- Email: [email protected]
Time-based retrieval of soft maps for environmental change detection
Information Processing and Management 39 (2003) 323–327 www.elsevier.com/locate/infoproman
Time-based retrieval of soft maps for environmental change detection Paola Carrara
a,*
, Giuseppe Fresta
b,1
, Anna Rampini
a,2
a
b
CNR, Via IREA-/Bassini, 15, I 20133, Milan, Italy ISTI-CNR, Area della Ricerca,Via Moruzzi 1, I 56127 Pisa, Italy
Abstract This contribution aims to shortly describe a data structure for creating and managing archives of thematic maps derived by classifying remotely sensed images by soft techniques (soft maps). Unlike traditional models for managing spatial data, the main key feature for query formulation is time, thus improving the investigation on changes occurred in time ranges. The data structure originally extends time-based models to soft maps, thus promoting a retrieval by changes approach also in the monitoring of phenomena for which hard classifications are not sufficiently accurate nor complete. The data structure has been implemented in a system, which is also described, available today in a Windows version. It has been applied to the monitoring of variations of Alpine glaciers. Ó 2002 Elsevier Science Ltd. All rights reserved. Keywords: Time-based retrieval; Change detection; Archives of soft maps
The study of Earth changes and the effects of human activities on the environment requires tools to analyse modifications which occur in time ranges and are directly and immediately observable through remotely sensed images. The development of such tools, however, depend on the definition of models for storing and retrieving images, able to privilege a dynamic, careful and sensitive representation of the events under study. Traditional Geographic Information Systems (GIS) represent spatial information as frozen in time, and possible changes through time are usually neglected. In this way the study of time-dependent phenomena becomes a heavy process, which requires the use of more software packages, the production of an amount of intermediate *
Corresponding author. Tel.: +39-2-70643268. E-mail addresses: [email protected] (P. Carrara), [email protected] (G. Fresta), [email protected] (A. Rampini). 1 Tel.: +39-50-3152933. 2 Tel.: +39-2-23699275. 0306-4573/03/$ - see front matter Ó 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 3 0 6 - 4 5 7 3 ( 0 2 ) 0 0 0 5 5 - 9
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results and complete re-elaboration in correspondence to new data or analyses. To overcome these drawbacks, GISs are sometimes extended to consider time as an attribute of spatial data: this approach, however, overlooks the observation that space and time properties are conceptually different, so that the representation of variations through time needs the development of Ôad hocÕ models. Therefore, temporal models for the management of spatial information have been proposed, going from time-stamping layers (Armstrong, 1988) to time-stamping events or processes (Yuan, 1996). These models also impact on the choice of data collected and recorded: in an event-oriented view, for example, data representing changes correspond to a new entry in the collection, while in an update-oriented approach, entries correspond to the newest sources, discarding information previously stored. Time-based models, anyway, are based on time as fundamental key by which spatial information is accessed: in this way, temporal information emerges with top priority and searching changes through time becomes easier facilitating monitoring activities (Carrara, Antoninetti, Ghioldi, & Rampini, 1997; Langran, 1992; Peuquet, 1994; Peuquet & Duan, 1995). The models proposed in the literature manage thematic maps obtained by traditional methods (human photo-interpretation) or by automatic classification of remotely sensed images: in these maps each soil unit corresponds to a unique value representing one land-cover class (theme). These kind of maps do not provide levels of accuracy sufficient for many applications, in which it is interesting to capture the contributions of the various cover patterns to the overall value of each area. If we consider, for example, the problem of identifying rice crops in satellite images, the variations in class responses relate mainly to the contemporary and unavoidable presence of water and plants at different growing stages, that is, to the different percentage of the pure components, water and vegetation, visible in the area concerned. The use of thematic maps in which more values correspond to each unit, representing the degrees of membership to more classes, is a possible solution to suitably describe dishomogeneity, vagueness and ambiguity which are often intrinsic to natural phenomena (see for example Binaghi, Rampini, Brivio, & Schowengerdt, 1996; Burrough, 1996). These maps are usually the results of automatic classification by soft techniques of remotely sensed images (soft maps). The new challenge is, therefore, to extend existing temporal models to manage not only crisp maps, but also soft maps. The aim of this contribution is to propose a time based, event-oriented data structure having the novel ability to store and retrieve maps generated by soft techniques. The proposal is independent of the used classification as all soft maps can be archived and searched. The advantages consist in saving of memory space and in the easier management of queries related to variations in time. The approach wishes to promote a retrieval by content changes rather than a retrieval by content as indexing concerns spatial properties which changed through time. In fact, the data structure has been designed with the following aims: to encourage the emergence of time (the timestamps of stored events) as basic information; to record only occurred variations and their types; to manage, for each stored event, more images corresponding to the classes of interest. It is a four level structure (see Fig. 1): the first level accesses to the event vector which stores the timestamps of occurred variations; each element of the vector points to two other vectors: the first one stores, for each class of interest, the number of elementary areas whose value of membership to the class itself increased with respect to the previous event (rise-vector), while the second one records the number of elementary areas whose membership decreased (decrease-
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Fig. 1. Schema of the proposed data structure; l is the membership value of the elementary area with co-ordinates x, y to the class Ci , which increased at time t with respect to t 1 .
vector). The third level hosts the variation table, accessed from the rise- or decrease-vectors: it supplies the co-ordinate values of the locations changed at given time, rising or decreasing with respect to the various classes; the membership degree of each changed area is at last stored in the classified image itself, in correspondence to the co-ordinates recorded in the variation table. This data structure allows to archive, without waste of memory, only data regarding changes; furthermore its multi-layer organisation is suitable to manage with different performances queries referred to variations: you need only to access to the first level to know if any modification occurred in a given time range. On the other hand, queries searching for the membership values of areas to classes are the heaviest with respect to resources. Queries about the number of areas whose membership values increased or decreased and queries about co-ordinates of changed areas, have an intermediate effectiveness. The proposed data structure has been implemented in the system GeoTemp 3.00, developed under the Windows operating system; the search engine of GeoTemp allows to create archives of maps which were classified by soft techniques, and to express various kinds of queries, mainly based on temporal key, guided by a graphic interface. Many types of queries are possible, such as: the dates in which images changed; the classes varied and the kind of variations; the number (or surface) of changed areas. Queries can be simple if they refer to one class only, or composed if they combine investigations on more than one class. The retrieved results can be presented both in table and in image format. By dealing with soft maps, the user is not limited to queries such as: (a) ‘‘Did the class Ci vary at time tj with respect to time ti ?’’ but also the degree of class variation may be investigated, as in the query: (b) ‘‘Show me the areas in which the degree of membership to the class Ci decreased of the 50% in the time range ti –tj ’’ The engine has been applied to the monitoring of annual Alpine glacier variations by means of archived soft maps from remotely sensed images (one Landsat TM image per year, taken at the end of Summer) (Carrara & Rampini, 2001). In this field, several image classification techniques have been experimented and particularly accurate results have been obtained by using soft
326
P. Carrara et al. / Information Processing and Management 39 (2003) 323–327
classification techniques, such as neural network and fuzzy statistical-based approaches. The supervised fuzzy statistical classifier designed by Wang (1990) has been shown to be particularly suited for the representation of cover class mixture. The classifier bases estimates of the distribution of pixels in multi-spectral space on the concept of the probability measure of fuzzy events to produce an output of the proportions of individual components. The fuzzy partition of the spectral space is performed by finding the analytical formulation of membership functions for each class. Moreover, the degrees of membership to classes produced by this kind of classifier, represent the percentage of presence of cover classes in the area (Binaghi, Brivio, Ghezzi, Rampini, & Zilioli, 1999; Wang, 1990). Therefore, a query of kind (b) legitimately assumes in this application the meaning of: (b1) ‘‘Show me the areas in which the degree of percentage of the class Ci decreased of the 50% in the time range ti –tj ’’ In this context, a tool for the analysis of changes by comparing different percentage of covers may improve the accuracy in the evaluations of the glaciological and hydrological parameters: in particular it is important to know the parts of the glacier changed from snow to ice or from ice to rock or vice-versa. The result of the composed query: ‘‘Show areas in which the percentage of snow decreased of the 50% and contemporary the ice increased of 50% in the time range September 1989–September 1991’’, is presented in Fig. 2 together with the images of the Alpine glacier under consideration. The described experience shown that a time-based searching tool, like GeoTemp, improves the analysis of changes as it allows to flexibly inspect from various points of view the same set of maps with very easy tools and quickness, without generating intermediate data. The possibility of performing a spatio-temporal analysis associated with an exploration of topographic properties (which are stored in the archives as well) of the data, allows the user to inspect the causes of changes. Furthermore, GeoTemp easily manages updating as new data are automatically ac-
Fig. 2. The ‘‘Gigante Occidentale’’ glacier in Landsat TM images of September 1989 (a) and 1991 (b), and the result of the query (c) showing areas in which the percentage of snow decreased of the 50% and contemporary the ice increased of 50%.
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commodated in the corresponding archive, and are immediately ready for investigations. With respect to time-based approaches managing crisp classification, the proposed engine allows to evaluate phenomena at different spatial scales, affecting the internal content of the spatial units without any possibility to crisply determine the membership to a unique class. A new, Web version of the system is under development, where XML will be used both to define a support for information exchange, in accordance to the data types of the case study, to the nature of elaborations, to the usersÕ needs, and to allow separation of content from presentation (W3C, 2001). An expected advantage of the XML encoding derives from the language ability to maintain multiple definition and rendering layers over the same data.
References Armstrong, M. P. (1988). Temporality in spatial databases. In Proc. of GIS/LIS ’88 2 (pp. 880–889). Binaghi, E., Rampini, A., Brivio, P. A., & Schowengerdt, R. A., (Eds.), 1996. Special issue on non-conventional pattern analysis in remote sensing. Pattern Recognition Letters, 17(13), 1349–1359. Binaghi, E., Brivio, P. A., Ghezzi, P., Rampini, A., & Zilioli, E. (1999). Investigating the behaviour of neural and fuzzystatistical classifiers in sub-pixel land cover estimation. Canadian Journal of Remote Sensing, 25(2), 171–188. Burrough, P. A. (1996). Natural objects with indeterminate boundaries. In P. A. Burrough & A. U. Frank (Eds.), Geographic Objects with Indeterminate Boundaries, GISDATA series (pp. 3–28). London: Taylor & Francis. Carrara, P., Antoninetti, M., Ghioldi, K., & Rampini, A. (1997). Strutture dati spazio-temporali basate su eventi per il monitoraggio ambientale. In Proceedings of the Conf. Naz. ASITA (pp. 25–26), Parma (Italy), 30.9–3.10.1997. Carrara, P., & Rampini, A. (2001). An event-based archive of soft maps for the analysis of glacier changes. In 15th International Symposium Informatics for Environmental Protection (pp. 395–402), Zurich (CH), 10–12/10/2001. Langran, G. (1992). Time in Geographic Information Systems. London: Taylor & Francis. Peuquet, D. J. (1994). ItÕs about time: A conceptual framework for the representation of temporal dynamics in geographic information system. Annals of the Association of American Geographers, 84(3), 441–461. Peuquet, D. J., & Duan, N. (1995). An event-based spatiotemporal data model (ESTDM) for temporal analysis of geographical data. International Journal of GIS, 9(1), 7–24. Wang, F. (1990). Fuzzy supervised classification of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 28(2), 194–200. W3C (2001). Extensible Markup Language (XML), W3C,
Time-based retrieval of soft maps for environmental change detection Paola Carrara
a,*
, Giuseppe Fresta
b,1
, Anna Rampini
a,2
a
b
CNR, Via IREA-/Bassini, 15, I 20133, Milan, Italy ISTI-CNR, Area della Ricerca,Via Moruzzi 1, I 56127 Pisa, Italy
Abstract This contribution aims to shortly describe a data structure for creating and managing archives of thematic maps derived by classifying remotely sensed images by soft techniques (soft maps). Unlike traditional models for managing spatial data, the main key feature for query formulation is time, thus improving the investigation on changes occurred in time ranges. The data structure originally extends time-based models to soft maps, thus promoting a retrieval by changes approach also in the monitoring of phenomena for which hard classifications are not sufficiently accurate nor complete. The data structure has been implemented in a system, which is also described, available today in a Windows version. It has been applied to the monitoring of variations of Alpine glaciers. Ó 2002 Elsevier Science Ltd. All rights reserved. Keywords: Time-based retrieval; Change detection; Archives of soft maps
The study of Earth changes and the effects of human activities on the environment requires tools to analyse modifications which occur in time ranges and are directly and immediately observable through remotely sensed images. The development of such tools, however, depend on the definition of models for storing and retrieving images, able to privilege a dynamic, careful and sensitive representation of the events under study. Traditional Geographic Information Systems (GIS) represent spatial information as frozen in time, and possible changes through time are usually neglected. In this way the study of time-dependent phenomena becomes a heavy process, which requires the use of more software packages, the production of an amount of intermediate *
Corresponding author. Tel.: +39-2-70643268. E-mail addresses: [email protected] (P. Carrara), [email protected] (G. Fresta), [email protected] (A. Rampini). 1 Tel.: +39-50-3152933. 2 Tel.: +39-2-23699275. 0306-4573/03/$ - see front matter Ó 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 3 0 6 - 4 5 7 3 ( 0 2 ) 0 0 0 5 5 - 9
324
P. Carrara et al. / Information Processing and Management 39 (2003) 323–327
results and complete re-elaboration in correspondence to new data or analyses. To overcome these drawbacks, GISs are sometimes extended to consider time as an attribute of spatial data: this approach, however, overlooks the observation that space and time properties are conceptually different, so that the representation of variations through time needs the development of Ôad hocÕ models. Therefore, temporal models for the management of spatial information have been proposed, going from time-stamping layers (Armstrong, 1988) to time-stamping events or processes (Yuan, 1996). These models also impact on the choice of data collected and recorded: in an event-oriented view, for example, data representing changes correspond to a new entry in the collection, while in an update-oriented approach, entries correspond to the newest sources, discarding information previously stored. Time-based models, anyway, are based on time as fundamental key by which spatial information is accessed: in this way, temporal information emerges with top priority and searching changes through time becomes easier facilitating monitoring activities (Carrara, Antoninetti, Ghioldi, & Rampini, 1997; Langran, 1992; Peuquet, 1994; Peuquet & Duan, 1995). The models proposed in the literature manage thematic maps obtained by traditional methods (human photo-interpretation) or by automatic classification of remotely sensed images: in these maps each soil unit corresponds to a unique value representing one land-cover class (theme). These kind of maps do not provide levels of accuracy sufficient for many applications, in which it is interesting to capture the contributions of the various cover patterns to the overall value of each area. If we consider, for example, the problem of identifying rice crops in satellite images, the variations in class responses relate mainly to the contemporary and unavoidable presence of water and plants at different growing stages, that is, to the different percentage of the pure components, water and vegetation, visible in the area concerned. The use of thematic maps in which more values correspond to each unit, representing the degrees of membership to more classes, is a possible solution to suitably describe dishomogeneity, vagueness and ambiguity which are often intrinsic to natural phenomena (see for example Binaghi, Rampini, Brivio, & Schowengerdt, 1996; Burrough, 1996). These maps are usually the results of automatic classification by soft techniques of remotely sensed images (soft maps). The new challenge is, therefore, to extend existing temporal models to manage not only crisp maps, but also soft maps. The aim of this contribution is to propose a time based, event-oriented data structure having the novel ability to store and retrieve maps generated by soft techniques. The proposal is independent of the used classification as all soft maps can be archived and searched. The advantages consist in saving of memory space and in the easier management of queries related to variations in time. The approach wishes to promote a retrieval by content changes rather than a retrieval by content as indexing concerns spatial properties which changed through time. In fact, the data structure has been designed with the following aims: to encourage the emergence of time (the timestamps of stored events) as basic information; to record only occurred variations and their types; to manage, for each stored event, more images corresponding to the classes of interest. It is a four level structure (see Fig. 1): the first level accesses to the event vector which stores the timestamps of occurred variations; each element of the vector points to two other vectors: the first one stores, for each class of interest, the number of elementary areas whose value of membership to the class itself increased with respect to the previous event (rise-vector), while the second one records the number of elementary areas whose membership decreased (decrease-
P. Carrara et al. / Information Processing and Management 39 (2003) 323–327
325
Fig. 1. Schema of the proposed data structure; l is the membership value of the elementary area with co-ordinates x, y to the class Ci , which increased at time t with respect to t 1 .
vector). The third level hosts the variation table, accessed from the rise- or decrease-vectors: it supplies the co-ordinate values of the locations changed at given time, rising or decreasing with respect to the various classes; the membership degree of each changed area is at last stored in the classified image itself, in correspondence to the co-ordinates recorded in the variation table. This data structure allows to archive, without waste of memory, only data regarding changes; furthermore its multi-layer organisation is suitable to manage with different performances queries referred to variations: you need only to access to the first level to know if any modification occurred in a given time range. On the other hand, queries searching for the membership values of areas to classes are the heaviest with respect to resources. Queries about the number of areas whose membership values increased or decreased and queries about co-ordinates of changed areas, have an intermediate effectiveness. The proposed data structure has been implemented in the system GeoTemp 3.00, developed under the Windows operating system; the search engine of GeoTemp allows to create archives of maps which were classified by soft techniques, and to express various kinds of queries, mainly based on temporal key, guided by a graphic interface. Many types of queries are possible, such as: the dates in which images changed; the classes varied and the kind of variations; the number (or surface) of changed areas. Queries can be simple if they refer to one class only, or composed if they combine investigations on more than one class. The retrieved results can be presented both in table and in image format. By dealing with soft maps, the user is not limited to queries such as: (a) ‘‘Did the class Ci vary at time tj with respect to time ti ?’’ but also the degree of class variation may be investigated, as in the query: (b) ‘‘Show me the areas in which the degree of membership to the class Ci decreased of the 50% in the time range ti –tj ’’ The engine has been applied to the monitoring of annual Alpine glacier variations by means of archived soft maps from remotely sensed images (one Landsat TM image per year, taken at the end of Summer) (Carrara & Rampini, 2001). In this field, several image classification techniques have been experimented and particularly accurate results have been obtained by using soft
326
P. Carrara et al. / Information Processing and Management 39 (2003) 323–327
classification techniques, such as neural network and fuzzy statistical-based approaches. The supervised fuzzy statistical classifier designed by Wang (1990) has been shown to be particularly suited for the representation of cover class mixture. The classifier bases estimates of the distribution of pixels in multi-spectral space on the concept of the probability measure of fuzzy events to produce an output of the proportions of individual components. The fuzzy partition of the spectral space is performed by finding the analytical formulation of membership functions for each class. Moreover, the degrees of membership to classes produced by this kind of classifier, represent the percentage of presence of cover classes in the area (Binaghi, Brivio, Ghezzi, Rampini, & Zilioli, 1999; Wang, 1990). Therefore, a query of kind (b) legitimately assumes in this application the meaning of: (b1) ‘‘Show me the areas in which the degree of percentage of the class Ci decreased of the 50% in the time range ti –tj ’’ In this context, a tool for the analysis of changes by comparing different percentage of covers may improve the accuracy in the evaluations of the glaciological and hydrological parameters: in particular it is important to know the parts of the glacier changed from snow to ice or from ice to rock or vice-versa. The result of the composed query: ‘‘Show areas in which the percentage of snow decreased of the 50% and contemporary the ice increased of 50% in the time range September 1989–September 1991’’, is presented in Fig. 2 together with the images of the Alpine glacier under consideration. The described experience shown that a time-based searching tool, like GeoTemp, improves the analysis of changes as it allows to flexibly inspect from various points of view the same set of maps with very easy tools and quickness, without generating intermediate data. The possibility of performing a spatio-temporal analysis associated with an exploration of topographic properties (which are stored in the archives as well) of the data, allows the user to inspect the causes of changes. Furthermore, GeoTemp easily manages updating as new data are automatically ac-
Fig. 2. The ‘‘Gigante Occidentale’’ glacier in Landsat TM images of September 1989 (a) and 1991 (b), and the result of the query (c) showing areas in which the percentage of snow decreased of the 50% and contemporary the ice increased of 50%.
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commodated in the corresponding archive, and are immediately ready for investigations. With respect to time-based approaches managing crisp classification, the proposed engine allows to evaluate phenomena at different spatial scales, affecting the internal content of the spatial units without any possibility to crisply determine the membership to a unique class. A new, Web version of the system is under development, where XML will be used both to define a support for information exchange, in accordance to the data types of the case study, to the nature of elaborations, to the usersÕ needs, and to allow separation of content from presentation (W3C, 2001). An expected advantage of the XML encoding derives from the language ability to maintain multiple definition and rendering layers over the same data.
References Armstrong, M. P. (1988). Temporality in spatial databases. In Proc. of GIS/LIS ’88 2 (pp. 880–889). Binaghi, E., Rampini, A., Brivio, P. A., & Schowengerdt, R. A., (Eds.), 1996. Special issue on non-conventional pattern analysis in remote sensing. Pattern Recognition Letters, 17(13), 1349–1359. Binaghi, E., Brivio, P. A., Ghezzi, P., Rampini, A., & Zilioli, E. (1999). Investigating the behaviour of neural and fuzzystatistical classifiers in sub-pixel land cover estimation. Canadian Journal of Remote Sensing, 25(2), 171–188. Burrough, P. A. (1996). Natural objects with indeterminate boundaries. In P. A. Burrough & A. U. Frank (Eds.), Geographic Objects with Indeterminate Boundaries, GISDATA series (pp. 3–28). London: Taylor & Francis. Carrara, P., Antoninetti, M., Ghioldi, K., & Rampini, A. (1997). Strutture dati spazio-temporali basate su eventi per il monitoraggio ambientale. In Proceedings of the Conf. Naz. ASITA (pp. 25–26), Parma (Italy), 30.9–3.10.1997. Carrara, P., & Rampini, A. (2001). An event-based archive of soft maps for the analysis of glacier changes. In 15th International Symposium Informatics for Environmental Protection (pp. 395–402), Zurich (CH), 10–12/10/2001. Langran, G. (1992). Time in Geographic Information Systems. London: Taylor & Francis. Peuquet, D. J. (1994). ItÕs about time: A conceptual framework for the representation of temporal dynamics in geographic information system. Annals of the Association of American Geographers, 84(3), 441–461. Peuquet, D. J., & Duan, N. (1995). An event-based spatiotemporal data model (ESTDM) for temporal analysis of geographical data. International Journal of GIS, 9(1), 7–24. Wang, F. (1990). Fuzzy supervised classification of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 28(2), 194–200. W3C (2001). Extensible Markup Language (XML), W3C,