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GIS and Spatial Decision Support System for Environmental Degradation Monitoring
Abstract
Agricultural activities have expanded dramatically in Brazil during the last 30 years, causing significant damage to the original Cerrado ecosystem and threatening the Pantanal biome. The consequences are a significant damage to the original ecosystem with the consequent degradation of the riparian vegetation and widespread erosion, with river siltation and degradation of the natural resources, risking the sustainability of anthropic activities in the Pantanal lowlands of Mato Grosso do Sul, Brazil.
This work aims to describes the conception of the development of a Spatial Decision Support System (SDSS) to assist land use management of the Upper Taquari Basin, Mato Grosso do Sul, Brasil being a useful tool to environmental decision makers.
In order to attain that goal, the erosion processes and water contamination by agrochemicals is being monitored in three selected sub-basins. The information gathered feed a GIS system, which, along with Remote Sensing data sources, monitor land use, changes and the erosion process throughout the years. This system is part of the SDSS which applies Artificial Intelligence Techniques to, among others: provide information on the cost/benefit of different mitigation options; rank the municipalities with regards to environmental impact of agricultural activities and rank areas in terms of priority for interventions.
This work is part of a multi-institutional project involving: Embrapa Soils Research Center (Rio de Janeiro), Embrapa Pantanal (Corumbá, Mato Grosso do Sul), UCB –Catholic University of Brasilia (Brasilia), COINTA – Consortium for the Sustainable Development of the Upper Taquari Municipalities (Mato Grosso do Sul), UCDB – Dom Bosco Catholic University (Mato Grosso do Sul), UERJ – Rio de Janeiro State University - Dept. of Systems Engineering (Rio de Janeiro), PUC Rio – Catholic University of Rio de Janeiro – Dep. of Eletrical Engineering and Embrapa Beef Cattle (Mato Grosso do Sul).
1. Introduction
The Taquari river is a major contributor to the Brazilian Pantanal hydrological system. Its watershed covers approximately 80,000 km 2 of land, being approx. 30,000 km 2 located in the highlands of the Paraguay river basin. The lowlands have been occupied during the last 200 years by extensive cattle grazing on natural pastures, an economic activity that did not reduce significantly the Pantanal’s luxuriant biodiversity.
On the other hand, occupation during the last 30 years of the Cerrados in the highlands amplified degradation of soil and water resources, to a point that it is considered the principal menace to the Pantanal’s integrity. Substitution of the Cerrado vegetation in the Taquari watershed highlands 34 - 2 by pastures (mainly in sandy soils) and cash crops correlates with increased sediment deposition in the Taquari river bed.
As a result, at least 400,000 ha of native grazing pasture of the Pantanal lowlands have been lost, due to flooding. This has driven local farmers to abandon their lands or remove vegetation in upper strips of land, which are being transformed into exotic pastures. The combination of flooding and deforestation is leading to transformation of the landscape, and reduction of terrestrial biodiversity. Other negative environmental and economic impacts are:
In order to unravel the complexity of the Taquari issue we adopted a problem-oriented approach. What is considered a major problem to people located in the lowlands (siltation and floods, for example) may not be considered of utmost importance to those in the highlands (soil erosion). In order to streamline our problem analysis, we considered the highland portion of the Taquari watershed in our studies.
We propose a model that explains the Upper Taquari watershed land degradation problem and will feed a Spatial Decision Support System.
2. Methodology
Considering the above-mentioned scenario, it was a consensus that we should not study the flood process (consequences) in the low lands, but the causes related to that phenomenon, which were located in the upper Taquari watershed. In this way, the geographical approach was delimited. The erosion process at the highlands are very dynamic, consequently it was realized that we should model the problem as a multitemporal one.
The use of static Geographical Information System (GIS), where the information would be organized (diagnoses) and some pre-selected analyses (prognoses) done only at one static time, would not be enough.
It was necessary to have a GIS system linked to a monitoring system, where we could spatially visualize and update the spatial database.
In this way, a system that could automatically extract land use and land cover changes from multitemporal satellite images was considered to be essential. This system aims to be simple enough so that the municipalities do not need highly qualified technicians to deal with Remote Sensing or Digital Image Processing Techniques.
Algorithms for automatic land degradation extraction from multitemporal satellite images (MATHER, 1999; RICHARDS et al 1999; CHAVEZ, 1996; ALVES et al 1996) were developed as part of the SDSS information input.
A hybrid algorithm, consisting basically of the usual unsupervised classification method, where an existing classification of the same area is explored to estimate the initial position of the centroids of each cluster, was implemented. Experiments were carried out to compare the performance of the proposed hybrid method with the conventional unsupervised method.
The evaluation uses images obtained in 1996 and 1999 of two representative sub-basins subjected to a rapid process of environmental degradation during the last few years. Another method based on the use of linear regression procedure was also implemented (JOHNSON et al, 1998; SONG et al, 2001).
This method is based on the assumption of a linear relationship between image bands across time. Pixels that change from one class in the earlier image to a different class in the later image (change pixels) behave like outliers and affect the accuracy of these techniques. In this way, the linear regression approach investigates methods to filter the training data from outliers before estimating the coefficients of the linear relationship between image bands. The procedure discriminates between change and stable pixels by evaluating how well the linear model fits to them.
In order to gather information that will feed the Spatial Decision Support System, the erosion processes and water contamination by agrochemicals are being monitored in three selected micro-watersheds. This information is being used to model the behavior of each representative micro-watershed.
The information gathered feeds a GIS system that, along with Remote Sensing data sources form an automatic land degradation detection system, monitoring land use changes and erosion processes throughout the years. In order to unravel the complexity of the Taquari issue we adopted a problem-oriented approach (DE GROOT, 1992).
However, what is considered a major problem to people located in the lowlands (siltation and floods, for example) may not be considered of utmost importance to those in the highlands (soil erosion). In order to streamline our problem analysis, we considered the highland portion of the Taquari watershed in our studies.
3. Results
The results of this work are the development of the basis of the SDSS, which applies Artificial Intelligence techniques to, among others: rank the municipalities, or sub-basins, with regards to risk of severe erosion or contamination of water resources by agrochemicals; rank areas in terms of priority for mitigating actions.
These are the basis for the creation and design of a Spatial Decision Support System to deal with dynamic processes, such as land degradation in huge areas. Figure 1, shows the results of the conception of an Intelligent Spatial Decision Support System as described and its interrelationship with database, monitoring system and decision making support.
Figure 1- Conception of an Intelligent Spatial Decision Support System
Aiming to obtain a quantitative and qualitative knowledge model to explain the Upper Taquari watershed land degradation problem, a Workshop was organized. Participants were scientists with different discipline backgrounds and having professional links to the problem context.
The Strategic Situational Planning (SSP) framework was used (MATUS, 1983). It is a management tool based on the understanding that a problem is product of a complex social system that 34 - 4 carries a high uncertainty, where its multiple actors relate interactively under various dimensions, leading to cooperation as well as conflict.
The SSP framework contains the following modules (or moments): 1) Explanation Moment; 2) Normative Moment; 3) Strategic Moment; 4) Tactical-Operational and Monitoring Moment. We applied the Explanation Moment model to the Taquari watershed problem analysis. Its many variables were grouped in the following categories: rules, accumulation, fluxes, and descriptors.
The first step of the study was to define precisely the problem to be focused, i.e., what is the client’s expectation from the DSS that will be developed? In other words, which problem(s) should the DSS deal with in order to satisfy the client? It was decided that the target-problem is: The erosion process in the Upper Taquari watershed is highly intense and evolving.
The descriptors of the problem were then defined. These should be precise and quantifiable, so as to serve as a basis for monitoring and as a measure of success or failure of mitigation actions. They also define what should be thoroughly explained by the model. Descriptors cannot be mistaken for causes or consequences of a problem, and it shouldn’t be possible to build a causal relationship between them.
Soil management can reduce the transfer of potential energy (gravity) to kinetic energy of the soil particles (displacement or runoff), reducing erosion. Therefore, the higher kinetic energy a soil particle gains, the more intense will be the erosion process. According to this concept, sheet erosion is directly linked to the kinetic energy of rain drops as well as of surface runoff; gully erosion is related to the kinetic energy of rain drops, surface and sub-surface runoff; and river bank erosion is a result of the kinetic energy of the river flow.
Accumulation and rules were defined by group analyses of the many causes that concur to the occurrence of the problem. The categories to which each of those causes should be assigned were then defined, and their inter-relations constructed in the form of a fluxogram.
Critical nodes (CN) were selected from the many causes identified. In order to be a CN, a variable should be amenable to alterations by human intervention, producing a significant impact over one or more descriptors of the problem. The CNs are practical centres of action, and not a mere consequence of other causes.
Future work will define and quantify which objectives are to be achieved by changing current status of critical nodes, establish their indicators, and generate a prototype of the DSS. The result of this was a comprehensive explanation of the causes of the land degradation problem in the Upper Taquari watershed, characterized by the various types of erosion present in the region (Figure 2) and used to design the solutions to the problem in a SSD context.
Figure 2. Representation of the inter- relations among causal variables. Red boxes enclose selected Critical Nodes
References
GIS and Spatial Decision Support System for Environmental Degradation Monitoring
Abstract
Agricultural activities have expanded dramatically in Brazil during the last 30 years, causing significant damage to the original Cerrado ecosystem and threatening the Pantanal biome. The consequences are a significant damage to the original ecosystem with the consequent degradation of the riparian vegetation and widespread erosion, with river siltation and degradation of the natural resources, risking the sustainability of anthropic activities in the Pantanal lowlands of Mato Grosso do Sul, Brazil.
This work aims to describes the conception of the development of a Spatial Decision Support System (SDSS) to assist land use management of the Upper Taquari Basin, Mato Grosso do Sul, Brasil being a useful tool to environmental decision makers.
In order to attain that goal, the erosion processes and water contamination by agrochemicals is being monitored in three selected sub-basins. The information gathered feed a GIS system, which, along with Remote Sensing data sources, monitor land use, changes and the erosion process throughout the years. This system is part of the SDSS which applies Artificial Intelligence Techniques to, among others: provide information on the cost/benefit of different mitigation options; rank the municipalities with regards to environmental impact of agricultural activities and rank areas in terms of priority for interventions.
This work is part of a multi-institutional project involving: Embrapa Soils Research Center (Rio de Janeiro), Embrapa Pantanal (Corumbá, Mato Grosso do Sul), UCB –Catholic University of Brasilia (Brasilia), COINTA – Consortium for the Sustainable Development of the Upper Taquari Municipalities (Mato Grosso do Sul), UCDB – Dom Bosco Catholic University (Mato Grosso do Sul), UERJ – Rio de Janeiro State University - Dept. of Systems Engineering (Rio de Janeiro), PUC Rio – Catholic University of Rio de Janeiro – Dep. of Eletrical Engineering and Embrapa Beef Cattle (Mato Grosso do Sul).
1. Introduction
The Taquari river is a major contributor to the Brazilian Pantanal hydrological system. Its watershed covers approximately 80,000 km 2 of land, being approx. 30,000 km 2 located in the highlands of the Paraguay river basin. The lowlands have been occupied during the last 200 years by extensive cattle grazing on natural pastures, an economic activity that did not reduce significantly the Pantanal’s luxuriant biodiversity.
On the other hand, occupation during the last 30 years of the Cerrados in the highlands amplified degradation of soil and water resources, to a point that it is considered the principal menace to the Pantanal’s integrity. Substitution of the Cerrado vegetation in the Taquari watershed highlands 34 - 2 by pastures (mainly in sandy soils) and cash crops correlates with increased sediment deposition in the Taquari river bed.
As a result, at least 400,000 ha of native grazing pasture of the Pantanal lowlands have been lost, due to flooding. This has driven local farmers to abandon their lands or remove vegetation in upper strips of land, which are being transformed into exotic pastures. The combination of flooding and deforestation is leading to transformation of the landscape, and reduction of terrestrial biodiversity. Other negative environmental and economic impacts are:
- Increasing magnitude, duration and unpredictability of floods;
- Changes in land use by farmers (eg. introduction of buffalo);
- Construction of polders and dams;
- Difficulties to navigation;
- Alterations in the ecology of the floodplains.
In order to unravel the complexity of the Taquari issue we adopted a problem-oriented approach. What is considered a major problem to people located in the lowlands (siltation and floods, for example) may not be considered of utmost importance to those in the highlands (soil erosion). In order to streamline our problem analysis, we considered the highland portion of the Taquari watershed in our studies.
We propose a model that explains the Upper Taquari watershed land degradation problem and will feed a Spatial Decision Support System.
2. Methodology
Considering the above-mentioned scenario, it was a consensus that we should not study the flood process (consequences) in the low lands, but the causes related to that phenomenon, which were located in the upper Taquari watershed. In this way, the geographical approach was delimited. The erosion process at the highlands are very dynamic, consequently it was realized that we should model the problem as a multitemporal one.
The use of static Geographical Information System (GIS), where the information would be organized (diagnoses) and some pre-selected analyses (prognoses) done only at one static time, would not be enough.
It was necessary to have a GIS system linked to a monitoring system, where we could spatially visualize and update the spatial database.
In this way, a system that could automatically extract land use and land cover changes from multitemporal satellite images was considered to be essential. This system aims to be simple enough so that the municipalities do not need highly qualified technicians to deal with Remote Sensing or Digital Image Processing Techniques.
Algorithms for automatic land degradation extraction from multitemporal satellite images (MATHER, 1999; RICHARDS et al 1999; CHAVEZ, 1996; ALVES et al 1996) were developed as part of the SDSS information input.
A hybrid algorithm, consisting basically of the usual unsupervised classification method, where an existing classification of the same area is explored to estimate the initial position of the centroids of each cluster, was implemented. Experiments were carried out to compare the performance of the proposed hybrid method with the conventional unsupervised method.
The evaluation uses images obtained in 1996 and 1999 of two representative sub-basins subjected to a rapid process of environmental degradation during the last few years. Another method based on the use of linear regression procedure was also implemented (JOHNSON et al, 1998; SONG et al, 2001).
This method is based on the assumption of a linear relationship between image bands across time. Pixels that change from one class in the earlier image to a different class in the later image (change pixels) behave like outliers and affect the accuracy of these techniques. In this way, the linear regression approach investigates methods to filter the training data from outliers before estimating the coefficients of the linear relationship between image bands. The procedure discriminates between change and stable pixels by evaluating how well the linear model fits to them.
In order to gather information that will feed the Spatial Decision Support System, the erosion processes and water contamination by agrochemicals are being monitored in three selected micro-watersheds. This information is being used to model the behavior of each representative micro-watershed.
The information gathered feeds a GIS system that, along with Remote Sensing data sources form an automatic land degradation detection system, monitoring land use changes and erosion processes throughout the years. In order to unravel the complexity of the Taquari issue we adopted a problem-oriented approach (DE GROOT, 1992).
However, what is considered a major problem to people located in the lowlands (siltation and floods, for example) may not be considered of utmost importance to those in the highlands (soil erosion). In order to streamline our problem analysis, we considered the highland portion of the Taquari watershed in our studies.
3. Results
The results of this work are the development of the basis of the SDSS, which applies Artificial Intelligence techniques to, among others: rank the municipalities, or sub-basins, with regards to risk of severe erosion or contamination of water resources by agrochemicals; rank areas in terms of priority for mitigating actions.
These are the basis for the creation and design of a Spatial Decision Support System to deal with dynamic processes, such as land degradation in huge areas. Figure 1, shows the results of the conception of an Intelligent Spatial Decision Support System as described and its interrelationship with database, monitoring system and decision making support.
Figure 1- Conception of an Intelligent Spatial Decision Support System
Aiming to obtain a quantitative and qualitative knowledge model to explain the Upper Taquari watershed land degradation problem, a Workshop was organized. Participants were scientists with different discipline backgrounds and having professional links to the problem context.
The Strategic Situational Planning (SSP) framework was used (MATUS, 1983). It is a management tool based on the understanding that a problem is product of a complex social system that 34 - 4 carries a high uncertainty, where its multiple actors relate interactively under various dimensions, leading to cooperation as well as conflict.
The SSP framework contains the following modules (or moments): 1) Explanation Moment; 2) Normative Moment; 3) Strategic Moment; 4) Tactical-Operational and Monitoring Moment. We applied the Explanation Moment model to the Taquari watershed problem analysis. Its many variables were grouped in the following categories: rules, accumulation, fluxes, and descriptors.
The first step of the study was to define precisely the problem to be focused, i.e., what is the client’s expectation from the DSS that will be developed? In other words, which problem(s) should the DSS deal with in order to satisfy the client? It was decided that the target-problem is: The erosion process in the Upper Taquari watershed is highly intense and evolving.
The descriptors of the problem were then defined. These should be precise and quantifiable, so as to serve as a basis for monitoring and as a measure of success or failure of mitigation actions. They also define what should be thoroughly explained by the model. Descriptors cannot be mistaken for causes or consequences of a problem, and it shouldn’t be possible to build a causal relationship between them.
-
d1 - From 1979/82 to 1997 there was an increase in the production of sediments in the watershed
d2 - X% of the watershed area present a high rates of sheet erosion
d3 - Erosion is higher in the cultivated pastures over sandy soils
d4 - There is a steady increase in the number and sizes of gullies
d5 - The waterways are widening
Soil management can reduce the transfer of potential energy (gravity) to kinetic energy of the soil particles (displacement or runoff), reducing erosion. Therefore, the higher kinetic energy a soil particle gains, the more intense will be the erosion process. According to this concept, sheet erosion is directly linked to the kinetic energy of rain drops as well as of surface runoff; gully erosion is related to the kinetic energy of rain drops, surface and sub-surface runoff; and river bank erosion is a result of the kinetic energy of the river flow.
Accumulation and rules were defined by group analyses of the many causes that concur to the occurrence of the problem. The categories to which each of those causes should be assigned were then defined, and their inter-relations constructed in the form of a fluxogram.
Critical nodes (CN) were selected from the many causes identified. In order to be a CN, a variable should be amenable to alterations by human intervention, producing a significant impact over one or more descriptors of the problem. The CNs are practical centres of action, and not a mere consequence of other causes.
Future work will define and quantify which objectives are to be achieved by changing current status of critical nodes, establish their indicators, and generate a prototype of the DSS. The result of this was a comprehensive explanation of the causes of the land degradation problem in the Upper Taquari watershed, characterized by the various types of erosion present in the region (Figure 2) and used to design the solutions to the problem in a SSD context.
Figure 2. Representation of the inter- relations among causal variables. Red boxes enclose selected Critical Nodes
References
- ALVES, D. S., KALIL, E.M. & MOREIRA, J.C., 1996. Characterizing Land Use Dynamics in Amazon Using Multitemporal Imagery and Segmentation Techniques. In: XVIII ISPR. Vienna, pp 123-130.
- CHAVEZ, P. S. Jr., 1996. Image –Based Atmospheric Corrections – Revisited and Improved, PE&RS, pp. 1025-1036.
- D’AGOSTINI, L.R. 1999. Erosão: o problema mais que o processo. Florianópolis, Ed. UFSC, 131 p.
- DE GROOT, W.T. 1992. Environmental Science Theory: Concepts and methods in a one-world, problem-oriented paradigm. In: Studies in Environmental Science, 52. 331-340. Elsevier, Amsterdam.
- JOHNSON, R. A. & Wichern, D.W. 1998. Applied Multivariate Statistical Analysis, Prentice Hall, 4 th Ed.
- MATHER, P.M. 1999. Computer Processing of Remotely Sensed Images. An Introduction. Ed. Wiley, Second edition.
- MATUS, C. 1983. Normative and Situational Planning. Trimestre Economico 50(199): 1721-1781.
- RICHARDS, J.A. & JIA, X. 1999. Remote Sensing Digital Image Analysis – An Introduction, 3 rd Ed., Springer Verlag.
- SONG, C., WOODCOCK, C. E., Seto, K. C., Lenney, M. P., Macomber, S. A. (2001), “Classification and change Detection Using Landsat TM Data: When and How to Correct
- Atmospheric Effects?”, Remote Sensing and Environment, vol. 75, pp. 230-244.