Over the last decade, coral reef remote sensing research has focused on habitat map development. Advances in field methods, spatial and spectral resolution of remote sensing data, and algorithm development have led to more detailed map categories and to heightened map accuracy. Studies have provided guidance for practitioners in areas such as imagery selection, algorithm application, and class selection methods, but the product has remained relatively unchanged – a habitat map showing the spatial distribution of a range of substrate classes, classified primarily on the basis of their spectral signature. However, the application of such a product in a management context has not been elaborated by the remote sensing community. The research described in this thesis addresses the challenge that the application of remotely-sensed coral reef information in a coral reef management environment elicits. In such an environment, the coral reef manager asks: "What can the map do to help me?", while the remote sensing scientist asks: "What type of information do you need?". The research described here aims to reconcile these two points of view, by answering the research question of this thesis: How can coral reef remotely-sensed information address stakeholder-specific coral reef management objectives? This question was answered through the development of a four-stage strategy. The strategy includes: 1) developing a traditional habitat map, 2) investigating stakeholder receptivity to the habitat map, 3) linking stakeholder interests with habitat data, and 4) illustrating the linked habitat data in what we term a management map. The strategy was applied on Bunaken Island, Indonesia, and involved the collection of both qualitative and quantitative data sets. The research was relevant to the communities on Bunaken Island, as they are directly responsible for the management of the coral reef resources surrounding Bunaken Island, and they are regularly planning and implementing coral reef management projects. The effectiveness of the four-stage strategy was evaluated in a framework that compares potential and actual uses of habitat maps and management maps in coral reef management projects. It was shown that management maps are superior to habitat maps for a wide range of management purposes. This research has provided two main contributions to the field of coral reef remote sensing and management. The first is the four-stage strategy that results in the development of management maps, and the second is the framework for evaluating the effectiveness of the management maps. This research seeks to traverse the gap between producers and users of coral reef remotely-sensed information. The recommendations made from this research addresses coral reef management procedures, action research, and cross-cultural communication. Each recommendation is founded on collaboration between scientist and manager. Such collaboration is crucial for successful application of remotely-sensed information to management.

The Niagara Escarpment is one of Southern Ontario’s most important landscapes. Due to the nature of the landform and its location, the Escarpment is subject to various development pressures including urban expansion, mineral resource extraction, agricultural practices and recreation. In 1985, Canada’s first large scale environmentally based land use plan was put in place to ensure that only development that is compatible with the Escarpment occurred within the Niagara Escarpment Plan (NEP). The southern extent of the NEP is of particular interest in this study, since a portion of the Plan is located within the rapidly expanding Greater Toronto Area (GTA). The Plan area located in the Regional Municipalities of Hamilton and Halton represent both urban and rural geographical areas respectively, and are both experiencing development pressures and subsequent changes in land cover. Monitoring initiatives on the NEP have been established, but have done little to identify consistent techniques for monitoring land cover on the Niagara Escarpment. Land cover information is an important part of planning and environmental monitoring initiatives. Remote sensing has the potential to provide frequent and accurate land cover information over various spatial scales. The goal of this research was to examine land cover change in the Regional Municipalities of Hamilton and Halton portions of the NEP. This was achieved through the creation of land cover maps for each region using Landsat 5 Thematic Mapper (TM) remotely sensed data. These maps aided in determining the qualitative and quantitative changes that had occurred in the Plan area over a 20 year time period from 1986 to 2006. Change was also examined based on the NEP’s land use designations, to determine if the Plan policy has been effective in protecting the Escarpment. To obtain land cover maps, five different supervised classification methods were explored: Minimum Distance, Mahalanobis Distance, Maximum Likelihood, Object-oriented and Support Vector Machine. Seven land cover classes were mapped (forest, water, recreation, bare agricultural fields, vegetated agricultural fields, urban and mineral resource extraction areas) at a regional scale. SVM proved most successful at mapping land cover on the Escarpment, providing classification maps with an average accuracy of 86.7%. Land cover change analysis showed promising results with an increase in the forested class and only slight increases to the urban and mineral resource extraction classes. Negatively, there was a decrease in agricultural land overall. An examination of land cover change based on the NEP land use designations showed little change, other than change that is regulated under Plan policies, proving the success of the NEP for protecting vital Escarpment lands insofar as this can be revealed through remote sensing. Land cover should be monitored in the NEP consistently over time to ensure changes in the Plan area are compatible with the Niagara Escarpment. Remote sensing is a tool that can provide this information to the Niagara Escarpment Commission (NEC) in a timely, comprehensive and cost-effective way. The information gained from remotely sensed data can aid in environmental monitoring and policy planning into the future.

Warming induced changes in Arctic vegetation have to date been studied through observational and experimental field studies, leaving significant uncertainty about the representativeness of selected field sites as well as how these field scale findings scale up to the entire pan-Arctic. The purposes of this thesis were therefore to 1) analyze remotely-sensed/modeled temperature, Normalized Difference Vegeta- tion Indices (NDVI) and plant Net Primary Productivity (NPP) to assess coarse- scale changes (1982–2006) in vegetation; and 2) compare field, remote sensing and model outputs to estimate limitations, challenges and disagreements between data formats. The following data sources were used: • Advanced Very High Resolution Radiometer Polar Pathfinder Extended (APP- x, temperature & albedo) • Moderate Resolution Imaging Spectroradiometer (MODIS, Normalized Dif- ference Vegetation Index (NDVI) & Enhanced Vegetation Index (EVI) ) • Landsat Enhanced Thematic Mapper (Landsat ETM, NDVI) • Global Inventory Modeling and Mapping Studies (GIMMS, NDVI) • Global Productivity Efficiency Model (GloPEM, Net Primary Productivity (NPP)) Over the pan-Arctic (1982-2007), increases in temperature, total annual NPP and maximum annual NDVI were observed. Increases in NDVI and NPP were found to be closely related to increases in temperature according to non-parametric Sen’ slope and Mann Kendall tau tests. Variations in phenology were largely non- significant but related to increases in growing season temperature. Snow melt onset and spring onset correspond closely. MODIS, Landsat and GIMMS NDVI data sets agree well, and MODIS EVI and NDVI are very similar for spring and summer at Fosheim Peninsula. GloPEM NPP and field estimates of NPP are poorly correlated, whereas GIMMS NDVI and GloPEM NPP are well correlated, indicating a need for better calibration of model NPP to field data. In summary, increases in pan-Arctic biological productivity indicators were ob- served, and were found to be closely related to recent circumpolar warming. How- ever, these changes appear to be focused in regions from which recent field studies have found significant ecological changes (Alaska), and coarse resolution remote sensing estimates of ecological changes have been less marked in other regions. Dis- crepancies between results from model, field data and remote sensing, as well as central questions remaining about the impact of increases in productivity on soil- vegetation-atmosphere feedbacks, indicate a clear need for continued research into warming induced changes in pan-Arctic vegetation.

Global warming and climate change are closely related to the amount of CO2 in the air. Forest ecosystem plays very important role in the global carbon cycle; CO2 from the atmosphere is taken up by vegetation and stored as plant biomass. Therefore, quantifying biomass and carbon sequestration in tropical forests has a significant concern within the United Nations Framework Convention on Climate Change (UNFCC), Kyoto Protocol and Reducing Emission from Deforestation and Forest Degradation (REDD) program for the purpose of the improvement of national carbon accounting as well as for addressing the potential areas for carbon credits, basis for payment for environmental services. The aim of research is to estimate biomass and carbon stocks in tropical forests using remote sensing data for dry forest of central highlands of Vietnam. This result showed that from satellite images of the SPOT, satellites could build the land cover map, carbon map and biomass map of Yok Don National Park, Central Highlands of Vietnam. Through which also the biomass (above ground biomass and below ground biomass) of each type of forest can be calculated. For instance the biomass of the dry forest (Dry Dipterocarp Forest) is 153.49 tones x ha-1, biomass of rich forest is 343.35 tones x ha-1, biomass of medium forest is 210.34 tones x ha-1 and biomass of poor forest & scrub are 33.56 tones x ha-1. / Sự ấm lên toàn cầu và biến đổi khí hậu có liên quan chặt chẽ với tổng lượng CO2 trong không khí. Hệ sinh thái rừng có vai trò rất quan trọng trong chu trình các bon toàn cầu; khí CO2 trong khí quyển được hấp thụ bởi thảm thực vật dưới dạng sinh khối. Vì vậy, việc xác định sinh khối và carbon tích trữ trong rừng nhiệt đới đã có được sự quan tâm đáng kể trong Công ước của Liên hiệp quốc về biến đổi khí hậu (UNFCC), Nghị định thư Kyoto và Chương trình giảm phát thải từ phá rừng và suy thoái rừng (REDD) gần đây, nhằm cho mục đích cải thiện việc tính toán lượng các bon tích trữ cũng như giải quyết các vấn đề tiềm năng cho tín dụng các bon, làm cơ sở cho việc thanh toán cho các dịch vụ môi trường. Mục đích của nghiên cứu này là ước lượng sinh khối và các bon lưu trữ trong các khu rừng nhiệt đới bằng cách sử dụng dữ liệu viễn thám, mà ở nghiên cứu này là cho rừng khộp Tây Nguyên của Việt Nam. Kết quả cho thấy rằng, từ ảnh vệ tinh SPOT có thể xây dựng bản đồ lớp phủ thực vật, bản đồ các bon và bản độ sinh khối của Vườn quốc gia Yok Đôn, Tây Nguyên Việt Nam. Qua đó đã tính toán được sinh khối (bao gồm cả trên mặt đất và dưới mặt đất) như: đối với sinh khối của rừng khô cây họ dầu (Dry Dipterocarp Forest) là 153,59 tấn/ha, sinh khối rừng giàu là 343,35 tấn/ha, sinh khối rừng trung bình là 210,34 tấn/ha và sinh khối rừng nghèo&cây bụi là 33,56 tấn/ha.

<p>Remote sensing technologies offer an innovative way to study tropical forests and complement research from forest inventory plots. From a logistic standpoint, remote sensing mitigates some of the physical, political, and economic challenges that complicate terrestrial studies of tropical forests. From a biological standpoint, remotely sensed data can elucidate ecological phenomena and dynamics of tropical forests that may be challenging to observe on the ground. I conducted a digital aerial survey during the peak blooming period for <em>Dipteryx panamensis</em>. <em>D. panamensis</em> is a canopy emergent, keystone tree species within the lowland Atlantic forests of Costa Rica. The aerial survey occurred over Maquenque National Wildlife Refuge, which is situated in the proposed San Juan-La Selva Biological Corridor. Maquenque was decreed in 2005 to protect the last breeding habitat for the endangered Great Green Macaw (<em>Ara ambigua</em>) in Costa Rica. The Great Green Macaw depends on <em>D. panamensis</em> as its primary food and nesting resource. However, because of Costa Rica's complex deforestation history, the Great Green Macaw remains imperiled due to habitat fragmentation, degradation, and loss. Only 25 to 35 breeding pairs and 250 individuals likely persist in the country. My research had three main objectives. First, I evaluated a classification technique that capitalized on the unique spectral signature of blooming <em>D. panamensis</em> in order to detect this species using a pattern recognition approach. Second, I determined the landscape density and distribution of this tree species using results from the aerial survey and classification. I also noted environmental factors predicting potential <em>D. panamensis</em> habitat using a classification and regression tree (CART) model, and I subsequently calculated how much potential habitat exists in Costa Rica. Third, I identified environmental conditions that indicate potential breeding habitat for the Great Green Macaw using a CART model, and I calculated how much potential breeding habitat exists in Costa Rica. Results of the classification work indicate that <em>D. panamensis</em> can be identified based on its unique spectral signature. In particular, the IHS (intensity, hue, saturation) color space was effective at resolving this tree. Results of the density analysis suggest that canopy emergent <em>D. panamensis</em> trees occur at densities as high as 2 trees/hectare. This work demonstrates that <em>D. panamensis</em> exists in denser patches than previously determined from forest inventory plots. Environmental conditions that will support high densities of <em>D. panamensis</em> habitat occur between 45 and 125 meters in elevation and on soil with an acid and clay profile. Nearly 240,000 hectares could support high density patches of <em>D. panamensis</em> in Costa Rica. Potential Great Green Macaw breeding habitat is defined by the density of <em>D. panamensis</em> trees within its 550-hectare breeding territory. Approximately 67,000 hectares of Great Green Macaw breeding territory exist in Costa Rica. Ultimately, the conservation of both <em>D. panamensis</em> and the Great Green Macaw in Costa Rica may require a multinational partnership between Costa Rica and Nicaragua.</p> / Dissertation

<p>Human and natural disturbance affect the Amazon basin at several spatial and temporal scales. In this thesis, I used satellite-detected hot pixels to examine patterns of human-caused disturbance and protected areas in the Brazilian Amazon from 1996-2006. Deforestation fires, as measured by hot pixels, declined exponentially with increasing distance from roads. Fewer deforestation fires occurred within protected areas than outside and this difference was greatest near roads. However, even within reserves, more deforestation fires occurred in regions with high human impact than in those with lower impact. El Niño-related droughts affected deforestation fires most outside of reserves and near roads. There was no significant difference in fire occurrence among inhabited and uninhabited reserve types. </p><p>Within this context of disturbance in the Brazilian Amazon basin, I examined relatively undisturbed savanna-like `campina' ecosystems. I reviewed the literature on campinas and discussed their variation and their significance for beta diversity. As one of two case studies, I assessed spatio-temporal patterns of disturbance (fire and blowdowns), and vegetation change from 1987 to 2007 in campinas in the central Brazilian Amazon using Landsat imagery. In 2001 images, an increase in open areas corresponded with significantly more visible signs of disturbance, likely precipitated by the 1997-98 El Niño. Bird community data indicated a trend of more generalist/savanna species in more frequently disturbed campinas. </p><p>As the second case study, I used daily 500 m resolution MODIS reflectance data to assess seasonal and inter-annual flooding in ~33,000 km2 of campinas in the Negro river basin. Flooding cycles of these wetland campinas critically influence regional ecosystem processes. Flooded areas ranged from 15,000 km2 at the end of the rainy season (August-Oct) to little, if any, open water in the driest times (Jan-Mar). Predictable seasonal flood pulses occurred, but also displayed high inter-annual variability. This variability was weakly correlated with the Multivariate El Niño Southern Oscillation Index (MEI). </p><p>Campina ecosystems are an important, but largely overlooked, component of the biodiversity of the Amazon basin. My research shows that climate, particularly ENSO-associated droughts, strongly affects campinas even in remote areas, just as it increases fire frequencies in more populated regions of the Amazon.</p> / Dissertation

<p>An ecosystem is a community of organisms interacting with its environment, and landscapes are spatially interactive ecosystems. Earth's burgeoning human population demands ever more from finite ecosystems; but if managed well, landscapes can sustain their provision of resources and services and adapt to fulfill the changing human appetite. Management relies on sound information, and managing landscape change requires reliable spatio-temporal databases of ecologically relevant information. Remote sensing technologies fill this niche, providing increasingly large and diverse datasets, but the algorithms to extract information from the data must be developed. I developed and compared three remotely sensed measurements of forest canopy height to one another and to in situ field measurements. Both the precision and the accuracy (as well as the cost) of the measurements sorted along an axis of spatial scale, with Light Detection and Ranging (lidar) measurements proving most reliable at fine scales but prohibitively expensive over large areas and various radar technologies more appropriate for larger areas, especially when calibrated to the more accurate and precise lidar measurements. I also adapted traditional, single-time landcover classification algorithms to extract dense time series of categorical landcover maps from archival multi-spectral satellite images. These measurements greatly expand the potential spatio-temporal scope of landscape ecology and management, facilitating a shift away from data-imposed reliance on "space-for-time substitution" and loosely connected case studies toward robust, statistical analysis based on consistent information.</p> / Dissertation

<p>This dissertation investigates the limnology of the southwestern Peruvian Amazon centered on the Madre de Dios department by examining first the geomorphology and then the ecology and biogeochemistry of the region's fluvial systems. </p><p>Madre de Dios, Peru is world renowned for its prolific biodiversity and its location within the Andes biodiversity hotspot. It is also a site of study regarding the development of the Fitzcarrald Arch and that feature's geomorphological importance as the drainage center for the headwaters of the Madeira River - the Amazon's largest tributary and as well as its role as a physical divider of genetic evolution in the Amazon. Though each of these has been studied by a variety of prominent researchers, the ability to investigate all the aspects of this unique region is hampered by the lack of a regional geomorphological map. This study aims to fill that gap by using remote sensing techniques on digital elevation models, satellite imagery and soil, geology and geoecological maps already in publication to create a geomorphological map. The resulting map contains ten distinct landform types that exemplify the dominance of fluvial processes in shaping this landscape. The river terraces of the Madre de Dios River are delineated in their entirety as well as the various dissected relief units and previously undefined units. The demarcation of the boundaries of these geomorphic units will provide invaluable assistance to the selection of field sites by future researchers as well as insights into the origin of the high biodiversity indices of this region and aid in planning for biodiversity conservation. </p><p>Secondly this study examines 25 tropical floodplain lakes along 300 km of the Manu River within the Manu National Park in the Madre de Dios department. Alternative stable state and regime shifts in shallow lakes typically have been examined in lakes in temperate and boreal regions and within anthropogenically disturbed basins but have rarely been studied in tropical or in undisturbed regions. In contrast this study focuses on a tropical region of virtually no human disturbance and evaluates the effects of hydrological variability on ecosystem structure and dynamics. Using satellite imagery a 23 yr timeline of ecological regime shifts in Amazon oxbow lakes or "cochas" is reconstructed. The study shows that almost 25% of the river's floodplain lakes experience periodic abrupt vegetative changes with an average 3.4% existing in an alternative stable state in any given year. State changes typically occur from a stable phytoplankton-dominated state to a short lived, <3 yr, floating macrophytic state and often occur independent of regional flooding. We theorize that multiple dynamics, both internal and external, drive vegetative regime shifts in the Manu but insufficient data yet exists in this remote region to identify the key processes. </p><p>To complete the investigation of tropical limnology the third study compares and contrasts the nutrient-productivity ration of floodplain and non-floodplain lakes globally and regionally. For over 70 years a strong positive relationship between sestonic chlorophyll-a (Chl-a) and total phosphorus (TP) has been established with phosphorus generally viewed as the most limiting factor to productivity. Most of these studies, however, have focused on northern, temperate regions where the lakes are typically postglacial, isolated and fed by small streams. Relatively little work has been done on floodplain lakes which are semi or permanently connected to the river. This study examines the relationship between nutrients and productivity in floodplain lakes globally through an extensive literature synthesis. Values for total phosphorus, total nitrogen and chlorophyll-a were collected for 523 floodplain lakes, represented by 288 data points while 551 data points were collected for 5444 non-floodplain lakes. Analysis revealed that globally, floodplain lakes do not show any significant difference in the total phosphorus/chlorophyll-a relationship from that found in non-floodplain lakes but significant differences are seen between tropical and temperate lakes. We propose that the term `floodplain' lake should serve as purely a geographical descriptor and that it is lacking as an ecological indicator. Instead factors such as precipitation seasonality, hydrological connectivity and regional flooding regimes are better indicators of high or low productivity in floodplain lakes.</p> / Dissertation

Whenever a new sensor or system comes online, engineers and analysts responsible for processing the measured data turn first to methods that are tried and true on existing systems. This is a natural, if not wholly logical approach, and is exactly what has happened in the advent of hyperspectral imagery (HSI) exploitation. However, a closer look at the assumptions made by the approaches published in the literature has not been undertaken. This thesis analyzes three key aspects of HSI exploitation: statistical data modeling, covariance estimation from training data, and dimension reduction. These items are part of standard processing schemes, and it is worthwhile to understand and quantify the impact that various assumptions for these items have on target detectability and detection statistics. First, the accuracy and applicability of the standard Gaussian (i.e., Normal) model is evaluated, and it is shown that the elliptically contoured t-distribution (EC-t) sometimes offers a better statistical model for HSI data. A finite mixture approach for EC-t is developed in which all parameters are estimated simultaneously without a priori information. Then the effects of making a poor covariance estimate are shown by including target samples in the training data. Multiple test cases with ground targets are explored. They show that the magnitude of the deleterious effect of covariance contamination on detection statistics depends on algorithm type and target signal characteristics. Next, the two most widely used dimension reduction approaches are tested. It is demonstrated that, in many cases, significant dimension reduction can be achieved with only a minor loss in detection performance. In addition, a concise development of key HSI detection algorithms is presented, and the state-of-the-art in adaptive detectors is benchmarked for land mine targets. Methods for detection and identification of airborne gases using hyperspectral imagery are discussed, and this application is highlighted as an excellent opportunity for future work.

Traditional remote sensing image classification has primarily relied on image spectral information and texture information was ignored or not fully utilized. Existing remote sensing software packages have very limited functionalities with respect to texture information extraction and utilization. This research focuses on the use of multi-scale image texture analysis techniques using Gabor filter banks and Wavelet transformations. Gabor filter banks model texture as irradiance patterns in an image over a limited range of spatial frequencies and orientations. Using Gabor filters, each image texture can be differentiated with respect to its dominant spatial frequency and orientation. Wavelet transformations are useful for decomposition of an image into a set of images based on an orthonormal basis. Dyadic transformations are applied to generate a multi-scale image pyramid which can be used for texture analysis. The analysis of texture is carried out using both artificial textures and remotely sensed image corresponding to natural scenes. This research has shown that texture can be extracted and incorporated in conventional classification algorithms to improve the accuracy of classified results. The applicability of Gabor filter banks and Wavelets is explored for classifying and segmenting remote sensing imagery for geographical applications. A qualitative and quantitative comparison between statistical texture indicators and multi-scale texture indicators has been performed. Multi-scale texture indicators derived from Gabor filter banks have been found to be very effective due to the nature of their configurability to target specific textural frequencies and orientations in an image. Wavelet transformations have been found to be effective tools in image texture analysis as they help identify the ideal scale at which texture indicators need to be measured and reduce the computation time taken to derive statistical texture indicators. A robust set of software tools for texture analysis has been developed using the popular .NET and ArcObjects. ArcObjects has been chosen as the API of choice, as these tools can be seamlessly integrated into ArcGIS. This will aid further exploration of image texture analysis by the remote sensing community.