Journal articles on the topic 'Pastures South Australia Remote sensing'
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1
Donald, G. E., S. G. Gherardi, A. Edirisinghe, S. P. Gittins, D. A. Henry, and G. Mata. "Using MODIS imagery, climate and soil data to estimate pasture growth rates on farms in the south-west of Western Australia." Animal Production Science 50, no. 6 (2010): 611. http://dx.doi.org/10.1071/an09159.
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Remote sensing of vegetation and its monitoring using the normalised difference vegetation index (NDVI) offers the opportunity to provide a coverage of agricultural land at a large scale. The availability of MODIS NDVI at a resolution of 250 m provided the opportunity to evaluate the hypothesis that pasture growth rate (PGR) of individual paddocks can be accurately predicted using a model based on MODIS NDVI in combination with climate and soil data and a light-use efficiency model. Model estimates of PGR were compared with field measurements of PGR recorded in grazing enclosure cages collected over 3 years from six farms located across the south-west region of Western Australia. The estimates attained from the model explained 70% of the variation in PGR for individual paddocks on farms over the 3 years of the study, with an average error at the paddock scale of 10.4 kg DM/ha.day over all growing seasons and years. Across all farms studied, there was generally good agreement between satellite-derived PGR and ground-based measurements, although estimates of PGR varied between years and farms. The model explained 47% of the variation in pasture growth early in the season (from break of season to end of July), compared with 62% late in the season (from August to pasture senescence). The present study demonstrated that PGR for individual paddocks can be predicted at weekly intervals from MODIS imagery, climate and soil data and a light-use efficiency model at an accuracy sufficient to facilitate on-farm pasture and livestock management.
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Jana, Raghavendra B., Ali Ershadi, and Matthew F. McCabe. "Examining the relationship between intermediate-scale soil moisture and terrestrial evaporation within a semi-arid grassland." Hydrology and Earth System Sciences 20, no. 10 (September 30, 2016): 3987–4004. http://dx.doi.org/10.5194/hess-20-3987-2016.
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Abstract. Interactions between soil moisture and terrestrial evaporation affect water cycle behaviour and responses between the land surface and the atmosphere across scales. With strong heterogeneities at the land surface, the inherent spatial variability in soil moisture makes its representation via point-scale measurements challenging, resulting in scale mismatch when compared to coarser-resolution satellite-based soil moisture or evaporation estimates. The Cosmic Ray Neutron Probe (CRNP) was developed to address such issues in the measurement and representation of soil moisture at intermediate scales. Here, we present a study to assess the utility of CRNP soil moisture observations in validating model evaporation estimates. The CRNP soil moisture product from a pasture in the semi-arid central west region of New South Wales, Australia, was compared to evaporation derived from three distinct approaches, including the Priestley–Taylor (PT-JPL), Penman–Monteith (PM-Mu), and Surface Energy Balance System (SEBS) models, driven by forcing data from local meteorological station data and remote sensing retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Pearson's correlations, quantile–quantile (Q–Q) plots, and analysis of variance (ANOVA) were used to qualitatively and quantitatively evaluate the temporal distributions of soil moisture and evaporation over the study site. The relationships were examined against nearly 2 years of observation data, as well as for different seasons and for defined periods of analysis. Results highlight that while direct correlations of raw data were not particularly instructive, the Q–Q plots and ANOVA illustrate that the root-zone soil moisture represented by the CRNP measurements and the modelled evaporation estimates reflect similar distributions under most meteorological conditions. The PT-JPL and PM-Mu model estimates performed contrary to expectation when high soil moisture and cold temperatures were present, while SEBS model estimates displayed a disconnect from the soil moisture distribution in summers with long dry spells. Importantly, no single evaporation model matched the statistical distribution of the measured soil moisture for the entire period, highlighting the challenges in effectively capturing evaporative flux response within changing landscapes. One of the outcomes of this work is that the analysis points to the feasibility of using intermediate-scale soil moisture measurements to evaluate gridded estimates of evaporation, exploiting the independent, yet physically linked nature of these hydrological variables.
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Hill, MJ, GE Donald, PJ Vickery, and EP Furnival. "Integration of satellite remote sensing, simple bioclimatic models and GIS for assessment of pastoral development for a commercial grazing enterprise." Australian Journal of Experimental Agriculture 36, no. 3 (1996): 309. http://dx.doi.org/10.1071/ea9960309.
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Satellite imagery, climatic, edaphic and topographic data, and a simple bioclimatic model were used to analyse the pastoral potential of a large cattle grazing enterprise, 'Cooplacurripa', on the central coast of New South Wales. Landsat Thematic Mapper and Spot XS satellite images were processed to create pasture growth status maps describing timbered and cleared areas, and priority areas for phosphorus fertiliser application. A digital elevation model was used to describe slope and aspect, and as a basis for interpolation of monthly surfaces of rainfall, temperature, radiation and evaporation from the Australian Climate Surfaces. These data were combined with a simple growth index model to create production surfaces for tropical, subtropical and temperature pasture types. The property was classified into zones defined by the seasonal growth patterns of the 3 pasture classes. The database was used to identify potential areas for further pasture improvement by a series of logical rules and map overlays to define pasture suitability for currently timbered areas with a slope of less than 20�. The northern section of the property with the highest rainfall, best suitability for temperate pasture and good road access was chosen for land capability and fauna impact studies on the basis of this analysis. The study provides an example of the application of spatial data to practical management issues in pastoral agriculture.
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Bailey, Adam, Rosalind King, Simon Holford, Joshua Sage, Guillaume Backe, and Martin Hand. "Remote sensing of subsurface fractures in the Otway Basin, South Australia." Journal of Geophysical Research: Solid Earth 119, no. 8 (August 2014): 6591–612. http://dx.doi.org/10.1002/2013jb010843.
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Power, Hannah E., Michael A. Kinsela, Caio E. Stringari, Murray J. Kendall, and David J. Hanslow. "WAVE OVERWASH ON A ROCK PLATFORM: REMOTE SENSING AND PRESSURE SENSOR OBSERVATIONS." Coastal Engineering Proceedings, no. 36 (December 30, 2018): 29. http://dx.doi.org/10.9753/icce.v36.waves.29.
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Open ocean rocky shore platforms are typically exposed to high wave energy and are often the location of recreational activities from sightseeing and walking to fishing (Kennedy et al. 2017). The exposure of these environments, combined with the use for recreation, results in a high level of risk for those who use the rock platform. In Australia, for example, 19% of coastal fatalities occur on rock coasts, most commonly when individuals fall from microtidal semi-horizontal platforms into the ocean (SLSA, 2014a,b). Managing the hazards and resultant risk on rocky shore platforms requires a different approach to that taken for sandy beaches as the sites are typically remote. Here we explore the wave overwash hazards on a remote but high visitation rocky shore platform 40 km south of Sydney, Australia.
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Riquelme, Linda, David H. Duncan, Libby Rumpff, and Peter Anton Vesk. "Using Remote Sensing to Estimate Understorey Biomass in Semi-Arid Woodlands of South-Eastern Australia." Remote Sensing 14, no. 10 (May 13, 2022): 2358. http://dx.doi.org/10.3390/rs14102358.
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Monitoring ground layer biomass, and therefore forage availability, is important for managing large, vertebrate herbivore populations for conservation. Remote sensing allows for frequent observations over broad spatial scales, capturing changes in biomass over the landscape and through time. In this study, we explored different satellite-derived vegetation indices (VIs) for their utility in estimating understorey biomass in semi-arid woodlands of south-eastern Australia. Relationships between VIs and understorey biomass data have not been established in these particular semi-arid communities. Managers want to use forage availability to inform cull targets for western grey kangaroos (Macropus fuliginosus), to minimise the risk that browsing poses to regeneration in threatened woodland communities when grass biomass is low. We attempted to develop relationships between VIs and understorey biomass data collected over seven seasons across open and wooded vegetation types. Generalised Linear Mixed Models (GLMMs) were used to describe relationships between understorey biomass and VIs. Total understorey biomass (live and dead, all growth forms) was best described using the Tasselled Cap (TC) greenness index. The combined TC brightness and Modified Soil Adjusted Vegetation Index (MSAVI) ranked best for live understorey biomass (all growth forms), and grass (live and dead) biomass was best described by a combination of TC brightness and greenness indices. Models performed best for grass biomass, explaining 70% of variation in external validation when predicting to the same sites in a new season. However, we found empirical relationships were not transferrable to data collected from new sites. Including other variables (soil moisture, tree cover, and dominant understorey growth form) improved model performance when predicting to new sites. Anticipating a drop in forage availability is critical for the management of grazing pressure for woodland regeneration, however, predicting understorey biomass through space and time is a challenge. Whilst remotely sensed VIs are promising as an easily-available source of vegetation information, additional landscape-scale data are required before they can be considered a cost-efficient method of understorey biomass estimation in this semi-arid landscape.
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Hewson, R., D. Robson, A. Carlton, P. Gilmore, and Louis-Noel Moresi. "Geological application of ASTER remote sensing within sparsely outcropping terrain, Central New South Wales, Australia." Cogent Geoscience 3, no. 1 (January 1, 2017): 1319259. http://dx.doi.org/10.1080/23312041.2017.1319259.
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Hammad Abu Ghurah, Mousa, Mohd Khairul Amri Kamarudi, Noorjima Abd Wahab, Hafizan Juahir, Muhammad Barzani Gasim, Fathurrahman Lananan, Khairul Nizam Abdul Maulud, et al. "Assessment of Urban Growth and Sprawl Using GIS and Remote Sensing Techniques in South Ghor Region, Al-Karak, Jordan." International Journal of Engineering & Technology 7, no. 3.14 (July 25, 2018): 5. http://dx.doi.org/10.14419/ijet.v7i3.14.16853.
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The land is a primary natural resource and is essential for human practice and life on earth. Improvement of land use and land cover is very much helpful to policymakers, city planners and for the sustainability of urban ecosystems. The urbanization process (urban growth and sprawl) is one of the important prime factors of land cover/use change, and it is related to the growth and increase of populations and economic activities. This study aims to assess the urban growth and sprawl in South Ghor, Jordan. Supervised classification techniques were applied for LULC classification using four Satellite images (1972, 1989, 1999 and 2016). The land was classified into four categories agricultural land, pastures and bare land, urban area and water bodies. The supervised classification techniques show that urban area and agricultural land have increased while water bodies and bare land have decreased. They also show the rapid urban growth and sprawl in the study areas.
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Hwang, Charnsmorn, Chih-Hua Chang, Michael Burch, Milena Fernandes, and Tim Kildea. "Effects of Epiphytes and Depth on Seagrass Spectral Profiles: Case Study of Gulf St. Vincent, South Australia." International Journal of Environmental Research and Public Health 16, no. 15 (July 29, 2019): 2701. http://dx.doi.org/10.3390/ijerph16152701.
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Seagrasses are a crucial indicator species of coastal marine ecosystems that provide substratum, shelter, and food for epiphytic algae, invertebrates, and fishes. More accurate mapping of seagrasses is essential for their survival as a long-lasting natural resource. Before reflectance spectra could properly be used as remote sensing endmembers, factors that may obscure the detection of reflectance signals must be assessed. The objectives in this study are to determine the influence of (1) epiphytes, (2) water depth, and (3) seagrass genus on the detection of reflectance spectral signals. The results show that epiphytes significantly dampen bottom-type reflectance throughout most of the visible light spectrum, excluding 670–679 nm; the depth does influence reflectance, with the detection of deeper seagrasses being easier, and as the depth increases, only Heterozostera increase in the exact “red edge” wavelength at which there is a rapid change in the near-infrared (NIR) spectrum. These findings helped improve the detection of seagrass endmembers during remote sensing, thereby helping protect the natural resource of seagrasses.
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Aravena, Ricardo A., Mitchell B. Lyons, Adam Roff, and David A. Keith. "A Colourimetric Approach to Ecological Remote Sensing: Case Study for the Rainforests of South-Eastern Australia." Remote Sensing 13, no. 13 (June 29, 2021): 2544. http://dx.doi.org/10.3390/rs13132544.
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To facilitate the simplification, visualisation and communicability of satellite imagery classifications, this study applied visual analytics to validate a colourimetric approach via the direct and scalable measurement of hue angle from enhanced false colour band ratio RGB composites. A holistic visual analysis of the landscape was formalised by creating and applying an ontological image interpretation key from an ecological-colourimetric deduction for rainforests within the variegated landscapes of south-eastern Australia. A workflow based on simple one-class, one-index density slicing was developed to implement this deductive approach to mapping using freely available Sentinel-2 imagery and the super computing power from Google Earth Engine for general public use. A comprehensive accuracy assessment based on existing field observations showed that the hue from a new false colour blend combining two band ratio RGBs provided the best overall results, producing a 15 m classification with an overall average accuracy of 79%. Additionally, a new index based on a band ratio subtraction performed better than any existing vegetation index typically used for tropical evergreen forests with comparable results to the false colour blend. The results emphasise the importance of the SWIR1 band in discriminating rainforests from other vegetation types. While traditional vegetation indices focus on productivity, colourimetric measurement offers versatile multivariate indicators that can encapsulate properties such as greenness, wetness and brightness as physiognomic indicators. The results confirmed the potential for the large-scale, high-resolution mapping of broadly defined vegetation types.
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Van Niel, Thomas G., and Tim R. McVicar. "Determining temporal windows for crop discrimination with remote sensing: a case study in south-eastern Australia." Computers and Electronics in Agriculture 45, no. 1-3 (December 2004): 91–108. http://dx.doi.org/10.1016/j.compag.2004.06.003.
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Hunter, John. "Grasslands on Coastal Headlands in New South Wales, south eastern Australia." Vegetation Classification and Survey 1 (June 16, 2020): 111–22. http://dx.doi.org/10.3897/vcs/2020/48228.
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Aims: To use unsupervised techniques to produce a hierarchical classification of grasslands on coastal headlands of New South Wales in eastern Australia. Methods: A dataset of 520 vegetation plots scored on cover and placed across grasslands on coastal headlands (ca. 2000 km of coastline). Vegetation assemblages were identified with the aid of a clustering method based on group averaging and tested using similarity profile analysis (SIMPROF) using Bray-Curtis similarity. A hierarchical schema was developed based on EcoVeg hierarchy and was circumscribed using positive and negative diagnostic taxa via similarity percentage analysis (SIMPER) and importance based on summed cover scores and frequency. Mapping the occurrences grasslands was initially constructed using remote sensing which was verified and modified with on ground observations. Results: One group Themeda – Pultenaea – Zoysia – Cynodon grasslands and heathy grasslands was defined to include all coastal headland grassland vegetation of the New South Wales, and within this, three alliances and ten associations. Only one of the circumscribed associations is represented within the current state classification schema. In total 107 ha were mapped of which 68 ha occurred within secure conservation tenure. Conclusions: A number of unique and rare grassland assemblages on coastal headlands have to date gone undescribed. The most common alliance constitutes approximately 87% of extant grassland occurrences but is currently the only type listed as endangered and afforded protection. Although Poa spp. are listed as a threat to Themeda dominated assemblages on headlands data from this study suggest that this is unlikely to be the case. Taxonomic reference: PlantNET (http://plantnet/10rbgsyd.nsw.gov.au/; accessed June 2019). Abbreviations: BC Act = Biodiversity Conservation Act; NMDS = non-metric multidimensional scaling; NSW = New South Wales; PCT = Plant Community Type; SIMPER = similarity percentage analysis; SIMPROF = Similarity profile analysis.
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Ahmed, Alaa, Abdullah Alrajhi, and Abdulaziz S. Alquwaizany. "Identification of Groundwater Potential Recharge Zones in Flinders Ranges, South Australia Using Remote Sensing, GIS, and MIF Techniques." Water 13, no. 18 (September 17, 2021): 2571. http://dx.doi.org/10.3390/w13182571.
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In Australia, water resource management is a major environmental, biological, and socio-economic issue, and will be an essential component of future development. The Hawker Area of the central Flinders Ranges, South Australia suffers from a lack of reliable data to help with water resource management and decision making. The present study aimed to delineate and assess groundwater recharge potential (GWRP) zones using an integration between the remote sensing (RS), geographic information system (GIS), and multi-influencing factors (MIF) approaches in the Hawker Area of the Flinders Ranges, South Australia. Many thematic layers such as lithology, drainage density, slope, and lineament density were established in a GIS environment for the purpose of identifying groundwater recharge potential zones. A knowledge base ranking from 1 to 5 was assigned to each individual thematic layer and its categories, depending on each layer’s importance to groundwater recharge potential zones. All of the thematic layers were integrated to create a combined groundwater potential map of the study area using weighting analysis in ArcGIS software. The groundwater potential zones were categorized into three classes, good, moderate, and low. The resulting zones were verified using available water data and showed a relative consistency with the interpretations. The findings of this study indicated that the most effective groundwater potential recharge zones are located where the lineament density is high, the drainage density is low, and the slope is gentle. The least effective areas for groundwater recharge are underlain by shale and siltstone. The results indicated that there were interrelationships between the groundwater recharge potential factors and the general hydrology characteristics scores of the catchment. MIF analysis using GIS mapping techniques proved to be a very useful tool in the evaluation of hydrogeological systems and could enable decision makers to evaluate, better manage, and protect a hydrogeological system using a single platform.
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Ullah, Fahim, Sara Imran Khan, Hafiz Suliman Munawar, Zakria Qadir, and Siddra Qayyum. "UAV Based Spatiotemporal Analysis of the 2019–2020 New South Wales Bushfires." Sustainability 13, no. 18 (September 13, 2021): 10207. http://dx.doi.org/10.3390/su131810207.
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Bushfires have been a key concern for countries such as Australia for a long time. These must be mitigated to eradicate the associated harmful effects on the climate and to have a sustainable and healthy environment for wildlife. The current study investigates the 2019–2020 bushfires in New South Wales (NSW) Australia. The bush fires are mapped using Geographical Information Systems (GIS) and remote sensing, the hotpots are monitored, and damage is assessed. Further, an Unmanned Aerial Vehicles (UAV)-based bushfire mitigation framework is presented where the bushfires can be mapped and monitored instantly using UAV swarms. For the GIS and remote sensing, datasets of the Australian Bureau of Meteorology and VIIRS fire data products are used, whereas the paths of UAVs are optimized using the Particle Swarm Optimization (PSO) algorithm. The mapping results of 2019–2020 NSW bushfires show that 50% of the national parks of NSW were impacted by the fires, resulting in damage to 2.5 million hectares of land. The fires are highly clustered towards the north and southeastern cities of NSW and its border region with Victoria. The hotspots are in the Deua, Kosciu Sako, Wollemi, and Yengo National Parks. The current study is the first step towards addressing a key issue of bushfire disasters, in the Australian context, that can be adopted by its Rural Fire Service (RFS), before the next fire season, to instantly map, assess, and subsequently mitigate the bushfire disasters. This will help move towards a smart and sustainable environment.
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Pagay, Vinay, and Catherine M. Kidman. "Evaluating Remotely-Sensed Grapevine (Vitis vinifera L.) Water Stress Responses Across a Viticultural Region." Agronomy 9, no. 11 (October 25, 2019): 682. http://dx.doi.org/10.3390/agronomy9110682.
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The evolving spatial and temporal knowledge about vineyard performance through the use of remote sensing offers new perspectives for vine water status studies. This paper describes the application of aerial thermal imaging to evaluate vine water status to improve irrigation scheduling decisions, water use efficiency, and overall winegrape quality in the Coonawarra viticultural region of South Australia. Airborne infrared images were acquired during the 2016 and 2017 growing seasons in the region of Coonawarra, South Australia. Several thermal indices of crop water status (CWSI, Ig, (Tc-Ta)) were calculated that correlated with conventional soil and vine water status measures (Ψpd, Ψs, gs). CWSI and Ig could discriminate between the two cultivars used in this study, Cabernet Sauvignon (CAS) and Shiraz (SHI), as did the conventional water stress measures. The relationship between conventional vine water status measures appeared stronger with CWSI in the warmer and drier season (2016) compared to the cooler and wetter season (2017), where Ig and (Tc-Ta) showed stronger correlations. The study identified CWSI, Ig and (Tc-Ta) to be reliable indicators of vine water status under a variety of environmental conditions. This is the first study to report on high resolution vine water status at a regional scale in Australia using a combination of remote and direct sensing methods. This methodology is promising for aerial surveillance of vine water status across multiple blocks and cultivars to inform irrigation scheduling.
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Gibson, Rebecca, Tim Danaher, Warwick Hehir, and Luke Collins. "A remote sensing approach to mapping fire severity in south-eastern Australia using sentinel 2 and random forest." Remote Sensing of Environment 240 (April 2020): 111702. http://dx.doi.org/10.1016/j.rse.2020.111702.
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Shaikh, M., D. Green, and H. Cross. "A remote sensing approach to determine environmental flows for wetlands of the Lower Darling River, New South Wales, Australia." International Journal of Remote Sensing 22, no. 9 (January 2001): 1737–51. http://dx.doi.org/10.1080/01431160118063.
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Gale, Matthew G., Geoffrey J. Cary, Marta Yebra, Adam J. Leavesley, and Albert I. J. M. Van Dijk. "Comparison of contrasting optical and LiDAR fire severity remote sensing methods in a heterogeneous forested landscape in south-eastern Australia." International Journal of Remote Sensing 43, no. 7 (April 3, 2022): 2559–80. http://dx.doi.org/10.1080/01431161.2022.2064197.
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Murphy, R. J., A. J. Underwood, T. J. Tolhurst, and M. G. Chapman. "Field-based remote-sensing for experimental intertidal ecology: Case studies using hyperspatial and hyperspectral data for New South Wales (Australia)." Remote Sensing of Environment 112, no. 8 (August 2008): 3353–65. http://dx.doi.org/10.1016/j.rse.2007.09.016.
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Tran, Bang Nguyen, Mihai A. Tanase, Lauren T. Bennett, and Cristina Aponte. "High-severity wildfires in temperate Australian forests have increased in extent and aggregation in recent decades." PLOS ONE 15, no. 11 (November 18, 2020): e0242484. http://dx.doi.org/10.1371/journal.pone.0242484.
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Wildfires have increased in size and frequency in recent decades in many biomes, but have they also become more severe? This question remains under-examined despite fire severity being a critical aspect of fire regimes that indicates fire impacts on ecosystem attributes and associated post-fire recovery. We conducted a retrospective analysis of wildfires larger than 1000 ha in south-eastern Australia to examine the extent and spatial pattern of high-severity burned areas between 1987 and 2017. High-severity maps were generated from Landsat remote sensing imagery. Total and proportional high-severity burned area increased through time. The number of high-severity patches per year remained unchanged but variability in patch size increased, and patches became more aggregated and more irregular in shape. Our results confirm that wildfires in southern Australia have become more severe. This shift in fire regime may have critical consequences for ecosystem dynamics, as fire-adapted temperate forests are more likely to be burned at high severities relative to historical ranges, a trend that seems set to continue under projections of a hotter, drier climate in south-eastern Australia.
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Hannaford, Peter. "Foreword." Australian Journal of Physics 46, no. 1 (1993): 1. http://dx.doi.org/10.1071/ph930001.
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This special issue contains selected papers of Plenary and Keynote Lectures presented at the Tenth National Congress of the Australian Institute of Physics, held at the University of Melbourne from 10 to 14 February, 1992. The Congress was attended by nearly 1000 delegates, including numerous distinguished physici~ts from Australia and abroad, who were treated to a smorgasbord of physics ranging from astrophysics to particle physics. The Congress was organised around a series of fifteen separate sections, representing various branches of physics in which there is active Australian interest, and incorporated the First Conference of the Vacuum Society of Australia; the Fifth Gaseous Electronics Meeting; the Fourteenth AINSE Nuclear and Particle Physics Conference; the 1992 Physics Teachers Conference; the Third Australasian Conference on Remote Sensing of Atmospheres and Oceans; and the South Pacific Solar-Terrestrial and Space Physics Workshop.
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Bi, Xu, Bianrong Chang, Fen Hou, Zihan Yang, Qi Fu, and Bo Li. "Assessment of Spatio-Temporal Variation and Driving Mechanism of Ecological Environment Quality in the Arid Regions of Central Asia, Xinjiang." International Journal of Environmental Research and Public Health 18, no. 13 (July 2, 2021): 7111. http://dx.doi.org/10.3390/ijerph18137111.
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Grassland ecosystems are increasingly threatened by pressures from climate change and intensified human activity, especially in the arid region of Central Asia. A comprehensive understanding of the ecological environment changes is crucial for humans to implement environmental protection measures to adapt to climate change and alleviate the contradiction between humans and land. In this study, fractional vegetation coverage (FVC), leaf area index (LAI), gross primary productivity of vegetation (GPP), land surface temperature (LST), and wetness (WET) were retrieved from Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite remote sensing products in 2008 and 2018. Principal component analysis (PCA) was used to establish the MODIS data-based ecological index (MODEI) in the study area, and the spatial differentiation characteristics and driving mechanism of ecological quality in the last ten years were explored. The results showed that: (1) FVC, GPP, LAI, and WET had positive effects on the ecological environment, while LST had a negative impact on the ecological environment. FVC and GPP were more significant than other indicators. (2) The MODEI showed a spatial pattern of “excellent in the north and poor in the south” and changed from north to south in the study area. (3) From 2008 to 2018, the average MODEI of Fuyun County increased from 0.292 to 0.303, indicating that the ecological quality in Fuyun County became better overall. The improved areas were mainly located in the summer pastures at higher elevations. In comparison, the deteriorated areas were concentrated in the spring and autumn pastures and winter pastures at lower elevations. The areas where the ecological environment had obviously improved and degraded were distributed along the banks of the Irtysh River and the Ulungur River. (4) With the increase in precipitation and the decrease in grazing pressure, the MODEI of summer pasture was improved. The deterioration of ecological environment quality in spring and autumn pastures and winter pastures was related to the excessive grazing pressure. The more significant changes in the MODEI on both sides of the river were associated with implementing the herdsmen settlement project. On the one hand, the implementation of newly settled villages increased the area of construction land on both sides of the river, which led to the deterioration of ecological quality; on the other hand, due to the increase in cropland land and the planting of artificial grasses along the river, the ecological quality was improved. The study offers significant information for managers to make more targeted ecological restoration efforts in ecologically fragile areas.
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Coops, N., M. Stanford, K. Old, M. Dudzinski, D. Culvenor, and C. Stone. "Assessment of Dothistroma Needle Blight of Pinus radiata Using Airborne Hyperspectral Imagery." Phytopathology® 93, no. 12 (December 2003): 1524–32. http://dx.doi.org/10.1094/phyto.2003.93.12.1524.
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Dothistroma needle blight is a serious foliar disease in Australian Pinus radiata plantations causing defoliation, decreased productivity and, in extreme cases, tree death. Conventional methods of monitoring forest health such as aerial survey and ground assessments are labor intensive, time consuming, and subjective. Remote sensing provides a synoptic view of the canopy and can indicate areas affected by damaging agents such as pests and pathogens. Hyperspectral airborne remote sensing imagery (CASI-2) was acquired over pine stands in southern New South Wales, Australia which had been ground assessed and ranked on an individual tree basis, according to the extent of Dothistroma needle blight. A series of spectral indices were tested using two different approaches for extracting crown-scale reflectance measurements and relating these to ground-based estimates of severity. Dothistroma needle blight is most severe in the lower crown and statistically significant relationships were found between crown reflectance values and ground estimates using a ‘halo’ approach (which ignored each tree crown's brightest central pixels). Independent accuracy assessment of the method indicated that the technique could successfully detect three levels of Dothistroma needle blight infection with an accuracy of over 70%.
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Hwang, Charnsmorn, Chih-Hua Chang, Michael Burch, Milena Fernandes, and Tim Kildea. "Spectral Deconvolution for Dimension Reduction and Differentiation of Seagrasses: Case Study of Gulf St. Vincent, South Australia." Sustainability 11, no. 13 (July 5, 2019): 3695. http://dx.doi.org/10.3390/su11133695.
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Seagrasses are a vulnerable and declining coastal habitat, which provide shelter and substrate for aquatic microbiota, invertebrates, and fishes. More accurate mapping of seagrasses is imperative for their sustainability but is hindered by the lack of data on reflectance spectra representing the optical signatures of individual species. Objectives of this study are: (1) To determine distinct characteristics of spectral profiles for sand versus three temperate seagrasses (Posidonia, Amphibolis, and Heterozostera); (2) to evaluate the most efficient derivative analysis method of spectral reflectance profiles for determining benthic types; and to assess the influences of (3) site location and (4) the water column on spectral responses. Results show that 566:689 and 566:600 bandwidth ratios are useful in separating seagrasses from sand and from detritus and algae, respectively; first-derivative reflectance spectra generally is the most efficient method, especially with deconvolution analyses further helping to reveal and isolate 11 key wavelength dimensions; and differences between sites and water column composition, which can include suspended particulate matter, both have no effect on endmembers. These findings helped develop a spectral reflectance library that can be used as an endmember reference for remote sensing, thereby providing continued monitoring, assessment, and management of seagrasses.
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Stocks, Jerom R., Michael P. Rodgers, Joe B. Pera, and Dean M. Gilligan. "Monitoring aquatic plants: An evaluation of hydroacoustic, on-site digitising and airborne remote sensing techniques." Knowledge & Management of Aquatic Ecosystems, no. 420 (2019): 27. http://dx.doi.org/10.1051/kmae/2019016.
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Aquatic macrophytes are often monitored to detect change in ecosystem function and state, as well as assessing the effectiveness of invasive aquatic plant management. This study compares seven methodologies to monitor the distribution and abundances of aquatic macrophytes. Four line transect methodologies and three spatial mapping techniques were employed in parallel over a broad turbidity gradient in two lentic habitats of south-eastern Australia. The methodologies examined included hydroacoustic surveys, on-site digitising, and digitisation of airborne remote sensing imagery. Variation in estimates of macrophyte coverage were observed between methodologies. Consistency in the collection and interpretation of data was greatest for the line transect methodologies and the digitisation of satellite imagery. Duel-frequency identification sonar proved to be an effective novel hydroacoustic technique to monitor macrophyte abundances over broad spatial scales. Single beam sonar transects was also an objective, repeatable and scalable methodology. Videography and on-site handheld PDA mapping were of limited utility due to restrictions imposed by turbidity. The utility of sidescan sonar could be improved when used in conjunction with on-site handheld PDA mapping. This study outlines important considerations when selecting a methodology to monitor macrophyte distribution and abundance. Results indicate that no one specific method can be employed across all macrophyte monitoring studies. The method or combination of methods employed during macrophyte monitoring studies is dependent upon the study objectives, budget and environmental conditions of the study site.
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Ahmed, Alaa, Abdullah Alrajhi, Abdulaziz Alquwaizany, Ali Al Maliki, and Guna Hewa. "Flood Susceptibility Mapping Using Watershed Geomorphic Data in the Onkaparinga Basin, South Australia." Sustainability 14, no. 23 (December 6, 2022): 16270. http://dx.doi.org/10.3390/su142316270.
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In the near future, natural disasters and associated risks are expected to increase, mainly because of the impact of climate change. Australia is considered one of the most vulnerable areas for natural disasters, including flooding. Therefore, an evaluation of the morphometric characteristics of the Onkaparinga basin in South Australia was undertaken using the integration of remote sensing and geospatial techniques to identify its impact on flash floods. The Shuttle Radar Topography Mission (SRTM) and Landsat images with other available geologic, topographic, and secondary data were analysed in geographic information system (GIS) to outline the drainage basins, estimate the morphometric parameters, and rank the parameters to demarcate the flash flood susceptibility zones of the basin. The main goal was to develop a flash flood susceptibility map showing the different hazard zones within the study areas. The results showed that 10.87%, 24.27%, and 64.85% are classified as low, moderate, and highly susceptible for flooding, respectively. These findings were then verified against secondary data relating to the historic flood events of the area. About 30.77% of the historical floods are found located within the high to extremely susceptible zones. Moreover, a significant correlation has been found between the high precipitation concentration index (PCI) and the irregular rainfall and high potential for flooding. Finally, the social and economic vulnerability was applied to determine the impact of the flood hazards. The result indicates a widespread threat to the economy, environment, and community in the study area. This study can be utilized to support and assist decision makers with planning and the devotion of alleviation measures to reducing and avoiding catastrophic flooding events, especially in highly susceptible areas in the world, such as South Australian basins.
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Niel, T. G. Van, and T. R. McVicar. "A simple method to improve field-level rice identification: toward operational monitoring with satellite remote sensing." Australian Journal of Experimental Agriculture 43, no. 4 (2003): 379. http://dx.doi.org/10.1071/ea02182.
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Discriminating crops by remote sensing remains reasonably complex and expensive for many agricultural land managers. The current study was conducted to facilitate the operational use of remote sensing for field-level rice monitoring in Australia by determining (i) whether existing methods relating to simple moisture-based rice classification could be further simplified, and (ii) whether the high accuracies resulting from that moisture-based methodology could be further increased. First, the impact of removing the most complicated processing step, atmospheric correction, on rice classification accuracies was assessed for the 2000–01 summer growing season at the Coleambally Irrigation Area, New South Wales. The primary error sources of rice classification were then identified and simple rules developed in an attempt to reduce errors associated with confusion between unharvested winter cereals and flooded rice paddies early in the summer growing season. These newly defined rules were then used on imagery acquired in the subsequent summer growing season (2001–02) in order to assess their repeatability. The assessment of atmospheric correction showed that during the critical time frame associated with high rice identification (October–November), using non-atmospherically corrected data increased overall accuracy, although the improvement was small (about 1%). Overall accuracy also increased for every case tested for both growing seasons as a result of the rule-based classification (ranging from about 1 to 14%), revealing that the methods were sufficiently repeatable. This study moves per-field rice monitoring at the Coleambally Irrigation Area closer to an operational application and shows that simple rule-based remote sensing classifications can be very effective when site practices are known.
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Shavorin, Vitalij A., and Andrej E. Kuleshov. "MODERN METHODS OF GROUND-BASED INTERFEROMETRY IN MONITORING ADJACENT ROCK MASSES IN OPEN PIT MINING." Interexpo GEO-Siberia 1 (May 21, 2021): 111–18. http://dx.doi.org/10.33764/2618-981x-2021-1-111-118.
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This article analyzes modern monitoring methods using ground-based interferometric radars. Currently, there are only a few companies on the world market that offer interferometric scanning solutions. These are IDS (Italy), part of the Hexagon group, Groundprobe (Australia), Reutech (South Africa) and the very rare LISA (JRC-Lisalab) and GPRI (Gamma Remote Sensing). Such radars can be used in the open pit mine development of mineral resources for monitoring the safety of adjacent rock masses, dumps and dams. The article considers the general principle of interferometric radars’ operation and differences between them. It also describes different types of aerials, which are being used nowadays in ground-based interferometric scanning. The analysis results enabled to formulate the recommendations for using different type radars depending on the strategy of geotechnical monitoring.
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Tesche, Matthias, and Vincent Noel. "Locations for the best lidar view of mid-level and high clouds." Atmospheric Measurement Techniques 15, no. 14 (July 21, 2022): 4225–40. http://dx.doi.org/10.5194/amt-15-4225-2022.
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Abstract. Mid-level altocumulus clouds (Ac) and high cirrus clouds (Ci) can be considered natural laboratories for studying cloud glaciation in the atmosphere. While their altitude makes them difficult to access with in situ instruments, they can be conveniently observed from the ground with active remote-sensing instruments such as lidar and radar. However, active remote sensing of Ac and Ci at visible wavelengths with lidar requires a clear line of sight between the instrument and the target cloud. It is therefore advisable to carefully assess potential locations for deploying ground-based lidar instruments in field experiments or for long-term observations that are focused on mid- or high-level clouds. Here, observations of clouds with two spaceborne lidars are used to assess where ground-based lidar measurements of mid- and high-level clouds are least affected by the light-attenuating effect of low-level clouds. It is found that cirrus can be best observed in the tropics, the Tibetan Plateau, the western part of North America, the Atacama region, the southern tip of South America, Greenland, Antarctica, and parts of western Europe. For the observation of altocumulus, a ground-based lidar is best placed at Greenland, Antarctica, the western flank of the Andes and Rocky Mountains, the Amazon, central Asia, Siberia, western Australia, or the southern half of Africa.
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Attiya, Ali A., and Brian G. Jones. "Impact of Smoke Plumes Transport on Air Quality in Sydney during Extensive Bushfires (2019) in New South Wales, Australia Using Remote Sensing and Ground Data." Remote Sensing 14, no. 21 (November 3, 2022): 5552. http://dx.doi.org/10.3390/rs14215552.
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Smoke aerosol dispersion and transport have a significant impact on air quality levels and can be examined by environmental monitoring and modelling techniques. The purpose of this study is to determine the characteristics of the smoke aerosols and the level of air quality during November and December 2019 under the influence of extensive bushfires in the Sydney area, New South Wales (NSW), Australia. To achieve this goal, air quality and meteorological data were analysed in combination with remote sensing satellite measurements. Meteorological and air quality data were obtained from the Bureau of Meteorology (BOM) and Environmental Protection Agency monitoring sites in NSW. In Richmond the daily maximum average hourly concentration of particulate matter (PM10) was 848.9 μg/m3 at 07:00 UTC on 26 November 2019 and 785 μg/m3 at 07:00 UTC on 12 December 2019. On 10 December 2019, the highest PM10 recorded in the Sydney region was 961.5 μg/m3 in St Marys at 01:00 UTC, while the highest PM2.5 concentration was 714.6 μg/m3 in Oakdale in southwest Sydney at 18:00 UTC. These values all decreased again to the standard level (<50 μg/m3) in a few days. The potential sources of smoke aerosols originated from bushfires to the northwest of Sydney (Blue Mountains) as well as from southwest and northwest NSW. The smoke plumes were revealed by the combined AOD values from Aqua and Terra sensors on the MODIS satellite. In each case, the smoke travelled towards the east coast of Australia and out over the Pacific Ocean. The NAAPS model displays the existence of smoke at ground level, while the CALIPSO satellite data showed that the plumes extended 14 km up into the stratosphere layer. Backward trajectories obtained from the HYSPLIT model agree well with the movement of smoke plumes observed in the MODIS satellite images.
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Nguyen, Hiep Duc, Merched Azzi, Stephen White, David Salter, Toan Trieu, Geoffrey Morgan, Mahmudur Rahman, et al. "The Summer 2019–2020 Wildfires in East Coast Australia and Their Impacts on Air Quality and Health in New South Wales, Australia." International Journal of Environmental Research and Public Health 18, no. 7 (March 29, 2021): 3538. http://dx.doi.org/10.3390/ijerph18073538.
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The 2019–2020 summer wildfire event on the east coast of Australia was a series of major wildfires occurring from November 2019 to end of January 2020 across the states of Queensland, New South Wales (NSW), Victoria and South Australia. The wildfires were unprecedent in scope and the extensive character of the wildfires caused smoke pollutants to be transported not only to New Zealand, but also across the Pacific Ocean to South America. At the peak of the wildfires, smoke plumes were injected into the stratosphere at a height of up to 25 km and hence transported across the globe. The meteorological and air quality Weather Research and Forecasting with Chemistry (WRF-Chem) model is used together with the air quality monitoring data collected during the bushfire period and remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellites to determine the extent of the wildfires, the pollutant transport and their impacts on air quality and health of the exposed population in NSW. The results showed that the WRF-Chem model using Fire Emission Inventory (FINN) from National Center for Atmospheric Research (NCAR) to simulate the dispersion and transport of pollutants from wildfires predicted the daily concentration of PM2.5 having the correlation (R2) and index of agreement (IOA) from 0.6 to 0.75 and 0.61 to 0.86, respectively, when compared with the ground-based data. The impact on health endpoints such as mortality and respiratory and cardiovascular diseases hospitalizations across the modelling domain was then estimated. The estimated health impact on each of the Australian Bureau of Statistics (ABS) census districts (SA4) of New South Wales was calculated based on epidemiological assumptions of the impact function and incidence rate data from the 2016 ABS and NSW Department of Health statistical health records. Summing up all SA4 census district results over NSW, we estimated that there were 247 (CI: 89, 409) premature deaths, 437 (CI: 81, 984) cardiovascular diseases hospitalizations and 1535 (CI: 493, 2087) respiratory diseases hospitalizations in NSW over the period from 1 November 2019 to 8 January 2020. The results are comparable with a previous study based only on observation data, but the results in this study provide much more spatially and temporally detailed data with regard to the health impact from the summer 2019–2020 wildfires.
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Cammarano, Davide, Glenn Fitzgerald, Bruno Basso, Deli Chen, Peter Grace, and Garry O'Leary. "Remote estimation of chlorophyll on two wheat cultivars in two rainfed environments." Crop and Pasture Science 62, no. 4 (2011): 269. http://dx.doi.org/10.1071/cp10100.
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For this study we hypothesise that the use of canopy chlorophyll content index (CCCI) and crop greenness will be useful in assessing crop nutritional status and provide a robust management tool by growth stage DC30 for fertiliser application across multiple sites without being confounded by soil and biomass differences. The objectives of this study were: (i) to study the robustness of the CCCI and greenness as a measure of crop N content at two different locations, and (ii) to validate the model developed for crop nitrogen (N) determination. Data were collected from two rain-fed field sites cropped to wheat, one in Southern Italy (Foggia) and the other in the south-eastern wheat belt of Australia (Horsham). Data collection was conducted during the growing season in 2006–07 (December–June) for the Italian site and during the 2006 and 2007 (June–December) growing seasons for the Australian site. Measurements included crop biophysical properties (leaf area index (LAI), biomass, crop N concentration), hyperspectral remote sensing data, and SPAD (chlorophyll meter) determination. An independent dataset including SPAD, biomass, and remotely sensed data from Horsham (Australia) was used to test the validity of the model developed. Results showed that there is good correlation between SPAD and crop N content. The relationship between greenness (measured as LAI*SPAD) and CCCI was fitted with an exponential model and was not affected by biomass accumulation or soil reflectance (r2 = 0.85; y = 15.1e4.5424x; P < 0.001). When this model was tested on the independent dataset it yielded good results for the estimation of greenness (y = 1.22x − 54.87; r2 = 0.90; P < 0.001; root mean square error 32.2; relative error 15%). In conclusion, SPAD measurements combined with LAI could be used as a crop nutritional management tool by DC30 for fertiliser application across multiple sites.
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Wu, Qi, Nuredin Habili, Fiona Constable, Maher Al Rwahnih, Darius E. Goszczynski, Yeniu Wang, and Vinay Pagay. "Virus Pathogens in Australian Vineyards with an Emphasis on Shiraz Disease." Viruses 12, no. 8 (July 28, 2020): 818. http://dx.doi.org/10.3390/v12080818.
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Grapevine viruses are found throughout the viticultural world and have detrimental effects on vine productivity and grape and wine quality. This report provides a comprehensive and up-to-date review on grapevine viruses in Australia with a focus on “Shiraz Disease” (SD) and its two major associated viruses, grapevine virus A (GVA) and grapevine leafroll-associated virus 3 (GLRaV-3). Sensitive grapevine cultivars like Shiraz infected with GVA alone or with a co-infection of a leafroll virus, primarily GLRaV-3, show symptoms of SD leading to significant yield and quality reductions in Australia and in South Africa. Symptom descriptors for SD will be outlined and a phylogenetic tree will be presented indicating the SD-associated isolates of GVA in both countries belong to the same clade. Virus transmission, which occurs through infected propagation material, grafting, and naturally vectored by mealybugs and scale insects, will be discussed. Laboratory and field-based indexing will also be discussed along with management strategies including rogueing and replanting certified stock that decrease the incidence and spread of SD. Finally, we present several cases of SD incidence in South Australian vineyards and their effects on vine productivity. We conclude by offering strategies for virus detection and management that can be adopted by viticulturists. Novel technologies such as high throughput sequencing and remote sensing for virus detection will be outlined.
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Ahmed, Alaa, Chathuri Ranasinghe-Arachchilage, Abdullah Alrajhi, and Guna Hewa. "Comparison of Multicriteria Decision-Making Techniques for Groundwater Recharge Potential Zonation: Case Study of the Willochra Basin, South Australia." Water 13, no. 4 (February 18, 2021): 525. http://dx.doi.org/10.3390/w13040525.
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In semi-arid regions, groundwater resources play a crucial role in all economic, environmental, and social processes. However, the occurrence, movement, and recharge of these hidden and valuable resources vary from place to place. Therefore, better management practices and mapping of groundwater recharge potential zones are needed for the sustainable groundwater resources. For an example, groundwater resources in Willochra Basin are vitally important for drinking, irrigation, and stock use. This study shows the significance of the application of three decision-making approaches, including multi-influencing factor, analytical hierarchy process, and frequency ratio techniques in the identification of groundwater potential zones. A total of seven criteria, including lithology, slope, soil texture, land-use, rainfall, drainage density, and lineament density, were extracted from conventional and remote sensing data sources. The parameters and their assigned weights were integrated using Geographic Information System (GIS) software to generate recharge potential maps. The resultant maps were evaluated using the area under the curve method. The results showed that the southern regions of the Willochra Basin are more promising for groundwater recharge potential. The map produced using the frequency ratio model was the most efficient (84%), followed by the multi-influencing factor model (70%) and then the analytical hierarchy process technique (62%). The area under the curve method agreed when evaluated using published weights and rating values.
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Handcock, R. N., D. L. Gobbett, L. A. González, G. J. Bishop-Hurley, and S. L. McGavin. "Combining multi-spectral proximal sensors and digital cameras for monitoring grazed tropical pastures." Biogeosciences Discussions 12, no. 21 (November 11, 2015): 18007–51. http://dx.doi.org/10.5194/bgd-12-18007-2015.
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Abstract. Timely and accurate monitoring of pasture biomass and ground-cover is necessary in livestock production systems to ensure productive and sustainable management of forage for livestock. Interest in the use of proximal sensors for monitoring pasture status in grazing systems has increased, since such sensors can return data in near real-time, and have the potential to be deployed on large properties where remote sensing may not be suitable due to issues such as spatial scale or cloud cover. However, there are unresolved challenges in developing calibrations to convert raw sensor data to quantitative biophysical values, such as pasture biomass or vegetation ground-cover, to allow meaningful interpretation of sensor data by livestock producers. We assessed the use of multiple proximal sensors for monitoring tropical pastures with a pilot deployment of sensors at two sites on Lansdown Research Station near Townsville, Australia. Each site was monitored by a Skye SKR-four-band multi-spectral sensor (every 1 min), a digital camera (every 30 min), and a soil moisture sensor (every 1 min), each operated over 18 months. Raw data from each sensor were processed to calculate a number of multispectral vegetation indices. Visual observations of pasture characteristics, including above-ground standing biomass and ground cover, were made every 2 weeks. A methodology was developed to manage the sensor deployment and the quality control of the data collected. The data capture from the digital cameras was more reliable than the multi-spectral sensors, which had up to 63 % of data discarded after data cleaning and quality control. We found a strong relationship between sensor and pasture measurements during the wet season period of maximum pasture growth (January to April), especially when data from the multi-spectral sensors were combined with weather data. RatioNS34 (a simple band ratio between the near infrared (NIR) and lower shortwave infrared (SWIR) bands) and rainfall since 1 September explained 91 % of the variation in above-ground standing biomass (RSE = 593 kg DM ha−1, p < 0.01). RatioNS34 together with rainfall explained 95 % of the variation in the percentage of green vegetation observed in 2-dimensions (%Green2D) (RSE = 6 %, p < 0.01). The Green Leaf Algorithm index derived from the digital camera images and the rainfall accumulated since the 1 September explained 91 % of the variation in %Green2D (RSE = 9 %, p < 0.01, df = 20), but had a poor relationship with biomass. Although proximal sensors observe only a small area of the pasture, they deliver continual and timely pasture measurements to inform timely decision-making on-farm.
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Ndalila, Mercy N., Grant J. Williamson, Paul Fox-Hughes, Jason Sharples, and David M. J. S. Bowman. "Evolution of a pyrocumulonimbus event associated with an extreme wildfire in Tasmania, Australia." Natural Hazards and Earth System Sciences 20, no. 5 (May 27, 2020): 1497–511. http://dx.doi.org/10.5194/nhess-20-1497-2020.
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Abstract. Extreme fires have substantial adverse effects on society and natural ecosystems. Such events can be associated with the intense coupling of fire behaviour with the atmosphere, resulting in extreme fire characteristics such as pyrocumulonimbus cloud (pyroCb) development. Concern that anthropogenic climate change is increasing the occurrence of pyroCbs globally is driving more focused research into these meteorological phenomena. Using 6 min scans from a nearby weather radar, we describe the development of a pyroCb during the afternoon of 4 January 2013 above the Forcett–Dunalley fire in south-eastern Tasmania. We relate storm development to (1) near-surface weather using the McArthur forest fire danger index (FFDI) and the C-Haines index, the latter of which is a measure of the vertical atmospheric stability and dryness, both derived from gridded weather reanalysis for Tasmania (BARRA-TA); and (2) a chronosequence of fire severity derived from remote sensing. We show that the pyroCb rapidly developed over a 24 min period on the afternoon of 4 January, with the cloud top reaching a height of 15 km. The pyroCb was associated with a highly unstable lower atmosphere (C-Haines value of 10–11) and severe–marginally extreme (FFDI 60–75) near-surface fire weather, and it formed over an area of forest that was severely burned (total crown defoliation). We use spatial patterns of elevated fire weather in Tasmania and fire weather during major runs of large wildfires in Tasmania for the period from 2007 to 2016 to geographically and historically contextualise this pyroCb event. Although the Forcett–Dunalley fire is the only known record of a pyroCb in Tasmania, our results show that eastern and south-eastern Tasmania are prone to the conjunction of high FFDI and C-Haines values that have been associated with pyroCb development. Our findings have implications for fire weather forecasting and wildfire management, and they highlight the vulnerability of south-east Tasmania to extreme fire events.
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Tao, Yuanyuan, and Qianxin Wang. "Quantitative Recognition and Characteristic Analysis of Production-Living-Ecological Space Evolution for Five Resource-Based Cities: Zululand, Xuzhou, Lota, Surf Coast and Ruhr." Remote Sensing 13, no. 8 (April 17, 2021): 1563. http://dx.doi.org/10.3390/rs13081563.
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The accurate identification of PLES changes and the discovery of their evolution characteristics is a key issue to improve the ability of the sustainable development for resource-based urban areas. However, the current methods are unsuitable for the long-term and large-scale PLES investigation. In this study, a modified method of PLES recognition is proposed based on the remote sensing image classification and land function evaluation technology. A multi-dimensional index system is constructed, which can provide a comprehensive evaluation for PLES evolution characteristics. For validation of the proposed methods, the remote sensing image, geographic information, and socio-economic data of five resource-based urbans (Zululand in South Africa, Xuzhou in China, Lota in Chile, Surf Coast in Australia, and Ruhr in Germany) from 1975 to 2020 are collected and tested. The results show that the data availability and calculation efficiency are significantly improved by the proposed method, and the recognition precision is better than 87% (Kappa coefficient). Furthermore, the PLES evolution characteristics show obvious differences at the different urban development stages. The expansions of production, living, and ecological space are fastest at the mining, the initial, and the middle ecological restoration stages, respectively. However, the expansion of living space is always increasing at any stage, and the disorder expansion of living space has led to the decrease of integration of production and ecological spaces. Therefore, the active polices should be formulated to guide the transformation of the living space expansion from jumping-type and spreading-type to filling-type, and the renovation of abandoned industrial and mining lands should be encouraged.
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Brennan, Jameson R., Patricia S. Johnson, and Niall P. Hanan. "Comparing stability in random forest models to map Northern Great Plains plant communities in pastures occupied by prairie dogs using Pleiades imagery." Biogeosciences 17, no. 5 (March 11, 2020): 1281–92. http://dx.doi.org/10.5194/bg-17-1281-2020.
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Abstract. Black-tailed prairie dogs (Cynomys ludovicianus) have been described as a keystone species and are important for grassland conservation, yet many concerns exist over the impact of prairie dogs on plant biomass production and consequently livestock production. The ability to map plant communities in pastures colonized by prairie dogs can provide land managers with an opportunity to optimize rangeland production while balancing conservation goals. The aim of this study was to test the ability of random forest (RF) models to classify five plant communities located on and off prairie dog towns in mixed-grass prairie landscapes of north central South Dakota, assess the stability of RF models among different years, and determine the utility of utilizing remote sensing techniques to identify prairie dog colony extent. During 2015 and 2016, Pleiades satellites were tasked to image the study site for a total of five monthly collections each summer (June–October). Training polygons were mapped in 2016 for the five plant communities and used to train RF models. Both the 2015 and 2016 RF models had low (1 %) out-of-bag error rates. However, comparisons between the predicted plant community maps using the 2015 imagery and one created with the 2016 imagery indicate over 32.9 % of pixels changed plant community class between 2015 and 2016. The results show that while RF models may predict with a high degree of accuracy, overlap of plant communities and interannual differences in rainfall may cause instability in fitted models. A final RF model combining both 2015 and 2016 data yielded the lowest error rates and was also highly accurate in determining prairie dog colony boundaries.
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Malerba, Martino E., Nicholas Wright, and Peter I. Macreadie. "A Continental-Scale Assessment of Density, Size, Distribution and Historical Trends of Farm Dams Using Deep Learning Convolutional Neural Networks." Remote Sensing 13, no. 2 (January 18, 2021): 319. http://dx.doi.org/10.3390/rs13020319.
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Farm dams are a ubiquitous limnological feature of agricultural landscapes worldwide. While their primary function is to capture and store water, they also have disproportionally large effects on biodiversity and biogeochemical cycling, with important relevance to several Sustainable Development Goals (SDGs). However, the abundance and distribution of farm dams is unknown in most parts of the world. Therefore, we used artificial intelligence and remote sensing data to address this critical global information gap. Specifically, we trained a deep learning convolutional neural network (CNN) on high-definition satellite images to detect farm dams and carry out the first continental-scale assessment on density, distribution and historical trends. We found that in Australia there are 1.765 million farm dams that occupy an area larger than Rhode Island (4678 km2) and store over 20 times more water than Sydney Harbour (10,990 GL). The State of New South Wales recorded the highest number of farm dams (654,983; 37% of the total) and Victoria the highest overall density (1.73 dams km−2). We also estimated that 202,119 farm dams (11.5%) remain omitted from any maps, especially in South Australia, Western Australia and the Northern Territory. Three decades of historical records revealed an ongoing decrease in the construction rate of farm dams, from >3% per annum before 2000, to ~1% after 2000, to <0.05% after 2010—except in the Australian Capital Territory where rates have remained relatively high. We also found systematic trends in construction design: farm dams built in 2015 are on average 50% larger in surface area and contain 66% more water than those built in 1989. To facilitate sharing information on sustainable farm dam management with authorities, scientists, managers and local communities, we developed AusDams.org—a free interactive portal to visualise and generate statistics on the physical, environmental and ecological impacts of farm dams.
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Handcock, Rebecca N., D. L. Gobbett, Luciano A. González, Greg J. Bishop-Hurley, and Sharon L. McGavin. "A pilot project combining multispectral proximal sensors and digital cameras for monitoring tropical pastures." Biogeosciences 13, no. 16 (August 22, 2016): 4673–95. http://dx.doi.org/10.5194/bg-13-4673-2016.
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Abstract. Timely and accurate monitoring of pasture biomass and ground cover is necessary in livestock production systems to ensure productive and sustainable management. Interest in the use of proximal sensors for monitoring pasture status in grazing systems has increased, since data can be returned in near real time. Proximal sensors have the potential for deployment on large properties where remote sensing may not be suitable due to issues such as spatial scale or cloud cover. There are unresolved challenges in gathering reliable sensor data and in calibrating raw sensor data to values such as pasture biomass or vegetation ground cover, which allow meaningful interpretation of sensor data by livestock producers. Our goal was to assess whether a combination of proximal sensors could be reliably deployed to monitor tropical pasture status in an operational beef production system, as a precursor to designing a full sensor deployment. We use this pilot project to (1) illustrate practical issues around sensor deployment, (2) develop the methods necessary for the quality control of the sensor data, and (3) assess the strength of the relationships between vegetation indices derived from the proximal sensors and field observations across the wet and dry seasons. Proximal sensors were deployed at two sites in a tropical pasture on a beef production property near Townsville, Australia. Each site was monitored by a Skye SKR-four-band multispectral sensor (every 1 min), a digital camera (every 30 min), and a soil moisture sensor (every 1 min), each of which were operated over 18 months. Raw data from each sensor was processed to calculate multispectral vegetation indices. The data capture from the digital cameras was more reliable than the multispectral sensors, which had up to 67 % of data discarded after data cleaning and quality control for technical issues related to the sensor design, as well as environmental issues such as water incursion and insect infestations. We recommend having a system with both sensor types to aid in data interpretation and troubleshooting technical issues. Non-destructive observations of pasture characteristics, including above-ground standing biomass and fractional ground cover, were made every 2 weeks. This simplified data collection was designed for multiple years of sampling at the remote site, but had the disadvantage of high measurement uncertainty. A bootstrapping method was used to explore the strength of the relationships between sensor and pasture observations. Due to the uncertainty in the field observations, the relationships between sensor and field data are not confirmational and should be used only to inform the design of future work. We found the strongest relationships occurred during the wet season period of maximum pasture growth (January to April), with generally poor relationships outside of this period. Strong relationships were found with multispectral indices that were sensitive to the green and dry components of the vegetation, such as those containing the band in the lower shortwave infrared (SWIR) region of the electromagnetic spectrum. During the wet season the bias-adjusted bootstrap point estimate of the R2 between above-ground biomass and the normalized ratio between the SWIR and red bands (NVI-SR) was 0.72 (95 % CI of 0.28 to 0.98), while that for the percentage of green vegetation observed in three dimensions and a simple ratio between the near infrared and SWIR bands (RatioNS34) was 0.81 (95 % CI of 0.53 to 1.00). Relationships between field data and the vegetation index derived from the digital camera images were generally weaker than from the multispectral sensor data, except for green vegetation observations in two and three dimensions. Our successful pilot of multiple proximal sensors supports the design of future deployments in tropical pastures and their potential for operational use. The stringent rules we developed for data cleaning can be more broadly applied to other sensor projects to ensure quality data. Although proximal sensors observe only a small area of the pasture, they deliver continual and timely pasture measurements to inform timely on-farm decision-making.
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Yang, Xihua, Qinggaozi Zhu, Mitch Tulau, Sally McInnes-Clarke, Liying Sun, and Xiaoping Zhang. "Near real-time monitoring of post-fire erosion after storm events: a case study in Warrumbungle National Park, Australia." International Journal of Wildland Fire 27, no. 6 (2018): 413. http://dx.doi.org/10.1071/wf18011.
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Wildfires in national parks can lead to severe damage to property and infrastructure, and adverse impacts on the environment. This is especially pronounced if wildfires are followed by intense storms, such as the fire in Warrumbungle National Park in New South Wales, Australia, in early 2013. The aims of this study were to develop and validate a methodology to predict erosion risk at near real-time after storm events, and to provide timely information for monitoring of the extent, magnitude and impact of hillslope erosion to assist park management. We integrated weather radar-based estimates of rainfall erosivity with the revised universal soil loss equation (RUSLE) and remote sensing to predict soil loss from individual storm events after the fire. Other RUSLE factors were estimated from high resolution digital elevation models (LS factor), satellite data (C factor) and recent digital soil maps (K factor). The accuracy was assessed against field measurements at twelve soil plots across the Park and regular field survey during the 5-year period after the fire (2013–17). Automated scripts in a geographical information system have been developed to process large quantity spatial data and produce time-series erosion risk maps which show spatial and temporal changes in hillslope erosion and groundcover across the Park at near real time.
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Mancilla-Ruiz, Diana, Francisco de la Barrera, Sergio González, and Ana Huaico. "The Effects of a Megafire on Ecosystem Services and the Pace of Landscape Recovery." Land 10, no. 12 (December 15, 2021): 1388. http://dx.doi.org/10.3390/land10121388.
Full textAbstract:
(1) Background: Megafires have affected several regions in the world (e.g., Australia, California), including, in 2017, the central and south-central zones of Chile. These areas represent real laboratories to monitor the impacts on the sustainability of landscapes and their recovery after fires. The present research examines the modification of dynamics and the provision of ecosystem services by a megafire in a Mediterranean landscape in central Chile, combining remote sensing technologies and ecosystem service assessments. (2) Methods: Land cover and spectral indices (NBRI, BAIS-2, NDVI, and EVI) were measured using Sentinel-2 imagery, while the provision of ecosystem services was evaluated using an expert-based matrix. (3) Results: The megafire affected forest plantations, formerly the dominant land cover, as well as other ecosystems, e.g., native forests. After five years, the landscape is dominated by exotic shrublands and grasslands. (4) Conclusions: The megafire caused a loss of 50% of the landscape’s capacity to supply ecosystem services. Given that native forests are the best provider of ecosystem services in this landscape, restoration is a key to recovering landscape sustainability.
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Bartley, Rebecca, Jeff P. Corfield, Aaron A. Hawdon, Anne E. Kinsey-Henderson, Brett N. Abbott, Scott N. Wilkinson, and Rex J. Keen. "Can changes to pasture management reduce runoff and sediment loss to the Great Barrier Reef? The results of a 10-year study in the Burdekin catchment, Australia." Rangeland Journal 36, no. 1 (2014): 67. http://dx.doi.org/10.1071/rj13013.
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Excess sediments from agricultural areas are having a detrimental impact on the Great Barrier Reef, and threaten the long-term viability of rangeland grazing. Changes to grazing management have been promoted as a mechanism for reducing excess sediment loss from grazed rangelands. This paper summarises the results of a 10-year study (2002–11) on a property in the Burdekin catchment that investigated the role of reduced stocking rates and rotational wet season resting on hill-slope and catchment runoff and sediment yields. Ground cover and pasture biomass were evaluated using on-ground surveys and remote sensing. During this study, average ground cover increased from ~35 to ~80% but pasture biomass was low due to the dominance of Bothriochloa pertusa (77% of composition). The percentage of deep-rooted perennial species increased from ~7% of pasture composition in 2002 to ~15% in 2011. This is still considerably lower than the percentage that occupied this property in 1979 (~78%). The increased ground cover resulted in progressively lower hill-slope runoff coefficients for the first event in each wet season, but annual catchment runoff did not respond significantly to the increasing ground cover during the study. Hill-slope and catchment sediment concentrations did decline with the increased ground cover, yet catchment sediment yields increased proportionally to annual runoff due to the contribution of sub-surface (scald, gully and bank) erosion. This study has demonstrated that changes to grazing management can reduce sediment concentrations leaving B. pertusa-dominated pastures, as B. pertusa is an effective controller of surface erosion. To further reduce the runoff that is fuelling gully and bank erosion, the proportion of deep-rooted native perennial grasses needs to be increased. It is argued that more than 10 years will be required to restore healthy eco-hydrological function to these previously degraded and low productivity rangelands. Even longer timescales will be needed to meet current targets for water quality.
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Bartley, Rebecca, Jeff P. Corfield, Aaron A. Hawdon, Anne E. Kinsey-Henderson, Brett N. Abbott, Scott N. Wilkinson, and Rex J. Keen. "Corrigendum to: Can changes to pasture management reduce runoff and sediment loss to the Great Barrier Reef? The results of a 10-year study in the Burdekin catchment, Australia." Rangeland Journal 36, no. 3 (2014): 311. http://dx.doi.org/10.1071/rj13013_co.
Full textAbstract:
Excess sediments from agricultural areas are having a detrimental impact on the Great Barrier Reef, and threaten the long-term viability of rangeland grazing. Changes to grazing management have been promoted as a mechanism for reducing excess sediment loss from grazed rangelands. This paper summarises the results of a 10-year study (2002–11) on a property in the Burdekin catchment that investigated the role of reduced stocking rates and rotational wet season resting on hill-slope and catchment runoff and sediment yields. Ground cover and pasture biomass were evaluated using on-ground surveys and remote sensing. During this study, average ground cover increased from ~35 to ~80% but pasture biomass was low due to the dominance of Bothriochloa pertusa (77% of composition). The percentage of deep-rooted perennial species increased from ~7% of pasture composition in 2002 to ~15% in 2011. This is still considerably lower than the percentage that occupied this property in 1979 (~78%). The increased ground cover resulted in progressively lower hill-slope runoff coefficients for the first event in each wet season, but annual catchment runoff did not respond significantly to the increasing ground cover during the study. Hill-slope and catchment sediment concentrations did decline with the increased ground cover, yet catchment sediment yields increased proportionally to annual runoff due to the contribution of sub-surface (scald, gully and bank) erosion. This study has demonstrated that changes to grazing management can reduce sediment concentrations leaving B. pertusa-dominated pastures, as B. pertusa is an effective controller of surface erosion. To further reduce the runoff that is fuelling gully and bank erosion, the proportion of deep-rooted native perennial grasses needs to be increased. It is argued that more than 10 years will be required to restore healthy eco-hydrological function to these previously degraded and low productivity rangelands. Even longer timescales will be needed to meet current targets for water quality.
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Lu, Hua, Ian P. Prosser, Chris J. Moran, John C. Gallant, Graeme Priestley, and Janelle G. Stevenson. "Predicting sheetwash and rill erosion over the Australian continent." Soil Research 41, no. 6 (2003): 1037. http://dx.doi.org/10.1071/sr02157.
Full textAbstract:
Soil erosion is a major environmental issue in Australia. It reduces land productivity and has off-site effects of decreased water quality. Broad-scale spatially distributed soil erosion estimation is essential for prioritising erosion control programs and as a component of broader assessments of natural resource condition. This paper describes spatial modelling methods and results that predict sheetwash and rill erosion over the Australian continent using the revised universal soil loss equation (RUSLE) and spatial data layers for each of the contributing environmental factors. The RUSLE has been used before in this way but here we advance the quality of estimation. We use time series of remote sensing imagery and daily rainfall to incorporate the effects of seasonally varying cover and rainfall intensity, and use new digital maps of soil and terrain properties. The results are compared with a compilation of Australian erosion plot data, revealing an acceptable consistency between predictions and observations. The modelling results show that: (1) the northern part of Australia has greater erosion potential than the south; (2) erosion potential differs significantly between summer and winter; (3) the average erosion rate is 4.1 t/ha.year over the continent and about 2.9 × 109 tonnes of soil is moved annually which represents 3.9% of global soil erosion from 5% of world land area; and (4) the erosion rate has increased from 4 to 33 times on average for agricultural lands compared with most natural vegetated lands.
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Xie, Senyang, Zhi Huang, and Xiao Hua Wang. "Remotely Sensed Seasonal Shoreward Intrusion of the East Australian Current: Implications for Coastal Ocean Dynamics." Remote Sensing 13, no. 5 (February 25, 2021): 854. http://dx.doi.org/10.3390/rs13050854.
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For decades, the presence of a seasonal intrusion of the East Australian Current (EAC) has been disputed. In this study, with a Topographic Position Index (TPI)-based image processing technique, we use a 26-year satellite Sea Surface Temperature (SST) dataset to quantitatively map the EAC off northern New South Wales (NSW, Australia, 28–32°S and ~154°E). Our mapping products have enabled direct measurement (“distance” and “area”) of the EAC’s shoreward intrusion, and the results show that the EAC intrusion exhibits seasonal cycles, moving closer to the coast in austral summer than in winter. The maximum EAC-to-coast distance usually occurs during winter, ranging from 30 to 40 km. In contrast, the minimum distance usually occurs during summer, ranging from 15 to 25 km. Further spatial analyses indicate that the EAC undergoes a seasonal shift upstream of 29°40′S and seasonal widening downstream. This is the first time that the seasonality of the EAC intrusion has been confirmed by long-term remote-sensing observation. The findings provide new insights into seasonal upwelling and shelf circulation previously observed off the NSW coast.
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Karunaratne, S. B., T. F. A. Bishop, J. S. Lessels, J. A. Baldock, and I. O. A. Odeh. "A space–time observation system for soil organic carbon." Soil Research 53, no. 6 (2015): 647. http://dx.doi.org/10.1071/sr14178.
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In this paper, we present a framework for a space–time observation system for soil organic carbon (STOS-SOC). We propose that the RothC model be embedded within the STOS-SOC, which is driven by satellite-derived inputs and readily available geospatial inputs, such as digital soil maps. In particular, advances in remote sensing have enabled the development of satellite products that represent key inputs into soil carbon models, examples being evapotranspiration and biomass inputs to soil, which characterise space–time variations in management and land use. Starting from an initial calibrated base for prediction, as new observations are acquired, data assimilation techniques could be used to optimise calibration algorithms and predicted model outputs. We present initial results obtained from the implementation of the proposed STOS-SOC approach to the 1445-km2 Cox’s Creek catchment in northern New South Wales, Australia. Our results showed that use of satellite-derived biomass inputs with a MODIS satellite product (MOD17A3) improved the accuracy of simulations by 16% compared with carbon inputs derived through other methods normally adopted in the spatialisation of the RothC model. We further discuss the possibility of improving the capabilities of the STOS-SOC for future applications.
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Singh, Sachchidanand, Harikesh Singh, Vishal Sharma, Vaibhav Shrivastava, Pankaj Kumar, Shruti Kanga, Netrananda Sahu, Gowhar Meraj, Majid Farooq, and Suraj Kumar Singh. "Impact of Forest Fires on Air Quality in Wolgan Valley, New South Wales, Australia—A Mapping and Monitoring Study Using Google Earth Engine." Forests 13, no. 1 (December 21, 2021): 4. http://dx.doi.org/10.3390/f13010004.
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Forests are an important natural resource and are instrumental in sustaining environmental sustainability. Burning biomass in forests results in greenhouse gas emissions, many of which are long-lived. Precise and consistent broad-scale monitoring of fire intensity is a valuable tool for analyzing climate and ecological changes related to fire. Remote sensing and geographic information systems provide an opportunity to improve current practice’s accuracy and performance. Spectral indices techniques such as normalized burn ratio (NBR) have been used to identify burned areas utilizing satellite data, which aid in distinguishing burnt areas using their standard spectral responses. For this research, we created a split-panel web-based Google Earth Engine app for the geo-visualization of the region severely affected by forest fire using Sentinel 2 weekly composites. Then, we classified the burn severity in areas affected by forest fires in Wolgan Valley, New South Wales, Australia, and the surrounding area through Difference Normalized Burn Ratio (dNBR). The result revealed that the region’s burnt area increased to 6731 sq. km in December. We also assessed the impact of long-term rainfall and land surface temperature (LST) trends over the study region to justify such incidents. We further estimated the effect of such incidents on air quality by analyzing the changes in the column number density of carbon monoxide and nitrogen oxides. The result showed a significant increase of about 272% for Carbon monoxide and 45% for nitrogen oxides. We conclude that, despite fieldwork constraints, the usage of different NBR and web-based application platforms may be highly useful for forest management to consider the propagation of fire regimes.
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Proctor, R., K. Roberts, and B. J. Ward. "A data delivery system for IMOS, the Australian Integrated Marine Observing System." Advances in Geosciences 28 (September 27, 2010): 11–16. http://dx.doi.org/10.5194/adgeo-28-11-2010.
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Abstract. The Integrated Marine Observing System (IMOS, www.imos.org.au), an AUD $150 m 7-year project (2007–2013), is a distributed set of equipment and data-information services which, among many applications, collectively contribute to meeting the needs of marine climate research in Australia. The observing system provides data in the open oceans around Australia out to a few thousand kilometres as well as the coastal oceans through 11 facilities which effectively observe and measure the 4-dimensional ocean variability, and the physical and biological response of coastal and shelf seas around Australia. Through a national science rationale IMOS is organized as five regional nodes (Western Australia – WAIMOS, South Australian – SAIMOS, Tasmania – TASIMOS, New SouthWales – NSWIMOS and Queensland – QIMOS) surrounded by an oceanic node (Blue Water and Climate). Operationally IMOS is organized as 11 facilities (Argo Australia, Ships of Opportunity, Southern Ocean Automated Time Series Observations, Australian National Facility for Ocean Gliders, Autonomous Underwater Vehicle Facility, Australian National Mooring Network, Australian Coastal Ocean Radar Network, Australian Acoustic Tagging and Monitoring System, Facility for Automated Intelligent Monitoring of Marine Systems, eMarine Information Infrastructure and Satellite Remote Sensing) delivering data. IMOS data is freely available to the public. The data, a combination of near real-time and delayed mode, are made available to researchers through the electronic Marine Information Infrastructure (eMII). eMII utilises the Australian Academic Research Network (AARNET) to support a distributed database on OPeNDAP/THREDDS servers hosted by regional computing centres. IMOS instruments are described through the OGC Specification SensorML and where-ever possible data is in CF compliant netCDF format. Metadata, conforming to standard ISO 19115, is automatically harvested from the netCDF files and the metadata records catalogued in the OGC GeoNetwork Metadata Entry and Search Tool (MEST). Data discovery, access and download occur via web services through the IMOS Ocean Portal (http://imos.aodn.org.au) and tools for the display and integration of near real-time data are in development.
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Ahmed, A. A. Masrur, Ekta Sharma, S. Janifer Jabin Jui, Ravinesh C. Deo, Thong Nguyen-Huy, and Mumtaz Ali. "Kernel Ridge Regression Hybrid Method for Wheat Yield Prediction with Satellite-Derived Predictors." Remote Sensing 14, no. 5 (February 25, 2022): 1136. http://dx.doi.org/10.3390/rs14051136.
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Wheat dominates the Australian grain production market and accounts for 10–15% of the world’s 100 million tonnes annual global wheat trade. Accurate wheat yield prediction is critical to satisfying local consumption and increasing exports regionally and globally to meet human food security. This paper incorporates remote satellite-based information in a wheat-growing region in South Australia to estimate the yield by integrating the kernel ridge regression (KRR) method coupled with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the grey wolf optimisation (GWO). The hybrid model, ‘GWO-CEEMDAN-KRR,’ employing an initial pool of 23 different satellite-based predictors, is seen to outperform all the benchmark models and all the feature selection (ant colony, atom search, and particle swarm optimisation) methods that are implemented using a set of carefully screened satellite variables and a feature decomposition or CEEMDAN approach. A suite of statistical metrics and infographics comparing the predicted and measured yield shows a model prediction error that can be reduced by ~20% by employing the proposed GWO-CEEMDAN-KRR model. With the metrics verifying the accuracy of simulations, we also show that it is possible to optimise the wheat yield to achieve agricultural profits by quantifying and including the effects of satellite variables on potential yield. With further improvements in the proposed methodology, the GWO-CEEMDAN-KRR model can be adopted in agricultural yield simulation that requires remote sensing data to establish the relationships between crop health, yield, and other productivity features to support precision agriculture.