Τα μεσογειακά οικοσυστήματα, με τη δομή και σύνθεση που γνωρίζουμε, οφείλουν την ύπαρξή τους, στα επεισόδια των πυρκαγιών, που συμβαίνουν ανά τακτά χρονικά διαστήματα. Στις μεγάλες δασικές πυρκαγιές, οφείλεται το όλο και μεγαλύτερο ποσοστό της συνολικής καμένης έκτασης ανά έτος στη χώρα μας, και κυρίως, κατά τις τελευταίες δύο δεκαετίες, σε αυτές που συμβαίνουν, όταν επικρατούν ακραία μετεωρολογικά φαινόμενα το καλοκαίρι. Ο νομός Ηλείας, θεωρείται από τους πλέον πυρόπληκτους της Ελλάδας. Για τον λόγο αυτό, επιλέξαμε να μελετήσουμε μια περιοχή, μεσογειακού οικοσυστήματος του Νομού, η οποία έχει πληγεί πολλές φορές στο παρελθόν από τις πυρκαγιές και όλα τα επακόλουθα που τις συνοδεύουν, έκτασης 111.912.638 m . Αυτή η μελέτη, επιχειρεί να παρουσιάσει τις αλλαγές των χρήσεων γης, αλλά και της αναγέννησης μετά από πυρκαγιές, χρησιμοποιώντας τα δορυφορικά στοιχεία της τηλεπισκόπησης, που συλλέχθηκαν σε δύο διαφορετικές χρονικές στιγμές, σε συνδυασμό με τα Συστήματα Γεωγραφικών Πληροφοριών (GIS). Καταγράψαμε την επικρατούσα εικόνα της βλάστησης και αναγέννησης, καθώς και οποιαδήποτε άλλη χρήσιμη, οικολογικής σημασίας πληροφορία, σε 45 δειγματοληπτικές επιφάνειες. Μετά την ολοκλήρωση του προσδιορισμού των φυτικών taxa, όλες οι φυτοληψίες καταχωρήθηκαν στη βάση δεδομένων Turboveg for Windows. Για την ομαδοποίηση των δειγματοληψιών, χρησιμοποιήθηκε η μέθοδος Twinspan. Στη συνέχεια, μέσω του στατιστικού πακέτου PC-ORD 5.0, χρησιμοποιήθηκε, η μέθοδος Detrended Correspondence Analysis (DCA) για τη δημιουργία της γραφικής τους απεικόνισης, ενώ για τον προσδιορισμό των σχέσεων μεταξύ των περιβαλλοντικών μεταβλητών και των μονάδων βλάστησης της περιοχής, έγινε χρήση της μεθόδου Canonical Correspondence analysis (CCA). Οι περιβαλλοντικές μεταβλητές που χρησιμοποιήθηκαν είναι: υπόστρωμα, υψόμετρο, κλίση, προσανατολισμός, αριθμός πυρκαγιών. Με τη χρήση του ArcGIS 9.3 έγιναν θεματικοί χάρτες των καλύψεων/χρήσεων γης πριν και μετά την φωτιά του 2007. Επίσης, έγινε απεικόνιση του ανάγλυφου της περιοχής μελέτης, με τη χρήση ισοϋψών καμπύλων. Έτσι δημιουργήθηκε το Ψηφιακό Υψομετρικό Μοντέλο Εδάφους (ΨΥΜΕ ή DEM: Digital Elevation Model) για την περιοχή. Επίσης παράχθηκε το ΤΙΝ της περιοχής (Triangulated Irregular Network ή Δίκτυο ακανόνιστων τριγώνων). Από το ΤΙΝ παράχθηκαν ο χάρτης κλίσεων, ο χάρτης εκθέσεων και ο χάρτης υψομέτρων. Για να πραγματοποιηθούν τα παραπάνω χρησιμοποιήθηκε η εφαρμογή 3D Analyst στο περιβάλλον του ArcMap, πρόγραμμα ArcGIS 9.3. Τέλος, έγινε Μεταταξινομική σύγκριση (POST- CLASSIFICATION COMPARISON) δορυφορικών εικόνων του Landsat 7 από την περιοχή μελέτης, των ετών 2006 και 2011. Χρησιμοποιήσαμε τα Γεωγραφικά Συστήματα Πληροφοριών και την Τηλεπισκόπηση, επειδή συνιστούν ένα πολύ σημαντικό εργαλείο στη διαχείριση του περιβάλλοντος. Η διαχρονική παρακολούθηση των μεταβολών που συμβαίνουν, μέσω αυτών των συστημάτων, προσφέρει σημαντικά πλεονεκτήματα στον καθορισμό των μελλοντικών δράσεων. Τα αποτελέσματα, μέσα από τη χρήση αυτών των τεχνολογιών, έδειξαν αλλαγές στην εικόνα της βλάστησης πριν και μετά από τις καταστροφικές πυρκαγιές του 2007. Προέκυψε ότι, η αναγέννηση της Χαλεπίου πεύκης (Pinus halepensis) είναι ιδιαιτέρως ικανοποιητική, γεγονός που αποδίδεται αφενός, στους προσαρμοστικούς μηχανισμούς που διαθέτει το συγκεκριμένο είδος και αφετέρου, στις ευνοϊκές συνθήκες ανάπτυξης που επικρατούν στην περιοχή. / Mediterranean ecosystems, with their known structure and composition, owe their existence to the repeated at regular intervals fires. Large forest fires contribute to the increasing percentage of the total area burned per year in our country, especially those of the last two decades which occur when extreme weather conditions prevail in summer. The prefecture of Ilia is considered as one of the most fire affected regions in Greece. For this reason we chose to study an area of a prefecture’s Mediterranean ecosystem expanding over 111.912.638m2, which has been affected several times in the past by fires and all the consequences that accompany them. This study attempts to present the changes in land uses and forest regeneration after fires, using satellite remote sensing data collected at two different times in combination with Geographic Information Systems (GIS). We recorded the prevailing vegetation and regeneration status, as well as any other useful information of ecological importance, by means of 45 sampling plots. After the plant taxa identification, all samplings data were registered in the Turboveg for Windows database and the Twinspan (Two-way indicator species analysis) method was used in order to group the plots. Furthermore, the DCA (Detrended Correspondence Analysis) method was used for the creation of a graphic display of the vegetation data, while, in order to determine the relationships between environmental variables and vegetation units of the area, the CCA (Canonical Correspondence analysis) method was used, both through the statistical package PC-ORD 5.0. The environmental variables used are: substrate, elevation, slope, exposure and number of fires. Using ArcGIS 9.3 we made thematic land use/cover maps before and after the fire of 2007. The 3-D reconstruction of the study area was also made using contour curves, and from this the Digital Elevation Model (DEM) for the study area was derived. The TIN (Triangulated Irregular Network, Network jagged triangles) of the study area was also created as well as the derived slope-, exposure- and altitude maps. To accomplish all the above, we used the 3D Analyst application in the environment of ArcMap, program ArcGIS 9.3. Finally, we compared images (post-classification comparison) of the Landsat 7 from the study area for the years 2006 and 2011. We used Geographic Information Systems and Remote Sensing, since they are very important technologies, used as tool for environmental management. Monitoring through these systems the changes that occur over time, we obtain significant advantages in determining future actions. The results obtained through the use of these technologies, have shown changes in the vegetation before and after the devastating fires of 2007; furthermore, the regeneration of Pinus halepensis is very satisfactory, due both to the adaptive mechanisms of the species and to the favorable growing conditions prevailing in the study area.

The Yorkshire Dales National Park contains some of the UK’s most extensive and well-preserved prehistoric landscapes. Of particular interest are a number of coaxial field systems, which cover hundreds of hectares and exhibit significant time-depth, yet remain little studied and poorly understood in relation to comparable resources elsewhere in Britain and north western Europe. This research aims to address this situation, bringing together existing disparate source materials for the first time, alongside supplementary field observation, to develop a detailed record of the coaxial landscapes. Using a Geographic Information System to manage, interpret and interrogate the combined datasets, analysis focuses on form and character, and explores prehistoric use of the iconic landscape. The study seeks to enhance our knowledge and understanding of the landscapes’ place in space and time, setting them against the backdrop of systems elsewhere, and attempts to place them within the context of later prehistoric society. The research, conducted in association with the Yorkshire Dales National Park Authority, also informs the management and public understanding of the archaeological resource of the Dales via the Historic Environment Record.

Este trabalho trata da organização de um algoritmo, sem preocupação com a linguagem de programação que possa ser utilizada, para desenvolver um método para o traçado da linha divisória de uma bacia hidrográfica, para aplicações em SIG. O método escolhido foi o de análise dos oitos pixels à volta de um pixel já pertencente ao divisor, constituindo uma variação do processo conhecido como D8 (determinístico dos oito vizinhos), que só é possível ser aplicado em sistemas do tipo raster. A partir dessa análise desenvolveu-se uma metodologia para a definição do próximo pixel que pertencerá ao divisor de águas da bacia. A avaliação do algoritmo foi feita utilizando-se um mapa fictício, criado para incluir as mais prováveis dificuldades encontradas na definição do divisor de uma bacia através de programa computacional. O traçado obtido, acompanhando as etapas lógicas do algoritmo, mostrou-se adequado quando comparado com a linha divisória da bacia traçada manualmente. Um mapa digitalizado a partir de rede local real é utilizado como exemplo de aplicação do algoritmo desenvolvido. / This work is about the organizing of an algorithm, without any regard with the programming language that may be used, in order to develop a method for delineation of watershed boundaries, for GIS applications. The method chosen was the analysis of eight pixels that surround another pixel belonging to the watershed, being a variation of the process known as D8 (deterministic of the eight neighbors), which can only be applied in raster systems. Considering this analysis, a methodology was developed for establishing the next pixel that will belong to the watershed. The algorithm evaluation was done with a fictitious map, created to include the most probable difficulties in establishing the divisor of a watershed through a computer program. The obtained line, following the logical steps of the algorithm, was suitable when compared with the dividing line of the watershed done manually. A digital map of a real place is used as an example of application of the developed algorithm.

Groundwater level models have an important role in the development and application of water management and policies. Understanding the temporal and spatial variations of groundwater levels in the Gorran water protection area in Nynäshamn is important for developing management strategies. Geostatistical analysis with several different methods was used to compare groundwater level records for 13 observation wells in Gorran from 1994 to 2012. The performance of the different methods was evaluated by using mean absolute error (MAE) and root mean square error (RMSE) metrics. The results showed that geostatistical methods had a higher general accuracy when utilizing the Empirical Bayesian kriging (EBK) method in Gorran. EBK was the best method with highest precision and lowest mean absolute error. Cross-validation was also applied to evaluate the best (smallest) root mean square error (RMSE). A predicted potentiometric groundwater level was estimated from the basis of the available digital elevation model to extend the observation area within the same geological specification. / Grundvattennivåns modeller har en viktig roll i utvecklingen och tillämpningen av vattenförvaltning och politik. Genom att förstå de tidsmässiga och geografiska variationer grundvattennivån i Gorran skyddat område är viktigt för att utveckla hanteringens strategier.Geostatistical analys med olika modeller som Inverse Distanse Weighted, Radial Basic Funktion och kriging modeller användes för att jämföra grundvattennivåns rekord under 13 observations brunnar 1994 till 2012. Prestandan hos metoder utvärderades med hjälp av medelabsolutfelet (MAE) och rot medelkvadratfelet (RMSE). Resultatet visade att geostatistiska metoder hade högre noggrannhet och Empirical Bayesian kriging (EBK) var den bästa metoden med högsta precision och lägst medelabsolutfel. Cross-validering applicerades också för att utvärdera de bästa minsta kvadratiska medelvärdet (RMSE). Förutsatt potentiometrisk grundvattennivå genomfördes från digital höjdmodell för att förlänga observations område inom samma geologiska specifikation.

Master of Science / Department of Horticulture and Natural Resources / Charles J. Barden / Windbreaks were originally promoted across the Great Plains region of the U.S to reduce wind erosion in general. A review paper published nearly 30 years ago showed yield increases for a variety of crops associated with windbreaks. However, with the widespread use of no-till systems in all farming and advanced crop genetics, the question is “Do windbreaks still provide a yield benefit?” This study compared multiple years of data from protected and unprotected fields across Kansas and few sites in Nebraska looking at relative crop yield differences of five crops: soybeans, wheat, corn, sorghum and sunflowers. Georeferenced data already existed, generated by automated combine yield monitors, and stored on farmer's computers. There were three sets of data collected for each field. The first level is general field level information, using aerial photography and on-site observations to measure the characteristics of the windbreak (length, height and density). The second was from the yield monitor; this data was analyzed with ArcGIS 10.3.1 to visualize windbreak interaction with crop yield. Multiple means comparisons (protected versus unprotected) through two sample T-tests were conducted to determine if the yield in protected areas of fields was significantly different from the yield in unprotected areas. The third data-layer is climate data that was factored into yield analysis to compare wet, normal and dry growing seasons through a Chi-Square 2x2 test analysis. Optical density of windbreaks from leaf-on/off ground-based photos was assessed using SigmaScan Pro 5.0 software as possibly an important factor influencing the windbreak effect. Finally, the yield loss was estimated from the windbreak footprint to see if yield increases are enough to compensate for the area taken out of crop production. Results showed that soybeans (81 crop/years) had the most positive response to windbreak effect with a yield increase 46% of the time, with a 16% average yield increase. Sorghum (31 crop/years) had the highest average yield increase with 25%. Narrow windbreaks (1 to 2 tree rows with an average of 52 ft. width) and those on the north edge of fields resulted in yield increases which compensated for the footprint of the windbreak more often than wider windbreaks on the south edges of fields. Significant yield increases were less than the decreases in the protected area. There was no evidence to show the windbreak effect on yield had any association with critical month precipitation for any crop or orientation group. According to the results obtained, modern hybrids and varieties are possibly less responsive to yield increases due to windbreak effect than older crop varieties. Future studies should collect more data from fields with different windbreak widths distributed more widely across the region to confirm these results. Overall, this project updated our knowledge of windbreak/crop yield interactions and may possibly influence their future role as a conservation practice in the Great Plains.

The bushmeat phenomenon is spreading throughout the African continent. Wildlife species are slain and conservation areas are under increasing strain. Although studies have been conducted in east, central and western Africa, limited resources regarding this phenomenon exist. This is the first study to be conducted in the southern African region. Initially the study was intended for the whole of South Africa, but due to a lack of co-operation from other provincial governments and the unavailability of data, the study was focussed on conservation areas within KwaZulu-Natal. Data regarding bushmeat occurrences were collected from Ezemvelo KZN Wildlife and the South African Police Services. With the help of a Geographical Information System, the data was overlain with other datasets such as the National Census data and a real-world model was created to establish the correlation and relationship between the bushmeat phenomenon in southern Africa compared to other regions of the continent. The results confirmed a steady increase in the number of reported bushmeat occurrences in KwaZulu-Natal from year to year. The increase in the number of bushmeat occurrences in KwaZulu-Natal can be attributed to an increase in population and poverty as well as the lack of a substantial alternative protein source. Bushmeat are primarily targeted by members of impoverished communities in the surrounding rural areas. Bushmeat are sold at local commercial markets as a subsistence protein source for consumption by the local communities. There is no evidence of a significant international commercial trade in bushmeat in the study area. Poachers travel great distances on foot to hunt inside conservation areas where the targeted animal species are still relatively abundant. Snares are utilised as the most preferred method for poaching in all areas. Hunters also utilise traditional weapons and dogs, but the use of firearms are relatively limited. According to the information derived from the analysis, Nyala and Wildebeest were the most targeted species during poaching activities. The highest percentage of bushmeat occurrences in KwaZulu-Natal take place in Mkhuze- and Ndumo Game Reserves. One of the most troubling results of the study was the police action associated with bushmeat occurrences. In most cases no Police Action was involved and only a limited amount of bushmeat incidents resulted in arrests being made. More effective policing by the South African Police Services are recommended as well as an increase in judiciary participation. More effective documentation of bushmeat occurrences by utilising a Global Positioning System to accurately log the specific locations as well as standardised documentation procedures are recommended for all field officers in all protected areas and regions. This will enable more effective monitoring of the bushmeat phenomenon. It is essential to address the key socio-economic problems as the main cause for the increase in the number of bushmeat occurrences in KwaZulu-Natal. The illicit bushmeat trade in the areas surrounding Mkhuze- and Ndumo Game Reserve can only be brought under control once the quality of life for the surrounding communities improves and more employment opportunities are created. Community conservation and awareness programmes as well as a better understanding of, and an increase in partnerships with surrounding communities will help to alleviate the bushmeat problem. / Dissertation (MSc)--University of Pretoria, 2007. / Geography, Geoinformatics and Meteorology / MSc / Unrestricted

This research presents a novel method to assess the availability and geographic accessibility of Thusong Service Centres and Thusong Service Clusters in Limpopo province. The distances that people in urban and rural areas travel to their nearest Thusong Service Centre or Cluster were compared. Accessibility in rural areas was optimised by identifying additional locations where accessibility was inadequate and where the establishment of additional Thusong Service Centres and Clusters could improve accessibility. The novel method to measure geographic accessibility considers factors such as population size and density, as well as the availability of roads. Since rural areas in Limpopo province tend to have limited road infrastructure, people are likely to travel between settlements on unofficial trails and footpaths that are not mapped. To include an approximated representation of these trails and footpaths in the overall travel network, a triangular irregular network was created to connect rural settlements to each other, as well as to the road network. Natural barriers, such as mountain ranges and large rivers or dams were also considered. Optimal locations were identified through different facility location procedures in a geographic information system. The objective was to identify the minimum number of additional locations for Service Centres that would maximise accessibility in the province. Results emanating from this research indicate that accessibility to Thusong Service Centres and Clusters is uneven between urban and rural settlements. People in rural areas need to travel significantly further to their nearest Service Centre compared to those living in urban areas. In order to increase accessibility in rural areas and to achieve a more equitable distribution of Service Centres across the province, five optimal locations were identified. / Dissertation (MA)--University of Pretoria, 2017. / Geography, Geoinformatics and Meteorology / MA / Unrestricted

Myers first identified the world’s 25 biodiversity hotspots and pioneered innovative ideas about the usefulness of biodiversity models for establishing long-term resource conservation strategies at global scales. Since Myers, most of the subsequent studies using hotspot science for biodiversity modeling have used large spatial scales like countries, provinces or states, and other biogeoraphic regions. The California Floristic Province continues to be one of the recognized global biodiversity hotspots. Our study site, San Luis Obispo County is within this hotspot and we created a map of plant biodiversity hotspots at the county scale using GIS technology. We wanted to determine the effectiveness and applicability of biodiversity hotspot mapping at this scale with anticipation that the map will serve as a new tool for establishing long-term resource conservation strategies in the County. Our plant biodiversity hotspot map is based on distribution data collected from herbarium specimens of San Luis Obispo County’s rare flora. These data were extracted from the Hoover Herbarium at Cal Poly and manually digitized into GIS. We built a model with GIS to identify, locate, and quantify the resultant hotspots from the data. The overall approach was successful at identifying and quantifying the attributes and geographic extents of plant biodiversity hotspots at the county scale. Our results are highly applicable for establishing local and regional plant conservation priorities at lower resolutions, which is frequently where land acquisition and reserve establishment occurs. We conclude that biodiversity hotspot modeling with GIS is an effective tool that can be applied to many other finer-scale biological inventories for conservation purposes.

The Yorkshire Dales National Park contains some of the UK’s most extensive and well-preserved prehistoric landscapes. Of particular interest are a number of coaxial field systems, which cover hundreds of hectares and exhibit significant time-depth, yet remain little studied and poorly understood in relation to comparable resources elsewhere in Britain and north western Europe. This research aims to address this situation, bringing together existing disparate source materials for the first time, alongside supplementary field observation, to develop a detailed record of the coaxial landscapes. Using a Geographic Information System to manage, interpret and interrogate the combined datasets, analysis focuses on form and character, and explores prehistoric use of the iconic landscape. The study seeks to enhance our knowledge and understanding of the landscapes’ place in space and time, setting them against the backdrop of systems elsewhere, and attempts to place them within the context of later prehistoric society. The research, conducted in association with the Yorkshire Dales National Park Authority, also informs the management and public understanding of the archaeological resource of the Dales via the Historic Environment Record. / Arts and Humanities Research Council Collaborative Doctoral Award.

Building facades, serving mainly to protect occupants and structural components from natural forces, require periodic inspections for the detection and assessment of building façade anomalies. Over the past years, a growing trend of utilizing camera-equipped drones for periodical building facade inspection has emerged. Building façade anomalies, such as cracks and erosion, can be detected through analyzing drone-captured video, photographs, and infrared images. Such anomalies are known to have an impact on various building performance aspects, e.g., thermal, energy, moisture control issues. Current research efforts mainly focus on the design of drone flight schema for building inspection, 3D building model reconstruction through drone-captured images, and the detection of specific façade anomalies with these images. However, there are several research gaps impeding the improvement of automation level during the processes of building façade inspection with UAV (Unmanned Aerial Vehicle). These gaps are (1) lack effective ways to store multi-type data captured by drones with the connection to the spatial information of building facades, (2) lack high-performance tools for UAV-image analysis for the automated detection of building façade anomalies, and (3) lack a comprehensive management (i.e., storage, retrieval, analysis, and display) of large amounts and multi-media information for cyclic façade inspection. When seeking inspirations from nature, the process of drone-based facade inspection can be compared with caching birds' foraging food through spatial memory, visual sensing, and remarkable memories. This dissertation aims at investigating ways to improve the management of UAV-captured data and the automation level of drone-based façade anomaly inspection with inspirations from caching birds' foraging behavior. Firstly, a 2D spatial model of building façades was created in the geographic information system (GIS) for the registration and storage of UAV-images to assign façade spatial information to each image. Secondly, computational methods like computer vision and deep learning neural networks were applied to develop algorithms for automated extraction of visual features of façade anomalies within UAV-captured images. Thirdly, a GIS-based database was designed for the comprehensive management of heterogeneous inspection data, such as the spatial, multi-spectral, and temporal data. This research will improve the automation level of storage, retrieval, analysis, and documentation of drone-captured images to support façade inspection during a building's service lifecycle. It has promising potential for supporting the decision-making of early-intervention or maintenance strategies to prevent façade failures and improve building performance. / Doctor of Philosophy / Building facades require periodic inspections and maintenance to protect occupants and structures from natural forces like the sun, wind, rain, and snow. Over the past years, a growing trend of utilizing drones for periodical building facade inspection has emerged. Building façade anomalies, such as cracks and corrosion, can be detected from the drone-captured photographs or video. Such anomalies are known to have an impact on various building performance aspects, such as moisture issues, abnormal heat loss, and additional energy consumptions. Existing practices for detecting façade anomalies from drone-captured photographs mainly rely on manual checking by going through numerous façade images and repetitively zooming in and out these high-resolution images, which is time-consuming and labor-intensive with potential risks of human errors. Besides, this manual checking process impedes the management of drone-captured data and the documentation of façade inspection activities. At the same time, the emerging technologies of computer vision (CV) and artificial intelligence (AI) have provided many opportunities to improve the automation level of façade anomaly detection and documentation. Previous research efforts have explored the image-based generation of 3D building models using computer vision techniques, as well as image-based detection of specific anomalies using deep learning techniques. However, few studies have looked into the comprehensive management, including the storage, retrieval, analysis, and display, of drone-captured images with the spatial coordinate information of building facades; there is also a lack of high-performance image analytics tools for the automated detection of building façade anomalies. This dissertation aims at investigating ways to improve the automation level of analyzing and managing drone-captured images as well as documenting building façade inspection information. To achieve this goal, a building façade model was created in the geographic information system (GIS) for the semi-automated registration and storage of drone-captured images with spatial coordinates by using computer vision techniques. Secondly, deep learning was applied for automated detection of façade anomalies in drone-captured images. Thirdly, a GIS-based database was designed as the platform for the automated analysis and management of heterogeneous data for drone-captured images, façade model information, and detected façade anomalies. This research will improve the automation level of drone-based façade inspection throughout a building's service lifecycle. It has promising potential for supporting the decision-making of maintenance strategies to prevent façade failures and improve building performance.