Academic literature on the topic 'Agricultural mapping South Australia Remote sensing'
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Journal articles on the topic "Agricultural mapping South Australia Remote sensing"
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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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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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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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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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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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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, 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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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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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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Lee, Sunmin, Yunjung Hyun, Saro Lee, and Moung-Jin Lee. "Groundwater Potential Mapping Using Remote Sensing and GIS-Based Machine Learning Techniques." Remote Sensing 12, no. 7 (April 8, 2020): 1200. http://dx.doi.org/10.3390/rs12071200.
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Adequate groundwater development for the rural population is essential because groundwater is an important source of drinking water and agricultural water. In this study, ensemble models of decision tree-based machine learning algorithms were used with geographic information system (GIS) to map and test groundwater yield potential in Yangpyeong-gun, South Korea. Groundwater control factors derived from remote sensing data were used for mapping, including nine topographic factors, two hydrological factors, forest type, soil material, land use, and two geological factors. A total of 53 well locations with both specific capacity (SPC) data and transmissivity (T) data were selected and randomly divided into two classes for model training (70%) and testing (30%). First, the frequency ratio (FR) was calculated for SPC and T, and then the boosted classification tree (BCT) method of the machine learning model was applied. In addition, an ensemble model, FR-BCT, was applied to generate and compare groundwater potential maps. Model performance was evaluated using the receiver operating characteristic (ROC) method. To test the model, the area under the ROC curve was calculated; the curve for the predicted dataset of SPC showed values of 80.48% and 87.75% for the BCT and FR-BCT models, respectively. The accuracy rates from T were 72.27% and 81.49% for the BCT and FR-BCT models, respectively. Both the BCT and FR-BCT models measured the contributions of individual groundwater control factors, which showed that soil was the most influential factor. The machine learning techniques used in this study showed effective modeling of groundwater potential in areas where data are relatively scarce. The results of this study may be used for sustainable development of groundwater resources by identifying areas of high groundwater potential.
Dissertations / Theses on the topic "Agricultural mapping South Australia Remote sensing"
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Lau, Ian Christopher. "Regolith-landform and mineralogical mapping of the White Dam Prospect, eastern Olary Domain, South Australia, using integrated remote sensing and spectral techniques." Title page, abstract and table of contents only, 2004. http://hdl.handle.net/2440/37972.
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The research contained within this thesis was directed at examining the spectral properties of regolith-dominated terrains using airborne and proximal hyperspectral instruments. The focus of the investigation was to identify the mineralogy of the regolith and determine if surficial materials were indicative of the underlying bedrock in the regolithdominated terrain of the eastern Olary Domain, South Australia. The research area was constrained to a 250 km2 area around the Cu-Au mineralisation of the White Dam Prosect. Integrated remote sensing, using airborne hyperspectral datasets (HyMap), Landsat imagery and gamma-ray spectroscopy data, was performed to map regolith-landforms and extract information on surficial materials. Detailed calibration of the HyMap dataset, using a modified model-based/empirical line calibration technique, was required prior to information extraction. The White Dam area was able to be divided into: alluvial regolith-dominated; in situ regolith-dominated; and bedrock-dominated terrains, based on mineralogical interpretations of the regolith, using the remotely sensed hyperspectral data. Alluvial regions were characterised by large abundances of vegetation and soils with a hematite-rich mineralogy. Highly weathered areas of in situ material were discriminated by the presence of goethite and kaolinite of various crystallinities, whereas the bedrock-dominated regions displayed white mica-/muscovite-rich mineralogy. Areas flanking bedrock exposures commonly consisted of shallow muscovite-rich soils containing regolith carbonate accumulations. Traditional mineral mapping processes were performed on the HyMap data and were able to extract endmembers of regolith and other surficial materials. The Mixture Tuned Matched Filter un-mixing process was successful at classifying regolith materials and minerals. Spectral indices performed on masked data were effective at identifying the key regolith mineralogical features of the HyMap imagery and proved less time consuming than un-mixing processes. Processed HyMap imagery was able to identify weathering halos, highlighted in mineralogical changes, around bedrock exposures. Proximal spectral measurements and XRD analyses of samples collected from the White Dam Prospect were used to create detailed mineralogical dispersion maps of the surface and costean sections. Regolith materials of the logged sections were found to correlate with the spectrally-derived mineral dispersion profiles. The HyLogger drill core scanning instrument was used to examine the mineralogy of the fresh bedrock, which contrasted with the weathering-derived near-surface regolith materials. The overall outcomes of the thesis showed that hyperspectral techniques were useful for charactering the mineralogy of surficial materials and mapping regolith-landforms.Thesis (Ph.D.)--School of Earth & Environmental Sciences, 2004.
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Mahoney, Sean Douglas. "Remote sensing techniques for geological and regolith mapping, interpretations in basement rock and thinly covered terrains adjacent to the Tarcoola mine site, north-western South Australia /." Title page, contents and abstract only, 2002. http://web4.library.adelaide.edu.au/theses/09SB/09sbm2165.pdf.
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Siddiqi, Muhammad Junaid. "Remote sensing and GIS techniques for monitoring and predicting land degradation and impacts of engineering solutions in an area." Thesis, Curtin University, 2009. http://hdl.handle.net/20.500.11937/2427.
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Salinity, waterlogging and drought are major causes of land degradation and serious threats to sustainable agricultural productivity in the south-west agricultural region of Western Australia (WA). The spectral signatures of pasture plants under drought, waterlogging and nutrient deficiency were examined in a greenhouse study using both a field and an analytical laboratory spectrometer (400 to 2,500 nm wavelength). Drought stress group (RWC, 39.9%) has the highest reflectance of 48.2%, waterlogging group (RWC, 49.7%) with a magnitude of 43.1% reflectance and control group (RWC, 61.5%) has the lowest of 41.9%. The highest separability based on magnitude among control, waterlogging and drought stress groups is located at reflection band at 1,666 nm, 1,818 nm and 2,216 nm and at 1,450 nm absorption bands. Remote sensing and GIS techniques were used to predict risks of soil salinity and waterlogging in the study area. Time-sequenced Landsat TM satellite data and groundwater data were analysed to delineate areas where major changes in soil salinity, waterlogging have taken place before and after engineering interventions of deep drains.The rainfall data analysis of all cities in the south-west of Western Australia indicate that annual rainfall has been decreasing since 1969 for some cities in the region and rainfall is decreasing in some cities since 1975. The winter season rainfall shows a downward trend and summer season rainfall shows an upward trend linked to an increase in the frequency of summer storm events in the south-west, in the Wheatbelt and in the east of Western Australia. The annual rainfall and summer season rainfalls have been increasing in the north of Western Australia and both annual rainfall and summer season rainfalls show an increasing trend. Climate change was studied for northern, eastern, Wheatbelt and south-west of WA and its impacts of on surface runoff, groundwater recharge, and land degradation were studied.Deep open drains were monitored in the two major drainage districts of Narembeen and Dumbleyung in Wheatbelt of Western Australia. The efficacy of drains in mitigating the problems of waterlogging and salinity in the area was studied. Information on monitoring of drains in six coastal districts in Western Australia, from Australia and other countries was collated and a coastal drainage best management practices ‘BMP Toolbox’ has been developed.
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McCloy, K. R. (Keith R. ). "Development and evaluation of a remote sensing algorithm suitable for mapping environments containing significant spatial variability : with particular reference to pastures." 1987. http://web4.library.adelaide.edu.au/theses/09PH/09phm127.pdf.
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McCloy, Keith R. "Development and evaluation of a remote sensing algorithm suitable for mapping environments containing significant spatial variability : with particular reference to pastures / by Keith R. McCloy." 1987. http://hdl.handle.net/2440/18902.
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Includes bibliographical references (leaves 176-179)xiii, 202 leaves : ill. (some col.) ; 30 cm.
Title page, contents and abstract only. The complete thesis in print form is available from the University Library.
Thesis (Ph.D.)--University of Adelaide, 1989
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McCloy, Keith R. "Development and evaluation of a remote sensing algorithm suitable for mapping environments containing significant spatial variability : with particular reference to pastures / by Keith R. McCloy." Thesis, 1987. http://hdl.handle.net/2440/18902.
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Lau, Ian Christopher. "Regolith-landform and mineralogical mapping of the White Dam Prospect, eastern Olary Domain, South Australia, using integrated remote sensing and spectral techniques." Thesis, 2004. http://hdl.handle.net/2440/37972.
Full textAbstract:
The research contained within this thesis was directed at examining the spectral properties of regolith-dominated terrains using airborne and proximal hyperspectral instruments. The focus of the investigation was to identify the mineralogy of the regolith and determine if surficial materials were indicative of the underlying bedrock in the regolithdominated terrain of the eastern Olary Domain, South Australia. The research area was constrained to a 250 km2 area around the Cu-Au mineralisation of the White Dam Prosect. Integrated remote sensing, using airborne hyperspectral datasets (HyMap), Landsat imagery and gamma-ray spectroscopy data, was performed to map regolith-landforms and extract information on surficial materials. Detailed calibration of the HyMap dataset, using a modified model-based/empirical line calibration technique, was required prior to information extraction. The White Dam area was able to be divided into: alluvial regolith-dominated; in situ regolith-dominated; and bedrock-dominated terrains, based on mineralogical interpretations of the regolith, using the remotely sensed hyperspectral data. Alluvial regions were characterised by large abundances of vegetation and soils with a hematite-rich mineralogy. Highly weathered areas of in situ material were discriminated by the presence of goethite and kaolinite of various crystallinities, whereas the bedrock-dominated regions displayed white mica-/muscovite-rich mineralogy. Areas flanking bedrock exposures commonly consisted of shallow muscovite-rich soils containing regolith carbonate accumulations. Traditional mineral mapping processes were performed on the HyMap data and were able to extract endmembers of regolith and other surficial materials. The Mixture Tuned Matched Filter un-mixing process was successful at classifying regolith materials and minerals. Spectral indices performed on masked data were effective at identifying the key regolith mineralogical features of the HyMap imagery and proved less time consuming than un-mixing processes. Processed HyMap imagery was able to identify weathering halos, highlighted in mineralogical changes, around bedrock exposures. Proximal spectral measurements and XRD analyses of samples collected from the White Dam Prospect were used to create detailed mineralogical dispersion maps of the surface and costean sections. Regolith materials of the logged sections were found to correlate with the spectrally-derived mineral dispersion profiles. The HyLogger drill core scanning instrument was used to examine the mineralogy of the fresh bedrock, which contrasted with the weathering-derived near-surface regolith materials. The overall outcomes of the thesis showed that hyperspectral techniques were useful for charactering the mineralogy of surficial materials and mapping regolith-landforms.Thesis (Ph.D.)--School of Earth & Environmental Sciences, 2004.
Book chapters on the topic "Agricultural mapping South Australia Remote sensing"
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Hewson, Robert, and Thomas Cudahy. "Issues Affecting Geological Mapping with ASTER Data: A Case Study of the Mt Fitton Area, South Australia." In Land Remote Sensing and Global Environmental Change, 273–300. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6749-7_13.
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Fraser, David. "Mapping Areas Susceptible to Soil Salinity in the Irrigation Region of Southern New South Wales, Australia." In Remote Sensing of Soil Salinization. CRC Press, 2008. http://dx.doi.org/10.1201/9781420065039.pt2.
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Lorenț, Adrian, Marius Petrila, Vladimir Gancz, Florin Capalb, Bogdan Apostol, and Nicolae-Ovidiu Badea. "Quick assessment of burn area and burn severity on black locust stands using Sentinel 2 imagery in South-West Romania." In Advances in Forest Fire Research 2022, 1282–91. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_194.
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In Romania forest fires have a strong annual and seasonal variability, having a distribution pattern correlated with human activities like stubble burning practices. The most fire affected region is located in the southwest of Romania, in the counties of Mehedinți and Gorj with mosaic landscapes of forests with agricultural land and human settlements. Although the year 2021 was generally a mild fire season at national level, almost 60% of the forests burned area occurred in Mehedinți, county whose surface covers 2.06% of the national territory. The affected area is determined by measurements with GPS receivers or by an approximate estimate of the percentage of affected area in a forest parcel. The accuracy of those determinations is not being verified through other procedures like remote sensing-based methods, thus there is a need for independent evaluation of the reported fire impact. The aim of this research is to perform a quick robust enough assessment of the areas affected by forest fires in August 2021 and their severity in a forested area covered mostly by black locust (Robinia pseudoacacia), located in the southwestern part of Mehedinți county, using Sentinel 2 imagery and in field validation. In order to assess the degree of damage of the forest stands, Sentinel 2 satellite images were used, before, during and after the fire events. Additionally, official RGB and CIR aerial orthophoto images with a spatial resolution of 0.5 m from 2015 were used together with ultra-high resolution orthoimages obtained by processing RGB aerial photographs taken post-fire event with an UAV. Several remote sensing-based indices mostly used in forest fire-science literature were tested in order to extract the affected burn area and burn severity, among which the Difference Normalized Burn Ratio (dNBR) proved to give the best estimates. A field inspection was carried out in order to verify whether the burn severity classes are corresponding to the ground truth impact of the fires. By comparing the dNBR mapping with terrain observations, we found that burn severity is corelated with canopy density and height of the trees, at least in black locust stands, who naturally have lower canopy density than other deciduous tree species. The correlation between dNBR and the density of the trees is negative, that means low density shows a false high severity on dNBR map and vice versa because the burnt grass and understory layer is more visible from above and denser in sparse forest stands. Also, low height of the trees induces high severity on the dNBR, the flames and heat produced by the burnt understory reaching easily to the canopy level. These observations were afterwards used to adjust the burn severity map.
Conference papers on the topic "Agricultural mapping South Australia Remote sensing"
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Rudiger, Christoph, Alessandra Monerris, David McJannet, Luigi Renzullo, Mariette Vreugdenhil, and Wolfgang Wagner. "Comparison of Different High-Resolution Soil Moisture Products Across an Agricultural Landscape in South-Eastern Australia." In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. http://dx.doi.org/10.1109/igarss.2018.8518208.
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