Simulating the spread of selection-driven genotypes using landscape resistance models for desert bighorn sheep.

PubMed

Creech, Tyler G; Epps, Clinton W; Landguth, Erin L; Wehausen, John D; Crowhurst, Rachel S; Holton, Brandon; Monello, Ryan J

2017-01-01

Landscape genetic studies based on neutral genetic markers have contributed to our understanding of the influence of landscape composition and configuration on gene flow and genetic variation. However, the potential for species to adapt to changing landscapes will depend on how natural selection influences adaptive genetic variation. We demonstrate how landscape resistance models can be combined with genetic simulations incorporating natural selection to explore how the spread of adaptive variation is affected by landscape characteristics, using desert bighorn sheep (Ovis canadensis nelsoni) in three differing regions of the southwestern United States as an example. We conducted genetic sampling and least-cost path modeling to optimize landscape resistance models independently for each region, and then simulated the spread of an adaptive allele favored by selection across each region. Optimized landscape resistance models differed between regions with respect to landscape variables included and their relationships to resistance, but the slope of terrain and the presence of water barriers and major roads had the greatest impacts on gene flow. Genetic simulations showed that differences among landscapes strongly influenced spread of adaptive genetic variation, with faster spread (1) in landscapes with more continuously distributed habitat and (2) when a pre-existing allele (i.e., standing genetic variation) rather than a novel allele (i.e., mutation) served as the source of adaptive genetic variation. The combination of landscape resistance models and genetic simulations has broad conservation applications and can facilitate comparisons of adaptive potential within and between landscapes.

Simulating the spread of selection-driven genotypes using landscape resistance models for desert bighorn sheep

PubMed Central

Epps, Clinton W.; Landguth, Erin L.; Wehausen, John D.; Crowhurst, Rachel S.; Holton, Brandon; Monello, Ryan J.

2017-01-01

Landscape genetic studies based on neutral genetic markers have contributed to our understanding of the influence of landscape composition and configuration on gene flow and genetic variation. However, the potential for species to adapt to changing landscapes will depend on how natural selection influences adaptive genetic variation. We demonstrate how landscape resistance models can be combined with genetic simulations incorporating natural selection to explore how the spread of adaptive variation is affected by landscape characteristics, using desert bighorn sheep (Ovis canadensis nelsoni) in three differing regions of the southwestern United States as an example. We conducted genetic sampling and least-cost path modeling to optimize landscape resistance models independently for each region, and then simulated the spread of an adaptive allele favored by selection across each region. Optimized landscape resistance models differed between regions with respect to landscape variables included and their relationships to resistance, but the slope of terrain and the presence of water barriers and major roads had the greatest impacts on gene flow. Genetic simulations showed that differences among landscapes strongly influenced spread of adaptive genetic variation, with faster spread (1) in landscapes with more continuously distributed habitat and (2) when a pre-existing allele (i.e., standing genetic variation) rather than a novel allele (i.e., mutation) served as the source of adaptive genetic variation. The combination of landscape resistance models and genetic simulations has broad conservation applications and can facilitate comparisons of adaptive potential within and between landscapes. PMID:28464013

Heritage landscape structure analysis in surrounding environment of the Grand Canal Yangzhou section

NASA Astrophysics Data System (ADS)

Xu, Huan

2018-03-01

The Yangzhou section of the Grand Canal is selected for a case study in this paper. The ZY-3 satellite images of 2016 are adopted as the data source. RS and GIS are used to analyze the landscape classification of the surrounding landscape of the Grand Canal, and the classification results are precisely evaluated. Next, the overall features of the landscape pattern are analyzed. The results showed that the overall accuracy is 82.5% and the Kappa coefficient is 78.17% in the Yangzhou section. The producer’s accuracy of the water landscape is the highest, followed by that of the other landscape, farmland landscape, garden and forest landscape, architectural landscape. The user’s accuracy of different landscape types can be ranked in a descending order, as the water landscape, farmland landscape, road landscape, architectural landscape, other landscape, garden and forest landscape. The farmland landscape and the architectural landscape are the top advantageous landscape types of the heritage site. The research findings can provide basic data for landscape protection, management and sustainable development of the Grand Canal Yangzhou section.

Assessment of land use and land cover change using spatiotemporal analysis of landscape: case study in south of Tehran.

PubMed

Sabr, Abutaleb; Moeinaddini, Mazaher; Azarnivand, Hossein; Guinot, Benjamin

2016-12-01

In the recent years, dust storms originating from local abandoned agricultural lands have increasingly impacted Tehran and Karaj air quality. Designing and implementing mitigation plans are necessary to study land use/land cover change (LUCC). Land use/cover classification is particularly relevant in arid areas. This study aimed to map land use/cover by pixel- and object-based image classification methods, analyse landscape fragmentation and determine the effects of two different classification methods on landscape metrics. The same sets of ground data were used for both classification methods. Because accuracy of classification plays a key role in better understanding LUCC, both methods were employed. Land use/cover maps of the southwest area of Tehran city for the years 1985, 2000 and 2014 were obtained from Landsat digital images and classified into three categories: built-up, agricultural and barren lands. The results of our LUCC analysis showed that the most important changes in built-up agricultural land categories were observed in zone B (Shahriar, Robat Karim and Eslamshahr) between 1985 and 2014. The landscape metrics obtained for all categories pictured high landscape fragmentation in the study area. Despite no significant difference was evidenced between the two classification methods, the object-based classification led to an overall higher accuracy than using the pixel-based classification. In particular, the accuracy of the built-up category showed a marked increase. In addition, both methods showed similar trends in fragmentation metrics. One of the reasons is that the object-based classification is able to identify buildings, impervious surface and roads in dense urban areas, which produced more accurate maps.

Assessment of landscape diversity and determination of landscape hotspots - a case of Slovenia

NASA Astrophysics Data System (ADS)

Perko, Drago; Ciglič, Rok; Hrvatin, Mauro

2017-04-01

Areas with high landscape diversity can be regarded as landscape hotspots, and vice versa areas with low landscape diversity can be marked as landscape coldspots. The main purpose of this paper is to use quantitative geoinformatical approach and identify parts of our test area (the country of Slovenia) that can be described as very diverse according to natural landscapes and natural elements. We used different digital raster data of natural elements and landscape classifications and defined landscape diversity and landscape hotspots. We defined diversity for each raster pixel by counting the number of different unique types of landscape elements and types of landscapes in its neighborhood. Namely, the method was used separately to define diversity according to natural elements (types of relief forms, rocks, and vegetation) and diversity according to existing geographical landscape classifications of Slovenia (types of landscapes). In both cases one-tenth of Slovenia's surface with the highest landscape diversity was defined as landscape hotspots. The same applies to the coldspots. Additionally we tested the same method of counting different types of landscapes in certain radius also for the area of Europe in order to find areas that are more diverse at continental level. By doing so we were able to find areas that have similar level of diversity as Slovenia according to different European landscape classifications. Areas with landscape diversity may have an advantage in economic development, especially in tourism. Such areas are also important for biodiversity, habitat, and species diversity. On the other hand, localities where various natural influences mix can also be areas where it is hard to transfer best practices from one place to another because of the varying responses of the landscapes to human intervention. Thus it is important to know where areas with high landscape diversity are.

[Land use and land cover charnge (LUCC) and landscape service: Evaluation, mapping and modeling].

PubMed

Song, Zhang-jian; Cao, Yu; Tan, Yong-zhong; Chen, Xiao-dong; Chen, Xian-peng

2015-05-01

Studies on ecosystem service from landscape scale aspect have received increasing attention from researchers all over the world. Compared with ecosystem scale, it should be more suitable to explore the influence of human activities on land use and land cover change (LUCC), and to interpret the mechanisms and processes of sustainable landscape dynamics on landscape scale. Based on comprehensive and systematic analysis of researches on landscape service, this paper firstly discussed basic concepts and classification of landscape service. Then, methods of evaluation, mapping and modeling of landscape service were analyzed and concluded. Finally, future trends for the research on landscape service were proposed. It was put forward that, exploring further connotation and classification system of landscape service, improving methods and quantitative indicators for evaluation, mapping and modelling of landscape service, carrying out long-term integrated researches on landscape pattern-process-service-scale relationships and enhancing the applications of theories and methods on landscape economics and landscape ecology are very important fields of the research on landscape service in future.

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...

Accuracy of Remotely Sensed Classifications For Stratification of Forest and Nonforest Lands

Treesearch

Raymond L. Czaplewski; Paul L. Patterson

2001-01-01

We specify accuracy standards for remotely sensed classifications used by FIA to stratify landscapes into two categories: forest and nonforest. Accuracy must be highest when forest area approaches 100 percent of the landscape. If forest area is rare in a landscape, then accuracy in the nonforest stratum must be very high, even at the expense of accuracy in the forest...

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...

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...

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

DOE PAGES

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

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

A new multi-scale geomorphological landscape GIS for the Netherlands

NASA Astrophysics Data System (ADS)

Weerts, Henk; Kosian, Menne; Baas, Henk; Smit, Bjorn

2013-04-01

At present, the Cultural Heritage Agency of the Netherlands is developing a nationwide landscape Geographical Information System (GIS). In this new conceptual approach, the Agency puts together several multi-scale landscape classifications in a GIS. The natural physical landscapes lie at the basis of this GIS, because these landscapes provide the natural boundary conditions for anthropogenic. At the local scale a nationwide digital geomorphological GIS is available in the Netherlands. This map, that was originally mapped at 1:50,000 from the late 1970's to the 1990's, is based on geomorphometrical (observable and measurable in the field), geomorphological and, lithological and geochronological criteria. When used at a national scale, the legend of this comprehensive geomorphological map is very complex which hampers use in e.g. planning practice or predictive archaeology. At the national scale several landscape classifications have been in use in the Netherlands since the early 1950's, typically ranging in the order of 10 -15 landscape units for the entire country. A widely used regional predictive archaeological classification has 13 archaeo-landscapes. All these classifications have been defined "top-down" and their actual content and boundaries have only been broadly defined. Thus, these classifications have little or no meaning at a local scale. We have tried to combine the local scale with the national scale. To do so, we first defined national physical geographical regions based on the new 2010 national geological map 1:500,000. We also made sure there was a reference with the European LANMAP2 classification. We arrived at 20 landscape units at the national scale, based on (1) genesis, (2) large-scale geomorphology, (3) lithology of the shallow sub-surface and (4) age. These criteria that were chosen because the genesis of the landscape largely determines its (scale of) morphology and lithology that in turn determine hydrological conditions. All together, they define the natural boundary conditions for anthropogenic use. All units have been defined, mapped and described based on these criteria. This enables the link with the European LANMAP2 GIS. The unit "Till-plateau sand region" for instance runs deep into Germany and even Poland. At the local scale, the boundaries of the national units can be defined and precisely mapped by linking them to the 1:50,000 geomorphological map polygons. Each national unit consists of a typical assemblage of local geomorphological units. So, the newly developed natural physical landscape map layer can be used from the local to the European scale.

Classification of Farmland Landscape Structure in Multiple Scales

NASA Astrophysics Data System (ADS)

Jiang, P.; Cheng, Q.; Li, M.

2017-12-01

Farmland is one of the basic terrestrial resources that support the development and survival of human beings and thus plays a crucial role in the national security of every country. Pattern change is the intuitively spatial representation of the scale and quality variation of farmland. Through the characteristic development of spatial shapes as well as through changes in system structures, functions and so on, farmland landscape patterns may indicate the landscape health level. Currently, it is still difficult to perform positioning analyses of landscape pattern changes that reflect the landscape structure variations of farmland with an index model. Depending on a number of spatial properties such as locations and adjacency relations, distance decay, fringe effect, and on the model of patch-corridor-matrix that is applied, this study defines a type system of farmland landscape structure on the national, provincial, and city levels. According to such a definition, the classification model of farmland landscape-structure type at the pixel scale is developed and validated based on mathematical-morphology concepts and on spatial-analysis methods. Then, the laws that govern farmland landscape-pattern change in multiple scales are analyzed from the perspectives of spatial heterogeneity, spatio-temporal evolution, and function transformation. The result shows that the classification model of farmland landscape-structure type can reflect farmland landscape-pattern change and its effects on farmland production function. Moreover, farmland landscape change in different scales displayed significant disparity in zonality, both within specific regions and in urban-rural areas.

[Dynamic changes of landscape pattern and hemeroby in Ximen Island wetland, Zhejiang Province, China].

PubMed

Xiao, Cui; Xie, Xue-Fen; Wu, Tao; Jiang, Guo-Jun; Bian, Hua-Jing; Xu, Wei

2014-11-01

Abstract: The hemeroby type classification system of Ximen Island wetland of Zhejiang Province was established based on the multiple datasets: SOPT-5 image data with a spatial resolution of 5 m in 2007 and 2010, its wetland land cover and land use status, the National Land Use Classification (on trail), and sea area use classification of marine industry standards as well as remote sensing data features. Meanwhile, the dynamic relationship between the landscape pattern and the degree of hemeroby in Ximen Island was investigated with the landscape indices and hemeroby index (HI) derived from the landscape pattern index and GIS spatial analysis. The results showed that the wetland landscape spatial heterogeneity, fragmentation and dominance index dropped, and the landscape shape index complexity was low. The human disturbance center developed from a dispersion type to a concentration type. The landscape type of the disturbance center was bare land and settlement. The HI rose up from the sea to the land. Settlement, wharf and traffic land had the highest HI. The HI of the mudflat cultivation, mudflats and raft-cultivation dramatically changed. Marine-terrestrial interlaced zone showed a low total HI with unstable characteristics. The number of patches declined of undisturbed, partially disturbed and completely disturbed landscapes. Mean patch areas of partially disturbed and completely disturbed landscapes increased, and that of the undisturbed decreased. Mean shape index of the undisturbed landscape decreased, while the partially disturbed and completely disturbed landscapes showed a trend of shape complication.

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...

Analysis of landscape character for visual resource management

Treesearch

Paul F. Anderson

1979-01-01

Description, classification and delineation of visual landscape character are initial steps in developing visual resource management plans. Landscape characteristics identified as key factors in visual landscape analysis include land cover/land use and landform. Landscape types, which are combinations of landform and surface features, were delineated for management...

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

Multi-scale curvature for automated identification of glaciated mountain landscapes

NASA Astrophysics Data System (ADS)

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

2014-03-01

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.

The dynamics of human-induced land cover change in miombo ecosystems of southern Africa

NASA Astrophysics Data System (ADS)

Jaiteh, Malanding Sambou

Understanding human-induced land cover change in the miombo require the consistent, geographically-referenced, data on temporal land cover characteristics as well as biophysical and socioeconomic drivers of land use, the major cause of land cover change. The overall goal of this research to examine the applications of high-resolution satellite remote sensing data in studying the dynamics of human-induced land cover change in the miombo. Specific objectives are to: (1) evaluate the applications of computer-assisted classification of Landsat Thematic Mapper (TM) data for land cover mapping in the miombo and (2) analyze spatial and temporal patterns of landscape change locations in the miombo. Stepwise Thematic Classification, STC (a hybrid supervised-unsupervised classification) procedure for classifying Landsat TM data was developed and tested using Landsat TM data. Classification accuracy results were compared to those from supervised and unsupervised classification. The STC provided the highest classification accuracy i.e., 83.9% correspondence between classified and referenced data compared to 44.2% and 34.5% for unsupervised and supervised classification respectively. Improvements in the classification process can be attributed to thematic stratification of the image data into spectrally homogenous (thematic) groups and step-by-step classification of the groups using supervised or unsupervised classification techniques. Supervised classification failed to classify 18% of the scene evidence that training data used did not adequately represent all of the variability in the data. Application of the procedure in drier miombo produced overall classification accuracy of 63%. This is much lower than that of wetter miombo. The results clearly demonstrate that digital classification of Landsat TM can be successfully implemented in the miombo without intensive fieldwork. Spatial characteristics of land cover change in agricultural and forested landscapes in central Malawi were analyzed for the period 1984 to 1995 spatial pattern analysis methods. Shifting cultivation areas, Agriculture in forested landscape, experienced highest rate of woodland cover fragmentation with mean patch size of closed woodland cover decreasing from 20ha to 7.5ha. Permanent bare (cropland and settlement) in intensive agricultural matrix landscapes increased 52% largely through the conversion of fallow areas. Protected National Park area remained fairly unchanged although closed woodland area increased by 4%, mainly from regeneration of open woodland. This study provided evidence that changes in spatial characteristics in the miombo differ with landscape. Land use change (i.e. conversion to cropland) is the primary driving force behind changes in landscape spatial patterns. Also, results revealed that exclusion of intense human use (i.e. cultivation and woodcutting) through regulations and/or fencing increased both closed woodland area (through regeneration of open woodland) and overall connectivity in the landscape. Spatial characteristics of land cover change were analyzed at locations in Malawi (wetter miombo) and Zimbabwe (drier miombo). Results indicate land cover dynamics differ both between and within case study sites. In communal areas in the Kasungu scene, land cover change is dominated by woodland fragmentation to open vegetation. Change in private commercial lands was dominantly expansion of bare (settlement and cropland) areas primarily at the expense of open vegetation (fallow land).

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 behaved LPM through a Fourier system analysis of the entire class map, or any subset of the class map (e.g. the watershed) is the focus of this work. The Fourier approach provides four improvements for LPM. First, the approach reduces any correlation between metrics by developing them within an independent (i.e. orthogonal) Fourier vector space; a Fourier vector space that includes relevant physically representative parameters ( i.e. between class Euclidean distance). Second, accounting for LCLU classification accuracy the LPM measurement precision and measurement accuracy are reported. Third, the mathematics of this approach makes it possible to compare image data captured at separate pixel resolutions or even from separate landscape scenes. Fourth, Fourier interpreted landscape pattern measurement can be a measure of the entire landscape shape, of individual landscape cover change, or as exchanges between class map subsets by operating on the entire class map, subset of class map, or separate subsets of class map[s] respectively. These LCLUC LPM are examined along the 1991-1992 and 2000-2001 records of National Land Cover Database Landsat data products. Those LPM results are used in a predictive fecal coliform model at the South Carolina watershed level in the context of past (validation study) change. Finally, the proposed LPM ability to be used as ecologically relevant environmental indicators is tested by correlating metrics with other, well known LI that consistently reveal causal processes in the literature.

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.

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). Conclusions Despite all the difficulties of studying habitat classifications (more implicit, more variable knowledge than knowledge on species), conducting landscape ethnoecological research will inevitably reveal a deeper human understanding of biological organization at a supraspecific level, where natural discontinuities are less sharp than at the species or population level. PMID:22853549

Fine resolution probabilistic land cover classification of landscapes in the southeastern United States

Treesearch

Joseph St. Peter; John Hogland; Nathaniel Anderson; Jason Drake; Paul Medley

2018-01-01

Land cover classification provides valuable information for prioritizing management and conservation operations across large landscapes. Current regional scale land cover geospatial products within the United States have a spatial resolution that is too coarse to provide the necessary information for operations at the local and project scales. This paper describes a...

Research needs for our national landscapes

Treesearch

Elwood L. Shafer

1979-01-01

The prevailing research problem for our national landscapes is: How shall we organize, control, and coordinate public and private development so as to protect, maintain, improve, and manage those landscape features that we value most? Research questions discussed include: environmental/political conflicts, taxation and zoning, landscape classification, public...

Mapping the distribution of the main host for plague in a complex landscape in Kazakhstan: An object-based approach using SPOT-5 XS, Landsat 7 ETM+, SRTM and multiple Random Forests

NASA Astrophysics Data System (ADS)

Wilschut, L. I.; Addink, E. A.; Heesterbeek, J. A. P.; Dubyanskiy, V. M.; Davis, S. A.; Laudisoit, A.; Begon, M.; Burdelov, L. A.; Atshabar, B. B.; de Jong, S. M.

2013-08-01

Plague is a zoonotic infectious disease present in great gerbil populations in Kazakhstan. Infectious disease dynamics are influenced by the spatial distribution of the carriers (hosts) of the disease. The great gerbil, the main host in our study area, lives in burrows, which can be recognized on high resolution satellite imagery. In this study, using earth observation data at various spatial scales, we map the spatial distribution of burrows in a semi-desert landscape. The study area consists of various landscape types. To evaluate whether identification of burrows by classification is possible in these landscape types, the study area was subdivided into eight landscape units, on the basis of Landsat 7 ETM+ derived Tasselled Cap Greenness and Brightness, and SRTM derived standard deviation in elevation. In the field, 904 burrows were mapped. Using two segmented 2.5 m resolution SPOT-5 XS satellite scenes, reference object sets were created. Random Forests were built for both SPOT scenes and used to classify the images. Additionally, a stratified classification was carried out, by building separate Random Forests per landscape unit. Burrows were successfully classified in all landscape units. In the ‘steppe on floodplain’ areas, classification worked best: producer's and user's accuracy in those areas reached 88% and 100%, respectively. In the ‘floodplain’ areas with a more heterogeneous vegetation cover, classification worked least well; there, accuracies were 86 and 58% respectively. Stratified classification improved the results in all landscape units where comparison was possible (four), increasing kappa coefficients by 13, 10, 9 and 1%, respectively. In this study, an innovative stratification method using high- and medium resolution imagery was applied in order to map host distribution on a large spatial scale. The burrow maps we developed will help to detect changes in the distribution of great gerbil populations and, moreover, serve as a unique empirical data set which can be used as input for epidemiological plague models. This is an important step in understanding the dynamics of plague.

Study on Ecological Risk Assessment of Guangxi Coastal Zone Based on 3s Technology

NASA Astrophysics Data System (ADS)

Zhong, Z.; Luo, H.; Ling, Z. Y.; Huang, Y.; Ning, W. Y.; Tang, Y. B.; Shao, G. Z.

2018-05-01

This paper takes Guangxi coastal zone as the study area, following the standards of land use type, divides the coastal zone of ecological landscape into seven kinds of natural wetland landscape types such as woodland, farmland, grassland, water, urban land and wetlands. Using TM data of 2000-2015 such 15 years, with the CART decision tree algorithm, for analysis the characteristic of types of landscape's remote sensing image and build decision tree rules of landscape classification to extract information classification. Analyzing of the evolution process of the landscape pattern in Guangxi coastal zone in nearly 15 years, we may understand the distribution characteristics and change rules. Combined with the natural disaster data, we use of landscape index and the related risk interference degree and construct ecological risk evaluation model in Guangxi coastal zone for ecological risk assessment results of Guangxi coastal zone.

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.

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.

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...

Modern landscape processes affecting archaeological sites along the Colorado River corridor downstream of Glen Canyon Dam, Glen Canyon National Recreation Area, Arizona

USGS Publications Warehouse

East, Amy E.; Sankey, Joel B.; Fairley, Helen C.; Caster, Joshua J.; Kasprak, Alan

2017-08-29

The landscape of the Colorado River through Glen Canyon National Recreation Area formed over many thousands of years and was modified substantially after the completion of Glen Canyon Dam in 1963. Changes to river flow, sediment supply, channel base level, lateral extent of sedimentary terraces, and vegetation in the post-dam era have modified the river-corridor landscape and have altered the effects of geologic processes that continue to shape the landscape and its cultural resources. The Glen Canyon reach of the Colorado River downstream of Glen Canyon Dam hosts many archaeological sites that are prone to erosion in this changing landscape. This study uses field evaluations from 2016 and aerial photographs from 1952, 1973, 1984, and 1996 to characterize changes in potential windblown sand supply and drainage configuration that have occurred over more than six decades at 54 archaeological sites in Glen Canyon and uppermost Marble Canyon. To assess landscape change at these sites, we use two complementary geomorphic classification systems. The first evaluates the potential for aeolian (windblown) transport of river-derived sand from the active river channel to higher elevation archaeological sites. The second identifies whether rills, gullies, or arroyos (that is, overland drainages that erode the ground surface) exist at the archaeological sites as well as the geomorphic surface, and therefore the relative base level, to which those flow paths drain. Results of these assessments are intended to aid in the management of irreplaceable archaeological resources by the National Park Service and stakeholders of the Glen Canyon Dam Adaptive Management Program.

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) ...

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.

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. Published by Elsevier Ltd.

Determination of the ecological connectivity between landscape patches obtained using the knowledge engineer (expert) classification technique

NASA Astrophysics Data System (ADS)

Selim, Serdar; Sonmez, Namik Kemal; Onur, Isin; Coslu, Mesut

2017-10-01

Connection of similar landscape patches with ecological corridors supports habitat quality of these patches, increases urban ecological quality, and constitutes an important living and expansion area for wild life. Furthermore, habitat connectivity provided by urban green areas is supporting biodiversity in urban areas. In this study, possible ecological connections between landscape patches, which were achieved by using Expert classification technique and modeled with probabilistic connection index. Firstly, the reflection responses of plants to various bands are used as data in hypotheses. One of the important features of this method is being able to use more than one image at the same time in the formation of the hypothesis. For this reason, before starting the application of the Expert classification, the base images are prepared. In addition to the main image, the hypothesis conditions were also created for each class with the NDVI image which is commonly used in the vegetation researches. Besides, the results of the previously conducted supervised classification were taken into account. We applied this classification method by using the raster imagery with user-defined variables. Hereupon, to provide ecological connections of the tree cover which was achieved from the classification, we used Probabilistic Connection (PC) index. The probabilistic connection model which is used for landscape planning and conservation studies via detecting and prioritization critical areas for ecological connection characterizes the possibility of direct connection between habitats. As a result we obtained over % 90 total accuracy in accuracy assessment analysis. We provided ecological connections with PC index and we created inter-connected green spaces system. Thus, we offered and implicated green infrastructure system model takes place in the agenda of recent years.

Application of Piedmont Landscape Ecosystem Classification as a Reference For A Vegetation and Herpetofaunal Survey on Lake Thurmond, SC

Treesearch

Victor B. Shelburne; Lawrence R. Gering; J. Drew Lanham; Gregory P. Smith; Thomas M. Floyd; Eran S. Kilpatrick

2002-01-01

Application of a Piedmont landscape ecosystem classification methodology was used as a basis for a survey of vegetation and herpetofaunal communities on a 343 hectare (846 acre) tract on Lake Thurmond near Plum Branch, SC. The site is located in the Carolina Slate Belt of the Midlands Plateau Region of the Piedmont province. A total of 160 plots were established and 30...

76 FR 77769 - North Finger Grazing Authorization Project, Malheur National Forest, Grant County, OR

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-14

... allotments within the North Finger Landscape. These allotments are within the Upper Deer Creek, Basin Creek... is to authorize grazing on all or portions of the North Finger landscape in such a manner that will... incorporating adaptive management strategies across the North Finger landscape. Adaptive Management is defined...

Multi-scale curvature for automated identification of glaciated mountain landscapes☆

PubMed Central

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

2014-01-01

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

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 catchments and continents. PMID:25396410

Hydrologic landscape regionalisation using deductive classification and random forests.

PubMed

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 catchments and continents.

Verification of hydrologic landscape derived basin-scale classifications in the Pacific Northwest

Treesearch

Keith Sawicz

2016-01-01

The interaction between the physical and climatic attributes of a basin (form) control how water is partitioned, stored, and conveyed through a catchment (function). Hydrologic Landscapes (HLs) were previously...

Development of a Digital Aquifer Permeability Map for the Pacific Southwest in Support of Hydrologic Landscape Classification: Methods

EPA Science Inventory

Researchers at the U.S. Environmental Protection Agency’s Western Ecology Division have been developing hydrologic landscape maps for selected U.S. states in an effort to create a method to identify the intrinsic watershed attributes of landscapes in regions with little dat...

Effect of host species on the topography of fitness landscape for a plant RNA virus.

PubMed

Cervera, Héctor; Lalić, Jasna; Elena, Santiago F

2016-08-31

Adaptive fitness landscapes are a fundamental concept in evolutionary biology that relate the genotype of individuals with their fitness. At the end, the evolutionary fate of evolving populations depends on the topography of the landscape, that is, the number of accessible mutational pathways and of possible fitness peaks (i.e, adaptive solutions). For long time, fitness landscapes were only theoretical constructions due to a lack of precise information on the mapping between genotypes and phenotypes. In recent years, however, efforts have been devoted to characterize the properties of empirical fitness landscapes for individual proteins or for microbes adapting to artificial environments. In a previous study, we had characterized the properties of the empirical fitness landscape defined by the first five mutations fixed during adaptation of tobacco etch potyvirus (TEV) to a new experimental host, Arabidopsis thaliana Here we evaluate the topography of this landscape in the ancestral host Nicotiana tabacum Comparing the topographies of the landscape in the two hosts, we found that some features remain similar, such as the existence of fitness holes and the prevalence of epistasis, including cases of sign and of reciprocal sign that create rugged, uncorrelated and highly random topographies. However, we also observed significant differences in the fine-grained details among both landscapes due to changes in the fitness and epistatic interactions of some genotypes. Our results support the idea that not only fitness tradeoffs between hosts but also topographical incongruences among fitness landscapes in alternative hosts may contribute to virus specialization. Despite its importance for understanding virus' evolutionary dynamics, very little is known about the topography of virus adaptive fitness landscapes and even less is known about the effect that different host species and environmental conditions may have of this topography. To bring this gap, we have evaluated the topography of a small fitness landscape formed by all genotypes that result from every possible combination of the five mutations fixed during adaptation of TEV to its novel host A. thaliana To assess the effect that host species may have on this topography, we evaluated the fitness of every genotype both in the ancestral and novel hosts. We found both landscapes share some macroscopic properties such as the existence of holes and being highly rugged and uncorrelated, yet they differ in microscopic details due to changes in the magnitude and sign of fitness and epistatic effects. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

Patterns among the ashes: Exploring the relationship between landscape pattern and the emerald ash borer

Treesearch

Susan J. Crocker; Dacia M. Meneguzzo; Greg C. Liknes

2010-01-01

Landscape metrics, including host abundance and population density, were calculated using forest inventory and land cover data to assess the relationship between landscape pattern and the presence or absence of the emerald ash borer (EAB) (Agrilus planipennis Fairmaire). The Random Forests classification algorithm in the R statistical environment was...

Multiple-factor classification of a human-modified forest landscape in the Hsuehshan Mountain Range, Taiwan.

PubMed

Berg, Kevan J; Icyeh, Lahuy; Lin, Yih-Ren; Janz, Arnold; Newmaster, Steven G

2016-12-01

Human actions drive landscape heterogeneity, yet most ecosystem classifications omit the role of human influence. This study explores land use history to inform a classification of forestland of the Tayal Mrqwang indigenous people of Taiwan. Our objectives were to determine the extent to which human action drives landscape heterogeneity. We used interviews, field sampling, and multivariate analysis to relate vegetation patterns to environmental gradients and human modification across 76 sites. We identified eleven forest classes. In total, around 70 % of plots were at lower elevations and had a history of shifting cultivation, terrace farming, and settlement that resulted in alder, laurel, oak, pine, and bamboo stands. Higher elevation mixed conifer forests were least disturbed. Arboriculture and selective harvesting were drivers of other conspicuous forest patterns. The findings show that past land uses play a key role in shaping forests, which is important to consider when setting targets to guide forest management.

Completing the land resource hierarchy

USDA-ARS?s Scientific Manuscript database

The Land Resource Hierarchy of the NRCS is a hierarchal landscape classification consisting of resource areas which represent both conceptual and spatially discrete landscape units stratifying agency programs and practices. The Land Resource Hierarchy (LRH) scales from discrete points (soil pedon an...

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. ...

Community Landscapes: An Integrative Approach to Determine Overlapping Network Module Hierarchy, Identify Key Nodes and Predict Network Dynamics

PubMed Central

Kovács, István A.; Palotai, Robin; Szalay, Máté S.; Csermely, Peter

2010-01-01

Background Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. Methodology/Principal Findings Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1) determine pervasively overlapping modules with high resolution; (2) uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3) allow the determination of key network nodes and (4) help to predict network dynamics. Conclusions/Significance The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction. PMID:20824084

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…

Applying landscape genetics to the microbial world.

PubMed

Dudaniec, Rachael Y; Tesson, Sylvie V M

2016-07-01

Landscape genetics, which explicitly quantifies landscape effects on gene flow and adaptation, has largely focused on macroorganisms, with little attention given to microorganisms. This is despite overwhelming evidence that microorganisms exhibit spatial genetic structuring in relation to environmental variables. The increasing accessibility of genomic data has opened up the opportunity for landscape genetics to embrace the world of microorganisms, which may be thought of as 'the invisible regulators' of the macroecological world. Recent developments in bioinformatics and increased data accessibility have accelerated our ability to identify microbial taxa and characterize their genetic diversity. However, the influence of the landscape matrix and dynamic environmental factors on microorganism genetic dispersal and adaptation has been little explored. Also, because many microorganisms coinhabit or codisperse with macroorganisms, landscape genomic approaches may improve insights into how micro- and macroorganisms reciprocally interact to create spatial genetic structure. Conducting landscape genetic analyses on microorganisms requires that we accommodate shifts in spatial and temporal scales, presenting new conceptual and methodological challenges not yet explored in 'macro'-landscape genetics. We argue that there is much value to be gained for microbial ecologists from embracing landscape genetic approaches. We provide a case for integrating landscape genetic methods into microecological studies and discuss specific considerations associated with the novel challenges this brings. We anticipate that microorganism landscape genetic studies will provide new insights into both micro- and macroecological processes and expand our knowledge of species' distributions, adaptive mechanisms and species' interactions in changing environments. © 2016 John Wiley & Sons Ltd.

The fragmented nature of tundra landscape

NASA Astrophysics Data System (ADS)

Virtanen, Tarmo; Ek, Malin

2014-04-01

The vegetation and land cover structure of tundra areas is fragmented when compared to other biomes. Thus, satellite images of high resolution are required for producing land cover classifications, in order to reveal the actual distribution of land cover types across these large and remote areas. We produced and compared different land cover classifications using three satellite images (QuickBird, Aster and Landsat TM5) with different pixel sizes (2.4 m, 15 m and 30 m pixel size, respectively). The study area, in north-eastern European Russia, was visited in July 2007 to obtain ground reference data. The QuickBird image was classified using supervised segmentation techniques, while the Aster and Landsat TM5 images were classified using a pixel-based supervised classification method. The QuickBird classification showed the highest accuracy when tested against field data, while the Aster image was generally more problematic to classify than the Landsat TM5 image. Use of smaller pixel sized images distinguished much greater levels of landscape fragmentation. The overall mean patch sizes in the QuickBird, Aster, and Landsat TM5-classifications were 871 m2, 2141 m2 and 7433 m2, respectively. In the QuickBird classification, the mean patch size of all the tundra and peatland vegetation classes was smaller than one pixel of the Landsat TM5 image. Water bodies and fens in particular occur in the landscape in small or elongated patches, and thus cannot be realistically classified from larger pixel sized images. Land cover patterns vary considerably at such a fine-scale, so that a lot of information is lost if only medium resolution satellite images are used. It is crucial to know the amount and spatial distribution of different vegetation types in arctic landscapes, as carbon dynamics and other climate related physical, geological and biological processes are known to vary greatly between vegetation types.

Ten Years of Landscape Genomics: Challenges and Opportunities.

PubMed

Li, Yong; Zhang, Xue-Xia; Mao, Run-Li; Yang, Jie; Miao, Cai-Yun; Li, Zhuo; Qiu, Ying-Xiong

2017-01-01

Landscape genomics is a relatively new discipline that aims to reveal the relationship between adaptive genetic imprints in genomes and environmental heterogeneity among natural populations. Although the interest in landscape genomics has increased since this term was coined, studies on this topic remain scarce. Landscape genomics has become a powerful method to scan and determine the genes responsible for the complex adaptive evolution of species at population (mostly) and individual (more rarely) level. This review outlines the sampling strategies, molecular marker types and research categories in 37 articles published during the first 10 years of this field (i.e., 2007-2016). We also address major challenges and future directions for landscape genomics. This review aims to promote interest in conducting additional studies in landscape genomics.

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.

Solutions for characterising natural landscapes in New Zealand using geographical information systems.

PubMed

Brabyn, Lars

2005-07-01

This paper explores solutions for characterising naturalness in relation to visual landscapes using Geographical Information System (GIS). It is argued that planners need to identify natural landscapes and monitor changes in their extent. Just like the indices that have been developed to describe the state of the economy, indices need to be developed that monitor the state of natural landscapes. The complications in characterising natural landscapes are outlined but it is argued that there is a need to develop definitions of natural landscapes that can be operationalised with a GIS. This will have the advantages of the efficiency of the technology and that the definition will be explicit and the implementation will be independent of the operator. Several GIS solutions are provided and these are an analysis of landcover, a density analysis of roads and utilities, and an analysis of property sizes. The analysis of property sizes is sensitive to many human modifications of the landscape because many developments begin with the subdivision of properties. However, it is argued in this paper that no one definition will suffice and that all three methods provide different, yet important, insights into natural landscape character. An aggregate classification of naturalness based on the majority value of the indices is demonstrated as well as a range of techniques for expressing the uncertainty of the aggregate classification.

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.

Multiscale landscape genomic models to detect signatures of selection in the alpine plant Biscutella laevigata.

PubMed

Leempoel, Kevin; Parisod, Christian; Geiser, Céline; Joost, Stéphane

2018-02-01

Plant species are known to adapt locally to their environment, particularly in mountainous areas where conditions can vary drastically over short distances. The climate of such landscapes being largely influenced by topography, using fine-scale models to evaluate environmental heterogeneity may help detecting adaptation to micro-habitats. Here, we applied a multiscale landscape genomic approach to detect evidence of local adaptation in the alpine plant Biscutella laevigata . The two gene pools identified, experiencing limited gene flow along a 1-km ridge, were different in regard to several habitat features derived from a very high resolution (VHR) digital elevation model (DEM). A correlative approach detected signatures of selection along environmental gradients such as altitude, wind exposure, and solar radiation, indicating adaptive pressures likely driven by fine-scale topography. Using a large panel of DEM-derived variables as ecologically relevant proxies, our results highlighted the critical role of spatial resolution. These high-resolution multiscale variables indeed indicate that the robustness of associations between genetic loci and environmental features depends on spatial parameters that are poorly documented. We argue that the scale issue is critical in landscape genomics and that multiscale ecological variables are key to improve our understanding of local adaptation in highly heterogeneous landscapes.

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...

Spatial analysis of instream nitrogen loads and factors controlling nitrogen delivery to streams in the southeastern United States using spatially referenced regression on watershed attributes (SPARROW) and regional classification frameworks

USGS Publications Warehouse

Hoos, A.B.; McMahon, G.

2009-01-01

Understanding how nitrogen transport across the landscape varies with landscape characteristics is important for developing sound nitrogen management policies. We used a spatially referenced regression analysis (SPARROW) to examine landscape characteristics influencing delivery of nitrogen from sources in a watershed to stream channels. Modelled landscape delivery ratio varies widely (by a factor of 4) among watersheds in the southeastern United States - higher in the western part (Tennessee, Alabama, and Mississippi) than in the eastern part, and the average value for the region is lower compared to other parts of the nation. When we model landscape delivery ratio as a continuous function of local-scale landscape characteristics, we estimate a spatial pattern that varies as a function of soil and climate characteristics but exhibits spatial structure in residuals (observed load minus predicted load). The spatial pattern of modelled landscape delivery ratio and the spatial pattern of residuals coincide spatially with Level III ecoregions and also with hydrologic landscape regions. Subsequent incorporation into the model of these frameworks as regional scale variables improves estimation of landscape delivery ratio, evidenced by reduced spatial bias in residuals, and suggests that cross-scale processes affect nitrogen attenuation on the landscape. The model-fitted coefficient values are logically consistent with the hypothesis that broad-scale classifications of hydrologic response help to explain differential rates of nitrogen attenuation, controlling for local-scale landscape characteristics. Negative model coefficients for hydrologic landscape regions where the primary flow path is shallow ground water suggest that a lower fraction of nitrogen mass will be delivered to streams; this relation is reversed for regions where the primary flow path is overland flow.

Spatial analysis of instream nitrogen loads and factors controlling nitrogen delivery to streams in the southeastern United States using spatially referenced regression on watershed attributes (SPARROW) and regional classification frameworks

USGS Publications Warehouse

Hoos, Anne B.; McMahon, Gerard

2009-01-01

Understanding how nitrogen transport across the landscape varies with landscape characteristics is important for developing sound nitrogen management policies. We used a spatially referenced regression analysis (SPARROW) to examine landscape characteristics influencing delivery of nitrogen from sources in a watershed to stream channels. Modelled landscape delivery ratio varies widely (by a factor of 4) among watersheds in the southeastern United States—higher in the western part (Tennessee, Alabama, and Mississippi) than in the eastern part, and the average value for the region is lower compared to other parts of the nation. When we model landscape delivery ratio as a continuous function of local-scale landscape characteristics, we estimate a spatial pattern that varies as a function of soil and climate characteristics but exhibits spatial structure in residuals (observed load minus predicted load). The spatial pattern of modelled landscape delivery ratio and the spatial pattern of residuals coincide spatially with Level III ecoregions and also with hydrologic landscape regions. Subsequent incorporation into the model of these frameworks as regional scale variables improves estimation of landscape delivery ratio, evidenced by reduced spatial bias in residuals, and suggests that cross-scale processes affect nitrogen attenuation on the landscape. The model-fitted coefficient values are logically consistent with the hypothesis that broad-scale classifications of hydrologic response help to explain differential rates of nitrogen attenuation, controlling for local-scale landscape characteristics. Negative model coefficients for hydrologic landscape regions where the primary flow path is shallow ground water suggest that a lower fraction of nitrogen mass will be delivered to streams; this relation is reversed for regions where the primary flow path is overland flow.

An ecological classification system for the central hardwoods region: The Hoosier National Forest

Treesearch

James E. Van Kley; George R. Parker

1993-01-01

This study, a multifactor ecological classification system, using vegetation, soil characteristics, and physiography, was developed for the landscape of the Hoosier National Forest in Southern Indiana. Measurements of ground flora, saplings, and canopy trees from selected stands older than 80 years were subjected to TWINSPAN classification and DECORANA ordination....

Quantitative classification of a historic northern Wisconsin (U.S.A.) landscape: mapping forests at regional scales

Treesearch

Lisa A. Schulte; David J. Mladenoff; Erik V. Nordheim

2002-01-01

We developed a quantitative and replicable classification system to improve understanding of historical composition and structure within northern Wisconsin's forests. The classification system was based on statistical cluster analysis and two forest metrics, relative dominance (% basal area) and relative importance (mean of relative dominance and relative density...

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 ...

Defining the landscape of adaptive genetic diversity.

PubMed

Eckert, Andrew J; Dyer, Rodney J

2012-06-01

Whether they are used to describe fitness, genome architecture or the spatial distribution of environmental variables, the concept of a landscape has figured prominently in our collective reasoning. The tradition of landscapes in evolutionary biology is one of fitness mapped onto axes defined by phenotypes or molecular sequence states. The characteristics of these landscapes depend on natural selection, which is structured across both genomic and environmental landscapes, and thus, the bridge among differing uses of the landscape concept (i.e. metaphorically or literally) is that of an adaptive phenotype and its distribution across geographical landscapes in relation to selective pressures. One of the ultimate goals of evolutionary biology should thus be to construct fitness landscapes in geographical space. Natural plant populations are ideal systems with which to explore the feasibility of attaining this goal, because much is known about the quantitative genetic architecture of complex traits for many different plant species. What is less known are the molecular components of this architecture. In this issue of Molecular Ecology, Parchman et al. (2012) pioneer one of the first truly genome-wide association studies in a tree that moves us closer to this form of mechanistic understanding for an adaptive phenotype in natural populations of lodgepole pine (Pinus contorta Dougl. ex Loud.). © 2012 Blackwell Publishing Ltd.

Efficient retrieval of landscape Hessian: Forced optimal covariance adaptive learning

NASA Astrophysics Data System (ADS)

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 (≳104). 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.

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.

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...

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...

Adaptive landscape and functional diversity of Neotropical cichlids: implications for the ecology and evolution of Cichlinae (Cichlidae; Cichliformes).

PubMed

Arbour, J H; López-Fernández, H

2014-11-01

Morphological, lineage and ecological diversity can vary substantially even among closely related lineages. Factors that influence morphological diversification, especially in functionally relevant traits, can help to explain the modern distribution of disparity across phylogenies and communities. Multivariate axes of feeding functional morphology from 75 species of Neotropical cichlid and a stepwise-AIC algorithm were used to estimate the adaptive landscape of functional morphospace in Cichlinae. Adaptive landscape complexity and convergence, as well as the functional diversity of Cichlinae, were compared with expectations under null evolutionary models. Neotropical cichlid feeding function varied primarily between traits associated with ram feeding vs. suction feeding/biting and secondarily with oral jaw muscle size and pharyngeal crushing capacity. The number of changes in selective regimes and the amount of convergence between lineages was higher than expected under a null model of evolution, but convergence was not higher than expected under a similarly complex adaptive landscape. Functional disparity was compatible with an adaptive landscape model, whereas the distribution of evolutionary change through morphospace corresponded with a process of evolution towards a single adaptive peak. The continentally distributed Neotropical cichlids have evolved relatively rapidly towards a number of adaptive peaks in functional trait space. Selection in Cichlinae functional morphospace is more complex than expected under null evolutionary models. The complexity of selective constraints in feeding morphology has likely been a significant contributor to the diversity of feeding ecology in this clade. © 2014 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.

Chapter 3: Simulating fire hazard across landscapes through time: integrating state-and-transition models with the Fuel Characteristic Classification System

Treesearch

Jessica E. Halofsky; Stephanie K. Hart; Miles A. Hemstrom; Joshua S. Halofsky; Morris C. Johnson

2014-01-01

Information on the effects of management activities such as fuel reduction treatments and of processes such as vegetation growth and disturbance on fire hazard can help land managers prioritize treatments across a landscape to best meet management goals. State-and-transition models (STMs) allow landscape-scale simulations that incorporate effects of succession,...

Simple adaptive sparse representation based classification schemes for EEG based brain-computer interface applications.

PubMed

Shin, Younghak; Lee, Seungchan; Ahn, Minkyu; Cho, Hohyun; Jun, Sung Chan; Lee, Heung-No

2015-11-01

One of the main problems related to electroencephalogram (EEG) based brain-computer interface (BCI) systems is the non-stationarity of the underlying EEG signals. This results in the deterioration of the classification performance during experimental sessions. Therefore, adaptive classification techniques are required for EEG based BCI applications. In this paper, we propose simple adaptive sparse representation based classification (SRC) schemes. Supervised and unsupervised dictionary update techniques for new test data and a dictionary modification method by using the incoherence measure of the training data are investigated. The proposed methods are very simple and additional computation for the re-training of the classifier is not needed. The proposed adaptive SRC schemes are evaluated using two BCI experimental datasets. The proposed methods are assessed by comparing classification results with the conventional SRC and other adaptive classification methods. On the basis of the results, we find that the proposed adaptive schemes show relatively improved classification accuracy as compared to conventional methods without requiring additional computation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Mapping dominant runoff processes: an evaluation of different approaches using similarity measures and synthetic runoff simulations

NASA Astrophysics Data System (ADS)

Antonetti, Manuel; Buss, Rahel; Scherrer, Simon; Margreth, Michael; Zappa, Massimiliano

2016-07-01

The identification of landscapes with similar hydrological behaviour is useful for runoff and flood predictions in small ungauged catchments. An established method for landscape classification is based on the concept of dominant runoff process (DRP). The various DRP-mapping approaches differ with respect to the time and data required for mapping. Manual approaches based on expert knowledge are reliable but time-consuming, whereas automatic GIS-based approaches are easier to implement but rely on simplifications which restrict their application range. To what extent these simplifications are applicable in other catchments is unclear. More information is also needed on how the different complexities of automatic DRP-mapping approaches affect hydrological simulations. In this paper, three automatic approaches were used to map two catchments on the Swiss Plateau. The resulting maps were compared to reference maps obtained with manual mapping. Measures of agreement and association, a class comparison, and a deviation map were derived. The automatically derived DRP maps were used in synthetic runoff simulations with an adapted version of the PREVAH hydrological model, and simulation results compared with those from simulations using the reference maps. The DRP maps derived with the automatic approach with highest complexity and data requirement were the most similar to the reference maps, while those derived with simplified approaches without original soil information differed significantly in terms of both extent and distribution of the DRPs. The runoff simulations derived from the simpler DRP maps were more uncertain due to inaccuracies in the input data and their coarse resolution, but problems were also linked with the use of topography as a proxy for the storage capacity of soils. The perception of the intensity of the DRP classes also seems to vary among the different authors, and a standardised definition of DRPs is still lacking. Furthermore, we argue not to use expert knowledge for only model building and constraining, but also in the phase of landscape classification.

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

Learning free energy landscapes using artificial neural networks.

PubMed

Sidky, Hythem; Whitmer, Jonathan K

2018-03-14

Existing adaptive bias techniques, which seek to estimate free energies and physical properties from molecular simulations, are limited by their reliance on fixed kernels or basis sets which hinder their ability to efficiently conform to varied free energy landscapes. Further, user-specified parameters are in general non-intuitive yet significantly affect the convergence rate and accuracy of the free energy estimate. Here we propose a novel method, wherein artificial neural networks (ANNs) are used to develop an adaptive biasing potential which learns free energy landscapes. We demonstrate that this method is capable of rapidly adapting to complex free energy landscapes and is not prone to boundary or oscillation problems. The method is made robust to hyperparameters and overfitting through Bayesian regularization which penalizes network weights and auto-regulates the number of effective parameters in the network. ANN sampling represents a promising innovative approach which can resolve complex free energy landscapes in less time than conventional approaches while requiring minimal user input.

Learning free energy landscapes using artificial neural networks

NASA Astrophysics Data System (ADS)

Sidky, Hythem; Whitmer, Jonathan K.

2018-03-01

Existing adaptive bias techniques, which seek to estimate free energies and physical properties from molecular simulations, are limited by their reliance on fixed kernels or basis sets which hinder their ability to efficiently conform to varied free energy landscapes. Further, user-specified parameters are in general non-intuitive yet significantly affect the convergence rate and accuracy of the free energy estimate. Here we propose a novel method, wherein artificial neural networks (ANNs) are used to develop an adaptive biasing potential which learns free energy landscapes. We demonstrate that this method is capable of rapidly adapting to complex free energy landscapes and is not prone to boundary or oscillation problems. The method is made robust to hyperparameters and overfitting through Bayesian regularization which penalizes network weights and auto-regulates the number of effective parameters in the network. ANN sampling represents a promising innovative approach which can resolve complex free energy landscapes in less time than conventional approaches while requiring minimal user input.

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...

Using community archetypes to better understand differential community adaptation to wildfire risk.

PubMed

Carroll, Matthew; Paveglio, Travis

2016-06-05

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'. © 2016 The Author(s).

[Molecular Genetics as Best Evidence in Glioma Diagnostics].

PubMed

Masui, Kenta; Komori, Takashi

2016-03-01

The development of a genomic landscape of gliomas has led to the internally consistent, molecularly-based classifiers. However, development of a biologically insightful classification to guide therapy is still ongoing. Further, tumors are heterogeneous, and they change and adapt in response to drugs. The challenge of developing molecular classifiers that provide meaningful ways to stratify patients for therapy remains a major challenge for the field. Therefore, by incorporating molecular markers into the new World Health Organization (WHO) classification of tumors of the central nervous system, the traditional principle of diagnosis based on histologic criteria will be replaced by a multilayered approach combining histologic features and molecular information in an "integrated diagnosis", to define tumor entities as narrowly as possible. We herein review the current status of diagnostic molecular markers for gliomas, focusing on IDH mutation, ATRX mutation, 1p/19q co-deletion, and TERT promoter mutation in adult tumors, as well as BRAF and H3F3A aberrations in pediatric gliomas, the combination of which will be a promising endeavor to render molecular genetics as a best evidence in the glioma diagnositics.

Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation.

PubMed

Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi

2016-12-16

Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency.

Combinatorial vector fields and the valley structure of fitness landscapes.

PubMed

Stadler, Bärbel M R; Stadler, Peter F

2010-12-01

Adaptive (downhill) walks are a computationally convenient way of analyzing the geometric structure of fitness landscapes. Their inherently stochastic nature has limited their mathematical analysis, however. Here we develop a framework that interprets adaptive walks as deterministic trajectories in combinatorial vector fields and in return associate these combinatorial vector fields with weights that measure their steepness across the landscape. We show that the combinatorial vector fields and their weights have a product structure that is governed by the neutrality of the landscape. This product structure makes practical computations feasible. The framework presented here also provides an alternative, and mathematically more convenient, way of defining notions of valleys, saddle points, and barriers in landscape. As an application, we propose a refined approximation for transition rates between macrostates that are associated with the valleys of the landscape.

Rare ecomorphological convergence on a complex adaptive landscape: Body size and diet mediate evolution of jaw shape in squirrels (Sciuridae).

PubMed

Zelditch, Miriam Leah; Ye, Ji; Mitchell, Jonathan S; Swiderski, Donald L

2017-03-01

Convergence is widely regarded as compelling evidence for adaptation, often being portrayed as evidence that phenotypic outcomes are predictable from ecology, overriding contingencies of history. However, repeated outcomes may be very rare unless adaptive landscapes are simple, structured by strong ecological and functional constraints. One such constraint may be a limitation on body size because performance often scales with size, allowing species to adapt to challenging functions by modifying only size. When size is constrained, species might adapt by changing shape; convergent shapes may therefore be common when size is limiting and functions are challenging. We examine the roles of size and diet as determinants of jaw shape in Sciuridae. As expected, size and diet have significant interdependent effects on jaw shape and ecomorphological convergence is rare, typically involving demanding diets and limiting sizes. More surprising is morphological without ecological convergence, which is equally common between and within dietary classes. Those cases, like rare ecomorphological convergence, may be consequences of evolving on an adaptive landscape shaped by many-to-many relationships between ecology and function, many-to-one relationships between form and performance, and one-to-many relationships between functionally versatile morphologies and ecology. On complex adaptive landscapes, ecological selection can yield different outcomes. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.

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...

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...

Using hydrologic landscape classification to assess streamflow vulnerability to changes in climate

EPA Science Inventory

Identifying regions with similar hydrology is useful for assessing water quality and quantity across the U.S., especially areas that are difficult or costly to monitor. For example, hydrologic landscapes (HLs) have been used to map streamflow variability and assess the spatial di...

An assessment of streamflow vulnerability to climate using Hydrologic Landscape classification

EPA Science Inventory

Identifying regions with similar hydrology is useful for assessing water quality and quantity across the U.S., especially areas that are difficult or costly to monitor. For example, hydrologic landscapes (HLs) have been used to map streamflow variability and assess the spatial di...

Assessing the habitat suitability of agricultural landscapes for characteristic breeding bird guilds using landscape metrics.

PubMed

Borges, Friederike; Glemnitz, Michael; Schultz, Alfred; Stachow, Ulrich

2017-04-01

Many of the processes behind the decline of farmland birds can be related to modifications in landscape structure (composition and configuration), which can partly be expressed quantitatively with measurable or computable indices, i.e. landscape metrics. This paper aims to identify statistical relationships between the occurrence of birds and the landscape structure. We present a method that combines two comprehensive procedures: the "landscape-centred approach" and "guild classification". Our study is based on more than 20,000 individual bird observations based on a 4-year bird monitoring approach in a typical agricultural area in the north-eastern German lowlands. Five characteristic bird guilds, each with three characteristic species, are defined for the typical habitat types of that area: farmland, grassland, hedgerow, forest and settlement. The suitability of each sample plot for each guild is indicated by the level of persistence (LOP) of occurrence of three respective species. Thus, the sample plots can be classified as "preferred" or "less preferred" depending on the lower and upper quartiles of the LOP values. The landscape structure is characterized by 16 different landscape metrics expressing various aspects of landscape composition and configuration. For each guild, the three landscape metrics with the strongest rank correlation with the LOP values and that are not mutually dependent were identified. For four of the bird guilds, the classification success was better than 80%, compared with only 66% for the grassland bird guild. A subset of six landscape metrics proved to be the most meaningful and sufficiently classified the sample areas with respect to bird guild suitability. In addition, derived logistic functions allowed the production of guild-specific habitat suitability maps for the whole landscape. The analytical results show that the proposed approach is appropriate to assess the habitat suitability of agricultural landscapes for characteristic bird guilds.

Adaptive sleep-wake discrimination for wearable devices.

PubMed

Karlen, Walter; Floreano, Dario

2011-04-01

Sleep/wake classification systems that rely on physiological signals suffer from intersubject differences that make accurate classification with a single, subject-independent model difficult. To overcome the limitations of intersubject variability, we suggest a novel online adaptation technique that updates the sleep/wake classifier in real time. The objective of the present study was to evaluate the performance of a newly developed adaptive classification algorithm that was embedded on a wearable sleep/wake classification system called SleePic. The algorithm processed ECG and respiratory effort signals for the classification task and applied behavioral measurements (obtained from accelerometer and press-button data) for the automatic adaptation task. When trained as a subject-independent classifier algorithm, the SleePic device was only able to correctly classify 74.94 ± 6.76% of the human-rated sleep/wake data. By using the suggested automatic adaptation method, the mean classification accuracy could be significantly improved to 92.98 ± 3.19%. A subject-independent classifier based on activity data only showed a comparable accuracy of 90.44 ± 3.57%. We demonstrated that subject-independent models used for online sleep-wake classification can successfully be adapted to previously unseen subjects without the intervention of human experts or off-line calibration.

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

PubMed

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

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.

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

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...

Assessing landscape vulnerability to wildfire in the USA

Treesearch

Nicole M. Vaillant; Crystal A. Kolden; Alistair M. S. Smith

2016-01-01

Wildfire is an ever present, natural process shaping landscapes. Having the ability to accurately measure and predict wildfire occurrence and impacts to ecosystem goods and services, both retrospectively and prospectively, is critical for adaptive management of landscapes. Landscape vulnerability is a concept widely utilized in the ecosystem management literature that...

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...

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 ...

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...

Industrial Landscapes: Perception and Classification as Learning Activities

ERIC Educational Resources Information Center

Peters, Gary; Larkin, Robert P.

1977-01-01

Suggests a high school or college level program of subjective perception and evaluation of industrial landscapes. Slides of local or national industrial sites can be rated and classified as pleasing or unpleasing in terms of variables such as architectural style of building, smokestacks, age, and visible pollution. (AV)

Developing an ecosystem diversity framework for landscape assessment

Treesearch

Robert D. Pfister; Michael D. Sweet

2000-01-01

Ecological diversity is being addressed in various research and management efforts, but a common foundation is not explicitly defined or displayed. A formal Ecosystem Diversity Framework (EDF) would improve landscape analysis and communication across multiple scales. The EDF represents a multiple-component vegetation classification system with inherent flexibility for...

Gradient modeling of conifer species using random forests

Treesearch

Jeffrey S. Evans; Samuel A. Cushman

2009-01-01

Landscape ecology often adopts a patch mosaic model of ecological patterns. However, many ecological attributes are inherently continuous and classification of species composition into vegetation communities and discrete patches provides an overly simplistic view of the landscape. If one adopts a nichebased, individualistic concept of biotic communities then it may...

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…

Regional landscape ecosystems of Michigan, Minnesota and Wisconsin: a working map and classification.

Treesearch

Dennis A. Albert

1995-01-01

Describes the landscape ecosystems (ecoregions) of Michigan, Minnesota, and Wisconsin and includes maps of all three states. Regional descriptions include climate, bedrock geology, landforms, lakes and streams, soils, presettlement vegetation, natural disturbance, present vegetation and land use, rare biota, natural areas, public land managers, and conservation...

A regional perspective of the physiographic provinces of the southeastern United States

Treesearch

James H. Miller; K.S. Robinson

1995-01-01

Abstract.  A landscape classification system using defined units for physiography, landform, and soils is needed to organize ecological information and to serve as an aid for landscape management.  To assist in this effort a composite physiographic map is presented to 12 southeastern states.

Assessing connectivity in salmonid fishes with DNA microsatellite markers

Treesearch

Helen Neville; Jason Dunham; Mary Peacock

2006-01-01

Connectivity is a key consideration for the management and conservation of any species, but empirical characterizations of connectivity can be extremely challenging. Assessments of connectivity require biologically realistic classifications of landscape structure (Kotliar and Wiens 1990), and an understanding of how landscape structure affects migration, dispersal, and...

A Programmatic Approach to Determine Eligibility of Prehistoric Sites in the San Diego Subregion, Southern Coast Archeological Region, California, for the National Register of Historic Places

DTIC Science & Technology

2007-08-01

Inland Highland .......................................................................................22 3 Prehistoric Human Adaptations ...highlands on the base has provided valuable insight regarding late Holocene adaptations in these landscapes (Hale and Becker 2005; Reddy 1997a, 2000; York...interpretations of the prehistoric human adaptations in the four landscape categories. Major research issues relevant to the study and the study area are

The dynamics of adapting, unregulated populations and a modified fundamental theorem.

PubMed

O'Dwyer, James P

2013-01-06

A population in a novel environment will accumulate adaptive mutations over time, and the dynamics of this process depend on the underlying fitness landscape: the fitness of and mutational distance between possible genotypes in the population. Despite its fundamental importance for understanding the evolution of a population, inferring this landscape from empirical data has been problematic. We develop a theoretical framework to describe the adaptation of a stochastic, asexual, unregulated, polymorphic population undergoing beneficial, neutral and deleterious mutations on a correlated fitness landscape. We generate quantitative predictions for the change in the mean fitness and within-population variance in fitness over time, and find a simple, analytical relationship between the distribution of fitness effects arising from a single mutation, and the change in mean population fitness over time: a variant of Fisher's 'fundamental theorem' which explicitly depends on the form of the landscape. Our framework can therefore be thought of in three ways: (i) as a set of theoretical predictions for adaptation in an exponentially growing phase, with applications in pathogen populations, tumours or other unregulated populations; (ii) as an analytically tractable problem to potentially guide theoretical analysis of regulated populations; and (iii) as a basis for developing empirical methods to infer general features of a fitness landscape.

Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation

PubMed Central

Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi

2016-01-01

Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency. PMID:27999261

LCMS landscape change monitoring system—results from an information needs assessment

Treesearch

Kevin Megown; Brian Schwind; Don Evans; Mark Finco

2015-01-01

Understanding changes in land use and land cover over space and time provides an important means to evaluate complex interactions between human and biophysical systems, to project future conditions, and to design mitigation and adaptive management strategies. Assessing and monitoring landscape change is evolving into a foundational element of climate change adaptation...

Use of Binary Partition Tree and energy minimization for object-based classification of urban land cover

NASA Astrophysics Data System (ADS)

Li, Mengmeng; Bijker, Wietske; Stein, Alfred

2015-04-01

Two main challenges are faced when classifying urban land cover from very high resolution satellite images: obtaining an optimal image segmentation and distinguishing buildings from other man-made objects. For optimal segmentation, this work proposes a hierarchical representation of an image by means of a Binary Partition Tree (BPT) and an unsupervised evaluation of image segmentations by energy minimization. For building extraction, we apply fuzzy sets to create a fuzzy landscape of shadows which in turn involves a two-step procedure. The first step is a preliminarily image classification at a fine segmentation level to generate vegetation and shadow information. The second step models the directional relationship between building and shadow objects to extract building information at the optimal segmentation level. We conducted the experiments on two datasets of Pléiades images from Wuhan City, China. To demonstrate its performance, the proposed classification is compared at the optimal segmentation level with Maximum Likelihood Classification and Support Vector Machine classification. The results show that the proposed classification produced the highest overall accuracies and kappa coefficients, and the smallest over-classification and under-classification geometric errors. We conclude first that integrating BPT with energy minimization offers an effective means for image segmentation. Second, we conclude that the directional relationship between building and shadow objects represented by a fuzzy landscape is important for building extraction.

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,...

Epigenetic Inheritance across the Landscape.

PubMed

Whipple, Amy V; Holeski, Liza M

2016-01-01

The study of epigenomic variation at the landscape-level in plants may add important insight to studies of adaptive variation. A major goal of landscape genomic studies is to identify genomic regions contributing to adaptive variation across the landscape. Heritable variation in epigenetic marks, resulting in transgenerational plasticity, can influence fitness-related traits. Epigenetic marks are influenced by the genome, the environment, and their interaction, and can be inherited independently of the genome. Thus, epigenomic variation likely influences the heritability of many adaptive traits, but the extent of this influence remains largely unknown. Here, we summarize the relevance of epigenetic inheritance to ecological and evolutionary processes, and review the literature on landscape-level patterns of epigenetic variation. Landscape-level patterns of epigenomic variation in plants generally show greater levels of isolation by distance and isolation by environment then is found for the genome, but the causes of these patterns are not yet clear. Linkage between the environment and epigenomic variation has been clearly shown within a single generation, but demonstrating transgenerational inheritance requires more complex breeding and/or experimental designs. Transgenerational epigenetic variation may alter the interpretation of landscape genomic studies that rely upon phenotypic analyses, but should have less influence on landscape genomic approaches that rely upon outlier analyses or genome-environment associations. We suggest that multi-generation common garden experiments conducted across multiple environments will allow researchers to understand which parts of the epigenome are inherited, as well as to parse out the relative contribution of heritable epigenetic variation to the phenotype.

Epigenetic Inheritance across the Landscape

PubMed Central

Whipple, Amy V.; Holeski, Liza M.

2016-01-01

The study of epigenomic variation at the landscape-level in plants may add important insight to studies of adaptive variation. A major goal of landscape genomic studies is to identify genomic regions contributing to adaptive variation across the landscape. Heritable variation in epigenetic marks, resulting in transgenerational plasticity, can influence fitness-related traits. Epigenetic marks are influenced by the genome, the environment, and their interaction, and can be inherited independently of the genome. Thus, epigenomic variation likely influences the heritability of many adaptive traits, but the extent of this influence remains largely unknown. Here, we summarize the relevance of epigenetic inheritance to ecological and evolutionary processes, and review the literature on landscape-level patterns of epigenetic variation. Landscape-level patterns of epigenomic variation in plants generally show greater levels of isolation by distance and isolation by environment then is found for the genome, but the causes of these patterns are not yet clear. Linkage between the environment and epigenomic variation has been clearly shown within a single generation, but demonstrating transgenerational inheritance requires more complex breeding and/or experimental designs. Transgenerational epigenetic variation may alter the interpretation of landscape genomic studies that rely upon phenotypic analyses, but should have less influence on landscape genomic approaches that rely upon outlier analyses or genome–environment associations. We suggest that multi-generation common garden experiments conducted across multiple environments will allow researchers to understand which parts of the epigenome are inherited, as well as to parse out the relative contribution of heritable epigenetic variation to the phenotype. PMID:27826318

Biweekly disturbance capture and attribution: case study in western Alberta grizzly bear habitat

NASA Astrophysics Data System (ADS)

Hilker, Thomas; Coops, Nicholas C.; Gaulton, Rachel; Wulder, Michael A.; Cranston, Jerome; Stenhouse, Gordon

2011-01-01

An increasing number of studies have demonstrated the impact of landscape disturbance on ecosystems. Satellite remote sensing can be used for mapping disturbances, and fusion techniques of sensors with complimentary characteristics can help to improve the spatial and temporal resolution of satellite-based mapping techniques. Classification of different disturbance types from satellite observations is difficult, yet important, especially in an ecological context as different disturbance types might have different impacts on vegetation recovery, wildlife habitats, and food resources. We demonstrate a possible approach for classifying common disturbance types by means of their spatial characteristics. First, landscape level change is characterized on a near biweekly basis through application of a data fusion model (spatial temporal adaptive algorithm for mapping reflectance change) and a number of spatial and temporal characteristics of the predicted disturbance patches are inferred. A regression tree approach is then used to classify disturbance events. Our results show that spatial and temporal disturbance characteristics can be used to classify disturbance events with an overall accuracy of 86% of the disturbed area observed. The date of disturbance was identified as the most powerful predictor of the disturbance type, together with the patch core area, patch size, and contiguity.

On the use of interaction error potentials for adaptive brain computer interfaces.

PubMed

Llera, A; van Gerven, M A J; Gómez, V; Jensen, O; Kappen, H J

2011-12-01

We propose an adaptive classification method for the Brain Computer Interfaces (BCI) which uses Interaction Error Potentials (IErrPs) as a reinforcement signal and adapts the classifier parameters when an error is detected. We analyze the quality of the proposed approach in relation to the misclassification of the IErrPs. In addition we compare static versus adaptive classification performance using artificial and MEG data. We show that the proposed adaptive framework significantly improves the static classification methods. Copyright © 2011 Elsevier Ltd. All rights reserved.

Neighbourhood-Scale Urban Forest Ecosystem Classification

Treesearch

James W.N. Steenberg; Andrew A. Millward; Peter N. Duinker; David J. Nowak; Pamela J. Robinson

2015-01-01

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...

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...

Variational and PDE-Based Methods for Big Data Analysis, Classification and Image Processing Using Graphs

DTIC Science & Technology

2015-01-01

explain the accuracy and speed increase. Exploring the underlying connections of the energy evolution of these methods and the energy landscape for the...unwanted trivial global minimizers from the energy landscape . Note that the second eigenvector of the Laplacian already provides a solution to a cut...von Brecht. Convergence and energy landscape for Cheeger cut clustering. Advances in Neural Information Processing Systems, 25:1394– 1402, 2012. [13] X

Landscape habitats [Chapter 2

Treesearch

C. L. Simmons

1994-01-01

This landscape habitat description is based on a ground reconnaissance of the Lost Lake, West Glacier Lake, and East Glacier Lake portions of GLEES conducted during 10 days in July-September 1986 and on subsequent photo interpretation of 1:6000 scale color-infrared photographs. A ground check was conducted in July-August 1987. The classification used is a physiognomic...

Predicting plant species diversity in a longleaf pine landscape

Treesearch

L. Katherine Kirkman; P. Charles Goebel; Brian J. Palik; Larry T. West

2004-01-01

In this study, we used a hierarchical, multifactor ecological classification system to examine how spatial patterns of biodiversity develop in one of the most species-rich ecosystems in North America, the fire-maintained longleaf pine-wiregrass ecosystem and associated depressional wetlands and riparian forests. Our goal was to determine which landscape features are...

Examining fire-prone forest landscapes as coupled human and natural systems

Treesearch

Thomas A. Spies; Eric M. White; Jeffrey D. Kline; A. Paige Fisher; Alan Ager; John Bailey; John Bolte; Jennifer Koch; Emily Platt; Christine S. Olsen; Derric Jacobs; Bruce Shindler; Michelle M. Steen-Adams; Roger Hammer

2014-01-01

Fire-prone landscapes are not well studied as coupled human and natural systems (CHANS) and present many challenges for understanding and promoting adaptive behaviors and institutions. Here, we explore how heterogeneity, feedbacks, and external drivers in this type of natural hazard system can lead to complexity and can limit the development of more adaptive approaches...

Using aerial images for establishing a workflow for the quantification of water management measures

NASA Astrophysics Data System (ADS)

Leuschner, Annette; Merz, Christoph; van Gasselt, Stephan; Steidl, Jörg

2017-04-01

Quantified landscape characteristics, such as morphology, land use or hydrological conditions, play an important role for hydrological investigations as landscape parameters directly control the overall water balance. A powerful assimilation and geospatial analysis of remote sensing datasets in combination with hydrological modeling allows to quantify landscape parameters and water balances efficiently. This study focuses on the development of a workflow to extract hydrologically relevant data from aerial image datasets and derived products in order to allow an effective parametrization of a hydrological model. Consistent and self-contained data source are indispensable for achieving reasonable modeling results. In order to minimize uncertainties and inconsistencies, input parameters for modeling should be extracted from one remote-sensing dataset mainly if possbile. Here, aerial images have been chosen because of their high spatial and spectral resolution that permits the extraction of various model relevant parameters, like morphology, land-use or artificial drainage-systems. The methodological repertoire to extract environmental parameters range from analyses of digital terrain models, multispectral classification and segmentation of land use distribution maps and mapping of artificial drainage-systems based on spectral and visual inspection. The workflow has been tested for a mesoscale catchment area which forms a characteristic hydrological system of a young moraine landscape located in the state of Brandenburg, Germany. These dataset were used as input-dataset for multi-temporal hydrological modelling of water balances to detect and quantify anthropogenic and meteorological impacts. ArcSWAT, as a GIS-implemented extension and graphical user input interface for the Soil Water Assessment Tool (SWAT) was chosen. The results of this modeling approach provide the basis for anticipating future development of the hydrological system, and regarding system changes for the adaption of water resource management decisions.

Geospatial analysis of lake and landscape interactions within the Toolik Lake region, North Slope of Alaska

NASA Astrophysics Data System (ADS)

Pathak, Prasad A.

The Arctic region of Alaska is experiencing severe impacts of climate change. The Arctic lakes ecosystems are bound to undergo alterations in its trophic structure and other chemical properties. However, landscape factors controlling the lake influxes were not studied till date. This research has examined the currently existing lake landscape interactions using Remote Sensing and GIS technology. The statistical modeling was carried out using Regression and CART methods. Remote sensing data was applied to derive the required landscape indices. Remote sensing in the Arctic Alaska faces many challenges including persistent cloud cover, low sun angle and limited snow free period. Tundra vegetation types are interspersed and intricate to classify unlike managed forest stands. Therefore, historical studies have remained underachieved with respect thematic accuracies. However, looking at vegetation communities at watershed level and the implementation of expert classification system achieved the accuracies up to 90%. The research has highlighted the probable role of interactions between vegetation root zones, nutrient availability within active zone, as well as importance of permafrost thawing. Multiple regression analyses and Classification Trees were developed to understand relationships between landscape factors with various chemical parameters as well as chlorophyll readings. Spatial properties of Shrubs and Riparian complexes such as complexity of individual patches at watershed level and within proximity of water channels were influential on Chlorophyll production of lakes. Till-age had significant impact on Total Nitrogen contents. Moreover, relatively young tills exhibited significantly positive correlation with concentration of various ions and conductivity of lakes. Similarly, density of patches of Heath complexes was found to be important with respect to Total Phosphorus contents in lakes. All the regression models developed in this study were significant at 95% confidence level. However, the classification trees could not achieve high predictabilities due to limited number of lakes sampled. Keywords: Landscape factors, Lake primary productivity, Arctic, Climate change, Regression, CART

Ecosystem services in changing landscapes: An introduction

Treesearch

Louis Iverson; Cristian Echeverria; Laura Nahuelhual; Sandra Luque

2014-01-01

The concept of ecosystem services from landscapes is rapidly gaining momentum as a language to communicate values and benefits to scientists and lay alike. Landscape ecology has an enormous contribution to make to this field, and one could argue, uniquely so. Tools developed or adapted for landscape ecology are being increasingly used to assist with the quantification...

Using community archetypes to better understand differential community adaptation to wildfire risk

PubMed Central

Carroll, Matthew; Paveglio, Travis

2016-01-01

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

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...

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...

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...

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.

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.

Landscape genomics of Sphaeralcea ambigua in the Mojave Desert: a multivariate, spatially-explicit approach to guide ecological restoration

USGS Publications Warehouse

Shryock, Daniel F.; Havrilla, Caroline A.; DeFalco, Lesley; Esque, Todd C.; Custer, Nathan; Wood, Troy E.

2015-01-01

Local adaptation influences plant species’ responses to climate change and their performance in ecological restoration. Fine-scale physiological or phenological adaptations that direct demographic processes may drive intraspecific variability when baseline environmental conditions change. Landscape genomics characterize adaptive differentiation by identifying environmental drivers of adaptive genetic variability and mapping the associated landscape patterns. We applied such an approach to Sphaeralcea ambigua, an important restoration plant in the arid southwestern United States, by analyzing variation at 153 amplified fragment length polymorphism loci in the context of environmental gradients separating 47 Mojave Desert populations. We identified 37 potentially adaptive loci through a combination of genome scan approaches. We then used a generalized dissimilarity model (GDM) to relate variability in potentially adaptive loci with spatial gradients in temperature, precipitation, and topography. We identified non-linear thresholds in loci frequencies driven by summer maximum temperature and water stress, along with continuous variation corresponding to temperature seasonality. Two GDM-based approaches for mapping predicted patterns of local adaptation are compared. Additionally, we assess uncertainty in spatial interpolations through a novel spatial bootstrapping approach. Our study presents robust, accessible methods for deriving spatially-explicit models of adaptive genetic variability in non-model species that will inform climate change modelling and ecological restoration.

Library of Congress Classification Adapted for Children's Sound Recordings.

ERIC Educational Resources Information Center

Perkins, John W.; And Others

This publication describes how the Library of Congress classification adapted for children's books by the Inglewood Public Library can also be used in the classification of children's sound recordings. One of the major features of the classification system is its applicability to various media, as it provides a method by which the same work can be…

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…

Landscape genomics in Atlantic salmon (Salmo salar): searching for gene-environment interactions driving local adaptation.

PubMed

Vincent, Bourret; Dionne, Mélanie; Kent, Matthew P; Lien, Sigbjørn; Bernatchez, Louis

2013-12-01

A growing number of studies are examining the factors driving historical and contemporary evolution in wild populations. By combining surveys of genomic variation with a comprehensive assessment of environmental parameters, such studies can increase our understanding of the genomic and geographical extent of local adaptation in wild populations. We used a large-scale landscape genomics approach to examine adaptive and neutral differentiation across 54 North American populations of Atlantic salmon representing seven previously defined genetically distinct regional groups. Over 5500 genome-wide single nucleotide polymorphisms were genotyped in 641 individuals and 28 bulk assays of 25 pooled individuals each. Genome scans, linkage map, and 49 environmental variables were combined to conduct an innovative landscape genomic analysis. Our results provide valuable insight into the links between environmental variation and both neutral and potentially adaptive genetic divergence. In particular, we identified markers potentially under divergent selection, as well as associated selective environmental factors and biological functions with the observed adaptive divergence. Multivariate landscape genetic analysis revealed strong associations of both genetic and environmental structures. We found an enrichment of growth-related functions among outlier markers. Climate (temperature-precipitation) and geological characteristics were significantly associated with both potentially adaptive and neutral genetic divergence and should be considered as candidate loci involved in adaptation at the regional scale in Atlantic salmon. Hence, this study significantly contributes to the improvement of tools used in modern conservation and management schemes of Atlantic salmon wild populations. © 2013 The Author(s). Evolution © 2013 The Society for the Study of Evolution.

Chapter 6 - Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

Treesearch

Jennifer L. Long; Melanie Miller; James P. Menakis; Robert E. Keane

2006-01-01

The Landscape Fire and Resource Management Planning Tools Prototype Project, or LANDFIRE Prototype Project, required a system for classifying vegetation composition, biophysical settings, and vegetation structure to facilitate the mapping of vegetation and wildland fuel characteristics and the simulation of vegetation dynamics using landscape modeling. We developed...

Water balances of two Piedmont headwater catchments: implications for regional hydrologic landscape classification

Treesearch

C. Dreps; G. Sun; J. Boggs

2014-01-01

In the Piedmont of North Carolina, a traditionally water-rich region, reservoirs that serve over 1 million people are under increasing pressure due to naturally occurring droughts and increasing land development. Innovative development approaches aim to maintain hydrologic conditions of the undisturbed landscape, but are based on insufficient target information. This...

Discriminative clustering on manifold for adaptive transductive classification.

PubMed

Zhang, Zhao; Jia, Lei; Zhang, Min; Li, Bing; Zhang, Li; Li, Fanzhang

2017-10-01

In this paper, we mainly propose a novel adaptive transductive label propagation approach by joint discriminative clustering on manifolds for representing and classifying high-dimensional data. Our framework seamlessly combines the unsupervised manifold learning, discriminative clustering and adaptive classification into a unified model. Also, our method incorporates the adaptive graph weight construction with label propagation. Specifically, our method is capable of propagating label information using adaptive weights over low-dimensional manifold features, which is different from most existing studies that usually predict the labels and construct the weights in the original Euclidean space. For transductive classification by our formulation, we first perform the joint discriminative K-means clustering and manifold learning to capture the low-dimensional nonlinear manifolds. Then, we construct the adaptive weights over the learnt manifold features, where the adaptive weights are calculated through performing the joint minimization of the reconstruction errors over features and soft labels so that the graph weights can be joint-optimal for data representation and classification. Using the adaptive weights, we can easily estimate the unknown labels of samples. After that, our method returns the updated weights for further updating the manifold features. Extensive simulations on image classification and segmentation show that our proposed algorithm can deliver the state-of-the-art performance on several public datasets. Copyright © 2017 Elsevier Ltd. All rights reserved.

[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.

Correlation of fitness landscapes from three orthologous TIM barrels originates from sequence and structure constraints

PubMed Central

Chan, Yvonne H.; Venev, Sergey V.; Zeldovich, Konstantin B.; Matthews, C. Robert

2017-01-01

Sequence divergence of orthologous proteins enables adaptation to environmental stresses and promotes evolution of novel functions. Limits on evolution imposed by constraints on sequence and structure were explored using a model TIM barrel protein, indole-3-glycerol phosphate synthase (IGPS). Fitness effects of point mutations in three phylogenetically divergent IGPS proteins during adaptation to temperature stress were probed by auxotrophic complementation of yeast with prokaryotic, thermophilic IGPS. Analysis of beneficial mutations pointed to an unexpected, long-range allosteric pathway towards the active site of the protein. Significant correlations between the fitness landscapes of distant orthologues implicate both sequence and structure as primary forces in defining the TIM barrel fitness landscape and suggest that fitness landscapes can be translocated in sequence space. Exploration of fitness landscapes in the context of a protein fold provides a strategy for elucidating the sequence-structure-fitness relationships in other common motifs. PMID:28262665

Identifying the characteristic of SundaParahiyangan landscape for a model of sustainable agricultural landscape

NASA Astrophysics Data System (ADS)

Dahlan, M. Z.; Nurhayati, H. S. A.; Mugnisjah, W. Q.

2017-10-01

This study was an explorative study of the various forms of traditional ecological knowledge (TEK) of Sundanese people in the context of sustainable agriculture. The qualitative method was used to identify SundaParahiyangan landscape by using Rapid Participatory Rural Appraisal throughsemi-structured interviews, focus group discussions, and field survey. The Landscape Characteristic Assessment and Community Sustainability Assessment were used to analyze the characteristic of landscape to achieve the sustainable agricultural landscape criteria proposed by US Department of Agriculture. The results revealed that the SundaParahiyangan agricultural landscape has a unique characteristic as a result of the long-term adaptation of agricultural society to theirlandscape through a learning process for generations. In general, this character was reflected in the typical of Sundanese’s agroecosystems such as forest garden, mixed garden, paddy field, and home garden. In addition, concept of kabuyutan is one of the TEKs related to understanding and utilization of landscape has been adapted on revitalizing the role of landscape surrounding the agroecosystem as the buffer zone by calculating and designating protected areas. To support the sustainability of production area, integrated practices of agroforestry with low-external-input and sustainable agriculture (LEISA) system can be applied in utilizing and managing agricultural resources.

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…

Using an Ecological Land Hierarchy to Predict Seasonal-Wetland Abundance in Upland Forests

Treesearch

Brian J. Palik; Richard Buech; Leanne Egeland

2003-01-01

Hierarchy theory, when applied to landscapes, predicts that broader-scale ecosystems constrain the development of finer-scale, nested ecosystems. This prediction finds application in hierarchical land classifications. Such classifications typically apply to physiognomically similar ecosystems, or ecological land units, e.g., a set of multi-scale forest ecosystems. We...

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

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.

Free-energy landscapes from adaptively biased methods: Application to quantum systems

NASA Astrophysics Data System (ADS)

Calvo, F.

2010-10-01

Several parallel adaptive biasing methods are applied to the calculation of free-energy pathways along reaction coordinates, choosing as a difficult example the double-funnel landscape of the 38-atom Lennard-Jones cluster. In the case of classical statistics, the Wang-Landau and adaptively biased molecular-dynamics (ABMD) methods are both found efficient if multiple walkers and replication and deletion schemes are used. An extension of the ABMD technique to quantum systems, implemented through the path-integral MD framework, is presented and tested on Ne38 against the quantum superposition method.

Landscape genetics: combining landscape ecology and population genetics

Treesearch

Stephanie Manel; Michael K. Schwartz; Gordon Luikart; Pierre Taberlet

2003-01-01

Understanding the processes and patterns of gene flow and local adaptation requires a detailed knowledge of how landscape characteristics structure populations. This understanding is crucial, not only for improving ecological knowledge, but also for managing properly the genetic diversity of threatened and endangered populations. For nearly 80 years, population...

"Educational Landscaping": A Method for Raising Awareness about Language and Communication

ERIC Educational Resources Information Center

Scarvaglieri, Claudio

2017-01-01

The article introduces "Educational Landscaping" (EL) as a measure of qualification in educational institutions to support the development of teachers' language awareness (LA). In EL, participating teachers are invited to investigate, analyse and, if necessary, adapt the linguistic landscape of their educational institution. The article…

Speaker normalization and adaptation using second-order connectionist networks.

PubMed

Watrous, R L

1993-01-01

A method for speaker normalization and adaption using connectionist networks is developed. A speaker-specific linear transformation of observations of the speech signal is computed using second-order network units. Classification is accomplished by a multilayer feedforward network that operates on the normalized speech data. The network is adapted for a new talker by modifying the transformation parameters while leaving the classifier fixed. This is accomplished by backpropagating classification error through the classifier to the second-order transformation units. This method was evaluated for the classification of ten vowels for 76 speakers using the first two formant values of the Peterson-Barney data. The results suggest that rapid speaker adaptation resulting in high classification accuracy can be accomplished by this method.

Using an agent-based model to examine forest management outcomes in a fire-prone landscape in Oregon, USA

Treesearch

Thomas A. Spies; Eric White; Alan Ager; Jeffrey D. Kline; John P. Bolte; Emily K. Platt; Keith A. Olsen; Robert J. Pabst; Ana M. G. Barros; John D. Bailey; Susan Charnley; Anita T. Morzillo; Jennifer Koch; Michelle M. Steen-Adams; Peter H. Singleton; James Sulzman; Cynthia Schwartz; Blair Csuti

2017-01-01

Fire-prone landscapes present many challenges for both managers and policy makers in developing adaptive behaviors and institutions. We used a coupled human and natural systems framework and an agent-based landscape model to examine how alternative management scenarios affect fire and ecosystem services metrics in a fire-prone multiownership landscape in the eastern...

Human preference for ecological units: patterns of dispersed campsites within landtype associations on the Chippewa National Forest

Treesearch

Lisa Whitcomb; Dennis Parker; Bob Carr; Paul Gobster; Herb Schroeder

2002-01-01

Forest Service landscape architects sought a method for determining if people showed a preference for certain landscape-scale ecosystems and if ecological classification units could be used in visual resource management. A study was conducted on the Chippewa National Forest to test whether there was a systematic relationship between dispersed campsite locations and...

Applicability of Hydrologic Landscapes for Model Calibration ...

EPA Pesticide Factsheets

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 techniques were applied to the HL assessment unit composition in watersheds across the Pacific Northwest to aggregate the hydrologic behavior of the Hydrologic Landscapes from the assessment unit scale to the watershed scale. This non-trivial solution both emphasizes HL classifications within the watershed that provide that majority of moisture surplus/deficit and considers the relative position (upstream vs. downstream) of these HL classifications. A clustering algorithm was applied to the HL-based characterization of assessment units within 185 watersheds to help organize watersheds into nine classes hypothesized to have similar hydrologic behavior. The HL-based classes were used to organize and describe hydrologic behavior information about watershed classes and both predictions and validations were independently performed with regard to the general magnitude of six hydroclimatic signature values. A second cluster analysis was then performed using the independently calculated signature values as similarity metrics, and it was found that the six signature clusters showed substantial overlap in watershed class membership to those in the HL-based classes. One hypothesis set forward from thi

Land cover and forest formation distributions for St. Kitts, Nevis, St. Eustatius, Grenada and Barbados from decision tree classification of cloud-cleared satellite imagery. Caribbean Journal of Science. 44(2):175-198.

Treesearch

E.H. Helmer; T.A. Kennaway; D.H. Pedreros; M.L. Clark; H. Marcano-Vega; L.L. Tieszen; S.R. Schill; C.M.S. Carrington

2008-01-01

Satellite image-based mapping of tropical forests is vital to conservation planning. Standard methods for automated image classification, however, limit classification detail in complex tropical landscapes. In this study, we test an approach to Landsat image interpretation on four islands of the Lesser Antilles, including Grenada and St. Kitts, Nevis and St. Eustatius...

[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.

Temporal and spatial changes of land use and landscape in a coal mining area in Xilingol grassland

NASA Astrophysics Data System (ADS)

Guan, Chunzhu; Zhang, Baolin; Li, Jiannan; Zhao, Junling

2017-01-01

Coal mining, particularly surface mining, inevitably disturbs land. According to Landsat images acquired over Xilingol grassland in 2005, 2009 and 2015, land uses were divided into seven classes, i. e., open stope, stripping area, waste-dump area, mine industrial area, farmland, urban area and the original landscape (grassland), using supervised classification and human-computer interactive interpretation. The overall classification accuracies were 97.72 %, 98.43 % and 96.73 %, respectively; the Kappa coefficients were 0.95, 0.97 and 0.95, respectively. Analysis on LUCC (Land Use and Cover Change) showed that surface coal mining disturbed grassland ecosystem: grassland decreased by 8661.15 hm2 in 2005-2015. The area and proportion of mining operation areas (open stope, stripping area, waste-dump area, mine industrial field) increased, but those of grassland decreased continuously. Transfer matrix of land use changes showed that waste-dump had the largest impacts in mining disturbance, and that effective reclamation of waste-dump areas would mitigate eco-environment destruction, as would be of great significance to protect fragile grassland eco-system. Six landscape index showed that landscape fragmentation increased, and the influences of human activity on landscape was mainly reflected in the expansion of mining area and urban area. Remote sensing monitoring of coal surface mining in grassland would accurately demonstrate the dynamics and trend of LUCC, providing scientific supports for ecological reconstruction in surface mining area.

Forest land cover change (1975-2000) in the Greater Border Lakes region

Treesearch

Peter T. Wolter; Brian R. Sturtevant; Brian R. Miranda; Sue M. Lietz; Phillip A. Townsend; John Pastor

2012-01-01

This document and accompanying maps describe land cover classifications and change detection for a 13.8 million ha landscape straddling the border between Minnesota, and Ontario, Canada (greater Border Lakes Region). Land cover classifications focus on discerning Anderson Level II forest and nonforest cover to track spatiotemporal changes in forest cover. Multi-...

Mapping fuels at multiple scales: landscape application of the fuel characteristic classification system.

Treesearch

D. McKenzie; C.L. Raymond; L.-K.B. Kellogg; R.A. Norheim; A.G. Andreu; A.C. Bayard; K.E. Kopper; E. Elman

2007-01-01

Fuel mapping is a complex and often multidisciplinary process, involving remote sensing, ground-based validation, statistical modeling, and knowledge-based systems. The scale and resolution of fuel mapping depend both on objectives and availability of spatial data layers. We demonstrate use of the Fuel Characteristic Classification System (FCCS) for fuel mapping at two...

An overview of cancer research in South African academic and research institutions, 2013 - 2014.

PubMed

Moodley, Jennifer; Stefan, D Cristina; Sewram, Vikash; Ruff, Paul; Freeman, Melvyn; Asante-Shongwe, Kwanele

2016-05-10

Cancer is emerging as a critical public health problem in South Africa (SA). Recognising the importance of research in addressing the cancer burden, the Ministerial Advisory Committee on the Prevention and Control of Cancer (MACC) research working group undertook a review of the current cancer research landscape in SA and related this to the cancer burden. Academic and research institutions in SA were contacted to provide information on the titles of all current and recently completed (2013/2014) cancer research projects. Three MACC research working group members used the project titles to independently classify the projects by type of research (basic, clinical and public health - projects could be classified in more than one category) and disease site. A more detailed classification of projects addressing the five most common cancers diagnosed in males and females in SA was conducted using an adapted Common Scientific Outline (CSO) categorisation. Information was available on 556 cancer research projects. Overall, 301 projects were classified as clinical, 254 as basic science and 71 as public health research. The most common cancers being researched were cancers of the breast (n=95 projects) and cervix (n=43), leukaemia (n=36), non-Hodgkin's lymphoma (n=35) and lung cancer (n=23). Classification of the five most common cancers in males and females in SA, using the adapted CSO categories, showed that the majority of projects related to treatment, with relatively few projects on prevention, survivorship and patient perspectives. Our findings established that there is a dearth of public health cancer research in SA.

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.

Evaluating the statistical performance of less applied algorithms in classification of worldview-3 imagery data in an urbanized landscape

NASA Astrophysics Data System (ADS)

Ranaie, Mehrdad; Soffianian, Alireza; Pourmanafi, Saeid; Mirghaffari, Noorollah; Tarkesh, Mostafa

2018-03-01

In recent decade, analyzing the remotely sensed imagery is considered as one of the most common and widely used procedures in the environmental studies. In this case, supervised image classification techniques play a central role. Hence, taking a high resolution Worldview-3 over a mixed urbanized landscape in Iran, three less applied image classification methods including Bagged CART, Stochastic gradient boosting model and Neural network with feature extraction were tested and compared with two prevalent methods: random forest and support vector machine with linear kernel. To do so, each method was run ten time and three validation techniques was used to estimate the accuracy statistics consist of cross validation, independent validation and validation with total of train data. Moreover, using ANOVA and Tukey test, statistical difference significance between the classification methods was significantly surveyed. In general, the results showed that random forest with marginal difference compared to Bagged CART and stochastic gradient boosting model is the best performing method whilst based on independent validation there was no significant difference between the performances of classification methods. It should be finally noted that neural network with feature extraction and linear support vector machine had better processing speed than other.

Classification of adaptive memetic algorithms: a comparative study.

PubMed

Ong, Yew-Soon; Lim, Meng-Hiot; Zhu, Ning; Wong, Kok-Wai

2006-02-01

Adaptation of parameters and operators represents one of the recent most important and promising areas of research in evolutionary computations; it is a form of designing self-configuring algorithms that acclimatize to suit the problem in hand. Here, our interests are on a recent breed of hybrid evolutionary algorithms typically known as adaptive memetic algorithms (MAs). One unique feature of adaptive MAs is the choice of local search methods or memes and recent studies have shown that this choice significantly affects the performances of problem searches. In this paper, we present a classification of memes adaptation in adaptive MAs on the basis of the mechanism used and the level of historical knowledge on the memes employed. Then the asymptotic convergence properties of the adaptive MAs considered are analyzed according to the classification. Subsequently, empirical studies on representatives of adaptive MAs for different type-level meme adaptations using continuous benchmark problems indicate that global-level adaptive MAs exhibit better search performances. Finally we conclude with some promising research directions in the area.

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…

Shore zone land use and land cover: Central Atlantic Regional Ecological Test Site

USGS Publications Warehouse

Dolan, R.; Hayden, B.P.; Vincent, C.L.

1974-01-01

Anderson's 1972 United States Geological Survey classification in modified form was applied to the barrier-island coastline within the CARETS region. High-altitude, color-infrared photography of December, 1972, and January, 1973, served as the primary data base in this study. The CARETS shore zone studied was divided into six distinct geographical regions; area percentages for each class in the modified Anderson classification are presented. Similarities and differences between regions are discussed within the framework of man's modification of these landscapes. The results of this study are presented as a series of 19 maps of land-use categories. Recommendations are made for a remote-sensing system for monitoring the CARETS shore zone within the context of the dynamics of the landscapes studied.

A framework to assess landscape structural capacity to provide regulating ecosystem services in West Africa.

PubMed

Inkoom, Justice Nana; Frank, Susanne; Greve, Klaus; Fürst, Christine

2018-03-01

The Sudanian savanna landscapes of West Africa are amongst the world's most vulnerable areas to climate change impacts. Inappropriate land use and agriculture management practices continuously impede the capacity of agricultural landscapes to provide ecosystem services (ES). Given the absence of practical assessment techniques to evaluate the landscape's capacity to provide regulating ES in this region, the goal of this paper is to propose an integrative assessment framework which combines remote sensing, geographic information systems, expert weighting and landscape metrics-based assessment. We utilized Analytical Hierarchical Process and Likert scale for the expert weighting of landscape capacity. In total, 56 experts from several land use and landscape management related departments participated in the assessment. Further, we adapted the hemeroby concept to define areas of naturalness while landscape metrics including Patch Density, Shannon's Diversity, and Shape Index were utilized for structural assessment. Lastly, we tested the reliability of expert weighting using certainty measurement rated by experts themselves. Our study focused on four regulating ES including flood control, pest and disease control, climate control, and wind erosion control. Our assessment framework was tested on four selected sites in the Vea catchment area of Ghana. The outcome of our study revealed that highly heterogeneous landscapes have a higher capacity to provide pest and disease control, while less heterogeneous landscapes have a higher potential to provide climate control. Further, we could show that the potential capacities to provide ecosystem services are underestimated by 15% if landscape structural aspects assessed through landscape metrics are not considered. We conclude that the combination of adapted land use and an optimized land use pattern could contribute considerably to lower climate change impacts in West African agricultural landscapes. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

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

Environmental mapping and monitoring of Iceland by remote sensing (EMMIRS)

NASA Astrophysics Data System (ADS)

Pedersen, Gro B. M.; Vilmundardóttir, Olga K.; Falco, Nicola; Sigurmundsson, Friðþór S.; Rustowicz, Rose; Belart, Joaquin M.-C.; Gísladóttir, Gudrun; Benediktsson, Jón A.

2016-04-01

Iceland is exposed to rapid and dynamic landscape changes caused by natural processes and man-made activities, which impact and challenge the country. Fast and reliable mapping and monitoring techniques are needed on a big spatial scale. However, currently there is lack of operational advanced information processing techniques, which are needed for end-users to incorporate remote sensing (RS) data from multiple data sources. Hence, the full potential of the recent RS data explosion is not being fully exploited. The project Environmental Mapping and Monitoring of Iceland by Remote Sensing (EMMIRS) bridges the gap between advanced information processing capabilities and end-user mapping of the Icelandic environment. This is done by a multidisciplinary assessment of two selected remote sensing super sites, Hekla and Öræfajökull, which encompass many of the rapid natural and man-made landscape changes that Iceland is exposed to. An open-access benchmark repository of the two remote sensing supersites is under construction, providing high-resolution LIDAR topography and hyperspectral data for land-cover and landform classification. Furthermore, a multi-temporal and multi-source archive stretching back to 1945 allows a decadal evaluation of landscape and ecological changes for the two remote sensing super sites by the development of automated change detection techniques. The development of innovative pattern recognition and machine learning-based approaches to image classification and change detection is one of the main tasks of the EMMIRS project, aiming to extract and compute earth observation variables as automatically as possible. Ground reference data collected through a field campaign will be used to validate the implemented methods, which outputs are then inferred with geological and vegetation models. Here, preliminary results of an automatic land-cover classification based on hyperspectral image analysis are reported. Furthermore, the EMMIRS project investigates the complex landscape dynamics between geological and ecological processes. This is done through cross-correlation of mapping results and implementation of modelling techniques that simulate geological and ecological processes in order to extrapolate the landscape evolution

Microtopographic characterization of ice-wedge polygon landscape in Barrow, Alaska: a digital map of troughs, rims, centers derived from high resolution (0.25 m) LiDAR data

DOE Data Explorer

Gangodagamage, Chandana; Wullschleger, Stan

2014-07-03

The dataset represents microtopographic characterization of the ice-wedge polygon landscape in Barrow, Alaska. Three microtopographic features are delineated using 0.25 m high resolution digital elevation dataset derived from LiDAR. The troughs, rims, and centers are the three categories in this classification scheme. The polygon troughs are the surface expression of the ice-wedges that are in lower elevations than the interior polygon. The elevated shoulders of the polygon interior immediately adjacent to the polygon troughs are the polygon rims for the low center polygons. In case of high center polygons, these features are the topographic highs. In this classification scheme, both topographic highs and rims are considered as polygon rims. The next version of the dataset will include more refined classification scheme including separate classes for rims ad topographic highs. The interior part of the polygon just adjacent to the polygon rims are the polygon centers.

Adaptive phase k-means algorithm for waveform classification

NASA Astrophysics Data System (ADS)

Song, Chengyun; Liu, Zhining; Wang, Yaojun; Xu, Feng; Li, Xingming; Hu, Guangmin

2018-01-01

Waveform classification is a powerful technique for seismic facies analysis that describes the heterogeneity and compartments within a reservoir. Horizon interpretation is a critical step in waveform classification. However, the horizon often produces inconsistent waveform phase, and thus results in an unsatisfied classification. To alleviate this problem, an adaptive phase waveform classification method called the adaptive phase k-means is introduced in this paper. Our method improves the traditional k-means algorithm using an adaptive phase distance for waveform similarity measure. The proposed distance is a measure with variable phases as it moves from sample to sample along the traces. Model traces are also updated with the best phase interference in the iterative process. Therefore, our method is robust to phase variations caused by the interpretation horizon. We tested the effectiveness of our algorithm by applying it to synthetic and real data. The satisfactory results reveal that the proposed method tolerates certain waveform phase variation and is a good tool for seismic facies analysis.

Disturbances catalyze the adaptation of forest ecosystems to changing climate conditions

PubMed Central

Thom, Dominik; Rammer, Werner; Seidl, Rupert

2016-01-01

The rates of anthropogenic climate change substantially exceed those at which forest ecosystems – dominated by immobile, long-lived organisms – are able to adapt. The resulting maladaptation of forests has potentially detrimental effects on ecosystem functioning. Furthermore, as many forest-dwelling species are highly dependent on the prevailing tree species, a delayed response of the latter to a changing climate can contribute to an extinction debt and mask climate-induced biodiversity loss. However, climate change will likely also intensify forest disturbances. Here, we tested the hypothesis that disturbances foster the reorganization of ecosystems and catalyze the adaptation of forest composition to climate change. Our specific objectives were (i) to quantify the rate of autonomous forest adaptation to climate change, (ii) examine the role of disturbance in the adaptation process, and (iii) investigate spatial differences in climate-induced species turnover in an unmanaged mountain forest landscape (Kalkalpen National Park, Austria). Simulations with a process-based forest landscape model were performed for 36 unique combinations of climate and disturbance scenarios over 1000 years. We found that climate change strongly favored European beech and oak species (currently prevailing in mid- to low-elevation areas), with novel species associations emerging on the landscape. Yet, it took between 357 and 706 years before the landscape attained a dynamic equilibrium with the climate system. Disturbances generally catalyzed adaptation and decreased the time needed to attain equilibrium by up to 211 years. However, while increasing disturbance frequency and severity accelerated adaptation, increasing disturbance size had the opposite effect. Spatial analyses suggest that particularly the lowest and highest elevation areas will be hotspots of future species change. We conclude that the growing maladaptation of forests to climate and the long lead times of autonomous adaptation need to be considered more explicitly in the ongoing efforts to safeguard biodiversity and ecosystem services provisioning. PMID:27633953

Disturbances catalyze the adaptation of forest ecosystems to changing climate conditions.

PubMed

Thom, Dominik; Rammer, Werner; Seidl, Rupert

2017-01-01

The rates of anthropogenic climate change substantially exceed those at which forest ecosystems - dominated by immobile, long-lived organisms - are able to adapt. The resulting maladaptation of forests has potentially detrimental effects on ecosystem functioning. Furthermore, as many forest-dwelling species are highly dependent on the prevailing tree species, a delayed response of the latter to a changing climate can contribute to an extinction debt and mask climate-induced biodiversity loss. However, climate change will likely also intensify forest disturbances. Here, we tested the hypothesis that disturbances foster the reorganization of ecosystems and catalyze the adaptation of forest composition to climate change. Our specific objectives were (i) to quantify the rate of autonomous forest adaptation to climate change, (ii) examine the role of disturbance in the adaptation process, and (iii) investigate spatial differences in climate-induced species turnover in an unmanaged mountain forest landscape (Kalkalpen National Park, Austria). Simulations with a process-based forest landscape model were performed for 36 unique combinations of climate and disturbance scenarios over 1000 years. We found that climate change strongly favored European beech and oak species (currently prevailing in mid- to low-elevation areas), with novel species associations emerging on the landscape. Yet, it took between 357 and 706 years before the landscape attained a dynamic equilibrium with the climate system. Disturbances generally catalyzed adaptation and decreased the time needed to attain equilibrium by up to 211 years. However, while increasing disturbance frequency and severity accelerated adaptation, increasing disturbance size had the opposite effect. Spatial analyses suggest that particularly the lowest and highest elevation areas will be hotspots of future species change. We conclude that the growing maladaptation of forests to climate and the long lead times of autonomous adaptation need to be considered more explicitly in the ongoing efforts to safeguard biodiversity and ecosystem services provisioning. © 2016 John Wiley & Sons Ltd.

Toward a 30m resolution time series of historical global urban expansion: exploring variation in North American cities

NASA Astrophysics Data System (ADS)

Stuhlmacher, M.; Wang, C.; Georgescu, M.; Tellman, B.; Balling, R.; Clinton, N. E.; Collins, L.; Goldblatt, R.; Hanson, G.

2016-12-01

Global representations of modern day urban land use and land cover (LULC) extent are becoming increasingly prevalent. Yet considerable uncertainties in the representation of built environment extent (i.e. global classifications generated from 250m resolution MODIS imagery or the United States' National Land Cover Database) remain because of the lack of a systematic, globally consistent methodological approach. We aim to increase resolution, accuracy, and improve upon past efforts by establishing a data-driven definition of the urban landscape, based on Landsat 5, 7 & 8 imagery and ancillary data sets. Continuous and discrete machine learning classification algorithms have been developed in Google Earth Engine (GEE), a powerful online cloud-based geospatial storage and parallel-computing platform. Additionally, thousands of ground truth points have been selected from high resolution imagery to fill in the previous lack of accurate data to be used for training and validation. We will present preliminary classification and accuracy assessments for select cities in the United States and Mexico. Our approach has direct implications for development of projected urban growth that is grounded on realistic identification of urbanizing hot-spots, with consequences for local to regional scale climate change, energy demand, water stress, human health, urban-ecological interactions, and efforts used to prioritize adaptation and mitigation strategies to offset large-scale climate change. Future work to apply the built-up detection algorithm globally and yearly is underway in a partnership between GEE, University of California in San Diego, and Arizona State University.

Modeling forest harvesting effects on landscape pattern in the Northwest Wisconsin Pine Barrens

Treesearch

Volker C. Radeloff; David J. Mladenoff; Eric J. Gustafson; Robert M. Scheller; Patrick A. Zollner; Hong S. Heilman; H. Resit Akcakaya

2006-01-01

Forest management shapes landscape patterns, and these patterns often differ significantly from those typical for natural disturbance regimes. This may affect wildlife habitat and other aspects of ecosystem function. Our objective was to examine the effects of different forest management decisions on landscape pattern in a fire adapted ecosystem. We used a factorial...

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…

Assessment of Landscape Fragmentation Associated With Urban Centers Using ASTER Data

NASA Astrophysics Data System (ADS)

Stefanov, W. L.

2002-12-01

The role of humans as an integral part of the environment and ecosystem processes has only recently been accepted into mainstream ecological thought. The realization that virtually all ecosystems on Earth have experienced some degree of human alteration or impact has highlighted the need to incorporate humans (and their environmental effects) into ecosystem models. A logical starting point for investigation of human ecosystem dynamics is examination of the land cover characteristics of large urban centers. Land cover and land use changes associated with urbanization are important drivers of local geological, hydrological, ecological, and climatic change. Quantification and monitoring of urban land cover/land use change is part of the primary mission of the ASTER instrument on board the NASA Terra satellite, and comprises the fundamental research objective of the Urban Environmental Monitoring (UEM) Program at Arizona State University. The UEM program seeks to acquire day/night, visible through thermal infrared data twice per year for 100 global urban centers (with an emphasis on semi-arid cities) over the nominal six-year life of the Terra mission. Data have been acquired for the majority of the target urban centers and are used to compare landscape fragmentation patterns on the basis of land cover classifications. Land cover classifications of urban centers are obtained using visible through mid-infrared reflectance and emittance spectra together with calculated vegetation index and spatial variance texture information (all derived from raw ASTER data). This information is combined within a classification matrix, using an expert system framework, to obtain final pixel classifications. Landscape fragmentation is calculated using a pixel per unit area metric for comparison between 55 urban centers with varying geographic and climatic settings including North America, South America, Europe, central and eastern Asia, and Australia. Temporal variations in land cover and landscape fragmentation are assessed for 9 urban centers (Albuquerque, New Mexico, USA; Baghdad, Iraq; Las Vegas, Nevada, USA; Lisbon, Portugal; Madrid, Spain; San Francisco, California, USA; Tokyo, Japan; and Vancouver, Canada). These data provide a useful baseline for comparison of human-dominated ecosystem land cover and associated regional landscape fragmentation. Continued collection of ASTER data throughout the duration of the Terra mission will enable further investigation of urban ecosystem trends.

Relationship between tourism development and vegetated landscapes in Luya Mountain Nature Reserve, Shanxi, China.

PubMed

Cheng, Zhan-Hong; Zhang, Jin-Tun

2005-09-01

The relationship between tourism development and vegetated landscapes is analyzed for the Luya Mountain Nature Reserve (LMNR), Shanxi, China, in this study. Indices such as Sensitive Level (SL), Landscape Importance Value (LIV), information index of biodiversity (H'), Shade-tolerant Species Proportion (SSP), and Tourism Influencing Index (TII) are used to characterize vegetated landscapes, the impact of tourism, and their relationship. Their relationship is studied by Two-Way Indicator Species Analysis (TWINSPAN) and Detrended Correspondence Analysis (DCA). TWINSPAN gives correct and rapid partition to the classification, and DCA ordination shows the changing tendency of all vegetation types based on tourism development. These results reflect the ecological relationship between tourism development and vegetated landscapes. In Luya Mountain Nature Reserve, most plant communities are in good or medium condition, which shows that these vegetated landscapes can support more tourism. However, the occurrence of the bad condition shows that there is a severe contradiction between tourism development and vegetated landscapes.

Adaptation of Bird Communities to Farmland Abandonment in a Mountain Landscape

PubMed Central

Guilherme, João Lopes; Miguel Pereira, Henrique

2013-01-01

Widespread farmland abandonment has led to significant landscape transformations of many European mountain areas. These semi-natural multi-habitat landscapes are important reservoirs of biodiversity and their abandonment has important conservation implications. In multi-habitat landscapes the adaptation of communities depends on the differential affinity of the species to the available habitats. We use nested species-area relationships (SAR) to model species richness patterns of bird communities across scales in a mountain landscape, in NW Portugal. We compare the performance of the classic-SAR and the countryside-SAR (i.e. multi-habitat) models at the landscape scale, and compare species similarity decay (SSD) at the regional scale. We find a considerable overlap of bird communities in the different land-uses (farmland, shrubland and oak forest) at the landscape scale. Analysis of the classic and countryside SAR show that specialist species are strongly related to their favourite habitat. Farmland and shrubland have higher regional SSD compared to oak forests. However, this is due to the opportunistic use of farmlands by generalist birds. Forest specialists display significant regional turnover in oak forest. Overall, the countryside-SAR model had a better fit to the data showing that habitat composition determines species richness across scales. Finally, we use the countryside-SAR model to forecast bird diversity under four scenarios of land-use change. Farmland abandonment scenarios show little impact on bird diversity as the model predicts that the complete loss of farmland is less dramatic, in terms of species diversity loss, than the disappearance of native Galicio-Portuguese oak forest. The affinities of species to non-preferred habitats suggest that bird communities can adapt to land-use changes derived from farmland abandonment. Based on model predictions we argue that rewilding may be a suitable management option for many European mountain areas. PMID:24023892

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

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.

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 data on species distribution, vegetation patterns, soil properties and on their correlations. Including the DTM-based variables increased the accuracy (κ) from 0.73 to 0.79 for Level 1. These findings show that the structural and spectral attributes of ALS echoes can be used for the classification of open landscapes, especially those where vegetation is influenced by elevation, such as coastal salt marshes, sand dunes, karst or alluvial areas; in these cases, ALS has a distinct advantage over other remotely sensed data.

Adaptive evolutionary walks require neutral intermediates in RNA fitness landscapes.

PubMed

Rendel, Mark D

2011-01-01

In RNA fitness landscapes with interconnected networks of neutral mutations, neutral precursor mutations can play an important role in facilitating the accessibility of epistatic adaptive mutant combinations. I use an exhaustively surveyed fitness landscape model based on short sequence RNA genotypes (and their secondary structure phenotypes) to calculate the minimum rate at which mutants initially appearing as neutral are incorporated into an adaptive evolutionary walk. I show first, that incorporating neutral mutations significantly increases the number of point mutations in a given evolutionary walk when compared to estimates from previous adaptive walk models. Second, that incorporating neutral mutants into such a walk significantly increases the final fitness encountered on that walk - indeed evolutionary walks including neutral steps often reach the global optimum in this model. Third, and perhaps most importantly, evolutionary paths of this kind are often extremely winding in their nature and have the potential to undergo multiple mutations at a given sequence position within a single walk; the potential of these winding paths to mislead phylogenetic reconstruction is briefly considered. Copyright © 2010 Elsevier Inc. All rights reserved.

Testing the Potential of Vegetation Indices for Land Use/cover Classification Using High Resolution Data

NASA Astrophysics Data System (ADS)

Karakacan Kuzucu, A.; Bektas Balcik, F.

2017-11-01

Accurate and reliable land use/land cover (LULC) information obtained by remote sensing technology is necessary in many applications such as environmental monitoring, agricultural management, urban planning, hydrological applications, soil management, vegetation condition study and suitability analysis. But this information still remains a challenge especially in heterogeneous landscapes covering urban and rural areas due to spectrally similar LULC features. In parallel with technological developments, supplementary data such as satellite-derived spectral indices have begun to be used as additional bands in classification to produce data with high accuracy. The aim of this research is to test the potential of spectral vegetation indices combination with supervised classification methods and to extract reliable LULC information from SPOT 7 multispectral imagery. The Normalized Difference Vegetation Index (NDVI), the Ratio Vegetation Index (RATIO), the Soil Adjusted Vegetation Index (SAVI) were the three vegetation indices used in this study. The classical maximum likelihood classifier (MLC) and support vector machine (SVM) algorithm were applied to classify SPOT 7 image. Catalca is selected region located in the north west of the Istanbul in Turkey, which has complex landscape covering artificial surface, forest and natural area, agricultural field, quarry/mining area, pasture/scrubland and water body. Accuracy assessment of all classified images was performed through overall accuracy and kappa coefficient. The results indicated that the incorporation of these three different vegetation indices decrease the classification accuracy for the MLC and SVM classification. In addition, the maximum likelihood classification slightly outperformed the support vector machine classification approach in both overall accuracy and kappa statistics.

An Investigation of Automatic Change Detection for Topographic Map Updating

NASA Astrophysics Data System (ADS)

Duncan, P.; Smit, J.

2012-08-01

Changes to the landscape are constantly occurring and it is essential for geospatial and mapping organisations that these changes are regularly detected and captured, so that map databases can be updated to reflect the current status of the landscape. The Chief Directorate of National Geospatial Information (CD: NGI), South Africa's national mapping agency, currently relies on manual methods of detecting changes and capturing these changes. These manual methods are time consuming and labour intensive, and rely on the skills and interpretation of the operator. It is therefore necessary to move towards more automated methods in the production process at CD: NGI. The aim of this research is to do an investigation into a methodology for automatic or semi-automatic change detection for the purpose of updating topographic databases. The method investigated for detecting changes is through image classification as well as spatial analysis and is focussed on urban landscapes. The major data input into this study is high resolution aerial imagery and existing topographic vector data. Initial results indicate the traditional pixel-based image classification approaches are unsatisfactory for large scale land-use mapping and that object-orientated approaches hold more promise. Even in the instance of object-oriented image classification generalization of techniques on a broad-scale has provided inconsistent results. A solution may lie with a hybrid approach of pixel and object-oriented techniques.

Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach

NASA Astrophysics Data System (ADS)

Bellón, Beatriz; Bégué, Agnès; Lo Seen, Danny; Lebourgeois, Valentine; Evangelista, Balbino Antônio; Simões, Margareth; Demonte Ferraz, Rodrigo Peçanha

2018-06-01

Cropping systems' maps at fine scale over large areas provide key information for further agricultural production and environmental impact assessments, and thus represent a valuable tool for effective land-use planning. There is, therefore, a growing interest in mapping cropping systems in an operational manner over large areas, and remote sensing approaches based on vegetation index time series analysis have proven to be an efficient tool. However, supervised pixel-based approaches are commonly adopted, requiring resource consuming field campaigns to gather training data. In this paper, we present a new object-based unsupervised classification approach tested on an annual MODIS 16-day composite Normalized Difference Vegetation Index time series and a Landsat 8 mosaic of the State of Tocantins, Brazil, for the 2014-2015 growing season. Two variants of the approach are compared: an hyperclustering approach, and a landscape-clustering approach involving a previous stratification of the study area into landscape units on which the clustering is then performed. The main cropping systems of Tocantins, characterized by the crop types and cropping patterns, were efficiently mapped with the landscape-clustering approach. Results show that stratification prior to clustering significantly improves the classification accuracies for underrepresented and sparsely distributed cropping systems. This study illustrates the potential of unsupervised classification for large area cropping systems' mapping and contributes to the development of generic tools for supporting large-scale agricultural monitoring across regions.

The ecological classification of coastal wet longleaf pine (pinus palustris) of Florida from reference conditions

Treesearch

George L. McCaskill; Jose. Shibu

2012-01-01

Tropical storms, fire, and urbanization have produced a heavily fragmented forested landscape along Florida’s Gulf coast. The longleaf pine forest, one of the most threatened ecosystems in the US, makes up a major part of this fragmented landscape. These three disturbance regimes have produced a mosaic of differently-aged pine patches of single or two cohort structures...

Integration of visual quality considerations in development of Israeli vegetation management policy.

PubMed

Misgav, A; Amir, S

2001-06-01

This article deals with the visual quality of Mediterranean vegetation groups in northern Israel, the public's preference of these groups as a visual resource, and the policy options for their management. The study is based on a sample of 44 Mediterranean vegetation groups and three population groups of local residents, who were interviewed using a questionnaire and photographs of the vegetation groups. The results of the research showed that plant classification methods based on flora composition, habitat, and external appearance were found to be suitable for visual plant classification and for the evaluation of visual preference of vegetation groups by the interviewed public. The vegetation groups of planted pine forests and olive groves, characterizing a cultured vegetation landscape, were preferred over typical Mediterranean landscapes such as scrub and grassed scrub. The researchers noted a marked difference between the two products of vegetation management policy, one that proposes the conservation and restoration of the variety of native Mediterranean vegetation landscape, and a second that advanced the development of the cultured landscape of planted olive groves and pines forests, which were highly preferred by the public. The authors suggested the development of an integrated vegetation management policy that would combine both needs and thus reduce the gap between the policy proposed by planners and the local population's visual preference.

The edge-preservation multi-classifier relearning framework for the classification of high-resolution remotely sensed imagery

NASA Astrophysics Data System (ADS)

Han, Xiaopeng; Huang, Xin; Li, Jiayi; Li, Yansheng; Yang, Michael Ying; Gong, Jianya

2018-04-01

In recent years, the availability of high-resolution imagery has enabled more detailed observation of the Earth. However, it is imperative to simultaneously achieve accurate interpretation and preserve the spatial details for the classification of such high-resolution data. To this aim, we propose the edge-preservation multi-classifier relearning framework (EMRF). This multi-classifier framework is made up of support vector machine (SVM), random forest (RF), and sparse multinomial logistic regression via variable splitting and augmented Lagrangian (LORSAL) classifiers, considering their complementary characteristics. To better characterize complex scenes of remote sensing images, relearning based on landscape metrics is proposed, which iteratively quantizes both the landscape composition and spatial configuration by the use of the initial classification results. In addition, a novel tri-training strategy is proposed to solve the over-smoothing effect of relearning by means of automatic selection of training samples with low classification certainties, which always distribute in or near the edge areas. Finally, EMRF flexibly combines the strengths of relearning and tri-training via the classification certainties calculated by the probabilistic output of the respective classifiers. It should be noted that, in order to achieve an unbiased evaluation, we assessed the classification accuracy of the proposed framework using both edge and non-edge test samples. The experimental results obtained with four multispectral high-resolution images confirm the efficacy of the proposed framework, in terms of both edge and non-edge accuracy.

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

Evolutionary Effect on the Embodied Beauty of Landscape Architectures.

PubMed

Zhang, Wei; Tang, Xiaoxiang; He, Xianyou; Chen, Guangyao

2018-01-01

According to the framework of evolutionary aesthetics, a sense of beauty is related to environmental adaptation and plasticity of human beings, which has adaptive value and biological foundations. Prior studies have demonstrated that organisms derive benefits from the landscape. In this study, we investigated whether the benefits of landscape might elicit a stronger sense of beauty and what the nature of this sense of beauty is. In two experiments, when viewing classical landscape and nonlandscape architectures photographs, participants rated the aesthetic scores (Experiment 1) and had a two-alternative forced choice aesthetic judgment by pressing the reaction button located near to (15 cm) or far from (45 cm) the presenting stimuli (Experiment 2). The results showed that reaction of aesthetic ratings for classical landscape architectures was faster than those of classical nonlandscape architectures. Furthermore, only the reaction of beautiful judgment of classical landscape architecture photograph was significantly faster when the reaction button was in the near position to the presenting photograph than those in the position of far away from the presenting photograph. This finding suggests a facilitated effect for the aesthetic perception of classical landscape architectures due to their corresponding components including water and green plants with strong evolutionary implications. Furthermore, this sense of beauty for classical landscape architectures might be the embodied approach to beauty based on the viewpoint of evolutionary aesthetics and embodied cognition.

Framework and tools for agricultural landscape assessment relating to water quality protection.

PubMed

Gascuel-Odoux, Chantal; Massa, Florence; Durand, Patrick; Merot, Philippe; Troccaz, Olivier; Baudry, Jacques; Thenail, Claudine

2009-05-01

While many scientific studies show the influence of agricultural landscape patterns on water cycle and water quality, only a few of these have proposed scientifically based and operational methods to improve water management. Territ'eau is a framework developed to adapt agricultural landscapes to water quality protection, using components such as farmers' fields, seminatural areas, and human infrastructures, which can act as sources, sinks, or buffers on water quality. This framework allows us to delimit active areas contributing to water quality, defined by the following three characteristics: (i) the dominant hydrological processes and their flow pathways, (ii) the characteristics of each considered pollutant, and (iii) the main landscape features. These areas are delineated by analyzing the flow connectivity from the stream to the croplands, by assessing the buffer functions of seminatural areas according to their flow pathways. Hence, this framework allows us to identify functional seminatural areas in terms of water quality and assess their limits and functions; it helps in proposing different approaches for changing agricultural landscape, acting on agricultural practices or systems, and/or conserving or rebuilding seminatural areas in controversial landscapes. Finally, it allows us to objectivize the functions of the landscape components, for adapting these components to new environmental constraints.

Dynamics of a temperate deciduous forest under landscape-scale management: Implications for adaptability to climate change

Treesearch

Matthew G. Olson; Benjamin O. Knapp; John M. Kabrick

2017-01-01

Landscape forest management is an approach to meeting diverse objectives that collectively span multiple spatial scales. It is critical that we understand the long-term effects of landscape management on the structure and composition of forest tree communities to ensure that these practices are sustainable. Furthermore, it is increasingly important to also consider...

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 function, suggest that hydrologic-process-based classifications should be incorporated into environmental process models and can be used to define application-specific maps of hydrologic function. PMID:26121466

The Blurred Line between Form and Process: A Comparison of Stream Channel Classification Frameworks

PubMed Central

Kasprak, Alan; Hough-Snee, Nate

2016-01-01

Stream classification provides a means to understand the diversity and distribution of channels and floodplains that occur across a landscape while identifying links between geomorphic form and process. Accordingly, stream classification is frequently employed as a watershed planning, management, and restoration tool. At the same time, there has been intense debate and criticism of particular frameworks, on the grounds that these frameworks classify stream reaches based largely on their physical form, rather than direct measurements of their component hydrogeomorphic processes. Despite this debate surrounding stream classifications, and their ongoing use in watershed management, direct comparisons of channel classification frameworks are rare. Here we implement four stream classification frameworks and explore the degree to which each make inferences about hydrogeomorphic process from channel form within the Middle Fork John Day Basin, a watershed of high conservation interest within the Columbia River Basin, U.S.A. We compare the results of the River Styles Framework, Natural Channel Classification, Rosgen Classification System, and a channel form-based statistical classification at 33 field-monitored sites. We found that the four frameworks consistently classified reach types into similar groups based on each reach or segment’s dominant hydrogeomorphic elements. Where classified channel types diverged, differences could be attributed to the (a) spatial scale of input data used, (b) the requisite metrics and their order in completing a framework’s decision tree and/or, (c) whether the framework attempts to classify current or historic channel form. Divergence in framework agreement was also observed at reaches where channel planform was decoupled from valley setting. Overall, the relative agreement between frameworks indicates that criticism of individual classifications for their use of form in grouping stream channels may be overstated. These form-based criticisms may also ignore the geomorphic tenet that channel form reflects formative hydrogeomorphic processes across a given landscape. PMID:26982076

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 function, suggest that hydrologic-process-based classifications should be incorporated into environmental process models and can be used to define application-specific maps of hydrologic function.

Is Nigeria losing its natural vegetation and landscape? Assessing the landuse-landcover change trajectories and effects in Onitsha using remote sensing and GIS

NASA Astrophysics Data System (ADS)

Nwaogu, Chukwudi; Okeke, Onyedikachi J.; Fadipe, Olusola O.; Bashiru, Kehinde A.; Pechanec, Vilém

2017-12-01

Onitsha is one of the largest commercial cities in Africa with its population growth rate increasing arithmetically for the past two decades. This situation has direct and indirect effects on the natural resources including vegetation and water. The study aimed at assessing land use-land cover (LULC) change and its effects on the vegetation and landscape from 1987 to 2015 using geoinformatics. Supervised and unsupervised classifications including maximum likelihood algorithm were performed using ENVI 4.7 and ArcGIS 10.1 versions. The LULC was classified into 7 classes: built-up areas (settlement), waterbody, thick vegetation, light vegetation, riparian vegetation, sand deposit (bare soil) and floodplain. The result revealed that all the three vegetation types decreased in areas throughout the study period while, settlement, sand deposit and floodplain areas have remarkable increase of about 100% in 2015 when compared with the total in 1987. Number of dominant plant species decreased continuously during the study. The overall classification accuracies in 1987, 2002 and 2015 was 90.7%, 92.9% and 95.5% respectively. The overall kappa coefficient of the image classification for 1987, 2002 and 2015 was 0.98, 0.93 and 0.96 respectively. In general, the average classification was above 90%, a proof that the classification was reliable and acceptable.

Mapping Vegetation Community Types in a Highly-Disturbed Landscape: Integrating Hiearchical Object-Based Image Analysis with Digital Surface Models

NASA Astrophysics Data System (ADS)

Snavely, Rachel A.

Focusing on the semi-arid and highly disturbed landscape of San Clemente Island, California, this research tests the effectiveness of incorporating a hierarchal object-based image analysis (OBIA) approach with high-spatial resolution imagery and light detection and range (LiDAR) derived canopy height surfaces for mapping vegetation communities. The study is part of a large-scale research effort conducted by researchers at San Diego State University's (SDSU) Center for Earth Systems Analysis Research (CESAR) and Soil Ecology and Restoration Group (SERG), to develop an updated vegetation community map which will support both conservation and management decisions on Naval Auxiliary Landing Field (NALF) San Clemente Island. Trimble's eCognition Developer software was used to develop and generate vegetation community maps for two study sites, with and without vegetation height data as input. Overall and class-specific accuracies were calculated and compared across the two classifications. The highest overall accuracy (approximately 80%) was observed with the classification integrating airborne visible and near infrared imagery having very high spatial resolution with a LiDAR derived canopy height model. Accuracies for individual vegetation classes differed between both classification methods, but were highest when incorporating the LiDAR digital surface data. The addition of a canopy height model, however, yielded little difference in classification accuracies for areas of very dense shrub cover. Overall, the results show the utility of the OBIA approach for mapping vegetation with high spatial resolution imagery, and emphasizes the advantage of both multi-scale analysis and digital surface data for accuracy characterizing highly disturbed landscapes. The integrated imagery and digital canopy height model approach presented both advantages and limitations, which have to be considered prior to its operational use in mapping vegetation communities.

Gradient Analysis and Classification of Carolina Bay Vegetation: A Framework for Bay Wetlands Conservation and Restoration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Diane De Steven,Ph.D.; Maureen Tone,PhD.

1997-10-01

This report address four project objectives: (1) Gradient model of Carolina bay vegetation on the SRS--The authors use ordination analyses to identify environmental and landscape factors that are correlated with vegetation composition. Significant factors can provide a framework for site-based conservation of existing diversity, and they may also be useful site predictors for potential vegetation in bay restorations. (2) Regional analysis of Carolina bay vegetation diversity--They expand the ordination analyses to assess the degree to which SRS bays encompass the range of vegetation diversity found in the regional landscape of South Carolina's western Upper Coastal Plain. Such comparisons can indicatemore » floristic status relative to regional potentials and identify missing species or community elements that might be re-introduced or restored. (3) Classification of vegetation communities in Upper Coastal Plain bays--They use cluster analysis to identify plant community-types at the regional scale, and explore how this classification may be functional with respect to significant environmental and landscape factors. An environmentally-based classification at the whole-bay level can provide a system of templates for managing bays as individual units and for restoring bays to desired plant communities. (4) Qualitative model for bay vegetation dynamics--They analyze present-day vegetation in relation to historic land uses and disturbances. The distinctive history of SRS bays provides the possibility of assessing pathways of post-disturbance succession. They attempt to develop a coarse-scale model of vegetation shifts in response to changing site factors; such qualitative models can provide a basis for suggesting management interventions that may be needed to maintain desired vegetation in protected or restored bays.« less

Soil erosion modelling for NSW coastal catchments using RUSLE in a GIS environment

NASA Astrophysics Data System (ADS)

Yang, Xihua; Chapman, Greg

2006-10-01

In this study, hillslope erosion risk has been estimated for all eastern New South Wales (NSW) catchments, Australia using Revised Universal Soil Loss Equation (RUSLE) in a geographic information system (GIS) environment. Rainfall-runoff erosivity (R) factor was interpolated from NSW rainfall-erosivity contour (isoerodent) data. Soil erodibility (K) factor was based on the soil regolith stability and sediment yield classification. The classification was derived from soil landscape and related soil map data. The slope length and steepness (LS) factor was derived from high resolution digital elevation model (DEM). A fully-automated program using Arc Macro Language (AML) produced RUSLE-based LS factor grids for all coastal catchments. The outputs are comparable to the range of LS values summarised in the literature. Cover and management (C) factor and conservation support-practices (P) factor were set to one. They are intended to be allocated according to land use, ground cover and erosion control provisions for particular land management actions. The resulting erosion risk map, with pixel size of 25-m, provides unprecedented resolution of relative expected sheet and rill erosion across all NSW costal catchments and can be adapted for a range of erosion control purposes such as bushfire hazard reduction and comprehensive costal assessment.

CLustre: semi-automated lineament clustering for palaeo-glacial reconstruction

NASA Astrophysics Data System (ADS)

Smith, Mike; Anders, Niels; Keesstra, Saskia

2016-04-01

Palaeo glacial reconstructions, or "inversions", using evidence from the palimpsest landscape are increasingly being undertaken with larger and larger databases. Predominant in landform evidence is the lineament (or drumlin) where the biggest datasets number in excess of 50,000 individual forms. One stage in the inversion process requires the identification of lineaments that are generically similar and then their subsequent interpretation in to a coherent chronology of events. Here we present CLustre, a semi-authomated algorithm that clusters lineaments using a locally adaptive, region growing, method. This is initially tested using 1,500 model runs on a synthetic dataset, before application to two case studies (where manual clustering has been undertaken by independent researchers): (1) Dubawnt Lake, Canada and (2) Victoria island, Canada. Results using the synthetic data show that classifications are robust in most scenarios, although specific cases of cross-cutting lineaments may lead to incorrect clusters. Application to the case studies showed a very good match to existing published work, with differences related to limited numbers of unclassified lineaments and parallel cross-cutting lineaments. The value in CLustre comes from the semi-automated, objective, application of a classification method that is repeatable. Once classified, summary statistics of lineament groups can be calculated and then used in the inversion.

Situating adaptation: How governance challenges and perceptions of uncertainty influence adaptation in the Rocky Mountains

Treesearch

Carina Wyborn; Laurie Yung; Daniel Murphy; Daniel R. Williams

2015-01-01

Adaptation is situated within multiple, interacting social, political, and economic forces. Adaptation pathways envision adaptation as a continual pathway of change and response embedded within this broader sociopolitical context. Pathways emphasize that current decisions are both informed by past actions and shape the landscape of future options. This research...

Spatial education: improving conservation delivery through space-structured decision making

USGS Publications Warehouse

Moore, Clinton T.; Shaffer, Terry L.; Gannon, Jill J.

2013-01-01

Adaptive management is a form of structured decision making designed to guide management of natural resource systems when their behaviors are uncertain. Where decision making can be replicated across units of a landscape, learning can be accelerated, and biological processes can be understood in a larger spatial context. Broad-based partnerships among land management agencies, exemplified by Landscape Conservation Cooperatives (conservation partnerships created through the U.S. Department of the Interior), are potentially ideal environments for implementing spatially structured adaptive management programs.

Multifunctional energy landscape for a DNA G-quadruplex: An evolved molecular switch

NASA Astrophysics Data System (ADS)

Cragnolini, Tristan; Chakraborty, Debayan; Šponer, Jiří; Derreumaux, Philippe; Pasquali, Samuela; Wales, David J.

2017-10-01

We explore the energy landscape for a four-fold telomere repeat, obtaining interconversion pathways between six experimentally characterised G-quadruplex topologies. The results reveal a multi-funnel system, with a variety of intermediate configurations and misfolded states. This organisation is identified with the intrinsically multi-functional nature of the system, suggesting a new paradigm for the classification of such biomolecules and clarifying issues regarding apparently conflicting experimental results.

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

USDA-ARS?s Scientific Manuscript database

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...

Adaptation approaches for conserving ecosystems services and biodiversity in dynamic landscapes caused by climate change

Treesearch

Oswald J. Schmitz; Anne M. Trainor

2014-01-01

Climate change stands to cause animal species to shift their geographic ranges. This will cause ecosystems to become reorganized across landscapes as species migrate into and out of specific locations with attendant impacts on values and services that ecosystems provide to humans. Conservation in an era of climate change needs to ensure that landscapes are resilient by...

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.

Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer

NASA Astrophysics Data System (ADS)

Werdiningsih, Indah; Zaman, Badrus; Nuqoba, Barry

2017-08-01

This paper presents classification of brain cancer using wavelet transformation and Adaptive Neighborhood Based Modified Backpropagation (ANMBP). Three stages of the processes, namely features extraction, features reduction, and classification process. Wavelet transformation is used for feature extraction and ANMBP is used for classification process. The result of features extraction is feature vectors. Features reduction used 100 energy values per feature and 10 energy values per feature. Classifications of brain cancer are normal, alzheimer, glioma, and carcinoma. Based on simulation results, 10 energy values per feature can be used to classify brain cancer correctly. The correct classification rate of proposed system is 95 %. This research demonstrated that wavelet transformation can be used for features extraction and ANMBP can be used for classification of brain cancer.

Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in northern Minnesota

USGS Publications Warehouse

Corcoran, Jennifer M.; Knight, Joseph F.; Gallant, Alisa L.

2013-01-01

Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota.

Accuracy of land use change detection using support vector machine and maximum likelihood techniques for open-cast coal mining areas.

PubMed

Karan, Shivesh Kishore; Samadder, Sukha Ranjan

2016-08-01

One objective of the present study was to evaluate the performance of support vector machine (SVM)-based image classification technique with the maximum likelihood classification (MLC) technique for a rapidly changing landscape of an open-cast mine. The other objective was to assess the change in land use pattern due to coal mining from 2006 to 2016. Assessing the change in land use pattern accurately is important for the development and monitoring of coalfields in conjunction with sustainable development. For the present study, Landsat 5 Thematic Mapper (TM) data of 2006 and Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) data of 2016 of a part of Jharia Coalfield, Dhanbad, India, were used. The SVM classification technique provided greater overall classification accuracy when compared to the MLC technique in classifying heterogeneous landscape with limited training dataset. SVM exceeded MLC in handling a difficult challenge of classifying features having near similar reflectance on the mean signature plot, an improvement of over 11 % was observed in classification of built-up area, and an improvement of 24 % was observed in classification of surface water using SVM; similarly, the SVM technique improved the overall land use classification accuracy by almost 6 and 3 % for Landsat 5 and Landsat 8 images, respectively. Results indicated that land degradation increased significantly from 2006 to 2016 in the study area. This study will help in quantifying the changes and can also serve as a basis for further decision support system studies aiding a variety of purposes such as planning and management of mines and environmental impact assessment.

Local Fitness Landscapes Predict Yeast Evolutionary Dynamics in Directionally Changing Environments.

PubMed

Gorter, Florien A; Aarts, Mark G M; Zwaan, Bas J; de Visser, J Arjan G M

2018-01-01

The fitness landscape is a concept that is widely used for understanding and predicting evolutionary adaptation. The topography of the fitness landscape depends critically on the environment, with potentially far-reaching consequences for evolution under changing conditions. However, few studies have assessed directly how empirical fitness landscapes change across conditions, or validated the predicted consequences of such change. We previously evolved replicate yeast populations in the presence of either gradually increasing, or constant high, concentrations of the heavy metals cadmium (Cd), nickel (Ni), and zinc (Zn), and analyzed their phenotypic and genomic changes. Here, we reconstructed the local fitness landscapes underlying adaptation to each metal by deleting all repeatedly mutated genes both by themselves and in combination. Fitness assays revealed that the height, and/or shape, of each local fitness landscape changed considerably across metal concentrations, with distinct qualitative differences between unconditionally (Cd) and conditionally toxic metals (Ni and Zn). This change in topography had particularly crucial consequences in the case of Ni, where a substantial part of the individual mutational fitness effects changed in sign across concentrations. Based on the Ni landscape analyses, we made several predictions about which mutations had been selected when during the evolution experiment. Deep sequencing of population samples from different time points generally confirmed these predictions, demonstrating the power of landscape reconstruction analyses for understanding and ultimately predicting evolutionary dynamics, even under complex scenarios of environmental change. Copyright © 2018 by the Genetics Society of America.

Geomorphology Classification of Shandong Province Based on Digital Elevation Model in the 1 Arc-second Format of Shuttle Radar Topography Mission Data

NASA Astrophysics Data System (ADS)

Fu, Jundong; Zhang, Guangcheng; Wang, Lei; Xia, Nuan

2018-01-01

Based on gigital elevation model in the 1 arc-second format of shuttle radar topography mission data, using the window analysis and mean change point analysis of geographic information system (GIS) technology, programmed with python modules this, automatically extracted and calculated geomorphic elements of Shandong province. The best access to quantitatively study area relief amplitude of statistical area. According to Chinese landscape classification standard, the landscape type in Shandong province was divided into 8 types: low altitude plain, medium altitude plain, low altitude platform, medium altitude platform, low altitude hills, medium altitude hills, low relief mountain, medium relief mountain and the percentages of Shandong province’s total area are as follows: 12.72%, 0.01%, 36.38%, 0.24%, 17.26%, 15.64%, 11.1%, 6.65%. The results of landforms are basically the same as the overall terrain of Shandong Province, Shandong province’s total area, and the study can quantitatively and scientifically provide reference for the classification of landforms in Shandong province.

Classification-Assisted Memetic Algorithms for Equality-Constrained Optimization Problems

NASA Astrophysics Data System (ADS)

Handoko, Stephanus Daniel; Kwoh, Chee Keong; Ong, Yew Soon

Regressions has successfully been incorporated into memetic algorithm (MA) to build surrogate models for the objective or constraint landscape of optimization problems. This helps to alleviate the needs for expensive fitness function evaluations by performing local refinements on the approximated landscape. Classifications can alternatively be used to assist MA on the choice of individuals that would experience refinements. Support-vector-assisted MA were recently proposed to alleviate needs for function evaluations in the inequality-constrained optimization problems by distinguishing regions of feasible solutions from those of the infeasible ones based on some past solutions such that search efforts can be focussed on some potential regions only. For problems having equality constraints, however, the feasible space would obviously be extremely small. It is thus extremely difficult for the global search component of the MA to produce feasible solutions. Hence, the classification of feasible and infeasible space would become ineffective. In this paper, a novel strategy to overcome such limitation is proposed, particularly for problems having one and only one equality constraint. The raw constraint value of an individual, instead of its feasibility class, is utilized in this work.

Selection towards different adaptive optima drove the early diversification of locomotor phenotypes in the radiation of Neotropical geophagine cichlids.

PubMed

Astudillo-Clavijo, Viviana; Arbour, Jessica H; López-Fernández, Hernán

2015-05-01

Simpson envisaged a conceptual model of adaptive radiation in which lineages diversify into "adaptive zones" within a macroevolutionary adaptive landscape. However, only a handful of studies have empirically investigated this adaptive landscape and its consequences for our interpretation of the underlying mechanisms of phenotypic evolution. In fish radiations the evolution of locomotor phenotypes may represent an important dimension of ecomorphological diversification given the implications of locomotion for feeding and habitat use. Neotropical geophagine cichlids represent a newly identified adaptive radiation and provide a useful system for studying patterns of locomotor diversification and the implications of selective constraints on phenotypic divergence in general. We use multivariate ordination, models of phenotypic evolution and posterior predictive approaches to investigate the macroevolutionary adaptive landscape and test for evidence of early divergence of locomotor phenotypes in Geophagini. The evolution of locomotor phenotypes was characterized by selection towards at least two distinct adaptive peaks and the early divergence of modern morphological disparity. One adaptive peak included the benthic and epibenthic invertivores and was characterized by fishes with deep, laterally compressed bodies that optimize precise, slow-swimming manoeuvres. The second adaptive peak resulted from a shift in adaptive optima in the species-rich ram-feeding/rheophilic Crenicichla-Teleocichla clade and was characterized by species with streamlined bodies that optimize fast starts and rapid manoeuvres. Evolutionary models and posterior predictive approaches favoured an early shift to a new adaptive peak over decreasing rates of evolution as the underlying process driving the early divergence of locomotor phenotypes. The influence of multiple adaptive peaks on the divergence of locomotor phenotypes in Geophagini is compatible with the expectations of an ecologically driven adaptive radiation. This study confirms that the diversification of locomotor phenotypes represents an important dimension of phenotypic evolution in the geophagine adaptive radiation. It also suggests that the commonly observed early burst of phenotypic evolution during adaptive radiations may be better explained by the concentration of shifts to new adaptive peaks deep in the phylogeny rather than overall decreasing rates of evolution.

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.

Geotechnology and landscape ecology applied to the selection of potential forest fragments for seed harvesting.

PubMed

Santos, Alexandre Rosa Dos; Antonio Alvares Soares Ribeiro, Carlos; de Oliveira Peluzio, Telma Machado; Esteves Peluzio, João Batista; de Queiroz, Vagner Tebaldi; Figueira Branco, Elvis Ricardo; Lorenzon, Alexandre Simões; Domingues, Getulio Fonseca; Marcatti, Gustavo Eduardo; de Castro, Nero Lemos Martins; Teixeira, Thaisa Ribeiro; Dos Santos, Gleissy Mary Amaral Dino Alves; Santos Mota, Pedro Henrique; Ferreira da Silva, Samuel; Vargas, Rozimelia; de Carvalho, José Romário; Macedo, Leandro Levate; da Silva Araújo, Cintia; de Almeida, Samira Luns Hatum

2016-12-01

The Atlantic Forest biome is recognized for its biodiversity and is one of the most threatened biomes on the planet, with forest fragmentation increasing due to uncontrolled land use, land occupation, and population growth. The most serious aspect of the forest fragmentation process is the edge effect and the loss of biodiversity. In this context, the aim of this study was to evaluate the dynamics of forest fragmentation and select potential forest fragments with a higher degree of conservation for seed harvesting in the Itapemirim river basin, Espírito Santo State, Brazil. Image classification techniques, forest landscape ecology, and multi-criteria analysis were used to evaluate the evolution of forest fragmentation to develop the landscape metric indexes, and to select potential forest fragments for seed harvesting for the years 1985 and 2013. According to the results, there was a reduction of 2.55% of the occupancy of the fragments in the basin between the years 1985 and 2013. For the years 1985 and 2013, forest fragment units 2 and 3 were spatialized with a high potential for seed harvesting, representing 6.99% and 16.01% of the total fragments, respectively. The methodology used in this study has the potential to be used to support decisions for the selection of potential fragments for seed harvesting because selecting fragments in different environments by their spatial attributes provides a greater degree of conservation, contributing to the protection and conscious management of the forests. The proposed methodology can be adapted to other areas and different biomes of the world. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery

NASA Technical Reports Server (NTRS)

Fagan, Matthew E.; Defries, Ruth S.; Sesnie, Steven E.; Arroyo-Mora, J. Pablo; Soto, Carlomagno; Singh, Aditya; Townsend, Philip A.; Chazdon, Robin L.

2015-01-01

An efficient means to map tree plantations is needed to detect tropical land use change and evaluate reforestation projects. To analyze recent tree plantation expansion in northeastern Costa Rica, we examined the potential of combining moderate-resolution hyperspectral imagery (2005 HyMap mosaic) with multitemporal, multispectral data (Landsat) to accurately classify (1) general forest types and (2) tree plantations by species composition. Following a linear discriminant analysis to reduce data dimensionality, we compared four Random Forest classification models: hyperspectral data (HD) alone; HD plus interannual spectral metrics; HD plus a multitemporal forest regrowth classification; and all three models combined. The fourth, combined model achieved overall accuracy of 88.5%. Adding multitemporal data significantly improved classification accuracy (p less than 0.0001) of all forest types, although the effect on tree plantation accuracy was modest. The hyperspectral data alone classified six species of tree plantations with 75% to 93% producer's accuracy; adding multitemporal spectral data increased accuracy only for two species with dense canopies. Non-native tree species had higher classification accuracy overall and made up the majority of tree plantations in this landscape. Our results indicate that combining occasionally acquired hyperspectral data with widely available multitemporal satellite imagery enhances mapping and monitoring of reforestation in tropical landscapes.

Adaptation to elevated CO 2 in different biodiversity contexts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kleynhans, Elizabeth J.; Otto, Sarah P.; Reich, Peter B.

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. For this study, 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 CO 2, in communities of low or high species richness. Using biomass as amore » fitness proxy, we find evidence for local adaptation to elevated CO 2, 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 CO 2.« less

Adaptation to elevated CO 2 in different biodiversity contexts

DOE PAGES

Kleynhans, Elizabeth J.; Otto, Sarah P.; Reich, Peter B.; ...

2016-08-11

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. For this study, 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 CO 2, in communities of low or high species richness. Using biomass as amore » fitness proxy, we find evidence for local adaptation to elevated CO 2, 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 CO 2.« less

Social and natural sciences differ in their research strategies, adapted to work for different knowledge landscapes.

PubMed

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.

Thermodynamical interpretation of an adaptive walk on a Mt. Fuji-type fitness landscape: Einstein relation-like formula holds in a stochastic evolution.

PubMed

Aita, Takuyo; Husimi, Yuzuru

2003-11-21

We have theoretically studied the statistical properties of adaptive walks (or hill-climbing) on a Mt. Fuji-type fitness landscape in the multi-dimensional sequence space through mathematical analysis and computer simulation. The adaptive walk is characterized by the "mutation distance" d as the step-width of the walker and the "population size" N as the number of randomly generated d-fold point mutants to be screened. In addition to the fitness W, we introduced the following quantities analogous to thermodynamical concepts: "free fitness" G(W) is identical with W+T x S(W), where T is the "evolutionary temperature" T infinity square root of d/lnN and S(W) is the entropy as a function of W, and the "evolutionary force" X is identical with d(G(W)/T)/dW, that is caused by the mutation and selection pressure. It is known that a single adaptive walker rapidly climbs on the fitness landscape up to the stationary state where a "mutation-selection-random drift balance" is kept. In our interpretation, the walker tends to the maximal free fitness state, driven by the evolutionary force X. Our major findings are as follows: First, near the stationary point W*, the "climbing rate" J as the expected fitness change per generation is described by J approximately L x X with L approximately V/2, where V is the variance of fitness distribution on a local landscape. This simple relationship is analogous to the well-known Einstein relation in Brownian motion. Second, the "biological information gain" (DeltaG/T) through adaptive walk can be described by combining the Shannon's information gain (DeltaS) and the "fitness information gain" (DeltaW/T).

Maize Cropping Systems Mapping Using RapidEye Observations in Agro-Ecological Landscapes in Kenya.

PubMed

Richard, Kyalo; Abdel-Rahman, Elfatih M; Subramanian, Sevgan; Nyasani, Johnson O; Thiel, Michael; Jozani, Hosein; Borgemeister, Christian; Landmann, Tobias

2017-11-03

Cropping systems information on explicit scales is an important but rarely available variable in many crops modeling routines and of utmost importance for understanding pests and disease propagation mechanisms in agro-ecological landscapes. In this study, high spatial and temporal resolution RapidEye bio-temporal data were utilized within a novel 2-step hierarchical random forest (RF) classification approach to map areas of mono- and mixed maize cropping systems. A small-scale maize farming site in Machakos County, Kenya was used as a study site. Within the study site, field data was collected during the satellite acquisition period on general land use/land cover (LULC) and the two cropping systems. Firstly, non-cropland areas were masked out from other land use/land cover using the LULC mapping result. Subsequently an optimized RF model was applied to the cropland layer to map the two cropping systems (2nd classification step). An overall accuracy of 93% was attained for the LULC classification, while the class accuracies (PA: producer's accuracy and UA: user's accuracy) for the two cropping systems were consistently above 85%. We concluded that explicit mapping of different cropping systems is feasible in complex and highly fragmented agro-ecological landscapes if high resolution and multi-temporal satellite data such as 5 m RapidEye data is employed. Further research is needed on the feasibility of using freely available 10-20 m Sentinel-2 data for wide-area assessment of cropping systems as an important variable in numerous crop productivity models.

Impact of CO2 emissions on the geoecological state of landscapes of the British Isles: carbon footprint versus the assimilation potential

NASA Astrophysics Data System (ADS)

Romanova, Emma; Bulokhov, Anton; Arshinova, Marina

2017-04-01

The geoecological state of landscapes is determined by the type and intensity of anthropogenic impacts, the ability of geosystems to sustain them and the number of population living within a particular landscape unit. The main sources of CO2 emissions are thermal power plants, industrial facilities, transport and waste utilization. In Great Britain 163 enterprises produce 254.7 MMT CO2Eq. and 20 enterprises in Ireland - 17.8 MMT CO2Eq. Total transport emissions are 122 MMT CO2Eq. Utilization of solid wastes collected on the British Isles produces about 4.2 MMT CO2Eq. The spatial pattern of CO2 sources within the landscapes is particularly mosaic. Among the indicators which characterize the capacity of landscapes to neutralize wastes the assimilation potential (AP) is particularly important. The neutralization is based on the process of sequestration of gaseous substances, i.e. their accumulation in leaves, branches and stocks during respiration and growth of trees and in water bodies by aquatic organisms. Thus the AP is calculated basing on the area of forests and wetlands which perform the regulating services in landscapes. Total absorbing capacity of forests of the British Isles is 6.805 MMT CO2Eq. Inland waters cover 0.01% of the territory and their assimilating role is minor. The evaluation procedure includes several analytical steps: 1) inventory of the volumes of CO2 emissions by all anthropogenic sources within the borders of natural geosystems; 2) calculation of the area of CO2 assimilation in landscapes and the maximum possible volumes of CO2 sequestration; 3) comparison of the volumes of emissions and the assimilation potential of each landscape, classification of landscapes into debtors (with the deficit of AP) and creditors (with surplus AP); 4) calculation of population in each landscape; 5) risk assessment for the inhabitants living within landscapes-debtors; 6) classification and mapping of landscapes according to their geoecological state. The assimilation potential of landscapes-creditors is higher, than it is necessary for the neutralization of CO2 emissions; they are capable of the positive biotic regulation of carbon cycle. But the most landscapes in England are debtors - their AP is sometimes well below the amount of CO2 emissions, so they cannot neutralize wastes completely any more. Such geosystems reach critical thresholds of environmental services exploitation, their biota turns from a carbon pool into a source of its drain, thus endangering the regulatory abilities of landscapes. The geoecological situation in these geocomplexes creates the risk of serious diseases for inhabitants, and such landscapes are considered as unfavorable for living. According to the calculations to neutralize all CO2 emissions produced within the British Isles they need an area 16 times larger than the available one. Hence the transition to a low-carbon energy regime to mitigate CO2 emission within landscapes-debtors is a most actual challenge.

Hydrologic landscape units and adaptive management of intermountain wetlands

USGS Publications Warehouse

Custer, Stephen G.; Sojda, R.S.

2006-01-01

daptive management is often proposed to assist in the management of national wildlife refuges and allows the exploration of alternatives as well as the addition of ne w knowledge as it becomes available. The hydrological landscape unit can be a good foundation for such efforts. Red Rock Lakes National Wildlife Refuge (NWR) is in an intermountain basin dominated by vertical tectonics in the Northern Rocky Mountains. A geographic information system was used to define the boundaries for the hydrologic landscape units there. Units identified include alluvial fan, interfan, stream alluvi um and basin flat. Management alternatives can be informed by ex amination of processes that occu r on the units. For example, an ancient alluvial fan unit related to Red Rock Creek appear s to be isolated from stream flow today, with recharge dominated by precipitation and bedrock springs; while other alluvial fan units in the area have shallow ground water recharged from mountain streams and precipitation. The scale of hydrologic processes in interfan units differs from that in alluvial fan hydrologic landscape units. These differences are important when the refuge is evaluating habitat management activities. Hydrologic landscape units provide scientific unde rpinnings for the refuge’s comprehensive planning process. New geologic, hydrologic, and biologic knowledge can be integrated into the hydrologic landscape unit definition and improve adaptive management.

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…

Meeting review. Uncovering the genetic basis of adaptive change: on the intersection of landscape genomics and theoretical population genetics.

PubMed

Joost, Stéphane; Vuilleumier, Séverine; Jensen, Jeffrey D; Schoville, Sean; Leempoel, Kevin; Stucki, Sylvie; Widmer, Ivo; Melodelima, Christelle; Rolland, Jonathan; Manel, Stéphanie

2013-07-01

A workshop recently held at the École Polytechnique Fédérale de Lausanne (EPFL, Switzerland) was dedicated to understanding the genetic basis of adaptive change, taking stock of the different approaches developed in theoretical population genetics and landscape genomics and bringing together knowledge accumulated in both research fields. Indeed, an important challenge in theoretical population genetics is to incorporate effects of demographic history and population structure. But important design problems (e.g. focus on populations as units, focus on hard selective sweeps, no hypothesis-based framework in the design of the statistical tests) reduce their capability of detecting adaptive genetic variation. In parallel, landscape genomics offers a solution to several of these problems and provides a number of advantages (e.g. fast computation, landscape heterogeneity integration). But the approach makes several implicit assumptions that should be carefully considered (e.g. selection has had enough time to create a functional relationship between the allele distribution and the environmental variable, or this functional relationship is assumed to be constant). To address the respective strengths and weaknesses mentioned above, the workshop brought together a panel of experts from both disciplines to present their work and discuss the relevance of combining these approaches, possibly resulting in a joint software solution in the future.

Social learning solves the problem of narrow-peaked search landscapes: experimental evidence in humans.

PubMed

Acerbi, Alberto; Tennie, Claudio; Mesoudi, Alex

2016-09-01

The extensive use of social learning is considered a major reason for the ecological success of humans. Theoretical considerations, models and experiments have explored the evolutionary basis of social learning, showing the conditions under which learning from others is more adaptive than individual learning. Here we present an extension of a previous experimental set-up, in which individuals go on simulated 'hunts' and their success depends on the features of a 'virtual arrowhead' they design. Individuals can modify their arrowhead either by individual trial and error or by copying others. We study how, in a multimodal adaptive landscape, the smoothness of the peaks influences learning. We compare narrow peaks, in which solutions close to optima do not provide useful feedback to individuals, to wide peaks, where smooth landscapes allow an effective hill-climbing individual learning strategy. We show that individual learning is more difficult in narrow-peaked landscapes, but that social learners perform almost equally well in both narrow- and wide-peaked search spaces. There was a weak trend for more copying in the narrow than wide condition, although as in previous experiments social information was generally underutilized. Our results highlight the importance of tasks' design space when studying the adaptiveness of high-fidelity social learning.

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

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 platform. Our final steps in this process have been to compare the series of expert-derived agricultural landscapes with a set of probability maps for smallholder farming developed from available country-level secondary datasets in order to fill area gaps. We see this analysis and participatory process as closely related to previous work by Eakin et al. (2011) in the region. It provides an example of how expert input and participatory mapping can help meet the pressing need for a deeper contextual understanding of the vulnerability of smallholder farmers to climate change in a data-poor environment. The methods and results of our research also offer an applied example how to approach analysis of adaptive capacity and vulnerability within agricultural systems, and we have purposefully focused on a set of methods that can easily transfer to other countries within regions such as the Tropics. Reference: Eakin, H., L.A. Bojórquez-Tapia, R. Monterde Diaz, E. Castellanos, J. Haggar. 2011. Adaptive Capacity and Social-Environmental Change: Theoretical and Operational Modeling of Smallholder Coffee Systems Response in Mesoamerican Pacific Rim. Environmental Management 47:352-367.

Land cover heterogeneity and soil respiration in a west Greenland tundra landscape

NASA Astrophysics Data System (ADS)

Bradley-Cook, J. I.; Burzynski, A.; Hammond, C. R.; Virginia, R. A.

2011-12-01

Multiple direct and indirect pathways underlie the association between land cover classification, temperature and soil respiration. Temperature is a main control of the biological processes that constitute soil respiration, yet the effect of changing atmospheric temperatures on soil carbon flux is unresolved. This study examines associations amongst land cover, soil carbon characteristics, soil respiration, and temperature in an Arctic tundra landscape in western Greenland. We used a 1.34 meter resolution multi-spectral WorldView2 satellite image to conduct an unsupervised multi-staged ISODATA classification to characterize land cover heterogeneity. The four band image was taken on July 10th, 2010, and captures an 18 km by 15 km area in the vicinity of Kangerlussuaq. The four major terrestrial land cover classes identified were: shrub-dominated, graminoid-dominated, mixed vegetation, and bare soil. The bare soil class was comprised of patches where surface soil has been deflated by wind and ridge-top fellfield. We hypothesize that soil respiration and soil carbon storage are associated with land cover classification and temperature. We set up a hierarchical field sampling design to directly observe spatial variation between and within land cover classes along a 20 km temperature gradient extending west from Russell Glacier on the margin of the Greenland Ice Sheet. We used the land cover classification map and ground verification to select nine sites, each containing patches of the four land cover classes. Within each patch we collected soil samples from a 50 cm pit, quantified vegetation, measured active layer depth and determined landscape characteristics. From a subset of field sites we collected additional 10 cm surface soil samples to estimate soil heterogeneity within patches and measured soil respiration using a LiCor 8100 Infrared Gas Analyzer. Soil respiration rates varied with land cover classes, with values ranging from 0.2 mg C/m^2/hr in the bare soil class to over 5 mg C/m^2/hr in the graminoid-dominated class. These findings suggest that shifts in land cover vegetation types, especially soil and vegetation loss (e.g. from wind deflation), can alter landscape soil respiration. We relate soil respiration measurements to soil, vegetation, and permafrost characteristics to understand how ecosystem properties and processes vary at the landscape scale. A long-term goal of this research is to develop a spatially explicit model of soil organic matter, soil respiration, and temperature sensitivity of soil carbon dynamics for a western Greenland permafrost tundra ecosystems.

The potential and flux landscape theory of evolution.

PubMed

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

2012-08-14

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 offer a theoretical basis for explaining the corresponding Red Queen hypothesis proposed by Van Valen. Our work provides a theoretical foundation for evolutionary dynamics.

Local adaptation in Trinidadian guppies alters stream ecosystem structure at landscape scales despite high environmental variability

USGS Publications Warehouse

Simon, Troy N.; Bassar, Ronald D.; Binderup, Andrew J.; Flecker, Alex S.; Freeman, Mary C.; Gilliam, James F.; Marshall, Michael C.; Thomas, Steve A.; Travis, Joseph; Reznick, David N.; Pringle, Catherine M.

2017-01-01

While previous studies have shown that evolutionary divergence alters ecological processes in small-scale experiments, a major challenge is to assess whether such evolutionary effects are important in natural ecosystems at larger spatial scales. At the landscape scale, across eight streams in the Caroni drainage, we found that the presence of locally adapted populations of guppies (Poecilia reticulata) is associated with reduced algal biomass and increased invertebrate biomass, while the opposite trends were true in streams with experimentally introduced populations of non-locally adapted guppies. Exclusion experiments conducted in two separate reaches of a single stream showed that guppies with locally adapted phenotypes significantly reduced algae with no effect on invertebrates, while non-adapted guppies had no effect on algae but significantly reduced invertebrates. These divergent effects of phenotype on stream ecosystems are comparable in strength to the effects of abiotic factors (e.g., light) known to be important drivers of ecosystem condition. They also corroborate the results of previous experiments conducted in artificial streams. Our results demonstrate that local adaptation can produce phenotypes with significantly different effects in natural ecosystems at a landscape scale, within a tropical watershed, despite high variability in abiotic factors: five of the seven physical and chemical parameters measured across the eight study streams varied by more than one order of magnitude. Our findings suggest that ecosystem structure is, in part, an evolutionary product and not simply an ecological pattern.

Wetland Hydrological Connectivity: A Classification Approach and Continental Assessment

EPA Science Inventory

Connectivity has become a major focus of hydrological and ecological studies. Connectivity influences fluxes between landscape elements, while isolation reduces flows between elements. Thus connectivity can be an important characteristic controlling ecosystem services. Hydrolo...

Landscape metrics for three-dimension urban pattern recognition

NASA Astrophysics Data System (ADS)

Liu, M.; Hu, Y.; Zhang, W.; Li, C.

2017-12-01

Understanding how landscape pattern determines population or ecosystem dynamics is crucial for managing our landscapes. Urban areas are becoming increasingly dominant social-ecological systems, so it is important to understand patterns of urbanization. Most studies of urban landscape pattern examine land-use maps in two dimensions because the acquisition of 3-dimensional information is difficult. We used Brista software based on Quickbird images and aerial photos to interpret the height of buildings, thus incorporating a 3-dimensional approach. We estimated the feasibility and accuracy of this approach. A total of 164,345 buildings in the Liaoning central urban agglomeration of China, which included seven cities, were measured. Twelve landscape metrics were proposed or chosen to describe the urban landscape patterns in 2- and 3-dimensional scales. The ecological and social meaning of landscape metrics were analyzed with multiple correlation analysis. The results showed that classification accuracy compared with field surveys was 87.6%, which means this method for interpreting building height was acceptable. The metrics effectively reflected the urban architecture in relation to number of buildings, area, height, 3-D shape and diversity aspects. We were able to describe the urban characteristics of each city with these metrics. The metrics also captured ecological and social meanings. The proposed landscape metrics provided a new method for urban landscape analysis in three dimensions.

Monitoring adaptive genetic responses to environmental change

Treesearch

Michael M. Hansen; Isabelle Olivieri; Donald M. Waller; Einar E. Nielsen; F. W. Allendorf; M. K. Schwartz; C. S. Baker; D. P. Gregovich; J. A. Jackson; K. C. Kendall; L. Laikre; K. McKelvey; M. C. Neel; N. Ryman; R. Short Bull; J. B. Stetz; D. A. Tallmon; C. D. Vojta; R. S. Waples

2012-01-01

Widespread environmental changes including climate change, selective harvesting and landscape alterations now greatly affect selection regimes for most organisms. How animals and plants can adapt to these altered environments via contemporary evolution is thus of strong interest. We discuss how to use genetic monitoring to study adaptive responses via repeated analysis...

Management for adaptation

Treesearch

John Innes; Linda A. Joyce; Seppo Kellomaki; Bastiaan Louman; Aynslie Ogden; Ian Thompson; Matthew Ayres; Chin Ong; Heru Santoso; Brent Sohngen; Anita Wreford

2009-01-01

This chapter develops a framework to explore examples of adaptation options that could be used to ensure that the ecosystem services provided by forests are maintained under future climates. The services are divided into broad areas within which managers can identify specific management goals for individual forests or landscapes. Adaptation options exist for the major...

Online adaptive decision trees: pattern classification and function approximation.

PubMed

Basak, Jayanta

2006-09-01

Recently we have shown that decision trees can be trained in the online adaptive (OADT) mode (Basak, 2004), leading to better generalization score. OADTs were bottlenecked by the fact that they are able to handle only two-class classification tasks with a given structure. In this article, we provide an architecture based on OADT, ExOADT, which can handle multiclass classification tasks and is able to perform function approximation. ExOADT is structurally similar to OADT extended with a regression layer. We also show that ExOADT is capable not only of adapting the local decision hyperplanes in the nonterminal nodes but also has the potential of smoothly changing the structure of the tree depending on the data samples. We provide the learning rules based on steepest gradient descent for the new model ExOADT. Experimentally we demonstrate the effectiveness of ExOADT in the pattern classification and function approximation tasks. Finally, we briefly discuss the relationship of ExOADT with other classification models.

Development of neural network techniques for finger-vein pattern classification

NASA Astrophysics Data System (ADS)

Wu, Jian-Da; Liu, Chiung-Tsiung; Tsai, Yi-Jang; Liu, Jun-Ching; Chang, Ya-Wen

2010-02-01

A personal identification system using finger-vein patterns and neural network techniques is proposed in the present study. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis and classification. The biometric system for verification consists of a combination of feature extraction using principal component analysis and pattern classification using both back-propagation network and adaptive neuro-fuzzy inference systems. Finger-vein features are first extracted by principal component analysis method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed adaptive neuro-fuzzy inference system in the pattern classification, the back-propagation network is compared with the proposed system. The experimental results indicated the proposed system using adaptive neuro-fuzzy inference system demonstrated a better performance than the back-propagation network for personal identification using the finger-vein patterns.

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-…

Analysis of Patent Databases Using VxInsight

DOE Office of Scientific and Technical Information (OSTI.GOV)

BOYACK,KEVIN W.; WYLIE,BRIAN N.; DAVIDSON,GEORGE S.

2000-12-12

We present the application of a new knowledge visualization tool, VxInsight, to the mapping and analysis of patent databases. Patent data are mined and placed in a database, relationships between the patents are identified, primarily using the citation and classification structures, then the patents are clustered using a proprietary force-directed placement algorithm. Related patents cluster together to produce a 3-D landscape view of the tens of thousands of patents. The user can navigate the landscape by zooming into or out of regions of interest. Querying the underlying database places a colored marker on each patent matching the query. Automatically generatedmore » labels, showing landscape content, update continually upon zooming. Optionally, citation links between patents may be shown on the landscape. The combination of these features enables powerful analyses of patent databases.« less

Predicting local adaptation in fragmented plant populations: implications for restoration genetics

PubMed Central

Pickup, Melinda; Field, David L; Rowell, David M; Young, Andrew G

2012-01-01

Understanding patterns and correlates of local adaptation in heterogeneous landscapes can provide important information in the selection of appropriate seed sources for restoration. We assessed the extent of local adaptation of fitness components in 12 population pairs of the perennial herb Rutidosis leptorrhynchoides (Asteraceae) and examined whether spatial scale (0.7–600 km), environmental distance, quantitative (QST) and neutral (FST) genetic differentiation, and size of the local and foreign populations could predict patterns of adaptive differentiation. Local adaptation varied among populations and fitness components. Including all population pairs, local adaptation was observed for seedling survival, but not for biomass, while foreign genotype advantage was observed for reproduction (number of inflorescences). Among population pairs, local adaptation increased with QST and local population size for biomass. QST was associated with environmental distance, suggesting ecological selection for phenotypic divergence. However, low FST and variation in population structure in small populations demonstrates the interaction of gene flow and drift in constraining local adaptation in R. leptorrhynchoides. Our study indicates that for species in heterogeneous landscapes, collecting seed from large populations from similar environments to candidate sites is likely to provide the most appropriate seed sources for restoration. PMID:23346235

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

Mapping urban climate zones and quantifying climate behaviors--an application on Toulouse urban area (France).

PubMed

Houet, Thomas; Pigeon, Grégoire

2011-01-01

Facing the concern of the population to its environment and to climatic change, city planners are now considering the urban climate in their choices of planning. The use of climatic maps, such Urban Climate Zone‑UCZ, is adapted for this kind of application. The objective of this paper is to demonstrate that the UCZ classification, integrated in the World Meteorological Organization guidelines, first can be automatically determined for sample areas and second is meaningful according to climatic variables. The analysis presented is applied on Toulouse urban area (France). Results show first that UCZ differentiate according to air and surface temperature. It has been possible to determine the membership of sample areas to an UCZ using landscape descriptors automatically computed with GIS and remote sensed data. It also emphasizes that climate behavior and magnitude of UCZ may vary from winter to summer. Finally we discuss the influence of climate data and scale of observation on UCZ mapping and climate characterization. Copyright © 2011 Elsevier Ltd. All rights reserved.

Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

DOE Office of Scientific and Technical Information (OSTI.GOV)

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

Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics tomore » help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. In conclusion, our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.« less

Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

DOE PAGES

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

2014-10-01

Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics tomore » help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. In conclusion, our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.« less

Wetland Hydrological Connectivity: A Classification Approach and United States Assessment

EPA Science Inventory

Connectivity has become a major focus of hydrological and ecological studies. Connectivity influences fluxes between landscape elements, while isolation reduces flows between elements. Thus connectivity can be an important characteristic controlling ecosystem services. Hydrolo...

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…

Eucalyptus as a landscape tree

Treesearch

W. Douglas Hamilton

1983-01-01

Ninety-two species of Eucalyptus were evaluated at the University of California re- search station in San Jose. The purpose: to find acceptable new street and park trees. Growth rates and horticultural characteristics were noted. Forty-three species were studied in locations statewide to evaluate site adaptation and landscape usefulness; flooded, cold, dry, saline....

'Seasons in the Sun’, a colorful new little Bluestem for landscapes

USDA-ARS?s Scientific Manuscript database

Schizachyrium scoparium, little bluestem, is a warm-season perennial grass native to much of North America. This drought-tolerant plant is tough and adaptable. It is becoming more popular in landscaping due to its low maintenance and attractive foliage, as well as increasing interest in using native...

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.

Modeling brook trout presence and absence from landscape variables using four different analytical methods

USGS Publications Warehouse

Steen, Paul J.; Passino-Reader, Dora R.; Wiley, Michael J.

2006-01-01

As a part of the Great Lakes Regional Aquatic Gap Analysis Project, we evaluated methodologies for modeling associations between fish species and habitat characteristics at a landscape scale. To do this, we created brook trout Salvelinus fontinalis presence and absence models based on four different techniques: multiple linear regression, logistic regression, neural networks, and classification trees. The models were tested in two ways: by application to an independent validation database and cross-validation using the training data, and by visual comparison of statewide distribution maps with historically recorded occurrences from the Michigan Fish Atlas. Although differences in the accuracy of our models were slight, the logistic regression model predicted with the least error, followed by multiple regression, then classification trees, then the neural networks. These models will provide natural resource managers a way to identify habitats requiring protection for the conservation of fish species.

Genomic landscape of gastric cancer: molecular classification and potential targets.

PubMed

Guo, Jiawei; Yu, Weiwei; Su, Hui; Pang, Xiufeng

2017-02-01

Gastric cancer imposes a considerable health burden worldwide, and its mortality ranks as the second highest for all types of cancers. The limited knowledge of the molecular mechanisms underlying gastric cancer tumorigenesis hinders the development of therapeutic strategies. However, ongoing collaborative sequencing efforts facilitate molecular classification and unveil the genomic landscape of gastric cancer. Several new drivers and tumorigenic pathways in gastric cancer, including chromatin remodeling genes, RhoA-related pathways, TP53 dysregulation, activation of receptor tyrosine kinases, stem cell pathways and abnormal DNA methylation, have been revealed. These newly identified genomic alterations await translation into clinical diagnosis and targeted therapies. Considering that loss-of-function mutations are intractable, synthetic lethality could be employed when discussing feasible therapeutic strategies. Although many challenges remain to be tackled, we are optimistic regarding improvements in the prognosis and treatment of gastric cancer in the near future.

Plant landscape design simulating natural community by using AHP method based on TWINSPAN classification

NASA Astrophysics Data System (ADS)

Wang, Li Han

2018-06-01

Taking the forest vegetation in Zijin Mountain (Purple Mountain) Area of Nanjing as the research object, based on the simulation natural and semi natural plant communities, the systematic research on the construction of Nanjing regional plant landscape is carried out by the method such as literature and theory, investigation and evaluation, discussion and reference. On the basis of TWINSPAN classification, the species composition (flora and geographical composition), community structure, species diversity, interspecific relationship and ecological niche of Zijin Mountain natural vegetation are studied and analyzed as a basis for simulation design and planting. Then, from the three levels of ornamental value, resource development and utilization potential and biological characteristics, a comprehensive evaluation system used for wild ornamental plant resources in Zijin Mountain is built. Finally, some suggestions on the planting species of deep forest vegetation in Zijin Mountain are put forward.

Fine- and coarse-filter conservation strategies in a time of climate change.

PubMed

Tingley, Morgan W; Darling, Emily S; Wilcove, David S

2014-08-01

As species adapt to a changing climate, so too must humans adapt to a new conservation landscape. Classical frameworks have distinguished between fine- and coarse-filter conservation strategies, focusing on conserving either the species or the landscapes, respectively, that together define extant biodiversity. Adapting this framework for climate change, conservationists are using fine-filter strategies to assess species vulnerability and prioritize the most vulnerable species for conservation actions. Coarse-filter strategies seek to conserve either key sites as determined by natural elements unaffected by climate change, or sites with low climate velocity that are expected to be refugia for climate-displaced species. Novel approaches combine coarse- and fine-scale approaches--for example, prioritizing species within pretargeted landscapes--and accommodate the difficult reality of multiple interacting stressors. By taking a diversified approach to conservation actions and decisions, conservationists can hedge against uncertainty, take advantage of new methods and information, and tailor actions to the unique needs and limitations of places, thereby ensuring that the biodiversity show will go on. © 2014 New York Academy of Sciences.

Self-Learning Adaptive Umbrella Sampling Method for the Determination of Free Energy Landscapes in Multiple Dimensions

PubMed Central

Wojtas-Niziurski, Wojciech; Meng, Yilin; Roux, Benoit; Bernèche, Simon

2013-01-01

The potential of mean force describing conformational changes of biomolecules is a central quantity that determines the function of biomolecular systems. Calculating an energy landscape of a process that depends on three or more reaction coordinates might require a lot of computational power, making some of multidimensional calculations practically impossible. Here, we present an efficient automatized umbrella sampling strategy for calculating multidimensional potential of mean force. The method progressively learns by itself, through a feedback mechanism, which regions of a multidimensional space are worth exploring and automatically generates a set of umbrella sampling windows that is adapted to the system. The self-learning adaptive umbrella sampling method is first explained with illustrative examples based on simplified reduced model systems, and then applied to two non-trivial situations: the conformational equilibrium of the pentapeptide Met-enkephalin in solution and ion permeation in the KcsA potassium channel. With this method, it is demonstrated that a significant smaller number of umbrella windows needs to be employed to characterize the free energy landscape over the most relevant regions without any loss in accuracy. PMID:23814508

Multi-disciplinary scientists as global change adaptation anchors: Filling the gaps in the Boundary Organization paradigm

NASA Astrophysics Data System (ADS)

Terando, A. J.; Collazo, J.

2017-12-01

Boundary organizations, entities that facilitate the co-production and translation of scientific research in decision making processes, have been promoted as a means to assist global change adaptation, particularly in the areas of landscape conservation and natural resource management. However, scientists can and often still must perform a similar role and act as anchoring agents within wicked adaptation problems that involve a myriad of actors, values, scientific uncertainties, governance structures, and multidisciplinary research needs. We illustrate one such case study in Puerto Rico's Bosque Modelo (Model Forest) where we discuss an ongoing scientific effort to undertake a multi-objective landscape conservation design project that intersects with the Bosque Modelo geography and goals. Perspectives are provided from two research ecologists, one with a background in terrestrial ecology who has worked at the intersection of science, conservation, and government for over 30 years, and the other with a multi-disciplinary background in earth sciences, climatology, and terrestrial ecology. We frame our discussion around the learning process that accompanies the development of global change scenarios that are both useful and useable for a wide spectrum of scientists, and the likelihood that scientifically informed adaptive management actions will ultimately be implemented in this complex and changing landscape.

Classification of multiple sclerosis lesions using adaptive dictionary learning.

PubMed

Deshpande, Hrishikesh; Maurel, Pierre; Barillot, Christian

2015-12-01

This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

Preliminary Assessment of JERS-1 SAR to Discriminating Boreal Landscape Features for the Boreal Forest Mapping Project

NASA Technical Reports Server (NTRS)

McDonald, Kyle; Williams, Cynthia; Podest, Erika; Chapman, Bruce

1999-01-01

This paper presents an overview of the JERS-1 North American Boreal Forest Mapping Project and a preliminary assessment of JERS-1 SAR imagery for application to discriminating features applicable to boreal landscape processes. The present focus of the JERS-1 North American Boreal Forest Mapping Project is the production of continental scale wintertime and summertime SAR mosaics of the North American boreal forest for distribution to the science community. As part of this effort, JERS-1 imagery has been collected over much of Alaska and Canada during the 1997-98 winter and 1998 summer seasons. To complete the mosaics, these data will be augmented with data collected during previous years. These data will be made available to the scientific community via CD ROM containing these and similar data sets compiled from companion studies of Asia and Europe. Regional landscape classification with SAR is important for the baseline information it will provide about distribution of woodlands, positions of treeline, current forest biomass, distribution of wetlands, and extent of major rivercourses. As well as setting the stage for longer term change detection, comparisons across several years provides additional baseline information about short-term landscape change. Rapid changes, including those driven by fire, permafrost heat balance, flooding, and insect outbreaks can dominate boreal systems. We examine JERS-1 imagery covering selected sites in Alaska and Canada to assess quality and applicability to such relevant ecological and hydrological issues. The data are generally of high quality and illustrate many potential applications. A texture-based classification scheme is applied to selected regions to assess the applicability of these data for distinguishing distribution of such landcover types as wetland, tundra, woodland and forested landscapes.

Effects of Tree-crop Farming on Land-cover Transitions in a Mosaic Landscape in the Eastern Region of Ghana.

PubMed

Asubonteng, Kwabena; Pfeffer, Karin; Ros-Tonen, Mirjam; Verbesselt, Jan; Baud, Isa

2018-05-11

Tree crops such as cocoa and oil palm are important to smallholders' livelihoods and national economies of tropical producer countries. Governments seek to expand tree-crop acreages and improve yields. Existing literature has analyzed socioeconomic and environmental effects of tree-crop expansion, but its spatial effects on the landscape are yet to be explored. This study aims to assess the effects of tree-crop farming on the composition and the extent of land-cover transitions in a mixed cocoa/oil palm landscape in Ghana. Land-cover maps of 1986 and 2015 produced through ISODATA, and maximum likelihood classification were validated with field reference, Google Earth data, and key respondent interviews. Post-classification change detection was conducted and the transition matrix analyzed using intensity analysis. Cocoa and oil palm areas have increased in extent by 8.9% and 11.2%, respectively, mainly at the expense of food-crop land and forest. The intensity of forest loss to both tree crops is at a lower intensity than the loss of food-crop land. There were transitions between cocoa and oil palm, but the gains in oil palm outweigh those of cocoa. Cocoa and oil palm have increased in area and dominance. The main cover types converted to tree-crop areas are food-crop land and off-reserve forest. This is beginning to have serious implications for food security and livelihood options that depend on ecosystem services provided by the mosaic landscape. Tree-crop policies should take account of the geographical distribution of tree-commodity production at landscape level and its implications for food production and ecosystems services.

Visions of Restoration in Fire-Adapted Forest Landscapes: Lessons from the Collaborative Forest Landscape Restoration Program.

PubMed

Urgenson, Lauren S; Ryan, Clare M; Halpern, Charles B; Bakker, Jonathan D; Belote, R Travis; Franklin, Jerry F; Haugo, Ryan D; Nelson, Cara R; Waltz, Amy E M

2017-02-01

Collaborative approaches to natural resource management are becoming increasingly common on public lands. Negotiating a shared vision for desired conditions is a fundamental task of collaboration and serves as a foundation for developing management objectives and monitoring strategies. We explore the complex socio-ecological processes involved in developing a shared vision for collaborative restoration of fire-adapted forest landscapes. To understand participant perspectives and experiences, we analyzed interviews with 86 respondents from six collaboratives in the western U.S., part of the Collaborative Forest Landscape Restoration Program established to encourage collaborative, science-based restoration on U.S. Forest Service lands. Although forest landscapes and group characteristics vary considerably, collaboratives faced common challenges to developing a shared vision for desired conditions. Three broad categories of challenges emerged: meeting multiple objectives, collaborative capacity and trust, and integrating ecological science and social values in decision-making. Collaborative groups also used common strategies to address these challenges, including some that addressed multiple challenges. These included use of issue-based recommendations, field visits, and landscape-level analysis; obtaining support from local agency leadership, engaging facilitators, and working in smaller groups (sub-groups); and science engagement. Increased understanding of the challenges to, and strategies for, developing a shared vision of desired conditions is critical if other collaboratives are to learn from these efforts.

Visions of Restoration in Fire-Adapted Forest Landscapes: Lessons from the Collaborative Forest Landscape Restoration Program

NASA Astrophysics Data System (ADS)

Urgenson, Lauren S.; Ryan, Clare M.; Halpern, Charles B.; Bakker, Jonathan D.; Belote, R. Travis; Franklin, Jerry F.; Haugo, Ryan D.; Nelson, Cara R.; Waltz, Amy E. M.

2017-02-01

Collaborative approaches to natural resource management are becoming increasingly common on public lands. Negotiating a shared vision for desired conditions is a fundamental task of collaboration and serves as a foundation for developing management objectives and monitoring strategies. We explore the complex socio-ecological processes involved in developing a shared vision for collaborative restoration of fire-adapted forest landscapes. To understand participant perspectives and experiences, we analyzed interviews with 86 respondents from six collaboratives in the western U.S., part of the Collaborative Forest Landscape Restoration Program established to encourage collaborative, science-based restoration on U.S. Forest Service lands. Although forest landscapes and group characteristics vary considerably, collaboratives faced common challenges to developing a shared vision for desired conditions. Three broad categories of challenges emerged: meeting multiple objectives, collaborative capacity and trust, and integrating ecological science and social values in decision-making. Collaborative groups also used common strategies to address these challenges, including some that addressed multiple challenges. These included use of issue-based recommendations, field visits, and landscape-level analysis; obtaining support from local agency leadership, engaging facilitators, and working in smaller groups (sub-groups); and science engagement. Increased understanding of the challenges to, and strategies for, developing a shared vision of desired conditions is critical if other collaboratives are to learn from these efforts.

Land classification of the standing stone state forest and state park on the eastern highland rim in Tennessee: the interaction of geology, topography, and soils

Treesearch

Glendon W. Smalley; Carlie McCowan; S. David Todd; Phillip M. Morrissey; J. Andrew McBride

2013-01-01

This paper summarizes the application of a land classification system developed by the senior author to the Standing Stone State Forest and State Park (SSSF&SP) on the Eastern Highland Rim. Landtypes are the most detailed level in the hierarchical system and represent distinct units of the landscape (mapped at a scale of 1:24,000) as defined by climate, geology,...

An incremental knowledge assimilation system (IKAS) for mine detection

NASA Astrophysics Data System (ADS)

Porway, Jake; Raju, Chaitanya; Varadarajan, Karthik Mahesh; Nguyen, Hieu; Yadegar, Joseph

2010-04-01

In this paper we present an adaptive incremental learning system for underwater mine detection and classification that utilizes statistical models of seabed texture and an adaptive nearest-neighbor classifier to identify varied underwater targets in many different environments. The first stage of processing uses our Background Adaptive ANomaly detector (BAAN), which identifies statistically likely target regions using Gabor filter responses over the image. Using this information, BAAN classifies the background type and updates its detection using background-specific parameters. To perform classification, a Fully Adaptive Nearest Neighbor (FAAN) determines the best label for each detection. FAAN uses an extremely fast version of Nearest Neighbor to find the most likely label for the target. The classifier perpetually assimilates new and relevant information into its existing knowledge database in an incremental fashion, allowing improved classification accuracy and capturing concept drift in the target classes. Experiments show that the system achieves >90% classification accuracy on underwater mine detection tasks performed on synthesized datasets provided by the Office of Naval Research. We have also demonstrated that the system can incrementally improve its detection accuracy by constantly learning from new samples.

Comparing Pixel- and Object-Based Approaches in Effectively Classifying Wetland-Dominated Landscapes

EPA Science Inventory

Wetland ecosystems straddle both terrestrial and aquatic habitats, performing many ecological functions directly and indirectly benefitting humans. However, global wetland losses are substantial. Satellite remote sensing and classification informs wise wetland management and moni...

Wetland Hydrologic Connectivity to Downstream Waters: A Classification Approach and National Assessment

EPA Science Inventory

Connectivity has become a major focus of hydrological and ecological studies. Connectivity influences fluxes between landscape elements, while isolation reduces flows between elements. Thus connectivity can be an important characteristic controlling ecosystem services. Hydrolo...

Development and validation of a method to estimate the potential wind erosion risk in Germany

NASA Astrophysics Data System (ADS)

Funk, Roger; Deumlich, Detlef; Völker, Lidia

2017-04-01

The introduction of the Cross Compliance (CC) regulations for soil protection resulted in the demand for the classification of the the wind erosion risk on agricultural areas in Germany nationwide. A spatial highly resolved method was needed based on uniform data sets and validation principles, which provides a fair and equivalent procedure for all affected farmers. A GIS-procedure was developed, which derives the site specific wind erosion risk from the main influencing factors: soil texture, wind velocity, wind direction and landscape structure following the German standard DIN 19706. The procedure enables different approaches in the Federal States and comparable classification results. Here, we present the approach of the Federal State of Brandenburg. In the first step a complete soil data map was composed in a grid size of 10 x 10 m. Data were taken from 1.) the Soil quality Appraisal (scale 1:10.000), 2.) the Medium-scale Soil Mapping (MMK, 1:25.000), 3.) extrapolating the MMK, 4.) new Soil quality Appraisal (new areas after coal-mining). Based on the texture and carbon content the wind erosion susceptibility was divided in 6 classes. This map was combined with data of the annual average wind velocity resulting in an increase of the risk classes for wind velocities > 5 ms-1 and a decrease for < 3 ms-1. The sheltering effect of landscape structure is regarded by allocating a height to each landscape element, corresponding to the described features in the digital "Biotope and Land Use Map". The "hill shade" procedure of ArcGIS was used to set virtual shadows behind the landscape elements for eight directions. The relative frequency of wind from each direction was used as a weighting factor and multiplied with the numerical values of the shadowed cells. Depending on the distance to the landscape element the shadowing effect was combined with the risk classes. The results show that the wind erosion risk is obviously reduced by integrating landscape structures into the risk assessment. After the renewed classification for the entire Federal State, about 60% of the area in the highest, and 40% in the medium risk classes changed into lower classes. The area of the highest potential risk class decreased from 40% to 17% in relation to the total area. A validation of this approach was made by data of the Digital Surface Model (DSM, first pulse) from laser scanning of an area of 144 km2 with a spatial resolution of 1 x 1 m. It could be shown that the allocated height values of the landscape elements were correct in 75% per cent, too low in 15% and too high in 11% off all cases. The current landscape element map of the Federal State of Brandenburg will be replaced, when the DSM is available for the entire area in the near future.

Assessment of geostatistical features for object-based image classification of contrasted landscape vegetation cover

NASA Astrophysics Data System (ADS)

de Oliveira Silveira, Eduarda Martiniano; de Menezes, Michele Duarte; Acerbi Júnior, Fausto Weimar; Castro Nunes Santos Terra, Marcela; de Mello, José Márcio

2017-07-01

Accurate mapping and monitoring of savanna and semiarid woodland biomes are needed to support the selection of areas of conservation, to provide sustainable land use, and to improve the understanding of vegetation. The potential of geostatistical features, derived from medium spatial resolution satellite imagery, to characterize contrasted landscape vegetation cover and improve object-based image classification is studied. The study site in Brazil includes cerrado sensu stricto, deciduous forest, and palm swamp vegetation cover. Sentinel 2 and Landsat 8 images were acquired and divided into objects, for each of which a semivariogram was calculated using near-infrared (NIR) and normalized difference vegetation index (NDVI) to extract the set of geostatistical features. The features selected by principal component analysis were used as input data to train a random forest algorithm. Tests were conducted, combining spectral and geostatistical features. Change detection evaluation was performed using a confusion matrix and its accuracies. The semivariogram curves were efficient to characterize spatial heterogeneity, with similar results using NIR and NDVI from Sentinel 2 and Landsat 8. Accuracy was significantly greater when combining geostatistical features with spectral data, suggesting that this method can improve image classification results.

Predictive mapping of soil organic carbon in wet cultivated lands using classification-tree based models: the case study of Denmark.

PubMed

Bou Kheir, Rania; Greve, Mogens H; Bøcher, Peder K; Greve, Mette B; Larsen, René; McCloy, Keith

2010-05-01

Soil organic carbon (SOC) is one of the most important carbon stocks globally and has large potential to affect global climate. Distribution patterns of SOC in Denmark constitute a nation-wide baseline for studies on soil carbon changes (with respect to Kyoto protocol). This paper predicts and maps the geographic distribution of SOC across Denmark using remote sensing (RS), geographic information systems (GISs) and decision-tree modeling (un-pruned and pruned classification trees). Seventeen parameters, i.e. parent material, soil type, landscape type, elevation, slope gradient, slope aspect, mean curvature, plan curvature, profile curvature, flow accumulation, specific catchment area, tangent slope, tangent curvature, steady-state wetness index, Normalized Difference Vegetation Index (NDVI), Normalized Difference Wetness Index (NDWI) and Soil Color Index (SCI) were generated to statistically explain SOC field measurements in the area of interest (Denmark). A large number of tree-based classification models (588) were developed using (i) all of the parameters, (ii) all Digital Elevation Model (DEM) parameters only, (iii) the primary DEM parameters only, (iv), the remote sensing (RS) indices only, (v) selected pairs of parameters, (vi) soil type, parent material and landscape type only, and (vii) the parameters having a high impact on SOC distribution in built pruned trees. The best constructed classification tree models (in the number of three) with the lowest misclassification error (ME) and the lowest number of nodes (N) as well are: (i) the tree (T1) combining all of the parameters (ME=29.5%; N=54); (ii) the tree (T2) based on the parent material, soil type and landscape type (ME=31.5%; N=14); and (iii) the tree (T3) constructed using parent material, soil type, landscape type, elevation, tangent slope and SCI (ME=30%; N=39). The produced SOC maps at 1:50,000 cartographic scale using these trees are highly matching with coincidence values equal to 90.5% (Map T1/Map T2), 95% (Map T1/Map T3) and 91% (Map T2/Map T3). The overall accuracies of these maps once compared with field observations were estimated to be 69.54% (Map T1), 68.87% (Map T2) and 69.41% (Map T3). The proposed tree models are relatively simple, and may be also applied to other areas. Copyright 2010 Elsevier Ltd. All rights reserved.

Landscape genetic approaches to guide native plant restoration in the Mojave Desert

USGS Publications Warehouse

Shryock, Daniel F.; Havrilla, Caroline A.; DeFalco, Lesley; Esque, Todd C.; Custer, Nathan; Wood, Troy E.

2016-01-01

Restoring dryland ecosystems is a global challenge due to synergistic drivers of disturbance coupled with unpredictable environmental conditions. Dryland plant species have evolved complex life-history strategies to cope with fluctuating resources and climatic extremes. Although rarely quantified, local adaptation is likely widespread among these species and potentially influences restoration outcomes. The common practice of reintroducing propagules to restore dryland ecosystems, often across large spatial scales, compels evaluation of adaptive divergence within these species. Such evaluations are critical to understanding the consequences of large-scale manipulation of gene flow and to predicting success of restoration efforts. However, genetic information for species of interest can be difficult and expensive to obtain through traditional common garden experiments. Recent advances in landscape genetics offer marker-based approaches for identifying environmental drivers of adaptive genetic variability in non-model species, but tools are still needed to link these approaches with practical aspects of ecological restoration. Here, we combine spatially-explicit landscape genetics models with flexible visualization tools to demonstrate how cost-effective evaluations of adaptive genetic divergence can facilitate implementation of different seed sourcing strategies in ecological restoration. We apply these methods to Amplified Fragment Length Polymorphism (AFLP) markers genotyped in two Mojave Desert shrub species of high restoration importance: the long-lived, wind-pollinated gymnosperm Ephedra nevadensis, and the short-lived, insect-pollinated angiosperm Sphaeralcea ambigua. Mean annual temperature was identified as an important driver of adaptive genetic divergence for both species. Ephedra showed stronger adaptive divergence with respect to precipitation variability, while temperature variability and precipitation averages explained a larger fraction of adaptive divergence in Sphaeralcea. We describe multivariate statistical approaches for interpolating spatial patterns of adaptive divergence while accounting for potential bias due to neutral genetic structure. Through a spatial bootstrapping procedure, we also visualize patterns in the magnitude of model uncertainty. Finally, we introduce an interactive, distance-based mapping approach that explicitly links marker-based models of adaptive divergence with local or admixture seed sourcing strategies, promoting effective native plant restoration.

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 values in landscape ecological security, with the most decreased number 0.52 in Zhaoqing. Results of this study offer important information for regional eco-construction and natural resource exploitation.

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 world.

Global hierarchical classification of deepwater and wetland environments from remote sensing products

NASA Astrophysics Data System (ADS)

Fluet-Chouinard, E.; Lehner, B.; Aires, F.; Prigent, C.; McIntyre, P. B.

2017-12-01

Global surface water maps have improved in spatial and temporal resolutions through various remote sensing methods: open water extents with compiled Landsat archives and inundation with topographically downscaled multi-sensor retrievals. These time-series capture variations through time of open water and inundation without discriminating between hydrographic features (e.g. lakes, reservoirs, river channels and wetland types) as other databases have done as static representation. Available data sources present the opportunity to generate a comprehensive map and typology of aquatic environments (deepwater and wetlands) that improves on earlier digitized inventories and maps. The challenge of classifying surface waters globally is to distinguishing wetland types with meaningful characteristics or proxies (hydrology, water chemistry, soils, vegetation) while accommodating limitations of remote sensing data. We present a new wetland classification scheme designed for global application and produce a map of aquatic ecosystem types globally using state-of-the-art remote sensing products. Our classification scheme combines open water extent and expands it with downscaled multi-sensor inundation data to capture the maximal vegetated wetland extent. The hierarchical structure of the classification is modified from the Cowardin Systems (1979) developed for the USA. The first level classification is based on a combination of landscape positions and water source (e.g. lacustrine, riverine, palustrine, coastal and artificial) while the second level represents the hydrologic regime (e.g. perennial, seasonal, intermittent and waterlogged). Class-specific descriptors can further detail the wetland types with soils and vegetation cover. Our globally consistent nomenclature and top-down mapping allows for direct comparison across biogeographic regions, to upscale biogeochemical fluxes as well as other landscape level functions.

Inclusion of the fitness sharing technique in an evolutionary algorithm to analyze the fitness landscape of the genetic code adaptability.

PubMed

Santos, José; Monteagudo, Ángel

2017-03-27

The canonical code, although prevailing in complex genomes, is not universal. It was shown the canonical genetic code superior robustness compared to random codes, but it is not clearly determined how it evolved towards its current form. The error minimization theory considers the minimization of point mutation adverse effect as the main selection factor in the evolution of the code. We have used simulated evolution in a computer to search for optimized codes, which helps to obtain information about the optimization level of the canonical code in its evolution. A genetic algorithm searches for efficient codes in a fitness landscape that corresponds with the adaptability of possible hypothetical genetic codes. The lower the effects of errors or mutations in the codon bases of a hypothetical code, the more efficient or optimal is that code. The inclusion of the fitness sharing technique in the evolutionary algorithm allows the extent to which the canonical genetic code is in an area corresponding to a deep local minimum to be easily determined, even in the high dimensional spaces considered. The analyses show that the canonical code is not in a deep local minimum and that the fitness landscape is not a multimodal fitness landscape with deep and separated peaks. Moreover, the canonical code is clearly far away from the areas of higher fitness in the landscape. Given the non-presence of deep local minima in the landscape, although the code could evolve and different forces could shape its structure, the fitness landscape nature considered in the error minimization theory does not explain why the canonical code ended its evolution in a location which is not an area of a localized deep minimum of the huge fitness landscape.

Monitoring conservation success in a large oak woodland landscape

Treesearch

Rich Reiner; Emma Underwood; John-O Niles

2002-01-01

Monitoring is essential in understanding the success or failure of a conservation project and provides the information needed to conduct adaptive management. Although there is a large body of literature on monitoring design, it fails to provide sufficient information to practitioners on how to organize and apply monitoring when implementing landscape-scale conservation...

California spotted owl, songbird, and small mammal responses to landscape fuel treatments

Treesearch

Scott L. Stephens; Seth W. Bigelow; Ryan D. Burnett; Brandon M. Collins; Claire. V. Gallagher; John Keane; Douglas A. Kelt; Malcolm P. North; Lance J. Roberts; Peter A. Stine; Dirk H. Van Vuren

2014-01-01

A principal challenge of federal forest management has been maintaining and improving habitat for sensitive species in forests adapted to frequent, low- to moderate-intensity fire regimes that have become increasingly vulnerable to uncharacteristically severe wildfires. To enhance forest resilience, a coordinated landscape fuel network was installed in the northern...

Collaborative implementation for ecological restoration on US public lands: implications for legal context, accountability, and adaptive management

Treesearch

William H. Butler; Ashley Monroe; Sarah McCaffrey

2015-01-01

The Collaborative Forest Landscape Restoration Program (CFLRP), established in 2009, encourages collaborative landscape scale ecosystem restoration efforts on United States Forest Service (USFS) lands. Although the USFS employees have experience engaging in collaborative planning, CFLRP requires collaboration in implementation, a domain where little prior experience...

Predicting landscape vegetation dynamics using state-and-transition simulation models

Treesearch

Colin J. Daniel; Leonardo Frid

2012-01-01

This paper outlines how state-and-transition simulation models (STSMs) can be used to project changes in vegetation over time across a landscape. STSMs are stochastic, empirical simulation models that use an adapted Markov chain approach to predict how vegetation will transition between states over time, typically in response to interactions between succession,...

Detecting consistent patterns of directional adaptation using differential selection codon models.

PubMed

Parto, Sahar; Lartillot, Nicolas

2017-06-23

Phylogenetic codon models are often used to characterize the selective regimes acting on protein-coding sequences. Recent methodological developments have led to models explicitly accounting for the interplay between mutation and selection, by modeling the amino acid fitness landscape along the sequence. However, thus far, most of these models have assumed that the fitness landscape is constant over time. Fluctuations of the fitness landscape may often be random or depend on complex and unknown factors. However, some organisms may be subject to systematic changes in selective pressure, resulting in reproducible molecular adaptations across independent lineages subject to similar conditions. Here, we introduce a codon-based differential selection model, which aims to detect and quantify the fine-grained consistent patterns of adaptation at the protein-coding level, as a function of external conditions experienced by the organism under investigation. The model parameterizes the global mutational pressure, as well as the site- and condition-specific amino acid selective preferences. This phylogenetic model is implemented in a Bayesian MCMC framework. After validation with simulations, we applied our method to a dataset of HIV sequences from patients with known HLA genetic background. Our differential selection model detects and characterizes differentially selected coding positions specifically associated with two different HLA alleles. Our differential selection model is able to identify consistent molecular adaptations as a function of repeated changes in the environment of the organism. These models can be applied to many other problems, ranging from viral adaptation to evolution of life-history strategies in plants or animals.

A hierarchical approach to forest landscape pattern characterization.

PubMed

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.

Deep neural network-based domain adaptation for classification of remote sensing images

NASA Astrophysics Data System (ADS)

Ma, Li; Song, Jiazhen

2017-10-01

We investigate the effectiveness of deep neural network for cross-domain classification of remote sensing images in this paper. In the network, class centroid alignment is utilized as a domain adaptation strategy, making the network able to transfer knowledge from the source domain to target domain on a per-class basis. Since predicted labels of target data should be used to estimate the centroid of each class, we use overall centroid alignment as a coarse domain adaptation method to improve the estimation accuracy. In addition, rectified linear unit is used as the activation function to produce sparse features, which may improve the separation capability. The proposed network can provide both aligned features and an adaptive classifier, as well as obtain label-free classification of target domain data. The experimental results using Hyperion, NCALM, and WorldView-2 remote sensing images demonstrated the effectiveness of the proposed approach.

Adaptation or Resistance: a classification of responses to sea-level rise

NASA Astrophysics Data System (ADS)

Cooper, J. A.

2016-02-01

Societal responses to sea level rise and associated coastal change are apparently diverse in nature and motivation. Most are commonly referred to as 'adaptation'. Based on a review of current practice, however, it is argued that many of these responses do not involve adaptation, but are rather resisting change. There are several instances where formerly adaptive initiatives involving human adaptability are being replaced by initiatives that resist change. A classification is presented that recognises a continuum of responses ranging from adaptation to resistance, depending upon the willingness to change human activities to accommodate environmental change. In many cases climate change adaptation resources are being used for projects that are purely resistant and which foreclose future adaptation options. It is argued that a more concise definition of adaptation is needed if coastal management is to move beyond the current position of holding the shoreline, other tah n in a few showcase examples.

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 forces.

Classification capacity of a modular neural network implementing neurally inspired architecture and training rules.

PubMed

Poirazi, Panayiota; Neocleous, Costas; Pattichis, Costantinos S; Schizas, Christos N

2004-05-01

A three-layer neural network (NN) with novel adaptive architecture has been developed. The hidden layer of the network consists of slabs of single neuron models, where neurons within a slab--but not between slabs--have the same type of activation function. The network activation functions in all three layers have adaptable parameters. The network was trained using a biologically inspired, guided-annealing learning rule on a variety of medical data. Good training/testing classification performance was obtained on all data sets tested. The performance achieved was comparable to that of SVM classifiers. It was shown that the adaptive network architecture, inspired from the modular organization often encountered in the mammalian cerebral cortex, can benefit classification performance.

Colonizing Dynamic Alluvial and Coastal Landscapes in the Holocene

NASA Astrophysics Data System (ADS)

Kidder, T.; Liu, X.; Ervin, K.

2017-12-01

Throughout the Holocene humans have had to adapt to dynamic, rapidly changing alluvial and coastal landscapes. Understanding when people inhabit a given environment is an important starting point for exploring human adaptations, but increasingly we need to consider how, and especially why certain environments are used—or not used— so we can understand the consequences of these human actions. Using four case studies—one from the Yellow River Valley, China, one from coastal Jiangsu, China, one from the Mississippi River Valley (Mississippi, USA) and one from the Mississippi River delta (Louisiana , USA)—we develop a model of how humans at various stages of cultural development colonize new environments. Using archaeological data and ecological modeling we investigate the relationship between the timing of landscape colonization and the ecological richness and predictability of any given environment. As new landscapes emerge and mature humans adopt different strategies for exploiting these novel environments that begins with episodic use and increasingly shifts to stable, long-term habitation. The early phase of landscape colonization appears to be the most significant period because it shapes human environmental practices and sets each culture on a trajectory of socio-cultural development. Thus, human-environment interaction is a critical part of the emergence of cultural patterns that shapes the past, present, and even the future.

Modeling urban land development as a continuum to address fine-grained habitat heterogeneity

Treesearch

P.N. Manley; S.A. Parks; Lori Campbell; M.D. Schlesinger

2009-01-01

Natural landscapes are increasingly subjected to impacts associated with urbanization, resulting in loss and degradation of native ecosystems and biodiversity. Traditional classification approaches to the characterization of urbanization may prove inadequate in some human-modified...

A TEST OF WATERSHED CLASSIFICATION SYSTEMS FOR ECOLOGICAL RISK ASSESSMENT

EPA Science Inventory

To facilitate extrapolation among watersheds, ecological risk assessments should be based on a model of underlying factors influencing watershed response, particularly vulnerability. We propose a conceptual model of landscape vulnerability to serve as a basis for watershed classi...

Implementing Process-Oriented, Guided-Inquiry Learning for the First Time: Adaptations and Short-Term Impacts on Students' Attitude and Performance

ERIC Educational Resources Information Center

Chase, Anthony; Pakhira, Deblina; Stains, Marilyne

2013-01-01

Innovative, research-based instructional practices are critical to transforming the conventional undergraduate instructional landscape into a student-centered learning environment. Research on dissemination of innovation indicates that instructors often adapt rather than adopt these practices. These adaptations can lead to the loss of critical…

The role of parasite-driven selection in shaping landscape genomic structure in red grouse (Lagopus lagopus scotica).

PubMed

Wenzel, Marius A; Douglas, Alex; James, Marianne C; Redpath, Steve M; Piertney, Stuart B

2016-01-01

Landscape genomics promises to provide novel insights into how neutral and adaptive processes shape genome-wide variation within and among populations. However, there has been little emphasis on examining whether individual-based phenotype-genotype relationships derived from approaches such as genome-wide association (GWAS) manifest themselves as a population-level signature of selection in a landscape context. The two may prove irreconcilable as individual-level patterns become diluted by high levels of gene flow and complex phenotypic or environmental heterogeneity. We illustrate this issue with a case study that examines the role of the highly prevalent gastrointestinal nematode Trichostrongylus tenuis in shaping genomic signatures of selection in red grouse (Lagopus lagopus scotica). Individual-level GWAS involving 384 SNPs has previously identified five SNPs that explain variation in T. tenuis burden. Here, we examine whether these same SNPs display population-level relationships between T. tenuis burden and genetic structure across a small-scale landscape of 21 sites with heterogeneous parasite pressure. Moreover, we identify adaptive SNPs showing signatures of directional selection using F(ST) outlier analysis and relate population- and individual-level patterns of multilocus neutral and adaptive genetic structure to T. tenuis burden. The five candidate SNPs for parasite-driven selection were neither associated with T. tenuis burden on a population level, nor under directional selection. Similarly, there was no evidence of parasite-driven selection in SNPs identified as candidates for directional selection. We discuss these results in the context of red grouse ecology and highlight the broader consequences for the utility of landscape genomics approaches for identifying signatures of selection. © 2015 John Wiley & Sons Ltd.

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. © The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A pivot mutation impedes reverse evolution across an adaptive landscape for drug resistance in Plasmodium vivax.

PubMed

Ogbunugafor, C Brandon; Hartl, Daniel

2016-01-25

The study of reverse evolution from resistant to susceptible phenotypes can reveal constraints on biological evolution, a topic for which evolutionary theory has relatively few general principles. The public health catastrophe of antimicrobial resistance in malaria has brought these constraints on evolution into a practical realm, with one proposed solution: withdrawing anti-malarial medication use in high resistance settings, built on the assumption that reverse evolution occurs readily enough that populations of pathogens may revert to their susceptible states. While past studies have suggested limits to reverse evolution, there have been few attempts to properly dissect its mechanistic constraints. Growth rates were determined from empirical data on the growth and resistance from a set of combinatorially complete set of mutants of a resistance protein (dihydrofolate reductase) in Plasmodium vivax, to construct reverse evolution trajectories. The fitness effects of individual mutations were calculated as a function of drug environment, revealing the magnitude of epistatic interactions between mutations and genetic backgrounds. Evolution across the landscape was simulated in two settings: starting from the population fixed for the quadruple mutant, and from a polymorphic population evenly distributed between double mutants. A single mutation of large effect (S117N) serves as a pivot point for evolution to high resistance regions of the landscape. Through epistatic interactions with other mutations, this pivot creates an epistatic ratchet against reverse evolution towards the wild type ancestor, even in environments where the wild type is the most fit of all genotypes. This pivot mutation underlies the directional bias in evolution across the landscape, where evolution towards the ancestor is precluded across all examined drug concentrations from various starting points in the landscape. The presence of pivot mutations can dictate dynamics of evolution across adaptive landscape through epistatic interactions within a protein, leaving a population trapped on local fitness peaks in an adaptive landscape, unable to locate ancestral genotypes. This irreversibility suggests that the structure of an adaptive landscape for a resistance protein should be understood before considering resistance management strategies. This proposed mechanism for constraints on reverse evolution corroborates evidence from the field indicating that phenotypic reversal often occurs via compensatory mutation at sites independent of those associated with the forward evolution of resistance. Because of this, molecular methods that identify resistance patterns via single SNPs in resistance-associated markers might be missing signals for resistance and compensatory mutation throughout the genome. In these settings, whole genome sequencing efforts should be used to identify resistance patterns, and will likely reveal a more complicated genomic signature for resistance and susceptibility, especially in settings where anti-malarial medications have been used intermittently. Lastly, the findings suggest that, given their role in dictating the dynamics of evolution across the landscape, pivot mutations might serve as future targets for therapy.

I-CAN: the classification and prediction of support needs.

PubMed

Arnold, Samuel R C; Riches, Vivienne C; Stancliffe, Roger J

2014-03-01

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 Disability, Washington, DC). While the methods and instruments to measure intelligence and adaptive behaviour are well established and generally accepted, the measurement and classification of support needs is still in its infancy. This article explores the measurement and classification of support needs. A study is presented comparing scores on the ICF (WHO, 2001) based I-CAN v4.2 support needs assessment and planning tool with expert clinical judgment using a proposed classification of support needs. A logical classification algorithm was developed and validated on a separate sample. Good internal consistency (range 0.73-0.91, N = 186) and criterion validity (κ = 0.94, n = 49) were found. Further advances in our understanding and measurement of support needs could change the way we assess, describe and classify disability. © 2013 John Wiley & Sons Ltd.

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

Identifying small depressional wetlands and using a topographic position index to infer hydroperiod regimes for pond-breeding amphibians

USGS Publications Warehouse

Riley, Jeffrey W.; Calhoun, Daniel L.; Barichivich, William J.; Walls, Susan C.

2017-01-01

Small, seasonal pools and temporary ponds (<4.0 ha) are the most numerous and biologically diverse wetlands in many natural landscapes. Thus, accurate determination of their numbers and spatial characteristics is beneficial for conservation and management of biodiversity associated with these freshwater systems. We examined the utility of a topographic position index (TPI) landscape classification to identify and classify depressional wetlands. We also assessed relationships between topographic characteristics and ponded duration of known wetlands to allow hydrological characteristics to be extended to non-monitored locations in similar landscapes. Our results indicate that this approach was successful at identifying wetlands, but did have higher errors of commission (10%) than omission (5%). Additionally, the TPI procedure provided a reasonable means to correlate general ponded duration characteristics (long/short) with wetland topography. Although results varied by hydrologic class, permanent/long ponded duration wetlands were more often classified correctly (80%) than were short ponded duration wetlands (67%). However, classification results were improved to 100 and 75% for permanent/long and short ponded duration wetlands, respectively, by removing wetlands occurring on an abrupt marine terrace that erroneously inflated pond topographic characteristics. Our study presents an approach for evaluating wetland suitability for species or guilds that are associated with key habitat characteristics, such as hydroperiod.

Development of a Digital Aquifer Permeability Map for the ...

EPA Pesticide Factsheets

Researchers at the U.S. Environmental Protection Agency’s Western Ecology Division have been developing hydrologic landscape maps for selected U.S. states in an effort to create a method to identify the intrinsic watershed attributes of landscapes in regions with little data. Each hydrologic landscape unit is assigned a categorical value from five key indices of macro-scale hydrologic behavior, including annual climate, climate seasonality, aquifer permeability, terrain, and soil permeability. The aquifer permeability index requires creation of a from-scratch dataset for each state. The permeability index for the Pacific Southwest (California, Nevada, and Arizona) expands and modifies the permeability index for the Pacific Northwest (Oregon, Washington, and Idaho), which preceded it. The permeability index was created by assigning geologic map units to one of 18 categories with presumed similar values of permeability to create a hydrolithologic map. The hydrolithologies were then further categorized into permeability index classifications of high, low, unknown and surface water. Unconsolidated, carbonate, volcanic, and undifferentiated units are classified more conservatively to better address uncertainty in source data. High vs. low permeability classifications are assigned qualitatively but follow a threshold guideline of 8.5x10-2 m/day hydraulic conductivity. Estimates of permeability from surface lithology is the current best practice for broad-sca

An on-line BCI for control of hand grasp sequence and holding using adaptive probabilistic neural network.

PubMed

Hazrati, Mehrnaz Kh; Erfanian, Abbas

2008-01-01

This paper presents a new EEG-based Brain-Computer Interface (BCI) for on-line controlling the sequence of hand grasping and holding in a virtual reality environment. The goal of this research is to develop an interaction technique that will allow the BCI to be effective in real-world scenarios for hand grasp control. Moreover, for consistency of man-machine interface, it is desirable the intended movement to be what the subject imagines. For this purpose, we developed an on-line BCI which was based on the classification of EEG associated with imagination of the movement of hand grasping and resting state. A classifier based on probabilistic neural network (PNN) was introduced for classifying the EEG. The PNN is a feedforward neural network that realizes the Bayes decision discriminant function by estimating probability density function using mixtures of Gaussian kernels. Two types of classification schemes were considered here for on-line hand control: adaptive and static. In contrast to static classification, the adaptive classifier was continuously updated on-line during recording. The experimental evaluation on six subjects on different days demonstrated that by using the static scheme, a classification accuracy as high as the rate obtained by the adaptive scheme can be achieved. At the best case, an average classification accuracy of 93.0% and 85.8% was obtained using adaptive and static scheme, respectively. The results obtained from more than 1500 trials on six subjects showed that interactive virtual reality environment can be used as an effective tool for subject training in BCI.

Explaining spatial variability in stream habitats using both natural and management-influenced landscape predictors

Treesearch

K.J. Anlauf; D.W. Jensen; K.M. Burnett; E.A. Steel; K. Christiansen; J.C. Firman; B.E. Feist; D.P. Larsen

2011-01-01

1. The distribution and composition of in-stream habitats are reflections of landscape scale geomorphic and climatic controls. Correspondingly, Pacific salmon (Oncorhynchus spp.) are largely adapted to and constrained by the quality and complexity of those in-stream habitat conditions. The degree to which lands have been fragmented and managed can...

Ecological adaptation of the endemic Shepherdia rotundifolia to conditions in its Colorado Plateau range

Treesearch

Chalita Sriladda; Roger Kjelgren; Heidi Kratsch; Thomas Monaco; Steven Larson; FenAnn Shen

2014-01-01

Due to limited water supplies, use of drought-tolerant species to conserve water in irrigated urban landscapes is increasingly important in the Intermountain West. The Colorado Plateau endemic shrub Shepherdia rotundifolia Parry is a potential candidate for use in sustainable urban low-water landscapes (LWLs) for its aesthetic and drought-tolerant qualities. However,...

Landscape genetic analyses reveal fine-scale effects of forest fragmentation in an insular tropical bird.

PubMed

Khimoun, Aurélie; Peterman, William; Eraud, Cyril; Faivre, Bruno; Navarro, Nicolas; Garnier, Stéphane

2017-10-01

Within the framework of landscape genetics, resistance surface modelling is particularly relevant to explicitly test competing hypotheses about landscape effects on gene flow. To investigate how fragmentation of tropical forest affects population connectivity in a forest specialist bird species, we optimized resistance surfaces without a priori specification, using least-cost (LCP) or resistance (IBR) distances. We implemented a two-step procedure in order (i) to objectively define the landscape thematic resolution (level of detail in classification scheme to describe landscape variables) and spatial extent (area within the landscape boundaries) and then (ii) to test the relative role of several landscape features (elevation, roads, land cover) in genetic differentiation in the Plumbeous Warbler (Setophaga plumbea). We detected a small-scale reduction of gene flow mainly driven by land cover, with a negative impact of the nonforest matrix on landscape functional connectivity. However, matrix components did not equally constrain gene flow, as their conductivity increased with increasing structural similarity with forest habitat: urban areas and meadows had the highest resistance values whereas agricultural areas had intermediate resistance values. Our results revealed a higher performance of IBR compared to LCP in explaining gene flow, reflecting suboptimal movements across this human-modified landscape, challenging the common use of LCP to design habitat corridors and advocating for a broader use of circuit theory modelling. Finally, our results emphasize the need for an objective definition of landscape scales (landscape extent and thematic resolution) and highlight potential pitfalls associated with parameterization of resistance surfaces. © 2017 John Wiley & Sons Ltd.

Convergent evolution of phenotypic integration and its alignment with morphological diversification in Caribbean Anolis ecomorphs.

PubMed

Kolbe, Jason J; Revell, Liam J; Szekely, Brian; Brodie, Edmund D; Losos, Jonathan B

2011-12-01

The adaptive landscape and the G-matrix are keys concepts for understanding how quantitative characters evolve during adaptive radiation. In particular, whether the adaptive landscape can drive convergence of phenotypic integration (i.e., the pattern of phenotypic variation and covariation summarized in the P-matrix) is not well studied. We estimated and compared P for 19 morphological traits in eight species of Caribbean Anolis lizards, finding that similarity in P among species was not correlated with phylogenetic distance. However, greater similarity in P among ecologically similar Anolis species (i.e., the trunk-ground ecomorph) suggests the role of convergent natural selection. Despite this convergence and relatively deep phylogenetic divergence, a large portion of eigenstructure of P is retained among our eight focal species. We also analyzed P as an approximation of G to test for correspondence with the pattern of phenotypic divergence in 21 Caribbean Anolis species. These patterns of covariation were coincident, suggesting that either genetic constraint has influenced the pattern of among-species divergence or, alternatively, that the adaptive landscape has influenced both G and the pattern of phenotypic divergence among species. We provide evidence for convergent evolution of phenotypic integration for one class of Anolis ecomorph, revealing yet another important dimension of evolutionary convergence in this group. No Claim to original U.S. government works.

Image Classification for Web Genre Identification

DTIC Science & Technology

2012-01-01

recognition and landscape detection using the computer vision toolkit OpenCV1. For facial recognition , we researched the possibilities of using the...method for connecting these names with a face/personal photo and logo respectively. [2] METHODOLOGY For this project, we focused primarily on facial

State-and-transition models for heterogeneous landscapes: A strategy for development and application

USDA-ARS?s Scientific Manuscript database

Interpretation of assessment and monitoring data requires information about reference conditions and ecological resilience. Reference conditions used as benchmarks can be specified via potential-based land classifications (e.g., ecological sites) that describe the plant communities potentially obser...

Creation of a Digital Aquifer Permeability Map for the Pacific Northwest

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 as climate change. One of the greatest challenges to this effort is obtaining consistent aquifer permea...

EFFECTS OF LANDSCAPE CHARACTERISTICS ON LAND-COVER CLASS ACCURACY

EPA Science Inventory

Utilizing land-cover data gathered as part of the National Land-Cover Data (NLCD) set accuracy assessment, several logistic regression models were formulated to analyze the effects of patch size and land-cover heterogeneity on classification accuracy. Specific land-cover ...

Integration of adaptive guided filtering, deep feature learning, and edge-detection techniques for hyperspectral image classification

NASA Astrophysics Data System (ADS)

Wan, Xiaoqing; Zhao, Chunhui; Gao, Bing

2017-11-01

The integration of an edge-preserving filtering technique in the classification of a hyperspectral image (HSI) has been proven effective in enhancing classification performance. This paper proposes an ensemble strategy for HSI classification using an edge-preserving filter along with a deep learning model and edge detection. First, an adaptive guided filter is applied to the original HSI to reduce the noise in degraded images and to extract powerful spectral-spatial features. Second, the extracted features are fed as input to a stacked sparse autoencoder to adaptively exploit more invariant and deep feature representations; then, a random forest classifier is applied to fine-tune the entire pretrained network and determine the classification output. Third, a Prewitt compass operator is further performed on the HSI to extract the edges of the first principal component after dimension reduction. Moreover, the regional growth rule is applied to the resulting edge logical image to determine the local region for each unlabeled pixel. Finally, the categories of the corresponding neighborhood samples are determined in the original classification map; then, the major voting mechanism is implemented to generate the final output. Extensive experiments proved that the proposed method achieves competitive performance compared with several traditional approaches.

Freeware Versus Commercial Office Productivity Software

DTIC Science & Technology

2016-12-01

adapting Google’s widely popular freeware for government agency usage. This study analyzes the proposed benefits of using freeware, specifically... computing , ESI 15. NUMBER OF PAGES 73 16. PRICE CODE 17. SECURITY CLASSIFICATION OF REPORT Unclassified 18. SECURITY CLASSIFICATION OF...announced the launch of Google Apps for Government, adapting Google’s widely popular freeware for government agency usage. This study analyzes the

Assessing the relationship between surface urban heat islands and landscape patterns across climatic zones in China.

PubMed

Yang, Qiquan; Huang, Xin; Li, Jiayi

2017-08-24

The urban heat island (UHI) effect exerts a great influence on the Earth's environment and human health and has been the subject of considerable attention. Landscape patterns are among the most important factors relevant to surface UHIs (SUHIs); however, the relationship between SUHIs and landscape patterns is poorly understood over large areas. In this study, the surface UHI intensity (SUHII) is defined as the temperature difference between urban and suburban areas, and the landscape patterns are quantified by the urban-suburban differences in several typical landscape metrics (ΔLMs). Temperature and land-cover classification datasets based on satellite observations were applied to analyze the relationship between SUHII and ΔLMs in 332 cities/city agglomerations distributed in different climatic zones of China. The results indicate that SUHII and its correlations with ΔLMs are profoundly influenced by seasonal, diurnal, and climatic factors. The impacts of different land-cover types on SUHIs are different, and the landscape patterns of the built-up and vegetation (including forest, grassland, and cultivated land) classes have the most significant effects on SUHIs. The results of this study will help us to gain a deeper understanding of the relationship between the SUHI effect and landscape patterns.

[Changes of wetland landscape pattern in Dayang River Estuary based on high-resolution remote sensing image].

PubMed

Wu, Tao; Zhao, Dong-zhi; Zhang, Feng-shou; Wei, Bao-quan

2011-07-01

Based on the comprehensive consideration of the high resolution characteristics of remote sensing data and the current situation of land cover and land use in Dayang River Estuary wetland, a classification system with different resolutions of wetland landscape in the Estuary was established. The landscape pattern indices and landscape transition matrix were calculated by using the high resolution remote sensing data, and the dynamic changes of the landscape pattern from 1984 to 2008 were analyzed. In the study period, the wetland landscape components changed drastically. Wetland landscape transferred from natural wetland into artificial wetland, and wetland core regional area decreased. Natural wetland's largest patch area index descended, and the fragmentation degree ascended; while artificial wetland area expanded, its patch number decreased, polymerization degree increased, and the maximum patch area index had an obvious increasing trend. Increasing human activities, embankment construction, and reclamation for aquaculture were the main causes for the decrease of wetland area and the degradation of the ecological functions of Dayang River Estuary. To constitute long-term scientific and reasonable development plan, establish wetland nature reserves, protect riverway, draft strict inspective regimes for aquaculture reclamation, and energetically develop resource-based tourism industry would be the main strategies for the protection of the estuarine wetland.

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.

Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing

NASA Astrophysics Data System (ADS)

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

2015-11-01

Natural and semi-natural habitats in agricultural landscapes are likely to come under increasing pressure with the global population set to exceed 9 billion by 2050. These non-cropped habitats are primarily made up of trees, hedgerows and grassy margins and their amount, quality and spatial configuration can have strong implications for the delivery and sustainability of various ecosystem services. In this study high spatial resolution (0.5 m) colour infrared aerial photography (CIR) was used in object based image analysis for the classification of non-cropped habitat in a 10,029 ha area of southeast England. Three classification scenarios were devised using 4 and 9 class scenarios. The machine learning algorithm Random Forest (RF) was used to reduce the number of variables used for each classification scenario by 25.5 % ± 2.7%. Proportion of votes from the 4 class hierarchy was made available to the 9 class scenarios and where the highest ranked variables in all cases. This approach allowed for misclassified parent objects to be correctly classified at a lower level. A single object hierarchy with 4 class proportion of votes produced the best result (kappa 0.909). Validation of the optimum training sample size in RF showed no significant difference between mean internal out-of-bag error and external validation. As an example of the utility of this data, we assessed habitat suitability for a declining farmland bird, the yellowhammer (Emberiza citronella), which requires hedgerows associated with grassy margins. We found that ˜22% of hedgerows were within 200 m of margins with an area >183.31 m2. The results from this analysis can form a key information source at the environmental and policy level in landscape optimisation for food production and ecosystem service sustainability.

Identification and characterization of agro-ecological infrastructures by remote sensing

NASA Astrophysics Data System (ADS)

Ducrot, D.; Duthoit, S.; d'Abzac, A.; Marais-Sicre, C.; Chéret, V.; Sausse, C.

2015-10-01

Agro-Ecological Infrastructures (AEIs) include many semi-natural habitats (hedgerows, grass strips, grasslands, thickets…) and play a key role in biodiversity preservation, water quality and erosion control. Indirect biodiversity indicators based on AEISs are used in many national and European public policies to analyze ecological processes. The identification of these landscape features is difficult and expensive and limits their use. Remote sensing has a great potential to solve this problem. In this study, we propose an operational tool for the identification and characterization of AEISs. The method is based on segmentation, contextual classification and fusion of temporal classifications. Experiments were carried out on various temporal and spatial resolution satellite data (20-m, 10-m, 5-m, 2.5-m, 50-cm), on three French regions southwest landscape (hilly, plain, wooded, cultivated), north (open-field) and Brittany (farmland closed by hedges). The results give a good idea of the potential of remote sensing image processing methods to map fine agro-ecological objects. At 20-m spatial resolution, only larger hedgerows and riparian forests are apparent. Classification results show that 10-m resolution is well suited for agricultural and AEIs applications, most hedges, forest edges, thickets can be detected. Results highlight the multi-temporal data importance. The future Sentinel satellites with a very high temporal resolution and a 10-m spatial resolution should be an answer to AEIs detection. 2.50-m resolution is more precise with more details. But treatments are more complicated. At 50-cm resolution, accuracy level of details is even higher; this amplifies the difficulties previously reported. The results obtained allow calculation of statistics and metrics describing landscape structures.

Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing.

PubMed

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

2015-11-01

Natural and semi-natural habitats in agricultural landscapes are likely to come under increasing pressure with the global population set to exceed 9 billion by 2050. These non-cropped habitats are primarily made up of trees, hedgerows and grassy margins and their amount, quality and spatial configuration can have strong implications for the delivery and sustainability of various ecosystem services. In this study high spatial resolution (0.5 m) colour infrared aerial photography (CIR) was used in object based image analysis for the classification of non-cropped habitat in a 10,029 ha area of southeast England. Three classification scenarios were devised using 4 and 9 class scenarios. The machine learning algorithm Random Forest (RF) was used to reduce the number of variables used for each classification scenario by 25.5 % ± 2.7%. Proportion of votes from the 4 class hierarchy was made available to the 9 class scenarios and where the highest ranked variables in all cases. This approach allowed for misclassified parent objects to be correctly classified at a lower level. A single object hierarchy with 4 class proportion of votes produced the best result (kappa 0.909). Validation of the optimum training sample size in RF showed no significant difference between mean internal out-of-bag error and external validation. As an example of the utility of this data, we assessed habitat suitability for a declining farmland bird, the yellowhammer ( Emberiza citronella ), which requires hedgerows associated with grassy margins. We found that ∼22% of hedgerows were within 200 m of margins with an area >183.31 m 2 . The results from this analysis can form a key information source at the environmental and policy level in landscape optimisation for food production and ecosystem service sustainability.

How will climate change affect spatial planning in agricultural and natural environments? Examples from three Dutch case study regions

NASA Astrophysics Data System (ADS)

Blom-Zandstra, Margaretha; Paulissen, Maurice; Agricola, Herman; Schaap, Ben

2009-11-01

Climate change will place increasing pressure on the functioning of agricultural and natural areas in the Netherlands. Strategies to adapt these areas to stress are likely to require changes in landscape structure and management. In densely populated countries such as the Netherlands, the increased pressure of climate change on agricultural and natural areas will inevitably lead, through the necessity of spatial adaptation measures, to spatial conflicts between the sectors of agriculture and nature. An integrated approach to climate change adaptation may therefore be beneficial in limiting such sectoral conflicts. We explored the conflicting and synergistic properties of different climate adaptation strategies for agricultural and natural environments in the Netherlands. To estimate the feasibility and effectiveness of the strategies, we focussed on three case study regions with contrasting landscape structural, natural and agricultural characteristics. For each region, we estimated the expected climate-related threats and associated trade-offs for arable farming and natural areas for 2040. We describe a number of spatial and integrated adaptation strategies to mitigate these threats. Formulating adaptation strategies requires consultation of different stakeholders and deliberation between different interests. We discuss some trade-offs involved in this decision-making.

Adaptations between ecotypes and along environmental gradients in Panicum virgatum

USDA-ARS?s Scientific Manuscript database

Determining the patterns and mechanisms of adaptation to different habitats across the natural landscape is of fundamental importance to understanding the differentiation of populations and the evolution of new species. Most recent studies of habitat-mediated natural selection in the wild have focus...

Combined use of SAR and optical data for environmental assessments around refugee camps in semiarid landscapes

NASA Astrophysics Data System (ADS)

Braun, A.; Hochschild, V.

2015-04-01

Over 15 million people were officially considered as refugees in the year 2012 and another 28 million as internally displaced people (IDPs). Natural disasters, climatic and environmental changes, violent regional conflicts and population growth force people to migrate in all parts of this world. This trend is likely to continue in the near future, as political instabilities increase and land degradation progresses. EO4HumEn aims at developing operational services to support humanitarian operations during crisis situations by means of dedicated geo-spatial information products derived from Earth observation and GIS data. The goal is to develop robust, automated methods of image analysis routines for population estimation, identification of potential groundwater extraction sites and monitoring the environmental impact of refugee/IDP camps. This study investigates the combination of satellite SAR data with optical sensors and elevation information for the assessment of the environmental conditions around refugee camps. In order to estimate their impact on land degradation, land cover classifications are required which target dynamic landscapes. We performed a land use / land cover classification based on a random forest algorithm and 39 input prediction rasters based on Landsat 8 data and additional layers generated from radar texture and elevation information. The overall accuracy was 92.9 %, while optical data had the highest impact on the final classification. By analysing all combinations of the three input datasets we additionally estimated their impact on single classification outcomes and land cover classes.

GIS interpolations of witness tree records (1839-1866) for northern Wisconsin at multiple scales

USGS Publications Warehouse

He, H.S.; Mladenoff, D.J.; Sickley, T.A.; Guntenspergen, G.R.

2000-01-01

To construct forest landscape of pre-European settlement periods, we developed a GIS interpolation approach to convert witness tree records of the U.S. General Land Office (GLO) survey from point to polygon data, which better described continuously distributed vegetation. The witness tree records (1839-1866) were processed for a 3-million ha landscape in northern Wisconsin, U.S.A. at different scales. We provided implications of processing results at each scale. Compared with traditional GLO mapping that has fixed mapping scales and generalized classifications, our approach allows presettlement forest landscapes to be analysed at the individual species level and reconstructed under various classifications. We calculated vegetation indices including relative density, dominance, and importance value for each species, and quantitatively described the possible outcomes when GLO records are analysed at three different scales (resolution). The 1 x 1-section resolution preserved spatial information but derived the most conservative estimates of species distributions measured in percentage area, which increased at coarser resolutions. Such increases under the 2 x 2-section resolution were in the order of three to four times for the least common species, two to three times for the medium to most common species, and one to two times for the most common or highly contagious species. We marred the distributions of hemlock and sugar maple from the pre-European settlement period based on their witness tree locations and reconstructed presettlement forest landscapes based on species importance values derived for all species. The results provide a unique basis to further study land cover changes occurring after European settlement.

In Search of Effective Scales for Stream Management: Does Agroecoregion, Watershed, or Their Intersection Best Explain the Variance in Stream Macroinvertebrate Communities?

NASA Astrophysics Data System (ADS)

Dovciak, A. L.; Perry, J. A.

2002-09-01

Our lack of understanding of relationships between stream biotic communities and surrounding landscape conditions makes it difficult to determine the spatial scale at which management practices are best assessed. We investigated these relationships in the Minnesota River Basin, which is divided into major watersheds and agroecoregions which are based on soil type, geologic parent material, landscape slope steepness, and climatic factors affecting crop productivity. We collected macroinvertebrate and stream habitat data from 68 tributaries among three major watersheds and two agroecoregions. We tested the effectiveness of the two landscape classification systems (i.e., watershed, agroecoregion) in explaining variance in habitat and macroinvertebrate metrics, and analyzed the relative influence on macroinvertebrates of local habitat versus regional characteristics. Macroinvertebrate community composition was most strongly influenced by local habitat; the variance in habitat conditions was best explained at the scale of intersection of major watershed and agroecoregion (i.e., stream habitat conditions were most homogeneous within the physical regions of intersection of these two landscape classification systems). Our results are consistent with findings of other authors that most variation in macroinvertebrate community data from large agricultural catchments is attributable to local physical conditions. Our results are the first to test the hypothesis and demonstrate that the scale of intersection best explains these variances. The results suggest that management practices adjusted for both watershed and ecoregion characteristics, with the goal of improving physical habitat characteristics of local streams, may lead to better basin-wide water quality conditions and stream biological integrity.

AdOn HDP-HMM: An Adaptive Online Model for Segmentation and Classification of Sequential Data.

PubMed

Bargi, Ava; Xu, Richard Yi Da; Piccardi, Massimo

2017-09-21

Recent years have witnessed an increasing need for the automated classification of sequential data, such as activities of daily living, social media interactions, financial series, and others. With the continuous flow of new data, it is critical to classify the observations on-the-fly and without being limited by a predetermined number of classes. In addition, a model should be able to update its parameters in response to a possible evolution in the distributions of the classes. This compelling problem, however, does not seem to have been adequately addressed in the literature, since most studies focus on offline classification over predefined class sets. In this paper, we present a principled solution for this problem based on an adaptive online system leveraging Markov switching models and hierarchical Dirichlet process priors. This adaptive online approach is capable of classifying the sequential data over an unlimited number of classes while meeting the memory and delay constraints typical of streaming contexts. In this paper, we introduce an adaptive ''learning rate'' that is responsible for balancing the extent to which the model retains its previous parameters or adapts to new observations. Experimental results on stationary and evolving synthetic data and two video data sets, TUM Assistive Kitchen and collated Weizmann, show a remarkable performance in terms of segmentation and classification, particularly for sequences from evolutionary distributions and/or those containing previously unseen classes.

How cells explore shape space: a quantitative statistical perspective of cellular morphogenesis.

PubMed

Yin, Zheng; Sailem, Heba; Sero, Julia; Ardy, Rico; Wong, Stephen T C; Bakal, Chris

2014-12-01

Through statistical analysis of datasets describing single cell shape following systematic gene depletion, we have found that the morphological landscapes explored by cells are composed of a small number of attractor states. We propose that the topology of these landscapes is in large part determined by cell-intrinsic factors, such as biophysical constraints on cytoskeletal organization, and reflects different stable signaling and/or transcriptional states. Cell-extrinsic factors act to determine how cells explore these landscapes, and the topology of the landscapes themselves. Informational stimuli primarily drive transitions between stable states by engaging signaling networks, while mechanical stimuli tune, or even radically alter, the topology of these landscapes. As environments fluctuate, the topology of morphological landscapes explored by cells dynamically adapts to these fluctuations. Finally we hypothesize how complex cellular and tissue morphologies can be generated from a limited number of simple cell shapes. © 2014 WILEY Periodicals, Inc.

Loss surface of XOR artificial neural networks

NASA Astrophysics Data System (ADS)

Mehta, Dhagash; Zhao, Xiaojun; Bernal, Edgar A.; Wales, David J.

2018-05-01

Training an artificial neural network involves an optimization process over the landscape defined by the cost (loss) as a function of the network parameters. We explore these landscapes using optimization tools developed for potential energy landscapes in molecular science. The number of local minima and transition states (saddle points of index one), as well as the ratio of transition states to minima, grow rapidly with the number of nodes in the network. There is also a strong dependence on the regularization parameter, with the landscape becoming more convex (fewer minima) as the regularization term increases. We demonstrate that in our formulation, stationary points for networks with Nh hidden nodes, including the minimal network required to fit the XOR data, are also stationary points for networks with Nh+1 hidden nodes when all the weights involving the additional node are zero. Hence, smaller networks trained on XOR data are embedded in the landscapes of larger networks. Our results clarify certain aspects of the classification and sensitivity (to perturbations in the input data) of minima and saddle points for this system, and may provide insight into dropout and network compression.

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…

Local soil classification and crop suitability: Implications for the historical land use and soil management in Monti di Trapani (Sicily)

NASA Astrophysics Data System (ADS)

Garcia-Vila, Margarita; Corselli, Rocco; Bonet, María Teresa; Lopapa, Giuseppe; Pillitteri, Valentina; Fereres, Elias

2017-04-01

In the past, the lack of technologies (e.g. synthetic fertilizers) to overcome biophysical limitations has played a central role in land use planning. Thus, landscape management and agronomic practices are reactions to local knowledge and perceptions on natural resources, particularly soil. In the framework of the European research project MEMOLA (FP7), the role of local farmers knowledge and perceptions on soil for the historical land use through the spatial distribution of crops and the various management practices have been assessed in three different areas of Monti di Trapani region (Sicily). The identification of the soil classification systems of farmers and the criteria on which it is based, linked to the evaluation of the farmers' ability to identify and map the different soil types, was a key step. Nevertheless, beyond the comparison of the ethnopedological classification approach versus standard soil classification systems, the study also aims at understanding local soil management and land use decisions. The applied methodology was based on an interdisciplinary approach, combining soil science methods and participatory appraisal tools, particularly: i) semi-structured interviews; ii) soil sampling and analysis; iii) discussion groups; and iv) a workshop with local edafologists and agronomists. A rich local glossary of terms associated with the soil conditions and an own soil classification system have been identified in the region. Also, a detailed soil map, including process of soil degradation and soil capability, has been generated. This traditional soil knowledge has conditioned the management and the spatial distribution of the crops, and therefore the configuration of the landscape, until the 1990s. Acknowledgements This work has been funded by the European Union project MEMOLA (Grant agreement no: 613265).

Columbia River Estuary ecosystem classification—Concept and application

USGS Publications Warehouse

Simenstad, Charles A.; Burke, Jennifer L.; O'Connor, Jim E.; Cannon, Charles; Heatwole, Danelle W.; Ramirez, Mary F.; Waite, Ian R.; Counihan, Timothy D.; Jones, Krista L.

2011-01-01

This document describes the concept, organization, and application of a hierarchical ecosystem classification that integrates saline and tidal freshwater reaches of estuaries in order to characterize the ecosystems of large flood plain rivers that are strongly influenced by riverine and estuarine hydrology. We illustrate the classification by applying it to the Columbia River estuary (Oregon-Washington, USA), a system that extends about 233 river kilometers (rkm) inland from the Pacific Ocean. More than three-quarters of this length is tidal freshwater. The Columbia River Estuary Ecosystem Classification ("Classification") is based on six hierarchical levels, progressing from the coarsest, regional scale to the finest, localized scale: (1) Ecosystem Province; (2) Ecoregion; (3) Hydrogeomorphic Reach; (4) Ecosystem Complex; (5) Geomorphic Catena; and (6) Primary Cover Class. We define and map Levels 1-3 for the entire Columbia River estuary with existing geospatial datasets, and provide examples of Levels 4-6 for one hydrogeomorphic reach. In particular, three levels of the Classification capture the scales and categories of ecosystem structure and processes that are most tractable to estuarine research, monitoring, and management. These three levels are the (1) eight hydrogeomorphic reaches that embody the formative geologic and tectonic processes that created the existing estuarine landscape and encompass the influence of the resulting physiography on interactions between fluvial and tidal hydrology and geomorphology across 230 kilometers (km) of estuary, (2) more than 15 ecosystem complexes composed of broad landforms created predominantly by geologic processes during the Holocene, and (3) more than 25 geomorphic catenae embedded within ecosystem complexes that represent distinct geomorphic landforms, structures, ecosystems, and habitats, and components of the estuarine landscape most likely to change over short time periods.

Predicting and quantifying soil processes using “geomorphon” landform Classification

USDA-ARS?s Scientific Manuscript database

Soil development and behavior vary spatially at multiple observation scales. Predicting and quantifying soil properties and processes via a catena integrates predictable landscape scale variation relevant to both management decisions and soil survey. Soil maps generally convey variation as a set of ...

Can ecological land classification increase the utility of vegetation monitoring data

USDA-ARS?s Scientific Manuscript database

Vegetation dynamics in rangelands and other ecosystems are known to be mediated by topoedaphic properties. Vegetation monitoring programs, however, often do not consider the impact of soils and other sources of landscape heterogeneity on the temporal patterns observed. Ecological sites (ES) comprise...

Operationalizing ecological resilience at a landscape scale: A framework and case study from Silicon Valley

NASA Astrophysics Data System (ADS)

Beller, E.; Robinson, A.; Grossinger, R.; Grenier, L.; Davenport, A.

2015-12-01

Adaptation to climate change requires redesigning our landscapes and watersheds to maximize ecological resilience at large scales and integrated across urban areas, wildlands, and a diversity of ecosystem types. However, it can be difficult for environmental managers and designers to access, interpret, and apply resilience concepts at meaningful scales and across a range of settings. To address this gap, we produced a Landscape Resilience Framework that synthesizes the latest science on the qualitative mechanisms that drive resilience of ecological functions to climate change and other large-scale stressors. The framework is designed to help translate resilience science into actionable ecosystem conservation and restoration recommendations and adaptation strategies by providing a concise but comprehensive list of considerations that will help integrate resilience concepts into urban design, conservation planning, and natural resource management. The framework is composed of seven principles that represent core attributes which determine the resilience of ecological functions within a landscape. These principles are: setting, process, connectivity, redundancy, diversity/complexity, scale, and people. For each principle we identify several key operationalizable components that help illuminate specific recommendations and actions that are likely to contribute to landscape resilience for locally appropriate species, habitats, and biological processes. We are currently using the framework to develop landscape-scale recommendations for ecological resilience in the heavily urbanized Silicon Valley, California, in collaboration with local agencies, companies, and regional experts. The resilience framework is being applied across the valley, including urban, suburban, and wildland areas and terrestrial and aquatic ecosystems. Ultimately, the framework will underpin the development of strategies that can be implemented to bolster ecological resilience from a site to landscape scale.

Modeling Adaptive Educational Methods with IMS Learning Design

ERIC Educational Resources Information Center

Specht, Marcus; Burgos, Daniel

2007-01-01

The paper describes a classification system for adaptive methods developed in the area of adaptive educational hypermedia based on four dimensions: What components of the educational system are adapted? To what features of the user and the current context does the system adapt? Why does the system adapt? How does the system get the necessary…

The 20th-Century Topographic Survey as Source Data for Long-Term Landscape Studies at Local and Regional Scales

USGS Publications Warehouse

Varanka, Dalia

2006-01-01

Historical topographic maps are the only systematically collected data resource covering the entire nation for long-term landscape change studies over the 20th century for geographical and environmental research. The paper discusses aspects of the historical U.S. Geological Survey topographic maps that present constraints on the design of a database for such studies. Problems involved in this approach include locating the required maps, understanding land feature classification differences between topographic vs. land use/land cover maps, the approximation of error between different map editions of the same area, and the identification of true changes on the landscape between time periods. Suggested approaches to these issues are illustrated using an example of such a study by the author.

Quantifying the Energy Landscape Statistics in Proteins - a Relaxation Mode Analysis

NASA Astrophysics Data System (ADS)

Cai, Zhikun; Zhang, Yang

Energy landscape, the hypersurface in the configurational space, has been a useful concept in describing complex processes that occur over a very long time scale, such as the multistep slow relaxations of supercooled liquids and folding of polypeptide chains into structured proteins. Despite extensive simulation studies, its experimental characterization still remains a challenge. To address this challenge, we developed a relaxation mode analysis (RMA) for liquids under a framework analogous to the normal mode analysis for solids. Using RMA, important statistics of the activation barriers of the energy landscape becomes accessible from experimentally measurable two-point correlation functions, e.g. using quasi-elastic and inelastic scattering experiments. We observed a prominent coarsening effect of the energy landscape. The results were further confirmed by direct sampling of the energy landscape using a metadynamics-like adaptive autonomous basin climbing computation. We first demonstrate RMA in a supercooled liquid when dynamical cooperativity emerges in the landscape-influenced regime. Then we show this framework reveals encouraging energy landscape statistics when applied to proteins.

Impact of atmospheric correction and image filtering on hyperspectral classification of tree species using support vector machine

NASA Astrophysics Data System (ADS)

Shahriari Nia, Morteza; Wang, Daisy Zhe; Bohlman, Stephanie Ann; Gader, Paul; Graves, Sarah J.; Petrovic, Milenko

2015-01-01

Hyperspectral images can be used to identify savannah tree species at the landscape scale, which is a key step in measuring biomass and carbon, and tracking changes in species distributions, including invasive species, in these ecosystems. Before automated species mapping can be performed, image processing and atmospheric correction is often performed, which can potentially affect the performance of classification algorithms. We determine how three processing and correction techniques (atmospheric correction, Gaussian filters, and shade/green vegetation filters) affect the prediction accuracy of classification of tree species at pixel level from airborne visible/infrared imaging spectrometer imagery of longleaf pine savanna in Central Florida, United States. Species classification using fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH) atmospheric correction outperformed ATCOR in the majority of cases. Green vegetation (normalized difference vegetation index) and shade (near-infrared) filters did not increase classification accuracy when applied to large and continuous patches of specific species. Finally, applying a Gaussian filter reduces interband noise and increases species classification accuracy. Using the optimal preprocessing steps, our classification accuracy of six species classes is about 75%.

The genomic landscape of rapid repeated evolutionary adaptation to toxic pollution in wild fish

EPA Science Inventory

Atlantic killifish populations have rapidly adapted to normally lethal levels of pollution in four urban estuaries. Through analysis of 384 whole killifish genome sequences and comparative transcriptomics in four pairs of sensitive and tolerant populations, we identify the aryl h...

Beyond naturalness: Adapting wilderness stewardship to an era of rapid global change

Treesearch

David N. Cole

2012-01-01

Climate change and its effects are writ large across wilderness landscapes. They always have been and always will be (see Figure 1). But contemporary change is different. For the first time, the pace and direction of climate change appear to be driven significantly by human activities (IPCC 2007), and this change is playing out across landscapes already affected by...

A hydrologically explicit, spatially exact, classification of landforms for Canada at 1:500,000 scale.

NASA Astrophysics Data System (ADS)

MacMillan, Robert A.; Geng, Xiaoyuan; Smith, Scott; Zawadzka, Joanna; Hengl, Tom

2016-04-01

A new approach for classifying landform types has been developed and applied to all of Canada using a 250 m DEM. The resulting LandMapR classification has been designed to provide a stable and consistent spatial fabric to act as initial proto-polygons to be used in updating the current 1:1 M scale Soil Landscapes of Canada map to 1:500,000 scale. There is a desire to make the current SLC polygon fabric more consistent across the country, more correctly aligned to observable hydrological and landscape features, more spatially exact, more detailed and more interpretable. The approach is essentially a modification of the Hammond (1954) criteria for classifying macro landform types as implemented for computerized analysis by Dikau (1989, 1991) and Brabyn (1998). The major modification is that the key input variables of local relief and relative position in the landscape are computed for specific hillslopes that occur between individual, explicitly defined, channels and divides. While most approaches, including Dikau et al., (1991) and SOTER (Dobos et al., 2005) compute relative relief and landscape position within a neighborhood analysis window (NAW) of some fixed size (9,600 m and 1 km respectively) the LandMapR method assesses these variables based on explicit analysis of flow paths between locally defined divides and channels (or lakes). We have modified the Hammond criteria by splitting the lowest relief class of 0-30 m into 4 classes of 0-0 m, 0-1 m, 1-10 m and 10-30 m) in order to be able to better differentiate subtle landform features in areas of low relief. Essentially this enables recognition of lakes and open water (0 relief and 0 slope), shorelines and littoral zones (0-1 m), nearly flat, low-relief landforms (1-10 m) and low relief undulating plains (10-30 m). We also modified the Hammond approach for separating upper versus lower landform positions used to differentiate flat areas in uplands from flat lowlands. We instead differentiate 3 relative slope positions of channel valley, toe slope and upper slope consistently and exhaustively and so can identify any flat areas that occur in any of these three landform positions. We did not find it necessary to use slope gradient as a criteria for defining and delineating classes because relief acts as a surrogate for slope and each relief class exhibits a narrow and definable range of slope gradients. Dominant slope gradient (or other attributes) can be computed, post classification, for each defined polygon, if there is a need to further classify by slope or other attribute. This simplifies classification and also reduces pixilation in the classification arising from considering too many local criteria in the class definitions. The resulting polygons provide an extremely detailed classification of relief and landform position at the level of individual hillslopes across all of Canada. The polygon boundaries explicitly follow major identifiable drainage networks and work their way upslope to delineate interfluves that occupy upslope positions at all levels of relief. The detailed LandMapR polygon classifications nest consistently within more general regions defined by the original Hammond-Dikau procedures. Initial visual analysis reveals a strong and consistent spatial relationship between observable changes in slope, vegetation and drainage regime and LandMapR landform polygon boundaries. More detailed quantitative assessment of the accuracy and utility of the LandMapR polygon classes is planned.

Collaborative implementation for ecological restoration on US Public Lands: implications for legal context, accountability, and adaptive management.

PubMed

Butler, William H; Monroe, Ashley; McCaffrey, Sarah

2015-03-01

The Collaborative Forest Landscape Restoration Program (CFLRP), established in 2009, encourages collaborative landscape scale ecosystem restoration efforts on United States Forest Service (USFS) lands. Although the USFS employees have experience engaging in collaborative planning, CFLRP requires collaboration in implementation, a domain where little prior experience can be drawn on for guidance. The purpose of this research is to identify the ways in which CFLRP's collaborative participants and agency personnel conceptualize how stakeholders can contribute to implementation on landscape scale restoration projects, and to build theory on dynamics of collaborative implementation in environmental management. This research uses a grounded theory methodology to explore collaborative implementation from the perspectives and experiences of participants in landscapes selected as part of the CFLRP in 2010. Interviewees characterized collaborative implementation as encompassing three different types of activities: prioritization, enhancing treatments, and multiparty monitoring. The paper describes examples of activities in each of these categories and then identifies ways in which collaborative implementation in the context of CFLRP (1) is both hindered and enabled by overlapping legal mandates about agency collaboration, (2) creates opportunities for expanded accountability through informal and relational means, and, (3) creates feedback loops at multiple temporal and spatial scales through which monitoring information, prioritization, and implementation actions shape restoration work both within and across projects throughout the landscape creating more robust opportunities for adaptive management.

Collaborative Implementation for Ecological Restoration on US Public Lands: Implications for Legal Context, Accountability, and Adaptive Management

NASA Astrophysics Data System (ADS)

Butler, William H.; Monroe, Ashley; McCaffrey, Sarah

2015-03-01

The Collaborative Forest Landscape Restoration Program (CFLRP), established in 2009, encourages collaborative landscape scale ecosystem restoration efforts on United States Forest Service (USFS) lands. Although the USFS employees have experience engaging in collaborative planning, CFLRP requires collaboration in implementation, a domain where little prior experience can be drawn on for guidance. The purpose of this research is to identify the ways in which CFLRP's collaborative participants and agency personnel conceptualize how stakeholders can contribute to implementation on landscape scale restoration projects, and to build theory on dynamics of collaborative implementation in environmental management. This research uses a grounded theory methodology to explore collaborative implementation from the perspectives and experiences of participants in landscapes selected as part of the CFLRP in 2010. Interviewees characterized collaborative implementation as encompassing three different types of activities: prioritization, enhancing treatments, and multiparty monitoring. The paper describes examples of activities in each of these categories and then identifies ways in which collaborative implementation in the context of CFLRP (1) is both hindered and enabled by overlapping legal mandates about agency collaboration, (2) creates opportunities for expanded accountability through informal and relational means, and, (3) creates feedback loops at multiple temporal and spatial scales through which monitoring information, prioritization, and implementation actions shape restoration work both within and across projects throughout the landscape creating more robust opportunities for adaptive management.

An extended global Earth system data record on daily landscape freeze-thaw status determined from satellite passive microwave remote sensing

NASA Astrophysics Data System (ADS)

Kim, Youngwook; Kimball, John S.; Glassy, Joseph; Du, Jinyang

2017-02-01

The landscape freeze-thaw (FT) signal determined from satellite microwave brightness temperature (Tb) observations has been widely used to define frozen temperature controls on land surface water mobility and ecological processes. Calibrated 37 GHz Tb retrievals from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), and SSM/I Sounder (SSMIS) were used to produce a consistent and continuous global daily data record of landscape FT status at 25 km grid cell resolution. The resulting FT Earth system data record (FT-ESDR) is derived from a refined classification algorithm and extends over a larger domain and longer period (1979-2014) than prior FT-ESDR releases. The global domain encompasses all land areas affected by seasonal frozen temperatures, including urban, snow- and ice-dominant and barren land, which were not represented by prior FT-ESDR versions. The FT retrieval is obtained using a modified seasonal threshold algorithm (MSTA) that classifies daily Tb variations in relation to grid-cell-wise FT thresholds calibrated using surface air temperature data from model reanalysis. The resulting FT record shows respective mean annual spatial classification accuracies of 90.3 and 84.3 % for evening (PM) and morning (AM) overpass retrievals relative to global weather station measurements. Detailed data quality metrics are derived characterizing the effects of sub-grid-scale open water and terrain heterogeneity, as well as algorithm uncertainties on FT classification accuracy. The FT-ESDR results are also verified against other independent cryospheric data, including in situ lake and river ice phenology, and satellite observations of Greenland surface melt. The expanded FT-ESDR enables new investigations encompassing snow- and ice-dominant land areas, while the longer record and favorable accuracy allow for refined global change assessments that can better distinguish transient weather extremes, landscape phenological shifts, and climate anomalies from longer-term trends extending over multiple decades. The dataset is freely available online (doi:10.5067/MEASURES/CRYOSPHERE/nsidc-0477.003).

High spatial resolution mapping of land cover types in a priority area for conservation in the Brazilian savanna

NASA Astrophysics Data System (ADS)

Ribeiro, F.; Roberts, D. A.; Hess, L. L.; Davis, F. W.; Caylor, K. K.; Nackoney, J.; Antunes Daldegan, G.

2017-12-01

Savannas are heterogeneous landscapes consisting of highly mixed land cover types that lack clear distinct boundaries. The Brazilian Cerrado is a Neotropical savanna considered a biodiversity hotspot for conservation due to its biodiversity richness and rapid transformation of its landscape by crop and pasture activities. The Cerrado is one of the most threatened Brazilian biomes and only 2.2% of its original extent is strictly protected. Accurate mapping and monitoring of its ecosystems and adjacent land use are important to select areas for conservation and to improve our understanding of the dynamics in this biome. Land cover mapping of savannas is difficult due to spectral similarity between land cover types resulting from similar vegetation structure, floristically similar components, generalization of land cover classes, and heterogeneity usually expressed as small patch sizes within the natural landscape. These factors are the major contributor to misclassification and low map accuracies among remote sensing studies in savannas. Specific challenges to map the Cerrado's land cover types are related to the spectral similarity between classes of land use and natural vegetation, such as natural grassland vs. cultivated pasture, and forest ecosystem vs. crops. This study seeks to classify and evaluate the land cover patterns across an area ranked as having extremely high priority for future conservation in the Cerrado. The main objective of this study is to identify the representativeness of each vegetation type across the landscape using high to moderate spatial resolution imagery using an automated scheme. A combination of pixel-based and object-based approaches were tested using RapidEye 3A imagery (5m spatial resolution) to classify the Cerrado's major land cover types. The random forest classifier was used to map the major ecosystems present across the area, and demonstrated to have an effective result with 68% of overall accuracy. Post-classification modification was performed to refine information to the major physiognomic groups of each ecosystem type. In this step, we used segmentation in eCognition, considering the random forest classification as input as well as other environmental layers (e.g. slope, soil types), which improved the overall classification to 75%.

Navigating the Interface Between Landscape Genetics and Landscape Genomics.

PubMed

Storfer, Andrew; Patton, Austin; Fraik, Alexandra K

2018-01-01

As next-generation sequencing data become increasingly available for non-model organisms, a shift has occurred in the focus of studies of the geographic distribution of genetic variation. Whereas landscape genetics studies primarily focus on testing the effects of landscape variables on gene flow and genetic population structure, landscape genomics studies focus on detecting candidate genes under selection that indicate possible local adaptation. Navigating the transition between landscape genomics and landscape genetics can be challenging. The number of molecular markers analyzed has shifted from what used to be a few dozen loci to thousands of loci and even full genomes. Although genome scale data can be separated into sets of neutral loci for analyses of gene flow and population structure and putative loci under selection for inference of local adaptation, there are inherent differences in the questions that are addressed in the two study frameworks. We discuss these differences and their implications for study design, marker choice and downstream analysis methods. Similar to the rapid proliferation of analysis methods in the early development of landscape genetics, new analytical methods for detection of selection in landscape genomics studies are burgeoning. We focus on genome scan methods for detection of selection, and in particular, outlier differentiation methods and genetic-environment association tests because they are the most widely used. Use of genome scan methods requires an understanding of the potential mismatches between the biology of a species and assumptions inherent in analytical methods used, which can lead to high false positive rates of detected loci under selection. Key to choosing appropriate genome scan methods is an understanding of the underlying demographic structure of study populations, and such data can be obtained using neutral loci from the generated genome-wide data or prior knowledge of a species' phylogeographic history. To this end, we summarize recent simulation studies that test the power and accuracy of genome scan methods under a variety of demographic scenarios and sampling designs. We conclude with a discussion of additional considerations for future method development, and a summary of methods that show promise for landscape genomics studies but are not yet widely used.

Navigating the Interface Between Landscape Genetics and Landscape Genomics

PubMed Central

Storfer, Andrew; Patton, Austin; Fraik, Alexandra K.

2018-01-01

As next-generation sequencing data become increasingly available for non-model organisms, a shift has occurred in the focus of studies of the geographic distribution of genetic variation. Whereas landscape genetics studies primarily focus on testing the effects of landscape variables on gene flow and genetic population structure, landscape genomics studies focus on detecting candidate genes under selection that indicate possible local adaptation. Navigating the transition between landscape genomics and landscape genetics can be challenging. The number of molecular markers analyzed has shifted from what used to be a few dozen loci to thousands of loci and even full genomes. Although genome scale data can be separated into sets of neutral loci for analyses of gene flow and population structure and putative loci under selection for inference of local adaptation, there are inherent differences in the questions that are addressed in the two study frameworks. We discuss these differences and their implications for study design, marker choice and downstream analysis methods. Similar to the rapid proliferation of analysis methods in the early development of landscape genetics, new analytical methods for detection of selection in landscape genomics studies are burgeoning. We focus on genome scan methods for detection of selection, and in particular, outlier differentiation methods and genetic-environment association tests because they are the most widely used. Use of genome scan methods requires an understanding of the potential mismatches between the biology of a species and assumptions inherent in analytical methods used, which can lead to high false positive rates of detected loci under selection. Key to choosing appropriate genome scan methods is an understanding of the underlying demographic structure of study populations, and such data can be obtained using neutral loci from the generated genome-wide data or prior knowledge of a species' phylogeographic history. To this end, we summarize recent simulation studies that test the power and accuracy of genome scan methods under a variety of demographic scenarios and sampling designs. We conclude with a discussion of additional considerations for future method development, and a summary of methods that show promise for landscape genomics studies but are not yet widely used. PMID:29593776

Domain Adaptation for Alzheimer’s Disease Diagnostics

PubMed Central

Wachinger, Christian; Reuter, Martin

2016-01-01

With the increasing prevalence of Alzheimer’s disease, research focuses on the early computer-aided diagnosis of dementia with the goal to understand the disease process, determine risk and preserving factors, and explore preventive therapies. By now, large amounts of data from multi-site studies have been made available for developing, training, and evaluating automated classifiers. Yet, their translation to the clinic remains challenging, in part due to their limited generalizability across different datasets. In this work, we describe a compact classification approach that mitigates overfitting by regularizing the multinomial regression with the mixed ℓ1/ℓ2 norm. We combine volume, thickness, and anatomical shape features from MRI scans to characterize neuroanatomy for the three-class classification of Alzheimer’s disease, mild cognitive impairment and healthy controls. We demonstrate high classification accuracy via independent evaluation within the scope of the CADDementia challenge. We, furthermore, demonstrate that variations between source and target datasets can substantially influence classification accuracy. The main contribution of this work addresses this problem by proposing an approach for supervised domain adaptation based on instance weighting. Integration of this method into our classifier allows us to assess different strategies for domain adaptation. Our results demonstrate (i) that training on only the target training set yields better results than the naïve combination (union) of source and target training sets, and (ii) that domain adaptation with instance weighting yields the best classification results, especially if only a small training component of the target dataset is available. These insights imply that successful deployment of systems for computer-aided diagnostics to the clinic depends not only on accurate classifiers that avoid overfitting, but also on a dedicated domain adaptation strategy. PMID:27262241

Forest climate change Vulnerability and Adaptation Assessment in Himalayas

NASA Astrophysics Data System (ADS)

Chitale, V. S.; Shrestha, H. L.; Agarwal, N. K.; Choudhurya, D.; Gilani, H.; Dhonju, H. K.; Murthy, M. S. R.

2014-11-01

Forests offer an important basis for creating and safeguarding more climate-resilient communities over Hindu Kush Himalayan region. The forest ecosystem vulnerability assessment to climate change and developing knowledge base to identify and support relevant adaptation strategies is realized as an urgent need. The multi scale adaptation strategies portray increasing complexity with the increasing levels in terms of data requirements, vulnerability understanding and decision making to choose a particular adaptation strategy. We present here how such complexities could be addressed and adaptation decisions could be either directly supported by open source remote sensing based forestry products or geospatial analysis and modelled products. The forest vulnerability assessment under climate change scenario coupled with increasing forest social dependence was studied using IPCC Landscape scale Vulnerability framework in Chitwan-Annapurna Landscape (CHAL) situated in Nepal. Around twenty layers of geospatial information on climate, forest biophysical and forest social dependence data was used to assess forest vulnerability and associated adaptation needs using self-learning decision tree based approaches. The increase in forest fires, evapotranspiration and reduction in productivity over changing climate scenario was observed. The adaptation measures on enhancing productivity, improving resilience, reducing or avoiding pressure with spatial specificity are identified to support suitable decision making. The study provides spatial analytical framework to evaluate multitude of parameters to understand vulnerabilities and assess scope for alternative adaptation strategies with spatial explicitness.

Climate change mitigation and adaptation in the land use sector: from complementarity to synergy.

PubMed

Duguma, Lalisa A; Minang, Peter A; van Noordwijk, Meine

2014-09-01

Currently, mitigation and adaptation measures are handled separately, due to differences in priorities for the measures and segregated planning and implementation policies at international and national levels. There is a growing argument that synergistic approaches to adaptation and mitigation could bring substantial benefits at multiple scales in the land use sector. Nonetheless, efforts to implement synergies between adaptation and mitigation measures are rare due to the weak conceptual framing of the approach and constraining policy issues. In this paper, we explore the attributes of synergy and the necessary enabling conditions and discuss, as an example, experience with the Ngitili system in Tanzania that serves both adaptation and mitigation functions. An in-depth look into the current practices suggests that more emphasis is laid on complementarity-i.e., mitigation projects providing adaptation co-benefits and vice versa rather than on synergy. Unlike complementarity, synergy should emphasize functionally sustainable landscape systems in which adaptation and mitigation are optimized as part of multiple functions. We argue that the current practice of seeking co-benefits (complementarity) is a necessary but insufficient step toward addressing synergy. Moving forward from complementarity will require a paradigm shift from current compartmentalization between mitigation and adaptation to systems thinking at landscape scale. However, enabling policy, institutional, and investment conditions need to be developed at global, national, and local levels to achieve synergistic goals.

Understanding Student Language: An Unsupervised Dialogue Act Classification Approach

ERIC Educational Resources Information Center

Ezen-Can, Aysu; Boyer, Kristy Elizabeth

2015-01-01

Within the landscape of educational data, textual natural language is an increasingly vast source of learning-centered interactions. In natural language dialogue, student contributions hold important information about knowledge and goals. Automatically modeling the dialogue act of these student utterances is crucial for scaling natural language…

2D/3D video content adaptation decision engine based on content classification and user assessment

NASA Astrophysics Data System (ADS)

Fernandes, Rui; Andrade, M. T.

2017-07-01

Multimedia adaptation depends on several factors, such as the content itself, the consumption device and its characteristics, the transport and access networks and the user. An adaptation decision engine, in order to provide the best possible Quality of Experience to a user, needs to have information about all variables that may influence its decision. For the aforementioned factors, we implement content classification, define device classes, consider limited bandwidth scenarios and categorize user preferences based on a subjective quality evaluation test. The results of these actions generate vital information to pass to the adaptation decision engine so that its operation may provide the indication of the most suitable adaptation to perform that delivers the best possible outcome for the user under the existing constraints.

Validating the Danish adaptation of the World Health Organization's International Classification for Patient Safety classification of patient safety incident types

PubMed Central

Mikkelsen, Kim Lyngby; Thommesen, Jacob; Andersen, Henning Boje

2013-01-01

Objectives Validation of a Danish patient safety incident classification adapted from the World Health Organizaton's International Classification for Patient Safety (ICPS-WHO). Design Thirty-three hospital safety management experts classified 58 safety incident cases selected to represent all types and subtypes of the Danish adaptation of the ICPS (ICPS-DK). Outcome Measures Two measures of inter-rater agreement: kappa and intra-class correlation (ICC). Results An average number of incident types used per case per rater was 2.5. The mean ICC was 0.521 (range: 0.199–0.809) and the mean kappa was 0.513 (range: 0.193–0.804). Kappa and ICC showed high correlation (r = 0.99). An inverse correlation was found between the prevalence of type and inter-rater reliability. Results are discussed according to four factors known to determine the inter-rater agreement: skill and motivation of raters; clarity of case descriptions; clarity of the operational definitions of the types and the instructions guiding the coding process; adequacy of the underlying classification scheme. Conclusions The incident types of the ICPS-DK are adequate, exhaustive and well suited for classifying and structuring incident reports. With a mean kappa a little above 0.5 the inter-rater agreement of the classification system is considered ‘fair’ to ‘good’. The wide variation in the inter-rater reliability and low reliability and poor discrimination among the highly prevalent incident types suggest that for these types, precisely defined incident sub-types may be preferred. This evaluation of the reliability and usability of WHO's ICPS should be useful for healthcare administrations that consider or are in the process of adapting the ICPS. PMID:23287641

Mapping regional livelihood benefits from local ecosystem services assessments in rural Sahel

PubMed Central

Sinare, Hanna; Enfors Kautsky, Elin; Ouedraogo, Issa; Gordon, Line J.

2018-01-01

Most current approaches to landscape scale ecosystem service assessments rely on detailed secondary data. This type of data is seldom available in regions with high levels of poverty and strong local dependence on provisioning ecosystem services for livelihoods. We develop a method to extrapolate results from a previously published village scale ecosystem services assessment to a higher administrative level, relevant for land use decision making. The method combines remote sensing (using a hybrid classification method) and interviews with community members. The resulting landscape scale maps show the spatial distribution of five different livelihood benefits (nutritional diversity, income, insurance/saving, material assets and energy, and crops for consumption) that illustrate the strong multifunctionality of the Sahelian landscapes. The maps highlight the importance of a diverse set of sub-units of the landscape in supporting Sahelian livelihoods. We see a large potential in using the resulting type of livelihood benefit maps for guiding future land use decisions in the Sahel. PMID:29389965

Mapping regional livelihood benefits from local ecosystem services assessments in rural Sahel.

PubMed

Malmborg, Katja; Sinare, Hanna; Enfors Kautsky, Elin; Ouedraogo, Issa; Gordon, Line J

2018-01-01

Most current approaches to landscape scale ecosystem service assessments rely on detailed secondary data. This type of data is seldom available in regions with high levels of poverty and strong local dependence on provisioning ecosystem services for livelihoods. We develop a method to extrapolate results from a previously published village scale ecosystem services assessment to a higher administrative level, relevant for land use decision making. The method combines remote sensing (using a hybrid classification method) and interviews with community members. The resulting landscape scale maps show the spatial distribution of five different livelihood benefits (nutritional diversity, income, insurance/saving, material assets and energy, and crops for consumption) that illustrate the strong multifunctionality of the Sahelian landscapes. The maps highlight the importance of a diverse set of sub-units of the landscape in supporting Sahelian livelihoods. We see a large potential in using the resulting type of livelihood benefit maps for guiding future land use decisions in the Sahel.

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)

Virtual-system-coupled adaptive umbrella sampling to compute free-energy landscape for flexible molecular docking.

PubMed

Higo, Junichi; Dasgupta, Bhaskar; Mashimo, Tadaaki; Kasahara, Kota; Fukunishi, Yoshifumi; Nakamura, Haruki

2015-07-30

A novel enhanced conformational sampling method, virtual-system-coupled adaptive umbrella sampling (V-AUS), was proposed to compute 300-K free-energy landscape for flexible molecular docking, where a virtual degrees of freedom was introduced to control the sampling. This degree of freedom interacts with the biomolecular system. V-AUS was applied to complex formation of two disordered amyloid-β (Aβ30-35 ) peptides in a periodic box filled by an explicit solvent. An interpeptide distance was defined as the reaction coordinate, along which sampling was enhanced. A uniform conformational distribution was obtained covering a wide interpeptide distance ranging from the bound to unbound states. The 300-K free-energy landscape was characterized by thermodynamically stable basins of antiparallel and parallel β-sheet complexes and some other complex forms. Helices were frequently observed, when the two peptides contacted loosely or fluctuated freely without interpeptide contacts. We observed that V-AUS converged to uniform distribution more effectively than conventional AUS sampling did. © 2015 Wiley Periodicals, Inc.

The topology of evolutionary novelty and innovation in macroevolution

PubMed Central

2017-01-01

Sewall Wright's fitness landscape introduced the concept of evolutionary spaces in 1932. George Gaylord Simpson modified this to an adaptive, phenotypic landscape in 1944 and since then evolutionary spaces have played an important role in evolutionary theory through fitness and adaptive landscapes, phenotypic and functional trait spaces, morphospaces and related concepts. Although the topology of such spaces is highly variable, from locally Euclidean to pre-topological, evolutionary change has often been interpreted as a search through a pre-existing space of possibilities, with novelty arising by accessing previously inaccessible or difficult to reach regions of a space. Here I discuss the nature of evolutionary novelty and innovation within the context of evolutionary spaces, and argue that the primacy of search as a conceptual metaphor ignores the generation of new spaces as well as other changes that have played important evolutionary roles. This article is part of the themed issue ‘Process and pattern in innovations from cells to societies’. PMID:29061895

Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska

NASA Astrophysics Data System (ADS)

Lara, Mark J.; Nitze, Ingmar; Grosse, Guido; McGuire, A. David

2018-04-01

Arctic tundra landscapes are composed of a complex mosaic of patterned ground features, varying in soil moisture, vegetation composition, and surface hydrology over small spatial scales (10-100 m). The importance of microtopography and associated geomorphic landforms in influencing ecosystem structure and function is well founded, however, spatial data products describing local to regional scale distribution of patterned ground or polygonal tundra geomorphology are largely unavailable. Thus, our understanding of local impacts on regional scale processes (e.g., carbon dynamics) may be limited. We produced two key spatiotemporal datasets spanning the Arctic Coastal Plain of northern Alaska (~60,000 km2) to evaluate climate-geomorphological controls on arctic tundra productivity change, using (1) a novel 30 m classification of polygonal tundra geomorphology and (2) decadal-trends in surface greenness using the Landsat archive (1999-2014). These datasets can be easily integrated and adapted in an array of local to regional applications such as (1) upscaling plot-level measurements (e.g., carbon/energy fluxes), (2) mapping of soils, vegetation, or permafrost, and/or (3) initializing ecosystem biogeochemistry, hydrology, and/or habitat modeling.

Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska.

PubMed

Lara, Mark J; Nitze, Ingmar; Grosse, Guido; McGuire, A David

2018-04-10

Arctic tundra landscapes are composed of a complex mosaic of patterned ground features, varying in soil moisture, vegetation composition, and surface hydrology over small spatial scales (10-100 m). The importance of microtopography and associated geomorphic landforms in influencing ecosystem structure and function is well founded, however, spatial data products describing local to regional scale distribution of patterned ground or polygonal tundra geomorphology are largely unavailable. Thus, our understanding of local impacts on regional scale processes (e.g., carbon dynamics) may be limited. We produced two key spatiotemporal datasets spanning the Arctic Coastal Plain of northern Alaska (~60,000 km 2 ) to evaluate climate-geomorphological controls on arctic tundra productivity change, using (1) a novel 30 m classification of polygonal tundra geomorphology and (2) decadal-trends in surface greenness using the Landsat archive (1999-2014). These datasets can be easily integrated and adapted in an array of local to regional applications such as (1) upscaling plot-level measurements (e.g., carbon/energy fluxes), (2) mapping of soils, vegetation, or permafrost, and/or (3) initializing ecosystem biogeochemistry, hydrology, and/or habitat modeling.

Optimizing Input/Output Using Adaptive File System Policies

NASA Technical Reports Server (NTRS)

Madhyastha, Tara M.; Elford, Christopher L.; Reed, Daniel A.

1996-01-01

Parallel input/output characterization studies and experiments with flexible resource management algorithms indicate that adaptivity is crucial to file system performance. In this paper we propose an automatic technique for selecting and refining file system policies based on application access patterns and execution environment. An automatic classification framework allows the file system to select appropriate caching and pre-fetching policies, while performance sensors provide feedback used to tune policy parameters for specific system environments. To illustrate the potential performance improvements possible using adaptive file system policies, we present results from experiments involving classification-based and performance-based steering.

Historical framework to explain long-term coupled human and natural system feedbacks: application to a multiple-ownership forest landscape in the northern Great Lakes region, USA

Treesearch

Michelle M. Steen-Adams; Nancy Langston; Mark D. O. Adams; David J. Mladenoff

2015-01-01

Current and future human and forest landscape conditions are influenced by the cumulative, unfolding history of socialecological interactions. Examining past system responses, especially unintended consequences, can reveal valuable insights that promote learning and adaptation in forest policy and management. Temporal couplings are complex, however; they can be...

Cases of Coastal Zone Change and Land Use/Land Cover Change: a learning module that goes beyond the "how" of doing image processing and change detection to asking the "why" about what are the "driving forces" of global change.

NASA Astrophysics Data System (ADS)

Ford, R. E.

2006-12-01

In 2006 the Loma Linda University ESSE21 Mesoamerican Project (Earth System Science Education for the 21st Century) along with partners such as the University of Redlands and California State University, Pomona, produced an online learning module that is designed to help students learn critical remote sensing skills-- specifically: ecosystem characterization, i.e. doing a supervised or unsupervised classification of satellite imagery in a tropical coastal environment. And, it would teach how to measure land use / land cover change (LULC) over time and then encourage students to use that data to assess the Human Dimensions of Global Change (HDGC). Specific objectives include: 1. Learn where to find remote sensing data and practice downloading, pre-processing, and "cleaning" the data for image analysis. 2. Use Leica-Geosystems ERDAS Imagine or IDRISI Kilimanjaro to analyze and display the data. 3. Do an unsupervised classification of a LANDSAT image of a protected area in Honduras, i.e. Cuero y Salado, Pico Bonito, or Isla del Tigre. 4. Virtually participate in a ground-validation exercise that would allow one to re-classify the image into a supervised classification using the FAO Global Land Cover Network (GLCN) classification system. 5. Learn more about each protected area's landscape, history, livelihood patterns and "sustainability" issues via virtual online tours that provide ground and space photos of different sites. This will help students in identifying potential "training sites" for doing a supervised classification. 6. Study other global, US, Canadian, and European land use/land cover classification systems and compare their advantages and disadvantages over the FAO/GLCN system. 7. Learn to appreciate the advantages and disadvantages of existing LULC classification schemes and adapt them to local-level user needs. 8. Carry out a change detection exercise that shows how land use and/or land cover has changed over time for the protected area of your choice. The presenter will demonstrate the module, assess the collaborative process which created it, and describe how it has been used so far by users in the US as well as in Honduras and elsewhere via a series joint workshops held in Mesoamerica. Suggestions for improvement will be requested. See the module and related content resources at: http://resweb.llu.edu/rford/ESSE21/LUCCModule/

A new approach to the identification of Landscape Quality Objectives (LQOs) as a set of indicators.

PubMed

Sowińska-Świerkosz, Barbara Natalia; Chmielewski, Tadeusz J

2016-12-15

The objective of the paper is threefold: (1) to introduce Landscape Quality Objectives (LQOs) as a set of indicators; (2) to present a method of linking social and expert opinion in the process of the formulation of landscape indicators; and (3) to present a methodological framework for the identification of LQOs. The implementation of these goals adopted a six-stage procedure based on the use of landscape units: (1) GIS analysis; (2) classification; (3) social survey; (4) expert value judgement; (5) quality assessment; and (6) guidelines formulation. The essence of the research was the presentation of features that determine landscape quality according to public opinion as a set of indicators. The results showed that 80 such indicators were identified, of both a qualitative (49) and a quantitative character (31). Among the analysed units, 60% (18 objects) featured socially expected (and confirmed by experts) levels of landscape quality, and 20% (6 objects) required overall quality improvement in terms of both public and expert opinion. The adopted procedure provides a new tool for integrating social responsibility into environmental management. The advantage of the presented method is the possibility of its application in the territories of various European countries. It is flexible enough to be based on cartographic studies, landscape research methods, and environmental quality standards existing in a given country. Copyright © 2016 Elsevier Ltd. All rights reserved.

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,…

CLAss-Specific Subspace Kernel Representations and Adaptive Margin Slack Minimization for Large Scale Classification.

PubMed

Yu, Yinan; Diamantaras, Konstantinos I; McKelvey, Tomas; Kung, Sun-Yuan

2018-02-01

In kernel-based classification models, given limited computational power and storage capacity, operations over the full kernel matrix becomes prohibitive. In this paper, we propose a new supervised learning framework using kernel models for sequential data processing. The framework is based on two components that both aim at enhancing the classification capability with a subset selection scheme. The first part is a subspace projection technique in the reproducing kernel Hilbert space using a CLAss-specific Subspace Kernel representation for kernel approximation. In the second part, we propose a novel structural risk minimization algorithm called the adaptive margin slack minimization to iteratively improve the classification accuracy by an adaptive data selection. We motivate each part separately, and then integrate them into learning frameworks for large scale data. We propose two such frameworks: the memory efficient sequential processing for sequential data processing and the parallelized sequential processing for distributed computing with sequential data acquisition. We test our methods on several benchmark data sets and compared with the state-of-the-art techniques to verify the validity of the proposed techniques.

Theoretical Perspectives on the Statics and Dynamics of Species’ Borders in Patchy Environments

PubMed Central

Holt, Robert D.; Barfield, Michael

2016-01-01

Understanding range limits is a fundamental problem in ecology and evolutionary biology. In 1963, Mayr argued that “contaminating” gene flow from central populations constrained adaptation in marginal populations, preventing range expansion, while in 1984, Bradshaw suggested that absence of genetic variation prevented species from occurring everywhere. Understanding stability of range boundaries requires unraveling the interplay of demography, gene flow, and evolution of populations in concrete landscape settings. We walk through a set of interrelated spatial scenarios that illustrate interesting complexities of this interplay. To motivate our individual-based model results, we consider a hypothetical zooplankter in a landscape of discrete water bodies coupled by dispersal. We examine how patterns of dispersal influence adaptation in sink habitats where conditions are outside the species’ niche. The likelihood of observing niche evolution (and thus range expansion) over any given timescale depends on (1) the degree of initial maladaptation; (2) pattern (pulsed vs. continuous, uni- vs. bidirectional), timing (juvenile vs. adult), and rate of dispersal (and hence population size); (3) mutation rate; (4) sexuality; and (5) the degree of heterogeneity in the occupied range. We also show how the genetic architecture of polygenic adaptation is influenced by the interplay of selection and dispersal in heterogeneous landscapes. PMID:21956092

Characterizing fuels in treated areas.

Treesearch

Roger D. Ottmar; Clinton S. Wright

2002-01-01

Small-log utilization or thinning operations followed by a fuel treatment such as prescribed fire can be used to change the composition and structure of fuelbeds, thereby mitigating deleterious fire effects and reducing the potential for catastrophic wildfires in some forested landscapes. We are developing a national system, Fuel Characteristic Classification (FCC),...

Skylab

NASA Image and Video Library

1973-01-01

This EREP photograph of the Uncompahgre Plateau area of Colorado illustrates the land use classification using the hierarchical numbering system to depict land forms and vegetative patterns. The numerator is a three-digit number with decimal components identifying the vegetation analog or land use conditions. The denominator uses a three-component decimal system for landscape characterization.

A new landscape classification system for monitoring and assessment of pastures

USDA-ARS?s Scientific Manuscript database

Pasturelands in the United States span a broad range of climate, soils, physical sites, and management. Rather than treat each site as a unique entity, this diversity must be classified into basic units for research and management purposes. A similar system based on ecological principles is needed f...

Energy dissipation for flat-sloped stepped spillways using new inception point relationship

USDA-ARS?s Scientific Manuscript database

Transforming from a rural to an urban landscape has created a change in hazard classification for many earthen embankments. As a result, these facilities provide inadequate spillway capacity for the upgraded hazard rating. To bring these dams into compliance with state and federal dam safety regul...

CLASSIFYING OREGON ESTUARIES BY HABITAT: ANALYSIS OF EXISTING DATA AND A PROPOSAL FOR A PILOT STUDY

EPA Science Inventory

Because many estuarine resources are linked to benthic habitats, classification of estuaries by habitat types may prove a relevant approach for grouping estuaries with similar ecological values and vulnerability to landscape alterations. As a first step, we evaluated whether pub...

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.

Monitoring changes in landscape pattern: use of Ikonos and Quickbird images.

PubMed

Alphan, Hakan; Çelik, Nil

2016-02-01

This paper aimed to analyze short-term changes in landscape pattern that primarily results from building development in the east coast of Mersin Province (Turkey). Three sites were selected. Ikonos (2003) and Quickbird (2009) images for these sites were classified, and land cover transformations were quantitatively analyzed using cross-tabulation of classification results. Changes in landscape structure were assessed by comparing the calculated values of area/edge and shape metrics for the earlier and later dates. Area/edge metrics included percentage of land and edge density, while shape metrics included perimeter-area ratio, fractal dimension, and related circumscribing circle (RCC) metrics. Orchards and buildings were dominating land cover classes. Variations in patch edge, size, and shapes were also analyzed and discussed. Degradation of prime agricultural areas due to building development and implications of such development on habitat fragmentation were highlighted.

An unbiased adaptive sampling algorithm for the exploration of RNA mutational landscapes under evolutionary pressure.

PubMed

Waldispühl, Jérôme; Ponty, Yann

2011-11-01

The analysis of the relationship between sequences and structures (i.e., how mutations affect structures and reciprocally how structures influence mutations) is essential to decipher the principles driving molecular evolution, to infer the origins of genetic diseases, and to develop bioengineering applications such as the design of artificial molecules. Because their structures can be predicted from the sequence data only, RNA molecules provide a good framework to study this sequence-structure relationship. We recently introduced a suite of algorithms called RNAmutants which allows a complete exploration of RNA sequence-structure maps in polynomial time and space. Formally, RNAmutants takes an input sequence (or seed) to compute the Boltzmann-weighted ensembles of mutants with exactly k mutations, and sample mutations from these ensembles. However, this approach suffers from major limitations. Indeed, since the Boltzmann probabilities of the mutations depend of the free energy of the structures, RNAmutants has difficulties to sample mutant sequences with low G+C-contents. In this article, we introduce an unbiased adaptive sampling algorithm that enables RNAmutants to sample regions of the mutational landscape poorly covered by classical algorithms. We applied these methods to sample mutations with low G+C-contents. These adaptive sampling techniques can be easily adapted to explore other regions of the sequence and structural landscapes which are difficult to sample. Importantly, these algorithms come at a minimal computational cost. We demonstrate the insights offered by these techniques on studies of complete RNA sequence structures maps of sizes up to 40 nucleotides. Our results indicate that the G+C-content has a strong influence on the size and shape of the evolutionary accessible sequence and structural spaces. In particular, we show that low G+C-contents favor the apparition of internal loops and thus possibly the synthesis of tertiary structure motifs. On the other hand, high G+C-contents significantly reduce the size of the evolutionary accessible mutational landscapes.

Distribution of global fallouts cesium-137 in taiga and tundra catenae at the Ob River basin

NASA Astrophysics Data System (ADS)

Semenkov, I. N.; Usacheva, A. A.; Miroshnikov, A. Yu.

2015-03-01

The classification of soil catenae at the Ob River basin is developed and applied. This classification reflects the diverse geochemical conditions that led to the formation of certain soil bodies, their combinations and the migration fields of chemical elements. The soil and geochemical diversity of the Ob River basin catenae was analyzed. The vertical and lateral distribution of global fallouts cesium-137 was studied using the example of the four most common catenae types in Western Siberia tundra and taiga. In landscapes of dwarf birches and dark coniferous forests on gleysols, cryosols, podzols, and cryic-stagnosols, the highest 137Cs activity density and specific activity are characteristic of the upper soil layer of over 30% ash, while the moss-grass-shrub cover is characterized by low 137Cs activity density and specific activity. In landscapes of dwarf birches and pine woods on podzols, the maximum specific activity of cesium-137 is typical for moss-grass-shrub cover, while the maximum reserves are concentrated in the upper soil layer of over 30% ash. Bog landscapes and moss-grass-shrub cover are characterized by a minimum activity of 137Cs, and its reserves in soil generally decrease exponentially with depth. The cesium-137 penetration depth increases in oligotrophic histosols from northern to middle taiga landscapes from 10-15 to 40 cm. 137Cs is accumulated in oligotrophic histosols for increases in pH from 3.3 to 4.0 and in concretionary interlayers of pisoplinthic-cryic-histic-stagnosols. Cryogenic movement, on the one hand, leads to burying organic layers enriched in 137Cs and, on the other hand, to deducing specific activity when mixed with low-active material from lower soil layers.

Metapopulation modelling of riparian tree species persistence in river networks under climate change.

PubMed

Van Looy, Kris; Piffady, Jérémy

2017-11-01

Floodplain landscapes are highly fragmented by river regulation resulting in habitat degradation and flood regime perturbation, posing risks to population persistence. Climate change is expected to pose supplementary risks in this context of fragmented landscapes, and especially for river systems adaptation management programs are developed. The association of habitat quality and quantity with the landscape dynamics and resilience to human-induced disturbances is still poorly understood in the context of species survival and colonization processes, but essential to prioritize conservation and restoration actions. We present a modelling approach that elucidates network connectivity and landscape dynamics in spatial and temporal context to identify vital corridors and conservation priorities in the Loire river and its tributaries. Alteration of flooding and flow regimes is believed to be critical to population dynamics in river ecosystems. Still, little is known of critical levels of alteration both spatially and temporally. We applied metapopulation modelling approaches for a dispersal-limited tree species, white elm; and a recruitment-limited tree species, black poplar. In different model steps the connectivity and natural dynamics of the river landscape are confronted with physical alterations (dams/dykes) to species survival and then future scenarios for climatic changes and potential adaptation measures are entered in the model and translated in population persistence over the river basin. For the two tree species we highlighted crucial network zones in relation to habitat quality and connectivity. Where the human impact model already shows currently restricted metapopulation development, climate change is projected to aggravate this persistence perspective substantially. For both species a significant drawback to the basin population is observed, with 1/3 for elm and ¼ for poplar after 25 years already. But proposed adaptation measures prove effective to even bring metapopulation strength and persistence up to a level above the current level. Copyright © 2016 Elsevier Ltd. All rights reserved.

Self-organized multi-species vegetation patterns: the role of connectivity of environmental niches in natural water harvesting ecosystems

NASA Astrophysics Data System (ADS)

Callegaro, Chiara; Ursino, Nadia

2016-04-01

Self-organizing vegetation patterns are natural water harvesting systems in arid and semi-arid regions of the world and should be imitated when designing man-managed water-harvesting systems for rain-fed crop. Disconnected vegetated and bare zones, functioning as a source-sink system of resources, sustain vegetation growth and reduce water and soil losses. Mechanisms such as soil crusting over bare areas and soil loosening in vegetated areas feed back to the local net facilitation effect and contribute to maintain the patterned landscape structure. Dis-connectivity of run-off production and run-on infiltration sites reduces runoff production at the landscape scale, and increases water retention in the vegetated patches. What is the effect of species adaptation to different resource niches on the landscape structure? A minimal model for two coexisting species and soil moisture balance was formulated, to improve our understanding of the effects of species differentiation on the dynamics of plants and water at single-pattern and landscape scale within a tiger bush type ecosystem. A basic assumption of our model was that soil moisture availability is a proxy for the environmental niche of plant species. Connectivity and dis-connectivity of specific niches of adaptation of two differing plant species was an input parameter of our model, in order to test the effect of coexistence on the ecosystem structure. The ecosystem structure is the model outcome, including: patterns persistence of coexisting species; patterns persistence of one species with exclusion of the other; patterns decline with just one species surviving in a non organized structure; bare landscape with loss of both species. Results suggest that pattern-forming-species communities arise as a result of complementary niche adaptation (niche dis-connecivity), whereas niche superposition (niche connectivity) may lead to impoverishment of environmental resources and loss of vegetation cover and diversity.

A novel clinical decision support system using improved adaptive genetic algorithm for the assessment of fetal well-being.

PubMed

Ravindran, Sindhu; Jambek, Asral Bahari; Muthusamy, Hariharan; Neoh, Siew-Chin

2015-01-01

A novel clinical decision support system is proposed in this paper for evaluating the fetal well-being from the cardiotocogram (CTG) dataset through an Improved Adaptive Genetic Algorithm (IAGA) and Extreme Learning Machine (ELM). IAGA employs a new scaling technique (called sigma scaling) to avoid premature convergence and applies adaptive crossover and mutation techniques with masking concepts to enhance population diversity. Also, this search algorithm utilizes three different fitness functions (two single objective fitness functions and multi-objective fitness function) to assess its performance. The classification results unfold that promising classification accuracy of 94% is obtained with an optimal feature subset using IAGA. Also, the classification results are compared with those of other Feature Reduction techniques to substantiate its exhaustive search towards the global optimum. Besides, five other benchmark datasets are used to gauge the strength of the proposed IAGA algorithm.

The development of quick, robust, quantitative phenotypic assays for describing the host–nonhost landscape to stripe rust

PubMed Central

Dawson, Andrew M.; Bettgenhaeuser, Jan; Gardiner, Matthew; Green, Phon; Hernández-Pinzón, Inmaculada; Hubbard, Amelia; Moscou, Matthew J.

2015-01-01

Nonhost resistance is often conceptualized as a qualitative separation from host resistance. Classification into these two states is generally facile, as they fail to fully describe the range of states that exist in the transition from host to nonhost. This poses a problem when studying pathosystems that cannot be classified as either host or nonhost due to their intermediate status relative to these two extremes. In this study, we investigate the efficacy of the Poaceae-stripe rust (Puccinia striiformis Westend.) interaction for describing the host–nonhost landscape. First, using barley (Hordeum vulgare L.) and Brachypodium distachyon (L.) P. Beauv. We observed that macroscopic symptoms of chlorosis and leaf browning were associated with hyphal colonization by P. striiformis f. sp. tritici, respectively. This prompted us to adapt a protocol for visualizing fungal structures into a phenotypic assay that estimates the percent of leaf colonized. Use of this assay in intermediate host and intermediate nonhost systems found the frequency of infection decreases with evolutionary divergence from the host species. Similarly, we observed that the pathogen’s ability to complete its life cycle decreased faster than its ability to colonize leaf tissue, with no incidence of pustules observed in the intermediate nonhost system and significantly reduced pustule formation in the intermediate host system as compared to the host system, barley-P. striiformis f. sp. hordei. By leveraging the stripe rust pathosystem in conjunction with macroscopic and microscopic phenotypic assays, we now hope to dissect the genetic architecture of intermediate host and intermediate nonhost resistance using structured populations in barley and B. distachyon. PMID:26579142

[Object-oriented remote sensing image classification in epidemiological studies of visceral leishmaniasis in urban areas].

PubMed

Almeida, Andréa Sobral de; Werneck, Guilherme Loureiro; Resendes, Ana Paula da Costa

2014-08-01

This study explored the use of object-oriented classification of remote sensing imagery in epidemiological studies of visceral leishmaniasis (VL) in urban areas. To obtain temperature and environmental information, an object-oriented classification approach was applied to Landsat 5 TM scenes from the city of Teresina, Piauí State, Brazil. For 1993-1996, VL incidence rates correlated positively with census tracts covered by dense vegetation, grass/pasture, and bare soil and negatively with areas covered by water and densely populated areas. In 2001-2006, positive correlations were found with dense vegetation, grass/pasture, bare soil, and densely populated areas and negative correlations with occupied urban areas with some vegetation. Land surface temperature correlated negatively with VL incidence in both periods. Object-oriented classification can be useful to characterize landscape features associated with VL in urban areas and to help identify risk areas in order to prioritize interventions.

Forest adaptation resources: Climate change tools and approaches for land managers

Treesearch

Chris Swanston; Maria, eds. Janowiak

2012-01-01

The forests of northern Wisconsin, a defining feature of the region's landscape, are expected to undergo numerous changes in response to the changing climate. This document provides a collection of resources designed to help forest managers incorporate climate change considerations into management and devise adaptation tactics. It was developed in northern...

Adaptive variation in Pinus ponderosa from Intermountain regions. II. Middle Columbia River system

Treesearch

Gerald Rehfeldt

1986-01-01

Seedling populations were grown and compared in common environments. Statistical analyses detected genetic differences between populations for numerous traits reflecting growth potential and periodicity of shoot elongation. Multiple regression models described an adaptive landscape in which populations from low elevations have a high growth potential while those from...

Integrated Framework for an Urban Climate Adaptation Tool

NASA Astrophysics Data System (ADS)

Omitaomu, O.; Parish, E. S.; Nugent, P.; Mei, R.; Sylvester, L.; Ernst, K.; Absar, M.

2015-12-01

Cities have an opportunity to become more resilient to future climate change through investments made in urban infrastructure today. However, most cities lack access to credible high-resolution climate change projection information needed to assess and address potential vulnerabilities from future climate variability. Therefore, we present an integrated framework for developing an urban climate adaptation tool (Urban-CAT). Urban-CAT consists of four modules. Firstly, it provides climate projections at different spatial resolutions for quantifying urban landscape. Secondly, this projected data is combined with socio-economic data using leading and lagging indicators for assessing landscape vulnerability to climate extremes (e.g., urban flooding). Thirdly, a neighborhood scale modeling approach is presented for identifying candidate areas for adaptation strategies (e.g., green infrastructure as an adaptation strategy for urban flooding). Finally, all these capabilities are made available as a web-based tool to support decision-making and communication at the neighborhood and city levels. In this paper, we present some of the methods that drive each of the modules and demo some of the capabilities available to-date using the City of Knoxville in Tennessee as a case study.

Development of an Integrated Team Training Design and Assessment Architecture to Support Adaptability in Healthcare Teams

DTIC Science & Technology

2016-10-01

and implementation of embedded, adaptive feedback and performance assessment. The investigators also initiated work designing a Bayesian Belief ...training; Teamwork; Adaptive performance; Leadership; Simulation; Modeling; Bayesian belief networks (BBN) 16. SECURITY CLASSIFICATION OF: 17. LIMITATION...Trauma teams Team training Teamwork Adaptability Adaptive performance Leadership Simulation Modeling Bayesian belief networks (BBN) 6

Landscape sensitivity in a dynamic environment

NASA Astrophysics Data System (ADS)

Lin, Jiun-Chuan; Jen, Chia-Horn

2010-05-01

Landscape sensitivity at different scales and topics is presented in this study. Methodological approach composed most of this paper. According to the environmental records in the south eastern Asia, the environment change is highly related with five factors, such as scale of influence area, background of environment characters, magnitude and frequency of events, thresholds of occurring hazards and influence by time factor. This paper tries to demonstrate above five points from historical and present data. It is found that landscape sensitivity is highly related to the degree of vulnerability of the land and the processes which put on the ground including human activities. The scale of sensitivity and evaluation of sensitivities is demonstrated in this paper by the data around east Asia. The methods of classification are mainly from the analysis of environmental data and the records of hazards. From the trend of rainfall records, rainfall intensity and change of temperature, the magnitude and frequency of earthquake, dust storm, days of draught, number of hazards, there are many coincidence on these factors with landscape sensitivities. In conclusion, the landscape sensitivities could be classified as four groups: physical stable, physical unstable, unstable, extremely unstable. This paper explain the difference.

Identification of environmental covariates of West Nile virus vector mosquito population abundance.

PubMed

Trawinski, Patricia R; Mackay, D Scott

2010-06-01

The rapid spread of West Nile virus (WNv) in North America is a major public health concern. Culex pipiens-restuans is the principle mosquito vector of WNv in the northeastern United States while Aedes vexans is an important bridge vector of the virus in this region. Vector mosquito abundance is directly dependent on physical environmental factors that provide mosquito habitats. The objective of this research is to determine landscape elements that explain the population abundance and distribution of WNv vector mosquitoes using stepwise linear regression. We developed a novel approach for examining a large set of landscape variables based on a land use and land cover classification by selecting variables in stages to minimize multicollinearity. We also investigated the distance at which landscape elements influence abundance of vector populations using buffer distances of 200, 400, and 1000 m. Results show landscape effects have a significant impact on Cx. pipiens-estuans population distribution while the effects of landscape features are less important for prediction of Ae. vexans population distributions. Cx. pipiens-restuans population abundance is positively correlated with human population density, housing unit density, and urban land use and land cover classes and negatively correlated with age of dwellings and amount of forested land.

The Sequential Probability Ratio Test and Binary Item Response Models

ERIC Educational Resources Information Center

Nydick, Steven W.

2014-01-01

The sequential probability ratio test (SPRT) is a common method for terminating item response theory (IRT)-based adaptive classification tests. To decide whether a classification test should stop, the SPRT compares a simple log-likelihood ratio, based on the classification bound separating two categories, to prespecified critical values. As has…

Challenges and opportunities for large landscape-scale management in a shifting climate: The importance of nested adaptation responses across geospatial and temporal scales

Treesearch

Gary M. Tabor; Anne Carlson; Travis Belote

2014-01-01

The Yellowstone to Yukon Conservation Initiative (Y2Y) was established over 20 years ago as an experiment in large landscape conservation. Initially, Y2Y emerged as a response to large scale habitat fragmentation by advancing ecological connectivity. It also laid the foundation for large scale multi-stakeholder conservation collaboration with almost 200 non-...

[Knowledge produced from the outcomes of the "Nursing Outcomes Classification--NOC": integrative review].

PubMed

da Silva, Natália Chantal Magalhães; de Souza Oliveira, Ana Railka; de Carvalho, Emília Campos

2015-12-01

To identify the knowledge produced from the outcomes of the Nursing Outcomes Classification (NOC). A literature review using the integrative databases: Latin American and Caribbean Health Sciences (LILACS), US National Library of Medicine (PubMed), Cumulative Index to Nursing & Allied Health Literature (CINAHL) and Scopus Info Site (SCOPUS), during the months of August and September 2014. The review consisted of 21 articles that addressed different issues: Translation and Cultural adaptation (4.77%); Applicability in clinical practice (33.33%); and, Validation (63.90%). Analysis of these articles showed that the knowledge produced from the Nursing Outcomes Classification includes translation and cultural adaptation, evaluation of applicability and validation of its items. Considering the continuous evolution of this classification, periodic reviews should be carried out to identify the knowledge, use and effects of the NOC.

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 science gaps and future research needs.

Evolution at ‘Sutures’ and ‘Centers’: Recombination Can Aid Adaptation of Spatially Structured Populations on Rugged Fitness Landscapes

PubMed Central

Cooper, Jacob D.; Kerr, Benjamin

2016-01-01

Epistatic interactions among genes can give rise to rugged fitness landscapes, in which multiple “peaks” of high-fitness allele combinations are separated by “valleys” of low-fitness genotypes. How populations traverse rugged fitness landscapes is a long-standing question in evolutionary biology. Sexual reproduction may affect how a population moves within a rugged fitness landscape. Sex may generate new high-fitness genotypes by recombination, but it may also destroy high-fitness genotypes by shuffling the genes of a fit parent with a genetically distinct mate, creating low-fitness offspring. Either of these opposing aspects of sex require genotypic diversity in the population. Spatially structured populations may harbor more diversity than well-mixed populations, potentially amplifying both positive and negative effects of sex. On the other hand, spatial structure leads to clumping in which mating is more likely to occur between like types, diminishing the effects of recombination. In this study, we use computer simulations to investigate the combined effects of recombination and spatial structure on adaptation in rugged fitness landscapes. We find that spatially restricted mating and offspring dispersal may allow multiple genotypes inhabiting suboptimal peaks to coexist, and recombination at the “sutures” between the clusters of these genotypes can create genetically novel offspring. Sometimes such an offspring genotype inhabits a new peak on the fitness landscape. In such a case, spatially restricted mating allows this fledgling subpopulation to avoid recombination with distinct genotypes, as mates are more likely to be the same genotype. Such population “centers” can allow nascent peaks to establish despite recombination. Spatial structure may therefore allow an evolving population to enjoy the creative side of sexual recombination while avoiding its destructive side. PMID:27973606

Assessments of habitat preferences and quality depend on spatial scale and metrics of fitness

USGS Publications Warehouse

Chalfoun, A.D.; Martin, T.E.

2007-01-01

1. Identifying the habitat features that influence habitat selection and enhance fitness is critical for effective management. Ecological theory predicts that habitat choices should be adaptive, such that fitness is enhanced in preferred habitats. However, studies often report mismatches between habitat preferences and fitness consequences across a wide variety of taxa based on a single spatial scale and/or a single fitness component. 2. We examined whether habitat preferences of a declining shrub steppe songbird, the Brewer's sparrow Spizella breweri, were adaptive when multiple reproductive fitness components and spatial scales (landscape, territory and nest patch) were considered. 3. We found that birds settled earlier and in higher densities, together suggesting preference, in landscapes with greater shrub cover and height. Yet nest success was not higher in these landscapes; nest success was primarily determined by nest predation rates. Thus landscape preferences did not match nest predation risk. Instead, nestling mass and the number of nesting attempts per pair increased in preferred landscapes, raising the possibility that landscapes were chosen on the basis of food availability rather than safe nest sites. 4. At smaller spatial scales (territory and nest patch), birds preferred different habitat features (i.e. density of potential nest shrubs) that reduced nest predation risk and allowed greater season-long reproductive success. 5. Synthesis and applications. Habitat preferences reflect the integration of multiple environmental factors across multiple spatial scales, and individuals may have more than one option for optimizing fitness via habitat selection strategies. Assessments of habitat quality for management prescriptions should ideally include analysis of diverse fitness consequences across multiple ecologically relevant spatial scales. ?? 2007 The Authors.

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 layers: the more sensitive landscapes were those of intensive cropping. This shows the importance of considering not only the plough layer, but also the vertical distribution of SOC stocks to assess the variation in SOC dynamics under land use, landscape management or climate change. Finally, rural hedgerow landscapes were proved to be quite well adapted for soil protection in a context of climate change, focusing on both carbon storage and soil erosion.

A contour-based shape descriptor for biomedical image classification and retrieval

NASA Astrophysics Data System (ADS)

You, Daekeun; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-12-01

Contours, object blobs, and specific feature points are utilized to represent object shapes and extract shape descriptors that can then be used for object detection or image classification. In this research we develop a shape descriptor for biomedical image type (or, modality) classification. We adapt a feature extraction method used in optical character recognition (OCR) for character shape representation, and apply various image preprocessing methods to successfully adapt the method to our application. The proposed shape descriptor is applied to radiology images (e.g., MRI, CT, ultrasound, X-ray, etc.) to assess its usefulness for modality classification. In our experiment we compare our method with other visual descriptors such as CEDD, CLD, Tamura, and PHOG that extract color, texture, or shape information from images. The proposed method achieved the highest classification accuracy of 74.1% among all other individual descriptors in the test, and when combined with CSD (color structure descriptor) showed better performance (78.9%) than using the shape descriptor alone.

Testing Multivariate Adaptive Regression Splines (MARS) as a Method of Land Cover Classification of TERRA-ASTER Satellite Images.

PubMed

Quirós, Elia; Felicísimo, Angel M; Cuartero, Aurora

2009-01-01

This work proposes a new method to classify multi-spectral satellite images based on multivariate adaptive regression splines (MARS) and compares this classification system with the more common parallelepiped and maximum likelihood (ML) methods. We apply the classification methods to the land cover classification of a test zone located in southwestern Spain. The basis of the MARS method and its associated procedures are explained in detail, and the area under the ROC curve (AUC) is compared for the three methods. The results show that the MARS method provides better results than the parallelepiped method in all cases, and it provides better results than the maximum likelihood method in 13 cases out of 17. These results demonstrate that the MARS method can be used in isolation or in combination with other methods to improve the accuracy of soil cover classification. The improvement is statistically significant according to the Wilcoxon signed rank test.

Space Object Classification and Characterization Via Multiple Model Adaptive Estimation

DTIC Science & Technology

2014-07-14

BRDF ) which models light distribution scattered from the surface due to the incident light. The BRDF at any point on the surface is a function of two...uu B vu B nu obs I u sun I u I hu (b) Reflection Geometry Fig. 2: Reflection Geometry and Space Object Shape Model of the BRDF is ρdiff(i...Space Object Classification and Characterization Via Multiple Model Adaptive Estimation Richard Linares Director’s Postdoctoral Fellow Space Science

Utilizing feedback in adaptive SAR ATR systems

NASA Astrophysics Data System (ADS)

Horsfield, Owen; Blacknell, David

2009-05-01

Existing SAR ATR systems are usually trained off-line with samples of target imagery or CAD models, prior to conducting a mission. If the training data is not representative of mission conditions, then poor performance may result. In addition, it is difficult to acquire suitable training data for the many target types of interest. The Adaptive SAR ATR Problem Set (AdaptSAPS) program provides a MATLAB framework and image database for developing systems that adapt to mission conditions, meaning less reliance on accurate training data. A key function of an adaptive system is the ability to utilise truth feedback to improve performance, and it is this feature which AdaptSAPS is intended to exploit. This paper presents a new method for SAR ATR that does not use training data, based on supervised learning. This is achieved by using feature-based classification, and several new shadow features have been developed for this purpose. These features allow discrimination of vehicles from clutter, and classification of vehicles into two classes: targets, comprising military combat types, and non-targets, comprising bulldozers and trucks. The performance of the system is assessed using three baseline missions provided with AdaptSAPS, as well as three additional missions. All performance metrics indicate a distinct learning trend over the course of a mission, with most third and fourth quartile performance levels exceeding 85% correct classification. It has been demonstrated that these performance levels can be maintained even when truth feedback rates are reduced by up to 55% over the course of a mission.

National Program for Inspection of Non-Federal Dams. Batterson Park Pond Dam (CT 00262), Connecticut River Basin, Farmington/New Britain, Connecticut. Phase I Inspection Report.

DTIC Science & Technology

1978-12-01

Division to inspect and report on selected dams in the State of Connecticut. Authorization and notice to proceed were issued to Storch Engineers under a...operable however. c. Size Classification - The size classification of I the dam is intermediate. The storage (2,520 acre-feet) governs the...Landscape Architects Planners - Environental Consultants 13ATFRMN ?ARtK P*Nr3 DAm CAPAC MlY CUR~VE ELEV .DP/ R AvJQ ATkrp oi .V0i 30-70 31 F q * .7 CO

A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition

PubMed Central

Huang, Qi; Yang, Dapeng; Jiang, Li; Zhang, Huajie; Liu, Hong; Kotani, Kiyoshi

2017-01-01

Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle). PMID:28608824

A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition.

PubMed

Huang, Qi; Yang, Dapeng; Jiang, Li; Zhang, Huajie; Liu, Hong; Kotani, Kiyoshi

2017-06-13

Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle).

Automated riverine landscape characterization: GIS-based tools for watershed-scale research, assessment, and management.

PubMed

Williams, Bradley S; D'Amico, Ellen; Kastens, Jude H; Thorp, James H; Flotemersch, Joseph E; Thoms, Martin C

2013-09-01

River systems consist of hydrogeomorphic patches (HPs) that emerge at multiple spatiotemporal scales. Functional process zones (FPZs) are HPs that exist at the river valley scale and are important strata for framing whole-watershed research questions and management plans. Hierarchical classification procedures aid in HP identification by grouping sections of river based on their hydrogeomorphic character; however, collecting data required for such procedures with field-based methods is often impractical. We developed a set of GIS-based tools that facilitate rapid, low cost riverine landscape characterization and FPZ classification. Our tools, termed RESonate, consist of a custom toolbox designed for ESRI ArcGIS®. RESonate automatically extracts 13 hydrogeomorphic variables from readily available geospatial datasets and datasets derived from modeling procedures. An advanced 2D flood model, FLDPLN, designed for MATLAB® is used to determine valley morphology by systematically flooding river networks. When used in conjunction with other modeling procedures, RESonate and FLDPLN can assess the character of large river networks quickly and at very low costs. Here we describe tool and model functions in addition to their benefits, limitations, and applications.

Adaptive coding of MSS imagery. [Multi Spectral band Scanners

NASA Technical Reports Server (NTRS)

Habibi, A.; Samulon, A. S.; Fultz, G. L.; Lumb, D.

1977-01-01

A number of adaptive data compression techniques are considered for reducing the bandwidth of multispectral data. They include adaptive transform coding, adaptive DPCM, adaptive cluster coding, and a hybrid method. The techniques are simulated and their performance in compressing the bandwidth of Landsat multispectral images is evaluated and compared using signal-to-noise ratio and classification consistency as fidelity criteria.

Cognitive Nonlinear Radar

DTIC Science & Technology

2013-01-01

intelligently selecting waveform parameters using adaptive algorithms. The adaptive algorithms optimize the waveform parameters based on (1) the EM...the environment. 15. SUBJECT TERMS cognitive radar, adaptive sensing, spectrum sensing, multi-objective optimization, genetic algorithms, machine...detection and classification block diagram. .........................................................6 Figure 5. Genetic algorithm block diagram

Adaptive video-based vehicle classification technique for monitoring traffic : [executive summary].

DOT National Transportation Integrated Search

2015-08-01

Federal Highway Administration (FHWA) recommends axle-based classification standards to map : passenger vehicles, single unit trucks, and multi-unit trucks, at Automatic Traffic Recorder (ATR) stations : statewide. Many state Departments of Transport...

Dynamics of habitat selection in birds: adaptive response to nest predation depends on multiple factors.

PubMed

Devries, J H; Clark, R G; Armstrong, L M

2018-05-01

According to theory, habitat selection by organisms should reflect underlying habitat-specific fitness consequences and, in birds, reproductive success has a strong impact on population growth in many species. Understanding processes affecting habitat selection also is critically important for guiding conservation initiatives. Northern pintails (Anas acuta) are migratory, temperate-nesting birds that breed in greatest concentrations in the prairies of North America and their population remains below conservation goals. Habitat loss and changing land use practices may have decoupled formerly reliable fitness cues with respect to nest habitat choices. We used data from 62 waterfowl nesting study sites across prairie Canada (1997-2009) to examine nest survival, a primary fitness metric, at multiple scales, in combination with estimates of habitat selection (i.e., nests versus random points), to test for evidence of adaptive habitat choices. We used the same habitat covariates in both analyses. Pintail nest survival varied with nest initiation date, nest habitat, pintail breeding pair density, landscape composition and annual moisture. Selection of nesting habitat reflected patterns in nest survival in some cases, indicating adaptive selection, but strength of habitat selection varied seasonally and depended on population density and landscape composition. Adaptive selection was most evident late in the breeding season, at low breeding densities and in cropland-dominated landscapes. Strikingly, at high breeding density, habitat choice appears to become maladaptive relative to nest predation. At larger spatial scales, the relative availability of habitats with low versus high nest survival, and changing land use practices, may limit the reproductive potential of pintails.

Closed-loop adaptation of neurofeedback based on mental effort facilitates reinforcement learning of brain self-regulation.

PubMed

Bauer, Robert; Fels, Meike; Royter, Vladislav; Raco, Valerio; Gharabaghi, Alireza

2016-09-01

Considering self-rated mental effort during neurofeedback may improve training of brain self-regulation. Twenty-one healthy, right-handed subjects performed kinesthetic motor imagery of opening their left hand, while threshold-based classification of beta-band desynchronization resulted in proprioceptive robotic feedback. The experiment consisted of two blocks in a cross-over design. The participants rated their perceived mental effort nine times per block. In the adaptive block, the threshold was adjusted on the basis of these ratings whereas adjustments were carried out at random in the other block. Electroencephalography was used to examine the cortical activation patterns during the training sessions. The perceived mental effort was correlated with the difficulty threshold of neurofeedback training. Adaptive threshold-setting reduced mental effort and increased the classification accuracy and positive predictive value. This was paralleled by an inter-hemispheric cortical activation pattern in low frequency bands connecting the right frontal and left parietal areas. Optimal balance of mental effort was achieved at thresholds significantly higher than maximum classification accuracy. Rating of mental effort is a feasible approach for effective threshold-adaptation during neurofeedback training. Closed-loop adaptation of the neurofeedback difficulty level facilitates reinforcement learning of brain self-regulation. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

A simple approach for a spatial terrestrial exposure assessment of the insecticide fenoxycarb, based on a high-resolution landscape analysis.

PubMed

Thomas, Kai; Resseler, Herbert; Spatz, Robert; Hendley, Paul; Sweeney, Paul; Urban, Martin; Kubiak, Roland

2016-11-01

The objective was to refine the standard regulatory exposure scenario used in plant protection product authorisations by developing a more realistic landscape-related GIS-based exposure assessment for terrestrial non-target arthropods. We quantified the proportion of adjacent off-target area in agricultural landscapes potentially exposed to insecticide drift from applications of the active substance fenoxycarb. High-resolution imagery, landscape classification and subsequent stepwise analysis of a whole landscape using drift and interception functions were applied to selected areas in representative fruit-producing regions in Germany. Even under worst-case assumptions regarding treated area, use rate and drift, less than 12% of the non-agricultural habitat area would potentially be exposed to fenoxycarb drift above regulatory acceptable concentrations. Additionally, if the filtering effect of tall vegetation were taken into account, this number would decrease to 6.6%. Further refinements to landscape elements and application conditions indicate that less than 5% of the habitat area might be exposed above regulatory acceptable concentrations, meaning that 95% of the non-agricultural habitat area will be unimpacted (i.e. no unacceptable effects) and can serve as refuge for recolonisation. Approaches and tools are proposed for standardisable and transparent refinements in regulatory risk assessments on the landscape level. © 2016 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. © 2016 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

First steps towards a novel European forest fuel classification systems and a European forest fuel map

NASA Astrophysics Data System (ADS)

Sebastián-López, Ana; Urbieta, Itziar R.; de La Fuente Blanco, David; García Mateo, Rubén.; Moreno Rodríguez, José Manuel; Eftichidis, George; Varela, Vassiliki; Cesari, Véronique; Mário Ribeiro, Luís.; Viegas, Domingos Xavier; Lanorte, Antonio; Lasaponara, Rosa; Camia, Andrea; San Miguel, Jesús

2010-05-01

Forest fires burn at the local scale, but their massive occurrence causes effects which have global dimensions. Furthermore climate change projections associate global warming to a significant increase in forest fire activity. Warmer and drier conditions are expected to increase the frequency, duration and intensity of fires, and greater amounts of fuel associated with forest areas in decline may cause more frequent and larger fires. These facts create the need for establishing strategies for harmonizing fire danger rating, fire risk assessment, and fire prevention policies at a supranational level. Albeit forest fires are a permanent threat for European ecosystems, particularly in the south, there is no commonly accepted fuel classification scheme adopted for operational use by the Member States of the EU. The European Commission (EC) DG Environment and JRC have launched a set of studies following a resolution of the European Parliament on the further development and enhancement of the European Forest Fire Information System (EFFIS), the EC focal point for information on forest fires in Europe. One of the studies that are being funded is the FUELMAP project. The objective of FUELMAP is to develop a novel fuel classification system and a new European fuel map that will be based on a comprehensive classification of fuel complexes representing the various vegetation types across EU27, plus Switzerland, Croatia and Turkey. The overall work plan is grounded on a throughout knowledge of European forest landscapes and the key features of fuel situations occurring in natural areas. The method makes extended use of existing databases available in the Member States and European Institutions. Specifically, our proposed classification combines relevant information on ecoregions, land cover and uses, potential and actual vegetation, and stand structure. GIS techniques are used in order to define the geographic extent of the classification units and for identifying the main driving factors that determine the spatial distribution of the resulting fuel complexes. Furthermore, relevant parameters influencing fire potential and effects such as fuel load, live/dead ratio, and fuels' size classes' distribution are considered. National- and local-scale datasets (vegetation maps, forest inventory plots, fuel maps...) will be also studied and compared. Local ground- truth data will be used to assess the accuracy of the classification and will contribute, along with literature values and experts' opinion, to characterize the fuels' physical properties. The resulting classification aims to support the characterization of the fire potential, serve as input in fire emissions models, and be used to assess the expected impact of fire in the European landscapes. The work plan includes the development of a GIS software tool to automatically update the fuel map from modified (up-to-date) input data layers. The fuel map of Europe is mainly intended to support the implementation of the EFFIS modules that can be enhanced by the use of improved information on forest fuel properties and spatial distribution, though it is also envisaged that the results of the project might be useful for other relevant applications at different spatial scales. To this purpose, the classification will be designed with a hierarchical and flexible structure for describing heterogeneous landscapes. The work is on-going and this presentation shows the first results towards the envisaged European fuel map.

Role of small oligomers on the amyloidogenic aggregation free-energy landscape.

PubMed

He, Xianglan; Giurleo, Jason T; Talaga, David S

2010-01-08

We combine atomic-force-microscopy particle-size-distribution measurements with earlier measurements on 1-anilino-8-naphthalene sulfonate, thioflavin T, and dynamic light scattering to develop a quantitative kinetic model for the aggregation of beta-lactoglobulin into amyloid. We directly compare our simulations to the population distributions provided by dynamic light scattering and atomic force microscopy. We combine species in the simulation according to structural type for comparison with fluorescence fingerprint results. The kinetic model of amyloidogenesis leads to an aggregation free-energy landscape. We define the roles of and propose a classification scheme for different oligomeric species based on their location in the aggregation free-energy landscape. We relate the different types of oligomers to the amyloid cascade hypothesis and the toxic oligomer hypothesis for amyloid-related diseases. We discuss existing kinetic mechanisms in terms of the different types of oligomers. We provide a possible resolution to the toxic oligomer-amyloid coincidence.

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.

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

United Kingdom health research analyses and the benefits of shared data.

PubMed

Carter, James G; Sherbon, Beverley J; Viney, Ian S

2016-06-24

To allow research organisations to co-ordinate activity to the benefit of national and international funding strategies requires assessment of the funding landscape; this, in turn, relies on a consistent approach for comparing expenditure on research. Here, we discuss the impact and benefits of the United Kingdom's Health Research Classification System (HRCS) in national landscaping analysis of health research and the pros and cons of performing large-scale funding analyses. The first United Kingdom health research analysis (2004/2005) brought together the 11 largest public and charity funders of health research to develop the HRCS and use this categorisation to examine United Kingdom health research. The analysis was revisited in 2009/2010 and again in 2014. The most recent quinquennial analysis in 2014 compiled data from 64 United Kingdom research organisations, accounting for 91% of all public/charitable health research funding in the United Kingdom. The three analyses summarise the United Kingdom's health research expenditure in 2004/2005, 2009/2010 and 2014, and can be used to identify changes in research activity and disease focus over this 10 year period. The 2004/2005 analysis provided a baseline for future reporting and evidence for a United Kingdom Government review that recommended the co-ordination of United Kingdom health research should be strengthened to accelerate the translation of basic research into clinical and economic benefits. Through the second and third analyses, we observed strategic prioritisation of certain health research activities and disease areas, with a strong trend toward increased funding for more translational research, and increases in specific areas such as research on prevention. The use of HRCS in the United Kingdom to analyse the research landscape has provided benefit both to individual participatory funders and in coordinating initiatives at a national level. A modest amount of data for each project is sufficient for a nationwide assessment of health research funding, but achieving coverage of the United Kingdom portfolio relies on sourcing these details from a large number of individual funding agencies. The effort needed to compile this data could be minimised if funders routinely shared or published this information in a standard and accessible way. The United Kingdom approach to landscaping analyses could be readily adapted to suit other groups or nations, and global availability of research funding data would support better national and international coordination of health research.

Functional Assessment of Genetic Variants with Outcomes Adapted to Clinical Decision-Making

PubMed Central

Thouvenot, Pierre; Ben Yamin, Barbara; Fourrière, Lou; Lescure, Aurianne; Boudier, Thomas; Del Nery, Elaine; Chauchereau, Anne; Goldgar, David E.; Stoppa-Lyonnet, Dominique; Nicolas, Alain; Millot, Gaël A.

2016-01-01

Understanding the medical effect of an ever-growing number of human variants detected is a long term challenge in genetic counseling. Functional assays, based on in vitro or in vivo evaluations of the variant effects, provide essential information, but they require robust statistical validation, as well as adapted outputs, to be implemented in the clinical decision-making process. Here, we assessed 25 pathogenic and 15 neutral missense variants of the BRCA1 breast/ovarian cancer susceptibility gene in four BRCA1 functional assays. Next, we developed a novel approach that refines the variant ranking in these functional assays. Lastly, we developed a computational system that provides a probabilistic classification of variants, adapted to clinical interpretation. Using this system, the best functional assay exhibits a variant classification accuracy estimated at 93%. Additional theoretical simulations highlight the benefit of this ready-to-use system in the classification of variants after functional assessment, which should facilitate the consideration of functional evidences in the decision-making process after genetic testing. Finally, we demonstrate the versatility of the system with the classification of siRNAs tested for human cell growth inhibition in high throughput screening. PMID:27272900

Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing

PubMed Central

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

2015-01-01

Natural and semi-natural habitats in agricultural landscapes are likely to come under increasing pressure with the global population set to exceed 9 billion by 2050. These non-cropped habitats are primarily made up of trees, hedgerows and grassy margins and their amount, quality and spatial configuration can have strong implications for the delivery and sustainability of various ecosystem services. In this study high spatial resolution (0.5 m) colour infrared aerial photography (CIR) was used in object based image analysis for the classification of non-cropped habitat in a 10,029 ha area of southeast England. Three classification scenarios were devised using 4 and 9 class scenarios. The machine learning algorithm Random Forest (RF) was used to reduce the number of variables used for each classification scenario by 25.5 % ± 2.7%. Proportion of votes from the 4 class hierarchy was made available to the 9 class scenarios and where the highest ranked variables in all cases. This approach allowed for misclassified parent objects to be correctly classified at a lower level. A single object hierarchy with 4 class proportion of votes produced the best result (kappa 0.909). Validation of the optimum training sample size in RF showed no significant difference between mean internal out-of-bag error and external validation. As an example of the utility of this data, we assessed habitat suitability for a declining farmland bird, the yellowhammer (Emberiza citronella), which requires hedgerows associated with grassy margins. We found that ∼22% of hedgerows were within 200 m of margins with an area >183.31 m2. The results from this analysis can form a key information source at the environmental and policy level in landscape optimisation for food production and ecosystem service sustainability. PMID:26664131

Pond Identification, Classification, and Inundation Dynamics at St. Marks National Wildlife Refuge in northwest Florida, USA

NASA Astrophysics Data System (ADS)

Riley, J. W.; Calhoun, D.; Barichivich, J.

2012-12-01

The persistence and resilience of amphibian communities is largely dependent on adequate breeding habitat. This is especially important for threatened and endangered species that may often exist as isolated populations and have specific requirements for breeding. A study currently being conducted by the U.S. Geological Survey is investigating the feasibility of a repatriation effort of the Striped Newt (Notophthalmus perstriatus), a federal candidate species, within the St. Marks National Wildlife Refuge (SMNWR) in northwest Florida. This amphibian species requires ponds that are free of fishes and, for this reason, generally chooses ephemeral ponds as breeding sites. The delineation of potential breeding habitat is a first step in selecting candidate areas for repatriation. To achieve this, a LIDAR (Light Detection and Ranging) derived digital elevation model (DEM) and a topographic position index (TPI) classification scheme was used to identify and classify isolated depressions across the landscape. The TPI evaluates the difference in elevation from a central DEM cell to the mean elevation of a neighborhood of surrounding DEM cells and is a robust tool for locating depressional features within a landscape. These candidate depression features were then screened to remove large perennial ponds and smaller connected ponds from further consideration. In addition, the perimeters of twenty-two field identified ephemeral ponds were surveyed with a high precision RTK GPS (Real Time Kinematic Global Positioning System) unit to provide a calibration dataset to evaluate the performance of the feature identification method. This set of ponds was also instrumented with water-level recorders to investigate inundation dynamics across a wide range of hydrologic conditions. We anticipate being able to classify pond hydroperiod—thus each pond's potential as breeding habitat—at the monitored locations through this combination of approaches. Using estimates of pond size, morphology, and landscape position derived from the DEM, the classifications will be extended to other ponds on the refuge.

Mapping Deforestation area in North Korea Using Phenology-based Multi-Index and Random Forest

NASA Astrophysics Data System (ADS)

Jin, Y.; Sung, S.; Lee, D. K.; Jeong, S.

2016-12-01

Forest ecosystem provides ecological benefits to both humans and wildlife. Growing global demand for food and fiber is accelerating the pressure on the forest ecosystem in whole world from agriculture and logging. In recently, North Korea lost almost 40 % of its forests to crop fields for food production and cut-down of forest for fuel woods between 1990 and 2015. It led to the increased damage caused by natural disasters and is known to be one of the most forest degraded areas in the world. The characteristic of forest landscape in North Korea is complex and heterogeneous, the major landscape types in the forest are hillside farm, unstocked forest, natural forest and plateau vegetation. Remote sensing can be used for the forest degradation mapping of a dynamic landscape at a broad scale of detail and spatial distribution. Confusion mostly occurred between hillside farmland and unstocked forest, but also between unstocked forest and forest. Most previous forest degradation that used focused on the classification of broad types such as deforests area and sand from the perspective of land cover classification. The objective of this study is using random forest for mapping degraded forest in North Korea by phenological based vegetation index derived from MODIS products, which has various environmental factors such as vegetation, soil and water at a regional scale for improving accuracy. The model created by random forest resulted in an overall accuracy was 91.44%. Class user's accuracy of hillside farmland and unstocked forest were 97.2% and 84%%, which indicate the degraded forest. Unstocked forest had relative low user accuracy due to misclassified hillside farmland and forest samples. Producer's accuracy of hillside farmland and unstocked forest were 85.2% and 93.3%, repectly. In this case hillside farmland had lower produce accuracy mainly due to confusion with field, unstocked forest and forest. Such a classification of degraded forest could supply essential information to decide the priority of forest management and restoration in degraded forest area.

Public acceptance of disturbance-based forest management: a study of the Blue River Landscape Strategy in the Central Cascades Adaptive Management Area.

Treesearch

Bruce Shindler; Angela L. Mallon

2009-01-01

This report examines public perspectives on disturbance-based management conducted in the central Cascade Range in Oregon as part of the Blue River Landscape Strategy. A mail survey to local residents was used to describe the public’s understanding of this form of management, identify perceived associated risks and potential barriers to implementation, and the overall...

The effect of landscape complexity and microclimate on the thermal tolerance of a pest insect.

PubMed

Alford, Lucy; Tougeron, Kévin; Pierre, Jean-Sébastien; Burel, Françoise; van Baaren, Joan

2017-03-21

Landscape changes are known to exacerbate the impacts of climate change. As such, understanding the combined effect of climate and landscape on agroecosystems is vital if we are to maintain the function of agroecosystems. This study aimed to elucidate the effects of agricultural landscape complexity on the microclimate and thermal tolerance of an aphid pest to better understand how landscape and climate may interact to affect the thermal tolerance of pest species within the context of global climate change. Meteorological data were measured at the landscape level, and cereal aphids (Sitobion avenae, Metopolophium dirhodum and Rhopalosiphum padi) sampled, from contrasting landscapes (simple and complex) in winter 2013/2014 and spring 2014 in cereal fields of Brittany, France. Aphids were returned to the laboratory and the effect of landscape of origin on aphid cold tolerance (as determined by CT min ) was investigated. Results revealed that local landscape complexity significantly affected microclimate, with simple homogenous landscapes being on average warmer, but with greater temperature variation. Landscape complexity was shown to impact aphid cold tolerance, with aphids from complex landscapes being more cold tolerant than those from simple landscapes in both winter and spring, but with differences among species. This study highlights that future changes to land use could have implications for the thermal tolerance and adaptability of insects. Furthermore, not all insect species respond in a similar way to microhabitat and microclimate, which could disrupt important predator-prey relationships and the ecosystem service they provide. © 2017 Institute of Zoology, Chinese Academy of Sciences.

Multitemporal L- and C-Band Synthetic Aperture Radar To Highlight Differences in Water Status Among Boreal Forest and Wetland Systems in the Yukon Flats, Interior Alaska

USGS Publications Warehouse

Balser, Andrew W.; Wylie, Bruce K.

2010-01-01

Tracking landscape-scale water status in high-latitude boreal systems is indispensible to understanding the fate of stored and sequestered carbon in a climate change scenario. Spaceborne synthetic aperture radar (SAR) imagery provides critical information for water and moisture status in Alaskan boreal environments at the landscape scale. When combined with results from optical sensor analyses, a complementary picture of vegetation, biomass, and water status emerges. Whereas L-band SAR showed better inherent capacity to map water status, C-band had much more temporal coverage in this study. Analysis through the use of L- and C-band SARs combined with Landsat Enhanced Thematic Mapper Plus (ETM+) enables landscape stratification by vegetation and by seasonal and interannual hydrology. Resultant classifications are highly relevant to biogeochemistry at the landscape scale. These results enhance our understanding of ecosystem processes relevant to carbon balance and may be scaled up to inform regional carbon flux estimates and better parameterize general circulation models (GCMs).

Adaptive video-based vehicle classification technique for monitoring traffic.

DOT National Transportation Integrated Search

2015-08-01

This report presents a methodology for extracting two vehicle features, vehicle length and number of axles in order : to classify the vehicles from video, based on Federal Highway Administration (FHWA)s recommended vehicle : classification scheme....

Fitness landscape transformation through a single amino acid change in the rho terminator.

PubMed

Freddolino, Peter L; Goodarzi, Hani; Tavazoie, Saeed

2012-05-01

Regulatory networks allow organisms to match adaptive behavior to the complex and dynamic contingencies of their native habitats. Upon a sudden transition to a novel environment, the mismatch between the native behavior and the new niche provides selective pressure for adaptive evolution through mutations in elements that control gene expression. In the case of core components of cellular regulation and metabolism, with broad control over diverse biological processes, such mutations may have substantial pleiotropic consequences. Through extensive phenotypic analyses, we have characterized the systems-level consequences of one such mutation (rho*) in the global transcriptional terminator Rho of Escherichia coli. We find that a single amino acid change in Rho results in a massive change in the fitness landscape of the cell, with widely discrepant fitness consequences of identical single locus perturbations in rho* versus rho(WT) backgrounds. Our observations reveal the extent to which a single regulatory mutation can transform the entire fitness landscape of the cell, causing a massive change in the interpretation of individual mutations and altering the evolutionary trajectories which may be accessible to a bacterial population.

Pairwise Classifier Ensemble with Adaptive Sub-Classifiers for fMRI Pattern Analysis.

PubMed

Kim, Eunwoo; Park, HyunWook

2017-02-01

The multi-voxel pattern analysis technique is applied to fMRI data for classification of high-level brain functions using pattern information distributed over multiple voxels. In this paper, we propose a classifier ensemble for multiclass classification in fMRI analysis, exploiting the fact that specific neighboring voxels can contain spatial pattern information. The proposed method converts the multiclass classification to a pairwise classifier ensemble, and each pairwise classifier consists of multiple sub-classifiers using an adaptive feature set for each class-pair. Simulated and real fMRI data were used to verify the proposed method. Intra- and inter-subject analyses were performed to compare the proposed method with several well-known classifiers, including single and ensemble classifiers. The comparison results showed that the proposed method can be generally applied to multiclass classification in both simulations and real fMRI analyses.

Adaptive governance, ecosystem management, and natural capital.

PubMed

Schultz, Lisen; Folke, Carl; Österblom, Henrik; Olsson, Per

2015-06-16

To gain insights into the effects of adaptive governance on natural capital, we compare three well-studied initiatives; a landscape in Southern Sweden, the Great Barrier Reef in Australia, and fisheries in the Southern Ocean. We assess changes in natural capital and ecosystem services related to these social-ecological governance approaches to ecosystem management and investigate their capacity to respond to change and new challenges. The adaptive governance initiatives are compared with other efforts aimed at conservation and sustainable use of natural capital: Natura 2000 in Europe, lobster fisheries in the Gulf of Maine, North America, and fisheries in Europe. In contrast to these efforts, we found that the adaptive governance cases developed capacity to perform ecosystem management, manage multiple ecosystem services, and monitor, communicate, and respond to ecosystem-wide changes at landscape and seascape levels with visible effects on natural capital. They enabled actors to collaborate across diverse interests, sectors, and institutional arrangements and detect opportunities and problems as they developed while nurturing adaptive capacity to deal with them. They all spanned local to international levels of decision making, thus representing multilevel governance systems for managing natural capital. As with any governance system, internal changes and external drivers of global impacts and demands will continue to challenge the long-term success of such initiatives.

Soil environment is a key driver of adaptation in Medicago truncatula: new insights from landscape genomics.

PubMed

Guerrero, Jimena; Andrello, Marco; Burgarella, Concetta; Manel, Stephanie

2018-07-01

Spatial differences in environmental selective pressures interact with the genomes of organisms, ultimately leading to local adaptation. Landscape genomics is an emergent research area that uncovers genome-environment associations, thus allowing researchers to identify candidate loci for adaptation to specific environmental variables. In the present study, we used latent factor mixed models (LFMMs) and Moran spectral outlier detection/randomization (MSOD-MSR) to identify candidate loci for adaptation to 10 environmental variables (climatic, soil and atmospheric) among 43 515 single nucleotide polymorphisms (SNPs) from 202 accessions of the model legume Medicago truncatula. Soil variables were associated with a large number of candidate loci identified through both LFMMs and MSOD-MSR. Genes tagged by candidate loci associated with drought and salinity are involved in the response to biotic and abiotic stresses, while those tagged by candidates associated with soil nitrogen and atmospheric nitrogen, participate in the legume-rhizobia symbiosis. Candidate SNPs identified through both LFMMs and MSOD-MSR explained up to 56% of variance in flowering traits. Our findings highlight the importance of soil in driving adaptation in the system and elucidate the basis of evolutionary potential of M. truncatula to respond to global climate change and anthropogenic disruption of the nitrogen cycle. © 2018 The Authors New Phytologist © 2018 New Phytologist Trust.

Social Networks and Adaptation to Environmental Change: The Case of Central Oregon's Fire-Prone Forest Landscape

NASA Astrophysics Data System (ADS)

Fischer, A.

2012-12-01

Social networks are the patterned interactions among individuals and organizations through which people refine their beliefs and values, negotiate meanings for things and develop behavioral intentions. The structure of social networks has bearing on how people communicate information, generate and retain knowledge, make decisions and act collectively. Thus, social network structure is important for how people perceive, shape and adapt to the environment. We investigated the relationship between social network structure and human adaptation to wildfire risk in the fire-prone forested landscape of Central Oregon. We conducted descriptive and non-parametric social network analysis on data gathered through interviews to 1) characterize the structure of the network of organizations involved in forest and wildfire issues and 2) determine whether network structure is associated with organizations' beliefs, values and behaviors regarding fire and forest management. Preliminary findings indicate that fire protection and forest-related organizations do not frequently communicate or cooperate, suggesting that opportunities for joint problem-solving, innovation and collective action are limited. Preliminary findings also suggest that organizations with diverse partners are more likely to hold adaptive beliefs about wildfire and work cooperatively. We discuss the implications of social network structure for adaptation to changing environmental conditions such as wildfire risk.

Adaptive governance, ecosystem management, and natural capital

PubMed Central

Schultz, Lisen; Folke, Carl; Österblom, Henrik; Olsson, Per

2015-01-01

To gain insights into the effects of adaptive governance on natural capital, we compare three well-studied initiatives; a landscape in Southern Sweden, the Great Barrier Reef in Australia, and fisheries in the Southern Ocean. We assess changes in natural capital and ecosystem services related to these social–ecological governance approaches to ecosystem management and investigate their capacity to respond to change and new challenges. The adaptive governance initiatives are compared with other efforts aimed at conservation and sustainable use of natural capital: Natura 2000 in Europe, lobster fisheries in the Gulf of Maine, North America, and fisheries in Europe. In contrast to these efforts, we found that the adaptive governance cases developed capacity to perform ecosystem management, manage multiple ecosystem services, and monitor, communicate, and respond to ecosystem-wide changes at landscape and seascape levels with visible effects on natural capital. They enabled actors to collaborate across diverse interests, sectors, and institutional arrangements and detect opportunities and problems as they developed while nurturing adaptive capacity to deal with them. They all spanned local to international levels of decision making, thus representing multilevel governance systems for managing natural capital. As with any governance system, internal changes and external drivers of global impacts and demands will continue to challenge the long-term success of such initiatives. PMID:26082542

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. © 2014 John Wiley & Sons Ltd/CNRS.

Quantifying the Availability of Tidewater Glacial Ice as Habitat for Harbor Seals in a Tidewater Glacial Fjord in Alaska Using Object-Based Image Analysis of Airborne Visible Imagery

NASA Astrophysics Data System (ADS)

Prakash, A.; Haselwimmer, C. E.; Gens, R.; Womble, J. N.; Ver Hoef, J.

2013-12-01

Tidewater glaciers are prominent landscape features that play a significant role in landscape and ecosystem processes along the southeastern and southcentral coasts of Alaska. Tidewater glaciers calve large icebergs that serve as an important substrate for harbor seals (Phoca vitulina richardii) for resting, pupping, nursing young, molting, and avoiding predators. Many of the tidewater glaciers in Alaska are retreating, which may influence harbor seal populations. Our objectives are to investigate the relationship between ice conditions and harbor seal distributions, which are poorly understood, in John's Hopkins Inlet, Glacier Bay National Park, Alaska, using a combination of airborne remote sensing and statistical modeling techniques. We present an overview of some results from Object-Based Image Analysis (OBIA) for classification of a time series of very high spatial resolution (4 cm pixels) airborne imagery acquired over John's Hopkins Inlet during the harbor seal pupping season in June and during the molting season in August from 2007 - 2012. Using OBIA we have developed a workflow to automate processing of the large volumes (~1250 images/survey) of airborne visible imagery for 1) classification of ice products (e.g. percent ice cover, percent brash ice, percent ice bergs) at a range of scales, and 2) quantitative determination of ice morphological properties such as iceberg size, roundness, and texture that are not found in traditional per-pixel classification approaches. These ice classifications and morphological variables are then used in statistical models to assess relationships with harbor seal abundance and distribution. Ultimately, understanding these relationships may provide novel perspectives on the spatial and temporal variation of harbor seals in tidewater glacial fjords.

Landscape object-based analysis of wetland plant functional types: the effects of spatial scale, vegetation classes and classifier methods

NASA Astrophysics Data System (ADS)

Dronova, I.; Gong, P.; Wang, L.; Clinton, N.; Fu, W.; Qi, S.

2011-12-01

Remote sensing-based vegetation classifications representing plant function such as photosynthesis and productivity are challenging in wetlands with complex cover and difficult field access. Recent advances in object-based image analysis (OBIA) and machine-learning algorithms offer new classification tools; however, few comparisons of different algorithms and spatial scales have been discussed to date. We applied OBIA to delineate wetland plant functional types (PFTs) for Poyang Lake, the largest freshwater lake in China and Ramsar wetland conservation site, from 30-m Landsat TM scene at the peak of spring growing season. We targeted major PFTs (C3 grasses, C3 forbs and different types of C4 grasses and aquatic vegetation) that are both key players in system's biogeochemical cycles and critical providers of waterbird habitat. Classification results were compared among: a) several object segmentation scales (with average object sizes 900-9000 m2); b) several families of statistical classifiers (including Bayesian, Logistic, Neural Network, Decision Trees and Support Vector Machines) and c) two hierarchical levels of vegetation classification, a generalized 3-class set and more detailed 6-class set. We found that classification benefited from object-based approach which allowed including object shape, texture and context descriptors in classification. While a number of classifiers achieved high accuracy at the finest pixel-equivalent segmentation scale, the highest accuracies and best agreement among algorithms occurred at coarser object scales. No single classifier was consistently superior across all scales, although selected algorithms of Neural Network, Logistic and K-Nearest Neighbors families frequently provided the best discrimination of classes at different scales. The choice of vegetation categories also affected classification accuracy. The 6-class set allowed for higher individual class accuracies but lower overall accuracies than the 3-class set because individual classes differed in scales at which they were best discriminated from others. Main classification challenges included a) presence of C3 grasses in C4-grass areas, particularly following harvesting of C4 reeds and b) mixtures of emergent, floating and submerged aquatic plants at sub-object and sub-pixel scales. We conclude that OBIA with advanced statistical classifiers offers useful instruments for landscape vegetation analyses, and that spatial scale considerations are critical in mapping PFTs, while multi-scale comparisons can be used to guide class selection. Future work will further apply fuzzy classification and field-collected spectral data for PFT analysis and compare results with MODIS PFT products.

Adaptive Skills and Academic Achievement in Latino Students

ERIC Educational Resources Information Center

Raines, Tara C.; Gordon, Melissa; Harrell-Williams, Leigh; Diliberto, Rachele A.; Parke, Elyse M.

2017-01-01

Interventions developed to improve adaptive skills can improve academic achievement. The authors expanded this line of research by examining the relationship between performance on a state proficiency exam and adaptive skills classifications on the Behavioral Assessment System for Children, Second Edition parent and teacher reports. Participants…

Can Evolution Supply What Ecology Demands?

PubMed

Kokko, Hanna; Chaturvedi, Anurag; Croll, Daniel; Fischer, Martin C; Guillaume, Frédéric; Karrenberg, Sophie; Kerr, Ben; Rolshausen, Gregor; Stapley, Jessica

2017-03-01

A simplistic view of the adaptive process pictures a hillside along which a population can climb: when ecological 'demands' change, evolution 'supplies' the variation needed for the population to climb to a new peak. Evolutionary ecologists point out that this simplistic view can be incomplete because the fitness landscape changes dynamically as the population evolves. Geneticists meanwhile have identified complexities relating to the nature of genetic variation and its architecture, and the importance of epigenetic variation is under debate. In this review, we highlight how complexity in both ecological 'demands' and the evolutionary 'supply' influences organisms' ability to climb fitness landscapes that themselves change dynamically as evolution proceeds, and encourage new synthetic effort across research disciplines towards ecologically realistic studies of adaptation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Fusion and Sense Making of Heterogeneous Sensor Network and Other Sources

DTIC Science & Technology

2017-03-16

multimodal fusion framework that uses both training data and web resources for scene classification, the experimental results on the benchmark datasets...show that the proposed text-aided scene classification framework could significantly improve classification performance. Experimental results also show...human whose adaptability is achieved by reliability- dependent weighting of different sensory modalities. Experimental results show that the proposed

Channel Classification across Arid West Landscapes in Support of OHW Delineation

DTIC Science & Technology

2013-01-01

8 Figure 5. National Hydrography Dataset for Chinle Creek, AZ...the OHW boundary is determined by observing recent physical evidence subsequent to flow. Channel morphology and physical features associated with the...data from the National Hydrography Dataset (NHD) (USGS 2010). The NHD digital ERDC/CRREL TR-13-3 9 stream data were downloaded as a line

Global synthesis of the classifications, distributions, benefits and issues of terracing

Treesearch

Wei Wei; Die Chen; Lixin Wang; Stefani Daryanto; Liding Chen; Yang Yu; Yonglong Lu; Ge Sun; Tianjiao Feng

2016-01-01

For thousands of years, humans have created different types of terraces in different sloping conditions, meant to mitigate flood risks, reduce soil erosion and conserve water. These anthropogenic landscapes can be found in tropical and subtropical rainforests, deserts, and arid and semiarid mountains across the globe. Despite the long history, the roles of and the...

Landscape scale mapping of forest inventory data by nearest neighbor classification

Treesearch

Andrew Lister

2009-01-01

One of the goals of the Forest Service, U.S. Department of Agriculture's Forest Inventory and Analysis (FIA) program is large-area mapping. FIA scientists have tried many methods in the past, including geostatistical methods, linear modeling, nonlinear modeling, and simple choropleth and dot maps. Mapping methods that require individual model-based maps to be...

Classification and spatial analysis of eastern hemlock health using remote sensing and GIS

Treesearch

Laurent R. Bonneau; Kathleen S. Shields; Daniel L. Civco; David R. Mikus

2000-01-01

Over the past decade hemlock stands in southern Connecticut have undergone significant decline coincident with the arrival in 1985 of an exotic insect pest, the hemlock woolly adelgid (Adelges tsugae Annand). The objective of this study was to evaluate image enhancement techniques for rating the health of hemlocks at the landscape level using...

Post-fire tree establishment patterns at the alpine treeline ecotone: Mount Rainier National Park, Washington, USA

Treesearch

Kirk M. Stueve; Dawna L. Cerney; Regina M. Rochefort; Laurie L. Kurth

2009-01-01

We performed classification analysis of 1970 satellite imagery and 2003 aerial photography to delineate establishment. Local site conditions were calculated from a LIDAR-based DEM, ancillary climate data, and 1970 tree locations in a GIS. We used logistic regression on a spatially weighted landscape matrix to rank variables.

Comparison of statistical and theoretical habitat models for conservation planning: the benefit of ensemble prediction

Treesearch

D. Todd Jones-Farrand; Todd M. Fearer; Wayne E. Thogmartin; Frank R. Thompson; Mark D. Nelson; John M. Tirpak

2011-01-01

Selection of a modeling approach is an important step in the conservation planning process, but little guidance is available. We compared two statistical and three theoretical habitat modeling approaches representing those currently being used for avian conservation planning at landscape and regional scales: hierarchical spatial count (HSC), classification and...

Preliminary fuel characterization of the chauga ridges region of the Southern Appalachian Mountains

Treesearch

Aaron D. Stottlemyer; Victor B. Shelburne; Thomas A. Waldrop; Sandra Rideout-Hanzak; William C. Bridges

2006-01-01

Many areas of the southern Appalachian Mountains contain large amounts of dead and/or ericaceous fuel. Fuel information critical in modeling fire behavior and its effects is not available to forest managers in the southern Appalachian Mountains, and direct measurement is often impractical due to steep, remote topography. An existing landscape ecosystem classification (...

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

USDA-ARS?s Scientific Manuscript database

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 recent ground truth data could be used to extrapolate pre...

Co-adapting societal and ecological interactions following large disturbances in urban park woodlands

Treesearch

Margaret Carreiro; Wayne Zipperer

2011-01-01

The responses of urban park woodlands to large disturbances provide the opportunity to identify and examine linkages in social-ecological systems in urban landscapes.We propose that the Panarchy model consisting of hierarchically nested adaptive cycles provides a useful framework to evaluate those linkages.We use two case studies as examples – Cherokee Park in...

Restoration of fire in managed forests: a model to prioritize landscapes and analyze tradeoffs

Treesearch

Alan A. Ager; Nicole M. Vaillant; Andrew McMahan

2013-01-01

Ongoing forest restoration on public lands in the western US is a concerted effort to counter the growing incidence of uncharacteristic wildfire in fire-adapted ecosystems. Restoration projects cover 725,000 ha annually, and include thinning and underburning to remove ladder and surface fuel, and seeding of fire-adapted native grasses and shrubs. The backlog of areas...

Diversity and habitat relationships of hypogeous fungi. III. Factors influencing the occurrence of fire-adapted species

Treesearch

Andrew W. Claridge; James M. Trappe; Douglas J. Mills; Debbie L. Claridge

2009-01-01

Among the huge array of hypogeous ectomycorrhizal fungi so far documented from Australia, six genera and more than 30 species occur within the family Mesophelliaceae, all of which show various adaptations for surviving in fire-prone landscapes. These mostly endemic fungi are critical to postfire reestablishment of regenerating vegetation, and their fruit-bodies provide...

Collaborative restoration effects on forest structure in ponderosa pine-dominated forests of Colorado

Treesearch

Jeffery B. Cannon; Kevin J. Barrett; Benjamin M. Gannon; Robert N. Addington; Mike A. Battaglia; Paula J. Fornwalt; Gregory H. Aplet; Antony S. Cheng; Jeffrey L. Underhill; Jennifer S. Briggs; Peter M. Brown

2018-01-01

In response to large, severe wildfires in historically fire-adapted forests in the western US, policy initiatives, such as the USDA Forest Service’s Collaborative Forest Landscape Restoration Program (CFLRP), seek to increase the pace and scale of ecological restoration. One required component of this program is collaborative adaptive management, in which monitoring...

Learning, memory, and the role of neural network architecture.

PubMed

Hermundstad, Ann M; Brown, Kevin S; Bassett, Danielle S; Carlson, Jean M

2011-06-01

The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems.

Characterizing the spatial structure of endangered species habitat using geostatistical analysis of IKONOS imagery

USGS Publications Warehouse

Wallace, C.S.A.; Marsh, S.E.

2005-01-01

Our study used geostatistics to extract measures that characterize the spatial structure of vegetated landscapes from satellite imagery for mapping endangered Sonoran pronghorn habitat. Fine spatial resolution IKONOS data provided information at the scale of individual trees or shrubs that permitted analysis of vegetation structure and pattern. We derived images of landscape structure by calculating local estimates of the nugget, sill, and range variogram parameters within 25 ?? 25-m image windows. These variogram parameters, which describe the spatial autocorrelation of the 1-m image pixels, are shown in previous studies to discriminate between different species-specific vegetation associations. We constructed two independent models of pronghorn landscape preference by coupling the derived measures with Sonoran pronghorn sighting data: a distribution-based model and a cluster-based model. The distribution-based model used the descriptive statistics for variogram measures at pronghorn sightings, whereas the cluster-based model used the distribution of pronghorn sightings within clusters of an unsupervised classification of derived images. Both models define similar landscapes, and validation results confirm they effectively predict the locations of an independent set of pronghorn sightings. Such information, although not a substitute for field-based knowledge of the landscape and associated ecological processes, can provide valuable reconnaissance information to guide natural resource management efforts. ?? 2005 Taylor & Francis Group Ltd.

Aridification of the Indian Subcontinent during the Holocene: Implications for Landscape Evolution, Sedimentation, Carbon Cycle, and Human Civilizations

DTIC Science & Technology

2012-06-01

of flora for both sites. For the Godavari River basin the  13 Cwax record shows a gradual increase in aridity- adapted vegetation from ~4,000 until...1,700 years ago followed by the persistence of aridity- adapted plants to the present. The oxygen isotopic composition of planktonic foraminifera...for a gradual increase in the proportion of aridity- adapted vegetation from ~4,000 until 1,700 years ago followed by the persistence of aridity

The Origins of Order: Self-Organization and Selection in Evolution

NASA Astrophysics Data System (ADS)

Kauffman, Stuart A.

The following sections are included: * Introduction * Fitness Landscapes in Sequence Space * The NK Model of Rugged Fitness Landscapes * The NK Model of Random Epistatic Interactions * The Rank Order Statistics on K = N - 1 Random Landscapes * The number of local optima is very large * The expected fraction of fitter 1-mutant neighbors dwindles by 1/2 on each improvement step * Walks to local optima are short and vary as a logarithmic function of N * The expected time to reach an optimum is proportional to the dimensionality of the space * The ratio of accepted to tried mutations scales as lnN/N * Any genotype can only climb to a small fraction of the local optima * A small fraction of the genotypes can climb to any one optimum * Conflicting constraints cause a "complexity catastrophe": as complexity increase accessible adaptive peaks fall toward the mean fitness * The "Tunable" NK Family of Correlated Landscapes * Other Combinatorial Optimization Problems and Their Landscapes * Summary * References

Knowledge production and learning for sustainable landscapes: seven steps using social-ecological systems as laboratories.

PubMed

Angelstam, Per; Elbakidze, Marine; Axelsson, Robert; Dixelius, Malcolm; Törnblom, Johan

2013-03-01

There are multiple challenges regarding use and governance of landscapes' goods, functions and intangible values for ecosystem health and human well-being. One group of challenges is to measure and assess principal sustainability dimensions through performance targets, so stakeholders have transparent information about states and trends. Another group is to develop adaptive governance at multiple levels, and management of larger geographical areas across scales. Addressing these challenges, we present a framework for transdisciplinary research using multiple landscapes as place-based case studies that integrates multiple research disciplines and non-academic actors: (1) identify a suite of landscapes, and for each (2) review landscape history, (3) map stakeholders, use and non-use values, products and land use, (4) analyze institutions, policies and the system of governance, (5) measure ecological, economic, social and cultural sustainability, (6) assess sustainability dimensions and governance, and finally (7) make comparisons and synthesize. Collaboration, communication and dissemination are additional core features. We discuss barriers bridges and bridges for applying this approach.

Principles for ecological classification

Treesearch

Dennis H. Grossman; Patrick Bourgeron; Wolf-Dieter N. Busch; David T. Cleland; William Platts; G. Ray; C. Robins; Gary Roloff

1999-01-01

The principal purpose of any classification is to relate common properties among different entities to facilitate understanding of evolutionary and adaptive processes. In the context of this volume, it is to facilitate ecosystem stewardship, i.e., to help support ecosystem conservation and management objectives.

The ERTS-1 investigation (ER-600). Volume 5: ERTS-1 urban land use analysis

NASA Technical Reports Server (NTRS)

Erb, R. B.

1974-01-01

The Urban Land Use Team conducted a year's investigation of ERTS-1 MSS data to determine the number of Land Use categories in the Houston, Texas, area. They discovered unusually low classification accuracies occurred when a spectrally complex urban scene was classified with extensive rural areas containing spectrally homogeneous features. Separate computer processing of only data in the urbanized area increased classification accuracies of certain urban land use categories. Even so, accuracies of urban landscape were in the 40-70 percent range compared to 70-90 percent for the land use categories containing more homogeneous features (agriculture, forest, water, etc.) in the nonurban areas.

Geomorphology applied to landscape analysis for planning and management of natural spaces. Case study: Las Batuecas-S. de Francia and Quilamas natural parks, (Salamanca, Spain).

PubMed

Martínez-Graña, A M; Silva, P G; Goy, J L; Elez, J; Valdés, V; Zazo, C

2017-04-15

Geomorphology is fundamental to landscape analysis, as it represents the main parameter that determines the land spatial configuration and facilitates reliefs classification. The goal of this article is the elaboration of thematic maps that enable the determination of different landscape units and elaboration of quality and vulnerability synthetic maps for landscape fragility assessment prior to planning human activities. For two natural spaces, the final synthetic maps were created with direct (visual-perceptual features) and indirect (cartographic models and 3D simulations) methods from thematic maps with GIS technique. This enabled the creation of intrinsic and extrinsic landscape quality maps showing sectors needing most preservation, as well as intrinsic and extrinsic landscape fragility maps (environment response capacity or vulnerability towards human actions). The resulting map shows absorption capacity for areas of maximum and/or minimum human intervention. Sectors of high absorption capacity (minimum need for preservation) are found where the incidence of human intervention is minimum: escarpment bottoms, fitted rivers, sinuous high lands with thick vegetation coverage and valley interiors, or those areas with high landscape quality, low fragility and high absorption capacity, whose average values are found across lower hillsides of some valleys, and sectors with low absorption capacity (areas needing most preservation) found mainly in the inner parts of natural spaces: peaks and upper hillsides, synclines flanks and scattered areas. For the integral analysis of landscape, a mapping methodology has been set. It comprises a valid criterion for rational and sustainable planning, management and protection of natural spaces. Copyright © 2017 Elsevier B.V. All rights reserved.

Hydrological resiliency in the Western Boreal Plains: classification of hydrological responses using wavelet analysis to assess landscape resilience

NASA Astrophysics Data System (ADS)

Probert, Samantha; Kettridge, Nicholas; Devito, Kevin; Hannah, David; Parkin, Geoff

2017-04-01

The Boreal represents a system of substantial resilience to climate change, with minimal ecological change over the past 6000 years. However, unprecedented climatic warming, coupled with catchment disturbances could exceed thresholds of hydrological function in the Western Boreal Plains. Knowledge of ecohydrological and climatic feedbacks that shape the resilience of boreal forests has advanced significantly in recent years, but this knowledge is yet to be applied and understood at landscape scales. Hydrological modelling at the landscape scale is challenging in the WBP due to diverse, non-topographically driven hydrology across the mosaic of terrestrial and aquatic ecosystems. This study functionally divides the geologic and ecological components of the landscape into Hydrologic Response Areas (HRAs) and wetland, forestland, interface and pond Hydrologic Units (HUs) to accurately characterise water storage and infer transmission at multiple spatial and temporal scales. Wavelet analysis is applied to pond and groundwater levels to describe the patterns of water storage in response to climate signals; to isolate dominant controls on hydrological responses and to assess the relative importance of physical controls between wet and dry climates. This identifies which components of the landscape exhibit greater magnitude and frequency of variability to wetting and drying trends, further to testing the hierarchical framework for hydrological storage controls of: climate, bedrock geology, surficial geology, soil, vegetation, and topography. Classifying HRA and HU hydrological function is essential to understand and predict water storage and redistribution through drought cycles and wet periods. This work recognises which landscape components are most sensitive under climate change and disturbance and also creates scope for hydrological resiliency research in Boreal systems by recognising critical landscape components and their role in landscape collapse or catastrophic shift in ecosystem function under future climatic scenarios.

Learnings from Opportunistic Wetlands: The Role of Substrate and Landscape Position on Reconstructed Landforms in a Sub-humid Climate

NASA Astrophysics Data System (ADS)

Little-Devito, M.; Chasmer, L.; Devito, K.; Kettridge, N.; Lukenbach, M. C.; Mendoza, C. A.

2017-12-01

Wetlands are important features in large-scale reclamation projects, and are integral to sustaining landscape eco-hydrological function and meeting reclamation goals. Despite a sub-humid climate, opportunistic wetlands are appearing on reconstructed landforms, and present an opportunity to understand the requirements for wetland construction, relative wetland succession, and their role in functioning landscapes. The relative importance and relationship between local and landscape-scale factors in determining initial wetland formation, as well as the relative occurrence and wetland type found on newly reclaimed landscapes was studied using both field and active (LiDAR) remote sensing methods. A random transect survey approach was used to characterize vegetation communities, soil and hydrologic characteristics, and local and landscape physiographic position across reconstructed landforms. Transects were also used to validate a broader area LiDAR-based classification. Preliminary findings suggest a higher frequency of opportunistic wetlands than anticipated. Soil texture of constructed landforms was important in determining the significance of local and landscape factors. On fine-textured constructed landforms, regardless of landscape position, wetlands formed on flat areas and in shallow depressions where soils had low water storage that promoted frequent surface saturation. Wetlands were less frequent on coarse-textured landforms and their location was controlled by landscape-scale factors, being restricted to the toes of slopes and areas intersecting the groundwater table. Wetlands found across all material types were predominantly Salix sp. and Carex sp. swamps with Typha sp. marsh complexes. This may indicate a potential initial phase of wetland succession and paludification in the Boreal Plains. These findings have important implications for understanding general wetland development, the initial phase of wetland paludification, and will aid the development of a geomorphic framework to better inform wetland construction and promote sustainable forest-wetland complexes similar to those found in natural landscapes.

Chlorobaculum tepidum Modulates Amino Acid Composition in Response to Energy Availability, as Revealed by a Systematic Exploration of the Energy Landscape of Phototrophic Sulfur Oxidation.

PubMed

Levy, Amalie T; Lee, Kelvin H; Hanson, Thomas E

2016-11-01

Microbial sulfur metabolism, particularly the formation and consumption of insoluble elemental sulfur (S 0 ), is an important biogeochemical engine that has been harnessed for applications ranging from bioleaching and biomining to remediation of waste streams. Chlorobaculum tepidum, a low-light-adapted photoautolithotrophic sulfur-oxidizing bacterium, oxidizes multiple sulfur species and displays a preference for more reduced electron donors: sulfide > S 0 > thiosulfate. To understand this preference in the context of light energy availability, an "energy landscape" of phototrophic sulfur oxidation was constructed by varying electron donor identity, light flux, and culture duration. Biomass and cellular parameters of C. tepidum cultures grown across this landscape were analyzed. From these data, a correction factor for colorimetric protein assays was developed, enabling more accurate biomass measurements for C. tepidum, as well as other organisms. C. tepidum's bulk amino acid composition correlated with energy landscape parameters, including a tendency toward less energetically expensive amino acids under reduced light flux. This correlation, paired with an observation of increased cell size and storage carbon production under electron-rich growth conditions, suggests that C. tepidum has evolved to cope with changing energy availability by tuning its proteome for energetic efficiency and storing compounds for leaner times. How microbes cope with and adapt to varying energy availability is an important factor in understanding microbial ecology and in designing efficient biotechnological processes. We explored the response of a model phototrophic organism, Chlorobaculum tepidum, across a factorial experimental design that enabled simultaneous variation and analysis of multiple growth conditions, what we term the "energy landscape." C. tepidum biomass composition shifted toward less energetically expensive amino acids at low light levels. This observation provides experimental evidence for evolved efficiencies in microbial proteomes and emphasizes the role that energy flux may play in the adaptive responses of organisms. From a practical standpoint, our data suggest that bulk biomass amino acid composition could provide a simple proxy to monitor and identify energy stress in microbial systems. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

Learning from conservation planning for the U.S. National Wildlife Refuges.

PubMed

Meretsky, Vicky J; Fischman, Robert L

2014-10-01

The U.S. National Wildlife Refuge System has nearly completed its first round of unit-level, comprehensive conservation plans (CCPs) and will soon begin required revisions. Laws and policies governing refuge planning emphasize ecological integrity, landscape-scale conservation, and adaptive management. We evaluated 185 CCPs completed during 2005-2011, which cover 324 of 555 national wildlife refuges. We reviewed CCP prescriptions addressing 5 common conservation issues (habitat and game, nongame, imperiled, and invasive species) and 3 specialized topics (landscape-scale conservation, climate change, and environmental quality). Common conservation issues received prescriptions in >90% of CCPs. Specialized topics received more variable treatment. Prescriptions for aquatic connectivity, water quantity, and climate-change impacts increased over the study period. Except for climate change, direct actions were the most common type of management prescription, followed by plans or studies. Most CCPs stated a commitment to adaptive management and prescribed monitoring for common conservation objectives; other aspects of planning for adaptive management were often lacking, despite strong support for adaptive management in the conservation planning literature. To better address refuge-specific threats, we recommend that revised plans explicitly match identified refuge issues with prescriptions, particularly for under-represented concerns such as novel pests and pathogens. We recommend incorporating triggers into monitoring frameworks and specifying actions that will occur when threshold values are reached to improve support for adaptive management. Revised CCPs should better reflect work that refuges already undertake to extend conservation objectives beyond their borders and better engage with regional conservation efforts to continue this work. More thorough landscape-scale threat assessments and explicit prioritization of planned actions would further improve conservation effectiveness. Excellent examples of all recommended practices exist within the CCPs we examined; sharing best planning practices would improve planning efficiency within the refuge system. © 2014 The Authors. Conservation Biology published by Wiley Periodicals, Inc., on behalf of the Society for Conservation Biology.

Reducing risks by transforming landscapes: Cross-scale effects of land-use changes on ecosystem services.

PubMed

Fedele, Giacomo; Locatelli, Bruno; Djoudi, Houria; Colloff, Matthew J

2018-01-01

Globally, anthropogenic environmental change is exacerbating the already vulnerable conditions of many people and ecosystems. In order to obtain food, water, raw materials and shelter, rural people modify forests and other ecosystems, affecting the supply of ecosystem services that contribute to livelihoods and well-being. Despite widespread awareness of the nature and extent of multiple impacts of land-use changes, there remains limited understanding of how these impacts affect trade-offs among ecosystem services and their beneficiaries across spatial scales. We assessed how rural communities in two forested landscapes in Indonesia have changed land uses over the last 20 years to adapt their livelihoods that were at risk from multiple hazards. We estimated the impact of these adaptation strategies on the supply of ecosystem services by comparing different benefits provided to people from these land uses (products, water, carbon, and biodiversity), using forest inventories, remote sensing, and interviews. Local people converted forests to rubber plantations, reforested less productive croplands, protected forests on hillsides, and planted trees in gardens. Our results show that land-use decisions were propagated at the landscape scale due to reinforcing loops, whereby local actors perceived that such decisions contributed positively to livelihoods by reducing risks and generating co-benefits. When land-use changes become sufficiently widespread, they affect the supply of multiple ecosystem services, with impacts beyond the local scale. Thus, adaptation implemented at the local-scale may not address development and climate adaptation challenges at regional or national scale (e.g. as part of UN Sustainable Development Goals or actions taken under the UNFCCC Paris Agreement). A better understanding of the context and impacts of local ecosystem-based adaptation is fundamental to the scaling up of land management policies and practices designed to reduce risks and improve well-being for people at different scales.

A 3D convolutional neural network approach to land cover classification using LiDAR and multi-temporal Landsat imagery

NASA Astrophysics Data System (ADS)

Xu, Z.; Guan, K.; Peng, B.; Casler, N. P.; Wang, S. W.

2017-12-01

Landscape has complex three-dimensional features. These 3D features are difficult to extract using conventional methods. Small-footprint LiDAR provides an ideal way for capturing these features. Existing approaches, however, have been relegated to raster or metric-based (two-dimensional) feature extraction from the upper or bottom layer, and thus are not suitable for resolving morphological and intensity features that could be important to fine-scale land cover mapping. Therefore, this research combines airborne LiDAR and multi-temporal Landsat imagery to classify land cover types of Williamson County, Illinois that has diverse and mixed landscape features. Specifically, we applied a 3D convolutional neural network (CNN) method to extract features from LiDAR point clouds by (1) creating occupancy grid, intensity grid at 1-meter resolution, and then (2) normalizing and incorporating data into a 3D CNN feature extractor for many epochs of learning. The learned features (e.g., morphological features, intensity features, etc) were combined with multi-temporal spectral data to enhance the performance of land cover classification based on a Support Vector Machine classifier. We used photo interpretation for training and testing data generation. The classification results show that our approach outperforms traditional methods using LiDAR derived feature maps, and promises to serve as an effective methodology for creating high-quality land cover maps through fusion of complementary types of remote sensing data.

Variability of lotic macroinvertebrate assemblages and stream habitat characteristics across hierarchical landscape classifications.

PubMed

Mykrä, Heikki; Heino, Jani; Muotka, Timo

2004-09-01

Streams are naturally hierarchical systems, and their biota are affected by factors effective at regional to local scales. However, there have been only a few attempts to quantify variation in ecological attributes across multiple spatial scales. We examined the variation in several macroinvertebrate metrics and environmental variables at three hierarchical scales (ecoregions, drainage systems, streams) in boreal headwater streams. In nested analyses of variance, significant spatial variability was observed for most of the macroinvertebrate metrics and environmental variables examined. For most metrics, ecoregions explained more variation than did drainage systems. There was, however, much variation attributable to residuals, suggesting high among-stream variation in macroinvertebrate assemblage characteristics. Nonmetric multidimensional scaling (NMDS) and multiresponse permutation procedure (MRPP) showed that assemblage composition differed significantly among both drainage systems and ecoregions. The associated R-statistics were, however, very low, indicating wide variation among sites within the defined landscape classifications. Regional delineations explained most of the variation in stream water chemistry, ecoregions being clearly more influential than drainage systems. For physical habitat characteristics, by contrast, the among-stream component was the major source of variation. Distinct differences attributable to stream size were observed for several metrics, especially total number of taxa and abundance of algae-scraping invertebrates. Although ecoregions clearly account for a considerable amount of variation in macroinvertebrate assemblage characteristics, we suggest that a three-tiered classification system (stratification through ecoregion and habitat type, followed by assemblage prediction within these ecologically meaningful units) will be needed for effective bioassessment of boreal running waters.

Towards psychologically adaptive brain-computer interfaces

NASA Astrophysics Data System (ADS)

Myrden, A.; Chau, T.

2016-12-01

Objective. Brain-computer interface (BCI) performance is sensitive to short-term changes in psychological states such as fatigue, frustration, and attention. This paper explores the design of a BCI that can adapt to these short-term changes. Approach. Eleven able-bodied individuals participated in a study during which they used a mental task-based EEG-BCI to play a simple maze navigation game while self-reporting their perceived levels of fatigue, frustration, and attention. In an offline analysis, a regression algorithm was trained to predict changes in these states, yielding Pearson correlation coefficients in excess of 0.45 between the self-reported and predicted states. Two means of fusing the resultant mental state predictions with mental task classification were investigated. First, single-trial mental state predictions were used to predict correct classification by the BCI during each trial. Second, an adaptive BCI was designed that retrained a new classifier for each testing sample using only those training samples for which predicted mental state was similar to that predicted for the current testing sample. Main results. Mental state-based prediction of BCI reliability exceeded chance levels. The adaptive BCI exhibited significant, but practically modest, increases in classification accuracy for five of 11 participants and no significant difference for the remaining six despite a smaller average training set size. Significance. Collectively, these findings indicate that adaptation to psychological state may allow the design of more accurate BCIs.

Fifty-year spatiotemporal analysis of landscape changes in the Mont Saint-Hilaire UNESCO Biosphere Reserve (Quebec, Canada).

PubMed

Béliveau, Marc; Germain, Daniel; Ianăş, Ana-Neli

2017-05-01

Diachronic analysis with a GIS-based classification of land-use changes based on aerial photographs, orthophotos, topographic maps, geotechnical reports, urban plans, and using landscape metrics has permitted insight into the driving forces responsible for landscape fragmentation in the Mont Saint-Hilaire (MSH) Biosphere Reserve over the period 1958-2015. Although the occurrence of exogenous factors, such as extreme weather and fires, can have a significant influence on the fragmentation of the territory in time and space, the accelerated development of the built environment (+470%) is nevertheless found to be primarily responsible for landscape fragmentation and the loss of areas formerly occupied by orchards, agriculture, and woodlands. The landscape metrics used corroborate these results, with a simplification of the shape of polygons, and once again reveal the difficulties of harmonizing different land uses. MSH has become somewhat of a forest island in a sea of residential development and agriculture. To counter this isolation of fragmented habitat components, forest corridors have been proposed and developed for the Biosphere Reserve and particularly for the core area. Two corridors, to the north and south, are used to connect the protected area and other wooded areas at the regional scale, in order to promote genetic exchange between populations of various species. In that regard, the forest buffer zone around the hill continues to play a key role and has great ecological value for species and ecological preservation and conservation. However, appropriate management and landscape preservation actions should recognize and focus on landscape composition and the associated geographical configuration.

Mediterranean Land Use and Land Cover Classification Assessment Using High Spatial Resolution Data

NASA Astrophysics Data System (ADS)

Elhag, Mohamed; Boteva, Silvena

2016-10-01

Landscape fragmentation is noticeably practiced in Mediterranean regions and imposes substantial complications in several satellite image classification methods. To some extent, high spatial resolution data were able to overcome such complications. For better classification performances in Land Use Land Cover (LULC) mapping, the current research adopts different classification methods comparison for LULC mapping using Sentinel-2 satellite as a source of high spatial resolution. Both of pixel-based and an object-based classification algorithms were assessed; the pixel-based approach employs Maximum Likelihood (ML), Artificial Neural Network (ANN) algorithms, Support Vector Machine (SVM), and, the object-based classification uses the Nearest Neighbour (NN) classifier. Stratified Masking Process (SMP) that integrates a ranking process within the classes based on spectral fluctuation of the sum of the training and testing sites was implemented. An analysis of the overall and individual accuracy of the classification results of all four methods reveals that the SVM classifier was the most efficient overall by distinguishing most of the classes with the highest accuracy. NN succeeded to deal with artificial surface classes in general while agriculture area classes, and forest and semi-natural area classes were segregated successfully with SVM. Furthermore, a comparative analysis indicates that the conventional classification method yielded better accuracy results than the SMP method overall with both classifiers used, ML and SVM.

Comparison analysis for classification algorithm in data mining and the study of model use

NASA Astrophysics Data System (ADS)

Chen, Junde; Zhang, Defu

2018-04-01

As a key technique in data mining, classification algorithm was received extensive attention. Through an experiment of classification algorithm in UCI data set, we gave a comparison analysis method for the different algorithms and the statistical test was used here. Than that, an adaptive diagnosis model for preventive electricity stealing and leakage was given as a specific case in the paper.

Walking pattern analysis and SVM classification based on simulated gaits.

PubMed

Mao, Yuxiang; Saito, Masaru; Kanno, Takehiro; Wei, Daming; Muroi, Hiroyasu

2008-01-01

Three classes of walking patterns, normal, caution and danger, were simulated by tying elastic bands to joints of lower body. In order to distinguish one class from another, four local motions suggested by doctors were investigated stepwise, and differences between levels were evaluated using t-tests. The human adaptability in the tests was also evaluated. We improved average classification accuracy to 84.50% using multiclass support vector machine classifier and concluded that human adaptability is a factor that can cause obvious bias in contiguous data collections.

A Review of Land-Cover Mapping Activities in Coastal Alabama and Mississippi

USGS Publications Warehouse

Smith, Kathryn E.L.; Nayegandhi, Amar; Brock, John C.

2010-01-01

INTRODUCTION Land-use and land-cover (LULC) data provide important information for environmental management. Data pertaining to land-cover and land-management activities are a common requirement for spatial analyses, such as watershed modeling, climate change, and hazard assessment. In coastal areas, land development, storms, and shoreline modification amplify the need for frequent and detailed land-cover datasets. The northern Gulf of Mexico coastal area is no exception. The impact of severe storms, increases in urban area, dramatic changes in land cover, and loss of coastal-wetland habitat all indicate a vital need for reliable and comparable land-cover data. Four main attributes define a land-cover dataset: the date/time of data collection, the spatial resolution, the type of classification, and the source data. The source data are the foundation dataset used to generate LULC classification and are typically remotely sensed data, such as aerial photography or satellite imagery. These source data have a large influence on the final LULC data product, so much so that one can classify LULC datasets into two general groups: LULC data derived from aerial photography and LULC data derived from satellite imagery. The final LULC data can be converted from one format to another (for instance, vector LULC data can be converted into raster data for analysis purposes, and vice versa), but each subsequent dataset maintains the imprint of the source medium within its spatial accuracy and data features. The source data will also influence the spatial and temporal resolution, as well as the type of classification. The intended application of the LULC data typically defines the type of source data and methodology, with satellite imagery being selected for large landscapes (state-wide, national data products) and repeatability (environmental monitoring and change analysis). The coarse spatial scale and lack of refined land-use categories are typical drawbacks to satellite-based land-use classifications. Aerial photography is typically selected for smaller landscapes (watershed-basin scale), for greater definition of the land-use categories, and for increased spatial resolution. Disadvantages of using photography include time-consuming digitization, high costs for imagery collection, and lack of seasonal data. Recently, the availability of high-resolution satellite imagery has generated a new category of LULC data product. These new datasets have similar strengths to the aerial-photo-based LULC in that they possess the potential for refined definition of land-use categories and increased spatial resolution but also have the benefit of satellite-based classifications, such as repeatability for change analysis. LULC classification based on high-resolution satellite imagery is still in the early stages of development but merits greater attention because environmental-monitoring and landscape-modeling programs rely heavily on LULC data. This publication summarizes land-use and land-cover mapping activities for Alabama and Mississippi coastal areas within the U.S. Geological Survey (USGS) Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazard Susceptibility Project boundaries. Existing LULC datasets will be described, as well as imagery data sources and ancillary data that may provide ground-truth or satellite training data for a forthcoming land-cover classification. Finally, potential areas for a high-resolution land-cover classification in the Alabama-Mississippi region will be identified.

Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska

PubMed Central

Lara, Mark J.; Nitze, Ingmar; Grosse, Guido; McGuire, A. David

2018-01-01

Arctic tundra landscapes are composed of a complex mosaic of patterned ground features, varying in soil moisture, vegetation composition, and surface hydrology over small spatial scales (10–100 m). The importance of microtopography and associated geomorphic landforms in influencing ecosystem structure and function is well founded, however, spatial data products describing local to regional scale distribution of patterned ground or polygonal tundra geomorphology are largely unavailable. Thus, our understanding of local impacts on regional scale processes (e.g., carbon dynamics) may be limited. We produced two key spatiotemporal datasets spanning the Arctic Coastal Plain of northern Alaska (~60,000 km2) to evaluate climate-geomorphological controls on arctic tundra productivity change, using (1) a novel 30 m classification of polygonal tundra geomorphology and (2) decadal-trends in surface greenness using the Landsat archive (1999–2014). These datasets can be easily integrated and adapted in an array of local to regional applications such as (1) upscaling plot-level measurements (e.g., carbon/energy fluxes), (2) mapping of soils, vegetation, or permafrost, and/or (3) initializing ecosystem biogeochemistry, hydrology, and/or habitat modeling. PMID:29633984

Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska

USGS Publications Warehouse

Lara, Mark J.; Nitze, Ingmar; Grosse, Guido; McGuire, A. David

2018-01-01

Arctic tundra landscapes are composed of a complex mosaic of patterned ground features, varying in soil moisture, vegetation composition, and surface hydrology over small spatial scales (10–100 m). The importance of microtopography and associated geomorphic landforms in influencing ecosystem structure and function is well founded, however, spatial data products describing local to regional scale distribution of patterned ground or polygonal tundra geomorphology are largely unavailable. Thus, our understanding of local impacts on regional scale processes (e.g., carbon dynamics) may be limited. We produced two key spatiotemporal datasets spanning the Arctic Coastal Plain of northern Alaska (~60,000 km2) to evaluate climate-geomorphological controls on arctic tundra productivity change, using (1) a novel 30 m classification of polygonal tundra geomorphology and (2) decadal-trends in surface greenness using the Landsat archive (1999–2014). These datasets can be easily integrated and adapted in an array of local to regional applications such as (1) upscaling plot-level measurements (e.g., carbon/energy fluxes), (2) mapping of soils, vegetation, or permafrost, and/or (3) initializing ecosystem biogeochemistry, hydrology, and/or habitat modeling.

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

Promoting sustainable economic development is the basis of the Green Economy: a new vision of Agriculture, Environmental and Regional policy, shared by the wine sector, especially on some crucial issues, such as reducing the consumption of agricultural land, recognition as economically important of the green agricultural production space, spreading of organic farming, adoption of good agricultural practices. Sustainability, in fact , is not just about the use of analysis tools (carbonfoot print, Waterfoot print, etc .) but is about innovations to be introduced in the entire production process, protection of biodiversity, ethic work in the vineyard and winery. It means to disseminate as much as possible all those practices that can enable a more efficient land management also considering the recent climate changes: introduction of agro-energy and precision agriculture, rational use of water resources, creation of an observatory on temperatures and an interactive mapping system, viticultural zoning and municipal planning to make concrete balance between vitality in agronomic sector and landscape quality. Realizing such a regional geopedological mapping about agricultural soil, will allow companies to display a real-time access to all the data needed for a sustainable management of the funds, not only it would be an important tool to support the technical choices of farmers, enhancing their potential and optimizing production in relation to the current climate crisis, but would have a strong impact in terms of managing and saving water and energy resources. A strong efficacy in this context should be recognized at the "Urban Regulation Plans of the Wine Cities", which have developed since 2007 on the base of the guidelines promoted by the Italian Association Città del Vino, in order to enhance the quality of wine districts. The foundations of this multidisciplinary tool are based on: • in-depth knowledge of the characteristics of the wine territory; • unity and 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.

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.

A Structure-Adaptive Hybrid RBF-BP Classifier with an Optimized Learning Strategy

PubMed Central

Wen, Hui; Xie, Weixin; Pei, Jihong

2016-01-01

This paper presents a structure-adaptive hybrid RBF-BP (SAHRBF-BP) classifier with an optimized learning strategy. SAHRBF-BP is composed of a structure-adaptive RBF network and a BP network of cascade, where the number of RBF hidden nodes is adjusted adaptively according to the distribution of sample space, the adaptive RBF network is used for nonlinear kernel mapping and the BP network is used for nonlinear classification. The optimized learning strategy is as follows: firstly, a potential function is introduced into training sample space to adaptively determine the number of initial RBF hidden nodes and node parameters, and a form of heterogeneous samples repulsive force is designed to further optimize each generated RBF hidden node parameters, the optimized structure-adaptive RBF network is used for adaptively nonlinear mapping the sample space; then, according to the number of adaptively generated RBF hidden nodes, the number of subsequent BP input nodes can be determined, and the overall SAHRBF-BP classifier is built up; finally, different training sample sets are used to train the BP network parameters in SAHRBF-BP. Compared with other algorithms applied to different data sets, experiments show the superiority of SAHRBF-BP. Especially on most low dimensional and large number of data sets, the classification performance of SAHRBF-BP outperforms other training SLFNs algorithms. PMID:27792737

The Kinzua Quality Deer Cooperative: can adaptive management and local stakeholder engagement sustain reduced impact of ungulate browsers in forest systems?

Treesearch

Susan L. Stout; Alejandro A. Royo; David S. deCalesta; Kevin McAleese; James C. Finley

2013-01-01

The Kinzua Quality Deer Cooperative (KQDC) was established in 2000 to test new approaches to stewardship of white-tailed deer and forest habitat on a 30 000 hectare landscape in northwest Pennsylvania, USA. Partners included land managers, scientists, educators, tourism promoters,and hunters. KQDC goals were adaptive management of the deer herd, improved habitat...

The red-cockaded woodpecker's role in the southern pine ecosystem, population trends and relationships with southern pine beetles

Treesearch

Richard N. Conner; D. Craig Rudolph; Daniel Saenz; Robert N. Coulson

1997-01-01

This study reviews the overall ecological role of the Red-cockaded Woodpecker (Picoides borealis)in the southern pine ecosystem. It is the only North American woodpecker species to become well adapted to a landscape that was relatively devoid of the substrate typically used by woodpeckers for cavity excavation (i.e. snags and decayed, living hardwoods). Its adaptation...

Lipogenesis in a wing-polymorphic cricket: Canalization versus morph-specific plasticity as a function of nutritional heterogeneity.

PubMed

Zera, Anthony J; Clark, Rebecca; Behmer, Spence

2016-12-01

The influence of variable nutritional input on life history adaptation is a central, but incompletely understood aspect of life history physiology. The wing-polymorphic cricket, Gryllus firmus, has been extensively studied with respect to the biochemical basis of life history adaptation, in particular, modification of lipid metabolism that underlies the enhanced accumulation of lipid flight fuel in the dispersing morph [LW(f)=long wings with functional flight muscles] relative to the flightless (SW=short-winged) morph. To date, biochemical studies have been undertaken almost exclusively using a single laboratory diet. Thus, the extent to which nutritional heterogeneity, likely experienced in the field, influences this key morph adaptation is unknown. We used the experimental approach of the Geometric Framework for Nutrition and employed 13 diets that differed in the amounts and ratios of protein and carbohydrate to assess how nutrient amount and balance affects morph-specific lipid biosynthesis. Greater lipid biosynthesis and allocation to the soma in the LW(f) compared with the SW morph (1) occurred across the entire protein-carbohydrate landscape and (2) is likely an important contributor to elevated somatic lipid in the LW(f) morph across the entire protein-carbohydrate landscape. Nevertheless, dietary carbohydrate strongly affected lipid biosynthesis in a morph-specific manner (to a greater degree in the LW(f) morph). Lipogenesis in the SW morph may be constrained due to its more limited lipid storage capacity compared to the LW(f) morph. Elevated activity of NADP + -isocitrate dehydrogenase (NADP + -IDH), an enzyme that produces reducing equivalents for lipid biosynthesis, was correlated with and may be an important cause of the increased lipogenesis in the LW(f) morph across most, but not all regions of the protein-carbohydrate landscape. By contrast, ATP-citrate lyase (ACL), an enzyme that catalyzes the first step in the pathway of fatty acid biosynthesis, showed complex morph-specific patterns of activity that were strongly contingent upon diet. Morph-specific patterns of NADP + -IDH and ACL activities across the nutrient landscape were much more complex than expected from previous studies on a single diet. Collectively, our results indicate that the biochemical basis of an important life history adaptation, morph-specific lipogenesis, can be canalized in the face of substantial nutritional heterogeneity. However, in some regions of the protein-carbohydrate landscape, it is strongly modulated in a morph-specific manner. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

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.

Individual dispersal, landscape connectivity and ecological networks.

PubMed

Baguette, Michel; Blanchet, Simon; Legrand, Delphine; Stevens, Virginie M; Turlure, Camille

2013-05-01

Connectivity is classically considered an emergent property of landscapes encapsulating individuals' flows across space. However, its operational use requires a precise understanding of why and how organisms disperse. Such movements, and hence landscape connectivity, will obviously vary according to both organism properties and landscape features. We review whether landscape connectivity estimates could gain in both precision and generality by incorporating three fundamental outcomes of dispersal theory. Firstly, dispersal is a multi-causal process; its restriction to an 'escape reaction' to environmental unsuitability is an oversimplification, as dispersing individuals can leave excellent quality habitat patches or stay in poor-quality habitats according to the relative costs and benefits of dispersal and philopatry. Secondly, species, populations and individuals do not always react similarly to those cues that trigger dispersal, which sometimes results in contrasting dispersal strategies. Finally, dispersal is a major component of fitness and is thus under strong selective pressures, which could generate rapid adaptations of dispersal strategies. Such evolutionary responses will entail spatiotemporal variation in landscape connectivity. We thus strongly recommend the use of genetic tools to: (i) assess gene flow intensity and direction among populations in a given landscape; and (ii) accurately estimate landscape features impacting gene flow, and hence landscape connectivity. Such approaches will provide the basic data for planning corridors or stepping stones aiming at (re)connecting local populations of a given species in a given landscape. This strategy is clearly species- and landscape-specific. But we suggest that the ecological network in a given landscape could be designed by stacking up such linkages designed for several species living in different ecosystems. This procedure relies on the use of umbrella species that are representative of other species living in the same ecosystem. © 2012 The Authors. Biological Reviews © 2012 Cambridge Philosophical Society.

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

PubMed

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

2015-03-19

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.

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).

A global view of shifting cultivation: Recent, current, and future extent

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heinimann, Andreas; Mertz, Ole; Frolking, Steve

Mosaic landscapes under shifting cultivation, with their dynamic mix of managed and natural land covers, often fall through the cracks in remote sensing-based land cover and land use classifications, as these are unable to adequately capture such landscapes' dynamic nature and complex spectral and spatial signatures. But information about such landscapes is urgently needed to improve the outcomes of global earth system modelling and large-scale carbon and greenhouse gas accounting. This study combines existing global Landsat-based deforestation data covering the years 2000 to 2014 with very high-resolution satellite imagery to visually detect the specific spatio-temporal pattern of shifting cultivation atmore » a one-degree cell resolution worldwide. The accuracy levels of our classification were high with an overall accuracy above 87%. We estimate the current global extent of shifting cultivation and compare it to other current global mapping endeavors as well as results of literature searches. Based on an expert survey, we make a first attempt at estimating past trends as well as possible future trends in the global distribution of shifting cultivation until the end of the 21 st century. With 62% of the investigated one-degree cells in the humid and sub-humid tropics currently showing signs of shifting cultivation$-$the majority in the Americas (41%) and Africa (37%)$-$this form of cultivation remains widespread, and it would be wrong to speak of its general global demise in the last decades. We estimate that shifting cultivation landscapes currently cover roughly 280 million hectares worldwide, including both cultivated fields and fallows. While only an approximation, this estimate is clearly smaller than the areas mentioned in the literature which range up to 1,000 million hectares. Based on our expert survey and historical trends we estimate a possible strong decrease in shifting cultivation over the next decades, raising issues of livelihood security and resilience among people currently depending on shifting cultivation.« less

A global view of shifting cultivation: Recent, current, and future extent

DOE PAGES

Heinimann, Andreas; Mertz, Ole; Frolking, Steve; ...

2017-09-08

Mosaic landscapes under shifting cultivation, with their dynamic mix of managed and natural land covers, often fall through the cracks in remote sensing-based land cover and land use classifications, as these are unable to adequately capture such landscapes' dynamic nature and complex spectral and spatial signatures. But information about such landscapes is urgently needed to improve the outcomes of global earth system modelling and large-scale carbon and greenhouse gas accounting. This study combines existing global Landsat-based deforestation data covering the years 2000 to 2014 with very high-resolution satellite imagery to visually detect the specific spatio-temporal pattern of shifting cultivation atmore » a one-degree cell resolution worldwide. The accuracy levels of our classification were high with an overall accuracy above 87%. We estimate the current global extent of shifting cultivation and compare it to other current global mapping endeavors as well as results of literature searches. Based on an expert survey, we make a first attempt at estimating past trends as well as possible future trends in the global distribution of shifting cultivation until the end of the 21 st century. With 62% of the investigated one-degree cells in the humid and sub-humid tropics currently showing signs of shifting cultivation$-$the majority in the Americas (41%) and Africa (37%)$-$this form of cultivation remains widespread, and it would be wrong to speak of its general global demise in the last decades. We estimate that shifting cultivation landscapes currently cover roughly 280 million hectares worldwide, including both cultivated fields and fallows. While only an approximation, this estimate is clearly smaller than the areas mentioned in the literature which range up to 1,000 million hectares. Based on our expert survey and historical trends we estimate a possible strong decrease in shifting cultivation over the next decades, raising issues of livelihood security and resilience among people currently depending on shifting cultivation.« less

Vocal development in a Waddington landscape

PubMed Central

Teramoto, Yayoi; Takahashi, Daniel Y; Holmes, Philip; Ghazanfar, Asif A

2017-01-01

Vocal development is the adaptive coordination of the vocal apparatus, muscles, the nervous system, and social interaction. Here, we use a quantitative framework based on optimal control theory and Waddington’s landscape metaphor to provide an integrated view of this process. With a biomechanical model of the marmoset monkey vocal apparatus and behavioral developmental data, we show that only the combination of the developing vocal tract, vocal apparatus muscles and nervous system can fully account for the patterns of vocal development. Together, these elements influence the shape of the monkeys’ vocal developmental landscape, tilting, rotating or shifting it in different ways. We can thus use this framework to make quantitative predictions regarding how interfering factors or experimental perturbations can change the landscape within a species, or to explain comparative differences in vocal development across species DOI: http://dx.doi.org/10.7554/eLife.20782.001 PMID:28092262

Landscape variation in species diversity and succession as related to topography, soils and human disturbance

Treesearch

Jeffery N. Pearcy; David M. Hix; Stacy A. Drury

1995-01-01

Three hundred and thirty-two plots have been sampled on the Wayne National Forest of southeastern Ohio, for the purpose of developing an ecological classification system (ECS). The ECS will be based on the herbaceous and woody vegetation, soils and topography of mature (80-140 year-old), relatively-undisturbed forests. Species diversity changes little across this...

Analyzing risks to protected areas using the human modification framework: a Colorado case study

Treesearch

David M. Theobald; Alisa Wade; Grant Wilcox; Nate Peterson

2010-01-01

A framework that organizes natural and protected areas is often used to help understand the potential risks to natural areas and aspects of their ecological and human dimensions. The spatial (or landscape) context of these dynamics is also a critical, but, rarely considered, factor. Common classification systems include the U.S. Geological (USGS) Gap Analysis Program...

Fuel characterization in the southern Appalachian Mountains: an application of landscape ecosystem classification

Treesearch

Aaron D. Stottlemeyer; Victor B. Shelburne; Thomas A. Waldrop; Sandra Rideout-Hanzak; William C. Bridges

2009-01-01

Prescribed fire has been widely used in the south-eastern United States to meet forest management objectives, but has only recently been reintroduced to the southern Appalachian Mountains. Fuel information is not available to forest managers in this region and direct measurement is often impractical owing to steep, remote topography. The objective of the present study...

Testing and classification of individual plants for fire behaviour: plant selection for the wildland–urban interface

Treesearch

Robert H. White; Wayne C. Zipperer

2010-01-01

Knowledge of how species differ in their flammability characteristics is needed to develop more reliable lists of plants recommended for landscaping homes in the wildland–urban interface (WUI). As indicated by conflicting advice in such lists, such characterisation is not without difficulties and disagreements. The flammability of vegetation is often described as...

Dynamic stratification of the landscape of Mexico: analysis of vegetation patterns observed with multitemporal remotely sensed images

Treesearch

Franz Mora; Louis R. Iverson; Louis R. Iverson

1997-01-01

Rapid deforestation in Mexico, when coupled with poor access to current and consistent ecological information across the country underscores the need for an ecological classification system that can be readily updated as new data become available. In this study, regional vegetation resources in Mexico were evaluated using remotely sensed information. Multitemporal...

Nebraska Vocational Agribusiness Curriculum for City Schools. Horticulture. Agricultural Mechanics. A Curriculum Guide. 11th Grade.

ERIC Educational Resources Information Center

Nebraska Univ., Lincoln. Dept. of Agricultural Education.

Designed for use with high school juniors, this agribusiness curriculum for city schools contains thirty-two units of instruction in the areas of horticulture and agricultural mechanics. Among the units included in the curriculum are (1) Planting Media, (2) Fertilizer, (3) Plant Classification, (4) Turf Grass Management, (5) Landscape Design, (6)…

Hierarchical subdivisions of the Columbia Plateau and Blue Mountains ecoregions, Oregon and Washington.

Treesearch

Sharon E. Clarke; Sandra A. Bryce

1997-01-01

This document presents two spatial scales of a hierarchical, ecoregional framework and provides a connection to both larger and smaller scale ecological classifications. The two spatial scales are subregions (1:250,000) and landscape-level ecoregions (1:100,000), or Level IV and Level V ecoregions. Level IV ecoregions were developed by the Environmental Protection...

Monitoring urban tree cover using object-based image analysis and public domain remotely sensed data

Treesearch

L. Monika Moskal; Diane M. Styers; Meghan Halabisky

2011-01-01

Urban forest ecosystems provide a range of social and ecological services, but due to the heterogeneity of these canopies their spatial extent is difficult to quantify and monitor. Traditional per-pixel classification methods have been used to map urban canopies, however, such techniques are not generally appropriate for assessing these highly variable landscapes....

Mapping Human-Dominated Landscapes: the Distribution and Yield of Major Crops of the World

NASA Astrophysics Data System (ADS)

Monfreda, C.; Ramankutty, N.; Foley, J. A.

2005-12-01

Croplands cover 18 million km2, an area the size of South America, and provide ecosystem goods and services essential to human well-being. Most global land-cover classifications group the diversity of croplands into a single or very few categories, thereby excluding critical information to answer key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information on land-use practices is even more limited. The relative lack of information about agricultural landscapes results partly from difficulties in using satellite data to identify individual crop types and land-use practices at a global scale. We address limitations common to remote-sensing classifications by distributing national, state, and county level statistics across a recently updated global dataset of cropland cover at 5 minute resolution. The resulting datasets depict the fractional harvested area and yield of twenty distinct crop types: maize, wheat, rice, sorghum, millet, barley, oats, soybeans, sunflower, rapeseed/canola, pulses, groundnuts/peanuts, oil palm, cassava, potatoes, sugar cane, sugar beets, tobacco, coffee, and cotton. These datasets represent the state of agriculture circa the year 2000 and will be made available for applications in ecological analysis, modeling, visualization, and education.

Gap Shape Classification using Landscape Indices and Multivariate Statistics

PubMed Central

Wu, Chih-Da; Cheng, Chi-Chuan; Chang, Che-Chang; Lin, Chinsu; Chang, Kun-Cheng; Chuang, Yung-Chung

2016-01-01

This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy gap classification. The gaps for the two periods were optimally classified into three categories. In general, gap type 1 had a more complex shape, gap type 2 was more elongated and gap type 3 had the largest gaps that were more regular in shape. The results were evaluated using Wilks’ lambda as satisfactory (p < 0.001). The agreement rate of confusion matrices exceeded 96%. Differences in gap characteristics between the classified gap types that were determined using a one-way ANOVA showed a statistical significance in all patch indices (p = 0.00), except for the Euclidean nearest neighbor distance (ENN) in 2002. Taken together, these results demonstrated the feasibility and applicability of the proposed methodology to classify the shape of a gap. PMID:27901127

Gap Shape Classification using Landscape Indices and Multivariate Statistics.

PubMed

Wu, Chih-Da; Cheng, Chi-Chuan; Chang, Che-Chang; Lin, Chinsu; Chang, Kun-Cheng; Chuang, Yung-Chung

2016-11-30

This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy gap classification. The gaps for the two periods were optimally classified into three categories. In general, gap type 1 had a more complex shape, gap type 2 was more elongated and gap type 3 had the largest gaps that were more regular in shape. The results were evaluated using Wilks' lambda as satisfactory (p < 0.001). The agreement rate of confusion matrices exceeded 96%. Differences in gap characteristics between the classified gap types that were determined using a one-way ANOVA showed a statistical significance in all patch indices (p = 0.00), except for the Euclidean nearest neighbor distance (ENN) in 2002. Taken together, these results demonstrated the feasibility and applicability of the proposed methodology to classify the shape of a gap.

Ecological Design of Fernery based on Bioregion Classification System in Ecopark Cibinong Science Center Botanic Gardens, Indonesia

NASA Astrophysics Data System (ADS)

Nafar, S.; Gunawan, A.

2017-10-01

Indonesia as mega biodiversity country has a wide variety of ferns. However, the natural habitats of ferns are currently degrading, particularly in lowlands due to the increasing level of urban-sprawl and industrial zones development. Therefore, Ecology Park (Ecopark) Cibinong Science Center-Botanic Gardens as an ex-situ conservation area is expected to be the best location to conserve the lowland ferns. The purpose of this study is to design a fernery through an ecological landscape design process. The main concept is The Journey of Fern, this concept aiming on providing users experiences in fernery by associating conservational, educational, and recreational aspects. Ecological landscape design as general is applied by the principal of reduce, reuse, and recycle (3R). Bioregion classification system is applied by grouping the plants based on the characteristics of light, water, soil, air, and temperature. The design concept is inspired by the morphology of fern and its growth patterns which is transformed into organic and geometric forms. The result of this study is a design of fernery which consist of welcome area, recreation area, service area, and conservation education area as the main area that providing 66 species of ferns.

Delineation of marsh types of the Texas coast from Corpus Christi Bay to the Sabine River in 2010

USGS Publications Warehouse

Enwright, Nicholas M.; Hartley, Stephen B.; Brasher, Michael G.; Visser, Jenneke M.; Mitchell, Michael K.; Ballard, Bart M.; Parr, Mark W.; Couvillion, Brady R.; Wilson, Barry C.

2014-01-01

Coastal zone managers and researchers often require detailed information regarding emergent marsh vegetation types for modeling habitat capacities and needs of marsh-reliant wildlife (such as waterfowl and alligator). Detailed information on the extent and distribution of marsh vegetation zones throughout the Texas coast has been historically unavailable. In response, the U.S. Geological Survey, in cooperation and collaboration with the U.S. Fish and Wildlife Service via the Gulf Coast Joint Venture, Texas A&M University-Kingsville, the University of Louisiana-Lafayette, and Ducks Unlimited, Inc., has produced a classification of marsh vegetation types along the middle and upper Texas coast from Corpus Christi Bay to the Sabine River. This study incorporates approximately 1,000 ground reference locations collected via helicopter surveys in coastal marsh areas and about 2,000 supplemental locations from fresh marsh, water, and “other” (that is, nonmarsh) areas. About two-thirds of these data were used for training, and about one-third were used for assessing accuracy. Decision-tree analyses using Rulequest See5 were used to classify emergent marsh vegetation types by using these data, multitemporal satellite-based multispectral imagery from 2009 to 2011, a bare-earth digital elevation model (DEM) based on airborne light detection and ranging (lidar), alternative contemporary land cover classifications, and other spatially explicit variables believed to be important for delineating the extent and distribution of marsh vegetation communities. Image objects were generated from segmentation of high-resolution airborne imagery acquired in 2010 and were used to refine the classification. The classification is dated 2010 because the year is both the midpoint of the multitemporal satellite-based imagery (2009–11) classified and the date of the high-resolution airborne imagery that was used to develop image objects. Overall accuracy corrected for bias (accuracy estimate incorporates true marginal proportions) was 91 percent (95 percent confidence interval [CI]: 89.2–92.8), with a kappa statistic of 0.79 (95 percent CI: 0.77–0.81). The classification performed best for saline marsh (user’s accuracy 81.5 percent; producer’s accuracy corrected for bias 62.9 percent) but showed a lesser ability to discriminate intermediate marsh (user’s accuracy 47.7 percent; producer’s accuracy corrected for bias 49.5 percent). Because of confusion in intermediate and brackish marsh classes, an alternative classification containing only three marsh types was created in which intermediate and brackish marshes were combined into a single class. Image objects were reattributed by using this alternative three-marsh-type classification. Overall accuracy, corrected for bias, of this more general classification was 92.4 percent (95 percent CI: 90.7–94.2), and the kappa statistic was 0.83 (95 percent CI: 0.81–0.85). Mean user’s accuracy for marshes within the four-marsh-type and three-marsh-type classifications was 65.4 percent and 75.6 percent, respectively, whereas mean producer’s accuracy was 56.7 percent and 65.1 percent, respectively. This study provides a more objective and repeatable method for classifying marsh types of the middle and upper Texas coast at an extent and greater level of detail than previously available for the study area. The seamless classification produced through this work is now available to help State agencies (such as the Texas Parks and Wildlife Department) and landscape-scale conservation partnerships (such as the Gulf Coast Prairie Landscape Conservation Cooperative and the Gulf Coast Joint Venture) to develop and (or) refine conservation plans targeting priority natural resources. Moreover, these data may improve projections of landscape change and serve as a baseline for monitoring future changes resulting from chronic and episodic stressors.

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.

Combining dispersal, landscape connectivity and habitat suitability to assess climate-induced changes in the distribution of Cunningham's skink, Egernia cunninghami.

PubMed

Ofori, Benjamin Y; Stow, Adam J; Baumgartner, John B; Beaumont, Linda J

2017-01-01

The ability of species to track their climate niche is dependent on their dispersal potential and the connectivity of the landscape matrix linking current and future suitable habitat. However, studies modeling climate-driven range shifts rarely address the movement of species across landscapes realistically, often assuming "unlimited" or "no" dispersal. Here, we incorporate dispersal rate and landscape connectivity with a species distribution model (Maxent) to assess the extent to which the Cunningham's skink (Egernia cunninghami) may be capable of tracking spatial shifts in suitable habitat as climate changes. Our model was projected onto four contrasting, but equally plausible, scenarios describing futures that are (relative to now) hot/wet, warm/dry, hot/with similar precipitation and warm/wet, at six time horizons with decadal intervals (2020-2070) and at two spatial resolutions: 1 km and 250 m. The size of suitable habitat was projected to decline 23-63% at 1 km and 26-64% at 250 m, by 2070. Combining Maxent output with the dispersal rate of the species and connectivity of the intervening landscape matrix showed that most current populations in regions projected to become unsuitable in the medium to long term, will be unable to shift the distance necessary to reach suitable habitat. In particular, numerous populations currently inhabiting the trailing edge of the species' range are highly unlikely to be able to disperse fast enough to track climate change. Unless these populations are capable of adaptation they are likely to be extirpated. We note, however, that the core of the species distribution remains suitable across the broad spectrum of climate scenarios considered. Our findings highlight challenges faced by philopatric species and the importance of adaptation for the persistence of peripheral populations under climate change.

Losing a heritage hedgerow landscape. Biocultural diversity conservation in a changing social-ecological Mediterranean system.

PubMed

Arnaiz-Schmitz, Cecilia; Herrero-Jáuregui, Cristina; Schmitz, María F

2018-05-09

Traditional rural landscapes host a biocultural heritage acquired by rural societies, developed in a secular adaptation with nature. Hedgerows play a key role in preserving biocultural diversity and associated ecosystem services. Despite their benefits, in some European regions inappropriate hedge management has led to a drastic degradation of hedgerows, with significant effects on natural and biocultural diversity, landscape connectivity and sustainable flow of ecosystem services. In Central Spain, an ancient hedgerow landscape constitutes a valuable natural and cultural heritage recognized by the establishment of different protection categories. We quantify the main tendency of change of this landscape over time, detecting a process of rural social-ecological decoupling both inside and outside protected areas. The hedgerow network has progressively been degraded and destructured together with the decline and local extinction of woody species, all of them of traditional use and some recorded in red lists for species conservation. This reveals weaknesses in the design and management plans of protected areas that should be effective in conserving the heritage of cultural landscapes and their valuable biocultural diversity and provision of ecosystem services. There is a need to elaborate regulations for the protection of hedgerow landscapes in the Spanish legislation, based on social-ecological relationships. Copyright © 2018. Published by Elsevier B.V.