PEMODELAN KONTUR MENGGUNAKAN INTERPOLASI FRAKTAL

Pemodelan fraktal berkembang dengan pesat dan semakin banyak dibutuhkan untuk memecahkan permasalahan pembuatan gambar objek alam seperti bukit, batuan,dll. Interpolasi fraktal merupakan metode untuk mencari grafik fungsi fraktal yang melalui beberapa titik interpolasi. Pertama, dirancang metode untuk melakukan pemodelan kontur dengan menggunakan interpolasi fraktal 2D dan 3D, fungsi interpolasi yang digunakan adalah transformasi affine sedangkan pemodelan konturnya dilakukan dengan menggambar grafik interpolasi berkali kali dengan suatu iterasi berdasar parameter tertentu, kemudian ditentukan parameter apa saja yang dapat diubah secara interkatif. Akhirnya dibangun suatu program komputer yang mampu melakukan pemodelan kontur secara interaktif. Hasil uji coba program komputer yang dibuat menunjukkan bahwa kontur dapat dimodelkan dengan baik menggunakan grafik interpolasi 2D dan 3D, parameter yang dapat diubah secara interaktif untuk mendapatkan model kontur yang beragam adalah parameter faktor skala vertikal, dimensi dan koordinat titik interpolasinya

Geomorphic evolution of the Great Yarmouth coastal system : spit sediment dynamics and forcing mechanisms

Little is known of the volume of sediment held within the Great Yarmouth spit or its short-term fluctuations in sediment storage capacity. Such gaps in coastal sediment budgets mean that the effects of predicted future increases in relative sea level and storminess are difficult to forecast. The current study addressed this by examining the late-Holocene geomorphic evolution of the Great Yarmouth spit, providing a value for the volume of sediment stored within the feature and placing current morphological changes within an historical context. The specific aims of this research were to investigate: (1) spit volume, (2) spit morphological change (3) potential forcings for this change and (4) likely future morphological trends. Short-term fluctuations in the Great Yarmouth spit’s sediment storage capacity were identified by investigating morphological changes within the feature’s coastal zone from map and aerial photograph evidence. These fluctuations appear to be site-specific across the Great Yarmouth spit and adjacent areas. Indeed, between 1800 and 2007 Winterton-on-Sea experienced accretion whilst coastline retreat followed by a period of relative stability typified trends at Caister-on-Sea. North Denes shows sea-ward advance of the coastline but this occurs after a phase of erosion prior to 1890. Great Yarmouth Pleasure Beach and Gorleston-on-Sea have remained relatively stable after a similar phase of pre-1890 erosion. All sites investigated are currently (2008 data) displaying either maintenance of or an increase in sediment storage capacity within the stable spit area (that above normal tidal conditions) with the exception of Caister Point which is undergoing erosion

Benthic habitat mapping in coastal waters of south–east Australia

The Victorian Marine Mapping Project will improve knowledge on the location, spatial distribution, condition and extent of marine habitats and associated biodiversity in Victorian State waters. This information will guide informed decision making, enable priority setting, and assist in targeted natural resource management planning. This project entails benthic habitat mapping over 500 square kilometers of Victorian State waters using multibeam sonar, towed video and image classification techniques. Information collected includes seafloor topography, seafloor softness and hardness (reflectivity), and information on geology and benthic flora and fauna assemblages collectively comprising habitat. Computerized semi-automated classification techniques are also being developed to provide a cost effective approach to rapid mapping and assessment of coastal habitats.Habitat mapping is important for understanding and communicating the distribution of natural values within the marine environment. The coastal fringe of Victoria encompasses a rich and diverse ecosystem representative of coastal waters of South-east Australia. To date, extensive knowledge of these systems is limited due to the lack of available data. Knowledge of the distribution and extent of habitat is required to target management activities most effectively, and provide the basis to monitor and report on their status in the future.<br /

Quantifying the effects of variable selection, spatial scale and spatial data quality in marine benthic habitat mapping

Mapping benthic habitats has become critical in many contexts like conservation and management. While marine habitat mapping methods strongly rely on tools and methods from geography and geomatics, habitat mapping practitioners with a background outside of these specialized areas do not always have a full understanding of the spatial concepts behind these tools and methods. This phenomenon is amplified when marine geomorphometry, the science used to quantify seafloor terrain characteristics, is integrated into the marine habitat mapping workflow. This dissertation reviews the use of spatial concepts in the field of marine benthic habitat mapping; many concepts are poorly understood or poorly implemented in the habitat mapping workflow, among which spatial scale and spatial data quality stand out as being of particular importance. While geomorphometry is commonly used in marine benthic habitat mapping, no framework existed to test which terrain attributes should be used as surrogates of species distribution, leading to an inability to compare results from different studies. This dissertation explores different options for terrain attribute selection and proposes an optimal combination that can be used as standard in all habitat mapping studies. This selection is then tested using two approaches to benthic habitat mapping and is shown to perform better than others. Bathymetric data, the primary input for marine geomorphometry analyses and one of the main data inputs for habitat mapping, are commonly impacted by data acquisition artefacts. Very little work has been done on trying to understand how these artefacts propagate throughout the habitat mapping workflow. The impact of artefacts on the bathymetry and its derived terrain attributes is described, and it is shown that artefacts modify the spatial and statistical distributions of depth and terrain attribute values. However, when these affected data are used in habitat mapping, their impact is not always predictable. Some artefacts were found to sometimes inflate measures of accuracy and performance and sometimes decrease them. Overall, habitat maps were shown to be very sensitive to the effects of variable selection, spatial scale and data quality, and as such have serious implications when they are used to inform decision-making, for instance in marine conservation and management. This dissertation raises awareness about these issues and highlights the need for careful integration of spatial data in habitat mapping practices

The efficient use of data from different sources for production and application of digital elevation models

The emphasis of the investigation reported in this thesis is on the use of digital elevation data of two resolutions originating from two different sources. The high resolution DEM was captured from aerial photographs (first source) at a scale of 1:30,000 and the low resolution DEM was captured from SPOT images (second source). It is well known that the resolution of DEM data depends a great deal on the scale of the images used. The technique for capturing DEMs is static measurement of the spot heights in a regular grid. The grid spacing of the high resolution DEM was 30 m, and of the low resolution DEM was 100 m. The aims of this thesis are as follows: 1. To assess the feasibility of using SPOT stereodata as a source of height information and merged with data from aerial photography. This is carried out by comparison of the elevation data derived from SPOT with the digital elevation data derived from aerial photography. From the comparison of these two sources of height information, some results are derived which show the possible heighting accuracy levels which can realistically be achieved. A systematic error in the estimated average of the elevation differences was found and many tests have been carried out to find the reasons for the presence of this systematic error. 2. To develop methods to manipulate the captured data. 2.1. Gross error (blunder) detection. Blunders made during the data capturing procedure affect the accuracy of the final product. Therefore it is necessary to trap and to remove them. A pointwise local self-checking blunder detection algorithm was developed in order to check the grid elevation data, particularly those which are derived from the second source. 2.2. Data coordinates transformation. The data must be transformed into a common projection in order to be directly comparable. The projection and coordinate systems employed are studied in this project, and the errors caused by the transformations are estimated. 2.3. Data merging. Data of different reliability have to be merged into a single set of data. In this project data from two different sources are merged in order to create a final product of known and uniform accuracy. The effect of the lower resolution source on the high resolution source was studied, in dense and in sparse form. 2.4. Data structure. To structure the data by changing the format in order to be in an acceptable form for DEM creation and display, through the commercially available Laser-Scan package DTMCREATE. 3. DEM production and contouring. To produce DEMs from the initial data and that derived from the two merged sources, and to find the accuracy of the interpolation procedure by comparing the derived interpolated data with the high resolution DEM which has been derived from aerial photography. Finally to interpolate contours directly from the "raw" SPOT data and to compare them with those derived from the aerial photography in order to find out the feasibility and capability of using SPOT data in contouring for topographic maps

Machine learning methods for discriminating natural targets in seabed imagery

The research in this thesis concerns feature-based machine learning processes and methods for discriminating qualitative natural targets in seabed imagery. The applications considered, typically involve time-consuming manual processing stages in an industrial setting. An aim of the research is to facilitate a means of assisting human analysts by expediting the tedious interpretative tasks, using machine methods. Some novel approaches are devised and investigated for solving the application problems. These investigations are compartmentalised in four coherent case studies linked by common underlying technical themes and methods. The first study addresses pockmark discrimination in a digital bathymetry model. Manual identification and mapping of even a relatively small number of these landform objects is an expensive process. A novel, supervised machine learning approach to automating the task is presented. The process maps the boundaries of ≈ 2000 pockmarks in seconds - a task that would take days for a human analyst to complete. The second case study investigates different feature creation methods for automatically discriminating sidescan sonar image textures characteristic of Sabellaria spinulosa colonisation. Results from a comparison of several textural feature creation methods on sonar waterfall imagery show that Gabor filter banks yield some of the best results. A further empirical investigation into the filter bank features created on sonar mosaic imagery leads to the identification of a useful configuration and filter parameter ranges for discriminating the target textures in the imagery. Feature saliency estimation is a vital stage in the machine process. Case study three concerns distance measures for the evaluation and ranking of features on sonar imagery. Two novel consensus methods for creating a more robust ranking are proposed. Experimental results show that the consensus methods can improve robustness over a range of feature parameterisations and various seabed texture classification tasks. The final case study is more qualitative in nature and brings together a number of ideas, applied to the classification of target regions in real-world sonar mosaic imagery. A number of technical challenges arose and these were surmounted by devising a novel, hybrid unsupervised method. This fully automated machine approach was compared with a supervised approach in an application to the problem of image-based sediment type discrimination. The hybrid unsupervised method produces a plausible class map in a few minutes of processing time. It is concluded that the versatile, novel process should be generalisable to the discrimination of other subjective natural targets in real-world seabed imagery, such as Sabellaria textures and pockmarks (with appropriate features and feature tuning.) Further, the full automation of pockmark and Sabellaria discrimination is feasible within this framework

Object-based mapping of temperate marine habitats from multi-resolution remote sensing data

PhD ThesisHabitat maps are needed to inform marine spatial planning but current methods of field survey and data interpretation are time-consuming and subjective. Object-based image analysis (OBIA) and remote sensing could deliver objective, cost-effective solutions informed by ecological knowledge. OBIA enables development of automated workflows to segment imagery, creating ecologically meaningful objects which are then classified based on spectral or geometric properties, relationships to other objects and contextual data. Successfully applied to terrestrial and tropical marine habitats for over a decade, turbidity and lack of suitable remotely sensed data had limited OBIA’s use in temperate seas to date. This thesis evaluates the potential of OBIA and remote sensing to inform designation, management and monitoring of temperate Marine Protected Areas (MPAs) through four studies conducted in English North Sea MPAs. An initial study developed OBIA workflows to produce circalittoral habitat maps from acoustic data using sequential threshold-based and nearest neighbour classifications. These methods produced accurate substratum maps over large areas but could not reliably predict distribution of species communities from purely physical data under largely homogeneous environmental conditions. OBIA methods were then tested in an intertidal MPA with fine-scale habitat heterogeneity using high resolution imagery collected by unmanned aerial vehicle. Topographic models were created from the imagery using photogrammetry. Validation of these models through comparison with ground truth measurements showed high vertical accuracy and the ability to detect decimetre-scale features. The topographic and spectral layers were interpreted simultaneously using OBIA, producing habitat maps at two thematic scales. Classifier comparison showed that Random Forests Abstract ii outperformed the nearest neighbour approach, while a knowledge-based rule set produced accurate results but requires further research to improve reproducibility. The final study applied OBIA methods to aerial and LiDAR time-series, demonstrating that despite considerable variability in the data, pre- and post-classification change detection methods had sufficient accuracy to monitor deviation from a background level of natural environmental fluctuation. This thesis demonstrates the potential of OBIA and remote sensing for large-scale rapid assessment, detailed surveillance and change detection, providing insight to inform choice of classifier, sampling protocol and thematic scale which should aid wider adoption of these methods in temperate MPAs.Natural Environment Research Council and Natural Englan

Modelling scour around submerged objects with TELEMAC3D - GAIA

Hydrodynamic

Online proceedings of the papers submitted to the 2020 TELEMAC-MASCARET User Conference October 2020

Hydrodynamic