Journal articles on the topic 'Remote sensing South Australia'
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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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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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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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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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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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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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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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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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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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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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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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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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Putri Yuliana and Pakhrur Razi. "Mapping Coastline Changes In The Mentawai Islands Using Remote Sensing." Georest 1, no. 1 (August 4, 2022): 1–6. http://dx.doi.org/10.57265/georest.v1i1.2.
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The Mentawai Islands are an active deformation zone caused by the movement of the Indo-Australian plate under the Eurasian plate at a speed of 5-6 cm/year. The movement of these plates that occur continuously can be a source of earthquake disasters in the future. However, the information on how much the value of the distance to the coastline changes and the direction of the change has not been well mapped. In this study, a mapping of shoreline changes caused by plate movement along the coast in the Mentawai Islands was carried out using Landsat Imagery. The method used in this research is the Overlay method between Landsat Imagery 2005,2010, and 2020 using ArcGIS software and the Digital Shoreline Analysis System (DSAS) which is used to determine the distance between shoreline changes and the direction of the change. Based on the results of the study, it was found that the coast of the Mentawai Islands showed a change in the coastline with the average distance of change, namely Siberut Island of -2,109 m, Sipora Island at -2,979 m, North Pagai Island at -3,282 m, and South Pagai Island -1,557 m. From the results obtained, the distance of significant shoreline changes that occur in South Pagai Island with the direction of change towards the Northeast.
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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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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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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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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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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.
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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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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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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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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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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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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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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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Higgisson, William, Adrian Cobb, Alica Tschierschke, and Fiona Dyer. "The Role of Environmental Water and Reedbed Condition on the Response of Phragmites australis Reedbeds to Flooding." Remote Sensing 14, no. 8 (April 13, 2022): 1868. http://dx.doi.org/10.3390/rs14081868.
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Globally, wetlands have experienced significant declines in area and condition. Reedbeds are a key attribute of many wetlands and are typically composed of Phragmites australis (common reed), a globally distributed emergent aquatic perennial grass. Environmental water is increasingly used to support functioning river and floodplain ecosystems, including reedbeds, where maintaining wetland vegetation condition is a common objective. Drone-based remote sensing allows for the consistent collection of high-quality data in locations such as wetlands where access is limited. We used unoccupied aerial vehicles (UAVs) and convolutional neural networks (CNNs) to estimate the cover of Phragmites australis and examine the role of reedbed condition and prior environmental watering in the response of reedbeds to flooding. Data were collected from a large inland reedbed in semi-arid western New South Wales, Australia between October 2019 and March 2021 using UAVs and processed using CNNs. Prior to the flood event, sites that had received environmental water had a significantly greater cover of Phragmites australis. The sites that were not managed with environmental water had very low cover (<1%) of reeds prior to the flood event and transitioned from a Critical condition to a Poor or Medium condition following flooding. Using UAVs and CNNs we demonstrated the role environmental water plays in filling the gaps between large flood events and maintaining the condition and resilience of reedbeds.
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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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Rattray, Alex, Daniel Ierodiaconou, Laurie Laurenson, Shoaib Burq, and Marcus Reston. "Hydro-acoustic remote sensing of benthic biological communities on the shallow South East Australian continental shelf." Estuarine, Coastal and Shelf Science 84, no. 2 (September 2009): 237–45. http://dx.doi.org/10.1016/j.ecss.2009.06.023.
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Klein, Igor, Natascha Oppelt, and Claudia Kuenzer. "Application of Remote Sensing Data for Locust Research and Management—A Review." Insects 12, no. 3 (March 9, 2021): 233. http://dx.doi.org/10.3390/insects12030233.
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Recently, locust outbreaks around the world have destroyed agricultural and natural vegetation and caused massive damage endangering food security. Unusual heavy rainfalls in habitats of the desert locust (Schistocerca gregaria) and lack of monitoring due to political conflicts or inaccessibility of those habitats lead to massive desert locust outbreaks and swarms migrating over the Arabian Peninsula, East Africa, India and Pakistan. At the same time, swarms of the Moroccan locust (Dociostaurus maroccanus) in some Central Asian countries and swarms of the Italian locust (Calliptamus italicus) in Russia and China destroyed crops despite developed and ongoing monitoring and control measurements. These recent events underline that the risk and damage caused by locust pests is as present as ever and affects 100 million of human lives despite technical progress in locust monitoring, prediction and control approaches. Remote sensing has become one of the most important data sources in locust management. Since the 1980s, remote sensing data and applications have accompanied many locust management activities and contributed to an improved and more effective control of locust outbreaks and plagues. Recently, open-access remote sensing data archives as well as progress in cloud computing provide unprecedented opportunity for remote sensing-based locust management and research. Additionally, unmanned aerial vehicle (UAV) systems bring up new prospects for a more effective and faster locust control. Nevertheless, the full capacity of available remote sensing applications and possibilities have not been exploited yet. This review paper provides a comprehensive and quantitative overview of international research articles focusing on remote sensing application for locust management and research. We reviewed 110 articles published over the last four decades, and categorized them into different aspects and main research topics to summarize achievements and gaps for further research and application development. The results reveal a strong focus on three species—the desert locust, the migratory locust (Locusta migratoria), and the Australian plague locust (Chortoicetes terminifera)—and corresponding regions of interest. There is still a lack of international studies for other pest species such as the Italian locust, the Moroccan locust, the Central American locust (Schistocerca piceifrons), the South American locust (Schistocerca cancellata), the brown locust (Locustana pardalina) and the red locust (Nomadacris septemfasciata). In terms of applied sensors, most studies utilized Advanced Very-High-Resolution Radiometer (AVHRR), Satellite Pour l’Observation de la Terre VEGETATION (SPOT-VGT), Moderate-Resolution Imaging Spectroradiometer (MODIS) as well as Landsat data focusing mainly on vegetation monitoring or land cover mapping. Application of geomorphological metrics as well as radar-based soil moisture data is comparably rare despite previous acknowledgement of their importance for locust outbreaks. Despite great advance and usage of available remote sensing resources, we identify several gaps and potential for future research to further improve the understanding and capacities of the use of remote sensing in supporting locust outbreak- research and management.
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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.
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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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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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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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Johnston, RM, and MM Barson. "Remote sensing of Australian wetlands: An evaluation of Landsat TM data for inventory and classification." Marine and Freshwater Research 44, no. 2 (1993): 235. http://dx.doi.org/10.1071/mf9930235.
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This study aimed to develop simple remote-sensing techniques suitable for mapping and monitoring wetlands, using Landsat TM imagery of inland wetland sites in Victoria and New South Wales. A range of classification methods was examined in attempts to map the location and extent of wetlands and their vegetation types. Multi-temporal imagery (winter/spring and summer) was used to display seasonal variability in water regime and vegetation status. Simple density slicing of the mid-infrared band (TM5) from imagery taken during wet conditions was useful for mapping the location and extent of inundated areas. None of the classification methods tested reproduced field maps of dominant vegetation species; however, density slicing of multi-temporal imagery produced classes based on seasonal variation in water regime and vegetation status that are useful for reconnaissance mapping and for examining variability in previously mapped units. Satellite imagery is unlikely to replace aerial photography for detailed mapping of wetland vegetation types, particularly where ecological gradients are steep, as in many riverine systems. However, it has much to offer in monitoring changes in water regime and in reconnaissance mapping at regional scales.
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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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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.
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(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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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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Cogato, Alessia, Vinay Pagay, Francesco Marinello, Franco Meggio, Peter Grace, and Massimiliano De Antoni Migliorati. "Assessing the Feasibility of Using Sentinel-2 Imagery to Quantify the Impact of Heatwaves on Irrigated Vineyards." Remote Sensing 11, no. 23 (December 2, 2019): 2869. http://dx.doi.org/10.3390/rs11232869.
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Heatwaves are common in many viticultural regions of Australia. We evaluated the potential of satellite-based remote sensing to detect the effects of high temperatures on grapevines in a South Australian vineyard over the 2016–2017 and 2017–2018 seasons. The study involved: (i) comparing the normalized difference vegetation index (NDVI) from medium- and high-resolution satellite images; (ii) determining correlations between environmental conditions and vegetation indices (Vis); and (iii) identifying VIs that best indicate heatwave effects. Pearson’s correlation and Bland–Altman testing showed a significant agreement between the NDVI of high- and medium-resolution imagery (R = 0.74, estimated difference −0.093). The band and the VI most sensitive to changes in environmental conditions were 705 nm and enhanced vegetation index (EVI), both of which correlated with relative humidity (R = 0.65 and R = 0.62, respectively). Conversely, SWIR (short wave infrared, 1610 nm) exhibited a negative correlation with growing degree days (R = −0.64). The analysis of heat stress showed that green and red edge bands—the chlorophyll absorption ratio index (CARI) and transformed chlorophyll absorption ratio index (TCARI)—were negatively correlated with thermal environmental parameters such as air and soil temperature and growing degree days (GDDs). The red and red edge bands—the soil-adjusted vegetation index (SAVI) and CARI2—were correlated with relative humidity. To the best of our knowledge, this is the first study demonstrating the effectiveness of using medium-resolution imagery for the detection of heat stress on grapevines in irrigated vineyards.
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Helmholz, P., S. Zlatanova, J. Barton, and M. Aleksandrov. "GEOINFORMATION FOR DISASTER MANAGEMENT 2020 (Gi4DM2020): PREFACE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-3/W1-2020 (November 18, 2020): 1–3. http://dx.doi.org/10.5194/isprs-archives-xliv-3-w1-2020-1-2020.
Full textAbstract:
Abstract. Across the world, nature-triggered disasters fuelled by climate change are worsening. Some two billion people have been affected by the consequences of natural hazards over the last ten years, 95% of which were weather-related (such as floods and windstorms). Fires swept across large parts of California, and in Australia caused unprecedented destruction to lives, wildlife and bush. This picture is likely to become the new normal, and indeed may worsen if unchecked. The Intergovernmental Panel on Climate Change (IPCC) estimates that in some locations, disaster that once had a once-in-a-century frequency may become annual events by 2050.Disaster management needs to keep up. Good cooperation and coordination of crisis response operations are of critical importance to react rapidly and adequately to any crisis situation, while post-disaster recovery presents opportunities to build resilience towards reducing the scale of the next disaster. Technology to support crisis response has advanced greatly in the last few years. Systems for early warning, command and control and decision-making have been successfully implemented in many countries and regions all over the world. Efforts to improve humanitarian response, in particular in relation to combating disasters in rapidly urbanising cities, have also led to better approaches that grapple with complexity and uncertainty.The challenges however are daunting. Many aspects related to the efficient collection and integration of geo-information, applied semantics and situational awareness for disaster management are still open, while agencies, organisations and governmental authorities need to improve their practices for building better resilience.Gi4DM 2020 marked the 13th edition of the Geoinformation for Disaster Management series of conferences. The first conference was held in 2005 in the aftermath of the 2004 Indian Ocean earthquake and tsunami which claimed the lives of over 220,000 civilians. The 2019-20 Australian Bushfire Season saw some 18.6 million Ha of bushland burn, 5,900 buildings destroyed and nearly three billion vertebrates killed. Gi4DM 2020 then was held during Covid-19 pandemic, which took the lives of more than 1,150,000 people by the time of the conference. The pandemic affected the organisation of the conference, but the situation also provided the opportunity to address important global problems.The fundamental goal of the Gi4DM has always been to provide a forum where emergency responders, disaster managers, urban planners, stakeholders, researchers, data providers and system developers can discuss challenges, share experience, discuss new ideas and demonstrate technology. The 12 previous editions of Gi4DM conferences were held in Delft, the Netherlands (March 2005), Goa, India (September 2006), Toronto, Canada (May 2007), Harbin, China (August 2008), Prague, Czech Republic (January 2009), Torino, Italy (February 2010), Antalya, Turkey (May 2011), Enschede, the Netherlands (December, 2012), Hanoi, Vietnam (December 2013), Montpellier, France (2015), Istanbul, Turkey (2018) and Prague, Czech Republic (2019). Through the years Gi4DM has been organised in cooperation with different international bodies such as ISPRS, UNOOSA, ICA, ISCRAM, FIG, IAG, OGC and WFP and supported by national organisations.Gi4DM 2020 was held as part of Climate Change and Disaster Management: Technology and Resilience for a Troubled World. The event took place through the whole week of 30th of November to 4th of December, Sydney, Australia and included three events: Gi4DM 2020, NSW Surveying and Spatial Sciences Institute (NSW SSSI) annual meeting and Urban Resilience Asia Pacific 2 (URAP2).The event explored two interlinked aspects of disaster management in relation to climate change. The first was geo-information technologies and their application for work in crisis situations, as well as sensor and communication networks and their roles for improving situational awareness. The second aspect was resilience, and its role and purpose across the entire cycle of disaster management, from pre-disaster preparedness to post-disaster recovery including challenges and opportunities in relation to rapid urbanisation and the role of security in improved disaster management practices.This volume consists of 22 scientific papers. These were selected on the basis of double-blind review from among the 40 short papers submitted to the Gi4DM 2020 conference. Each paper was reviewed by two scientific reviewers. The authors of the papers were encouraged to revise, extend and adapt their papers to reflect the comments of the reviewers and fit the goals of this volume. The selected papers concentrate on monitoring and analysis of various aspects related to Covid-19 (4), emergency response (4), earthquakes (3), flood (2), forest fire, landslides, glaciers, drought, land cover change, crop management, surface temperature, address standardisation and education for disaster management. The presented methods range from remote sensing, LiDAR and photogrammetry on different platforms to GIS and Web-based technologies. Figure 1 illustrates the covered topics via wordcount of keywords and titles.The Gi4DM 2020 program consisted of scientific presentations, keynote speeches, panel discussions and tutorials. The four keynotes speakers Prof Suzan Cutter (Hazard and Vulnerability Research Institute, USC, US), Jeremy Fewtrell (NSW Fire and Rescue, Australia), Prof Orhan Altan (Ad-hoc Committee on RISK and Disaster Management, GeoUnions, Turkey) and Prof Philip Gibbins (Fenner School of Environment and Society, ANU, Australia) concentrated on different aspects of disaster and risk management in the context of climate change. Eight tutorials offered exciting workshops and hands-on on: Semantic web tools and technologies within Disaster Management, Structure-from-motion photogrammetry, Radar Remote Sensing, Dam safety: Monitoring subsidence with SAR Interferometry, Location-based Augmented Reality apps with Unity and Mapbox, Visualising bush fires datasets using open source, Making data smarter to manage disasters and emergency situational awareness and Response using HERE Location Services. The scientific sessions were blended with panel discussions to provide more opportunities to exchange ideas and experiences, connect people and researchers from all over the world.The editors of this volume acknowledge all members of the scientific committee for their time, careful review and valuable comments: Abdoulaye Diakité (Australia), Alexander Rudloff (Germany), Alias Abdul Rahman (Malaysia), Alper Yilmaz (USA), Amy Parker (Australia), Ashraf Dewan (Australia), Bapon Shm Fakhruddin (New Zealand), Batuhan Osmanoglu (USA), Ben Gorte (Australia), Bo Huang (Hong Kong), Brendon McAtee (Australia), Brian Lee (Australia), Bruce Forster (Australia), Charity Mundava (Australia), Charles Toth (USA), Chris Bellman (Australia), Chris Pettit (Australia), Clive Fraser (Australia), Craig Glennie (USA), David Belton (Australia), Dev Raj Paudyal (Australia), Dimitri Bulatov (Germany), Dipak Paudyal (Australia), Dorota Iwaszczuk (Germany), Edward Verbree (The Netherlands), Eliseo Clementini (Italy), Fabio Giulio Tonolo (Italy), Fazlay Faruque (USA), Filip Biljecki (Singapore), Petra Helmholz (Australia), Francesco Nex (The Netherlands), Franz Rottensteiner (Germany), George Sithole (South Africa), Graciela Metternicht (Australia), Haigang Sui (China), Hans-Gerd Maas (Germany), Hao Wu (China), Huayi Wu (China), Ivana Ivanova (Australia), Iyyanki Murali Krishna (India), Jack Barton (Australia), Jagannath Aryal (Australia), Jie Jiang (China), Joep Compvoets (Belgium), Jonathan Li (Canada), Kourosh Khoshelham (Australia), Krzysztof Bakuła (Poland), Lars Bodum (Denmark), Lena Halounova (Czech Republic), Madhu Chandra (Germany), Maria Antonia Brovelli (Italy), Martin Breunig (Germany), Martin Tomko (Australia), Mila Koeva (The Netherlands), Mingshu Wang (The Netherlands), Mitko Aleksandrov (Australia), Mulhim Al Doori (UAE), Nancy Glenn (Australia), Negin Nazarian (Australia), Norbert Pfeifer (Austria), Norman Kerle (The Netherlands), Orhan Altan (Turkey), Ori Gudes (Australia), Pawel Boguslawski (Poland), Peter van Oosterom (The Netherlands), Petr Kubíček (Czech Republic), Petros Patias (Greece), Piero Boccardo (Italy), Qiaoli Wu (China), Qing Zhu (China), Riza Yosia Sunindijo (Australia), Roland Billen (Belgium), Rudi Stouffs (Singapore), Scott Hawken (Australia), Serene Coetzee (South Africa), Shawn Laffan (Australia), Shisong Cao (China), Sisi Zlatanova (Australia), Songnian Li (Canada), Stephan Winter (Australia), Tarun Ghawana (Australia), Ümit Işıkdağ (Turkey), Wei Li (Australia), Wolfgang Reinhardt (Germany), Xianlian Liang (Finland) and Yanan Liu (China).The editors would like to express their gratitude to all contributors, who made this volume possible. Many thanks go to all supporting organisations: ISPRS, SSSI, URAP2, Blackash, Mercury and ISPRS Journal of Geoinformation. The editors are grateful to the continued support of the involved Universities: The University of New South Wales, Curtin University, Australian National University and The University of Melbourne.
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Helmholz, P., S. Zlatanova, J. Barton, and M. Aleksandrov. "GEOINFORMATION FOR DISASTER MANAGEMENT 2020 (GI4DM2020): PREFACE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences VI-3/W1-2020 (November 17, 2020): 1–2. http://dx.doi.org/10.5194/isprs-annals-vi-3-w1-2020-1-2020.
Full textAbstract:
Abstract. Across the world, nature-triggered disasters fuelled by climate change are worsening. Some two billion people have been affected by the consequences of natural hazards over the last ten years, 95% of which were weather-related (such as floods and windstorms). Fires swept across large parts of California, and in Australia caused unprecedented destruction to lives, wildlife and bush. This picture is likely to become the new normal, and indeed may worsen if unchecked. The Intergovernmental Panel on Climate Change (IPCC) estimates that in some locations, disaster that once had a once-in-a-century frequency may become annual events by 2050.Disaster management needs to keep up. Good cooperation and coordination of crisis response operations are of critical importance to react rapidly and adequately to any crisis situation, while post-disaster recovery presents opportunities to build resilience towards reducing the scale of the next disaster. Technology to support crisis response has advanced greatly in the last few years. Systems for early warning, command and control and decision-making have been successfully implemented in many countries and regions all over the world. Efforts to improve humanitarian response, in particular in relation to combating disasters in rapidly urbanising cities, have also led to better approaches that grapple with complexity and uncertainty.The challenges however are daunting. Many aspects related to the efficient collection and integration of geo-information, applied semantics and situational awareness for disaster management are still open, while agencies, organisations and governmental authorities need to improve their practices for building better resilience.Gi4DM 2020 marked the 13th edition of the Geoinformation for Disaster Management series of conferences. The first conference was held in 2005 in the aftermath of the 2004 Indian Ocean earthquake and tsunami which claimed the lives of over 220,000 civilians. The 2019-20 Australian Bushfire Season saw some 18.6 million Ha of bushland burn, 5,900 buildings destroyed and nearly three billion vertebrates killed. Gi4DM 2020 then was held during Covid-19 pandemic, which took the lives of more than 1,150,000 people by the time of the conference. The pandemic affected the organisation of the conference, but the situation also provided the opportunity to address important global problems.The fundamental goal of the Gi4DM has always been to provide a forum where emergency responders, disaster managers, urban planners, stakeholders, researchers, data providers and system developers can discuss challenges, share experience, discuss new ideas and demonstrate technology. The 12 previous editions of Gi4DM conferences were held in Delft, the Netherlands (March 2005), Goa, India (September 2006), Toronto, Canada (May 2007), Harbin, China (August 2008), Prague, Czech Republic (January 2009), Torino, Italy (February 2010), Antalya, Turkey (May 2011), Enschede, the Netherlands (December, 2012), Hanoi, Vietnam (December 2013), Montpellier, France (2015), Istanbul, Turkey (2018) and Prague, Czech Republic (2019). Through the years Gi4DM has been organised in cooperation with different international bodies such as ISPRS, UNOOSA, ICA, ISCRAM, FIG, IAG, OGC and WFP and supported by national organisations.Gi4DM 2020 was held as part of Climate Change and Disaster Management: Technology and Resilience for a Troubled World. The event took place through the whole week of 30th of November to 4th of December, Sydney, Australia and included three events: Gi4DM 2020, NSW Surveying and Spatial Sciences Institute (NSW SSSI) annual meeting and Urban Resilience Asia Pacific 2 (URAP2).The event explored two interlinked aspects of disaster management in relation to climate change. The first was geo-information technologies and their application for work in crisis situations, as well as sensor and communication networks and their roles for improving situational awareness. The second aspect was resilience, and its role and purpose across the entire cycle of disaster management, from pre-disaster preparedness to post-disaster recovery including challenges and opportunities in relation to rapid urbanisation and the role of security in improved disaster management practices.This volume consists of 16 peer-reviewed scientific papers. These were selected on the basis of double-blind review from among the 25 full papers submitted to the Gi4DM 2020 conference. Each paper was reviewed by three scientific reviewers. The authors of the papers were encouraged to revise, extend and adapt their papers to reflect the comments of the reviewers and fit the goals of this volume. The selected papers concentrate on monitoring and analysis of forest fire (3), landslides (3), flood (2), earthquake, avalanches, water pollution, heat, evacuation and urban sustainability, applying a variety of remote sensing, GIS and Web-based technologies. Figure 1 illustrates the scope of the covered topics though the word count of keywords and titles.The Gi4DM 2020 program consisted of scientific presentations, keynote speeches, panel discussions and tutorials. The four keynotes speakers Prof Suzan Cutter (Hazard and Vulnerability Research Institute, USC, US), Jeremy Fewtrell (NSW Fire and Rescue, Australia), Prof Orhan Altan (Ad-hoc Committee on RISK and Disaster Management, GeoUnions, Turkey) and Prof Philip Gibbins (Fenner School of Environment and Society, ANU, Australia) concentrated on different aspects of disaster and risk management in the context of climate change. Eight tutorials offered exciting workshops and hands-on on: Semantic web tools and technologies within Disaster Management, Structure-from-motion photogrammetry, Radar Remote Sensing, Dam safety: Monitoring subsidence with SAR Interferometry, Location-based Augmented Reality apps with Unity and Mapbox, Visualising bush fires datasets using open source, Making data smarter to manage disasters and emergency situational awareness and Response using HERE Location Services. The scientific sessions were blended with panel discussions to provide more opportunities to exchange ideas and experiences, connect people and researchers from all over the world.The editors of this volume acknowledge all members of the scientific committee for their time, careful review and valuable comments: Abdoulaye Diakité (Australia), Alexander Rudloff (Germany), Alias Abdul Rahman (Malaysia), Alper Yilmaz (USA), Amy Parker (Australia), Ashraf Dewan (Australia), Bapon Shm Fakhruddin (New Zealand), Batuhan Osmanoglu (USA), Ben Gorte (Australia), Bo Huang (Hong Kong), Brendon McAtee (Australia), Brian Lee (Australia), Bruce Forster (Australia), Charity Mundava (Australia), Charles Toth (USA), Chris Bellman (Australia), Chris Pettit (Australia), Clive Fraser (Australia), Craig Glennie (USA), David Belton (Australia), Dev Raj Paudyal (Australia), Dimitri Bulatov (Germany), Dipak Paudyal (Australia), Dorota Iwaszczuk (Germany), Edward Verbree (The Netherlands), Eliseo Clementini (Italy), Fabio Giulio Tonolo (Italy), Fazlay Faruque (USA), Filip Biljecki (Singapore), Petra Helmholz (Australia), Francesco Nex (The Netherlands), Franz Rottensteiner (Germany), George Sithole (South Africa), Graciela Metternicht (Australia), Haigang Sui (China), Hans-Gerd Maas (Germany), Hao Wu (China), Huayi Wu (China), Ivana Ivanova (Australia), Iyyanki Murali Krishna (India), Jack Barton (Australia), Jagannath Aryal (Australia), Jie Jiang (China), Joep Compvoets (Belgium), Jonathan Li (Canada), Kourosh Khoshelham (Australia), Krzysztof Bakuła (Poland), Lars Bodum (Denmark), Lena Halounova (Czech Republic), Madhu Chandra (Germany), Maria Antonia Brovelli (Italy), Martin Breunig (Germany), Martin Tomko (Australia), Mila Koeva (The Netherlands), Mingshu Wang (The Netherlands), Mitko Aleksandrov (Australia), Mulhim Al Doori (UAE), Nancy Glenn (Australia), Negin Nazarian (Australia), Norbert Pfeifer (Austria), Norman Kerle (The Netherlands), Orhan Altan (Turkey), Ori Gudes (Australia), Pawel Boguslawski (Poland), Peter van Oosterom (The Netherlands), Petr Kubíček (Czech Republic), Petros Patias (Greece), Piero Boccardo (Italy), Qiaoli Wu (China), Qing Zhu (China), Riza Yosia Sunindijo (Australia), Roland Billen (Belgium), Rudi Stouffs (Singapore), Scott Hawken (Australia), Serene Coetzee (South Africa), Shawn Laffan (Australia), Shisong Cao (China), Sisi Zlatanova (Australia), Songnian Li (Canada), Stephan Winter (Australia), Tarun Ghawana (Australia), Ümit Işıkdağ (Turkey), Wei Li (Australia), Wolfgang Reinhardt (Germany), Xianlian Liang (Finland) and Yanan Liu (China).The editors would like to express their gratitude to all contributors, who made this volume possible. Many thanks go to all supporting organisations: ISPRS, SSSI, URAP2, Blackash, Mercury and ISPRS Journal of Geoinformation. The editors are grateful to the continued support of the involved Universities: The University of New South Wales, Curtin University, Australian National University and The University of Melbourne.
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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.
Full textAbstract:
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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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.
Full textAbstract:
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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Andela, Niels, Guido R. van der Werf, Johannes W. Kaiser, Thijs T. van Leeuwen, Martin J. Wooster, and Caroline E. R. Lehmann. "Biomass burning fuel consumption dynamics in the tropics and subtropics assessed from satellite." Biogeosciences 13, no. 12 (June 28, 2016): 3717–34. http://dx.doi.org/10.5194/bg-13-3717-2016.
Full textAbstract:
Abstract. Landscape fires occur on a large scale in (sub)tropical savannas and grasslands, affecting ecosystem dynamics, regional air quality and concentrations of atmospheric trace gasses. Fuel consumption per unit of area burned is an important but poorly constrained parameter in fire emission modelling. We combined satellite-derived burned area with fire radiative power (FRP) data to derive fuel consumption estimates for land cover types with low tree cover in South America, Sub-Saharan Africa, and Australia. We developed a new approach to estimate fuel consumption, based on FRP data from the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS) and the geostationary Spinning Enhanced Visible and Infrared Imager (SEVIRI) in combination with MODIS burned-area estimates. The fuel consumption estimates based on the geostationary and polar-orbiting instruments showed good agreement in terms of spatial patterns. We used field measurements of fuel consumption to constrain our results, but the large variation in fuel consumption in both space and time complicated this comparison and absolute fuel consumption estimates remained more uncertain. Spatial patterns in fuel consumption could be partly explained by vegetation productivity and fire return periods. In South America, most fires occurred in savannas with relatively long fire return periods, resulting in comparatively high fuel consumption as opposed to the more frequently burning savannas in Sub-Saharan Africa. Strikingly, we found the infrequently burning interior of Australia to have higher fuel consumption than the more productive but frequently burning savannas in northern Australia. Vegetation type also played an important role in explaining the distribution of fuel consumption, by affecting both fuel build-up rates and fire return periods. Hummock grasslands, which were responsible for a large share of Australian biomass burning, showed larger fuel build-up rates than equally productive grasslands in Africa, although this effect might have been partially driven by the presence of grazers in Africa or differences in landscape management. Finally, land management in the form of deforestation and agriculture also considerably affected fuel consumption regionally. We conclude that combining FRP and burned-area estimates, calibrated against field measurements, is a promising approach in deriving quantitative estimates of fuel consumption. Satellite-derived fuel consumption estimates may both challenge our current understanding of spatiotemporal fuel consumption dynamics and serve as reference datasets to improve biogeochemical modelling approaches. Future field studies especially designed to validate satellite-based products, or airborne remote sensing, may further improve confidence in the absolute fuel consumption estimates which are quickly becoming the weakest link in fire emission estimates.
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Jenkins, Meaghan E., Michael Bedward, Owen Price, and Ross A. Bradstock. "Modelling Bushfire Fuel Hazard Using Biophysical Parameters." Forests 11, no. 9 (August 24, 2020): 925. http://dx.doi.org/10.3390/f11090925.
Full textAbstract:
Environmental gradients or biophysical parameters such as climate, topography and geology drive landscape-scale vegetation structure, species distribution and productivity. These gradients have the potential to provide detailed, fine-scale spatial prediction of the accumulation of bushfire fuels and hence fire hazard by elucidating patterns in field information in a consistent and repeatable way. Rapid visual assessment of bushfire fuel hazard via ratings provides fire and land management agencies with a measure of the probability of first attack success and general suppression difficulty of bushfires at a location. This study used generalised additive modelling to examine how measures of fuel hazard, recorded for locations in New South Wales, Australia, varied in response to environmental gradients and whether these gradients could be used to predict fuel hazard at a landscape scale. We found that time since last fire, temperature and precipitation were strong predictors of fuel hazard. Our model predictions for fuel hazard outperformed current operational methods; however, both methods tended to overestimate lower fuel hazard and underestimate higher fuel hazard. Biophysical modelling of fuel hazard provides significant advancement for predicting fuel hazard. These models have the capability to be improved and developed as additional fuel hazard data, fire history mapping and remote sensing of environmental variables advance both spatially and temporally.