Predicting the distribution of Eastern Grey Kangaroos by remote sensing assessment of food resources.

This study demonstrates how the distribution of animals can be described using remotely sensed data at a scale in the order of square kilometers. Kangaroo distribution has been monitored at regional scales using aerial surveys and detailed field study. This study attempts to fill the gap between local and regional scales by using Landsat derived vegetation characteristics to provide animal distribution details at local scale. Field surveys of Eastern Grey kangaroos and vegetation biomass were undertaken at the Warrumbungle National Park, New South Wales, Australia. The distribution and abundance of kangaroos and plant biomass were compared with remotely sensed vegetation characteristics taken from Landsat TM imagery. The distribution of green, short (< 5cm) blade grass biomass (the preferred kangaroo food resource) was patchy and positively correlated with kangaroo density and Landsat spectral bands 1, 2, 3 and a principal component combination of bands 1-7 (excluding band 6). Total population density was positively correlated with blade grass biomass and Landsat band 3. The dispersion of kangaroos within habitats was patchy, even though the Landsat image defined habitats as being homogeneous. This study clearly demonstrates the value of Landsat data to environmental management in the past and present

Application of cooperative neuro - evolution of Elman recurrent networks for a two - dimensional cyclone track prediction for the South Pacific region

This paper presents a two-dimensional time series prediction approach for cyclone track prediction using cooperative neuro-evolution of Elman recurrent networks in the South Pacific region. The latitude and longitude of tracks of cyclone lifetime is taken into consideration for past three decades to build a robust forecasting system. The proposed method performs one step ahead prediction of the cyclone position which is essentially a two-dimensional time series prediction problem. The results show that the Elman recurrent network is able to achieve very good accuracy in terms of prediction of the tracks which can be used as means of taking precautionary measures

Groundwater detection using resistivity at Nubutautau village in Viti Levu in Fiji

A geophysical method, electrical resistivity tomography, was applied to identify potential groundwater-bearing zones around Nubutautau village on Viti Levu island, Fiji. Apparent resistivity data of the subsurface were collected through an electrode assembly along survey lines by injecting current into the subsurface using an ABEM Terrameter LS2. The apparent resistivity data were inverted using Res2DINVx64 software to produce the final electrical resistivity through an iterative process to compare the resistivity of layers and draw analogical hydrogeological results. Analysis revealed the presence of two potential groundwater-bearing zones as potential targets for future drilling. The two targets indicated the presence of potentially saturated vertical fractures through which infiltrating rainwater percolates through the volcanic rock towards a deeper basal aquifer. The identification of the two potential targets demonstrated great potential of this geophysical technique to effectively inform drilling operations. A scientific approach can increase the successful delivery of water security interventions in remote, drought-prone communities of the Pacific

Tree diversity, vegetation structure and management of mangrove systems on Viti Levu, Fiji Islands

Mangrove forest ecosystems are critical natural resources, particularly in the South Pacific region. Mangrove forests in Viti Levu, Fiji’s main island, are threatened by infrastructure development activities and population growth. Consequently, the protection and restoration of mangrove forest are of utmost importance. This study investigated the diversity and structure of mangrove forest on Viti Levu to determine the most appropriate species for use in restoration projects. These species could enhance the management of mangroves in Fiji. Five sites were selected: Ellington Wharf (EW), Naboutini Village (NV), Nasese (NA), Suva City Council Park (SCCP) and the University of the South Pacific Upper Campus (UUC). The variations in the number of tree stumps from site-tosite highlighted differences in the degree of anthropogenic disturbances, EW was classified as an undisturbed site while NA was highly disturbed. The sites were examined using systematic line transects with random starting points. Continuous belt transects were established, along which 10 m × 10 m segments were selected as the primary plots (n = 100 primary plots). Tree species, stand structure, tree diameter and height, stem abundance, stand volume, basal area and natural regeneration were recorded at all sites. Five mangrove species (Rhizophora stylosa Griff., Bruguiera gymnorhiza (L.) Lam., Excoecaria agallocha (L.), Rhizophora samoensis (Hochr.) Salvoza, and Rhizophora × selala (Salvoza) Toml.) were identified. The species importance value indices were highest for R. stylosa at EW (264.0) and for B. gymnorhiza) at NV (175.2). All sites had at least some level of human disturbance but R. stylosa and B. gymnorhiza thrived regardless of the extent of anthropogenic impacts. Subsequently, R. stylosa and B. gymnorhiza are recommended for mangrove forest ecosystem restoration programmes in Fiji

Spatial Information Technology and Natural Resources Management: A Tool TO-ASIST

The Task Orientated - Application of a Spatial Information Systems Toolbox (TO-ASIST) is proffered as a new concept in the delivery of remote sensing and GIS technologies to natural resource managers. These technologies, which may collectively be termed spatial information technology (SIT), have much to offer natural resources management but are not extensively used. This thesis reports the theoretical and experimental development of the TO-ASIST concept as a link between SIT and natural resources management. This is achieved by demonstrating how spatial information technology may assist in the process of natural resources management in theory and in practice, and how the technology may be made more accessible to managers and their staff using a TO-ASIST. A TO-ASIST is developed using a new computer programming paradigm. It is apparent that natural resources management is moving towards a more holistic approach in which individual natural resources are viewed as smaller components of a bigger picture. The need for information about these resources, their location, condition and relationship to one another was identified as an area of priority. Information of this kind is needed to establish a base line inventory but also to monitor resources over time. Monitoring allows the impact of resource developments to be re-assessed thus incrementally improving our knowledge of the environment, that in turn will assist in better decision making in the future. This is the essence of adaptive management

Planning for whole-farm systems research at a credible scale: subdividing land into farmlets with equivalent initial conditions

Most research comparing different farming systems has been conducted on relatively uniform plots at small scales made necessary by the desire for sufficient replication of the systems and cost limitations. This paper describes an alternative approach to plan the allocation of land to three unreplicated whole-farm management systems such that each farmlet had equivalent starting conditions and yet was at a scale credible to both livestock producers and researchers. The paddocks of each farmlet were distributed across the landscape in a ‘patchwork quilt’ pattern after six iterations of a mapping exercise using a Geographic Information System. Allocation of paddocks took into account those variables of the landscape and natural resource capacity that were not able to be altered. An important benefit of the procedure was that it ensured that the farmlets were co-located with contiguous paddock boundaries so that all farmlets experienced the same climatic as well as biophysical conditions. An electromagnetic survey was conducted of the entire property and used in conjunction with a detailed soils map in order to classify areas into soil conductivity groupings. Equivalent areas of each soil type were allocated across the three farmlets. Similarly, land was distributed according to its topography so that no farmlet would be compromised by being allocated more low lying, flood-prone land than any other farmlet. The third factor used to allocate land to each farmlet was the prior fertiliser history of the original paddocks. This process ensured that each farmlet was objectively allocated equivalent areas of soil type, topography and fertiliser history thus avoiding initial bias among the farmlets. After the plan for all paddocks of each farmlet was finalised, new paddock boundaries were drawn and where necessary, fencing was removed, modified and added, along with re-arranged watering points. The farmlet treatments commenced in July 2000 when the first pasture establishment and differential fertiliser applications were carried out. Evidence from the electromagnetic survey and the Landsat imagery confirmed that the distribution of hydrologic soil conductivity and vegetation greenness were similar between all farmlets just before the commencement of the experiment

Segmenting mangrove ecosystems drone images using SLIC superpixels

Mangrove ecosystems play a very important ecological role on land-ocean interfaces in tropical regions. These ecosystems comprise of various tree species and aquatic animals, protecting the environment and providing a habitat that supports many living organisms including humans. The identification of image regions in mangrove ecosystems plays a significant role in ecosystem monitoring and conservation. Recent studies have suggested oversegmentation of colour images using superpixels as a solution to the segmentation of image regions. This study used the SLIC superpixel algorithm and k-means clustering to segment images taken from a camera mounted on a drone from a mangrove ecosystem in Fiji. The SLIC superpixel algorithm performed well to demarcate image regions with similar colour and texture information into patches and to use k-means for the segmentation of the whole image. These results lend support to the use of superpixel algorithms for the segmentation of mangrove ecosystems. Understanding how superpixels can be used for the segmentation of drone images will assist conservation efforts in mangrove ecosystems