A CASE STUDY OF LANDSCAPE ANALYSIS WITH HIGH-RESOLUTION SATELLITE IMAGERY IN OLEIROS (A CORUÑA, SPAIN)

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1 A CASE STUDY OF LANDSCAPE ANALYSIS WITH HIGH-RESOLUTION SATELLITE IMAGERY IN OLEIROS (A CORUÑA, SPAIN) Gil Docampo, Mª Luz a ; Corbelle Rico, Eduardo b ; López Rodríguez, Andrés c a, b, c Departamento de Enxeñería Agroforestal, Universidade de Santiago de Compostela, Spain. Corresponding author: M.L. Gil Docampo, mlgild@lugo.usc.es During recent years, the availability of new high-resolution satellite imagery has changed remote sensing into a competitive technique in fields that were previously reserved for aerial photography. Nowadays, civilian platforms such as IKONOS, QuickBird and SPOT 5 allow capturing data over large surfaces with short revisit times. The use of this kind of imagery in geographic information systems along with other cartographic data significantly increases the capacity to carry out studies at a regional and/or local scale. A common characteristic of landscape studies is usually their great complexity and their need for large amounts of time and resources, which often made their use limited to expensive engineering projects. In this communication we present an example of landscape analysis that can be easily applied to smaller projects, by means of a simplified theoretical approach as well as simplified data (just a QuickBird image and a digital elevation model) and methods. The result is a map of homogeneous landscape units, developed through the study of visibility, land use and man made objects. 1. INTRODUCTION Landscape is a physical reality of difficult study because different interpretations of the term are commonly used by the different disciplines involved (e.g. landscape architects, agricultural, forestry and civil engineers, geographers). Furthermore, the duality between the physical reality of the landscape and what an external observer perceives is another factor that adds complexity to the issue. Technicians and researchers have tried to eliminate subjective interpretations by focusing on the objective (physical) reality: geology, relief, vegetation, wildlife and other similar landscape features have been regularly used in landscape studies, constituting a good approach for land use planning studies. Nevertheless it should be kept in mind that the effects of every planned activity are (literally) seen either by local inhabitants or visitors, and that, for this reason, the study of the total landscape (physical territory) does not always avoid the need for the study of the visual landscape as it is perceived by observers (MOPT 1992). Generally speaking, the studies of the landscape can be carried out using two possible strategies: (a) by directly identifying the different homogeneous units that form the landscape, or (b) by first inventorying a number of landscape features and take them into account to generate homogeneous landscape units. The first strategy requires an extensive knowledge of the area in study and tends to result in a more subjective process. On the other hand, strategy (b) allows for a more systematic approach and therefore it provides results that are generally more objective. An absolutely objective study cannot be obtained, though, as the selection of landscape features that should be inventoried still depends on decisions made by the technician of group of technicians involved. Traditionally, this kind of studies required the use of many different sources of information, e.g. cartographic and bibliographic data and field surveys. The larger the amount of sources used, the more time and resources are needed. One of the advantages of satellite imagery over traditional sources is that it contains lots of information, thus allowing to reduce the number of conventional sources and simplifying the whole process (Gil and Cañas, 2001). 2. OBJECTIVES AND DATA SET The objective of this work is the classification of the study area (located in the municipality of Oleiros, in the Northwestern coast of Spain, figure 1) in several homogeneous landscape units, using a simplified theoretical approach and a simplified data set: a QuickBird satellite image and a digital elevation model were the only needed materials. The final result would be a thematic map of landscape units, generated by the use of remote sensing and GIS techniques.

2 The image was acquired following normal ordering procedures and delivered as standard product, which has radiometric, sensor, and geometric corrections applied to it and is mapped to a cartographic projection (Digital Globe, 2004). The most relevant characteristics of the image are shown on table 1. Figure 1. QuickBird multispectral image of the study area. Product type Standard Sensor elevation angle º Pixel size 2.44 m (multispectral) Date of acquisition by sensor Time of acquisition 11:34 UTC Situation (geographic coordinates) Latitude: N 43º 24 N 43º 20 Longitude: W 8º 21 W 8º 17 Bands Blue, Green, Red, Near Infrared Table 1. QuickBird image main characteristics The digital elevation model was generated from digitised contour lines extracted from 1:5,000 digital maps (provided by the Oleiros town council), with a vertical interval of 5 metres between contours. 3. METHODOLOGY Cañas (1995) proposed a systematic methodology for the study of the landscape. This proposal classifies the landscape features that can be individually inventoried into three categories: Terrain relief. Vegetation or land use. Human activities and man made objects. In the present work we followed the cited approach, although it was slightly modified to fit the specific conditions of the study area. The terrain relief is fairly homogeneous throughout the entire area, so it would not make sense to create different areas based on altitude or slope. Instead, the most important effect of the relief is its effect of the visibility of the sea. On the other hand, the whole area has been transformed by human activities during the last centuries and certain man made features are present everywhere, as it is the case of the road network. Because of that, buildings were selected as the most important man made elements that can be useful to differenciate between landscape units.

3 The work was carried out in two different phases: (1) the selection and study of landscape features and, (2) the combination of these features to obtain homogeneous landscape units. The considered landscape features were the following ones: Visibility of the sea, considering two possible states: the sea is visible, the sea is not visible. Land use, considering three possible states: agricultural use, forestry use, other (e.g. roads, built areas). Distance to buildings, considering two possible states: buildings are located at less than 50 metres, buildings are located further than 50 metres Visibility of the sea Visibility can be defined as the degree in which something can be seen from a given place and it is not only conditioned by the existence of a free of obstacles line of sight but by the atmospheric conditions as well. In certain cases, more factors should be taken into account, such as the characteristics of the observed object (i.e. its texture, shape and colour) and whether the observer already knows about its existence (in which case it would be easier for him/her to locate it) or not. As a consequence of these factors visibility range for a given object may vary considerably, but it can be assumed that it would be around 9 12 kilometres during most of the year (although it could reach 40 kilometres or more in extraordinary conditions) (CEDEX 1998). In our case we wanted to identify the parts of the study area from where it is possible to see the sea, or what is the same, the parts of the study area that can be seen from the sea surface -this being the way in which the software estimates it-. To generate a map of sea-visibility areas we used the 3-D analysis capabilities of the commercial software ArcView TM 3.2. To this end an open polygon line was created some 10 kilometres away from the shoreline, representing the average visibility range: this line is used by the software to generate the visibility areas (an example of results can be seen in figure 2). Obviously, the existence of vegetation or buildings can restrict the theoretical visibility Land use Figure 2. Visibility of the sea (areas shaded in blue are those from where the sea can be seen) The land use map was created by means of a classification of the QuickBird multispectral image. The objective of the classification process is to assign all pixels in an image to a finite number of categories or classes of data, based on the spectral information contained in each pixel. Classification methods can be divided into two groups depending on the degree of operator intervention: in unsupervised methods the operator only decides the number of information classes to be created, while in supervised methods the operator closely controls the process by selecting recognizable regions as training areas for any of the intended information classes (PCI 2003). As each approach has its own advantages and disadvantages it is advisable to carry out the classification process in two stages: creating first an unsupervised classification to gain preliminary knowledge about the spectral information present in the image and using this knowledge to further refine results by means of a supervised classification.

4 Unsupervised classification results. The unsupervised classification process identified six information classes, in which % of the pixels in the image were included. The analysis of these classes revealed that: The surface of the sea was clearly separated into one information class (class 1). Most of the forested areas were included into one information class (class 2). Classes 3 and 4 grouped all crops and agricultural fields. Most of the built areas were included in class 5. The areas covered by clouds were grouped in class 6. The conclusion of this analysis is that it is possible to carry out a separated identification of the land use categories previously mentioned: forestry, agricultural use, other (built areas) Supervised classification results. All the spectral bands of the QuickBird image (green, blue, red, near infrared) were selected as the input spectral information, which allowed to make use of all the information contained in the image. In accordance with the results of the previous phase (unsupervised classification), four information classes were created, along with their respective training areas: (1) forested areas, (2) agricultural fields (bare land), (3) crops and pastures, and (4) built areas (a fifth class or null class was allowed, to include all the pixels that could not be assigned to any of the previous categories). To be considered representative of their respective information class, training areas are recommended to contain a number of pixels from 10 to 100 times the number of input spectral channels (Jensen 1996), which in this case makes a number between 40 and 400 pixels. All the training areas were verified to meet this specification. The spectral separability of the four information classes is shown in figure Green Blue Red Near infrared Forested areas Bare agricultural land Crops and pastures Built areas Figure 3. Spectral separability of the information classes (mean digital level values for each spectral band). The accuracy of the supervised classification results was assessed using 227 check points, which were selected using a systematic approach (the nodes of a 250 metre grid). It was found that the final accuracy of the classification was 88 %, as it is shown in table 2. Real land use Assigned land use (number of pixels) Omission Forestry Agriculture Other Null class Total error Forestry % Agriculture % Other % Total

5 Commission error 7.8 % 1.6 % 8.3 % Total accuracy: % 227 Table 2. Accuracy assessment of the supervised classification results Distance to buildings The distance to buildings map was created with two possible categories -less and more than 50 metres-, creating a 50 metres buffer around the buildings that were identified in the supervised classification of the QuickBird image. An example of the results can be found in figure 4. Figure 4. Distance to buildings (in the areas shaded in blue the distance is less than 50 metres) Landscape units The map of landscape units is the result of integrating the information obtained about each of the three considered landscape features. Considering the possible values of each feature there were 12 potential homogeneous landscape units, as they are defined in table 3. Landscape features Visibility of the sea Distance to buildings Yes No Less than 50 m More than 50 m Less than 50 m More than 50 m Landscape units Land use Agricultural Unit 1 Forestry Unit 2 Other Unit 3 Agricultural Unit 4 Forestry Unit 5 Other Unit 6 Agricultural Unit 7 Forestry Unit 8 Other Unit 9 Agricultural Unit 10 Forestry Unit 11 Other Unit 12 Table 3. Homogeneous landscape units and their relationship with landscape features.

6 4. RESULTS The three previously studied landscape features were integrated into homogeneous landscape units, with all the potentially existent landscape units (12) being actually present. As it can be seen in table 4, the landscape units numbers 3, 6, 9 and 12 (those in which land use was classified as other ) occupy a large portion of the study area. This situation is due to the existence of some areas covered by clouds in the original QuickBird image. It can also be clearly appreciated in figure 5, where those landscape units are represented in different shades of violet. Landscape unit Area (hectares) 1 94, , ,1 4 49, , ,9 7 57,3 8 78, , , , ,7 Table 4. Area occupied by each landscape unit. Figure 5. Map of homogeneous landscape units and original QuickBird image. 5. CONCLUSIONS The present case study shows the feasibility of carrying out a simple landscape analysis that is suitable for low cost engineering projects. The final result is a thematic map of landscape units. The materials used comprised a highresolution satellite image (a QuickBird multispectral image in this case) and detailed digital maps covering the study area (to generate a detailed digital elevation model). We would like to emphasize the fact that satellite imagery can be used in place of other information sources, specially thematic maps (e.g. land use maps), thus reducing the total number of sources needed. The actual selection of the landscape features that should be taken into consideration depends on each specific case, more specifically on the characteristics of the area in study as well as on the purpose of the project (e.g. the construction of a road, a suitability analysis for the placement of an industrial plant, etc.).

7 References Cañas Guerrero, I., Introducción al paisaje. Unicopia, Lugo, Spain. Centro de Estudios y Experimentación de Obras Públicas (CEDEX), Las obras públicas en el paisaje. Ministerio de Fomento, Madrid, Spain. Digital Globe, QuickBird Imagery Products. Product Guide, revision 4.2, Gil Docampo, M. L., Cañas Guerrero, I., Aplicaciones de las imágenes espaciales al estudio del paisaje. In: Fundación Alfonso Martín Escudero (ed.), Gestión sostenible de paisajes rurales. Mundi-Prensa, Madrid, Spain. Jensen, J. R., Introductory Digital Image Processing. A Remote Sensing Perspective, 2 nd edition. Prentice Hall, New Jersey, USA. Ministerio de Obras Públicas y Transportes (MOPT), Guía para la elaboración de estudios del medio físico. MOPT, Madrid, Spain. PCI Geomatics, Geomatica Focus User Guide. PCI, Ontario, Canada. Aknowledgements The authors would like to thank the Alfonso Martín Escudero Foundation (Spain), for providing the financial support for this research, included in the research project entitled Development of a methodology for design and preservation of greenways. We also would like to thank the active cooperation of the Oleiros town council, for providing some of the data used in this study. Brief note about the presenting author Eduardo Corbelle Rico graduated as a forestry engineer (2004) in the University of Santiago de Compostela (Spain) where he currently continues his studies as a PhD student. He worked as a collaboration scholar in research projects in the Department of Agricultural Engineering of this University during the period of march to september His current research fields are geometric correction of high-resolution satellite imagery and remote sensing of forested areas.