Geoscience Australia is conducting a series of national risk assessments for a range of natural hazards such as severe winds. The impact of severe wind varies considerably between equivalent structures located at different sites due to local roughness of the upwind terrain, shielding provided by upwind structures and topographic factors.Terrain surface roughness information is a critical spatial input to generate wind multipliers. It is generally the first spatial field to be evaluated, as it is utilised in both the generation of the terrain and topographic wind multiplier. Landsat imagery was employed to generate a terrain surface roughness product for six major metropolitan areas across Australia. It was necessary to investigate the applicability of multi-sensor approaches to generate a regional/national terrain surface roughness map based on the Australian/New Zealand wind loading standard (AS/NZS 1170.2). This paper discusses the methodology that developed a procedure to derive terrain surface roughness from various multi-source satellite images. MODIS, Landsat, and IKONOS imagery were acquired (from 12 September -26 November 2002) covering a significant portion of the New South Wales, Australia. An object-based image segmentation and classification technique was tested for seven bands of MODIS, six bands of Landsat Thematic Mapper, and four bands of IKONOS. Eleven terrain categories were identified using this technique which achieved classification accuracies of 79% and 93% over metropolitan (Sydney) and rural/urban areas respectively. It was revealed that the object-based image classification enhances the quality of the terrain product compared to traditional spectralbased maximum likelihood classification methods. To further improve the derivation of terrain roughness classification results, an integrated textural-spectral analysis merged Synthetic Aperture Radar and optical datasets provided in a study by [1]. A comparison with results derived from textural-spectral classification showed considerable improvement over the results from earlier classification techniques.
There is an increasing application for bushfire spread models in planning for prescribed burning and for the generation of fire risk assessment maps in fire prone communities. An evaluation of FARSITE and FlamMap bushfire spread models developed by the Fire Sciences Laboratory at Missoula involved a comparison of fire simulator models over two Californian landscapes representing different terrain and vegetation regimes. The paper includes a discussion on the models, the assumptions and limitations resulting from their application, and also the assembly of data to build landscape files and model outputs over the two test areas. The spatial datasets used in this study are sourced from the USGS EROS Data Center's LANDFIRE database at 30-metre pixel resolution. Information about fuel which has been derived from satellite imagery, terrain modelling and biophysical and local field knowledge was used to build Anderson's 13 Fire Behavior Fuel Models (FBFM13) and Scott and Burgan's 40 Fire Behavior Fuel Models (FBFM40). These were ingested into the FARSITE fire growth simulation model and FlamMap fire potential simulator. The FBFM40 provides a better representation of fuel across the landscape, leading to an improvement in surface fire intensity prediction and increased precision in determining crown fire behaviour. The FARSITE/FlamMap were used to model fire behaviour, and WindWizard simulated wind speed and direction scenarios across the Woodacre and Glen Ellen regions near San Francisco, California. FARSITE and FlamMap are two separate fire simulation models that use the same input datasets (vegetation/ground cover type, crown stand height, crown base height, crown bulk density, temperature, humidity, precipitation, slope, aspect, elevation, wind speed and direction). In this study, the actual fire perimeters were not available to compare the overestimated and underestimated fire growth perimeters/areas after and before using gridded wind data into the fire simulation. However, previous studies [1] demonstrate that incorporation of gridded wind data clearly improves prediction of fire growth perimeters. The preliminary evaluation of FARSITE/FlamMap simulations appropriately predicts fire growth process and assesses resources at risk, suggesting the need for further experiments in areas of different terrain and vegetation, and with varied weather conditions. In future research, it is proposed to evaluate how these models compare with existing Australian fire models by using the example of recent Australian wildfire events.
The automation of map generalisation in this study involves an expert system approach that consists of four main components including knowledge acquisition, an inference engine, knowledge representation and a user interface. The acquired knowledge was then utilised to build a knowledge-based solution: a ‘Generalisation Expert System’ (GES) developed in Java, Python and C programming environments for the delivery of generalised geographical features. Its capabilities are demonstrated in a case study through generalising several line and polyline databases over the study area in Canberra, Australia. The cartographic and GIS software communities will benefit from this study through access to a set of tools, guidelines and protocols that incorporate a standardised cartographic generalisation methodology. The results of the trials utilising GES were analysed: a series of generalisation routines were performed to assess the quality of simplification results for different spatial layers. Cartometric measures such as the total length and number of line or polyline segments were used as indices of generalisation to quantify generalisation performance for the target small scale. For example, there are 101,228 segments in 1:250,000 scale and 9,491 segments in 1:500,000 scale contours over the study area. This requires a reduction in the complexity and the density of elevation data. Changes in the representation of contour features at 1:250,000 and 1:500,000 scales as a result of generalisation were quantified. Outputs from map derivation have been analysed applying the Radical Law, this determines the retained number of objects for a given scale change and the number of objects of the original source map. Testing demonstrated that the implemented algorithms in GES are able to extract characteristic vertices on the original entity lines and polylines (e.g. for roads) while excluding non-characteristic lines and polylines to reduce irrelevant computation. This study has demonstrated reasonable improvements in Vertex Reduction, Classification and Merge, Enhanced Douglas-Peucker and Douglas-Peucker-Peschier algorithms. The test results show that GES generalises line features accurately while still maintaining their geometric relations. Existing generalisation software requires advanced technical skills from users; GES however, has a basic and user friendly Graphical User Interface (GUI) which is an advantage to users with basic technical skills and understanding of spatial data management. Changes to geographic parameters should be updated in multi-scale maps and spatial databases in near real-time. GES can be developed as a potential tool for generalising large-scale maps into smaller scales, and creating maps of different themes across a variety of scales. Test results have also demonstrated that the methodology developed improves the efficiency of line and polyline generalisation. This study aims to contribute to generalisation system design and the production of a clear framework for users. Experiments presented in this book can be applied to real world problems such as the generalisation of road networks and area features using GES. Future research should be directed towards developing web mapping platforms with generalisation functionality at varying scales.
Mashhad is one of the important metropolitans in the northeast of Iran with over 25 million tourists per year. After evaluating the physical space of Mashhad in terms of semantics and identity with the aim of promoting tourism, 127 valuable places with cultural, historical, and religious values were identified, assessed, and analyzed using a Geographic Information Systems (GIS). Therefore, the appropriate distribution of tourist routes for travelers to visit the city of Mashhad was done and 10 corridors and zones were selected. The basis of this choice was the existence of a valuable place at origin and destination of the routes and the existence of appropriate tourism, commercial, welfare and cultural infrastructure and ability to access various uses. Percentage and number of valuable places in each proposed route necessarily meant percentage of the total, excluding repetition in other routes.
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