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Sample records for adaptive landscape classification

  1. An Adaptive Landscape Classification Procedure using Geoinformatics and Artificial Neural Networks

    SciTech Connect

    Coleman, Andre Michael

    2008-06-01

    The Adaptive Landscape Classification Procedure (ALCP), which links the advanced geospatial analysis capabilities of Geographic Information Systems (GISs) and Artificial Neural Networks (ANNs) and particularly Self-Organizing Maps (SOMs), is proposed as a method for establishing and reducing complex data relationships. Its adaptive and evolutionary capability is evaluated for situations where varying types of data can be combined to address different prediction and/or management needs such as hydrologic response, water quality, aquatic habitat, groundwater recharge, land use, instrumentation placement, and forecast scenarios. The research presented here documents and presents favorable results of a procedure that aims to be a powerful and flexible spatial data classifier that fuses the strengths of geoinformatics and the intelligence of SOMs to provide data patterns and spatial information for environmental managers and researchers. This research shows how evaluation and analysis of spatial and/or temporal patterns in the landscape can provide insight into complex ecological, hydrological, climatic, and other natural and anthropogenic-influenced processes. Certainly, environmental management and research within heterogeneous watersheds provide challenges for consistent evaluation and understanding of system functions. For instance, watersheds over a range of scales are likely to exhibit varying levels of diversity in their characteristics of climate, hydrology, physiography, ecology, and anthropogenic influence. Furthermore, it has become evident that understanding and analyzing these diverse systems can be difficult not only because of varying natural characteristics, but also because of the availability, quality, and variability of spatial and temporal data. Developments in geospatial technologies, however, are providing a wide range of relevant data, and in many cases, at a high temporal and spatial resolution. Such data resources can take the form of high

  2. From landscape to domain: Soils role in landscape classifications

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Soil landscape classifications are designed to divide landscapes into units with significance for the provisioning and regulating of ecosystem services and the development of conservation plans for natural resources. More specifically, such classifications serve as the basis for stratifying manageme...

  3. Oregon Hydrologic Landscapes: A Classification Framework

    EPA Science Inventory

    There is a growing need for hydrologic classification systems that can provide a basis for broad-scale assessments of the hydrologic functions of landscapes and watersheds and their responses to stressors such as climate change. We developed a hydrologic landscape (HL) classifica...

  4. Land classification based on hydrological landscape units

    NASA Astrophysics Data System (ADS)

    Gharari, S.; Fenicia, F.; Hrachowitz, M.; Savenije, H. H. G.

    2011-05-01

    This paper presents a new type of hydrological landscape classification based on dominant runoff mechanisms. Three landscape classes are distinguished: wetland, hillslope and plateau, corresponding to three dominant hydrological regimes: saturation excess overland flow, storage excess sub-surface flow, and deep percolation. Topography, geology and land use hold the key to identifying these landscapes. The height above the nearest drain (HAND) and the surface slope, which can be readily obtained from a digital elevation model, appear to be the dominant topographical parameters for hydrological classification. In this paper several indicators for classification are tested as well as their sensitivity to scale and sample size. It appears that the best results are obtained by the simple use of HAND and slope. The results obtained compare well with field observations and the topographical wetness index. The new approach appears to be an efficient method to "read the landscape" on the basis of which conceptual models can be developed.

  5. Hydrological Land Classification Based on Landscape Units

    NASA Astrophysics Data System (ADS)

    Gharari, S.; hrachowitz, M.; Fenicia, F.; Savenije, H.

    2011-12-01

    Landscape classification in meaningful hydrological units has important implications for hydrological modeling. Conceptual hydrological models, such as HBV- type models, are most commonly designed to represent catchments in a lumped or semi-distributed way at best, i.e. treating them as single entities or sometimes accounting for topographical and land cover variability by introducing some level of stratification. These oversimplifications can frequently lead to substantial misrepresentations of flow generating processes in the catchments in question, as feedback processes between topography, land cover and hydrology in different landscape units are poorly represented. By making use of readily available topographical information, hydrological units can be identified based on the concept of ''Height above Nearest Drainage'' (HAND; Rennó et al., 2008). These units are characterized by distinct hydrological behavior, and they can be represented using different model structures (Savenije, 2010). We selected the Wark Catchment in Grand Duchy of Luxembourg and identified three landscape units: plateau, wetland and hillslope. The original HAND was compared to other, similar models for landscape classification, which make use of other topographical indicators. The models were applied to a 5±5 m2 DEM, and were tested using data collected in the field. The comparison between the models showed that HAND is a more appropriate hydrological descriptor than other models. The map of the classified landscape was set in a probabilistic framework and was then used to determine the proportion of the individual units in the catchment. Different model structures were then assigned to the individual units and were used to model total runoff.

  6. Hydrologic Landscape Classification to Estimate Bristol Bay Watershed Hydrology

    EPA Science Inventory

    The use of hydrologic landscapes has proven to be a useful tool for broad scale assessment and classification of landscapes across the United States. These classification systems help organize larger geographical areas into areas of similar hydrologic characteristics based on cl...

  7. ASSESSMENT OF LANDSCAPE CHARACTERISTICS ON THEMATIC IMAGE CLASSIFICATION ACCURACY

    EPA Science Inventory

    Landscape characteristics such as small patch size and land cover heterogeneity have been hypothesized to increase the likelihood of misclassifying pixels during thematic image classification. However, there has been a lack of empirical evidence, to support these hypotheses. This...

  8. Hydrologic classification of Bristol Bay, Alaska using hydrologic landscapes

    NASA Astrophysics Data System (ADS)

    Todd, J.; Wigington, P. J.; Sproles, E.

    2013-12-01

    The use of hydrologic landscapes has proven to be a useful tool for broad scale assessment and classification of landscapes across the United States. These classification systems help organize larger geographical areas into areas of similar hydrologic characteristics based on climate, terrain and underlying geology. Such characterization of landscapes into areas of common hydrologic patterning is particularly instructive in regions where site specific hydrologic data is sparse or spatially incomplete. By using broad scale landscape metrics to organize the landscape into discrete, characterized units, natural resources managers can gain valuable understanding of landscape patterning and how locations may be differentially affected by a variety of environmental stressors ranging from land use change to management of salmon resources to climate change. Further, the heterogeneity of aquatic habitats and undisturbed hydrologic regimes within this area are a known principal driver for its region-wide fisheries stability. The use of hydrologic landscapes offers an opportunity to better characterize the hydrologic and landscape influences on structuring biotic populations at a regional scale. We have undertaken a hydrologic landscape approach for the Bristol Bay region of Alaska to gain a better understanding of the overall hydrologic environment found in this region since its hydrologic patterning plays a principal role in structuring its world-renowned salmon fishery. Heretofore, a characterization of the entire Bristol Bay region into discrete hydrologic units has not been undertaken. Our classification structure includes indices of annual climate and seasonality, terrain, and geology. Following categorization of landscape units, we compared hydrologic landscape units to locations of available long term streamflow for characterization of expected hydrologic behavior. This demonstration of hydrologic landscapes in Bristol Bay, Alaska shows the utility of using large

  9. The sensory ecology of adaptive landscapes

    PubMed Central

    Jordan, Lyndon A.; Ryan, Michael J.

    2015-01-01

    In complex environments, behavioural plasticity depends on the ability of an animal to integrate numerous sensory stimuli. The multidimensionality of factors interacting to shape plastic behaviour means it is difficult for both organisms and researchers to predict what constitutes an adaptive response to a given set of conditions. Although researchers may be able to map the fitness pay-offs of different behavioural strategies in changing environments, there is no guarantee that the study species will be able to perceive these pay-offs. We thus risk a disconnect between our own predictions about adaptive behaviour and what is behaviourally achievable given the umwelt of the animal being studied. This may lead to erroneous conclusions about maladaptive behaviour in circumstances when the behaviour exhibited is the most adaptive possible given sensory limitations. With advances in the computational resources available to behavioural ecologists, we can now measure vast numbers of interactions among behaviours and environments to create adaptive behavioural surfaces. These surfaces have massive heuristic, predictive and analytical potential in understanding adaptive animal behaviour, but researchers using them are destined to fail if they ignore the sensory ecology of the species they study. Here, we advocate the continued use of these approaches while directly linking them to perceptual space to ensure that the topology of the generated adaptive landscape matches the perceptual reality of the animal it intends to study. Doing so will allow predictive models of animal behaviour to reflect the reality faced by the agents on adaptive surfaces, vastly improving our ability to determine what constitutes an adaptive response for the animal in question. PMID:26018831

  10. The sensory ecology of adaptive landscapes.

    PubMed

    Jordan, Lyndon A; Ryan, Michael J

    2015-05-01

    In complex environments, behavioural plasticity depends on the ability of an animal to integrate numerous sensory stimuli. The multidimensionality of factors interacting to shape plastic behaviour means it is difficult for both organisms and researchers to predict what constitutes an adaptive response to a given set of conditions. Although researchers may be able to map the fitness pay-offs of different behavioural strategies in changing environments, there is no guarantee that the study species will be able to perceive these pay-offs. We thus risk a disconnect between our own predictions about adaptive behaviour and what is behaviourally achievable given the umwelt of the animal being studied. This may lead to erroneous conclusions about maladaptive behaviour in circumstances when the behaviour exhibited is the most adaptive possible given sensory limitations. With advances in the computational resources available to behavioural ecologists, we can now measure vast numbers of interactions among behaviours and environments to create adaptive behavioural surfaces. These surfaces have massive heuristic, predictive and analytical potential in understanding adaptive animal behaviour, but researchers using them are destined to fail if they ignore the sensory ecology of the species they study. Here, we advocate the continued use of these approaches while directly linking them to perceptual space to ensure that the topology of the generated adaptive landscape matches the perceptual reality of the animal it intends to study. Doing so will allow predictive models of animal behaviour to reflect the reality faced by the agents on adaptive surfaces, vastly improving our ability to determine what constitutes an adaptive response for the animal in question. PMID:26018831

  11. Hydrologic Classification of Bristol Bay, Alaska Using Hydrologic Landscapes

    NASA Astrophysics Data System (ADS)

    Todd, J.; Wigington, P. J., Jr.; Sproles, E. A.

    2014-12-01

    The use of hydrologic landscapes has proven to be a useful tool for broad scale assessment and classification of landscapes across the United States. These classification systems help organize larger geographical areas into areas of similar hydrologic characteristics based on climate, terrain and underlying geology. Such characterization of landscapes into areas of common hydrologic patterning is particularly instructive where site specific hydrologic data is sparse or spatially incomplete. By using broad scale landscape metrics to organize the landscape into discrete, characterized units, natural resources managers can gain valuable understanding of landscape patterning and how locations may be differentially affected by a variety of environmental stressors ranging from land use change to climate change. The heterogeneity of aquatic habitats and undisturbed hydrologic regimes within Bristol Bay are a known principal driver for its overall fisheries stability and the use of hydrologic landscapes offers the ability to better characterize the hydrologic and landscape influences on structuring biotic populations at a regional scale. Here we classify the entire Bristol Bay region into discrete hydrologic landscape units based on indices of annual climate and seasonality, terrain, and geology. We then compared hydrologic landscape units to locations of available long term streamflow for characterization of expected hydrologic behavior where streamflow data was lacking. This demonstration of hydrologic landscapes in Bristol Bay, Alaska shows the utility of using large-scale datasets on climate, terrain and geology to infer broad scale hydrologic patterning within a data poor area. Disclaimer: The authors' views expressed here do not necessarily reflect views or policies of USEPA.

  12. Seri Landscape Classification and Spatial Reference

    ERIC Educational Resources Information Center

    O'Meara, Carolyn

    2010-01-01

    This thesis contributes to the growing field of ethnophysiography, a new subfield of cognitive anthropology that aims to determine the universals and variation in the categorization of landscape objects across cultures. More specifically, this work looks at the case of the Seri people of Sonora, Mexico to investigate the way they categorize…

  13. Hydrological landscape classification: investigating the performance of HAND based landscape classifications in a central European meso-scale catchment

    NASA Astrophysics Data System (ADS)

    Gharari, S.; Hrachowitz, M.; Fenicia, F.; Savenije, H. H. G.

    2011-11-01

    This paper presents a detailed performance and sensitivity analysis of a recently developed hydrological landscape classification method based on dominant runoff mechanisms. Three landscape classes are distinguished: wetland, hillslope and plateau, corresponding to three dominant hydrological regimes: saturation excess overland flow, storage excess sub-surface flow, and deep percolation. Topography, geology and land use hold the key to identifying these landscapes. The height above the nearest drainage (HAND) and the surface slope, which can be easily obtained from a digital elevation model, appear to be the dominant topographical controls for hydrological classification. In this paper several indicators for classification are tested as well as their sensitivity to scale and resolution of observed points (sample size). The best results are obtained by the simple use of HAND and slope. The results obtained compared well with the topographical wetness index. The HAND based landscape classification appears to be an efficient method to ''read the landscape'' on the basis of which conceptual models can be developed.

  14. Hydrologic Landscape Regionalisation Using Deductive Classification and Random Forests

    PubMed Central

    Brown, Stuart C.; Lester, Rebecca E.; Versace, Vincent L.; Fawcett, Jonathon; Laurenson, Laurie

    2014-01-01

    Landscape classification and hydrological regionalisation studies are being increasingly used in ecohydrology to aid in the management and research of aquatic resources. We present a methodology for classifying hydrologic landscapes based on spatial environmental variables by employing non-parametric statistics and hybrid image classification. Our approach differed from previous classifications which have required the use of an a priori spatial unit (e.g. a catchment) which necessarily results in the loss of variability that is known to exist within those units. The use of a simple statistical approach to identify an appropriate number of classes eliminated the need for large amounts of post-hoc testing with different number of groups, or the selection and justification of an arbitrary number. Using statistical clustering, we identified 23 distinct groups within our training dataset. The use of a hybrid classification employing random forests extended this statistical clustering to an area of approximately 228,000 km2 of south-eastern Australia without the need to rely on catchments, landscape units or stream sections. This extension resulted in a highly accurate regionalisation at both 30-m and 2.5-km resolution, and a less-accurate 10-km classification that would be more appropriate for use at a continental scale. A smaller case study, of an area covering 27,000 km2, demonstrated that the method preserved the intra- and inter-catchment variability that is known to exist in local hydrology, based on previous research. Preliminary analysis linking the regionalisation to streamflow indices is promising suggesting that the method could be used to predict streamflow behaviour in ungauged catchments. Our work therefore simplifies current classification frameworks that are becoming more popular in ecohydrology, while better retaining small-scale variability in hydrology, thus enabling future attempts to explain and visualise broad-scale hydrologic trends at the scale of

  15. Adaptation and extinction in experimentally fragmented landscapes.

    PubMed

    Fakheran, Sima; Paul-Victor, Cloé; Heichinger, Christian; Schmid, Bernhard; Grossniklaus, Ueli; Turnbull, Lindsay A

    2010-11-01

    Competition and disturbance are potent ecological forces that shape evolutionary trajectories. These forces typically work in opposition: when disturbance is infrequent, densities are high and competition is intense. In contrast, frequent disturbance creates a low-density environment in which competition is weak and good dispersal essential. We exploited recent advances in genomic research to quantify the response to selection by these powerful ecological forces at the phenotypic and molecular genetic level in experimental landscapes. We grew the annual plant Arabidopsis thaliana in discrete patches embedded in a hostile matrix and varied the number and size of patches and the intensity of disturbance, by creating both static and dynamic landscapes. In static landscapes all patches were undisturbed, whereas in dynamic landscapes all patches were destroyed in each generation, forcing seeds to disperse to new locations. We measured the resulting changes in phenotypic, genetic, and genotypic diversity after five generations of selection. Simulations revealed that the observed loss of genetic diversity dwarfed that expected under drift, with dramatic diversity loss, particularly from dynamic landscapes. In line with ecological theory, static landscapes favored good competitors; however, competitive ability was linked to growth rate and not, as expected, to seed mass. In dynamic landscapes, there was strong selection for increased dispersal ability in the form of increased inflorescence height and reduced seed mass. The most competitive genotypes were almost eliminated from highly disturbed landscapes, raising concern over the impact of increased levels of human-induced disturbance in natural landscapes. PMID:20956303

  16. Adaptive learning based heartbeat classification.

    PubMed

    Srinivas, M; Basil, Tony; Mohan, C Krishna

    2015-01-01

    Cardiovascular diseases (CVD) are a leading cause of unnecessary hospital admissions as well as fatalities placing an immense burden on the healthcare industry. A process to provide timely intervention can reduce the morbidity rate as well as control rising costs. Patients with cardiovascular diseases require quick intervention. Towards that end, automated detection of abnormal heartbeats captured by electronic cardiogram (ECG) signals is vital. While cardiologists can identify different heartbeat morphologies quite accurately among different patients, the manual evaluation is tedious and time consuming. In this chapter, we propose new features from the time and frequency domains and furthermore, feature normalization techniques to reduce inter-patient and intra-patient variations in heartbeat cycles. Our results using the adaptive learning based classifier emulate those reported in existing literature and in most cases deliver improved performance, while eliminating the need for labeling of signals by domain experts. PMID:26484555

  17. Adaptive environment classification system for hearing aids.

    PubMed

    Lamarche, Luc; Giguère, Christian; Gueaieb, Wail; Aboulnasr, Tyseer; Othman, Hisham

    2010-05-01

    An adaptive sound classification framework is proposed for hearing aid applications. The long-term goal is to develop fully trainable instruments in which both the acoustical environments encountered in daily life and the hearing aid settings preferred by the user in each environmental class could be learned. Two adaptive classifiers are described, one based on minimum distance clustering and one on Bayesian classification. Through unsupervised learning, the adaptive systems allow classes to split or merge based on changes in the ongoing acoustical environments. Performance was evaluated using real-world sounds from a wide range of acoustical environments. The systems were first initialized using two classes, speech and noise, followed by a testing period when a third class, music, was introduced. Both systems were successful in detecting the presence of an additional class and estimating its underlying parameters, reaching a testing accuracy close to the target rates obtained from best-case scenarios derived from non-adaptive supervised versions of the classifiers (about 3% lower performance). The adaptive Bayesian classifier resulted in a 4% higher overall accuracy upon splitting adaptation than the minimum distance classifier. Merging accuracy was found to be the same in the two systems and within 1%-2% of the best-case supervised versions. PMID:21117761

  18. Transcriptome Analysis Reveals Signature of Adaptation to Landscape Fragmentation

    PubMed Central

    Ikonen, Suvi; Auvinen, Petri; Paulin, Lars; Koskinen, Patrik; Holm, Liisa; Taipale, Minna; Duplouy, Anne; Ruokolainen, Annukka; Saarnio, Suvi; Sirén, Jukka; Kohonen, Jukka; Corander, Jukka; Frilander, Mikko J.; Ahola, Virpi; Hanski, Ilkka

    2014-01-01

    We characterize allelic and gene expression variation between populations of the Glanville fritillary butterfly (Melitaea cinxia) from two fragmented and two continuous landscapes in northern Europe. The populations exhibit significant differences in their life history traits, e.g. butterflies from fragmented landscapes have higher flight metabolic rate and dispersal rate in the field, and higher larval growth rate, than butterflies from continuous landscapes. In fragmented landscapes, local populations are small and have a high risk of local extinction, and hence the long-term persistence at the landscape level is based on frequent re-colonization of vacant habitat patches, which is predicted to select for increased dispersal rate. Using RNA-seq data and a common garden experiment, we found that a large number of genes (1,841) were differentially expressed between the landscape types. Hexamerin genes, the expression of which has previously been shown to have high heritability and which correlate strongly with larval development time in the Glanville fritillary, had higher expression in fragmented than continuous landscapes. Genes that were more highly expressed in butterflies from newly-established than old local populations within a fragmented landscape were also more highly expressed, at the landscape level, in fragmented than continuous landscapes. This result suggests that recurrent extinctions and re-colonizations in fragmented landscapes select a for specific expression profile. Genes that were significantly up-regulated following an experimental flight treatment had higher basal expression in fragmented landscapes, indicating that these butterflies are genetically primed for frequent flight. Active flight causes oxidative stress, but butterflies from fragmented landscapes were more tolerant of hypoxia. We conclude that differences in gene expression between the landscape types reflect genomic adaptations to landscape fragmentation. PMID:24988207

  19. Transcriptome analysis reveals signature of adaptation to landscape fragmentation.

    PubMed

    Somervuo, Panu; Kvist, Jouni; Ikonen, Suvi; Auvinen, Petri; Paulin, Lars; Koskinen, Patrik; Holm, Liisa; Taipale, Minna; Duplouy, Anne; Ruokolainen, Annukka; Saarnio, Suvi; Sirén, Jukka; Kohonen, Jukka; Corander, Jukka; Frilander, Mikko J; Ahola, Virpi; Hanski, Ilkka

    2014-01-01

    We characterize allelic and gene expression variation between populations of the Glanville fritillary butterfly (Melitaea cinxia) from two fragmented and two continuous landscapes in northern Europe. The populations exhibit significant differences in their life history traits, e.g. butterflies from fragmented landscapes have higher flight metabolic rate and dispersal rate in the field, and higher larval growth rate, than butterflies from continuous landscapes. In fragmented landscapes, local populations are small and have a high risk of local extinction, and hence the long-term persistence at the landscape level is based on frequent re-colonization of vacant habitat patches, which is predicted to select for increased dispersal rate. Using RNA-seq data and a common garden experiment, we found that a large number of genes (1,841) were differentially expressed between the landscape types. Hexamerin genes, the expression of which has previously been shown to have high heritability and which correlate strongly with larval development time in the Glanville fritillary, had higher expression in fragmented than continuous landscapes. Genes that were more highly expressed in butterflies from newly-established than old local populations within a fragmented landscape were also more highly expressed, at the landscape level, in fragmented than continuous landscapes. This result suggests that recurrent extinctions and re-colonizations in fragmented landscapes select a for specific expression profile. Genes that were significantly up-regulated following an experimental flight treatment had higher basal expression in fragmented landscapes, indicating that these butterflies are genetically primed for frequent flight. Active flight causes oxidative stress, but butterflies from fragmented landscapes were more tolerant of hypoxia. We conclude that differences in gene expression between the landscape types reflect genomic adaptations to landscape fragmentation. PMID:24988207

  20. Efficient retrieval of landscape Hessian: forced optimal covariance adaptive learning.

    PubMed

    Shir, Ofer M; Roslund, Jonathan; Whitley, Darrell; Rabitz, Herschel

    2014-06-01

    Knowledge of the Hessian matrix at the landscape optimum of a controlled physical observable offers valuable information about the system robustness to control noise. The Hessian can also assist in physical landscape characterization, which is of particular interest in quantum system control experiments. The recently developed landscape theoretical analysis motivated the compilation of an automated method to learn the Hessian matrix about the global optimum without derivative measurements from noisy data. The current study introduces the forced optimal covariance adaptive learning (FOCAL) technique for this purpose. FOCAL relies on the covariance matrix adaptation evolution strategy (CMA-ES) that exploits covariance information amongst the control variables by means of principal component analysis. The FOCAL technique is designed to operate with experimental optimization, generally involving continuous high-dimensional search landscapes (≳30) with large Hessian condition numbers (≳10^{4}). This paper introduces the theoretical foundations of the inverse relationship between the covariance learned by the evolution strategy and the actual Hessian matrix of the landscape. FOCAL is presented and demonstrated to retrieve the Hessian matrix with high fidelity on both model landscapes and quantum control experiments, which are observed to possess nonseparable, nonquadratic search landscapes. The recovered Hessian forms were corroborated by physical knowledge of the systems. The implications of FOCAL extend beyond the investigated studies to potentially cover other physically motivated multivariate landscapes. PMID:25019911

  1. Adaptive landscapes: Top-down and bottom-up perspectives

    NASA Astrophysics Data System (ADS)

    Kerr, Benjamin

    Sewall Wright introduced the metaphor of the adaptive landscape, a map from genotype to fitness, more than 80 years ago to help describe his view of adaptive evolution. This metaphor has been immensely popular and has been used in a variety of incarnations. However, a systematic study of the genotype-fitness map presents significant problems. The space of possible genotypes is vast, and the mapping is likely dependent on both environment and the composition of genotypes in a population. In this talk, I will discuss some of these problems and present experimental strategies for uncovering features of adaptive landscapes. In particular, I will discuss how population structure can be used as an experimental variable to elucidate landscape topography and how a combination of experimental evolution and genetic engineering can reveal important landscape features in changing environments. I will also present some potential applications of this work to the problem of antibiotic resistance and potential implications for evolutionary rescue in the face of global climate change. For some of these topics, the classic notion of the adaptive landscape must itself be adapted; however, I propose that there are fruitful ways to continue to apply this metaphor.

  2. Adaptive Gaussian pattern classification. Final report

    SciTech Connect

    Priebe, C.E.; Marchette, D.J.

    1988-08-01

    A massively parallel architecture for pattern classification is described. The architecture is based on the field of density estimation. It makes use of a variant of the adaptive-kernel estimator to approximate the distributions of the classes as a sum of Gaussian distributions. These Gaussians are learned using a moved-mean, moving-covariance learning scheme. A temporal ordering scheme is implemented using decay at the input level, allowing the network to learn to recognize sequences. The learning scheme requires a single pass through the data, giving the architecture the capability of real-time learning. The first part of the paper develops the adaptive-kernel estimator. The parallel architecture is then described, and issues relevant to implementation are discussed. Finally, applications to robotic sensor fusion, intended word recognition, and vision are described.

  3. Mapping land cover in urban residential landscapes using fine resolution imagery and object-oriented classification

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A knowledge of different types of land cover in urban residential landscapes is important for building social and economic city-wide policies including landscape ordinances and water conservation programs. Urban landscapes are typically heterogeneous, so classification of land cover in these areas ...

  4. Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes.

    PubMed

    Forester, Brenna R; Jones, Matthew R; Joost, Stéphane; Landguth, Erin L; Lasky, Jesse R

    2016-01-01

    The spatial structure of the environment (e.g. the configuration of habitat patches) may play an important role in determining the strength of local adaptation. However, previous studies of habitat heterogeneity and local adaptation have largely been limited to simple landscapes, which poorly represent the multiscale habitat structure common in nature. Here, we use simulations to pursue two goals: (i) we explore how landscape heterogeneity, dispersal ability and selection affect the strength of local adaptation, and (ii) we evaluate the performance of several genotype-environment association (GEA) methods for detecting loci involved in local adaptation. We found that the strength of local adaptation increased in spatially aggregated selection regimes, but remained strong in patchy landscapes when selection was moderate to strong. Weak selection resulted in weak local adaptation that was relatively unaffected by landscape heterogeneity. In general, the power of detection methods closely reflected levels of local adaptation. False-positive rates (FPRs), however, showed distinct differences across GEA methods based on levels of population structure. The univariate GEA approach had high FPRs (up to 55%) under limited dispersal scenarios, due to strong isolation by distance. By contrast, multivariate, ordination-based methods had uniformly low FPRs (0-2%), suggesting these approaches can effectively control for population structure. Specifically, constrained ordinations had the best balance of high detection and low FPRs and will be a useful addition to the GEA toolkit. Our results provide both theoretical and practical insights into the conditions that shape local adaptation and how these conditions impact our ability to detect selection. PMID:26576498

  5. Dynamic LiDAR-NDVI classification of fluvial landscape units

    NASA Astrophysics Data System (ADS)

    Ramírez-Núñez, Carolina; Parrot, Jean-François

    2015-04-01

    The lower basin of the Coatzacoalcos River is a wide floodplain in which, during the wet season, local and major flooding are distinguished. Both types of floods, intermittent and regional, are important in terms of resources; the regional flood sediments enrich the soils of the plains and intermittent floods allow obtaining aquatic resources for subsistence during the heatwave. In the floodplain different abandoned meanders and intermittent streams are quickly colonized by aquatic vegetation. However, from the 1990s, the Coatzacoalcos River floodplain has important topographic changes due to mining, road and bridges construction; erosion and sedimentation requires continuous parcel boundaries along with the increasing demand of channel reparation, embankments, levees and bridges associated to tributaries. NDVI data, LiDAR point cloud and various types of flood simulations taking into account the DTM are used to classify the dynamic landscape units. These units are associated to floods in relation with water resources, agriculture and livestock. In the study area, the first returns of the point cloud allow extracting vegetation strata. The last returns correspond to the bare earth surface, especially in this area with few human settlements. The surface that is not covered by trees or by aquatic vegetation, correspond to crops, pastures and bare soils. The classification is obtained by using the NDVI index coupled with vegetation strata and water bodies. The result shows that 47.96% of the area does not present active vegetation and it includes 31.53% of bare soils. Concerning the active vegetation, pastures, bushes and trees represent respectively 25.59%, 11.14% and 13.25%. The remaining 1.25% is distributed between water bodies with aquatic vegetation, trees and shrubs. Dynamic landscape units' classification represents a tool for monitoring water resources in a fluvial plain. This approach can be also applied to forest management, environmental services and

  6. Adaptive data embedding framework for multiclass classification.

    PubMed

    Mu, Tingting; Jiang, Jianmin; Wang, Yan; Goulermas, John Y

    2012-08-01

    The objective of this paper is the design of an engine for the automatic generation of supervised manifold embedding models. It proposes a modular and adaptive data embedding framework for classification, referred to as DEFC, which realizes in different stages including initial data preprocessing, relation feature generation and embedding computation. For the computation of embeddings, the concepts of friend closeness and enemy dispersion are introduced, to better control at local level the relative positions of the intraclass and interclass data samples. These are shown to be general cases of the global information setup utilized in the Fisher criterion, and are employed for the construction of different optimization templates to drive the DEFC model generation. For model identification, we use a simple but effective bilevel evolutionary optimization, which searches for the optimal model and its best model parameters. The effectiveness of DEFC is demonstrated with experiments using noisy synthetic datasets possessing nonlinear distributions and real-world datasets from different application fields. PMID:24807525

  7. Adaptive Landscapes of Resistance Genes Change as Antibiotic Concentrations Change.

    PubMed

    Mira, Portia M; Meza, Juan C; Nandipati, Anna; Barlow, Miriam

    2015-10-01

    Most studies on the evolution of antibiotic resistance are focused on selection for resistance at lethal antibiotic concentrations, which has allowed the detection of mutant strains that show strong phenotypic traits. However, solely focusing on lethal concentrations of antibiotics narrowly limits our perspective of antibiotic resistance evolution. New high-resolution competition assays have shown that resistant bacteria are selected at relatively low concentrations of antibiotics. This finding is important because sublethal concentrations of antibiotics are found widely in patients undergoing antibiotic therapies, and in nonmedical conditions such as wastewater treatment plants, and food and water used in agriculture and farming. To understand the impacts of sublethal concentrations on selection, we measured 30 adaptive landscapes for a set of TEM β-lactamases containing all combinations of the four amino acid substitutions that exist in TEM-50 for 15 β-lactam antibiotics at multiple concentrations. We found that there are many evolutionary pathways within this collection of landscapes that lead to nearly every TEM-genotype that we studied. While it is known that the pathways change depending on the type of β-lactam, this study demonstrates that the landscapes including fitness optima also change dramatically as the concentrations of antibiotics change. Based on these results we conclude that the presence of multiple concentrations of β-lactams in an environment result in many different adaptive landscapes through which pathways to nearly every genotype are available. Ultimately this may increase the diversity of genotypes in microbial populations. PMID:26113371

  8. Greedy adaptive walks on a correlated fitness landscape.

    PubMed

    Park, Su-Chan; Neidhart, Johannes; Krug, Joachim

    2016-05-21

    We study adaptation of a haploid asexual population on a fitness landscape defined over binary genotype sequences of length L. We consider greedy adaptive walks in which the population moves to the fittest among all single mutant neighbors of the current genotype until a local fitness maximum is reached. The landscape is of the rough mount Fuji type, which means that the fitness value assigned to a sequence is the sum of a random and a deterministic component. The random components are independent and identically distributed random variables, and the deterministic component varies linearly with the distance to a reference sequence. The deterministic fitness gradient c is a parameter that interpolates between the limits of an uncorrelated random landscape (c=0) and an effectively additive landscape (c→∞). When the random fitness component is chosen from the Gumbel distribution, explicit expressions for the distribution of the number of steps taken by the greedy walk are obtained, and it is shown that the walk length varies non-monotonically with the strength of the fitness gradient when the starting point is sufficiently close to the reference sequence. Asymptotic results for general distributions of the random fitness component are obtained using extreme value theory, and it is found that the walk length attains a non-trivial limit for L→∞, different from its values for c=0 and c=∞, if c is scaled with L in an appropriate combination. PMID:26953649

  9. Plasticity, memory and the adaptive landscape of the genotype

    PubMed Central

    l, C. P

    1998-01-01

    The adaptive value of epigenetic inheritance systems is investigated in a simple mathematical framework. These systems enable the environmentally induced phenotypes to be transmitted between generations. The frequencies of the different epigenetic variants are determined by the plasticity and the efficiency of transmission (called memory). Plasticity and memory are genetically determined. This paper studies the evolution of a quantitative character, its plasticity and memory, on the adaptive landscape. Due to the dual inheritance of the character, selection acts on two levels: on the phenotypes of the same genotype, and on the different genotypes. Plasticity generates the raw material, and memory increases the strength of phenotypic selection. If the character is far from the peak of the landscape, then dual inheritance of the character can be advantageous for the genotype. Near the peak it is more favourable to suppress phenotypic variation. This would lead to genetic assimilation.

  10. IMPACTS OF PATCH SIZE AND LANDSCAPE HETEROGENEITY ON THEMATIC IMAGE CLASSIFICATION ACCURACY

    EPA Science Inventory

    Impacts of Patch Size and Landscape Heterogeneity on Thematic Image Classification Accuracy.
    Currently, most thematic accuracy assessments of classified remotely sensed images oily account for errors between the various classes employed, at particular pixels of interest, thu...

  11. Adaptation in protein fitness landscapes is facilitated by indirect paths

    PubMed Central

    Wu, Nicholas C; Dai, Lei; Olson, C Anders; Lloyd-Smith, James O; Sun, Ren

    2016-01-01

    The structure of fitness landscapes is critical for understanding adaptive protein evolution. Previous empirical studies on fitness landscapes were confined to either the neighborhood around the wild type sequence, involving mostly single and double mutants, or a combinatorially complete subgraph involving only two amino acids at each site. In reality, the dimensionality of protein sequence space is higher (20L) and there may be higher-order interactions among more than two sites. Here we experimentally characterized the fitness landscape of four sites in protein GB1, containing 204 = 160,000 variants. We found that while reciprocal sign epistasis blocked many direct paths of adaptation, such evolutionary traps could be circumvented by indirect paths through genotype space involving gain and subsequent loss of mutations. These indirect paths alleviate the constraint on adaptive protein evolution, suggesting that the heretofore neglected dimensions of sequence space may change our views on how proteins evolve. DOI: http://dx.doi.org/10.7554/eLife.16965.001 PMID:27391790

  12. Hydrologic landscape classification evaluates streamflow vulnerability to climate change in Oregon, USA

    EPA Science Inventory

    Classification can allow assessments of the hydrologic functions of landscapes and their responses to stressors. Here we demonstrate the use of a hydrologic landscape (HL) approach to assess vulnerability to potential future climate change at statewide and basin scales. The HL ...

  13. Use of hydrologic landscape classification to diagnose streamflow predictability in Oregon

    EPA Science Inventory

    We implement a spatially lumped rainfall-runoff model to predict daily streamflow at 88 catchments within Oregon, USA and analyze its performance within the context of Oregon Hydrologic Landscapes (OHL) classification. OHL classification is used to characterize the physio-climat...

  14. Land Cover Classification in a Complex Urban-Rural Landscape with Quickbird Imagery

    PubMed Central

    Moran, Emilio Federico.

    2010-01-01

    High spatial resolution images have been increasingly used for urban land use/cover classification, but the high spectral variation within the same land cover, the spectral confusion among different land covers, and the shadow problem often lead to poor classification performance based on the traditional per-pixel spectral-based classification methods. This paper explores approaches to improve urban land cover classification with Quickbird imagery. Traditional per-pixel spectral-based supervised classification, incorporation of textural images and multispectral images, spectral-spatial classifier, and segmentation-based classification are examined in a relatively new developing urban landscape, Lucas do Rio Verde in Mato Grosso State, Brazil. This research shows that use of spatial information during the image classification procedure, either through the integrated use of textural and spectral images or through the use of segmentation-based classification method, can significantly improve land cover classification performance. PMID:21643433

  15. Prospects for hydrologic classification of landscapes and watersheds

    EPA Science Inventory

    The ecological functions of streams and associated riparian zones are strongly influenced by the hydrological attributes of watersheds and landscapes in which they occur. Oregon hydrologic landscape regions (HLRs) have been defined based on four types of GIS data: 1) climate, 2) ...

  16. Mapping agricultural landscapes and characterizing adaptive capacity in Central America

    NASA Astrophysics Data System (ADS)

    Holland, M. B.; Imbach, P. A.; Bouroncle, C.; Donatti, C.; Leguia, E.; Martinez, M.; Medellin, C.; Saborio-Rodriguez, M.; Shamer, S.; Zamora, J.

    2013-12-01

    One of the key challenges in developing adaptation strategies for smallholder farmers in developing countries is that of a data-poor environment, where spatially-explicit information about where the most vulnerable smallholder communities are located is lacking. Developing countries tend to lack consistent and reliable maps on agricultural land use, and have limited information available on smallholder adaptive capacity. We developed a novel participatory and expert mapping process to overcome these barriers and develop detailed national-scale maps that allow for a characterization of unique agricultural landscapes based on profiles of adaptive capacity for smallholder agriculture in each area. This research focuses specifically on the Central American nations of Costa Rica, Guatemala, and Honduras, where our focus is on coffee and basic grains as the two main cropping systems. Here we present the methodology and results of a series of in-depth interviews and participatory mapping sessions with experts working within the broader agricultural sector in each country. We held individual interviews and mapping sessions with approximately thirty experts from each country, and used a detailed survey instrument for each mapping session to both spatially identify distinct agricultural landscapes, and to further characterize each area based on specific farm practices and social context. The survey also included a series of questions to help us assess the relative adaptive capacity of smallholder agriculture within each landscape. After all expert mapping sessions were completed in each country we convened an expert group to assist in both validating and refining the set of landscapes already defined. We developed a characterization of adaptive capacity by aggregating indicators into main assets-based criteria (e.g. land tenure, access to credit, access to technical assistance, sustainable farm practices) derived from further expert weighting of indicators through an online

  17. Application of Unsupervised Clustering using Sparse Representations on Learned Dictionaries to develop Land Cover Classifications in Arctic Landscapes

    NASA Astrophysics Data System (ADS)

    Rowland, J. C.; Moody, D. I.; Brumby, S.; Gangodagamage, C.

    2012-12-01

    Techniques for automated feature extraction, including neuroscience-inspired machine vision, are of great interest for landscape characterization and change detection in support of global climate change science and modeling. Successful application of novel unsupervised feature extraction and clustering algorithms for use in Land Cover Classification requires the ability to determine what landscape attributes are represented by automated clustering. A closely related challenge is learning how to precondition the input data streams to the unsupervised classification algorithms in order to obtain clusters that represent Land Cover category of relevance to landsurface change and modeling applications. We present results from an ongoing effort to apply novel clustering methodologies developed primarily for neuroscience machine vision applications to the environmental sciences. We use a Hebbian learning rule to build spectral-textural dictionaries that are adapted to the data. We learn our dictionaries from millions of overlapping image patches and then use a pursuit search to generate sparse classification features. These sparse representations of pixel patches are used to perform unsupervised k-means clustering. In our application, we use 8-band multispectral Worldview-2 data from three arctic study areas: Barrow, Alaska; the Selawik River, Alaska; and a watershed near the Mackenzie River delta in northwest Canada. Our goal is to develop a robust classification methodology that will allow for the automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties (e.g. soil moisture and inundation), and topographic/geomorphic characteristics. The challenge of developing a meaningful land cover classification includes both learning how optimize the clustering algorithm and successfully interpreting the results. In applying the unsupervised clustering, we have the flexibility of selecting both the window

  18. Incorporating landscape classifications in hydrological conceptual models A case study for a central European meso-scale catchment

    NASA Astrophysics Data System (ADS)

    Gharari, S.; Hrachowitz, M.; Fenicia, F.; Savenije, H. H. G.

    2012-04-01

    catchment. The classified landscapes were used to assign different model structures to the individual hydrological response units. As an example deep percolation was defined as dominant process for plateaus, rapid subsurface flow as dominant process for hillslopes and saturation overland flow as dominant process for wetlands. The modeled runoffs from each hydrological unit were combined in a parallel set-up to proportionally contribute to the total catchment runoff. The hydrological units are, in addition, linked by a common groundwater reservoir. The parallel hydrological units, although increasing the number of parameters, have the benefit of comparative calibration. As an example, one may consider the lag time of wetland to be shorter than the lag time of water traveling to the outlet from a plateau. Moreover, due to the dominance of forest on hillslopes in this catchment, hillslope interception should be higher than interception on plateaus which are mainly used for agriculture in the Wark catchment. Furthermore fluxes and processes can be compared. For example, actual evaporation from wetland can potentially be higher than other entities within a catchment as wetland is water logged and evaporation thus less supply limited than on plateaus. To include all the comparisons and criteria in calibration, an evolutionary algorithm was used. The algorithm was adapted and applied in a way that in subsequent steps more and more comparative criteria are forced to be satisfied. At the end of the calibration it is expected that all the criteria should be satisfied. Including landscape classification into hydrological models seems to be a powerful tool which not only allows to consider and to make use of crucial feedback processes controlling the evolution of the hydrological system together with the eco-system but may also lead to more detailed information on how a catchment may work than a simple lumped model.

  19. Mutual Information Item Selection in Adaptive Classification Testing

    ERIC Educational Resources Information Center

    Weissman, Alexander

    2007-01-01

    A general approach for item selection in adaptive multiple-category classification tests is provided. The approach uses mutual information (MI), a special case of the Kullback-Leibler distance, or relative entropy. MI works efficiently with the sequential probability ratio test and alleviates the difficulties encountered with using other local-…

  20. The Landscape of long non-coding RNA classification

    PubMed Central

    St Laurent, Georges; Wahlestedt, Claes; Kapranov, Philipp

    2015-01-01

    Advances in the depth and quality of transcriptome sequencing have revealed many new classes of long non-coding RNAs (lncRNAs). lncRNA classification has mushroomed to accommodate these new findings, even though the real dimensions and complexity of the non-coding transcriptome remain unknown. Although evidence of functionality of specific lncRNAs continues to accumulate, conflicting, confusing, and overlapping terminology has fostered ambiguity and lack of clarity in the field in general. The lack of fundamental conceptual un-ambiguous classification framework results in a number of challenges in the annotation and interpretation of non-coding transcriptome data. It also might undermine integration of the new genomic methods and datasets in an effort to unravel function of lncRNA. Here, we review existing lncRNA classifications, nomenclature, and terminology. Then we describe the conceptual guidelines that have emerged for their classification and functional annotation based on expanding and more comprehensive use of large systems biology-based datasets. PMID:25869999

  1. Oregon Hydrologic Landscapes: An Approach for Broadscale Hydrologic Classification

    EPA Science Inventory

    Gaged streams represent only a small percentage of watershed hydrologic conditions throughout the Unites States and globe, but there is a growing need for hydrologic classification systems that can serve as the foundation for broad-scale assessments of the hydrologic functions of...

  2. Classification tree models for predicting distributions of michigan stream fish from landscape variables

    USGS Publications Warehouse

    Steen, P.J.; Zorn, T.G.; Seelbach, P.W.; Schaeffer, J.S.

    2008-01-01

    Traditionally, fish habitat requirements have been described from local-scale environmental variables. However, recent studies have shown that studying landscape-scale processes improves our understanding of what drives species assemblages and distribution patterns across the landscape. Our goal was to learn more about constraints on the distribution of Michigan stream fish by examining landscape-scale habitat variables. We used classification trees and landscape-scale habitat variables to create and validate presence-absence models and relative abundance models for Michigan stream fishes. We developed 93 presence-absence models that on average were 72% correct in making predictions for an independent data set, and we developed 46 relative abundance models that were 76% correct in making predictions for independent data. The models were used to create statewide predictive distribution and abundance maps that have the potential to be used for a variety of conservation and scientific purposes. ?? Copyright by the American Fisheries Society 2008.

  3. Extreme learning machine and adaptive sparse representation for image classification.

    PubMed

    Cao, Jiuwen; Zhang, Kai; Luo, Minxia; Yin, Chun; Lai, Xiaoping

    2016-09-01

    Recent research has shown the speed advantage of extreme learning machine (ELM) and the accuracy advantage of sparse representation classification (SRC) in the area of image classification. Those two methods, however, have their respective drawbacks, e.g., in general, ELM is known to be less robust to noise while SRC is known to be time-consuming. Consequently, ELM and SRC complement each other in computational complexity and classification accuracy. In order to unify such mutual complementarity and thus further enhance the classification performance, we propose an efficient hybrid classifier to exploit the advantages of ELM and SRC in this paper. More precisely, the proposed classifier consists of two stages: first, an ELM network is trained by supervised learning. Second, a discriminative criterion about the reliability of the obtained ELM output is adopted to decide whether the query image can be correctly classified or not. If the output is reliable, the classification will be performed by ELM; otherwise the query image will be fed to SRC. Meanwhile, in the stage of SRC, a sub-dictionary that is adaptive to the query image instead of the entire dictionary is extracted via the ELM output. The computational burden of SRC thus can be reduced. Extensive experiments on handwritten digit classification, landmark recognition and face recognition demonstrate that the proposed hybrid classifier outperforms ELM and SRC in classification accuracy with outstanding computational efficiency. PMID:27389571

  4. Undercomplete learned dictionaries for land cover classification in multispectral imagery of Arctic landscapes using CoSA: clustering of sparse approximations

    NASA Astrophysics Data System (ADS)

    Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; Gangodagamage, Chandana

    2013-05-01

    Techniques for automated feature extraction, including neuroscience-inspired machine vision, are of great interest for landscape characterization and change detection in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methodologies to the environmental sciences, using state-of-theart adaptive signal processing, combined with compressive sensing and machine learning techniques. We use a Hebbian learning rule to build undercomplete spectral-textural dictionaries that are adapted to the data. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labels are automatically generated using our CoSA algorithm: unsupervised Clustering of Sparse Approximations. We demonstrate our method using multispectral Worldview-2 data from three Arctic study areas: Barrow, Alaska; the Selawik River, Alaska; and a watershed near the Mackenzie River delta in northwest Canada. Our goal is to develop a robust classification methodology that will allow for the automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and geomorphic characteristics. To interpret and assign land cover categories to the clusters we both evaluate the spectral properties of the clusters and compare the clusters to both field- and remote sensing-derived classifications of landscape attributes. Our work suggests that neuroscience-based models are a promising approach to practical pattern recognition problems in remote sensing.

  5. Classification of pasture habitats by Hungarian herders in a steppe landscape (Hungary)

    PubMed Central

    2012-01-01

    Background Landscape ethnoecology focuses on the ecological features of the landscape, how the landscape is perceived, and used by people who live in it. Though studying folk classifications of species has a long history, the comparative study of habitat classifications is just beginning. I studied the habitat classification of herders in a Hungarian steppe, and compared it to classifications of botanists and laymen. Methods For a quantitative analysis the picture sort method was used. Twenty-three pictures of 7-11 habitat types were sorted by 25 herders.’Density’ of pictures along the habitat gradient of the Hortobágy salt steppe was set as equal as possible, but pictures differed in their dominant species, wetness, season, etc. Before sorts, herders were asked to describe pictures to assure proper recognition of habitats. Results Herders classified the images into three main groups: (1) fertile habitats at the higher parts of the habitat gradient (partos, lit. on the shore); (2) saline habitats (szík, lit. salt or saline place), and (3) meadows and marshes (lapos, lit. flooded) at the lower end of the habitat gradient. Sharpness of delimitation changed along the gradient. Saline habitats were the most isolated from the rest. Botanists identified 6 groups. Laymen grouped habitats in a less coherent way. As opposed to my expectations, botanical classification was not more structured than that done by herders. I expected and found high correspondence between the classifications by herders, botanists and laymen. All tended to recognize similar main groups: wetlands, ”good grass” and dry/saline habitats. Two main factors could have been responsible for similar classifications: salient features correlated (e.g. salinity recognizable by herders and botanists but not by laymen correlated with the density of grasslands or height of vegetation recognizable also for laymen), or the same salient features were used as a basis for sorting (wetness, and abiotic stress

  6. How does the selection of landscape classification schemes affect the spatial pattern of natural landscapes? An assessment on a coastal wetland site in southern Italy.

    PubMed

    Tomaselli, V; Veronico, G; Sciandrello, S; Blonda, P

    2016-06-01

    It is widely known that thematic resolution affects spatial pattern and landscape metrics performances. In literature, data dealing with this issue usually refer to a specific class scheme with its thematic levels. In this paper, the effects of different land cover (LC) and habitat classification schemes on the spatial pattern of a coastal landscape were compared. One of the largest components of the Mediterranean wetland system was considered as the study site, and different schemes widely used in the EU were selected and harmonized with a common thematic resolution, suitable for habitat discrimination and monitoring. For each scheme, a thematic map was produced and, for each map, 28 landscape metrics were calculated. The landscape composition, already in terms of number of classes, class area, and number of patches, changes significantly among different classification schemes. Landscape complexity varies according to the class scheme considered and its underlying semantics, depending on how the different types aggregate or split when changing class scheme. Results confirm that the selection of a specific class scheme affects the spatial pattern of the derived landscapes and consequently the landscape metrics, especially at class level. Moreover, among the classification schemes considered, EUNIS seems to be the best choice for a comprehensive representation of both natural and anthropogenic classes. PMID:27194232

  7. Adaptive wavelets for visual object detection and classification

    NASA Astrophysics Data System (ADS)

    Aghdasi, Farzin

    1997-10-01

    We investigate the application of adaptive wavelets for the representation and classification of signals in digitized speech and medical images. A class of wavelet basis functions are used to extract features from the regions of interest. These features are then used in an artificial neural network to classify the region are containing the desired object or belonging to the background clutter. The dilation and shift parameters of the wavelet functions are not fixed. These parameters are included in the training scheme. In this way the wavelets are adaptive to the expected shape and size of the signals. The results indicate that adaptive wavelet functions may outperform the classical fixed wavelet analysis in detection of subtle objects.

  8. Implementation of Multispectral Image Classification on a Remote Adaptive Computer

    NASA Technical Reports Server (NTRS)

    Figueiredo, Marco A.; Gloster, Clay S.; Stephens, Mark; Graves, Corey A.; Nakkar, Mouna

    1999-01-01

    As the demand for higher performance computers for the processing of remote sensing science algorithms increases, the need to investigate new computing paradigms its justified. Field Programmable Gate Arrays enable the implementation of algorithms at the hardware gate level, leading to orders of m a,gnitude performance increase over microprocessor based systems. The automatic classification of spaceborne multispectral images is an example of a computation intensive application, that, can benefit from implementation on an FPGA - based custom computing machine (adaptive or reconfigurable computer). A probabilistic neural network is used here to classify pixels of of a multispectral LANDSAT-2 image. The implementation described utilizes Java client/server application programs to access the adaptive computer from a remote site. Results verify that a remote hardware version of the algorithm (implemented on an adaptive computer) is significantly faster than a local software version of the same algorithm implemented on a typical general - purpose computer).

  9. Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function

    PubMed Central

    Groenendyk, Derek G.; Ferré, Ty P.A.; Thorp, Kelly R.; Rice, Amy K.

    2015-01-01

    Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth’s surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing to bias and uncertainty in applications from slope stability to water resource management. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Hydrologic simulations based on common meteorological conditions were performed using HYDRUS-1D, spanning textures identified by the United States Department of Agriculture soil texture triangle. We consider these common conditions to be: drainage from saturation, infiltration onto a drained soil, and combined infiltration and drainage events. Using a k-means clustering algorithm, we created soil classifications based on the modeled hydrologic responses of these soils. The hydrologic-process-based classifications were compared to those based on soil texture and a single hydraulic property, Ks. Differences in classifications based on hydrologic response versus soil texture demonstrate that traditional soil texture classification is a poor predictor of hydrologic response. We then developed a QGIS plugin to construct soil maps combining a classification with georeferenced soil data from the Natural Resource Conservation Service. The spatial patterns of hydrologic response were more immediately informative, much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which hydrologic-process-based classifications can be made, along with the improved quantitative predictions of soil responses and visualization of landscape

  10. Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function.

    PubMed

    Groenendyk, Derek G; Ferré, Ty P A; Thorp, Kelly R; Rice, Amy K

    2015-01-01

    Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth's surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing to bias and uncertainty in applications from slope stability to water resource management. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Hydrologic simulations based on common meteorological conditions were performed using HYDRUS-1D, spanning textures identified by the United States Department of Agriculture soil texture triangle. We consider these common conditions to be: drainage from saturation, infiltration onto a drained soil, and combined infiltration and drainage events. Using a k-means clustering algorithm, we created soil classifications based on the modeled hydrologic responses of these soils. The hydrologic-process-based classifications were compared to those based on soil texture and a single hydraulic property, Ks. Differences in classifications based on hydrologic response versus soil texture demonstrate that traditional soil texture classification is a poor predictor of hydrologic response. We then developed a QGIS plugin to construct soil maps combining a classification with georeferenced soil data from the Natural Resource Conservation Service. The spatial patterns of hydrologic response were more immediately informative, much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which hydrologic-process-based classifications can be made, along with the improved quantitative predictions of soil responses and visualization of landscape

  11. Classification of transient signals using sparse representations over adaptive dictionaries

    NASA Astrophysics Data System (ADS)

    Moody, Daniela I.; Brumby, Steven P.; Myers, Kary L.; Pawley, Norma H.

    2011-06-01

    Automatic classification of broadband transient radio frequency (RF) signals is of particular interest in persistent surveillance applications. Because such transients are often acquired in noisy, cluttered environments, and are characterized by complex or unknown analytical models, feature extraction and classification can be difficult. We propose a fast, adaptive classification approach based on non-analytical dictionaries learned from data. Conventional representations using fixed (or analytical) orthogonal dictionaries, e.g., Short Time Fourier and Wavelet Transforms, can be suboptimal for classification of transients, as they provide a rigid tiling of the time-frequency space, and are not specifically designed for a particular signal class. They do not usually lead to sparse decompositions, and require separate feature selection algorithms, creating additional computational overhead. Pursuit-type decompositions over analytical, redundant dictionaries yield sparse representations by design, and work well for target signals in the same function class as the dictionary atoms. The pursuit search however has a high computational cost, and the method can perform poorly in the presence of realistic noise and clutter. Our approach builds on the image analysis work of Mairal et al. (2008) to learn a discriminative dictionary for RF transients directly from data without relying on analytical constraints or additional knowledge about the signal characteristics. We then use a pursuit search over this dictionary to generate sparse classification features. We demonstrate that our learned dictionary is robust to unexpected changes in background content and noise levels. The target classification decision is obtained in almost real-time via a parallel, vectorized implementation.

  12. An adaptive unsupervised hyperspectral classification method based on Gaussian distribution

    NASA Astrophysics Data System (ADS)

    Yue, Jiang; Wu, Jing-wei; Zhang, Yi; Bai, Lian-fa

    2014-11-01

    In order to achieve adaptive unsupervised clustering in the high precision, a method using Gaussian distribution to fit the similarity of the inter-class and the noise distribution is proposed in this paper, and then the automatic segmentation threshold is determined by the fitting result. First, according with the similarity measure of the spectral curve, this method assumes that the target and the background both in Gaussian distribution, the distribution characteristics is obtained through fitting the similarity measure of minimum related windows and center pixels with Gaussian function, and then the adaptive threshold is achieved. Second, make use of the pixel minimum related windows to merge adjacent similar pixels into a picture-block, then the dimensionality reduction is completed and the non-supervised classification is realized. AVIRIS data and a set of hyperspectral data we caught are used to evaluate the performance of the proposed method. Experimental results show that the proposed algorithm not only realizes the adaptive but also outperforms K-MEANS and ISODATA on the classification accuracy, edge recognition and robustness.

  13. Adaptive Landscape by Environment Interactions Dictate Evolutionary Dynamics in Models of Drug Resistance.

    PubMed

    Ogbunugafor, C Brandon; Wylie, C Scott; Diakite, Ibrahim; Weinreich, Daniel M; Hartl, Daniel L

    2016-01-01

    The adaptive landscape analogy has found practical use in recent years, as many have explored how their understanding can inform therapeutic strategies that subvert the evolution of drug resistance. A major barrier to applications of these concepts is a lack of detail concerning how the environment affects adaptive landscape topography, and consequently, the outcome of drug treatment. Here we combine empirical data, evolutionary theory, and computer simulations towards dissecting adaptive landscape by environment interactions for the evolution of drug resistance in two dimensions-drug concentration and drug type. We do so by studying the resistance mediated by Plasmodium falciparum dihydrofolate reductase (DHFR) to two related inhibitors-pyrimethamine and cycloguanil-across a breadth of drug concentrations. We first examine whether the adaptive landscapes for the two drugs are consistent with common definitions of cross-resistance. We then reconstruct all accessible pathways across the landscape, observing how their structure changes with drug environment. We offer a mechanism for non-linearity in the topography of accessible pathways by calculating of the interaction between mutation effects and drug environment, which reveals rampant patterns of epistasis. We then simulate evolution in several different drug environments to observe how these individual mutation effects (and patterns of epistasis) influence paths taken at evolutionary "forks in the road" that dictate adaptive dynamics in silico. In doing so, we reveal how classic metrics like the IC50 and minimal inhibitory concentration (MIC) are dubious proxies for understanding how evolution will occur across drug environments. We also consider how the findings reveal ambiguities in the cross-resistance concept, as subtle differences in adaptive landscape topography between otherwise equivalent drugs can drive drastically different evolutionary outcomes. Summarizing, we discuss the results with regards to their

  14. Adaptive Landscape by Environment Interactions Dictate Evolutionary Dynamics in Models of Drug Resistance

    PubMed Central

    Ogbunugafor, C. Brandon; Wylie, C. Scott; Diakite, Ibrahim; Weinreich, Daniel M.; Hartl, Daniel L.

    2016-01-01

    The adaptive landscape analogy has found practical use in recent years, as many have explored how their understanding can inform therapeutic strategies that subvert the evolution of drug resistance. A major barrier to applications of these concepts is a lack of detail concerning how the environment affects adaptive landscape topography, and consequently, the outcome of drug treatment. Here we combine empirical data, evolutionary theory, and computer simulations towards dissecting adaptive landscape by environment interactions for the evolution of drug resistance in two dimensions—drug concentration and drug type. We do so by studying the resistance mediated by Plasmodium falciparum dihydrofolate reductase (DHFR) to two related inhibitors—pyrimethamine and cycloguanil—across a breadth of drug concentrations. We first examine whether the adaptive landscapes for the two drugs are consistent with common definitions of cross-resistance. We then reconstruct all accessible pathways across the landscape, observing how their structure changes with drug environment. We offer a mechanism for non-linearity in the topography of accessible pathways by calculating of the interaction between mutation effects and drug environment, which reveals rampant patterns of epistasis. We then simulate evolution in several different drug environments to observe how these individual mutation effects (and patterns of epistasis) influence paths taken at evolutionary “forks in the road” that dictate adaptive dynamics in silico. In doing so, we reveal how classic metrics like the IC50 and minimal inhibitory concentration (MIC) are dubious proxies for understanding how evolution will occur across drug environments. We also consider how the findings reveal ambiguities in the cross-resistance concept, as subtle differences in adaptive landscape topography between otherwise equivalent drugs can drive drastically different evolutionary outcomes. Summarizing, we discuss the results with

  15. Polytomous Adaptive Classification Testing: Effects of Item Pool Size, Test Termination Criterion, and Number of Cutscores

    ERIC Educational Resources Information Center

    Gnambs, Timo; Batinic, Bernad

    2011-01-01

    Computer-adaptive classification tests focus on classifying respondents in different proficiency groups (e.g., for pass/fail decisions). To date, adaptive classification testing has been dominated by research on dichotomous response formats and classifications in two groups. This article extends this line of research to polytomous classification…

  16. Landscape Risk Factors for Lyme Disease in the Eastern Broadleaf Forest Province of the Hudson River Valley and the Effect of Explanatory Data Classification Resolution

    EPA Science Inventory

    This study assessed how landcover classification affects associations between landscape characteristics and Lyme disease rate. Landscape variables were derived from the National Land Cover Database (NLCD), including native classes (e.g., deciduous forest, developed low intensity)...

  17. Context dependence in complex adaptive landscapes: frequency and trait-dependent selection surfaces within an adaptive radiation of Caribbean pupfishes.

    PubMed

    Martin, Christopher H

    2016-06-01

    The adaptive landscape provides the foundational bridge between micro- and macroevolution. One well-known caveat to this perspective is that fitness surfaces depend on ecological context, including competitor frequency, traits measured, and resource abundance. However, this view is based largely on intraspecific studies. It is still unknown how context-dependence affects the larger features of peaks and valleys on the landscape which ultimately drive speciation and adaptive radiation. Here, I explore this question using one of the most complex fitness landscapes measured in the wild in a sympatric pupfish radiation endemic to San Salvador Island, Bahamas by tracking survival and growth of laboratory-reared F2 hybrids. I present new analyses of the effects of competitor frequency, dietary isotopes, and trait subsets on this fitness landscape. Contrary to expectations, decreasing competitor frequency increased survival only among very common phenotypes, whereas less common phenotypes rarely survived despite few competitors, suggesting that performance, not competitor frequency, shapes large-scale features of the fitness landscape. Dietary isotopes were weakly correlated with phenotype and growth, but did not explain additional survival variation. Nonlinear fitness surfaces varied substantially among trait subsets, revealing one-, two-, and three-peak landscapes, demonstrating the complexity of selection in the wild, even among similar functional traits. PMID:27130447

  18. Achieving effective landscape conservation: evolving demands adaptive metrics

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Rapid changes in demographics and on- and off-farm land use limit the impacts of U.S. conservation programs and present particular challenges to future conservation efforts. The fragmentation of landscape through urban, suburban, and peri-urban development, coincident with demographic shifts, has s...

  19. Classification in medical images using adaptive metric k-NN

    NASA Astrophysics Data System (ADS)

    Chen, C.; Chernoff, K.; Karemore, G.; Lo, P.; Nielsen, M.; Lauze, F.

    2010-03-01

    The performance of the k-nearest neighborhoods (k-NN) classifier is highly dependent on the distance metric used to identify the k nearest neighbors of the query points. The standard Euclidean distance is commonly used in practice. This paper investigates the performance of k-NN classifier with respect to different adaptive metrics in the context of medical imaging. We propose using adaptive metrics such that the structure of the data is better described, introducing some unsupervised learning knowledge in k-NN. We investigated four different metrics are estimated: a theoretical metric based on the assumption that images are drawn from Brownian Image Model (BIM), the normalized metric based on variance of the data, the empirical metric is based on the empirical covariance matrix of the unlabeled data, and an optimized metric obtained by minimizing the classification error. The spectral structure of the empirical covariance also leads to Principal Component Analysis (PCA) performed on it which results the subspace metrics. The metrics are evaluated on two data sets: lateral X-rays of the lumbar aortic/spine region, where we use k-NN for performing abdominal aorta calcification detection; and mammograms, where we use k-NN for breast cancer risk assessment. The results show that appropriate choice of metric can improve classification.

  20. A New Real-coded Genetic Algorithm with an Adaptive Mating Selection for UV-landscapes

    NASA Astrophysics Data System (ADS)

    Oshima, Dan; Miyamae, Atsushi; Nagata, Yuichi; Kobayashi, Shigenobu; Ono, Isao; Sakuma, Jun

    The purpose of this paper is to propose a new real-coded genetic algorithm (RCGA) named Networked Genetic Algorithm (NGA) that intends to find multiple optima simultaneously in deceptive globally multimodal landscapes. Most current techniques such as niching for finding multiple optima take into account big valley landscapes or non-deceptive globally multimodal landscapes but not deceptive ones called UV-landscapes. Adaptive Neighboring Search (ANS) is a promising approach for finding multiple optima in UV-landscapes. ANS utilizes a restricted mating scheme with a crossover-like mutation in order to find optima in deceptive globally multimodal landscapes. However, ANS has a fundamental problem that it does not find all the optima simultaneously in many cases. NGA overcomes the problem by an adaptive parent-selection scheme and an improved crossover-like mutation. We show the effectiveness of NGA over ANS in terms of the number of detected optima in a single run on Fletcher and Powell functions as benchmark problems that are known to have multiple optima, ill-scaledness, and UV-landscapes.

  1. Classification of vegetation in an open landscape using full-waveform airborne laser scanner data

    NASA Astrophysics Data System (ADS)

    Alexander, Cici; Deák, Balázs; Kania, Adam; Mücke, Werner; Heilmeier, Hermann

    2015-09-01

    Airborne laser scanning (ALS) is increasingly being used for the mapping of vegetation, although the focus so far has been on woody vegetation, and ALS data have only rarely been used for the classification of grassland vegetation. In this study, we classified the vegetation of an open alkali landscape, characterized by two Natura 2000 habitat types: Pannonic salt steppes and salt marshes and Pannonic loess steppic grasslands. We generated 18 variables from an ALS dataset collected in the growing (leaf-on) season. Elevation is a key factor determining the patterns of vegetation types in the landscape, and hence 3 additional variables were based on a digital terrain model (DTM) generated from an ALS dataset collected in the dormant (leaf-off) season. We classified the vegetation into 24 classes based on these 21 variables, at a pixel size of 1 m. Two groups of variables with and without the DTM-based variables were used in a Random Forest classifier, to estimate the influence of elevation, on the accuracy of the classification. The resulting classes at Level 4, based on associations, were aggregated at three levels - Level 3 (11 classes), Level 2 (8 classes) and Level 1 (5 classes) - based on species pool, site conditions and structure, and the accuracies were assessed. The classes were also aggregated based on Natura 2000 habitat types to assess the accuracy of the classification, and its usefulness for the monitoring of habitat quality. The vegetation could be classified into dry grasslands, wetlands, weeds, woody species and man-made features, at Level 1, with an accuracy of 0.79 (Cohen's kappa coefficient, κ). The accuracies at Levels 2-4 and the classification based on the Natura 2000 habitat types were κ: 0.76, 0.61, 0.51 and 0.69, respectively. Levels 1 and 2 provide suitable information for nature conservationists and land managers, while Levels 3 and 4 are especially useful for ecologists, geologists and soil scientists as they provide high resolution

  2. Convergent Evolution During Local Adaptation to Patchy Landscapes.

    PubMed

    Ralph, Peter L; Coop, Graham

    2015-11-01

    Species often encounter, and adapt to, many patches of similar environmental conditions across their range. Such adaptation can occur through convergent evolution if different alleles arise in different patches, or through the spread of shared alleles by migration acting to synchronize adaptation across the species. The tension between the two reflects the constraint imposed on evolution by the underlying genetic architecture versus how effectively selection and geographic isolation act to inhibit the geographic spread of locally adapted alleles. This paper studies the balance between these two routes to adaptation in a model of continuous environments with patchy selection pressures. We address the following questions: How long does it take for a novel allele to appear in a patch where it is locally adapted through mutation? Or, through migration from another, already adapted patch? Which is more likely to occur, as a function of distance between the patches? What population genetic signal is left by the spread of migrant alleles? To answer these questions we examine the family structure underlying migration-selection equilibrium surrounding an already adapted patch, treating those rare families that reach new patches as spatial branching processes. A main result is that patches further apart than a critical distance will likely evolve independent locally adapted alleles; this distance is proportional to the spatial scale of selection ([Formula: see text], where σ is the dispersal distance and sm is the selective disadvantage of these alleles between patches), and depends linearly on log(sm/μ), where μ is the mutation rate. This provides a way to understand the role of geographic separation between patches in promoting convergent adaptation and the genomic signals it leaves behind. We illustrate these ideas using the convergent evolution of cryptic coloration in the rock pocket mouse, Chaetodipus intermedius, as an empirical example. PMID:26571125

  3. Convergent Evolution During Local Adaptation to Patchy Landscapes

    PubMed Central

    2015-01-01

    Species often encounter, and adapt to, many patches of similar environmental conditions across their range. Such adaptation can occur through convergent evolution if different alleles arise in different patches, or through the spread of shared alleles by migration acting to synchronize adaptation across the species. The tension between the two reflects the constraint imposed on evolution by the underlying genetic architecture versus how effectively selection and geographic isolation act to inhibit the geographic spread of locally adapted alleles. This paper studies the balance between these two routes to adaptation in a model of continuous environments with patchy selection pressures. We address the following questions: How long does it take for a novel allele to appear in a patch where it is locally adapted through mutation? Or, through migration from another, already adapted patch? Which is more likely to occur, as a function of distance between the patches? What population genetic signal is left by the spread of migrant alleles? To answer these questions we examine the family structure underlying migration–selection equilibrium surrounding an already adapted patch, treating those rare families that reach new patches as spatial branching processes. A main result is that patches further apart than a critical distance will likely evolve independent locally adapted alleles; this distance is proportional to the spatial scale of selection (σ/sm, where σ is the dispersal distance and s m is the selective disadvantage of these alleles between patches), and depends linearly on log(s m/μ), where μ is the mutation rate. This provides a way to understand the role of geographic separation between patches in promoting convergent adaptation and the genomic signals it leaves behind. We illustrate these ideas using the convergent evolution of cryptic coloration in the rock pocket mouse, Chaetodipus intermedius, as an empirical example. PMID:26571125

  4. Towards catchment classification by means of environmental tracers and landscape analysis: The Attert catchment in Luxembourg

    NASA Astrophysics Data System (ADS)

    Wrede, S.; Pfister, L.; Krein, A.; Fenicia, F.; Bogaard, T. A.; Uhlenbrook, S.; Savenije, H. H. G.

    2010-05-01

    Until recently hydrological research has been mainly focusing on detailed investigations at small spatial scales, resulting in a set of more and more complex physically-based and spatially distributed hydrologic models. While much of the research effort today is targeted to advance these hydrological model predictions at the catchment scale, shortcomings remain to adequately capture the dominating hydrological processes across a range of scales that translate into the rainfall-runoff response of a catchment. Thus, studies addressing the fundamental relations between catchment structure and function are urgently needed, as they help catchment classification by advancing our knowledge about suitable catchment signatures and controls at different spatial and temporal scales. In our study in the nested Attert catchment in the Grand-Duchy of Luxembourg (Europe) we investigate how environmental tracer dynamics, hydrological response behavior and landscape analysis can help to identify such dominating controls on runoff generation across multiple scales. Techniques to characterize landscape structure and hydrological processes are complementary applied to identify scales in which shifts of the dominant hydrological processes occur. These dominating controls in turn provide a more integrated perspective of catchment structure and functioning that can be used for catchment classification based on functional response.

  5. Structured estimation - Sample size reduction for adaptive pattern classification

    NASA Technical Reports Server (NTRS)

    Morgera, S.; Cooper, D. B.

    1977-01-01

    The Gaussian two-category classification problem with known category mean value vectors and identical but unknown category covariance matrices is considered. The weight vector depends on the unknown common covariance matrix, so the procedure is to estimate the covariance matrix in order to obtain an estimate of the optimum weight vector. The measure of performance for the adapted classifier is the output signal-to-interference noise ratio (SIR). A simple approximation for the expected SIR is gained by using the general sample covariance matrix estimator; this performance is both signal and true covariance matrix independent. An approximation is also found for the expected SIR obtained by using a Toeplitz form covariance matrix estimator; this performance is found to be dependent on both the signal and the true covariance matrix.

  6. Cherokee Adaptation to the Landscape of the West and Overcoming the Loss of Culturally Significant Plants

    ERIC Educational Resources Information Center

    Vick, R. Alfred

    2011-01-01

    Plant species utilized by Cherokees have been documented by several authors. However, many of the traditional uses of plants were lost or forgotten in the generations following the Trail of Tears. The pressures of overcoming the physical and psychological impact of the removal, adapting to a new landscape, rebuilding a government, rebuilding…

  7. Natural selection fails to optimize mutation rates for long-term adaptation on rugged fitness landscapes.

    PubMed

    Clune, Jeff; Misevic, Dusan; Ofria, Charles; Lenski, Richard E; Elena, Santiago F; Sanjuán, Rafael

    2008-01-01

    The rate of mutation is central to evolution. Mutations are required for adaptation, yet most mutations with phenotypic effects are deleterious. As a consequence, the mutation rate that maximizes adaptation will be some intermediate value. Here, we used digital organisms to investigate the ability of natural selection to adjust and optimize mutation rates. We assessed the optimal mutation rate by empirically determining what mutation rate produced the highest rate of adaptation. Then, we allowed mutation rates to evolve, and we evaluated the proximity to the optimum. Although we chose conditions favorable for mutation rate optimization, the evolved rates were invariably far below the optimum across a wide range of experimental parameter settings. We hypothesized that the reason that mutation rates evolved to be suboptimal was the ruggedness of fitness landscapes. To test this hypothesis, we created a simplified landscape without any fitness valleys and found that, in such conditions, populations evolved near-optimal mutation rates. In contrast, when fitness valleys were added to this simple landscape, the ability of evolving populations to find the optimal mutation rate was lost. We conclude that rugged fitness landscapes can prevent the evolution of mutation rates that are optimal for long-term adaptation. This finding has important implications for applied evolutionary research in both biological and computational realms. PMID:18818724

  8. Natural Selection Fails to Optimize Mutation Rates for Long-Term Adaptation on Rugged Fitness Landscapes

    PubMed Central

    Clune, Jeff; Misevic, Dusan; Ofria, Charles; Lenski, Richard E.; Elena, Santiago F.; Sanjuán, Rafael

    2008-01-01

    The rate of mutation is central to evolution. Mutations are required for adaptation, yet most mutations with phenotypic effects are deleterious. As a consequence, the mutation rate that maximizes adaptation will be some intermediate value. Here, we used digital organisms to investigate the ability of natural selection to adjust and optimize mutation rates. We assessed the optimal mutation rate by empirically determining what mutation rate produced the highest rate of adaptation. Then, we allowed mutation rates to evolve, and we evaluated the proximity to the optimum. Although we chose conditions favorable for mutation rate optimization, the evolved rates were invariably far below the optimum across a wide range of experimental parameter settings. We hypothesized that the reason that mutation rates evolved to be suboptimal was the ruggedness of fitness landscapes. To test this hypothesis, we created a simplified landscape without any fitness valleys and found that, in such conditions, populations evolved near-optimal mutation rates. In contrast, when fitness valleys were added to this simple landscape, the ability of evolving populations to find the optimal mutation rate was lost. We conclude that rugged fitness landscapes can prevent the evolution of mutation rates that are optimal for long-term adaptation. This finding has important implications for applied evolutionary research in both biological and computational realms. PMID:18818724

  9. Spatial variation, mapping, and classification of moss families in semi-arid landscapes in NW Turkey.

    PubMed

    Abay, Gökhan; Gül, Ebru; Günlü, Alkan; Erşahin, Sabit; Ursavaş, Serhat

    2015-03-01

    Geostatistics and remote sensing techniques are frequently used in analyzing the spatial variability of terrestrial ecosystems. We analyzed spatial variation of moss families by geostatistics and Landsat imagery in a typical semi-arid landscape in North Central Anatolia, Turkey. We sampled 49 sites, chosen based on elevation, slope steepness, and slope aspect. Moss families were determined in a 10-m(2) representative area at each sampling site. The samples were transported to a laboratory and identified for moss families. In total, 10 families were found. Semivariogram analysis was performed to analyze the spatial structure of these families. The semivariogram analysis showed that the moss families were spatially dependent within 117 m in the study area. Thirteen thematic classes were categorized by Landsat Thematic Mapper (TM) image in the study area. The classification resulted in an overall kappa statistic of 0.8535, producer accuracy of 74.29, and user accuracy of 86.67. The family with the lowest classification accuracy was Orthotrichaceae (kappa of 0.6379, producer accuracy of 64.52, and user accuracy of 66.67). The moss families and the other classes were identified with a 0.78 kappa statistic value and an 80.74 % accuracy level by using the Landsat TM. The classification showed that Brachytheciaceae, Pottiaceae, Bryaceae, and Grimmiaceae were the most abundant moss families in this semi-arid environment. PMID:25647788

  10. Adaptation in tunably rugged fitness landscapes: the rough Mount Fuji model.

    PubMed

    Neidhart, Johannes; Szendro, Ivan G; Krug, Joachim

    2014-10-01

    Much of the current theory of adaptation is based on Gillespie's mutational landscape model (MLM), which assumes that the fitness values of genotypes linked by single mutational steps are independent random variables. On the other hand, a growing body of empirical evidence shows that real fitness landscapes, while possessing a considerable amount of ruggedness, are smoother than predicted by the MLM. In the present article we propose and analyze a simple fitness landscape model with tunable ruggedness based on the rough Mount Fuji (RMF) model originally introduced by Aita et al. in the context of protein evolution. We provide a comprehensive collection of results pertaining to the topographical structure of RMF landscapes, including explicit formulas for the expected number of local fitness maxima, the location of the global peak, and the fitness correlation function. The statistics of single and multiple adaptive steps on the RMF landscape are explored mainly through simulations, and the results are compared to the known behavior in the MLM model. Finally, we show that the RMF model can explain the large number of second-step mutations observed on a highly fit first-step background in a recent evolution experiment with a microvirid bacteriophage. PMID:25123507

  11. Adaptation in Tunably Rugged Fitness Landscapes: The Rough Mount Fuji Model

    PubMed Central

    Neidhart, Johannes; Szendro, Ivan G.; Krug, Joachim

    2014-01-01

    Much of the current theory of adaptation is based on Gillespie’s mutational landscape model (MLM), which assumes that the fitness values of genotypes linked by single mutational steps are independent random variables. On the other hand, a growing body of empirical evidence shows that real fitness landscapes, while possessing a considerable amount of ruggedness, are smoother than predicted by the MLM. In the present article we propose and analyze a simple fitness landscape model with tunable ruggedness based on the rough Mount Fuji (RMF) model originally introduced by Aita et al. in the context of protein evolution. We provide a comprehensive collection of results pertaining to the topographical structure of RMF landscapes, including explicit formulas for the expected number of local fitness maxima, the location of the global peak, and the fitness correlation function. The statistics of single and multiple adaptive steps on the RMF landscape are explored mainly through simulations, and the results are compared to the known behavior in the MLM model. Finally, we show that the RMF model can explain the large number of second-step mutations observed on a highly fit first-step background in a recent evolution experiment with a microvirid bacteriophage. PMID:25123507

  12. Landscape Hazards in Yukon Communities: Geological Mapping for Climate Change Adaptation Planning

    NASA Astrophysics Data System (ADS)

    Kennedy, K.; Kinnear, L.

    2010-12-01

    Climate change is considered to be a significant challenge for northern communities where the effects of increased temperature and climate variability are beginning to affect infrastructure and livelihoods (Arctic Climate Impact Assessment, 2004). Planning for and adapting to ongoing and future changes in climate will require the identification and characterization of social, economic, cultural, political and biophysical vulnerabilities. This pilot project addresses physical landscape vulnerabilities in two communities in the Yukon Territory through community-scale landscape hazard mapping and focused investigations of community permafrost conditions. Landscape hazards are identified by combining pre-existing data from public utilities and private-sector consultants with new geophysical techniques (ground penetrating radar and electrical resistivity), shallow drilling, surficial geological mapping, and permafrost characterization. Existing landscape vulnerabilities are evaluated based on their potential for hazard (low, medium or high) under current climate conditions, as well as under future climate scenarios. Detailed hazard maps and landscape characterizations for both communities will contribute to overall adaptation plans and allow for informed development, planning and mitigation of potentially threatening hazards in and around the communities.

  13. Adaptation and Coevolution on an Emergent Global Competitive Landscape

    NASA Astrophysics Data System (ADS)

    Fellman, Philip Vos; Post, Jonathan Vos; Wright, Roxana; Dasari, Usha

    Notions of Darwinian selection have been implicit in economic theory for at least sixty years. Richard Nelson and Sidney Winter have argued that while evolutionary thinking was prevalent in prewar economics, the postwar Neoclassical school became almost entirely preoccupied with equilibrium conditions and their mathematical conditions. One of the problems with the economic interpretation of firm selection through competition has been a weak grasp on an incomplete scientific paradigm. As I.F. Price notes: "The biological metaphor has long lurked in the background of management theory largely because the message of 'survival of the fittest' (usually wrongly attributed to Charles Darwin rather than Herbert Spencer) provides a seemingly natural model for market competition (e.g. Alchian 1950, Merrell 1984, Henderson 1989, Moore 1993), without seriously challenging the underlying paradigms of what an organisation is." [1] In this paper we examine the application of dynamic fitness landscape models to economic theory, particularly the theory of technology substitution, drawing on recent work by Kauffman, Arthur, McKelvey, Nelson and Winter, and Windrum and Birchenhall. In particular we use Professor Post's early work with John Holland on the genetic algorithm to explain some of the key differences between static and dynamic approaches to economic modeling.

  14. Free energy landscapes of short peptide chains using adaptively biased molecular dynamics

    NASA Astrophysics Data System (ADS)

    Karpusenka, Vadzim; Babin, Volodymyr; Roland, Christopher; Sagui, Celeste

    2009-03-01

    We present the results of a computational study of the free energy landscapes of short polypeptide chains, as a function of several reaction coordinates meant to distinguish between several known types of helices. The free energy landscapes were calculated using the recently developed adaptively biased molecular dynamics method followed up with equilibrium ``umbrella correction'' runs. Specific polypeptides investigated include small chains of pure and mixed alanine, glutamate, leucine, lysine and methionine (all amino acids with strong helix-forming propensities), as well as glycine, proline(having a low helix forming propensities), tyrosine, serine and arginine. Our results are consistent with the existing experimental and other theoretical evidence.

  15. Neonatal Brain Tissue Classification with Morphological Adaptation and Unified Segmentation

    PubMed Central

    Beare, Richard J.; Chen, Jian; Kelly, Claire E.; Alexopoulos, Dimitrios; Smyser, Christopher D.; Rogers, Cynthia E.; Loh, Wai Y.; Matthews, Lillian G.; Cheong, Jeanie L. Y.; Spittle, Alicia J.; Anderson, Peter J.; Doyle, Lex W.; Inder, Terrie E.; Seal, Marc L.; Thompson, Deanne K.

    2016-01-01

    Measuring the distribution of brain tissue types (tissue classification) in neonates is necessary for studying typical and atypical brain development, such as that associated with preterm birth, and may provide biomarkers for neurodevelopmental outcomes. Compared with magnetic resonance images of adults, neonatal images present specific challenges that require the development of specialized, population-specific methods. This paper introduces MANTiS (Morphologically Adaptive Neonatal Tissue Segmentation), which extends the unified segmentation approach to tissue classification implemented in Statistical Parametric Mapping (SPM) software to neonates. MANTiS utilizes a combination of unified segmentation, template adaptation via morphological segmentation tools and topological filtering, to segment the neonatal brain into eight tissue classes: cortical gray matter, white matter, deep nuclear gray matter, cerebellum, brainstem, cerebrospinal fluid (CSF), hippocampus and amygdala. We evaluated the performance of MANTiS using two independent datasets. The first dataset, provided by the NeoBrainS12 challenge, consisted of coronal T2-weighted images of preterm infants (born ≤30 weeks' gestation) acquired at 30 weeks' corrected gestational age (n = 5), coronal T2-weighted images of preterm infants acquired at 40 weeks' corrected gestational age (n = 5) and axial T2-weighted images of preterm infants acquired at 40 weeks' corrected gestational age (n = 5). The second dataset, provided by the Washington University NeuroDevelopmental Research (WUNDeR) group, consisted of T2-weighted images of preterm infants (born <30 weeks' gestation) acquired shortly after birth (n = 12), preterm infants acquired at term-equivalent age (n = 12), and healthy term-born infants (born ≥38 weeks' gestation) acquired within the first 9 days of life (n = 12). For the NeoBrainS12 dataset, mean Dice scores comparing MANTiS with manual segmentations were all above 0.7, except for the cortical gray

  16. [Ecological classification system of forest landscape in eastern mountainous region of Liaoning Province].

    PubMed

    Tang, Li-na; Wang, Qing-li; Dai, Li-min; Shao, Guo-fan

    2008-01-01

    Based on Digital Elevation Models (DEM) and satellite SPOT-5 data, and by using the spatial analysis function in Geographic Information System, a hierachical Ecological Classification System of forest landscape was developed for the eastern mountainous region of Liaoning Province, and the two lowest layers in the hierachical framework, Ecological Land Types (ELTs) and Ecological Land Type Phases (ELTPs), were mapped. The results indicated that there were 5 ELTs and 34 ELTPs. The boundaries of ELTs, which presented the potential vegetation distribution and potential forestry ecosystem productivity, were determined by environmental conditions quantified by DEM. ELTPs were classified by overlaying ELTs with forest vegetation data layers which were obtained from remotely sensed data, forest inventory data, and ground data. The ELTPs represented the divisions of land in terms of both natural and human-induced forest conditions, and therefore, were reliable units for forest inventories and management. ELTPs could function as conventional forest inventory sub-compartments. By this means, forestry departments could adjust forest management planning and forest management measures from the viewpoint of forest landscape scale to realize forest ecosystem management. PMID:18419066

  17. Perspectives on the use of landscape genetics to detect genetic adaptive variation in the field.

    PubMed

    Manel, Stéphanie; Joost, Stéphane; Epperson, Bryan K; Holderegger, Rolf; Storfer, Andrew; Rosenberg, Michael S; Scribner, Kim T; Bonin, Aurélie; Fortin, Marie-Josée

    2010-09-01

    Understanding the genetic basis of species adaptation in the context of global change poses one of the greatest challenges of this century. Although we have begun to understand the molecular basis of adaptation in those species for which whole genome sequences are available, the molecular basis of adaptation is still poorly understood for most non-model species. In this paper, we outline major challenges and future research directions for correlating environmental factors with molecular markers to identify adaptive genetic variation, and point to research gaps in the application of landscape genetics to real-world problems arising from global change, such as the ability of organisms to adapt over rapid time scales. High throughput sequencing generates vast quantities of molecular data to address the challenge of studying adaptive genetic variation in non-model species. Here, we suggest that improvements in the sampling design should consider spatial dependence among sampled individuals. Then, we describe available statistical approaches for integrating spatial dependence into landscape analyses of adaptive genetic variation. PMID:20723056

  18. Adaptive evolutionary artificial neural networks for pattern classification.

    PubMed

    Oong, Tatt Hee; Isa, Nor Ashidi Mat

    2011-11-01

    This paper presents a new evolutionary approach called the hybrid evolutionary artificial neural network (HEANN) for simultaneously evolving an artificial neural networks (ANNs) topology and weights. Evolutionary algorithms (EAs) with strong global search capabilities are likely to provide the most promising region. However, they are less efficient in fine-tuning the search space locally. HEANN emphasizes the balancing of the global search and local search for the evolutionary process by adapting the mutation probability and the step size of the weight perturbation. This is distinguishable from most previous studies that incorporate EA to search for network topology and gradient learning for weight updating. Four benchmark functions were used to test the evolutionary framework of HEANN. In addition, HEANN was tested on seven classification benchmark problems from the UCI machine learning repository. Experimental results show the superior performance of HEANN in fine-tuning the network complexity within a small number of generations while preserving the generalization capability compared with other algorithms. PMID:21968733

  19. Adaptive social recommendation in a multiple category landscape

    NASA Astrophysics Data System (ADS)

    Chen, Duanbing; Zeng, An; Cimini, Giulio; Zhang, Yi-Cheng

    2013-02-01

    People in the Internet era have to cope with the information overload, striving to find what they are interested in, and usually face this situation by following a limited number of sources or friends that best match their interests. A recent line of research, namely adaptive social recommendation, has therefore emerged to optimize the information propagation in social networks and provide users with personalized recommendations. Validation of these methods by agent-based simulations often assumes that the tastes of users can be represented by binary vectors, with entries denoting users' preferences. In this work we introduce a more realistic assumption that users' tastes are modeled by multiple vectors. We show that within this framework the social recommendation process has a poor outcome. Accordingly, we design novel measures of users' taste similarity that can substantially improve the precision of the recommender system. Finally, we discuss the issue of enhancing the recommendations' diversity while preserving their accuracy.

  20. Social and Natural Sciences Differ in Their Research Strategies, Adapted to Work for Different Knowledge Landscapes

    PubMed Central

    Jaffe, Klaus

    2014-01-01

    Do different fields of knowledge require different research strategies? A numerical model exploring different virtual knowledge landscapes, revealed two diverging optimal search strategies. Trend following is maximized when the popularity of new discoveries determine the number of individuals researching it. This strategy works best when many researchers explore few large areas of knowledge. In contrast, individuals or small groups of researchers are better in discovering small bits of information in dispersed knowledge landscapes. Bibliometric data of scientific publications showed a continuous bipolar distribution of these strategies, ranging from natural sciences, with highly cited publications in journals containing a large number of articles, to the social sciences, with rarely cited publications in many journals containing a small number of articles. The natural sciences seem to adapt their research strategies to landscapes with large concentrated knowledge clusters, whereas social sciences seem to have adapted to search in landscapes with many small isolated knowledge clusters. Similar bipolar distributions were obtained when comparing levels of insularity estimated by indicators of international collaboration and levels of country-self citations: researchers in academic areas with many journals such as social sciences, arts and humanities, were the most isolated, and that was true in different regions of the world. The work shows that quantitative measures estimating differences between academic disciplines improve our understanding of different research strategies, eventually helping interdisciplinary research and may be also help improve science policies worldwide. PMID:25426723

  1. Adaptive Capacity in Tanzanian Maasailand: Changing strategies to cope with drought in fragmented landscapes

    PubMed Central

    Riosmena, Fernando

    2014-01-01

    This study examines the ways in which the adaptive capacity of households to climatic events varies within communities and is mediated by institutional and landscape changes. We present qualitative and quantitative data from two Maasai communities differentially exposed to the devastating drought of 2009 in Northern Tanzania. We show how rangeland fragmentation combined with the decoupling of institutions and landscapes are affecting pastoralists ability to cope with drought. Our data highlight that mobility remains a key coping mechanism for pastoralists to avoid cattle loss during a drought. However, mobility is now happening in new ways that require not only large amounts of money but new forms of knowledge and connections outside of customary reciprocity networks. Those least affected by the drought, in terms of cattle lost, were those with large herds who were able to sell some of their cattle and to pay for private access to pastures outside of Maasai areas. Drawing on an entitlements framework, we argue that the new coping mechanisms are not available to all, could be making some households more vulnerable to climate change, and reduce the adaptive capacity of the overall system as reciprocity networks and customary institutions are weakened. As such, we posit that adaptive capacity to climate change is uneven within and across communities, is scale-dependent, and is intimately tied to institutional and landscape changes. PMID:25400331

  2. Adaptive Capacity in Tanzanian Maasailand: Changing strategies to cope with drought in fragmented landscapes.

    PubMed

    Goldman, Mara J; Riosmena, Fernando

    2013-06-01

    This study examines the ways in which the adaptive capacity of households to climatic events varies within communities and is mediated by institutional and landscape changes. We present qualitative and quantitative data from two Maasai communities differentially exposed to the devastating drought of 2009 in Northern Tanzania. We show how rangeland fragmentation combined with the decoupling of institutions and landscapes are affecting pastoralists ability to cope with drought. Our data highlight that mobility remains a key coping mechanism for pastoralists to avoid cattle loss during a drought. However, mobility is now happening in new ways that require not only large amounts of money but new forms of knowledge and connections outside of customary reciprocity networks. Those least affected by the drought, in terms of cattle lost, were those with large herds who were able to sell some of their cattle and to pay for private access to pastures outside of Maasai areas. Drawing on an entitlements framework, we argue that the new coping mechanisms are not available to all, could be making some households more vulnerable to climate change, and reduce the adaptive capacity of the overall system as reciprocity networks and customary institutions are weakened. As such, we posit that adaptive capacity to climate change is uneven within and across communities, is scale-dependent, and is intimately tied to institutional and landscape changes. PMID:25400331

  3. Experimental demonstration of an adaptive architecture for direct spectral imaging classification.

    PubMed

    Dunlop-Gray, Matthew; Poon, Phillip K; Golish, Dathon; Vera, Esteban; Gehm, Michael E

    2016-08-01

    Spectral imaging is a powerful tool for providing in situ material classification across a spatial scene. Typically, spectral imaging analyses are interested in classification, though often the classification is performed only after reconstruction of the spectral datacube. We present a computational spectral imaging system, the Adaptive Feature-Specific Spectral Imaging Classifier (AFSSI-C), which yields direct classification across the spatial scene without reconstruction of the source datacube. With a dual disperser architecture and a programmable spatial light modulator, the AFSSI-C measures specific projections of the spectral datacube which are generated by an adaptive Bayesian classification and feature design framework. We experimentally demonstrate multiple order-of-magnitude improvement of classification accuracy in low signal-to-noise (SNR) environments when compared to legacy spectral imaging systems. PMID:27505794

  4. Hydrologic landscape classification evaluates streamflow vulnerability to climate change in Oregon, USA

    NASA Astrophysics Data System (ADS)

    Leibowitz, S. G.; Comeleo, R. L.; Wigington, P. J., Jr.; Weaver, C. P.; Morefield, P. E.; Sproles, E. A.; Ebersole, J. L.

    2014-09-01

    Classification can allow for evaluations of the hydrologic functions of landscapes and their responses to stressors. Here we demonstrate the use of a hydrologic landscape (HL) approach to evaluate vulnerability to potential future climate change at statewide and basin scales in the state of Oregon. The HL classification has five components: climate, seasonality, aquifer permeability, terrain, and soil permeability. We evaluate changes when the 1971-2000 HL climate indices are recalculated using 2041-2070 simulation results from the ECHAM (European Centre HAMburg) and PCM (Parallel Climate Model) climate models with the A2, A1b, and B1 emission scenarios. Changes in climate class were modest (4-18%) statewide. However, there were major changes in seasonality class for five of the six realizations (excluding PCM_B1): Oregon shifts from being 13% snow-dominated to 4-6% snow-dominated under these five realizations, representing a 56-68% reduction in snowmelt-dominated area. At the basin scale, simulated changes for the Siletz Basin, in Oregon's Coast Range, include a small switch from very wet to wet climate, with no change in seasonality. However, there is a modest increase in fall and winter water due to increased precipitation. For the Sandy Basin, on the western slope of the Cascades, HL climate class does not change, but there are major changes in seasonality, especially for areas with low aquifer permeability, which experiences a 100% loss of spring seasonality. This would reduce summer baseflow, but effects could potentially be mitigated by streamflow buffering effects provided by groundwater in the high aquifer permeability portions of the upper Sandy. The Middle Fork John Day Basin (MFJD), in northeastern Oregon, is snowmelt-dominated. The basin experiences a net loss of wet and moist climate area, along with an increase in dry climate area. The MFJD also experiences major shifts from spring to winter seasonality, representing a 20-60% reduction in snowmelt

  5. On-board multispectral classification study. Volume 2: Supplementary tasks. [adaptive control

    NASA Technical Reports Server (NTRS)

    Ewalt, D.

    1979-01-01

    The operational tasks of the onboard multispectral classification study were defined. These tasks include: sensing characteristics for future space applications; information adaptive systems architectural approaches; data set selection criteria; and onboard functional requirements for interfacing with global positioning satellites.

  6. Diagnostic Classification Models and Multidimensional Adaptive Testing: A Commentary on Rupp and Templin

    ERIC Educational Resources Information Center

    Frey, Andreas; Carstensen, Claus H.

    2009-01-01

    On a general level, the objective of diagnostic classifications models (DCMs) lies in a classification of individuals regarding multiple latent skills. In this article, the authors show that this objective can be achieved by multidimensional adaptive testing (MAT) as well. The authors discuss whether or not the restricted applicability of DCMs can…

  7. Exploring indigenous landscape classification across different dimensions: a case study from the Bolivian Amazon

    PubMed Central

    Riu-Bosoms, Carles; Vidal-Amat, Teresa; Duane, Andrea; Fernandez-Llamazares, Alvaro; Guèze, Maximilien; Luz, Ana C.; Macía, Manuel J.; Paneque-Gálvez, Jaime; Reyes-García, Victoria

    2013-01-01

    Decisions on landscape management are often dictated by government officials based on their own understandings of how landscape should be used and managed, but rarely considering local peoples’ understandings of the landscape they inhabit. We use data collected through free listings, field transects, and interviews to describe how an Amazonian group of hunter-horticulturalists, the Tsimane’, classify and perceive the importance of different elements of the landscape across the ecological, socioeconomic, and spiritual dimensions. The Tsimane’ recognize nine folk ecotopes (i.e., culturally-recognized landscape units) and use a variety of criteria (including geomorphological features and landscape uses) to differentiate ecotopes from one another. The Tsimane’ rank different folk ecotopes in accordance with their perceived ecological, socioeconomic, and spiritual importance. Understanding how local people perceive their landscape contributes towards a landscape management planning paradigm that acknowledges the continuing contributions to management of landscape inhabitants, as well as their cultural and land use rights. PMID:25821282

  8. Effects of Estimation Bias on Multiple-Category Classification with an IRT-Based Adaptive Classification Procedure

    ERIC Educational Resources Information Center

    Yang, Xiangdong; Poggio, John C.; Glasnapp, Douglas R.

    2006-01-01

    The effects of five ability estimators, that is, maximum likelihood estimator, weighted likelihood estimator, maximum a posteriori, expected a posteriori, and Owen's sequential estimator, on the performances of the item response theory-based adaptive classification procedure on multiple categories were studied via simulations. The following…

  9. Testing Adaptive Hypotheses of Convergence with Functional Landscapes: A Case Study of Bone-Cracking Hypercarnivores

    PubMed Central

    Tseng, Zhijie Jack

    2013-01-01

    Morphological convergence is a well documented phenomenon in mammals, and adaptive explanations are commonly employed to infer similar functions for convergent characteristics. I present a study that adopts aspects of theoretical morphology and engineering optimization to test hypotheses about adaptive convergent evolution. Bone-cracking ecomorphologies in Carnivora were used as a case study. Previous research has shown that skull deepening and widening are major evolutionary patterns in convergent bone-cracking canids and hyaenids. A simple two-dimensional design space, with skull width-to-length and depth-to-length ratios as variables, was used to examine optimized shapes for two functional properties: mechanical advantage (MA) and strain energy (SE). Functionality of theoretical skull shapes was studied using finite element analysis (FEA) and visualized as functional landscapes. The distribution of actual skull shapes in the landscape showed a convergent trend of plesiomorphically low-MA and moderate-SE skulls evolving towards higher-MA and moderate-SE skulls; this is corroborated by FEA of 13 actual specimens. Nevertheless, regions exist in the landscape where high-MA and lower-SE shapes are not represented by existing species; their vacancy is observed even at higher taxonomic levels. Results highlight the interaction of biomechanical and non-biomechanical factors in constraining general skull dimensions to localized functional optima through evolution. PMID:23734244

  10. EXTENDING AQUATIC CLASSIFICATION TO THE LANDSCAPE SCALE HYDROLOGY-BASED STRATEGIES

    EPA Science Inventory

    Aquatic classification of single water bodies (lakes, wetlands, estuaries) is often based on geologic origin, while stream classification has relied on multiple factors related to landform, geomorphology, and soils. We have developed an approach to aquatic classification based o...

  11. Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System

    PubMed Central

    Hosseini, Monireh Sheikh; Zekri, Maryam

    2012-01-01

    Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated. PMID:23493054

  12. Classification of chemical chaperones based on their effect on protein folding landscapes.

    PubMed

    Dandage, Rohan; Bandyopadhyay, Anannya; Jayaraj, Gopal Gunanathan; Saxena, Kanika; Dalal, Vijit; Das, Aritri; Chakraborty, Kausik

    2015-03-20

    Various small molecules present in biological systems can assist protein folding in vitro and are known as chemical chaperones. De novo design of chemical chaperones with higher activity than currently known examples is desirable to ameliorate protein misfolding and aggregation in multiple contexts. However, this development has been hindered by limited knowledge of their activities. It is thought that chemical chaperones are typically poor solvents for a protein backbone and hence facilitate native structure formation. However, it is unknown if different chemical chaperones can act differently to modulate folding energy landscapes. Using a model slow folding protein, double-mutant Maltose-binding protein (DM-MBP), we show that a canonical chemical chaperone, trimethylamine-N-oxide (TMAO), accelerates refolding by decreasing the flexibility of the refolding intermediate (RI). Among a number of small molecules that chaperone DM-MBP folding, proline and serine stabilize the transition state (TS) enthalpically, while trehalose behaves like TMAO and increases the rate of barrier crossing through nonenthalpic processes. We propose a two-group classification of chemical chaperones based upon their thermodynamic effect on RI and TS, which is also supported by single molecule Förster resonance energy transfer (smFRET) studies. Interestingly, for a different test protein, the molecular mechanisms of the two groups of chaperones are not conserved. This provides a glimpse into the complexity of chemical chaperoning activity of osmolytes. Future work would allow us to engineer synergism between the two classes to design more efficient chemical chaperones to ameliorate protein misfolding and aggregation problems. PMID:25493352

  13. COMPARE: classification of morphological patterns using adaptive regional elements.

    PubMed

    Fan, Yong; Shen, Dinggang; Gur, Ruben C; Gur, Raquel E; Davatzikos, Christos

    2007-01-01

    This paper presents a method for classification of structural brain magnetic resonance (MR) images, by using a combination of deformation-based morphometry and machine learning methods. A morphological representation of the anatomy of interest is first obtained using a high-dimensional mass-preserving template warping method, which results in tissue density maps that constitute local tissue volumetric measurements. Regions that display strong correlations between tissue volume and classification (clinical) variables are extracted using a watershed segmentation algorithm, taking into account the regional smoothness of the correlation map which is estimated by a cross-validation strategy to achieve robustness to outliers. A volume increment algorithm is then applied to these regions to extract regional volumetric features, from which a feature selection technique using support vector machine (SVM)-based criteria is used to select the most discriminative features, according to their effect on the upper bound of the leave-one-out generalization error. Finally, SVM-based classification is applied using the best set of features, and it is tested using a leave-one-out cross-validation strategy. The results on MR brain images of healthy controls and schizophrenia patients demonstrate not only high classification accuracy (91.8% for female subjects and 90.8% for male subjects), but also good stability with respect to the number of features selected and the size of SVM kernel used. PMID:17243588

  14. Synthetic shuffling and in vitro selection reveal the rugged adaptive fitness landscape of a kinase ribozyme

    PubMed Central

    Curtis, Edward A.; Bartel, David P.

    2013-01-01

    The relationship between genotype and phenotype is often described as an adaptive fitness landscape. In this study, we used a combination of recombination, in vitro selection, and comparative sequence analysis to characterize the fitness landscape of a previously isolated kinase ribozyme. Point mutations present in improved variants of this ribozyme were recombined in vitro in more than 1014 different arrangements using synthetic shuffling, and active variants were isolated by in vitro selection. Mutual information analysis of 65 recombinant ribozymes isolated in the selection revealed a rugged fitness landscape in which approximately one-third of the 91 pairs of positions analyzed showed evidence of correlation. Pairs of correlated positions overlapped to form densely connected networks, and groups of maximally connected nucleotides occurred significantly more often in these networks than they did in randomized control networks with the same number of links. The activity of the most efficient recombinant ribozyme isolated from the synthetically shuffled pool was 30-fold greater than that of any of the ribozymes used to build it, which indicates that synthetic shuffling can be a rich source of ribozyme variants with improved properties. PMID:23798664

  15. Bayesian classification of polarimetric SAR images using adaptive a priori probabilities

    NASA Technical Reports Server (NTRS)

    Van Zyl, J. J.; Burnette, C. F.

    1992-01-01

    The problem of classifying earth terrain by observed polarimetric scattering properties is tackled with an iterative Bayesian scheme using a priori probabilities adaptively. The first classification is based on the use of fixed and not necessarily equal a priori probabilities, and successive iterations change the a priori probabilities adaptively. The approach is applied to an SAR image in which a single water body covers 10 percent of the image area. The classification accuracy for ocean, urban, vegetated, and total area increase, and the percentage of reclassified pixels decreases greatly as the iteration number increases. The iterative scheme is found to improve the a posteriori classification accuracy of maximum likelihood classifiers by iteratively using the local homogeneity in polarimetric SAR images. A few iterations can improve the classification accuracy significantly without sacrificing key high-frequency detail or edges in the image.

  16. Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification.

    PubMed

    Algamal, Zakariya Yahya; Lee, Muhammad Hisyam

    2015-12-01

    Cancer classification and gene selection in high-dimensional data have been popular research topics in genetics and molecular biology. Recently, adaptive regularized logistic regression using the elastic net regularization, which is called the adaptive elastic net, has been successfully applied in high-dimensional cancer classification to tackle both estimating the gene coefficients and performing gene selection simultaneously. The adaptive elastic net originally used elastic net estimates as the initial weight, however, using this weight may not be preferable for certain reasons: First, the elastic net estimator is biased in selecting genes. Second, it does not perform well when the pairwise correlations between variables are not high. Adjusted adaptive regularized logistic regression (AAElastic) is proposed to address these issues and encourage grouping effects simultaneously. The real data results indicate that AAElastic is significantly consistent in selecting genes compared to the other three competitor regularization methods. Additionally, the classification performance of AAElastic is comparable to the adaptive elastic net and better than other regularization methods. Thus, we can conclude that AAElastic is a reliable adaptive regularized logistic regression method in the field of high-dimensional cancer classification. PMID:26520484

  17. Classification of local- and landscape-scale ecological types in the Southern Appalachian mountains

    SciTech Connect

    McNab, W.H.

    1996-12-31

    Five local ecological types based on vegetative communities and two landscape types based on groups of communities, were identified by integrating landform, soil, and vegetation components using multivariate techniques. Evaluation and several topographic and soil variables were highly correlated with types of both scales. Landscape ecological types based only on landform and soil variables without vegetation did not correspond with types developed using vegetation.

  18. Adaptive binary material classification of an unknown object using polarimetric images degraded by atmospheric turbulence

    NASA Astrophysics Data System (ADS)

    Kim, Mu J.; Hyde, Milo W.

    2012-10-01

    An improved binary material-classification algorithm using passive polarimetric imagery degraded by atmospheric turbulence is presented. The technique implements a modified version of an existing polarimetric blind-deconvolution algorithm in order to remove atmospheric distortion and correctly classify the unknown object. The classification decision, dielectric or metal in this case, is based on degree of linear polarization (DoLP) estimates provided by the blind-deconvolution algorithm augmented by two DoLP priors - one statistically modeling the polarization behavior of metals and the other statistically modeling the polarization behavior of dielectrics. The DoLP estimate which maximizes the log-likelihood function determines the image pixel's classification. The method presented here significantly improves upon a similar published polarimetric classification method by adaptively updating the DoLP priors as more information becomes available about the scene. This new adaptive method significantly extends the range of validity of the existing polarimetric classification technique to near-normal collection geometries where most polarimetric material classifiers perform poorly. In this paper, brief reviews of the polarimetric blind-deconvolution algorithm and the functional forms of the DoLP priors are provided. Also provided is the methodology for making the algorithm adaptive including three techniques for updating the DoLP priors using in-progress DoLP estimates. Lastly, the proposed technique is experimentally validated by comparing classification results of two dielectric and metallic samples obtained using the new method to those obtained using the existing technique.

  19. Enhanced land use/cover classification of heterogeneous tropical landscapes using support vector machines and textural homogeneity

    NASA Astrophysics Data System (ADS)

    Paneque-Gálvez, Jaime; Mas, Jean-François; Moré, Gerard; Cristóbal, Jordi; Orta-Martínez, Martí; Luz, Ana Catarina; Guèze, Maximilien; Macía, Manuel J.; Reyes-García, Victoria

    2013-08-01

    Land use/cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land use/cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims at establishing an efficient classification approach to accurately map all broad land use/cover classes in a large, heterogeneous tropical area, as a basis for further studies (e.g., land use/cover change, deforestation and forest degradation). Specifically, we first compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbor and four different support vector machines - SVM), and hybrid (unsupervised-supervised) classifiers, using hard and soft (fuzzy) accuracy assessments. We then assess, using the maximum likelihood algorithm, what textural indices from the gray-level co-occurrence matrix lead to greater classification improvements at the spatial resolution of Landsat imagery (30 m), and rank them accordingly. Finally, we use the textural index that provides the most accurate classification results to evaluate whether its usefulness varies significantly with the classifier used. We classified imagery corresponding to dry and wet seasons and found that SVM classifiers outperformed all the rest. We also found that the use of some textural indices, but particularly homogeneity and entropy, can significantly improve classifications. We focused on the use of the homogeneity index, which has so far been neglected in land use/cover classification efforts, and found that this index along with reflectance bands significantly increased the overall accuracy of all the classifiers, but particularly of SVM. We observed that improvements in producer's and user's accuracies through the inclusion of homogeneity were different

  20. Research on adaptive segmentation and activity classification method of filamentous fungi image in microbe fermentation

    NASA Astrophysics Data System (ADS)

    Cai, Xiaochun; Hu, Yihua; Wang, Peng; Sun, Dujuan; Hu, Guilan

    2009-10-01

    The paper presents an adaptive segmentation and activity classification method for filamentous fungi image. Firstly, an adaptive structuring element (SE) construction algorithm is proposed for image background suppression. Based on watershed transform method, the color labeled segmentation of fungi image is taken. Secondly, the fungi elements feature space is described and the feature set for fungi hyphae activity classification is extracted. The growth rate evaluation of fungi hyphae is achieved by using SVM classifier. Some experimental results demonstrate that the proposed method is effective for filamentous fungi image processing.

  1. Energy Landscape Reveals That the Budding Yeast Cell Cycle Is a Robust and Adaptive Multi-stage Process

    PubMed Central

    Lv, Cheng; Li, Xiaoguang; Li, Fangting; Li, Tiejun

    2015-01-01

    Quantitatively understanding the robustness, adaptivity and efficiency of cell cycle dynamics under the influence of noise is a fundamental but difficult question to answer for most eukaryotic organisms. Using a simplified budding yeast cell cycle model perturbed by intrinsic noise, we systematically explore these issues from an energy landscape point of view by constructing an energy landscape for the considered system based on large deviation theory. Analysis shows that the cell cycle trajectory is sharply confined by the ambient energy barrier, and the landscape along this trajectory exhibits a generally flat shape. We explain the evolution of the system on this flat path by incorporating its non-gradient nature. Furthermore, we illustrate how this global landscape changes in response to external signals, observing a nice transformation of the landscapes as the excitable system approaches a limit cycle system when nutrients are sufficient, as well as the formation of additional energy wells when the DNA replication checkpoint is activated. By taking into account the finite volume effect, we find additional pits along the flat cycle path in the landscape associated with the checkpoint mechanism of the cell cycle. The difference between the landscapes induced by intrinsic and extrinsic noise is also discussed. In our opinion, this meticulous structure of the energy landscape for our simplified model is of general interest to other cell cycle dynamics, and the proposed methods can be applied to study similar biological systems. PMID:25794282

  2. Non-parametric transient classification using adaptive wavelets

    NASA Astrophysics Data System (ADS)

    Varughese, Melvin M.; von Sachs, Rainer; Stephanou, Michael; Bassett, Bruce A.

    2015-11-01

    Classifying transients based on multiband light curves is a challenging but crucial problem in the era of GAIA and Large Synoptic Sky Telescope since the sheer volume of transients will make spectroscopic classification unfeasible. We present a non-parametric classifier that predicts the transient's class given training data. It implements two novel components: the use of the BAGIDIS wavelet methodology - a characterization of functional data using hierarchical wavelet coefficients - as well as the introduction of a ranked probability classifier on the wavelet coefficients that handles both the heteroscedasticity of the data in addition to the potential non-representativity of the training set. The classifier is simple to implement while a major advantage of the BAGIDIS wavelets is that they are translation invariant. Hence, BAGIDIS does not need the light curves to be aligned to extract features. Further, BAGIDIS is non-parametric so it can be used effectively in blind searches for new objects. We demonstrate the effectiveness of our classifier against the Supernova Photometric Classification Challenge to correctly classify supernova light curves as Type Ia or non-Ia. We train our classifier on the spectroscopically confirmed subsample (which is not representative) and show that it works well for supernova with observed light-curve time spans greater than 100 d (roughly 55 per cent of the data set). For such data, we obtain a Ia efficiency of 80.5 per cent and a purity of 82.4 per cent, yielding a highly competitive challenge score of 0.49. This indicates that our `model-blind' approach may be particularly suitable for the general classification of astronomical transients in the era of large synoptic sky surveys.

  3. Adaptive frequency estimation by MUSIC (Multiple Signal Classification) method

    NASA Astrophysics Data System (ADS)

    Karhunen, Juha; Nieminen, Esko; Joutsensalo, Jyrki

    During the last years, the eigenvector-based method called MUSIC has become very popular in estimating the frequencies of sinusoids in additive white noise. Adaptive realizations of the MUSIC method are studied using simulated data. Several of the adaptive realizations seem to give in practice equally good results as the nonadaptive standard realization. The only exceptions are instantaneous gradient type algorithms that need considerably more samples to achieve a comparable performance. A new method is proposed for constructing initial estimates to the signal subspace. The method improves often dramatically the performance of instantaneous gradient type algorithms. The new signal subspace estimate can also be used to define a frequency estimator directly or to simplify eigenvector computation.

  4. Verification of Hydrologic Landscape Derived Basin-Scale Classifications in the Pacific Northwest

    EPA Science Inventory

    The interaction between the physical properties of a catchment (form) and climatic forcing of precipitation and energy control how water is partitioned, stored, and conveyed through a catchment (function). Hydrologic Landscapes (HLs) were previously developed across Oregon and de...

  5. Application of adaptive and neural network computational techniques to Traffic Volume and Classification Monitoring

    SciTech Connect

    Mead, W.C.; Fisher, H.N.; Jones, R.D.; Bisset, K.R.; Lee, L.A.

    1993-09-01

    We are developing a Traffic Volume and Classification Monitoring (TVCM) system based on adaptive and neural network computational techniques. The value of neutral networks in this application lies in their ability to learn from data and to form a mapping of arbitrary topology. The piezoelectric strip and magnetic loop sensors typically used for TVCM provide signals that are complicated and variable, and that correspond in indirect ways with the desired FWHA 13-class classification system. Further, the wide variety of vehicle configurations adds to the complexity of the classification task. Our goal is to provide a TVCM system featuring high accuracy, adaptability to wide sensor and envirorunental variations, and continuous fault detection. We have instrumented an experimental TVCM site, developed PC-based on-line data acquisition software, collected a large database of vehicles` signals together with accurate ground truth determination, and analyzed the data off-line with a neural net classification system that can distinguish between class 2 (automobiles) and class 3 (utility vehicles) with better than 90% accuracy. The neural network used, called the Connectionist Hyperprism Classification (CHC) network, features simple basis functions; rapid, linear training algorithms for basis function amplitudes and widths; and basis function elimination that enhances network speed and accuracy. Work is in progress to extend the system to other classes, to quantify the system`s adaptability, and to develop automatic fault detection techniques.

  6. Targeting the adaptive molecular landscape of castration-resistant prostate cancer

    PubMed Central

    Wyatt, Alexander W; Gleave, Martin E

    2015-01-01

    Castration and androgen receptor (AR) pathway inhibitors induce profound and sustained responses in advanced prostate cancer. However, the inevitable recurrence is associated with reactivation of the AR and progression to a more aggressive phenotype termed castration-resistant prostate cancer (CRPC). AR reactivation can occur directly through genomic modification of the AR gene, or indirectly via co-factor and co-chaperone deregulation. This mechanistic heterogeneity is further complicated by the stress-driven induction of a myriad of overlapping cellular survival pathways. In this review, we describe the heterogeneous and evolvable molecular landscape of CRPC and explore recent successes and failures of therapeutic strategies designed to target AR reactivation and adaptive survival pathways. We also discuss exciting areas of burgeoning anti-tumour research, and their potential to improve the survival and management of patients with CRPC. PMID:25896606

  7. Space-based RF signal classification using adaptive wavelet features

    SciTech Connect

    Caffrey, M.; Briles, S.

    1995-04-01

    RF signals are dispersed in frequency as they propagate through the ionosphere. For wide-band signals, this results in nonlinearly- chirped-frequency, transient signals in the VHF portion of the spectrum. This ionospheric dispersion provide a means of discriminating wide-band transients from other signals (e.g., continuous-wave carriers, burst communications, chirped-radar signals, etc.). The transient nature of these dispersed signals makes them candidates for wavelet feature selection. Rather than choosing a wavelet ad hoc, we adaptively compute an optimal mother wavelet via a neural network. Gaussian weighted, linear frequency modulate (GLFM) wavelets are linearly combined by the network to generate our application specific mother wavelet, which is optimized for its capacity to select features that discriminate between the dispersed signals and clutter (e.g., multiple continuous-wave carriers), not for its ability to represent the dispersed signal. The resulting mother wavelet is then used to extract features for a neutral network classifier. The performance of the adaptive wavelet classifier is the compared to an FFT based neural network classifier.

  8. Climate Change Impact Assessment and Adaptation Options in Vulnerable Agro-Landscapes in East-Africa

    NASA Astrophysics Data System (ADS)

    Manful, D.; Tscherning, K.; Kersebaum, K.; Dietz, J.; Dietrich, O.; Gomani, C.; Böhm, H.; Büchner, M.; Lischeid, G.,; Ojoyi, M.,

    2009-04-01

    Climate change poses a risk to the livelihoods of large populations in the developing world, especially in Africa. In East Africa, climate change is expected to affect the spatial distribution and quantity of precipitation. The proposed project will assess aspects of climate impacts and adaptation options in Tanzania. The project will attempt to quantify (1) projected impacts including: variability in temperature, rainfall, flooding and drought (2) the affect changes in 1. will have on specific sectors namely agriculture (food security), water resources and ecosystem services. The cumulative effects of diminished surface and ground water flow on agricultural production coupled with increasing demand for food due to increase in human pressure will also be evaluated. Expected outputs of the project include (1) downscaled climate change scenarios for different IPCC emission scenarios (2) model based estimations of climate change impacts on hydrological cycle and assessment of land use options (3) scenarios of sustainable livelihoods and resilient agro-landscapes under climate change (4) assessment of adaptive practices and criteria for best adaptation practices. The presentation will focus on novel approaches that focus on the use of agro-ecosystem models to predict local and regional impacts of climate variability on food with specific needs of the end-user factored into model set-up process. In other words, model configurations adapted to the information needs of a specific end-user or audience are evaluated. The perception of risk within different end-users (small scale farmer versus a regional or state level policy maker) are explicitly taken into consideration with the overarching aim of maximizing the impact of the results obtained from computer-based simulations.

  9. Classification of osteosarcoma T-ray responses using adaptive and rational wavelets for feature extraction

    NASA Astrophysics Data System (ADS)

    Ng, Desmond; Wong, Fu Tian; Withayachumnankul, Withawat; Findlay, David; Ferguson, Bradley; Abbott, Derek

    2007-12-01

    In this work we investigate new feature extraction algorithms on the T-ray response of normal human bone cells and human osteosarcoma cells. One of the most promising feature extraction methods is the Discrete Wavelet Transform (DWT). However, the classification accuracy is dependant on the specific wavelet base chosen. Adaptive wavelets circumvent this problem by gradually adapting to the signal to retain optimum discriminatory information, while removing redundant information. Using adaptive wavelets, classification accuracy, using a quadratic Bayesian classifier, of 96.88% is obtained based on 25 features. In addition, the potential of using rational wavelets rather than the standard dyadic wavelets in classification is explored. The advantage it has over dyadic wavelets is that it allows a better adaptation of the scale factor according to the signal. An accuracy of 91.15% is obtained through rational wavelets with 12 coefficients using a Support Vector Machine (SVM) as the classifier. These results highlight adaptive and rational wavelets as an efficient feature extraction method and the enormous potential of T-rays in cancer detection.

  10. Fast Model Adaptation for Automated Section Classification in Electronic Medical Records.

    PubMed

    Ni, Jian; Delaney, Brian; Florian, Radu

    2015-01-01

    Medical information extraction is the automatic extraction of structured information from electronic medical records, where such information can be used for improving healthcare processes and medical decision making. In this paper, we study one important medical information extraction task called section classification. The objective of section classification is to automatically identify sections in a medical document and classify them into one of the pre-defined section types. Training section classification models typically requires large amounts of human labeled training data to achieve high accuracy. Annotating institution-specific data, however, can be both expensive and time-consuming; which poses a big hurdle for adapting a section classification model to new medical institutions. In this paper, we apply two advanced machine learning techniques, active learning and distant supervision, to reduce annotation cost and achieve fast model adaptation for automated section classification in electronic medical records. Our experiment results show that active learning reduces the annotation cost and time by more than 50%, and distant supervision can achieve good model accuracy using weakly labeled training data only. PMID:26262005

  11. An alternative to current psychiatric classifications: a psychological landscape hypothesis based on an integrative, dynamical and multidimensional approach

    PubMed Central

    2014-01-01

    Background Mental disorders as defined by current classifications are not fully supported by scientific evidence. It is unclear whether main disorders should be broken down into separate categories or disposed along a continuous spectrum. In the near future, new classes of mental disorders could be defined through associations of so-called abnormalities observed at the genetic, molecular and neuronal circuitry levels. Methods We propose an alternative hypothesis to these classifications based on an integrative, dynamical and multidimensional approach. Results We suggest that observed data collected in the general population can be used to build a psychological landscape. Innovative techniques issued from information processing and system dynamics can prove helpful in this task. Information preserving techniques can reduce the high dimensional data collected and provide an intrinsic map for psychological characteristics or behaviors. Dynamical patterns called attractors, which are linked to each other through continuous pathways, can be identified. Specific attractors can define mental disorders. Their causal structure can be investigated with causal networks. Conclusions Powerful and reliable tools are available so that an alternative to current psychiatric classifications can be built based on a genuine biopsychosocial model. The proposed model is ready to be tested on real data. PMID:25033795

  12. Multi-peaked adaptive landscape for chikungunya virus evolution predicts continued fitness optimization in Aedes albopictus mosquitoes.

    PubMed

    Tsetsarkin, Konstantin A; Chen, Rubing; Yun, Ruimei; Rossi, Shannan L; Plante, Kenneth S; Guerbois, Mathilde; Forrester, Naomi; Perng, Guey Chuen; Sreekumar, Easwaran; Leal, Grace; Huang, Jing; Mukhopadhyay, Suchetana; Weaver, Scott C

    2014-01-01

    Host species-specific fitness landscapes largely determine the outcome of host switching during pathogen emergence. Using chikungunya virus (CHIKV) to study adaptation to a mosquito vector, we evaluated mutations associated with recently evolved sub-lineages. Multiple Aedes albopictus-adaptive fitness peaks became available after CHIKV acquired an initial adaptive (E1-A226V) substitution, permitting rapid lineage diversification observed in nature. All second-step mutations involved replacements by glutamine or glutamic acid of E2 glycoprotein amino acids in the acid-sensitive region, providing a framework to anticipate additional A. albopictus-adaptive mutations. The combination of second-step adaptive mutations into a single, 'super-adaptive' fitness peak also predicted the future emergence of CHIKV strains with even greater transmission efficiency in some current regions of endemic circulation, followed by their likely global spread. PMID:24933611

  13. Adaptive road crack detection system by pavement classification.

    PubMed

    Gavilán, Miguel; Balcones, David; Marcos, Oscar; Llorca, David F; Sotelo, Miguel A; Parra, Ignacio; Ocaña, Manuel; Aliseda, Pedro; Yarza, Pedro; Amírola, Alejandro

    2011-01-01

    This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement. PMID:22163717

  14. Adaptive Road Crack Detection System by Pavement Classification

    PubMed Central

    Gavilán, Miguel; Balcones, David; Marcos, Oscar; Llorca, David F.; Sotelo, Miguel A.; Parra, Ignacio; Ocaña, Manuel; Aliseda, Pedro; Yarza, Pedro; Amírola, Alejandro

    2011-01-01

    This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement. PMID:22163717

  15. EVALUATION OF THE USE OF LANDSCAPE CLASSIFICATIONS FOR THE PREDICTION OF FRESHWATER BIOTA: SYNTHESIS AND RECOMMENDATIONS

    EPA Science Inventory

    This paper summarizes and synthesizes the collective results that emerged from the series of papers published in this issue of J-NABS, and places these results in the context of previously published literature describing variation in aquatic biota at landscape spatial scales. Cla...

  16. Using Hydrologic Landscape Classification to Evaluate the Hydrologic Effects of Climate in the Southwestern United States

    EPA Science Inventory

    Hydrologic landscapes (HLs) have been an active area of research at regional and national scales in the United States. The concept has been used to make spatially distributed assessments of variability in streamflow and climatic response in Oregon, Alaska, and the Pacific Northwe...

  17. Classification of crops across heterogeneous agricultural landscape in Kenya using AisaEAGLE imaging spectroscopy data

    NASA Astrophysics Data System (ADS)

    Piiroinen, Rami; Heiskanen, Janne; Mõttus, Matti; Pellikka, Petri

    2015-07-01

    Land use practices are changing at a fast pace in the tropics. In sub-Saharan Africa forests, woodlands and bushlands are being transformed for agricultural use to produce food for the rapidly growing population. The objective of this study was to assess the prospects of mapping the common agricultural crops in highly heterogeneous study area in south-eastern Kenya using high spatial and spectral resolution AisaEAGLE imaging spectroscopy data. Minimum noise fraction transformation was used to pack the coherent information in smaller set of bands and the data was classified with support vector machine (SVM) algorithm. A total of 35 plant species were mapped in the field and seven most dominant ones were used as classification targets. Five of the targets were agricultural crops. The overall accuracy (OA) for the classification was 90.8%. To assess the possibility of excluding the remaining 28 plant species from the classification results, 10 different probability thresholds (PT) were tried with SVM. The impact of PT was assessed with validation polygons of all 35 mapped plant species. The results showed that while PT was increased more pixels were excluded from non-target polygons than from the polygons of the seven classification targets. This increased the OA and reduced salt-and-pepper effects in the classification results. Very high spatial resolution imagery and pixel-based classification approach worked well with small targets such as maize while there was mixing of classes on the sides of the tree crowns.

  18. Study on Landscape Freeze/Thaw Classification and its Spatial Scale Effects using Satellite L-band radar observations over Alaska

    NASA Astrophysics Data System (ADS)

    Du, J.; Kimball, J. S.; Azarderakhsh, M.; Dunbar, R.; Moghaddam, M.; McDonald, K. C.; Kim, Y.

    2013-12-01

    Spatial and temporal variability in landscape freeze-thaw (FT) status at higher latitudes and elevations significantly impacts land surface water mobility and surface energy partitioning, with major consequences for regional climate, hydrological, ecological and biogeochemical processes. With the development of new generation space-borne remote sensing instruments, future L-band missions including the NASA Soil Moisture Active and Passive (SMAP) mission will provide new operational retrievals of landscape FT state dynamics at relatively fine (3 km) spatial resolution. We applied theoretical simulations of L-band radar backscatter using first-order radiative transfer models with two-layer and three-layer modeling schemes to develop a modified seasonal threshold algorithm (STA) and FT classification over Alaska using finer scale (100 m resolution) satellite Phased Array L-band Synthetic Aperture Radar (PALSAR) observations. An Alaska FT map for April, 2007 was generated from PALSAR observations and showed a regionally consistent, but finer FT spatial pattern than an alternative surface air temperature based classification derived from global reanalysis data. Validation of the STA based FT classification against regional soil climate stations indicated approximately 80% and 70% spatial classification accuracy in relation to respective in situ station air temperature and soil temperature measurement based FT estimates. The STA FT classification method is found to be reliable for most of the major Alaska land cover types except for barren land. An investigation of relative spatial scale effects on FT classification accuracy indicates that the relationship between pixel size and relative FT spatial classification error follows a general logarithmic function. The optimum resolution for accurate FT classification is expected to depend on the landscape FT spatial heterogeneity. However, our results indicate that the regional FT spatial scaling error is less than 12.8% and

  19. An adaptive strategy of classification for detecting hypoglycemia using only two EEG channels.

    PubMed

    Nguyen, Lien B; Nguyen, Anh V; Ling, Sai Ho; Nguyen, Hung T

    2012-01-01

    Hypoglycemia is the most common but highly feared side effect of the insulin therapy for patients with Type 1 Diabetes Mellitus (T1DM). Severe episodes of hypoglycemia can lead to unconsciousness, coma, and even death. The variety of hypoglycemic symptoms arises from the activation of the autonomous central nervous system and from reduced cerebral glucose consumption. In this study, electroencephalography (EEG) signals from five T1DM patients during an overnight clamp study were measured and analyzed. By applying a method of feature extraction using Fast Fourier Transform (FFT) and classification using neural networks, we establish that hypoglycemia can be detected non-invasively using EEG signals from only two channels. This paper demonstrates that a significant advantage can be achieved by implementing adaptive training. By adapting the classifier to a previously unseen person, the classification results can be improved from 60% sensitivity and 54% specificity to 75% sensitivity and 67% specificity. PMID:23366685

  20. Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents.

    PubMed

    Ubeyli, Elif Derya

    2009-03-01

    This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electrocardiogram (ECG) signals. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, and atrial fibrillation beat) obtained from the PhysioBank database were classified by four ANFIS classifiers. To improve diagnostic accuracy, the fifth ANFIS classifier (combining ANFIS) was trained using the outputs of the four ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the ECG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the ECG signals. PMID:19084286

  1. Adaptive landscapes and emergent phenotypes: why do cancers have high glycolysis?

    PubMed

    Gillies, Robert J; Gatenby, Robert A

    2007-06-01

    Investigating the causes of increased aerobic glycolysis in tumors (Warburg Effect) has gone in and out of fashion many times since it was first described almost a century ago. The field is currently in ascendance due to two factors. Over a million FDG-PET studies have unequivocally identified increased glucose uptake as a hallmark of metastatic cancer in humans. These observations, combined with new molecular insights with HIF-1alpha and c-myc, have rekindled an interest in this important phenotype. A preponderance of work has been focused on the molecular mechanisms underlying this effect, with the expectation that a mechanistic understanding may lead to novel therapeutic approaches. There is also an implicit assumption that a mechanistic understanding, although fundamentally reductionist, will nonetheless lead to a more profound teleological understanding of the need for altered metabolism in invasive cancers. In this communication, we describe an alternative approach that begins with teleology; i.e. adaptive landscapes and selection pressures that promote emergence of aerobic glycolysis during the somatic evolution of invasive cancer. Mathematical models and empirical observations are used to define the adaptive advantage of aerobic glycolysis that would explain its remarkable prevalence in human cancers. These studies have led to the hypothesis that increased consumption of glucose in metastatic lesions is not used for substantial energy production via Embden-Meyerhoff glycolysis, but rather for production of acid, which gives the cancer cells a competitive advantage for invasion. Alternative hypotheses, wherein the glucose is used for generation of reducing equivalents (NADPH) or anabolic precursors (ribose) are also discussed. PMID:17624581

  2. Diagnosis of streamflow prediction skills in Oregon using Hydrologic Landscape Classification

    EPA Science Inventory

    A complete understanding of why rainfall-runoff models provide good streamflow predictions at catchments in some regions, but fail to do so in other regions, has still not been achieved. Here, we argue that a hydrologic classification system is a robust conceptual tool that is w...

  3. Where and why do models fail? Perspectives from Oregon Hydrologic Landscape classification

    EPA Science Inventory

    A complete understanding of why rainfall-runoff models provide good streamflow predictions at catchments in some regions, but fail to do so in other regions, has still not been achieved. Here, we argue that a hydrologic classification system is a robust conceptual tool that is w...

  4. Object-based forest classification to facilitate landscape-scale conservation in the Mississippi Alluvial Valley

    USGS Publications Warehouse

    Mitchell, Michael; Wilson, R. Randy; Twedt, Daniel J.; Mini, Anne E.; James, J. Dale

    2016-01-01

    The Mississippi Alluvial Valley is a floodplain along the southern extent of the Mississippi River extending from southern Missouri to the Gulf of Mexico. This area once encompassed nearly 10 million ha of floodplain forests, most of which has been converted to agriculture over the past two centuries. Conservation programs in this region revolve around protection of existing forest and reforestation of converted lands. Therefore, an accurate and up to date classification of forest cover is essential for conservation planning, including efforts that prioritize areas for conservation activities. We used object-based image analysis with Random Forest classification to quickly and accurately classify forest cover. We used Landsat band, band ratio, and band index statistics to identify and define similar objects as our training sets instead of selecting individual training points. This provided a single rule-set that was used to classify each of the 11 Landsat 5 Thematic Mapper scenes that encompassed the Mississippi Alluvial Valley. We classified 3,307,910±85,344 ha (32% of this region) as forest. Our overall classification accuracy was 96.9% with Kappa statistic of 0.96. Because this method of forest classification is rapid and accurate, assessment of forest cover can be regularly updated and progress toward forest habitat goals identified in conservation plans can be periodically evaluated.

  5. Helicopter and aircraft detection and classification using adaptive beamforming and tracking techniques

    NASA Astrophysics Data System (ADS)

    van Koersel, Antonius C.; Beerens, S. P.

    2002-08-01

    Measurements of different types of aircraft are performed and used to obtain information on target characteristics and develop an algorithm to perform classification between jet aircraft, propeller aircraft and helicopters. To obtain a larger detection range, reduce background noise and to reduce classification errors in a multi-target environment, a real time adaptive beamformer algorithm is developed for a three microphone array. The output of the beamformer is submitted to a tracking algorithm. Acoustic signals from identified tracks are submitted to the classification algorithms. The algorithm is tested on data recorded during various field trials. The objective of the research, which is part of a research program for the Dutch Army, is to detect the passage of an aircraft with one or more mechanical wave sensors, either acoustic or seismic. After detection of a target, classification of the type of aircraft is requested (for example: helicopter-jet-propeller-rpv). If possible type identification is also requested. Earlier work showed promising results for detection and classification of helicopter targets. The projects resulted in an algorithm that can detect and classify helicopters, but it was developed to reject other targets. The chosen approach is to combine new aircraft detection and beamforming algorithms with the existing algorithms.

  6. Wavelength-adaptive dehazing using histogram merging-based classification for UAV images.

    PubMed

    Yoon, Inhye; Jeong, Seokhwa; Jeong, Jaeheon; Seo, Doochun; Paik, Joonki

    2015-01-01

    Since incoming light to an unmanned aerial vehicle (UAV) platform can be scattered by haze and dust in the atmosphere, the acquired image loses the original color and brightness of the subject. Enhancement of hazy images is an important task in improving the visibility of various UAV images. This paper presents a spatially-adaptive dehazing algorithm that merges color histograms with consideration of the wavelength-dependent atmospheric turbidity. Based on the wavelength-adaptive hazy image acquisition model, the proposed dehazing algorithm consists of three steps: (i) image segmentation based on geometric classes; (ii) generation of the context-adaptive transmission map; and (iii) intensity transformation for enhancing a hazy UAV image. The major contribution of the research is a novel hazy UAV image degradation model by considering the wavelength of light sources. In addition, the proposed transmission map provides a theoretical basis to differentiate visually important regions from others based on the turbidity and merged classification results. PMID:25808767

  7. Wavelength-Adaptive Dehazing Using Histogram Merging-Based Classification for UAV Images

    PubMed Central

    Yoon, Inhye; Jeong, Seokhwa; Jeong, Jaeheon; Seo, Doochun; Paik, Joonki

    2015-01-01

    Since incoming light to an unmanned aerial vehicle (UAV) platform can be scattered by haze and dust in the atmosphere, the acquired image loses the original color and brightness of the subject. Enhancement of hazy images is an important task in improving the visibility of various UAV images. This paper presents a spatially-adaptive dehazing algorithm that merges color histograms with consideration of the wavelength-dependent atmospheric turbidity. Based on the wavelength-adaptive hazy image acquisition model, the proposed dehazing algorithm consists of three steps: (i) image segmentation based on geometric classes; (ii) generation of the context-adaptive transmission map; and (iii) intensity transformation for enhancing a hazy UAV image. The major contribution of the research is a novel hazy UAV image degradation model by considering the wavelength of light sources. In addition, the proposed transmission map provides a theoretical basis to differentiate visually important regions from others based on the turbidity and merged classification results. PMID:25808767

  8. A scale- and orientation-adaptive extension of Local Binary Patterns for texture classification

    PubMed Central

    Hegenbart, Sebastian; Uhl, Andreas

    2015-01-01

    Local Binary Patterns (LBPs) have been used in a wide range of texture classification scenarios and have proven to provide a highly discriminative feature representation. A major limitation of LBP is its sensitivity to affine transformations. In this work, we present a scale- and rotation-invariant computation of LBP. Rotation-invariance is achieved by explicit alignment of features at the extraction level, using a robust estimate of global orientation. Scale-adapted features are computed in reference to the estimated scale of an image, based on the distribution of scale normalized Laplacian responses in a scale-space representation. Intrinsic-scale-adaption is performed to compute features, independent of the intrinsic texture scale, leading to a significantly increased discriminative power for a large amount of texture classes. In a final step, the rotation- and scale-invariant features are combined in a multi-resolution representation, which improves the classification accuracy in texture classification scenarios with scaling and rotation significantly. PMID:26240440

  9. A tortoise–hare pattern seen in adapting structured and unstructured populations suggests a rugged fitness landscape in bacteria

    PubMed Central

    Nahum, Joshua R.; Godfrey-Smith, Peter; Harding, Brittany N.; Marcus, Joseph H.; Carlson-Stevermer, Jared; Kerr, Benjamin

    2015-01-01

    In the context of Wright’s adaptive landscape, genetic epistasis can yield a multipeaked or “rugged” topography. In an unstructured population, a lineage with selective access to multiple peaks is expected to fix rapidly on one, which may not be the highest peak. In a spatially structured population, on the other hand, beneficial mutations take longer to spread. This slowdown allows distant parts of the population to explore the landscape semiindependently. Such a population can simultaneously discover multiple peaks, and the genotype at the highest discovered peak is expected to dominate eventually. Thus, structured populations sacrifice initial speed of adaptation for breadth of search. As in the fable of the tortoise and the hare, the structured population (tortoise) starts relatively slow but eventually surpasses the unstructured population (hare) in average fitness. In contrast, on single-peak landscapes that lack epistasis, all uphill paths converge. Given such “smooth” topography, breadth of search is devalued and a structured population only lags behind an unstructured population in average fitness (ultimately converging). Thus, the tortoise–hare pattern is an indicator of ruggedness. After verifying these predictions in simulated populations where ruggedness is manipulable, we explore average fitness in metapopulations of Escherichia coli. Consistent with a rugged landscape topography, we find a tortoise–hare pattern. Further, we find that structured populations accumulate more mutations, suggesting that distant peaks are higher. This approach can be used to unveil landscape topography in other systems, and we discuss its application for antibiotic resistance, engineering problems, and elements of Wright’s shifting balance process. PMID:25964348

  10. Automatic classification of schizophrenia using resting-state functional language network via an adaptive learning algorithm

    NASA Astrophysics Data System (ADS)

    Zhu, Maohu; Jie, Nanfeng; Jiang, Tianzi

    2014-03-01

    A reliable and precise classification of schizophrenia is significant for its diagnosis and treatment of schizophrenia. Functional magnetic resonance imaging (fMRI) is a novel tool increasingly used in schizophrenia research. Recent advances in statistical learning theory have led to applying pattern classification algorithms to access the diagnostic value of functional brain networks, discovered from resting state fMRI data. The aim of this study was to propose an adaptive learning algorithm to distinguish schizophrenia patients from normal controls using resting-state functional language network. Furthermore, here the classification of schizophrenia was regarded as a sample selection problem where a sparse subset of samples was chosen from the labeled training set. Using these selected samples, which we call informative vectors, a classifier for the clinic diagnosis of schizophrenia was established. We experimentally demonstrated that the proposed algorithm incorporating resting-state functional language network achieved 83.6% leaveone- out accuracy on resting-state fMRI data of 27 schizophrenia patients and 28 normal controls. In contrast with KNearest- Neighbor (KNN), Support Vector Machine (SVM) and l1-norm, our method yielded better classification performance. Moreover, our results suggested that a dysfunction of resting-state functional language network plays an important role in the clinic diagnosis of schizophrenia.

  11. Comparative landscape genetics and the adaptive radiation of Darwin's finches: the role of peripheral isolation.

    PubMed

    Petren, K; Grant, P R; Grant, B R; Keller, L F

    2005-09-01

    We use genetic divergence at 16 microsatellite loci to investigate how geographical features of the Galápagos landscape structure island populations of Darwin's finches. We compare the three most genetically divergent groups of Darwin's finches comprising morphologically and ecologically similar allopatric populations: the cactus finches (Geospiza scandens and Geospiza conirostris), the sharp-beaked ground finches (Geospiza difficilis) and the warbler finches (Certhidea olivacea and Certhidea fusca). Evidence of reduced genetic diversity due to drift was limited to warbler finches on small, peripheral islands. Evidence of low levels of recent interisland migration was widespread throughout all three groups. The hypothesis of distance-limited dispersal received the strongest support in cactus and sharp-beaked ground finches as evidenced by patterns of isolation by distance, while warbler finches showed a weaker relationship. Support for the hypothesis that gene flow constrains morphological divergence was only found in one of eight comparisons within these groups. Among warbler finches, genetic divergence was relatively high while phenotypic divergence was low, implicating stabilizing selection rather than constraint due to gene flow. We conclude that the adaptive radiation of Darwin's finches has occurred in the presence of ongoing but low levels of gene flow caused by distance-dependent interisland dispersal. Gene flow does not constrain phenotypic divergence, but may augment genetic variation and facilitate evolution due to natural selection. Both microsatellites and mtDNA agree in that subsets of peripheral populations of two older groups are genetically more similar to other species that underwent dramatic morphological change. The apparent decoupling of morphological and molecular evolution may be accounted for by a modification of Lack's two-stage model of speciation: relative ecological stasis in allopatry followed by secondary contact, ecological

  12. Hand movements classification for myoelectric control system using adaptive resonance theory.

    PubMed

    Jahani Fariman, H; Ahmad, Siti A; Hamiruce Marhaban, M; Alijan Ghasab, M; Chappell, Paul H

    2016-03-01

    This research proposes an exploratory study of a simple, accurate, and computationally efficient movement classification technique for prosthetic hand application. Surface myoelectric signals were acquired from the four muscles, namely, flexor carpi ulnaris, extensor carpi radialis, biceps brachii, and triceps brachii, of four normal-limb subjects. The signals were segmented, and the features were extracted with a new combined time-domain feature extraction method. Fuzzy C-means clustering method and scatter plot were used to evaluate the performance of the proposed multi-feature versus Hudgins' multi-feature. The movements were classified with a hybrid Adaptive Resonance Theory-based neural network. Comparative results indicate that the proposed hybrid classifier not only has good classification accuracy (89.09%) but also a significantly improved computation time. PMID:26581764

  13. Multi-source adaptation joint kernel sparse representation for visual classification.

    PubMed

    Tao, JianWen; Hu, Wenjun; Wen, Shiting

    2016-04-01

    Most of the existing domain adaptation learning (DAL) methods relies on a single source domain to learn a classifier with well-generalized performance for the target domain of interest, which may lead to the so-called negative transfer problem. To this end, many multi-source adaptation methods have been proposed. While the advantages of using multi-source domains of information for establishing an adaptation model have been widely recognized, how to boost the robustness of the computational model for multi-source adaptation learning has only recently received attention. To address this issue for achieving enhanced performance, we propose in this paper a novel algorithm called multi-source Adaptation Regularization Joint Kernel Sparse Representation (ARJKSR) for robust visual classification problems. Specifically, ARJKSR jointly represents target dataset by a sparse linear combination of training data of each source domain in some optimal Reproduced Kernel Hilbert Space (RKHS), recovered by simultaneously minimizing the inter-domain distribution discrepancy and maximizing the local consistency, whilst constraining the observations from both target and source domains to share their sparse representations. The optimization problem of ARJKSR can be solved using an efficient alternative direction method. Under the framework ARJKSR, we further learn a robust label prediction matrix for the unlabeled instances of target domain based on the classical graph-based semi-supervised learning (GSSL) diagram, into which multiple Laplacian graphs constructed with the ARJKSR are incorporated. The validity of our method is examined by several visual classification problems. Results demonstrate the superiority of our method in comparison to several state-of-the-arts. PMID:26894961

  14. Texture descriptors based on adaptive neighborhoods for classification of pigmented skin lesions

    NASA Astrophysics Data System (ADS)

    González-Castro, Víctor; Debayle, Johan; Wazaefi, Yanal; Rahim, Mehdi; Gaudy-Marqueste, Caroline; Grob, Jean-Jacques; Fertil, Bernard

    2015-11-01

    Different texture descriptors are proposed for the automatic classification of skin lesions from dermoscopic images. They are based on color texture analysis obtained from (1) color mathematical morphology (MM) and Kohonen self-organizing maps (SOMs) or (2) local binary patterns (LBPs), computed with the use of local adaptive neighborhoods of the image. Neither of these two approaches needs a previous segmentation process. In the first proposed descriptor, the adaptive neighborhoods are used as structuring elements to carry out adaptive MM operations which are further combined by using Kohonen SOM; this has been compared with a nonadaptive version. In the second one, the adaptive neighborhoods enable geometrical feature maps to be defined, from which LBP histograms are computed. This has also been compared with a classical LBP approach. A receiver operating characteristics analysis of the experimental results shows that the adaptive neighborhood-based LBP approach yields the best results. It outperforms the nonadaptive versions of the proposed descriptors and the dermatologists' visual predictions.

  15. Collaborative adaptive landscape management (CALM) in rangelands: Discussion of general principles

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The management of rangeland landscapes involves broad spatial extents, mixed land ownership, and multiple resource objectives. Management outcomes depend on biophysical heterogeneity, highly variable weather conditions, land use legacies, and spatial processes such as wildlife movement, hydrological...

  16. Human action classification using adaptive key frame interval for feature extraction

    NASA Astrophysics Data System (ADS)

    Lertniphonphan, Kanokphan; Aramvith, Supavadee; Chalidabhongse, Thanarat H.

    2016-01-01

    Human action classification based on the adaptive key frame interval (AKFI) feature extraction is presented. Since human movement periods are different, the action intervals that contain the intensive and compact motion information are considered in this work. We specify AKFI by analyzing an amount of motion through time. The key frame is defined to be the local minimum interframe motion, which is computed by using frame differencing between consecutive frames. Once key frames are detected, the features within a segmented period are encoded by adaptive motion history image and key pose history image. The action representation consists of the local orientation histogram of the features during AKFI. The experimental results on Weizmann dataset, KTH dataset, and UT Interaction dataset demonstrate that the features can effectively classify action and can classify irregular cases of walking compared to other well-known algorithms.

  17. Research on a Pulmonary Nodule Segmentation Method Combining Fast Self-Adaptive FCM and Classification

    PubMed Central

    Liu, Hui; Zhang, Cai-Ming; Su, Zhi-Yuan; Wang, Kai; Deng, Kai

    2015-01-01

    The key problem of computer-aided diagnosis (CAD) of lung cancer is to segment pathologically changed tissues fast and accurately. As pulmonary nodules are potential manifestation of lung cancer, we propose a fast and self-adaptive pulmonary nodules segmentation method based on a combination of FCM clustering and classification learning. The enhanced spatial function considers contributions to fuzzy membership from both the grayscale similarity between central pixels and single neighboring pixels and the spatial similarity between central pixels and neighborhood and improves effectively the convergence rate and self-adaptivity of the algorithm. Experimental results show that the proposed method can achieve more accurate segmentation of vascular adhesion, pleural adhesion, and ground glass opacity (GGO) pulmonary nodules than other typical algorithms. PMID:25945120

  18. Research on a pulmonary nodule segmentation method combining fast self-adaptive FCM and classification.

    PubMed

    Liu, Hui; Zhang, Cai-Ming; Su, Zhi-Yuan; Wang, Kai; Deng, Kai

    2015-01-01

    The key problem of computer-aided diagnosis (CAD) of lung cancer is to segment pathologically changed tissues fast and accurately. As pulmonary nodules are potential manifestation of lung cancer, we propose a fast and self-adaptive pulmonary nodules segmentation method based on a combination of FCM clustering and classification learning. The enhanced spatial function considers contributions to fuzzy membership from both the grayscale similarity between central pixels and single neighboring pixels and the spatial similarity between central pixels and neighborhood and improves effectively the convergence rate and self-adaptivity of the algorithm. Experimental results show that the proposed method can achieve more accurate segmentation of vascular adhesion, pleural adhesion, and ground glass opacity (GGO) pulmonary nodules than other typical algorithms. PMID:25945120

  19. Deep classification of a large cryo-EM dataset defines the conformational landscape of the 26S proteasome

    PubMed Central

    Unverdorben, Pia; Beck, Florian; Śledź, Paweł; Schweitzer, Andreas; Pfeifer, Günter; Plitzko, Jürgen M.; Baumeister, Wolfgang; Förster, Friedrich

    2014-01-01

    The 26S proteasome is a 2.5 MDa molecular machine that executes the degradation of substrates of the ubiquitin–proteasome pathway. The molecular architecture of the 26S proteasome was recently established by cryo-EM approaches. For a detailed understanding of the sequence of events from the initial binding of polyubiquitylated substrates to the translocation into the proteolytic core complex, it is necessary to move beyond static structures and characterize the conformational landscape of the 26S proteasome. To this end we have subjected a large cryo-EM dataset acquired in the presence of ATP and ATP-γS to a deep classification procedure, which deconvolutes coexisting conformational states. Highly variable regions, such as the density assigned to the largest subunit, Rpn1, are now well resolved and rendered interpretable. Our analysis reveals the existence of three major conformations: in addition to the previously described ATP-hydrolyzing (ATPh) and ATP-γS conformations, an intermediate state has been found. Its AAA-ATPase module adopts essentially the same topology that is observed in the ATPh conformation, whereas the lid is more similar to the ATP-γS bound state. Based on the conformational ensemble of the 26S proteasome in solution, we propose a mechanistic model for substrate recognition, commitment, deubiquitylation, and translocation into the core particle. PMID:24706844

  20. Deep classification of a large cryo-EM dataset defines the conformational landscape of the 26S proteasome.

    PubMed

    Unverdorben, Pia; Beck, Florian; Śledź, Paweł; Schweitzer, Andreas; Pfeifer, Günter; Plitzko, Jürgen M; Baumeister, Wolfgang; Förster, Friedrich

    2014-04-15

    The 26S proteasome is a 2.5 MDa molecular machine that executes the degradation of substrates of the ubiquitin-proteasome pathway. The molecular architecture of the 26S proteasome was recently established by cryo-EM approaches. For a detailed understanding of the sequence of events from the initial binding of polyubiquitylated substrates to the translocation into the proteolytic core complex, it is necessary to move beyond static structures and characterize the conformational landscape of the 26S proteasome. To this end we have subjected a large cryo-EM dataset acquired in the presence of ATP and ATP-γS to a deep classification procedure, which deconvolutes coexisting conformational states. Highly variable regions, such as the density assigned to the largest subunit, Rpn1, are now well resolved and rendered interpretable. Our analysis reveals the existence of three major conformations: in addition to the previously described ATP-hydrolyzing (ATPh) and ATP-γS conformations, an intermediate state has been found. Its AAA-ATPase module adopts essentially the same topology that is observed in the ATPh conformation, whereas the lid is more similar to the ATP-γS bound state. Based on the conformational ensemble of the 26S proteasome in solution, we propose a mechanistic model for substrate recognition, commitment, deubiquitylation, and translocation into the core particle. PMID:24706844

  1. Unveiling Undercover Cropland Inside Forests Using Landscape Variables: A Supplement to Remote Sensing Image Classification

    PubMed Central

    Ayanu, Yohannes; Conrad, Christopher; Jentsch, Anke; Koellner, Thomas

    2015-01-01

    The worldwide demand for food has been increasing due to the rapidly growing global population, and agricultural lands have increased in extent to produce more food crops. The pattern of cropland varies among different regions depending on the traditional knowledge of farmers and availability of uncultivated land. Satellite images can be used to map cropland in open areas but have limitations for detecting undergrowth inside forests. Classification results are often biased and need to be supplemented with field observations. Undercover cropland inside forests in the Bale Mountains of Ethiopia was assessed using field observed percentage cover of land use/land cover classes, and topographic and location parameters. The most influential factors were identified using Boosted Regression Trees and used to map undercover cropland area. Elevation, slope, easterly aspect, distance to settlements, and distance to national park were found to be the most influential factors determining undercover cropland area. When there is very high demand for growing food crops, constrained under restricted rights for clearing forest, cultivation could take place within forests as an undercover. Further research on the impact of undercover cropland on ecosystem services and challenges in sustainable management is thus essential. PMID:26098107

  2. Detection and Classification of Motor Vehicle Noise in a Forested Landscape

    NASA Astrophysics Data System (ADS)

    Brown, Casey L.; Reed, Sarah E.; Dietz, Matthew S.; Fristrup, Kurt M.

    2013-11-01

    Noise emanating from human activity has become a common addition to natural soundscapes and has the potential to harm wildlife and erode human enjoyment of nature. In particular, motor vehicles traveling along roads and trails produce high levels of both chronic and intermittent noise, eliciting varied responses from a wide range of animal species. Anthropogenic noise is especially conspicuous in natural areas where ambient background sound levels are low. In this article, we present an acoustic method to detect and analyze motor vehicle noise. Our approach uses inexpensive consumer products to record sound, sound analysis software to automatically detect sound events within continuous recordings and measure their acoustic properties, and statistical classification methods to categorize sound events. We describe an application of this approach to detect motor vehicle noise on paved, gravel, and natural-surface roads, and off-road vehicle trails in 36 sites distributed throughout a national forest in the Sierra Nevada, CA, USA. These low-cost, unobtrusive methods can be used by scientists and managers to detect anthropogenic noise events for many potential applications, including ecological research, transportation and recreation planning, and natural resource management.

  3. Charting the landscape of priority problems in psychiatry, part 1: classification and diagnosis.

    PubMed

    Stephan, Klaas E; Bach, Dominik R; Fletcher, Paul C; Flint, Jonathan; Frank, Michael J; Friston, Karl J; Heinz, Andreas; Huys, Quentin J M; Owen, Michael J; Binder, Elisabeth B; Dayan, Peter; Johnstone, Eve C; Meyer-Lindenberg, Andreas; Montague, P Read; Schnyder, Ulrich; Wang, Xiao-Jing; Breakspear, Michael

    2016-01-01

    Contemporary psychiatry faces major challenges. Its syndrome-based disease classification is not based on mechanisms and does not guide treatment, which largely depends on trial and error. The development of therapies is hindered by ignorance of potential beneficiary patient subgroups. Neuroscientific and genetics research have yet to affect disease definitions or contribute to clinical decision making. In this challenging setting, what should psychiatric research focus on? In two companion papers, we present a list of problems nominated by clinicians and researchers from different disciplines as candidates for future scientific investigation of mental disorders. These problems are loosely grouped into challenges concerning nosology and diagnosis (this Personal View) and problems related to pathogenesis and aetiology (in the companion Personal View). Motivated by successful examples in other disciplines, particularly the list of Hilbert's problems in mathematics, this subjective and eclectic list of priority problems is intended for psychiatric researchers, helping to re-focus existing research and providing perspectives for future psychiatric science. PMID:26573970

  4. Classification of diabetes maculopathy images using data-adaptive neuro-fuzzy inference classifier.

    PubMed

    Ibrahim, Sulaimon; Chowriappa, Pradeep; Dua, Sumeet; Acharya, U Rajendra; Noronha, Kevin; Bhandary, Sulatha; Mugasa, Hatwib

    2015-12-01

    Prolonged diabetes retinopathy leads to diabetes maculopathy, which causes gradual and irreversible loss of vision. It is important for physicians to have a decision system that detects the early symptoms of the disease. This can be achieved by building a classification model using machine learning algorithms. Fuzzy logic classifiers group data elements with a degree of membership in multiple classes by defining membership functions for each attribute. Various methods have been proposed to determine the partitioning of membership functions in a fuzzy logic inference system. A clustering method partitions the membership functions by grouping data that have high similarity into clusters, while an equalized universe method partitions data into predefined equal clusters. The distribution of each attribute determines its partitioning as fine or coarse. A simple grid partitioning partitions each attribute equally and is therefore not effective in handling varying distribution amongst the attributes. A data-adaptive method uses a data frequency-driven approach to partition each attribute based on the distribution of data in that attribute. A data-adaptive neuro-fuzzy inference system creates corresponding rules for both finely distributed and coarsely distributed attributes. This method produced more useful rules and a more effective classification system. We obtained an overall accuracy of 98.55%. PMID:26109519

  5. Sampling network stratification by terrain classification in eroded agricultural landscapes at plot scale

    NASA Astrophysics Data System (ADS)

    Penížek, Vít; Zádorová, Tereza; Kodešová, Radka

    2015-04-01

    The description of soil properties variability is important aspect in land management and food production at plot scale. We describe novelty approach for design of sampling network on agricultural plots with high relief variability. The terrain properties were used for improved spatial prediction of soil properties including design of the sampling network. Regular sampling network, random sampling network, systematic unaligned sampling network and stratified sampling network schemes were compared to prove the advantages of relief based stratified sampling networks. The study was performed for humus horizon depth prediction on agriculture plot of 6.5 ha with dissected relief where originally homogenous soil cover was differentiated by erosion and sedimentation into mosaic of Chernozem, Regosol and colluvial soils. Moreover the comparison was done on three levels of sampling density (65, 40 and 24 sampling points). The stratification of sampling network was based on unsupervised relief classification. The performance of the soil properties prediction based on different sampling network was assesed by RMSE calculation based on predicted values versus validation dataset. According the RMSE, the stratified sampling network performed the best (7.4 cm) comparing the regular sampling network (10.8 cm), random sampling network (17.7 cm) and systematic unaligned sampling network (11.2 cm). The accuracy of the soil properties spatial prediction decreased with the decreasing number of sampling points, but the stratified network performed significantly better that other used methods. The study showed that, for soil properties spatial variability description at certain accuracy level, relief-based stratified network can contain 25 % less sampling points comparing to regular network. This leads to potential financial and person cost reduction for the soil survey. The study was supported by grant nr. 13-07516P of the Czech science foundation and by grant nr. QJ1230319 of the

  6. Classification

    ERIC Educational Resources Information Center

    Clary, Renee; Wandersee, James

    2013-01-01

    In this article, Renee Clary and James Wandersee describe the beginnings of "Classification," which lies at the very heart of science and depends upon pattern recognition. Clary and Wandersee approach patterns by first telling the story of the "Linnaean classification system," introduced by Carl Linnacus (1707-1778), who is…

  7. Multiclass Classification by Adaptive Network of Dendritic Neurons with Binary Synapses Using Structural Plasticity

    PubMed Central

    Hussain, Shaista; Basu, Arindam

    2016-01-01

    The development of power-efficient neuromorphic devices presents the challenge of designing spike pattern classification algorithms which can be implemented on low-precision hardware and can also achieve state-of-the-art performance. In our pursuit of meeting this challenge, we present a pattern classification model which uses a sparse connection matrix and exploits the mechanism of nonlinear dendritic processing to achieve high classification accuracy. A rate-based structural learning rule for multiclass classification is proposed which modifies a connectivity matrix of binary synaptic connections by choosing the best “k” out of “d” inputs to make connections on every dendritic branch (k < < d). Because learning only modifies connectivity, the model is well suited for implementation in neuromorphic systems using address-event representation (AER). We develop an ensemble method which combines several dendritic classifiers to achieve enhanced generalization over individual classifiers. We have two major findings: (1) Our results demonstrate that an ensemble created with classifiers comprising moderate number of dendrites performs better than both ensembles of perceptrons and of complex dendritic trees. (2) In order to determine the moderate number of dendrites required for a specific classification problem, a two-step solution is proposed. First, an adaptive approach is proposed which scales the relative size of the dendritic trees of neurons for each class. It works by progressively adding dendrites with fixed number of synapses to the network, thereby allocating synaptic resources as per the complexity of the given problem. As a second step, theoretical capacity calculations are used to convert each neuronal dendritic tree to its optimal topology where dendrites of each class are assigned different number of synapses. The performance of the model is evaluated on classification of handwritten digits from the benchmark MNIST dataset and compared with other

  8. Multiclass Classification by Adaptive Network of Dendritic Neurons with Binary Synapses Using Structural Plasticity.

    PubMed

    Hussain, Shaista; Basu, Arindam

    2016-01-01

    The development of power-efficient neuromorphic devices presents the challenge of designing spike pattern classification algorithms which can be implemented on low-precision hardware and can also achieve state-of-the-art performance. In our pursuit of meeting this challenge, we present a pattern classification model which uses a sparse connection matrix and exploits the mechanism of nonlinear dendritic processing to achieve high classification accuracy. A rate-based structural learning rule for multiclass classification is proposed which modifies a connectivity matrix of binary synaptic connections by choosing the best "k" out of "d" inputs to make connections on every dendritic branch (k < < d). Because learning only modifies connectivity, the model is well suited for implementation in neuromorphic systems using address-event representation (AER). We develop an ensemble method which combines several dendritic classifiers to achieve enhanced generalization over individual classifiers. We have two major findings: (1) Our results demonstrate that an ensemble created with classifiers comprising moderate number of dendrites performs better than both ensembles of perceptrons and of complex dendritic trees. (2) In order to determine the moderate number of dendrites required for a specific classification problem, a two-step solution is proposed. First, an adaptive approach is proposed which scales the relative size of the dendritic trees of neurons for each class. It works by progressively adding dendrites with fixed number of synapses to the network, thereby allocating synaptic resources as per the complexity of the given problem. As a second step, theoretical capacity calculations are used to convert each neuronal dendritic tree to its optimal topology where dendrites of each class are assigned different number of synapses. The performance of the model is evaluated on classification of handwritten digits from the benchmark MNIST dataset and compared with other spike

  9. SSD-Optimized Workload Placement with Adaptive Learning and Classification in HPC Environments

    SciTech Connect

    Wan, Lipeng; Lu, Zheng; Cao, Qing; Wang, Feiyi; Oral, H Sarp; Settlemyer, Bradley W

    2014-01-01

    In recent years, non-volatile memory devices such as SSD drives have emerged as a viable storage solution due to their increasing capacity and decreasing cost. Due to the unique capability and capacity requirements in large scale HPC (High Performance Computing) storage environment, a hybrid config- uration (SSD and HDD) may represent one of the most available and balanced solutions considering the cost and performance. Under this setting, effective data placement as well as movement with controlled overhead become a pressing challenge. In this paper, we propose an integrated object placement and movement framework and adaptive learning algorithms to address these issues. Specifically, we present a method that shuffle data objects across storage tiers to optimize the data access performance. The method also integrates an adaptive learning algorithm where real- time classification is employed to predict the popularity of data object accesses, so that they can be placed on, or migrate between SSD or HDD drives in the most efficient manner. We discuss preliminary results based on this approach using a simulator we developed to show that the proposed methods can dynamically adapt storage placements and access pattern as workloads evolve to achieve the best system level performance such as throughput.

  10. EEG-Based BCI System Using Adaptive Features Extraction and Classification Procedures

    PubMed Central

    Mangia, Anna Lisa; Cappello, Angelo

    2016-01-01

    Motor imagery is a common control strategy in EEG-based brain-computer interfaces (BCIs). However, voluntary control of sensorimotor (SMR) rhythms by imagining a movement can be skilful and unintuitive and usually requires a varying amount of user training. To boost the training process, a whole class of BCI systems have been proposed, providing feedback as early as possible while continuously adapting the underlying classifier model. The present work describes a cue-paced, EEG-based BCI system using motor imagery that falls within the category of the previously mentioned ones. Specifically, our adaptive strategy includes a simple scheme based on a common spatial pattern (CSP) method and support vector machine (SVM) classification. The system's efficacy was proved by online testing on 10 healthy participants. In addition, we suggest some features we implemented to improve a system's “flexibility” and “customizability,” namely, (i) a flexible training session, (ii) an unbalancing in the training conditions, and (iii) the use of adaptive thresholds when giving feedback.

  11. Adapted Verbal Feedback, Instructor Interaction and Student Emotions in the Landscape Architecture Studio

    ERIC Educational Resources Information Center

    Smith, Carl A.; Boyer, Mark E.

    2015-01-01

    In light of concerns with architectural students' emotional jeopardy during traditional desk and final-jury critiques, the authors pursue alternative approaches intended to provide more supportive and mentoring verbal assessment in landscape architecture studios. In addition to traditional studio-based critiques throughout a semester, we provide…

  12. Expanding the prevention armamentarium portfolio: a framework for promoting HIV-Conversant Communities within a complex, adaptive epidemiological landscape.

    PubMed

    Burman, Christopher J; Aphane, Marota; Mtapuri, Oliver; Delobelle, Peter

    2015-01-01

    The article describes a design journey that culminated in an HIV-Conversant Community Framework that is now being piloted in the Limpopo Province of South Africa. The objective of the initiative is to reduce the aggregate community viral load by building capacity at multiple scales that strengthens peoples' HIV-related navigational skill sets-while simultaneously opening a 'chronic situation' schema. The framework design is based upon a transdisciplinary methodological combination that synthesises ideas and constructs from complexity science and the management sciences as a vehicle through which to re-conceptualise HIV prevention. This resulted in a prototype that included the following constructs: managing HIV-prevention in a complex, adaptive epidemiological landscape; problematising and increasing the scope of the HIV knowledge armamentarium through education that focuses on the viral load and Langerhans cells; disruptive innovation and safe-fail probes followed by the facilitation of path creations and pattern management implementation techniques. These constructs are underpinned by a 'middle-ground' prevention approach which is designed to bridge the prevention 'fault line', enabling a multi-ontology conceptualisation of the challenge to be developed. The article concludes that stepping outside of the 'ordered' epistemological parameters of the existing prevention 'messaging' mind-set towards a more systemic approach that emphasises agency, structure and social practices as a contribution to 'ending AIDS by 2030' is worthy of further attention if communities are to engage more adaptively with the dynamic HIV landscape in South Africa. PMID:25888256

  13. Landscape risk factors for Lyme disease in the eastern broadleaf forest province of the Hudson River valley and the effect of explanatory data classification resolution.

    PubMed

    Messier, Kyle P; Jackson, Laura E; White, Jennifer L; Hilborn, Elizabeth D

    2015-01-01

    This study assessed how landcover classification affects associations between landscape characteristics and Lyme disease rate. Landscape variables were derived from the National Land Cover Database (NLCD), including native classes (e.g., deciduous forest, developed low intensity) and aggregate classes (e.g., forest, developed). Percent of each landcover type, median income, and centroid coordinates were calculated by census tract. Regression results from individual and aggregate variable models were compared with the dispersion parameter-based R(2) (Rα(2)) and AIC. The maximum Rα(2) was 0.82 and 0.83 for the best aggregate and individual model, respectively. The AICs for the best models differed by less than 0.5%. The aggregate model variables included forest, developed, agriculture, agriculture-squared, y-coordinate, y-coordinate-squared, income and income-squared. The individual model variables included deciduous forest, deciduous forest-squared, developed low intensity, pasture, y-coordinate, y-coordinate-squared, income, and income-squared. Results indicate that regional landscape models for Lyme disease rate are robust to NLCD landcover classification resolution. PMID:25779905

  14. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation.

    PubMed

    Fitzpatrick, Matthew C; Keller, Stephen R

    2015-01-01

    Local adaptation is a central feature of most species occupying spatially heterogeneous environments, and may factor critically in responses to environmental change. However, most efforts to model the response of species to climate change ignore intraspecific variation due to local adaptation. Here, we present a new perspective on spatial modelling of organism-environment relationships that combines genomic data and community-level modelling to develop scenarios regarding the geographic distribution of genomic variation in response to environmental change. Rather than modelling species within communities, we use these techniques to model large numbers of loci across genomes. Using balsam poplar (Populus balsamifera) as a case study, we demonstrate how our framework can accommodate nonlinear responses of loci to environmental gradients. We identify a threshold response to temperature in the circadian clock gene GIGANTEA-5 (GI5), suggesting that this gene has experienced strong local adaptation to temperature. We also demonstrate how these methods can map ecological adaptation from genomic data, including the identification of predicted differences in the genetic composition of populations under current and future climates. Community-level modelling of genomic variation represents an important advance in landscape genomics and spatial modelling of biodiversity that moves beyond species-level assessments of climate change vulnerability. PMID:25270536

  15. Factors shaping the adaptive landscape for arboviruses: implications for the emergence of disease

    PubMed Central

    Coffey, Lark L; Forrester, Naomi; Tsetsarkin, Konstantin; Vasilakis, Nikos; Weaver, Scott C

    2013-01-01

    Many examples of the emergence or re-emergence of infectious diseases involve the adaptation of zoonotic viruses to new amplification hosts or to humans themselves. These include several instances of simple mutational adaptations, often to hosts closely related to the natural reservoirs. However, based on theoretical grounds, arthropod-borne viruses, or arboviruses, may face several challenges for adaptation to new hosts. Here, we review recent findings regarding adaptive evolution of arboviruses and its impact on disease emergence. We focus on the zoonotic alphaviruses Venezuelan equine encephalitis and chikungunya viruses, which have undergone adaptive evolution that mediated recent outbreaks of disease, as well as the flaviviruses dengue and West Nile viruses, which have emerged via less dramatic adaptive mechanisms. PMID:23374123

  16. Using ethnographic, landscape history and climate data to identify smallholder adaptation strategies to tidal regime changes in the Amazon Estuary

    NASA Astrophysics Data System (ADS)

    Vogt, N. D.; Fernandes, K. D.; Pinedo-Vasquez, M.

    2013-12-01

    Although climate change is predicted to negatively impact production of smallholder farmers in tropical estuaries, how changes in the local climate will impact tidal dynamics specifically relevant to the Amazon River estuarine populations is not clear. We argue that using ethnographic and landscape history data can improve the linkages between climate studies and changes in tidal patterns relevant to local populations. Survey data collected from local elders describe spatial and temporal variations in the local hydro-climatic conditions over recent decades and how farmers are adapting their resource-use patterns to these changes. We also analyze how they adapt resource-use system to unpredictable events. The ethnographic and landscape history information are then used to guide climate studies by identifying how to aggregate climate and tidal data to seasons of production relevant to the study population. Climate studies often aggregate data into astronomical seasons not taking into account local production calendars, which may mask long term trends or patterns of extreme events underway that affect local production. The climate deviations are then correlated to large-scale forcings, such as the El Niño Southern Oscillation (ENSO), to verify whether seasonal climate forecast can be used to predict events to which local populations are most vulnerable. We have applied this approach to identify and analyze extremes changes in the local climate regimens in the Amazon Estuary in both north and south channels using over 40 years of river heightand precipitation data. We present the most significant changes underway, climate drivers of them, and discuss how smallholder farmers are able to adapt to the challenges and opportunities produced by ongoing changes in the local hydro-climatic patterns.

  17. Medical image classification using spatial adjacent histogram based on adaptive local binary patterns.

    PubMed

    Liu, Dong; Wang, Shengsheng; Huang, Dezhi; Deng, Gang; Zeng, Fantao; Chen, Huiling

    2016-05-01

    Medical image recognition is an important task in both computer vision and computational biology. In the field of medical image classification, representing an image based on local binary patterns (LBP) descriptor has become popular. However, most existing LBP-based methods encode the binary patterns in a fixed neighborhood radius and ignore the spatial relationships among local patterns. The ignoring of the spatial relationships in the LBP will cause a poor performance in the process of capturing discriminative features for complex samples, such as medical images obtained by microscope. To address this problem, in this paper we propose a novel method to improve local binary patterns by assigning an adaptive neighborhood radius for each pixel. Based on these adaptive local binary patterns, we further propose a spatial adjacent histogram strategy to encode the micro-structures for image representation. An extensive set of evaluations are performed on four medical datasets which show that the proposed method significantly improves standard LBP and compares favorably with several other prevailing approaches. PMID:27058283

  18. Self-Adaptive MOEA Feature Selection for Classification of Bankruptcy Prediction Data

    PubMed Central

    Gaspar-Cunha, A.; Recio, G.; Costa, L.; Estébanez, C.

    2014-01-01

    Bankruptcy prediction is a vast area of finance and accounting whose importance lies in the relevance for creditors and investors in evaluating the likelihood of getting into bankrupt. As companies become complex, they develop sophisticated schemes to hide their real situation. In turn, making an estimation of the credit risks associated with counterparts or predicting bankruptcy becomes harder. Evolutionary algorithms have shown to be an excellent tool to deal with complex problems in finances and economics where a large number of irrelevant features are involved. This paper provides a methodology for feature selection in classification of bankruptcy data sets using an evolutionary multiobjective approach that simultaneously minimise the number of features and maximise the classifier quality measure (e.g., accuracy). The proposed methodology makes use of self-adaptation by applying the feature selection algorithm while simultaneously optimising the parameters of the classifier used. The methodology was applied to four different sets of data. The obtained results showed the utility of using the self-adaptation of the classifier. PMID:24707201

  19. Self-adaptive MOEA feature selection for classification of bankruptcy prediction data.

    PubMed

    Gaspar-Cunha, A; Recio, G; Costa, L; Estébanez, C

    2014-01-01

    Bankruptcy prediction is a vast area of finance and accounting whose importance lies in the relevance for creditors and investors in evaluating the likelihood of getting into bankrupt. As companies become complex, they develop sophisticated schemes to hide their real situation. In turn, making an estimation of the credit risks associated with counterparts or predicting bankruptcy becomes harder. Evolutionary algorithms have shown to be an excellent tool to deal with complex problems in finances and economics where a large number of irrelevant features are involved. This paper provides a methodology for feature selection in classification of bankruptcy data sets using an evolutionary multiobjective approach that simultaneously minimise the number of features and maximise the classifier quality measure (e.g., accuracy). The proposed methodology makes use of self-adaptation by applying the feature selection algorithm while simultaneously optimising the parameters of the classifier used. The methodology was applied to four different sets of data. The obtained results showed the utility of using the self-adaptation of the classifier. PMID:24707201

  20. An adaptive threshold based image processing technique for improved glaucoma detection and classification.

    PubMed

    Issac, Ashish; Partha Sarathi, M; Dutta, Malay Kishore

    2015-11-01

    Glaucoma is an optic neuropathy which is one of the main causes of permanent blindness worldwide. This paper presents an automatic image processing based method for detection of glaucoma from the digital fundus images. In this proposed work, the discriminatory parameters of glaucoma infection, such as cup to disc ratio (CDR), neuro retinal rim (NRR) area and blood vessels in different regions of the optic disc has been used as features and fed as inputs to learning algorithms for glaucoma diagnosis. These features which have discriminatory changes with the occurrence of glaucoma are strategically used for training the classifiers to improve the accuracy of identification. The segmentation of optic disc and cup based on adaptive threshold of the pixel intensities lying in the optic nerve head region. Unlike existing methods the proposed algorithm is based on an adaptive threshold that uses local features from the fundus image for segmentation of optic cup and optic disc making it invariant to the quality of the image and noise content which may find wider acceptability. The experimental results indicate that such features are more significant in comparison to the statistical or textural features as considered in existing works. The proposed work achieves an accuracy of 94.11% with a sensitivity of 100%. A comparison of the proposed work with the existing methods indicates that the proposed approach has improved accuracy of classification glaucoma from a digital fundus which may be considered clinically significant. PMID:26321351

  1. Improving brain-computer interface classification using adaptive common spatial patterns.

    PubMed

    Song, Xiaomu; Yoon, Suk-Chung

    2015-06-01

    Common Spatial Patterns (CSP) is a widely used spatial filtering technique for electroencephalography (EEG)-based brain-computer interface (BCI). It is a two-class supervised technique that needs subject-specific training data. Due to EEG nonstationarity, EEG signal may exhibit significant intra- and inter-subject variation. As a result, spatial filters learned from a subject may not perform well for data acquired from the same subject at a different time or from other subjects performing the same task. Studies have been performed to improve CSP's performance by adding regularization terms into the training. Most of them require target subjects' training data with known class labels. In this work, an adaptive CSP (ACSP) method is proposed to analyze single trial EEG data from single and multiple subjects. The method does not estimate target data's class labels during the adaptive learning and updates spatial filters for both classes simultaneously. The proposed method was evaluated based on a comparison study with the classic CSP and several CSP-based adaptive methods using motor imagery EEG data from BCI competitions. Experimental results indicate that the proposed method can improve the classification performance as compared to the other methods. For circumstances where true class labels of target data are not instantly available, it was examined if adding classified target data to training data would improve the ACSP learning. Experimental results show that it would be better to exclude them from the training data. The proposed ACSP method can be performed in real-time and is potentially applicable to various EEG-based BCI applications. PMID:25909828

  2. Noise-Robust Spectral Signature Classification in Non-resolved Object Detection using Feedback Controlled Adaptive Learning

    NASA Astrophysics Data System (ADS)

    Schmalz, M.; Key, G.

    2012-09-01

    Accurate spectral signature classification is key to reliable nonresolved detection and recognition of spaceborne objects. In classical signature-based recognition applications, classification accuracy has been shown to depend on accurate spectral endmember discrimination. Unfortunately, signatures are corrupted by noise and clutter that can be nonergodic in astronomical imaging practice. In previous work, we have shown that object class separation and classifier refinement results can be severely corrupted by input noise, leading to suboptimal classification. We have also shown that computed pattern recognition, like its human counterpart, can benefit from processes such as learning or forgetting, which in spectral signature classification can support adaptive tracking of input nonergodicities. In this paper, we model learning as the acquisition or insertion of a new pattern into a classifier's knowledge base. For example, in neural nets (NNs), this insertion process could correspond to the superposition of a new pattern onto the NN weight matrix. Similarly, we model forgetting as the deletion of a pattern currently stored in the classifier knowledge base, for example, as a pattern deletion operation on the NN weight matrix, which is a difficult goal with classical neural nets (CNNs). In particular, this paper discusses the implementation of feedback control for pattern insertion and deletion in lattice associative memories (LAMs) and dynamically adaptive statistical data fusion (DASDAF) paradigms, in support of signature classification. It is shown that adaptive classifiers based on LNN or DASDAF technology can achieve accurate signature classification in the presence of nonergodic Gaussian and non-Gaussian noise, at low signal-to-noise ratio (SNR). Demonstration involves classification of multiple closely spaced, noise corrupted signatures from a NASA database of space material signatures at SNR > 0.1:1.

  3. Pathogen population bottlenecks and adaptive landscapes: overcoming the barriers to disease emergence.

    PubMed

    Geoghegan, Jemma L; Senior, Alistair M; Holmes, Edward C

    2016-08-31

    Emerging diseases are a major challenge to public health. Revealing the evolutionary processes that allow novel pathogens to adapt to new hosts, also the potential barriers to host adaptation, is central to understanding the drivers of disease emergence. In particular, it is unclear how the genetics and ecology of pathogens interact to shape the likelihood of successful cross-species transmission. To better understand the determinants of host adaptation and emergence, we modelled key aspects of pathogen evolutionary dynamics at both intra- and inter-host scales, using parameter values similar to those observed in influenza virus. We considered the possibility of acquiring the necessary host adaptive mutations both before ('off-the-shelf' emergence) and after ('tailor-made' emergence) a virus is transmitted from a donor to a new recipient species. Under both scenarios, population bottlenecks at inter-host transmission act as a major barrier to host adaptation, greatly limiting the number of adaptive mutations that are able to cross the species barrier. In addition, virus emergence is hindered if the fitness valley between the donor and recipient hosts is either too steep or too shallow. Overall, our results reveal where in evolutionary parameter space a virus could adapt to and become transmissible in a new species. PMID:27581875

  4. Expanding the prevention armamentarium portfolio: A framework for promoting HIV-Conversant Communities within a complex, adaptive epidemiological landscape

    PubMed Central

    Burman, Christopher J.; Aphane, Marota; Mtapuri, Oliver; Delobelle, Peter

    2015-01-01

    Abstract The article describes a design journey that culminated in an HIV-Conversant Community Framework that is now being piloted in the Limpopo Province of South Africa. The objective of the initiative is to reduce the aggregate community viral load by building capacity at multiple scales that strengthens peoples' HIV-related navigational skill sets—while simultaneously opening a ‘chronic situation’ schema. The framework design is based upon a transdisciplinary methodological combination that synthesises ideas and constructs from complexity science and the management sciences as a vehicle through which to re-conceptualise HIV prevention. This resulted in a prototype that included the following constructs: managing HIV-prevention in a complex, adaptive epidemiological landscape; problematising and increasing the scope of the HIV knowledge armamentarium through education that focuses on the viral load and Langerhans cells; disruptive innovation and safe-fail probes followed by the facilitation of path creations and pattern management implementation techniques. These constructs are underpinned by a ‘middle-ground’ prevention approach which is designed to bridge the prevention ‘fault line’, enabling a multi-ontology conceptualisation of the challenge to be developed. The article concludes that stepping outside of the ‘ordered’ epistemological parameters of the existing prevention ‘messaging’ mind-set towards a more systemic approach that emphasises agency, structure and social practices as a contribution to ‘ending AIDS by 2030’ is worthy of further attention if communities are to engage more adaptively with the dynamic HIV landscape in South Africa. PMID:25888256

  5. Landscape genomics reveal signatures of local adaptation in barley (Hordeum vulgare L.)

    PubMed Central

    Abebe, Tiegist D.; Naz, Ali A.; Léon, Jens

    2015-01-01

    Land plants are sessile organisms that cannot escape the adverse climatic conditions of a given environment. Hence, adaptation is one of the solutions to surviving in a challenging environment. This study was aimed at detecting adaptive loci in barley landraces that are affected by selection. To that end, a diverse population of barley landraces was analyzed using the genotyping by sequencing approach. Climatic data for altitude, rainfall and temperature were collected from 61 weather sites near the origin of selected landraces across Ethiopia. Population structure analysis revealed three groups whereas spatial analysis accounted significant similarities at shorter geographic distances (< 40 Km) among barley landraces. Partitioning the variance between climate variables and geographic distances indicated that climate variables accounted for most of the explainable genetic variation. Markers by climatic variables association analysis resulted in altogether 18 and 62 putative adaptive loci using Bayenv and latent factor mixed model (LFMM), respectively. Subsequent analysis of the associated SNPs revealed putative candidate genes for plant adaptation. This study highlights the presence of putative adaptive loci among barley landraces representing original gene pool of the farming communities. PMID:26483825

  6. Classification

    NASA Astrophysics Data System (ADS)

    Oza, Nikunj

    2012-03-01

    A supervised learning task involves constructing a mapping from input data (normally described by several features) to the appropriate outputs. A set of training examples— examples with known output values—is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. Within supervised learning, one type of task is a classification learning task, in which each output is one or more classes to which the input belongs. For example, we may have data consisting of observations of sunspots. In a classification learning task, our goal may be to learn to classify sunspots into one of several types. Each example may correspond to one candidate sunspot with various measurements or just an image. A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot. This model can then be used to classify previously unseen sunspots based on the candidate’s measurements. The generalization performance of a learned model (how closely the target outputs and the model’s predicted outputs agree for patterns that have not been presented to the learning algorithm) would provide an indication of how well the model has learned the desired mapping. More formally, a classification learning algorithm L takes a training set T as its input. The training set consists of |T| examples or instances. It is assumed that there is a probability distribution D from which all training examples are drawn independently—that is, all the training examples are independently and identically distributed (i.i.d.). The ith training example is of the form (x_i, y_i), where x_i is a vector of values of several features and y_i represents the class to be predicted.* In the sunspot classification example given above, each training example

  7. Adaptable Neighbours: Movement Patterns of GPS-Collared Leopards in Human Dominated Landscapes in India

    PubMed Central

    Odden, Morten; Athreya, Vidya; Rattan, Sandeep; Linnell, John D. C.

    2014-01-01

    Understanding the nature of the interactions between humans and wildlife is of vital importance for conflict mitigation. We equipped five leopards with GPS-collars in Maharashtra (4) and Himachal Pradesh (1), India, to study movement patterns in human-dominated landscapes outside protected areas. An adult male and an adult female were both translocated 52 km, and exhibited extensive, and directional, post release movements (straight line movements: male  = 89 km in 37 days, female  = 45 km in 5 months), until they settled in home ranges of 42 km2 (male) and 65 km2 (female). The three other leopards, two adult females and a young male were released close to their capture sites and used small home ranges of 8 km2 (male), 11 km2 and 15 km2 (females). Movement patterns were markedly nocturnal, with hourly step lengths averaging 339±9.5 m (SE) during night and 60±4.1 m during day, and night locations were significantly closer to human settlements than day locations. However, more nocturnal movements were observed among those three living in the areas with high human population densities. These visited houses regularly at nighttime (20% of locations <25 m from houses), but rarely during day (<1%). One leopard living in a sparsely populated area avoided human settlements both day and night. The small home ranges of the leopards indicate that anthropogenic food resources may be plentiful although wild prey is absent. The study provides clear insights into the ability of leopards to live and move in landscapes that are extremely modified by human activity. PMID:25390067

  8. ADAPT: building conceptual models of the physical and biological processes across permafrost landscapes

    NASA Astrophysics Data System (ADS)

    Allard, M.; Vincent, W. F.; Lemay, M.

    2012-12-01

    Fundamental and applied permafrost research is called upon in Canada in support of environmental protection, economic development and for contributing to the international efforts in understanding climatic and ecological feedbacks of permafrost thawing under a warming climate. The five year "Arctic Development and Adaptation to Permafrost in Transition" program (ADAPT) funded by NSERC brings together 14 scientists from 10 Canadian universities and involves numerous collaborators from academia, territorial and provincial governments, Inuit communities and industry. The geographical coverage of the program encompasses all of the permafrost regions of Canada. Field research at a series of sites across the country is being coordinated. A common protocol for measuring ground thermal and moisture regime, characterizing terrain conditions (vegetation, topography, surface water regime and soil organic matter contents) is being applied in order to provide inputs for designing a general model to provide an understanding of transfers of energy and matter in permafrost terrain, and the implications for biological and human systems. The ADAPT mission is to produce an 'Integrated Permafrost Systems Science' framework that will be used to help generate sustainable development and adaptation strategies for the North in the context of rapid socio-economic and climate change. ADAPT has three major objectives: to examine how changing precipitation and warming temperatures affect permafrost geosystems and ecosystems, specifically by testing hypotheses concerning the influence of the snowpack, the effects of water as a conveyor of heat, sediments, and carbon in warming permafrost terrain and the processes of permafrost decay; to interact directly with Inuit communities, the public sector and the private sector for development and adaptation to changes in permafrost environments; and to train the new generation of experts and scientists in this critical domain of research in Canada

  9. Image classification with densely sampled image windows and generalized adaptive multiple kernel learning.

    PubMed

    Yan, Shengye; Xu, Xinxing; Xu, Dong; Lin, Stephen; Li, Xuelong

    2015-03-01

    We present a framework for image classification that extends beyond the window sampling of fixed spatial pyramids and is supported by a new learning algorithm. Based on the observation that fixed spatial pyramids sample a rather limited subset of the possible image windows, we propose a method that accounts for a comprehensive set of windows densely sampled over location, size, and aspect ratio. A concise high-level image feature is derived to effectively deal with this large set of windows, and this higher level of abstraction offers both efficient handling of the dense samples and reduced sensitivity to misalignment. In addition to dense window sampling, we introduce generalized adaptive l(p)-norm multiple kernel learning (GA-MKL) to learn a robust classifier based on multiple base kernels constructed from the new image features and multiple sets of prelearned classifiers from other classes. With GA-MKL, multiple levels of image features are effectively fused, and information is shared among different classifiers. Extensive evaluation on benchmark datasets for object recognition (Caltech256 and Caltech101) and scene recognition (15Scenes) demonstrate that the proposed method outperforms the state-of-the-art under a broad range of settings. PMID:24968365

  10. Convective cloud identification and classification in daytime satellite imagery using standard deviation limited adaptive clustering

    NASA Astrophysics Data System (ADS)

    Berendes, Todd A.; Mecikalski, John R.; MacKenzie, Wayne M.; Bedka, Kristopher M.; Nair, U. S.

    2008-10-01

    This paper describes a statistical clustering approach toward the classification of cloud types within meteorological satellite imagery, specifically, visible and infrared data. The method is based on the Standard Deviation Limited Adaptive Clustering (SDLAC) procedure, which has been used to classify a variety of features within both polar orbiting and geostationary imagery, including land cover, volcanic ash, dust, and clouds of various types. In this study, the focus is on classifying cumulus clouds of various types (e.g., "fair weather, "towering, and newly glaciated cumulus, in addition to cumulonimbus). The SDLAC algorithm is demonstrated by showing examples using Geostationary Operational Environmental Satellite (GOES) 12, Meteosat Second Generation's (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI), and the Moderate Resolution Infrared Spectrometer (MODIS). Results indicate that the method performs well, classifying cumulus similarly between MODIS, SEVIRI, and GOES, despite the obvious channel and resolution differences between these three sensors. The SDLAC methodology has been used in several research activities related to convective weather forecasting, which offers some proof of concept for its value.

  11. Classification

    NASA Technical Reports Server (NTRS)

    Oza, Nikunj C.

    2011-01-01

    A supervised learning task involves constructing a mapping from input data (normally described by several features) to the appropriate outputs. Within supervised learning, one type of task is a classification learning task, in which each output is one or more classes to which the input belongs. In supervised learning, a set of training examples---examples with known output values---is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. For example, we may have data consisting of observations of sunspots. In a classification learning task, our goal may be to learn to classify sunspots into one of several types. Each example may correspond to one candidate sunspot with various measurements or just an image. A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot. This model can then be used to classify previously unseen sunspots based on the candidate's measurements. This chapter discusses methods to perform machine learning, with examples involving astronomy.

  12. Genetic regulatory network motifs constrain adaptation through curvature in the landscape of mutational (co)variance.

    PubMed

    Hether, Tyler D; Hohenlohe, Paul A

    2014-04-01

    Systems biology is accumulating a wealth of understanding about the structure of genetic regulatory networks, leading to a more complete picture of the complex genotype-phenotype relationship. However, models of multivariate phenotypic evolution based on quantitative genetics have largely not incorporated a network-based view of genetic variation. Here we model a set of two-node, two-phenotype genetic network motifs, covering a full range of regulatory interactions. We find that network interactions result in different patterns of mutational (co)variance at the phenotypic level (the M-matrix), not only across network motifs but also across phenotypic space within single motifs. This effect is due almost entirely to mutational input of additive genetic (co)variance. Variation in M has the effect of stretching and bending phenotypic space with respect to evolvability, analogous to the curvature of space-time under general relativity, and similar mathematical tools may apply in each case. We explored the consequences of curvature in mutational variation by simulating adaptation under divergent selection with gene flow. Both standing genetic variation (the G-matrix) and rate of adaptation are constrained by M, so that G and adaptive trajectories are curved across phenotypic space. Under weak selection the phenotypic mean at migration-selection balance also depends on M. PMID:24219635

  13. MRI-based treatment plan simulation and adaptation for ion radiotherapy using a classification-based approach

    PubMed Central

    2013-01-01

    Background In order to benefit from the highly conformal irradiation of tumors in ion radiotherapy, sophisticated treatment planning and simulation are required. The purpose of this study was to investigate the potential of MRI for ion radiotherapy treatment plan simulation and adaptation using a classification-based approach. Methods Firstly, a voxelwise tissue classification was applied to derive pseudo CT numbers from MR images using up to 8 contrasts. Appropriate MR sequences and parameters were evaluated in cross-validation studies of three phantoms. Secondly, ion radiotherapy treatment plans were optimized using both MRI-based pseudo CT and reference CT and recalculated on reference CT. Finally, a target shift was simulated and a treatment plan adapted to the shift was optimized on a pseudo CT and compared to reference CT optimizations without plan adaptation. Results The derivation of pseudo CT values led to mean absolute errors in the range of 81 - 95 HU. Most significant deviations appeared at borders between air and different tissue classes and originated from partial volume effects. Simulations of ion radiotherapy treatment plans using pseudo CT for optimization revealed only small underdosages in distal regions of a target volume with deviations of the mean dose of PTV between 1.4 - 3.1% compared to reference CT optimizations. A plan adapted to the target volume shift and optimized on the pseudo CT exhibited a comparable target dose coverage as a non-adapted plan optimized on a reference CT. Conclusions We were able to show that a MRI-based derivation of pseudo CT values using a purely statistical classification approach is feasible although no physical relationship exists. Large errors appeared at compact bone classes and came from an imperfect distinction of bones and other tissue types in MRI. In simulations of treatment plans, it was demonstrated that these deviations are comparable to uncertainties of a target volume shift of 2 mm in two directions

  14. Dynamic transition of transcription and chromatin landscape during fission yeast adaptation to glucose starvation.

    PubMed

    Oda, Arisa; Takemata, Naomichi; Hirata, Yoshito; Miyoshi, Tomoichiro; Suzuki, Yutaka; Sugano, Sumio; Ohta, Kunihiro

    2015-05-01

    Shortage of glucose, the primary energy source for all organisms, is one of the most critical stresses influencing cell viability. Glucose starvation promptly induces changes in mRNA and noncoding RNA (ncRNA) transcription. We previously reported that glucose starvation induces long ncRNA (lncRNA) transcription in the 5' segment of a fission yeast gluconeogenesis gene (fbp1+), which leads to stepwise chromatin alteration around the fbp1+ promoter and to subsequent robust gene activation. Here, we analyzed genomewide transcription by strand-specific RNA sequencing, together with chromatin landscape by immunoprecipitation sequencing (ChIP-seq). Clustering analysis showed that distinct mRNAs and ncRNAs are induced at the early, middle and later stages of cellular response to glucose starvation. The starvation-induced transcription depends substantially on the stress-responsive transcription factor Atf1. Using a new computer program that examines dynamic changes in expression patterns, we identified ncRNAs with similar behavior to the fbp1+ lncRNA. We confirmed that there are continuous lncRNAs associated with local reduction of histone density. Overlapping with the regions for transcription of these lncRNAs, antisense RNAs are antagonistically transcribed under glucose-rich conditions. These results suggest that Atf1-dependent integrated networks of mRNA and lncRNA govern drastic changes in cell physiology in response to glucose starvation. PMID:25728061

  15. A Monte Carlo simulation based two-stage adaptive resonance theory mapping approach for offshore oil spill vulnerability index classification.

    PubMed

    Li, Pu; Chen, Bing; Li, Zelin; Zheng, Xiao; Wu, Hongjing; Jing, Liang; Lee, Kenneth

    2014-09-15

    In this paper, a Monte Carlo simulation based two-stage adaptive resonance theory mapping (MC-TSAM) model was developed to classify a given site into distinguished zones representing different levels of offshore Oil Spill Vulnerability Index (OSVI). It consisted of an adaptive resonance theory (ART) module, an ART Mapping module, and a centroid determination module. Monte Carlo simulation was integrated with the TSAM approach to address uncertainties that widely exist in site conditions. The applicability of the proposed model was validated by classifying a large coastal area, which was surrounded by potential oil spill sources, based on 12 features. Statistical analysis of the results indicated that the classification process was affected by multiple features instead of one single feature. The classification results also provided the least or desired number of zones which can sufficiently represent the levels of offshore OSVI in an area under uncertainty and complexity, saving time and budget in spill monitoring and response. PMID:25044043

  16. Adaptive classification of marine ecosystems: Identifying biologically meaningful regions in the marine environment

    NASA Astrophysics Data System (ADS)

    Gregr, Edward J.; Bodtker, Karin M.

    2007-03-01

    The move to ecosystem-based management of marine fisheries and endangered species would be greatly facilitated by a quantitative method for identifying marine ecosystems that captures temporal dynamics at meso-scale (10s or 100s of kilometers) resolutions. Understanding the dynamics of ecosystem boundaries, which may differ according to the species of interest or the management objectives, is a fundamental challenge of ecosystem-based management. We present an adaptive ecosystem classification that begins to address these challenges. To demonstrate the approach, we quantitatively bounded distinct, biologically meaningful marine regions in the North Pacific Ocean based on physical oceanography. We identified the regions by applying image classification algorithms to a comprehensive description of the ocean's surface, derived from an oceanographic circulation model. Our resulting maps illustrate 15 distinct marine regions. The size and location of these regions related well to previously described water masses in the North Pacific. We investigated seasonal and long-term changes in the pattern of regions and their boundaries by dividing the oceanographic data into four seasons and two 10-year time periods, one on either side of the 1976-1977 North Pacific Ocean climate regime shift. We compared our results for each season across the regime shift and for sequential seasons within regimes using the Kappa Index of Agreement and the index of Average Mutual Information. Seasonal patterns were more similar between regimes than from one season to the next within a regime, while the magnitude of seasonal transitions appeared to differ before and after the regime shift. We assessed the biological relevance of the identified regions using seasonal maps derived from remotely sensed chlorophyll- a concentrations ([chl-a]). We used Kruskal-Wallis and Wilcoxon rank sum tests to evaluate the correspondence between the [chl-a] maps and our post-regime shift regions. There was a

  17. Key landscape ecology metrics for assessing climate change adaptation options: rate of change and patchiness of impacts

    USGS Publications Warehouse

    López-Hoffman, Laura; Breshears, David D.; Allen, Craig D.; Miller, Marc L.

    2013-01-01

    Under a changing climate, devising strategies to help stakeholders adapt to alterations to ecosystems and their services is of utmost importance. In western North America, diminished snowpack and river flows are causing relatively gradual, homogeneous (system-wide) changes in ecosystems and services. In addition, increased climate variability is also accelerating the incidence of abrupt and patchy disturbances such as fires, floods and droughts. This paper posits that two key variables often considered in landscape ecology—the rate of change and the degree of patchiness of change—can aid in developing climate change adaptation strategies. We use two examples from the “borderland” region of the southwestern United States and northwestern Mexico. In piñon-juniper woodland die-offs that occurred in the southwestern United States during the 2000s, ecosystem services suddenly crashed in some parts of the system while remaining unaffected in other locations. The precise timing and location of die-offs was uncertain. On the other hand, slower, homogeneous change, such as the expected declines in water supply to the Colorado River delta, will likely impact the entire ecosystem, with ecosystem services everywhere in the delta subject to alteration, and all users likely exposed. The rapidity and spatial heterogeneity of faster, patchy climate change exemplified by tree die-off suggests that decision-makers and local stakeholders would be wise to operate under a Rawlsian “veil of ignorance,” and implement adaptation strategies that allow ecosystem service users to equitably share the risk of sudden loss of ecosystem services before actual ecosystem changes occur. On the other hand, in the case of slower, homogeneous, system-wide impacts to ecosystem services as exemplified by the Colorado River delta, adaptation strategies can be implemented after the changes begin, but will require a fundamental rethinking of how ecosystems and services are used and valued. In

  18. Who runs fastest in an adaptive landscape: sexual versus asexual reproduction

    NASA Astrophysics Data System (ADS)

    Holmström, Kerstin; Jensen, Henrik Jeldtoft

    2004-06-01

    We compare the speed with which a sexual, respectively, an asexual, population is able to respond to a biased selective pressure. Our model focuses on the Weismann hypothesis that the extra variation caused by crossing-over and recombination during sexual reproduction allows a sexual population to adapt faster. We find, however, that the extra variation amongst the progeny produced during sexual reproduction for most model parameters is unable to overcome the effect that parents with a high individual fitness in general must mate with individuals of lower individual fitness resulting in a moderate reproductive fitness for the pair.

  19. Classification of short-lived objects using an interactive adaptable assistance system

    NASA Astrophysics Data System (ADS)

    El Bekri, Nadia; Angele, Susanne; Peinsipp-Byma, Elisabeth

    2015-05-01

    "Although we know that it is not a familiar object, after a while we can say what it resembles". The core task of an aerial image analyst is to recognize different object types based on certain clearly classified characteristics from aerial or satellite images. An interactive recognition assistance system compares selected features with a fixed set of reference objects (core data set). Therefore it is mainly designed to evaluate durable single objects like a specific type of ship or vehicle. Aerial image analysts on missions realized a changed warfare over the time. The task was not anymore to classify and thereby recognize a single durable object. The problem was that they had to classify strong variable objects and the reference set did not match anymore. In order to approach this new scope we introduce a concept to a further development of the interactive assistance system to be able to handle also short-lived, not clearly classifiable and strong variable objects like for example dhows. Dhows are the type of ships that are often used during pirate attacks at the coast of West Africa. Often these ships were build or extended by the pirates themselves. They follow no particular pattern as the standard construction of a merchant ship. In this work we differ between short-lived and durable objects. The interactive adaptable assistance system is supposed to assist image analysts with the classification of objects, which are new and not listed in the reference set of objects yet. The human interaction and perception is an important factor in order to realize this task and achieve the goal of recognition. Therefore we had to model the possibility to classify short-lived objects with appropriate procedures taking into consideration all aspects of short-lived objects. In this paper we will outline suitable measures and the possibilities to categorize short-lived objects via simple basic shapes as well as a temporary data storage concept for shortlived objects. The

  20. An analysis of historic and projected climate scenarios in the Western united States using hydrologic landscape classification

    EPA Science Inventory

    Identifying areas of similar hydrology within the United States and its regions (Hydrologic landscapes - HLs) is an active area of research. HLs have been used to make spatially distributed assessments of variability in streamflow and climatic response in Oregon, Alaska, and the ...

  1. An analysis of historic and projected climate scenarios in the Western United States using hydrologic landscape classification.

    EPA Science Inventory

    : Identifying areas of similar hydrology within the United States and its regions (hydrologic landscapes - HLs) is an active area of research. HLs are being used to construct spatially distributed assessments of variability in streamflow and climatic response in Oregon, Alaska, a...

  2. Evolutionary Dynamics on Protein Bi-stability Landscapes can Potentially Resolve Adaptive Conflicts

    PubMed Central

    Sikosek, Tobias; Bornberg-Bauer, Erich; Chan, Hue Sun

    2012-01-01

    Experimental studies have shown that some proteins exist in two alternative native-state conformations. It has been proposed that such bi-stable proteins can potentially function as evolutionary bridges at the interface between two neutral networks of protein sequences that fold uniquely into the two different native conformations. Under adaptive conflict scenarios, bi-stable proteins may be of particular advantage if they simultaneously provide two beneficial biological functions. However, computational models that simulate protein structure evolution do not yet recognize the importance of bi-stability. Here we use a biophysical model to analyze sequence space to identify bi-stable or multi-stable proteins with two or more equally stable native-state structures. The inclusion of such proteins enhances phenotype connectivity between neutral networks in sequence space. Consideration of the sequence space neighborhood of bridge proteins revealed that bi-stability decreases gradually with each mutation that takes the sequence further away from an exactly bi-stable protein. With relaxed selection pressures, we found that bi-stable proteins in our model are highly successful under simulated adaptive conflict. Inspired by these model predictions, we developed a method to identify real proteins in the PDB with bridge-like properties, and have verified a clear bi-stability gradient for a series of mutants studied by Alexander et al. (Proc Nat Acad Sci USA 2009, 106:21149–21154) that connect two sequences that fold uniquely into two different native structures via a bridge-like intermediate mutant sequence. Based on these findings, new testable predictions for future studies on protein bi-stability and evolution are discussed. PMID:23028272

  3. Adaptation of neurological practice and policy to a changing US health-care landscape.

    PubMed

    Gorelick, Philip B

    2016-04-01

    Health care in the USA is undergoing a drastic transformation under the Patient Protection and Affordable Care Act. The Patient Protection and Affordable Care Act is driving major health-care policy changes by connecting payment for traditional health-care services to value-based care initiatives and emphasising population health and innovative mechanisms to deliver care. Under the Patient Protection and Affordable Care Act, neurological practice will need to adapt and transform. Therefore, neurological policy should consider employing a new framework for neurological residency training, developing interdisciplinary team approaches to neurological subspecialty care, and strengthening the primary care-neurological specialty care interface to avoid redundancies and other medical waste. Additionally, neurological policy will need to support a more robust review of diagnostic and care pathway use to reduce avoidable expenditures, and test and implement bundled payments for key neurological diagnoses. In view of an anticipated 19% shortage of US neurologists in the next 10 years, development of new neurological policy under the Patient Protection and Affordable Care Act is paramount. PMID:26971663

  4. Using high resolution CIR imagery in the classification of non-cropped areas in agricultural landscapes in the UK

    NASA Astrophysics Data System (ADS)

    O'Connell, Jerome; Bradter, Ute; Benton, Tim G.

    2013-10-01

    With global food demand on course to double in the next 50 years the pressures of agricultural intensification on ecosystem services in highly managed landscapes are increasing. Within an agricultural landscape non-cropped areas are a key component of ecological heterogeneity and the sustainability of ecosystem services. Management of the landscape for both production of food and ecosystem services requires configuring the non-cropped areas in an optimal way, which, in turn requires large scale information on the distribution of non-cropped areas. In this study the Canny edge detection algorithm was used to delineate 93% of all boundaries within 422 ha of agricultural land in south east England. The resulting image was used in conjunction with vegetation indices derived from Color Infra Red (CIR) aerial photography and auxiliary landuse data in an Object Orientated (OO) Knowledge Based Classifier (KBC) to identify non-cropped areas. An overall accuracy of 94.27% (Kappa 0.91) for the KBC compared favorably with 63.04% (Kappa 0.55) for a pixel based hybrid classifier of the same area.

  5. The population genomic landscape of human genetic structure, admixture history and local adaptation in Peninsular Malaysia.

    PubMed

    Deng, Lian; Hoh, Boon Peng; Lu, Dongsheng; Fu, Ruiqing; Phipps, Maude E; Li, Shilin; Nur-Shafawati, Ab Rajab; Hatin, Wan Isa; Ismail, Endom; Mokhtar, Siti Shuhada; Jin, Li; Zilfalil, Bin Alwi; Marshall, Christian R; Scherer, Stephen W; Al-Mulla, Fahd; Xu, Shuhua

    2014-09-01

    Peninsular Malaysia is a strategic region which might have played an important role in the initial peopling and subsequent human migrations in Asia. However, the genetic diversity and history of human populations--especially indigenous populations--inhabiting this area remain poorly understood. Here, we conducted a genome-wide study using over 900,000 single nucleotide polymorphisms (SNPs) in four major Malaysian ethnic groups (MEGs; Malay, Proto-Malay, Senoi and Negrito), and made comparisons of 17 world-wide populations. Our data revealed that Peninsular Malaysia has greater genetic diversity corresponding to its role as a contact zone of both early and recent human migrations in Asia. However, each single Orang Asli (indigenous) group was less diverse with a smaller effective population size (N(e)) than a European or an East Asian population, indicating a substantial isolation of some duration for these groups. All four MEGs were genetically more similar to Asian populations than to other continental groups, and the divergence time between MEGs and East Asian populations (12,000--6,000 years ago) was also much shorter than that between East Asians and Europeans. Thus, Malaysian Orang Asli groups, despite their significantly different features, may share a common origin with the other Asian groups. Nevertheless, we identified traces of recent gene flow from non-Asians to MEGs. Finally, natural selection signatures were detected in a batch of genes associated with immune response, human height, skin pigmentation, hair and facial morphology and blood pressure in MEGs. Notable examples include SYN3 which is associated with human height in all Orang Asli groups, a height-related gene (PNPT1) and two blood pressure-related genes (CDH13 and PAX5) in Negritos. We conclude that a long isolation period, subsequent gene flow and local adaptations have jointly shaped the genetic architectures of MEGs, and this study provides insight into the peopling and human migration

  6. Field heritability of a plant adaptation to fire in heterogeneous landscapes.

    PubMed

    Castellanos, M C; González-Martínez, S C; Pausas, J G

    2015-11-01

    The strong association observed between fire regimes and variation in plant adaptations to fire suggests a rapid response to fire as an agent of selection. It also suggests that fire-related traits are heritable, a precondition for evolutionary change. One example is serotiny, the accumulation of seeds in unopened fruits or cones until the next fire, an important strategy for plant population persistence in fire-prone ecosystems. Here, we evaluate the potential of this trait to respond to natural selection in its natural setting. For this, we use a SNP marker approach to estimate genetic variance and heritability of serotiny directly in the field for two Mediterranean pine species. Study populations were large and heterogeneous in climatic conditions and fire regime. We first estimated the realized relatedness among trees from genotypes, and then partitioned the phenotypic variance in serotiny using Bayesian animal models that incorporated environmental predictors. As expected, field heritability was smaller (around 0.10 for both species) than previous estimates under common garden conditions (0.20). An estimate on a subset of stands with more homogeneous environmental conditions was not different from that in the complete set of stands, suggesting that our models correctly captured the environmental variation at the spatial scale of the study. Our results highlight the importance of measuring quantitative genetic parameters in natural populations, where environmental heterogeneity is a critical aspect. The heritability of serotiny, although not high, combined with high phenotypic variance within populations, confirms the potential of this fire-related trait for evolutionary change in the wild. PMID:26460597

  7. Adaptive neuro-fuzzy inference system for classification of background EEG signals from ESES patients and controls.

    PubMed

    Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang

    2014-01-01

    Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3-9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved. PMID:24790547

  8. Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls

    PubMed Central

    Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang

    2014-01-01

    Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved. PMID:24790547

  9. Osteoarthritis Classification Using Self Organizing Map Based on Gabor Kernel and Contrast-Limited Adaptive Histogram Equalization

    PubMed Central

    Anifah, Lilik; Purnama, I Ketut Eddy; Hariadi, Mochamad; Purnomo, Mauridhi Hery

    2013-01-01

    Localization is the first step in osteoarthritis (OA) classification. Manual classification, however, is time-consuming, tedious, and expensive. The proposed system is designed as decision support system for medical doctors to classify the severity of knee OA. A method has been proposed here to localize a joint space area for OA and then classify it in 4 steps to classify OA into KL-Grade 0, KL-Grade 1, KL-Grade 2, KL-Grade 3 and KL-Grade 4, which are preprocessing, segmentation, feature extraction, and classification. In this proposed system, right and left knee detection was performed by employing the Contrast-Limited Adaptive Histogram Equalization (CLAHE) and the template matching. The Gabor kernel, row sum graph and moment methods were used to localize the junction space area of knee. CLAHE is used for preprocessing step, i.e.to normalize the varied intensities. The segmentation process was conducted using the Gabor kernel, template matching, row sum graph and gray level center of mass method. Here GLCM (contrast, correlation, energy, and homogeinity) features were employed as training data. Overall, 50 data were evaluated for training and 258 data for testing. Experimental results showed the best performance by using gabor kernel with parameters α=8, θ=0, Ψ=[0 π/2], γ=0,8, N=4 and with number of iterations being 5000, momentum value 0.5 and α0=0.6 for the classification process. The run gave classification accuracy rate of 93.8% for KL-Grade 0, 70% for KL-Grade 1, 4% for KL-Grade 2, 10% for KL-Grade 3 and 88.9% for KL-Grade 4. PMID:23525188

  10. Osteoarthritis classification using self organizing map based on gabor kernel and contrast-limited adaptive histogram equalization.

    PubMed

    Anifah, Lilik; Purnama, I Ketut Eddy; Hariadi, Mochamad; Purnomo, Mauridhi Hery

    2013-01-01

    Localization is the first step in osteoarthritis (OA) classification. Manual classification, however, is time-consuming, tedious, and expensive. The proposed system is designed as decision support system for medical doctors to classify the severity of knee OA. A method has been proposed here to localize a joint space area for OA and then classify it in 4 steps to classify OA into KL-Grade 0, KL-Grade 1, KL-Grade 2, KL-Grade 3 and KL-Grade 4, which are preprocessing, segmentation, feature extraction, and classification. In this proposed system, right and left knee detection was performed by employing the Contrast-Limited Adaptive Histogram Equalization (CLAHE) and the template matching. The Gabor kernel, row sum graph and moment methods were used to localize the junction space area of knee. CLAHE is used for preprocessing step, i.e.to normalize the varied intensities. The segmentation process was conducted using the Gabor kernel, template matching, row sum graph and gray level center of mass method. Here GLCM (contrast, correlation, energy, and homogeinity) features were employed as training data. Overall, 50 data were evaluated for training and 258 data for testing. Experimental results showed the best performance by using gabor kernel with parameters α=8, θ=0, Ψ=[0 π/2], γ=0,8, N=4 and with number of iterations being 5000, momentum value 0.5 and α0=0.6 for the classification process. The run gave classification accuracy rate of 93.8% for KL-Grade 0, 70% for KL-Grade 1, 4% for KL-Grade 2, 10% for KL-Grade 3 and 88.9% for KL-Grade 4. PMID:23525188

  11. Response and adaptation of grapevine cultivars to hydrological conditions forced by a changing climate in a complex landscape

    NASA Astrophysics Data System (ADS)

    De Lorenzi, Francesca; Bonfante, Antonello; Alfieri, Silvia Maria; Monaco, Eugenia; De Mascellis, Roberto; Manna, Piero; Menenti, Massimo

    2014-05-01

    Soil water availability is one of the main components of the terroir concept, influencing crop yield and fruit composition in grapes. The aim of this work is to analyze some elements of the "natural environment" of terroir (climate and soil) in combination with the intra-specific biodiversity of yield responses of grapevine to water availability. From a reference (1961-90) to a future (2021-50) climate case, the effects of climate evolution on soil water availability are assessed and, regarding soil water regime as a predictor variable, the potential spatial distribution of wine-producing cultivars is determined. In a region of Southern Italy (Valle Telesina, 20,000 ha), where a terroir classification has been produced (Bonfante et al., 2011), we applied an agro-hydrological model to determine water availability indicators. Simulations were performed in 60 soil typological units, over the entire study area, and water availability (= hydrological) indicators were determined. Two climate cases were considered: reference (1961-90) and future (2021-2050), the former from climatic statistics on observed variables, and the latter from statistical downscaling of predictions by general circulation models (AOGCM) under A1B SRES scenario. Climatic data consist of daily time series of maximum and minimum temperature, and daily rainfall on a grid with a spatial resolution of 35 km. Spatial and temporal variability of hydrological indicators was addressed. With respect to temporal variability, both inter-annual and intra-annual (i.e. at different stages of crop cycle) variability were analyzed. Some cultivar-specific relations between hydrological indicators and characteristics of must quality were established. Moreover, for several wine-producing cultivars, hydrological requirements were determined by means of yield response functions to soil water availability, through the re-analysis of experimental data derived from scientific literature. The standard errors of estimated

  12. Necessity to adapt land use and land cover classification systems to readily accept radar data

    NASA Technical Reports Server (NTRS)

    Drake, B.

    1977-01-01

    A hierarchial, four level, standardized system for classifying land use/land cover primarily from remote-sensor data (USGS system) is described. The USGS system was developed for nonmicrowave imaging sensors such as camera systems and line scanners. The USGS system is not compatible with the land use/land cover classifications at different levels that can be made from radar imagery, and particularly from synthetic-aperture radar (SAR) imagery. The use of radar imagery for classifying land use/land cover at different levels is discussed, and a possible revision of the USGS system to more readily accept land use/land cover classifications from radar imagery is proposed.

  13. Methods for improving accuracy and extending results beyond periods covered by traditional ground-truth in remote sensing classification of a complex landscape

    NASA Astrophysics Data System (ADS)

    Mueller-Warrant, George W.; Whittaker, Gerald W.; Banowetz, Gary M.; Griffith, Stephen M.; Barnhart, Bradley L.

    2015-06-01

    Successful development of approaches to quantify impacts of diverse landuse and associated agricultural management practices on ecosystem services is frequently limited by lack of historical and contemporary landuse data. We hypothesized that ground truth data from one year could be used to extrapolate previous or future landuse in a complex landscape where cropping systems do not generally change greatly from year to year because the majority of crops are established perennials or the same annual crops grown on the same fields over multiple years. Prior to testing this hypothesis, it was first necessary to classify 57 major landuses in the Willamette Valley of western Oregon from 2005 to 2011 using normal same year ground-truth, elaborating on previously published work and traditional sources such as Cropland Data Layers (CDL) to more fully include minor crops grown in the region. Available remote sensing data included Landsat, MODIS 16-day composites, and National Aerial Imagery Program (NAIP) imagery, all of which were resampled to a common 30 m resolution. The frequent presence of clouds and Landsat7 scan line gaps forced us to conduct of series of separate classifications in each year, which were then merged by choosing whichever classification used the highest number of cloud- and gap-free bands at any given pixel. Procedures adopted to improve accuracy beyond that achieved by maximum likelihood pixel classification included majority-rule reclassification of pixels within 91,442 Common Land Unit (CLU) polygons, smoothing and aggregation of areas outside the CLU polygons, and majority-rule reclassification over time of forest and urban development areas. Final classifications in all seven years separated annually disturbed agriculture, established perennial crops, forest, and urban development from each other at 90 to 95% overall 4-class validation accuracy. In the most successful use of subsequent year ground-truth data to classify prior year landuse, an

  14. Domain adaptation for land use classification: A spatio-temporal knowledge reusing method

    NASA Astrophysics Data System (ADS)

    Liu, Yilun; Li, Xia

    2014-12-01

    Land use classification requires a significant amount of labeled data, which may be difficult and time consuming to obtain. On the other hand, without a sufficient number of training samples, conventional classifiers are unable to produce satisfactory classification results. This paper aims to overcome this issue by proposing a new model, TrCbrBoost, which uses old domain data to successfully train a classifier for mapping the land use types of target domain when new labeled data are unavailable. TrCbrBoost adopts a fuzzy CBR (Case Based Reasoning) model to estimate the land use probabilities for the target (new) domain, which are subsequently used to estimate the classifier performance. Source (old) domain samples are used to train the classifiers of a revised TrAdaBoost algorithm in which the weight of each sample is adjusted according to the classifier's performance. This method is tested using time-series SPOT images for land use classification. Our experimental results indicate that TrCbrBoost is more effective than traditional classification models, provided that sufficient amount of old domain data is available. Under these conditions, the proposed method is 9.19% more accurate.

  15. Travelling in the eastern Mediterranean with landscape character assessment

    NASA Astrophysics Data System (ADS)

    Abu Jaber, N.; Abunnasr, Y.; Abu Yahya, A.; Boulad, N.; Christou, O.; Dimitropoulos, G.; Dimopoulos, T.; Gkoltsiou, K.; Khreis, N.; Manolaki, P.; Michael, K.; Odeh, T.; Papatheodoulou, A.; Sorotou, A.; Sinno, S.; Suliman, O.; Symons, N.; Terkenli, T.; Trigkas, Vassilis; Trovato, M. G.; Victora, M.; Zomeni, M.; Vogiatzakis, I. N.

    2015-06-01

    Following its application in Northern Europe, Landscape Character Assessment has also been implemented in Euro-Mediterranean countries as a tool for classifying, describing and assessing landscapes. Many landscape classifications employed in the Euro-Mediterranean area are similar in philosophy and application to the ones developed in Northern Europe. However, many aspects of landform, climate, land-use and ecology, as well as socio-economic context are distinctive of Mediterranean landscapes. The paper discusses the conceptual and methodological issues faced during landscape mapping and characterisation in four East-Mediterranean countries (within the MEDSCAPES project): Cyprus, Greece, Jordan and Lebanon. The major hurdles to overcome during the first phase of methodology development include variation in availability, quality, scale and coverage of spatial datasets between countries and also terminology semantics around landscapes. For example, the concept of landscape - a well-defined term in Greek and English - did not exist in Arabic. Another issue is the use of relative terms like 'high mountains,' `uplands' `lowlands' or ' hills'. Such terms, which are regularly used in landscape description, were perceived slightly differently in the four participating countries. In addition differences exist in nomenclature and classification systems used by each country for the dominant landscape-forming factors i.e. geology, soils and land use- but also in the cultural processes shaping the landscapes - compared both to each other and to the Northern-European norms. This paper argues for the development of consistent, regionally adapted, relevant and standardised methodologies if the results and application of LCA in the eastern Mediterranean region are to be transferable and comparable between countries.

  16. Testing the hydrological landscape unit classification system and other terrain analysis measures for predicting low-flow nitrate and chloride in watersheds.

    PubMed

    Poor, Cara J; McDonnell, Jeffrey J; Bolte, John

    2008-11-01

    Elevated nitrate concentrations in streamwater are a major environmental management problem. While land use exerts a large control on stream nitrate, hydrology often plays an equally important role. To date, predictions of low-flow nitrate in ungauged watersheds have been poor because of the difficulty in describing the uniqueness of watershed hydrology over large areas. Clearly, hydrologic response varies depending on the states and stocks of water, flow pathways, and residence times. How to capture the dominant hydrological controls that combine with land use to define streamwater nitrate concentration is a major research challenge. This paper tests the new Hydrologic Landscape Regions (HLRs) watershed classification scheme of Wolock and others (Environmental Management 34:S71-S88, 2004) to address the question: Can HLRs be used as a way to predict low-flow nitrate? We also test a number of other indexes including inverse-distance weighting of land use and the well-known topographic index (TI) to address the question: How do other terrain and land use measures compare to HLR in terms of their ability to predict low-flow nitrate concentration? We test this for 76 watersheds in western Oregon using the U.S. Environmental Protection Agency's Environmental Monitoring and Assessment Program and Regional Environmental Monitoring and Assessment Program data. We found that HLRs did not significantly improve nitrate predictions beyond the standard TI and land-use metrics. Using TI and inverse-distance weighting did not improve nitrate predictions; the best models were the percentage land use-elevation models. We did, however, see an improvement of chloride predictions using HLRs, TI, and inverse-distance weighting; adding HLRs and TI significantly improved model predictions and the best models used inverse-distance weighting and elevation. One interesting result of this study is elevation consistently predicted nitrate better than TI or the hydrologic classification

  17. Using ASTER Imagery in Land Use/cover Classification of Eastern Mediterranean Landscapes According to CORINE Land Cover Project

    PubMed Central

    Yüksel, Alaaddin; Akay, Abdullah E.; Gundogan, Recep

    2008-01-01

    The satellite imagery has been effectively utilized for classifying land cover types and detecting land cover conditions. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor imagery has been widely used in classification process of land cover. However, atmospheric corrections have to be made by preprocessing satellite sensor imagery since the electromagnetic radiation signals received by the satellite sensors can be scattered and absorbed by the atmospheric gases and aerosols. In this study, an ASTER sensor imagery, which was converted into top-of-atmosphere reflectance (TOA), was used to classify the land use/cover types, according to COoRdination of INformation on the Environment (CORINE) land cover nomenclature, for an area representing the heterogonous characteristics of eastern Mediterranean regions in Kahramanmaras, Turkey. The results indicated that using the surface reflectance data of ASTER sensor imagery can provide accurate (i.e. overall accuracy and kappa values of 83.2% and 0.79, respectively) and low-cost cover mapping as a part of inventory for CORINE Land Cover Project.

  18. CRISPRmap: an automated classification of repeat conservation in prokaryotic adaptive immune systems.

    PubMed

    Lange, Sita J; Alkhnbashi, Omer S; Rose, Dominic; Will, Sebastian; Backofen, Rolf

    2013-09-01

    Central to Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)-Cas systems are repeated RNA sequences that serve as Cas-protein-binding templates. Classification is based on the architectural composition of associated Cas proteins, considering repeat evolution is essential to complete the picture. We compiled the largest data set of CRISPRs to date, performed comprehensive, independent clustering analyses and identified a novel set of 40 conserved sequence families and 33 potential structure motifs for Cas-endoribonucleases with some distinct conservation patterns. Evolutionary relationships are presented as a hierarchical map of sequence and structure similarities for both a quick and detailed insight into the diversity of CRISPR-Cas systems. In a comparison with Cas-subtypes, I-C, I-E, I-F and type II were strongly coupled and the remaining type I and type III subtypes were loosely coupled to repeat and Cas1 evolution, respectively. Subtypes with a strong link to CRISPR evolution were almost exclusive to bacteria; nevertheless, we identified rare examples of potential horizontal transfer of I-C and I-E systems into archaeal organisms. Our easy-to-use web server provides an automated assignment of newly sequenced CRISPRs to our classification system and enables more informed choices on future hypotheses in CRISPR-Cas research: http://rna.informatik.uni-freiburg.de/CRISPRmap. PMID:23863837

  19. CRISPRmap: an automated classification of repeat conservation in prokaryotic adaptive immune systems

    PubMed Central

    Lange, Sita J.; Alkhnbashi, Omer S.; Rose, Dominic; Will, Sebastian; Backofen, Rolf

    2013-01-01

    Central to Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)-Cas systems are repeated RNA sequences that serve as Cas-protein–binding templates. Classification is based on the architectural composition of associated Cas proteins, considering repeat evolution is essential to complete the picture. We compiled the largest data set of CRISPRs to date, performed comprehensive, independent clustering analyses and identified a novel set of 40 conserved sequence families and 33 potential structure motifs for Cas-endoribonucleases with some distinct conservation patterns. Evolutionary relationships are presented as a hierarchical map of sequence and structure similarities for both a quick and detailed insight into the diversity of CRISPR-Cas systems. In a comparison with Cas-subtypes, I-C, I-E, I-F and type II were strongly coupled and the remaining type I and type III subtypes were loosely coupled to repeat and Cas1 evolution, respectively. Subtypes with a strong link to CRISPR evolution were almost exclusive to bacteria; nevertheless, we identified rare examples of potential horizontal transfer of I-C and I-E systems into archaeal organisms. Our easy-to-use web server provides an automated assignment of newly sequenced CRISPRs to our classification system and enables more informed choices on future hypotheses in CRISPR-Cas research: http://rna.informatik.uni-freiburg.de/CRISPRmap. PMID:23863837

  20. Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization

    PubMed Central

    Zhang, Yudong; Wu, Lenan

    2011-01-01

    This paper proposes a hybrid crop classifier for polarimetric synthetic aperture radar (SAR) images. The feature sets consisted of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM) based texture features. Then, the features were reduced by principle component analysis (PCA). Finally, a two-hidden-layer forward neural network (NN) was constructed and trained by adaptive chaotic particle swarm optimization (ACPSO). K-fold cross validation was employed to enhance generation. The experimental results on Flevoland sites demonstrate the superiority of ACPSO to back-propagation (BP), adaptive BP (ABP), momentum BP (MBP), Particle Swarm Optimization (PSO), and Resilient back-propagation (RPROP) methods. Moreover, the computation time for each pixel is only 1.08 × 10−7 s. PMID:22163872

  1. Studying the Effect of Adaptive Momentum in Improving the Accuracy of Gradient Descent Back Propagation Algorithm on Classification Problems

    NASA Astrophysics Data System (ADS)

    Rehman, Muhammad Zubair; Nawi, Nazri Mohd.

    Despite being widely used in the practical problems around the world, Gradient Descent Back-propagation algorithm comes with problems like slow convergence and convergence to local minima. Previous researchers have suggested certain modifications to improve the convergence in gradient Descent Back-propagation algorithm such as careful selection of input weights and biases, learning rate, momentum, network topology, activation function and value for 'gain' in the activation function. This research proposed an algorithm for improving the working performance of back-propagation algorithm which is 'Gradient Descent with Adaptive Momentum (GDAM)' by keeping the gain value fixed during all network trials. The performance of GDAM is compared with 'Gradient Descent with fixed Momentum (GDM)' and 'Gradient Descent Method with Adaptive Gain (GDM-AG)'. The learning rate is fixed to 0.4 and maximum epochs are set to 3000 while sigmoid activation function is used for the experimentation. The results show that GDAM is a better approach than previous methods with an accuracy ratio of 1.0 for classification problems like Wine Quality, Mushroom and Thyroid disease.

  2. A novel approach for SEMG signal classification with adaptive local binary patterns.

    PubMed

    Ertuğrul, Ömer Faruk; Kaya, Yılmaz; Tekin, Ramazan

    2016-07-01

    Feature extraction plays a major role in the pattern recognition process, and this paper presents a novel feature extraction approach, adaptive local binary pattern (aLBP). aLBP is built on the local binary pattern (LBP), which is an image processing method, and one-dimensional local binary pattern (1D-LBP). In LBP, each pixel is compared with its neighbors. Similarly, in 1D-LBP, each data in the raw is judged against its neighbors. 1D-LBP extracts feature based on local changes in the signal. Therefore, it has high a potential to be employed in medical purposes. Since, each action or abnormality, which is recorded in SEMG signals, has its own pattern, and via the 1D-LBP these (hidden) patterns may be detected. But, the positions of the neighbors in 1D-LBP are constant depending on the position of the data in the raw. Also, both LBP and 1D-LBP are very sensitive to noise. Therefore, its capacity in detecting hidden patterns is limited. To overcome these drawbacks, aLBP was proposed. In aLBP, the positions of the neighbors and their values can be assigned adaptively via the down-sampling and the smoothing coefficients. Therefore, the potential to detect (hidden) patterns, which may express an illness or an action, is really increased. To validate the proposed feature extraction approach, two different datasets were employed. Achieved accuracies by the proposed approach were higher than obtained results by employed popular feature extraction approaches and the reported results in the literature. Obtained accuracy results were brought out that the proposed method can be employed to investigate SEMG signals. In summary, this work attempts to develop an adaptive feature extraction scheme that can be utilized for extracting features from local changes in different categories of time-varying signals. PMID:26718556

  3. An adaptive image segmentation process for the classification of lung biopsy images

    NASA Astrophysics Data System (ADS)

    McKee, Daniel W.; Land, Walker H., Jr.; Zhukov, Tatyana; Song, Dansheng; Qian, Wei

    2006-03-01

    The purpose of this study was to develop a computer-based second opinion diagnostic tool that could read microscope images of lung tissue and classify the tissue sample as normal or cancerous. This problem can be broken down into three areas: segmentation, feature extraction and measurement, and classification. We introduce a kernel-based extension of fuzzy c-means to provide a coarse initial segmentation, with heuristically-based mechanisms to improve the accuracy of the segmentation. The segmented image is then processed to extract and quantify features. Finally, the measured features are used by a Support Vector Machine (SVM) to classify the tissue sample. The performance of this approach was tested using a database of 85 images collected at the Moffitt Cancer Center and Research Institute. These images represent a wide variety of normal lung tissue samples, as well as multiple types of lung cancer. When used with a subset of the data containing images from the normal and adenocarcinoma classes, we were able to correctly classify 78% of the images, with a ROC A Z of 0.758.

  4. Improved Correlation of the Neuropathologic Classification According to Adapted World Health Organization Classification and Outcome After Radiotherapy in Patients With Atypical and Anaplastic Meningiomas

    SciTech Connect

    Combs, Stephanie E.; Schulz-Ertner, Daniela; Debus, Juergen; Deimling, Andreas von; Hartmann, Christian

    2011-12-01

    Purpose: To evaluate the correlation between the 1993 and 2000/2007 World Health Organization (WHO) classification with the outcome in patients with high-grade meningiomas. Patients and Methods: Between 1985 and 2004, 73 patients diagnosed with atypical or anaplastic meningiomas were treated with radiotherapy. Sections from the paraffin-embedded tumor material from 66 patients (90%) from 13 different pathology departments were re-evaluated according to the first revised WHO classification from 1993 and the revised classifications from 2000/2007. In 4 cases, the initial diagnosis meningioma was not reproducible (5%). Therefore, 62 patients with meningiomas were analyzed. Results: All 62 tumors were reclassified according to the 1993 and 2000/2007 WHO classification systems. Using the 1993 system, 7 patients were diagnosed with WHO grade I meningioma (11%), 23 with WHO grade II (37%), and 32 with WHO grade III meningioma (52%). After scoring using the 2000/2007 system, we found 17 WHO grade I meningiomas (27%), 32 WHO grade II meningiomas (52%), and 13 WHO grade III meningiomas (21%). According to the 1993 classification, the difference in overall survival was not statistically significant among the histologic subgroups (p = .96). Using the 2000/2007 WHO classifications, the difference in overall survival became significant (p = .02). Of the 62 reclassified patients 29 developed tumor progression (47%). No difference in progression-free survival was observed among the histologic subgroups (p = .44). After grading according to the 2000/2007 WHO classifications, significant differences in progression-free survival were observed among the three histologic groups (p = .005). Conclusion: The new 2000/2007 WHO classification for meningiomas showed an improved correlation between the histologic grade and outcome. This classification therefore provides a useful basis to determine the postoperative indication for radiotherapy. According to our results, a comparison of the

  5. Classification of ring artifacts for their effective removal using type adaptive correction schemes.

    PubMed

    Anas, Emran Mohammad Abu; Lee, Soo Yeol; Hasan, Kamrul

    2011-06-01

    High resolution tomographic images acquired with a digital X-ray detector are often degraded by the so called ring artifacts. In this paper, a detail analysis including the classification, detection and correction of these ring artifacts is presented. At first, a novel idea for classifying rings into two categories, namely type I and type II rings, is proposed based on their statistical characteristics. The defective detector elements and the dusty scintillator screens result in type I ring and the mis-calibrated detector elements lead to type II ring. Unlike conventional approaches, we emphasize here on the separate detection and correction schemes for each type of rings for their effective removal. For the detection of type I ring, the histogram of the responses of the detector elements is used and a modified fast image inpainting algorithm is adopted to correct the responses of the defective pixels. On the other hand, to detect the type II ring, first a simple filtering scheme is presented based on the fast Fourier transform (FFT) to smooth the sum curve derived form the type I ring corrected projection data. The difference between the sum curve and its smoothed version is then used to detect their positions. Then, to remove the constant bias suffered by the responses of the mis-calibrated detector elements with view angle, an estimated dc shift is subtracted from them. The performance of the proposed algorithm is evaluated using real micro-CT images and is compared with three recently reported algorithms. Simulation results demonstrate superior performance of the proposed technique as compared to the techniques reported in the literature. PMID:21513928

  6. The 'Functional Landscape Approach': Building a socio-ecological evidence base for its contribution to adaptation and resilience in wetland catchments.

    NASA Astrophysics Data System (ADS)

    Carrie, Rachael; Dixon, Alan

    2015-04-01

    Sustainable land management is increasingly taking a landscape approach to advocate simultaneously for local and multiple stakeholder-negotiated development and environmental objectives. Landscape approaches advance earlier frameworks that failed to acknowledge or reconcile either biodiversity or societal trade-offs, and that often tended toward externally-derived or imposed management interventions. Most recently, the management of land to balance biodiversity, food security and ecosystem services outcomes has been informed by socio-ecological systems thinking that seeks to promote an interdisciplinary understanding of any given 'landscape' where environmental and social factors continually interact in complex, adaptive and resilient ways. Reflecting these concepts, and integrating local and external scientific knowledge, the Functional Landscape Approach (FLA) was developed by Wetland Action, focussing on wetland systems in rural sub-Saharan Africa to contribute to environmentally sensitive and climate resilient strategies for safeguarding essential ecosystem services and improving livelihoods and well-being. In particular, the FLA stresses the ways in which land productivity can be improved through supporting, strengthening or re-establishing functional linkages between wetlands and their catchments and provides a basis for local identification of specific interventions to improve the sustainability of land use. Crucially, it also emphasises the need for community-based institutional support and the importance of incentives through market linkages and value-chain development. In this paper we will describe our experiences of developing and implementing the FLA in Ethiopia, Zambia and Malawi over the past two decades. Drawing on successful and less-successful elements of participatory planning, monitoring and evaluation, and the facilitation of long-term sustainable benefits, we will discuss some of the accomplishments and challenges that can be associated with

  7. Climate variables explain neutral and adaptive variation within salmonid metapopulations: the importance of replication in landscape genetics.

    PubMed

    Hand, Brian K; Muhlfeld, Clint C; Wade, Alisa A; Kovach, Ryan P; Whited, Diane C; Narum, Shawn R; Matala, Andrew P; Ackerman, Michael W; Garner, Brittany A; Kimball, John S; Stanford, Jack A; Luikart, Gordon

    2016-02-01

    Understanding how environmental variation influences population genetic structure is important for conservation management because it can reveal how human stressors influence population connectivity, genetic diversity and persistence. We used riverscape genetics modelling to assess whether climatic and habitat variables were related to neutral and adaptive patterns of genetic differentiation (population-specific and pairwise FST ) within five metapopulations (79 populations, 4583 individuals) of steelhead trout (Oncorhynchus mykiss) in the Columbia River Basin, USA. Using 151 putatively neutral and 29 candidate adaptive SNP loci, we found that climate-related variables (winter precipitation, summer maximum temperature, winter highest 5% flow events and summer mean flow) best explained neutral and adaptive patterns of genetic differentiation within metapopulations, suggesting that climatic variation likely influences both demography (neutral variation) and local adaptation (adaptive variation). However, we did not observe consistent relationships between climate variables and FST across all metapopulations, underscoring the need for replication when extrapolating results from one scale to another (e.g. basin-wide to the metapopulation scale). Sensitivity analysis (leave-one-population-out) revealed consistent relationships between climate variables and FST within three metapopulations; however, these patterns were not consistent in two metapopulations likely due to small sample sizes (N = 10). These results provide correlative evidence that climatic variation has shaped the genetic structure of steelhead populations and highlight the need for replication and sensitivity analyses in land and riverscape genetics. PMID:26677031

  8. Climate variables explain neutral and adaptive variation within salmonid metapopulations: The importance of replication in landscape genetics

    USGS Publications Warehouse

    Hand, Brian K; Muhlfeld, Clint C.; Wade, Alisa A.; Kovach, Ryan; Whited, Diane C.; Narum, Shawn R; Matala, Andrew P; Ackerman, Michael W.; Garner, B. A.; Kimball, John S; Stanford, Jack A.; Luikart, Gordon

    2016-01-01

    Understanding how environmental variation influences population genetic structure is important for conservation management because it can reveal how human stressors influence population connectivity, genetic diversity and persistence. We used riverscape genetics modelling to assess whether climatic and habitat variables were related to neutral and adaptive patterns of genetic differentiation (population-specific and pairwise FST) within five metapopulations (79 populations, 4583 individuals) of steelhead trout (Oncorhynchus mykiss) in the Columbia River Basin, USA. Using 151 putatively neutral and 29 candidate adaptive SNP loci, we found that climate-related variables (winter precipitation, summer maximum temperature, winter highest 5% flow events and summer mean flow) best explained neutral and adaptive patterns of genetic differentiation within metapopulations, suggesting that climatic variation likely influences both demography (neutral variation) and local adaptation (adaptive variation). However, we did not observe consistent relationships between climate variables and FST across all metapopulations, underscoring the need for replication when extrapolating results from one scale to another (e.g. basin-wide to the metapopulation scale). Sensitivity analysis (leave-one-population-out) revealed consistent relationships between climate variables and FST within three metapopulations; however, these patterns were not consistent in two metapopulations likely due to small sample sizes (N = 10). These results provide correlative evidence that climatic variation has shaped the genetic structure of steelhead populations and highlight the need for replication and sensitivity analyses in land and riverscape genetics.

  9. Applications of network analysis for adaptive management of artificial drainage systems in landscapes vulnerable to sea level rise

    NASA Astrophysics Data System (ADS)

    Poulter, Benjamin; Goodall, Jonathan L.; Halpin, Patrick N.

    2008-08-01

    SummaryThe vulnerability of coastal landscapes to sea level rise is compounded by the existence of extensive artificial drainage networks initially built to lower water tables for agriculture, forestry, and human settlements. These drainage networks are found in landscapes with little topographic relief where channel flow is characterized by bi-directional movement across multiple time-scales and related to precipitation, wind, and tidal patterns. The current configuration of many artificial drainage networks exacerbates impacts associated with sea level rise such as salt-intrusion and increased flooding. This suggests that in the short-term, drainage networks might be managed to mitigate sea level rise related impacts. The challenge, however, is that hydrologic processes in regions where channel flow direction is weakly related to slope and topography require extensive parameterization for numerical models which is limited where network size is on the order of a hundred or more kilometers in total length. Here we present an application of graph theoretic algorithms to efficiently investigate network properties relevant to the management of a large artificial drainage system in coastal North Carolina, USA. We created a digital network model representing the observation network topology and four types of drainage features (canal, collector and field ditches, and streams). We applied betweenness-centrality concepts (using Dijkstra's shortest path algorithm) to determine major hydrologic flowpaths based off of hydraulic resistance. Following this, we identified sub-networks that could be managed independently using a community structure and modularity approach. Lastly, a betweenness-centrality algorithm was applied to identify major shoreline entry points to the network that disproportionately control water movement in and out of the network. We demonstrate that graph theory can be applied to solving management and monitoring problems associated with sea level rise

  10. Functional decoupling between flowers and leaves in the Ameroglossum pernambucense complex can facilitate local adaptation across a pollinator and climatic heterogeneous landscape.

    PubMed

    Wanderley, A M; Galetto, L; Machado, I C S

    2016-03-01

    Decoupling between floral and leaf traits is expected in plants with specialized pollination systems to assure a precise flower-pollinator fit, irrespective of leaf variation associated with environmental heterogeneity (functional modularity). Nonetheless, developmental interactions among floral traits also decouple flowers from leaves regardless of selection pressures (developmental modularity). We tested functional modularity in the hummingbird-pollinated flowers of the Ameroglossum pernambucense complex while controlling for developmental modularity. Using two functional traits responsible for flower-pollinator fit [floral tube length (TL) and anther-nectary distance (AN)], one floral trait not linked to pollination [sepal length (SL), control for developmental modularity] and one leaf trait [leaf length (LL)], we found evidence of flower functional modularity. Covariation between TL and AN was ca. two-fold higher than the covariation of either of these traits with sepal and leaf lengths, and variations in TL and AN, important for a precise flower-pollinator fit, were smaller than SL and LL variations. Furthermore, we show that previously reported among-population variation of flowers associated with local pollinator phenotypes was independent from SL and LL variations. These results suggest that TL and AN are functionally linked to fit pollinators and sufficiently decoupled from developmentally related floral traits (SL) and vegetative traits (LL). These results support previous evidences of population differentiation due to local adaptation in the A. pernambucense complex and shed light on the role of flower-leaf decoupling for local adaptation in species distributed across biotic and abiotic heterogeneous landscapes. PMID:26663030

  11. Adaptation.

    PubMed

    Broom, Donald M

    2006-01-01

    The term adaptation is used in biology in three different ways. It may refer to changes which occur at the cell and organ level, or at the individual level, or at the level of gene action and evolutionary processes. Adaptation by cells, especially nerve cells helps in: communication within the body, the distinguishing of stimuli, the avoidance of overload and the conservation of energy. The time course and complexity of these mechanisms varies. Adaptive characters of organisms, including adaptive behaviours, increase fitness so this adaptation is evolutionary. The major part of this paper concerns adaptation by individuals and its relationships to welfare. In complex animals, feed forward control is widely used. Individuals predict problems and adapt by acting before the environmental effect is substantial. Much of adaptation involves brain control and animals have a set of needs, located in the brain and acting largely via motivational mechanisms, to regulate life. Needs may be for resources but are also for actions and stimuli which are part of the mechanism which has evolved to obtain the resources. Hence pigs do not just need food but need to be able to carry out actions like rooting in earth or manipulating materials which are part of foraging behaviour. The welfare of an individual is its state as regards its attempts to cope with its environment. This state includes various adaptive mechanisms including feelings and those which cope with disease. The part of welfare which is concerned with coping with pathology is health. Disease, which implies some significant effect of pathology, always results in poor welfare. Welfare varies over a range from very good, when adaptation is effective and there are feelings of pleasure or contentment, to very poor. A key point concerning the concept of individual adaptation in relation to welfare is that welfare may be good or poor while adaptation is occurring. Some adaptation is very easy and energetically cheap and

  12. Do we know how to reconcile preservation of landscapes with adaptation of agriculture to climate change? A case-study in a hilly area in Southern Italy

    NASA Astrophysics Data System (ADS)

    Menenti, Massimo; Alfieri, Silvia; Basile, Angelo; Bonfante, Antonello; Monaco, Eugenia; Riccardi, Maria; De Lorenzi, Francesca

    2013-04-01

    Limited impacts of climate change on agricultural yields are unlikely to induce any significant changes in current landscapes. Larger impacts, unacceptable on economic or social ground, are likely to trigger interventions towards adaptation of agricultural production systems by reducing or removing vulnerabilities to climate variability and change. Such interventions may require a transition to a different production system, i.e. complete substitution of current crops, or displacement of current crops at their current location towards other locations, e.g. at higher elevations within the landscape. We have assessed the impacts of climate change and evaluated options for adaptation of a valley in Southern Italy, dominated by vine and olive orchards with a significant presence of wheat. We have first estimated the climatic requirements of several varieties for each dominant species. Next, to identify options for adaptation we have evaluated the compatibility of such requirements with indicators of a reference (current) climate and of future climate. This climate - compatibility assessment was done for each soil unit within the valley, leading to maps of locations where each crop is expected to be compatible with climate. This leads to identify both potential crop substitutions within the entire valley and crop displacements from one location to another within the valley. Two climate scenarios were considered: reference (1961-90) and future (2021-2050) climate, the former from climatic statistics, and the latter from statistical downscaling of general circulation models (AOGCM). Climatic data consists of daily time series of maximum and minimum temperature, and daily rainfall on a grid with a spatial resolution of 35 km. We evaluated the adaptive capacity of the "Valle Telesina" (Campania Region, Southern Italy). A mechanistic model of water flow in the soil-plant-atmosphere system (SWAP) was used to describe the hydrological conditions in response to climate for each

  13. Reducing the overall HIV-burden in South Africa: is 'reviving ABC' an appropriate fit for a complex, adaptive epidemiological HIV landscape?

    PubMed

    Burman, Christopher J; Aphane, Marota; Delobelle, Peter

    2015-01-01

    This article questions the recommendations to 'revive ABC (abstain, be faithful, condomise)' as a mechanism to 'educate' people in South Africa about HIV prevention as the South African National HIV Prevalence, Incidence and Behaviour Survey, 2012, suggests. We argue that ABC was designed as a response to a particular context which has now radically changed. In South Africa the contemporary context reflects the mass roll-out of antiretroviral treatment; significant bio-medical knowledge gains; a generalised population affected by HIV that has made sense of and embodied those diverse experiences; and a government committed to confronting the epidemic. We suggest that the situation can now be plausibly conceptualised as a complex, adaptive epidemiological landscape that could benefit from an expansion of the existing, 'descriptive' prevention paradigm towards strategies that focus on the dynamics of transmission. We argue for this shift by proposing a theoretical framework based on complexity theory and pattern management. We interrogate one educational prevention heuristic that emphasises the importance of risk-reduction through the lens of transmission, called A-3B-4C-T. We argue that this type of approach provides expansive opportunities for people to engage with the epidemic in contextualised, innovative ways that supersede the opportunities afforded by ABC. We then suggest that framing the prevention imperative through the lens of 'dynamic prevention' at scale opens more immediate opportunities, as well as developing a future-oriented mind-set, than the 'descriptive prevention' parameters can facilitate. The parameters of the 'descriptive prevention' paradigm, that maintain - and partially reinforce - the presence of ABC, do not have the flexibility required to develop the armamentarium of tools required to contribute to the management of a complex epidemiological landscape. Uncritically adhering to both the 'descriptive paradigm', and ABC, represents an

  14. [Landscape and ecological genomics].

    PubMed

    2013-10-01

    Landscape genomics is the modern version of landscape genetics, a discipline that arose approximately 10 years ago as a combination of population genetics, landscape ecology, and spatial statistics. It studies the effects of environmental variables on gene flow and other microevolutionary processes that determine genetic connectivity and variations in populations. In contrast to population genetics, it operates at the level of individual specimens rather than at the level of population samples. Another important difference between landscape genetics and genomics and population genetics is that, in the former, the analysis of gene flow and local adaptations takes quantitative account of landforms and features of the matrix, i.e., hostile spaces that separate species habitats. Landscape genomics is a part of population ecogenomics, which, along with community genomics, is a major part of ecological genomics. One of the principal purposes of landscape genomics is the identification and differentiation of various genome-wide and locus-specific effects. The approaches and computation tools developed for combined analysis of genomic and landscape variables make it possible to detect adaptation-related genome fragments, which facilitates the planning of conservation efforts and the prediction of species' fate in response to expected changes in the environment. PMID:25508669

  15. [Landscape and ecological genomics].

    PubMed

    Tetushkin, E Ia

    2013-10-01

    Landscape genomics is the modern version of landscape genetics, a discipline that arose approximately 10 years ago as a combination of population genetics, landscape ecology, and spatial statistics. It studies the effects of environmental variables on gene flow and other microevolutionary processes that determine genetic connectivity and variations in populations. In contrast to population genetics, it operates at the level of individual specimens rather than at the level of population samples. Another important difference between landscape genetics and genomics and population genetics is that, in the former, the analysis of gene flow and local adaptations takes quantitative account of landforms and features of the matrix, i.e., hostile spaces that separate species habitats. Landscape genomics is a part of population ecogenomics, which, along with community genomics, is a major part of ecological genomics. One of the principal purposes of landscape genomics is the identification and differentiation of various genome-wide and locus-specific effects. The approaches and computation tools developed for combined analysis of genomic and landscape variables make it possible to detect adaptation-related genome fragments, which facilitates the planning of conservation efforts and the prediction of species' fate in response to expected changes in the environment. PMID:25474890

  16. Multi-temporal land cover classification of the Konya Basin, south-central Turkey, based on a LANDSAT TM-derived NDVI/NDMI time series: satellite remote sensing in support of landscape-scale soil biogeochemistry research

    NASA Astrophysics Data System (ADS)

    Mayes, M. T.; Ozdogan, M.; Marin-Spiotta, E.

    2010-12-01

    Recently, terrestrial biogeochemists and soil scientists have called for new approaches to study human impacts on soil biogeochemical properties at landscape-wide, 100-1000 km2 spatial scales (Trumbore and Czimczik 2008). Here, we use satellite remote sensing to map land cover across a 16,000 km2 region in the Konya Basin, south-central Turkey, in support of research into agricultural and pastoral land use impacts on soil biogeochemistry. Our land cover classification is based on time series analysis of Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) data, derived from eight LANDSAT TM images spanning the 2006-2007 growing seasons. Using a hierarchical, binary-split classification approach and a support vector machine (SVM) algorithm, we map five land cover classes that correspond with the following dominant land-use categories: 1) annual cultivated row-crops, 2) perennial orchards/cultivated woody vegetation, 3) fallow fields, 4) uncultivated woody vegetation, 5) steppe vegetation/rangeland. The final map has an overall classification accuracy of 87.4% (kappa = 0.842), determined via traditional confusion-matrix analysis and over 150 site visits during summer 2010. Classes 1 and 2, which have the highest per-pixel NDVI and NDMI sums across image dates, attain the highest producer and consumer accuracies (>95%). We also compare the relative contributions and efficacy of NDVI and NDMI in separating land cover classes, and the influence of radiometric correction and calibration across image dates on classification accuracies. Our results support previous research showing that NDVI time series can effectively classify agricultural landscapes in semi-arid to arid environments (Simonneaux et al. 2008; Pax-Lenny et al. 1996). By combining our land cover map with other geospatial information in a GIS, we demonstrate how satellite remote sensing can help expand spatial scales of terrestrial biogeochemistry research from

  17. Segmentation and object-oriented classification of wetlands in a karst Florida landscape using multi-season Landsat-7 ETM+ Imagery

    EPA Science Inventory

    Segmentation and object-oriented processing of single-season and multi-season Landsat-7 ETM+ data was utilized for the classification of wetlands in a 1560 km2 study area of north central Florida. This segmentation and object-oriented classification outperformed the traditional ...

  18. Adapt

    NASA Astrophysics Data System (ADS)

    Bargatze, L. F.

    2015-12-01

    Active Data Archive Product Tracking (ADAPT) is a collection of software routines that permits one to generate XML metadata files to describe and register data products in support of the NASA Heliophysics Virtual Observatory VxO effort. ADAPT is also a philosophy. The ADAPT concept is to use any and all available metadata associated with scientific data to produce XML metadata descriptions in a consistent, uniform, and organized fashion to provide blanket access to the full complement of data stored on a targeted data server. In this poster, we present an application of ADAPT to describe all of the data products that are stored by using the Common Data File (CDF) format served out by the CDAWEB and SPDF data servers hosted at the NASA Goddard Space Flight Center. These data servers are the primary repositories for NASA Heliophysics data. For this purpose, the ADAPT routines have been used to generate data resource descriptions by using an XML schema named Space Physics Archive, Search, and Extract (SPASE). SPASE is the designated standard for documenting Heliophysics data products, as adopted by the Heliophysics Data and Model Consortium. The set of SPASE XML resource descriptions produced by ADAPT includes high-level descriptions of numerical data products, display data products, or catalogs and also includes low-level "Granule" descriptions. A SPASE Granule is effectively a universal access metadata resource; a Granule associates an individual data file (e.g. a CDF file) with a "parent" high-level data resource description, assigns a resource identifier to the file, and lists the corresponding assess URL(s). The CDAWEB and SPDF file systems were queried to provide the input required by the ADAPT software to create an initial set of SPASE metadata resource descriptions. Then, the CDAWEB and SPDF data repositories were queried subsequently on a nightly basis and the CDF file lists were checked for any changes such as the occurrence of new, modified, or deleted

  19. Complex Adaptive Systems, soil degradation and land sensitivity to desertification: A multivariate assessment of Italian agro-forest landscape.

    PubMed

    Salvati, Luca; Mavrakis, Anastasios; Colantoni, Andrea; Mancino, Giuseppe; Ferrara, Agostino

    2015-07-15

    Degradation of soils and sensitivity of land to desertification are intensified in last decades in the Mediterranean region producing heterogeneous spatial patterns determined by the interplay of factors such as climate, land-use changes, and human pressure. The present study hypothesizes that rising levels of soil degradation and land sensitivity to desertification are reflected into increasingly complex (and non-linear) relationships between environmental and socioeconomic variables. To verify this hypothesis, the Complex Adaptive Systems (CAS) framework was used to explore the spatiotemporal dynamics of eleven indicators derived from a standard assessment of soil degradation and land sensitivity to desertification in Italy. Indicators were made available on a detailed spatial scale (773 agricultural districts) for various years (1960, 1990, 2000 and 2010) and analyzed through a multi-dimensional exploratory data analysis. Our results indicate that the number of significant pair-wise correlations observed between indicators increased with the level of soil and land degradation, although with marked differences between northern and southern Italy. 'Fast' and 'slow' factors underlying soil and land degradation, and 'rapidly-evolving' or 'locked' agricultural districts were identified according to the rapidity of change estimated for each of the indicators studied. In southern Italy, 'rapidly-evolving' districts show a high level of soil degradation and land sensitivity to desertification during the whole period of investigation. On the contrary, those districts in northern Italy are those experiencing a moderate soil degradation and land sensitivity to desertification with the highest increase in the level of sensitivity over time. The study framework contributes to the assessment of complex local systems' dynamics in affluent but divided countries. Results may inform thematic strategies for the mitigation of land and soil degradation in the framework of action

  20. Making sense in a complex landscape: how the Cynefin Framework from Complex Adaptive Systems Theory can inform health promotion practice.

    PubMed

    Van Beurden, Eric K; Kia, Annie M; Zask, Avigdor; Dietrich, Uta; Rose, Lauren

    2013-03-01

    Health promotion addresses issues from the simple (with well-known cause/effect links) to the highly complex (webs and loops of cause/effect with unpredictable, emergent properties). Yet there is no conceptual framework within its theory base to help identify approaches appropriate to the level of complexity. The default approach favours reductionism--the assumption that reducing a system to its parts will inform whole system behaviour. Such an approach can yield useful knowledge, yet is inadequate where issues have multiple interacting causes, such as social determinants of health. To address complex issues, there is a need for a conceptual framework that helps choose action that is appropriate to context. This paper presents the Cynefin Framework, informed by complexity science--the study of Complex Adaptive Systems (CAS). It introduces key CAS concepts and reviews the emergence and implications of 'complex' approaches within health promotion. It explains the framework and its use with examples from contemporary practice, and sets it within the context of related bodies of health promotion theory. The Cynefin Framework, especially when used as a sense-making tool, can help practitioners understand the complexity of issues, identify appropriate strategies and avoid the pitfalls of applying reductionist approaches to complex situations. The urgency to address critical issues such as climate change and the social determinants of health calls for us to engage with complexity science. The Cynefin Framework helps practitioners make the shift, and enables those already engaged in complex approaches to communicate the value and meaning of their work in a system that privileges reductionist approaches. PMID:22128193

  1. Potentials of RapidEye time series for improved classification of crop rotations in heterogeneous agricultural landscapes: experiences from irrigation systems in Central Asia

    NASA Astrophysics Data System (ADS)

    Conrad, Christopher; Machwitz, Miriam; Schorcht, Gunther; Löw, Fabian; Fritsch, Sebastian; Dech, Stefan

    2011-11-01

    In Central Asia, more than eight Million ha of agricultural land are under irrigation. But severe degradation problems and unreliable water distribution have caused declining yields during the past decades. Reliable and area-wide information about crops can be seen as important step to elaborate options for sustainable land and water management. Experiences from RapidEye classifications of crop in Central Asia are exemplarily shown during a classification of eight crop classes including three rotations with winter wheat, cotton, rice, and fallow land in the Khorezm region of Uzbekistan covering 230,000 ha of irrigated land. A random forest generated by using 1215 field samples was applied to multitemporal RapidEye data acquired during the vegetation period 2010. But RapidEye coverage varied and did not allow for generating temporally consistent mosaics covering the entire region. To classify all 55,188 agricultural parcels in the region three classification zones were classified separately. The zoning allowed for including at least three observation periods into classification. Overall accuracy exceeded 85 % for all classification zones. Highest accuracies of 87.4 % were achieved by including five spatiotemporal composites of RapidEye. Class-wise accuracy assessments showed the usefulness of selecting time steps which represent relevant phenological phases of the vegetation period. The presented approach can support regional crop inventory. Accurate classification results in early stages of the cropping season permit recalculation of crop water demands and reallocation of irrigation water. The high temporal and spatial resolution of RapidEye can be concluded highly beneficial for agricultural land use classifications in entire Central Asia.

  2. Hydrologic Landscape Characterization for the Pacific Northwest, USA

    EPA Science Inventory

    Hydrologic classification can help address some of the challenges facing catchment hydrology. Wigington et al. (2013) developed a hydrologic landscape (HL) approach to classification that was applied to the state of Oregon. Several characteristics limited its applicability outs...

  3. Do Ecological Niche Model Predictions Reflect the Adaptive Landscape of Species?: A Test Using Myristica malabarica Lam., an Endemic Tree in the Western Ghats, India

    PubMed Central

    Nagaraju, Shivaprakash K.; Gudasalamani, Ravikanth; Barve, Narayani; Ghazoul, Jaboury; Narayanagowda, Ganeshaiah Kotiganahalli; Ramanan, Uma Shaanker

    2013-01-01

    Ecological niche models (ENM) have become a popular tool to define and predict the “ecological niche” of a species. An implicit assumption of the ENMs is that the predicted ecological niche of a species actually reflects the adaptive landscape of the species. Thus in sites predicted to be highly suitable, species would have maximum fitness compared to in sites predicted to be poorly suitable. As yet there are very few attempts to address this assumption. Here we evaluate this assumption. We used Bioclim (DIVA GIS version 7.3) and Maxent (version 3.3.2) to predict the habitat suitability of Myristica malabarica Lam., an economically important tree occurring in the Western Ghats, India. We located populations of the trees naturally occurring in different habitat suitability regimes (from highly suitable to poorly suitable) and evaluated them for their regeneration ability and genetic diversity. We also evaluated them for two plant functional traits, fluctuating asymmetry – an index of genetic homeostasis, and specific leaf weight – an index of primary productivity, often assumed to be good surrogates of fitness. We show a significant positive correlation between the predicted habitat quality and plant functional traits, regeneration index and genetic diversity of populations. Populations at sites predicted to be highly suitable had a higher regeneration and gene diversity compared to populations in sites predicted to be poor or unsuitable. Further, individuals in the highly suitable sites exhibited significantly less fluctuating asymmetry and significantly higher specific leaf weight compared to individuals in the poorly suitable habitats. These results for the first time provide an explicit test of the ENM with respect to the plant functional traits, regeneration ability and genetic diversity of populations along a habitat suitability gradient. We discuss the implication of these resultsfor designing viable species conservation and restoration programs. PMID

  4. Image-based ATR utilizing adaptive clutter filter detection, LLRT classification, and Volterra fusion with application to side-looking sonar

    NASA Astrophysics Data System (ADS)

    Aridgides, Tom; Fernández, Manuel

    2010-04-01

    An improved automatic target recognition (ATR) processing string has been developed. The overall processing string consists of pre-processing, subimage adaptive clutter filtering, detection, feature extraction, optimal subset feature selection, feature orthogonalization and classification processing blocks. The objects that are classified by three distinct ATR strings are fused using the classification confidence values and their expansions as features, and using "summing" or log-likelihood-ratio-test (LLRT) based fusion rules. These three ATR processing strings were individually developed and tuned by researchers from different companies. The utility of the overall processing strings and their fusion was demonstrated with an extensive side-looking sonar dataset. In this paper we describe a new processing improvement: six additional classification features are extracted, using primarily target shadow information and a feature extraction window whose length is now made variable as a function of range. This new ATR processing improvement resulted in a 3:1 reduction in false alarms. Two advanced fusion algorithms are subsequently applied: First, a nonlinear Volterra expansion (2nd order) feature-LLRT fusion algorithm is employed. Second, a repeated application of a subset Volterra feature selection / feature orthogonalization / LLRT fusion block is utilized. It is shown that cascaded Volterra feature- LLRT fusion of the ATR processing strings outperforms baseline "summing" and single-stage Volterra feature-LLRT fusion algorithms, yielding significant improvements over the best single ATR processing string results, and providing the capability to correctly call the majority of targets while maintaining a very low false alarm rate.

  5. Laser Raman detection of platelets for early and differential diagnosis of Alzheimer’s disease based on an adaptive Gaussian process classification algorithm

    NASA Astrophysics Data System (ADS)

    Luo, Yusheng; Du, Z. W.; Yang, Y. J.; Chen, P.; Tian, Q.; Shang, X. L.; Liu, Z. C.; Yao, X. Q.; Wang, J. Z.; Wang, X. H.; Cheng, Y.; Peng, J.; Shen, A. G.; Hu, J. M.

    2013-04-01

    Early and differential diagnosis of Alzheimer’s disease (AD) has puzzled many clinicians. In this work, laser Raman spectroscopy (LRS) was developed to diagnose AD from platelet samples from AD transgenic mice and non-transgenic controls of different ages. An adaptive Gaussian process (GP) classification algorithm was used to re-establish the classification models of early AD, advanced AD and the control group with just two features and the capacity for noise reduction. Compared with the previous multilayer perceptron network method, the GP showed much better classification performance with the same feature set. Besides, spectra of platelets isolated from AD and Parkinson’s disease (PD) mice were also discriminated. Spectral data from 4 month AD (n = 39) and 12 month AD (n = 104) platelets, as well as control data (n = 135), were collected. Prospective application of the algorithm to the data set resulted in a sensitivity of 80%, a specificity of about 100% and a Matthews correlation coefficient of 0.81. Samples from PD (n = 120) platelets were also collected for differentiation from 12 month AD. The results suggest that platelet LRS detection analysis with the GP appears to be an easier and more accurate method than current ones for early and differential diagnosis of AD.

  6. "Once You Go to a White School, You Kind of Adapt": Black Adolescents and the Racial Classification of Schools

    ERIC Educational Resources Information Center

    Ispa-Landa, Simone; Conwell, Jordan

    2015-01-01

    Studies of when youth classify academic achievement in racial terms have focused on the racial classification of behaviors and individuals. However, institutions--including schools--may also be racially classified. Drawing on a comparative interview study, we examine the school contexts that prompt urban black students to classify schools in…

  7. Using landscape limnology to classify freshwater ecosystems for multi-ecosystem management and conservation

    USGS Publications Warehouse

    Soranno, Patricia A.; Cheruvelil, Kendra Spence; Webster, Katherine E.; Bremigan, Mary T.; Wagner, Tyler; Stow, Craig A.

    2010-01-01

    Governmental entities are responsible for managing and conserving large numbers of lake, river, and wetland ecosystems that can be addressed only rarely on a case-by-case basis. We present a system for predictive classification modeling, grounded in the theoretical foundation of landscape limnology, that creates a tractable number of ecosystem classes to which management actions may be tailored. We demonstrate our system by applying two types of predictive classification modeling approaches to develop nutrient criteria for eutrophication management in 1998 north temperate lakes. Our predictive classification system promotes the effective management of multiple ecosystems across broad geographic scales by explicitly connecting management and conservation goals to the classification modeling approach, considering multiple spatial scales as drivers of ecosystem dynamics, and acknowledging the hierarchical structure of freshwater ecosystems. Such a system is critical for adaptive management of complex mosaics of freshwater ecosystems and for balancing competing needs for ecosystem services in a changing world.

  8. Adaption by Rewiring Epigenetic Landscapes

    PubMed Central

    Liu, Yifei; Xiao, Andrew

    2016-01-01

    Embryonic stem cells (ESCs) generally rely on repressive histone modifications to silence retrotransposons, rather than DNA methylation as in differentiated cells. In this issue of Cell Stem Cell, He et al. (2015) show that Daxx/Atrx repress transposons in ESCs devoid of 5mC, demonstrating dynamic reorganization of epigenetic networks and crosstalk between distinct repressive mechanisms to maintain transposon silencing. PMID:26340521

  9. Laser Raman detection for oral cancer based on an adaptive Gaussian process classification method with posterior probabilities

    NASA Astrophysics Data System (ADS)

    Du, Zhanwei; Yang, Yongjian; Bai, Yuan; Wang, Lijun; Su, Le; Chen, Yong; Li, Xianchang; Zhou, Xiaodong; Jia, Jun; Shen, Aiguo; Hu, Jiming

    2013-03-01

    The existing methods for early and differential diagnosis of oral cancer are limited due to the unapparent early symptoms and the imperfect imaging examination methods. In this paper, the classification models of oral adenocarcinoma, carcinoma tissues and a control group with just four features are established by utilizing the hybrid Gaussian process (HGP) classification algorithm, with the introduction of the mechanisms of noise reduction and posterior probability. HGP shows much better performance in the experimental results. During the experimental process, oral tissues were divided into three groups, adenocarcinoma (n = 87), carcinoma (n = 100) and the control group (n = 134). The spectral data for these groups were collected. The prospective application of the proposed HGP classification method improved the diagnostic sensitivity to 56.35% and the specificity to about 70.00%, and resulted in a Matthews correlation coefficient (MCC) of 0.36. It is proved that the utilization of HGP in LRS detection analysis for the diagnosis of oral cancer gives accurate results. The prospect of application is also satisfactory.

  10. Study of settlement distribution pattern in the Kolkheti lowland (Black Sea coast of Georgia) starting from early Bronze Age - natural and human influence and adaptation to landscape evolution

    NASA Astrophysics Data System (ADS)

    Elashvili, Mikheil; Akhvlediani, Dimitri; Navrozashvili, Levan; Sukhishvili, Lasha; Kirkitadze, Giorgi; Kelterbaum, Daniel; Laermans, Hannes

    2015-04-01

    archaeological datasets are collected in the joint-venture project and in addition with known historical and old topographic maps of the region they represent a good start for the research. There are typical ancient settlements in the Kolkheti lowland, called locally "Dikhagudzuba", which are still identifiable on aerial imagery. Their structure, physical dimensions and locations were analyzed from aerial and on site studies. Data from existing archaeological studies and recent field works were analyzed to create a reliable database on the distribution of Bronze Age settlements. Changes in paleoclimate, sea level and river deltas represent the main components to form a paleolandscape of the study area. Based on the results of recent fieldwork and the analyses of regional historical maps in addition with the general geological and geomorphological settings paleogeographical scenarios were constructed. Proposed models of past landscape changes and human settlement pattern were merged and analyzed. From one hand the human settlement distribution (taking into account tells relation with the local landscape of the same period) help us to identify the best suitable scenario from the set of paleolandscape patterns. Moreover, paleogeographical scenarios provide a better understanding on the erection of human settlements in the past, and their influence and adaptation to ongoing changes.

  11. Form classification

    NASA Astrophysics Data System (ADS)

    Reddy, K. V. Umamaheswara; Govindaraju, Venu

    2008-01-01

    The problem of form classification is to assign a single-page form image to one of a set of predefined form types or classes. We classify the form images using low level pixel density information from the binary images of the documents. In this paper, we solve the form classification problem with a classifier based on the k-means algorithm, supported by adaptive boosting. Our classification method is tested on the NIST scanned tax forms data bases (special forms databases 2 and 6) which include machine-typed and handwritten documents. Our method improves the performance over published results on the same databases, while still using a simple set of image features.

  12. Adapting Landscape Mosaics of medIteranean Rainfed Agrosystems for a sustainable management of crop production, water and soil resources: the ALMIRA project.

    NASA Astrophysics Data System (ADS)

    Jacob, Frédéric; Mekki, Insaf; Chikhaoui, Mohamed

    2014-05-01

    In the context of mitigating the pressures induced by global change combined with demography and market pressures, there is increasing societal demand and scientific need to understand the functioning of Mediterranean Rainfed Agrosystems (MRAs) for their potential to provide various environmental and economic services of importance such as food production, preservation of employment and local knowhow, downstream water delivery or mitigation of rural exodus. Efficient MRAs management strategies that allow for compromises between economic development and natural resources preservation are needed. Such strategies require innovative system based research, integration across approaches and scales. One of the major challenges is to make all contributions from different disciplines converging towards a reproducible transdisciplinary approach. The objective of this communication is to present the ALMIRA project, a Tunisian - Moroccan - French project which lasts four years (2014 - 2017). The communication details the societal context, the scientific positioning and the related work hypothesis, the study areas, the project structure, the expected outcomes and the partnership which capitalizes on long term collaborations. ALMIRA aims to explore the modulation of landscape mosaics within MRAs to optimize landscape services. To explore this new lever, ALMIRA proposes to design, implement and test a new Integrated Assessment Modelling approach that explicitly i) includes innovations and action means into prospective scenarii for landscape evolutions, and ii) addresses landscape mosaics and processes of interest from the agricultural field to the resource governance catchment. This requires tackling methodological challenges in relation to i) the design of spatially explicit landscape evolution scenarii, ii) the coupling of biophysical processes related to agricultural catchment hydrology, iii) the digital mapping of landscape properties and iv) the economic assessment of the

  13. Equivalent Diagnostic Classification Models

    ERIC Educational Resources Information Center

    Maris, Gunter; Bechger, Timo

    2009-01-01

    Rupp and Templin (2008) do a good job at describing the ever expanding landscape of Diagnostic Classification Models (DCM). In many ways, their review article clearly points to some of the questions that need to be answered before DCMs can become part of the psychometric practitioners toolkit. Apart from the issues mentioned in this article that…

  14. A new adaptive classifier using iterative filtering. [classification of remotely sensed data in visible and near infrared bands

    NASA Technical Reports Server (NTRS)

    Actkinson, A. L.

    1974-01-01

    To cope with signature variability, an algorithm has been defined which will adaptively classify remotely sensed data in the visible and near infrared band. The signal is divided into a space-dependent component and a target-dependent component. The target-dependent component is assumed fixed across the image for each target type. The space-dependent component is estimated iteratively by a weighted, least-squares algorithm. Included are the derivations of the sensor model and the two-dimensional, estimation algorithm.

  15. Modelling the Relationship Between Land Surface Temperature and Landscape Patterns of Land Use Land Cover Classification Using Multi Linear Regression Models

    NASA Astrophysics Data System (ADS)

    Bernales, A. M.; Antolihao, J. A.; Samonte, C.; Campomanes, F.; Rojas, R. J.; dela Serna, A. M.; Silapan, J.

    2016-06-01

    The threat of the ailments related to urbanization like heat stress is very prevalent. There are a lot of things that can be done to lessen the effect of urbanization to the surface temperature of the area like using green roofs or planting trees in the area. So land use really matters in both increasing and decreasing surface temperature. It is known that there is a relationship between land use land cover (LULC) and land surface temperature (LST). Quantifying this relationship in terms of a mathematical model is very important so as to provide a way to predict LST based on the LULC alone. This study aims to examine the relationship between LST and LULC as well as to create a model that can predict LST using class-level spatial metrics from LULC. LST was derived from a Landsat 8 image and LULC classification was derived from LiDAR and Orthophoto datasets. Class-level spatial metrics were created in FRAGSTATS with the LULC and LST as inputs and these metrics were analysed using a statistical framework. Multi linear regression was done to create models that would predict LST for each class and it was found that the spatial metric "Effective mesh size" was a top predictor for LST in 6 out of 7 classes. The model created can still be refined by adding a temporal aspect by analysing the LST of another farming period (for rural areas) and looking for common predictors between LSTs of these two different farming periods.

  16. Mars Landscapes

    NASA Video Gallery

    Spacecraft have studied the Martian surface for decades, giving Earthlings insights into the history, climate and geology of our nearest neighbor, Mars. These images are from "Mars Landscapes," a v...

  17. Landscape Architecture.

    ERIC Educational Resources Information Center

    American School and University, 1985

    1985-01-01

    Members of the American Society of Landscape Architects shape open spaces on the campuses of Georgetown University, District of Columbia; the University of Missouri; Auraria Higher Education Center, Colorado; and the University of Michigan. (MLF)

  18. Structural identification of a prototype pre-stressable leaf-spring based adaptive tuned mass damper: Nonlinear characterization and classification

    NASA Astrophysics Data System (ADS)

    Rizos, Demosthenis; Feltrin, Glauco; Motavalli, Masoud

    2011-01-01

    The current paper focuses on a prototype adaptive TMD. Its design concept is based on pre-stressable leaf-springs that are controlled by piezoceramic (PZT) stack actuators. Experiments performed on the prototype showed that it is continuously tunable in a broad frequency range. Moreover, they revealed that the device exhibits structural nonlinearities. The current paper focuses on the structural identification of the prototype and attempts for the first time to characterize and classify the observed nonlinearities. Several experiments at different PZT voltage levels are performed. The results indicate PZT voltage dependent nonlinear softening and hardening stiffness. Based upon these observations, static experiments and proper data-pooling techniques, an effective "global" model for the nonlinear stiffness is derived. The estimated nonlinear model is finally validated upon static experiments as well as more realistic operational cases, that are vibrations of the prototype under typical ground excitation.

  19. Adaptive and context-aware detection and classification of potential QoS degradation events in biomedical wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Abreu, Carlos; Miranda, Francisco; Mendes, Paulo M.

    2016-06-01

    The use of wireless sensor networks in healthcare has the potential to enhance the services provided to citizens. In particular, they play an important role in the development of state-of-the-art patient monitoring applications. Nevertheless, due to the critical nature of the data conveyed by such patient monitoring applications, they have to fulfil high standards of quality of service in order to obtain the confidence of all players in the healthcare industry. In such context, vis-à-vis the quality of service being provided by the wireless sensor network, this work presents an adaptive and context-aware method to detect and classify performance degradation events. The proposed method has the ability to catch the most significant and damaging variations on the metrics being used to quantify the quality of service provided by the network without overreacting to small and innocuous variations on the metric's value.

  20. Monitoring of Agricultural Landscape in Norway

    NASA Astrophysics Data System (ADS)

    Wallin, H. G.; Engan, G.

    2012-07-01

    An overall societal aim is to ensure a sustainable use and management of agricultural landscapes. This requires continuous delivery of reliable and up-to-date information to decision-makers. To be able to deliver this information, a monitoring program for agricultural landscapes was initiated in Norway 13 years ago. The program documents and reports on land use / land cover changes from data captured through interpretation of true colour aerial photos using stereo instruments. The monitoring programme is based on a sample of 1000 squares of 1 × 1 km and the entire sample of squares is photographed over a five-year period. Each square is then mapped repeatedly every fifth year to record changes. Aerial photo interpretation is based on a custom classification system which is built up hierarchically, with three levels. The first level comprises seven land type classes: Agricultural land, Bare ground, Semi-natural open vegetation, Unforested wetland vegetation, Forest, Urban areas and Water. These land classes are further divided into 24 land types at level two, and approximately 100 land types at level 3. In addition to land type units we map both line elements like stone fences and point elements like buildings and solitary threes. By use of indicators that describe status and change focusing on themes of particular policy interest, we can report on whether policy aims are being fulfilled or not. Four indicator themes have been in focus hitherto: landscape spatial structure, biological diversity, cultural heritage and accessibility. Our data is stored in databases and most of the data quality check/structure process and analyses are now being made in open source software like PostGIS and PostSQL. To assess the accuracy of the photo-interpretation, ground truthing is carried out on 10 % of the squares. The results of this operation document the benefits of having access to photos of the same area from two different years. The program is designed first and foremost to

  1. Catchment classification by means of hydrological models

    NASA Astrophysics Data System (ADS)

    Hellebrand, Hugo; Ley, Rita; Casper, Markus

    2013-04-01

    An important hydrological objective is catchment classification that will serve as a basis for the regionalisation of discharge parameters or model parameters. The main task of this study is the development and assessment of two classification approaches with respect to their efficiency in catchment classification. The study area in western Germany comprises about 80 catchments that range in size from 8 km2 up to 1500 km2, covering a wide range of geological substrata, soils, landscapes and mean annual precipitation. In a first approach Self Organising Maps (SOMs) use discharge characteristics or catchment characteristics to classify the catchments of the study area. Next, a reference hydrological model calibrates the catchments of the study area and tests the possibilities of parameter transfer. Compared to the transfer of parameters outside a class, for most catchments the model performance improves when parameters within a class are transferred. Thus, it should be possible to distinguish catchment classes by means of a hydrological model. The classification results of the SOM are compared to the classification results of the reference hydrological model in order to determine the latter validity. The second approach builds on the first approach in such a way that it uses the Superflex Modelling Framework instead of only one reference model. Within this framework multiple conceptual model structures can be calibrated and adapted. Input data for each calibration of a catchment are hourly time series of runoff, precipitation and evaporation for at least eight years. The calibration of multiple models for each catchment and their comparison allows for the assessment of the influence of different model structures on model performance. Learning loops analyse model performance and adapt model structures accordingly with a view to performance improvement. The result of the modelling exercise is a best performing model structure for each catchment that serves as a basis

  2. Landscape diversity

    Technology Transfer Automated Retrieval System (TEKTRAN)

    While biodiversity is usually considered at the species level, maintenance of biodiversity requires management at higher levels of organization, particularly at the landscape scale. It is difficult to manage for each threatened species individually. Alternatively, management can focus on the ecosyst...

  3. Planning Construction Research of Modern Urban Landscape

    NASA Astrophysics Data System (ADS)

    Xiao, Z. Q.; Chen, W.

    With the development and expansion of the city's traditional urban landscape planning methods have been difficult to adapt to the requirements of modern urban development, in the new urban construction, planning what kind of urban landscape is a new research topic. The article discusses the principles of modern urban landscape planning and development, promote the adoption of new concepts and theories, building more regional characteristics, more humane, more perfect, more emphasis on urban landscape pattern natural ecological protection and construction can sustainable development of urban living environment, and promote the development and construction of the city.

  4. The farmer as a landscape steward: Comparing local understandings of landscape stewardship, landscape values, and land management actions.

    PubMed

    Raymond, Christopher M; Bieling, Claudia; Fagerholm, Nora; Martin-Lopez, Berta; Plieninger, Tobias

    2016-03-01

    We develop a landscape stewardship classification which distinguishes between farmers' understanding of landscape stewardship, their landscape values, and land management actions. Forty semi-structured interviews were conducted with small-holder (<5 acres), medium-holders (5-100 acres), and large-holders (>100 acres) in South-West Devon, UK. Thematic analysis revealed four types of stewardship understandings: (1) an environmental frame which emphasized the farmers' role in conserving or restoring wildlife; (2) a primary production frame which emphasized the farmers' role in taking care of primary production assets; (3) a holistic frame focusing on farmers' role as a conservationist, primary producer, and manager of a range of landscape values, and; (4) an instrumental frame focusing on the financial benefits associated with compliance with agri-environmental schemes. We compare the landscape values and land management actions that emerged across stewardship types, and discuss the global implications of the landscape stewardship classification for the engagement of farmers in landscape management. PMID:26346276

  5. A comparison of grazing behavior between desert adapted Mexican Criollo cattle and temperate british breeds using two diverse landscapes in New Mexico and Chihuahua.

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This study was designed to test how grazing behaviors differ between desert adapted Mexican criollo cattle and temperate British beef breeds, to learn how each breed interacts with environments common to the southwestern US and northwestern Mexico. Additionally, criollo cattle may be a better breed ...

  6. Vulnerability of Oregon hydrologic landscapes and streamflow to climate change

    EPA Science Inventory

    Hydrologic classification systems can provide a basis for broadscale assessments of the hydrologic functions of landscapes and watersheds and their responses to stressors. Such assessments could be particularly useful in determining hydrologic vulnerability from climate change. ...

  7. Landscape complexity and vegetation dynamics in Riding Mountain National Park, Canada

    NASA Astrophysics Data System (ADS)

    Walker, David John

    spatial complexity over time. Fragmentation and habitat losses in the region surrounding RMNP were found to be high, with only half of the forest present in 1950 remaining in the 1990's. Scale-invariant spatial dispersion of forest fragments decreased between the 1950's and 1990's. Thus, the study area is becoming increasingly isolated from other natural forested areas within the region. In creating maps of land cover for these analyses, it was found that structural composition of the canopy was often more important than floristics in determining spectral reflectance in Landsat data. A rule-based optimization procedure using multivariate analysis was developed to maximize the relationship between vegetation on the ground and spectral reflectance. Because of the high degree of spatial complexity in these systems, an alternative approach to map accuracy assessment utilizing multiple discriminant analysis (MDA) was developed. It was found that closed conifer stands composed of different softwood species were not easily discriminated during classification because of identical spectral signatures at the stand-level. It is suggested that the highly structured architecture and conical form of conifer stands results in the anechoic interception and absorption of light. This light interception strategy may have adaptive advantages in regions where sun angle is low, or where cloud cover is high, such as in the boreal forest and montane environments. The results of these investigations into landscape pattern suggest that ecosystem dynamics in the boreal forest produce scale-invariant landscape complexity.

  8. A solution to the challenge of optimization on ''golf-course''-like fitness landscapes.

    PubMed

    Melo, Hygor Piaget M; Franks, Alexander; Moreira, André A; Diermeier, Daniel; Andrade, José S; Amaral, Luís A Nunes

    2013-01-01

    Genetic algorithms (GAs) have been used to find efficient solutions to numerous fundamental and applied problems. While GAs are a robust and flexible approach to solve complex problems, there are some situations under which they perform poorly. Here, we introduce a genetic algorithm approach that is able to solve complex tasks plagued by so-called ''golf-course''-like fitness landscapes. Our approach, which we denote variable environment genetic algorithms (VEGAs), is able to find highly efficient solutions by inducing environmental changes that require more complex solutions and thus creating an evolutionary drive. Using the density classification task, a paradigmatic computer science problem, as a case study, we show that more complex rules that preserve information about the solution to simpler tasks can adapt to more challenging environments. Interestingly, we find that conservative strategies, which have a bias toward the current state, evolve naturally as a highly efficient solution to the density classification task under noisy conditions. PMID:24223800

  9. A Solution to the Challenge of Optimization on ''Golf-Course''-Like Fitness Landscapes

    PubMed Central

    Melo, Hygor Piaget M.; Franks, Alexander; Moreira, André A.; Diermeier, Daniel; Andrade, José S.; Amaral, Luís A. N. u. n. e. s.

    2013-01-01

    Genetic algorithms (GAs) have been used to find efficient solutions to numerous fundamental and applied problems. While GAs are a robust and flexible approach to solve complex problems, there are some situations under which they perform poorly. Here, we introduce a genetic algorithm approach that is able to solve complex tasks plagued by so-called ''golf-course''-like fitness landscapes. Our approach, which we denote variable environment genetic algorithms (VEGAs), is able to find highly efficient solutions by inducing environmental changes that require more complex solutions and thus creating an evolutionary drive. Using the density classification task, a paradigmatic computer science problem, as a case study, we show that more complex rules that preserve information about the solution to simpler tasks can adapt to more challenging environments. Interestingly, we find that conservative strategies, which have a bias toward the current state, evolve naturally as a highly efficient solution to the density classification task under noisy conditions. PMID:24223800

  10. Improving classification of psychoses.

    PubMed

    Lawrie, Stephen M; O'Donovan, Michael C; Saks, Elyn; Burns, Tom; Lieberman, Jeffrey A

    2016-04-01

    Psychosis has been recognised as an abnormal state in need of care throughout history and by diverse cultures. Present classifications of psychotic disorder remain based on the presence of specific psychotic symptoms, relative to affective and other symptoms, and their sequence and duration. Although extant diagnostic classifications have restricted validity, they have proven reliability and most clinicians and some patients find them useful. Moreover, these classifications have yet to be replaced by anything better. We propose that an expansion of the subgrouping approach inherent to classification will provide incremental improvement to present diagnostic constructs-as has worked in the rest of medicine. We also propose that subgroups could be created both within and across present diagnostic classifications, taking into consideration the potential value of continuous measures (eg, duration of psychotic symptoms and intelligence quotient). Health-care workers also need to work with service users and carers to develop and adapt approaches to diagnosis that are seen as helpful. PMID:27063387

  11. Multistage Adaptive Testing for a Large-Scale Classification Test: Design, Heuristic Assembly, and Comparison with Other Testing Modes. ACT Research Report Series, 2012 (6)

    ERIC Educational Resources Information Center

    Zheng, Yi; Nozawa, Yuki; Gao, Xiaohong; Chang, Hua-Hua

    2012-01-01

    Multistage adaptive tests (MSTs) have gained increasing popularity in recent years. MST is a balanced compromise between linear test forms (i.e., paper-and-pencil testing and computer-based testing) and traditional item-level computer-adaptive testing (CAT). It combines the advantages of both. On one hand, MST is adaptive (and therefore more…

  12. Driving the Landscape

    NASA Astrophysics Data System (ADS)

    Haff, P. K.

    2012-12-01

    destination—whereas the natural evolution of landscape has no such goal. Goals will become an essential feature of landscape prediction. The presence of a goal potentially increases our ability to predict, provided it is possible to use feedback (i.e., management) to nudge the system back in the "right" direction when it starts to stray. Under a regime of accelerating technology the closest we can get to predicting the longer term future of landscape is adaptive management, which at large scale is really geoengineer the system. The goal presumably would be to maintain a condition conducive to human well-being, for example to maintain a suitable fraction of global arable land. A successful "prediction" would be to stay within an envelope of states consistent with that goal. We cannot say, however, in what specific state the landscape will be at any time beyond the near future; this will depend on the future sequence of management decisions, which are, like the system they are managing, unpredictable, except shortly before they are implemented. The landscape of the future will thus likely be the result of a series of quick fixes to previous trends in landscape change. Similar comments apply to the prediction, or management, of climate. There is of course no guarantee that it will be possible to stay within the desired envelope of well-being.

  13. Classification Schemes: Developments and Survival.

    ERIC Educational Resources Information Center

    Pocock, Helen

    1997-01-01

    Discusses the growth, survival and future of library classification schemes. Concludes that to survive, a scheme must constantly update its policies, and readily adapt itself to accommodate growing disciplines and changing terminology. (AEF)

  14. Improving land cover classification using input variables derived from a geographically weighted principal components analysis

    NASA Astrophysics Data System (ADS)

    Comber, Alexis J.; Harris, Paul; Tsutsumida, Narumasa

    2016-09-01

    This study demonstrates the use of a geographically weighted principal components analysis (GWPCA) of remote sensing imagery to improve land cover classification accuracy. A principal components analysis (PCA) is commonly applied in remote sensing but generates global, spatially-invariant results. GWPCA is a local adaptation of PCA that locally transforms the image data, and in doing so, can describe spatial change in the structure of the multi-band imagery, thus directly reflecting that many landscape processes are spatially heterogenic. In this research the GWPCA localised loadings of MODIS data are used as textural inputs, along with GWPCA localised ranked scores and the image bands themselves to three supervised classification algorithms. Using a reference data set for land cover to the west of Jakarta, Indonesia the classification procedure was assessed via training and validation data splits of 80/20, repeated 100 times. For each classification algorithm, the inclusion of the GWPCA loadings data was found to significantly improve classification accuracy. Further, but more moderate improvements in accuracy were found by additionally including GWPCA ranked scores as textural inputs, data that provide information on spatial anomalies in the imagery. The critical importance of considering both spatial structure and spatial anomalies of the imagery in the classification is discussed, together with the transferability of the new method to other studies. Research topics for method refinement are also suggested.

  15. Landscaping for energy efficiency

    SciTech Connect

    1995-04-01

    This publication by the National Renewable Energy Laboratory addresses the use of landscaping for energy efficiency. The topics of the publication include minimizing energy expenses; landscaping for a cleaner environment; climate, site, and design considerations; planning landscape; and selecting and planting trees and shrubs. A source list for more information on landscaping for energy efficiency and a reading list are included.

  16. Classification Options

    ERIC Educational Resources Information Center

    Exceptional Children, 1978

    1978-01-01

    The interview presents opinions of Nicholas Hobbs on the classification of exceptional children, including topics such as ecologically oriented classification systems, the role of parents, and need for revision of teacher preparation programs. (IM)

  17. Epistasis and the Structure of Fitness Landscapes: Are Experimental Fitness Landscapes Compatible with Fisher's Geometric Model?

    PubMed

    Blanquart, François; Bataillon, Thomas

    2016-06-01

    The fitness landscape defines the relationship between genotypes and fitness in a given environment and underlies fundamental quantities such as the distribution of selection coefficient and the magnitude and type of epistasis. A better understanding of variation in landscape structure across species and environments is thus necessary to understand and predict how populations will adapt. An increasing number of experiments investigate the properties of fitness landscapes by identifying mutations, constructing genotypes with combinations of these mutations, and measuring the fitness of these genotypes. Yet these empirical landscapes represent a very small sample of the vast space of all possible genotypes, and this sample is often biased by the protocol used to identify mutations. Here we develop a rigorous statistical framework based on Approximate Bayesian Computation to address these concerns and use this flexible framework to fit a broad class of phenotypic fitness models (including Fisher's model) to 26 empirical landscapes representing nine diverse biological systems. Despite uncertainty owing to the small size of most published empirical landscapes, the inferred landscapes have similar structure in similar biological systems. Surprisingly, goodness-of-fit tests reveal that this class of phenotypic models, which has been successful so far in interpreting experimental data, is a plausible in only three of nine biological systems. More precisely, although Fisher's model was able to explain several statistical properties of the landscapes-including the mean and SD of selection and epistasis coefficients-it was often unable to explain the full structure of fitness landscapes. PMID:27052568

  18. Multi-scale curvature for automated identification of glaciated mountain landscapes.

    PubMed

    Prasicek, Günther; Otto, Jan-Christoph; Montgomery, David R; Schrott, Lothar

    2014-03-15

    Erosion by glacial and fluvial processes shapes mountain landscapes in a long-recognized and characteristic way. Upland valleys incised by fluvial processes typically have a V-shaped cross-section with uniform and moderately steep slopes, whereas glacial valleys tend to have a U-shaped profile with a changing slope gradient. We present a novel regional approach to automatically differentiate between fluvial and glacial mountain landscapes based on the relation of multi-scale curvature and drainage area. Sample catchments are delineated and multiple moving window sizes are used to calculate per-cell curvature over a variety of scales ranging from the vicinity of the flow path at the valley bottom to catchment sections fully including valley sides. Single-scale curvature can take similar values for glaciated and non-glaciated catchments but a comparison of multi-scale curvature leads to different results according to the typical cross-sectional shapes. To adapt these differences for automated classification of mountain landscapes into areas with V- and U-shaped valleys, curvature values are correlated with drainage area and a new and simple morphometric parameter, the Difference of Minimum Curvature (DMC), is developed. At three study sites in the western United States the DMC thresholds determined from catchment analysis are used to automatically identify 5 × 5 km quadrats of glaciated and non-glaciated landscapes and the distinctions are validated by field-based geological and geomorphological maps. Our results demonstrate that DMC is a good predictor of glacial imprint, allowing automated delineation of glacially and fluvially incised mountain landscapes. PMID:24748703

  19. Manifold alignment for classification of multitemporal hyperspectral image data

    NASA Astrophysics Data System (ADS)

    Yang, Hsiu-Han

    Analyzing remotely sensed images to obtain land cover classification maps is an effective approach for acquiring information over landscapes that can be accomplished over extended areas with limited ground surveys. Further, with advances in remote sensing technology, spaceborne hyperspectral sensors provide the capability to acquire a set of images that have both high spectral and temporal resolution. These images are suitable for monitoring and analyzing environmental changes with subtle spectral characteristics. However, inherent characteristics of multitemporal hyperspectral images, including high dimensionality, nonlinearity, and nonstationarity phenomena over time and across large areas, pose several challenges for classification. This research addresses the issues of classification tasks in the presence of spectral shifts within multitemporal hyperspectral images by leveraging the concept of the data manifold. Although manifold learning has been applied successfully in single image hyperspectral data classification to address high dimensionality and nonlinear spectral responses, research related to manifold learning for multitemporal classification studies is limited. The proposed approaches utilize spectral signatures and spatial proximity to construct similar "local" geometries of temporal images. By aligning these underlying manifolds optimally, the impacts of nonstationary effects are mitigated and classification is accomplished in a representative temporal data manifold. "Global" manifolds learned from temporal hyperspectral images have a major advantage in faithful representation of the data in an image, such as retaining relationships between different classes. Local manifolds are favored in discriminating difficult classes and for computation efficiency. A new hybrid global-local manifold alignment method that combines the advantages of global and local manifolds for effective multitemporal image classification is also proposed. Results illustrate the

  20. Land cover classification in multispectral satellite imagery using sparse approximations on learned dictionaries

    NASA Astrophysics Data System (ADS)

    Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; Altmann, Garrett L.

    2014-05-01

    Techniques for automated feature extraction, including neuroscience-inspired machine vision, are of great interest for landscape characterization and change detection in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methodologies to the environmental sciences, using state-of-theart adaptive signal processing, combined with compressive sensing and machine learning techniques. We use a modified Hebbian learning rule to build spectral-textural dictionaries that are tailored for classification. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labels are automatically generated using CoSA: unsupervised Clustering of Sparse Approximations. We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska (USA). Our goal is to develop a robust classification methodology that will allow for automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties (e.g., soil moisture and inundation), and topographic/geomorphic characteristics. In this paper, we explore learning from both raw multispectral imagery, as well as normalized band difference indexes. We explore a quantitative metric to evaluate the spectral properties of the clusters, in order to potentially aid in assigning land cover categories to the cluster labels.

  1. Assessment of Classification Accuracies of SENTINEL-2 and LANDSAT-8 Data for Land Cover / Use Mapping

    NASA Astrophysics Data System (ADS)

    Hale Topaloğlu, Raziye; Sertel, Elif; Musaoğlu, Nebiye

    2016-06-01

    This study aims to compare classification accuracies of land cover/use maps created from Sentinel-2 and Landsat-8 data. Istanbul metropolitan city of Turkey, with a population of around 14 million, having different landscape characteristics was selected as study area. Water, forest, agricultural areas, grasslands, transport network, urban, airport- industrial units and barren land- mine land cover/use classes adapted from CORINE nomenclature were used as main land cover/use classes to identify. To fulfil the aims of this research, recently acquired dated 08/02/2016 Sentinel-2 and dated 22/02/2016 Landsat-8 images of Istanbul were obtained and image pre-processing steps like atmospheric and geometric correction were employed. Both Sentinel-2 and Landsat-8 images were resampled to 30m pixel size after geometric correction and similar spectral bands for both satellites were selected to create a similar base for these multi-sensor data. Maximum Likelihood (MLC) and Support Vector Machine (SVM) supervised classification methods were applied to both data sets to accurately identify eight different land cover/ use classes. Error matrix was created using same reference points for Sentinel-2 and Landsat-8 classifications. After the classification accuracy, results were compared to find out the best approach to create current land cover/use map of the region. The results of MLC and SVM classification methods were compared for both images.

  2. Landscape analysis: Theoretical considerations and practical needs

    USGS Publications Warehouse

    Godfrey, A.E.; Cleaves, E.T.

    1991-01-01

    Numerous systems of land classification have been proposed. Most have led directly to or have been driven by an author's philosophy of earth-forming processes. However, the practical need of classifying land for planning and management purposes requires that a system lead to predictions of the results of management activities. We propose a landscape classification system composed of 11 units, from realm (a continental mass) to feature (a splash impression). The classification concerns physical aspects rather than economic or social factors; and aims to merge land inventory with dynamic processes. Landscape units are organized using a hierarchical system so that information may be assembled and communicated at different levels of scale and abstraction. Our classification uses a geomorphic systems approach that emphasizes the geologic-geomorphic attributes of the units. Realm, major division, province, and section are formulated by subdividing large units into smaller ones. For the larger units we have followed Fenneman's delineations, which are well established in the North American literature. Areas and districts are aggregated into regions and regions into sections. Units smaller than areas have, in practice, been subdivided into zones and smaller units if required. We developed the theoretical framework embodied in this classification from practical applications aimed at land use planning and land management in Maryland (eastern Piedmont Province near Baltimore) and Utah (eastern Uinta Mountains). ?? 1991 Springer-Verlag New York Inc.

  3. Landscape analysis: Theoretical considerations and practical needs

    NASA Astrophysics Data System (ADS)

    Godfrey, Andrew E.; Cleaves, Emery T.

    1991-03-01

    Numerous systems of land classification have been proposed. Most have led directly to or have been driven by an author's philosophy of earth-forming processes. However, the practical need of classifying land for planning and management purposes requires that a system lead to predictions of the results of management activities. We propose a landscape classification system composed of 11 units, from realm (a continental mass) to feature (a splash impression). The classification concerns physical aspects rather than economic or social factors; and aims to merge land inventory with dynamic processes. Landscape units are organized using a hierarchical system so that information may be assembled and communicated at different levels of scale and abstraction. Our classification uses a geomorphic systems approach that emphasizes the geologic-geomorphic attributes of the units. Realm, major division, province, and section are formulated by subdividing large units into smaller ones. For the larger units we have followed Fenneman's delineations, which are well established in the North American literature. Areas and districts are aggregated into regions and regions into sections. Units smaller than areas have, in practice, been subdivided into zones and smaller units if required. We developed the theoretical framework embodied in this classification from practical applications aimed at land use planning and land management in Maryland (eastern Piedmont Province near Baltimore) and Utah (eastern Uinta Mountains).

  4. Consensus in landscape preference judgments: the effects of landscape visual aesthetic quality and respondents' characteristics.

    PubMed

    Kalivoda, Ondřej; Vojar, Jiří; Skřivanová, Zuzana; Zahradník, Daniel

    2014-05-01

    Landscape's visual aesthetic quality (VAQ) has been widely regarded as a valuable resource worthy of protection. Although great effort has been devoted to determining the factors driving aesthetic preferences, public consensus in judgments has been neglected in the vast majority of such studies. Therefore, the aim of our study was to analyze three main possible sources of judgment variance: landscape VAQ, landscape type, and variability among respondents. Based upon an extensive perception-based investigation including more than 400 hikers as respondents, we found that variance in respondents' judgments differed significantly among assessed landscape scenes. We discovered a significant difference in judgment variances within each investigated respondent characteristic (gender, age, education level, occupational classification, and respondent's type of residence). Judgment variance was at the same time affected by landscape VAQ itself - the higher the VAQ, the better the consensus. While differences caused by characteristics indicate subjectivity of aesthetic values, the knowledge that people better find consensus for positively perceived landscapes provides a cogent argument for legal protection of valuable landscape scenes. PMID:24594757

  5. The Campus Landscape.

    ERIC Educational Resources Information Center

    du Von, Jay

    1966-01-01

    All across the country, landscaping and site development are coming to the fore as essential and integral parts of university planning and development. This reprint concentrates on the function of landscape architecture, and briefly examines some of the major responsibilities of the landscape architect in planning a campus. Included are--(1)…

  6. Landscape Management: Field Supervisor.

    ERIC Educational Resources Information Center

    Newton, Deborah; Newton, Steve

    This module is the third volume in a series of instructional materials on landscape management. The materials are designed to help teachers train students in the job skills they will need in landscape occupations. The module contains six instructional units that cover the following topics: orientation; basic landscape design principles; irrigation…

  7. Random Forest Classification for Surficial Material Mapping in Northern Canada

    NASA Astrophysics Data System (ADS)

    Parkinson, William

    There is a need at the Geological Survey of Canada to apply improved accuracy assessments of satellite image classification and to support remote predictive mapping techniques for geological map production and field operations. Most existing image classification algorithms, however, lack any robust capabilities for assessing classification accuracy and its variability throughout the landscape. In this study, a random forest classification workflow is introduced to improve understanding of overall image classification accuracy and to better describe its spatial variability across a heterogeneous landscape in Northern Canada. Random Forest model is a stochastic implementation of classification and regression trees, which is computationally efficient, effectively handles outlier bias can be used on non-parametric data sources. A variable selection methodology and stochastic accuracy assessment for Random Forest is introduced. Random forest provides an enhanced classification compared to the standard maximum likelihood algorithms improving predictive capacity of satellite imagery for surficial material mapping.

  8. Using remote sensing products to classify landscape. A multi-spatial resolution approach

    NASA Astrophysics Data System (ADS)

    García-Llamas, Paula; Calvo, Leonor; Álvarez-Martínez, José Manuel; Suárez-Seoane, Susana

    2016-08-01

    The European Landscape Convention encourages the inventory and characterization of landscapes for environmental management and planning actions. Among the range of data sources available for landscape classification, remote sensing has substantial applicability, although difficulties might arise when available data are not at the spatial resolution of operational interest. We evaluated the applicability of two remote sensing products informing on land cover (the categorical CORINE map at 30 m resolution and the continuous NDVI spectral index at 1 km resolution) in landscape classification across a range of spatial resolutions (30 m, 90 m, 180 m, 1 km), using the Cantabrian Mountains (NW Spain) as study case. Separate landscape classifications (using topography, urban influence and land cover as inputs) were accomplished, one per each land cover dataset and spatial resolution. Classification accuracy was estimated through confusion matrixes and uncertainty in terms of both membership probability and confusion indices. Regarding landscape classifications based on CORINE, both typology and number of landscape classes varied across spatial resolutions. Classification accuracy increased from 30 m (the original resolution of CORINE) to 90m, decreasing towards coarser resolutions. Uncertainty followed the opposite pattern. In the case of landscape classifications based on NDVI, the identified landscape patterns were geographically structured and showed little sensitivity to changes across spatial resolutions. Only the change from 1 km (the original resolution of NDVI) to 180 m improved classification accuracy. The value of confusion indices increased with resolution. We highlight the need for greater effort in selecting data sources at the suitable spatial resolution, matching regional peculiarities and minimizing error and uncertainty.

  9. Combining QuickBird, LiDAR, and GIS topography indices to identify a single native tree species in a complex landscape using an object-based classification approach

    NASA Astrophysics Data System (ADS)

    Pham, Lien T. H.; Brabyn, Lars; Ashraf, Salman

    2016-08-01

    There are now a wide range of techniques that can be combined for image analysis. These include the use of object-based classifications rather than pixel-based classifiers, the use of LiDAR to determine vegetation height and vertical structure, as well terrain variables such as topographic wetness index and slope that can be calculated using GIS. This research investigates the benefits of combining these techniques to identify individual tree species. A QuickBird image and low point density LiDAR data for a coastal region in New Zealand was used to examine the possibility of mapping Pohutukawa trees which are regarded as an iconic tree in New Zealand. The study area included a mix of buildings and vegetation types. After image and LiDAR preparation, single tree objects were identified using a range of techniques including: a threshold of above ground height to eliminate ground based objects; Normalised Difference Vegetation Index and elevation difference between the first and last return of LiDAR data to distinguish vegetation from buildings; geometric information to separate clusters of trees from single trees, and treetop identification and region growing techniques to separate tree clusters into single tree crowns. Important feature variables were identified using Random Forest, and the Support Vector Machine provided the classification. The combined techniques using LiDAR and spectral data produced an overall accuracy of 85.4% (Kappa 80.6%). Classification using just the spectral data produced an overall accuracy of 75.8% (Kappa 67.8%). The research findings demonstrate how the combining of LiDAR and spectral data improves classification for Pohutukawa trees.

  10. FLEX-TOPO: Proof of concept in a central European landscape

    NASA Astrophysics Data System (ADS)

    Gharari, Shervan; Hrachowitz, Markus; Fenicia, Fabrizio; Gao, Hongkai; Euser, Tanja; Savenije, Huub

    2013-04-01

    Landscape classification into meaningful hydrological units has important implications for hydrological modeling. Conceptual hydrological models, such as HBV-type models, are most commonly designed to represent catchments in a lumped or semi-distributed way at best, i.e. treating them as single entities or sometimes accounting for topographical and land cover variability by introducing some level of stratification. Moreover, such models often combine different dominant runoff mechanisms (such as Hortonian overland flow, saturation overland flow and rapid subsurface flow) into one mechanism, so as to avoid large numbers of parameters. These oversimplifications can frequently lead to substantial misrepresentations of flow generating processes in the catchments in question, as feedback processes between topography, land cover and hydrology in different landscape units can arguably lead to distinct hydrological patterns. By making use of readily available topographical information, hydrological units can be identified based on the concept of "Height above Nearest Drainage" (HAND; Rennó et al., 2008; Nobre et al., 2011). These hydrological units are characterized by different hydrological behavior with different dominant runoff generating mechanisms and can thus be assigned different model structures (Savenije, 2010). In this study we classified the Wark Catchment in Grand Duchy of Luxembourg into three distinct landscape units: plateau, wetland and hillslope, on the basis of a 5×5 m2 DEM. A revised and extended version of HAND gave preliminary estimates of uncertainty in the landscape unit identification as they were implemented in a stochastic framework. As the transition thresholds between the landscape units are a priori unknown, they were calibrated against landscape units observed in the field using a single probability based objective function. As a result, each grid cell of the DEM was characterized by a certain probability of being a certain landscape unit

  11. Effects of landscape characteristics on land-cover class accuracy

    USGS Publications Warehouse

    Smith, Jonathan H.; Stehman, Stephen V.; Wickham, James D.; Yang, Limin

    2003-01-01

    The effects of patch size and land-cover heterogeneity on classification accuracy were evaluated using reference data collected for the National Land-Cover Data (NLCD) set accuracy assessment. Logistic regression models quantified the relationship between classification accuracy and these landscape variables for each land-cover class at both the Anderson Levels I and II classification schemes employed in the NLCD. The general relationships were consistent, with the odds of correctly classifying a pixel increasing as patch size increased and decreasing as heterogeneity increased. Specific characteristics of these relationships, however, showed considerable diversity among the various classes. Odds ratios are reported to document these relationships. Interaction between the two landscape variables was not a significant influence on classification accuracy, indicating that the effect of heterogeneity was not impacted by the sample being in a small or large patch. Landscape variables remained significant predictors of class-specific accuracy even when adjusted for regional differences in the mapping and assessment processes or landscape characteristics. The land-cover class-specific analyses provide insight into sources of classification error and a capacity for predicting error based on a pixel's mapped land-cover class, patch size and surrounding land-cover heterogeneity.

  12. Endodontic classification.

    PubMed

    Morse, D R; Seltzer, S; Sinai, I; Biron, G

    1977-04-01

    Clinical and histopathologic findings are mixed in current endodontic classifications. A new system, based on symptomatology, may be more useful in clincial practice. The classifications are vital asymptomatic, hypersensitive dentin, inflamed-reversible, inflamed/dengenerating without area-irreversible, inflamed/degenerating with area-irreversible, necrotic without area, and necrotic with area. PMID:265327

  13. Vulnerability of Oregon Hydrologic Landscapes and Streamflow to Climate Change - 5/20/2014

    EPA Science Inventory

    Hydrologic classification systems can provide a basis for broadscale assessments of the hydrologic functions of landscapes and watersheds and their responses to stressors. Such assessments could be particularly useful in determining hydrologic vulnerability from climate change. A...

  14. SOME THOUGHTS ON USING A LANDSCAPE FRAMEWORK TO ADDRESS CUMULATIVE IMPACTS ON WETLAND FOOD CHAIN SUPPORT

    EPA Science Inventory

    A landscape-level approach is derived to hierarchically separate the nation's wetlands into ecological regions. his classification scheme allows for the predetermination of the environmental constraints and possible natural limits of the wetlands food chain support. iscussion fol...

  15. Applicability of Hydrologic Landscapes for Model Calibration at the Watershed Scale in the Pacific Northwest

    EPA Science Inventory

    The Pacific Northwest Hydrologic Landscapes (PNW HL) at the assessment unit scale has provided a solid conceptual classification framework to relate and transfer hydrologically meaningful information between watersheds without access to streamflow time series. A collection of tec...

  16. Landscape ecological security assessment based on projection pursuit in Pearl River Delta.

    PubMed

    Gao, Yang; Wu, Zhifeng; Lou, Quansheng; Huang, Huamei; Cheng, Jiong; Chen, Zhangli

    2012-04-01

    Regional landscape ecological security is an important issue for ecological security, and has a great influence on national security and social sustainable development. The Pearl River Delta (PRD) in southern China has experienced rapid economic development and intensive human activities in recent years. This study, based on landscape analysis, provides a method to discover the alteration of character among different landscape types and to understand the landscape ecological security status. Based on remotely sensed products of the Landsat 5 TM images in 1990 and the Landsat 7 ETM+ images in 2005, landscape classification maps of nine cities in the PRD were compiled by implementing Remote Sensing and Geographic Information System technology. Several indices, including aggregation, crush index, landscape shape index, Shannon's diversity index, landscape fragile index, and landscape security adjacent index, were applied to analyze spatial-temporal characteristics of landscape patterns in the PRD. A landscape ecological security index based on these outcomes was calculated by projection pursuit using genetic algorithm. The landscape ecological security of nine cities in the PRD was thus evaluated. The main results of this research are listed as follows: (1) from 1990 to 2005, the aggregation index, crush index, landscape shape index, and Shannon's diversity index of nine cities changed little in the PRD, while the landscape fragile index and landscape security adjacent index changed obviously. The landscape fragile index of nine cities showed a decreasing trend; however, the landscape security adjacent index has been increasing; (2) from 1990 to 2005, landscape ecology of the cities of Zhuhai and Huizhou maintained a good security situation. However, there was a relatively low value of ecological security in the cities of Dongguan and Foshan. Except for Foshan and Guangzhou, whose landscape ecological security situation were slightly improved, the cities had reduced

  17. Spatial transferability of landscape-based hydrological models

    NASA Astrophysics Data System (ADS)

    Gao, Hongkai; Hrachowitz, Markus; Fenicia, Fabrizio; Gharari, Shervan; Sriwongsitanon, Nutchanart; Savenije, Hubert

    2015-04-01

    Landscapes, mainly distinguished by land surface topography and vegetation cover, are crucial in defining runoff generation mechanisms, interception capacity and transpiration processes. Landscapes information provides modelers with a way to take into account catchment heterogeneity, while simultaneously keeping model complexity low. A landscape-based hydrological modelling framework (FLEX-Topo), with parallel model structures, was developed and tested in various catchments with diverse climate, topography and land cover conditions. Landscape classification is the basic and most crucial procedure to create a tailor-made model for a certain catchment, as it explicitly relates hydrologic similarity to landscape similarity, which is the base of this type of models. Therefore, the study catchment is classified into different landscapes units that fulfil similar hydrological function, based on classification criteria such as the height above the nearest drainage, slope, aspect and land cover. At present, to suggested model includes four distinguishable landscapes: hillslopes, terraces/plateaus, riparian areas, and glacierized areas. Different parallel model structures are then associated with the different landscape units to describe their different dominant runoff generation mechanisms. These hydrological units are parallel and only connected by groundwater reservoir. The transferability of this landscape-based model can then be compared with the transferability of a lumped model. In this study, FLEX-Topo was developed and tested in three study sites: two cold-arid catchments in China (the upper Heihe River and the Urumqi Glacier No1 catchment), and one tropical catchment in Thailand (the upper Ping River). Stringent model tests indicate that FLEX-Topo, allowing for more process heterogeneity than lumped model formulations, exhibits higher capabilities to be spatially transferred. Furthermore, the simulated water balances, including internal fluxes, hydrograph

  18. Predictability of evolution in complex fitness landscapes

    NASA Astrophysics Data System (ADS)

    Krug, Joachim

    2013-03-01

    Evolutionary adaptations arise from an intricate interplay of deterministic selective forces and random reproductive or mutational events, and the relative roles of these two types of influences is the subject of a long-standing controversy. In general, the predictability of adaptive trajectories is governed by the genetic constraints imposed by the structure of the underlying fitness landscape as well as by the supply rate and effect size of beneficial mutations. On the level of single mutational steps, evolutionary predictability depends primarily on the distribution of fitness effects, with heavy-tailed distributions giving rise to highly predictable behavior. The genetic constraints imposed by the fitness landscape can be quantified through the statistical properties of accessible mutational pathways along which fitness increases monotonically. I will report on recent progress in the understanding of evolutionary accessibility in model landscapes and compare the predictions of the models to empirical data. Finally, I will describe extensive Wright-Fisher-type simulations of asexual adaptation on an empirical fitness landscape. By quantifying predictability through the entropies of the distributions of evolutionary trajectories and endpoints we show that, contrary to common wisdom, the predictability of evolution depends non-monotonically on population size. Supported by DFG within SFB 680 and SPP 1590.

  19. Application of adaptive boosting to EP-derived multilayer feed-forward neural networks (MLFN) to improve benign/malignant breast cancer classification

    NASA Astrophysics Data System (ADS)

    Land, Walker H., Jr.; Masters, Timothy D.; Lo, Joseph Y.; McKee, Dan

    2001-07-01

    A new neural network technology was developed for improving the benign/malignant diagnosis of breast cancer using mammogram findings. A new paradigm, Adaptive Boosting (AB), uses a markedly different theory in solutioning Computational Intelligence (CI) problems. AB, a new machine learning paradigm, focuses on finding weak learning algorithm(s) that initially need to provide slightly better than random performance (i.e., approximately 55%) when processing a mammogram training set. Then, by successive development of additional architectures (using the mammogram training set), the adaptive boosting process improves the performance of the basic Evolutionary Programming derived neural network architectures. The results of these several EP-derived hybrid architectures are then intelligently combined and tested using a similar validation mammogram data set. Optimization focused on improving specificity and positive predictive value at very high sensitivities, where an analysis of the performance of the hybrid would be most meaningful. Using the DUKE mammogram database of 500 biopsy proven samples, on average this hybrid was able to achieve (under statistical 5-fold cross-validation) a specificity of 48.3% and a positive predictive value (PPV) of 51.8% while maintaining 100% sensitivity. At 97% sensitivity, a specificity of 56.6% and a PPV of 55.8% were obtained.

  20. Classification of Fuel Types Using Envisat Data

    NASA Astrophysics Data System (ADS)

    Wozniak, Edyta; Nasilowska, Sylwia

    2010-12-01

    Forest fires have an important impact on landscape structure and ecosystems biodiversity. Moreover, wild land fires have strong influence on forest planning and management. Furthermore, forest fires affect not only woodworking industry but also arable fields and inhabitants life too. A precise knowledge of the spatial distribution of fuels is necessary to predict, analyse and model fire behaviour. Modelling of fire spread is difficult and complicated because it depends on many factors. First of all, it depends on undergrowth and brushwood moisture and thickness, and tree species. There are many fuel types classification developed for regional environmental condition. The main drawback of implemented systems is utility for particular region of interest. That causes a need of permanent, consequent and more accurate researches in specific habitat not only in continental scale. In this paper a new system is proposed. It organizes fuels into three major groups (coniferous, deciduous wood and open) and four subcategories which describes a fuel structure (trees lower then 4m, trees higher than 4 m: without bushes; with low bushes lower them 2m; with high bushes higher then 2m). This classification is adapted into Polish lowlands environmental condition. The classification was carried out on the base of 120 training plots, which were determinate during a field experiment in north-eastern Poland. The plots discriminate homogeneous parts of forest which correspond to fuel classes. In the study we used the ENVISAT Alternating Polarization (HH/HV) image. The most popular classifiers were tried out and the maximum likelihood method resulted the most efficient. To map fuel types many methods are employed. The use of remote sensing systems gives the possibility of low- costs and time-consuming fuels mapping and updating. The employ of SAR systems permits mapping independently of weather condition. The microwave data has the potential to estimate fuel loads and map fuel types. The

  1. Landscape ecology of eastern coyotes based on large-scale estimates of abundance.

    PubMed

    Kays, Roland W; Gompper, Matthew E; Ray, Justina C

    2008-06-01

    Since their range expansion into eastern North America in the mid-1900s, coyotes (Canis latrans) have become the region's top predator. Although widespread across the region, coyote adaptation to eastern forests and use of the broader landscape are not well understood. We studied the distribution and abundance of coyotes by collecting coyote feces from 54 sites across a diversity of landscapes in and around the Adirondacks of northern New York. We then genotyped feces with microsatellites and found a close correlation between the number of detected individuals and the total number of scats at a site. We created habitat models predicting coyote abundance using multi-scale vegetation and landscape data and ranked them with an information-theoretic model selection approach. These models allow us to reject the hypothesis that eastern forests are unsuitable habitat for coyotes as their abundance was positively correlated with forest cover and negatively correlated with measures of rural non-forest landscapes. However, measures of vegetation structure turned out to be better predictors of coyote abundance than generalized "forest vs. open" classification. The best supported models included those measures indicative of disturbed forest, especially more open canopies found in logged forests, and included natural edge habitats along water courses. These forest types are more productive than mature forests and presumably host more prey for coyotes. A second model with only variables that could be mapped across the region highlighted the lower density of coyotes in areas with high human settlement, as well as positive relationships with variables such as snowfall and lakes that may relate to increased numbers and vulnerability of deer. The resulting map predicts coyote density to be highest along the southwestern edge of the Adirondack State Park, including Tug Hill, and lowest in the mature forests and more rural areas of the central and eastern Adirondacks. Together, these

  2. Another Paper Landscape?

    ERIC Educational Resources Information Center

    Radlak, Ted

    2001-01-01

    Describes the University of Toronto's extensive central campus revitalization plan to create lush landscapes that add to the school's image and attractiveness. Drawings and photographs are included. (GR)

  3. Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries

    DOE PAGESBeta

    Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; Altmann, Garrett L.

    2014-12-09

    We present results from an ongoing effort to extend neuromimetic machine vision algorithms to multispectral data using adaptive signal processing combined with compressive sensing and machine learning techniques. Our goal is to develop a robust classification methodology that will allow for automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and topographic/geomorphic characteristics. We use a Hebbian learning rule to build spectral-textural dictionaries that are tailored for classification. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labelsmore » are automatically generated using unsupervised clustering of sparse approximations (CoSA). We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska. We explore learning from both raw multispectral imagery and normalized band difference indices. We explore a quantitative metric to evaluate the spectral properties of the clusters in order to potentially aid in assigning land cover categories to the cluster labels. In this study, our results suggest CoSA is a promising approach to unsupervised land cover classification in high-resolution satellite imagery.« less

  4. Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries

    SciTech Connect

    Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; Altmann, Garrett L.

    2014-12-09

    We present results from an ongoing effort to extend neuromimetic machine vision algorithms to multispectral data using adaptive signal processing combined with compressive sensing and machine learning techniques. Our goal is to develop a robust classification methodology that will allow for automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and topographic/geomorphic characteristics. We use a Hebbian learning rule to build spectral-textural dictionaries that are tailored for classification. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labels are automatically generated using unsupervised clustering of sparse approximations (CoSA). We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska. We explore learning from both raw multispectral imagery and normalized band difference indices. We explore a quantitative metric to evaluate the spectral properties of the clusters in order to potentially aid in assigning land cover categories to the cluster labels. In this study, our results suggest CoSA is a promising approach to unsupervised land cover classification in high-resolution satellite imagery.

  5. Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries

    NASA Astrophysics Data System (ADS)

    Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; Altmann, Garrett L.

    2014-01-01

    We present results from an ongoing effort to extend neuromimetic machine vision algorithms to multispectral data using adaptive signal processing combined with compressive sensing and machine learning techniques. Our goal is to develop a robust classification methodology that will allow for automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and topographic/geomorphic characteristics. We use a Hebbian learning rule to build spectral-textural dictionaries that are tailored for classification. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labels are automatically generated using unsupervised clustering of sparse approximations (CoSA). We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska. We explore learning from both raw multispectral imagery and normalized band difference indices. We explore a quantitative metric to evaluate the spectral properties of the clusters in order to potentially aid in assigning land cover categories to the cluster labels. Our results suggest CoSA is a promising approach to unsupervised land cover classification in high-resolution satellite imagery.

  6. Exploring the fitness landscape of poliovirus

    NASA Astrophysics Data System (ADS)

    Bianco, Simone; Acevedo, Ashely; Andino, Raul; Tang, Chao

    2012-02-01

    RNA viruses are known to display extraordinary adaptation capabilities to different environments, due to high mutation rates. Their very dynamical evolution is captured by the quasispecies concept, according to which the viral population forms a swarm of genetic variants linked through mutation, which cooperatively interact at a functional level and collectively contribute to the characteristics of the population. The description of the viral fitness landscape becomes paramount towards a more thorough understanding of the virus evolution and spread. The high mutation rate, together with the cooperative nature of the quasispecies, makes it particularly challenging to explore its fitness landscape. I will present an investigation of the dynamical properties of poliovirus fitness landscape, through both the adoption of new experimental techniques and theoretical models.

  7. A Hierarchical Approach to Forest Landscape Pattern Characterization

    NASA Astrophysics Data System (ADS)

    Wang, Jialing; Yang, Xiaojun

    2012-01-01

    Landscape spatial patterns have increasingly been considered to be essential for environmental planning and resources management. In this study, we proposed a hierarchical approach for landscape classification and evaluation by characterizing landscape spatial patterns across different hierarchical levels. The case study site is the Red Hills region of northern Florida and southwestern Georgia, well known for its biodiversity, historic resources, and scenic beauty. We used one Landsat Enhanced Thematic Mapper image to extract land-use/-cover information. Then, we employed principal-component analysis to help identify key class-level landscape metrics for forests at different hierarchical levels, namely, open pine, upland pine, and forest as a whole. We found that the key class-level landscape metrics varied across different hierarchical levels. Compared with forest as a whole, open pine forest is much more fragmented. The landscape metric, such as CONTIG_MN, which measures whether pine patches are contiguous or not, is more important to characterize the spatial pattern of pine forest than to forest as a whole. This suggests that different metric sets should be used to characterize landscape patterns at different hierarchical levels. We further used these key metrics, along with the total class area, to classify and evaluate subwatersheds through cluster analysis. This study demonstrates a promising approach that can be used to integrate spatial patterns and processes for hierarchical forest landscape planning and management.

  8. The global risk landscape

    NASA Astrophysics Data System (ADS)

    2015-03-01

    Initiatives aimed at preserving or enhancing the state of the environment are created in a broad political landscape influenced by, among other things, perceived risks. We take a brief look at this risk landscape in the run up to Paris 2015.

  9. BATS AND BT INSECT RESISTANCE ON AGRICULTURAL LANDSCAPES

    EPA Science Inventory

    A landscape model that utilizes land cover classification data, insect life history, insect movement, and bat foraging pressure is developed that addresses the implementation of genetically modified crops in the Winter Garden region of Texas. The principal strategy for delaying r...

  10. Geomorpho-Landscapes

    NASA Astrophysics Data System (ADS)

    Farabollini, Piero; Lugeri, Francesca; Amadio, Vittorio

    2014-05-01

    Landscape is the object of human perceptions, being the image of spatial organization of elements and structures: mankind lives the first approach with the environment, viewing and feeling the landscape. Many definitions of landscape have been given over time: in this case we refer to the Landscape defined as the result of interaction among physical, biotic and anthropic phenomena acting in a different spatial-temporal scale (Foreman & Godron) Following an Aristotelic approach in studying nature, we can assert that " Shape is synthesis": so it is possible to read the land features as the expression of the endogenous and exogenous processes that mould earth surfaces; moreover, Landscape is the result of the interaction of natural and cultural components, and conditions the spatial-temporal development of a region. The study of the Landscape offers results useful in order to promote sustainable development, ecotourism, enhancement of natural and cultural heritage, popularization of the scientific knowledge. In Italy, a very important GIS-based tool to represent the territory is the "Carta della Natura" ("Map of Nature", presently coordinated by the ISPRA) that aims at assessing the state of the whole Italian territory, analyzing Landscape. The methodology follows a holistic approach, taking into consideration all the components of a landscape and then integrating the information. Each individual landscape, studied at different scales, shows distinctive elements: structural, which depend on physical form and specific spatial organization; functional, which depend on relationships created between biotic and abiotic elements, and dynamic, which depend on the successive evolution of the structure. The identification of the landscape units, recognized at different scales of analysis, allows an evaluation of the state of the land, referring to the dual risk/resource which characterizes the Italian country. An interesting opportunity is to discover those areas of unusual

  11. Computerized Classification Testing with the Rasch Model

    ERIC Educational Resources Information Center

    Eggen, Theo J. H. M.

    2011-01-01

    If classification in a limited number of categories is the purpose of testing, computerized adaptive tests (CATs) with algorithms based on sequential statistical testing perform better than estimation-based CATs (e.g., Eggen & Straetmans, 2000). In these computerized classification tests (CCTs), the Sequential Probability Ratio Test (SPRT) (Wald,…

  12. How Good Are Statistical Models at Approximating Complex Fitness Landscapes?

    PubMed

    du Plessis, Louis; Leventhal, Gabriel E; Bonhoeffer, Sebastian

    2016-09-01

    Fitness landscapes determine the course of adaptation by constraining and shaping evolutionary trajectories. Knowledge of the structure of a fitness landscape can thus predict evolutionary outcomes. Empirical fitness landscapes, however, have so far only offered limited insight into real-world questions, as the high dimensionality of sequence spaces makes it impossible to exhaustively measure the fitness of all variants of biologically meaningful sequences. We must therefore revert to statistical descriptions of fitness landscapes that are based on a sparse sample of fitness measurements. It remains unclear, however, how much data are required for such statistical descriptions to be useful. Here, we assess the ability of regression models accounting for single and pairwise mutations to correctly approximate a complex quasi-empirical fitness landscape. We compare approximations based on various sampling regimes of an RNA landscape and find that the sampling regime strongly influences the quality of the regression. On the one hand it is generally impossible to generate sufficient samples to achieve a good approximation of the complete fitness landscape, and on the other hand systematic sampling schemes can only provide a good description of the immediate neighborhood of a sequence of interest. Nevertheless, we obtain a remarkably good and unbiased fit to the local landscape when using sequences from a population that has evolved under strong selection. Thus, current statistical methods can provide a good approximation to the landscape of naturally evolving populations. PMID:27189564

  13. A checklist for ecological management of landscapes for conservation.

    PubMed

    Lindenmayer, David; Hobbs, Richard J; Montague-Drake, Rebecca; Alexandra, Jason; Bennett, Andrew; Burgman, Mark; Cale, Peter; Calhoun, Aram; Cramer, Viki; Cullen, Peter; Driscoll, Don; Fahrig, Lenore; Fischer, Joern; Franklin, Jerry; Haila, Yrjo; Hunter, Malcolm; Gibbons, Philip; Lake, Sam; Luck, Gary; MacGregor, Chris; McIntyre, Sue; Nally, Ralph Mac; Manning, Adrian; Miller, James; Mooney, Hal; Noss, Reed; Possingham, Hugh; Saunders, Denis; Schmiegelow, Fiona; Scott, Michael; Simberloff, Dan; Sisk, Tom; Tabor, Gary; Walker, Brian; Wiens, John; Woinarski, John; Zavaleta, Erika

    2008-01-01

    The management of landscapes for biological conservation and ecologically sustainable natural resource use are crucial global issues. Research for over two decades has resulted in a large literature, yet there is little consensus on the applicability or even the existence of general principles or broad considerations that could guide landscape conservation. We assess six major themes in the ecology and conservation of landscapes. We identify 13 important issues that need to be considered in developing approaches to landscape conservation. They include recognizing the importance of landscape mosaics (including the integration of terrestrial and aquatic areas), recognizing interactions between vegetation cover and vegetation configuration, using an appropriate landscape conceptual model, maintaining the capacity to recover from disturbance and managing landscapes in an adaptive framework. These considerations are influenced by landscape context, species assemblages and management goals and do not translate directly into on-the-ground management guidelines but they should be recognized by researchers and resource managers when developing guidelines for specific cases. Two crucial overarching issues are: (i) a clearly articulated vision for landscape conservation and (ii) quantifiable objectives that offer unambiguous signposts for measuring progress. PMID:17927771

  14. How Good Are Statistical Models at Approximating Complex Fitness Landscapes?

    PubMed Central

    du Plessis, Louis; Leventhal, Gabriel E.; Bonhoeffer, Sebastian

    2016-01-01

    Fitness landscapes determine the course of adaptation by constraining and shaping evolutionary trajectories. Knowledge of the structure of a fitness landscape can thus predict evolutionary outcomes. Empirical fitness landscapes, however, have so far only offered limited insight into real-world questions, as the high dimensionality of sequence spaces makes it impossible to exhaustively measure the fitness of all variants of biologically meaningful sequences. We must therefore revert to statistical descriptions of fitness landscapes that are based on a sparse sample of fitness measurements. It remains unclear, however, how much data are required for such statistical descriptions to be useful. Here, we assess the ability of regression models accounting for single and pairwise mutations to correctly approximate a complex quasi-empirical fitness landscape. We compare approximations based on various sampling regimes of an RNA landscape and find that the sampling regime strongly influences the quality of the regression. On the one hand it is generally impossible to generate sufficient samples to achieve a good approximation of the complete fitness landscape, and on the other hand systematic sampling schemes can only provide a good description of the immediate neighborhood of a sequence of interest. Nevertheless, we obtain a remarkably good and unbiased fit to the local landscape when using sequences from a population that has evolved under strong selection. Thus, current statistical methods can provide a good approximation to the landscape of naturally evolving populations. PMID:27189564

  15. Planetary Landscape Geography

    NASA Astrophysics Data System (ADS)

    Hargitai, H.

    INTRODUCTION Landscape is one of the most often used category in physical ge- ography. The term "landshap" was introduced by Dutch painters in the 15-16th cen- tury. [1] The elements that build up a landscape (or environment) on Earth consists of natural (biogenic and abiogenic - lithologic, atmospheric, hydrologic) and artificial (antropogenic) factors. Landscape is a complex system of these different elements. The same lithology makes different landscapes under different climatic conditions. If the same conditions are present, the same landscape type will appear. Landscapes build up a hierarchic system and cover the whole surface. On Earth, landscapes can be classified and qualified according to their characteristics: relief forms (morphology), and its potential economic value. Aesthetic and subjective parameters can also be considered. Using the data from landers and data from orbiters we can now classify planetary landscapes (these can be used as geologic mapping units as well). By looking at a unknown landscape, we can determine the processes that created it and its development history. This was the case in the Pathfinder/Sojourner panoramas. [2]. DISCUSSION Planetary landscape evolution. We can draw a raw landscape develop- ment history by adding the different landscape building elements to each other. This has a strong connection with the planet's thermal evolution (age of the planet or the present surface materials) and with orbital parameters (distance from the central star, orbit excentricity etc). This way we can build a complex system in which we use differ- ent evolutional stages of lithologic, atmospheric, hydrologic and biogenic conditions which determine the given - Solar System or exoplanetary - landscape. Landscape elements. "Simple" landscapes can be found on asteroids: no linear horizon is present (not differentiated body, only impact structures), no atmosphere (therefore no atmospheric scattering - black sky as part of the landscape) and no

  16. Union of phylogeography and landscape genetics

    PubMed Central

    Rissler, Leslie J.

    2016-01-01

    Phylogeography and landscape genetics have arisen within the past 30 y. Phylogeography is said to be the bridge between population genetics and systematics, and landscape genetics the bridge between landscape ecology and population genetics. Both fields can be considered as simply the amalgamation of classic biogeography with genetics and genomics; however, they differ in the temporal, spatial, and organismal scales addressed and the methodology used. I begin by briefly summarizing the history and purview of each field and suggest that, even though landscape genetics is a younger field (coined in 2003) than phylogeography (coined in 1987), early studies by Dobzhansky on the “microgeographic races” of Linanthus parryae in the Mojave Desert of California and Drosophila pseudoobscura across the western United States presaged the fields by over 40 y. Recent advances in theory, models, and methods have allowed researchers to better synthesize ecological and evolutionary processes in their quest to answer some of the most basic questions in biology. I highlight a few of these novel studies and emphasize three major areas ripe for investigation using spatially explicit genomic-scale data: the biogeography of speciation, lineage divergence and species delimitation, and understanding adaptation through time and space. Examples of areas in need of study are highlighted, and I end by advocating a union of phylogeography and landscape genetics under the more general field: biogeography. PMID:27432989

  17. Union of phylogeography and landscape genetics.

    PubMed

    Rissler, Leslie J

    2016-07-19

    Phylogeography and landscape genetics have arisen within the past 30 y. Phylogeography is said to be the bridge between population genetics and systematics, and landscape genetics the bridge between landscape ecology and population genetics. Both fields can be considered as simply the amalgamation of classic biogeography with genetics and genomics; however, they differ in the temporal, spatial, and organismal scales addressed and the methodology used. I begin by briefly summarizing the history and purview of each field and suggest that, even though landscape genetics is a younger field (coined in 2003) than phylogeography (coined in 1987), early studies by Dobzhansky on the "microgeographic races" of Linanthus parryae in the Mojave Desert of California and Drosophila pseudoobscura across the western United States presaged the fields by over 40 y. Recent advances in theory, models, and methods have allowed researchers to better synthesize ecological and evolutionary processes in their quest to answer some of the most basic questions in biology. I highlight a few of these novel studies and emphasize three major areas ripe for investigation using spatially explicit genomic-scale data: the biogeography of speciation, lineage divergence and species delimitation, and understanding adaptation through time and space. Examples of areas in need of study are highlighted, and I end by advocating a union of phylogeography and landscape genetics under the more general field: biogeography. PMID:27432989

  18. AEDS Property Classification Code Manual.

    ERIC Educational Resources Information Center

    Association for Educational Data Systems, Washington, DC.

    The control and inventory of property items using data processing machines requires a form of numerical description or code which will allow a maximum of description in a minimum of space on the data card. An adaptation of a standard industrial classification system is given to cover any expendable warehouse item or non-expendable piece of…

  19. Land use classification in Bolivia

    NASA Technical Reports Server (NTRS)

    Brockmann, C. E.; Brooner, W. G.

    1975-01-01

    The Bolivian LANDSAT Program is an integrated, multidisciplinary project designed to provide thematic analysis of LANDSAT, Skylab, and other remotely sensed data for natural resource management and development in Bolivia, is discussed. Among the first requirements in the program is the development of a legend, and appropriate methodologies, for the analysis and classification of present land use based on landscape cover. The land use legend for Bolivia consists of approximately 80 categories in a hierarchical organization which may be collapsed for generalization, or expanded for greater detail. The categories, and their definitions, provide for both a graphic and textual description of the complex and diverse landscapes found in Bolivia, and are designed for analysis from LANDSAT and other remotely sensed data at scales of 1:1,000,000 and 1:250,000. Procedures and example products developed are described and illustrated, for the systematic analysis and mapping of present land use for all of Bolivia.

  20. Land Use and Landscape Pattern In Mesoscale Watersheds: Analysis and Assessment of Their Interactions and Impacts On Processes With Regard To The Eu Water Framework Directive

    NASA Astrophysics Data System (ADS)

    Volk, M.; Steinhardt, U.; Lausch, A.

    The implementation of the EU water framework directive requires the comprehensive consideration of environmental impacts within whole watersheds during the next 9 years in order to ensure a sound quality and availability of both surface and groundwater. Especially the change of landscape pattern caused by land use alterations has strong impacts on the water balance and the waterbound fluxes within landscapes. Thus, land use systems and cultivation practices unadapted to the natural conditions of the landscapes can lead to environmental impairments of water and soil affecting the regulation functions. In order to develop concepts for adapted land use systems with respect to the goals of the EU water framework directive, integrated investigation and assessment approaches on different spatio-temporal scales have to be developed. The authors suggest a hierachical nested approach applied on the example of the Saale watershed (ca. 23.000 km2) and some of its subbasins of different sizes (8 km2, 360km2, 5.000 km2). The approach is structured into four main steps: · Investigation and assessment of the effects of actual and future trends of regional, national and european land use changes for the study areas (probability according to the landscape conditions) · Investigation of the effects of these land use changes on the landscape structure · Investigation of the impact respectively the interactions between the (resulted) changes of the landscape structure and fluxes of water and material (retention capability, intensity, duration and range of processes, etc.) · Development of a multiscalar parameter system in order to assess the impact of land use changes on the regulation functions (with particular view on water quantity and quality) and to derive adapted land use systems for their protection (suitability and sensitivity of landscapes for land use types). The approach requires the development and coupling of "classic" methods of landscape analysis with innovative GIS

  1. Elaboration of the third-generation world map of terrestrial landscapes as a model of the landscape sphere of the Earth

    NASA Astrophysics Data System (ADS)

    Romanova, Emma; Alexeeva, Nina; Arshinova, Marina; Klimanova, Oksana; Kovaleva, Tatiana; Kondratieva, Tatiana; Alyautdinov, Ali

    2016-04-01

    The first fundamental investigation aimed at the elaboration of the global map of terrestrial landscapes has resulted in a series of maps for the Physical-Geographical Atlas of the World (1964). Typological classification of landscapes and the concept of the zonal differentiation of terrestrial landscapes of the Earth became a basis for the maps of physical-geographical regions of individual continents and the global map of landscape types at the scale of 1:80 Mln. The next stage of research in the sphere of small-scale landscape regionalization and mapping of both natural and natural-anthropogenic landscapes has produced the global maps of Geographical Belts and Zonal Types of Terrestrial Landscapes (1988) and Present-Day Landscapes of the World (1992) at the scale of 1:15 Mln. By the end of the 1990-s similar maps of individual continents were compiled for the Nature and Resources of the Earth digital atlas. Recent decades saw further development of the idea of zone - sector - belt structure of the Earth's landscape sphere which includes several hierarchically subordinated natural-territorial levels. New theoretical studies and emergence of extensive information materials allowed starting the elaboration of a new (third-generation) map at the scales of 1:15 Mln to 1:5 Mln. A new classification of landscape units was suggested basing on the analysis of principal landscape-forming factors (climatic, lithogene and biogenic). A new cartographical model was developed specifying the following hierarchical levels: geographical belts, sectors, natural zones and sub-zones, classes and subclasses of landscapes. Classification criteria used for landscape systematization and mapping include both natural parameters (radiation balance, heat and moisture supply, structure of the vegetative period, biological productivity of vegetation, etc.) and anthropogenic indicators, thus providing for the evaluation of the geoecological state of landscapes (ecosystems of regional dimension

  2. Appendix E: Research papers. Use of remote sensing in landscape stratification for environmental impact assessment

    NASA Technical Reports Server (NTRS)

    Stanturf, J. A.; Heimbuch, D. G.

    1980-01-01

    A refinement to the matrix approach to environmental impact assessment is to use landscape units in place of separate environmental elements in the analysis. Landscape units can be delineated by integrating remotely sensed data and available single-factor data. A remote sensing approach to landscape stratification is described and the conditions under which it is superior to other approaches that require single-factor maps are indicated. Flowcharts show the steps necessary to develop classification criteria, delineate units and a map legend, and use the landscape units in impact assessment. Application of the approach to assessing impacts of a transmission line in Montana is presented to illustrate the method.

  3. Classifying Classification

    ERIC Educational Resources Information Center

    Novakowski, Janice

    2009-01-01

    This article describes the experience of a group of first-grade teachers as they tackled the science process of classification, a targeted learning objective for the first grade. While the two-year process was not easy and required teachers to teach in a new, more investigation-oriented way, the benefits were great. The project helped teachers and…

  4. Boundary dynamics in landscapes

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Landscapes consist of a mosaic of distinct vegetation types and their intervening boundaries with distinct characteristics. Boundaries can exist along abrupt environmental gradients or along gradual changes that are reinforced by feedback mechanisms between plants and soil properties. Boundaries can...

  5. Synergistic Techniques for Better Understanding and Classifying the Environmental Structure of Landscapes

    NASA Astrophysics Data System (ADS)

    Bryan, Brett A.

    2006-01-01

    The desire to capture natural regions in the landscape has been a goal of geographic and environmental classification and ecological land classification (ELC) for decades. Since the increased adoption of data-centric, multivariate, computational methods, the search for natural regions has become the search for the best classification that optimally trades off classification complexity for class homogeneity. In this study, three techniques are investigated for their ability to find the best classification of the physical environments of the Mt. Lofty Ranges in South Australia: AutoClass-C (a Bayesian classifier), a Kohonen Self-Organising Map neural network, and a k-means classifier with homogeneity analysis. AutoClass-C is specifically designed to find the classification that optimally trades off classification complexity for class homogeneity. However, AutoClass analysis was not found to be assumption-free because it was very sensitive to the user-specified level of relative error of input data. The AutoClass results suggest that there may be no way of finding the best classification without making critical assumptions as to the level of class heterogeneity acceptable in the classification when using continuous environmental data. Therefore, rather than relying on adjusting abstract parameters to arrive at a classification of suitable complexity, it is better to quantify and visualize the data structure and the relationship between classification complexity and class homogeneity. Individually and when integrated, the Self-Organizing Map and k-means classification with homogeneity analysis techniques also used in this study facilitate this and provide information upon which the decision of the scale of classification can be made. It is argued that instead of searching for the elusive classification of natural regions in the landscape, it is much better to understand and visualize the environmental structure of the landscape and to use this knowledge to select the

  6. Landscape evolution (A Review)

    PubMed Central

    Sharp, Robert P.

    1982-01-01

    Landscapes are created by exogenic and endogenic processes acting along the interface between the lithosphere and the atmosphere and hydrosphere. Various landforms result from the attack of weathering and erosion upon the highly heterogeneous lithospheric surface. Landscapes are dynamic, acutely sensitive to natural and artificial perturbation. Undisturbed, they can evolve through a succession of stages to a plain of low relief. Often, the progression of an erosion cycle is interrupted by tectonic or environmental changes; thus, many landscapes preserve vestiges of earlier cycles useful in reconstructing the recent history of Earth's surface. Landforms are bounded by slopes, so their evolution is best understood through study of slopes and the complex of factors controlling slope character and development. The substrate, biosphere, climatic environment, and erosive processes are principal factors. Creep of the disintegrated substrate and surface wash by water are preeminent. Some slopes attain a quasisteady form and recede parallel to themselves (backwearing); others become ever gentler with time (downwearing). The lovely convex/rectilinear/concave profile of many debris-mantled slopes reflects an interplay between creep and surface wash. Landscapes of greatest scenic attraction are usually those in which one or two genetic factors have strongly dominated or those perturbed by special events. Nature has been perturbing landscapes for billions of years, so mankind can learn about landscape perturbation from natural examples. Images

  7. Termination Criteria for Computerized Classification Testing

    ERIC Educational Resources Information Center

    Thompson, Nathan A.

    2011-01-01

    Computerized classification testing (CCT) is an approach to designing tests with intelligent algorithms, similar to adaptive testing, but specifically designed for the purpose of classifying examinees into categories such as "pass" and "fail." Like adaptive testing for point estimation of ability, the key component is the termination criterion,…

  8. Application of ant colony optimization to image classification using a Markov model with non-stationary neighborhoods

    NASA Astrophysics Data System (ADS)

    Le Hégarat-Mascle, S.; Kallel, A.; Descombes, X.

    2005-10-01

    In global classifications using Markov Random Field (MRF) modelling, the neighbourhood form is generally considered as independent of its location in the image. Such an approach may lead to classification errors for pixels located at the segment borders. The solution proposed here consists in relaxing the assumption of fixed-form neighbourhood. However this non-stationary neighbourhood modelling is useful only if an efficient heuristic can be defined to perform the optimization. Ant colony optimization (ACO) is currently a popular algorithm. It models upon the behavior of social insects for computing strategies: the information gathered by simple autonomous mobile agents, called ants, is shared and exploited for problem solving. Here we propose to use the ACO and to exploit its ability of self-organization. The ants collect information through the image, from one pixel to the others. The choice of the path is a function of the pixel label, favouring paths within a same image segment. We show that this corresponds to an automatic adaptation of the neighbourhood to the segment form. Performance of this new approach is illustrated on a simulated image and on actual remote sensing images, SPOT4/HRV, representing agricultural areas. In the studied examples, we found that it outperforms the fixed-form neighbourhood used in classical MRF classifications. The advantage of having a neighborhood shape that automatically adapts to the image segment clearly appears in these cases of images containing fine elements, lanes or thin fields, but also complex natural landscape structures.

  9. Quantifying landscape resilience using vegetation indices

    NASA Astrophysics Data System (ADS)

    Eddy, I. M. S.; Gergel, S. E.

    2014-12-01

    Landscape resilience refers to the ability of systems to adapt to and recover from disturbance. In pastoral landscapes, degradation can be measured in terms of increased desertification and/or shrub encroachment. In many countries across Central Asia, the use and resilience of pastoral systems has changed markedly over the past 25 years, influenced by centralized Soviet governance, private property rights and recently, communal resource governance. In Kyrgyzstan, recent governance reforms were in response to the increasing degradation of pastures attributed to livestock overgrazing. Our goal is to examine and map the landscape-level factors that influence overgrazing throughout successive governance periods. Here, we map and examine some of the spatial factors influencing landscape resilience in agro-pastoral systems in the Kyrgyzstan Republic where pastures occupy >50% of the country's area. We ask three questions: 1) which mechanisms of pasture degradation (desertification vs. shrub encroachment), are detectable using remote sensing vegetation indices?; 2) Are these degraded pastures associated with landscape features that influence herder mobility and accessibility (e.g., terrain, distance to other pastures)?; and 3) Have these patterns changed through successive governance periods? Using a chronosequence of Landsat imagery (1999-2014), NDVI and other VIs were used to identify trends in pasture condition during the growing season. Least-cost path distances as well as graph theoretic indices were derived from topographic factors to assess landscape connectivity (from villages to pastures and among pastures). Fieldwork was used to assess the feasibility and accuracy of this approach using the most recent imagery. Previous research concluded that low herder mobility hindered pasture use, thus we expect the distance from pasture to village to be an important predictor of pasture condition. This research will quantify the magnitude of pastoral degradation and test

  10. Epistasis and the Structure of Fitness Landscapes: Are Experimental Fitness Landscapes Compatible with Fisher’s Geometric Model?

    PubMed Central

    Blanquart, François; Bataillon, Thomas

    2016-01-01

    The fitness landscape defines the relationship between genotypes and fitness in a given environment and underlies fundamental quantities such as the distribution of selection coefficient and the magnitude and type of epistasis. A better understanding of variation in landscape structure across species and environments is thus necessary to understand and predict how populations will adapt. An increasing number of experiments investigate the properties of fitness landscapes by identifying mutations, constructing genotypes with combinations of these mutations, and measuring the fitness of these genotypes. Yet these empirical landscapes represent a very small sample of the vast space of all possible genotypes, and this sample is often biased by the protocol used to identify mutations. Here we develop a rigorous statistical framework based on Approximate Bayesian Computation to address these concerns and use this flexible framework to fit a broad class of phenotypic fitness models (including Fisher’s model) to 26 empirical landscapes representing nine diverse biological systems. Despite uncertainty owing to the small size of most published empirical landscapes, the inferred landscapes have similar structure in similar biological systems. Surprisingly, goodness-of-fit tests reveal that this class of phenotypic models, which has been successful so far in interpreting experimental data, is a plausible in only three of nine biological systems. More precisely, although Fisher’s model was able to explain several statistical properties of the landscapes—including the mean and SD of selection and epistasis coefficients—it was often unable to explain the full structure of fitness landscapes. PMID:27052568

  11. Seismic event classification system

    DOEpatents

    Dowla, F.U.; Jarpe, S.P.; Maurer, W.

    1994-12-13

    In the computer interpretation of seismic data, the critical first step is to identify the general class of an unknown event. For example, the classification might be: teleseismic, regional, local, vehicular, or noise. Self-organizing neural networks (SONNs) can be used for classifying such events. Both Kohonen and Adaptive Resonance Theory (ART) SONNs are useful for this purpose. Given the detection of a seismic event and the corresponding signal, computation is made of: the time-frequency distribution, its binary representation, and finally a shift-invariant representation, which is the magnitude of the two-dimensional Fourier transform (2-D FFT) of the binary time-frequency distribution. This pre-processed input is fed into the SONNs. These neural networks are able to group events that look similar. The ART SONN has an advantage in classifying the event because the types of cluster groups do not need to be pre-defined. The results from the SONNs together with an expert seismologist's classification are then used to derive event classification probabilities. 21 figures.

  12. Seismic event classification system

    DOEpatents

    Dowla, Farid U.; Jarpe, Stephen P.; Maurer, William

    1994-01-01

    In the computer interpretation of seismic data, the critical first step is to identify the general class of an unknown event. For example, the classification might be: teleseismic, regional, local, vehicular, or noise. Self-organizing neural networks (SONNs) can be used for classifying such events. Both Kohonen and Adaptive Resonance Theory (ART) SONNs are useful for this purpose. Given the detection of a seismic event and the corresponding signal, computation is made of: the time-frequency distribution, its binary representation, and finally a shift-invariant representation, which is the magnitude of the two-dimensional Fourier transform (2-D FFT) of the binary time-frequency distribution. This pre-processed input is fed into the SONNs. These neural networks are able to group events that look similar. The ART SONN has an advantage in classifying the event because the types of cluster groups do not need to be pre-defined. The results from the SONNs together with an expert seismologist's classification are then used to derive event classification probabilities.

  13. Differential responses of cryptic bat species to the urban landscape.

    PubMed

    Lintott, Paul R; Barlow, Kate; Bunnefeld, Nils; Briggs, Philip; Gajas Roig, Clara; Park, Kirsty J

    2016-04-01

    Urbanization is a key global driver in the modification of land use and has been linked to population declines even in widespread and relatively common species. Cities comprise a complex assortment of habitat types yet we know relatively little about the effects of their composition and spatial configuration on species distribution. Although many bat species exploit human resources, the majority of species are negatively impacted by urbanization. Here, we use data from the National Bat Monitoring Programme, a long-running citizen science scheme, to assess how two cryptic European bat species respond to the urban landscape. A total of 124 × 1 km(2) sites throughout Britain were surveyed. The landscape surrounding each site was mapped and classified into discrete biotope types (e.g., woodland). Generalized linear models were used to assess differences in the response to the urban environment between the two species, and which landscape factors were associated with the distributions of P. pipistrellus and P. pygmaeus. The relative prevalence of P. pygmaeus compared to P. pipistrellus was greater in urban landscapes with a higher density of rivers and lakes, whereas P. pipistrellus was frequently detected in landscapes comprising a high proportion of green space (e.g., parklands). Although P. pipistrellus is thought to be well adapted to the urban landscape, we found a strong negative response to urbanization at a relatively local scale (1 km), whilst P. pygmaeus was detected more regularly in wooded urban landscapes containing freshwater. These results show differential habitat use at a landscape scale of two morphologically similar species, indicating that cryptic species may respond differently to anthropogenic disturbance. Even species considered relatively common and well adapted to the urban landscape may respond negatively to the built environment highlighting the future challenges involved in maintaining biodiversity within an increasingly urbanized

  14. Ecologically-Relevant Maps of Landforms and Physiographic Diversity for Climate Adaptation Planning

    PubMed Central

    Theobald, David M.; Harrison-Atlas, Dylan; Monahan, William B.; Albano, Christine M.

    2015-01-01

    Key to understanding the implications of climate and land use change on biodiversity and natural resources is to incorporate the physiographic platform on which changes in ecological systems unfold. Here, we advance a detailed classification and high-resolution map of physiography, built by combining landforms and lithology (soil parent material) at multiple spatial scales. We used only relatively static abiotic variables (i.e., excluded climatic and biotic factors) to prevent confounding current ecological patterns and processes with enduring landscape features, and to make the physiographic classification more interpretable for climate adaptation planning. We generated novel spatial databases for 15 landform and 269 physiographic types across the conterminous United States of America. We examined their potential use by natural resource managers by placing them within a contemporary climate change adaptation framework, and found our physiographic databases could play key roles in four of seven general adaptation strategies. We also calculated correlations with common empirical measures of biodiversity to examine the degree to which the physiographic setting explains various aspects of current biodiversity patterns. Additionally, we evaluated the relationship between landform diversity and measures of climate change to explore how changes may unfold across a geophysical template. We found landform types are particularly sensitive to spatial scale, and so we recommend using high-resolution datasets when possible, as well as generating metrics using multiple neighborhood sizes to both minimize and characterize potential unknown biases. We illustrate how our work can inform current strategies for climate change adaptation. The analytical framework and classification of landforms and parent material are easily extendable to other geographies and may be used to promote climate change adaptation in other settings. PMID:26641818

  15. Ecologically-Relevant Maps of Landforms and Physiographic Diversity for Climate Adaptation Planning.

    PubMed

    Theobald, David M; Harrison-Atlas, Dylan; Monahan, William B; Albano, Christine M

    2015-01-01

    Key to understanding the implications of climate and land use change on biodiversity and natural resources is to incorporate the physiographic platform on which changes in ecological systems unfold. Here, we advance a detailed classification and high-resolution map of physiography, built by combining landforms and lithology (soil parent material) at multiple spatial scales. We used only relatively static abiotic variables (i.e., excluded climatic and biotic factors) to prevent confounding current ecological patterns and processes with enduring landscape features, and to make the physiographic classification more interpretable for climate adaptation planning. We generated novel spatial databases for 15 landform and 269 physiographic types across the conterminous United States of America. We examined their potential use by natural resource managers by placing them within a contemporary climate change adaptation framework, and found our physiographic databases could play key roles in four of seven general adaptation strategies. We also calculated correlations with common empirical measures of biodiversity to examine the degree to which the physiographic setting explains various aspects of current biodiversity patterns. Additionally, we evaluated the relationship between landform diversity and measures of climate change to explore how changes may unfold across a geophysical template. We found landform types are particularly sensitive to spatial scale, and so we recommend using high-resolution datasets when possible, as well as generating metrics using multiple neighborhood sizes to both minimize and characterize potential unknown biases. We illustrate how our work can inform current strategies for climate change adaptation. The analytical framework and classification of landforms and parent material are easily extendable to other geographies and may be used to promote climate change adaptation in other settings. PMID:26641818

  16. Geomorpho-Landscapes

    NASA Astrophysics Data System (ADS)

    Farabollini, Piero; Lugeri, Francesca; Amadio, Vittorio

    2014-05-01

    Landscape is the object of human perceptions, being the image of spatial organization of elements and structures: mankind lives the first approach with the environment, viewing and feeling the landscape. Many definitions of landscape have been given over time: in this case we refer to the Landscape defined as the result of interaction among physical, biotic and anthropic phenomena acting in a different spatial-temporal scale (Foreman & Godron) Following an Aristotelic approach in studying nature, we can assert that " Shape is synthesis": so it is possible to read the land features as the expression of the endogenous and exogenous processes that mould earth surfaces; moreover, Landscape is the result of the interaction of natural and cultural components, and conditions the spatial-temporal development of a region. The study of the Landscape offers results useful in order to promote sustainable development, ecotourism, enhancement of natural and cultural heritage, popularization of the scientific knowledge. In Italy, a very important GIS-based tool to represent the territory is the "Carta della Natura" ("Map of Nature", presently coordinated by the ISPRA) that aims at assessing the state of the whole Italian territory, analyzing Landscape. The methodology follows a holistic approach, taking into consideration all the components of a landscape and then integrating the information. Each individual landscape, studied at different scales, shows distinctive elements: structural, which depend on physical form and specific spatial organization; functional, which depend on relationships created between biotic and abiotic elements, and dynamic, which depend on the successive evolution of the structure. The identification of the landscape units, recognized at different scales of analysis, allows an evaluation of the state of the land, referring to the dual risk/resource which characterizes the Italian country. An interesting opportunity is to discover those areas of unusual

  17. A Context for Classification in Child Psychiatry

    PubMed Central

    Carrey, Normand; Gregson, John

    2008-01-01

    Objective To provide a context for classification in child psychiatry over last 45 years including debate over different approaches. Method The context for classification of child psychiatric disorders has changed drastically since the introduction of categorical classification and the multi-axial formulation in the Diagnostic and Statistical Manual (DSM) and the International Classification of Disease (ICD). The authors review some historical factors including the shift in psychiatry to a universal classification system spanning the lifespan. Results The adaptation of categorical and universal diagnosis has resulted in a series of child-adult lifespan continuities and discontinuities about how problems are conceptualized within the categorical, multi-axial system. Conclusion There is need for a more flexible classification system to incorporate emerging data from longitudinal and gene-environment (GxE) interaction studies within the framework of attachment, developmental and systems theory. PMID:18516306

  18. Evolutionary Accessibility in Tunably Rugged Fitness Landscapes

    NASA Astrophysics Data System (ADS)

    Franke, Jasper; Krug, Joachim

    2012-09-01

    The adaptive evolution of a population under the influence of mutation and selection is strongly influenced by the structure of the underlying fitness landscape, which encodes the interactions between mutations at different genetic loci. Theoretical studies of such landscapes have been carried out for several decades, but only recently experimental fitness measurements encompassing all possible combinations of small sets of mutations have become available. The empirical studies have spawned new questions about the accessibility of optimal genotypes under natural selection. Depending on population dynamic parameters such as mutation rate and population size, evolutionary accessibility can be quantified through the statistics of accessible mutational pathways (along which fitness increases monotonically), or through the study of the basin of attraction of the optimal genotype under greedy (steepest ascent) dynamics. Here we investigate these two measures of accessibility in the framework of Kauffman's LK-model, a paradigmatic family of random fitness landscapes with tunable ruggedness. The key parameter governing the strength of genetic interactions is the number K of interaction partners of each of the L sites in the genotype sequence. In general, accessibility increases with increasing genotype dimensionality L and decreases with increasing number of interactions K. Remarkably, however, we find that some measures of accessibility behave non-monotonically as a function of K, indicating a special role of the most sparsely connected, non-trivial cases K=1 and 2. The relation between models for fitness landscapes and spin glasses is also addressed.

  19. Disorder on the landscape

    SciTech Connect

    Podolsky, Dmitry; Jokela, Niko; Majumder, Jaydeep E-mail: majumder@mnnit.ac.in

    2008-05-15

    Disorder on the string theory landscape may significantly affect dynamics of eternal inflation leading to the possibility for some vacua on the landscape to become dynamically preferable over others. We systematically study effects of a generic disorder on the landscape, starting by identifying a sector with built-in disorder-a set of de Sitter vacua corresponding to compactifications of the type IIB string theory on Calabi-Yau manifolds with a number of warped Klebanov-Strassler throats attached randomly to the bulk part of the Calabi-Yau. Further, we derive a continuum limit of the vacuum dynamics equations on the landscape. Using methods of the dynamical renormalization group we determine the late-time behavior of the probability distribution for an observer to measure a given value of the cosmological constant. We find the diffusion of the probability distribution to significantly slow down in sectors of the landscape where the number of nearest-neighboring vacua for any given vacuum is small. We discuss the relation of this slowdown with the phenomenon of Anderson localization in disordered media.

  20. Development of a reproducible method for determining quantity of water and its configuration in a marsh landscape

    USGS Publications Warehouse

    Suir, Glenn M.; Evers, D. Elaine; Steyer, Gregory D.; Sasser, Charles E.

    2013-01-01

    Coastal Louisiana is a dynamic and ever-changing landscape. From 1956 to 2010, over 3,734 km2 of Louisiana's coastal wetlands have been lost due to a combination of natural and human-induced activities. The resulting landscape constitutes a mosaic of conditions from highly deteriorated to relatively stable with intact landmasses. Understanding how and why coastal landscapes change over time is critical to restoration and rehabilitation efforts. Historically, changes in marsh pattern (i.e., size and spatial distribution of marsh landmasses and water bodies) have been distinguished using visual identification by individual researchers. Difficulties associated with this approach include subjective interpretation, uncertain reproducibility, and laborious techniques. In order to minimize these limitations, this study aims to expand existing tools and techniques via a computer-based method, which uses geospatial technologies for determining shifts in landscape patterns. Our method is based on a raster framework and uses landscape statistics to develop conditions and thresholds for a marsh classification scheme. The classification scheme incorporates land and water classified imagery and a two-part classification system: (1) ratio of water to land, and (2) configuration and connectivity of water within wetland landscapes to evaluate changes in marsh patterns. This analysis system can also be used to trace trajectories in landscape patterns through space and time. Overall, our method provides a more automated means of quantifying landscape patterns and may serve as a reliable landscape evaluation tool for future investigations of wetland ecosystem processes in the northern Gulf of Mexico.

  1. Modeling animal landscapes.

    PubMed

    Porter, W P; Ostrowski, S; Williams, J B

    2010-01-01

    There is an increasing need to assess the effects of climate and land-use change on habitat quality, ideally from a mechanistic basis. The symposium "Molecules to Migration: Pressures of Life" at the Fourth International Conference in Africa for Comparative Physiology and Biochemistry, Maasai Mara National Reserve, Kenya, 2008, illustrated how the principles of biophysical ecology can capture the mechanistic links between organisms, climate, and other habitat features. These principles provide spatially explicit assessments of habitat quality from a physiological perspective (i.e., "animal landscapes") that can be validated independently of the data used to derive and parameterize them. The contents of this symposium showcased how the modeling of animal landscapes can be used to assess key issues in applied and theoretical ecology. The presentations included applications to amphibians, reptiles, birds, and mammals. The rare Arabian oryx on the Arabian Peninsula is used as an example for energetic calculations and their implications for behavior on the landscape. PMID:20670170

  2. Landscape of superconducting membranes

    SciTech Connect

    Denef, Frederik; Hartnoll, Sean A.

    2009-06-15

    The AdS/CFT correspondence may connect the landscape of string vacua and the 'atomic landscape' of condensed matter physics. We study the stability of a landscape of IR fixed points of N=2 large N gauge theories in 2+1 dimensions, dual to Sasaki-Einstein compactifications of M theory, toward a superconducting state. By exhibiting instabilities of charged black holes in these compactifications, we show that many of these theories have charged operators that condense when the theory is placed at a finite chemical potential. We compute a statistical distribution of critical superconducting temperatures for a subset of these theories. With a chemical potential of 1 mV, we find critical temperatures ranging between 0.24 and 165 K.

  3. Landscapes, tourism, and conservation

    PubMed

    Burger

    2000-04-17

    One key aspect of global change is a decrease in ecological integrity as more and more landscapes are developed, leaving a mosaic of intact refuges and degraded patches that may not be sufficient for conserving biodiversity. While increases in human population and shifts in the distribution of people affect land use, the temporary movement of people can have major implications for conservation and biodiversity. Three examples are presented where recreation/tourism can enhance the conservation of land on a landscape scale, leading to habitat protection and biodiversity preservation: (1) Shorebirds often require a matrix of different habitat types during migratory stopovers, and ecotourism can serve as a catalyst for landscape scale protection of habitat. (2) Riparian habitats can serve as corridors to link diverse habitat patches, as well as serving as biodiversity hotspots. (3) Remediation and rehabilitation of contaminated lands, such as those of the US Department of Energy, aimed at developing recreational activities on the uncontaminated portions, can be the most economical form of re-development with no increase in human or ecological risk. Since large areas on many DOE sites have been undisturbed since the Second World War, when they were acquired, they contain unique or valuable ecosystems that serve an important role within their regional landscapes. In all three cases the judicious development of recreational/tourist interests can encourage both the conservation of habitats and the wise management of habitats on a landscape scale. While some species or habitats are too fragile for sustained tourism, many can be managed so that species, ecosystems and ecotourists flourish. By contributing to the economic base of regions, ecotourists/recreationists can influence the protection of land and biodiversity on a landscape scale, contributing to ecosystem management. The human dimensions of land preservation and biodiversity protection are key to long

  4. Probabilistic drought classification using gamma mixture models

    NASA Astrophysics Data System (ADS)

    Mallya, Ganeshchandra; Tripathi, Shivam; Govindaraju, Rao S.

    2015-07-01

    Drought severity is commonly reported using drought classes obtained by assigning pre-defined thresholds on drought indices. Current drought classification methods ignore modeling uncertainties and provide discrete drought classification. However, the users of drought classification are often interested in knowing inherent uncertainties in classification so that they can make informed decisions. Recent studies have used hidden Markov models (HMM) for quantifying uncertainties in drought classification. The HMM method conceptualizes drought classes as distinct hydrological states that are not observed (hidden) but affect observed hydrological variables. The number of drought classes or hidden states in the model is pre-specified, which can sometimes result in model over-specification problem. This study proposes an alternate method for probabilistic drought classification where the number of states in the model is determined by the data. The proposed method adapts Standard Precipitation Index (SPI) methodology of drought classification by employing gamma mixture model (Gamma-MM) in a Bayesian framework. The method alleviates the problem of choosing a suitable distribution for fitting data in SPI analysis, quantifies modeling uncertainties, and propagates them for probabilistic drought classification. The method is tested on rainfall data over India. Comparison of the results with standard SPI show important differences particularly when SPI assumptions on data distribution are violated. Further, the new method is simpler and more parsimonious than HMM based drought classification method and can be a viable alternative for probabilistic drought classification.

  5. Construction Standard of Farmland Landscapeconstruction Standard of Farmland Landscape

    NASA Astrophysics Data System (ADS)

    Fu, Meichen; Zhang, Jianjun

    Precision agriculture is an important choice for the future agriculture. It is the base for precision agriculture development to change the state of small-scale farmland production and weak agricultural foundation in China gradually. Combined with the poorness of village in China, the variation of farmland and the dominance of small-scale peasant economy, this paper analyzed the adaptability of farmland landscape pattern to precision agriculture based on literatures and farmland landscape survey. With the requirements of precision agricultural production, this paper put forward the standards on cultivated field scale and shape, farmland corridor structure, cultivated field matrix and farmland landscape protection in order to make farmland landscape suitable for precision agriculture and to provide references for the sustainable development of precision agriculture in China.

  6. Mapping the Ancient Maya Landscape from Space

    NASA Technical Reports Server (NTRS)

    Sever, Tom

    2003-01-01

    This project uses new satellite and airborne imagery in combination with remote sensing, GIS, and GPS technology to understand the dynamics of how the Maya successfully interacted with their karst topographic landscape for several centuries in the northern Peten region of Guatemala. The ancient Maya attained one of the greatest population densities in human history in the tropical forest of the Peten, Guatemala, and it was in this region that the Maya civilization began, flourished, and abruptly disappeared for unknown reasons around AD 800. How the Maya were able to successfully manage water and feed this dense population is not known at this time. However, a recent NASA-funded project was the first to investigate large seasonal swamps (bajos) that make up 40 percent of the landscape. Through the use of remote sensing, ancient Maya features such as cities, roadways, canals and water reservoirs have been detected and verified through ground reconnaissance. The results of this research cast new light on the adaptation of the ancient Maya to their environment. Micro-environmental variation within the wetlands was elucidated and the different vegetational associations identified in the satellite imagery. More than 70 new archeological sites within and at the edges of the bajo were mapped and tested. Modification of the landscape by the Maya in the form of dams and reservoirs in the Holmul River and its tributaries and possible drainage canals in bajos was demonstrated. The recent acquisition of one-meter IKONOS imagery and high resolution STAR-3i radar imagery (2.5m backscatter/ 10m DEM), opens new possibilities for understanding how a civilization was able to survive for centuries upon a karst topographic landscape and their human-induced effects upon the local climate. This understanding is critical for the current population that is presently experiencing rapid population growth and destroying the landscape through non-traditional farming and grazing techniques

  7. Shaping the Landscape.

    ERIC Educational Resources Information Center

    Naturescope, 1987

    1987-01-01

    Provides background information on various agents that change the landscape. Includes teaching activities on weathering, water, wind and ice erosion, plate tectonics, sedimentation, deposition, mountain building, and determining contour lines. Contains reproducible handouts and worksheets for two of the activities. (TW)

  8. Performance Technology Landscape.

    ERIC Educational Resources Information Center

    Addison, Roger M.

    2003-01-01

    Describes a performance technology landscape that has been developed for performance improvement institutes. Defines performance technology, including identification of value; definition of outcomes; performance analysis; valuation of effectiveness; focusing on results; systemic approach; adding value; aligning workers, activity, the organization,…

  9. Desert landscape irrigation

    SciTech Connect

    Quinones, R.

    1995-06-01

    Industrialization can take place in an arid environment if a long term, overall water management program is developed. The general rule to follow is that recharge must equal or exceed use. The main problem encountered in landscape projects is that everyone wants a lush jungle setting, tall shade trees, ferns, with a variety of floral arrangements mixed in. What we want, what we can afford, and what we get are not always the same. Vegetation that requires large quantities of water are not native to any desert. Surprisingly; there are various types of fruit trees, and vegetables that will thrive in the desert. Peaches, plums, nut trees, do well with drip irrigation as well as tomatoes. Shaded berry plans will also do well, the strawberry being one. In summary; if we match our landscape to our area, we can then design our irrigation system to maintain our landscape and grow a variety of vegetation in any arid or semiarid environment. The application of science and economics to landscaping has now come of age.

  10. Biofuels from urban landscapes

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Biomass from urban landscapes is an untapped resource. Lawn thatch and clippings, fallen leaves and tree limbs are all potential sources of biofuels. Most cities already collect and transport these materials to disposal sites; but, alternatively could collect and transport these materials to a loc...

  11. Landscape in Literature.

    ERIC Educational Resources Information Center

    Salter, Christopher L.; Lloyd, William J.

    One of a series of Resource Papers for College Geography, this thematic study guide focuses on literary setting and the personal space of fictional characters as an approach to comparative literary study, and concurrently uses fictional treatments of landscape and place as a means to encourage greater sensitivity to geographical and architectural…

  12. A Curious Landscape

    NASA Technical Reports Server (NTRS)

    2004-01-01

    This 'postcard' from the panoramic camera on the Mars Exploration Rover Opportunity shows the view of the martian landscape southwest of the rover. The image was taken in the late martian afternoon at Meridiani Planum on Mars, where Opportunity landed at approximately 9:05 p.m. PST on Saturday, Jan. 24.

  13. Landscape Management: Field Specialist.

    ERIC Educational Resources Information Center

    Newton, Deborah; Newton, Steve

    This module is the second volume in a series of three publications on landscape management. The module contains five instructional units that cover the following topics: orientation; equipment; irrigation systems and maintenance; plant material identification and pests; and turf identification and pests. Each instructional unit follows a standard…

  14. Moving into Landscapes

    ERIC Educational Resources Information Center

    Nelson, Cindy

    2008-01-01

    This article describes a lesson, designed for second graders, that begins with the teacher showing and talking about a few landscape fundamentals: horizon line, depth, and the mood or feeling that a work of art inspires. A class discussion ensues about how an artist's images can make one feel, how they can convey calmness, warmth, anxiety, or a…

  15. Landscapes. Artists' Workshop Series.

    ERIC Educational Resources Information Center

    King, Penny; Roundhill, Clare

    This instructional resource, designed to be used by and with elementary level students, provides inspiration for landscape painting by presenting the work of six different artists. These include: "Fuji in Clear Weather" (Katsushika Hokusai, 1823-29); "The Tree of Life" (Gustav Klimt, c. 1905-1909); "The Waterlily Pond" (Claude Monet, 1899);…

  16. LANDSCAPE SCIENCES OVERVIEW

    EPA Science Inventory

    The primary aim of the Landscape Sciences Program (LSP) is to develop methodologies to evaluate the status, trends, and vulnerability of ecological resources (primarily water) at site, watershed, regional, and national scales, and to evaluate the major stressors and exposures to...

  17. LANDSCAPE MANAGEMENT PRACTICES

    EPA Science Inventory

    USDA Conservation Practices are applied at various scales ranging from a portion of a field or a specific farm operation to the watershed or landscape scale. The Conservation Effects Assessment Project is a joint effort of USDA Conservation and Research agencies to determine the...

  18. Campus Landscape: Functions, Forms, Features.

    ERIC Educational Resources Information Center

    Dober, Richard P.

    This guide provides information, instruction, and ideas on planning and designing every aspect of the campus landscape, from parking lots to playing fields. Using real-world examples of classic and contemporary campus landscapes, it features coverage of landscape restoration and regeneration; provides an assessment matrix for consistent, effective…

  19. Classification in Australia.

    ERIC Educational Resources Information Center

    McKinlay, John

    Despite some inroads by the Library of Congress Classification and short-lived experimentation with Universal Decimal Classification and Bliss Classification, Dewey Decimal Classification, with its ability in recent editions to be hospitable to local needs, remains the most widely used classification system in Australia. Although supplemented at…

  20. Geomorphology of anthropogenic landscapes

    NASA Astrophysics Data System (ADS)

    Sofia, Giulia; Tarolli, Paolo

    2015-04-01

    The construction of urban areas and the development of road networks leave a significant signature on the Earth surface, providing a geomorphological evidence to support the idea that humans are nowadays a geomorphic agent having deep effects on the morphological organization of the landscape. The reconstruction or identification of anthropogenic topographies, therefore, provides a mechanism for quantifying anthropogenic changes to the landscape systems in the Anthropocene. Following this research line, the present study tests the effectiveness of a recently published topographic index, the Slope Local Length of Autocorrelation (SLLAC, Sofia et al. 2014) to portrait anthropogenic geomorphology, focusing in particular on road network density, and urban complexity (UCI). At first, the research considers the increasing of anthropic structures and the resulting changes in the SLLAC and in two derived parameters (mean SLLAC per km2 and SLLAC roughness, or Surface Peak Curvature -Spc). As a second step, considering the SLLAC derived indices, the anthropogenic geomorphology is automatically depicted using a k-means clustering algorithm. In general, the increasing of road network density or of the UCI is positively correlated to the mean SLLAC per km2, while the Spc is negatively correlated to the increasing of the anthropic structures. Areas presenting different road network organization are effectively captured considering multiple combinations of the defined parameters. Landscapes with small scattered towns, and a network with long roads in a dendritic shape (with hierarchical branching) are characterized simultaneously by high mean SLLAC and low Spc. Large and complex urban areas served by rectilinear networks with numerous short straight lines and right angles, have either a maximized mean SLLAC or a minimized Spc or both. In all cases, the anthropogenic landscape identified by the procedure is comparable to the ones identified manually from orthophoto, with the

  1. Assessment Issues, Placement Litigation, and the Future of Mild Mental Retardation Classification and Programming.

    ERIC Educational Resources Information Center

    Reschly, Daniel J.

    1988-01-01

    The article examines issues concerning mild mental retardation (MMR) classification and programing including placement bias litigation, MMR diagnostic construct and classification criteria, general intellectual functioning, adaptive behavior, sociocultural status, pseudo reforms through changes in assessment, the Learning Potential Assessment…

  2. An Object-Based Method for Chinese Landform Types Classification

    NASA Astrophysics Data System (ADS)

    Ding, Hu; Tao, Fei; Zhao, Wufan; Na, Jiaming; Tang, Guo'an

    2016-06-01

    Landform classification is a necessary task for various fields of landscape and regional planning, for example for landscape evaluation, erosion studies, hazard prediction, et al. This study proposes an improved object-based classification for Chinese landform types using the factor importance analysis of random forest and the gray-level co-occurrence matrix (GLCM). In this research, based on 1km DEM of China, the combination of the terrain factors extracted from DEM are selected by correlation analysis and Sheffield's entropy method. Random forest classification tree is applied to evaluate the importance of the terrain factors, which are used as multi-scale segmentation thresholds. Then the GLCM is conducted for the knowledge base of classification. The classification result was checked by using the 1:4,000,000 Chinese Geomorphological Map as reference. And the overall classification accuracy of the proposed method is 5.7% higher than ISODATA unsupervised classification, and 15.7% higher than the traditional object-based classification method.

  3. 23 CFR 752.4 - Landscape development.

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    ... 23 Highways 1 2011-04-01 2011-04-01 false Landscape development. 752.4 Section 752.4 Highways... ROADSIDE DEVELOPMENT § 752.4 Landscape development. (a) Landscape development, which includes landscaping... landscaping and environmental design. (b) Landscape development should have provisions for plant...

  4. 23 CFR 752.4 - Landscape development.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... 23 Highways 1 2010-04-01 2010-04-01 false Landscape development. 752.4 Section 752.4 Highways... ROADSIDE DEVELOPMENT § 752.4 Landscape development. (a) Landscape development, which includes landscaping... landscaping and environmental design. (b) Landscape development should have provisions for plant...

  5. 23 CFR 752.4 - Landscape development.

    Code of Federal Regulations, 2014 CFR

    2014-04-01

    ... 23 Highways 1 2014-04-01 2014-04-01 false Landscape development. 752.4 Section 752.4 Highways... ROADSIDE DEVELOPMENT § 752.4 Landscape development. (a) Landscape development, which includes landscaping... landscaping and environmental design. (b) Landscape development should have provisions for plant...

  6. 23 CFR 752.4 - Landscape development.

    Code of Federal Regulations, 2012 CFR

    2012-04-01

    ... 23 Highways 1 2012-04-01 2012-04-01 false Landscape development. 752.4 Section 752.4 Highways... ROADSIDE DEVELOPMENT § 752.4 Landscape development. (a) Landscape development, which includes landscaping... landscaping and environmental design. (b) Landscape development should have provisions for plant...

  7. 23 CFR 752.4 - Landscape development.

    Code of Federal Regulations, 2013 CFR

    2013-04-01

    ... 23 Highways 1 2013-04-01 2013-04-01 false Landscape development. 752.4 Section 752.4 Highways... ROADSIDE DEVELOPMENT § 752.4 Landscape development. (a) Landscape development, which includes landscaping... landscaping and environmental design. (b) Landscape development should have provisions for plant...

  8. Applying GIS to Interpret Erosional History of Landscapes

    NASA Astrophysics Data System (ADS)

    Huerta, A. D.; Swanson, C. D.

    2011-12-01

    The morphometry of mountain ranges is an expression of complex interactions between tectonics, climate, and erosion. Understanding the erosive processes that have shaped a landscape can help us understand the climate and tectonic setting through which it evolved. In particular, the ability to distinguish the degree to which a landscape has been affected by fluvial vs. glacial processes would provide critical information towards unraveling past climate and tectonics, and increase our ability to predict future climate variations. Typically, the effect of glacial and fluvial processes on mountainous landscapes is assessed through field research, while an automated technique for classification of large regions has remained elusive. Given that glacial valleys typically have steeper side slopes, flatter valley profiles, and exhibit characteristic landforms such as arêtes and cirques, we use the standard GIS metrics of curvature and slope to distinguish mountainous landscapes that are dominated by glacial erosion vs landscapes that are dominated by fluvial erosion. Preliminary analysis of a test region in the Sawtooth Mts., Idaho indicates that these two metrics, curvature and slope, can be used to distinguish the degree of glacial vs. fluvial erosion. Previous work in the region (Amerson, et al., 2008) recognized valleys dominated by fluvial erosion and valleys dominated by glacial erosion. DEM analysis of the area reveal a higher density of cells with values of slope greater than 45 degrees in the area of glacial influence than in the area of fluvial influence. As glacial influence increases, the strength of the signal increases. A similar pattern is observed with curvature values. Using these signals, we are confident that relative influence of glaciation can be determined with GIS. The ability to analyze landscapes using standard GIS methods allows orogenic scale characterization of landscapes, and increases efficiency of large-scale analysis of landscape evolution

  9. Using graph approach for managing connectivity in integrative landscape modelling

    NASA Astrophysics Data System (ADS)

    Rabotin, Michael; Fabre, Jean-Christophe; Libres, Aline; Lagacherie, Philippe; Crevoisier, David; Moussa, Roger

    2013-04-01

    FLUID-landr library has been developed in order i) to be used with no GIS expert skills needed (common gis formats can be read and simplified spatial management is provided), ii) to easily develop adapted rules of landscape discretization and graph creation to follow spatialized model requirements and iii) to allow model developers to manage dynamic and complex spatial topology. Graph management in OpenFLUID are shown with i) examples of hydrological modelizations on complex farmed landscapes and ii) the new implementation of Geo-MHYDAS tool based on the OpenFLUID-landr library, which allows to discretize a landscape and create graph structure for the MHYDAS model requirements.

  10. Optimization of Landscape Services under Uncoordinated Management by Multiple Landowners

    PubMed Central

    Porto, Miguel; Correia, Otília; Beja, Pedro

    2014-01-01

    objectives. It may thus be adapted to other socio-ecological systems, particularly where specific patterns of landscape heterogeneity are to be maintained despite imperfect management by multiple landowners. PMID:24465833

  11. Classification and knowledge

    NASA Technical Reports Server (NTRS)

    Kurtz, Michael J.

    1989-01-01

    Automated procedures to classify objects are discussed. The classification problem is reviewed, and the relation of epistemology and classification is considered. The classification of stellar spectra and of resolved images of galaxies is addressed.

  12. Remote Sensing Information Classification

    NASA Technical Reports Server (NTRS)

    Rickman, Douglas L.

    2008-01-01

    This viewgraph presentation reviews the classification of Remote Sensing data in relation to epidemiology. Classification is a way to reduce the dimensionality and precision to something a human can understand. Classification changes SCALAR data into NOMINAL data.

  13. Analyzing landscape changes in the Bafa Lake Nature Park of Turkey using remote sensing and landscape structure metrics.

    PubMed

    Esbah, Hayriye; Deniz, Bulent; Kara, Baris; Kesgin, Birsen

    2010-06-01

    Bafa Lake Nature Park is one of Turkey's most important legally protected areas. This study aimed at analyzing spatial change in the park environment by using object-based classification technique and landscape structure metrics. SPOT 2X (1994) and ASTER (2005) images are the primary research materials. Results show that artificial surfaces, low maqui, garrigue, and moderately high maqui covers have increased and coniferous forests, arable lands, permanent crop, and high maqui covers have decreased; coniferous forest, high maqui, grassland, and saline areas are in a disappearance stage of the land transformation; and the landscape pattern is more fragmented outside the park boundaries. The management actions should support ongoing vegetation regeneration, mitigate transformation of vegetation structure to less dense and discontinuous cover, control the dynamics at the agricultural-natural landscape interface, and concentrate on relatively low but steady increase of artificial surfaces. PMID:19496005

  14. Lipid landscapes and pipelines in membrane homeostasis.

    PubMed

    Holthuis, Joost C M; Menon, Anant K

    2014-06-01

    The lipid composition of cellular organelles is tailored to suit their specialized tasks. A fundamental transition in the lipid landscape divides the secretory pathway in early and late membrane territories, allowing an adaptation from biogenic to barrier functions. Defending the contrasting features of these territories against erosion by vesicular traffic poses a major logistical problem. To this end, cells evolved a network of lipid composition sensors and pipelines along which lipids are moved by non-vesicular mechanisms. We review recent insights into the molecular basis of this regulatory network and consider examples in which malfunction of its components leads to system failure and disease. PMID:24899304

  15. Simulations of Fluvial Landscapes

    NASA Astrophysics Data System (ADS)

    Cattan, D.; Birnir, B.

    2013-12-01

    The Smith-Bretherton-Birnir (SBB) model for fluvial landsurfaces consists of a pair of partial differential equations, one governing water flow and one governing the sediment flow. Numerical solutions of these equations have been shown to provide realistic models in the evolution of fluvial landscapes. Further analysis of these equations shows that they possess scaling laws (Hack's Law) that are known to exist in nature. However, the simulations are highly dependent on the numerical methods used; with implicit methods exhibiting the correct scaling laws, but the explicit methods fail to do so. These equations, and the resulting models, help to bridge the gap between the deterministic and the stochastic theories of landscape evolution. Slight modifications of the SBB equations make the results of the model more realistic. By modifying the sediment flow equation, the model obtains more pronounced meandering rivers. Typical landsurface with rivers.

  16. Sharing a disparate landscape

    NASA Astrophysics Data System (ADS)

    Ali-Khan, Carolyne

    2010-06-01

    Working across boundaries of power, identity, and political geography is fraught with difficulties and contradictions. In Tali Tal and Iris Alkaher's, " Collaborative environmental projects in a multicultural society: Working from within separate or mutual landscapes?" the authors describe their efforts to do this in the highly charged atmosphere of Israel. This forum article offers a response to their efforts. Writing from a framework of critical pedagogy, I use the concepts of space and time to anchor my analysis, as I examine the issue of power in this Jew/Arab collaborative environmental project. This response problematizes "sharing" in a landscape fraught with disparities. It also looks to further Tal and Alkaher's work by geographically and politically grounding it in the broader current conflict and by juxtaposing sustainability with equity.

  17. Learning and Domain Adaptation

    NASA Astrophysics Data System (ADS)

    Mansour, Yishay

    Domain adaptation is a fundamental learning problem where one wishes to use labeled data from one or several source domains to learn a hypothesis performing well on a different, yet related, domain for which no labeled data is available. This generalization across domains is a very significant challenge for many machine learning applications and arises in a variety of natural settings, including NLP tasks (document classification, sentiment analysis, etc.), speech recognition (speakers and noise or environment adaptation) and face recognition (different lighting conditions, different population composition).

  18. Adaptive background model

    NASA Astrophysics Data System (ADS)

    Lu, Xiaochun; Xiao, Yijun; Chai, Zhi; Wang, Bangping

    2007-11-01

    An adaptive background model aiming at outdoor vehicle detection is presented in this paper. This model is an improved model of PICA (pixel intensity classification algorithm), it classifies pixels into K-distributions by color similarity, and then a hypothesis that the background pixel color appears in image sequence with a high frequency is used to evaluate all the distributions to determine which presents the current background color. As experiments show, the model presented in this paper is a robust, adaptive and flexible model, which can deal with situations like camera motions, lighting changes and so on.

  19. [Landscape character assessment framework in rural area: A case study in Qiaokou, Chang-sha, China].

    PubMed

    Zhang, Qian; Liu, Wen-ping; Yu, Zhen-rong

    2015-05-01

    Based on the concept and methods of landscape character assessment (LCA) in England, this paper applied a complete process of landscape character assessment with a case study in Qiaokou Town, which is located in a typical southern paddy fields area in Changsha City. We drew the landscape character map of Qiaokou Town through desk classification and field survey, identified and compared the key characters of each character area, and proposed suggestions on the improvement and stewardship of landscape characters. The results showed that Qiaokou could be divided into 2 landscape character types and 7 landscape character areas with the main differences in cropland and vegetation pattern as well as aesthetic characters. The case study indicated that LCA could be a critical tool to identify the characteristics in rural area, and provide helpful guidance to protect, restore and maintain the unique culture and characters of rural landscape, which is useful for targeted rural landscape development. In the future, we suggested that the assessment on the effects of landscape construction measures on the ecosystem services should be incorporated in LCA research as well. PMID:26571675

  20. Multitemporal spatial pattern analysis of Tulum's tropical coastal landscape

    NASA Astrophysics Data System (ADS)

    Ramírez-Forero, Sandra Carolina; López-Caloca, Alejandra; Silván-Cárdenas, José Luis

    2011-11-01

    The tropical coastal landscape of Tulum in Quintana Roo, Mexico has a high ecological, economical, social and cultural value, it provides environmental and tourism services at global, national, regional and local levels. The landscape of the area is heterogeneous and presents random fragmentation patterns. In recent years, tourist services of the region has been increased promoting an accelerate expansion of hotels, transportation and recreation infrastructure altering the complex landscape. It is important to understand the environmental dynamics through temporal changes on the spatial patterns and to propose a better management of this ecological area to the authorities. This paper addresses a multi-temporal analysis of land cover changes from 1993 to 2000 in Tulum using Thematic Mapper data acquired by Landsat-5. Two independent methodologies were applied for the analysis of changes in the landscape and for the definition of fragmentation patterns. First, an Iteratively Multivariate Alteration Detection (IR-MAD) algorithm was used to detect and localize land cover change/no-change areas. Second, the post-classification change detection evaluated using the Support Vector Machine (SVM) algorithm. Landscape metrics were calculated from the results of IR-MAD and SVM. The analysis of the metrics indicated, among other things, a higher fragmentation pattern along roadways.

  1. Crop pathogen emergence and evolution in agro-ecological landscapes

    PubMed Central

    Papaïx, Julien; Burdon, Jeremy J; Zhan, Jiasui; Thrall, Peter H

    2015-01-01

    Remnant areas hosting natural vegetation in agricultural landscapes can impact the disease epidemiology and evolutionary dynamics of crop pathogens. However, the potential consequences for crop diseases of the composition, the spatial configuration and the persistence time of the agro-ecological interface – the area where crops and remnant vegetation are in contact – have been poorly studied. Here, we develop a demographic–genetic simulation model to study how the spatial and temporal distribution of remnant wild vegetation patches embedded in an agricultural landscape can drive the emergence of a crop pathogen and its subsequent specialization on the crop host. We found that landscape structures that promoted larger pathogen populations on the wild host facilitated the emergence of a crop pathogen, but such landscape structures also reduced the potential for the pathogen population to adapt to the crop. In addition, the evolutionary trajectory of the pathogen population was determined by interactions between the factors describing the landscape structure and those describing the pathogen life histories. Our study contributes to a better understanding of how the shift of land-use patterns in agricultural landscapes might influence crop diseases to provide predictive tools to evaluate management practices. PMID:25926883

  2. Crop pathogen emergence and evolution in agro-ecological landscapes.

    PubMed

    Papaïx, Julien; Burdon, Jeremy J; Zhan, Jiasui; Thrall, Peter H

    2015-04-01

    Remnant areas hosting natural vegetation in agricultural landscapes can impact the disease epidemiology and evolutionary dynamics of crop pathogens. However, the potential consequences for crop diseases of the composition, the spatial configuration and the persistence time of the agro-ecological interface - the area where crops and remnant vegetation are in contact - have been poorly studied. Here, we develop a demographic-genetic simulation model to study how the spatial and temporal distribution of remnant wild vegetation patches embedded in an agricultural landscape can drive the emergence of a crop pathogen and its subsequent specialization on the crop host. We found that landscape structures that promoted larger pathogen populations on the wild host facilitated the emergence of a crop pathogen, but such landscape structures also reduced the potential for the pathogen population to adapt to the crop. In addition, the evolutionary trajectory of the pathogen population was determined by interactions between the factors describing the landscape structure and those describing the pathogen life histories. Our study contributes to a better understanding of how the shift of land-use patterns in agricultural landscapes might influence crop diseases to provide predictive tools to evaluate management practices. PMID:25926883

  3. The potential and flux landscape theory of evolution

    NASA Astrophysics Data System (ADS)

    Zhang, Feng; Xu, Li; Zhang, Kun; Wang, Erkang; Wang, Jin

    2012-08-01

    We established the potential and flux landscape theory for evolution. We found explicitly the conventional Wright's gradient adaptive landscape based on the mean fitness is inadequate to describe the general evolutionary dynamics. We show the intrinsic potential as being Lyapunov function(monotonically decreasing in time) does exist and can define the adaptive landscape for general evolution dynamics for studying global stability. The driving force determining the dynamics can be decomposed into gradient of potential landscape and curl probability flux. Non-zero flux causes detailed balance breaking and measures how far the evolution from equilibrium state. The gradient of intrinsic potential and curl flux are perpendicular to each other in zero fluctuation limit resembling electric and magnetic forces on electrons. We quantified intrinsic energy, entropy and free energy of evolution and constructed non-equilibrium thermodynamics. The intrinsic non-equilibrium free energy is a Lyapunov function. Both intrinsic potential and free energy can be used to quantify the global stability and robustness of evolution. We investigated an example of three allele evolutionary dynamics with frequency dependent selection (detailed balance broken). We uncovered the underlying single, triple, and limit cycle attractor landscapes. We found quantitative criterions for stability through landscape topography. We also quantified evolution pathways and found paths do not follow potential gradient and are irreversible due to non-zero flux. We generalized the original Fisher's fundamental theorem to the general (i.e., frequency dependent selection) regime of evolution by linking the adaptive rate with not only genetic variance related to the potential but also the flux. We show there is an optimum potential where curl flux resulting from biotic interactions of individuals within a species or between species can sustain an endless evolution even if the physical environment is unchanged. We

  4. Incorporating Bioenergy in Sustainable Landscape Designs Workshop Two: Agricultural Landscapes

    SciTech Connect

    Negri, M. Cristina; Ssegane, H.

    2015-08-01

    The Bioenergy Technologies Office hosted two workshops on Incorporating Bioenergy in Sustainable Landscape Designs with Oak Ridge and Argonne National Laboratories in 2014. The second workshop focused on agricultural landscapes and took place in Argonne, IL from June 24—26, 2014. The workshop brought together experts to discuss how landscape design can contribute to the deployment and assessment of sustainable bioenergy. This report summarizes the discussions that occurred at this particular workshop.

  5. A Novel Modulation Classification Approach Using Gabor Filter Network

    PubMed Central

    Ghauri, Sajjad Ahmed; Qureshi, Ijaz Mansoor; Cheema, Tanveer Ahmed; Malik, Aqdas Naveed

    2014-01-01

    A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN). The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS) algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR) on AWGN channel. PMID:25126603

  6. THE EFFECTS OF HABITAT RESOLUTION ON MODELS OF AVIAN DIVERSITY AND DISTRIBUTIONS: A COMPARISON OF TWO LAND-COVER CLASSIFICATIONS

    EPA Science Inventory

    The quantification of pattern is a key element of landscape analyses. One aspect of this quantification of particular importance to landscape ecologists regards the classification of continuous variables to produce categorical variables such as land-cover type or elevation strat...

  7. Landscape Construction in Dynamical Systems

    NASA Astrophysics Data System (ADS)

    Tang, Ying; Yuan, Ruoshi; Wang, Gaowei; Ao, Ping

    The idea of landscape has been recently applied to study various of biological problems. We demonstrate that a dynamical structure built into nonlinear dynamical systems allows us to construct such a global optimization landscape, which serves as the Lyapunov function for the ordinary differential equation. We find exact constructions on the landscape for a class of dynamical systems, including a van der Pol type oscillator, competitive Lotka-Volterra systems, and a chaotic system. The landscape constructed provides a new angle for understanding and modelling biological network dynamics.

  8. Terrain classification for a UGV

    NASA Astrophysics Data System (ADS)

    Sarwal, Alok; Baker, Chris; Rosenblum, Mark

    2005-05-01

    This work addresses the issue of Terrain Classification that can be applied for path planning for an Unmanned Ground Vehicle (UGV) platform. We are interested in classification of features such as rocks, bushes, trees and dirt roads. Currently, the data is acquired from a color camera mounted on the UGV as we can add range data from a second sensor in the future. The classification is accomplished by first, coarse segmenting a frame and then refining the initial segmentations through a convenient user interface. After the first frame, temporal information is exploited to improve the quality of the image segmentation and help classification adapt to changes due to ambient lighting, shadows, and scene changes as the platform moves. The Mean Shift Classifier algorithm provides segmentation of the current frame data. We have tested the above algorithms with four sequence of frames acquired in an environment with terrain representative of the type we expect to see in the field. A comparison of the results from this algorithm was done with accurate manually-segmented (ground-truth) data, for each frame in the sequence.

  9. I-CAN: The Classification and Prediction of Support Needs

    ERIC Educational Resources Information Center

    Arnold, Samuel R. C.; Riches, Vivienne C.; Stancliffe, Roger J.

    2014-01-01

    Background: Since 1992, the diagnosis and classification of intellectual disability has been dependent upon three constructs: intelligence, adaptive behaviour and support needs (Luckasson "et al." 1992. Mental Retardation: Definition, Classification and Systems of Support. American Association on Intellectual and Developmental…

  10. Enriching User-Oriented Class Associations for Library Classification Schemes.

    ERIC Educational Resources Information Center

    Pu, Hsiao-Tieh; Yang, Chyan

    2003-01-01

    Explores the possibility of adding user-oriented class associations to hierarchical library classification schemes. Analyses a log of book circulation records from a university library in Taiwan and shows that classification schemes can be made more adaptable by analyzing circulation patterns of similar users. (Author/LRW)

  11. Using community archetypes to better understand differential community adaptation to wildfire risk.

    PubMed

    Carroll, Matthew; Paveglio, Travis

    2016-06-01

    One of the immediate challenges of wildfire management concerns threats to human safety and property in residential areas adjacent to non-cultivated vegetation. One approach for relieving this problem is to increase human community 'adaptiveness' to deal with the risk and reality of fire in a variety of landscapes. The challenge in creating 'fire-adapted communities' (FACs) is the great diversity in character and make-up of populations at risk from wildfire. This paper outlines a recently developed categorization scheme for Wildland-Urban Interface (WUI) communities based on a larger conceptual approach for understanding how social diversity is likely to influence the creation of FACs. The WUI categorization scheme situates four community archetypes on a continuum that recognizes dynamic change in human community functioning. We use results from the WUI classification scheme to outline key characteristics associated with each archetype and results from recent case studies to demonstrate the diversity across WUI communities. Differences among key characteristics of local social context will likely result in the need for different adaptation strategies to wildfire. While the WUI archetypes described here may not be broadly applicable to other parts of the world, we argue that the conceptual approach and strategies for systematically documenting local influences on wildfire adaptation have potential for broad application.This article is part of the themed issue 'The interaction of fire and mankind'. PMID:27216514

  12. Probing the String Landscape

    ScienceCinema

    Keith Dienes

    2010-01-08

    We are currently in the throes of a potentially huge paradigm shift in physics. Motivated by recent developments in string theory and the discovery of the so-called "string landscape", physicists are beginning to question the uniqueness of fundamental theories of physics and the methods by which such theories might be understood and investigated. In this colloquium, I will give a non-technical introduction to the nature of this paradigm shift and how it developed. I will also discuss some of the questions to which it has led, and the nature of the controversies it has spawned.

  13. Wind-Eroded Landscape

    NASA Technical Reports Server (NTRS)

    2005-01-01

    5 August 2005 This Mars Global Surveyor (MGS) Mars Orbiter Camera (MOC) image shows a dust-mantled, wind-eroded landscape in the Medusae Sulci region of Mars. Wind eroded the bedrock in this region, and then, later, windblown dust covered much of the terrain.

    Location near: 5.7oS, 160.2oW Image width: width: 3 km (1.9 mi) Illumination from: lower left Season: Southern Spring

  14. Probing the String Landscape

    SciTech Connect

    Keith Dienes

    2009-12-01

    We are currently in the throes of a potentially huge paradigm shift in physics. Motivated by recent developments in string theory and the discovery of the so-called "string landscape", physicists are beginning to question the uniqueness of fundamental theories of physics and the methods by which such theories might be understood and investigated. In this colloquium, I will give a non-technical introduction to the nature of this paradigm shift and how it developed. I will also discuss some of the questions to which it has led, and the nature of the controversies it has spawned.

  15. Intrinsically Disordered Energy Landscapes

    NASA Astrophysics Data System (ADS)

    Chebaro, Yassmine; Ballard, Andrew J.; Chakraborty, Debayan; Wales, David J.

    2015-05-01

    Analysis of an intrinsically disordered protein (IDP) reveals an underlying multifunnel structure for the energy landscape. We suggest that such ‘intrinsically disordered’ landscapes, with a number of very different competing low-energy structures, are likely to characterise IDPs, and provide a useful way to address their properties. In particular, IDPs are present in many cellular protein interaction networks, and several questions arise regarding how they bind to partners. Are conformations resembling the bound structure selected for binding, or does further folding occur on binding the partner in a induced-fit fashion? We focus on the p53 upregulated modulator of apoptosis (PUMA) protein, which adopts an -helical conformation when bound to its partner, and is involved in the activation of apoptosis. Recent experimental evidence shows that folding is not necessary for binding, and supports an induced-fit mechanism. Using a variety of computational approaches we deduce the molecular mechanism behind the instability of the PUMA peptide as a helix in isolation. We find significant barriers between partially folded states and the helix. Our results show that the favoured conformations are molten-globule like, stabilised by charged and hydrophobic contacts, with structures resembling the bound state relatively unpopulated in equilibrium.

  16. Cancer Genome Landscapes

    PubMed Central

    Vogelstein, Bert; Papadopoulos, Nickolas; Velculescu, Victor E.; Zhou, Shibin; Diaz, Luis A.; Kinzler, Kenneth W.

    2013-01-01

    Over the past decade, comprehensive sequencing efforts have revealed the genomic landscapes of common forms of human cancer. For most cancer types, this landscape consists of a small number of “mountains” (genes altered in a high percentage of tumors) and a much larger number of “hills” (genes altered infrequently). To date, these studies have revealed ~140 genes that, when altered by intragenic mutations, can promote or “drive” tumorigenesis. A typical tumor contains two to eight of these “driver gene” mutations; the remaining mutations are passengers that confer no selective growth advantage. Driver genes can be classified into 12 signaling pathways that regulate three core cellular processes: cell fate, cell survival, and genome maintenance. A better understanding of these pathways is one of the most pressing needs in basic cancer research. Even now, however, our knowledge of cancer genomes is sufficient to guide the development of more effective approaches for reducing cancer morbidity and mortality. PMID:23539594

  17. Landscape Evolution of Titan

    NASA Technical Reports Server (NTRS)

    Moore, Jeffrey

    2012-01-01

    Titan may have acquired its massive atmosphere relatively recently in solar system history. The warming sun may have been key to generating Titan's atmosphere over time, starting from a thin atmosphere with condensed surface volatiles like Triton, with increased luminosity releasing methane, and then large amounts of nitrogen (perhaps suddenly), into the atmosphere. This thick atmosphere, initially with much more methane than at present, resulted in global fluvial erosion that has over time retreated towards the poles with the removal of methane from the atmosphere. Basement rock, as manifested by bright, rough, ridges, scarps, crenulated blocks, or aligned massifs, mostly appears within 30 degrees of the equator. This landscape was intensely eroded by fluvial processes as evidenced by numerous valley systems, fan-like depositional features and regularly-spaced ridges (crenulated terrain). Much of this bedrock landscape, however, is mantled by dunes, suggesting that fluvial erosion no longer dominates in equatorial regions. High midlatitude regions on Titan exhibit dissected sedimentary plains at a number of localities, suggesting deposition (perhaps by sediment eroded from equatorial regions) followed by erosion. The polar regions are mainly dominated by deposits of fluvial and lacustrine sediment. Fluvial processes are active in polar areas as evidenced by alkane lakes and occasional cloud cover.

  18. The Sahara's Diverse Landscape

    NASA Technical Reports Server (NTRS)

    2002-01-01

    Vast stretches of uninterrupted sand are only one kind of Saharan landscape. This true-color MODIS image from November 9, 2001, reveals a diversity of land surface features, including ancient lava flows and volcanoes. Beginning at upper left and moving clockwise are the countries of Algeria, Tunisia, Libya, Chad, and Niger. Evidence of previous volcanic activity in the Sahara can be found in northeastern Chad, in particular, in a region known as Tibesti. Reaching up out of the surrounding desert, the dark rock of the Tibesti Plateau stands out in dark brown against the sand. Scattered throughout the region are the circular cones and calderas of several volcanoes. The dark remains of a lava flow mark the location of the Tousside volcano. North of Tibesti, in Libya, more dark-colored lava beds leave their mark on the landscape. Variety exists in Algeria, where the Grand Erg Oriental desert (far upper left) is hemmed in to the south by the Tinrhert Plateau. South of the Plateau, desert resumes briefly, only to give way to a mountainous region traced with impermanent rivers. In northern Niger, a sinuous gray-green line marks the edge of an escarpment that separates the Mangueni Plateau to the north from the rock deserts to the south. Image courtesy Jacques Descloitres, MODIS Land Rapid Response Team at NASA GSFC

  19. Intrinsically Disordered Energy Landscapes

    PubMed Central

    Chebaro, Yassmine; Ballard, Andrew J.; Chakraborty, Debayan; Wales, David J.

    2015-01-01

    Analysis of an intrinsically disordered protein (IDP) reveals an underlying multifunnel structure for the energy landscape. We suggest that such ‘intrinsically disordered’ landscapes, with a number of very different competing low-energy structures, are likely to characterise IDPs, and provide a useful way to address their properties. In particular, IDPs are present in many cellular protein interaction networks, and several questions arise regarding how they bind to partners. Are conformations resembling the bound structure selected for binding, or does further folding occur on binding the partner in a induced-fit fashion? We focus on the p53 upregulated modulator of apoptosis (PUMA) protein, which adopts an -helical conformation when bound to its partner, and is involved in the activation of apoptosis. Recent experimental evidence shows that folding is not necessary for binding, and supports an induced-fit mechanism. Using a variety of computational approaches we deduce the molecular mechanism behind the instability of the PUMA peptide as a helix in isolation. We find significant barriers between partially folded states and the helix. Our results show that the favoured conformations are molten-globule like, stabilised by charged and hydrophobic contacts, with structures resembling the bound state relatively unpopulated in equilibrium. PMID:25999294

  20. Landscapes Impacted by Light

    NASA Astrophysics Data System (ADS)

    Arellano, B.; Roca, J.

    2016-06-01

    The gradual spread of urbanization, the phenomenon known under the term urban sprawl, has become one of the paradigms that have characterized the urban development since the second half of the twentieth century and early twenty-first century. However, there is no unanimous consensus about what means "urbanization". The plurality of forms of human settlement on the planet difficult to identify the urbanization processes. The arrival of electrification to nearly every corner of the planet is certainly the first and more meaningful indicator of artificialization of land. In this sense, the paper proposes a new methodology based on the analysis of the satellite image of nighttime lights designed to identify the highly impacted landscapes worldwide and to build an index of Land Impacted by Light per capita (LILpc) as an indicator of the level of urbanization. The used methodology allows the identification of different typologies of urbanized areas (villages, cities or metropolitan areas), as well as "rural", "rurban", "periurban" and "central" landscapes. The study identifies 186,134 illuminated contours (urbanized areas). In one hand, 404 of these contours could be consider as real "metropolitan areas"; and in the other hand, there are 161,821 contours with less than 5,000 inhabitants, which could be identify as "villages". Finally, the paper shows that 44.5 % live in rural areas, 15.5 % in rurban spaces, 26.2 % in suburban areas and only 18.4 % in central areas.

  1. Intrinsically disordered energy landscapes.

    PubMed

    Chebaro, Yassmine; Ballard, Andrew J; Chakraborty, Debayan; Wales, David J

    2015-01-01

    Analysis of an intrinsically disordered protein (IDP) reveals an underlying multifunnel structure for the energy landscape. We suggest that such 'intrinsically disordered' landscapes, with a number of very different competing low-energy structures, are likely to characterise IDPs, and provide a useful way to address their properties. In particular, IDPs are present in many cellular protein interaction networks, and several questions arise regarding how they bind to partners. Are conformations resembling the bound structure selected for binding, or does further folding occur on binding the partner in a induced-fit fashion? We focus on the p53 upregulated modulator of apoptosis (PUMA) protein, which adopts an α-helical conformation when bound to its partner, and is involved in the activation of apoptosis. Recent experimental evidence shows that folding is not necessary for binding, and supports an induced-fit mechanism. Using a variety of computational approaches we deduce the molecular mechanism behind the instability of the PUMA peptide as a helix in isolation. We find significant barriers between partially folded states and the helix. Our results show that the favoured conformations are molten-globule like, stabilised by charged and hydrophobic contacts, with structures resembling the bound state relatively unpopulated in equilibrium. PMID:25999294

  2. Norwegian millstone quarry landscapes

    NASA Astrophysics Data System (ADS)

    Heldal, Tom; Meyer, Gurli; Grenne, Tor

    2013-04-01

    Rotary querns and millstones were used in Norway since just after the Roman Period until the last millstone was made in the 1930s. Throughout all this time millstone mining was fundamental for daily life: millstones were needed to grind grain, our most important food source. We can find millstone quarries in many places in the country from coast to mountain. Some of them cover many square kilometers and count hundreds of quarries as physical testimonies of a long and great production history. Other quarries are small and hardly visible. Some of this history is known through written and oral tradition, but most of it is hidden and must be reconstructed from the traces we can find in the landscape today. The Millstone project has put these quarry landscapes on the map, and conducted a range of case studies, including characterization of archaeological features connected to the quarrying, interpretation of quarrying techniques and evolution of such and establishing distribution and trade patterns by the aid of geological provenance. The project also turned out to be a successful cooperation between different disciplines, in particular geology and archaeology.

  3. Genomic Classification of Cutaneous Melanoma.

    PubMed

    2015-06-18

    We describe the landscape of genomic alterations in cutaneous melanomas through DNA, RNA, and protein-based analysis of 333 primary and/or metastatic melanomas from 331 patients. We establish a framework for genomic classification into one of four subtypes based on the pattern of the most prevalent significantly mutated genes: mutant BRAF, mutant RAS, mutant NF1, and Triple-WT (wild-type). Integrative analysis reveals enrichment of KIT mutations and focal amplifications and complex structural rearrangements as a feature of the Triple-WT subtype. We found no significant outcome correlation with genomic classification, but samples assigned a transcriptomic subclass enriched for immune gene expression associated with lymphocyte infiltrate on pathology review and high LCK protein expression, a T cell marker, were associated with improved patient survival. This clinicopathological and multi-dimensional analysis suggests that the prognosis of melanoma patients with regional metastases is influenced by tumor stroma immunobiology, offering insights to further personalize therapeutic decision-making. PMID:26091043

  4. Complex Landscape Terms in Seri

    ERIC Educational Resources Information Center

    O'Meara, Carolyn; Bohnemeyer, Jurgen

    2008-01-01

    The nominal lexicon of Seri is characterized by a prevalence of analytical descriptive terms. We explore the consequences of this typological trait in the landscape domain. The complex landscape terms of Seri classify geographic entities in terms of their material make-up and spatial properties such as shape, orientation, and merological…

  5. Fantasy Landscapes with a Message

    ERIC Educational Resources Information Center

    D'Amico, Elizabeth

    2005-01-01

    The author of this article describes using a Fantasy Landscapes lesson to get students expressing environmental issues through art. The Fantasy Landscapes lesson is an exploration of art elements and design principles through visual problem solving that links ideas, language, and theory to art. To get students thinking specifically about…

  6. Landscape Solutions to School Problems.

    ERIC Educational Resources Information Center

    Spitz, Katherine

    2002-01-01

    Discusses key lessons in school landscape design. Landscapes should: (1) include trees and plants that themselves provide hands-on teaching opportunities; (2) enhance health and safety in a number of ways while performing their other functions; (3) be sensitively designed relative to location to cut energy costs; and (4) be aesthetic as well as…

  7. Landscape in a Lacquer Box

    ERIC Educational Resources Information Center

    Savage, Martha

    2010-01-01

    A symbolic dry landscape garden of Eastern origin holds a special fascination for the author's middle-school students, which is why the author chose to create a project exploring this view of nature. A dry landscape garden, or "karesansui," is an arrangement of rocks, worn by nature and surrounded by a "sea" of sand, raked into patterns…

  8. Reading the Landscape--Geologically.

    ERIC Educational Resources Information Center

    Melvin, Ruth

    1982-01-01

    Although the landscape may be examined without background information, one's appreciation increases by using resources to interpret changing landscapes. Many geologic maps and road guides have been published for this purpose. The use of one such guide is described and sources of specific guides and maps are included. (Author/JN)

  9. Landscaping With Maintenance in Mind.

    ERIC Educational Resources Information Center

    Sorensen, Randy

    2000-01-01

    Examines school ground landscape design that enhances attractive of the school and provides for easier maintenance. Landscape design issues discussed include choice of grass, trees, and shrubs; irrigation; and safety and access. Other considerations for lessening maintenance problems for facility managers are also highlighted. (GR)

  10. On the Usefulness of Hydrologic Landscapes for Hydrologic Modeling and Water Management

    EPA Science Inventory

    Hydrologic Landscapes (HLs) are units that can be used in aggregate to describe the watershed-scale hydrologic response of an area through use of physical and climatic properties. The HL assessment unit is a useful classification tool to relate and transfer hydrologically meaning...

  11. On the Usefulness of Hydrologic Landscapes on Hydrologic Model calibration and Selection

    EPA Science Inventory

    Hydrologic Landscapes (HLs) are units that can be used in aggregate to describe the watershed-scale hydrologic response of an area through use of physical and climatic properties. The HL assessment unit is a useful classification tool to relate and transfer hydrologically meaning...

  12. Management and development of land in the name of the Green Economy: planning, landscape, efficiency, biodiversity

    NASA Astrophysics Data System (ADS)

    Benvenuti, Paolo

    2016-04-01

    preservation of landscapes; • relationship between the wine areas with the territory and its infrastructure; • participation in the process of territorial planning with operators and administrations; • relationship between wine and landscape, adaptation to climatic deterioration, renewable energy sources and energy efficiency; • new skills and new forms to manage the vineyard Depth knowledge of the characteristics of the territory wine passes through: • wine zoning, i.e. the identification of the more suitable terroirs for a wine: study of the climate, the soil, the vines of the interactions with the environment (Chart of vocations agroforestry); • soil classification; • analysis of the ecosystems (flora and fauna, biodiversity, forest, grasslands, crops); • identification of landscapes, from the analysis of types, morphology of the urban and rural landscape, processes of contextualization, etc. (Chart of landscape values) . The results achieved so far by the Italian municipalities that have adopted them, will soon be enhanced by enlargement of the methodological lines to new issues such as accessibility in wine territory, strengthening of local participation and the presence, promotion of wine as an integral part the local food planning, development of planning practices in the process of institutional reform.

  13. Fitness Landscapes of Functional RNAs.

    PubMed

    Kun, Ádám; Szathmáry, Eörs

    2015-01-01

    The notion of fitness landscapes, a map between genotype and fitness, was proposed more than 80 years ago. For most of this time data was only available for a few alleles, and thus we had only a restricted view of the whole fitness landscape. Recently, advances in genetics and molecular biology allow a more detailed view of them. Here we review experimental and theoretical studies of fitness landscapes of functional RNAs, especially aptamers and ribozymes. We find that RNA structures can be divided into critical structures, connecting structures, neutral structures and forbidden structures. Such characterisation, coupled with theoretical sequence-to-structure predictions, allows us to construct the whole fitness landscape. Fitness landscapes then can be used to study evolution, and in our case the development of the RNA world. PMID:26308059

  14. The new landscape for nonprofits.

    PubMed

    Ryan, W P

    1999-01-01

    For most of this century, society's caring functions have been the work of government and charities. But social services in the United States are in a period of transition. Today the U.S. government no longer considers nonprofits to be entitled--or even best qualified--to provide social services. Profit-seeking companies like Lockheed Martin are now winning contracts for such services. William Ryan describes how government outsourcing and a new business mind-set have changed the landscape of social services. The change raises fundamental questions about the mission and future of nonprofits. Ryan attributes the growth of for-profits in the social service industry to four factors: size, capital, mobility, and responsiveness. While those attributes give for-profits an advantage in acquiring new contracts, nonprofits have not yet lost their foothold. Ryan cites examples of organizations like the YWCA and Abraxas to demonstrate various ways that nonprofits are responding--from subcontracting to partnership to outright conversion to for-profit status. By playing in the new marketplace, nonprofits will be forced to reconfigure their operations and organizations in ways that could compromise their missions. Because nonprofits now find themselves sharing territory with for-profits, sometimes as collaborators and sometimes as competitors, the distinctions between these organizations will continue to blur. The point, Ryan argues, is not whether nonprofits can survive opposition from for-profits. Many have already adjusted to the new competitive environment. The real issue is whether nonprofits can adapt without compromising the qualities that distinguish them from for-profit organizations. PMID:10345388

  15. Landscape structure affects specialists but not generalists in naturally fragmented grasslands

    USGS Publications Warehouse

    Miller, Jesse E.D.; Damschen, Ellen Ingman; Harrison, Susan P.; Grace, James B.

    2015-01-01

    Understanding how biotic communities respond to landscape spatial structure is critically important for conservation management as natural landscapes become increasingly fragmented. However, empirical studies of the effects of spatial structure on plant species richness have found inconsistent results, suggesting that more comprehensive approaches are needed. In this study, we asked how landscape structure affects total plant species richness and the richness of a guild of specialized plants in a multivariate context. We sampled herbaceous plant communities at 56 dolomite glades (insular, fire-adapted grasslands) across the Missouri Ozarks, and used structural equation modeling (SEM) to analyze the relative importance of landscape structure, soil resource availability, and fire history for plant communities. We found that landscape spatial structure-defined as the area-weighted proximity of glade habitat surrounding study sites (proximity index)-had a significant effect on total plant species richness, but only after we controlled for environmental covariates. Richness of specialist species, but not generalists, was positively related to landscape spatial structure. Our results highlight that local environmental filters must be considered to understand the influence of landscape structure on communities, and that unique species guilds may respond differently to landscape structure than the community as a whole. These findings suggest that both local environment and landscape context should be considered when developing management strategies for species of conservation concern in fragmented habitats.

  16. Issues in using landscape indicators to assess land changes

    SciTech Connect

    Kline, Keith L; Dale, Virginia H

    2012-01-01

    Landscape indicators, when combined with information about environmental conditions (such as habitat potential, biodiversity, carbon and nutrient cycling, and erosion) and socioeconomic forces, can provide insights about changing ecosystem services. They also provide information about opportunities for improving natural resources management. Landscape indicators rely on data regarding land cover, land management and land functionality. Challenges in using landscape indicators to assess change and effects include (1) measures of land management and attributes that are reliable, robust and consistent for all areas on the Earth do not exist, and thus land cover is more frequently utilized; (2) multiple types of land cover and management are often found within a single landscape and are constantly changing, which complicates measurement and interpretation; and (3) while causal analysis is essential for understanding and interpreting changes in indicator values, the interactions among multiple causes and effects over time make accurate attribution among many drivers of change particularly difficult. Because of the complexity, sheer number of variables, and limitations of empirical data on land changes, models are often used to illustrate and estimate values for landscape indicators, and those models have several problems. Recommendations to improve our ability to assess the effects of changes in land management include refinement of questions to be more consistent with available information and the development of data sets based on systematic measurement over time of spatially explicit land qualities such as carbon and nutrient stocks, water and soil quality, net primary productivity, habitat and biodiversity. Well-defined and consistent land-classification systems that are capable of tracking changes in these and other qualities that matter to society need to be developed and deployed. Because landscapes are so dynamic, it is crucial to develop ways for the scientific

  17. Buildings Interoperability Landscape

    SciTech Connect

    Hardin, Dave; Stephan, Eric G.; Wang, Weimin; Corbin, Charles D.; Widergren, Steven E.

    2015-12-31

    Through its Building Technologies Office (BTO), the United States Department of Energy’s Office of Energy Efficiency and Renewable Energy (DOE-EERE) is sponsoring an effort to advance interoperability for the integration of intelligent buildings equipment and automation systems, understanding the importance of integration frameworks and product ecosystems to this cause. This is important to BTO’s mission to enhance energy efficiency and save energy for economic and environmental purposes. For connected buildings ecosystems of products and services from various manufacturers to flourish, the ICT aspects of the equipment need to integrate and operate simply and reliably. Within the concepts of interoperability lie the specification, development, and certification of equipment with standards-based interfaces that connect and work. Beyond this, a healthy community of stakeholders that contribute to and use interoperability work products must be developed. On May 1, 2014, the DOE convened a technical meeting to take stock of the current state of interoperability of connected equipment and systems in buildings. Several insights from that meeting helped facilitate a draft description of the landscape of interoperability for connected buildings, which focuses mainly on small and medium commercial buildings. This document revises the February 2015 landscape document to address reviewer comments, incorporate important insights from the Buildings Interoperability Vision technical meeting, and capture thoughts from that meeting about the topics to be addressed in a buildings interoperability vision. In particular, greater attention is paid to the state of information modeling in buildings and the great potential for near-term benefits in this area from progress and community alignment.

  18. New Classification of Headache

    PubMed Central

    Gawel, Marek J.

    1992-01-01

    The Headache Classification Committee of the International Headache Society has developed a new classification system for headache, cranial neuralgia, and facial pain. The value of the classification for the practising clinician is that it forces him or her to take a more careful history in order to determine the nature of the headache. This article reviews the classification system and gives examples of case histories and subsequent diagnoses. PMID:21221276

  19. Spatial Variability in Wheat Phenology Across a Landscape

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The World Meteorological Organization is publishing their 3rd edition of Guide to Agricultural Meteorological Practices. This chapter focuses on global wheat production practices and the role and management of agrometeorological factors. The sections include 1) classification of wheat, 2) adaptation...

  20. Classification of articulators.

    PubMed

    Rihani, A

    1980-03-01

    A simple classification in familiar terms with definite, clear characteristics can be adopted. This classification system is based on the number of records used and the adjustments necessary for the articulator to accept these records. The classification divides the articulators into nonadjustable, semiadjustable, and fully adjustable articulators (Table I). PMID:6928204

  1. Government Classification: An Overview.

    ERIC Educational Resources Information Center

    Brown, Karen M.

    Classification of government documents (confidential, secret, top secret) is a system used by the executive branch to, in part, protect national security and foreign policy interests. The systematic use of classification markings with precise definitions was established during World War I, and since 1936 major changes in classification have…

  2. Landscape characterization and biodiversity research

    SciTech Connect

    Dale, V.H.; Offerman, H.; Frohn, R.; Gardner, R.H.

    1995-03-01

    Rapid deforestation often produces landscape-level changes in forest characteristics and structure, including area, distribution, and forest habitat types. Changes in landscape pattern through fragmentation or aggregation of natural habitats can alter patterns of abundance for single species and entire communities. Examples of single-species effects include increased predation along the forest edge, the decline in the number of species with poor dispersal mechanisms, and the spread of exotic species that have deleterious effects (e.g., gypsy moth). A decrease in the size and number of natural habitat patches increases the probability of local extirpation and loss of diversity of native species, whereas a decline in connectivity between habitat patches can negatively affect species persistence. Thus, there is empirical justification for managing entire landscapes, not just individual habitat types, in order to insure that native plant and animal diversity is maintained. A landscape is defined as an area composed of a mosaic of interacting ecosystems, or patches, with the heterogeneity among the patches significantly affecting biotic and abiotic processes in the landscape. Patches comprising a landscape are usually composed of discrete areas of relatively homogeneous environmental conditions and must be defined in terms of the organisms of interest. A large body of theoretical work in landscape ecology has provided a wealth of methods for quantifying spatial characteristics of landscapes. Recent advances in remote sensing and geographic information systems allow these methods to be applied over large areas. The objectives of this paper are to present a brief overview of common measures of landscape characteristics, to explore the new technology available for their calculation, to provide examples of their application, and to call attention to the need for collection of spatially-explicit field data.

  3. Classification: Something to Think About.

    ERIC Educational Resources Information Center

    Isenberg, Joan P.; Jacobs, Judith E.

    1981-01-01

    Advocates the use of classification activities in the elementary school curriculum as a means of developing thinking skills in children. Critical preclassification skills, classification activities (including simple and multiple classification), and classification tasks and materials are discussed. (Author/RH)

  4. How soil shapes the landscape

    NASA Astrophysics Data System (ADS)

    Minasny, Budiman; Finke, Peter; Vanwalleghem, Tom Tom; Stockmann, Uta; McBratney, Alex

    2014-05-01

    There has been an increase in interest in quantitative modelling of soil genesis, which can provide prediction of environmental changes through numerical models. Modelling soil formation is a difficult task because soil itself is highly complex with interactions between water, inorganic materials and organic matter. This paper will provide a review on the research efforts of modelling soil genesis, their connection with landscape models and the inexorable genesis of the IUSS soil landscape modelling working group. Quantitative modelling soil formation using mechanistic models have begun in the 1980s such as the 'soil deficit' model by Kirkby (1985), Hoosbeek & Bryant's pedodynamic model (1992), and recently the SoilGen model by Finke (2008). These profile models considered the chemical reactions and physical processes in the soil at the horizon and pedon scale. The SoilGen model is an integration of sub-models, such as water and solute movement, heat transport, soil organic matter decomposition, mineral dissolution, ion exchange, adsorption, speciation, complexation and precipitation. The model can calculate with detail the chemical changes and materials fluxes in a profile and has been successfully applied. While they can simulate soil profile development in detail, there is still a gap how the processes act in the landscape. Meanwhile research in landscape formation in geomorphology is progressing steadily over time, slope development models model have been developed since 1970s (Ahnert, 1977). Soil was also introduced in a landscape, however soil processes are mainly modelled through weathering and transport processes (Minasny & McBratney 1999, 2001). Recently, Vanwalleghem et al. (2013) are able to combine selected physical, chemical and biological processes to simulate a full 3-D soil genesis in the landscape. Now there are research gaps between the 2 approaches: the landscape modellers increasingly recognise the importance of soil and need more detailed soil

  5. Classification of childhood psychopathology: a developmental perspective.

    PubMed

    Garber, J

    1984-02-01

    The absence of a comprehensive, objective, and reliable system for classifying emotional and behavior problems in children has slowed the advancement of knowledge in the field of developmental psychopathology. This paper provides a developmental framework for the classification of psychopathology in children and highlights the potential contributions that such classification may have toward the understanding of normal development. The salient issues derived from this developmental perspective are concerned with the continuity between childhood and adult psychopathology, and the definition of normality and adaptation in the context of development. The implications of the continuity issue for classification are that (a) the validity of adult criteria for use with children should be explored further, (b) the diagnosis of a childhood disorder at 1 point in time is not necessarily dependent upon there being episodes of the disorder at a later time, and (c) the focus of classification should not be limited to isolated behaviors and traits, but rather should emphasize patterns of adaptation. Moreover, it is suggested that judgments about normality and dysfunction should be made relative to what is expected given the child's age, sex, environmental context, developmental task, level of functioning, and phase in the progression through development. This paper provides a conceptual framework that will facilitate the construction of a more developmentally relevant system of classification. PMID:6705631

  6. Evolutionary Accessibility of Modular Fitness Landscapes

    NASA Astrophysics Data System (ADS)

    Schmiegelt, B.; Krug, J.

    2013-10-01

    A fitness landscape is a mapping from the space of genetic sequences, which is modeled here as a binary hypercube of dimension L, to the real numbers. We consider random models of fitness landscapes, where fitness values are assigned according to some probabilistic rule, and study the statistical properties of pathways to the global fitness maximum along which fitness increases monotonically. Such paths are important for evolution because they are the only ones that are accessible to an adapting population when mutations occur at a low rate. The focus of this work is on the block model introduced by A.S. Perelson and C.A. Macken (Proc. Natl. Acad. Sci. USA 92:9657, 1995) where the genome is decomposed into disjoint sets of loci (`modules') that contribute independently to fitness, and fitness values within blocks are assigned at random. We show that the number of accessible paths can be written as a product of the path numbers within the blocks, which provides a detailed analytic description of the path statistics. The block model can be viewed as a special case of Kauffman's NK-model, and we compare the analytic results to simulations of the NK-model with different genetic architectures. We find that the mean number of accessible paths in the different versions of the model are quite similar, but the distribution of the path number is qualitatively different in the block model due to its multiplicative structure. A similar statement applies to the number of local fitness maxima in the NK-models, which has been studied extensively in previous works. The overall evolutionary accessibility of the landscape, as quantified by the probability to find at least one accessible path to the global maximum, is dramatically lowered by the modular structure.

  7. Connecting Brabant's cover sand landscapes through landscape history

    NASA Astrophysics Data System (ADS)

    Heskes, Erik; van den Ancker, Hanneke; Jungerius, Pieter Dirk; Harthoorn, Jaap; Maes, Bert; Leenders, Karel; de Jongh, Piet; Kluiving, Sjoerd; van den Oetelaar, Ger

    2015-04-01

    Noord-Brabant has the largest variety of cover sand landscapes in The Netherlands, and probably in Western Europe. During the Last Ice Age the area was not covered by land ice and a polar desert developed in which sand dunes buried the existing river landscapes. Some of these polar dune landscapes experienced a geomorphological and soil development that remained virtually untouched up to the present day, such as the low parabolic dunes of the Strabrechtse Heide or the later and higher dunes of the Oisterwijkse Vennen. As Noord-Brabant lies on the fringe of a tectonic basin, the thickness of cover sand deposits in the Centrale Slenk, part of a rift through Europe, amounts up to 20 metres. Cover sand deposits along the fault lines cause the special phenomenon of 'wijst' to develop, in which the higher grounds are wetter than the boarding lower grounds. Since 4000 BC humans settled in these cover sand landscapes and made use of its small-scale variety. An example are the prehistoric finds on the flanks and the historic towns on top of the 'donken' in northwest Noord-Brabant, where the cover sand landscapes are buried by river and marine deposits and only the peaks of the dunes protrude as donken. Or the church of Handel that is built beside a 'wijst' source and a site of pilgrimage since living memory. Or the 'essen' and plaggen agriculture that developed along the stream valleys of Noord-Brabant from 1300 AD onwards, giving rise to geomorphological features as 'randwallen' and plaggen soils of more than a metre thickness. Each region of Brabant each has its own approach in attracting tourists and has not yet used this common landscape history to connect, manage and promote their territories. We propose a landscape-historical approach to develop a national or European Geopark Brabants' cover sand landscapes, in which each region focuses on a specific part of the landscape history of Brabant, that stretches from the Late Weichselian polar desert when the dune

  8. RS- and GIS-based study on landscape pattern change in the Poyang Lake wetland area, China

    NASA Astrophysics Data System (ADS)

    Chen, Xiaoling; Li, Hui; Bao, Shuming; Wu, Zhongyi; Fu, Weijuan; Cai, Xiaobin; Zhao, Hongmei; Guo, Peng

    2006-10-01

    As wetland has been recognized as an important component of ecosystem, it is received ever-increasing attention worldwide. Poyang Lake wetlands, the international wetlands and the largest bird habitat in Asia, play an important role in biodiversity and ecologic protection. However, with the rapid economic growth and urbanization, landscape patterns in the wetlands have dramatically changed in the past three decades. To better understand the wetland landscape dynamics, remote sensing, geographic information system technologies, and the FRAGSTATS landscape analysis program were used to measure landscape patterns. Statistical approach was employed to illustrate the driving forces. In this study, Landsat images (TM and ETM+) from 1989 and 2000 were acquired for the wetland area. The landscapes in the wetland area were classified as agricultural land, urban, wetland, forest, grassland, unused land, and water body using a combination of supervised and unsupervised classification techniques integrated with Digital Elevation Model (DEM). Landscape indices, which are popular for the quantitative analysis of landscape pattern, were then employed to analyze the landscape pattern changes between the two dates in a GIS. From this analysis an understanding of the spatial-temporal patterns of landscape evolution was generated. The results show that wetland area was reduced while fragmentation was increased over the study period. Further investigation was made to examine the relationship between landscape metrics and some other parameters such as urbanization to address the driving forces for those changes. The urban was chosen as center to conduct buffer analysis in a GIS to study the impact of human-induced activities on landscape pattern dynamics. It was found that the selected parameters were significantly correlated with the landscape metrics, which may well indicate the impact of human-induced activities on the wetland landscape pattern dynamics and account for the driving

  9. Mapping resource use over a Russian landscape: an integrated look at harvesting of a non-timber forest product in central Kamchatka

    NASA Astrophysics Data System (ADS)

    Hitztaler, Stephanie K.; Bergen, Kathleen M.

    2013-12-01

    Small-scale resource use became an important adaptive mechanism in remote logging communities in Russia at the onset of the post-Soviet period in 1991. We focused on harvesting of a non-timber forest product, lingonberry (Vaccinium vitis-idaea), in the forests of the Kamchatka Peninsula (Russian Far East). We employed an integrated geographical approach to make quantifiable connections between harvesting and the landscape, and to interpret these relationships in their broader contexts. Landsat TM images were used for a new classification; the resulting land-cover map was the basis for linking non-spatial data on harvesters’ gathering behaviors to spatial data within delineated lingonberry gathering sites. Several significant relationships emerged: (1) mature forests negatively affected harvesters’ initial choice to gather in a site, while young forests had a positive effect; (2) land-cover type was critical in determining how and why gathering occurred: post-disturbance young and maturing forests were significantly associated with higher gathering intensity and with the choice to market harvests; and (3) distance from gathering sites to villages and main roads also mattered: longer distances were significantly correlated to more time spent gathering and to increased marketing of harvests. We further considered our findings in light of the larger ecological and social dynamics at play in central Kamchatka. This unique study is an important starting point for conservation- and sustainable development-based work, and for additional research into the drivers of human-landscape interactions in the Russian Far East.

  10. Investigations in adaptive processing of multispectral data

    NASA Technical Reports Server (NTRS)

    Kriegler, F. J.; Horwitz, H. M.

    1973-01-01

    Adaptive data processing procedures are applied to the problem of classifying objects in a scene scanned by multispectral sensor. These procedures show a performance improvement over standard nonadaptive techniques. Some sources of error in classification are identified and those correctable by adaptive processing are discussed. Experiments in adaptation of signature means by decision-directed methods are described. Some of these methods assume correlation between the trajectories of different signature means; for others this assumption is not made.

  11. Comparison of Support Vector Machine, Neural Network, and CART Algorithms for the Land-Cover Classification Using Limited Training Data Points

    EPA Science Inventory

    Support vector machine (SVM) was applied for land-cover characterization using MODIS time-series data. Classification performance was examined with respect to training sample size, sample variability, and landscape homogeneity (purity). The results were compared to two convention...

  12. Modeling Soil-Landscape Relations in the Sonoran Desert, Arizona, USA

    NASA Astrophysics Data System (ADS)

    Regmi, N. R.; Rasmussen, C.

    2015-12-01

    Digital soil mapping (DSM) techniques that integrate remotely sensed surface topography and reflectance, and map soil-landscape associations have the potential in improve understanding of critical zone evolution and landscape processes. The goal of this study was to understand the soil-geomorphic evolution of Quaternary alluvial and eolian deposits in the Sonoran Desert using a data-driven DSM technique and mapping of soil-landscape relationships. An iterative principal component analysis (iPCA) data reduction routine was developed and implemented for a set of LiDAR elevation- and Landsat ETM+-derived environmental covariates that characterize soil-landscape variability. Principal components that explain more than 95% of the soil-landscape variability were then integrated and classified based on an ISODATA (Iterative Self-Organizing Data) unsupervised technique. The classified map was then segmented based on a region growing algorithm and multi-scale maps of soil-landscape relations were developed, which then compared with maps of major arid-region landforms that can be identified on aerial photographs and satellite images by their distinguishing tone and texture, and in the field by their distinguishing surface and sub-surface soil physical, chemical and biological properties. The approach identified and mapped the soil-landscape variability of alluvial and eolian landscapes, and illustrated the applicability of coupling covariate selection and integration by iPCA, ISODATA classifications of integrated layers, and image segmentation for effective spatial prediction of soil-landscape characteristics. The approach developed here is data-driven, cost- and time-effective, applicable for multi-scale mapping, allows incorporation of wide variety of covariates, and provides accurate quantitative prediction of wide range of soil-landscape attributes that are necessary for hydrologic models, land and ecosystem management decisions, and hazard assessment.

  13. Adaptive Management

    EPA Science Inventory

    Adaptive management is an approach to natural resource management that emphasizes learning through management where knowledge is incomplete, and when, despite inherent uncertainty, managers and policymakers must act. Unlike a traditional trial and error approach, adaptive managem...

  14. Spatiotemporal microbial evolution on antibiotic landscapes.

    PubMed

    Baym, Michael; Lieberman, Tami D; Kelsic, Eric D; Chait, Remy; Gross, Rotem; Yelin, Idan; Kishony, Roy

    2016-09-01

    A key aspect of bacterial survival is the ability to evolve while migrating across spatially varying environmental challenges. Laboratory experiments, however, often study evolution in well-mixed systems. Here, we introduce an experimental device, the microbial evolution and growth arena (MEGA)-plate, in which bacteria spread and evolved on a large antibiotic landscape (120 × 60 centimeters) that allowed visual observation of mutation and selection in a migrating bacterial front. While resistance increased consistently, multiple coexisting lineages diversified both phenotypically and genotypically. Analyzing mutants at and behind the propagating front, we found that evolution is not always led by the most resistant mutants; highly resistant mutants may be trapped behind more sensitive lineages. The MEGA-plate provides a versatile platform for studying microbial adaption and directly visualizing evolutionary dynamics. PMID:27609891

  15. Adaptive genetic variation and population differences

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Humans are physiologically and morphologically diverse. Such diversities have been shaped by demographic history and adaptation to local environments, including regional climate, landscape, food source, culture, and pathogens since their expansion within and out of Africa between 50,000 and 100,000 ...

  16. Studying Landforms through Landscape Painting.

    ERIC Educational Resources Information Center

    Glenn, William H.

    1981-01-01

    Using three specific works of art, the author demonstrates how a study of selected landscape paintings can be integrated into units on landforms in secondary school earth science and general science courses. (Author/SJL)

  17. Accidental inflation in the landscape

    SciTech Connect

    Blanco-Pillado, Jose J.; Metallinos, Konstantinos; Gomez-Reino, Marta E-mail: marta.gomez-reino.perez@cern.ch

    2013-02-01

    We study some aspects of fine tuning in inflationary scenarios within string theory flux compactifications and, in particular, in models of accidental inflation. We investigate the possibility that the apparent fine-tuning of the low energy parameters of the theory needed to have inflation can be generically obtained by scanning the values of the fluxes over the landscape. Furthermore, we find that the existence of a landscape of eternal inflation in this model provides us with a natural theory of initial conditions for the inflationary period in our vacuum. We demonstrate how these two effects work in a small corner of the landscape associated with the complex structure of the Calabi-Yau manifold P{sup 4}{sub [1,1,1,6,9]} by numerically investigating the flux vacua of a reduced moduli space. This allows us to obtain the distribution of observable parameters for inflation in this mini-landscape directly from the fluxes.

  18. Economic Growth and Landscape Change

    USGS Publications Warehouse

    Prato, Tony; Fagre, Dan

    2007-01-01

    Sustaining Rocky Mountain Landscapes provides a scientific basis for communities to develop policies for managing the growth and economic transformation of the CCE without sacrificing the quality of life and environment for which the land is renowned. This forthcoming edited volume focuses on five aspects of sustaining mountain landscapes in the CCE and similar regions in the Rocky Mountains. The five aspects are: 1) how social, economic, demographic and environmental forces are transforming ecosystem structure and function, 2) trends in use and conditions for human and environmental resources, 3) activating science, policy and education to enhance sustainable landscape management, 4) challenges to sustainable management of public and private lands, and 5) future prospects for achieving sustainable landscapes.

  19. Managing for Climate Change in Western Forest Ecosystems; The Role of Refugia in Adaptation Strategies (Invited)

    NASA Astrophysics Data System (ADS)

    Millar, C. I.; Morelli, T.

    2009-12-01

    Managing forested ecosystems in western North America for adaptation to climate change involves options that depend on resource objectives, landscape conditions, sensitivity to change, and social desires. Strategies range from preserving species and ecosystems in the face of change (resisting change); managing for resilience to change; realigning ecosystems that have been severely altered so that they can adapt successfully; and enabling species to respond to climate changes. We are exploring one extreme in this range of strategies, that is, to manage locations, species, communities, or ecosystems as refugia. This concept is familiar from the Quaternary literature as isolated locations where climates remained warm during cold glacial intervals and wherein species contracted and persisted in small populations. References to refugia have been made in the climate-adaptation literature but little elaborated, and applications have not been described. We are addressing this gap conceptually and in case-studies from national forest and national park environments in California. Using a classification of refugium categories, we extend the concept beyond the original use to include diverse locations and conditions where plant or animal species, or ecosystems of concern, would persist during future changing climatic backgrounds. These locations may be determined as refugial for reasons of local microclimate, substrate, elevation, topographic context, paleohistory, species ecology, or management capacity. Recognizing that species and ecosystems respond to climate change differently, refugium strategies are appropriate in some situations and not others. We describe favorable conditions for using refugium strategies and elaborate specific approaches in Sierra Nevada case studies.

  20. Adaptive compression of image data

    NASA Astrophysics Data System (ADS)

    Hludov, Sergei; Schroeter, Claus; Meinel, Christoph

    1998-09-01

    In this paper we will introduce a method of analyzing images, a criterium to differentiate between images, a compression method of medical images in digital form based on the classification of the image bit plane and finally an algorithm for adaptive image compression. The analysis of the image content is based on a valuation of the relative number and absolute values of the wavelet coefficients. A comparison between the original image and the decoded image will be done by a difference criteria calculated by the wavelet coefficients of the original image and the decoded image of the first and second iteration step of the wavelet transformation. This adaptive image compression algorithm is based on a classification of digital images into three classes and followed by the compression of the image by a suitable compression algorithm. Furthermore we will show that applying these classification rules on DICOM-images is a very effective method to do adaptive compression. The image classification algorithm and the image compression algorithms have been implemented in JAVA.

  1. Adaptive Transfer Function Networks

    SciTech Connect

    Goulding, J.R. |

    1993-06-01

    Real-time pattern classification and time-series forecasting applications continue to drive artificial neural network (ANN) technology. As ANNs increase in complexity, the throughput of digital computer simulations decreases. A novel ANN, the Adaptive Transfer Function Network (ATF-Net), directly addresses the issue of throughput. ATF-Nets are global mapping equations generated by the superposition of ensembles of neurodes having arbitrary continuous functions receiving encoded input data. ATF-Nets may be implemented on parallel digital computers. An example is presented which illustrates a four-fold increase in computational throughput.

  2. Adaptive Transfer Function Networks

    SciTech Connect

    Goulding, J.R. Portland State Univ., OR . Dept. of Electrical Engineering)

    1993-01-01

    Real-time pattern classification and time-series forecasting applications continue to drive artificial neural network (ANN) technology. As ANNs increase in complexity, the throughput of digital computer simulations decreases. A novel ANN, the Adaptive Transfer Function Network (ATF-Net), directly addresses the issue of throughput. ATF-Nets are global mapping equations generated by the superposition of ensembles of neurodes having arbitrary continuous functions receiving encoded input data. ATF-Nets may be implemented on parallel digital computers. An example is presented which illustrates a four-fold increase in computational throughput.

  3. Landscape evolution by soil redistribution in a Mediterranean agricultural context

    NASA Astrophysics Data System (ADS)

    Ciampalini, Rossano; Follain, Stéphane; Le Bissonnais, Yves

    2010-05-01

    climate changes on soil cover and landscape evolution. The model has been tested on a watershed unit (91 ha) located at Roujan (43°30'N - 3°19'E) in the south of France (Hérault, France). Its first justification is to allow the study of global changes affecting hydrosystems in agro-systems located in Mediterranean context. Mediterranean environment is well adapted to study the system vulnerability to anthropic and climatic pressure changes. The pedological dataset is based on the soils map (1/25 000) of the watershed established by Coulouma et al. (2008) refined with data collected during two pedological surveys implementing a 25 m square sampling scheme for soil description. The simulated soil depth evolution, compared to the present soil cover detailed by the surveying, seems to confirm the observed patterns in terms of soil redistribution and topographic evolution. Land use, cultural practices and agricultural landscape structure are also able to directly influence sediment fluxes, then the related landscape evolution. The present landscape structure, tested under the perspective of climate changing showed a mitigating effect in terms of soil erosion confirming the importance of a detailed representation of the system geometry in modelling practises.

  4. Impacts of Landscape Context on Patterns of Wind Downfall Damage in a Fragmented Amazonian Landscape

    NASA Astrophysics Data System (ADS)

    Schwartz, N.; Uriarte, M.; DeFries, R. S.; Gutierrez-Velez, V. H.; Fernandes, K.; Pinedo-Vasquez, M.

    2015-12-01

    Wind is a major disturbance in the Amazon and has both short-term impacts and lasting legacies in tropical forests. Observed patterns of damage across landscapes result from differences in wind exposure and stand characteristics, such as tree stature, species traits, successional age, and fragmentation. Wind disturbance has important consequences for biomass dynamics in Amazonian forests, and understanding the spatial distribution and size of impacts is necessary to quantify the effects on carbon dynamics. In November 2013, a mesoscale convective system was observed over the study area in Ucayali, Peru, a highly human modified and fragmented forest landscape. We mapped downfall damage associated with the storm in order to ask: how does the severity of damage vary within forest patches, and across forest patches of different sizes and successional ages? We applied spectral mixture analysis to Landsat images from 2013 and 2014 to calculate the change in non-photosynthetic vegetation fraction after the storm, and combined it with C-band SAR data from the Sentinel-1 satellite to predict downfall damage measured in 30 field plots using random forest regression. We then applied this model to map damage in forests across the study area. Using a land cover classification developed in a previous study, we mapped secondary and mature forest, and compared the severity of damage in the two. We found that damage was on average higher in secondary forests, but patterns varied spatially. This study demonstrates the utility of using multiple sources of satellite data for mapping wind disturbance, and adds to our understanding of the sources of variation in wind-related damage. Ultimately, an improved ability to map wind impacts and a better understanding of their spatial patterns can contribute to better quantification of carbon dynamics in Amazonian landscapes.

  5. PSEUDO-CODEWORD LANDSCAPE

    SciTech Connect

    CHERTKOV, MICHAEL; STEPANOV, MIKHAIL

    2007-01-10

    The authors discuss performance of Low-Density-Parity-Check (LDPC) codes decoded by Linear Programming (LP) decoding at moderate and large Signal-to-Noise-Ratios (SNR). Frame-Error-Rate (FER) dependence on SNR and the noise space landscape of the coding/decoding scheme are analyzed by a combination of the previously introduced instanton/pseudo-codeword-search method and a new 'dendro' trick. To reduce complexity of the LP decoding for a code with high-degree checks, {ge} 5, they introduce its dendro-LDPC counterpart, that is the code performing identifically to the original one under Maximum-A-Posteriori (MAP) decoding but having reduced (down to three) check connectivity degree. Analyzing number of popular LDPC codes and their dendro versions performing over the Additive-White-Gaussian-Noise (AWGN) channel, they observed two qualitatively different regimes: (i) error-floor sets early, at relatively low SNR, and (ii) FER decays with SNR increase faster at moderate SNR than at the largest SNR. They explain these regimes in terms of the pseudo-codeword spectra of the codes.

  6. Landscape Visualisation on the Internet

    NASA Astrophysics Data System (ADS)

    Imhof, M. P.; Cox, M. T.; Harvey, D. W.; Heemskerk, G. E.; Pettit, C. J.

    2012-07-01

    The Victorian Resources Online (VRO) website (http://www.dpi.vic.gov.au/vro) is the principal means for accessing landscapebased information in Victoria. In this paper we introduce a range of online landscape visualisations that have been developed to enhance existing static web content around the nature and distribution of Victoria's landforms and soils as well as associated processes. Flash is used to develop online visualisations that include interactive landscape panoramas, animations of soil and landscape processes and videos of experts explaining features in the field as well as landscape "flyovers". The use of interactive visualisations adds rich information multimedia content to otherwise static pages and offers the potential to improve user's appreciation and understanding of soil and landscapes. Visualisation is becoming a key component of knowledge management activities associated with VRO - proving useful for both "knowledge capture" (from subject matter specialists) and "knowledge transfer" to a diverse user base. A range of useful visualisation products have been made available online, with varying degrees of interactivity and suited to a variety of users. The use of video files, animation and interactive visualisations is adding rich information content to otherwise static web pages. These information products offer new possibilities to enhance learning of landscapes and the effectiveness of these will be tested as the next phase of development.

  7. Landscape and climate science and scenarios for Florida

    USGS Publications Warehouse

    Terando, Adam; Traxler, Steve; Collazo, Jaime

    2014-01-01

    The Peninsular Florida Landscape Conservation Cooperative (PFLCC) is part of a network of 22 Landscape Conservation Cooperatives (LCCs) that extend from Alaska to the Caribbean. LCCs are regional-applied conservation-science partnerships among Federal agencies, regional organizations, States, tribes, nongovernmental organizations (NGOs), private stakeholders, universities, and other entities within a geographic area. The goal of these conservation-science partnerships is to help inform managers and decision makers at a landscape scale to further the principles of adaptive management and strategic habitat conservation. A major focus for LCCs is to help conservation managers and decision makers respond to large-scale ecosystem and habitat stressors, such as climate change, habitat fragmentation, invasive species, and water scarcity. The purpose of the PFLCC is to facilitate planning, design, and implementation of conservation strategies for fish and wildlife species at the landscape level using the adaptive management framework of strategic habitat conservation—integrating planning, design, delivery, and evaluation. Florida faces a set of unique challenges when responding to regional and global stressors because of its unique ecosystems and assemblages of species, its geographic location at the crossroads of temperate and tropical climates, and its exposure to both rapid urbanization and rising sea levels as the climate warms. In response to these challenges, several landscape-scale science projects were initiated with the goal of informing decision makers about how potential changes in climate and the built environment could impact habitats and ecosystems of concern in Florida and the Southeast United States. In June 2012, the PFLCC, North Carolina State University, convened a workshop at the U.S. Geological Survey (USGS) Coastal and Marine Science Center in St. Petersburg to assess the results of these integrated assessments and to foster an open dialogue about

  8. [Dynamic changes of landscape pattern and eco-disturbance degree in Shuangtai estuary wetland of Liaoning Province, China].

    PubMed

    Chen, Ai-lian; Zhu, Bo-qin; Chen, Li-ding; Wu, Yan-hua; Sun, Ran-hao

    2010-05-01

    The main objective of establishing natural reserve is to protect its natural resources from human disturbances and maintain its critical ecological service values. This paper introduced the concept of hemeroby, and by using remote sensing technology, systematically assessed the dynamic changes of landscape pattern and eco-disturbance degree in Shuangtai estuary wetland of Liaoning Province, China. Firstly, a knowledge-based expert system was used to classify the landscape into three first-level types based on eco-disturbance degree, i.e., undisturbed, partially disturbed, or completely disturbed, which were further classified into 30 second-level categories. Secondly, questionnaire and experts knowledge were adopted to determine the hemeroby index for each landscape type and to formulate a landscape classification system. Finally, the landscape classification maps and hemeroby indices were derived by using the Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) data acquired on 30 April 1987, 7 June 1995, 12 June 2000, and 11 October 2006. The results indicated that from 1987 to 2006, the landscape patches in the study area became more fragmented, being most obvious for reed marsh. Undisturbed landscape type decreased in area, while partially and completely disturbed types were in adverse. The overall characteristics of the spatial distribution of hemeroby index were of most variable in the areas along the river and surrounding the estuary and being the highest in the areas surrounding the city, largely due to the rapid urbanization and the blooming fishery in the study area. PMID:20707090

  9. Remotely-Sensed Urban Wet-Landscapes AN Indicator of Coupled Effects of Human Impact and Climate Change

    NASA Astrophysics Data System (ADS)

    Ji, Wei

    2016-06-01

    This study proposes the concept of urban wet-landscapes (loosely-defined wetlands) as against dry-landscapes (mainly impervious surfaces). The study is to examine whether the dynamics of urban wet-landscapes is a sensitive indicator of the coupled effects of the two major driving forces of urban landscape change - human built-up impact and climate (precipitation) variation. Using a series of satellite images, the study was conducted in the Kansas City metropolitan area of the United States. A rule-based classification algorithm was developed to identify fine-scale, hidden wetlands that could not be appropriately detected based on their spectral differentiability by a traditional image classification. The spatial analyses of wetland changes were implemented at the scales of metropolitan, watershed, and sub-watershed as well as based on the size of surface water bodies in order to reveal urban wetland change trends in relation to the driving forces. The study identified that wet-landscape dynamics varied in trend and magnitude from the metropolitan, watersheds, to sub-watersheds. The study also found that increased precipitation in the region in the past decades swelled larger wetlands in particular while smaller wetlands decreased mainly due to human development activities. These findings suggest that wet-landscapes, as against the dry-landscapes, can be a more effective indicator of the coupled effects of human impact and climate change.

  10. Landscape structure affects specialists but not generalists in naturally fragmented grasslands.

    PubMed

    Miller, Jesse E D; Damschen, Ellen I; Harrison, Susan P; Grace, James B

    2015-12-01

    Understanding how biotic communities respond to landscape spatial structure is critically important for conservation management as natural habitats become increasingly fragmented. However, empirical studies of the effects of spatial structure on plant species richness have found inconsistent results, suggesting that more comprehensive approaches are needed. We asked how landscape structure affects total plant species richness and the richness of a guild of specialized plants in a multivariate context. We sampled herbaceous plant communities at 56 dolomite glades (insular, fire-adapted grasslands) across the Missouri Ozarks, USA, and used structural equation modeling (SEM) to analyze the relative importance of landscape structure, soil resource availability, and fire history for plant communities. We found that landscape spatial structure, defined as the area-weighted proximity of glade habitat surrounding study sites (proximity index), had a significant effect on total plant species richness, but only after we controlled for environmental covariates. Richness of specialist species, but not generalists, was positively related to landscape spatial structure. Our results highlight that local environmental filters must be considered to understand the influence of landscape structure on communities and that unique species guilds may respond differently to landscape structure than the community as a whole. These findings suggest that both local environment and landscape context should be considered when developing management strategies for species of conservation concern in fragmented habitats. PMID:26909437

  11. Efficient escape from local optima in a highly rugged fitness landscape by evolving RNA virus populations.

    PubMed

    Cervera, Héctor; Lalić, Jasna; Elena, Santiago F

    2016-08-17

    Predicting viral evolution has proven to be a particularly difficult task, mainly owing to our incomplete knowledge of some of the fundamental principles that drive it. Recently, valuable information has been provided about mutation and recombination rates, the role of genetic drift and the distribution of mutational, epistatic and pleiotropic fitness effects. However, information about the topography of virus' adaptive landscapes is still scarce, and to our knowledge no data has been reported so far on how its ruggedness may condition virus' evolvability. Here, we show that populations of an RNA virus move efficiently on a rugged landscape and scape from the basin of attraction of a local optimum. We have evolved a set of Tobacco etch virus genotypes located at increasing distances from a local adaptive optimum in a highly rugged fitness landscape, and we observed that few evolved lineages remained trapped in the local optimum, while many others explored distant regions of the landscape. Most of the diversification in fitness among the evolved lineages was explained by adaptation, while historical contingency and chance events contribution was less important. Our results demonstrate that the ruggedness of adaptive landscapes is not an impediment for RNA viruses to efficiently explore remote parts of it. PMID:27534955

  12. Shifting Fitness and Epistatic Landscapes Reflect Trade-offs along an Evolutionary Pathway.

    PubMed

    Steinberg, Barrett; Ostermeier, Marc

    2016-07-01

    Nature repurposes proteins via evolutionary processes. Such adaptation can come at the expense of the original protein's function, which is a trade-off of adaptation. We sought to examine other potential adaptive trade-offs. We measured the effect on ampicillin resistance of ~12,500 unique single amino acid mutants of the TEM-1, TEM-17, TEM-19, and TEM-15 β-lactamase alleles, which constitute an adaptive path in the evolution of cefotaxime resistance. These protein fitness landscapes were compared and used to calculate epistatic interactions between these mutations and the two mutations in the pathway (E104K and G238S). This series of protein fitness landscapes provides a systematic, quantitative description of pairwise/tertiary intragenic epistasis involving adaptive mutations. We find that the frequency of mutations exhibiting epistasis increases along the evolutionary pathway. Adaptation moves the protein to a region in the fitness landscape characterized by decreased mutational robustness and increased ruggedness, as measured by fitness effects of mutations and epistatic interactions for TEM-1's original function. This movement to such a "fitness territory" has evolutionary consequences and is an important adaptive trade-off and cost of adaptation. Our systematic study provides detailed insight into the relationships between mutation, protein structure, protein stability, and epistasis and quantitatively depicts the different costs inherent in the evolution of new functions. PMID:27173379

  13. Recursive heuristic classification

    NASA Technical Reports Server (NTRS)

    Wilkins, David C.

    1994-01-01

    The author will describe a new problem-solving approach called recursive heuristic classification, whereby a subproblem of heuristic classification is itself formulated and solved by heuristic classification. This allows the construction of more knowledge-intensive classification programs in a way that yields a clean organization. Further, standard knowledge acquisition and learning techniques for heuristic classification can be used to create, refine, and maintain the knowledge base associated with the recursively called classification expert system. The method of recursive heuristic classification was used in the Minerva blackboard shell for heuristic classification. Minerva recursively calls itself every problem-solving cycle to solve the important blackboard scheduler task, which involves assigning a desirability rating to alternative problem-solving actions. Knowing these ratings is critical to the use of an expert system as a component of a critiquing or apprenticeship tutoring system. One innovation of this research is a method called dynamic heuristic classification, which allows selection among dynamically generated classification categories instead of requiring them to be prenumerated.

  14. Security classification of information

    SciTech Connect

    Quist, A.S.

    1993-04-01

    This document is the second of a planned four-volume work that comprehensively discusses the security classification of information. The main focus of Volume 2 is on the principles for classification of information. Included herein are descriptions of the two major types of information that governments classify for national security reasons (subjective and objective information), guidance to use when determining whether information under consideration for classification is controlled by the government (a necessary requirement for classification to be effective), information disclosure risks and benefits (the benefits and costs of classification), standards to use when balancing information disclosure risks and benefits, guidance for assigning classification levels (Top Secret, Secret, or Confidential) to classified information, guidance for determining how long information should be classified (classification duration), classification of associations of information, classification of compilations of information, and principles for declassifying and downgrading information. Rules or principles of certain areas of our legal system (e.g., trade secret law) are sometimes mentioned to .provide added support to some of those classification principles.

  15. Safety-aware semi-supervised classification.

    PubMed

    Wang, Yunyun; Chen, Songcan

    2013-11-01

    Though semi-supervised classification learning has attracted great attention over past decades, semi-supervised classification methods may show worse performance than their supervised counterparts in some cases, consequently reducing their confidence in real applications. Naturally, it is desired to develop a safe semi-supervised classification method that never performs worse than the supervised counterparts. However, to the best of our knowledge, few researches have been devoted to safe semi-supervised classification. To address this problem, in this paper, we invent a safety-control mechanism for safe semi-supervised classification by adaptive tradeoff between semi-supervised and supervised classification in terms of unlabeled data. In implementation, based on our recent semi-supervised classification method based on class memberships (SSCCM), we develop a safety-aware SSCCM (SA-SSCCM). SA-SSCCM, on the one hand, exploits the unlabeled data to help learning (as SSCCM does) under the assumption that unlabeled data can help learning, and on the other hand, restricts its prediction to approach that of its supervised counterpart least-square support vector machine (LS-SVM) under the assumption that unlabeled data can hurt learning. Therefore, prediction by SA-SSCCM becomes a tradeoff between those by semi-supervised SSCCM and supervised LS-SVM, respectively, in terms of the unlabeled data. As in SSCCM, the optimization problem in SA-SSCCM can be efficiently solved by the alternating iterative strategy, and the iteration convergence can theoretically be guaranteed. Experiments over several real datasets show the promising performance of SA-SSCCM compared with LS-SVM, SSCCM, and off-the-shelf safe semi-supervised classification methods. PMID:24808610

  16. Genomic landscape of liposarcoma

    PubMed Central

    Kanojia, Deepika; Nagata, Yasunobu; Garg, Manoj; Lee, Dhong Hyun; Sato, Aiko; Yoshida, Kenichi; Sato, Yusuke; Sanada, Masashi; Mayakonda, Anand; Bartenhagen, Christoph; Klein, Hans-Ulrich; Doan, Ngan B.; Said, Jonathan W.; Mohith, S.; Gunasekar, Swetha; Shiraishi, Yuichi; Chiba, Kenichi; Tanaka, Hiroko; Miyano, Satoru; Myklebost, Ola; Yang, Henry; Dugas, Martin; Meza-Zepeda, Leonardo A.; Silberman, Allan W.; Forscher, Charles; Tyner, Jeffrey W.; Ogawa, Seishi; Koeffler, H. Phillip

    2015-01-01

    Liposarcoma (LPS) is the most common type of soft tissue sarcoma accounting for 20% of all adult sarcomas. Due to absence of clinically effective treatment options in inoperable situations and resistance to chemotherapeutics, a critical need exists to identify novel therapeutic targets. We analyzed LPS genomic landscape using SNP arrays, whole exome sequencing and targeted exome sequencing to uncover the genomic information for development of specific anti-cancer targets. SNP array analysis indicated known amplified genes (MDM2, CDK4, HMGA2) and important novel genes (UAP1, MIR557, LAMA4, CPM, IGF2, ERBB3, IGF1R). Carboxypeptidase M (CPM), recurrently amplified gene in well-differentiated/de-differentiated LPS was noted as a putative oncogene involved in the EGFR pathway. Notable deletions were found at chromosome 1p (RUNX3, ARID1A), chromosome 11q (ATM, CHEK1) and chromosome 13q14.2 (MIR15A, MIR16-1). Significantly and recurrently mutated genes (false discovery rate < 0.05) included PLEC (27%), MXRA5 (21%), FAT3 (24%), NF1 (20%), MDC1 (10%), TP53 (7%) and CHEK2 (6%). Further, in vitro and in vivo functional studies provided evidence for the tumor suppressor role for Neurofibromin 1 (NF1) gene in different subtypes of LPS. Pathway analysis of recurrent mutations demonstrated signaling through MAPK, JAK-STAT, Wnt, ErbB, axon guidance, apoptosis, DNA damage repair and cell cycle pathways were involved in liposarcomagenesis. Interestingly, we also found mutational and copy number heterogeneity within a primary LPS tumor signifying the importance of multi-region sequencing for cancer-genome guided therapy. In summary, these findings provide insight into the genomic complexity of LPS and highlight potential druggable pathways for targeted therapeutic approach. PMID:26643872

  17. Classification images: A review.

    PubMed

    Murray, Richard F

    2011-01-01

    Classification images have recently become a widely used tool in visual psychophysics. Here, I review the development of classification image methods over the past fifteen years. I provide some historical background, describing how classification images and related methods grew out of established statistical and mathematical frameworks and became common tools for studying biological systems. I describe key developments in classification image methods: use of optimal weighted sums based on the linear observer model, formulation of classification images in terms of the generalized linear model, development of statistical tests, use of priors to reduce dimensionality, methods for experiments with more than two response alternatives, a variant using multiplicative noise, and related methods for examining nonlinearities in visual processing, including second-order Volterra kernels and principal component analysis. I conclude with a selective review of how classification image methods have led to substantive findings in three representative areas of vision research, namely, spatial vision, perceptual organization, and visual search. PMID:21536726

  18. Classification of Pulmonary Hypertension.

    PubMed

    Oudiz, Ronald J

    2016-08-01

    The classification of pulmonary hypertension (PH) is an attempt to define subtypes of PH based on clinical presentation, underlying physiology, and treatment implications. Five groups of PH have been defined, and the classification scheme has been refined over the years to guide clinicians in the diagnosis and management of PH. Understanding the classification of PH is paramount before embarking on a work-up of patients with PH or suspected PH because treatment and outcome can vary greatly. PMID:27443133

  19. Classiology and soil classification

    NASA Astrophysics Data System (ADS)

    Rozhkov, V. A.

    2012-03-01

    Classiology can be defined as a science studying the principles and rules of classification of objects of any nature. The development of the theory of classification and the particular methods for classifying objects are the main challenges of classiology; to a certain extent, they are close to the challenges of pattern recognition. The methodology of classiology integrates a wide range of methods and approaches: from expert judgment to formal logic, multivariate statistics, and informatics. Soil classification assumes generalization of available data and practical experience, formalization of our notions about soils, and their representation in the form of an information system. As an information system, soil classification is designed to predict the maximum number of a soil's properties from the position of this soil in the classification space. The existing soil classification systems do not completely satisfy the principles of classiology. The violation of logical basis, poor structuring, low integrity, and inadequate level of formalization make these systems verbal schemes rather than classification systems sensu stricto. The concept of classification as listing (enumeration) of objects makes it possible to introduce the notion of the information base of classification. For soil objects, this is the database of soil indices (properties) that might be applied for generating target-oriented soil classification system. Mathematical methods enlarge the prognostic capacity of classification systems; they can be applied to assess the quality of these systems and to recognize new soil objects to be included in the existing systems. The application of particular principles and rules of classiology for soil classification purposes is discussed in this paper.

  20. Classification of Stellar Spectra

    NASA Astrophysics Data System (ADS)

    Garrison, R.; Murdin, P.

    2000-11-01

    How does a scientist approach the problem of trying to understand countless billions of objects? One of the first steps is to organize the data and set up a classification scheme which can provide the best insights into the nature of the objects. Perception and insight are the main purposes of classification. In astronomy, where there are `billions and billions' of stars, classification is an ong...

  1. Analysis of uncertainty in multi-temporal object-based classification

    NASA Astrophysics Data System (ADS)

    Löw, Fabian; Knöfel, Patrick; Conrad, Christopher

    2015-07-01

    Agricultural management increasingly uses crop maps based on classification of remotely sensed data. However, classification errors can translate to errors in model outputs, for instance agricultural production monitoring (yield, water demand) or crop acreage calculation. Hence, knowledge on the spatial variability of the classier performance is important information for the user. But this is not provided by traditional assessments of accuracy, which are based on the confusion matrix. In this study, classification uncertainty was analyzed, based on the support vector machines (SVM) algorithm. SVM was applied to multi-spectral time series data of RapidEye from different agricultural landscapes and years. Entropy was calculated as a measure of classification uncertainty, based on the per-object class membership estimations from the SVM algorithm. Permuting all possible combinations of available images allowed investigating the impact of the image acquisition frequency and timing, respectively, on the classification uncertainty. Results show that multi-temporal datasets decrease classification uncertainty for different crops compared to single data sets, but there was no "one-image-combination-fits-all" solution. The number and acquisition timing of the images, for which a decrease in uncertainty could be realized, proved to be specific to a given landscape, and for each crop they differed across different landscapes. For some crops, an increase of uncertainty was observed when increasing the quantity of images, even if classification accuracy was improved. Random forest regression was employed to investigate the impact of different explanatory variables on the observed spatial pattern of classification uncertainty. It was strongly influenced by factors related with the agricultural management and training sample density. Lower uncertainties were revealed for fields close to rivers or irrigation canals. This study demonstrates that classification uncertainty estimates

  2. Classification of Itch.

    PubMed

    Ständer, Sonja

    2016-01-01

    Chronic pruritus has diverse forms of presentation and can appear not only on normal skin [International Forum for the Study of Itch (IFSI) classification group II], but also in the company of dermatoses (IFSI classification group I). Scratching, a natural reflex, begins in response to itch. Enough damage can be done to the skin by scratching to cause changes in the primary clinical picture, often leading to a clinical picture predominated by the development of chronic scratch lesions (IFSI classification group III). An internationally recognized, standardized classification system was created by the IFSI to not only aid in clarifying terms and definitions, but also to harmonize the global nomenclature for itch. PMID:27578063

  3. Landscape pattern and transition under natural and anthropogenic disturbance in an arid region of northwestern China

    NASA Astrophysics Data System (ADS)

    Zhang, Yu; Wang, Tianwei; Cai, Chongfa; Li, Chongguang; Liu, Yaojun; Bao, Yuze; Guan, Wuhong

    2016-02-01

    There is a pressing need to determine the relationships between driving variables and landscape transformations. Human activities shape landscapes and turn them into complex assemblages of highly diverse structures. Other factors, including climate and topography, also play significant roles in landscape transitions, and identifying the interactions among the variables is critical to environmental management. This study analyzed the configurations and spatial-temporal processes of landscape changes from 1998 to 2011 under different anthropogenic disturbances, identified the main variables that determine the landscape patterns and transitions, and quantified the relationships between pairs of driver sets. Landsat images of Baicheng and Tekes from 1998, 2006 and 2011 were used to classify landscapes by supervised classification. Redundancy analysis (RDA) and variation partitioning were performed to identify the main driving forces and to quantify the unique, shared, and total explained variation of the sets of variables. The results indicate that the proportions of otherwise identical landscapes in Baicheng and Tekes were very different. The area of the grassland in Tekes was much larger than that of the cropland; however, the differences between the grassland and cropland in Baicheng were not as pronounced. Much of the grassland in Tekes was located in an area that was near residents, whereas most of the grassland in Baicheng was far from residents. The slope, elevation, annual precipitation, annual temperature, and distance to the nearest resident were strong driving forces influencing the patterns and transitions of the landscapes. The results of the variation partitioning indicated complex interrelationships among all of the pairs of driver sets. All of the variable sets had significant explanatory roles, most of which had both unique and shared variations with the others. The results of this study can assist policy makers and planners in implementing sustainable

  4. Hidden Randomness between Fitness Landscapes Limits Reverse Evolution

    NASA Astrophysics Data System (ADS)

    Tan, Longzhi; Serene, Stephen; Xiao Chao, Hui; Gore, Jeff

    2012-02-01

    Natural populations must constantly adapt to the ever-changing environment. A fundamental question in evolutionary biology is whether adaptations can be reversed by returning the population to its ancestral environment. Traditionally, reverse evolution is defined as restoring an ancestral phenotype (physical characteristics such as body size), and the classic Dollo's Law has hypothesized the impossibility of reversing complex adaptations. However, this ``law'' remains ambiguous unless reverse evolution can be studied at the level of genotypes (the underlying genome sequence). We measured the fitness landscapes of a bacterial antibiotic-resistance gene and analyzed the reversibility of evolution as a global, statistical feature of the landscapes. In both experiments and simulations, we find that an adaptation's reversibility declines as the number of mutations it involves increases, suggesting a probabilistic form of Dollo's Law at the molecular level. We also show computationally that slowly switching between environments facilitates reverse evolution in small populations, where clonal interference is negligible or moderate. This is an analogy to thermodynamics, where the reversibility of a physical process is maximized when conditions are modified infinitely slowly.

  5. Landscape structure and the genetic effects of a population collapse

    PubMed Central

    Caplins, Serena A.; Gilbert, Kimberly J.; Ciotir, Claudia; Roland, Jens; Matter, Stephen F.; Keyghobadi, Nusha

    2014-01-01

    Both landscape structure and population size fluctuations influence population genetics. While independent effects of these factors on genetic patterns and processes are well studied, a key challenge is to understand their interaction, as populations are simultaneously exposed to habitat fragmentation and climatic changes that increase variability in population size. In a population network of an alpine butterfly, abundance declined 60–100% in 2003 because of low over-winter survival. Across the network, mean microsatellite genetic diversity did not change. However, patch connectivity and local severity of the collapse interacted to determine allelic richness change within populations, indicating that patch connectivity can mediate genetic response to a demographic collapse. The collapse strongly affected spatial genetic structure, leading to a breakdown of isolation-by-distance and loss of landscape genetic pattern. Our study reveals important interactions between landscape structure and temporal demographic variability on the genetic diversity and genetic differentiation of populations. Projected future changes to both landscape and climate may lead to loss of genetic variability from the studied populations, and selection acting on adaptive variation will likely occur within the context of an increasing influence of genetic drift. PMID:25320176

  6. National-level progress on adaptation

    NASA Astrophysics Data System (ADS)

    Lesnikowski, Alexandra; Ford, James; Biesbroek, Robbert; Berrang-Ford, Lea; Heymann, S. Jody

    2016-03-01

    It is increasingly evident that adaptation will figure prominently in the post-2015 United Nations climate change agreement. As adaptation obligations under the United Nations Framework Convention on Climate Change evolve, more rigorous approaches to measuring adaptation progress among parties will be critical. In this Letter we elaborate on an emerging area of research referred to as `adaptation tracking’, which has potential to inform development of a global adaptation monitoring framework. We evaluate this potential by presenting evidence on policy change for 41 high-income countries between 2010 and 2014. We examine whether countries that were in early stages of adaptation planning in 2010 are making progress to close adaptation gaps, and how the landscape of adaptation in these countries has evolved. In total we find an 87% increase in reported adaptation policies and measures, and evidence that implementation of concrete adaptation initiatives is growing. Reflecting on the strengths and challenges of this early methodology, we further discuss how adaptation tracking practices could guide development of a robust framework for monitoring global adaptation progress and inform future research on policy change across countries.

  7. Adaptive SPECT

    PubMed Central

    Barrett, Harrison H.; Furenlid, Lars R.; Freed, Melanie; Hesterman, Jacob Y.; Kupinski, Matthew A.; Clarkson, Eric; Whitaker, Meredith K.

    2008-01-01

    Adaptive imaging systems alter their data-acquisition configuration or protocol in response to the image information received. An adaptive pinhole single-photon emission computed tomography (SPECT) system might acquire an initial scout image to obtain preliminary information about the radiotracer distribution and then adjust the configuration or sizes of the pinholes, the magnifications, or the projection angles in order to improve performance. This paper briefly describes two small-animal SPECT systems that allow this flexibility and then presents a framework for evaluating adaptive systems in general, and adaptive SPECT systems in particular. The evaluation is in terms of the performance of linear observers on detection or estimation tasks. Expressions are derived for the ideal linear (Hotelling) observer and the ideal linear (Wiener) estimator with adaptive imaging. Detailed expressions for the performance figures of merit are given, and possible adaptation rules are discussed. PMID:18541485

  8. Remote Sensing Data Binary Classification Using Boosting with Simple Classifiers

    NASA Astrophysics Data System (ADS)

    Nowakowski, Artur

    2015-10-01

    Boosting is a classification method which has been proven useful in non-satellite image processing while it is still new to satellite remote sensing. It is a meta-algorithm, which builds a strong classifier from many weak ones in iterative way. We adapt the AdaBoost.M1 boosting algorithm in a new land cover classification scenario based on utilization of very simple threshold classifiers employing spectral and contextual information. Thresholds for the classifiers are automatically calculated adaptively to data statistics. The proposed method is employed for the exemplary problem of artificial area identification. Classification of IKONOS multispectral data results in short computational time and overall accuracy of 94.4% comparing to 94.0% obtained by using AdaBoost.M1 with trees and 93.8% achieved using Random Forest. The influence of a manipulation of the final threshold of the strong classifier on classification results is reported.

  9. Multiple Scale Landscape Pattern Index Interpretation for the Persistent Monitoring of Land-Cover and Land-Use

    NASA Astrophysics Data System (ADS)

    Spivey, Alvin J.

    Mapping land-cover land-use change (LCLUC) over regional and continental scales, and long time scales (years and decades), can be accomplished using thematically identified classification maps of a landscape---a LCLU class map. Observations of a landscape's LCLU class map pattern can indicate the most relevant process, like hydrologic or ecologic function, causing landscape scale environmental change. Quantified as Landscape Pattern Metrics (LPM), emergent landscape patterns act as Landscape Indicators (LI) when physically interpreted. The common mathematical approach to quantifying observed landscape scale pattern is to have LPM measure how connected a class exists within the landscape, through nonlinear local kernel operations of edges and gradients in class maps. Commonly applied kernel-based LPM that consistently reveal causal processes are Dominance, Contagion, and Fractal Dimension. These kernel-based LPM can be difficult to interpret. The emphasis on an image pixel's edge by gradient operations and dependence on an image pixel's existence according to classification accuracy limit the interpretation of LPM. For example, the Dominance and Contagion kernel-based LPM very similarly measure how connected a landscape is. Because of this, their reported edge measurements of connected pattern correlate strongly, making their results ambiguous. Additionally, each of these kernel-based LPM are unscalable when comparing class maps from separate imaging system sensor scenarios that change the image pixel's edge position (i.e. changes in landscape extent, changes in pixel size, changes in orientation, etc), and can only interpret landscape pattern as accurately as the LCLU map classification will allow. This dissertation discusses the reliability of common LPM in light of imaging system effects such as: algorithm classification likelihoods, LCLU classification accuracy due to random image sensor noise, and image scale. A description of an approach to generating well

  10. LORICA - A new model for linking landscape and soil profile evolution: Development and sensitivity analysis

    NASA Astrophysics Data System (ADS)

    Temme, Arnaud J. A. M.; Vanwalleghem, Tom

    2016-05-01

    Soils and landscapes evolve in tandem. Landscape position is a strong determinant of vertical soil development, which has often been formalized in the catena concept. At the same time, soil properties are strong determinants of geomorphic processes such as overland erosion, landsliding and creep. We present a new soilscape evolution model; LORICA, to study these numerous interactions between soil and landscape development. The model is based on the existing landscape evolution model LAPSUS and the soil formation model MILESD. The model includes similar soil formation processes as MILESD, but the main novelties include the consideration of more layers and the dynamic adaption of the number of layers as a function of the soil profile's heterogeneity. New processes in the landscape evolution component include a negative feedback of vegetation and armouring and particle size selectivity of the erosion-deposition process. In order to quantify these different interactions, we present a full sensitivity analysis of the input parameters. First results show that the model successfully simulates various soil-landscape interactions, leading to outputs where the surface changes in the landscape clearly depend on soil development, and soil changes depend on landscape location. Sensitivity analysis of the model confirms that soil and landscape interact: variables controlling amount and position of fine clay have the largest effect on erosion, and erosion variables control among others the amount of chemical weathering. These results show the importance of particle size distribution, and especially processes controlling the presence of finer clay particles that are easily eroded, both for the resulting landscape form as for the resulting soil profiles. Further research will have to show whether this is specific to the boundary conditions of this study or a general phenomenon.

  11. Correlates of vernal pool occurrence in the Massachusetts USA, landscape

    USGS Publications Warehouse

    Grant, E.H.C.

    2005-01-01

    Vernal pool wetlands are at risk of destruction across the northeast United States, due in part to their diminutive size and short hydroperiods. These characteristics make it difficult to locate vernal pool habitats in the landscape during much of the year, and no efficient method exists for predicting their occurrence. A logistic regression procedure was used to identify large-scale variables that influence the presence of a potential vernal pool, including surficial geology, land use and land cover, soil classification, topography, precipitation, and surficial hydrologic features. The model was validated with locations of field-verified vernal pools. The model demonstrated that the probability of potential vernal pool occurrence is positively related to slope, negatively related to till/bedrock surficial geology, and negatively related to the proportion of cropland, urban/commercial, and high density residential development in the landscape. The relationship between vernal pool occurrence and large-scale variables suggests that these habitats do not occur at random in the landscape, and thus, protection in situ should be considered.

  12. Library Classification 2020

    ERIC Educational Resources Information Center

    Harris, Christopher

    2013-01-01

    In this article the author explores how a new library classification system might be designed using some aspects of the Dewey Decimal Classification (DDC) and ideas from other systems to create something that works for school libraries in the year 2020. By examining what works well with the Dewey Decimal System, what features should be carried…

  13. Linear Classification Functions.

    ERIC Educational Resources Information Center

    Huberty, Carl J.; Smith, Jerry D.

    Linear classification functions (LCFs) arise in a predictive discriminant analysis for the purpose of classifying experimental units into criterion groups. The relative contribution of the response variables to classification accuracy may be based on LCF-variable correlations for each group. It is proved that, if the raw response measures are…

  14. Energy Landscape of Social Balance

    NASA Astrophysics Data System (ADS)

    Marvel, Seth A.; Strogatz, Steven H.; Kleinberg, Jon M.

    2009-11-01

    We model a close-knit community of friends and enemies as a fully connected network with positive and negative signs on its edges. Theories from social psychology suggest that certain sign patterns are more stable than others. This notion of social “balance” allows us to define an energy landscape for such networks. Its structure is complex: numerical experiments reveal a landscape dimpled with local minima of widely varying energy levels. We derive rigorous bounds on the energies of these local minima and prove that they have a modular structure that can be used to classify them.

  15. Autofocused 3D classification of cryoelectron subtomograms.

    PubMed

    Chen, Yuxiang; Pfeffer, Stefan; Fernández, José Jesús; Sorzano, Carlos Oscar S; Förster, Friedrich

    2014-10-01

    Classification of subtomograms obtained by cryoelectron tomography (cryo-ET) is a powerful approach to study the conformational landscapes of macromolecular complexes in situ. Major challenges in subtomogram classification are the low signal-to-noise ratio (SNR) of cryo-tomograms, their incomplete angular sampling, the unknown number of classes and the typically unbalanced abundances of structurally distinct complexes. Here, we propose a clustering algorithm named AC3D that is based on a similarity measure, which automatically focuses on the areas of major structural discrepancy between respective subtomogram class averages. Furthermore, we incorporate a spherical-harmonics-based fast subtomogram alignment algorithm, which provides a significant speedup. Assessment of our approach on simulated data sets indicates substantially increased classification accuracy of the presented method compared to two state-of-the-art approaches. Application to experimental subtomograms depicting endoplasmic-reticulum-associated ribosomal particles shows that AC3D is well suited to deconvolute the compositional heterogeneity of macromolecular complexes in situ. PMID:25242455

  16. 2-Stage Classification Modeling

    Energy Science and Technology Software Center (ESTSC)

    1994-11-01

    CIRCUIT2.4 is used to design optimum two-stage classification configurations and operating conditions for energy conservation. It permits simulation of five basic grinding-classification circuits, including one single-stage and four two-stage classification arrangements. Hydrocyclones, spiral classifiers, and sieve band screens can be simulated, and the user may choose the combination of devices for the flowsheet simulation. In addition, the user may select from four classification modeling methods to achieve the goals of a simulation project using themore » most familiar concepts. Circuit performance is modeled based on classification parameters or equipment operating conditions. A modular approach was taken in designing the program, which allows future addition of other models with relatively minor changes.« less

  17. Martian Landscapes in Motion

    NASA Astrophysics Data System (ADS)

    Mattson, Sarah; McEwen, Alfred; Kirk, Randolph; Howington-Kraus, Elpitha; Chojnacki, Matthew; Runyon, Kirby; Cremonese, Gabriele; Re, Cristina

    2014-05-01

    RISE orthorectified image sequences makes it possible to conduct accurate change detection studies of active processes on Mars. Some examples of studies of active landscapes on Mars using HiRISE DTMs and orthoimage sequences include: dune and ripple motion (Bridges et al., 2012, Nature), recurring slope lineae (RSL) (McEwen et al., 2011, Science; McEwen et al., 2013, Nature Geoscience), gully activity (Dundas et al., 2012, Icarus), and polar processes (Hansen et al., 2011, Science; Portyankina et al. 2013, Icarus,). These studies encompass images from multiple Mars years and seasons. Sequences of orthoimages make it possible to generate animated gifs or movies to visualize temporal changes (http://www.uahirise.org/sim/). They can also be brought into geospatial software to quantitatively map and record changes. The ability to monitor the surface of Mars at high spatial resolution with frequent repeat images has opened up our insight into seasonal and interannual changes, further increasing our understanding of Mars as an active planet.

  18. Conceptual domain of the matrix in fragmented landscapes.

    PubMed

    Driscoll, Don A; Banks, Sam C; Barton, Philip S; Lindenmayer, David B; Smith, Annabel L

    2013-10-01

    In extensively modified landscapes, how the matrix is managed determines many conservation outcomes. Recent publications revise popular conceptions of a homogeneous and static matrix, yet we still lack an adequate conceptual model of the matrix. Here, we identify three core effects that influence patch-dependent species, through impacts associated with movement and dispersal, resource availability, and the abiotic environment. These core effects are modified by five 'dimensions': spatial and temporal variation in matrix quality; spatial scale; temporal scale of matrix variation; and adaptation. The conceptual domain of the matrix, defined as three core effects and their interaction with these five dimensions, provides a much-needed framework to underpin management of fragmented landscapes and highlights new research priorities. PMID:23883740

  19. Microfluidic Platform Generates Oxygen Landscapes for Localized Hypoxic Activation

    PubMed Central

    Rexius, Megan L.; Mauleon, Gerardo; Malik, Asrar B.; Rehman, Jalees; Eddington, David T.

    2014-01-01

    An open-well microfluidic platform generates an oxygen landscape using gas-perfused networks which diffuse across a membrane. The device enables real-time analysis of cellular and tissue responses to oxygen tension to define how cells adapt to heterogeneous oxygen conditions found in the physiological setting. We demonstrate that localized hypoxic activation of cells elicited specific metabolic and gene responses in human microvascular endothelial cells and bone marrow-derived mesenchymal stem cells. A robust demonstration of the compatibility of the device with standard laboratory techniques demonstrates the wide utility of the method. This platform is ideally suited to study real-time cell responses and cell-cell interactions within physiologically relevant oxygen landscapes. PMID:25315003

  20. Embedding landscape processes into triangulated irregular networks for distributed hydrogeomorphic modeling.

    NASA Astrophysics Data System (ADS)

    Vivoni, E. R.; Teles, V.; Ivanov, V. Y.; Bras, R. L.; Entekhabi, D.

    2003-04-01

    Landscape indices provide a simple and concise method for describing the potential for saturation, erosion or shallow landsliding based on spatially distributed terrain attributes. We present a methodology that combines the efficiency of a similarity landscape index with a multiple resolution computational mesh based on a triangulated irregular network (TIN). The method results in an adaptive landscape discretisation that resembles the a priori, steady-state prediction of basin behavior. The landscape index-derived TIN terrain model is used as a physically-based initialization for the CHILD and tRIBS distributed models. Based on a TIN data structure, CHILD simulates long-term landscape evolution, while tRIBS models short-term runoff processes over complex terrain in small to large-scale basins. The spatial partitioning provided by embedding a landscape process into the terrain model improves upon traditional methods for deriving TIN terrains. In particular, it offers high resolution in regions anticipated to impact basin hydrogeomorphic behavior. An evaluation of the new method is presented for three case studies: saturation excess-runoff, transport-limited erosion and rainfall-triggered shallow landsliding in the Baron Fork (OK), Owl Creek (TX) and Tolt River (WA) watersheds, respectively. Comparisons of the integrated and spatially-distributed basin response in each case highlight the advantages of coupling landscape indices and triangulated terrain resolution.

  1. Imaginative Landscapes: This World and Beyond.

    ERIC Educational Resources Information Center

    Moore, John Noell, Ed.

    2001-01-01

    Describes a variety of books that offer fictional and poetic landscapes--five historical novels set in disparate locales, a book set in medieval Denmark, another addressing the landscape of memory, and a novel about a poet-scientist. (SR)

  2. Isolation-by-distance in landscapes: considerations for landscape genetics.

    PubMed

    van Strien, M J; Holderegger, R; Van Heck, H J

    2015-01-01

    In landscape genetics, isolation-by-distance (IBD) is regarded as a baseline pattern that is obtained without additional effects of landscape elements on gene flow. However, the configuration of suitable habitat patches determines deme topology, which in turn should affect rates of gene flow. IBD patterns can be characterized either by monotonically increasing pairwise genetic differentiation (for example, FST) with increasing interdeme geographic distance (case-I pattern) or by monotonically increasing pairwise genetic differentiation up to a certain geographical distance beyond which no correlation is detectable anymore (case-IV pattern). We investigated if landscape configuration influenced the rate at which a case-IV pattern changed to a case-I pattern. We also determined at what interdeme distance the highest correlation was measured between genetic differentiation and geographic distance and whether this distance corresponded to the maximum migration distance. We set up a population genetic simulation study and assessed the development of IBD patterns for several habitat configurations and maximum migration distances. We show that the rate and likelihood of the transition of case-IV to case-I FST-distance relationships was strongly influenced by habitat configuration and maximum migration distance. We also found that the maximum correlation between genetic differentiation and geographic distance was not related to the maximum migration distance and was measured across all deme pairs in a case-I pattern and, for a case-IV pattern, at the distance where the FST-distance curve flattens out. We argue that in landscape genetics, separate analyses should be performed to either assess IBD or the landscape effects on gene flow. PMID:25052412

  3. Isolation-by-distance in landscapes: considerations for landscape genetics

    PubMed Central

    van Strien, M J; Holderegger, R; Van Heck, H J

    2015-01-01

    In landscape genetics, isolation-by-distance (IBD) is regarded as a baseline pattern that is obtained without additional effects of landscape elements on gene flow. However, the configuration of suitable habitat patches determines deme topology, which in turn should affect rates of gene flow. IBD patterns can be characterized either by monotonically increasing pairwise genetic differentiation (for example, FST) with increasing interdeme geographic distance (case-I pattern) or by monotonically increasing pairwise genetic differentiation up to a certain geographical distance beyond which no correlation is detectable anymore (case-IV pattern). We investigated if landscape configuration influenced the rate at which a case-IV pattern changed to a case-I pattern. We also determined at what interdeme distance the highest correlation was measured between genetic differentiation and geographic distance and whether this distance corresponded to the maximum migration distance. We set up a population genetic simulation study and assessed the development of IBD patterns for several habitat configurations and maximum migration distances. We show that the rate and likelihood of the transition of case-IV to case-I FST–distance relationships was strongly influenced by habitat configuration and maximum migration distance. We also found that the maximum correlation between genetic differentiation and geographic distance was not related to the maximum migration distance and was measured across all deme pairs in a case-I pattern and, for a case-IV pattern, at the distance where the FST–distance curve flattens out. We argue that in landscape genetics, separate analyses should be performed to either assess IBD or the landscape effects on gene flow. PMID:25052412

  4. Modeling Climate Change Impacts on Landscape Evolution, Fire, and Hydrology

    NASA Astrophysics Data System (ADS)

    Sheppard, B. S.; O Connor, C.; Falk, D. A.; Garfin, G. M.

    2015-12-01

    Landscape disturbances such as wildfire interact with climate variability to influence hydrologic regimes. We coupled landscape, fire, and hydrologic models and forced them using projected climate to demonstrate climate change impacts anticipated at Fort Huachuca in southeastern Arizona, USA. The US Department of Defense (DoD) recognizes climate change as a trend that has implications for military installations, national security and global instability. The goal of this DoD Strategic Environmental Research and Development Program (SERDP) project (RC-2232) is to provide decision making tools for military installations in the southwestern US to help them adapt to the operational realities associated with climate change. For this study we coupled the spatially explicit fire and vegetation dynamics model FireBGCv2 with the Automated Geospatial Watershed Assessment tool (AGWA) to evaluate landscape vegetation change, fire disturbance, and surface runoff in response to projected climate forcing. A projected climate stream for the years 2005-2055 was developed from the Multivariate Adaptive Constructed Analogs (MACA) 4 km statistical downscaling of the CanESM2 GCM using Representative Concentration Pathway (RCP) 8.5. AGWA, an ArcGIS add-in tool, was used to automate the parameterization and execution of the Soil Water Assessment Tool (SWAT) and the KINematic runoff and EROSion2 (KINEROS2) models based on GIS layers. Landscape raster data generated by FireBGCv2 project an increase in fire and drought associated tree mortality and a decrease in vegetative basal area over the years of simulation. Preliminary results from SWAT modeling efforts show an increase to surface runoff during years following a fire, and for future winter rainy seasons. Initial results from KINEROS2 model runs show that peak runoff rates are expected to increase 10-100 fold as a result of intense rainfall falling on burned areas.

  5. ASSESSING THE HYDROGEOLOGIC CLASSIFICATION SYSTEM IN MID-ATLANTIC COASTAL PLAIN STREAMS USING BENTHIC MACROINVERTEBRATES

    EPA Science Inventory

    Assessing classification systems that describe natural variation across regions is an important first step for developing indicators. We evaluated a hydrogeologic framework for first order streams in the mid-Atlantic Coastal Plain as part of the LIPS-MACS (Landscape Indicators f...

  6. IMPACTS OF PATCH SIZE AND LAND COVER HETEROGENEITY ON THEMATIC IMAGE CLASSIFICATION ACCURACY

    EPA Science Inventory


    Landscape characteristics such as small patch size and land cover heterogeneity have been hypothesized to increase the likelihood of miss-classifying pixels during thematic image classification. However, there has been a lack of empirical evidence to support these hypotheses,...

  7. Renewable energy from urban landscapes

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Utilizing biomass from urban landscapes could significantly contribute to the nation’s renewable energy needs. In 2007, an experiment was begun to evaluate the biomass potential from a bermudagrass, Cynodon dactylon var. dactylon (L.) Pers., lawn in Woodward, OK and to estimate the potential biomas...

  8. Flowers and Landscape by Serendipity.

    ERIC Educational Resources Information Center

    Pippin, Sandi

    2003-01-01

    Describes an art lesson in which students sketch drawings of flowers and use watercolor paper and other materials to paint a landscape. Explains that the students also learn about impressionism in this lesson. Discusses how the students prepare the paper and create their artwork. (CMK)

  9. Linguistic Landscape and Minority Languages

    ERIC Educational Resources Information Center

    Cenoz, Jasone; Gorter, Durk

    2006-01-01

    This paper focuses on the linguistic landscape of two streets in two multilingual cities in Friesland (Netherlands) and the Basque Country (Spain) where a minority language is spoken, Basque or Frisian. The paper analyses the use of the minority language (Basque or Frisian), the state language (Spanish or Dutch) and English as an international…

  10. SAVANNAH RIVER BASIN LANDSCAPE ANALYSIS

    EPA Science Inventory

    Scientists from the U.S. Environmental Protection Agency (EPA), Region 4, Science and Ecosystem Support Division, enlisted the assistance of the landscape ecology group of U.S. EPA, Office of Research and Development (ORD), National Exposure Research Laboratory, Environmental Sci...

  11. Axion landscape and natural inflation

    NASA Astrophysics Data System (ADS)

    Higaki, Tetsutaro; Takahashi, Fuminobu

    2015-05-01

    Multiple axions form a landscape in the presence of various shift symmetry breaking terms. Eternal inflation populates the axion landscape, continuously creating new universes by bubble nucleation. Slow-roll inflation takes place after the tunneling event, if a very flat direction with a super-Planckian decay constant arises due to the alignment mechanism. We study the vacuum structure as well as possible inflationary dynamics in the axion landscape scenario, and find that the inflaton dynamics is given by either natural or multi-natural inflation. In the limit of large decay constant, it is approximated by the quadratic chaotic inflation, which however is disfavored if there is a pressure toward shorter duration of inflation. Therefore, if the spectral index and the tensor-to-scalar ratio turn out to be different from the quadratic chaotic inflation, there might be observable traces of the bubble nucleation. Also, the existence of small modulations to the inflaton potential is a common feature in the axion landscape, which generates a sizable and almost constant running of the scalar spectral index over CMB scales. Non-Gaussianity of equilateral type can also be generated if some of the axions are coupled to massless gauge fields.

  12. Ornamental Landscape Grasses. Slide Script.

    ERIC Educational Resources Information Center

    Still, Steven M.; Adams, Denise W.

    This slide script to accompany the slide series, Ornamental Landscape Grasses, contains photographs of the 167 slides and accompanying narrative text intended for use in the study and identification of commercially important ornamental grasses and grasslike plants. Narrative text is provided for slides of 62 different perennial and annual species…

  13. LANDSCAPING YOUR HOME, TEACHER'S GUIDE.

    ERIC Educational Resources Information Center

    HEDGES, LOWELL E.

    THE PURPOSE OF THIS GUIDE IS TO ASSIST THE VOCATIONAL AGRICULTURE TEACHER TO DEVELOP A UNIT IN THE RELATIVELY SPECIALIZED FIELD OF HOME LANDSCAPING. IT WAS DEVELOPED BY A TEACHER IN CONSULTATION WITH HORTICULTURISTS AND TESTED IN THE CLASSROOM BEFORE PUBLICATION. THE OBJECTIVES OF THE UNIT ARE TO DEVELOP STUDENT ABILITY TO (1) UNDERSTAND THE NEED…

  14. Language's Landscape of the Mind.

    ERIC Educational Resources Information Center

    Tracy, Janet

    2000-01-01

    Describes how the author's 6 middle school students living in a village in the Yukon, 100 miles off the road system just below the arctic circle, enthusiastically wrote stories or poems about their lives. The students shared their works via an online electronic conferencing system with students from the unimaginably different landscape of the…

  15. Selected Landscape Plants. Slide Script.

    ERIC Educational Resources Information Center

    McCann, Kevin

    This slide script, part of a series of slide scripts designed for use in vocational agriculture classes, deals with commercially important woody ornamental landscape plants. Included in the script are narrations for use with a total of 253 slides illustrating 92 different plants. Several slides are used to illustrate each plant: besides a view of…

  16. A World of Peace and Military Landscapes.

    ERIC Educational Resources Information Center

    Brunn, Stanley D.

    1987-01-01

    Defines "peace landscapes" as areas having a virtual absence of conflict, such the border between the United States and Canada. Identifies "military landscapes" as those having intense military conflicts, as in the Iran-Iraq war. Examines the components of these landscapes and identifies the contributions geographers can make to better understand…

  17. Space Strategies for the New Learning Landscape

    ERIC Educational Resources Information Center

    Dugdale, Shirley

    2009-01-01

    The Learning Landscape is the total context for students' learning experiences and the diverse landscape of learning settings available today--from specialized to multipurpose, from formal to informal, and from physical to virtual. The goal of the Learning Landscape approach is to acknowledge this richness and maximize encounters among people,…

  18. The Changing Landscape of Higher Education

    ERIC Educational Resources Information Center

    Staley, David J.; Trinkle, Dennis A.

    2011-01-01

    The landscape of higher education--the growing variety of higher education institutions, the cultural environment, the competitive ecosystem--is changing rapidly and disruptively. The higher education landscape is metaphorically crossed with fault lines, those fissures in the landscape creating potential areas of dramatic change, and is as…

  19. Multifunctional landscapes--perspectives for the future.

    PubMed

    Brandt, Jesper

    2003-03-01

    New methods in landscape ecology to study the link between landscape heterogeneity and landscape functionality are needed. Heterogeneity is a basic characteristic of landscape, and landscape function is the capacity to change the structural heterogeneity of a landscape system. In most developed countries the industrialisation of agriculture has in general resulted in a change of agricultural landscapes from a small-grained heterogeneous pattern towards more monotonous and monofunctional landscapes. During the 1990's this trends seem to have changed due to a diversification of rural land use, and new trends in ubanisation. Whether these phases of landscape development should be expected in developing countries is a totally open question. Dealing with the study of multifunctionality of landscapes it is proposed to distinguish between ecological functionality of landscape ecosystems, functionality pertaining to land use and social functionality. Further, the relation between function, space and scale is important by the determination of spatial and time segregation as well as spatial and time integration of multifunctionality in landscapes. PMID:12765260

  20. Soil Respiration in Response to Landscape Position

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Variations in soil type, due to landscape position, may influence soil respiration. This study was conducted to determine how landscape position (summit, side-slope, and depression) influences heterotrophic and autotrophic soil respiration. Soil respiration was determined at three landscape positio...

  1. Characterizing fish community diversity across Virginia landscapes: Prerequisite for conservation

    USGS Publications Warehouse

    Angermeier, P.L.; Winston, M.R.

    1999-01-01

    The number of community types occurring within landscapes is an important, but often unprotected, component of biological diversity. Generally applicable protocols for characterizing community diversity need to be developed to facilitate conservation. We used several multivariate techniques to analyze geographic variation in the composition of fish communities in Virginia streams. We examined relationships between community composition and six landscape variables: drainage basin, physiography, stream order, elevation, channel slope, and map coordinates. We compared patterns at two scales (statewide and subdrainage-specific) to assess sensitivity of community classification to spatial scale. We also compared patterns based on characterizing communities by species composition vs. ecological composition. All landscape variables explained significant proportions of the variance in community composition. Statewide, they explained 32% of the variance in species composition and 48% of the variance in ecological composition. Typical communities in each drainage or physiography were statistically distinctive. Communities in different combinations of drainage, physiography, and stream size were even more distinctive, but composition was strongly spatially autocorrelated. Ecological similarity and species similarity of community pairs were strongly related, but replacement by ecologically similar species was common among drainage-physiography combinations. Landscape variables explained significant proportions of variance in community composition within selected subdrainages, but proportions were less than at the statewide scale, and the explanatory power of individual variables varied considerably among subdrainages. Community variation within subdrainages appeared to be much more closely related to environmental variation than to replacement among ecologically similar species. Our results suggest that taxonomic and ecological characterizations of community composition are

  2. Estimating biogeochemical fluxes across sagebrush-steppe landscapes with Thematic Mapper imagery

    NASA Technical Reports Server (NTRS)

    Reiners, W. A.; Strong, L. L.; Matson, P. A.; Burke, I. C.; Ojima, D. S.

    1989-01-01

    Thematic Mapper (TM) satellite data were coupled to an ecosystem simulation model to simulate variation in nitrogen mineralization over time and space in a sagebrush steppe. This system of data inputs and calculations provides estimates of ecosystem properties including rates of biogeochemical processes over extensive and complex landscapes, and under changing management and climatic conditions. The landscape surface was divided into three sagebrush ecosystem types plus one other class consisting of nonsagebrush vegetation. This classification presented a complex mosaic of ecosystem types that shifted markedly in composition from one end of the 933-sq km study area to the other. Annual N-mineralization rates ranged from 5 to 25 kg N/ha among the three sagebrush types. The most active type comprised 42 percent of the entire area but contributed 60 percent to the nitrogen mineralization throughout the landscape.

  3. Mapping Crop Patterns in Central US Agricultural Systems from 2000 to 2014 Based on Landsat Data: To What Degree Does Fusing MODIS Data Improve Classification Accuracies?

    NASA Astrophysics Data System (ADS)

    Zhu, L.; Radeloff, V.; Ives, A. R.; Barton, B.

    2015-12-01

    Deriving crop pattern with high accuracy is of great importance for characterizing landscape diversity, which affects the resilience of food webs in agricultural systems in the face of climatic and land cover changes. Landsat sensors were originally designed to monitor agricultural areas, and both radiometric and spatial resolution are optimized for monitoring large agricultural fields. Unfortunately, few clear Landsat images per year are available, which has limited the use of Landsat for making crop classification, and this situation is worse in cloudy areas of the Earth. Meanwhile, the MODerate Resolution Imaging Spectroradiometer (MODIS) data has better temporal resolution but cannot capture fine spatial heterogeneity of agricultural systems. Our question was to what extent fusing imagery from both sensors could improve crop classifications. We utilized the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm to simulate Landsat-like images at MODIS temporal resolution. Based on Random Forests (RF) classifier, we tested whether and by what degree crop maps from 2000 to 2014 of the Arlington Agricultural Research Station (Wisconsin, USA) were improved by integrating available clear Landsat images each year with synthetic images. We predicted that the degree to which classification accuracy can be improved by incorporating synthetic imagery depends on the number and acquisition time of clear Landsat images. Moreover, multi-season data are essential for mapping crop types by capturing their phenological dynamics, and STARFM-simulated images can be used to compensate for missing Landsat observations. Our study is helpful for eliminating the limits of the use of Landsat data in mapping crop patterns, and can provide a benchmark of accuracy when choosing STARFM-simulated images to make crop classification at broader scales.

  4. Adaptation to elevated CO2 in different biodiversity contexts

    PubMed Central

    Kleynhans, Elizabeth J.; Otto, Sarah P.; Reich, Peter B.; Vellend, Mark

    2016-01-01

    In the absence of migration, species persistence depends on adaption to a changing environment, but whether and how adaptation to global change is altered by community diversity is not understood. Community diversity may prevent, enhance or alter how species adapt to changing conditions by influencing population sizes, genetic diversity and/or the fitness landscape experienced by focal species. We tested the impact of community diversity on adaptation by performing a reciprocal transplant experiment on grasses that evolved for 14 years under ambient and elevated CO2, in communities of low or high species richness. Using biomass as a fitness proxy, we find evidence for local adaptation to elevated CO2, but only for plants assayed in a community of similar diversity to the one experienced during the period of selection. Our results indicate that the biological community shapes the very nature of the fitness landscape within which species evolve in response to elevated CO2. PMID:27510545

  5. Land Use Classification from Vhr Aerial Images Using Invariant Colour Components and Texture

    NASA Astrophysics Data System (ADS)

    Movia, A.; Beinat, A.; Sandri, T.

    2016-06-01

    Very high resolution (VHR) aerial images can provide detailed analysis about landscape and environment; nowadays, thanks to the rapid growing airborne data acquisition technology an increasing number of high resolution datasets are freely available. In a VHR image the essential information is contained in the red-green-blue colour components (RGB) and in the texture, therefore a preliminary step in image analysis concerns the classification in order to detect pixels having similar characteristics and to group them in distinct classes. Common land use classification approaches use colour at a first stage, followed by texture analysis, particularly for the evaluation of landscape patterns. Unfortunately RGB-based classifications are significantly influenced by image setting, as contrast, saturation, and brightness, and by the presence of shadows in the scene. The classification methods analysed in this work aim to mitigate these effects. The procedures developed considered the use of invariant colour components, image resampling, and the evaluation of a RGB texture parameter for various increasing sizes of a structuring element. To identify the most efficient solution, the classification vectors obtained were then processed by a K-means unsupervised classifier using different metrics, and the results were compared with respect to corresponding user supervised classifications. The experiments performed and discussed in the paper let us evaluate the effective contribution of texture information, and compare the most suitable vector components and metrics for automatic classification of very high resolution RGB aerial images.

  6. Delaware Alternative Classifications

    ERIC Educational Resources Information Center

    Miller, Jay

    1975-01-01

    This article discusses the species designation and taxonomies of Delaware and Algonkian and presents eight classifications of taxa by form, habitat, color, movement, sound, use, relationship, and appearance. Relevant research is also reviewed. (CLK)

  7. Multiscale assessment of landscape structure in heterogeneous forested area

    NASA Astrophysics Data System (ADS)

    Simoniello, T.; Pignatti, S.; Carone, M. T.; Fusilli, L.; Lanfredi, M.; Coppola, R.; Santini, F.

    2010-05-01

    The characterization of landscape structure in space or time is fundamental to infer ecological processes (Ingegnoli, 2002). Landscape pattern arrangements strongly influence forest ecological functioning and biodiversity, as an example landscape fragmentation can induce habitat degradation reducing forest species populations or limiting their recolonization. Such arrangements are spatially correlated and scale-dependent, therefore they have distinctive operational-scales at which they can be best characterized (Wu, 2004). In addition, the detail of the land cover classification can have substantial influences on resulting pattern quantification (Greenberg et al.2001). In order to evaluate the influence of the observational scales and labelling details, we investigated a forested area (Pollino National Park; southern Italy) by analyzing the patch arrangement derived from three remote sensing sensors having different spectral and spatial resolutions. In particular, we elaborated data from the hyperspectral MIVIS (102 bands; ~7m) and Hyperion (220 bands; 30m), and the multispectral Landsat-TM (7 bands; 30m). Moreover, to assess the landscape evolution we investigated the hierarchical structure of the study area (landscape, class, patch) by elaborating two Landsat-TM acquired in 1987 and 1998. Preprocessed data were classified by adopting a supervised procedure based on the Minimum Distance classifier. The obtained labelling correspond to Corine level 5 for the high resolution MIVIS data, to Corine level 4 for Hyperion and to an intermediate level 4-3 for TM data. The analysis was performed by taking into account patch density, diversity and evenness at landscape level; mean patch size and interdispersion at class level; patch structure and perimeter regularity at patch level. The three sensors described a landscape with a quite high level of richness and distribution. The high spectral and spatial resolution of MIVIS data provided the highest diversity level (SHDI

  8. Landscape-scale modelling of soil carbon dynamics under land use and climate change

    NASA Astrophysics Data System (ADS)

    Lacoste, Marine; Viaud, Valérie; Michot, Didier; Christian, Walter

    2013-04-01

    Soil organic carbon (SOC) sequestration is highly linked to soil use and farming practices, but also to soil redistributions, soil properties, and climate. In a global change context, landscape, farming practice and climate changes are expected; and they will most probably impact SOC dynamics. To assess their respective impacts, we modelled the SOC contents and stocks evolution at the scale of an agricultural landscape, by taking into account the soil redistribution by tillage and water processes. The simulations were conducted from 2010 to 2100 under different scenarios of landscape and climate. These scenarios combined different land uses associated to specific farming practices (mixed dairy with rotations of crops and grasslands, intensive cropping with only crops rotations or permanent grasslands), landscape managements (hedges planting or removal), and climates (business-as-usual climate and climate change, with temperature and precipitations increase). We used a spatially SOC dynamic model (adapted from RothC), coupled to a soil redistribution model (LandSoil). SOC dynamics were spatially modelled with a lateral resolution of 2-m and for soil organic layers up to 105 cm. Initial SOC stocks were described with a 2-m resolution map based on field data and produced with digital soil mapping methods. The major factor of change in SOC stocks was land use change, the second factor of importance was climate change, and finally landscape management: for the total SOC stocks (0-to-105 cm soil layer) the change of land use, climate and landscape management induced a respective mean absolute variation of 10 to 20 tC ha-1, 9 tC ha-1 and 0.4 tC ha-1. When considering the 0-to-105 cm soil layer, the different modelled landscapes showed the same sensitivity to climate change, with induced a mean decrease of 10 tC ha-1. However, the impact of climate change was found different according to the different modelled landscape when considering the 0-to-7.5 and 0-to-30 cm soil

  9. Supernova Photometric Lightcurve Classification

    NASA Astrophysics Data System (ADS)

    Zaidi, Tayeb; Narayan, Gautham

    2016-01-01

    This is a preliminary report on photometric supernova classification. We first explore the properties of supernova light curves, and attempt to restructure the unevenly sampled and sparse data from assorted datasets to allow for processing and classification. The data was primarily drawn from the Dark Energy Survey (DES) simulated data, created for the Supernova Photometric Classification Challenge. This poster shows a method for producing a non-parametric representation of the light curve data, and applying a Random Forest classifier algorithm to distinguish between supernovae types. We examine the impact of Principal Component Analysis to reduce the dimensionality of the dataset, for future classification work. The classification code will be used in a stage of the ANTARES pipeline, created for use on the Large Synoptic Survey Telescope alert data and other wide-field surveys. The final figure-of-merit for the DES data in the r band was 60% for binary classification (Type I vs II).Zaidi was supported by the NOAO/KPNO Research Experiences for Undergraduates (REU) Program which is funded by the National Science Foundation Research Experiences for Undergraduates Program (AST-1262829).

  10. Impacts of patch size and land-cover heterogeneity on thematic image classification accuracy

    USGS Publications Warehouse

    Smith, Jonathan H.; Wickham, James D.; Stehman, Stephen V.; Yang, Limin

    2002-01-01

    Landscape chamcteristics such as small patch size and landcover heterogeneity have been hypothesized to increase the likelihood of mis-classifying pixels during thematic image classification. However, there has been a lack of empirical evidence to support these hypotheses. This study utilizes data gathered as part of the accuracy assessment of the 1992 National Land Cover Data (NLCD) set to identify and quantify the impacts of land-cover heterogeneity and patch size on classification accuracy Logistic regression is employed to assess the impacts of these variables, as well as the impact of land-cover class information. The results reveal that accuracy decreases as landcover heterogeneity increases and as patch size decreases. These landscape variables remain significant factors in explaining classification accuracy even when adjusted for their confounding association with land-cover class information.

  11. Better image texture recognition based on SVM classification

    NASA Astrophysics Data System (ADS)

    Liu, Kuan; Lu, Bin; Wei, Yaxun

    2013-10-01

    Texture classification is very important in remote sensing images, X-ray photos, cell image interpretation and processing, and is also the active research areas of computer vision, image processing, image analysis, image retrieval, and so on. As to spatial domain image, texture analysis can use statistical methods to calculate the texture feature vector. In this paper, we use the gray level co-occurrence matrix and Gabor filter feature vector to calculate the feature vector. For the feature vector classification under normal circumstances we can use Bayesian method, KNN method, BP neural network. In this paper, we use a statistical classification method which is based on SVM method to classify images. Image classification generally includes image preprocessing, image feature extraction, image feature selection and image classification in four steps. In this paper, we use a gray-scale image, by calculating the image gray level cooccurrence matrix and Gabor filtering method to get feature extraction, and then use SVM to training and classification. From the test results, it shows that the SVM method is the better way to solve the problem of texture features for image classification and it shows strong adaptability and robustness for image classification.

  12. Neighbourhood-scale urban forest ecosystem classification.

    PubMed

    Steenberg, James W N; Millward, Andrew A; Duinker, Peter N; Nowak, David J; Robinson, Pamela J

    2015-11-01

    Urban forests are now recognized as essential components of sustainable cities, but there remains uncertainty concerning how to stratify and classify urban landscapes into units of ecological significance at spatial scales appropriate for management. Ecosystem classification is an approach that entails quantifying the social and ecological processes that shape ecosystem conditions into logical and relatively homogeneous management units, making the potential for ecosystem-based decision support available to urban planners. The purpose of this study is to develop and propose a framework for urban forest ecosystem classification (UFEC). The multifactor framework integrates 12 ecosystem components that characterize the biophysical landscape, built environment, and human population. This framework is then applied at the neighbourhood scale in Toronto, Canada, using hierarchical cluster analysis. The analysis used 27 spatially-explicit variables to quantify the ecosystem components in Toronto. Twelve ecosystem classes were identified in this UFEC application. Across the ecosystem classes, tree canopy cover was positively related to economic wealth, especially income. However, education levels and homeownership were occasionally inconsistent with the expected positive relationship with canopy cover. Open green space and stocking had variable relationships with economic wealth and were more closely related to population density, building intensity, and land use. The UFEC can provide ecosystem-based information for greening initiatives, tree planting, and the maintenance of the existing canopy. Moreover, its use has the potential to inform the prioritization of limited municipal resources according to ecological conditions and to concerns of social equity in the access to nature and distribution of ecosystem service supply. PMID:26311086

  13. Adapting ODC for Empirical Evaluation of Pre-Launch Anomalies

    NASA Technical Reports Server (NTRS)

    Lutz, Robyn; Mikulski, Carmen

    2003-01-01

    This slide presentation reviews the concept of using Orthogonal Defect Classification (ODC) to identify pre-launch anomalies in software. The goals of this work are: (1) To characterize pre-launch software anomalies, using data from multiple spacecraft projects, by means of a defect-analysis technology, Orthogonal Defect Classification (ODC). (2) To support transfer of ODC to NASA projects through applications and demonstrations. Approach: Analyzed anomaly data using adaptation of Orthogonal Defect Classification (ODC) method. This project has adapted ODC for NASA use and applied to NASA projects.

  14. Reserves, resilience and dynamic landscapes.

    PubMed

    Bengtsson, Janne; Angelstam, Per; Elmqvist, Thomas; Emanuelsson, Urban; Folke, Carl; Ihse, Margareta; Moberg, Fredrik; Nyström, Magnus

    2003-09-01

    In a world increasingly modified by human activities, the conservation of biodiversity is essential as insurance to maintain resilient ecosystems and ensure a sustainable flow of ecosystem goods and services to society. However, existing reserves and national parks are unlikely to incorporate the long-term and large-scale dynamics of ecosystems. Hence, conservation strategies have to actively incorporate the large areas of land that are managed for human use. For ecosystems to reorganize after large-scale natural and human-induced disturbances, spatial resilience in the form of ecological memory is a prerequisite. The ecological memory is composed of the species, interactions and structures that make ecosystem reorganization possible, and its components may be found within disturbed patches as well in the surrounding landscape. Present static reserves should be complemented with dynamic reserves, such as ecological fallows and dynamic successional reserves, that are part of ecosystem management mimicking natural disturbance regimes at the landscape level. PMID:14627367

  15. Landscape dynamics of northeastern forests

    NASA Technical Reports Server (NTRS)

    Canham, Charles D.; Silander, John A., Jr.; Civco, Daniel L.

    1994-01-01

    This project involves collaborative research with Stephen W. Pacala and Simon A. Levin of Princeton University to calibrate, test, and analyze models of heterogeneous forested landscapes containing a diverse array of habitats. The project is an extension of previous, NASA-supported research to develop a spatially-explicit model of forest dynamics at the scale of an individual forest stand (hectares to square kilometer spatial scales). That model (SORTIE) has been thoroughly parameterized from field studies in the modal upland environment of western Connecticut. Under our current funding, we are scaling-up the model and parameterizing it for the broad range of upland environments in the region. Our most basic goal is to understand the linkages between stand-level dynamics (as revealed in our previous research) and landscape-level dynamics of forest composition and structure.

  16. Geomorphology and surface hydrology applied to landscape reclamation in the strippable coal belts of northwestern New Mexico. Final report

    SciTech Connect

    Wells, S.G.

    1982-06-01

    The long-term success of reclamation of surface coal mines in the strippable coal belts of northwestern New Mexico is dependent upon the relative stability of undisturbed and restored landscapes. Areas of rapid modification, or relative instability, include headwaters of high-relief watersheds and areas of active base-level lowering. Stable landscapes are characterized by high infiltration rates, low sediment yields, low relief, and relatively dense vegetation. Landscape-classification schemes incorporating modern geomorphic processes and relative landscape ages serve as guides for reclaiming landscapes to stable forms. Evaluating the success of post-mining reclamation procedures requires that both internal (within reclaimed areas) and external (outside reclaimed areas) geomorphic variables be considered. Internal geomorphic variables include hillslope gradients and areal configurations, infiltration rates, degree of drainage integration, and surface roughness. External geomorphic variables include base-level changes, gully head-cutting rates, valley-fill geometry, and the ratio of bedrock to valley fill. Engineering designs are significant to internal variables; whereas, the geomorphic history of a watershed influences the external variables. Research at the McKinley Coal Mine in northwestern New Mexico suggests that external variables may pose the greatest threat to reclaimed landscapes. This report contains base-line information for preparing environmental documents, for designing optimum reclamation procedures and realistic goals, and for evaluating post-mining effects on the reclaimed landscape. Additionally, this report contains an annotated bibliography on surface coal-mining reclamation.

  17. Genomic Landscapes of Pancreatic Neoplasia

    PubMed Central

    Wood, Laura D.; Hruban, Ralph H.

    2015-01-01

    Pancreatic cancer is a deadly disease with a dismal prognosis. However, recent advances in sequencing and bioinformatic technology have led to the systematic characterization of the genomes of all major tumor types in the pancreas. This characterization has revealed the unique genomic landscape of each tumor type. This knowledge will pave the way for improved diagnostic and therapeutic approaches to pancreatic tumors that take advantage of the genetic alterations in these neoplasms. PMID:25812653

  18. Synonymous Genes Explore Different Evolutionary Landscapes

    PubMed Central

    Cambray, Guillaume; Mazel, Didier

    2008-01-01

    The evolutionary potential of a gene is constrained not only by the amino acid sequence of its product, but by its DNA sequence as well. The topology of the genetic code is such that half of the amino acids exhibit synonymous codons that can reach different subsets of amino acids from each other through single mutation. Thus, synonymous DNA sequences should access different regions of the protein sequence space through a limited number of mutations, and this may deeply influence the evolution of natural proteins. Here, we demonstrate that this feature can be of value for manipulating protein evolvability. We designed an algorithm that, starting from an input gene, constructs a synonymous sequence that systematically includes the codons with the most different evolutionary perspectives; i.e., codons that maximize accessibility to amino acids previously unreachable from the template by point mutation. A synonymous version of a bacterial antibiotic resistance gene was computed and synthesized. When concurrently submitted to identical directed evolution protocols, both the wild type and the recoded sequence led to the isolation of specific, advantageous phenotypic variants. Simulations based on a mutation isolated only from the synthetic gene libraries were conducted to assess the impact of sub-functional selective constraints, such as codon usage, on natural adaptation. Our data demonstrate that rational design of synonymous synthetic genes stands as an affordable improvement to any directed evolution protocol. We show that using two synonymous DNA sequences improves the overall yield of the procedure by increasing the diversity of mutants generated. These results provide conclusive evidence that synonymous coding sequences do experience different areas of the corresponding protein adaptive landscape, and that a sequence's codon usage effectively constrains the evolution of the encoded protein. PMID:19008944

  19. The immunological landscape in necrotising enterocolitis.

    PubMed

    Cho, Steven X; Berger, Philip J; Nold-Petry, Claudia A; Nold, Marcel F

    2016-01-01

    Necrotising enterocolitis (NEC) is an uncommon, but devastating intestinal inflammatory disease that predominantly affects preterm infants. NEC is sometimes dubbed the spectre of neonatal intensive care units, as its onset is insidiously non-specific, and once the disease manifests, the damage inflicted on the baby's intestine is already disastrous. Subsequent sepsis and multi-organ failure entail a mortality of up to 65%. Development of effective treatments for NEC has stagnated, largely because of our lack of understanding of NEC pathogenesis. It is clear, however, that NEC is driven by a profoundly dysregulated immune system. NEC is associated with local increases in pro-inflammatory mediators, e.g. Toll-like receptor (TLR) 4, nuclear factor-κB, tumour necrosis factor, platelet-activating factor (PAF), interleukin (IL)-18, interferon-gamma, IL-6, IL-8 and IL-1β. Deficiencies in counter-regulatory mechanisms, including IL-1 receptor antagonist (IL-1Ra), TLR9, PAF-acetylhydrolase, transforming growth factor beta (TGF-β)1&2, IL-10 and regulatory T cells likely facilitate a pro-inflammatory milieu in the NEC-afflicted intestine. There is insufficient evidence to conclude a predominance of an adaptive Th1-, Th2- or Th17-response in the disease. Our understanding of the accompanying regulation of systemic immunity remains poor; however, IL-1Ra, IL-6, IL-8 and TGF-β1 show promise as biomarkers. Here, we chart the emerging immunological landscape that underpins NEC by reviewing the involvement and potential clinical implications of innate and adaptive immune mediators and their regulation in NEC. PMID:27341512

  20. Energy landscapes and persistent minima

    NASA Astrophysics Data System (ADS)

    Carr, Joanne M.; Mazauric, Dorian; Cazals, Frédéric; Wales, David J.

    2016-02-01

    We consider a coarse-graining of high-dimensional potential energy landscapes based upon persistences, which correspond to lowest barrier heights to lower-energy minima. Persistences can be calculated efficiently for local minima in kinetic transition networks that are based on stationary points of the prevailing energy landscape. The networks studied here represent peptides, proteins, nucleic acids, an atomic cluster, and a glassy system. Minima with high persistence values are likely to represent some form of alternative structural morphology, which, if appreciably populated at the prevailing temperature, could compete with the global minimum (defined as infinitely persistent). Threshold values on persistences (and in some cases equilibrium occupation probabilities) have therefore been used in this work to select subsets of minima, which were then analysed to see how well they can represent features of the full network. Simplified disconnectivity graphs showing only the selected minima can convey the funnelling (including any multiple-funnel) characteristics of the corresponding full graphs. The effect of the choice of persistence threshold on the reduced disconnectivity graphs was considered for a system with a hierarchical, glassy landscape. Sets of persistent minima were also found to be useful in comparing networks for the same system sampled under different conditions, using minimum oriented spanning forests.