Spectral–texture feature extraction using statistical moments with application to object-based vegetation species classification

The use of appropriate features to characterise an output class or object is critical for all classification problems. In order to find optimal feature descriptors for vegetation species classification in a power line corridor monitoring application, this article evaluates the capability of several spectral and texture features. A new idea of spectral–texture feature descriptor is proposed by incorporating spectral vegetation indices in statistical moment features. The proposed method is evaluated against several classic texture feature descriptors. Object-based classification method is used and a support vector machine is employed as the benchmark classifier. Individual tree crowns are first detected and segmented from aerial images and different feature vectors are extracted to represent each tree crown. The experimental results showed that the proposed spectral moment features outperform or can at least compare with the state-of-the-art texture descriptors in terms of classification accuracy. A comprehensive quantitative evaluation using receiver operating characteristic space analysis further demonstrates the strength of the proposed feature descriptors.

Look before you leap : a confidence-based method for selecting species criticality while avoiding negative populations in τ-leaping

The stochastic simulation algorithm was introduced by Gillespie and in a different form by Kurtz. There have been many attempts at accelerating the algorithm without deviating from the behavior of the simulated system. The crux of the explicit τ-leaping procedure is the use of Poisson random variables to approximate the number of occurrences of each type of reaction event during a carefully selected time period, τ. This method is acceptable providing the leap condition, that no propensity function changes “significantly” during any time-step, is met. Using this method there is a possibility that species numbers can, artificially, become negative. Several recent papers have demonstrated methods that avoid this situation. One such method classifies, as critical, those reactions in danger of sending species populations negative. At most, one of these critical reactions is allowed to occur in the next time-step. We argue that the criticality of a reactant species and its dependent reaction channels should be related to the probability of the species number becoming negative. This way only reactions that, if fired, produce a high probability of driving a reactant population negative are labeled critical. The number of firings of more reaction channels can be approximated using Poisson random variables thus speeding up the simulation while maintaining the accuracy. In implementing this revised method of criticality selection we make use of the probability distribution from which the random variable describing the change in species number is drawn. We give several numerical examples to demonstrate the effectiveness of our new method.

Acoustic component detection for automatic species recognition in environmental monitoring

Automatic species recognition plays an important role in assisting ecologists to monitor the environment. One critical issue in this research area is that software developers need prior knowledge of specific targets people are interested in to build templates for these targets. This paper proposes a novel approach for automatic species recognition based on generic knowledge about acoustic events to detect species. Acoustic component detection is the most critical and fundamental part of this proposed approach. This paper gives clear definitions of acoustic components and presents three clustering algorithms for detecting four acoustic components in sound recordings; whistles, clicks, slurs, and blocks. The experiment result demonstrates that these acoustic component recognisers have achieved high precision and recall rate.

Impacts of experimental warming and fire on phenology of subalpine open-heath species

The present study examined experimentally the phenological responses of a range of plant species to rises in temperature. We used the climate-change field protocol of the International Tundra Experiment (ITEX), which measures plant responses to warming of 1 to 2°C inside small open-topped chambers. The field study was established on the Bogong High Plains, Australia, in subalpine open heathlands; the most common treeless plant community on the Bogong High Plains. The study included areas burnt by fire in 2003, and therefore considers the interactive effects of warming and fire, which have rarely been studied in high mountain environments. From November 2003 to March 2006, various phenological phases were monitored inside and outside chambers during the snow-free periods. Warming resulted in earlier occurrence of key phenological events in 7 of the 14 species studied. Burning altered phenology in 9 of 10 species studied, with both earlier and later phenological changes depending on the species. There were no common phenological responses to warming or burning among species of the same family, growth form or flowering type (i.e. early or late-flowering species), when all phenological events were examined. The proportion of plants that formed flower buds was influenced by fire in half of the species studied. The findings support previous findings of ITEX and other warming experiments; that is, species respond individualistically to experimental warming. The inter-year variation in phenological response, the idiosyncratic nature of the responses to experimental warming among species, and an inherent resilience to fire, may result in community resilience to short-term climate change. In the first 3 years of experimental warming, phenological responses do not appear to be driving community-level change. Our findings emphasise the value of examining multiple species in climate-change studies.