43,428 research outputs found

    Linking remote-sensing estimates of land cover and census statistics on land use to produce maps of land use of the conterminous United States

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    Human use of the land has a large effect on the structure of terrestrial ecosystems and the dynamics of biogeochemical cycles. For this reason, terrestrial ecosystem and biogeochemistry models require moderate resolution (e.g., ≤0.5°) information on land use in order to make realistic predictions. Few such data sets currently exist. To create a land use data set of sufficient resolution, we developed models relating land cover data derived from optical remote sensing and a census database on land use for the conterminous United States. The land cover product used was from the International Geosphere-Biosphere Programme DISCover global product, derived from 1 km advanced very high resolution radiometer imagery, with 16 land cover classes. Land use data at state-level resolution came from the U.S. Department of Agriculture\u27s Major Land Uses database, aggregated into four general land use categories: Cropland, Pasture/Range, Forest, and Other. We developed and applied models relating these data sets to generate maps of land use in 1992 for the conterminous United States at 0.5° spatial resolution

    Object preference by walking fruit flies, Drosophila melanogaster, is mediated by vision and graviperception

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    Walking fruit flies, Drosophila melanogaster, use visual information to orient towards salient objects in their environment, presumably as a search strategy for finding food, shelter or other resources. Less is known, however, about the role of vision or other sensory modalities such as mechanoreception in the evaluation of objects once they have been reached. To study the role of vision and mechanoreception in exploration behavior, we developed a large arena in which we could track individual fruit flies as they walked through either simple or more topologically complex landscapes. When exploring a simple, flat environment lacking three-dimensional objects, flies used visual cues from the distant background to stabilize their walking trajectories. When exploring an arena containing an array of cones, differing in geometry, flies actively oriented towards, climbed onto, and explored the objects, spending most of their time on the tallest, steepest object. A fly’s behavioral response to the geometry of an object depended upon the intrinsic properties of each object and not a relative assessment to other nearby objects. Furthermore, the preference was not due to a greater attraction towards tall, steep objects, but rather a change in locomotor behavior once a fly reached and explored the surface. Specifically, flies are much more likely to stop walking for long periods when they are perched on tall, steep objects. Both the vision system and the antennal chordotonal organs (Johnston’s organs) provide sufficient information about the geometry of an object to elicit the observed change in locomotor behavior. Only when both these sensory systems were impaired did flies not show the behavioral preference for the tall, steep objects

    Vegetation height products between 60° S and 60° N from ICESat GLAS data.

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    We present new coarse resolution (0.5� ×0.5�)vegetation height and vegetation-cover fraction data sets between 60� S and 60� N for use in climate models and ecological models. The data sets are derived from 2003–2009 measurements collected by the Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud and land Elevation Satellite (ICESat), the only LiDAR instrument that provides close to global coverage. Initial vegetation height is calculated from GLAS data using a development of the model of Rosette et al. (2008) with further calibration on desert sites. Filters are developed to identify and eliminate spurious observations in the GLAS data, e.g. data that are affected by clouds, atmosphere and terrain and as such result in erroneous estimates of vegetation height or vegetation cover. Filtered GLAS vegetation height estimates are aggregated in histograms from 0 to 70m in 0.5m intervals for each 0.5�×0.5�. The GLAS vegetation height product is evaluated in four ways. Firstly, the Vegetation height data and data filters are evaluated using aircraft LiDAR measurements of the same for ten sites in the Americas, Europe, and Australia. Application of filters to the GLAS vegetation height estimates increases the correlation with aircraft data from r =0.33 to r =0.78, decreases the root-mean-square error by a factor 3 to about 6m (RMSE) or 4.5m (68% error distribution) and decreases the bias from 5.7m to −1.3 m. Secondly, the global aggregated GLAS vegetation height product is tested for sensitivity towards the choice of data quality filters; areas with frequent cloud cover and areas with steep terrain are the most sensitive to the choice of thresholds for the filters. The changes in height estimates by applying different filters are, for the main part, smaller than the overall uncertainty of 4.5–6m established from the site measurements. Thirdly, the GLAS global vegetation height product is compared with a global vegetation height product typically used in a climate model, a recent global tree height product, and a vegetation greenness product and is shown to produce realistic estimates of vegetation height. Finally, the GLAS bare soil cover fraction is compared globally with the MODIS bare soil fraction (r = 0.65) and with bare soil cover fraction estimates derived from AVHRR NDVI data (r =0.67); the GLAS treecover fraction is compared with the MODIS tree-cover fraction (r =0.79). The evaluation indicates that filters applied to the GLAS data are conservative and eliminate a large proportion of spurious data, while only in a minority of cases at the cost of removing reliable data as well. The new GLAS vegetation height product appears more realistic than previous data sets used in climate models and ecological models and hence should significantly improve simulations that involve the land surface

    Feature integration in natural language concepts

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    Two experiments measured the joint influence of three key sets of semantic features on the frequency with which artifacts (Experiment 1) or plants and creatures (Experiment 2) were categorized in familiar categories. For artifacts, current function outweighed both originally intended function and current appearance. For biological kinds, appearance and behavior, an inner biological function, and appearance and behavior of offspring all had similarly strong effects on categorization. The data were analyzed to determine whether an independent cue model or an interactive model best accounted for how the effects of the three feature sets combined. Feature integration was found to be additive for artifacts but interactive for biological kinds. In keeping with this, membership in contrasting artifact categories tended to be superadditive, indicating overlapping categories, whereas for biological kinds, it was subadditive, indicating conceptual gaps between categories. It is argued that the results underline a key domain difference between artifact and biological concepts

    Quantifying urban forest structure with open-access remote sensing data sets

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    Future cities are set to face ever increasing population and climate pressures, ecosystem services offered by urban forests have been recognised as providing significant mitigation for these pressures. Therefore, the ability to accurately quantify the extent and structure of urban forests, across large and highly dynamic cities, is vital for determining the value of services provided and to assess the effectiveness of policy to promote these important assets. Current inventory methods used in urban forestry are mostly reliant on plot networks measuring a range of structural and demographic metrics; however, limited sampling (spatially and temporally) cannot fully capture the dynamics and spatial heterogeneity of the urban matrix. The rapid increase in the availability of open-access remote sensing data and processing tools offers an opportunity for monitoring and assessment of urban forest structure that is synoptic and at high spatial and temporal resolutions. Here we present a framework to estimate urban forest structure that uses open-access data and software, is robust to differences in data sources, is reproducible and is transferable between cities. The workflow is demonstrated by estimating three metrics of 3D forest structure (canopy cover, canopy height and tree density) across the Greater London area (1577 km^{2}). Random Forest was trained with open-access airborne LiDAR or iTree Eco inventory data, with predictor variables derived from Sentinel 2, climatic and topography data sets. Output were maps of forest structure at 100 m and 20 m resolution. Results indicate that forest structure can be accurately estimated across large urban areas; Greater London has a mean canopy cover of ∼16.5% (RMSE 11-17%), mean canopy height of 8.1–15.0 m (RMSE 4.9–6.2 m) m and is home to ∼4.6 M large trees (projected crown area >10 m^{2}Urban forest structureOpen-accessRemote sensingAirborne LiDARiTree EcoSentinel 2). Transferability to other cities is demonstrated using the UK city of Southampton, where estimates were generated from local and Greater London training data sets indicating application beyond geographic domains is feasible. The methods presented here can augment existing inventory practices and give city planners, urban forest managers and greenspace advocates across the globe tools to generate consistent and timely information to help assess and value urban forests

    Seedling Performance Associated with Live or Herbicide Treated Tall Fescue

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    Tall fescue is an important forage grass which can host systemic fungal endophytes. The association of host grass and endophyte is known to influence herbivore behavior and host plant competition for resources. Establishing legumes into existing tall fescue sods is a desirable means to acquire nitrogen and enhance the nutritive value of forage for livestock production. Competition from existing tall fescue typically must be controlled to ensure interseeding success. We used a soil-on-agar method to determine if soil from intact, living (L), or an herbicide killed (K) tall fescue sward influenced germination and seedling growth of three cultivars of tall fescue (E+, MaxQ, and E−) or legumes (alfalfa, red clover, and white clover). After 30 days, seedlings were larger and present in greater numbers when grown in L soil rather than K soil. Root growth of legumes (especially white clover) and tall fescue (especially MaxQ) were not as vigorous in K soil as L soil. While shoot biomass was similar for all cultivars of tall fescue in L soil, MaxQ produced less herbage when grown in K soil. Our data suggest establishing legumes or fescue cultivars may not be improved by first killing the existing fescue sod and seedling performance can exhibit significant interseasonal variation, related only to soil conditions

    Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies

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    We explore the trade-offs of performing linear algebra using Apache Spark, compared to traditional C and MPI implementations on HPC platforms. Spark is designed for data analytics on cluster computing platforms with access to local disks and is optimized for data-parallel tasks. We examine three widely-used and important matrix factorizations: NMF (for physical plausability), PCA (for its ubiquity) and CX (for data interpretability). We apply these methods to TB-sized problems in particle physics, climate modeling and bioimaging. The data matrices are tall-and-skinny which enable the algorithms to map conveniently into Spark's data-parallel model. We perform scaling experiments on up to 1600 Cray XC40 nodes, describe the sources of slowdowns, and provide tuning guidance to obtain high performance
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