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.
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Das, Sumanta, Malini Roy Choudhury, Subhasish Das, and M. Nagarajan. "Earth Observation and Geospatial techniques for Soil Salinity and Land Capability Assessment over Sundarban Bay of Bengal Coast, India." Geodesy and Cartography 65, no. 2 (December 1, 2016): 163–92. http://dx.doi.org/10.1515/geocart-2016-0012.
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Abstract To guarantee food security and job creation of small scale farmers to commercial farmers, unproductive farms in the South 24 PGS, West Bengal need land reform program to be restructured and evaluated for agricultural productivity. This study established a potential role of remote sensing and GIS for identification and mapping of salinity zone and spatial planning of agricultural land over the Basanti and Gosaba Islands(808.314sq. km) of South 24 PGS. District of West Bengal. The primary data i.e. soil pH, Electrical Conductivity (EC) and Sodium Absorption ratio (SAR) were obtained from soil samples of various GCP (Ground Control Points) locations collected at 50 mts. intervals by handheld GPS from 0–100 cm depths. The secondary information is acquired from the remotely sensed satellite data (LANDSAT ETM+) in different time scale and digital elevation model. The collected field samples were tested in the laboratory and were validated with Remote Sensing based digital indices analysisover the temporal satellite data to assess the potential changes due to over salinization. Soil physical properties such as texture, structure, depth and drainage condition is stored as attributes in a geographical soil database and linked with the soil map units. The thematic maps are integrated with climatic and terrain conditions of the area to produce land capability maps for paddy. Finally, The weighted overlay analysis was performed to assign theweights according to the importance of parameters taken into account for salineareaidentification and mapping to segregate higher, moderate, lower salinity zonesover the study area.
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Gao, Zitian, Danlu Guo, Dongryeol Ryu, and Andrew W. Western. "Enhancing the Accuracy and Temporal Transferability of Irrigated Cropping Field Classification Using Optical Remote Sensing Imagery." Remote Sensing 14, no. 4 (February 18, 2022): 997. http://dx.doi.org/10.3390/rs14040997.
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Mapping irrigated areas using remotely sensed imagery has been widely applied to support agricultural water management; however, accuracy is often compromised by the in-field heterogeneity of and interannual variability in crop conditions. This paper addresses these key issues. Two classification methods were employed to map irrigated fields using normalized difference vegetation index (NDVI) values derived from Landsat 7 and Landsat 8: a dynamic thresholding method (method one) and a random forest method (method two). To improve the representativeness of field-level NDVI aggregates, which are the key inputs in our methods, a Gaussian mixture model (GMM)-based filtering approach was adopted to remove noncrop pixels (e.g., trees and bare soils) and mixed pixels along the field boundary. To improve the temporal transferability of method one we dynamically determined the threshold value to account for the impact of interannual weather variability based on the dynamic range of NDVI values. In method two an innovative training sample pool was designed for the random forest modeling to enable automatic calibration for each season, which contributes to consistent performance across years. The irrigated field mapping was applied to a major irrigation district in Australia from 2011 to 2018, for summer and winter cropping seasons separately. The results showed that using GMM-based filtering can markedly improve field-level data quality and avoid up to 1/3 of omission errors for irrigated fields. Method two showed superior performance, exhibiting consistent and good accuracy (kappa > 0.9) for both seasons. The classified maps in wet winter seasons should be used with caution, because rainfall alone can largely meet plant water requirements, leaving the contribution of irrigation to the surface spectral signature weak. The approaches introduced are transferable to other areas, can support multiyear irrigated area mapping with high accuracy, and significantly reduced model development effort.
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Lamb, D. W. "The use of qualitative airborne multispectral imaging for managing agricultural crops - a case study in south-eastern Australia." Australian Journal of Experimental Agriculture 40, no. 5 (2000): 725. http://dx.doi.org/10.1071/ea99086.
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Charles Sturt University has operated an airborne multispectral imaging system as a research support and management tool over south-eastern Australian crops since 1994. Our experiences have demonstrated the utility, timeliness and cost-effectiveness of qualitative multispectral imagery for monitoring and managing spatial variability in a range of agricultural crops, yet to date the technology remains underutilised in Australia. Images showing variations in the texture of soils in paddocks are a useful indicator of the location of different soil zones for soil sampling, and can assist in siting of treatment plots within paddocks. Multispectral imagery can be used for a synoptic assessment of early weed pressure in fallow paddocks or seedling crops. Locating variability in crop emergence and, later, canopy vigour and biomass, are all potentially means of undertaking precision farming without the capital investment associated with yield mapping. However, like any remote monitoring tool, follow-up ground-truthing must always be used to establish or confirm the causes of observed variability. The use of the technology as part of a greater data acquisition strategy is recommended.
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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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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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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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Kolecka and Kozak. "Wall-to-wall parcel-level mapping of agricultural land abandonment in the Polish Carpathians." Land 8, no. 9 (August 26, 2019): 129. http://dx.doi.org/10.3390/land8090129.
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Accurate estimations of the extent of agricultural land abandonment (ALA) are critical to the sustainable management of agricultural resources and forestry, the understanding of ALA determinants, and the development of future agricultural policies. Although ALA is widespread in Europe, mapping it over large areas using remote sensing data is difficult as a result of the complexity of this phenomenon. This study aims to develop methods for a detailed wall-to-wall regional-scale mapping of ALA using vegetation height and secondary forest succession indicators. The rates and distribution of ALA were analyzed at the parcel and communal level in the Polish Carpathians using a high-resolution vegetation height model (VHM) derived from Light Detection and Ranging (LiDAR) point clouds and topographic data. Depending on the parcel-level secondary forest succession threshold (10, 20, and 50%), the regional ALA rates were 18.8, 9.0, and 2.1%, respectively. Regardless of the threshold, abandoned grasslands covered about three times more area than abandoned croplands. The highest ALA rates were observed in communes located in the western part of the study area, as well as east and south of Rzeszów. We found that areas receiving European Union Common Agricultural Policy payments very rarely showed signs of secondary forest succession and land abandonment. The developed method proved to be effective for detailed ALA mapping at various spatial 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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Sahar, Awad A., Muaid J. Rasheed, Dhia A. A. H. Uaid, and Ammar A. Jasim. "Mapping Sandy Areas and their changes using remote sensing. A Case Study at North-East Al-Muthanna Province, South of Iraq." Revista de Teledetección, no. 58 (July 21, 2021): 39. http://dx.doi.org/10.4995/raet.2021.13622.
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<p>Sandy areas are the main problem in regions of arid and semi-arid climate in the world that threaten urban life, buildings, agricultural, and even human health. Remote sensing is one of the technologies that can be used as an effective tool in dynamic features study of sandy areas and sand accumulations. In this study, two new indices were developed to separate the sandy areas from the non-sandy areas. The first one is called the Normalized Differential Sandy Areas Index (NDSAI) that has been based on the assumption that the sandy area has the lowest water content (moisture) than the other land cover classes. The second other is called the Sandy Areas Surface Temperature index (SASTI) which was built on the assumption that the surface temperature of sandy soil is the highest. The results of proposed indices have been compared with two indices that were previously proposed by other researchers, namely the Normalized Differential Sand Dune Index NDSI and the Eolain Mapping Index (EMI). The accuracy assessment of the sandy indices showed that the NDSAI provides very good performance with an overall accuracy of 89 %. The SASTI can isolate many sandy and non-sandy pixels with an overall accuracy about 86 %. The performance of the NDSI is low with an overall accuracy about 82 %. It fails to classify or isolate the vegetation area from the sandy area and might have better performance in desert environments. The performing of NDSAI that is calculated with the SWIR1 band of the Landsat satellite is better than the performing of NDSI that is calculated with the SWIR2 band of the same satellite. EMI performance is less robust than other methods as it is not useful for extracting sandy surfaces in area with different land covers. Change detection techniques were used by comparing the areas of the sandy lands for the periods from 1987 to 2017. The results showed an increase in sandy areas over four decades. The percentage of this increase was about 20 % to 30 % during 2002 and 2017 compared to 1987.</p>
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Musetsho, Khangwelo Desmond, Munyaradzi Chitakira, and Willem Nel. "Mapping Land-Use/Land-Cover Change in a Critical Biodiversity Area of South Africa." International Journal of Environmental Research and Public Health 18, no. 19 (September 27, 2021): 10164. http://dx.doi.org/10.3390/ijerph181910164.
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Land-use/land-cover (LULC) changes have implications for the long-term outlook of environmental processes, especially in the face of factors such as climate change. These changes can have serious consequences for humans. In this study, remote sensing and geographic information system methods were used to investigate LULC changes in a critical biodiversity area (CBA) in the northern sections of Limpopo Province in South Africa from 1990 to 2018 using data obtained from the South African National Land Cover project. In 1990, the dominant land cover comprised thickets and dense bush, followed by woodland and built-up areas, covering proportions of 40, 24 and 18% of the total land-cover area, respectively. Bare and forest areas were the least dominant classes during this time. In 2018, the dominant land cover was woodland, followed by built-up areas, comprising 71 and 20% of the total area, respectively. Subsistence agriculture is a land-cover class with a relatively higher area compared to water bodies, wetlands and other classes. Between 1990 and 2018, significant changes in land-cover were noted for thickets and dense bush, woodland, water bodies, subsistence agriculture and built-up areas. Woodland increased by over 1000 hectares (ha) per year, while thickets decreased by over 900 ha per year. Interviews were conducted with local residents to determine what they thought were the drivers behind the observed changes. According to these interviews, the drivers included deforestation, agricultural activities in wetlands, sand and gravel mining, among others. The study’s outcomes are critical for future land-use planning exercises and the long-term conservation of this CBA, an area rich in biodiversity and a strategic water source for the communities.
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Singh, Sachchidanand, Harikesh Singh, Vishal Sharma, Vaibhav Shrivastava, Pankaj Kumar, Shruti Kanga, Netrananda Sahu, Gowhar Meraj, Majid Farooq, and Suraj Kumar Singh. "Impact of Forest Fires on Air Quality in Wolgan Valley, New South Wales, Australia—A Mapping and Monitoring Study Using Google Earth Engine." Forests 13, no. 1 (December 21, 2021): 4. http://dx.doi.org/10.3390/f13010004.
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Forests are an important natural resource and are instrumental in sustaining environmental sustainability. Burning biomass in forests results in greenhouse gas emissions, many of which are long-lived. Precise and consistent broad-scale monitoring of fire intensity is a valuable tool for analyzing climate and ecological changes related to fire. Remote sensing and geographic information systems provide an opportunity to improve current practice’s accuracy and performance. Spectral indices techniques such as normalized burn ratio (NBR) have been used to identify burned areas utilizing satellite data, which aid in distinguishing burnt areas using their standard spectral responses. For this research, we created a split-panel web-based Google Earth Engine app for the geo-visualization of the region severely affected by forest fire using Sentinel 2 weekly composites. Then, we classified the burn severity in areas affected by forest fires in Wolgan Valley, New South Wales, Australia, and the surrounding area through Difference Normalized Burn Ratio (dNBR). The result revealed that the region’s burnt area increased to 6731 sq. km in December. We also assessed the impact of long-term rainfall and land surface temperature (LST) trends over the study region to justify such incidents. We further estimated the effect of such incidents on air quality by analyzing the changes in the column number density of carbon monoxide and nitrogen oxides. The result showed a significant increase of about 272% for Carbon monoxide and 45% for nitrogen oxides. We conclude that, despite fieldwork constraints, the usage of different NBR and web-based application platforms may be highly useful for forest management to consider the propagation of fire regimes.
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Sarwar, Abid, Sajid Rashid Ahmad, Muhammad Ishaq Asif Rehmani, Muhammad Asif Javid, Shazia Gulzar, Muhammad Ahmad Shehzad, Javeed Shabbir Dar, et al. "Mapping Groundwater Potential for Irrigation, by Geographical Information System and Remote Sensing Techniques: A Case Study of District Lower Dir, Pakistan." Atmosphere 12, no. 6 (May 24, 2021): 669. http://dx.doi.org/10.3390/atmos12060669.
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The changing climate and global warming have rendered existing surface water insufficient, which is projected to adversely influence the irrigated farming systems globally. Consequently, groundwater demand has increased significantly owing to increasing population and demand for plant-based foods especially in South Asia and Pakistan. This study aimed to determine the potential areas for groundwater use for agriculture sector development in the study area Lower Dir District. ArcGIS 10.4 was utilized for geospatial analysis, which is referred to as Multi Influencing Factor (MIF) methodology. Seven parameters including land cover, geology, soil, rainfall, underground faults (liniment) density, drainage density, and slope, were utilized for delineation purpose. Considering relative significance and influence of each parameter in the groundwater recharge rating and weightage was given and potential groundwater areas were classified into very high, high, good, and poor. The result of classification disclosed that the areas of 113.10, 659.38, 674.68, and 124.17 km2 had very high, high, good, and poor potential for groundwater agricultural uses, respectively. Field surveys for water table indicated groundwater potentiality, which was high for Kotkay and Lalqila union councils having shallow water table. However, groundwater potentiality was poor in Zimdara, Khal, and Talash, characterized with a very deep water table. Moreover, the study effectively revealed that remote sensing and GIS could be developed as potent tools for mapping potential sites for groundwater utilization. Furthermore, MIF technique could be a suitable approach for delineation of groundwater potential zone, which can be applied for further research in different areas.
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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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Bretcan, Petre, Daniel Dunea, Gabriel Vintescu, Danut Tanislav, Martina Zelenakova, Laurențiu Predescu, Gheorghe Șerban, et al. "Automated versus Manual Mapping of Gravel Pit Lakes from South-Eastern Romania for Detailed Morphometry and Vegetation." Water 14, no. 12 (June 9, 2022): 1858. http://dx.doi.org/10.3390/w14121858.
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In recent years, the accelerated development of the remote sensing domain and the improvement of the resolution and frequency of satellite images allowed the increase in the accuracy of the evaluation of morphometric characteristics and the spatiotemporal distribution of pit lakes, including the small ones. Our study quantitatively analyzes small-scale pit lakes in the piedmont and subsidence plains from contact with the Getic and Curvature Subcarpathians from Romania using the normalized difference water index (NDWI) and data series, with different resolutions, from Landsat 8, Google Earth, and Sentinel 2A. The problems encountered in extracting the contours of the gravel pit lakes were determined by the different resolution of the images, the uneven quality of the images exported from Google Earth, and an additional challenge was given by the diversity of the analyzed land surfaces, the land use, and the optical properties of the lakes. A comparison of the obtained NDWI values using data series from Sentinel 2A and Landsat 8 highlighted the importance of resolution and also showed a larger spectral difference between the identified water bodies and the surrounding land in favor of Sentinel 2A. Regarding the vegetation-derived indices, superior leaf area index (1.8–3) was recorded in low-lying plains and mixed areas (tall shrubs, wetlands, etc.) because the river banks have increased moisture that supports taller species with denser foliage and the sparsely vegetated areas are located in agricultural crops and in/near villages. Changes in vegetation richness and abundance can be spatiotemporally monitored using indices derived from the spectral bands of satellite imagery.
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Graf, Lukas, Heike Bach, and Dirk Tiede. "Semantic Segmentation of Sentinel-2 Imagery for Mapping Irrigation Center Pivots." Remote Sensing 12, no. 23 (December 1, 2020): 3937. http://dx.doi.org/10.3390/rs12233937.
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Estimating the number and size of irrigation center pivot systems (CPS) from remotely sensed data, using artificial intelligence (AI), is a potential information source for assessing agricultural water use. In this study, we identified two technical challenges in the neural-network-based classification: Firstly, an effective reduction of the feature space of the remote sensing data to shorten training times and increase classification accuracy is required. Secondly, the geographical transferability of the AI algorithms is a pressing issue if AI is to replace human mapping efforts one day. Therefore, we trained the semantic image segmentation algorithm U-NET on four spectral channels (U-NET SPECS) and the first three principal components (U-NET principal component analysis (PCA)) of ESA/Copernicus Sentinel-2 images on a study area in Texas, USA, and assessed the geographic transferability of the trained models to two other sites: the Duero basin, in Spain, and South Africa. U-NET SPECS outperformed U-NET PCA at all three study areas, with the highest f1-score at Texas (0.87, U-NET PCA: 0.83), and a value of 0.68 (U-NET PCA: 0.43) in South Africa. At the Duero, both models showed poor classification accuracy (f1-score U-NET PCA: 0.08; U-NET SPECS: 0.16) and segmentation quality, which was particularly evident in the incomplete representation of the center pivot geometries. In South Africa and at the Duero site, a high rate of false positive and false negative was observed, which made the model less useful, especially at the Duero test site. Thus, geographical invariance is not an inherent model property and seems to be mainly driven by the complexity of land-use pattern. We do not consider PCA a suited spectral dimensionality reduction measure in this. However, shorter training times and a more stable training process indicate promising prospects for reducing computational burdens. We therefore conclude that effective dimensionality reduction and geographic transferability are important prospects for further research towards the operational usage of deep learning algorithms, not only regarding the mapping of CPS.
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Zahir, Ibra Lebbe Mohamed, and Kafoor Nijamir. "Application of Geospatial Technology for Wetlands’ Mapping and Change-Detection: A Case Study in Selected Areas of South Eastern Coast in Ampara District, Sri Lanka." Sustainable Geoscience and Geotourism 1 (June 2018): 25–32. http://dx.doi.org/10.18052/www.scipress.com/sgg.1.25.
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In global context, the anthropogenic pressure increases the loss of wetland and its resources. Wetlands and estuaries are highly productive and act as critical habitats for a variety of plants, fish, shellfish, and other wildlife (Klemas, 2011). The detection and evaluation of the wetland with modern technology is an important phenomenon to conserve the wetland area and its ecosystem. Remote sensing (RS) has a long history of successful applications within the field of wetland delineation, using a multitude of satellite platforms and sensors (Allan, 2016). This paper is an attempt to object-based approach to derive the change detection inventory information of wetland for selected administrative areas of South Eastern coast in Ampara District within the period of 1991 to 2017 using Toposheets and Google Earth imagery. Further, it also explores the human activities which pressure on wetland including agricultural practices (land encroachment), new settlements, solid waste dumping, land cover changes and etc. Google Earth imagery of 1991 and 2017 were collected and subjected to the GIS analysis to find the result of this study. According to the results, agricultural and built-up area has increased in 1991 by (9.4 per cent), 2017 (16.4 per cent) and 1991 (0.1 per cent), 2017 (2.1 per cent) respectively whereas there has been a decrease in the forest and wetland areas in the years of 1991 (80.3 per cent), 2017 (72.7 per cent) and 1991 (3.5 per cent), 2017 (2.9 per cent) respectively.
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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.
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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.
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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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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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Defwaldi, Defwaldi. "PEMETAAN KESESUAIAN PEMANFAATAN LAHAN PERKEBUNAN SAWIT (STUDI KASUS : KAB. SOLOK SELATAN, SUMATERA BARAT)." Ensiklopedia of Journal 4, no. 2 (January 20, 2022): 169–75. http://dx.doi.org/10.33559/eoj.v4i2.1060.
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Indonesia is known as an agricultural country because most of the people's livelihood as farmers. The use of land for agriculture and plantations in a sustainable manner requires development planning based on complete data and information regarding the condition, quality and characteristics of the land. How to anticipate the occurrence of mismatches in the use of agricultural and plantation land with the type of plant can be done by determining the plantation land with a predetermined designation so that there are no errors in its use. The purpose of this study was to analyze the suitability of land use for oil palm plantations with soil conditions in South Solok Regency. The availability of spatial information is one of the supporting data in realizing the land use resilience program. Utilization of remote sensing is one way to obtain land spatial information. The method used in this research is land surface temperature, inverse distance weighted, slope and overlay methods. The data used in this study are rainfall data in 2020, Landsat 8 Oli Imagery Data, Shuttle Radar Topography Mission data and soil type data. Analysis of the data in the study carried out overlay techniques on mapping the suitability of land use for oil palm plantations in South Solok Regency. The results of this study indicate that the suitable land (S2) with an area of 68,049, 23 Ha, Fairly Appropriate (S3) with an area of 55,144.91 Ha and Unsuitable (N) with an area of 235,707, 46 Ha for oil palm plantations in South Solok Regency.
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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.
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Remote sensing of vegetation and its monitoring using the normalised difference vegetation index (NDVI) offers the opportunity to provide a coverage of agricultural land at a large scale. The availability of MODIS NDVI at a resolution of 250 m provided the opportunity to evaluate the hypothesis that pasture growth rate (PGR) of individual paddocks can be accurately predicted using a model based on MODIS NDVI in combination with climate and soil data and a light-use efficiency model. Model estimates of PGR were compared with field measurements of PGR recorded in grazing enclosure cages collected over 3 years from six farms located across the south-west region of Western Australia. The estimates attained from the model explained 70% of the variation in PGR for individual paddocks on farms over the 3 years of the study, with an average error at the paddock scale of 10.4 kg DM/ha.day over all growing seasons and years. Across all farms studied, there was generally good agreement between satellite-derived PGR and ground-based measurements, although estimates of PGR varied between years and farms. The model explained 47% of the variation in pasture growth early in the season (from break of season to end of July), compared with 62% late in the season (from August to pasture senescence). The present study demonstrated that PGR for individual paddocks can be predicted at weekly intervals from MODIS imagery, climate and soil data and a light-use efficiency model at an accuracy sufficient to facilitate on-farm pasture and livestock management.
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Cerbelaud, A., L. Roupioz, G. Blanchet, P. Breil, and X. Briottet. "SUPERVISED CLASSIFICATION METHODS FOR AUTOMATIC DAMAGE DETECTION CAUSED BY HEAVY RAINFALL USING MULTITEMPORAL HIGH RESOLUTION OPTICAL IMAGERY AND AUXILIARY DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 29, 2021): 693–700. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-693-2021.
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Abstract. In the context of climate change and rising frequency of extreme hydro-meteorological events around the world, flood risk management and mapping of heavy rainfall-related damages represent an ongoing critical challenge. For decades now, remote sensing has been largely used to investigate spatial and temporal changes in land use and water resources. Today, different satellite products provide fast and crucial knowledge for the study of hydrological disasters over large areas, possibly in remote regions, with high spatial resolution and high revisit frequency. Yet, until now, few works have sought to detect the full range of extreme rainfall-related damages with optical imagery, especially those caused by intense rainwater runoff beyond the direct vicinity of major waterways. The work presented in this paper focuses on the Aude severe weather event of October 15th, 2018, in the South of France, for which more than a thousand claims for agricultural disaster were registered, both related to river overflowing and rainwater runoff.The full resources of ground truths, contextual information, land use as well as digital elevation model (DEM) combined to high resolution and high frequency optical imagery (Sentinel-2, Pléiades) are used to develop an automatic damage detection method based on supervised classification algorithms. Through the combination of several indicators characterizing heterogeneous spectral variations among agricultural plots following the event, a Gaussian process classifier achieved various classification accuracies up to 90% on a large comparable and independent photo-interpreted validation sample. This work builds great expectations for applications in other areas with contrasted climate, topography and land cover.
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Twayana, Rabina, Sijan Bhandari, and Reshma Shrestha. "Analyzing Urban Growth Pattern and Driving Factors Using Remote Sensing and GIS: A Case Study of Banepa Municipality, Nepal." Journal on Geoinformatics, Nepal 20, no. 1 (December 1, 2020): 9–18. http://dx.doi.org/10.3126/njg.v20i1.39471.
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Nepal is considered one of the rapidly urbanizing countries in south Asia. Most of the urbanization is dominated in large and medium cities i.e., metropolitan, sub-metropolitan, and municipalities. Remote Sensing and Geographic Information System (GIS) technologies in the sector of urban land governance are growing day by day due to their capability of mapping, analyzing, detecting changes, etc. The main aim of this paper is to analyze the urban growth pattern in Banepa Municipality during three decades (1992-2020) using freely available Landsat imageries and explore driving factors for change in the urban landscape using the AHP model. The Banepa municipality is taken as a study area as it is one of the growing urban municipalities in the context of Nepal. The supervised image classification was applied to classify the acquired satellite image data. The generated results from this study illustrate that urbanization is gradually increasing from 1992 to 2012 while, majority of the urban expansion happened during 2012-2020, and it is still growing rapidly along the major roads in a concentric pattern. This study also demonstrates the responsible driving factors for continuous urban growth during the study period. Analytical Hierarchy Process (AHP) was adopted to analyze the impact of drivers which reveals that, Internal migration (57%) is major drivers for change in urban dynamics whereas, commercialization (25%), population density (16%), and real estate business (5%) are other respective drivers for alteration of urban land inside the municipality. To prevent rapid urbanization in this municipality, the concerned authorities must take initiative for proper land use planning and its implementation on time. Recently, Nepal Government has endorsed Land Use Act 2019 for preventing the conversion of agricultural land into haphazard urban growth.
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Kasmaeeyazdi, Sara, Enrico Dinelli, and Roberto Braga. "Mapping Co–Cr–Cu and Fe Occurrence in a Legacy Mining Waste Using Geochemistry and Satellite Imagery Analyses." Applied Sciences 12, no. 4 (February 12, 2022): 1928. http://dx.doi.org/10.3390/app12041928.
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Abandoned mining wastes are both an environmental challenge and a possible secondary raw material source. The characterization and monitoring of these sites are often expensive and cumbersome because of the need of repeated field surveys. Remote sensing data are a cost-effective alternative that helps in producing multiscale maps of mining wastes. These maps can be used to investigate and monitor the spatial patterns of different elements within the mining wastes. In this work, Sentinel-2 images are combined with the geochemical samples in order to map the distribution of iron, copper, chromium, and cobalt. The target area was the Vigonzano mining wastes in Northern Apennines (Italy) where there are a small number of geochemical analyses but a large amount of satellite image data. We used the multivariate geostatistical estimation method (Co-Kriging) that exploit the meaningful spatial correlation between the elements of interest and band ratios (obtained from Sentinel-2 images). The concentration maps highlighted subareas for Cu and Cr with an estimated grade of about 0.3% and 0.2%, respectively. In addition, the critical element Co showed an enrichment in the south-east part of the mining wastes, in a similar pattern as Cr. Instead, the obtained maps show Ce, La, Rb, and Nb depletion compared to the surrounding agricultural areas. The concentration maps were intended as a prefeasibility study to determine enriched areas for further detailed investigation.
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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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Wang, Lingyu, Quan Chen, Zhongfa Zhou, Xin Zhao, Jiancheng Luo, Tianjun Wu, Yingwei Sun, Wei Liu, Shu Zhang, and Wenhui Zhang. "Crops planting structure and karst rocky desertification analysis by Sentinel-1 data." Open Geosciences 13, no. 1 (January 1, 2021): 867–79. http://dx.doi.org/10.1515/geo-2020-0272.
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Abstract Accurate crop planting structure (CPS) information and its relationship with the surrounding special environment can provide strong support for the adjustment of agricultural structure in areas with limited cultivated land resources, and it will help regional food security, social economy, and ecological balance adjustment. However, due to the perennial cloudy, rainy, and scattered arable land in Karst mountainous areas, the monitoring of planting structure by traditional remote sensing methods is greatly limited. In this regard, we focus on synthetic aperture radar (SAR) remote sensing, which can penetrate clouds and rain, without light constraints to image. In this article, based on parcel-based temporal sequence SAR, the CPS in South China karst area was extracted by deep learning technology, and the spatial coupling relationship between CPS and karst rocky desertification (KRD) was analyzed. The results showed that: (a) The overall accuracy of CPS classification was 75.98%, which proved that the geo-parcel-based time series SAR has a good effect for the CPS mapping in the karst mountainous areas; (b) Through the analysis of the spatial relationship between the planting structure and KRD, we found that the lower KRD level caused the simpler CPS and the higher KRD grade caused more complex CPS and more richer landscape types. The spatial variation trend of CPS landscape indicates the process of water shortage and the deepening of KRD in farmland; (c) The landscape has higher connectivity (Contagion Index, CI 0.52–1.73) in lower KRD level and lower connectivity (CI 0.83–2.05) in higher KRD level, which shows that the degree of fragmentation and connection of CPS landscape is positively proportional to the degree of KRD. In this study, the planting structure extraction of crops under complex imaging environment was realized by using the farmland geo-parcels-based time series Sentinel-1 data, and the relationship between planting structure and KRD was analyzed. This study provides a new idea and method for the extraction of agricultural planting structure in the cloudy and rainy karst mountainous areas of Southwest China. The results of this study have certain guiding significance for the adjustment of regional agricultural planting structure and the balance of regional development.
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Galuszynski, Nicholas C., Robbert Duker, Alastair J. Potts, and Teja Kattenborn. "Automated mapping of Portulacaria afra canopies for restoration monitoring with convolutional neural networks and heterogeneous unmanned aerial vehicle imagery." PeerJ 10 (October 14, 2022): e14219. http://dx.doi.org/10.7717/peerj.14219.
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Ecosystem restoration and reforestation often operate at large scales, whereas monitoring practices are usually limited to spatially restricted field measurements that are (i) time- and labour-intensive, and (ii) unable to accurately quantify restoration success over hundreds to thousands of hectares. Recent advances in remote sensing technologies paired with deep learning algorithms provide an unprecedented opportunity for monitoring changes in vegetation cover at spatial and temporal scales. Such data can feed directly into adaptive management practices and provide insights into restoration and regeneration dynamics. Here, we demonstrate that convolutional neural network (CNN) segmentation algorithms can accurately classify the canopy cover of Portulacaria afra Jacq. in imagery acquired using different models of unoccupied aerial vehicles (UAVs) and under variable light intensities. Portulacaria afra is the target species for the restoration of Albany Subtropical Thicket vegetation, endemic to South Africa, where canopy cover is challenging to measure due to the dense, tangled structure of this vegetation. The automated classification strategy presented here is widely transferable to restoration monitoring as its application does not require any knowledge of the CNN model or specialist training, and can be applied to imagery generated by a range of UAV models. This will reduce the sampling effort required to track restoration trajectories in space and time, contributing to more effective management of restoration sites, and promoting collaboration between scientists, practitioners and landowners.
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Santana, Lucas Santos, Gabriel Araújo e. Silva Ferraz, Alberdan José da Silva Teodoro, Mozarte Santos Santana, Giuseppe Rossi, and Enrico Palchetti. "Advances in Precision Coffee Growing Research: A Bibliometric Review." Agronomy 11, no. 8 (August 5, 2021): 1557. http://dx.doi.org/10.3390/agronomy11081557.
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Precision coffee-growing technologies contribute to increased yield, operational efficiency, and final product quality. In addition, they strengthen coffee growing in the global agricultural scenario, which makes this activity increasingly competitive. Scientific research is essential for technological development and offering security regarding its application. For relevant research identification, bibliometric revision methods expose the best studies and their relationships with countries and authors, providing a complete map of research directions. This study identified the main contributions and contributors to academic research generation about precision coffee growing from 2000 to 2021. Bibliometric analysis was performed in VOSViewer software from the referential bases Scopus and Web of Science that identified 150 articles. Based on the number of citations, publications about precision coffee-growing showed Brazilian institutions at the top of the list, and Brazil’s close relationships with North American and South African institutions. Geostatistical analysis, remote sensing and spatial variability mapping of cultivation areas were used in most experimental research. A trend in research exploring machine learning technologies and autonomous systems was evident. The identification of the main agents of scientific development in precision coffee growing contributes to objective advances in the development and application of new management systems. Overall, this analysis represents wide precision coffee growing research providing valuable information for farmers, policymakers, and researchers.
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Rahman, Md Naimur. "Urban Expansion Analysis and Land Use Changes in Rangpur City Corporation Area, Bangladesh, using Remote Sensing (RS) and Geographic Information System (GIS) Techniques." Geosfera Indonesia 4, no. 3 (November 25, 2019): 217. http://dx.doi.org/10.19184/geosi.v4i3.13921.
Full textAbstract:
This study aim to attempt mapping out the Land Use or Land Cover (LULC) status of Regional Project Coordination Committee (RPCC) between 2009-2019 with a view of detecting the land consumption rate and the changes that has taken place using RS and GIS techniques; serving as a precursor to the further study on urban induced variations or change in weather pattern of the cityn Rangpur City Corporation(RCC) is the main administrative functional area for both of Rangpur City and Rangpur division and experiencing a rapid changes in the field of urban sprawl, cultural and physical landscape,city growth. These agents of Land use or Land cover (LULC) varieties are responsible for multi-dimensional problems such as traffic congestion, waterlogging, and solid waste disposal, loss of agricultural land. In this regard, this study fulfills LULC changes by using Geographical Information Systems (GIS) and Remote Sensing (RS) as well as field survey was conducted for the measurement of change detection. The sources of data were Landsat 7 ETM and landsat 8 OLI/TIRS of both C1 level 1. Then after correcting the data, geometrically and radiometrically change detection and combined classification (supervised & unsupervised) were used. The study finds LULC changes built-up area, water source, agricultural land, bare soil in a change of percentage is 17.23, 2.58, -9.94, -10.19 respectively between 2009 and 2019. Among these changes, bare soil is changed to a great extent, which indicates the expansion of urban areas is utilizing the land to a proper extent.
Keywords: Urban expansion; land use; land cover; remote sensing; geographic information system (GIS); Rangpur City Corporation(RCC).
References
Al Rifat, S. A., & Liu, W. (2019). Quantifying spatiotemporal patterns and major explanatory factors of urban expansion in miami metropolitan area during 1992-2016. Remote Sensing, 11(21) doi:10.3390/rs11212493
Arimoro AO, Fagbeja MA, Eedy W. (2002). The Need and Use of Geographic Information Systems for Environmental Impact Assessment in Africa: With Example from Ten Years Experience in Nigeria. AJEAM/RAGEE, 4(2), 16-27.
Belal, A.A. and Moghanm, F.S. (2011).Detecting Urban Growth Using Remote Sensing and GIS Techniques in Al Gharbiya Governorate, Egypt.The Egyptian Journal of Remote Sensing and Space Science, 14, 73-79. http://dx.doi.org/10.1016/j.ejrs.2011.09.001
Dewan, A.M. and Yamaguchi, Y. (2009). Using Remote Sensing and GIS to Detect and Monitor and Use and Land Cover Change in Dhaka Metropolitan of Bangladesh during 1960-2005. Environmental Monitor Assessment, 150, 237- 249. Retrieved from http://dx.doi.org/10.1007/s10661-008-0226-5
Djimadoumngar, K.-N., & Adegoke, J. (2018). Satellite-Based Assessment of Land Use and Land Cover (LULC) Changes around Lake Fitri, Republic of Chad. Journal of Sustainable Development, 11(5), 71. doi:10.5539/jsd.v11n5p71
Edwards, B., Frasch, T., & Jeyacheya, J. (2019). Evaluating the effectiveness of land-use zoning for the protection of built heritage in the bagan archaeological zone, Myanmar—A satellite remote-sensing approach. Land use Policy, 88 doi:10.1016/j.landusepol.2019.104174
Fallati, L., Savini, A., Sterlacchini, S., & Galli, P. (2017). Land use and land cover (LULC) of the Republic of the Maldives: first national map and LULC change analysis using remote-sensing data. Environmental Monitoring and Assessment, 189(8). doi:10.1007/s10661-017-6120-2
Fučík, P., Novák, P., & Žížala, D. (2014). A combined statistical approach for evaluation of the effects of land use, agricultural and urban activities on stream water chemistry in small tile-drained catchments of south bohemia, czech republic. Environmental Earth Sciences, 72(6), 2195-2216. doi:10.1007/s12665-014-3131-y
Elbeih, S. F., & El-Zeiny, A. M. (2018). Qualitative assessment of groundwater quality based on land use spectral retrieved indices: Case study sohag governorate, egypt. Remote Sensing Applications: Society and Environment, 10, 82-92. doi:10.1016/j.rsase.2018.03.001
Fasal, S. (2000). Urban expansion and loss of agricultural land – A GIS based study of Saharanpur City, India. Environment and Urbanization, 12(2), 133 – 149
He, S., Wang, X., Dong, J., Wei, B., Duan, H., Jiao, J., & Xie, Y. (2019). Three-dimensional urban expansion analysis of valley-type cities: A case study of chengguan district, lanzhou, china. Sustainability (Switzerland), 11(20) doi:10.3390/su11205663
Heimlich, R.E and W.D. Anderson. (2001). Development at the Urban Fringe and Beyond: Impacts on Agriculture and Rural Land. 803, Economic Research Service, U.S. Department of Agriculture, Washington D.C., pg 80
Im, N., Kawamura, K., Suwandana, E., & Sakuno, Y. (2014). Monitoring land use and land cover effects on water quality in cheung ek lake using ASTER images. American Journal of Environmental Sciences, 11(1), 1-12. doi:10.3844/ajessp.2015.1.12
Kalnay, E., & Cai, M. (2003). Impact of urbanization and land-use change on climate. Nature, 423(6939), 528-531. doi:10.1038/nature01675
Matlhodi, B., Kenabatho, P. K., Parida, B. P., & Maphanyane, J. G. (2019). Evaluating land use and land cover change in the gaborone dam catchment, botswana, from 1984-2015 using GIS and remote sensing. Sustainability (Switzerland), 11(19) doi:10.3390/su11195174
Uddin, M. M. M. (2015). Causal relationship between agriculture, industry and services sector for GDP growth in Bangladesh: An econometric investigation. Journal of Poverty, Investment and Development, 8.
Mondal, I., Srivastava, V. K., Roy, P. S., & Talukdar, G. (2014). Using logit model to identify the drivers of landuse landcover change in the lower gangetic basin, india. Paper presented at the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, , XL-8(1) 853-859. doi:10.5194/isprsarchives-XL-8-853-2014
Navale, V. B., & Mhaske, S. Y. (2019). Land use/land cover changes in sangamner city by using remote sensing and GIS. International Journal of Recent Technology and Engineering, 8(2), 4614-4621. doi:10.35940/ijrte.B3386.078219
Nicolson, L.D. (1987). The Greening of the cities; Routledge and Kegan Paul, London
Nong, D., Fox, J., Miura, T., & Saksena, S. (2015). Built-up Area Change Analysis in Hanoi Using Support Vector Machine Classification of Landsat Multi-Temporal Image Stacks and Population Data. Land, 4(4), 1213–1231. doi:10.3390/land4041213
Park, H., Fan, P., John, R., Ouyang, Z., & Chen, J. (2019). Spatiotemporal changes of informal settlements: Ger districts in ulaanbaatar, mongolia. Landscape and Urban Planning, 191 doi:10.1016/j.landurbplan.2019.103630
Rajeshwari D. (2006). Management of the Urban Environment Using Remote Sensing and Geographic Information Systems.J. Hum. Ecol., 20(4), 269-277. Retrieved from http://www.krepublishers.com/02_journals/JHE/
Rasul, A., Balzter, H., Ibrahim, G., Hameed, H., Wheeler, J., Adamu, B., … Najmaddin, P. (2018). Applying Built-Up and Bare-Soil Indices from Landsat 8 to Cities in Dry Climates. Land, 7(3), 81. doi:10.3390/land7030081
Risma, Zubair, H., & Paharuddin. (2019). Prediction of land use and land cover (LULC) changes using CA-Markov model in Mamuju Subdistrict. Journal of Physics: Conference Series, 1341, 082033. doi:10.1088/1742-6596/1341/8/082033
Schilling, K. E., Jha, M. K., Zhang, Y.-K., Gassman, P. W., & Wolter, C. F. (2008). Impact of land use and land cover change on the water balance of a large agricultural watershed: Historical effects and future directions. Water Resources Research, 44(7). doi:10.1029/2007wr006644
Copyright (c) 2019 Geosfera Indonesia Journal and Department of Geography Education, University of Jember
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Van Niekerk, Elna. "Visual interpretation of ASTER satellite data, Part II: Land use mapping in Mpumalanga,South Africa." Suid-Afrikaanse Tydskrif vir Natuurwetenskap en Tegnologie 26, no. 4 (September 22, 2007): 247–64. http://dx.doi.org/10.4102/satnt.v26i4.137.
Full textAbstract:
Since the initiation in 1960 of the era of satellite remote sensing to detect the different characteristics of the earth, a powerful tool was created to aid researchers. Many land-use studies were undertaken using Landsat MSS, Landsat TM and ETM, as well as SPOT satellite data. The application of these data to the mapping of land use and land cover at smaller scales was constrained by the limited spectral and/or spatial resolution of the data provided by these satellite sensors. In view of the relatively high cost of SPOT data, and uncertainty regarding the future continuation of the Landsat series, alternative data sources need to be investigated. In the absence of published previous research on this issue in South Africa, the purpose of this article is to investigate the value of visual interpretation of ASTER satellite images for the identification and mapping of land-use in an area in South Africa. The study area is situated in Mpumalanga, in the area of Witbank, around the Witbank and Doorndraai dams. This area is characterised by a variety of urban, rural and industrial land uses. Digital image processing of one Landsat 5 TM, one Landsat 7 ETM and one ASTER satellite image was undertaken, including atmospheric correction and georeferencing, natural colour composites, photo infrared colour composites (or false colour satellite images), band ratios, Normalised Difference Indices, as well as the Brightness, Greenness and Wetness Indices. The efficacy with which land use could be identified through the visual interpretation of the processed Landsat 5 TM, Landsat 7 TM and ASTER satellite images was compared. The published 1:50 000 topographical maps of the area were used for the purpose of initial verification. Findings of the visual interpretation process were verified by field visits to the study area. The study found that the ASTER satellite data produced clearer results and therefore have a higher mapping ability and capacity than the Landsat satellite data. Hence, it is anticipated that the use of the full range of the spectral resolution of the ASTER satellite data – which were not available for this study – in statistical pattern recognition and classification methods will enhance the value of the process. Statistical methods are often used to produce visual information which could be applied to prepare land-use change inventories. This should be addressed in future research projects. Should the Landsat programme be terminated, ASTER satellite data might provide the best alternative for a variety of research projects, but if the Landsat project is continued, the ASTER satellite data could be used very effectively in conjunction with the Landsat satellite data. Since it is foreseen that the ASTER satellite data will be available for at least the next 12 to 15 years, it will continue to provide exciting possibilities for the development of programmes to monitor land-use and land-use change. This could then be used by all three levels of government to reach their goals in terms of agricultural planning, town and regional planning and environmental management. These requirements are described in the Integrated Development Programmes (IDP) of the different local governments.
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Quesada-Román, Adolfo, Lilliam Quirós-Arias, and Juan Carlos Zamora-Pereira. "Interactions between Geomorphology and Production Chain of High-Quality Coffee in Costa Rica." Sustainability 14, no. 9 (April 27, 2022): 5265. http://dx.doi.org/10.3390/su14095265.
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High-altitude coffee has an international reputation due to its high quality, especially in countries with a long production history, such as Costa Rica. Specific geographical characteristics determine the regions where high-altitude coffee can be cultivated. Over the last two decades, new production conditions have promoted the growth of smallholder coffee farms in the Upper Buenavista Catchment (UBC) in the South of Costa Rica. To understand this phenomenon’s process, we initially performed a detailed geomorphological mapping of the high-elevation production sites in the UBC. Then, we used remote sensing to determine the coffee land cover (2005, 2012, and 2018) to compare their landforms. Furthermore, we analyzed the production–processing–market chain that has promoted coffee plantations since 2005. Our results show that coffee farmers chose more unstable and erosive areas with short-term production prospects to cultivate premium-priced coffee. Moreover, farmers have changed their role in the coffee sector, evolving from small producers to entrepreneurs with specialized knowledge. These actions may reduce economic risks and improve the household incomes of smallholder coffee producers. However, limited research has been conducted along the tropics about the relationships between landforms, socioeconomic drivers, and high-altitude coffee yield. Therefore, our results are essential to present geomorphology and applied geography as baselines in land-use planning for agricultural landscapes.
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Vinayan, Midhuna, B. Gurugnanam, and S. Bairavi. "Land use/Land cover Change Detection Study in Vythiri Taluk, Wayanad District, Kerala using Geospatial Technology." Disaster Advances 15, no. 4 (March 25, 2022): 26–33. http://dx.doi.org/10.25303/1504da026033.
Full textAbstract:
Mapping and monitoring the land use/land cover (LU/LC) changes in the Vythiri taluk of Wayanad district are essential for sustainable improvement and management. Remote sensing and GIS technology are used to track the changes in land use/ land cover of Vythiri taluk of Wayanad district for the year 2015–2021. Images from Landsat-8 data were used to generate LU/LC maps. The maximum likelihood supervised classification approach was used to produce the signature class of the important land use/ land cover category. Six major land use/ land cover classes were noticed viz. agricultural land, barren land, built-up area, forest, grassland and water body. The land use/ land cover changes in the results show that built-up area and grassland have increased by 19% (115km2) and 25% (18.1km2), while agricultural land and barren land have decreased by 7% (39.4km2) and 15% (93.3km2) respectively. The area of forest land and water bodies has shown no changes. The overall accuracy of the study is 80%, 83.3% and 83.3% and Kappa Coefficient is 75.8%, 79.5%, 79.2% for the years 2015, 2018 and 2021 respectively. The analyses reveal that land use/ land cover is changed continuously due to population growth, urbanization and natural disasters like flooding and landslides. Google earth images are also used to detect the land cover changes in the study area. The outcome of the study is helpful to policymakers for sustainable land use/land cover management in the Vythiri taluk of Wayanad district, Kerala, South India.
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Chabalala, Yingisani, Elhadi Adam, and Khalid Adem Ali. "Exploring the Effect of Balanced and Imbalanced Multi-Class Distribution Data and Sampling Techniques on Fruit-Tree Crop Classification Using Different Machine Learning Classifiers." Geomatics 3, no. 1 (January 18, 2023): 70–92. http://dx.doi.org/10.3390/geomatics3010004.
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Fruit-tree crops generate food and income for local households and contribute to South Africa’s gross domestic product. Timely and accurate phenotyping of fruit-tree crops is essential for innovating and achieving precision agriculture in the horticulture industry. Traditional methods for fruit-tree crop classification are time-consuming, costly, and often impossible to use for mapping heterogeneous horticulture systems. The application of remote sensing in smallholder agricultural landscapes is more promising. However, intercropping systems coupled with the presence of dispersed small agricultural fields that are characterized by common and uncommon crop types result in imbalanced samples, which may limit conventionally applied classification methods for phenotyping. This study assessed the influence of balanced and imbalanced multi-class distribution and data-sampling techniques on fruit-tree crop detection accuracy. Seven data samples were used as input to adaptive boosting (AdaBoost), gradient boosting (GB), random forest (RF), support vector machine (SVM), and eXtreme gradient boost (XGBoost) machine learning algorithms. A pixel-based approach was applied using Sentinel-2 (S2). The SVM algorithm produced the highest classification accuracy of 71%, compared with AdaBoost (67%), RF (65%), XGBoost (63%), and GB (62%), respectively. Individually, the majority of the crop types were classified with an F1 score of between 60% and 100%. In addition, the study assessed the effect of size and ratio of class imbalance in the training datasets on algorithms’ sensitiveness and stability. The results show that the highest classification accuracy of 71% could be achieved from an imbalanced training dataset containing only 60% of the original dataset. The results also showed that S2 data could be successfully used to map fruit-tree crops and provide valuable information for subtropical crop management and precision agriculture in heterogeneous horticultural landscapes.
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Symeonakis, E., K. Petroulaki, and T. Higginbottom. "LANDSAT-BASED WOODY VEGETATION COVER MONITORING IN SOUTHERN AFRICAN SAVANNAHS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 563–67. http://dx.doi.org/10.5194/isprs-archives-xli-b7-563-2016.
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Mapping woody cover over large areas can only be effectively achieved using remote sensing data and techniques. The longest continuously operating Earth-observation program, the Landsat series, is now freely-available as an atmospherically corrected, cloud masked surface reflectance product. The availability and length of the Landsat archive is thus an unparalleled Earth-observation resource, particularly for long-term change detection and monitoring. Here, we map and monitor woody vegetation cover in the Northwest Province of South Africa, an area of more than 100,000 km<sup>2</sup> covered by 11 Landsat scenes. We employ a multi-temporal approach with dry-season data from 7 epochs between 1990 to 2015. We use 0.5 m-pixel colour aerial photography to collect > 15,000 point samples for training and validating Random Forest classifications of (i) woody vegetation cover, (ii) other vegetation types (including grasses and agricultural land), and (iii) non-vegetated areas (i.e. urban areas and bare land). Overall accuracies for all years are around 80 % and overall kappa between 0.45 and 0.66. Woody vegetation covers a quarter of the Province and is the most accurately mapped class (balanced accuracies between 0.74-0.84 for the 7 epochs). There is a steady increase in woody vegetation cover over the 25-year-long period of study in the expense of the other vegetation types. We identify potential woody vegetation encroachment 'hot-spots' where mitigation measures might be required and thus provide a management tool for the prioritisation of such measures in degraded and food-insecure areas.
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Symeonakis, E., K. Petroulaki, and T. Higginbottom. "LANDSAT-BASED WOODY VEGETATION COVER MONITORING IN SOUTHERN AFRICAN SAVANNAHS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 563–67. http://dx.doi.org/10.5194/isprsarchives-xli-b7-563-2016.
Full textAbstract:
Mapping woody cover over large areas can only be effectively achieved using remote sensing data and techniques. The longest continuously operating Earth-observation program, the Landsat series, is now freely-available as an atmospherically corrected, cloud masked surface reflectance product. The availability and length of the Landsat archive is thus an unparalleled Earth-observation resource, particularly for long-term change detection and monitoring. Here, we map and monitor woody vegetation cover in the Northwest Province of South Africa, an area of more than 100,000 km<sup>2</sup> covered by 11 Landsat scenes. We employ a multi-temporal approach with dry-season data from 7 epochs between 1990 to 2015. We use 0.5 m-pixel colour aerial photography to collect > 15,000 point samples for training and validating Random Forest classifications of (i) woody vegetation cover, (ii) other vegetation types (including grasses and agricultural land), and (iii) non-vegetated areas (i.e. urban areas and bare land). Overall accuracies for all years are around 80 % and overall kappa between 0.45 and 0.66. Woody vegetation covers a quarter of the Province and is the most accurately mapped class (balanced accuracies between 0.74-0.84 for the 7 epochs). There is a steady increase in woody vegetation cover over the 25-year-long period of study in the expense of the other vegetation types. We identify potential woody vegetation encroachment 'hot-spots' where mitigation measures might be required and thus provide a management tool for the prioritisation of such measures in degraded and food-insecure areas.
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Yermolayev, Oleg, Evgeniya Platoncheva, and Benedict Essuman-Quainoo. "Spatial-Temporal Dynamics of the Ephemeral Gully Belt on the Plowed Slopes of River Basins in Natural and Anthropogenic Landscapes of the East of the Russian Plain." Geosciences 10, no. 5 (May 6, 2020): 167. http://dx.doi.org/10.3390/geosciences10050167.
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Erosion is the leading process of soil degradation on agricultural land. In the spectrum of erosion processes, the most unfavorable for soil degradation are the processes of linear (ephemeral and gully) erosion. An assessment of the dynamics of linear erosion in the intensive farming zone of the European part of Russia (EPR) is relevant due to the lack of generalized data on the development of this type of erosion in the post-Soviet period and also, due to the highest intensity of soil erosion in the ephemeral gully erosion. The development of information technologies and the availability of high-resolution and ultra-high-resolution satellite images make it possible to solve the problems of ephemeral gully erosion belts identification, and also makes it possible to trace the dynamics of development of stream erosion on arable lands over a period characterized by the greatest changes in the climate system and economic conditions in the post-Soviet period (1980s–2010s). The study was conducted on the eastern wing of the boreal ecotone of the Russian Plain within the southern border of these zones of mixed and broad-leaved forests, forest-steppe, and steppe landscapes using the basin approach. For the initial material, satellite images of medium (30 m) and high resolution (0.5–1.5 m) were used in the work. The study used methods of image interpretation such as remote sensing of the earth and geoinformation mapping. For 70 key areas (interfluve spaces of river basins), the study developed a method of geoinformation mapping of the ephemeral gully erosion belt dynamics on arable lands. In the same way, the research developed a system of quantitative indicators characterizing its development on arable slopes. The dynamics of ephemeral gully erosion was evaluated over three-time intervals: the 1980s, 2000s, and 2010s by determining the horizontal dissection (density) and density of ephemeral gully erosion. Over the past 30 years, in the direction from the south of the forest sub-zone to the forest-steppe and steppe landscapes, there was a sharp increase in the horizontal dissection and density of the ephemeral gully network: an average of 4.6 and 10 times, respectively. The ephemeral gully erosion belt advances toward the watershed because of the formation of new erosion in the upper parts of the ephemeral gully networks and its extension, while there is a noticeable reduction in the width of the erosion-weakly active belt-sheet and rill erosion.
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Ouaadi, Nadia, Lionel Jarlan, Saïd Khabba, Jamal Ezzahar, Michel Le Page, and Olivier Merlin. "Irrigation Amounts and Timing Retrieval through Data Assimilation of Surface Soil Moisture into the FAO-56 Approach in the South Mediterranean Region." Remote Sensing 13, no. 14 (July 7, 2021): 2667. http://dx.doi.org/10.3390/rs13142667.
Full textAbstract:
Agricultural water use represents more than 70% of the world’s freshwater through irrigation water inputs that are poorly known at the field scale. Irrigation monitoring is thus an important issue for optimizing water use in particular with regards to the water scarcity that the semi-arid regions are already facing. In this context, the aim of this study is to develop and evaluate a new approach to predict seasonal to daily irrigation timing and amounts at the field scale. The method is based on surface soil moisture (SSM) data assimilated into a simple land surface (FAO-56) model through a particle filter technique based on an ensemble of irrigation scenarios. The approach is implemented in three steps. First, synthetic experiments are designed to assess the impact of the frequency of observation, the errors on SSM and the a priori constraints on the irrigation scenarios for different irrigation techniques (flooding and drip). In a second step, the method is evaluated using in situ SSM measurements with different revisit times (3, 6 and 12 days) to mimic the available SSM product derived from remote sensing observation. Finally, SSM estimates from Sentinel-1 are used. Data are collected on different wheat fields grown in Morocco, for both flood and drip irrigation techniques in addition to rainfed fields used for an indirect evaluation of the method performance. Using in situ data, accurate results are obtained. With an observation every 6 days to mimic the Sentinel-1 revisit time, the seasonal amounts are retrieved with R > 0.98, RMSE < 32 mm and bias < 2.5 mm. Likewise, a good agreement is observed at the daily scale for flood irrigation as more than 70% of the detected irrigation events have a time difference from actual irrigation events shorter than 4 days. Over the drip irrigated fields, the statistical metrics are R = 0.74, RMSE = 24.8 mm and bias = 2.3 mm for irrigation amounts cumulated over 15 days. When using SSM products derived from Sentinel-1 data, the statistical metrics on 15-day cumulated amounts slightly dropped to R = 0.64, RMSE = 28.7 mm and bias = 1.9 mm. The metrics on the seasonal amount retrievals are close to assimilating in situ observations with R = 0.99, RMSE = 33.5 mm and bias = −18.8 mm. Finally, among four rainfed seasons, only one false event was detected. This study opens perspectives for the regional retrieval of irrigation amounts and timing at the field scale and for mapping irrigated/non irrigated areas.
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Gumma, Murali Krishna, Takuji W. Tsusaka, Irshad Mohammed, Geoffrey Chavula, N. V. P. R. Ganga Rao, Patrick Okori, Christopher O. Ojiewo, Rajeev Varshney, Moses Siambi, and Anthony Whitbread. "Monitoring Changes in the Cultivation of Pigeonpea and Groundnut in Malawi Using Time Series Satellite Imagery for Sustainable Food Systems." Remote Sensing 11, no. 12 (June 21, 2019): 1475. http://dx.doi.org/10.3390/rs11121475.
Full textAbstract:
Malawi, in south-eastern Africa, is one of the poorest countries in the world. Food security in the country hinges on rainfed systems in which maize and sorghum are staple cereals and groundnut and pigeonpea are now major grain legume crops. While the country has experienced a considerable reduction in forest lands, population growth and demand for food production have seen an increase in the area dedicated to agricultural crops. From 2010, pigeonpea developed into a major export crop, and is commonly intercropped with cereals or grown in double-up legume systems. Information on the spatial extent of these crops is useful for estimating food supply, understanding export potential, and planning policy changes as examples of various applications. Remote sensing analysis offers a number of efficient approaches to deliver spatial, reproducible data on land use and land cover (LULC) and changes therein. Moderate Resolution Imaging Spectroradiometer (MODIS) products (fortnightly and monthly) and derived phenological parameters assist in mapping cropland areas during the agricultural season, with explicit focus on redistributed farmland. Owing to its low revisit time and the availability of long-term period data, MODIS offers several advantages, e.g., the possibility of obtaining cloud-free Normalized Difference Vegetation Index (NDVI) profile and an analysis using one methodology applied to one sensor at regular acquisition dates, avoiding incomparable results. To assess the expansion of areas used in the production of pigeonpea and groundnut resulting from the release of new varieties, the spatial distribution of cropland areas was mapped using MODIS NDVI 16-day time-series products (MOD13Q1) at a spatial resolution of 250 m for the years 2010–2011 and 2016–2017. The resultant cropland extent map was validated using intensive ground survey data. Pigeonpea is mostly grown in the southern dry districts of Mulanje, Phalombe, Chiradzulu, Blantyre and Mwanza and parts of Balaka and Chikwawa as a groundnut-pigeonpea intercrop, and sorghum-pigeonpea intercrop in Mzimba district. By 2016, groundnut extent had increased in Mwanza, Mulanje, and Phalombe and fallen in Mzimba. The result indicates that the area planted with pigeonpea had increased by 29% (75,000 ha) from 2010–2011 to 2016–2017. Pigeonpea expansion in recent years has resulted from major export opportunities to Asian countries like India, and its consumption by Asian expatriates all over the world. This study provides useful information for policy changes and the prioritization of resources allocated to sustainable food production and to support smallholder farmers.
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O., Ademila, Akingboye A. S., and Ojamomi A. I. "Radiometric survey in geological mapping of basement complex area of parts of Southwestern Nigeria." VIETNAM JOURNAL OF EARTH SCIENCES 40, no. 3 (June 4, 2018): 288–98. http://dx.doi.org/10.15625/0866-7187/40/3/12619.
Full textAbstract:
Radiometric methods were used to investigate the radioactive properties of rocks in parts of southwestern Nigeria with a view to interpreting the geological structure and abundance of natural radioactive elements in the main type rocks. The airborne radiometric dataset of Ikole Sheet and ground radiometric data recorded from eight traverses in Akoko axis of the study area were processed. Results presented as maps and profiles displayed variations of high and low radioactive concentrations across the area. These maps showed moderate to very high concentrations and very low to low concentrations of the radioelements; uranium (4.5-13.0 ppm); (LLD-low limit of detection -3.0 ppm), Th (25.0-70.0 ppm); (8.5-16.0 ppm) and K (2.0-4.0 %); but the most often observed values are in the range 2.5-7.0 ppm, 22.0-30.0 ppm and 3.0-4.0% for U, Th, and K respectively. High concentrations imply that the rocks are crystalline, undeformed and are rich in feldspar and U-Th bearing minerals. While low radioactivity is attributed to varying geologic framework compositions; weathered materials or fluids formed as a result of intense metamorphism. The radiometric datasets proved valuable in delineating different rock types and serve as a complementary tool in identifying geochemical zoning of rocks in the area.ReferencesAjibade A.C. and Fitches W.R., 1988. The Nigerian Precambrian and the Pan-African Orogeny, Precambrian Geology of Nigeria, 45-53.Ajibade A.C., Woakes M. and Rahaman M.A., 1987.Proterozoic crustal development in Pan-African regime of Nigeria: In A. Croner (ed.) Proterozoic Lithospheric Evolution Geodynamics, 17, 259-231.Appleton J.D., Miles J.C.H., Green B.M.R, Larmour R., 2008. Pilot study of the application of Tellus airborne radiometric and soil geochemical data for radon mapping. Journal of Environmental Radioactivity, 99, 1687-1697.Arisekola T.M. and Ajenipa R.A., 2013. Geophysical data results preliminary application to uranium and thorium exploration. IAEA-CYTED-UNECE Workshop on UNFC-2009 at Santiago, Chile 9-12, July, 12.Bayowa O.G., Olorunfemi O.M., Akinluyi O.F. and Ademilua O.L., 2014.A Preliminary Approach to Groundwater Potential Appraisal of Ekiti State, Southwestern Nigeria. International Journal of Science and Technology (IJST), 4(3), 48-58.Bierwirth P.N., 1997. The use of airborne gamma-emission data for detecting soil properties.Proceedings of the Third International Airborne Remote Sensing Conference and Exhibition.Copenhagen, Denmark.Grasty R.L. and Multala J., 1991. A correlation technique for separating natural and man-made airborne gamma-ray spectra. In: Current Research, Part D, Geological Survey of Canada, 111-116.Grasty R.L., Minty B.R.S., 1995a. A guide to the technical specifications for airborne gamma ray surveys. Australian Geological Survey Organization, Record.Grasty R.L., Minty B.R.S., 1995b. The standardization of airborne gamma-ray surveys in Australia. Exploration Geophysics, 26, 276-283.IAEA, 1991. Airborne gamma ray spectrometer surveying, International Atomic Energy Agency, Technical Report Series, 323.IAEA, 2007.International Atomic Energy Agency. Safety Glossary, Terminology used in Nuclear Safety and Radiation Protection-2007 Edition.Jones H.A. and Hockey, 1964.The Geology of part of’ Southwestern Nigeria.Geological Survey, Nigeria bulletin, 31.Kearey P., Brooks M. and Hill I., 2002. An Introduction to Geophysical Exploration.3rd ed. Oxford: Blackwell Science, 262.Milsom J., 2003. Field Geophysics: The geological field guide series, John Milsom University College, London. Published by John Wiley and Sons Ltd. Third edition, 51-70.MontajTM Tutorial, 2004. Two - Dimensional frequency domain processing of potential field data.Nigeria Geological Survey Agency (NGSA), 2009. Geological map of Nigeria prepared by Nigeria Geological Survey Agency, 31, ShetimaMangono Crescent Utako District, Garki, Abuja, Nigeria.Omosanya K.O., Ariyo S.O., Kaigama U., Mosuro G.O., and Laniyan T.A., 2015. An outcrop evidence for polycyclic orogenies in the basement complex of Southwestern Nigeria. Journal of Geography and Geology, 7(3), 24-34.Oyawoye, M.O., 1972. The Basement Complex of Nigeria.In African Geology. T.F.J. Dessauvagie and A.J. Whiteman (Eds) Ibadan University Press, 67-99.Oyinloye A.O., 2011. Geology and Geotectonic Setting of the Basement Complex Rocks in Southwestern Nigeria: Implications on Provenance and Evolution. Earth and Environmental Sciences, 98-117. ISBN: 978-953-307-468-9.Rahaman M.A., 1981. Recent Advances in the Study of the Basement Complex of Nigeria.First Symposium on the Precambrian Geology of Nigeria, Summary.Rahaman M.A., Emofureta W.O. and Vachette M., 1983. The potassic-grades of the Igbeti area: Further evaluation of the polycyclic evolution of the Pan-African Belt in South-western Nigeria. Precambrian Resources, 22, 75-92.Woakes M., Rahaman M.A., Ajibade A.C., 1987. Some Metallogenetic Features of the Nigerian Basement. Journal of African Earth Sciences, 6(5), 655-664.
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Pryer, L. L., K. K. Romine, T. S. Loutit, and R. G. Barnes. "CARNARVON BASIN ARCHITECTURE AND STRUCTURE DEFINED BY THE INTEGRATION OF MINERAL AND PETROLEUM EXPLORATION TOOLS AND TECHNIQUES." APPEA Journal 42, no. 1 (2002): 287. http://dx.doi.org/10.1071/aj01016.
Full textAbstract:
The Barrow and Dampier Sub-basins of the Northern Carnarvon Basin developed by repeated reactivation of long-lived basement structures during Palaeozoic and Mesozoic tectonism. Inherited basement fabric specific to the terranes and mobile belts in the region comprise northwest, northeast, and north–south-trending Archaean and Proterozoic structures. Reactivation of these structures controlled the shape of the sub-basin depocentres and basement topography, and determined the orientation and style of structures in the sediments.The Lewis Trough is localised over a reactivated NEtrending former strike-slip zone, the North West Shelf (NWS) Megashear. The inboard Dampier Sub-basin reflects the influence of the fabric of the underlying Pilbara Craton. Proterozoic mobile belts underlie the Barrow Sub-basin where basement fabric is dominated by two structural trends, NE-trending Megashear structures offset sinistrally by NS-trending Pinjarra structures.The present-day geometry and basement topography of the basins is the result of accumulated deformation produced by three main tectonic phases. Regional NESW extension in the Devonian produced sinistral strikeslip on NE-trending Megashear structures. Large Devonian-Carboniferous pull-apart basins were introduced in the Barrow Sub-basin where Megashear structures stepped to the left and are responsible for the major structural differences between the Barrow and Dampier Sub-basins. Northwest extension in the Late Carboniferous to Early Permian marks the main extensional phase with extreme crustal attenuation. The majority of the Northern Carnarvon basin sediments were deposited during this extensional basin phase and the subsequent Triassic sag phase. Jurassic extension reactivated Permian faults during renewed NW extension. A change in extension direction occurred prior to Cretaceous sea floor spreading, manifest in basement block rotation concentrated in the Tithonian. This event changed the shape and size of basin compartments and altered fluid migration pathways.The currently mapped structural trends, compartment size and shape of the Barrow and Dampier Sub-basins of the Northern Carnarvon Basin reflect the “character” of the basement beneath and surrounding each of the subbasins.Basement character is defined by the composition, lithology, structure, grain, fabric, rheology and regolith of each basement terrane beneath or surrounding the target basins. Basement character can be discriminated and mapped with mineral exploration methods that use non-seismic data such as gravity, magnetics and bathymetry, and then calibrated with available seismic and well datasets. A range of remote sensing and geophysical datasets were systematically calibrated, integrated and interpreted starting at a scale of about 1:1.5 million (covering much of Western Australia) and progressing to scales of about 1:250,000 in the sub-basins. The interpretation produced a new view of the basement geology of the region and its influence on basin architecture and fill history. The bottom-up or basement-first interpretation process complements the more traditional top-down seismic and well-driven exploration methods, providing a consistent map-based regional structural model that constrains structural interpretation of seismic data.The combination of non-seismic and seismic data provides a powerful tool for mapping basement architecture (SEEBASE™: Structurally Enhanced view of Economic Basement); basement-involved faults (trap type and size); intra-sedimentary geology (igneous bodies, basement-detached faults, basin floor fans); primary fluid focussing and migration pathways and paleo-river drainage patterns, sediment composition and lithology.