Visual Landscape Character in the Approach of GeoDesign

Transcription

1 227 Visual Landscape Character in the Approach of GeoDesign Jan SZTEJN, Piotr ŁABĘDŹ and Paweł OZIMEK 1 Introduction One of the important landscape indicators used in the field of GeoDesign are the visual aspects of the landscape, including any change resulting from the implementation of projects and spatial transformations. The authors of this paper perceive there still exists a gap in the assessment used for bulk data, with which designers have to do in a situation of large area of attractive landscape. They attempted to develop a monitoring tool to support landscape and design in the space using a combination of the best of various legacy tools (CAD, GIS, BIM, e.g.), by supplementing general information about the area of visual resource assessment. An important new approach is to gather information about the visual resources of the landscape as seen not from few individual viewpoints, or paths, but with the entire surface of the land being developed. It could be helpful for obtaining content of several components connected with Geodesign, which were distinguished by Stephen Ervin (ERVIN 2012). First of all, as a tool for monitoring, it could give us layers of specific information about environment. Moreover, these layers contain visual relationships between space elements and provide essential inputs into the analysis and simulation functions. Using the same tool we can establish relationships between existing and designed elements of landscape. The study was conducted in based on the concept of the landscape character developed in the framework of LCA (Landscape Character Assessment), which is defined as: a clear, recognizable, coherent system of elements in the landscape, which makes the landscape different from the others... (SWANWICK 2002). Mentioned elements can be interpreted in the context of the visual character of a landscape described as: The visual expression of the spatial elements, structure and pattern in the landscape. The role of the researcher is the proper selection of these resources, which are visual indicators of a place. It can be interpreted as elements-per-view and degree of variation in Between-size view (ODE et al. 2008). In order to illustrate the visual character of the landscape it was decided to present the results in the form of maps. Traditional maps shows the distribution of individual landscape elements in the orthogonal projection well, it may also show some phenomena in relation to the unit area, for example the number of inhabitants per square km. It can provide good performance of the physical elements of a landscape, however to illustrate the visual landscape character it is indispensable to show a panorama from particular place on the map. To demonstrate the phenomenon in evenly arrangement it is needed to analyse panoramas spaced in systematically intervals on the ground. In practice, due to the large area and a significant number of respondents panoramas it would be impossible, and therefore it was needed to build three-dimensional model of the landscape for the entire study area and simulate visibility on it (OZIMEK et al. 2009).

2 228 J. Sztejn, P. Łabędź and P. Ozimek 2 Methods 2.1 Case Study Area The adopted classification of indicators was dictated by the specificity of the study area. The Investigation area covers the south western suburbs of Wroclaw and has a size of approximately 7 12 km. According to the micro-region division of Poland the area belongs to Wrocław Plain (318.53), a part of the Silesian Lowland (KONDRACKI 2002). The land here is almost entirely flat with small hills and the only distinctive haughtiness of Sleza mountain. The area is characterized by the agricultural landscape, where large surfaces agricultural crops are separated by small groups of green forests and mid-field. Traditional rural buildings are concentrated in small villages. The neighborhoods of Wroclaw promotes a dynamic suburbanisation phenomenon and the emergence of new buildings and new landscape elements. 2.2 Input Data In the investigation numerical terrain model in the form of TIN was used and also orthophotomaps, which were gain from a local administrative unit responsible for collecting, processing and dissemination of geodetic and cartographic data. The maps of investigation area was built on a grid of squares TEMKART developed by Klimczak (KLIMCZAK 2001). This system consists of a geometric units, which have resulted from the division of Polish territory by polygons with dimensions of meters. Sides of each unit are determined by the meridians and parallels. A digital code is assigned for each field representing its centroid. It allows to link a particular fields of TEMKART with the corresponding database record. The map was expanded by additional layers including buildings, roads, different types of greenery and water. These layers were produced from digital orthophotomap which was vectorised in order to give corresponding data. The cartographic elaboration and preparation of basis data for construction of the model was prepared in ArcGIS 9.3 in projected coordinate system PUWG_1992 and partly in the CAD environment. 2.3 Modelling Approach The first step of the work was generation of three-dimensional digital terrain model with the use of AutoCAD Civil 3D. Achieved model included the area of 240 km² and with the Sleza Mountain taken into account it raised to 500 km². Additional layers were included and whole data were exported to Autodesk 3ds Max 2011, which environment is effective in the case of renderings. Such constructed digital model allows unrestricted selection of components that are important for analysing and distinguishing them in space. In order to determine points for analysis, systematic sampling was performed. The frequency of points distribution depends on the level of detail required for the analysis. It should be chosen properly to match morphological dynamics and land cover. As a basis to determine frequency of points distribution, different maps can be used including: map of landform geodiversity containing information from map of landform energy, map of landform fragmentation or map of contemporary landform preservation (ZWOLIŃSKI). The highest geodiversity value on the map is treated as a determinant of points distribution frequency. Thereby it is less probable to omit the important places in the spaces between

3 Visual Landscape Character in the Approach of GeoDesign 229 samples. In the landscape attractive areas, the values of geodiversity are always high, therefore the number of samples is tremendous. Processing them requires an efficient computer system Visibility Map The visibility map was created in Autodesk 3ds Max During that process a point light was placed in each of the achieved points. The assumption was made that the light rays trajectories, calculated according to raytracing algorithms, are straight lines. Thus, one can draw a conclusion that if the fragment of model is lit by the light it is also visible from point where the light is situated. It led us to creation of a visibility graph, which shows the parts of the terrain that could be seen from single point. The resolution of the graph were set in the way that 1 pixel in the image corresponds 25 square meters of terrain. Fig. 1: The diagram showing steps of implementation of visibility map and landscape resource visibility map

4 230 J. Sztejn, P. Łabędź and P. Ozimek After image binarization the visibility graphs from all points, achieved by systematic sampling, were composed in arithmetic mean to create the visibility map. Procedure of histogram stretching was applied in achieved map to improve its quality. In final image we obtain information about passive exposure, which reveals the degree of visibility of whole analysed area. The lighter the pixel in the picture is there are more places the corresponding area is seen from. In order to achieve map of active exposure one can compose samples located in the open space. Visibility graphs made for the huge number of points take into account all the items affecting the landscape. Visibility map contains accumulated information about the nature of the landscape of whole analysed area. That type of maps can be applied to perform analyses of changes in landscape visibility character caused by terrain transformations or changes in the way of its management. It is important during map creation process that the denominator of arithmetic mean for different design states was the same. Then, the differences in maps achieved for the states before and after performing simulated transformation indicated the degree of impact on visibility. Making such analyses on digital models, on which one can perform simulations, is a part of GeoDesign Virtual Panoramas The second element is the virtual panoramas analysis. In each point of systematic sampling, a virtual wide-angle camera was placed at the height of 1,8 meter above the terrain to simulate human eye-level view. Its focal length was set to 18 mm, equivalent of 90 degrees field-of-view. A series of four renderings was performed from each point, to present a view in each side of the world. Model elements were simplified and marked easy recognizable colors from CMYK and RGB space. Each rendering was examined with regard to contain characteristic elements of the landscape of suburban area: buildings forests mid-field greenery roads water hedgerows In addition the Sleza Mountain was taken into account as a dominant in mostly flat landscape. No atmospheric haze or any different disturbing factors were taken into consideration. Calculated output data was percentage participation of each element that is seen from each of the centroids. It was determined by analysing the percentage participation of selected color in the picture. Thanks to the digital connector it was possible to link a mathematical result of the corresponding field of TEMKART network and drawing the appropriate value on the map. Classification of values on the map was presented using a logarithmic scale. This was due to the specificity of the results obtained, in which the largest number of observations recorded in the range from 0.0% to 1.0%. Due to mass amount of data authors used an effective and fast Quicksilver hardware rendering algorithm. It fits the task perfectly forasmuch used model was geometrically simple, despite it contains huge number of polygons and there was no need for images to

5 Visual Landscape Character in the Approach of GeoDesign 231 look realistic. Rendering were performed with the use of GPU Nvidia Quadro 5000, image operations, like binarization, histograms creation, colour segmentation were made in Matlab Image Processing Toolbox and Parallel Processing Toolbox. Fig. 2: An example of a virtual panorama ( above) and the same view in orthogonal projection (below) 3 Results 3.1 Visibility Map During the first stage of analysis almost 3500 visibility graphs were achieved. Afterwards they were averaged to create one visibility map, which histogram were stretched to improve its quality. To monitor landscape changes during time, the same procedure was applied to data obtained in three different years 1982, 2004 and Resulted maps for 1982 and 2009 are presented in Fig. 3 a and b. They revealed a progressive process of urbanization of analysed area. It is visible especially near the highway turn in the middle of the picture. There is a wide dark area in that region in 2009 (Fig 3.b), which didn t exists in 1982

6 232 J. Sztejn, P. Łabędź and P. Ozimek (Fig 3.a). Dark area means that there is slight number of points of which that area is seen from and, on the other hand, from that area meagre part of analysed terrain could be observed. There exists more similar regions, however they are not so obvious to perceive. After calculating difference between images representing years 2009 and 1982 (Fig 3.c) more congenial territories are disclosed. Enclosed example demonstrated usefulness of presented method in GeoDesign. One could perform such study to perform an objective estimation of changes in landscape character caused by planned investments or modifications that would affect it. Constructed digital model is highly adjustable to different types of GeoDesign tasks. a) b) c) Fig. 3: Achieved visibility maps of analysed area: a) in 1982; b) in 2009; c) difference between b and a. 3.2 Virtual Panoramas Owing to the visibility range analysis of the virtual panoramas a set of maps working in GIS was obtained, showing the degree of individual elements visibility of the landscape. (Fig. 5) All maps are stored in a geodatabase and can be enriched by any layer. The resulting map database consists of a set which shows the states of the landscape in the years 1982, 2004 and 2009 for each of the eight characteristic elements including the view of the four sites of the world for each of them. Presented phenomenon shows how an observer standing on the ground at some particular point can see the landscape individual resources (if an item is visible and its share in the panorama). Visual assessment of the landscape can be considered in qualitative terms, however, in this case it was achieved by means of quantitative analysis which gave tangible and objective results. Statistical analysis showed a trend of distribution of observations in the study panoramas. The visible surface of the analysed elements was located predominantly in the range from 0.0 to 0.05 (0% to 5%). The distribution of the data was concentrated closer to zero. An exception is the surface of the ground and the sky which is mainly from 0.4 to 0.6 (40% to 60%) (Fig. 4) The legends in the classification contain 10 intervals in a logarithmic scale with which it is easier to performance the nature of the phenomenon.

7 Visual Landscape Character in the Approach of GeoDesign 233 Fig. 4: A histogram showing the distribution of observations in a virtual panorama of the building layer (left) and terrain layer (right) Fig. 5: The landscape resource visibility maps showing the gradient of forests in the north direction 4 Discussion and Conclusions The virtual landscape model allows performing mass analysis of the visibility in relatively effortless way. It can be modified depending on needs (adding more layers, the analysis in subsequent years). In addition, measurable and equal indicators was used for all the analyzed points on the map, regardless of the test area size. Therefore, the results are objective and give a comparison, which would be impossible for expert analysis in the field, assuming a large area of research. However, this method requires an appropriate choice of parameters to build the model and assumptions in the performance simulation and

8 234 J. Sztejn, P. Łabędź and P. Ozimek analysis. This step requires expert work, which conduct the desirable classification of landscape indicators. An important element of the construction of the model is to match the grid TEMKART to specific sites. Grid and related observation points can be compacted depending on terrain, landscapes and adopted to resolutions of the map. The visibility map method which presents passive exposure is based on the binarization viewed area (the object is visible or not, without indicating the intensity of the phenomenon). Passive exposure assigns a higher value for the objects those are seen from larger number of points. This is useful for identifying most characteristic areas and landmarks. This result may be verified by means of the landscape resource visibility maps, which gives outcome in gradient value, therefore, shows the extent to which an element of the landscape is visible. These methods provide objective data to interpret the visual character of the landscape, monitor changes in the landscape by performing systematic analyses, planning and decision making in protection and forming the landscape. However it requires evaluation of an expert who knows the conditions of study area landscapes (natural, cultural, historical). Moreover, visibility maps should be confronted with the subjective perception evaluation and the conclusions can be applied in urban planning and landscape protection as well as providing a guidelines to form aesthetic perceptual aspects in LCA methodology. They also could affecting at the local community awareness about the resources and landscape changes. References Ervin, S. (2012), A System for GeoDesign. In: Buhmann, E. et al. (Eds.), Peer-reviewed Proceedings of Digital Landscape Architecture 2012 at Anhalt University of Applied Sciences. Berlin/Offenbach, Wichmann, Klimczak H. (2001), Studia rozmieszczania obiektów punktowych, liniowych i powierzchniowych na przykładzie obszarów leśnych i terenów zadrzewionych. In: Modelowanie kartograficzne w badaniach przydatności obszarów pod zalesienie, Wrocław. Kondracki J. (2002), Geografia regionalna Polski PWN. Warszawa. Ode, A., Tveit, M. & Fry G. (2008), Capturing Landscape Visual Character Using Indicators: Touching Base with Landscape Aesthetic Theory. In: Landscape Research, 33, Ozimek, P., Ozimek, A. & Łabędź, P (2009), Digital Analyses of Wind Farms Visual Apsects in South-East Poland. In: Digital Design in Landscape Architecture 2009, Proceedings at Anhalt University of Applied Sciences. Swanwick C. (2002), Landscape Character Assessment. Guidance for England and Scotland. The Countryside Agency, Scottish Natural Heritage. Zwoliński Z. (2009), The routine of landform geodiversity map design for the Polish Carpathian Mts. In: Geoecology of the eurasiatic Alpides (Eds. Łajczak, A. & E. Rojan), Landform Analysies, 11,