This thesis work is motivated by the potential and promise of image fusion technologies in the multi sensor image fusion system and applications. With specific focus on pixel level image fusion, the process after the image registration is processed, we develop graphic user interface for multi-sensor image fusion software using Microsoft visual studio and Microsoft Foundation Class library. In this thesis, we proposed and presented some image fusion algorithms with low computational cost, based upon spatial mixture analysis. The segment weighted average image fusion combines several low spatial resolution data source from different sensors to create high resolution and large size of fused image. This research includes developing a segment-based step, based upon stepwise divide and combine process. In the second stage of the process, the linear interpolation optimization is used to sharpen the image resolution. Implementation of these image fusion algorithms are completed based on the graphic user interface we developed. Multiple sensor image fusion is easily accommodated by the algorithm, and the results are demonstrated at multiple scales. By using quantitative estimation such as mutual information, we obtain the experiment quantifiable results. We also use the image morphing technique to generate fused image sequence, to simulate the results of image fusion. While deploying our pixel level image fusion algorithm approaches, we observe several challenges from the popular image fusion methods. While high computational cost and complex processing steps of image fusion algorithms provide accurate fused results, they also makes it hard to become deployed in system and applications that require real-time feedback, high flexibility and low computation ability

Weather data are huge. Traditional visualization techniques are limited to show temperature trends. Pixel-based approaches could be used to visualize the huge amount of weather data and in process show the temperature trends. A prototype using this approach is built to make temperature data more understandable in changing trends. It is implemented using a 2D representation and many popular interaction techniques. It is a lightweight and reusable tool to visualize temperatures.

For landscapes dominated by agriculture, land cover plays an important role in the balance between anthropogenic and natural forces. Therefore, the objective of this thesis is to describe two different methodologies that have been implemented to create high-resolution land cover classifications in a dominant agricultural landscape. First, an object-based segmentation approach will be presented, which was applied to historic, high resolution, panchromatic aerial photography. Second, a traditional per-pixel technique was applied to multi-temporal, multispectral, high resolution aerial photography, in combination with light detection and ranging (LIDAR) and independent component analysis (ICA). A critical analysis of each approach will be discussed in detail, as well as the ability of each methodology to generate landscape metrics that can accurately characterize the quality of the landscape. This will be done through the comparison of various landscape metrics derived from the different classifications approaches, with a goal of enhancing the literature concerning how these metrics vary across methodologies and across scales. This is a familiar problem encountered when analyzing land cover datasets over time, which are often at different scales or generated using different methodologies. The diversity of remotely sensed imagery, including varying spatial resolutions, landscapes, and extents, as well as the wide range of spatial metrics that can be created, has generated concern about the integrity of these metrics when used to make inferences about landscape quality. Finally, inferences will be made about land cover and land cover change dynamics for the state of Iowa based on insight gained throughout the process.

The main goal of this study was to map and measure forest change in the southwestern part of Madagascar near the city of Toliara in the period 1990-2010. Recent studies show that forest change in Madagascar on a regional scale does not only deal with forest loss, but also with forest growth However, it is unclear how the study area is dealing with these patterns. In order to select the right classification method, pixel-based classification was compared with object-based classification. The results of this study shows that the object-based classification method was the most suitable method for this landscape. However, the pixel-based approaches also resulted in accurate results. Furthermore, the study shows that in the period 1990–2010, 42% of the forest cover disappeared and was converted into bare soil and savannahs. Next to the change in forest, stable forest regions were fragmented. This has negative effects on the amount of suitable habitats for Malagasy fauna. Finally, the scaling structure in landscape patches was investigated. The study shows that the patch size distribution has long-tail properties and that these properties do not change in periods of deforestation.

In this project report a novel pixel-based approach to digital pathology is proposed. The algorithm directly decides the class of single pixels in an image without needing the larger context of neighbouring pixels. This allows researchers to circumvent complications that might arise from using classical cell segmentation methods based around counting cells - which then relies on the cell segmentation being close to perfect. Such issues are avoided by pixel-based approaches by instead directly measuring total area. The algorithm is tested on the BOMI2 Redox dataset consisting of 79 samples of multi-spectral images from lung cancer patients. The results of the algorithm are compared against ground truth data in the form of RNA sequencing data from the same patient cores as the images are taken. The algorithm achieves Spearman correlations in the range of R = [0.4,0.6], thereby serving as an initial testament to the validity of pixel-based methods. Furthermore an automatic method for deciding biomarker threshold values is proposed, based around finding the knee point of the biomarker histogram. The threshold values found by the algorithm on the BOMI2 Redox data set are reasonable. The method opens up for a standardised way of deciding thresholds in digital pathology, allowing easier comparison between research results from different researchers.

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

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The intensification of aridity due to anthropogenic climate change is likely to have a large impact on the growth and survival of plant species in the southwestern U.S. where species are already vulnerable to high temperatures and limited precipitation. Global climate change impacts plants through a rising temperature effect, CO2 effect, and land management. In order to forecast the impacts of global climate change, it is necessary to know the current conditions and create a baseline for future comparisons and to understand the factors and players that will affect what happens in the future. The objective of Chapter 1 is to create the very first high resolution, accurate, park-wide map that shows the distribution of dominant plants on the Colorado Plateau and serves as a baseline for future comparisons of species distribution. If we are going to forecast what species have already been impacted by global change or will likely be impacted in the future, we need to know their physiology. Chapter 2 surveys the physiology of the twelve most abundant non-tree species on the Colorado Plateau to help us forecast what climate change might do and to understand what has likely already occurred. Chapter 1. Our objective was to create an accurate species-level classification map using a combination of multispectral data from the World View-3 satellite and hyperspectral data from a handheld radiometer to compare pixel-based and object-based classification. We found that overall, both methods were successful in creating an accurate landscape map. Different functional types could be classified with fairly good accuracy in a pixel-based classification but to get more accurate species-level classification, object-based methods were more effective (0.915, kappa coefficient=0.905) than pixel-based classification (0.79, kappa coefficient=0.766). Although spectral reflectance values were important in classification, the addition of other features such as brightness, texture, number of pixels, size, shape, compactness, and asymmetry improved classification accuracy.Chapter 2. We sought to understand if patterns of gas exchange to changes in temperature and CO2 can explain why C3 shrubs are increasing, and C3 and C4 grasses are decreasing in the southwestern U.S. We conducted seasonal, leaf-level gas exchange surveys, and measured temperature response curves and A-Ci response curves of common shrub, forb, and grass species in perennial grassland ecosystems over the year. We found that the functional trait of being evergreen is increasingly more successful in climate changing conditions with warmer winter months. Grass species in our study did not differentiate by photosynthetic pathway; they were physiologically the same in all of our measurements. Increasing shrub species, Ephedra viridis and Coleogyne ramosissima displayed functional similarities in response to increasing temperature and CO2.

The intensification of aridity due to anthropogenic climate change is likely to have a large impact on the growth and survival of plant species in the southwestern U.S. where species are already vulnerable to high temperatures and limited precipitation. Global climate change impacts plants through a rising temperature effect, CO2 effect, and land management. In order to forecast the impacts of global climate change, it is necessary to know the current conditions and create a baseline for future comparisons and to understand the factors and players that will affect what happens in the future. The objective of Chapter 1 is to create the very first high resolution, accurate, park-wide map that shows the distribution of dominant plants on the Colorado Plateau and serves as a baseline for future comparisons of species distribution. If we are going to forecast what species have already been impacted by global change or will likely be impacted in the future, we need to know their physiology. Chapter 2 surveys the physiology of the twelve most abundant non-tree species on the Colorado Plateau to help us forecast what climate change might do and to understand what has likely already occurred. Chapter 1. Our objective was to create an accurate species-level classification map using a combination of multispectral data from the World View-3 satellite and hyperspectral data from a handheld radiometer to compare pixel-based and object-based classification. We found that overall, both methods were successful in creating an accurate landscape map. Different functional types could be classified with fairly good accuracy in a pixel-based classification but to get more accurate species-level classification, object-based methods were more effective (0.915, kappa coefficient=0.905) than pixel-based classification (0.79, kappa coefficient=0.766). Although spectral reflectance values were important in classification, the addition of other features such as brightness, texture, number of pixels, size, shape, compactness, and asymmetry improved classification accuracy.Chapter 2. We sought to understand if patterns of gas exchange to changes in temperature and CO2 can explain why C3 shrubs are increasing, and C3 and C4 grasses are decreasing in the southwestern U.S. We conducted seasonal, leaf-level gas exchange surveys, and measured temperature response curves and A-Ci response curves of common shrub, forb, and grass species in perennial grassland ecosystems over the year. We found that the functional trait of being evergreen is increasingly more successful in climate changing conditions with warmer winter months. Grass species in our study did not differentiate by photosynthetic pathway; they were physiologically the same in all of our measurements. Increasing shrub species, Ephedra viridis and Coleogyne ramosissima displayed functional similarities in response to increasing temperature and CO2.

Sand and mud traversed by tidal inlets and channels, which split in subtle branches, salt marshes at the coast, the tide, harsh weather conditions and a high diversity of fauna and flora characterize the ecosystem Wadden Sea. No other landscape on the Earth changes in such a dynamic manner. Therefore, land cover classification and monitoring of vulnerable ecosystems is one of the most important approaches in remote sensing and has drawn much attention in recent years. The Wadden Sea in the southeastern part of the North Sea is one such vulnerable ecosystem, which is highly dynamic and diverse. The tidal flats of the Wadden Sea are the zone of interaction between marine and terrestrial environments and are at risk due to climate change, pollution and anthropogenic pressure. Due to that, the European Union has implemented various directives, which formulate objectives such as achieving or maintaining a good environmental status respectively a favourable conservation status within a given time. In this context, a permanent observation for the estimation of the ecological condition is needed. Moreover, changes can be tracked or even foreseen and an appropriate response is possible. Therefore, it is important to distinguish between short-term changes, which are related to the dynamic manner of the ecosystem, and long-term changes, which are the result of extraneous influences. The accessibility both from sea and land is very poor, which makes monitoring and mapping of tidal flat environments from in situ measurements very difficult and cost-intensive. For the monitoring of big areas, time-saving applications are needed. In this context, remote sensing offers great possibilities, due to its provision of a large spatial coverage and non-intrusive measurements of the Earth’s surface. Previous studies in remote sensing have focused on the use of electro-optical and radar sensors for remote sensing of tidal flats, whereas microwave systems using synthetic aperture radar (SAR) can be a complementary tool for tidal flat observation, especially due to their high spatial resolution and all-weather imaging capability. Nevertheless, the repetitive tidal event and dynamic sedimentary processes make an integrated observation of tidal flats from multi-sourced datasets essential for mapping and monitoring. The main challenge for remote sensing of tidal flats is to isolate the sediment, vegetation or shellfish bed features in the spectral signature or backscatter intensity from interference by water, the atmosphere, fauna and flora. In addition, optically active materials, such as plankton, suspended matter and dissolved organics, affect the scattering and absorption of radiation. Tidal flats are spatially complex and temporally quite variable and thus mapping tidal land cover requires satellites or aircraft imagers with high spatial and temporal resolution and, in some cases, hyperspectral data. In this research, a hierarchical knowledge-based decision tree applied to multi-sensor remote sensing data is introduced and the results have been visually and numerically evaluated and subsequently analysed. The multi-sensor approach comprises electro-optical data from RapidEye, SAR data from TerraSAR-X and airborne LiDAR data in a decision tree. Moreover, spectrometric and ground truth data are implemented into the analysis. The aim is to develop an automatic or semi-automatic procedure for estimating the distribution of vegetation, shellfish beds and sediments south of the barrier island Norderney. The multi-sensor approach starts with a semi-automatic pre-processing procedure for the electro-optical data of RapidEye, LiDAR data, spectrometric data and ground truth data. The decision tree classification is based on a set of hierarchically structured algorithms that use object and texture features. In each decision, one satellite dataset is applied to estimate a specific class. This helps to overcome the drawbacks that arise from a combined usage of all remote sensing datasets for one class. This could be shown by the comparison of the decision tree results with a popular state-of-the-art supervised classification approach (random forest). Subsequent to the classification, a discrimination analysis of various sediment spectra, measured with a hyperspectral sensor, has been carried out. In this context, the spectral features of the tidal sediments were analysed and a feature selection method has been developed to estimate suitable wavelengths for discrimination with very high accuracy. The developed feature selection method ‘JMDFS’ (Jeffries-Matusita distance feature selection) is a filter-based supervised band elimination technique and is based on the local Euclidean distance and the Jeffries-Matusita distance. An iterative process is used to subsequently eliminate wavelengths and calculate a separability measure at the end of each iteration. If distinctive thresholds are achieved, the process stops and the remaining wavelengths are applied in the further analysis. The results have been compared with a standard feature selection method (ReliefF). The JMDFS method obtains similar results and runs 216 times faster. Both approaches are quantitatively and qualitatively evaluated using reference data and standard methodologies for comparison. The results show that the proposed approaches are able to estimate the land cover of the tidal flats and to discriminate the tidal sediments with moderate to very high accuracy. The accuracies of each land cover class vary according to the dataset used. Furthermore, it is shown that specific reflection features can be identified that help in discriminating tidal sediments and which should be used in further applications in tidal flats.