FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2181093697> ?p ?o ?g. }

• W2181093697 abstract "Unmanned Aircraft Systems (UAS) are an emerging technology in the field of remotesensing. Two fundamental differences of UAS when compared with traditional aerialremote sensing platforms are the operational altitude and payload capacity. The loweroperational altitude of UAS generates ultra-fine spatial resolution data (< 10 cm). Thesmall size of most UAS platforms allows scientific research groups to transport and operatethe platform within small focused study areas. However, a small size also places physicallimitations on UAS sensor payload carrying capacity. This requires a compromise betweensensor functionality, cost, and weight. Sensor feature reduction or miniaturisation achievesthis compromise but at the cost of data quality. This thesis advances UAS remote sensingthrough an exploration of the development, scale analysis and application of ultra-finespatial resolution UAS data.Two sites of remnant cold temperate saltmarsh vegetation in Tasmania were selectedto assess UAS remote sensing. Frequent salt water spray and tidal inundation withinsaltmarsh create a saline gradient that limits the establishment of larger canopy species.This has resulted in the dominance of salt and water-logging tolerant herbaceous andsmall woody shrub species. Despite the harsh environmental conditions, the combinationof land wash-off and tidal inundation both readily supply and redistribute nutrients,creating one of the most environmentally productive environments. Measuring the finescalevegetation distribution and productivity of cold temperate saltmarsh vegetationrequires the ultra-fine spatial resolution data of UAS.In this study, a sensor correction methodology was designed and implemented to reducethe effects of noise and distortion in the 6-band multispectral miniature multiple cameraarray (mini-MCA) produced by Tetracam. This methodology includes techniques forsensor noise reduction using dark offset subtraction, vignetting correction through atfield look-up tables, and lens distortion correction by implementing the Brown-Conrady model. The sensor correction framework is demonstrated through a real-world applicationon UAS-derived saltmarsh data. Chapter 2 demonstrates that sensor noise and distortionscan be satisfactorily corrected in 6-band Tetracam mini-MCA data acquired from a smallmultirotor UAS.Once image data are constructed, the next challenge lies in deconstructing the complexultra-fine spatial resolution UAS data to derive meaningful information. The increasedresolving power of UAS data provides spatial measurements of image features at scalespreviously too small to distinguish. This results in increased spatial complexity as finescalestructural variation becomes measurable. A key challenge is to disassemble andsimplify this fine-scale variation for the extraction of information. This is achieved throughtwo frameworks that provide a meaningful spatial generalisation using image texturemodels and geospatial object-based image analysis (GEOBIA).Image texture is defined as the replications, symmetries and patterns in tonal structure.Image texture models are used to quantify the tonal structure in a local neighbourhoodinto a single, statistical measure. The large number of available texture models andparameters, as well as the dependence of texture on image scale and context, complicatesthe optimal selection of image texture measures. In Chapter 3, a texture selectionmethodology is introduced to provide a rapid, broad assessment of image texture.The texture selection framework is illustrated using a 6-band multispectral dataset of asaltmarsh site. Four texture models are investigated: a simple first-order kernel, the greylevelco-occurrence matrix (GLCM), local binary pattern operator (LBP), and wavelets.Using image subsets, 693 texture measures are extracted from seven vegetation and nonvegetationgroundcover classes. A random forest ensemble classifier was used to quantifythe relative class-specific importance of individual texture measures. A correlation thresholdwas used to remove highly correlated, less important measures before forward inclusionwas used to identify the minimum optimal number of texture measures. The number ofrequired texture measures was linked with class spectral variation, with spectrally complicated classes requiring more measures. The performance of the measures was testedacross the entire image, with a recorded improvement of 17.2% in overall classificationaccuracy with the inclusion of selected texture measures.GEOBIA extends traditional pixel-based analysis through the segmentation of imageryinto meaningful objects. The results of the initial segmentation determine the units ofanalysis, and their accuracy is therefore paramount to the entire analysis. As with texture,image segmentation is dependent upon image structure and content. In Chapter 4,a methodology is presented utilising image subsets to identify class-specific relative scalesof image segmentation through identifying under- and over-segmentation. Reference objectswere used to compare image segmentation results against a meaningful real-worldabstraction. Under-segmentation was tested using spatial area metrics, and was quantified on a class-by-class basis whenever a subset recorded 100% omission in labelling.Over-segmentation was identified by extracting the statistical properties of objects andthen testing the separability using a random forest model. The insuficient spatial generalisationof over-segmentation resulted in reduced class separability. Furthermore, spatialaccuracy was limited by classification accuracy, as the need of spatial generalisation toachieve class separability required suitably large objects. It was found that this dependenceupon objects for spatial generalisation could be reduced through the incorporationof texture measures.Chapter 5 explores the scale potential of ultra-fine spatial resolution data. Field-levelbiomass modelling relies upon the construction of allometric models for the rapid estimationof biomass based upon easily measurable plant characteristics. Allometric modellingis regarded as the most accurate approach for estimating plant biomass, but its extensionto remotely sensed data has been limited by data resolution. Coarser data resolutionmay limit or exclude the ability to measure the parameters required of plant allometricbiomass models. The potential of ultra-fine resolution UAS data to measure allometricparameters is presented in Chapter 5, which is focused on fine-scale shrub biomass. Field derived allometric relationships are used to deconstruct shrub structure through imagesegmentation. Allometric parameters derived from the shrub components are then usedto estimate biomass.This thesis demonstrates a methodology to develop and analyse UAS remotely senseddata, illustrating the scale potential of ultra-fine spatial resolution data. The increasedcomplexity of fine-scale variability is a recognised problem associated with the improvedresolving power of image data. This variability is a central challenge for UAS remotesensing and the analysis of the ultra-fine data scale it generates. By developing a clearmethodology to construct and meaningfully disassemble ultra-fine resolution UAS data,this thesis provides a foundation which provides broader access to the novel scale nichethat UAS measurements fill." @default.
• W2181093697 created "2016-06-24" @default.
• W2181093697 creator A5018935651 @default.
• W2181093697 date "2014-01-01" @default.
• W2181093697 modified "2023-09-27" @default.
• W2181093697 title "Object-based image analysis of ultra-fine spatial resolution imagery acquired over a saltmarsh environment by an Unmanned Aircraft ASystem (UAS)" @default.
• W2181093697 hasPublicationYear "2014" @default.
• W2181093697 type Work @default.
• W2181093697 sameAs 2181093697 @default.
• W2181093697 citedByCount "1" @default.
• W2181093697 countsByYear W21810936972016 @default.
• W2181093697 crossrefType "dissertation" @default.
• W2181093697 hasAuthorship W2181093697A5018935651 @default.
• W2181093697 hasConcept C134066672 @default.
• W2181093697 hasConcept C158379750 @default.
• W2181093697 hasConcept C18903297 @default.
• W2181093697 hasConcept C205649164 @default.
• W2181093697 hasConcept C31258907 @default.
• W2181093697 hasConcept C39432304 @default.
• W2181093697 hasConcept C41008148 @default.
• W2181093697 hasConcept C62649853 @default.
• W2181093697 hasConcept C86803240 @default.
• W2181093697 hasConcept C87441765 @default.
• W2181093697 hasConceptScore W2181093697C134066672 @default.
• W2181093697 hasConceptScore W2181093697C158379750 @default.
• W2181093697 hasConceptScore W2181093697C18903297 @default.
• W2181093697 hasConceptScore W2181093697C205649164 @default.
• W2181093697 hasConceptScore W2181093697C31258907 @default.
• W2181093697 hasConceptScore W2181093697C39432304 @default.
• W2181093697 hasConceptScore W2181093697C41008148 @default.
• W2181093697 hasConceptScore W2181093697C62649853 @default.
• W2181093697 hasConceptScore W2181093697C86803240 @default.
• W2181093697 hasConceptScore W2181093697C87441765 @default.
• W2181093697 hasLocation W21810936971 @default.
• W2181093697 hasOpenAccess W2181093697 @default.
• W2181093697 hasPrimaryLocation W21810936971 @default.
• W2181093697 hasRelatedWork W1495220357 @default.
• W2181093697 hasRelatedWork W1983209869 @default.
• W2181093697 hasRelatedWork W1986805693 @default.
• W2181093697 hasRelatedWork W1997806695 @default.
• W2181093697 hasRelatedWork W2006888104 @default.
• W2181093697 hasRelatedWork W2015208520 @default.
• W2181093697 hasRelatedWork W2019292188 @default.
• W2181093697 hasRelatedWork W2090344194 @default.
• W2181093697 hasRelatedWork W2096878678 @default.
• W2181093697 hasRelatedWork W2100701194 @default.
• W2181093697 hasRelatedWork W2123720514 @default.
• W2181093697 hasRelatedWork W2168576593 @default.
• W2181093697 hasRelatedWork W23600966 @default.
• W2181093697 hasRelatedWork W2803583446 @default.
• W2181093697 hasRelatedWork W2998595112 @default.
• W2181093697 hasRelatedWork W3102533665 @default.
• W2181093697 hasRelatedWork W3198250645 @default.
• W2181093697 hasRelatedWork W329918055 @default.
• W2181093697 hasRelatedWork W98418520 @default.
• W2181093697 hasRelatedWork W75140462 @default.
• W2181093697 isParatext "false" @default.
• W2181093697 isRetracted "false" @default.
• W2181093697 magId "2181093697" @default.
• W2181093697 workType "dissertation" @default.