The ability to map nearshore habitat (i.e. submerged aquatic vegetation) is an integral component of marine conservation. The main goal of this thesis was to examine the ability of high resolution, multispectral satellite imagery and a single-beam acoustic ground discrimination system to map the location of marine habitats in Bag Harbour, found in the Gwaii Haanas National Marine Conservation Area Reserve. To meet this goal, two objectives were addressed: (1) Using the QTC View V sing-beam acoustic ground discrimination system, identify which frequency (50 kHz or 200 kHz) is best suited for mapping marine habitat; (2) evaluate the ability to map nearshore marine habitat using WorldView-2 high resolution, multispectral satellite imagery and compare the results of marine habitat maps derived from the acoustic and satellite datasets. Ground-truth data for both acoustic and satellite data were collected via towed underwater video camera on June 3rd and 4th, 2012. Acoustic data (50 and 200 kHz) were collected on June 23rd and 24th, 2012, respectively. The results of this study are organized into two papers. The first paper focuses on objective 1 where the QTC View V single-beam acoustic ground discrimination system was used to map nearshore habitat at a site within the Gwaii Haanas National Marine Conservation Area using two survey frequencies – 50 kHz and 200 kHz. The results show that the 200 kHz data outperformed the 50 kHz data set in both thematic and spatial accuracy. The 200 kHz dataset was able to identify two species of submerged aquatic vegetation, eelgrass (Zostera marina) and a red algae (Chondrocanthus exasperatus) while the 50 kHz dataset was only able to detect the distribution of eelgrass. The best overall accuracy achieved with the 200 kHz dataset was 86% for a habitat map with three classes (dense eelgrass, dense red algae and unvegetated substrate) compared to the 50 kHz habitat classification with two classes (dense eelgrass and unvegetated substrate) that had an overall accuracy of 70%. Neither dataset was capable if discerning the distribution of green algae (Ulva spp.) or brown algae (Fucus spp.), also present at the site. The second paper examines the benthic habitat maps created using WorldView-2 satellite imagery and the QTC View V single-beam acoustic ground discrimination system (AGDS) at 200 kHz (objective 2). Optical and acoustic remote sensing technologies both present unique capabilities of mapping nearshore habitat. Acoustic systems are able to map habitat in subtidal regions outside of the range of optical sensors while optical sensors such as WorldView-2 provide higher spatial and spectral resolution. The results of this study found that the WorldView-2 achieved the highest overall accuracy (75%) for mapping shallow (<3 m) benthic classes (green algae, brown algae, eelgrass and unvegetated substrate). The 200 kHz data were found to perform best in deeper (>3 m) regions and were able to detect the distribution of eelgrass, red algae and unvegetated substrate. A final habitat map was produced composed of these outputs to create a final, comprehensive habitat map of Bag Harbour. These results highlight the benefits and limitations of each remote sensing technology from a conservation management perspective. The main benefits of the WorldView-2 imagery stem from the high resolution (2 x 2 m) pixel resolution, with a single image covering many kilometers of coastline, and ability to discern habitats in the intertidal region that were undetectable by AGDS. However, the main limitation of this technology is the ability to acquire imagery under ideal conditions (low tide and calm seas). In contrast, the QTC View V system requires more hours spent collecting acoustic data in the field, is limited in the number of habitats it is able to detect and creates maps based on interpolated point data (compared to the continuous raster data of the WorldView-2 imagery). If, however, the objectives of the conservation management to create high resolution benthic habitat maps of subtidal habitats (e.g. eelgrass and benthic red algae) at a handful of sites (in contrast to continuous coastal coverage), the QTC View V system is more suitable. Whichever system is used ground-truth data are required to train and validate each dataset. / Graduate / 0799 / luba.reshitnyk@gmail.com

The main objective of this thesis is to create an appropriate methodological procedure for classifying damaged forest in the selected area of Šumava National Park. For this purpose, multispectral imagery WorldView-2 and Landsat 8 are used. Work emphasis on distribution of each phase of forest development affected by bark beetle. According to selected legend, involving multiple stages of damaged but also recovering forest, the images are classified by Neural Network, Support Vector Machine and object classification methods. Application of these methods on selected images required a suitable choice of parameters and rules to achieve optimal results. The results of this thesis compare and evaluate the final classification. Another outcome of this work is to evaluate the influence of the processed images WorldView-2 and Landsat 8 on the final classification performance. All work results are assessed by overall precision, error matrix and kappa coefficient. Powered by TCPDF (www.tcpdf.org)

>Magister Scientiae - MSc / The Cape Floristic Region, situated at the southern tip of Africa, is one of the world’s most botanically diverse regions. The biodiversity of this region faces various types of threats, which can be divided into three main categories, namely increasing urbanisation, agriculture expansion, and the spread of invasive alien vegetation. It has been shown that botanically diverse areas are more prone to invasion by invasive alien plant (IAP) species. The Hawequa conservation area, in the south-western Cape, is particularly botanically diverse, such that it is very prone to aggressive invasion by IAP species. Therefore, conservation management of the Hawequa conservation area urgently need to map, prioritise and clear IAP species. Due to the topographical complexity of this mountainous area, it is not possible to map the distribution of IAP species throughout the protected area by conventional field methods. Remote sensing may be able to provide a suitable alternative for mapping. The aim of this research was to assess various image classification methods,using two types of high-resolution imagery (colour aerial photography and WorldView-2 satellite imagery), in order to map the distribution of IAP species, including small stands and individuals. Specifically, the study will focus on mapping Pinus and Acacia spp. in a study site of approximately 9 225ha in the Hawequa conservation rea. Supervised classification was performed using two different protocols, namely per-pixel and per-field. For the per-pixel classification Iterative Self-Organising Data Analyses Technique (ISODATA) was used, a method supported by ERDAS Imagine. The per-field (object-based) classification was done using fractal net evolution approach (FNEA), a method supported by eCognition. The per-pixel classification mapped the extent of Pinus and Acacia spp. in the study area as 1 205.8 ha (13%) and 80.1 ha (0.9%) respectively, and the perfield classification as 1 120.9 ha (12.1%) and 96.8 ha (1.1%) respectively. Accuracy assessments performed on the resulting thematic maps generated from these two classification methods had a kappa coefficient of 0.700 for the per-pixel classification and 0.408 for the per-field classification. Even though the overall extent of IAP species for each of these methods is similar, the reliability of the actual thematic maps is vastly different. These findings suggest that mapping IAP species (especially Pinus spp.) stands and individuals in highly diverse natural veld, the traditional per-pixel classification still proves to be the best method when using high-resolution images. In the case of Acacia spp., which often occurs along rivers, it is more difficult to distinguish them from the natural riverine vegetation. Using WorldView-2 satellite images for large areas can be very expensive (approximately R120 per km2 in 2011), but in comparison with the cost of mapping and the subsequent clearing, especially in inaccessible areas, it might be a worthwhile investment. Alternative image sources such as the high resolution digital colour infrared aerial photography must be considered as a good source for mapping IAP species in high altitude areas.

Native palms, such as the Sabal palmetto, play an important role in maintaining the ecological balance in Florida. As a side-effect of modern globalization, new phytopathogens like Texas Phoenix Palm Decline have been introduced into forest systems that threaten native palms. This presents new challenges for forestry managers and geographers. Advances in remote sensing has assisted the practice of forestry by providing spatial metrics regarding the type, quantity, location, and the state of heath for trees for many years. This study provides spatial details regarding the general palm decline in Florida by taking advantage of the new developments in deep learning constructs coupled with high resolution WorldView-2 multispectral/temporal satellite imagery and LiDAR point cloud data. A novel approach using TensorFlow deep learning classification, multiband spatial statistics and indices, data reduction, and step-wise refinement masking yielded a significant improvement over Random Forest classification in a comparison analysis. The results from the TensorFlow deep learning were then used to develop an Empirical Bayesian Kriging continuous raster as an informative map regarding palm decline zones using Normalized Difference Vegetation Index Change. The significance from this research showed a large portion of the study area exhibiting palm decline and provides a new methodology for deploying TensorFlow learning for multispectral satellite imagery.

Woodlands constitute the subsistence base of the majority of people in the Sudano-Sahelian zone (SSZ), but low availability of in situ data on vegetation structure and composition hampers research and monitoring. This thesis explores the utility of remote sensing for mapping and analysing vegetation, primarily trees, in the SSZ. A comprehensive literature review was first conducted to describe how the application of remote sensing has developed in the SSZ between 1975 and 2014, and to identify important research gaps. Based on the gaps identified in the literature review, the capabilities of two new satellite systems (WorldView-2 and Landsat 8) for mapping woodland structure and composition were tested in an area in central Burkina Faso. The results shows that WorldView-2 represents a useful data source for mapping individual trees: 85.4% of the reference trees were detected in the WorldView-2 data and tree crown area was estimate with an average error of 45.6%. In addition, WorldView-2 data produced high classification accuracies for five locally important tree species. The highest overall classification accuracy (82.4%) was produced using multi-temporal WorldView-2 data. Landsat 8 data proved more suitable for mapping tree canopy cover as compared to aboveground biomass in the woodland landscape. Tree canopy cover and aboveground biomass was predicted with 41% and 66% root mean square error, respectively, at pixel level. This thesis demonstrates the potential of easily accessible data from two satellite systems for mapping important tree attributes in woodland areas, and discusses how the usefulness of remote sensing for analyzing vegetation can be further enhanced in the SSZ. / Merparten av befolkningen i Sudano-Sahel zonen (SSZ) är beroende av naturresurser och ekosystemtjänster från woodlands (öppen torrskog) för att säkra sin försörjning. Tillgången av fältmätningar av vegetationens struktur och sammansättning är mycket låg i detta område, vilket utgör ett problem för forskning och miljöövervakning. Denna avhandling undersöker nyttan av fjärranalys för att kartlägga och analysera vegetation, främst träd, i SSZ. En omfattande litteraturöversikt genomfördes först för att undersöka hur tillämpningen av fjärranalys har utvecklats i SSZ mellan 1975 och 2014, samt att identifiera viktiga forskningsluckor. Några av de luckor som konstaterades i litteraturgenomgången låg till grund för de följande studierna där två nya satellitsystem (Worldview-2 och Landsat 8) utvärderades för deras användbarhet att kartlägga trädtäckets struktur och artsammansättning i ett woodland-område i centrala Burkina Faso. Resultaten visar att Worldview-2 är en värdefull datakälla för kartering av enskilda träd: 85.4% av referensträden detekterades och trädkronornas storlek uppskattades med ett medelfel av 45.6%. Worldview-2-data producerade även hög klassificeringsnoggrannhet för de fem lokalt viktigaste trädslagen. Den högsta noggrannheten (82.4%) uppnåddes med multi-temporal Worldview-2-data. Landsat 8 data visade sig mer lämpade för kartering av krontäcke, jämfört med biomassa. Medelfelet för karteringen var 41% för krontäcke och 66% för biomassa, på pixelnivå. Avhandlingen visar att lättillgängliga data från två satellitsystem är användbara för kartläggning av viktiga trädattribut i woodlands, samt diskuterar hur nyttan av fjärranalys för vegetationsanalys kan ökas ytterligare i SSZ.

Possibilities of remote sensing in grassland vegetation and management interventions monitoring in the Giant Mountains Abstract The aim of this thesis was to evaluate suitability of WorldView-2 imagery for grassland associations classification in the model area of Giant Mountains. The classification was based both on the legend compiled by a botanist, and on the legend of Natura 2000. In order to eliminate the effects of other types of land cover on the classification accuracy, a mask of grasslands was created. Using discriminant analysis, the significance of spectral bands of WorldView-2, as well as signifikance of selected vegetation indices and components from Principal Component Analysis (PCA) - to distinguish particular classes of grassland vegetation were evaluated. Based on the results of discriminant analysis, classifications using neural networks method and also maximum likelihood method were performed in ENVI 4.7 version software. The results of the both method were compared Key words: remote sensing, meadows association, classification, Giant mountains, WorldView 2

Water quality and wetlands represent two vital elements of a healthy coastal ecosystem. Both experienced substantial declines in the U.S. during the 20th century. Overall coastal wetland cover decreased over 50% in the 20th century due to coastal development and water pollution. Management and legislative efforts have successfully addressed some of the problems and threats, but recent research indicates that the diffuse impacts of climate change and non-point source pollution may be the primary drivers of current and future water-quality and wetland stress. In order to respond to these pervasive threats, traditional management approaches need to adopt modern technological tools for more synoptic, frequent and fine-scale monitoring and assessment. In this dissertation, I explored some of the applications possible with new, commercial satellite imagery to better assess the status of coastal ecosystems. Large-scale land-cover change influences the quality of adjacent coastal water. Satellite imagery has been used to derive land-cover maps since the 1960’s. It provides multiple data points with which to evaluate the effects of land-cover change on water quality. The objective of the first chapter of this research was to determine how 40 years of land-cover change in the Tampa Bay watershed (6,500 km2) may have affected turbidity and chlorophyll concentration – two proxies for coastal water quality. Land cover classes were evaluated along with precipitation and wind stress as explanatory variables. Results varied between analyses for the entire estuary and those of segments within the bay. Changes in developed land percent cover best explained the turbidity and chlorophyll-concentration time series for the entire bay (R2 > 0.75, p < 0.02). The paucity of official land-cover maps (i.e. five maps) restricted the temporal resolution of the assessments. Furthermore, most estuaries along the Gulf of Mexico do not have forty years of water-quality time series with which to perform evaluations against land-cover change. Ocean-color satellite imagery was used to derive proxies for coastal water with near-daily satellite observations since 2000. The goal of chapter two was to identify drivers of turbidity variability for 11 National Estuary Program water bodies along the Gulf of Mexico. Land cover assessments could not be used as an explanatory variable because of the low temporal resolution (i.e. approximately one map per five-year period). Ocean color metrics were evaluated against atmospheric, meteorological, and oceanographic variables including precipitation, wind speed, U and V wind vectors, river discharge, and water level over weekly, monthly, seasonal and annual time steps. Climate indices like the North Atlantic Oscillation and El Niño Southern Oscillation index were also examined as possible drivers of long-term changes. Extreme turbidity events were defined by the 90th and 95th percentile observations over each time step. Wind speed, river discharge and El Niño best explained variability in turbidity time-series and extreme events (R2 > 0.2, p < 0.05), but this varied substantially between time steps and estuaries. The background land cover analyses conducted for coastal water quality studies showed that there are substantial discrepancies between the wetland extent estimates mapped by local, state and federal agencies. The third chapter of my research sought to examine these differences and evaluate the accuracy and precision of wetland maps using high spatial-resolution (i.e. two-meter) WorldView-2 satellite imagery. Ground validation data showed that wetlands mapped at two study sites in Tampa Bay were more accurately identified by WorldView-2 than by Landsat imagery (30-meter resolution). When compared to maps produced separately by the National Oceanic and Atmospheric Administration, Southwest Florida Water Management District, and National Wetland Inventory, we found that these historical land cover products overestimated by 2-10 times the actual extent of wetlands as identified in the WorldView-2 maps. We could find no study that had utilized more than six of these commercial images for a given project. Part of the problem is cost of the images, but there is also the cost of processing the images, which is typically done one at a time and with substantial human interaction. Chapter four explains an approach to automate the preprocessing and classification of imagery to detect wetlands within the Tampa Bay watershed (6,500 km2). Software scripts in Python, Matlab and Linux were used to ingest 130 WorldView-2 images and to generate maps that included wetlands, uplands, water, and bare and developed land. These maps proved to be more accurate at identifying forested wetland (78%) than those by NOAA, SWFWMD, and NWI (45-65%) based on ground validation data. Typical processing methods would have required 4-5 months to complete this work, but this protocol completed the 130 images in under 24 hours. Chapter five of the dissertation reviews coastal management case studies that have used satellite technologies. The objective was to illustrate the utility of this technology. The management sectors reviewed included coral reefs, wetlands, water quality, public health, and fisheries and aquaculture.

Possibilities of object based image analysis for monitoring of meadow vegetation and management in the Krkonoše Mountains National Park Abstract The main aim of the thesis was to evaluate possibilities of Object Based Image Analysis (OBIA) of WorldView-2 satellite image data and aerial optical scanner for meadow vegetation and managment types classification in Krkonoše Mountains National Park. The classification was based on legend prepared by botanist of the national park. The second goal was to compare classification accuracy of Object Based Image Analysis and neural net classification method that was used by Pomahačová (2012) for the same area and the same WorldView-2 data. OBIA for meadow vegetation was conducted using SVM algorithm and "Decision Tree" algorithm. The classification accuracy was estimated using reference points from the field. The thesis puts the requirements (optimal parameters and conditions) for successfull object based classification of mountain meadow vegetation into a new perspective. Key words: Object based classification, meadows, WorldView-2, aerial optical scanner, SVM, KRNAP

The interface between protected and communal lands presents certain challenges for wildlife conservation and the sustainability of local livelihoods. This is a particular case in South Africa, where foot-and-mouth disease (FMD), mainly carried by African buffalo (Syncerus caffer) is transmitted to cattle despite a fence surrounding the protected areas.The ultimate objective of this thesis was to improve knowledge of FMD transmission risk by analyzing behavioral patterns of African buffalo and cattle near the Kruger National Park, and by modelling at fine spatial scale the seasonal risk of contact between them. Since vegetation is considered as a primary bottom-up regulator of grazers distribution, I developed fine-scale seasonal mapping of vegetation. With that purpose, I explored the utility of WorldView-2 (WV-2) sensor, comparing object- (OBIA) and pixel-based image classification methods, and various traditional and advanced classification algorithms. All tested methods produced relatively high accuracy results (>77%), however OBIA with random forest and support vector machines performed significantly better, particularly for wet season imagery (93%).In order to investigate the buffalo and cattle seasonal home ranges and resource utilization distributions I combined the telemetry data with fine-scale maps on forage (vegetation components, and forage quality and quantity). I found that buffalo behaved more like bulk feeders at the scale of home ranges but were more selective within their home range, preferring quality forage over quantity. In contrast, cattle selected forage with higher quantity and quality during the dry season but behaved like bulk grazers in the wet season.Based on the resource utilization models, I generated seasonal cost (resistance) surfaces of buffalo and cattle movement through the landscape considering various scenarios. These surfaces were used to predict buffalo and cattle dispersal routes by applying a cumulative resistant kernels method. The final seasonal contact risks maps were developed by intersecting the cumulative resistant kernels layers of both species and by averaging all scenarios. The maps revealed important seasonal differences in the contact risk, with higher risk in the dry season and hotspots along a main river and the weakest parts of the fence. Results of this study can guide local decision makers in the allocation of resources for FMD mitigation efforts and provide guidelines to minimize overgrazing. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished