A landform classification method with G.I.S. for landscape visual analysis purposes

Transcription

1 Sustainable Development and Planning II, Vol A landform classification method with G.I.S. for landscape visual analysis purposes A. Tsouchlaraki Hellenic Open University, Greece Abstract The relief forms are essentially innumerous and their classification has been a multidisciplinary field of study. This paper develops a landform classification method, in order to meet visual analysis needs. The main feature of this classification is that it tries to predict perspective observation conditions and constitutes a modification of Hammond s method. Considering that we have a digital terrain model in the method proposed, the three parameters of Hammond s method are adopted (flat slope percentage, hypsometric difference, flat slope percentage in the upper or lower half of the hypsometric difference range) while one more parameter is added regarding the observation post. The four parameters are applied to movable windows of specific dimensions and of specific movement increment on the digital terrain model. GIS tools are used for this work. The study area of this paper is about the Lefka Ori mountain range of the island of Crete. From the results of the classification 33 relief categories are derived. In order to examine the degree to which these classification results meet landscape visual analysis purposes, certain positions were sampled, from which perspective digital imaging representations of the relief were created, with the use of a special algorithm. The results show that the proposed classification is considered successful, since it fulfils to a great extent the description of the visual impression of an observer for a specified relief form. A further examination may lead to the determination of all morphological combinations that can probably appear. Keywords: landform classification, quantitative parameters selection, landscape visual analysis, geographical information systems.

2 826 Sustainable Development and Planning II, Vol. 2 1 Introduction Landscape visual analysis attempted to reveal the factors that regulate the complexity and the differentiation of physical space, and to present the difference between the actual and the visual landscape. It differs from the usual geographical analysis, which is also an analysis under specialised knowledge, in that it does not deal with physical environment only as a reality, but also as a space of behaviour and perception. People when moving within a landscape do not analyse a static map or an aerial photo. Issues of perspective, relative position, movement, orientation, etc., help at interpreting perception; in this respect, landscape analysis has been trying to play this role [1]. There are cases when it is desirable to isolate and examine separately the relief from other elements that compose the visual environment. These cases include technical works that cause big and permanent changes in the relief, as well as works whose arrangement depend on the soil s morphology. The forms of the relief are practically indefinite in number. Landform classification is an issue that has preoccupied many scientists, and in particular surveying engineers [2, 3, 4]. The methods developed differ as to the way they approach the problem and the aim they serve. In order to meet the needs of visual analysis, this paper develops a landform classification method, based on existing experience, but modified to meet the needs of perspective and 3D perception. The method is applied on Lefka Ori mountain range of the island of Crete, due to the variety of its relief forms, since it combines an island and an appropriate inland environment. 2 Developing a landform classification method for visual analysis purposes The classification method developed is a modified form of Hammond s classification, which was selected as the basis; firstly because it is one of the most acknowledged relief classification methods [5] and secondly, because it uses few variables in the quantitative determination of forms, something that is essential in order to have a limited and logical number of categories. Considering that a digital terrain model is available, the method proposed adopts the three quantitative parameters of Hammond s classification, and one more. These four parameters are applied to moving windows of specific dimensions. Following this, a detailed description of the elements of the proposed classification method is made. 2.1 Review of Hammond s classification method According to Hammond s classification, in order to classify large areas, the study area is sub-divided into large square parts, 6x6 square miles, namely 10x10 km 2 approximately [5]. This size is neither big to include a large variety of forms, nor small to divide individual slopes. The quantitative parameters used

3 Sustainable Development and Planning II, Vol are based for their determination on the main stages of geomorphic process. These parameters are: 1. Flat slopes percentage. Slopes up to 8% are characterised as flat. 2. The maximum hypsometric difference observed within the square part. 3. The percentage of flat slopes observed at the upper or lower half of the maximum hypsometric difference range. Each of the variables mentioned above is divided in specific value intervals (Table 1), while 96 possible combinations may theoretically derive from the intervals of all variables. However, the application of Hammond s method to the Appalachia Mountains showed that only half of the combinations might probably appear, while most of them at a small percentage [5]. So he suggested a final classification in five categories: i) purely plane areas; ii) areas that combine plane and hilly parts (Β3b); iii) plateaux (B3c, B4cd); iv) mountains with small curvatures on their picks (C3bc, C4acd, C5cd); and v) mountains with great curvature (D3, D4, D5). Table 1: Hammond s classification. Parameters Code Value intervals Flat slopes percentage A > 80% B 50-80% C 20-50% D <20% Maximum hypsometric difference m m m m m 6 >900m Flat slope percentage at the upper or lower half a >75% at the upper half of the hypsometric difference range (this b 50-75% at the upper half parameter is not examined when flat slopes c 50-75% at the lower half cover less than 20% or when dh <30) d >75% at the lower half Cole et al examined the minimum possible size of the square part, where Hammond s method may be applied, without altering the classification. By this examination they proved that in parts smaller than 3x3 square miles, namely 5x5 km 2 approximately, categories alter in relation to the initial ones and, moreover, they define this size as the threshold for applying the method [5]. 2.2 Quantitative parameters in the classification method proposed The classification method proposed adopts the three quantitative parameters of prementioned Hammond s method, while one more variable is added for the observation post. The observation post is actually related to the relative elevation post between the observer and the objects he observes and it constitutes a significant element in landscape analysis, because it affects the viewshed offered by the position. Three types of positions are determined [6]: 1) higher, 2) equal, and 3) lower. At higher position, the observer is higher than the objects he

4 828 Sustainable Development and Planning II, Vol. 2 observes, while this position offers a larger viewshed and gives him the opportunity to better observe and perceive shapes and forms. At equal position, the observer stands at about the same elevation with the observation objects, while at the lower position he stands at a lower level. As we are interested in the observation post with regard to the whole view of the relief and not for a certain sub-area of the relief, we used the criterion of 1/3 of the maximum hypsometric difference observed in order to determine the position. That is, if h 0 is the elevation of the observation post, h min the minimum elevation observed in the window and dh the maximum hypsometric difference, then: h 0 - h min < 1 3 dh => relatively lower position 1 3 dh < h 0 - h min < 2 3 dh => relatively middle position h 0 - h min > 2 3 dh => relatively higher position This logic is based on three characteristic parts of a hill: a) the foot; b) the hillside; and c) the peak. Therefore, the position is characterised lower at the first third of the hypsometric difference, because it is expected to observe mainly the foot of the elements of the relief and there is in general a limited visibility. At the second third, the position is characterised as middle, because visibility will be greater than the one at the lower position, but at the same time narrower than the one offered by the higher position. Finally, at the last third, where peaks prevail and visibility has the maximum possible range, the position is characterised as higher. 2.3 Size and movement increment of the moving window The previous four quantitative parameters are applied to square parts of 5x5 km 2. This surface area meets two criteria: a) as mentioned above, Cole et al found that it is the threshold to which Hammond s method can be applied; and b) the 5 km distance practically constitutes the middle viewing zone for landscape analysts. Most landscape analysis systems do not examine distance as a constant variable, but they make use of a general distinction by dividing it into three main zones, i.e. close, middle and long distance. Various approaches have been formulated for determining these zones [6,7,8]. The close viewing zone is characterised by great details in the texture and the colours of the elements and covers information from the observation post up to a distance of approximately 1000m (or 800m according to other approaches) [6]. It should be noted at this point that a very close viewing zone, which is placed by many scientists at the first 100m and by others at the first 400 m, is not examined in landscape analysis or evaluation, because it is regarded as a general threshold for the convenient

5 Sustainable Development and Planning II, Vol stereoscopic observation of the objects [6]. The middle zone is the most important one in landscape analysis, mainly because it gives a complete perception of the shapes, the forms and the templates of the elements. This zone extends from 1 to 4 km, while according to some analysts it reaches 5 km. Due to the interference of the atmosphere, the colours in this zone are clear, while their contrast is reduced. Finally, the long zone is dominated by the horizon and serves as a general background, where only general information about the forms of the elements is perceived. This zone begins where the middle zone ends and extends to the point in which the atmospheric conditions offer visibility. Therefore, by determining square parts of 5x5 km 2, the middle viewing zone is taken into account for the classification of the relief. In contrast to Hammond s method, where the square parts determined are static, the method in question makes use of moving windows. This selection was made because the relative observation post within each part changes from point to point, while the researcher is interested in characterising the form of the relief, always in combination with the observation post. Hence, it should be stressed that following the application of the method, any landform category that could derive characterises the specified position under examination and not all other positions within the square part, as it is the case in Hammond s method. The movement increment of the window was set at 2 km. The selection of the 2 km was made based on the close viewing zone, which is set at approximately 1 km radially, around each view point. The movement increment practically creates a template with elements of 2x2 km 2 size. For each element of the template the observation posts are selected to be in the middle of the sides, while in each post the orientation towards the centre of the element of the template is examined (Figure 1). Figure 1: Dimensions and movement increment of movable window position and orientation.

6 830 Sustainable Development and Planning II, Vol. 2 The observation post is considered to be at the centre of the corresponding side of the moving window, as it is in Figure 1. This process is repeated for each element, and therefore the four main orientations are examined, i.e. from north, south, east and west to the centre of all the elements (see the black arrows in Figure 1). 2.4 Codification of the combinations For the final classification of the landforms and in combination with the observation post, a codification is required in order to have manageable results. The codification selected in this paper corresponds to a number at each value interval of each variable (Table 2). Table 2: Codification of the classification results. Parameters Value intervals Code Relative observation post Lower (< dh/3) 1000 Middle (dh/3 up to 2dh/3) 2000 Higher ( > 2dh/3) 3000 Window percentage with slopes > 80% 100 smaller than 8% 50-80% % 300 Maximum hypsometric difference (dh) Percentage of flat slopes (<8%) at the upper or lower half of the hypsometric difference range (dh) < 20% < dh < 30 m < dh < 90 m < dh < 150 m < dh < 300 m < dh < 900 m 50 dh > 900 m 60 when flat < 20% ή dh <30 0 > 75% at the upper half % at the upper half % at the lower half 3 > 75% at the lower half 4 The final code of each observation post derives from adding up the corresponding codes that represent the characteristics of a given relief form. For example, if for a certain position the code is 3454, this means that: - the observation post is the higher (code 3000); - less than 20% of the window is covered by flat slopes (code 400); - the hypsometric difference observed varies in the range of m (code 50); - more than 75% of flat slopes appear at the lower part of the hypsometric difference (code 4). By applying this codification technique, where alpharethmetic characters are avoided, the process of determining the appearance frequency of the various combinations becomes easier. Actually this codification derives from applying overlaying tools of a GIS, using as a background the elevation data of a digital terrain model.

7 Sustainable Development and Planning II, Vol Applying the classification method to the study area In order to apply the classification, the aforementioned process was planned and a record was created for the final co-ordinates of the observation posts. A digital terrain model was used for obtaining elevation data, which was created by map sheets of the Army Geographical Service, of a 1: scale, that is the most common scale in middle scale landscape analysis [9]. The data resolution was set at 50 m, thus meeting the research requirements. The application field of the works is depicted in Figure 2. Figure 2: Research field Lefka Ori of the island of Crete. The IDRISI GIS package was used for the processing of the information, for creating the DTM, calculating the slopes and all other derivative elements. Based on the DTM created for the study area, a total of 1033 observation posts orientations were examined. Certain elements of the template, perimetrically to the area s bounds, were not examined, because either further hypsometric information was needed, or there was a risk to include the sea, an element that lies beyond the scope of this paper. From the results four maps are created. Each map corresponds to one of the main orientations, i.e. N, E, S, W, and shows the serial number of the element of the template and the code of the relief category that is viewed from each element. The resulting relief categories are presented in Table 3, with their appearance frequencies in parenthesis. Practically 33 combinations were produced, out of a total of 96x3 = 288 theoretically possible combinations. There were no areas in which more than 80% of their surface was dominated by flat slopes, a fact that was expected as the region has no large plains.

8 832 Sustainable Development and Planning II, Vol. 2 Table 3: Relief classification categories in the study area (2) 2243 (2) 3243 (2) 1244 (3) 2244 (5) 3244 (2) 1254 (2) 2254 (2) (2) 3342 (1) 1343 (4) 2343 (3) 3343 (3) (2) (1) 1352 (1) 2352 (1) 3352 (3) 1353 (7) 2353 (12) 3353 (8) 1354 (39) 2354 (36) 3354 (20) 1364 (4) 2364 (5) 3364 (2) 1450 (49) 2450 (118) 3450 (96) 1460 (142) 2460 (231) 3460 (223) As Hammond also detected, a few combinations may probably appear; however, in this particular application in Lefka Ori, our purpose is not to draw conclusions about all possible combinations. Besides, this is already examined by Ηammond, with the use of a greater research field (section 2.1). The issues of particular interest in our case were: a) the way this new quantitative parameter and the use of a moving window affect the results of the classification; and b) the degree to which the results of the classification fulfill landscape visual analysis purposes. More specifically, in order to examine the second issue, certain positions were sampled for creating perspective digital imaging representations of the relief, with the use of a special algorithm developed in this regard [10]. According to this algorithm, for all photographic shootings we used the geometrical elements of a 35 mm camera, with normal lens f=50 mm, strictly horizontal shootings and a 1.5 m observation height. In order to create the images, a DTM of the area of the Lefka Ori was used, as mentioned above. The colour palette consists of tones that darken according to the shading and become grey according to the distance, in order to simulate the natural observation conditions. In Figure 3 eight images are indicatively presented out of the total images created. The printing size of the images is in this case much smaller than the one of a normal picture, due to limited publication space. Below each image there are two codes: a) the serial number of the element of the shooting template; and b) the category of the relief form, that was expected to been seen according to the classification. The serial number of the element of the template ends to one of the letters of the Latin alphabet, i.e. n, e, s, w, that represent respectively the north, east, south or west orientation of the shooting. 4 Output comments The results differentiate in relation to the ones that could have derived from Hammond s classification, exactly as the visual impression offered to the observer by image 3B in comparison to image 3A differentiates.

9 Sustainable Development and Planning II, Vol A B C 1 128n 129n 156n w 166n 102s e 229w Figure 3: Relief digital imaging representation in 8 selected positions. The images 3A and 3B depict mountains with great curvature, whereas in the first case the position is lower (image 3Α), and in the second one higher (image 3Β); therefore, the viewshed offered by each position differentiates. Similarly, the same applies to images 2A and 2B, but with a smaller differentiation, where the first one corresponds to a lower position and the second one to an equal observation post. A lower observation post offers also a different visual impression in the case of mountains with great curvatures (images 2A, 3Α), in relation to plateaux or mountains having small curvatures on the top (images 1A, 1B). In the case of images 1A and 1B, the category of the position is not readily perceived. If the code did not exist, we would have been concerned of whether the position is lower, equal or higher. This is due to the way we distinguish the observation posts, namely based on the relative hypsometric difference of each form and not on certain standardised value intervals, common for all forms. Therefore, it is expected that in plain areas or in areas dominated by high percentages of flat slopes, where the hypsometric difference is relatively small, the 1/3 of the hypsometric difference observed does not produce important visual differentiations in relation to its 2/3, especially when this hypsometric difference happens to appear after the middle viewing zone. On the contrary, in

10 834 Sustainable Development and Planning II, Vol. 2 mountainous areas the observation post differentiates significantly the visual impression of the result. This problem is however resolved by the second and the third digit of the codes, where it is obvious whether we are dealing with a mountain or plain area. Image 3A refers to a hypsometric difference higher than 900m, while images 1A and 1B to a hypsometric difference within the range of m. Images 1A and 1B are shootings towards the same direction of two horizontally (to axis X) continuous square parts (128 and 129). These two images show that the horizontal movement increment of the moving window is satisfying, as it is neither too small so as to cover the forms depicted, not too big to lose the coherence and the sequence of the forms. Images 1B and 1C are shootings towards the same direction of two vertically (to axis Υ) continuous square parts. It is obvious that the relief elements that are depicted in the first case in the middle viewing zone (1B), are represented in the following position in the long viewing zone. Therefore, the movement increment selected is considered to sufficiently secure, both horizontally and vertically, the coherence and the sequence of the relief forms. When comparing images 1A and 2C, that present the same relief characteristics, according to the three last digits of the code, and while we would have expected that image 2C depicts a similar plain landscape to image 1A, this looks more like a hilly rather than a plain landscape. This fact is due to two possible reasons: a) the different hypsometric differences of the two forms, which still belong to the same category (perhaps the two opposite sides of the range of the specified category); and b) the different horizontal distances of the minimum and maximum elevation values from the observation post. In the second case, even if hypsometric difference is the same in both forms, the perspective height varies and therefore the impression offered. 5 Conclusions From the abovementioned observations we can conclude that even within the same classification category, there is still a variety of forms. This is however inevitable because: a) classification presupposes generalisation; and b) the perspective observation conditions cannot be accurately measured and predicted by topographic maps (ground plan), without using a certain representation perspective. Besides, what matters in this case is to have the classification prior to the creation of the images, and without calculating the viewshed which is a time consuming procedure. The classification method in question tries to predict the perspective observation conditions and not to use them as entry elements. To summarise, the relief classification proposed is regarded as successful with regard to the objectives for which it was designed, since it describes sufficiently to a great extent the visual impression made to the observer by a certain relief form. A further research may lead to the classification of the relief throughout the Greek territory, but also of other areas, and to the determination of all the morphological combinations that may probably appear. Therefore we could

11 Sustainable Development and Planning II, Vol compare various areas in order to show the uniqueness of certain combinations and, based on the results, to proceed to a generalisation of the categories in greater ones. However, the results of this particular classification are useful in landscape analysis, even without a further generalisation, taken into account that the uniqueness of the relief form constitutes a criterion in visual landscape evaluation [8]. This information together with other data of other variables of the physical and visual environment, such as vegetation, water resources, etc., may even lead to the creation of complex landscape maps. The topic of this paper involves human perception and the physical environment. The relevance of the visual landscape classification and evaluation in terms of landscape dynamics, theoretical ecology and eco-informatics is obvious. It is difficult to model human perception given that there are many gaps in our understanding of human perception. The use of G.I.S. to model perception should be encouraged because of its relevance. References [1] Ananiadou-Tzimopoulou M., Landscape analysis in Designing. Contribution to Landscape Architecture Research. PhD. Dissertation. Scientific Journal of the Polytechnic School of the Aristotle University Thessaloniki (in Greek), [2] Bradyn L.K., Landscape classification using GIS and national digital databases. Landscape Research, Vol. 21, No 3, pp , [3] Bradyn L.K., Classification of macro landforms using GIS. ITTC journal, , pp [4] Dikau R., Brabb E.E., Mark R.M., Landform classification of New Mexico by computer. U.S. Department of the Interior, U.S. Geological Survey. Open file report , [5] Cole N, Ferraro M, Mallary R, Palmer J, Zube E., Visual Design Resources for Surface Mine Reclamation. Institute for Man and Environment / ARSTECHNICA: Center for Art and Technology University of Massachusetts. Amherst, [6] Felleman P.J., Landscape Visibility. Foundations for Visual Project Analysis. John Willey & Sons, N.Y., pp , 1986 [7] USDA Forest Service, The Visual Management System. Government Printing Office: Ag. Handbook 462. Washington, [8] USDA Forest Service, Landscape Aesthetics. Government Printing Office: Ag. Handbook 701. Washington, [9] Smardon CR, Felleman PJ, Palmer FJ., Decision Making Model for Visual Resource Management and Project Review. Foundations for Visual Project Analysis, John Willey & Sons, N.Y., pp N.Y., [10] Tsouchlaraki A., Digital Relief Visualisation in Landscape Analysis. Technika Chronika. Scientific edition of the Technical Chamber of Greece: I, issue 3, pp Athens (in Greek with English extended summary), 1996.