Investigating the distribution of acacia longifolia ssp. sophorae in south-west Victoria using satellite remote sensing and GIS

Mapping and analysis of the distribution of environmental weeds is an important component of strategic weed management. Such information is particularly important in managing 'native invaders', where invasion characteristics must be clearly understood prior to any management action being taken. This paper reports on an investigation of the current distribution of the native invader Acacia longifolia ssp. sophorae (Labill.) Court (coast wattle) in south-west Victoria, using remote sensing and Geographic Information Systems (GIS). Coast wattle was successfully mapped from Landsat ETM imagery using a supervised classification procedure, with 82%, of coast wattle shown on the map accurately depicting coast wattle on the ground. An estimated 11,448 ha were classified as supporting coast wattle, representing 12% of native vegetation in the study area. A more detailed GIS analysis in the Lower Glenelg National Park revealed coast wattle has invaded a limited number of vegetation types, and is more prevalent close to roads and within management zones associated with disturbance. The current regional extent of the species means widespread control is unlikely; hence the immediate focus should be on preventing further spread into areas where it is currently absent. Landsat imagery also proved to be a successful tool for mapping large scale coast wattle distribution, and could be used in long-term monitoring of the species.

Hydro-acoustic remote sensing of benthic biological communities on the shallow South East Australian continental shelf

Information regarding the composition and extent of benthic habitats on the South East Australian continental shelf is limited. In this habitat mapping study, multibeam echosounder (MBES) data are integrated with precisely geo-referenced video ground-truth data to quantify benthic biotic communities at Cape Nelson, Victoria, Australia. Using an automated decision tree classification approach, 5 representative biotic groups defined from video analysis were related to hydro-acoustically derived variables in the Cape Nelson survey area. Using a combination of multibeam bathymetry, backscatter and derivative products produced highest overall accuracy (87%) and kappa statistic (0.83). This study demonstrates that decision tree classifiers are capable of integrating variable data types for mapping distributions of benthic biological assemblages, which are important in maintaining biodiversity and other system services in the marine environment.

Retrospective application of remote sensing to the Tasmanian lakeland

This thesis describes the research undertaken for a degree of Master of Science in a retrospective study of airborne remotely sensed data registered in 1990 and 1993, and field captured data of aquatic humus concentrations for ~ 45 lakes in Tasmania. The aim was to investigate and describe the relationship between the remotely sensed data and the field data and to test the hypothesis that the remotely sensed data would establish further evidence of a limnological corridor of change running north-west to south- east. The airborne remotely sensed data consisted of data captured by the CSIRO Ocean Colour Scanner (OCS) and a newly developed Canadian scanner, a compact airborne spectrographic imager (CASI). The thesis investigates the relationship between the two kinds of data sources. The remotely sensed data was collected with the OCS scanner in 1990 (during one day) and with both the OCS and the CASI in 1993 (during three days). The OCS scanner registers data in 9 wavelength bands between 380 nm and 960 nm with a 10-20 nm bandwidth, and the CASI in 288 wavelength bands between 379.57 nm and 893.5 nm (ie. spectral mode) with a spectral resolution of 2.5 nm. The remotely sensed data were extracted from the original tapes with the help of the CSIRO and supplied software and digital sample areas (band value means) for each lake were subsequently extracted for data manipulation and statistical analysis. Field data was captured concurrently with the remotely sensed data in 1993 by lake hopping using a light aircraft with floats. The field data used for analysis with the remotely sensed data were the laboratory determined g440 values from the 1993 water samples collated with g440 values determined from earlier years. No spectro-radiometric data of the lakes, data of incoming irradiance or ancillary climatic data were captured during the remote sensing missions. The sections of the background chapter in the thesis provide a background to the research both in regards to remote sensing of water quality and the relationship between remotely sensed spectral data and water quality parameters, as well as a description of the Tasmanian lakes flown. The lakes were divided into four groups based on results from previous studies and optical parameters, especially aquatic humus concentrations as measured from field captured data. The four groups consist of the ‘green” clear water lakes mostly situated on the Central Plateau, the ‘brown” highly dystrophic lakes in western Tasmania, the ‘corridor” lakes situated along a corridor of change lying approximately between the two lines denoting the Jurassic edge and 1200 mm isohyet, and the ‘eastern, turbid” lakes make up the fourth group. The analytical part of the research work was mostly concerned with manipulating and analysing the CASI data because of its higher spectral resolution. The research explores methods to apply corrections to this data to reduce the disturbing effects of varying illumination and atmospheric conditions. Three different methods were attempted. In the first method two different standardisation formulas are applied to the data as well as ‘day correction” factors calculated from data from one of the lakes, Lake Rolleston, which had data captured for all three days of the remote sensing operations. The standardisation formulas were also applied to the OCS data. In second method an attempt to reduce the effects of the atmosphere was performed using spectro-radiometric captured in 1988 for one of the lakes flown, Great Lake. All the lake sample data were time normalised using general irradiance data obtained from the University of Tasmania and the sky portion as calculated from Great Lake upwelling irradiance data was then subtracted. The last method involved using two different band ratios to eliminate atmospheric effects. Statistical analysis was applied to the data resulting from the three methods to try to describe the relationship between the remotely sensed data and the field captured data. Discriminant analysis, cluster analysis and factor analysis using principal component analysis (pea) were applied to the remotely sensed data and the field data. The factor scores resulting from the pca were regressed against the field collated data of g440 as were the values resulting from last method. The results from the statistical analysis of the data from the first method show that the lakes group well (100%) against the predetermined groups using discriminant analysis applied to the remotely sensed CASI data. Most variance in the data are contained in the first factor resulting from pca regardless of data manipulation method. Regression of the factor scores against g440 field data show a strong non- linear relationship and a one-sided linear regression test is therefore considered an inappropriate analysis method to describe the dataset relationships. The research has shown that with the available data, correction and analysis methods, and within the scope of the Masters study, it was not possible to establish the relationships between the remotely sensed data and the field measured parameters as hoped. The main reason for this was the failure to retrieve remotely sensed lake signatures adequately corrected for atmospheric noise for comparison with the field data. This in turn is a result of the lack of detailed ancillary information needed to apply available established methods for noise reduction - to apply these methods we require field spectroradiometric measurements and environmental information of the varying conditions both within the study area and within the time frame of capture of the remotely sensed data.

Detecting patterns of change in benthic habitats by acoustic remote sensing

Changes in benthic habitats occur as a result of natural variation or human-induced processes. It is important to understand natural fine-scale inter-annual patterns of change to separate these signals from patterns of long-term change. Describing change from an acoustic remote sensing standpoint has been facilitated by the recent availability of full coverage swath acoustic datasets, but is limited by cost pressures associated with multiple surveys of the same area. We studied the use of landscape transition analysis as a means to differentiate seemingly random patterns of habitat change from systematic signals of habitat transition at a shallow (10 to 50 m depth) 18 km2 site on the temperate Australian continental shelf in 2006 and 2007. Supervised classifications for each year were accomplished using inde pendently collected highresolution swath acoustic and video reference data. Of the 4 representative biotic clas ses considered, signals of directional systematic changes occurred be tween a kelp-dominated class, a sessile invertebrate-dominated class and a mixed class of kelp and sessile invertebrates. We provide a detailed analysis of the components of the traditional change detection cross tabulation matrix, allowing identification of the strongest signals of systematic habitat transitions. Iden tifying patterns of habitat change is an important first step toward understanding the processes that drive them.

Monitoring protected areas using remote sensing technology

Due to irrational use of natural resources, human society is facing unprecedented threats. Remote sensing is one of the essential tools to determine changes in various forms of biological diversity over time. There are many methods to determine changes in protected areas, using satellite images. In this paper after introducing different change detection methods and their advantages and disadvantages, a hybrid method is used to analyse changes in forests and protected areas in a national park. Two Landsat images of Golestan National Park in Iran (taken in 1998 and 2010) were used. This hybrid approach combines Change Vector Analysis (CVA) for flagging the occurrence of changes, followed by signature extension to assign labels to changedpixels. The main objective of this paper is to propose a method for discovering and assessing environmental threats to natural treasures.

Evaluating coverage changes in national parks using a hybrid change detection algorithm and remote sensing

Remote sensing is a useful tool for detecting change over time.We introduce a hybrid change-detection method for forest and protected-area vegetation and demonstrate its use with two satellite images of Golestan National Park in northern Iran (1998 and 2010). We report on the advantages and disadvantages of the hybrid method relative to the standard change-detection method. In the proposed hybrid algorithm, the change vector analysis technique was used to determine changes in vegetation. Following this, we used postclassification comparison to determine the nature of the changes observed and their accuracy and to evaluate the effects of different parameters on the performance of the proposed method. We determined 85% accuracy for the proposed hybrid change-detection method, thus demonstrating a method for discovering and assessing environmental threats to natural treasures. © 2014 Society of Photo-Optical Instrumentation Engineers.

Use of geographical information system and remote sensing techniques for planning culture-based fisheries in non-perennial reservoirs of Sri Lanka

 The presence of a wide areal extent of small-sized village reservoirs offers a considerable potential for the development of culture-based fisheries (CBFs) in Sri Lanka. To this end, this study uses geographical information systems (GISs) and remote sensing (RS) techniques to determine the morphometric and biological characteristics most useful for classifying non-perennial reservoirs for CBF development and for assessing the influence of catchment land-use patterns on potential CBF yields. The reservoir shorelines at full water supply level were mapped with a Global Positioning System to determine shoreline length and reservoir areal extent. The ratio of shoreline length to reservoir extent, which was reported to be a powerful predictor variable of CBF yields, could be reliably quantified using RS techniques. The areal extent of reservoirs, quantified with RS techniques (RS extent), was used to estimate the ratio of forest cover plus scrubland cover to RS extent and was found to be significantly related to the CBF yield (R2 = 0.400; P < 0.05). The results of this study indicated that morphometric characteristics and catchment land-use patterns, which might be viewed as indices of biological productivity, can be quantified using RS and GIS techniques. © 2014 Wiley Publishing Asia Pty Ltd.

GIS and remote sensing-based spatial models for commercial fisheries assessment

By applying a novel set of multidisciplinary GIS, remote sensing and spatial modelling approaches, research presented in this thesis advances our knowledge of the distribution patterns, fishery and ecological status of an important commercial benthic macro-invertebrate, blacklip abalone, in south-east Australia.

Animal-borne sensors successfully capture the real-time thermal properties of ocean basins

Climate change is perhaps the most pressing and urgent environmental issue facing the world today. However our ability to predict and quantify the consequences of this change is severely limited by the paucity of in situ oceanographic measurements. Marine animals equipped with sophisticated oceanographic data loggers to study their behavior offer one solution to this problem because marine animals range widely across the world’s ocean basins and visit remote and often inaccessible locations. However, unlike the information being collected from conventional oceanographic sensing equipment, which has been validated, the data collected from instruments deployed on marine animals over long periods has not. This is the first long-term study to validate in situ oceanographic data collected by animal oceanographers. We compared the ocean temperatures collected by leatherback turtles (Dermochelys coriacea) in the Atlantic Ocean with the ARGO network of ocean floats and could find no systematic errors that could be ascribed to sensor instability. Animal-borne sensors allowed water temperature to be monitored across a range of depths, over entire ocean basins, and, importantly, over long periods and so will play a key role in assessing global climate change through improved monitoring of global temperatures. This finding is especially pertinent given recent international calls for the development and implementation of a comprehensive Earth observation system (see http://iwgeo.ssc.nasa.gov/documents.asp?s=review) that includes the use of novel techniques for monitoring and understanding ocean and climate interactions to address strategic environmental and societal needs.

Maximum contrast analysis for nonnegative blind source separation

In this paper, we propose a maximum contrast analysis (MCA) method for nonnegative blind source separation, where both the mixing matrix and the source signals are nonnegative. We first show that the contrast degree of the source signals is greater than that of the mixed signals. Motivated by this observation, we propose an MCA-based cost function. It is further shown that the separation matrix can be obtained by maximizing the proposed cost function. Then we derive an iterative determinant maximization algorithm for estimating the separation matrix. In the case of two sources, a closed-form solution exists and is derived. Unlike most existing blind source separation methods, the proposed MCA method needs neither the independence assumption, nor the sparseness requirement of the sources. The effectiveness of the new method is illustrated by experiments using X-ray images, remote sensing images, infrared spectral images, and real-world fluorescence microscopy images.

Corner inlet and Nooramunga habitat mapping project

Deakin University and the University of Tasmania were commissioned by Parks Victoria (PV) to create two updated habitat maps for areas within the Corner Inlet and Nooramunga Marine and Coastal Park and Ramsar area. The team obtained a ground-truth data set using in situ video and still photographs. This dataset was used to develop and assess predictive models of benthic marine habitat distributions incorporating data from both ALOS (Advanced Land Observation Satellite) imagery atmospherically corrected by CSIRO and LiDAR (Light Detection and Ranging) bathymetry. This report describes the results of the mapping effort as well as the methodology used to produce these habitat maps.

Overall accuracies of habitat classifications were good, returning overall accuracies >73 % and kappa values > 0.62 for both study localities. Habitats predicted with highest accuracies included Zosteraceae in Nooramunga (91 %), reef in Corner Inlet (80 %), and bare sediment (no-visible macrobiota/no-visible seagrass classes; both > 76 %). The majority of classification errors were due to the misclassification of areas of sparse seagrass as bare sediment. For the Corner Inlet study locality the no-visible macrobiota (10,698 ha), Posidonia (4,608 ha) and Zosteraceae (4,229 ha) habitat classes covered the most area. In Nooramunga no-visible seagrass (5,538 ha), Zosteraceae (4,060 ha) and wet saltmarsh (1,562 ha) habitat classes were most dominant.

In addition to the commissioned work preliminary change detection analyses were undertaken as part of this project. These analyses indicated shifts in habitat extents in both study localities since the late 1990s/2000. In particular, a post-classification analysis highlighted that there were considerable increases in seagrass habitat (primarily Zosteraceae) throughout the littoral zones and river/creek mouths of both study localities. Further, the numerous channel systems remained stable and were free of seagrass at both times. A substantial net loss of Posidonia in the Corner Inlet locality is likely but requires further investigation due to potential misclassifications between habitats in both the 1998 map (Roob et al. 1998) and the current mapping. While the unsupervised Independent Components Analysis (ICA) change detection technique indicated some changes in habitat extent and distribution, considerable areas of habitat change observed in the post-classification approach are questionable, and may reflect misclassifications rather than real change. A particular example of this is an apparent large decrease in Zosteraceae and increase in Posidonia being related to the classification of Posidonia beds as Zosteraceae in the 1998 mapping. Despite this, we believe that changes indicated by both the ICA and post-classification approaches have a high likelihood of being ‘actual’ change. A pattern of gains and losses of Zosteraceae in the region north of Stockyard channel is an example of this. Further analyses and refinements of approaches in change detection analyses such as would improve confidence in the location and extent of habitat changes over this time period.

This work has been successful in providing new baseline maps using a repeatable method meaning that any future changes in intertidal and shallow water marine habitats may be assessed in a consistent way with quantitative error assessments. In wider use, these maps should also allow improved conservation planning, advance fisheries and catchment management, and progress infrastructure planning to limit impacts on the Inlet environment.

Habitat classification of temperate marine macroalgal communities using bathymetric LiDAR

Here, we evaluated the potential of using bathymetric Light Detection and Ranging (LiDAR) to characterise shallow water (<30 m) benthic habitats of high energy subtidal coastal environments. Habitat classification, quantifying benthic substrata and macroalgal communities, was achieved in this study with the application of LiDAR and underwater video groundtruth data using automated classification techniques. Bathymetry and reflectance datasets were used to produce secondary terrain derivative surfaces (e.g., rugosity, aspect) that were assumed to influence benthic patterns observed. An automated decision tree classification approach using the Quick Unbiased Efficient Statistical Tree (QUEST) was applied to produce substrata, biological and canopy structure habitat maps of the study area. Error assessment indicated that habitat maps produced were primarily accurate (>70%), with varying results for the classification of individual habitat classes; for instance, producer accuracy for mixed brown algae and sediment substrata, was 74% and 93%, respectively. LiDAR was also successful for differentiating canopy structure of macroalgae communities (i.e., canopy structure classification), such as canopy forming kelp versus erect fine branching algae. In conclusion, habitat characterisation using bathymetric LiDAR provides a unique potential to collect baseline information about biological assemblages and, hence, potential reef connectivity over large areas beyond the range of direct observation. This research contributes a new perspective for assessing the structure of subtidal coastal ecosystems, providing a novel tool for the research and management of such highly dynamic marine environments. © 2014 by the authors; licensee MDPI, Basel, Switzerland.