A comparison of unsupervised classification procedures on LANDSAT MSS data for an area of complex surface conditions in Basilicata, Southern Italy

Two unsupervised classification procedures were applied to ratioed and unratioed LANDSAT multispectral scanner data of an area of spatially complex vegetation and terrain. An objective accuracy assessment was undertaken on each classification and comparison was made of the classification accuracies. The two unsupervised procedures use the same clustering algorithm. By on procedure the entire area is clustered and by the other a representative sample of the area is clustered and the resulting statistics are extrapolated to the remaining area using a maximum likelihood classifier. Explanation is given of the major steps in the classification procedures including image preprocessing; classification; interpretation of cluster classes; and accuracy assessment. Of the four classifications undertaken, the monocluster block approach on the unratioed data gave the highest accuracy of 80% for five coarse cover classes. This accuracy was increased to 84% by applying a 3 x 3 contextual filter to the classified image. A detailed description and partial explanation is provided for the major misclassification. The classification of the unratioed data produced higher percentage accuracies than for the ratioed data and the monocluster block approach gave higher accuracies than clustering the entire area. The moncluster block approach was additionally the most economical in terms of computing time

Continental land cover classification using meteorological satellite data

The use of the National Oceanic and Atmospheric Administration's advanced very high resolution radiometer satellite data for classifying land cover and monitoring of vegetation dynamics over an extremely large area is demonstrated for the continent of Africa. Data from 17 imaging periods of 21 consecutive days each were composited by a technique sensitive to the in situ green-leaf biomass to provide cloud-free imagery for the whole continent. Virtually cloud-free images were obtainable even for equatorial areas. Seasonal variation in the density and extent of green leaf vegetation corresponded to the patterns of rainfall associated with the inter-tropical convergence zone. Regional variations, such as the 1982 drought in east Africa, were also observed. Integration of the weekly satellite data with respect to time produced a remotely sensed assessment of biological activity based upon density and duration of green-leaf biomass. Two of the 21-day composited data sets were used to produce a general land cover classification. The resultant land cover distributions correspond well to those of existing maps

Monitoring sediment transfer processes on the desert margin

LANDSAT Thematic Mapper and Multispectral Scanner data have been used to construct change detection images for three playas in south-central Tunisia. Change detection images have been used to analyze changes in surface reflectance and absorption between wet and dry season (intra-annual change) and between different years (inter-annual change). Change detection imagery has been used to examine geomorphological changes on the playas. Changes in geomorphological phenomena are interpreted from changes in soil and foliar moisture levels, differences in reflectances between different salt and sediments and the spatial expression of geomorphological features. Intra-annual change phenomena that can be detected from multidate imagery are changes in surface moisture, texture and chemical composition, vegetation cover and the extent of aeolian activity. Inter-annual change phenomena are divisible into those restricted to marginal playa facies (sedimentation from sheetwash and alluvial fans, erosion from surface runoff and cliff retreat) and these are found in central playa facies which are related to the internal redistribution of water, salt and sediment

Using state space differential geometry for nonlinear blind source separation

Given a time series of multicomponent measurements of an evolving stimulus, nonlinear blind source separation (BSS) seeks to find a "source" time series, comprised of statistically independent combinations of the measured components. In this paper, we seek a source time series with local velocity cross correlations that vanish everywhere in stimulus state space. However, in an earlier paper the local velocity correlation matrix was shown to constitute a metric on state space. Therefore, nonlinear BSS maps onto a problem of differential geometry: given the metric observed in the measurement coordinate system, find another coordinate system in which the metric is diagonal everywhere. We show how to determine if the observed data are separable in this way, and, if they are, we show how to construct the required transformation to the source coordinate system, which is essentially unique except for an unknown rotation that can be found by applying the methods of linear BSS. Thus, the proposed technique solves nonlinear BSS in many situations or, at least, reduces it to linear BSS, without the use of probabilistic, parametric, or iterative procedures. This paper also describes a generalization of this methodology that performs nonlinear independent subspace separation. In every case, the resulting decomposition of the observed data is an intrinsic property of the stimulus' evolution in the sense that it does not depend on the way the observer chooses to view it (e.g., the choice of the observing machine's sensors). In other words, the decomposition is a property of the evolution of the "real" stimulus that is "out there" broadcasting energy to the observer. The technique is illustrated with analytic and numerical examples.Comment: Contains 14 pages and 3 figures. For related papers, see http://www.geocities.com/dlevin2001/ . New version is identical to original version except for URL in the bylin

On Bayesian Modelling of the Uncertainties in Palaeoclimate Reconstruction

We outline a model and algorithm to perform inference on the palaeoclimate and palaeoclimate volatility from pollen proxy data. We use a novel multivariate non-linear non-Gaussian state space model consisting of an observation equation linking climate to proxy data and an evolution equation driving climate change over time. The link from climate to proxy data is defined by a pre-calibrated forward model, as developed in Salter-Townshend and Haslett (2012) and Sweeney (2012). Climatic change is represented by a temporally-uncertain Normal-Inverse Gaussian Levy process, being able to capture large jumps in multivariate climate whilst remaining temporally consistent. The pre-calibrated nature of the forward model allows us to cut feedback between the observation and evolution equations and thus integrate out the state variable entirely whilst making minimal simplifying assumptions. A key part of this approach is the creation of mixtures of marginal data posteriors representing the information obtained about climate from each individual time point. Our approach allows for an extremely efficient MCMC algorithm, which we demonstrate with a pollen core from Sluggan Bog, County Antrim, Northern Ireland.Comment: 25 pages, 7 figure

The 2015 Global Climate Legislation Study: a review of climate change legislation in 99 countries: summary for policy-makers

This report summarises the main insights from the 2015 Global Climate Legislation Study. It is the fifth edition in a series dating back to 2010 (Townshend et al., 2011). The 2015 edition covers 98 countries plus the EU, up from 66 in 2014, which together account for 93 per cent of global greenhouse gas emissions. The study is intended as a source of information for legislators, researchers and policy-makers. It is hoped that parliaments considering climate change legislation will benefit from the growing body of experience reflected in the study. Facilitating knowledge exchange among parliamentarians was one of the primary motivations behind the Climate Legislation Study when the series was conceived by the Grantham Research Institute, LSE and GLOBE International in 2010. Since then there have been many examples of parliamentarians learning from, and being inspired by, each other through forums such as GLOBE and the Inter-Parliamentary Union – the two co-sponsors of the 2015 study

The effect of caffeine on subsequent sleep: A systematic review and meta-analysis

The consumption of caffeine in response to insufficient sleep may impair the onset and maintenance of subsequent sleep. This systematic review and meta-analysis investigated the effect of caffeine on the characteristics of night-time sleep, with the intent to identify the time after which caffeine should not be consumed prior to bedtime. A systematic search of the literature was undertaken with 24 studies included in the analysis. Caffeine consumption reduced total sleep time by 45 min and sleep efficiency by 7%, with an increase in sleep onset latency of 9 min and wake after sleep onset of 12 min. Duration (+6.1 min) and proportion (+1.7%) of light sleep (N1) increased with caffeine intake and the duration (-11.4 min) and proportion (-1.4%) of deep sleep (N3 and N4) decreased with caffeine intake. To avoid reductions in total sleep time, coffee (107 mg per 250 mL) should be consumed at least 8.8 h prior to bedtime and a standard serve of pre-workout supplement (217.5 mg) should be consumed at least 13.2 h prior to bedtime. The results of the present study provide evidence-based guidance for the appropriate consumption of caffeine to mitigate the deleterious effects on sleep

Annual Carbon Emissions from Deforestation in the Amazon Basin between 2000 and 2010

Funding for Open Access provided by the UMD Libraries Open Access Publishing Fund.Reducing emissions from deforestation and forest degradation (REDD+) is considered one of the most cost-effective strategies for mitigating climate change. However, historical deforestation and emission rates―critical inputs for setting reference emission levels for REDD+―are poorly understood. Here we use multi-source, time-series satellite data to quantify carbon emissions from deforestation in the Amazon basin on a year-to-year basis between 2000 and 2010.We first derive annual deforestation indicators by using the Moderate Resolution Imaging Spectroradiometer Vegetation Continuous Fields (MODIS VCF) product. MODIS indicators are calibrated by using a large sample of Landsat data to generate accurate deforestation rates, which are subsequently combined with a spatially explicit biomass dataset to calculate committed annual carbon emissions. Across the study area, the average deforestation and associated carbon emissions were estimated to be 1.59 ± 0.25M ha•yr−1 and 0.18 ± 0.07 Pg C•yr−1 respectively, with substantially different trends and inter-annual variability in different regions. Deforestation in the Brazilian Amazon increased between 2001 and 2004 and declined substantially afterwards, whereas deforestation in the Bolivian Amazon, the Colombian Amazon, and the Peruvian Amazon increased over the study period. The average carbon density of lost forests after 2005 was 130 Mg C•ha−1, ~11%lower than the average carbon density of remaining forests in year 2010 (144 Mg C•ha−1). Moreover, the average carbon density of cleared forests increased at a rate of 7 Mg C•ha−1•yr−1 from 2005 to 2010, suggesting that deforestation has been progressively encroaching into high-biomass lands in the Amazon basin. Spatially explicit, annual deforestation and emission estimates like the ones derived in this study are useful for setting baselines for REDD+ and other emission mitigation programs, and for evaluating the performance of such efforts

Statistical Challenges in Estimating Past Climate Changes

We review the statistical methods currently in use to estimate past changes in climate. These methods encompass the full gamut of statistical modeling approaches, ranging from simple regression up to nonparametric spatiotemporal Bayesian models. Often the full inferential challenge is broken down into many submodels each of which may involve multiple stochastic components, and occasionally mechanistic or process‐based models too. We argue that many of the traditional approaches are simplistic in their structure, handling, and presentation of uncertainty, and that newer models (which incorporate mechanistic aspects alongside statistical models) provide an exciting research agenda for the next decade. We hope that policy‐makers and those charged with predicting future climate change will increasingly use probabilistic paleoclimate reconstructions to calibrate their forecasts, learn about key natural climatological parameters, and make appropriate decisions concerning future climate change. Remarkably few statisticians have involved themselves with paleoclimate reconstruction, and we hope that this article inspires more to take up the challenge

The Prelude to the Deep Minimum between Solar Cycles 23 and 24: Interplanetary Scintillation Signatures in the Inner Heliosphere

Extensive interplanetary scintillation (IPS) observations at 327 MHz obtained between 1983 and 2009 clearly show a steady and significant drop in the turbulence levels in the entire inner heliosphere starting from around ~1995. We believe that this large-scale IPS signature, in the inner heliosphere, coupled with the fact that solar polar fields have also been declining since ~1995, provide a consistent result showing that the buildup to the deepest minimum in 100 years actually began more than a decade earlier.Comment: 9 pages, 4 figures, accepted for publication in Geophysical Research Letters on 28 September 201