Ecological legacies of drought, fire, and insect disturbance in western North American forests, The

Includes bibliographical references.2015 Fall.Temperate forest ecosystems are subject to various disturbances including insect agents, drought and fire, which can have profound effects on the structure of the ecosystem for many years after the event. Impacts of disturbance can vary widely, therefore an understanding of the legacies of an event are critical in the interpretation of contemporary forest patterns and those of the near future. The primary objective of this dissertation was to investigate the ecological legacies of drought, beetle outbreak and ensuing wildfire in two different ecosystems. A secondary objective of my research, data development, was motivated by a lack of available data which precluded ecological investigation of each disturbance. I studied the effects of drought on deciduous and coniferous forest along a forest-shrubland ecotone in the southern portion of the Wyoming Basin Ecoregion. The results show that forests in the region have experienced high levels of cumulative drought related mortality over the last decade. Negative trends were not consistent across forest type or distributed randomly across the study area. The patterns of long-term trends highlight areas of forest that are resistant, persistent or vulnerable to severe drought. In the second thread of my dissertation, I used multiple lines of evidence to retrospectively characterize a landscape scale mountain pine beetle disturbance from the 1970s in Glacier National Park. The lack of spatially explicit data on this disturbance was a major data gap since wildfire had removed some of the evidence from the landscape. I used this information to assess the influence of beetle severity on the burn severity of subsequent wildfires in the decades after the outbreak. Although many factors contribute to burn severity, my results indicate that beetle severity can positively influence burn severity of wildfire. This is likely due to the change in forest structure in the decades after the outbreak and not as a direct result of tree mortality from the outbreak. The long-term perspective of this study suggests that ecological legacies of high severity disturbance may continue to influence subsequent disturbance for many years after the initial event. This work also provides insight on future disturbance interactions associated with the recent mountain pine beetle outbreak that has impacted tens of millions of hectares in western North America over the last two decades

Advanced correlation-based character recognition applied to the Archimedes Palimpsest

The Archimedes Palimpsest is a manuscript containing the partial text of seven treatises by Archimedes that were copied onto parchment and bound in the tenth-century AD. This work is aimed at providing tools that allow scholars of ancient Greek mathematics to retrieve as much information as possible from images of the remaining degraded text. Acorrelation pattern recognition (CPR) system has been developed to recognize distorted versions of Greek characters in problematic regions of the palimpsest imagery, which have been obscured by damage from mold and fire, overtext, and natural aging. Feature vectors for each class of characters are constructed using a series of spatial correlation algorithms and corresponding performance metrics. Principal components analysis (PCA) is employed prior to classification to remove features corresponding to filtering schemes that performed poorly for the spatial characteristics of the selected region-of-interest. A probability is then assigned to each class, forming a character probability distribution based on relative distances from the class feature vectors to the ROI feature vector in principal component (PC) space. However, the current CPR system does not produce a single classification decision, as is common in most target detection problems, but instead has been designed to provide intermediate results that allow the user to apply his or her own decisions (or evidence) to arrive at a conclusion. To achieve this result, a probabilistic network has been incorporated into the recognition system. A probabilistic network represents a method for modeling the uncertainty in a system, and for this application, it allows information from the existing iv partial transcription and contextual knowledge from the user to be an integral part of the decision-making process. The CPR system was designed to provide a framework for future research in the area of spatial pattern recognition by accommodating a broad range of applications and the development of new filtering methods. For example, during preliminary testing, the CPR system was used to confirm the publication date of a fifteenth-century Hebrew colophon, and demonstrated success in the detection of registration markers in three-dimensional MRI breast imaging. In addition, a new correlation algorithm that exploits the benefits of linear discriminant analysis (LDA) and the inherent shift invariance of spatial correlation has been derived, implemented, and tested. Results show that this composite filtering method provides a high level of class discrimination while maintaining tolerance to withinclass distortions. With the integration of this algorithm into the existing filter library, this work completes each stage of a cyclic workflow using the developed CPR system, and provides the necessary tools for continued experimentation

A scaled, contextual perspective of woody structure and dynamics across a savanna riperian landscape

Sound understanding of the influence of scale and context on ecological patternprocess relationships is lacking in many systems. The hierarchical patch dynamics paradigm (HPDP) provides a framework for addressing spatio-temporal heterogeneity, but the range of systems in which, and scales at which, its principles apply are largely unknown. Furthermore, it does not explicitly account for the influence of spatial context. Recent developments in remote sensing science show potential for bridging this gap by enabling the exploration of landscape heterogeneity at multiple scales and across a wide range of systems and contexts, but the ecological application of these new techniques is lagging. The savanna riparian landscapes of the northern Kruger Park, South Africa, provided a unique platform in which to explore the influence of spatial context, and to test the pattern-process-scale and metastability principles of the HPDP, to further its potential as a unifying framework in landscape ecology. LiDAR and high-resolution aerial imagery were integrated through object-based image analysis to create spatial representations of woody structure (canopy height, canopy cover, canopy height diversity and canopy cover diversity) across a portion of the savanna landscape (60 000ha). Temporal change in woody cover and heterogeneity (number and size of woody patches) was assessed from a historical aerial photography record, that spanned 59 years from 1942 to 2001. Spatial relationships between environmental variables and patterns of woody structure and dynamics were tested at broad (100ha), medium (10ha) and fine-scales (1ha) through canonical correspondence analysis (CCA). The relative contribution of different categories of environmental variables, to the total explained variation in woody structure, was assessed at each scale through partial canonical correspondence analysis (PCCA). Spatial variation in environmental variables, and the influence of spatial context on woody structure-environment relationships, was explicitly tested through geographically weighted regression (GWR). LiDAR results provided an unprecedented basis from which to explore spatial patterns of woody structure in an African savanna. Standard approaches to generating normalized canopy models (nCM) from LiDAR suffered interpolation artifacts in the heterogeneous landscape, but an object-based image analysis technique was developed to overcome this shortfall. The fusion of LiDAR with aerial imagery greatly enhanced the structural description of the landscape, and the accuracy of canopy height estimates varied between different vegetation patch types. Woody structure and dynamics displayed distinct spatial trends across the landscape with high diversity and variability occurring in the alluvial riparian zones. Woody canopy height, canopy cover and cover dynamics exhibited scale variance in their relationship with environmental variables, but woody structural diversityenvironment relationships were scale invariant across the analysis patch hierarchy. These findings from different woody attributes both support and contradict the pattern-process-scale principle of the HPDP, which hypothesizes that ecological processes shift with scale, but that spatial variance measures exhibit stepwise patterns of change with scale, along a patch hierarchy. Percentage woody cover was stable over time across the landscape, despite high variability at smaller scales. However the metastability principle cannot be considered generally applicable in this system, as a broader view of the woody component revealed a marked decline in woody heterogeneity over time. Although losses of woody cover on the diverse alluvial substrates were countered by increases of cover in the uplands, analysis of current woody structure in the context of historical change revealed that the increases took place in the form of shrub encroachment and not the replacement of tall trees. The vertical structure of woody vegetation, and therefore both the biodiversity and ecological functioning of the system, has changed over time across the landscape. The metastability principle of theHPDP may not be applicable in spatially heterogeneous systems, where ecological processes act differentially across the landscape, but may apply within specific patch types at certain temporal scales. Spatially localized analysis models revealed significant spatial non-stationarity in the majority of processes correlated with woody structure, and showed that both the magnitude and direction of woody structure-environment relationships varied in different spatial contexts across the landscape. These results have fundamental implications for the manner in which both science and conservation measures are conducted in heterogeneous systems. Global analysis models, that assume stationarity, are widely accepted and employed in ecological research but may greatly misrepresent ecological relationships that are context-dependent. These findings question the level of system understanding that field studies can provide, by revealing the dangers of inferring patterns and relationships from measurements of limited spatial representation. Leveraging the latest remote sensing technologies, that provide large-extent but fine-grain coverage, in a scaled and context conscious manner, will enhance ecological understanding by spatially quantifying the full spectrum of system heterogeneity. The heterogeneous patterns, scaled relationships and context-dependent patterns identified in this study are challenging from both ecological research and biodiversity conservation points of view. Traditional approaches to science and conservation are ill equipped to address these issues. The HPDP provides an excellent conceptual construct for meeting such challenges, but the influence of spatial context needs to be more explicitly incorporated within the framework. A catchment-based hierarchy is suggested for guiding future research and conservation efforts in heterogeneous landscapes, where context-dependency of ecological processes may be the norm

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

Dataset shift in land-use classification for optical remote sensing

Multimodal dataset shifts consisting of both concept and covariate shifts are addressed in this study to improve texture-based land-use classification accuracy for optical panchromatic and multispectral remote sensing. Multitemporal and multisensor variances between train and test data are caused by atmospheric, phenological, sensor, illumination and viewing geometry differences, which cause supervised classification inaccuracies. The first dataset shift reduction strategy involves input modification through shadow removal before feature extraction with gray-level co-occurrence matrix and local binary pattern features. Components of a Rayleigh quotient-based manifold alignment framework is investigated to reduce multimodal dataset shift at the input level of the classifier through unsupervised classification, followed by manifold matching to transfer classification labels by finding across-domain cluster correspondences. The ability of weighted hierarchical agglomerative clustering to partition poorly separated feature spaces is explored and weight-generalized internal validation is used for unsupervised cardinality determination. Manifold matching solves the Hungarian algorithm with a cost matrix featuring geometric similarity measurements that assume the preservation of intrinsic structure across the dataset shift. Local neighborhood geometric co-occurrence frequency information is recovered and a novel integration thereof is shown to improve matching accuracy. A final strategy for addressing multimodal dataset shift is multiscale feature learning, which is used within a convolutional neural network to obtain optimal hierarchical feature representations instead of engineered texture features that may be sub-optimal. Feature learning is shown to produce features that are robust against multimodal acquisition differences in a benchmark land-use classification dataset. A novel multiscale input strategy is proposed for an optimized convolutional neural network that improves classification accuracy to a competitive level for the UC Merced benchmark dataset and outperforms single-scale input methods. All the proposed strategies for addressing multimodal dataset shift in land-use image classification have resulted in significant accuracy improvements for various multitemporal and multimodal datasets.Thesis (PhD)--University of Pretoria, 2016.National Research Foundation (NRF)University of Pretoria (UP)Electrical, Electronic and Computer EngineeringPhDUnrestricte

Human-Centric Machine Vision

Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans