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Land use mapping methodology using remote sensing for the regional planning directives in Segovia, Spain
Landscape and Urban Planning 62 (2003) 103–115
Land use mapping methodology using remote sensing for the regional planning directives in Segovia, Spain Francisco J. Tapiador a,∗ , Jose L. Casanova b a
Department of Geography, School of Geography and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK b Department of Applied Physics, University of Valladolid, Valladolid, Spain Accepted 6 June 2002
Abstract This study presents the methodology followed in the land use mapping at scale 1:50,000 for the Functional Area of Segovia, Spain. The study is included in the regional planning directives for Segovia, and makes a comprehensive use of the remote sensing techniques developed during the second half of the 1990s. The methodological precedents in this respect are analyzed, mainly the CORINE land cover project, together with the data sources and the techniques that were used to obtain the thematic information searched for. The less known methods are described and a complete methodological sequence of the work is offered. The main characteristics of the methodology are the high degree of automation, objectivity, possibility of direct contrasting and its capacity for quick updating. Diverse data sources have been used, such as cartographic vector information and satellite imagery. LANDSAT-TM and IRS-1D Pan were used as well as aerial oblique photography. All information was integrated into a Geographic Information System (GIS; named SIGIM-TD). Data fusion methods were also widely used to improve the spatial resolution of the images. A neural network was generated to provide an appropriate classification method. Results of the neural network-based classification are shown and a classification-fieldwork correlation of 0.887 was obtained, in contrast with coefficients of 0.334, 0.432, 0.234 and 0.678 achieved through other techniques. Graphic results of the work are presented together with the data sources used for the map elaboration. Finally, a discussion on the advances that these techniques represent is done. © 2002 Elsevier Science B.V. All rights reserved. Keywords: Regional planning directives; Land use; Remote sensing
1. Introduction The regional planning in Spain is a legal instrument aimed at optimizing a territory’s resources through public actions. The regional planning in Castilla y Leon region conforms to the 10/1998 act, 5 December, which states the principles on which the basic lines of action of the projects under this denomination are based. Through its articles, the act establishes the min∗ Corresponding author. Tel.: +44-121-414-5523. E-mail address: [email protected] (F.J. Tapiador).
0169-2046/02/$20.00 © 2002 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 9 - 2 0 4 6 ( 0 2 ) 0 0 1 2 6 - 3
imum contents and the formal structure that must be given to all regional planning projects, as much at a regional as at a sub-regional scale. One stage necessary to comply with is the quantification of the elements in the space under these directives’ scope. It is obvious that all regional planning tasks require a deep knowledge on the condition of the space on which the estimated actions will be carried out, since only in this way will it be possible to establish the actions required. In this sense, the elaboration of a land use map is one of the first actions to undertake, since it will allow a first approximation to the location and
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a quantification of the individualizing elements from which the features constituting a territory’s geographic structure will be later obtained. Regional planning for Segovia started in June 1999 and is currently (late 2001) in progress. A functional area was administratively defined as the geographical space economically and socially related with Segovia city. Several indicators, such as the economic rate of dependency of the villages were used to define the actual extents of the area using gravitational models. This implies a constrain in the implementation of the methodology since several administrations were involved, databases need to be homogenized and it was expected that the results will serve to re-define the area. The necessity to have a reliable map was highlighted in the early stages. Some properties was required, such as objectivity, accuracy, capacity of quick updating, explicit methodology capable of being contrasted and use of the most advanced technologies available. It also suggested the necessity of having a methodology as generic as possible, in the sense of exportable, which could be used afterwards in and out this area for other works in regional planning. As a precedent methodology, the results from the CORINE land cover program were mainly used. This large scale European program resulted in the land cover cartography for an area superior to 2 million square kilometers at a scale of 1:100,000). Minor works were also used, such as the Farm Use Map in the region of Segovia and a similar planning work for the Valladolid city metropolitan area. Both Segovia and Valladolid belong to the local governing body of Castilla y Leon (the largest interior region in Spain). The two first works share the common denominator of having being carried out through space remote sensing techniques, though they differ as much in their methodological as in their conceptual aspects: the different aims in both projects define their future contents. The use of remote sensing techniques implies an important break-through with respect to former works in the same area. In the present work, and considering the time elapsed since the realization of the preceding work, an important number of new techniques developed during the last five years had to be integrated, whose contribution to the work’s final quality has been noteworthy. They are mainly data fusion techniques and advanced statistics-based semi-automatic
classification techniques, such as neural networks that make it possible to obtain information not so long ago obscure or little used due to the lack of appropriate tools. It should be noted here that in this space there is no government body equivalent, as for example, the Ordnance Survey in Britain that could provide yearly information about land use. Finally, it must be pointed out that the map is created with an aim to be used as a cartographic base to be integrated in a Geographic Information System (GIS), essential for the joint consideration of the elements and factors affecting the configuration and dynamics of a territory from the perspective of a quantitative geographical analysis.
2. Study area The study area covers 3419 km2 . The number of municipalities to be mapped was 97. People directly affected by the ordination work are 105,000 without take into account the population from Madrid that uses this area for recreational or as second residence. These figures reflect the scale of the problem. Fig. 1 situates the area and the distribution of the population.
3. Methods 3.1. Precedents The CORINE land cover project shows a turning point in the history of land use maps (Anonymous, 1997). Both the economic and the spatial scale of the project, as well as the technical and human means involved made necessary a clear and complete methodological sequence. The idea is to enable an effective task segmentation and the adaptation of the procedures for very different territories. Besides the intensive use of satellite images as the interpretative base—apart from aerial photographs—represents an important landmark, considering the new capacities it conveys, such as the wide spatial coverage, objectivity, potential for quick updating and the improvement of visible wavelengths. However, the CORINE land cover project does not really reflect the land uses, but rather the land cover. That is, it does not refer to the socioeconomic function
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Fig. 1. Area of study. Municipalities and population are shown.
of the elements on the earth’s surface, but rather to its own nature. For the regional planning, the first approximation is essential, whereas the second one is restricted to environmental studies, which on the other hand was the CORINE land cover program’s objective. In spite of it, the mapping methodology followed shares some elements with that of a real land use mapping though taking into account that the work scale, the minimum unit of study and the legend are different. It is in the stages of preparation and interpretation of satellite information where the choices carried out in this program can be transposed to a study, such as the regional planning directives for Segovia.
One of the most important points in this work is the treatment and focusing of satellite images (LANDSAT-TM and MSS, SPOT HRV, XS and Pan). The approach followed was the assisted photo-interpretation of false color images. It implies the preparation of a series of cartographic products on which a highly specialized technical team will carry out a land cover classification according to their geographic knowledge and the prescribed legend. The pre-process of information from the “raw” image reception from the satellite, to the radiometrically georeferenced and corrected image, is essential for the final quality of the product. The consideration and detailed study of other factors, such as the minimum
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cartographic unit or the appropriate work scale are also required. The criteria followed for the delimitation of physiographic units and cover discrimination are also a contribution of the CORINE project to the present work, contribution that has been an illustrative guideline to a way of acting with regard to a determined problem rather than an effective directive. The problem of legend election had already been raised in previous works, such as the regional planning directives for Valladolid. The solutions then adopted have been a valuable support for the creation of these, improving some vague aspects and adapting the conclusions obtained to an area of very different characteristics. However, the most direct methodological precedent comes from a different source: the farming use map for the region of Segovia to a scale of 1:50,000. The main reason, apart from this work’s intrinsic quality, is the correspondence with the studied territory although it is obvious that they are different maps with different aims and functions. Concretely, the classification of the farming areas in our work owes to the latter the manner and criteria with which they were defined. 3.2. Methodological proposals The satellite images are the base on which the land use map has been realized. Besides, there was an important complementary cartographic base available: from the farm-use map for Segovia, mentioned above, to the called Topobase project, 1:50,000 by the local governing body of Castilla y Leon (topographical map of the region); or the geological map of Castilla y Leon, 1:400,000 and the Digital Elevation Model (DEM) of 50 m resolution, by the Environment Council of the local government in Castilla y Leon. Other kind of greater scale cartography was available in some sectors (master plans, subsidiary schemes, other kind of planning). This data had to be homogenized and filtered in order to prevent the loss of overall coherence. Likewise, a set of more than 300 oblique air photographs were taken during a series of low altitude flights (with an average of 500 m above ground level) over the zone under study, as well as a video of approximately 3 h of length filmed during one of these flights. They were integrated in a GIS together with the digital version of the land use map and other information. All these sources served as a support to the
fieldwork necessary for the establishment of the coverage in difficult classification zones. In addition, they present some advantages over the traditional method, such as a lower cost, a higher speed and data availability as well as the possibility of being contrasted by a large team of professionals in the laboratory, without losing reliability or resolution quality. Apart from these sources, since it is a land use map, and not a land cover map, the use of georeferenced alpha-numeric information became necessary for the establishment of the effective use of some of the discriminated elements. This is particularly necessary in the functional differentiation of urban areas, especially with regard to the type of housing, equipment and services. These categories, contained in the legend’s main groups, are difficult to estimate through remote sensing, not to say impossible. In order to delimit them in small-sized nucleus, it was proceed to cross the information available in the Local Inquiry on the Infrastructures and Equipment in Segovia (EIEL), with the information coming from other disperse or sector data. In the case of larger-sized nucleus, information from Town Halls was used. The operations were carried out with the help of a GIS especially orientated to regional planning, and which was also used in the photo-interpretation stage, once the different sets of supporting data had been integrated. This GIS (called SIGIM-TD) was programmed in C++ for this work. With regard to the work core, the satellite images, two different data sets were used. On the one hand, a set of three multi-spectral satellite images LANDSAT-TM, taken in different seasons along the year 1998 (end of spring, end of autumn, middle of winter): as it is known, the spatial resolution of these images is 30 m per pixel. On the other hand, a series of images from the Indian satellite IRS-1C (Pan) were used, corresponding to the end of the spring of 1997, with a spatial resolution of 5 m. The LANDSAT-TM images provide with the six bands normally used spectral information accurate enough as to differentiate cover types at a first level. The addition of a thermical band allows the differentiation of masse of water, vegetation and urban areas in a relatively easy way, as well as irrigated crops from dry ones. The problem is their relatively low spatial resolution, inadequate to differentiate elements with the degree of accuracy required. This information, however, is provided by the IRS satellite, which, with
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its 5 m of resolution, enables the almost complete discrimination of the physiographic structures studied here. The question is that this satellite operates in panchromatic, and therefore we lack enough spectral information for cover discrimination. The merging of the strong points of both data sets’ and the overcoming their weakness is resolved through a set of techniques, developed during the second half of the 1990s, known as data fusion, whose basis will be briefly given. Its use is justified, as already mentioned, by the necessity of spectral and spatial resolution improvement. Data fusion can be defined as sets of techniques aimed towards the obtaining of the maximum information possible out of heterogeneous or different contain sources (Wald, 1999). The term is applied to different procedures: from the inclusion of geographic information in the image processing through a GIS, to the fusion of different spectral and spatial resolution satellite images. This field of work is relatively new: most of the relevant works (in the sense of being capable of being applied) come from the 1990s, but are specially intensive in the second half of that decade. Within data fusion, there exists different methodologies and algorithms. The most used ones are Brovey’s algorithm (versus Vrabel, 1996), the HIS—Intensity, Hue, Saturation—(versus Carper et al., 1990), the PCS—Principal Components Substitution—(versus Göpfert, 1987), the ARSIS—Accroissement de la Résolution Spatiale par Injection de Structures—(versus Ranchin and Wald, 1996), the SVR—Synthetic Variable Ratio—(Pradines, 1986; Price, 1987; Munechika et al., 1993) and the modified SVR (Zhang, 1999). The two first have even been implemented in remote sensing commercial software. The theoretical basis lies in the injection of the physiographic information in the panchromatic images into the multi-spectral images, trying to preserve the radiance as far as possible (Hill et al., 1999). Results vary depending not only on the algorithm chosen, but also on the geographic area and on the image on which they will be applied, which implies an added difficulty (Chavez et al., 1991). It has been proved that the SVR modified presents a better behavior on urban areas, whereas the IHS is more appropriate for forests and farmlands. The behavior also differs depending on the images: the same algorithm can be very convenient to fuse SPOT Pan with LANDSAT-TM or with SPOT HRV, and present problems with IRS-1C Pan and LANDSAT-TM.
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For the present work, LANDSAT-TM and IRS-1D images were available. Several attempts were previously carried out on the chosen trial areas, aimed at deciding the most convenient method for visual coverage discrimination in those areas difficult to observe semi-automatically. Brovey’s algorithms, the PCS, the ARSIS, the IHS, our own improved version of the IHS, the SVR and the SVR modified, were successively tried out at the Remote Sensing Laboratory in the University of Valladolid. It was found that, even though the modified IHS version provided very good results and its computational costs were not too high, the improved SVR provided a much higher reliability, ensuring the almost complete preservation of the spectral response on different covers. Thus, it became possible to carry out ulterior semi-automatic classifications with a lesser degree of uncertainty. The difficulty of using this technique lies in the high cost of first time implementation, in the necessity of analyzing the corresponding correlations separately and in the length of time the process requires. Finally, the EDF method was used (Tapiador and Casanova, 2002). Results are shown in Fig. 2. Another technique used was neural networks for semi-automatic image classifications. The analysis of this methodology is complex, but can be summarized by saying that a neural network is a complex mathematical structure with automaton characteristics, that is, able to improve its own performance—its intelligence—as it elaborates the information previously provided to it. The foundational basis of neural networks can be found in (Fausett, 1994), and are used in fields as different as artificial vision, analysis and response against natural contingencies, in nuclear power stations and, obviously, in artificial intelligence (AI) and remote sensing. In this latter field, they are mainly used for coverage classification and automatic search and sensing of the effects of some natural events, such as forest fires. Neural networks present an outstanding variety. It is not a static technique. On the contrary, there must be a designing process of the most convenient network to be used according to the type of work on which it will be later applied. With regard to cover discrimination, they allow a supervised and non-supervised treatment, though it is in the first case where their potentialities are better shown. On an average, neural networks show a reliability of 85% when applied in a generic
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Fig. 2. Original satellite images and data fusion result.
way. When applied to every element in a legend individually, this percentage increases to 90%. Their main problem lies in the computational cost they require, which is very superior to that of any other technique and which could make them inoperative in small computer systems—in this kind of equipment they could take weeks before offering results. Another problem lies in the difficulty of real implementation as well as in the sharpening of the network once designed. This
last process is also very delicate and requires a good previous experience together with a deep knowledge of the mathematical basis on which they lie. For the land use mapping, a distributed ARTMAP (Carpenter et al., 1998) neural network was successively applied to each of the elements previously designated in the legend at a first level. The advantage of this way of acting is the accuracy enhancement, its disadvantage being the time needed. The network, written in
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C language, was tried with an average of 1% of the pixels integrating the images, using six bands obtained out of the LANDSAT-TM/IRS-1C Pan fused image. For the calculations intended to design and train the network, a vectorial supercomputer Fujitsu (VPP300E) kindly provided by the Supercomputation Center in Galicia (CEGSA) was used. The ulterior operations, applicable to this work were carried out in an Alpha Workstation. The subsequent fieldwork estimated the classification accuracy at around 89% for all the classes, being closed to 98% in the water sheets. This high level of accuracy is due to the inclusion of each class in the neural network one by one. A voting procedure was also included since the presentation ordering determines the final result of the ARTMAP nets. As in the previous case, some trials and comparisons were also carried out with different supervised classification methods. It was not considered convenient to go on into analyzing non-supervised techniques, since it seems quite clear, as much from the existing literature, as from previous experience, that they always offer worse results. Concretely, the common methods of maximum probability, Mahalanobis distance, of parallelepipeds and of minimum distance were tried. From the comparison among them (Table 1) it was concluded that the neural network presented much better properties as far as accuracy concerns. It has, as mentioned earlier, the disadvantage of its high computation cost, but this problem would be partly solved through the use of the VPP300E (6 CPUs, 12GB of memory and 14.4 GFLOPS of pick power). Considering the characteristics of this work regarding the time available the neural network appeared as the best choice. 3.3. Sequence of work In such an important work as this one, considering it has a real effect on the territories and their Table 1 Correlation’s of the classification methods tested Method used
Correlation coefficient
Maximum probability Mahalanobis distance Parallelepipeds Minimum distance Neural network
0.334 0.432 0.234 0.678 0.887
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inhabitants, it becomes necessary to establish the work sequence clearly. There are two main reasons for it. In the first place, it must have the capacity of being updated in the quickest possible way according to the administration needs. Secondly, being a scientific instrument, it must present a methodology that can be contrasted and allows discourse and evaluation by the scientific community. The work scheme was designed according to the time limits provided, as well as to the staff and sources availability. From the sequential point of view, the work was structured as follows. Firstly, a previous study of the methodologies used in former similar works (Anonymous, 1997). As above mentioned, it was proceeded to the analysis of the methodological fundaments of some previous related works in order to design a methodology of our own, appropriate to the sources available and to the regional planning directives objectives. Metadata, the data sources available were gathered next. Metadata are essential when it comes to evaluating the quality of the information available. In this first stage, it was carried out an inventory of them. The information was structured in an ORACLE database so that they could later be accessed more easily and reused in other works. The determination of the available data sources condition is the next task to accomplish. Depending on the available metadata, the access, the technology needed and the available information were evaluated as much from the technical point of view as from the economic one. Also, the establishment of the potential uses and objectives of the cartography generated is an essential stage in order to determine firstly, the legend to use, and secondly the discrimination techniques to follow. The legend of the map was determined using a hierarchic classification. According to the very map characteristics and to its potential estimated uses, it was proceed to design the legend (Table 2). This legend reflects a trade off between the risk of an excessive number of classes, which would make the map difficult to use, and the risk of an excessive class reduction, which would make it little useful. Low altitude flights (500 m above ground level) were carried out. Digital and analogical oblique photography were taken, and a video of the area studied was filmed. These images were integrated in the GIS after digital imaging pre-processing with standard equipments and
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Table 2 The legend of the map Water sheets Reservoirs, lakes, ponds and pools Rivers Channels Transport infrastructures Rails Highways Roads Stock areas Intensive Extensive Farm areas Crops for dry farming Crops for irrigated lands Fruits Grazing Pastures Forests Coniferous Leafy Industrial plants Isolated In industrial states Grouped Concentrated housing Concentrated housing Detached housing Traditional settlements Scattered housing Public spaces Parks Gardens Services Commercial Public services Offices, corporate headquarters Research centers and technological zones Facilities Sport and leisure Cultural and religious Others Unproductive land Uncultivated land Bare ground Other uses Airports Military facilities installations Other uses
restitution through a first adjustment of the raw control data, followed by an analytical aerial triangulation control processing using GPS, photogrammetric data capture with first-order analytical stereoplotters, data quality control and finally photogrammetric data translation into vectorial formats (DXF, SHP). These georeferenced images were used by the operators in conflictive areas when required. A 10% of the raw images were used as panoramic, landscape views to be used as visual aid in the GIS. In relation with the GIS two approaches were followed. First, ARC/VIEW GIS 3.1 under Alpha Workstation was used to provide the cartographical and database features required for the first stages of the work. In the next stages, a new GIS using the Map Objects LT OCX was programmed under C++. These decision were made considering the needs and requirements of the future users of the map. An study of the possibilities and performances of automatic classification techniques was carried out. This stage is the core of the methodology presented. The final map quality as well as the performance possibilities that will be obtained will depend on a correct choice. The first decision was the choice of the minimum unit of study. Such an election will condition as much the final map scale as the accessible resolution and it was also crucial in determining the economic assessment of the work. In this case, since a graphic outcome to a scale 1:50,000 was suggested, for calculation convenience a minimum unit of study of 10 ha was chosen for non-urban areas (in the CORINE land cover case this unit was established in 25 ha). The urban case remains subject to highly accurate cartographic availability. Neural networks design was the next task to be carried out. Since it was predictable that this technique would offer better results than any other classification method (Benediktsson et al., 1990), it was directly proceed to the designing of a network as described. Comparison of the network accuracy with respect to classic classification methods using confusion matrix and field randomly selected point were also accomplished. Results are shown in Table 1. Once established, this method is the most reliable, the merged images were classified through the resulting neural network model, applying it to each element in the legend liable to be discriminated (water, farming land, forests, built land and unproductive land). This procedure proved to be better that a one-stage classification.
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Fig. 3. CORINE land cover and new classification.
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After the classification, the results were integrated in the raster and vector GIS created ad hoc for the zone (SIGIM-TD). A first post-classification based in the inference rules and fuzzy logic was required. The auxiliary geographic information integrated in the GIS, such as high resolution vector information, was used to a complete determination of the elements in the legend. The idea behind this procedure is evaluate conflictive areas and label those that are not directly discernible by remote sensing techniques. This is the case of the facilities or services for example. A small theoretical evaluation analysis of the digitalization and shape-extracting automatic methods was also carried out, since, although most of the information had already reached this level of vector format, some details had still to be conveniently concluded. Some algorithms were applied to the tested areas to evaluate if automate procedures were able to avoid human intervention. Entropy-based estimators, frequency-domain filters and wavelet analysis were applied. Results proved that expert operators works were still required in the urban areas. As the last step in the classification sequence, a final selection of the suitable algorithmic sequence were made. A complete documentation of all the algorithms used with detailed discussion on the reasons of choice and the results obtained were also done to facilitate further improvements. This documentation task was considered very important since most of the work was planned to be updated in the future. The results of the classification were considered as appropriate in so far as hardly 10% of the surface was of uncertain assessment. However, taking into account that the work was not an statistical exercise but a work with real applications, it was considered necessary to improve this threshold through the use of qualified operators in photo-interpretation. Thus, data and programs were prepared and tested, and the task to be carried out by the operators determined. A methodology for a second post-classification was also prepared, using all the complementary geographical information. Relevant data from the councils’ databases were integrated into the GIS. To give an idea about the scale of the project, 97 councils were involved in this phase and more than 6 months were spent. The huge volume of data were homogenized into the ORACLE framework and prepared for the
queries from the cartographical part of the GIS. A final interpretative work sequence and the cartographic and photo-interpretation criteria were sorted out and documented. In particular, it was important to determine which criteria was followed to differentiate some physiographic units from others (Fig. 3) since any update of the map should follow the same rationale. Another set of tasks, less important but worth noting were the establishment of directives on the qualifications expected and the training provided to the operators in charge of updating the work; the advice for the design of the interfaces with the final users (using SIGIM-TD as prototype) and the determination of the quality controls to be carried out in the future.
4. Results and discussion The main result of this methodology was the map itself. Fig. 4 shows the index map of the sheets, with the classes aggregated for clarity. A total of 27 sheets were made. As already stated, Figs. 2 and 3 also show some results. The performances of the data fusion methods, providing a 5 m multi-spectral image to be classified can be readily seen. The use of neural networks to this task is noticeable, cause of the scale of the work, and the data fusion methodologies proved to be useful to increase the spatial resolution. Must be also noted that some novel elements have been proposed. First, the integration of vector and raster information in the land use generation process. The need of differentiates between some classes, as housing or states use requires this merging. The features of quick updating, objectivity and relatively accuracy of the map must to be stressed as well. More important, the establishment of an objective, easy, repeatable, moderately expensive, and quick methodology for this kind of works can be stated as a scientific proposal, subject to contrast and evaluation. This represents an improvement on the old techniques based in an expert person digitizing aerial photography. Errors can be minimized with this method, whilst feedback is more successful. However, a certain degree of human intervention is required. The map was widely used in the regional planning directives making process. Proposals and decisions about main directions of growth of the settlements,
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protected areas, facilities to improve in order to achieve a regional balance, and environmental issues were based on this map. The economical implications of the proposal could also be properly quantified with the digital version of the map, and the dissemination process improved with its aid. 5. Conclusions One of the conclusions implicit along this work is the necessity of an interdisciplinary team that contributes to the development of a work of these characteristics, a team coming from very different fields, such as private firms and the university academic world, but in need of complementation. The making of a map like this one, in which fields as different as applied physics, regional planning and remote sensing meet together, requires the strong integration of experts from every field, coordinated according to clear directives and methodology. With regard to the product in itself, it has been observed that the use of advanced remote sensing techniques implies an important breakthrough, qualitative as much as quantitative, in which the economical factor also plays an important role. The remote sensing applied to the regional planning represents important saving in costs, even though it requires a high investment at first. Besides, the possibility of contrasting is ensured since all documents of work are published and could even be subject to legal evaluation. Finally, it must be added that a good part of the usefulness of the methodology described lies in its updating capacity. Once the tasks to be developed are established, and with the experience acquired during the stage of realization, it is possible to produce new maps by just modifying the cartographic base—the satellite images. In this way, it is possible to obtain new products with a rotation cycle much shorter than in traditional techniques and with very superior properties as far as accuracy and objectivity are concerned. Acknowledgements The authors wish to acknowledge Antonio Hoyuela (architect) for his contributions to the first draft of this paper. Also acknowledged are the two anonymous
referees who provided valuable notes helping to improve the paper.
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Wald, L., 1999. Definition and terms of reference in data fusion. Int. Arch. Photogrammetry Remote Sens. 32, 1190–1193. Zhang, Y., 1999. A new merging method and its spectral and spatial effects. Int. J. Remote Sens. 20 (10), 2003–2014.
of some Spanish provinces, and in urban regeneration studies. His main research interest is the application of remote sensing in urban and regional quantitative geographical analysis.
Francisco J. Tapiador is currently a research fellow in the Department of Geography of the University of Birmingham, UK. After graduating in geography, he had his PhD in remote sensing in the department of applied physics of the University of Valladolid, Spain. He also received training in the Department of Geography of the University of Cork, Ireland) and in the Autonomous Universities of Madrid and Barcelona, Spain. He has worked as professional geographer in the regional planning guidelines
Jose L. Casanova is full professor and head of the Department of Applied Physics in the University of Valladolid, Spain. He obtained his PhD in 1973. He has published tens of papers in peer-reviewed remote sensing international journals in the last 10 years. He is former chairman of the Spanish Remote Sensing Society, and member of the Spanish National Space Commission. His main interest in the present is the environmental applications of remote sensing in real time.
Land use mapping methodology using remote sensing for the regional planning directives in Segovia, Spain Francisco J. Tapiador a,∗ , Jose L. Casanova b a
Department of Geography, School of Geography and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK b Department of Applied Physics, University of Valladolid, Valladolid, Spain Accepted 6 June 2002
Abstract This study presents the methodology followed in the land use mapping at scale 1:50,000 for the Functional Area of Segovia, Spain. The study is included in the regional planning directives for Segovia, and makes a comprehensive use of the remote sensing techniques developed during the second half of the 1990s. The methodological precedents in this respect are analyzed, mainly the CORINE land cover project, together with the data sources and the techniques that were used to obtain the thematic information searched for. The less known methods are described and a complete methodological sequence of the work is offered. The main characteristics of the methodology are the high degree of automation, objectivity, possibility of direct contrasting and its capacity for quick updating. Diverse data sources have been used, such as cartographic vector information and satellite imagery. LANDSAT-TM and IRS-1D Pan were used as well as aerial oblique photography. All information was integrated into a Geographic Information System (GIS; named SIGIM-TD). Data fusion methods were also widely used to improve the spatial resolution of the images. A neural network was generated to provide an appropriate classification method. Results of the neural network-based classification are shown and a classification-fieldwork correlation of 0.887 was obtained, in contrast with coefficients of 0.334, 0.432, 0.234 and 0.678 achieved through other techniques. Graphic results of the work are presented together with the data sources used for the map elaboration. Finally, a discussion on the advances that these techniques represent is done. © 2002 Elsevier Science B.V. All rights reserved. Keywords: Regional planning directives; Land use; Remote sensing
1. Introduction The regional planning in Spain is a legal instrument aimed at optimizing a territory’s resources through public actions. The regional planning in Castilla y Leon region conforms to the 10/1998 act, 5 December, which states the principles on which the basic lines of action of the projects under this denomination are based. Through its articles, the act establishes the min∗ Corresponding author. Tel.: +44-121-414-5523. E-mail address: [email protected] (F.J. Tapiador).
0169-2046/02/$20.00 © 2002 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 9 - 2 0 4 6 ( 0 2 ) 0 0 1 2 6 - 3
imum contents and the formal structure that must be given to all regional planning projects, as much at a regional as at a sub-regional scale. One stage necessary to comply with is the quantification of the elements in the space under these directives’ scope. It is obvious that all regional planning tasks require a deep knowledge on the condition of the space on which the estimated actions will be carried out, since only in this way will it be possible to establish the actions required. In this sense, the elaboration of a land use map is one of the first actions to undertake, since it will allow a first approximation to the location and
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a quantification of the individualizing elements from which the features constituting a territory’s geographic structure will be later obtained. Regional planning for Segovia started in June 1999 and is currently (late 2001) in progress. A functional area was administratively defined as the geographical space economically and socially related with Segovia city. Several indicators, such as the economic rate of dependency of the villages were used to define the actual extents of the area using gravitational models. This implies a constrain in the implementation of the methodology since several administrations were involved, databases need to be homogenized and it was expected that the results will serve to re-define the area. The necessity to have a reliable map was highlighted in the early stages. Some properties was required, such as objectivity, accuracy, capacity of quick updating, explicit methodology capable of being contrasted and use of the most advanced technologies available. It also suggested the necessity of having a methodology as generic as possible, in the sense of exportable, which could be used afterwards in and out this area for other works in regional planning. As a precedent methodology, the results from the CORINE land cover program were mainly used. This large scale European program resulted in the land cover cartography for an area superior to 2 million square kilometers at a scale of 1:100,000). Minor works were also used, such as the Farm Use Map in the region of Segovia and a similar planning work for the Valladolid city metropolitan area. Both Segovia and Valladolid belong to the local governing body of Castilla y Leon (the largest interior region in Spain). The two first works share the common denominator of having being carried out through space remote sensing techniques, though they differ as much in their methodological as in their conceptual aspects: the different aims in both projects define their future contents. The use of remote sensing techniques implies an important break-through with respect to former works in the same area. In the present work, and considering the time elapsed since the realization of the preceding work, an important number of new techniques developed during the last five years had to be integrated, whose contribution to the work’s final quality has been noteworthy. They are mainly data fusion techniques and advanced statistics-based semi-automatic
classification techniques, such as neural networks that make it possible to obtain information not so long ago obscure or little used due to the lack of appropriate tools. It should be noted here that in this space there is no government body equivalent, as for example, the Ordnance Survey in Britain that could provide yearly information about land use. Finally, it must be pointed out that the map is created with an aim to be used as a cartographic base to be integrated in a Geographic Information System (GIS), essential for the joint consideration of the elements and factors affecting the configuration and dynamics of a territory from the perspective of a quantitative geographical analysis.
2. Study area The study area covers 3419 km2 . The number of municipalities to be mapped was 97. People directly affected by the ordination work are 105,000 without take into account the population from Madrid that uses this area for recreational or as second residence. These figures reflect the scale of the problem. Fig. 1 situates the area and the distribution of the population.
3. Methods 3.1. Precedents The CORINE land cover project shows a turning point in the history of land use maps (Anonymous, 1997). Both the economic and the spatial scale of the project, as well as the technical and human means involved made necessary a clear and complete methodological sequence. The idea is to enable an effective task segmentation and the adaptation of the procedures for very different territories. Besides the intensive use of satellite images as the interpretative base—apart from aerial photographs—represents an important landmark, considering the new capacities it conveys, such as the wide spatial coverage, objectivity, potential for quick updating and the improvement of visible wavelengths. However, the CORINE land cover project does not really reflect the land uses, but rather the land cover. That is, it does not refer to the socioeconomic function
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Fig. 1. Area of study. Municipalities and population are shown.
of the elements on the earth’s surface, but rather to its own nature. For the regional planning, the first approximation is essential, whereas the second one is restricted to environmental studies, which on the other hand was the CORINE land cover program’s objective. In spite of it, the mapping methodology followed shares some elements with that of a real land use mapping though taking into account that the work scale, the minimum unit of study and the legend are different. It is in the stages of preparation and interpretation of satellite information where the choices carried out in this program can be transposed to a study, such as the regional planning directives for Segovia.
One of the most important points in this work is the treatment and focusing of satellite images (LANDSAT-TM and MSS, SPOT HRV, XS and Pan). The approach followed was the assisted photo-interpretation of false color images. It implies the preparation of a series of cartographic products on which a highly specialized technical team will carry out a land cover classification according to their geographic knowledge and the prescribed legend. The pre-process of information from the “raw” image reception from the satellite, to the radiometrically georeferenced and corrected image, is essential for the final quality of the product. The consideration and detailed study of other factors, such as the minimum
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cartographic unit or the appropriate work scale are also required. The criteria followed for the delimitation of physiographic units and cover discrimination are also a contribution of the CORINE project to the present work, contribution that has been an illustrative guideline to a way of acting with regard to a determined problem rather than an effective directive. The problem of legend election had already been raised in previous works, such as the regional planning directives for Valladolid. The solutions then adopted have been a valuable support for the creation of these, improving some vague aspects and adapting the conclusions obtained to an area of very different characteristics. However, the most direct methodological precedent comes from a different source: the farming use map for the region of Segovia to a scale of 1:50,000. The main reason, apart from this work’s intrinsic quality, is the correspondence with the studied territory although it is obvious that they are different maps with different aims and functions. Concretely, the classification of the farming areas in our work owes to the latter the manner and criteria with which they were defined. 3.2. Methodological proposals The satellite images are the base on which the land use map has been realized. Besides, there was an important complementary cartographic base available: from the farm-use map for Segovia, mentioned above, to the called Topobase project, 1:50,000 by the local governing body of Castilla y Leon (topographical map of the region); or the geological map of Castilla y Leon, 1:400,000 and the Digital Elevation Model (DEM) of 50 m resolution, by the Environment Council of the local government in Castilla y Leon. Other kind of greater scale cartography was available in some sectors (master plans, subsidiary schemes, other kind of planning). This data had to be homogenized and filtered in order to prevent the loss of overall coherence. Likewise, a set of more than 300 oblique air photographs were taken during a series of low altitude flights (with an average of 500 m above ground level) over the zone under study, as well as a video of approximately 3 h of length filmed during one of these flights. They were integrated in a GIS together with the digital version of the land use map and other information. All these sources served as a support to the
fieldwork necessary for the establishment of the coverage in difficult classification zones. In addition, they present some advantages over the traditional method, such as a lower cost, a higher speed and data availability as well as the possibility of being contrasted by a large team of professionals in the laboratory, without losing reliability or resolution quality. Apart from these sources, since it is a land use map, and not a land cover map, the use of georeferenced alpha-numeric information became necessary for the establishment of the effective use of some of the discriminated elements. This is particularly necessary in the functional differentiation of urban areas, especially with regard to the type of housing, equipment and services. These categories, contained in the legend’s main groups, are difficult to estimate through remote sensing, not to say impossible. In order to delimit them in small-sized nucleus, it was proceed to cross the information available in the Local Inquiry on the Infrastructures and Equipment in Segovia (EIEL), with the information coming from other disperse or sector data. In the case of larger-sized nucleus, information from Town Halls was used. The operations were carried out with the help of a GIS especially orientated to regional planning, and which was also used in the photo-interpretation stage, once the different sets of supporting data had been integrated. This GIS (called SIGIM-TD) was programmed in C++ for this work. With regard to the work core, the satellite images, two different data sets were used. On the one hand, a set of three multi-spectral satellite images LANDSAT-TM, taken in different seasons along the year 1998 (end of spring, end of autumn, middle of winter): as it is known, the spatial resolution of these images is 30 m per pixel. On the other hand, a series of images from the Indian satellite IRS-1C (Pan) were used, corresponding to the end of the spring of 1997, with a spatial resolution of 5 m. The LANDSAT-TM images provide with the six bands normally used spectral information accurate enough as to differentiate cover types at a first level. The addition of a thermical band allows the differentiation of masse of water, vegetation and urban areas in a relatively easy way, as well as irrigated crops from dry ones. The problem is their relatively low spatial resolution, inadequate to differentiate elements with the degree of accuracy required. This information, however, is provided by the IRS satellite, which, with
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its 5 m of resolution, enables the almost complete discrimination of the physiographic structures studied here. The question is that this satellite operates in panchromatic, and therefore we lack enough spectral information for cover discrimination. The merging of the strong points of both data sets’ and the overcoming their weakness is resolved through a set of techniques, developed during the second half of the 1990s, known as data fusion, whose basis will be briefly given. Its use is justified, as already mentioned, by the necessity of spectral and spatial resolution improvement. Data fusion can be defined as sets of techniques aimed towards the obtaining of the maximum information possible out of heterogeneous or different contain sources (Wald, 1999). The term is applied to different procedures: from the inclusion of geographic information in the image processing through a GIS, to the fusion of different spectral and spatial resolution satellite images. This field of work is relatively new: most of the relevant works (in the sense of being capable of being applied) come from the 1990s, but are specially intensive in the second half of that decade. Within data fusion, there exists different methodologies and algorithms. The most used ones are Brovey’s algorithm (versus Vrabel, 1996), the HIS—Intensity, Hue, Saturation—(versus Carper et al., 1990), the PCS—Principal Components Substitution—(versus Göpfert, 1987), the ARSIS—Accroissement de la Résolution Spatiale par Injection de Structures—(versus Ranchin and Wald, 1996), the SVR—Synthetic Variable Ratio—(Pradines, 1986; Price, 1987; Munechika et al., 1993) and the modified SVR (Zhang, 1999). The two first have even been implemented in remote sensing commercial software. The theoretical basis lies in the injection of the physiographic information in the panchromatic images into the multi-spectral images, trying to preserve the radiance as far as possible (Hill et al., 1999). Results vary depending not only on the algorithm chosen, but also on the geographic area and on the image on which they will be applied, which implies an added difficulty (Chavez et al., 1991). It has been proved that the SVR modified presents a better behavior on urban areas, whereas the IHS is more appropriate for forests and farmlands. The behavior also differs depending on the images: the same algorithm can be very convenient to fuse SPOT Pan with LANDSAT-TM or with SPOT HRV, and present problems with IRS-1C Pan and LANDSAT-TM.
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For the present work, LANDSAT-TM and IRS-1D images were available. Several attempts were previously carried out on the chosen trial areas, aimed at deciding the most convenient method for visual coverage discrimination in those areas difficult to observe semi-automatically. Brovey’s algorithms, the PCS, the ARSIS, the IHS, our own improved version of the IHS, the SVR and the SVR modified, were successively tried out at the Remote Sensing Laboratory in the University of Valladolid. It was found that, even though the modified IHS version provided very good results and its computational costs were not too high, the improved SVR provided a much higher reliability, ensuring the almost complete preservation of the spectral response on different covers. Thus, it became possible to carry out ulterior semi-automatic classifications with a lesser degree of uncertainty. The difficulty of using this technique lies in the high cost of first time implementation, in the necessity of analyzing the corresponding correlations separately and in the length of time the process requires. Finally, the EDF method was used (Tapiador and Casanova, 2002). Results are shown in Fig. 2. Another technique used was neural networks for semi-automatic image classifications. The analysis of this methodology is complex, but can be summarized by saying that a neural network is a complex mathematical structure with automaton characteristics, that is, able to improve its own performance—its intelligence—as it elaborates the information previously provided to it. The foundational basis of neural networks can be found in (Fausett, 1994), and are used in fields as different as artificial vision, analysis and response against natural contingencies, in nuclear power stations and, obviously, in artificial intelligence (AI) and remote sensing. In this latter field, they are mainly used for coverage classification and automatic search and sensing of the effects of some natural events, such as forest fires. Neural networks present an outstanding variety. It is not a static technique. On the contrary, there must be a designing process of the most convenient network to be used according to the type of work on which it will be later applied. With regard to cover discrimination, they allow a supervised and non-supervised treatment, though it is in the first case where their potentialities are better shown. On an average, neural networks show a reliability of 85% when applied in a generic
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Fig. 2. Original satellite images and data fusion result.
way. When applied to every element in a legend individually, this percentage increases to 90%. Their main problem lies in the computational cost they require, which is very superior to that of any other technique and which could make them inoperative in small computer systems—in this kind of equipment they could take weeks before offering results. Another problem lies in the difficulty of real implementation as well as in the sharpening of the network once designed. This
last process is also very delicate and requires a good previous experience together with a deep knowledge of the mathematical basis on which they lie. For the land use mapping, a distributed ARTMAP (Carpenter et al., 1998) neural network was successively applied to each of the elements previously designated in the legend at a first level. The advantage of this way of acting is the accuracy enhancement, its disadvantage being the time needed. The network, written in
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C language, was tried with an average of 1% of the pixels integrating the images, using six bands obtained out of the LANDSAT-TM/IRS-1C Pan fused image. For the calculations intended to design and train the network, a vectorial supercomputer Fujitsu (VPP300E) kindly provided by the Supercomputation Center in Galicia (CEGSA) was used. The ulterior operations, applicable to this work were carried out in an Alpha Workstation. The subsequent fieldwork estimated the classification accuracy at around 89% for all the classes, being closed to 98% in the water sheets. This high level of accuracy is due to the inclusion of each class in the neural network one by one. A voting procedure was also included since the presentation ordering determines the final result of the ARTMAP nets. As in the previous case, some trials and comparisons were also carried out with different supervised classification methods. It was not considered convenient to go on into analyzing non-supervised techniques, since it seems quite clear, as much from the existing literature, as from previous experience, that they always offer worse results. Concretely, the common methods of maximum probability, Mahalanobis distance, of parallelepipeds and of minimum distance were tried. From the comparison among them (Table 1) it was concluded that the neural network presented much better properties as far as accuracy concerns. It has, as mentioned earlier, the disadvantage of its high computation cost, but this problem would be partly solved through the use of the VPP300E (6 CPUs, 12GB of memory and 14.4 GFLOPS of pick power). Considering the characteristics of this work regarding the time available the neural network appeared as the best choice. 3.3. Sequence of work In such an important work as this one, considering it has a real effect on the territories and their Table 1 Correlation’s of the classification methods tested Method used
Correlation coefficient
Maximum probability Mahalanobis distance Parallelepipeds Minimum distance Neural network
0.334 0.432 0.234 0.678 0.887
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inhabitants, it becomes necessary to establish the work sequence clearly. There are two main reasons for it. In the first place, it must have the capacity of being updated in the quickest possible way according to the administration needs. Secondly, being a scientific instrument, it must present a methodology that can be contrasted and allows discourse and evaluation by the scientific community. The work scheme was designed according to the time limits provided, as well as to the staff and sources availability. From the sequential point of view, the work was structured as follows. Firstly, a previous study of the methodologies used in former similar works (Anonymous, 1997). As above mentioned, it was proceeded to the analysis of the methodological fundaments of some previous related works in order to design a methodology of our own, appropriate to the sources available and to the regional planning directives objectives. Metadata, the data sources available were gathered next. Metadata are essential when it comes to evaluating the quality of the information available. In this first stage, it was carried out an inventory of them. The information was structured in an ORACLE database so that they could later be accessed more easily and reused in other works. The determination of the available data sources condition is the next task to accomplish. Depending on the available metadata, the access, the technology needed and the available information were evaluated as much from the technical point of view as from the economic one. Also, the establishment of the potential uses and objectives of the cartography generated is an essential stage in order to determine firstly, the legend to use, and secondly the discrimination techniques to follow. The legend of the map was determined using a hierarchic classification. According to the very map characteristics and to its potential estimated uses, it was proceed to design the legend (Table 2). This legend reflects a trade off between the risk of an excessive number of classes, which would make the map difficult to use, and the risk of an excessive class reduction, which would make it little useful. Low altitude flights (500 m above ground level) were carried out. Digital and analogical oblique photography were taken, and a video of the area studied was filmed. These images were integrated in the GIS after digital imaging pre-processing with standard equipments and
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Table 2 The legend of the map Water sheets Reservoirs, lakes, ponds and pools Rivers Channels Transport infrastructures Rails Highways Roads Stock areas Intensive Extensive Farm areas Crops for dry farming Crops for irrigated lands Fruits Grazing Pastures Forests Coniferous Leafy Industrial plants Isolated In industrial states Grouped Concentrated housing Concentrated housing Detached housing Traditional settlements Scattered housing Public spaces Parks Gardens Services Commercial Public services Offices, corporate headquarters Research centers and technological zones Facilities Sport and leisure Cultural and religious Others Unproductive land Uncultivated land Bare ground Other uses Airports Military facilities installations Other uses
restitution through a first adjustment of the raw control data, followed by an analytical aerial triangulation control processing using GPS, photogrammetric data capture with first-order analytical stereoplotters, data quality control and finally photogrammetric data translation into vectorial formats (DXF, SHP). These georeferenced images were used by the operators in conflictive areas when required. A 10% of the raw images were used as panoramic, landscape views to be used as visual aid in the GIS. In relation with the GIS two approaches were followed. First, ARC/VIEW GIS 3.1 under Alpha Workstation was used to provide the cartographical and database features required for the first stages of the work. In the next stages, a new GIS using the Map Objects LT OCX was programmed under C++. These decision were made considering the needs and requirements of the future users of the map. An study of the possibilities and performances of automatic classification techniques was carried out. This stage is the core of the methodology presented. The final map quality as well as the performance possibilities that will be obtained will depend on a correct choice. The first decision was the choice of the minimum unit of study. Such an election will condition as much the final map scale as the accessible resolution and it was also crucial in determining the economic assessment of the work. In this case, since a graphic outcome to a scale 1:50,000 was suggested, for calculation convenience a minimum unit of study of 10 ha was chosen for non-urban areas (in the CORINE land cover case this unit was established in 25 ha). The urban case remains subject to highly accurate cartographic availability. Neural networks design was the next task to be carried out. Since it was predictable that this technique would offer better results than any other classification method (Benediktsson et al., 1990), it was directly proceed to the designing of a network as described. Comparison of the network accuracy with respect to classic classification methods using confusion matrix and field randomly selected point were also accomplished. Results are shown in Table 1. Once established, this method is the most reliable, the merged images were classified through the resulting neural network model, applying it to each element in the legend liable to be discriminated (water, farming land, forests, built land and unproductive land). This procedure proved to be better that a one-stage classification.
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Fig. 3. CORINE land cover and new classification.
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After the classification, the results were integrated in the raster and vector GIS created ad hoc for the zone (SIGIM-TD). A first post-classification based in the inference rules and fuzzy logic was required. The auxiliary geographic information integrated in the GIS, such as high resolution vector information, was used to a complete determination of the elements in the legend. The idea behind this procedure is evaluate conflictive areas and label those that are not directly discernible by remote sensing techniques. This is the case of the facilities or services for example. A small theoretical evaluation analysis of the digitalization and shape-extracting automatic methods was also carried out, since, although most of the information had already reached this level of vector format, some details had still to be conveniently concluded. Some algorithms were applied to the tested areas to evaluate if automate procedures were able to avoid human intervention. Entropy-based estimators, frequency-domain filters and wavelet analysis were applied. Results proved that expert operators works were still required in the urban areas. As the last step in the classification sequence, a final selection of the suitable algorithmic sequence were made. A complete documentation of all the algorithms used with detailed discussion on the reasons of choice and the results obtained were also done to facilitate further improvements. This documentation task was considered very important since most of the work was planned to be updated in the future. The results of the classification were considered as appropriate in so far as hardly 10% of the surface was of uncertain assessment. However, taking into account that the work was not an statistical exercise but a work with real applications, it was considered necessary to improve this threshold through the use of qualified operators in photo-interpretation. Thus, data and programs were prepared and tested, and the task to be carried out by the operators determined. A methodology for a second post-classification was also prepared, using all the complementary geographical information. Relevant data from the councils’ databases were integrated into the GIS. To give an idea about the scale of the project, 97 councils were involved in this phase and more than 6 months were spent. The huge volume of data were homogenized into the ORACLE framework and prepared for the
queries from the cartographical part of the GIS. A final interpretative work sequence and the cartographic and photo-interpretation criteria were sorted out and documented. In particular, it was important to determine which criteria was followed to differentiate some physiographic units from others (Fig. 3) since any update of the map should follow the same rationale. Another set of tasks, less important but worth noting were the establishment of directives on the qualifications expected and the training provided to the operators in charge of updating the work; the advice for the design of the interfaces with the final users (using SIGIM-TD as prototype) and the determination of the quality controls to be carried out in the future.
4. Results and discussion The main result of this methodology was the map itself. Fig. 4 shows the index map of the sheets, with the classes aggregated for clarity. A total of 27 sheets were made. As already stated, Figs. 2 and 3 also show some results. The performances of the data fusion methods, providing a 5 m multi-spectral image to be classified can be readily seen. The use of neural networks to this task is noticeable, cause of the scale of the work, and the data fusion methodologies proved to be useful to increase the spatial resolution. Must be also noted that some novel elements have been proposed. First, the integration of vector and raster information in the land use generation process. The need of differentiates between some classes, as housing or states use requires this merging. The features of quick updating, objectivity and relatively accuracy of the map must to be stressed as well. More important, the establishment of an objective, easy, repeatable, moderately expensive, and quick methodology for this kind of works can be stated as a scientific proposal, subject to contrast and evaluation. This represents an improvement on the old techniques based in an expert person digitizing aerial photography. Errors can be minimized with this method, whilst feedback is more successful. However, a certain degree of human intervention is required. The map was widely used in the regional planning directives making process. Proposals and decisions about main directions of growth of the settlements,
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protected areas, facilities to improve in order to achieve a regional balance, and environmental issues were based on this map. The economical implications of the proposal could also be properly quantified with the digital version of the map, and the dissemination process improved with its aid. 5. Conclusions One of the conclusions implicit along this work is the necessity of an interdisciplinary team that contributes to the development of a work of these characteristics, a team coming from very different fields, such as private firms and the university academic world, but in need of complementation. The making of a map like this one, in which fields as different as applied physics, regional planning and remote sensing meet together, requires the strong integration of experts from every field, coordinated according to clear directives and methodology. With regard to the product in itself, it has been observed that the use of advanced remote sensing techniques implies an important breakthrough, qualitative as much as quantitative, in which the economical factor also plays an important role. The remote sensing applied to the regional planning represents important saving in costs, even though it requires a high investment at first. Besides, the possibility of contrasting is ensured since all documents of work are published and could even be subject to legal evaluation. Finally, it must be added that a good part of the usefulness of the methodology described lies in its updating capacity. Once the tasks to be developed are established, and with the experience acquired during the stage of realization, it is possible to produce new maps by just modifying the cartographic base—the satellite images. In this way, it is possible to obtain new products with a rotation cycle much shorter than in traditional techniques and with very superior properties as far as accuracy and objectivity are concerned. Acknowledgements The authors wish to acknowledge Antonio Hoyuela (architect) for his contributions to the first draft of this paper. Also acknowledged are the two anonymous
referees who provided valuable notes helping to improve the paper.
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of some Spanish provinces, and in urban regeneration studies. His main research interest is the application of remote sensing in urban and regional quantitative geographical analysis.
Francisco J. Tapiador is currently a research fellow in the Department of Geography of the University of Birmingham, UK. After graduating in geography, he had his PhD in remote sensing in the department of applied physics of the University of Valladolid, Spain. He also received training in the Department of Geography of the University of Cork, Ireland) and in the Autonomous Universities of Madrid and Barcelona, Spain. He has worked as professional geographer in the regional planning guidelines
Jose L. Casanova is full professor and head of the Department of Applied Physics in the University of Valladolid, Spain. He obtained his PhD in 1973. He has published tens of papers in peer-reviewed remote sensing international journals in the last 10 years. He is former chairman of the Spanish Remote Sensing Society, and member of the Spanish National Space Commission. His main interest in the present is the environmental applications of remote sensing in real time.