Preliminary leaf area index estimates from airborne small footprint full-waveform LiDAR data

This study has compared preliminary estimates of effective leaf area index (LAI) derived from fish-eye lens photographs to those estimated from airborne full-waveform small-footprint LiDAR data for a forest dataset in Australia. The full-waveform data was decomposed and optimized using a trust-region-reflective algorithm to extract denser point clouds. LAI LiDAR estimates were derived in two ways (1) from the probability of discrete pulses reaching the ground without being intercepted (point method) and (2) from raw waveform canopy height profile processing adapted to small-footprint laser altimetry (waveform method) accounting for reflectance ratio between vegetation and ground. The best results, that matched hemispherical photography estimates, were achieved for the waveform method with a study area-adjusted reflectance ratio of 0.4 (RMSE of 0.15 and 0.03 at plot and site level, respectively). The point method generally overestimated, whereas the waveform method with an arbitrary reflectance ratio of 0.5 underestimated the fish-eye lens LAI estimates.

Advances in the stochastic modelling of satellite-derived rainfall estimates using a sparse calibration dataset

As satellite technology develops, satellite rainfall estimates are likely to become ever more important in the world of food security. It is therefore vital to be able to identify the uncertainty of such estimates and for end users to be able to use this information in a meaningful way. This paper presents new developments in the methodology of simulating satellite rainfall ensembles from thermal infrared satellite data. Although the basic sequential simulation methodology has been developed in previous studies, it was not suitable for use in regions with more complex terrain and limited calibration data. Developments in this work include the creation of a multithreshold, multizone calibration procedure, plus investigations into the causes of an overestimation of low rainfall amounts and the best way to take into account clustered calibration data. A case study of the Ethiopian highlands has been used as an illustration.

'he perceives himself as a caterpillar […]' constructions of the disabled subject in the critical response to Alfred Hitchcock's 'Rear Window'

This chapter focuses on critical responses to Alfred Hitchcock’s Rear Window, especially their construction of disability. The suggestion is that such criticism takes the disabled body to be both necessary and superfluous to the meaning of the film, a difficulty that, I argue, can be read more widely within film theory. Ever since Christian Metz’s ‘the Imaginary Signifier’, the condition of being ‘bound to a wheelchair’ is understood to have a resonance for theories of film spectatorship, but only ever in a sense that does away with the wheelchair as a mark of difference.

A comparison of duality and energy a posteriori estimates for $\mathrm {L}_{\infty }(0,T;\mathrm {L}_2(\varOmega ))$ in parabolic problems

We use the elliptic reconstruction technique in combination with a duality approach to prove a posteriori error estimates for fully discrete backward Euler scheme for linear parabolic equations. As an application, we combine our result with the residual based estimators from the a posteriori estimation for elliptic problems to derive space-error indicators and thus a fully practical version of the estimators bounding the error in the $ \mathrm {L}_{\infty }(0,T;\mathrm {L}_2(\varOmega ))$ norm. These estimators, which are of optimal order, extend those introduced by Eriksson and Johnson in 1991 by taking into account the error induced by the mesh changes and allowing for a more flexible use of the elliptic estimators. For comparison with previous results we derive also an energy-based a posteriori estimate for the $ \mathrm {L}_{\infty }(0,T;\mathrm {L}_2(\varOmega ))$-error which simplifies a previous one given by Lakkis and Makridakis in 2006. We then compare both estimators (duality vs. energy) in practical situations and draw conclusions.

Proposed feasibility oversight for pre-pack administration in England and Wales: window dressing or effective reform?

The 2014 Graham proposals aimed at reducing recidivism are unlikely to achieve the desired goals. It is argued that due consideration must be had for the future of the rescued entity. Further, both the viability review and the proposed capital structure of the rescued entity must be carefully assessed.

Assessing the performance of dynamical trajectory estimates

Estimating trajectories and parameters of dynamical systems from observations is a problem frequently encountered in various branches of science; geophysicists for example refer to this problem as data assimilation. Unlike as in estimation problems with exchangeable observations, in data assimilation the observations cannot easily be divided into separate sets for estimation and validation; this creates serious problems, since simply using the same observations for estimation and validation might result in overly optimistic performance assessments. To circumvent this problem, a result is presented which allows us to estimate this optimism, thus allowing for a more realistic performance assessment in data assimilation. The presented approach becomes particularly simple for data assimilation methods employing a linear error feedback (such as synchronization schemes, nudging, incremental 3DVAR and 4DVar, and various Kalman filter approaches). Numerical examples considering a high gain observer confirm the theory.

An Ensemble Smoother with Error Estimates

A smoother introduced earlier by van Leeuwen and Evensen is applied to a problem in which real obser vations are used in an area with strongly nonlinear dynamics. The derivation is new , but it resembles an earlier derivation by van Leeuwen and Evensen. Again a Bayesian view is taken in which the prior probability density of the model and the probability density of the obser vations are combined to for m a posterior density . The mean and the covariance of this density give the variance-minimizing model evolution and its errors. The assumption is made that the prior probability density is a Gaussian, leading to a linear update equation. Critical evaluation shows when the assumption is justified. This also sheds light on why Kalman filters, in which the same ap- proximation is made, work for nonlinear models. By reference to the derivation, the impact of model and obser vational biases on the equations is discussed, and it is shown that Bayes’ s for mulation can still be used. A practical advantage of the ensemble smoother is that no adjoint equations have to be integrated and that error estimates are easily obtained. The present application shows that for process studies a smoother will give superior results compared to a filter , not only owing to the smooth transitions at obser vation points, but also because the origin of features can be followed back in time. Also its preference over a strong-constraint method is highlighted. Further more, it is argued that the proposed smoother is more efficient than gradient descent methods or than the representer method when error estimates are taken into account

Evaluation of a polynya flux model by means of thermal infrared satellite estimates

We test the ability of a two-dimensional flux model to simulate polynya events with narrow open-water zones by comparing model results to ice-thickness and ice-production estimates derived from thermal infrared Moderate Resolution Imaging Spectroradiometer (MODIS) observations in conjunction with an atmospheric dataset. Given a polynya boundary and an atmospheric dataset, the model correctly reproduces the shape of an 11 day long event, using only a few simple conservation laws. Ice production is slightly overestimated by the model, owing to an underestimated ice thickness. We achieved best model results with the consolidation thickness parameterization developed by Biggs and others (2000). Observed regional discrepancies between model and satellite estimates might be a consequence of the missing representation of the dynamic of the thin-ice thickening (e.g. rafting). We conclude that this simplified polynya model is a valuable tool for studying polynya dynamics and estimating associated fluxes of single polynya events.

Independent uncertainty estimates for coefficient based sea surface temperature retrieval from the Along-Trck Scanning Radiometer instruments

We establish a methodology for calculating uncertainties in sea surface temperature estimates from coefficient based satellite retrievals. The uncertainty estimates are derived independently of in-situ data. This enables validation of both the retrieved SSTs and their uncertainty estimate using in-situ data records. The total uncertainty budget is comprised of a number of components, arising from uncorrelated (eg. noise), locally systematic (eg. atmospheric), large scale systematic and sampling effects (for gridded products). The importance of distinguishing these components arises in propagating uncertainty across spatio-temporal scales. We apply the method to SST data retrieved from the Advanced Along Track Scanning Radiometer (AATSR) and validate the results for two different SST retrieval algorithms, both at a per pixel level and for gridded data. We find good agreement between our estimated uncertainties and validation data. This approach to calculating uncertainties in SST retrievals has a wider application to data from other instruments and retrieval of other geophysical variables.

How much flower-rich habitat is enough for wild pollinators? Answering a key policy question with incomplete knowledge

In 2013, an opportunity arose in England to develop an agri-environment package for wild pollinators, as part of the new Countryside Stewardship scheme launched in 2015. It can be understood as a 'policy window', a rare and time-limited opportunity to change policy, supported by a narrative about pollinator decline and widely supported mitigating actions. An agri-environment package is a bundle of management options that together supply sufficient resources to support a target group of species. This paper documents information that was available at the time to develop such a package for wild pollinators. Four questions needed answering: (1) Which pollinator species should be targeted? (2) Which resources limit these species in farmland? (3) Which management options provide these resources? (4) What area of each option is needed to support populations of the target species? Focussing on wild bees, we provide tentative answers that were used to inform development of the package. There is strong evidence that floral resources can limit wild bee populations, and several sources of evidence identify a set of agri-environment options that provide flowers and other resources for pollinators. The final question could only be answered for floral resources, with a wide range of uncertainty. We show that the areas of some floral resource options in the basic Wild Pollinator and Farmland Wildlife Package (2% flower-rich habitat and 1 km flowering hedgerow), are sufficient to supply a set of six common pollinator species with enough pollen to feed their larvae at lowest estimates, using minimum values for estimated parameters where a range was available. We identify key sources of uncertainty, and stress the importance of keeping the Package flexible, so it can be revised as new evidence emerges about how to achieve the policy aim of supporting pollinators on farmland.

Uncertainties in tidally adjusted estimates of sea level rise flooding (bathtub model) for the Greater London

Sea-level rise (SLR) from global warming may have severe consequences for coastal cities, particularly when combined with predicted increases in the strength of tidal surges. Predicting the regional impact of SLR flooding is strongly dependent on the modelling approach and accuracy of topographic data. Here, the areas under risk of sea water flooding for London boroughs were quantified based on the projected SLR scenarios reported in Intergovernmental Panel on Climate Change (IPCC) fifth assessment report (AR5) and UK climatic projections 2009 (UKCP09) using a tidally-adjusted bathtub modelling approach. Medium- to very high-resolution digital elevation models (DEMs) are used to evaluate inundation extents as well as uncertainties. Depending on the SLR scenario and DEMs used, it is estimated that 3%–8% of the area of Greater London could be inundated by 2100. The boroughs with the largest areas at risk of flooding are Newham, Southwark, and Greenwich. The differences in inundation areas estimated from a digital terrain model and a digital surface model are much greater than the root mean square error differences observed between the two data types, which may be attributed to processing levels. Flood models from SRTM data underestimate the inundation extent, so their results may not be reliable for constructing flood risk maps. This analysis provides a broad-scale estimate of the potential consequences of SLR and uncertainties in the DEM-based bathtub type flood inundation modelling for London boroughs.

Use of a predictive model for food insecurity estimates in Brazil

In 2004 the National Household Survey (Pesquisa Nacional par Amostras de Domicilios - PNAD) estimated the prevalence of food and nutrition insecurity in Brazil. However, PNAD data cannot be disaggregated at the municipal level. The objective of this study was to build a statistical model to predict severe food insecurity for Brazilian municipalities based on the PNAD dataset. Exclusion criteria were: incomplete food security data (19.30%); informants younger than 18 years old (0.07%); collective households (0.05%); households headed by indigenous persons (0.19%). The modeling was carried out in three stages, beginning with the selection of variables related to food insecurity using univariate logistic regression. The variables chosen to construct the municipal estimates were selected from those included in PNAD as well as the 2000 Census. Multivariate logistic regression was then initiated, removing the non-significant variables with odds ratios adjusted by multiple logistic regression. The Wald Test was applied to check the significance of the coefficients in the logistic equation. The final model included the variables: per capita income; years of schooling; race and gender of the household head; urban or rural residence; access to public water supply; presence of children; total number of household inhabitants and state of residence. The adequacy of the model was tested using the Hosmer-Lemeshow test (p=0.561) and ROC curve (area=0.823). Tests indicated that the model has strong predictive power and can be used to determine household food insecurity in Brazilian municipalities, suggesting that similar predictive models may be useful tools in other Latin American countries.

Extended time weather forecasts contributes to agricultural productivity estimates

Weather conditions in critical periods of the vegetative crop development influence crop productivity, thus being a basic parameter for crop forecast. Reliable extended period weather forecasts may contribute to improve the estimation of agricultural productivity. The production of soybean plays an important role in the Brazilian economy, because this country is ranked among the largest producers of soybeans in the world. This culture can be significantly affected by water conditions, depending on the intensity of water deficit. This work explores the role of extended period weather forecasts for estimating soybean productivity in the southern part of Brazil, Passo Fundo, and Londrina (State of Rio Grande do Sul and Parana, respectively) in the 2005/2006 harvest. The goal was to investigate the possible contribution of precipitation forecasts as a substitute for the use of climatological data on crop forecasts. The results suggest that the use of meteorological forecasts generate more reliable productivity estimates during the growth period than those generated only through climatological information.

Downward longwave radiation estimates for clear and all-sky conditions in the Sertozinho region of So Paulo, Brazil

The accurate estimate of the surface longwave fluxes contribution is important for the calculation of the surface radiation budget, which in turn controls all the components of the surface energy budget, such as evaporation and the sensible heat fluxes. This study evaluates the performance of the various downward longwave radiation parameterizations for clear and all-sky days applied to the Sertozinho region in So Paulo, Brazil. Equations have been adjusted to the observations of longwave radiation. The adjusted equations were evaluated for every hour throughout the day and the results showed good fits for most of the day, except near dawn and sunset, followed by nighttime. The seasonal variation was studied by comparing the dry period against the rainy period in the dataset. The least square linear regressions resulted in coefficients equal to the coefficients found for the complete period, both in the dry period and in the rainy period. It is expected that the best fit equation to the observed data for this site be used to produce estimates in other regions of the State of So Paulo, where such information is not available.