On the convergence of EM-like algorithms for image segmentation using Markov random fields.

Inference of Markov random field images segmentation models is usually performed using iterative methods which adapt the well-known expectation-maximization (EM) algorithm for independent mixture models. However, some of these adaptations are ad hoc and may turn out numerically unstable. In this paper, we review three EM-like variants for Markov random field segmentation and compare their convergence properties both at the theoretical and practical levels. We specifically advocate a numerical scheme involving asynchronous voxel updating, for which general convergence results can be established. Our experiments on brain tissue classification in magnetic resonance images provide evidence that this algorithm may achieve significantly faster convergence than its competitors while yielding at least as good segmentation results.

Monte Carlo study of a kinetic lattice model with random diffusion of disorder

We report Monte Carlo results for a nonequilibrium Ising-like model in two and three dimensions. Nearest-neighbor interactions J change sign randomly with time due to competing kinetics. There follows a fast and random, i.e., spin-configuration-independent diffusion of Js, of the kind that takes place in dilute metallic alloys when magnetic ions diffuse. The system exhibits steady states of the ferromagnetic (antiferromagnetic) type when the probability p that J>0 is large (small) enough. No counterpart to the freezing phenomena found in quenched spin glasses occurs. We compare our results with existing mean-field and exact ones, and obtain information about critical behavior.

Geometrically accurate time series of archived aerial images and airborne lidar data in a forest environment

Abstract

Generating a raster map presentation of a forest resource by solving a transportation problem

Abstract

Mating system parameters in a high density population of andirobas in the Amazon forest

The objective of this work was to estimate the mating system parameters of a andiroba (Carapa guianensis) population using microsatellite markers and the mixed and correlated mating models. Twelve open‑pollinated progeny arrays of 15 individuals were sampled in an area with C. guianensis estimated density of 25.7 trees per hectare. Overall, the species has a mixed reproductive system, with a predominance of outcrossing. The multilocus outcrossing rate (t m = 0.862) was significantly lower than the unity, indicating that self‑pollination occurred. The rate of biparental inbreeding was substantial (t m ‑ t s = 0.134) and significantly different from zero. The correlation of selfing within progenies was high (r s = 0.635), indicating variation in the individual outcrossing rate. Consistent with this result, the estimate of the individual outcrossing rate ranged from 0.598 to 0.978. The multilocus correlation of paternity was low (r p(m) = 0.081), but significantly different from zero, suggesting that the progenies contain full‑sibs. The coancestry within progenies (Θ = 0.185) was higher and the variance effective size (Ne(v) = 2.7) was lower than expected for true half‑sib progenies (Θ = 0.125; Ne(v) = 4). These results suggest that, in order to maintain a minimum effective size of 150 individuals for breeding, genetic conservation, and environmental reforestation programs, seeds from at least 56 trees must be collected.

A random linear functional approach to efficiency bounds

This paper suggests a method for obtaining efficiency bounds in models containing either only infinite-dimensional parameters or both finite- and infinite-dimensional parameters (semiparametric models). The method is based on a theory of random linear functionals applied to the gradient of the log-likelihood functional and is illustrated by computing the lower bound for Cox's regression model

Scheduling forest road maintenance using the analytic hierarchy process and heuristics

Abstract

Landsat TM imagery and high altitude aerial photographs in estimation of forest characteristics

Abstract

Educating Pulp and Paper Engineers for the Global Forest Industry

Vol.23, No. 5, pp. 1024-1037, 2007.

Spatial predictions of land-use transitions and associated threats to biodiversity: the case of forest regrowth in mountain grasslands

Question Can we predict where forest regrowth caused by abandonment of agricultural activities is likely to occur? Can we assess how it may conflict with grassland diversity hotspots? Location Western Swiss Alps (4003210m a.s.l.). Methods We used statistical models to predict the location of land abandonment by farmers that is followed by forest regrowth in semi-natural grasslands of the Western Swiss Alps. Six modelling methods (GAM, GBM, GLM, RF, MDA, MARS) allowing binomial distribution were tested on two successive transitions occurring between three time periods. Models were calibrated using data on land-use change occurring between 1979 and 1992 as response, and environmental, accessibility and socio-economic variables as predictors, and these were validated for their capacity to predict the changes observed from 1992 to 2004. Projected probabilities of land-use change from an ensemble forecast of the six models were combined with a model of plant species richness based on a field inventory, allowing identification of critical grassland areas for the preservation of biodiversity. Results Models calibrated over the first land-use transition period predicted the second transition with reasonable accuracy. Forest regrowth occurs where cultivation costs are high and yield potential is low, i.e. on steeper slopes and at higher elevations. Overlaying species richness with land-use change predictions, we identified priority areas for the management and conservation of biodiversity at intermediate elevations. Conclusions Combining land-use change and biodiversity projections, we propose applied management measures for targeted/identified locations to limit the loss of biodiversity that could otherwise occur through loss of open habitats. The same approach could be applied to other types of land-use changes occurring in other ecosystems.

Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

The objective of this work was to compare random regression models for the estimation of genetic parameters for Guzerat milk production, using orthogonal Legendre polynomials. Records (20,524) of test-day milk yield (TDMY) from 2,816 first-lactation Guzerat cows were used. TDMY grouped into 10-monthly classes were analyzed for additive genetic effect and for environmental and residual permanent effects (random effects), whereas the contemporary group, calving age (linear and quadratic effects) and mean lactation curve were analized as fixed effects. Trajectories for the additive genetic and permanent environmental effects were modeled by means of a covariance function employing orthogonal Legendre polynomials ranging from the second to the fifth order. Residual variances were considered in one, four, six, or ten variance classes. The best model had six residual variance classes. The heritability estimates for the TDMY records varied from 0.19 to 0.32. The random regression model that used a second-order Legendre polynomial for the additive genetic effect, and a fifth-order polynomial for the permanent environmental effect is adequate for comparison by the main employed criteria. The model with a second-order Legendre polynomial for the additive genetic effect, and that with a fourth-order for the permanent environmental effect could also be employed in these analyses.

Advances in UAS for forest fire fighting

Peer reviewed

Project SKY-EYE: applying UAVs to forest fire fighter support and monitoring

Peer reviewed