Ancillary Information Research Articles

DAFNE: A Matlab toolbox for Bayesian multi-source remote sensing and ancillary data fusion, with application to flood mapping

High-resolution, remotely sensed images of the Earth surface have been proven to be of help in producing detailed flood maps, thanks to their synoptic overview of the flooded area and frequent revisits. However, flood scenarios can be complex situations, requiring the integration of different data in order to provide accurate and robust flood information. Several processing approaches have been recently proposed to efficiently combine and integrate heterogeneous information sources. In this paper, we introduce DAFNE, a Matlab®-based, open source toolbox, conceived to produce flood maps from remotely sensed and other ancillary information, through a data fusion approach. DAFNE is based on Bayesian Networks, and is composed of several independent modules, each one performing a different task. Multi-temporal and multi-sensor data can be easily handled, with the possibility of following the evolution of an event through multi-temporal output flood maps. Each DAFNE module can be easily modified or upgraded to meet different user needs. The DAFNE suite is presented together with an example of its application.

A Dynamic Enhancement With Background Reduction Algorithm: Overview and Application to Satellite‐Based Dust Storm Detection

AbstractThis paper describes a Dynamic Enhancement Background Reduction Algorithm (DEBRA) applicable to multispectral satellite imaging radiometers. DEBRA uses ancillary information about the clear‐sky background to reduce false detections of atmospheric parameters in complex scenes. Applied here to the detection of lofted dust, DEBRA enlists a surface emissivity database coupled with a climatological database of surface temperature to approximate the clear‐sky equivalent signal for selected infrared‐based multispectral dust detection tests. This background allows for suppression of false alarms caused by land surface features while retaining some ability to detect dust above those problematic surfaces. The algorithm is applicable to both day and nighttime observations and enables weighted combinations of dust detection tests. The results are provided quantitatively, as a detection confidence factor [0, 1], but are also readily visualized as enhanced imagery. Utilizing the DEBRA confidence factor as a scaling factor in false color red/green/blue imagery enables depiction of the targeted parameter in the context of the local meteorology and topography. In this way, the method holds utility to both automated clients and human analysts alike. Examples of DEBRA performance from notable dust storms and comparisons against other detection methods and independent observations are presented.

New Approaches Using Exponential Type Estimator With Cost Modelling for Population Mean on Successive Waves

Abstract The key and fundamental purpose of sampling over successive waves lies in the varying nature of study character, it so may happen with ancillary information if the time lag between two successive waves is sufficiently large. Keeping the varying nature of auxiliary information in consideration, modern approaches have been proposed to estimate population mean over two successive waves. Four exponential ratio type estimators have been designed. The properties of proposed estimators have been elaborated theoretically including the optimum rotation rate.Cost models have also been worked out to minimize the total cost of the survey design over two successive waves. Dominances of the proposed estimators have been shown over well-known existing estimators. Simulation algorithms have been designed and applied to corroborate the theoretical results.

Long‐term population dynamics in a multi‐species assemblage of large herbivores in East Africa

AbstractWildlife population declines in Africa are widespread. However, species‐specific population trends and dynamics in mammal community composition have rarely been described over long time periods. To describe population trends of 13 large herbivore species in Lake Manyara National Park (Tanzania) from 1959 to 2016 and to discover whether the herbivore community structure changed, we used general additive models and additional statistical methods to detect structural changes in the time series. Population dynamics were non‐linear and population growth rates were not correlated with precipitation anomalies. Relatively steep population declines of three megaherbivores occurred during the 1980s and early 1990s, resulting in severe reductions in African elephant and buffalo populations and the local extinction of black rhinoceros. These declines coincided with reported peaks of illegal hunting of these species and expansion of agriculture at the periphery of the park. Population densities of elephant and buffalo seem to have stabilized in recent times, yet have not recovered to previous densities. In contrast, eight species (giraffe, zebra, waterbuck, wildebeest, warthog, impala, bushbuck, and baboon) have apparently fared well (similar or higher densities in most recent compared to first decade), despite having undergone substantial fluctuations over the past 58 yr. Population fluctuations in these species were likely caused by disease outbreaks, heavy bush encroachment, and reduced competition with buffalo. Possibly, declines in megaherbivore densities (mainly elephants) facilitated bush encroachment. Albeit grazers are still dominating in the herbivore community, the proportion of browsers is currently increasing, likely encouraged by dense vegetation in the shrub layer in large parts of the park. Overall, herbivore biomass density has declined by ~40% compared to the baseline estimate in the first decade of the time series. Our analyses and ancillary information provide evidence that this overall decline in the herbivore assemblage was triggered by human‐induced reductions in megaherbivore population densities during the 1980s, either through excessive poaching, insularization of the park, or both. Likely, this had cascading and interacting effects on the vegetation structure and the herbivore assemblage. Thus, legacy effects of ineffective megaherbivore conservation efforts 30 yr ago are likely still affecting the ecology of this national park.

The MUSEHubbleUltra Deep Field Survey

Non-resonant Fe ii* (λ2365,λ2396,λ2612,λ2626) emission can potentially trace galactic winds in emission and provide useful constraints to wind models. From the 3.15′ × 3.15′ mosaic of theHubbleUltra Deep Field (UDF) obtained with the VLT/MUSE integral field spectrograph, we identify a statistical sample of 40 Fe ii* emitters and 50 Mg ii(λλ2796,2803) emitters from a sample of 271 [O ii]λλ3726,3729 emitters with reliable redshifts fromz= 0.85−1.50 down to 2 × 10-18(3σ) ergs s-1cm-2(for [O ii]), covering theM⋆range from 108−1011M⊙. The Fe ii* and Mg iiemitters follow the galaxy main sequence, but with a clear dichotomy. Galaxies with masses below 109M⊙and star formation rates (SFRs) of ≲ 1M⊙yr-1have Mg iiemission without accompanying Fe ii* emission, whereas galaxies with masses above 1010M⊙andSFRs ≳ 10M⊙yr-1have Fe ii* emission without accompanying Mg iiemission. Between these two regimes, galaxies have both Mg iiand Fe ii* emission, typically with Mg iiP Cygni profiles. Indeed, the Mg iiprofile shows a progression along the main sequence from pure emission to P Cygni profiles to strong absorption, due to resonant trapping. Combining the deep MUSE data with HST ancillary information, we find that galaxies with pure Mg iiemission profiles have lower SFR surface densities than those with either Mg iiP Cygni profiles or Fe ii* emission. These spectral signatures produced through continuum scattering and fluorescence, Mg iiP Cygni profiles and Fe ii* emission, are better candidates for tracing galactic outflows than pure Mg iiemission, which may originate from H iiregions. We compare the absorption and emission rest-frame equivalent widths for pairs of Fe iitransitions to predictions from outflow models and find that the observations consistently have less total re-emission than absorption, suggesting either dust extinction or non-isotropic outflow geometries.

An Interferometric Approach to Cross-Track Clutter Detection in Two-Channel VHF Radar Sounders

Surface cross-track clutter can corrupt both earth and planetary radar sounder (RS) observations preventing definitive interpretation of subsurface features, which are often of primary interest to geologists and planetary scientists. This clutter is usually identified either by manual or automatic techniques that require ancillary information about the topography of the surface, or by using multichannel RS systems with arrays of antennas. However, topographic information is not always available and multichannel systems are generally too massive and costly to mount on satellites for the planetary exploration. In this paper, we propose a novel approach to clutter discrimination that is independent of ancillary information and limits the hardware complexity of the RS system. This approach uses a two-channel RS and exploits cross-channel interferometric phase differences to discriminate the clutter. Our approach includes three main steps: 1) manual feature extraction and theoretical phase-difference estimation; 2) RS interferogram formation; and 3) comparison of theoretical and real phase difference distributions. The proposed method was validated on RS data acquired in Greenland and provides a proof of concept for the surface clutter discrimination using RS data.

Snowpack Density Retrieval Using Fully Polarimetric TerraSAR-X Data in the Himalayas

This paper focuses on the development of a novel algorithm for deriving snowpack density over the snow-covered region of the Himalayas. The analysis utilizes fully polarimetric TerraSAR-X synthetic aperture radar data sets, field observations, and other ancillary information for the retrieval of snowpack density. The algorithm involves the development of a new generalized hybrid decomposition model. The generalized volume scattering parameter from the decomposition model is inverted for snow density estimation. A few field data measurements’ campaigns were carried out, within near-real time of satellite passing over the area, to collect various parameters such as temperature, water content, and the density of the snowpack at varying depths. These field observations are further used for validation of the results obtained from the inversion algorithm. It is also found that the model-estimated snowpack density is highly congruent with the field-measured snowpack density. The mean absolute error of snowpack density, root-mean-square error, and index of agreement are found to be 9.9 kg/ $\text{m}^{3}$ , 10 kg/ $\text{m}^{3}$ , and 0.96, respectively, which are well within the acceptable range.

Improving Classification Accuracy of Multi-Temporal Landsat Images by Assessing the Use of Different Algorithms, Textural and Ancillary Information for a Mediterranean Semiarid Area from 2000 to 2015

The aim of this study was to evaluate three different strategies to improve classification accuracy in a highly fragmented semiarid area using, (i) different classification algorithms with parameter optimization in some cases; (ii) different feature sets including spectral, textural and terrain features; and (iii) different seasonal combinations of images. A three-way ANOVA was used to discern which of these approaches and their interactions significantly increases accuracy. Tukey–Kramer contrast using a heteroscedasticity-consistent estimation of the kappa covariances matrix was used to check for significant differences in accuracy. The experiment was carried out with Landsat TM, ETM and OLI images corresponding to the period 2000–2015. A combination of four images using random forest and the three feature sets was the best way to improve accuracy. Maximum likelihood, random forest and support vector machines do not significantly increase accuracy when textural information was added, but do so when terrain features were taken into account. On the other hand, sequential maximum a posteriori increased accuracy when textural features were used, but reduced accuracy substantially when terrain features were included. Random forest using the three feature subsets and sequential maximum a posteriori with spectral and textural features had the largest kappa values, around 0.9.

Galaxies into the Dark Ages

Abstract We consider the capabilities of current and future large facilities operating at 2–3 mm wavelength to detect and image the [C ii] 158 μm line from galaxies into the cosmic “dark ages” (z ∼ 10–20). The [C ii] line may prove to be a powerful tool in determining spectroscopic redshifts, and galaxy dynamics, for the first galaxies. We emphasize that the nature, and even existence, of such extreme redshift galaxies, remains at the frontier of open questions in galaxy formation. In 40 hr, the Atacama Large Millimeter Array has the sensitivity to detect the integrated [C ii] line emission from a moderate metallicity, active star-forming galaxy [ Z A = 0.2 Z ⊙ ; star formation rate ( SFR ) = 5 M ⊙ yr−1], at z = 10 at a significance of 6σ. The next-generation Very Large Array (ngVLA) will detect the integrated [C ii] line emission from a Milky Way–like SFR galaxy ( Z A = 0.2 Z ⊙ , SFR = 1 M ⊙ yr−1), at z = 15 at a significance of 6σ. Imaging simulations show that the ngVLA can determine rotation dynamics for active star-forming galaxies at z ∼ 15 , if they exist. Based on our very limited knowledge of the extreme redshift universe, we calculate the count rate in blind, volumetric surveys for [C ii] emission at z ∼ 10 –20. The detection rates in blind surveys will be slow (of the order of unity per 40 hr pointing). However, the observations are well suited to commensal searches. We compare [C ii] with the [O iii] 88 μm line, and other ancillary information in high z galaxies that would aid these studies.

Resources for Interpreting Variants in Precision Genomic Oncology Applications.

Precision genomic oncology—applying high throughput sequencing (HTS) at the point-of-care to inform clinical decisions—is a developing precision medicine paradigm that is seeing increasing adoption. Simultaneously, new developments in targeted agents and immunotherapy, when informed by rich genomic characterization, offer potential benefit to a growing subset of patients. Multiple previous studies have commented on methods for identifying both germline and somatic variants. However, interpreting individual variants remains a significant challenge, relying in large part on the integration of observed variants with biological knowledge. A number of data and software resources have been developed to assist in interpreting observed variants, determining their potential clinical actionability, and augmenting them with ancillary information that can inform clinical decisions and even generate new hypotheses for exploration in the laboratory. Here, we review available variant catalogs, variant and functional annotation software and tools, and databases of clinically actionable variants that can be used in an ad hoc approach with research samples or incorporated into a data platform for interpreting and formally reporting clinical results.

Wipe Sampling Method and Evaluation of Environmental Variables for Assessing Surface Contamination of 10 Antineoplastic Drugs by Liquid Chromatography/Tandem Mass Spectrometry

This paper describes a novel wipe sampling and high-performance liquid chromatography/tandem mass spectrometry (HPLC-MS/MS) method capable of simultaneously detecting 10 antineoplastic drugs (5-fluorouracil, oxaliplatin, methotrexate, vindesine, ifosfamide, cyclophosphamide, vincristine, vinblastine, docetaxel, and paclitaxel). The good overall recoveries and sensitivity values of this method along with the comparatively short run time (8 min) allows for its use in routine monitoring in health care facilities. The long-term behavior of the studied drugs on contaminated surfaces and the effect of surface roughness on drug recoveries were studied to gain insights about how these environmental variables influence the detection, cleaning, and occupational exposure of these drugs. Surfaces with higher roughness parameter (Ra) values (rougher) had the lowest recoveries while those with lower Ra (smoother) presented the highest recoveries. Long-term assessments evidence distinctive drug behaviors with oxaliplatin, vindesine, vincristine, and vinblastine being the less persistent drugs (~20% was recovered after 24 h) and docetaxel and paclitaxel the most persistent drugs with recoveries of 40% and 80% after 1 month. This information indicates the importance of collecting ancillary information about drug usage (throughput, timing, cleaning procedures, etc.) to interpret the results in the context of potential exposure. Finally, the method was successfully applied to evaluate trace surface contamination down to the single picogram per square centimeter in multiple work areas within three local health care centers on Vancouver Island, Canada.

Brown planthopper Nilaparvata lugens was concentrated at the rear of the typhoon Soudelor in Eastern China in August 2015.

Sometimes, extreme weather is vital for the population survival of migratory insects by causing sudden population collapse or outbreak. Several studies have shown that rice planthopper migration was significantly influenced by typhoons in eastern Asia. Most typhoons occur in the summer, especially in August. In August, brown planthopper Nilaparvata lugens (Stål) migrates northward or southward depending on wind direction, and thus typhoons can potentially influence its migration process and population distribution. However, this has not yet been studied. This paper reported a case study on the effects of Typhoon Soudelor on the summer migration of N. lugens in eastern China in 2015. The migration pathways of N. lugens were reconstructed for the period under the influence of a typhoon by calculating the trajectories and migration events in eight counties of the Yangtze River Valley region with ancillary information. Trajectory modelling showed that most migrants took short distance migrations (less than 200 km) under the influence of the Typhoon Soudelor. Numerous N. lugens migrants were concentrated and deposited at the rear of the typhoon during the last 5 days of Typhoon Soudelor on August 9-13 due to horizontal convergence, and this led to an outbreak population. These results indicated that the N. lugens population was redistributed by the typhoon in the summer and that the population dynamics at the rear of a typhoon should be kept under close surveillance. This study provided insight into migratory organisms adapting to atmospheric features.

Examining the Effect of Ancillary and Derived Geographical Data on Improvement of Per-Pixel Classification Accuracy of Different Landscapes

Effective conservation and management of natural resources requires up-to-date information of the land cover (LC) types and their dynamics. Multi-resolution remote sensing (RS) data coupled with additional ancillary topographical layers (both remotely acquired or derived from ground measurements) with appropriate classification strategies would be more effective in capturing LC dynamics and changes associated with the natural resources. Ancillary information would make the decision boundaries between the LC classes more widely separable, enabling classification with higher accuracy compared to conventional methods of RS data classification. In this work, we ascertain the possibility of improvement in classification accuracy of RS data with the addition of ancillary and derived geographical layers such as vegetation indices, temperature, digital elevation model, aspect, slope and texture, implemented in three different terrains of varying topography-urbanised landscape (Greater Bangalore), forested landscape (Western Ghats) and rugged terrain (Western Himalaya). The study showed that use of additional spatial ancillary and derived information significantly improved the classification accuracy compared to the classification of only original spectral bands. The analysis revealed that in a highly urbanised area with less vegetation cover and contrasting features, inclusion of elevation and texture increased the overall accuracy of IKONOS data classification to 88.72% (3.5% improvement), and inclusion of temperature, NDVI, EVI, elevation, slope, aspect, Panchromatic band along with texture measures, significantly increased the overall accuracy of Landsat ETM+ data classification to 83.15% (7.6% improvement). In a forested landscape with moderate elevation, temperature was useful in improving the overall accuracy by 6.7 to 88.26%, and in a rugged terrain with temperate climate, temperature, EVI, elevation, slope, aspect and Panchromatic band significantly improved the classification accuracy to 89.97% (10.84% improvement) compared to the classification of only original spectral bands, suggesting selection of appropriate ancillary data depending on the terrain.

A metadata reporting framework (FRAMES) for synthesis of ecohydrological observations

Metadata describe the ancillary information needed for data preservation and independent interpretation, comparison across heterogeneous datasets, and quality assessment and quality control (QA/QC). Environmental observations are vastly diverse in type and structure, can be taken across a wide range of spatiotemporal scales in a variety of measurement settings and approaches, and saved in multiple formats. Thus, well-organized, consistent metadata are required to produce usable data products from diverse environmental observations collected across field sites. However, existing metadata reporting protocols do not support the complex data synthesis and model-data integration needs of interdisciplinary earth system research. We developed a metadata reporting framework (FRAMES) to enable management and synthesis of observational data that are essential in advancing a predictive understanding of earth systems. FRAMES utilizes best practices for data and metadata organization enabling consistent data reporting and compatibility with a variety of standardized data protocols. We used an iterative scientist-centered design process to develop FRAMES, resulting in a data reporting format that incorporates existing field practices to maximize data-entry efficiency. Thus, FRAMES has a modular organization that streamlines metadata reporting and can be expanded to incorporate additional data types. With FRAMES's multi-scale measurement position hierarchy, data can be reported at observed spatial resolutions and then easily aggregated and linked across measurement types to support model-data integration. FRAMES is in early use by both data originators (persons generating data) and consumers (persons using data and metadata). In this paper, we describe FRAMES, identify lessons learned, and discuss areas of future development.

Reassessment of the temperature-emissivity separation from multispectral thermal infrared data: Introducing the impact of vegetation canopy by simulating the cavity effect with the SAIL-Thermique model

We investigated the use of multispectral thermal imagery to retrieve land surface emissivity and temperature. Conversely to concurrent methods, the temperature emissivity separation (TES) method simply requires single overpass without any ancillary information. This is possible since TES makes use of an empirical relationship that estimates the minimum emissivity ε-min from the emissivity spectral contrast captured over several channels, so-called maximum-minimum difference (MMD). In previous studies, the ε-min - MMD empirical relationship of TES was calibrated and validated for various sensor spectral configurations, where the proposed calibrations involved single or linearly mixed spectra of emissivity at the leaf or soil level. However, cavity effect should be taken into account at the vegetation canopy level, to avoid an underestimation of emissivity, especially for intermediate vegetation conditions between bare soil and full vegetation cover.The current study aimed to evaluate the performances of the TES method when applied to vegetation canopies with cavity effect. We used the SAIL-Thermique model to simulate a library of emissivity spectra for a wide range of soil and plant conditions, and we addressed the spectral configurations of recent and forthcoming sensors. We obtained good results for calibration and validation over the simulated library, except for full cover canopies because of the TES gray body problem. Consistent with previous studies, the calibration/validation results were better with more channels that capture emissivity spectral contrast more efficiently. Our TES calibrations provided larger ε-min values as compared to former studies, especially for intermediate vegetation cover. We explained this trend by the simulated spectral library that involved numerous vegetation canopies with cavity effect, thereby shifting up the ε-min - MMD empirical relationship. Consequently, our TES calibration provided larger (respectively lower) estimates of emissivity (respectively radiometric temperature) that were likely to be more realistic as compared to previous calibrations. Finally, SAIL-Thermique simulations permitted to show that increasing Leaf Area Index induced a displacement of the (ε-min, MMD) pairs along the empirical relationship. This was consistent with the TES underlying assumption, where any change in ε-min induces changes in MMD since ε-max is bounded on [0.98–1]. Further investigations should focus on validating the outcomes of the current study against ground-based measurements, and on assessing TES performances when accounting for instrumental and atmospheric perturbations.

Forward a spatio-temporal trend surface for long-term ground-measured albedo upscaling over heterogeneous land surface

ABSTRACTUpscaling ground albedo is challenged by the serious discrepancy between the heterogeneity of land surfaces and the small number of ground-based measurements. Conventional ground-based measurements cannot provide sufficient information on the characteristics of surface albedo at the scale of coarse pixels over heterogeneous land surfaces. One method of overcoming this problem is to introduce high-resolution albedo imagery as ancillary information for upscaling. However, due to the low frequency of updating of high-resolution albedo maps, upscaling time series of ground-based albedo measurements is difficult. This paper proposes a method that is based on the idea of conceptual universal scaling methodology for establishing a spatiotemporal trend surface using very few high-resolution images and time series of ground-based measurements for spatial-temporal upscaling of albedo. The construction of the spatiotemporal trend surface incorporates the spatial information provided by auxiliary remote sensing images and the temporal information provided by long time series of ground observations. This approach was illustrated by upscaling ground-based fine-scale albedo measurements to a coarse scale over the core study area in HiWATER. The results indicate that this method can characterize the spatiotemporal variations in surface albedo well, and the overall correlation coefficient was 0.702 during the study period.

A Stochastic Approach to Estimate Distribution of Built-Up Area in Regions with Thick Tree Cover

Buildings and other human-made constructions have been accepted as an indicator of human habitation and are identified as built-up area. Identification of built-up area in a region and its subsequent measurement is a key step in many fields of studies like urban planning, environmental studies, and population demography. Remote sensing techniques utilising medium resolution images (e.g. LISS III, Landsat) are extensively used for the extraction of the built-up area as high-resolution images are expensive, and its processing is difficult. Extraction of built land use from medium resolution images poses a challenge in regions like Western-Ghats, North-East regions of India, and countries in tropical region, due to the thick evergreen tree cover. The spectral signature of individual houses with a small footprint are easily overpowered by the overlapping tree canopy in a medium resolution image when the buildings are not clustered. Kerala is a typical case for this scenario. The research presented here proposes a stochastic-dasymetric process to aid in the built-up area recognition process by taking Kerala as a case study. The method utilises a set of ancillary information to derive a probability surface. The ancillary information used here includes distance from road junctions, distance from road network, population density, built-up space visible in the LISS III image, the population of the region, and the household size. The methodology employs logistic regression and Monte Carlo simulation in two sub processes. The algorithm estimates the built-up area expected in the region and distributes the estimated built-up area among pixels according to the probability estimated from the ancillary information. The output of the algorithm has two components. The first component is an example scenario of the built-up area distribution. The second component is a probability surface, where the value of each pixel denotes the probability of that pixel to have a significant built-up area within it. The algorithm is validated for regions in Kerala and found to be significant. The model correctly predicted the built-up pixel count count over a validation grid of 900 m in 95.2% of the cases. The algorithm is implemented using Python and ArcGIS.

Multi-Data Approach for remote sensing-based regional crop rotation mapping: A case study for the Rur catchment, Germany

Spatial land use information is one of the key input parameters for regional agro-ecosystem modeling. Furthermore, to assess the crop-specific management in a spatio-temporal context accurately, parcel-related crop rotation information is additionally needed. Such data is scarcely available for a regional scale, so that only modeled crop rotations can be incorporated instead. However, the spectrum of the occurring multiannual land use patterns on arable land remains unknown. Thus, this contribution focuses on the mapping of the actually practiced crop rotations in the Rur catchment, located in the western part of Germany. We addressed this by combining multitemporal multispectral remote sensing data, ancillary information and expert-knowledge on crop phenology in a GIS-based Multi-Data Approach (MDA). At first, a methodology for the enhanced differentiation of the major crop types on an annual basis was developed. Key aspects are (i) the usage of physical block data to separate arable land from other land use types, (ii) the classification of remote sensing scenes of specific time periods, which are most favorable for the differentiation of certain crop types, and (iii) the combination of the multitemporal classification results in a sequential analysis strategy. Annual crop maps of eight consecutive years (2008–2015) were combined to a crop sequence dataset to have a profound data basis for the mapping of crop rotations. In most years, the remote sensing data basis was highly fragmented. Nevertheless, our method enabled satisfying crop mapping results. As an example for the annual crop mapping workflow, the procedure and the result of 2015 are illustrated. For the generation of the crop sequence dataset, the eight annual crop maps were geometrically smoothened and integrated into a single vector data layer. The resulting dataset informs about the occurring crop sequence for individual areas on arable land, so that crop rotation schemes can be derived. The resulting dataset reveals that the spectrum of the practiced crop rotations is extremely heterogeneous and contains a large amount of crop sequences, which strongly diverge from model crop rotations. Consequently, the integration of remote sensing-based crop rotation data can considerably reduce uncertainties regarding the management in regional agro-ecosystem modeling. Finally, the developed methods and the results are discussed in detail.

A novel study of artificial bee colony with clustering technique on paddy rice image classification

Rice is a crop of global importance. To predict the area of paddy rice and thus its production, it draws great attraction of using data mining approaches on remote sensing data, which are well accepted. Many approaches based on supervised and unsupervised learning techniques have been developed over the years. Artificial bee colony (ABC) algorithm with a clustering technique is one of the most popular swarm-based algorithms. In this study, ABC algorithm is used to perform the rice image classification based on remote sensing imagery. This study comprises two stages. In the first part of the study, the ancillary information composed from the original spectra is applied to increase the performance of classification. As the other parts of the study, an efficient unsupervised classifier is developed to evaluate the performance of the incorporated ancillary information. This study integrates the ABC algorithm into a clustering process to build a land cover classifier system. On the other hand, a parallel approach using ant colony optimization (ACO) is studied for comparison. Two significant contributions are presented in this study: (1) a paddy rice image classifier is built with ABC algorithm and (2) the outcome of classifier using ABC algorithm outperforms that using ACO algorithm.

Personalized Medicine in the Paediatric Population: The Balance Between Pharmacogenetic Progress and Bioethics.

Personalized medicine (PM) is becoming increasingly important in contemporary clinical and research scenarios. In the context of PM, pharmacogenomics and pharmacogenetics are aimed at the genetic personalization of drug response. Extrinsic and intrinsic factors may explain interindividual variability in drug response. Among such factors, age seems to specifically intervene to modulate drug response since normal developmental changes may influence the exposure-response relation. Consequently, the potential benefit of pharmacogenomics (PGx) in the paediatric population is considerable. However, many challenges still exist in incorporating PGx into clinical practice. In fact, drug prescribing in the paediatric population is often based on extrapolation from clinical trials conducted on adults as there is often a lack of paediatric data. Children are not just 'small adults', as they have their own pharmacological characteristics in terms of drug metabolism and efficacy, adverse drug reactions and toxicity. Although children might potentially benefit from such research, many ethical concerns arise at the intersection of the spheres of drug development and genetic testing. Children require particular attention because of their vulnerability both in research and the clinical applications of PGx; furthermore, children range from preterm newborns and neonates to infants and toddlers and to adolescents, thus forming a further heterogeneous target group. In this paper, we focus on some ethically relevant concerns (i.e., informed consent, stigmatization, ancillary information) that might arise as a result of the possible application of PGx tests in both paediatric practice and research.