείς δραστηριότητες, όπως οι περιοχές της Αττικής και της Θεσσαλονίκης. Παρατηρείται ότι το 50-55% περίπου του εκτιμώμενου οπτικού βάθους οφείλεται σε ανθρωπογενείς πηγές ρύπανσης, ενώ το υπόλοιπο αναμένεται να οφείλεται σε φαινόμενα διασυνοριακής ρύπανσης. Ιδιαίτερο ενδιαφέρον έχουν τα αποτελέσματα για την περιοχή της Πτολεμαΐδας και της κεντρικής Πελοποννήσου. Εκεί, τα επίπεδα των τοπικών πηγών ρύπανσης που φαίνονται από τον Aqua και συνεισφέρουν στο συνολικό οπτικό βάθος των αιωρούμενων σωματιδίων είναι πολύ μεγαλύτερα σε σχέση με αυτά του Terra κατά +37.9% και +70.6% αντίστοιχα. Τις μεσημεριανές ώρες που διέρχεται ο δορυφόρος Aqua, η ατμόσφαιρα της ευρύτερης περιοχής της Πτολεμαΐδας και της κεντρικής Πελοποννήσου έχει πιθανώς επιφορτιστεί από αιωρούμενα σωματίδια, λόγω της εντατικής λειτουργίας των εργοστασίων ηλεκτρικής ενέργειας. Για αυτό το λόγο, τα επίπεδα τοπικών πηγών ρύπανσης του Aqua είναι μεγαλύτερα από αυτά του δορυφόρου Terra, ο οποίος διέρχεται πάνω από την Ελλάδα τις πρωινές ώρες. Για την περιοχή της Θεσσαλονίκης συγκρίθηκαν δορυφορικά δεδομένα MODIS με επίγειες μετρήσεις PM10 και PM2.5. Συγκρίνοντας το AOD550 με το PM10 δεν διακρίνεται ιδιαίτερη συσχέτιση μεταξύ αυτών. Μάλιστα, τα μέγιστα του AOD550 εμφανίζονται την άνοιξη και το καλοκαίρι ενώ τα αντίστοιχα μέγιστα των PM10 το φθινόπωρο και τον χειμώνα. Ομοίως οι ελάχιστες τιμές του AOD550 υπάρχουν το φθινόπωρο και το χειμώνα ενώ των PM10 την άνοιξη και το καλοκαίρι. Το γεγονός αυτό βασίζεται στο ότι, εντός των ορίων του Δήμου Θεσσαλονίκης, η βασική πηγή ρύπανσης είναι η κυκλοφορία των αυτοκινήτων. Επειδή κατά τους καλοκαιρινούς μήνες ο κυκλοφοριακός φόρτος είναι περιορισμένος, αναμένεται να είναι μικρότερες και οι συγκεντρώσεις των αιωρούμενων σωματιδίων. / The spatial and temporal distribution of particulate matter is strongly influenced by local sources and the atmospheric circulation in the wider region. In this study, the levels of the optical depth of aerosols in Greece are examined, as well as the contribution of local sources on pollution. The following essential steps were followed: 1. Data analysis and possible trends of the aerosol optical depth over various regions of Greece 2. Comparison of the aerosol optical depth values among polluted regions, such as large cities and areas with increased industrial activity, and regions where the influence of local sources of pollution is very small 3. Estimation of the contribution of local sources of pollution in the above mentioned results. 4. Comparison of satellite estimations with ground based data. The study was based on the dataset of aerosol optical properties from the MODIS (Moderate Resolution Imaging Spectoradiometer) instrument, located on Terra and Aqua satellites. The dataset covers a 9-year time period (February 2000 - September 2009) on the Terra spacecraft and 7 years (July 2002 - September 2009) on Aqua. The overpass times of the satellites Terra and Aqua above Greece are 9.35 ± 0.50 UTC and 11.34 ± 0.53 UTC, respectively. The average optical depth (AOD550) over Greece is 0.20±0.07 and 0.19±0.06, from the Terra and Aqua satellites respectively. The linear correlation coefficient between the satellite estimations of AOD550 from MODIS/Terra and MODIS/Aqua is 0.81. The time series analysis of aerosol optical depth reveals a seasonal variation with maximum levels occurring during March and April and during summertime. The seasonality could be attributed to the increased particulate matter of Sahara desert dust in the free troposphere and the transport of biomass burning during August, when the prevailing North East winds carry particulate matter from air polluted sites like the East Balkans. Generally, the higher AOD550 values appear in urban areas, such as the regions of Attica and Thessaloniki. Finally, it appears that 50-55% of the estimated optical depth could be attributed to anthropogenic sources. The results for the area of Ptolemais and central Peloponnese were examined thoroughly. Over those regions, the levels of local pollution sources that appear from Aqua, are much higher than those of Terra by +37.9% and +70.6% respectively. During the midday hours, when the Aqua satellite passes over the wider area of Ptolemais and central Peloponnese, the increased particulate matter could be attributed to the intensive operation of power plants. For this reason, the levels of local air pollution sources, observed from Aqua, are higher than those of Terra satellite, which passes over the sites in the early morning. For the region of Thessaloniki, the MODIS satellite data compared with ground based measurements of PM10 and PM2.5. When comparing AOD550 with PM10, there is no distinguished direct relation between them. On the contrary, the maximum satellite values of AOD550 appear during spring and summer, while the corresponding maximum values of PM10 are measured during autumn and winter. Likewise, the minimum AOD550 values appear in autumn and winter, while the PM10 appear during spring and summer. This is probably based on the fact that, in the limit area of the city of Thessalonica, the main source of pollution is car traffic. So, during summer months, when the traffic is low, the concentrations of particulate matter are expected to be lower.

Business model innovation has in recent year become more important for firms looking to gain competitive advantage on dynamic markets. Additionally, incorporating data into a firm’s business model has been shown to lead to improved performance. This development has led to interest in the connection between data utilisation and business model innovation. This thesis provides an in-depth case study of a Swedish space firm active within the satellite industry. The firm operates within an increasingly dynamic market, and ongoing disruptions in the form of new market entrants and rapid technological advancements has led to a search for new business opportunities. As a result, novel ways of utilising the increased amounts of data from space are of significant importance. While the firm is still realising profits utilising their incumbent business model, the firm must simultaneously explore new business opportunities to avoid extinction. The findings show that novel data utilisation, in the form of data processing, leads to business model innovation. Furthermore, the degree of business model transformation is dependent on how many of the business model's underlying elements are affected by data utilisation. Furthermore, the study concludes that a lack of trial-and-error learning impedes radical innovation efforts and hinders the development of ambidextrous capabilities within the firm. Lastly, the study finds a novel connection between the introduction of large-scale projects and improved ambidextrous capabilities. / Innovation av affärsmodeller har under senare år blivit alltmer viktigt för företag som vill uppnå ökad konkurrenskraft på dynamiska marknader. Vidare har det visat sig att företag som använder data för att förändra sin affärsmodell når bättre resultat än sina konkurrenter. Detta har lett till ett intresse för kopplingen mellan datautnyttjande och innovation av affärsmodeller. Detta examensarbete består av en fallstudie av ett svenskt rymdföretag, som har del av sin verksamhet inom satellitbranschen. Företaget verkar på en alltmer dynamisk marknad, och pågående störningar i form av nya marknadsaktörer och tekniska framsteg har lett till att företaget nu måste söka efter nya affärsmöjligheter. Som ett resultat av detta blir nya sätt att använda de ökade mängderna data från rymden av stor betydelse. Fastän företaget fortfarande framgångsrikt nyttjar sin befintliga affärsmodell, måste företaget samtidigt undersöka nya affärsmöjligheter för att undvika att hamna efter marknadsutvecklingen. Studiens resultat visar att nya sätt att använda data, i form av databehandling, leder till innovation av företagets affärsmodell. Dessutom beror graden av innovation på hur många av affärsmodellens underliggande byggstenar som påverkas av införandet av data. Studien drar vidare slutsatsen att en brist på lärande genom ’trial-and-error’ inom företaget hindrar radikala innovationsinsatser och leder till begränsade förutsättningar för att hantera organisatorisk ambidexteritet. Slutligen finner studien att storskaliga innovationsprojekt kan förbättra förutsättningarna för organisatorisk ambidexteritet.

With the advancement of technology and the development of more sophisticated remote sensing sensor systems, the use of satellite imagery has opened up various fields of exploration and application. There has been an increased interest in analysis of multi-temporal satellite image in the past few years because of the wide variety of possible applications of in both short-term and long-term image analysis. The type of changes that might be of interest can range from short-term phenomena such as flood assessment and crop growth stage, to long-term phenomena such as urban fringe development. This thesis studies flood assessment and land cover mapping of satellite images, and proposes nature inspired algorithms that can be easily implemented in realistic scenarios. Disaster monitoring using space technology is one of the key areas of research with vast potential; particularly flood based disasters are more challenging. Every year floods occur in many regions of the world and cause great losses. In order to monitor and assess such situations, decision-makers need accurate near real-time knowledge of the field situation. How to provide actual information to decision-makers for effective flood monitoring and mitigation is an important task, from the point of view of public welfare. Over-estimation of the flooded area leads to over-compensation to people, while under-estimation results in production loss and negative impacts on the population. Hence it is essential to assess the flood damage accurately, both in qualitative and quantitative terms. In such situations, land cover maps play a very critical role. Updating land cover maps is a time consuming and costlier operation when it is performed using traditional or manual methods. Hence, there is a need to find solutions for such problem through automation. Design of automatic systems dedicated to satellite image processing which involves change detection to discriminate areas of land cover change between imaging dates. The system integrates the spectral and spatial information with the techniques of image registration and pattern classification using nature inspired techniques. In the literature, various works have been carried out for solving the problem of image registration and pattern classification using conventional methods. Many researchers have proved, for different situations, that nature inspired techniques are promising in comparison with that of conventional methods. The main advantage of nature inspired technique over any other conventional methods is its stochastic nature, which converges to optimal solution for any dynamic variation in a given satellite image. Results are given in such terms as to delineate change in multi-date imagery using change-versus-no-change information to guide multi-date data analysis. The main objective of this study is to analyze spatio-temporal satellite data to bring out significant changes in the land cover map through automated image processing methods. In this study, for satellite image analysis of flood assessment and land cover mapping, the study areas and images considered are: Multi-temporal MODerate-resolution Imaging Spectroradiometer (MODIS) image around Krishna river basin in Andhra Pradesh India; Linear Imaging Self Scanning Sensor III (LISS III)and Synthetic Aperture Radar(SAR)image around Kosi river basin in Bihar, India; Landsat7thematicmapperimage from the southern part of India; Quick-Bird image of the central Bangalore, India; Hyperion image around Meerut city, Uttar Pradesh, India; and Indian pines hyperspectral image. In order to develop a flood assessment framework for this study, a database was created from remotely sensed images (optical and/or Synthetic Aperture Radar data), covering a period of time. The nature inspired techniques are used to find solutions to problems of image registration and pattern classification of a multi-sensor and multi-temporal satellite image. Results obtained are used to localize and estimate accurately the flood extent and also to identify the type of the inundated area based on land cover mapping. The nature inspired techniques used for satellite image processing are Artificial Neural Network (ANN), Genetic Algorithm (GA),Particle Swarm Optimization (PSO), Firefly Algorithm(FA),Glowworm Swarm Optimization(GSO)and Artificial Immune System (AIS). From the obtained results, we evaluate the performance of the methods used for image registration and pattern classification to compare the accuracy of satellite image processing using nature inspired techniques. In summary, the main contributions of this thesis include (a) analysis of flood assessment and land cover mapping using satellite images and (b) efficient image registration and pattern classification using nature inspired algorithms, which are more popular than conventional optimization methods because of their simplicity, parallelism and convergence of the population towards the optimal solution in a given search space.

This work is motivated by the question: how much complexity is appropriate for a cloud parameterization used in general circulation models (GCM). To approach this question, cloud parameterizations across the complexity range are explored using general circulation models and theoretical Monte-Carlo simulations. Their results are compared with high-resolution satellite observations and simulations that resolve the GCM subgrid-scale variability explicitly. A process-orientated evaluation is facilitated by GCM forecast simulations which reproduce the synoptic state. For this purpose novel methods were develop to a) conceptually relate the underlying saturation deficit probability density function (PDF) with its saturated cloudy part, b) analytically compute the vertical integrated liquid water path (LWP) variability, c) diagnose the relevant PDF-moments from cloud parameterizations, d) derive high-resolution LWP from satellite observations and e) deduce the LWP statistics by aggregating the LWP onto boxes equivalent to the GCM grid size. On this basis, this work shows that it is possible to evaluate the sub-grid scale variability of cloud parameterizations in terms of cloud variables. Differences among the PDF types increase with complexity, in particular the more advanced cloud parameterizations can make use of their double Gaussian PDF in conditions, where cumulus convection forms a separate mode with respect to the remainder of the grid-box. Therefore, it is concluded that the difference between unimodal and bimodal PDFs is more important, than the shape within each mode. However, the simulations and their evaluation reveals that the advanced parameterizations do not take full advantage of their abilities and their statistical relationships are broadly similar to less complex PDF shapes, while the results from observations and cloud resolving simulations indicate even more complex distributions. Therefore, this work suggests that the use of less complex PDF shapes might yield a better trade-off. With increasing model resolution initial weaknesses of simpler, e.g. unimodal PDFs, will be diminished. While cloud schemes for coarse-resolved models need to parameterize multiple cloud regimes per grid-box, higher spatial resolution of future GCMs will separate them better, so that the unimodal approximation improves.

Thesis (M.Sc. (Geography)) -- University of Limpopo, 2020 / This work aimed at assessing the implications of involving local communities in biodiversity conservation in Blouberg Nature Reserve (BNR) in Limpopo Province, South Africa. To achieve this objective, firstly biodiversity status before and after involving local communities in conservation initiatives was assessed using multi-temporal medium-resolution Landsat series data and species diversity indices. The results showed that there were significant variations (α = 0.05) in tree species diversity in BNR for before and after involving local communities. For example, tree species diversity was low after involving communities particularly for the years 1996 and 2019. Secondly, benefits and costs of involving local communities in biodiversity conservation as well as their investigate views, perceptions and attitudes BNR management were assessed. The study demonstrated local communities do not obtain sufficient benefits or incur numerous costs from the nature reserve. Despite this, there was considerable support for biodiversity conservation (84.2%) since household respondents still held positive attitudes towards biodiversity conservation in the reserve. For, example most of them indicated that they would report illegal activities to the authorities. However, despite lack of participation by the majority of the household respondents (89.6%) in biodiversity conservation, they demonstrated understanding of the relevance of nature conservation. In contrary, the BNR Manager stated that the local communities received benefits in the form of fuel-wood for special occasions such as funerals and bush meat sold at treasury approved tariffs during culling. However, illegal activities like poaching are still experienced in the nature reserve. Thus, the study underscores the relevance the integrating satellite data and qualitative information in assessing the ecological condition of PAs. Such information can help in biodiversity monitoring and decision-making on conservation of biodiversity. Keywords: biodiversity conservation; community-based natural resource management approach; ecological status; mapping; satellite data; spatial characterisation; species diversity; statistical analysis.

This work is motivated by the question: how much complexity is appropriate for a cloud parameterization used in general circulation models (GCM). To approach this question, cloud parameterizations across the complexity range are explored using general circulation models and theoretical Monte-Carlo simulations. Their results are compared with high-resolution satellite observations and simulations that resolve the GCM subgrid-scale variability explicitly. A process-orientated evaluation is facilitated by GCM forecast simulations which reproduce the synoptic state. For this purpose novel methods were develop to a) conceptually relate the underlying saturation deficit probability density function (PDF) with its saturated cloudy part, b) analytically compute the vertical integrated liquid water path (LWP) variability, c) diagnose the relevant PDF-moments from cloud parameterizations, d) derive high-resolution LWP from satellite observations and e) deduce the LWP statistics by aggregating the LWP onto boxes equivalent to the GCM grid size. On this basis, this work shows that it is possible to evaluate the sub-grid scale variability of cloud parameterizations in terms of cloud variables. Differences among the PDF types increase with complexity, in particular the more advanced cloud parameterizations can make use of their double Gaussian PDF in conditions, where cumulus convection forms a separate mode with respect to the remainder of the grid-box. Therefore, it is concluded that the difference between unimodal and bimodal PDFs is more important, than the shape within each mode. However, the simulations and their evaluation reveals that the advanced parameterizations do not take full advantage of their abilities and their statistical relationships are broadly similar to less complex PDF shapes, while the results from observations and cloud resolving simulations indicate even more complex distributions. Therefore, this work suggests that the use of less complex PDF shapes might yield a better trade-off. With increasing model resolution initial weaknesses of simpler, e.g. unimodal PDFs, will be diminished. While cloud schemes for coarse-resolved models need to parameterize multiple cloud regimes per grid-box, higher spatial resolution of future GCMs will separate them better, so that the unimodal approximation improves.

Knowledge about land use and land cover represents an important information basis for various planning applications. In particular, urban and suburban regions are subject to a high dynamic development. The detection and identification of changes is therefore an important instrument to follow and accompany the developments by planning. Here, aerial photography and, increasingly, satellite images serve as an important basis for information. The recognition and mapping of changes is still a time-consuming and cost-intensive matter which is mostly realized by visual interpretation of aerial photography and to an increasing degree of high- and ultra-high-resolution satellite images. Within the scope of the present work a new, robust and largely automated process based on a statistical change analysis is developed and presented. Basis for the data are multitemporal high-resolution satellite image data. The generated suspect areas, respectively areas of change, are supposed to function as clues in order to facilitate the process of the visual interpretation of multitemporal image datasets with regard to change mapping, since only marked areas of change have to undergo further examination. Consequently, this process can be used as a tool to ease and accelerate the updating of planning bases in general and maps in particular so far realised by visual interpretation. However, the automation of the process is not only supposed to serve the purpose of saving time and cost but also to bring the interpretation process to a higher level of objectivity. In order to improve the quality of the whole process, for the preprocessing of the image data selected methods of image processing have been integrated. Through the use of additional geo-information reference data for the automated calculation of the areas of change, a further refinement of the results can be reached. The obtained results in the first time-cut (1997-1998) can be proved and verified by a different data-take (1997-2000). To reach a convenient use and a good distribution of the developed method, the process has been implemented by means of the widespread image processing software ERDAS IMAGINE. This allows to make the developed method available for other users, since it can easily be integrated into the working environment of ERDAS IMAGINE. / Das Wissen um die Landnutzung und Landbedeckung ist für planerische Anwendungsgebiete eine wichtige Informationsgrundlage. Gerade urbane und suburbane Regionen unterliegen einer hohen Entwicklungsdynamik. Das Erkennen und Aufzeigen von Veränderungen ist somit ein wichtiges Instrument um Entwicklungen zu verfolgen und planerisch zu begleiten. Luft- und zunehmend Satellitenbilder dienen hierfür als wichtige Informationsgrundlage. Das Erkennen und Kartieren von Veränderungen ist nach wie vor eine zeitaufwändige und kostenintensive Angelegenheit, die überwiegend durch visuelle Interpretation von Luft- und zunehmend auch mit hoch- und höchstauflösenden Satellitenbildern realisiert wird. In dieser Arbeit wird ein neues, robustes, weitgehend automatisiertes, auf einem statistischen Ansatz beruhendes Verfahren der Veränderungsanalyse entwickelt und vorgestellt. Die Datengrundlage bilden multitemporale, hoch auflösende Satellitenbilddaten. Die generierten Verdachts- bzw. Veränderungsflächen sollen als Anhaltspunkte fungieren, um den Prozess der visuellen Interpretation von multitemporalen Bilddatensätzen in Hinsicht auf eine Veränderungskartierung zu erleichtern, da nur als Veränderungsflächen markierte Areale einer weiteren Untersuchung unterzogen werden müssen. Das Verfahren kann somit als Werkzeug dienen, die durch visuelle Interpretation realisierte Aktualisierung von Planungsgrundlagen bzw. Kartenwerken zu erleichtern und zu beschleunigen. Die Automatisierung des Verfahrens soll jedoch nicht allein dem Zweck der Zeit- und Kostenersparnis dienen, sondern auch den Interpretationsprozess objektiver gestalten. Um die Qualität des Verfahrens zu erhöhen, werden ausgewählte Methoden der Bildverarbeitung für die Vorverarbeitung der Bilder in das Verfahren integriert. Durch das Einbinden zusätzlicher Geobasisdaten in die automatisierte Berechnung der Veränderungsflächen kann eine weitere Verbesserung der Ergebnisse erzielt werden. Die Ergebnisse, der im ersten Zeitschnitt (1997-1998) untersuchten Datensätze, werden mit Hilfe eines weiteren Zeitschnitts (1997-2000) überprüft und verifiziert. Um eine unkomplizierte Anwendung und Verbreitung der Methode zu erreichen, wurde das Verfahren mit Hilfe der weit verbreiteten Bildverarbeitungssoftware ERDAS IMAGINE realisiert. Dies ermöglicht, das Verfahren auch anderen Nutzern zur Verfügung zu stellen, da es problemlos in die Arbeitsumgebung des Bildverarbeitungssystems ERDAS IMAGINE integriert werden kann