A new approach for detecting and mapping sewage impacts

Increased nitrogen loading has been implicated in eutrophication occurrences worldwide. Much of this loading is attributable to the growing human population along the world's coastlines. A significant component of this nitrogen input is from sewage effluent, and delineation of the distribution and biological impact of sewage-derived nitrogen is becoming increasingly important. Here, we show a technique that identifies the source, extent and fate of biologically available sewage nitrogen in coastal marine ecosystem. This method is based on the uptake of sewage nitrogen by marine plants and subsequent analysis of the sewage signature (elevated delta N-15) in plant tissues. Spatial analysis is used to create maps of delta N-15 and establish coefficient of variation estimates of the mapped values. We show elevated delta N-15 levels in marine plants near sewage outfalls in Moreton Bay, Australia, a semi-enclosed bay receiving multiple sewage inputs. These maps of sewage nitrogen distribution are being used to direct nutrient reduction strategies in the region and will assist in monitoring the effectiveness of environmental protection measures. (C) 2001 Elsevier Science Ltd. All rights reserved.

Inheritance and mapping of 11 avirulence genes in Phytophthora sojae

Two new crosses involving four races (races 7, 16, 17, and 25) of the soybean root and stem rot pathogen Phytophthora sojae were established (7/16 cross; 17/25 cross). An F-2 Population derived from each cross was used to determine the genetic basis of avirulence towards 11 different resistance genes in soybean. Avirulence was found to be dominant and determined by a single locus for Avr1b, 1d, 1k, 3b, 4, and 6, as expected for a simple gene-for-gene model. We also observed several cases of segregation, inconsistent with a single dominant gene being solely responsible for avirulence, which suggests that the genetic background of the different crosses can affect avirulence. Avr4 and 6 cosegregated in both the 7/16 and 17/25 crosses and, in the 7/16 cross, Avr1b and 1k were closely linked. Information from segregating RAPD, RFLP, and AFLP markers screened on F-2 progeny from the two new crosses and two crosses described previously (a total of 212 F-2 individuals, 53 from each cross) were used to construct an integrated genetic linkage map of P. sojae. This revised genetic linkage map consists of 386 markers comprising 35 RFLP, 236 RAPD, and 105 AFLP markers, as well as 10 avirulence genes. The map is composed of 21 major linkage groups and seven minor linkage groups covering a total map distance of 1640.4 cM. (C) 2002 Elsevier Science (USA). All rights reserved.

RatSLAM: A hippocampal model for simultaneous localisation and mapping

The work presents a new approach to the problem of simultaneous localization and mapping - SLAM - inspired by computational models of the hippocampus of rodents. The rodent hippocampus has been extensively studied with respect to navigation tasks, and displays many of the properties of a desirable SLAM solution. RatSLAM is an implementation of a hippocampal model that can perform SLAM in real time on a real robot. It uses a competitive attractor network to integrate odometric information with landmark sensing to form a consistent representation of the environment. Experimental results show that RatSLAM can operate with ambiguous landmark information and recover from both minor and major path integration errors.

Biologically inspired visual landmark processing for simultaneous localization and mapping

This paper illustrates a method for finding useful visual landmarks for performing simultaneous localization and mapping (SLAM). The method is based loosely on biological principles, using layers of filtering and pooling to create learned templates that correspond to different views of the environment. Rather than using a set of landmarks and reporting range and bearing to the landmark, this system maps views to poses. The challenge is to produce a system that produces the same view for small changes in robot pose, but provides different views for larger changes in pose. The method has been developed to interface with the RatSLAM system, a biologically inspired method of SLAM. The paper describes the method of learning and recalling visual landmarks in detail, and shows the performance of the visual system in real robot tests.

Hippocampal models for simultaneous localisation and mapping on an autonomous robot

To navigate successfully in a novel environment a robot needs to be able to Simultaneously Localize And Map (SLAM) its surroundings. The most successful solutions to this problem so far have involved probabilistic algorithms, but there has been much promising work involving systems based on the workings of part of the rodent brain known as the hippocampus. In this paper we present a biologically plausible system called RatSLAM that uses competitive attractor networks to carry out SLAM in a probabilistic manner. The system can effectively perform parameter self-calibration and SLAM in onedimension. Tests in two dimensional environments revealed the inability of the RatSLAM system to maintain multiple pose hypotheses in the face of ambiguous visual input. These results support recent rat experimentation that suggest current competitive attractor models are not a complete solution to the hippocampal modelling problem.

Probabilistic visual recognition of artificial landmarks for simultaneous localization and mapping

Probabilistic robotics most often applied to the problem of simultaneous localisation and mapping (SLAM), requires measures of uncertainty to accompany observations of the environment. This paper describes how uncertainty can be characterised for a vision system that locates coloured landmarks in a typical laboratory environment. The paper describes a model of the uncertainty in segmentation, the internal cameral model and the mounting of the camera on the robot. It explains the implementation of the system on a laboratory robot, and provides experimental results that show the coherence of the uncertainty model.

Estimation and mapping of foliar chemical levels in a coastal eucalypt forest using CASI data

Proceedings of the 11th Australasian Remote Sensing and Photogrammetry Conference

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.

Integrating high and moderate spatial resolution image data to estimate forest age structure

The collection of spatial information to quantify changes to the state and condition of the environment is a fundamental component of conservation or sustainable utilization of tropical and subtropical forests, Age is an important structural attribute of old-growth forests influencing biological diversity in Australia eucalypt forests. Aerial photograph interpretation has traditionally been used for mapping the age and structure of forest stands. However this method is subjective and is not able to accurately capture fine to landscape scale variation necessary for ecological studies. Identification and mapping of fine to landscape scale vegetative structural attributes will allow the compilation of information associated with Montreal Process indicators lb and ld, which seek to determine linkages between age structure and the diversity and abundance of forest fauna populations. This project integrated measurements of structural attributes derived from a canopy-height elevation model with results from a geometrical-optical/spectral mixture analysis model to map forest age structure at a landscape scale. The availability of multiple-scale data allows the transfer of high-resolution attributes to landscape scale monitoring. Multispectral image data were obtained from a DMSV (Digital Multi-Spectral Video) sensor over St Mary's State Forest in Southeast Queensland, Australia. Local scene variance levels for different forest tapes calculated from the DMSV data were used to optimize the tree density and canopy size output in a geometric-optical model applied to a Landsat Thematic Mapper (TU) data set. Airborne laser scanner data obtained over the project area were used to calibrate a digital filter to extract tree heights from a digital elevation model that was derived from scanned colour stereopairs. The modelled estimates of tree height, crown size, and tree density were used to produce a decision-tree classification of forest successional stage at a landscape scale. The results obtained (72% accuracy), were limited in validation, but demonstrate potential for using the multi-scale methodology to provide spatial information for forestry policy objectives (ie., monitoring forest age structure).

A simple, rapid method for mapping bathymetry of small wetland basins

Many tools exist for determining and mapping the bathymetry and topography of aquatic systems, such as freshwater wetlands. However, these tools often require time-consuming survey work to produce accurate maps. In particular, the large quantity of data necessary may be prohibitive for projects where determining bathymetry is not a central focus, but instead a necessary step in achieving some other goal. We present a method to produce bathymetric surface maps with a minimum amount of effort using global positioning system receiver and laser transit survey data. We also demonstrate that this method is surprisingly accurate, given the small amount of data we use to generate the bathymetry maps.