An Application of Large Scale Computing Facilities to the Processing of LANDSAT Digital Data in Australia

An early issue in the Australian Wide Scale Wheat Monitoring Project, started in November, 1978, was whether to use area sampling, as in the LACIE, or to be innovative and attempt whole scene processing. The availability of a large computing system and acknowledgment of the trends in price and performance of computers influenced a decision towards whole scene processing. The computing facilities used in this project are described. An interactive facility supported by software called ER-MAN II is installed on an IBM 3033 which simultaneously supports several hundred other interactive users. The pros and cons of using such a shared facility for this type of work are explored. The use of multi-temporal data has been the essence of the approach in this project. Reasons for its use, and its performance implications are discussed from the computing view point. Results to date indicate that shared use of a large facility is feasible and effective. In addition, some calculations may not be possible on small CPU\u27s. While the interactive processing of the combination of multi-temporal LANDSAT data and large areas is not common in Australia now, it is probable that its use will increase as the cost of computing equipment decreases

Crop Monitoring in Australia Using Digital Analysis of Landsat Data

This paper describes on-going co-operative research by the New South Wales (N.S.W.) Department of Agriculture and IBM Australia Limited. The aims of the project are to investigate if Landsat digital data can be used to map and monitor agricultural resources, particularly crop acreages and production, over large areas. From the outset, multi-temporal whole scene computation has been a feature of the technical approach. Software modifications for large data volumes were carried out allowing supervised maximum likelihood classification to be used throughout the work. Considerable amounts of agronomic ground truth have been collected over wide latitudes for use in training the computer system as well as for accuracy assessment of classification results. A number of Landsat scenes have been studied, with three to five acquisitions being registered for each. Preliminary analyses were conducted on historical data of Tamworth (N.S.W.) and Narrabri (N.S.W.) scenes. More intensive studies were undertaken for the 1980-81 wheat season in the Narrabri scene and are currently being undertaken in the Horsham (Victoria) scene. The techniques used and results from these analyses are discussed in this paper. Classification accuracy has been very encouraging showing excellent potential for use in crop area and production estimates. Various problems emerged which required special attention and these are discussed. They included loss of data because of cloud cover, registration accuracy, training techniques, best combinations of bands and dates for classification, confusion classes, computation of very large volumes of data and classification accuracy assessment. This work is demonstrating valuable applications of Landsat data in Australia. Technology transfer to other users is under way and it is anticipated that further technological development will lead to large scale adoption of remote sensing techniques for monitoring agricultural resources in Australia

Information theory analysis of Australian humpback whale song

Songs produced by migrating whales were recorded off the coast of Queensland, Australia, over six consecutive weeks in 2003. Forty-eight independent song sessions were analyzed using information theory techniques. The average length of the songs estimated by correlation analysis was approximately 100 units, with song sessions lasting from 300 to over 3100 units. Song entropy, a measure of structural constraints, was estimated using three different methodologies: (1) the independently identically distributed model, (2) a first-order Markov model, and (3) the nonparametric sliding window match length (SWML) method, as described by Suzuki et al. [(2006). “Information entropy of humpback whale song,” J. Acoust. Soc. Am. 119, 1849–1866]. The analysis finds that the song sequences of migrating Australian whales are consistent with the hierarchical structure proposed by Payne and McVay [(1971). “Songs of humpback whales,” Science 173, 587–597], and recently supported mathematically by Suzuki et al. (2006) for singers on the Hawaiian breeding grounds. Both the SWML entropy estimates and the song lengths for the Australian singers in 2003 were lower than that reported by Suzuki et al. (2006) for Hawaiian whales in 1976–1978; however, song redundancy did not differ between these two populations separated spatially and temporally. The average total information in the sequence of units in Australian song was approximately 35 bits/song. Aberrant songs (8%) yielded entropies similar to the typical songs

Determining spatial and temporal scales for management: lessons from whaling

Selection of the appropriate management unit is critical to the conservation of animal populations. Defining such units depends upon knowledge of population structure and upon the timescale being considered. Here, we examine the trajectory of eleven subpopulations of five species of baleen whales to investigate temporal and spatial scales in management. These subpopulations were all extirpated by commercial whaling, and no recovery or repopulation has occurred since. In these cases, time elapsed since commercial extinction ranges from four decades to almost four centuries. We propose that these subpopulations did not recover either because cultural memory of the habitat has been lost, because widespread whaling among adjacent stocks eliminated these as sources for repopulation, and/or because segregation following exploitation produced the abandonment of certain areas. Spatial scales associated with the extirpated subpopulations are frequently smaller than those typically employed in management. Overall, the evidence indicates that: (1) the time frame for management should be at most decadal in scope (i.e., \u3c100 yr) and based on both genetic and nongenetic evidence of population substructure, and (2) at least some stocks should be defined on a smaller spatial scale than they currently are

Diversity in sound pressure levels and estimated active space of resident killer whale vocalizations

Author Posting. © The Author, 2005. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Journal of Comparative Physiology A: Sensory, Neural, and Behavioral Physiology 192 (2006): 449-459, doi:10.1007/s00359-005-0085-2.Signal source intensity and detection range, which integrates source intensity with propagation loss, background noise and receiver hearing abilities, are important characteristics of communication signals. Apparent source levels were calculated for 819 pulsed calls and 24 whistles produced by free-ranging resident killer whales by triangulating the angles-of-arrival of sounds on two beamforming arrays towed in series. Levels in the 1-20 kHz band ranged from 131-168 dB re 1μPa @1m, with differences in the means of different sound classes (whistles: 140.2 ± 4.1 dB; variable calls: 146.6 ± 6.6 dB; stereotyped calls: 152.6 ± 5.9 dB), and among stereotyped call types. Repertoire diversity carried through to estimates of active space, with “long-range” stereotyped calls all containing overlapping, independently-modulated high-frequency components (mean estimated active space of 10-16km in sea state zero) and “short-range” sounds (5-9 km) included all stereotyped calls without a high-frequency component, whistles, and variable calls. Short-range sounds are reported to be more common during social and resting behaviors, while long-range stereotyped calls predominate in dispersed travel and foraging behaviors. These results suggest that variability in sound pressure levels may reflect diverse social and ecological functions of the acoustic repertoire of killer whales.Funding was provided by WHOI’s Ocean Ventures Fund and Rinehart Coastal Research Center and a Royal Society fellowship

Population Structure of Humpback Whales from Their Breeding Grounds in the South Atlantic and Indian Oceans

Although humpback whales are among the best-studied of the large whales, population boundaries in the Southern Hemisphere (SH) have remained largely untested. We assess population structure of SH humpback whales using 1,527 samples collected from whales at fourteen sampling sites within the Southwestern and Southeastern Atlantic, the Southwestern Indian Ocean, and Northern Indian Ocean (Breeding Stocks A, B, C and X, respectively). Evaluation of mtDNA population structure and migration rates was carried out under different statistical frameworks. Using all genetic evidence, the results suggest significant degrees of population structure between all ocean basins, with the Southwestern and Northern Indian Ocean most differentiated from each other. Effective migration rates were highest between the Southeastern Atlantic and the Southwestern Indian Ocean, followed by rates within the Southeastern Atlantic, and the lowest between the Southwestern and Northern Indian Ocean. At finer scales, very low gene flow was detected between the two neighbouring sub-regions in the Southeastern Atlantic, compared to high gene flow for whales within the Southwestern Indian Ocean. Our genetic results support the current management designations proposed by the International Whaling Commission of Breeding Stocks A, B, C, and X as four strongly structured populations. The population structure patterns found in this study are likely to have been influenced by a combination of long-term maternally directed fidelity of migratory destinations, along with other ecological and oceanographic features in the region