Magister Scientiae (Biodiversity and Conservation Biology) / Coastal upwelling is one of the most important oceanographic processes relating to ecosystem function at local and global spatial scales. To better understand how changes in upwelling trends may occur in the face of ongoing anthropogenically induced climate change it is important to quantify historical trends in climatic factors responsible for enabling coastal upwelling. However, a paucity of conclusive knowledge relating to patterns concerning changes in upwelling across the world’s oceans over time makes such analyses difficult. In this study I aimed to quantify these patterns by first identifying when upwelling events occur using a novel method for predictingthe behaviours of coastal upwelling systems over time. By using remotely sensed SST data of differing resolutions as well as several wind variables I was able to identify and quantify upwelling signals at several distances away from the coastline of various upwelling systems. Using this novel method of determining upwelling, I then compared upwelling patterns within all Eastern Boundary Upwelling Systems (EBUS) over a period of 37 years, with the assumption that climate change was likely to have driven variable wind patterns leading to a more intense upwelling over time. Overall, upwelling patterns and wind variables did not intensify overtime. This method of identifying upwelling may allow for the development of predictive capabilities to investigate investigate investigate upwelling trends in the future.

The Army National Guard Bureau has implemented a cooperative project with Utah State University to help with the use, display, and evaluation of environmental data for maintaining land condition. Camp Grayling, Michigan, is comprised of deciduous and evergreen forest types. Use of remote sensing for classification has been limited in this region due to the difficulty of species-level classification using single-date remote-sensing techniques . Also, remote sensing has traditionally focused on mapping homogenous zones rather than vegetation boundaries, while one of the concerns for land managers is the nature of vegetation edges (ecotones). This study analyzed each season and band from multiseasonal satellite imagery for their contribution to separating vegetation type and density classes. Then spectral reflectance values for each vegetation and density class were used in discriminant models that define vegetation cover types and densities. These models were then tested against points within 200 m of vegetation boundaries to determine the performance of the models at edges of vegetation types . The reflectance values for vegetation types on Landsat Thematic Mapper (TM), Landsat MultiSpectral Sensor (MSS), and Advanced Very High Resolution Radiometer (AVHRR) imagery were used. Single-band separability decreased with decreasing resolution of the remote sensing data, and the number of spectral bands that could separate means of vegetation and density cover classes was much greater than expected . Winter bands provided more separability than expected for density classes . A VHRR data were shown to provide very little separation and were not included in the discriminant analysis. In the evaluation of the discriminant models, both resubstitution and crossvalidation tests showed that TM and MSS were nearly equal in their ability to discriminate cover types and densities. At the vegetation boundary zones, classification accuracy increased with increasing distance from the edge. These results are encouraging for future classification and monitoring of ecotones using satellite imagery, as picture elements (pixels) of ecotones generally exhibit the characteristics of a mixing of the boundary vegetation types. Further investigation into fuzzy set classification and ecotone classification and monitoring appears warranted.

Over the last 50 to 150 years there has been widespread conversion of grassland to shrubland throughout the western United States. A major management concern on the Edwards Plateau is the encroachment of Ashe Juniper (Juniperus ashei). To facilitate brush management programs, I investigated relationships of two endangered species, the black-capped vireo (Vireo atricapillus) and the golden-cheeked warbler (Dendroica chrysoparia), with their habitats at the landscape level. GIS (Geographic Information Systems) and remotely sensed data, such as Landsat imagery, DEMs (Digital Elevation Maps), and DOQQs (Digital Ortho Quarter Quads) were used to evaluate vegetative and geomorphic features within both 100m- and 400m-radius areas surrounding occupied and (assumed) unoccupied sites. Stepwise-logistic regression was used to develop probability models for each species within a catchment and was then applied to the entire Leon River Watershed and evaluated for accuracy. Golden-cheeked warblers were identified in areas with mean juniper cover greater than 70%, mean departure from North (aspect), and maximum slope. For black-capped vireos, mean shrub cover, mean departure from North, and mean slope were important in habitat selection. Variables at the 400m spatial scale best identified areas of probable occurrence for both species, indicating that features of landscape surrounding a territory may play an important role in habitat selection.

Over the last 50 to 150 years there has been widespread conversion of grassland to shrubland throughout the western United States. A major management concern on the Edwards Plateau is the encroachment of Ashe Juniper (Juniperus ashei). To facilitate brush management programs, I investigated relationships of two endangered species, the black-capped vireo (Vireo atricapillus) and the golden-cheeked warbler (Dendroica chrysoparia), with their habitats at the landscape level. GIS (Geographic Information Systems) and remotely sensed data, such as Landsat imagery, DEMs (Digital Elevation Maps), and DOQQs (Digital Ortho Quarter Quads) were used to evaluate vegetative and geomorphic features within both 100m- and 400m-radius areas surrounding occupied and (assumed) unoccupied sites. Stepwise-logistic regression was used to develop probability models for each species within a catchment and was then applied to the entire Leon River Watershed and evaluated for accuracy. Golden-cheeked warblers were identified in areas with mean juniper cover greater than 70%, mean departure from North (aspect), and maximum slope. For black-capped vireos, mean shrub cover, mean departure from North, and mean slope were important in habitat selection. Variables at the 400m spatial scale best identified areas of probable occurrence for both species, indicating that features of landscape surrounding a territory may play an important role in habitat selection.

As well as the many benefits associated with the evolution of multispectral sensors into hyperspectral sensors there is also a considerable increase in storage space and the computational load to process the data. Consequently the remote sensing ommunity is investigating and developing statistical methods to alleviate these problems. / The research presented here investigates several approaches to reducing the dimensionality of hyperspectral remotely sensed data while maintaining the levels of accuracy achieved using the full dimensionality of the data. It was conducted with an emphasis on applications in maximum likelihood classification (MLC) of hyperspectral image data. An inherent characteristic of hyperspectral data is that adjacent bands are typically highly correlated and this results in a high level of redundancy in the data. The high correlations between adjacent bands can be exploited to realise significant reductions in the dimensionality of the data, for a negligible reduction in classification accuracy. / The high correlations between neighbouring bands is related to their response functions overlapping with each other by a large amount. The spectral band filter functions were modelled for the HyMap instrument that acquires hyperspectral data used in this study. The results were compared with measured filter function data from a similar, more recent HyMap instrument. The results indicated that on average HyMap spectral band filter functions exhibit overlaps with their neighbouring bands of approximately 60%. This is considerable and partly accounts for the high correlation between neighbouring spectral bands on hyperspectral instruments. / A hyperspectral HyMap image acquired over an agricultural region in the south west of Western Australia has been used for this research. The image is composed of 512 × 512 pixels, with each pixel having a spatial resolution of 3.5 m. The data was initially reduced from 128 spectral bands to 82 spectral bands by removing the highly overlapping spectral bands, those which exhibit high levels of noise and those bands located at strong atmospheric absorption wavelengths. The image was examined and found to contain 15 distinct spectral classes. Training data was selected for each of these classes and class spectral mean and covariance matrices were generated. / The discriminant function for MLC makes use of not only the measured pixel spectra but also the sample class covariance matrices. This thesis first examines reducing the parameterization of these covariance matrices for use by the MLC algorithm. The full dimensional spectra are still used for the classification but the number of parameters needed to describe the covariance information is significantly reduced. When a threshold of 0.04 was used in conjunction with the partial correlation matrices to identify low values in the inverse covariance matrices, the resulting classification accuracy was 96.42%. This was achieved using only 68% of the elements in the original covariance matrices. / Both wavelet techniques and cubic splines were investigated as a means of representing the measured pixel spectra with considerably fewer bands. Of the different mother wavelets used, it was found that the Daubechies-4 wavelet performed slightly better than the Haar and Daubechies-6 wavelets at generating accurate spectra with the least number of parameters. The wavelet techniques investigated produced more accurately modelled spectra compared with cubic splines with various knot selection approaches. A backward stepwise knot selection technique was identified to be more effective at approximating the spectra than using regularly spaced knots. A forward stepwise selection technique was investigated but was determined to be unsuited to this process. / All approaches were adapted to process an entire hyperspectral image and the subsequent images were classified using MLC. Wavelet approximation coefficients gave slightly better classification results than wavelet detail coefficients and the Haar wavelet proved to be a more superior wavelet for classification purposes. With 6 approximation coefficients, the Haar wavelet could be used to classify the data with an accuracy of 95.6%. For 11 approximation coefficients this figure increased to 96.1%. / First and second derivative spectra were also used in the classification of the image. The first and second derivatives were determined for each of the class spectral means and for each band the standard deviations were calculated of both the first and second derivatives. Bands were then ranked in order of decreasing standard deviation. Bands showing the highest standard deviations were identified and the derivatives were generated for the entire image at these wavelengths. The resulting first and second derivative images were then classified using MLC. Using 25 spectral bands classification accuracies of approximately 96% and 95% were achieved using the first and second derivative images respectively. These results are comparable with those from using wavelets although wavelets produced higher classification accuracies when fewer coefficients were used.

This research focuses on the development of a deterministic-probabilistic framework for agricultural land use and management, specifically for both annual crops, such as wheat, barley and maize, and permanent crops, such as vineyards. The goal is to predict crop greening and peak crop development progressively through the growing season, based on accumulating information as the crop develops and matures, and to provide an accompanying uncertainty statement (credible interval) with each prediction. The integrated area underneath the phenology curve can be associated, although not explicitly in our example, with per-area crop yield. The prediction model relies on remotely sensed data, including science data products from the Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) spaceborne instruments, field data from agro-meteorological stations, and statistical data from prior years. The development of the deterministic-probabilistic model focuses on northeastern Italy, a region of small agricultural plots set in a diverse physical landscape, which is typical of many areas of old-world and developing-nation agriculture. The estimation process uses the phenological cycle of the MODIS Enhanced Vegetation Index (EVI), extracted from the satellite imagery at 500 m spatial resolution. Landsat data, at 30-m spatial resolution, are fused with MODIS data, to provide fine-scale information better suited to small-field agriculture. By applying a piecewise logistic function to model the time trajectory of EVI values, crop development and peak greenness are estimated and characterized based on the main phenological stages determined from the remote imagery trained with ground station observations. The deterministic-probabilistic model is later validated with observations from reference testing stations and statistical crop and yield data obtained independently by administrative districts such as regional and national organizations. A temporal filter of the main phenological stages, here called a crop calendar, plays a critical role. A Bayesian approach to integrate stochastically the parameters related to a certain area provides a way to include the different datasets at the different dimensions and scales and to assess the probability to obtain a vegetation index within a given uncertainty. The model becomes, therefore, a typical generalized linear model problem, deterministically described by a piecewise logistic function, with the parameters describing the peak phenological curve estimated probabilistically, with their own uncertainty. / 2026-01-31T00:00:00Z

Philosophiae Doctor - PhD (Biodiversity and Conservation Biology) / Seawater temperature from regional to global scale is central to many measures of biodi- versity and continues to aid our understanding of the evolution and ecology of biolog- ical assemblages. Therefore, a clear understanding of the relationship between marine biodiversity and thermal structures is critical for effective conservation planning. In the an- thropocene, an epoch characterised by anthropogenic forcing on the climate system, future patterns in biodiversity and ecological functioning may be estimated from projected climate scenarios however; absent from many of these scenarios is the inclusion of extreme thermal events, known as marine heatwaves (MHWs). There is also a conspicuous absence in knowl- edge of the drivers for all but the most notorious of these events. Before the drivers of MHWs along the coast of South Africa could be determined, it was first necessary to validate the 129 in situ coastal seawater temperature time series that could be used to this end. In doing so it was found that time series created with older (longer), lower precision (0.5 Degrees Celsius) instruments were more useful than newer (shorter) time series produced with high precision (0.001 Degrees Celsius) instruments. With the in situ data validated, a history of the occurrence of MHWs along the coastline (nearshore) was created and compared against MHWs detected by remotely sensed data (offshore). This comparison showed that the forcing of offshore temperatures onto the nearshore was much lower than anticipated, with the rates of co-occurrence for events between the datasets along the coast ranging from 0.2 to 0.5. To accommodate this lack of consistency between datasets, a much larger mesoscale area was then taken around southern Africa when attempting to determine potential mesoscale drivers of MHWs along the coast. Using a self organising-map (SOM), it was possible to organise the synoptic scale oceanographic and atmospheric states during coastal MHWs into discernible groupings. It was found that the most common synoptic oceanographic pattern during coastal MHWs was Agulhas Leakage, and the most common atmospheric pattern was anomalously warmoverland air temperatures.With these patterns known it is now necessary to calculate how often they occur when no MHW has been detected. This work may then allow for the development of predictive capabilities that could help mitigate the damage caused by MHWs.

Human intervention and natural phenomenon cause change in land use day by day. Availability of accurate land use information is essential for many applications like natural resource management, planning and monitoring programs. Landuse Change has become a central component in current strategies for managing natural resources and monitoring environmental change. Because of the rapid development in the field of land use mapping, there is an increase in studies of land use change worldwide. Providing an accurate assessment of the extent and health of the world’s forest, grassland and agricultural resources has become an important priority. By printed maps without any statistics or only statistics without any map can not solve this visualization problem. Because printed maps have not attracted as much attention as statistics among the people because of it is limited applications (Himiyama, 2002). Remotely sensed data like aerial photographs and satellite imageries are undoubtedly the most ideal data for extracting land use change information. Satellite images are the most economical way of getting data for different times. The multitude of existing software helps getting information from satellite image also in manipulating the information. The approach used in this study to classify satellite images and change detection based on Satellite images Landsat MSS (1972), Landsat TM (1989) and Landsat ETM (1999) for using supervised classification methods like maximum likelihood (MAXLIKE), MAHALCLASS and time series analysis of CROSSTAB. After performed these hard and soft classifiers the research showed the significant Landuse change in the study area of Chakoria Sundarban mangrove forest. Remote sensing is the modern tools for detecting change pattern and behaviours of coastal environment (Saifuzzaman, 2000). So, those tools are used in the research work for better change analysis of the study area. For analyzing, evaluation and mapping environmental change detection of different years remotely sensed data have been undertaken. The present research provides some suggestions and recommendations as per research findings in order to optimize the utility of coastal resources and to maintain the sustainability of the resources, coastal land use control and there by stabilizing the coastal vulnerable area of chakoria Sundarban.

<p>Human intervention and natural phenomenon cause change in land use day by day. Availability of accurate land use information is essential for many applications like natural resource management, planning and monitoring programs. Landuse Change has become a central component in current strategies for managing natural resources and monitoring environmental change. Because of the rapid development in the field of land use mapping, there is an increase in studies of land use change worldwide. Providing an accurate assessment of the extent and health of the world’s forest, grassland and agricultural resources has become an important priority. By printed maps without any statistics or only statistics without any map can not solve this visualization problem. Because printed maps have not attracted as much attention as statistics among the people because of it is limited applications (Himiyama, 2002). Remotely sensed data like aerial photographs and satellite imageries are undoubtedly the most ideal data for extracting land use change information. Satellite images are the most economical way of getting data for different times. The multitude of existing software helps getting information from satellite image also in manipulating the information. The approach used in this study to classify satellite images and change detection based on Satellite images Landsat MSS (1972), Landsat TM (1989) and Landsat ETM (1999) for using supervised classification methods like maximum likelihood (MAXLIKE), MAHALCLASS and time series analysis of CROSSTAB. After performed these hard and soft classifiers the research showed the significant Landuse change in the study area of Chakoria Sundarban mangrove forest. Remote sensing is the modern tools for detecting change pattern and behaviours of coastal environment (Saifuzzaman, 2000). So, those tools are used in the research work for better change analysis of the study area. For analyzing, evaluation and mapping environmental change detection of different years remotely sensed data have been undertaken. The present research provides some suggestions and recommendations as per research findings in order to optimize the utility of coastal resources and to maintain the sustainability of the resources, coastal land use control and there by stabilizing the coastal vulnerable area of chakoria Sundarban.</p><p> </p>

Biological diversity is a key component in assessing ecosystem health. Alteration, degradation and loss of habitat due to human influence is currently the primary stressor resulting in decreases in diversity. Reliable assessment of large areas in terms of biological integrity are needed for conservation and preservation efforts. Remotely sensed data provide an integrated view of reflected electromagnetic energy over large areas of the earth. These energy patterns provide unique spectral signatures which can be correlated to land cover and habitat. This research sought relationships between traditional ecological measures and information gathered from satellite digital imagery. Reliable interpretation of earth surface characteristics relies largely on accurate rectification to a map projection and subsequent thematic classification. Use of the Global Positioning System (GPS) for rectification was superior than digitizing topographical maps. Differentially corrected GPS locations provided optimum rectification with SPOT satellite imagery while marginally better rectifications were obtained for Landsat MSS imagery using uncorrected GPS positions. SPOT imagery provided more accurate land cover classifications than did MSS. Detection of temporal land cover change using MSS imagery was hampered by confusion among intermediate successional classes. Confusion between upland and bottomland forest classes occurred with both SPOT and MSS. Landscape analyses using thematic maps produced from the previously discussed endeavors suggested that terrestrial habitat in the Ray Roberts Lake area became more fragmented and complex in shape. Habitat patches became smaller but more numerous. Forested areas were most effected and conservation efforts should focus on management strategies that promote vegetation succession and forest maturation. Remotely sensed SPOT data were successfully used to predict tree basal area. There were no significant relationships found with other in situ measures or between MSS data and any vegetation measures. Remote sensing provided information suitable for large scale projects concerning landscape-level ecological issues. Rectification and classification accuracies were the primary factors influencing meaningful interpretation. Project goals should determine the scale of remotely sensed data and acceptable level of accuracy.