Open, reproducible, and replicable research practices are a fundamental part of science. Training is often organized on a grassroots level, offered by early career researchers, for early career researchers. Buffet style courses that cover many topics can inspire participants to try new things; however, they can also be overwhelming. Participants who want to implement new practices may not know where to start once they return to their research team. We describe ten simple rules to guide participants of relevant training courses in implementing robust research practices in their own projects, once they return to their research group. This includes (1) prioritizing and planning which practices to implement, which involves obtaining support and convincing others involved in the research project of the added value of implementing new practices; (2) managing problems that arise during implementation; and (3) making reproducible research and open science practices an integral part of a future research career. We also outline strategies that course organizers can use to prepare participants for implementation and support them during this process.

Earth is an integrated, complex system with strong coupling among atmosphere, hydrosphere, biosphere, and lithosphere processes. The US Geological Survey has reported globally more than 17 earthquakes per year with a magnitude 7 and higher in the last 18 years. The remote sensing community is actively and quickly moving toward more advanced methodologies, linking remote sensing with in situ measurements and ancillary data for more precise mapping, faster analysis, and more effective forecasting and data delivery to the user community. The International Charter BSpace and Major Disasters was established to enable such collaboration in sensor tasking during times of crisis and is often activated in response to calls for assistance from authorized users. Insight is provided from a US perspective into sensor support for Charter activations and other disaster events.

BACKGROUND: The Ensembl web site has provided access to genomic information for almost 10 years. During this time the amount of data available through Ensembl has grown dramatically. At the same time, the World Wide Web itself has become a dramatically more important component of the scientific workflow and the way that scientists share and access data and scientific information. Since 2000, the Ensembl web interface has had three major updates and numerous smaller updates. These have largely been in response to expanding data types and valuable representations of existing data types. In 2007 it was realised that a radical new approach would be required in order to serve the project's future requirements, and development therefore focused on identifying suitable web technologies for implementation in the 2008 site redesign. RESULTS: By comparing the Ensembl website to well-known "Web 2.0" sites, we were able to identify two main areas in which cutting-edge technologies could be advantageously deployed: server efficiency and interface latency. We then evaluated the performance of the existing site using browser-based tools and Apache benchmarking, and selected appropriate technologies to overcome any issues found. Solutions included optimization of the Apache web server, introduction of caching technologies and widespread implementation of AJAX code. These improvements were successfully deployed on the Ensembl website in late 2008 and early 2009. CONCLUSIONS: Web 2.0 technologies provide a flexible and efficient way to access the terabytes of data now available from Ensembl, enhancing the user experience through improved website responsiveness and a rich, interactive interface.

Long-distance migratory shorebirds require wetland stopover sites where they can forage and deposit sufficient fat to complete their migration and, in the spring, reproduce. Conservation biologists are concerned that continental-scale reductions in wetland availability and quality due to human disturbance, climate change, and natural drought events are negatively impacting these species by eliminating critical stopovers along migratory flyways. We describe an individual-based migration model driven by remotely sensed land surface data, climate data assimilation models, and biological field data to examine the impact of changing environmental conditions on migration routes, temporal patterns, and fitness. We used an evolutionary programming approach to evaluate birds' adaptive responses to variation in refueling potential in the landscape. Birds' shifted their migratory routes and extended their stopovers as the mean quality of the landscape decreased and spatial variation in refueling potential increased. We discuss possible applications of our model for understanding inter-annual climate variation, climate change, and wetland loss.

We explore the fundamental idea that thermal and water-relation environments of birds, as estimated from satellite data and biophysical models, can define the constraints on their occurrences and richness. From experimental observations we know that species richness tends to track biological or environmental gradients. We develop individual bird energy budget models, driven by satellite data, and use these models to define the climate space niche of birds and their distributions across the geographic landscape.

The IEEE Geoscience and Remote Sensing Society's Awards were presented at the IGARSS'03 banquet on Thursday, July 24 in the Hotel Dieu in Toulouse France. The GRS-S President Charles Luther, assisted by the Awards Committee Chairman Werner Wiesbeck, presented the awards. To promote excellence in research and service, each year the Geoscience and Remote Sensing Society of IEEE recognizes individuals among its members by bestowing IEEE certificates and awards.

Several theoretical calculations are presented and our approach discussed for simulating overall composite scene thermal infrared exitance and canopy bi-directional reflectance of a forest canopy. Calculations are performed for selected wavelength bands of the DOE Multispectral Thermal Imagery and comparisons with atmospherically corrected MTI imagery are underway. NASA EO-1 Hyperion observations also are available and the favorable comparison of our reflective model results with these data are reported elsewhere.

The spread of invasive species is one of the most daunting environmental, economic, and human-health problems facing the United States and the World today. It is one of several grand challenge environmental problems being considered by NASA's Earth Science Vision for 2025. The invasive species problem is complex and presents many challenges. Developing an invasive species predictive capability could significantly advance the science and technology of ecological forecasting.

Independent component analysis (ICA) has shown success in blind source separation and channel equalization. Its applications to remotely sensed images have been investigated in recent years. Linear spectral mixture analysis (LSMA) has been widely used for subpixel detection and mixed pixel classification. It models an image pixel as a linear mixture of materials present in an image where the material abundance fractions are assumed to be unknown and nonrandom parameters. This paper considers an application of ICA to the LSMA, referred to as ICA-based linear spectral random mixture analysis (LSRMA), which describes an image pixel as a random source resulting from a random composition of multiple spectral signatures of distinct materials in the image. It differs from the LSMA in that the abundance fractions of the material spectral signatures in the LSRMA are now considered to be unknown but random independent signal sources. Two major advantages result from the LSRMA. First, it does not require prior knowledge of the materials to be used in the linear mixture model, as required for the LSMA. Second, and most importantly, the LSRMA models the abundance fraction of each material spectral signature as an independent random signal source so that the spectral variability of materials can be described by their corresponding abundance fractions and captured more effectively in a stochastic manner.

The authors describe a simple model to calculate brightness temperature and surface-energy balance for a forest-canopy system. The root mean-square (RMS) error in brightness temperature was 2.5/spl deg/C. The corresponding RMS errors for net radiation, latent, and sensible heat were 38.9, 30.7, and 41.1 W m/sup -2/ respectively.

Land surface temperature and emissivity products are currently being derived from satellite and aircraft remote sensing data using a variety of techniques to correct for atmospheric effects. Implicit in the commonly employed approaches is the assumption of isotropy in directional thermal infrared exitance. The authors' theoretical analyses indicate angular variations in apparent infrared temperature will typically yield land surface temperature errors ranging from 1 to 4/spl deg/C unless corrective measures are applied.

A steady-state thermal radiance model to compute thermal exitance and energy balance within forest canopies is described. The model treats fully leafed canopies as discrete ensembles of leaves partitioned into slope-angle and height classes. Short-wave energy flux absorbed within the canopy is estimated by solving simplified radiosity equations. Sensible heat exchange is estimated using a logarithmic wind profile above the canopy and a modified exponential profile within the canopy. The latent heat boundary layer resistance is estimated from site-specific measurements summarizing the effects of solar irradiance, air temperature, and vapor pressure deficit on stomatal conductance. Example comparisons for a dense spruce-fir forest study site in central Maine are given. For clear days the resulting root mean square error in modeled versus measured canopy temperatures was 1.2/spl deg/C. Corresponding errors in latent and sensible heat flux energy budget terms were 30 and 32 W/m/sup -2/, respectively. For partly cloudy days the root mean square error in predicted temperature was 1.0/spl deg/C and corresponding errors in latent and sensible heat were 40 and 110 W/m/sup -2/.>

Standard regression methods applied to canopies within a single homogeneous soil type yield good results for estimating leaf area index (LAI) but perform unacceptably when applied across soil boundaries. In contrast, the neural network reported generally yielded absolute percentage errors of>

Constrained-least-squares (CLS) and weighted-least-squares (WLS) mixing models for generating fraction images derived from remote sensing multispectral data are presented. An experiment considering three components within the pixels-eucalyptus, soil (understory), and shade-was performed. The generated fraction images for shade (shade image) derived from these two methods were compared by considering the performance and computer time. The derived shade images are related to the observed variation in forest structure, i.e. the fraction of inferred shade in the pixel is related to different eucalyptus ages.>

This paper identifies the basic notions involved in the automatic generation of logic diagrams. A system (ALDGS) for generating logic diagrams automatically is described and sample diagrams are given.