Integrating JERS-1 imaging radar and elevation models for mapping tropical rainforest communities in far North Queensland, Australia

The Wet Tropics World Heritage Area in Far North Queens- land, Australia consists predominantly of tropical rainforest and wet sclerophyll forest in areas of variable relief. Previous maps of vegetation communities in the area were produced by a labor-intensive combination of field survey and air-photo interpretation. Thus,. the aim of this work was to develop a new vegetation mapping method based on imaging radar that incorporates topographical corrections, which could be repeated frequently, and which would reduce the need for detailed field assessments and associated costs. The method employed G topographic correction and mapping procedure that was developed to enable vegetation structural classes to be mapped from satellite imaging radar. Eight JERS-1 scenes covering the Wet Tropics area for 1996 were acquired from NASDA under the auspices of the Global Rainforest Mapping Project. JERS scenes were geometrically corrected for topographic distortion using an 80 m DEM and a combination of polynomial warping and radar viewing geometry modeling. An image mosaic was created to cover the Wet Tropics region, and a new technique for image smoothing was applied to the JERS texture bonds and DEM before a Maximum Likelihood classification was applied to identify major land-cover and vegetation communities. Despite these efforts, dominant vegetation community classes could only be classified to low levels of accuracy (57.5 percent) which were partly explained by the significantly larger pixel size of the DEM in comparison to the JERS image (12.5 m). In addition, the spatial and floristic detail contained in the classes of the original validation maps were much finer than the JERS classification product was able to distinguish. In comparison to field and aerial photo-based approaches for mapping the vegetation of the Wet Tropics, appropriately corrected SAR data provides a more regional scale, all-weather mapping technique for broader vegetation classes. Further work is required to establish an appropriate combination of imaging radar with elevation data and other environmental surrogates to accurately map vegetation communities across the entire Wet Tropics.

Age and growth in olive ridley seaturtles (Lepidochelys olivacea) from the north-central Pacific: a skeletochronological analysis

The olive ridley is the most abundant seaturtle species in the world but little is known of the demography of this species. We used skeletochronological data on humerus diameter growth changes to estimate the age of North Pacific olive ridley seaturtles caught incidentally by pelagic longline fisheries operating near Hawaii and from dead turtles washed ashore on the main Hawaiian Islands. Two age estimation methods [ranking, correction factor (CF)] were used and yielded age estimates ranging from 5 to 38 and 7 to 24 years, respectively. Rank age-estimates are highly correlated (r = 0.93) with straight carapace length (SCL), CF age estimates are not (r = 0.62). We consider the CF age-estimates as biologically more plausible because of the disassociation of age and size. Using the CF age-estimates, we then estimate the median age at sexual maturity to be around 13 years old (mean carapace size c. 60 cm SCL) and found that somatic growth was negligible by 15 years of age. The expected age-specific growth rate function derived using numerical differentiation suggests at least one juvenile growth spurt at about 10–12 years of age when maximum age-specific growth rates, c. 5 cm SCL year−1, are apparent.

The cross-entropy method for continuous multi-extremal optimization

In recent years, the cross-entropy method has been successfully applied to a wide range of discrete optimization tasks. In this paper we consider the cross-entropy method in the context of continuous optimization. We demonstrate the effectiveness of the cross-entropy method for solving difficult continuous multi-extremal optimization problems, including those with non-linear constraints.