Journal of Coastal Research SI ICS2009 (Proceedings) Portugal ISSN

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1 Journal of Coastal Research SI ICS2009 (Proceedings) Portugal ISSN Landscape Simulation of Piranhas-Assu River (Rio Grande do Norte State, Brazil), from 1988 to 2024, Making Use of Cell Automata in Spatial Dynamic Models for Simulation of Future Scenarios A.M. Grigio, V.E. Amaro, M.A. Diodato and A.F. Castro. Departamento de Gestão Geology Department, CCET/ Departamento Engenharia Ambiental, FACEM/UERN, UFRN, C.P. 1639, Natal-RN, Florestal, Campus Cinobelina Campus central Br 110, km 46 Costa e Silva, Mossoró-RN, Brazil, alfredogrigio@uern.br Brazil, amaro@geologia.ufrn.br Elvas, BR 135, km 3, Bom Jesus-PI, Brazil, diodato@ufpi.br Departamento de Ciências Ambientais, UFERSA, BR Km 47 Costa e Silva, Mossoró-RN, Brazil, angelica@ufersa.edu.br ABSTRACT GRIGIO, A.M., AMARO, V.E., DIODATO, M.A. and CASTRO, A.F., Landscape simulation of Piranhas-Assu River (Rio Grande do Norte State, Brazil), from 1988 to 2024, making use of cell automata in spatial dynamic models for simulation of future scenarios. Journal of Coastal Research, SI 56 (Proceedings of the 10th International Coastal Symposium), Lisbon, Portugal, ISSN The lower course of Piranhas-Assu River, located in the coast of Rio Grande do Norte State, Brazil, arouses a keen interest as a study field, once it concentrates, besides petroleum and gas exploration, activities related to shrimp culture, salt and horticulture, factors that also deserve special attention. Therefore we search for understanding and studying the field study landscape area, aiming at putting, through simulation modes, future projections into practice. The work includes the research, analysis, interpretation of results, and the generation of simulation models, to analyze the landscape tendency. From Geographical Database, the necessary exploratory analyses were accomplished, in order to be used in the simulation model. Later, we conducted and executed the landscape simulation model into a specific software environment (DINAMICA-EGO /UFMG). At last, we carried out the analysis of the study field landscape tendencies. The lower course Piranhas-Assu River did not feature any intense dynamics in landscape changing. The class stability proved to be higher than transformations. The activities related to agriculture and cattle raising conduct the landscape dynamics. The producing of sea shrimp and petroleum also interfere in the landscape, even if in smaller proportions. The public policies, specially the ones related to the agrarian reform, largely determine the study field landscape dynamics. The landscape simulation, in a wider analysis, showed that the decisive agents for the spatial mobility of antropic activities, in the focused area, are related to the pre-existence of communities gifted to farming and cattle production, as well as access routes and drainage. ADITIONAL INDEX WORDS: Landscape simulation, Spatial Dynamic Models, Cell Automata INTRODUCTION The lower course of Piranhas-Assu River, located in the coast of Rio Grande do Norte State, Brazil (figure 1), arouses a keen interest as a study field, once it concentrates, besides petroleum and gas exploration, activities related to shrimp culture, salt and horticulture, factors that also deserve special attention. Cities where activities that involve meaningful environmental impact occur, as it happens with cities located in the estuaries of Piranhas- Assu River, must be able to handle tools which can discipline those activities in their territory. So it is necessary to develop a study to integrate, generate, and interpret information which are of concern to these areas, aiming at achieving objective answers which truly represent the areas under consideration, mainly the ones that present peculiar features because of the confluence of different and conflicting antropic activities (activities related to petroleum industries, shrimp culture, salt, horticulture and farming) in an environment of extreme natural dynamics. Environmental integrated studies have tried to organize the geographical space, considering the use of natural resources, renewable or not, in the search for an environmental balance. This way, the awareness of the stage in which the study field is, in its environmental condition, demands researchers to study, and society to be conscious, as a way to understand the interrelationship between men and the environment. Therefore, we have tried to recognize and study the dynamics of land use and occupation in the lower course of Piranhas-Assu River, through a multitemporal analysis of past times and the present, and accomplish future projections through simulation models. This work makes an effort at analyzing the landscape dynamics of the lower course of Piranhas-Assu River (Brazil), using a spatiotemporal dynamic spatial model, with the help of concepts and cellular automata methodologies. Besides, we search for identifying, mapping, and interpreting the evolution of land use and occupation, based on a modeling methodology to achieve a multitemporal understanding of images of remote sensors, to field recognition, and also to simulate future scenarios through the analysis of landscape use and occupation. The dynamic modeling (BURRHOUGH, 1988) attempts to transcend the limitations of geoprocessing technologies strongly based on a static, two-dimensional view of the world. The objective of dynamic models in GIS is to put the numeric simulation of time dependant processes into practice. 1508

2 Landscape simulation of Piranhas-Assu River with cell automata As defined by Burrough: a dynamic spatial model is a mathematic representation of a real world process in which certain language and oriented to Java written objects, and its current version runs in Windows 32 bits system. The modelling environment of DINAMICA EGO involves a number of operators called functores (functors). A functor can be understood as a process that operates on a group of input data on which a finite number of operations is applied, producing a new group of data as an output. Besides the conventional operators, ordinarily called functores, there are also the group operators, known as containers. The containers are peculiar, because they group and determine a specific behavior for the group of operators they contain. The functors and containers (figure 2) receive and send data to other functors and containers through a group of inputs and outputs called ports. Each port contains a type of associate data, for example: table, map, matrix, value, etc. (RODRIGUES et al., 2007). Figure 1. Location map of the lower course of Piranhas-Assu River. localization on earth s surface changes in response to variations in its directional forces. The models of spatial dynamic simulation are based on ecosystem models with extensions to accommodate the spatial heterogeneity and decision-making human processes. An approach to develop spatial dynamic simulation models is to represent the space as a matrix cell and apply the mathematical equations to each cell in the matrix simultaneously. Each cell in the model is connected to the neighbor cell, in a way that it is possible to establish a flow among the adjacent cells. This simplifies the system prediction mechanism significantly (PEDROSA, 2003). According to Stephen Wolfram (1983), one of the most reputed scholars on cellular automata, these are: mathematical idealizations of physical systems, in which space and time are discrete, and the attributes assume a group of values that are also discrete. A cellular automaton consists of a uniform regular grade (or matrix field ), commonly infinite in its extension, with a discrete variable in each location ( cell ). The cellular automaton evolves in discrete time intervals, with the variant value in a cell being affected by the variant values of the neighbor cells found in the interval of the earlier time. The variants in each cell are updated simultaneously ( synchronically ), being based on the variant values of their neighborhood in the interval of the previous time, and according to a pre-defined group of local rules. The simulation models are developed for the dynamic analysis of probabilistic transition, applying transitional rules developed from cellular automata in their calculation. They also apply methodologies from simulation engineering, resulted from a spatial approach though the calculation of dynamic variants and the application of logarithmical regression, or weights of evidence (GODOY, 2004). The DINAMICA EGO Environment for Geoprocessing Objects (SOARES-FILHO et al., 2007) was the tool used to develop and execute a spatial simulation model based on cellular automata. It is a landscape dynamics simulation program based on cellular automata executed through empirical logarithms of land use allocation, and was developed by the Centre of Remote Sensing of Universidade Federal de Minas Gerais ( The program was written in C++ Figure 2. Modelling environment of DINAMICA EGO with container and functor. The software DINAMICA EGO uses a group of maps as data input. There are also the cartographic variables, data components that can be divided in static and dynamic. The static maps are the assumed variants of controlling the change configuration. These variants are adjusted through the definition of their weights of evidence, aiming at generating the transition maps, that is, the change configuration (SOARES-FILHO, et al., 2003; SOARES FILHO et al., 2004). The use of dynamic variants is not applied to this work, because they do not exist. Therefore, only the static variants were taken into consideration. The last procedure to be executed in DINAMICA EGO for generating the simulations is the calibration process, that is, the model adjustment, which can be made from the researcher s landscape perception, along with the theoretical knowledge and the reflections in the result maps. METHOD The work is divided in stages which comprehend the research, the analysis and interpretation of results, and the generation of simulation models. The initial stage comprehends the gathering, treatment and insertion of topographic maps, satellite images and the Shuttle Radar Topography Mission (SRTM), from which the 1509

3 Grigio et al. Geographical Data Bank (GDB) was elaborated. From the GDB, we made the necessary exploratory analysis on the evolution of land use and occupation, and the preparation of the data to be used as a simulation model. Later, we carried out the construction of the landscape simulation model, which was calibrated and validated in a later stage. In the next step we made the simulations for future scenarios through the execution of the model in a specific software environment for such purpose. Following, the mentioned stages are listed, concisely: a) Data processing and feeding in a Geographical Information System (GIS) It comprehended the pre-processing, digitalization, digital image processing, as well as the vectorization of maps and images, and ground control. The satellite images used were: Landsat 5 TM, Órbita Ponto: (30/07/1988 e 28/09/1998); Landsat 7 - ETM+, Órbita Ponto: (05/04/2001 e 29/05/2003); CBERS_2_CCD1XS, Órbita Ponto: (14/08/2004); all of them with UTM, Datum SAD-69 projection, and 100x100 meter pixels. b) Thematic maps, data processing and analysis in a GIS environment aiming at preparing the previous step material for the following step, we elaborated the thematic maps: land use and occupation maps from 1988, 1998, 2001 e 2004; Geology Maps, Geomorphology, Soil/Soil Association, Geodiversity, Vegetation, Environmental Vulnerability, Ground Digital Model and Steepness. This step includes the exploratory analysis and the selection of the variables to be used in the model, which are: city distances, permanent drainage distances, road distances, ground digital model, steepness, initial landscape use 2004, geodiversity and environmental vulnerability. c) Modelling This step includes the construction of the model to be used in the simulation. It involves the making of the Transitional Matrix and the weights of evidence determination. The Transitional Matrix is generated from the land use and occupation crossing table in the years taken into account: 1988, 1998, 2001 e The software starts crossing the years 1988 and Subsequently, it generates and, finally, Each pair of data produces two matrixes, the Simple Matrix and the Multiple Matrix. The generation of the transitional matrixes enables the definition of transition variations, that is, the matrixes are analyzed and the transitions to be used are decided (Table 1). Table 1. Transition variables chosen for the simulation. Landscape modeling Transitions Transitions of landscape modeling 3 TO 5 Agricultural Activity to Cattle Raising 3 TO 6 Agricultural Activity to Caatinga Vegetation 5 TO 3 Cattle raising to Agricultural Activity 5 TO 6 Cattle Raising to Caatinga Vegetation 6 TO 3 Caatinga Vegetation to Agricultural Activity 6 TO 4 Caatinga Vegetation to Industrial Activity 6 TO 5 Caatinga Vegetation to Cattle Raising The Weights of evidence method is a Baysean method, traditionally used by professionals who deal with probabilities in order to indicate favourable areas to, for example, geological phenomena such as mineralization and seismic. (1) Where O {D} and O {D/B} are chance reasons, respectively, of a prior occurance of event D and the occurance of D given a spatial pattern B. Therefore, (2) W+ is the weight of evidence of an occurance of event D, given a spatial pattern B. d) Calibration and validation of the model it includes: a) weights of evidence correlation, b) generating the landscape probability maps, and c) calculating similarity between maps. To the correlation of the weights of evidence, the software DINAMICA EGO uses two statistic tests: Crammer Coefficient (V) and Uncertainty of Conjunct Data (U). As a result from the tests, one of the correlational variables can be eliminated or combined with the second in order to form a new variant that will replace both of them in the integrated model. The calibration of the model attempts to select the best group of input variables and the program internal parameters, as a way to best adjust the empirical data and the observed reality. This task involves two stages. First, a comparative visual analysis is executed, for each kind of change in land use, among the general tendencies of simulative preliminary results, the hints provided by the maps of use transition and probabilities of transition, and the directional information contained in the simultaneous superposition of different maps of explainable variables about the real map of land use in a vetorial format. The second calibration step is concerned with the adjustment of the simulation program internal parameters: spot size and changes, proportions of transitional algorithms and number of interactions. The software DINAMICA EGO presents a model for this process that, besides the insertion of the maps that take part in the simulation, is also necessary to the input of the following parameters: a) spot size and changes; b) proportions of transitional algorithms (Patcher and Expander); and, c) number of interactions. e) Generation of maps of future scenarios it is the moment in which the model is executed, it is the step that produces the maps of future scenarios. RESULTS The understanding of the landscape and its evolution, in a certain future time period, requires the historical dynamics of the studied area to be interpreted. In the spatial case of this work we evidenced, particularly, the beginning of cattle raising activities to the detriment of the native vegetation and, afterwards, changes from agricultural activity areas to cattle raising and vice versa, as well as the abandonment of certain areas to the caatinga vegetation (GRIGIO, 2008). The obtained results infer that the studied area did not present any intense dynamics in landscape changes once in the considered periods class stability proved to be superior to the transformations. The activities related to agriculture and cattle raising are the ones that induce, particularly, the landscape dynamics. The production of sea shrimp and petroleum also interfere in the landscape, although in smaller proportions. Through this analysis we could infer that the landscape of the studied area follows a rhythm that proved to be slower than expected, given the insertion of new economic activities such as crustacean breeding, petroleum production and fruit growing. The analysis of the spatiotemporal 1510

4 Landscape simulation of Piranhas-Assu River with cell automata evolution showed that after the initial impact caused by the installation of such enterprises, significant changes in those areas and activities did not happen. From the data contained in the simulation maps of landscape evolution we made up table 2 and figure 3. It shows the land use and occupation classes and their areas, for the initial landscape year (2004) and for the simulated years (2009, 2014, 2019 and 2024). indicate that there are no changes with the passing of time. The last column refers to the difference of the area between the initial landscape (2004) and the final simulated landscape (2024). In the area studied, the variables that dominate the landscape were the ones that correspond to farming and industrial activities, what ended up influencing the vegetative covering. Analyzing these variables and the passing of time in detail, we found out that in the first considered period ( ) there were considerable Table 2. of land use and occupation classes, for the years 2004, 2009, 2014, 2019 e 2024, and respective differences in percentage. Class * Initial Landscape Figure 3. Activities change map (2004 until 2024). It also presents the area differences, in percentages, between paired years. The values in black ink relate to the increase and decrease (with a negative mark) of the class area. The other values , ,0 1, ,4 0, ,9 0, ,0-0,3 0, , ,8-0, ,8 0, ,8 0, ,3 0,0-0, , ,3-4, ,1-1, ,1-0, ,8-0,6-6, , ,0 6, ,2 5, ,3 5, ,3 5,1 25, , ,1 23, ,2 5, ,2 1, ,4 0,0 32, , ,7-4, ,7-2, ,9-1, ,9-0,8-7, , ,6 0, ,2 0, ,5 0, ,8 0,0 0, , ,5 0, ,7 0, ,1 0, ,1 0,1 0, , ,8 0, ,8 0, ,8 0, ,8 0,0 0, , ,6 0, ,6 0, ,6 0, ,6 0,0 0, , ,8 0, ,8 0, ,8 0, ,8 0,0 0, , ,6 0, ,5 0, ,3 0, ,4 0,0 0,0 TOTAL , , , , ,2 - - * 1- Human Agglomerate. 2- Exposed Ground. 3- Agricultural Activity. 4- Industrial Activity. 5- Cattle Raising Activity. 6- Caatinga Vegetation. 7- Carnauba Vegetation. 8- Permanent Drainage. 9- Mangrove. 10- Ocean. 11- Creeping Vegetation. 12- Humid Zone. ** In black ink: increase or decrease of the area. increases and decreases in the antropic activities, if compared with later periods ( , e ). In the case of agricultural activities, there was a continuous decrease in the areas that, with the passing of time, resulted in an accumulated negative difference of 6,8%, between the period The industrial activity showed the same behavior, although with positive differences in areas where such activities are developed, what resulted in a difference of 25,0% between the years 2004 and The same pattern happened with cattle raising areas that showed a positive difference of 32,6%, between the years 2004 and The cattle raising activities showed the greatest differential when compared to the other variables, what suggests that this kind of activity has an important influence on the landscape dynamics in the lower course of river. In a temporal analysis of antropic evolution and the studied area remaining vegetation, during the whole period studied, from 1988 to 2024, we found a decreasing tendency in agricultural and industrial activities, while the cattle raising activities showed a particular increase between the years 2001 and 2014, keeping practically stable until The agricultural activity increased the occupied area in a growing mode until the year 2001, nevertheless it gradually decreased until the year 2024, ending with an area smaller than the one in the initial year of study, that is, The industrial activity showed a similar behavior, reaching its highest point in the occupied area in the year 2004, decreasing abruptly in the year 2014, and showing a little raise in the year The caatinga vegetation (table 3) shows that the biggest remaining area happened in the year 1998, showing a gradual decrease until the year

5 Grigio et al. Table 3. Land use and occupation during the period from 1988 to 2024, in the lower course of Piranhas-Assu River, Brazil. Use/Occupation Agricultural Activity , , , , , ,96 Industrial Activity , , , , , ,94 Cattle Raising Activity , , , , , ,35 Caatinga Vegetation , , , , , ,41 Total , , , , , ,66 CONCLUSIONS From the accomplished study, based on the historical activities of the region and through the analysis of probabilities of tendencies that affirmed the farming activities, particularly cattle raising, as determinant variables in the studied area, we evaluated the future landscape and its spatiotemporal evolution, through the achieved simulation. The simulation, in a wider analysis, shows that the determinant factors of spatial mobility and the transformations of antropic activities, in the focused area, are related to the pre-existence of communities with a vocation to farming. In this case, we refer to settlements made by the Colonization and Agrarian Reform National Institute (Instituto Nacional de Colonização e Reforma Agrária - INCRA), and to the existence of access routes and drainage. Therefore, we can assert that the public policies promoted by this federal organ greatly determine the landscape dynamics of the studied area. In a smaller, but not less important scale, the existence of a regional transport system contributed to the local economy, and helped with the installation of larger undertakings, such as fruit growing. Because it is treated as a complex system dynamic, historical and active -, we can relate it to the Chaos Theory that, according to Oliveira (2007), slight differences in the chosen initial configuration to the evolution of a dynamic system can lead to vastly distinct final states, what implies that, at long term, the system behavior becomes rigorously unpredictable or chaotic. This shows the importance of the chosen variables that will model the system, thus the simulations presented here reflect a future reality based on the present reality, and founded in the historical reality. In other words: the prognostic of the landscape evolution is based upon probabilities, evidenced by the analysis of the historical evolution in the focused region. On the other hand, the more distant in time the more unpredictable the system is, therefore this is not about making future projections at long term, but revaluing the system at short and medium terms. It is important to remind that the simulated landscapes can suffer significant changes from events that may happen in the future, for example, public policies that subsidize some particular agricultural culture, macro and micro economic changes, among other unpredictable events. However, with the updating of the data bank, the new variables included, it is possible to make up future simulations from this new reality, distinguishing the process as dynamic, susceptible of receiving the impression of new factors, variables and realities that affect the future reality. Besides, this is one of the strongest characteristics of complex systems: the perception of temporal dynamics and, particularly, the capacity to receive new information and incorporate them. LITERATURE CITED BURROUGH, P., Dynamic Modelling and Geocomputation. Geocomputation: A Primer. P. Longley, M. Batty and R. McDonnel. London, John Wiley & Sons. GODOY, M. M. G., Modelagem da Dinâmica de Ocupação do Solo no Bairro Savassi, Belo Horizonte, Brasil. Belo Horizonte, Minas Gerais: Universidade Federal de Minas Gerais, Master s thesis, 99p. GRIGIO, A. M. Evolução da Paisagem do baixo curso do Rio Piranhas-Assu ( ): Uso de Autômatos Celulares em Modelo Dinâmico Espacial para Simulação de Cenários Futuros. Natal, Rio Grande do Norte: Universidade Federal do Rio Grande do Norte, Ph.D.Thesis, 216p. OLIVEIRA, L. A., O tempo é do caos. Revista Caros Amigos Especial, XI(36), PEDROSA, B. M., Ambiente Computacional para Modelagem Dinâmica Espacial. São José dos Campos, São Paulo: Instituto Nacional de Pesquisas Espaciais INPE, Ph.D. thesis, 98p. RODRIGUES, H. O.; SOARES-FILHO, B. S. AND COSTA, W. L. S., Dinamica EGO, uma plataforma para modelagem de sistemas ambientais. Proceedings oh the 13th Simpósio Brasileiro de Sensoriamento Remoto (Florianópolis, Santa Catarina, Brasil), pp SOARES-FILHO, B. S.; CORRADI, L.; CERQUEIRA, G. and ARAÚJO, W. L, Simulating the spatial patterns of change through the use of the dinamica model. Proceedings of the 11 Simpósio Brasileiro de Sensoriamento Remoto (Belo Horizonte, Minas Gerais, Brasil) pp SOARES-FILHO, B. S. ; ALENCAR, A. ; NEPSTAD, D.; CERQUEIRA, G.; DIAZ, M. C.; SOLÓZARNO, L. and VOLL, E., Simulating the Response of Land-Cover Changes to Road Paving and Governance Along a Major Amazon Highway: The Santarém-Cuiabá Corridor. Global Change Biology, 10(5), SOARES-FILHO et. al. Programa DINAMICA EGO. Laboratório de Sensoriamento Remoto. Universidade Federal de Minas Gerais WOLFRAN, S. (ed.), Theory and Applications of Cellular Automata. Singapore: World Scientific Press. ACKNOWLEDGEMENTS The authors would like to thank the program of post-graduation in Geodynamics and Geophysics of Universidade Federal do Rio Grande do Norte for the infrastructure and for providing the Geoprocessing Laboratory (GEOPRO); the PETRORISCO Project (Network 05/01) environmental monitoring of risk areas and petroleum spill and its derivatives; the Interchange convention CAPES/DAAD/PROBAL n 150/02 Sea level variations during the Holocene, between Touros and Areia Branca (RN), the northeast of Brazil; and CAPES for the Doctorate scholarship. 1512