GEOTECHNOLOGIES AS A TOOL FOR ENVIRONMENTAL MONITORING

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From the beginning of my life I had my father as a reference point, who was always enthusiastic, studying, learning and passing on knowledge through the dialogue of his experiences, which gave me a taste for studying. Later on, the example was reinforced by my husband, the supervisor of this work, who, in addition to knowledge, added the desire to direct my studies towards the common good, always with creative and innovative ideas. I would also like to thank four women who supported me and took care of my children at some point while I was studying or travelling to conferences: my mother, my stepmother, my aunt and my faithful secretary. I would also like to thank all the colleagues who accompanied me on the sampling, laboratory analyses and image processing, with whom I shared experiences and long journeys, in the field, in the sun, in the mud, with wet clothes and a few falls, in the laboratory with analyses that seemed endless and in front of the computer late into the night recording and processing satellite images. I would also like to thank the laboratory technicians for their support and dedication to the work, who were always willing to help and carried out physicochemical and microbiological analyses of the water. I would like to thank the administrative technicians for their efficiency, agility and patience in resolving academic issues. I would like to thank the drivers for the information they passed on through their empirical knowledge of the region. Special thanks to the academic coordinator for his great management skills, competence, astuteness and, above all, his ability to listen to academic concerns. I would also like to thank the UNIVERSITY for giving me the opportunity to attend a free, high-quality higher education course, with access to laboratories, equipment and vehicles for sampling, as well as for the support provided through PBDA scholarships. 3 EPIGRAPH "I refuse to bury my dreams. I can't close my eyes to the flowers that will sprout. Even if they are timid, fragile, squalid... Even if they have to break through the reinforced, inhuman concrete in the inhuman heart. They will sprout and become what they are meant to be" Maria de Lourdes Scottini Heiden 4 SUMMARY REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS : TOOLS FOR PUBLIC AND AGRICULTURAL MANAGEMENT The management of physical space, in all its forms, can benefit from the inclusion of techniques and procedures based on new information technologies. The aim of this work is to investigate the possibilities of applying geographic information systems in order to contribute to some aspects of the management of urban and agricultural activities. Although it's not a new technology, because its principles have been used for a long time, GIS and GIS are tools that are currently widely available on the internet, even with free software options and free information. This work integrates field research, through limnological analyses, with the processing of Landsat5 satellite images and the use of the Georeferenced Information Processing System (SPRING). In addition to the fact that these tools have proved effective in visualising potentially polluting points, it is believed that this management model using information from the urban and rural areas of the municipality of Itaqui, in Rio Grande do Sul, could lead to new ideas and applications to help decision-makers. As a university, UNIPAMPA has the potential to work closely with the community and municipal authorities, where the tools presented can serve as support for visualising social, economic and environmental problems. Keywords: Limnological analyses, spring, low-cost research. 11 2.1 Remote Sensing (RS) Numerous definitions have emerged for the term remote sensing, but the definition given by Jensen (2011) is the one I like best. It may be considered minimal, but it is the most focussed on the legitimate functions of remote sensing: "Remote Sensing is the recording of information in the ultraviolet, visible, infrared and microwave regions of the electromagnetic spectrum, without contact, using instruments such as cameras, scanners, lasers, linear and/or matrix devices located on platforms such as aircraft or satellites, and analysing the information acquired by visual means or digital image processing." RS should be seen as a tool or technique similar to maths, as its sensors are able to measure the amount of electromagnetic energy emanating from an object or target on the Earth's surface, and data can then be extracted using algorithms based on maths and statistics (JENSEN, 2011). RS can be used for different purposes according to the interests of each researcher. The interesting thing about this tool is knowing how to use and interpret it according to the end product you want to obtain. For such analyses, it is necessary to know how radiation interacts with the environment to be studied, as detected by sensors. Therefore, in addition to understanding the behaviour and functions of the sensors, it is necessary to understand what happens when the radiation hits the environment and subsequently reaches the sensors; this interaction must be analysed and understood by the researcher. There are a large number of satellites orbiting in space; each satellite has sensors on board, which are devices that record the energy reflected or emitted by objects on the earth's surface. This energy is transformed into electrical signals, which are transmitted to the receiving stations that make up the ground segment on Earth. The signals are then processed and transformed into images. To capture data from the earth's surface, the sensors on board the satellites are always pointed towards the earth (FLORENZANO, 2008). 12 The information resulting from remote sensors can achieve a high degree of specificity, covering properties such as vegetation type, lengths, areas, slopes and others. However, it is the electromagnetic energy reflected or emitted by the targets that is used to deduce these real properties. This requires the use of visual or digital image processing techniques. SR information is transmitted via electromagnetic radiation, which can be characterised by wavelength (), usually in nanometres (nm) or micrometres (um), or frequency (v), in Hz, using the expression: v=c/ . The intervals of wavelengths (or frequencies) detected by a given sensor constitute the bands (MOREIRA, 2011). Therefore, the first information that researchers should seek before starting their research in remote sensing is the characteristics of the satellite, the type of sensor they are going to use, what combination of bands they are going to use in order to be successful and achieve the expected objectives. 2.2 Geographic Information System (GIS) All human activities and natural phenomena take place somewhere, in other words, they have a location in space, a positioning. This positioning can be given by latitude, longitude and altitude on the ground. Nowadays, mainly due to the creation and availability of the global positioning system and the use of new Information and Communication Technologies (ICTs), and geotechnologies as a whole, we are becoming more and more familiar with this connected and knowledgeable trend towards different geographical places and spaces. Google Earth (GE), for example, has revolutionised and popularised GIS, as it has made it possible to visualise space and different places on our planet globally. We can consider GE to be a great GIS, because in addition to SR data, satellite images and aerial images at different scales, it also allows data to be entered, such as photos, maps, charts, lines, points and polygons, and it also allows routes to be drawn linking two or more points on the ground. Topographic data such as that from the SRTM mission (Radar Shuttle Topographic Mission) is also used, making it possible to visualise in 13 three dimensions both natural (hills) and artificial (buildings). We are in the era of disciplined information management. In general, GIS can be used as a tool for planning, managing and optimising natural and financial resources, as it enables a vision and integration of data from different sources, transforming it into information that can be applied for various purposes, such as environmental, social, agricultural and even health purposes. GIS is generally accepted as a technology that has the necessary tools to carry out analyses with spatial data, offering alternatives for understanding the occupation and use of the physical environment, thus making up the universe of geotechnologies, alongside digital image processing (DIP) and geostatistics. According to Câmara (1995), GIS captures, models, manipulates, retrieves, queries, analyses and presents geographically referenced data. GIS technology can bring enormous benefits due to its ability to manipulate spatial information accurately, quickly and sophisticatedly. GIS is a relatively new technology, and in the last 30 years there has been a very rapid growth in the theoretical, technological and organisational aspects of communication theory. The diversity of use of GIS has allowed heterogeneous groups to formulate various concepts of GIS. The definition of MIS has also been hampered by the academic debate surrounding the main focus of MIS. Some definitions of GIS are presented below: More reductive: "TOOL with advanced geographic MODELLING capabilities" (Koshkarion, 1989) More comprehensive: "a set of automated functions that provide professionals with advanced capabilities and georeferenced information visualisation" (Azemoy et al., 1981). Context of use: "A SET OF PROCEDURES, manual or automated, used to store and manipulate information" (Aeronoff, 1989). Depending on the problem to be solved: "DECISION SUPPORT SYSTEM involving the integration of georeferenced information in a problem-solving environment" (Cowen, 1988) 14 We can therefore say that GIS is a system specialised in modifying and analysing geographic (geospatial) information. Figure 2.1 shows schematically how a GIS works. For a GIS to function properly, it will need hardware and software, where the user will enter geographically referenced information, such as satellite images, radar, drones, topographic surveys, tables, etc. In this way, the GIS will be able to manipulate, consult, visualise, archive and model geographic data and transform it into useful information for decisionmaking. Figure 2.1. Basic components of a Geographic Information System. 2.2.1. History of GIS If we think of a system that makes it possible to record geographical information, then we have the first evolutionary records of GIS in 35,000 BC, since human beings were already using illustrations on rocks to record and describe migration routes. Later, in the 1st century, mapping began to be used for urban planning in the Roman Empire. But it wasn't until the 18th century that European civilisation began systematic surveys. And the first maps with a reasonable level of precision appeared. This was due to the emergence of cartography as a science. At that time, cartography was defined as the art and science of producing maps. Today, we have added technology to this concept, and maps are both scientific documents and works of art. 15 In the 19th century, thematic maps were incorporated into London life between 1848 and 1854. In 1848, a fairly standardised world atlas was published; in 1854, a map was published that effectively solved a problem of endemic diseases, which was critical as London was suffering from a cholera epidemic at the time. Dr John Snow represented the cases of the cholera epidemic geographically, i.e. in a specialised way, and the government bodies took decisions that made it possible to deal with it in an orderly way and with a well-defined spatial vision, and it was possible to reverse the critical situation in which the population of London found itself. In the 20th century, the first GIS using computer resources appeared (1962). Developed in Canada (Canadian Geographic Information System - CGIS) and designed to serve more than one specific application, it stored maps in digital format It was used to map land use in Canada. The CGIS was capable of storing and retrieving data, reclassifying, changing scale of presentation, overlap between polygons and statistical reports. The cornerstone of GIS was finally laid! Over the course of the 20th century, GISs have improved and incorporated more and more technologies. Using platform configuration, using networked PCs, managing vector and raster data, creating Digital Elevation Models (DEM) and Numerical Terrain Models (NTM), analysing data mathematically using classical statistics, as well as using geostatistics, object-oriented databases, artificial intelligence and analysis in three dimensions. However, along the path of the evolution and use of GIS, there have been some misunderstandings. For example, in 1966, NY Governor Nelson Rockfeller invested in the LUNR System, which cost more than $750,000.00. With the aim of making an inventory of natural resources, the project lasted 3 years (1967-1970) and the database with 130 categories of land use quickly became obsolete. This led to frustration and even a delay in Canada resuming its investments in GIS technology. 16 2.2.1. Care with GIS It is important to emphasise that the use of GIS does not guarantee certainty and security that the final product corresponds to the correct solution alternatives. If there is no quality control of the database. If the database is inaccurate and/or full of errors, the final map may be an extremely colourful map, capable of impressing, but in practice it will be meaningless and unsuitable for use. According to Silva (2003), the GIS does not behave like a "magic wand". If the original data set is made up of "rubbish", the product derived from the operations carried out in the GIS environment will be organised "rubbish". If the original data is inaccurate, the end product will be organised "rubbish". 2.2.3. GIS in Environmental Monitoring Satellite images show us a stunningly beautiful planet. Yuri Gagarin, the first astronaut to see the Earth from space, said: "The blue planet!". This impressive beauty of our planet comes from our oceans, seas, polar ice caps, large rivers and lakes, clouds, all of which remind us of the presence of water on the planet. Earth is undoubtedly the water planet. This is the only planet in the solar system where water is found in its different states: solid, gaseous and liquid (TUNDISI and TUNDISI, 2011). Water is essential for life on Earth. Man and all living organisms depend on water to survive. Changes in the physical state of water in the hydrological cycle are essential and influence the processes that take place on the Earth's surface, including the development and maintenance of life. Over the years, the multiple use of water has increased and permanent withdrawals for various purposes have reduced water availability and produced degradation and pollution. The degradation of aquatic systems and water quality produces a series of environmental and social impacts. Another problem is the excessive discharge of untreated sewage and agricultural waste, which causes eutrophication of lakes, rivers, 17 dams and reservoirs, as well as increasing the cost of the treatment needed to produce potable water (TUNDISI, 2003). An aquatic system, although made up of a considerable body of water, can suffer fluctuations in water quality as a result of permanent or seasonal changes in land use, as in the case of agriculture, where crops are grown at a certain time of year. It is known that the dynamics and use of the land are constantly changing, which modifies its chemical and physical characteristics, causing a series of interactions between environments. Monitoring, planning and interpreting these dynamics are demands of government and research organisations. Understanding the dynamics and monitoring of land use makes it possible to better visualise problems and speed up decision-making. For these reasons, the integration of rangeland data into GIS is becoming increasingly desirable, and it is difficult to visualise them in isolation. According to Blashke et al, (2007), rapid environmental changes can no longer be recorded in a way that satisfies growing demands by means of conventional imaging. For sustainable decision-making or effective conflict management, there is a need for a database that represents an image of the current situation. It is therefore necessary to develop an object-related database and make it available to bodies such as town halls and secretariats, where they can be helped by the ease of observing weak points in the system, and once created, GISs can be supported by the town halls themselves, thus helping to make decisions quickly and effectively. The term Geographic Information System (GIS) refers to systems that perform computerised processing of geographic data. A GIS stores the geometry and attributes of data that is georeferenced, i.e. located on the earth's surface and in any cartographic projection. The main characteristic of the data processed in geoprocessing is the diversity of generating sources and formats presented. There are at least three main ways of using a GIS: 1. as a tool for producing maps; 2. as a support for spatial analysis of phenomena; or 3. as a geographic database with the functions of storing and retrieving spatial information (CÂMARA & MEDEIROS, 1998). 18 This work uses GIS and RS in the three forms described by Câmara and Medeiros (1998). 2.2.4 Spring The SPRING product (Sistema de Processamento de Informações Georreferenciadas) is a geographic database development software, which according to Lopes (2009) has the following characteristics: 1°)It operates as a geographic database without borders and supports a large volume of data (without scale, projection or zone limitations), maintaining the identity of geographic objects throughout the entire database; 2°)Manages both vector and raster data, and integrates remote sensing data into a GIS; 3°)Provides a user-friendly and powerful working environment through the combination of menus and windows with an easily programmable spatial language (LEGAL - Spatial Language for Algebraic Geoprocessing); 4°)It achieves complete scalability, i.e. it is able to operate with all its functionality in environments ranging from microcomputers to high-performance RISC workstations. SPRING is based on an object-orientated data model, from which its menu interface and the Spatial Language for Algebraic Geoprocessing (LEGAL) are derived. Innovative algorithms, such as those used for spatial indexing, image segmentation and the generation of triangular grids, guarantee adequate performance for the most varied applications. Designed for the RISC platform and with a standard OSF Motif graphic interface, SPRING has a highly interactive and user-friendly interface, as well as online documentation, both written in Portuguese, making it extremely easy to use and support. Based on these characteristics, SPRING has proved to be a highly attractive option in the area of geoprocessing, as it is now considered public domain software and can be purchased over the Internet ("http://www.dpi.inpe.br/spring"), simply by registering on INPE's own website. 19 2.2.4.1 Spring's relational mode Spring's relational mode works in an integrated way with the Geographic Information System. This integration is done through a RDBMS (Relational Database Management System). The RDBMS can also be called a "geo-relational" model, where the spatial and descriptive components of the geographic object are stored separately. Conventional attributes are stored in the database (in the form of tables) and spatial data is handled by a dedicated system. The connection is made by object identifiers (id). To retrieve an object, the two subsystems must be searched and the answer is a composite of the results (LOPES, 2009). This architecture is illustrated in Figure 2.2. Figure 1.2. Representation of a query in a relational database management system. Source: Lopes (2009). The main objectives of a DBMS are: 1°) make integrated data available to a wide variety of users through user-friendly interfaces; 2°) guarantee data privacy through security measures within the system; 3°) allow data to be shared in an organised way, acting as a mediator between applications and the database, thus guaranteeing control and reducing the level of redundancy and managing concurrent access; and 4°) enabling data independence in the sense of sparing the user physical details and organisation and storage (MEDEIROS & PIRES, 1998). Therefore, once structured, Spring's relational mode can be passed on to town halls and departments, such as the Department of the Environment and the Department of Agriculture. Bodies like these can then feed this database and rely on localised information, facilitating planning, management and decision-making at municipal 20 level. 2.3 Environmental Monitoring Remote sensing makes it possible to monitor agricultural and aquatic systems on a regular basis. According to Novo et al. (2007) "remote sensing is a tool that makes it possible to acquire information for spatial and temporal analysis of aquatic environments, integrating watershed and drainage". As irrigated rice fields have vegetation under water, it is important to understand these dynamics and the behaviour of reflected energy. The Western Frontier of RS has abundant quantities of water, whether through dams, artificial dams, natural reservoirs, rivers, streams or groundwater, which are used for various purposes, such as animal watering, cattle herds, which are characteristic of the region, as well as for agriculture, specifically irrigated rice, which has become the core of the Western Frontier, which is no different for the municipality of Itaqui, the focus of this study. This region is characterised by the cultivation of large areas of rice, where the taipas system predominates. Irrigation in the vast majority of crops is poorly planned, although the water is controlled. Flooding occurs from higher levels, with the water being conveyed by gravity, maintaining a water layer through the slabs built with a difference in level of 5 to 10 cm. The volume of water required by rice irrigated by flooding the soil is the sum of the water needed to saturate the soil, form a sheet, compensate for evapotranspiration and replace losses due to percolation and lateral flow. When calculating a crop's water needs, losses in the irrigation channels must also be included. The amount depends mainly on climatic conditions, crop management, the physical characteristics of the soil, the size and lining of the channels, the crop cycle, the location of the source and the depth of the water table. To meet rice's water needs, it is estimated that an average volume of water of 8,000 to 10,000 m3 /ha (flow rate of 1.0 to 1.4 L/s.ha) is currently being used, for an 27 CHAPTER 4 RESULTS AND DISCUSSION The results and discussions relating to this work are divided into four main parts: 1) description of the study area; 2) land use; and 3) results of the integration of limnological data and GIS. The Geographic Information System created is characterised by the stratification of information into levels such as: 1) images of the region; 2) land use; 3) hydrography, sewage network, road network; 4) sampling points; 5) industrial and commercial activity points; 6) history of collection points; 7) water conductivity; 8) pH; 9) dissolved oxygen; 10) redox potential; 11) microbiology, chlorides; and 12) organic matter (COD). This allows flexible combinations and efficient access to any geographical location in the database. Relationships between the entities contained in the information plans were also inferred by investigating the joint occurrence of conditions or locations represented in the digital model of the environment, in SPRING's relational query module. 4.1 Description of the study area Any management system must begin with a precise description of the study area, included within a georeferenced system. This construction was carried out using Landsat 5 satellite images as a basis, duly georeferenced using the screen registration procedure based on NASA's Geocover Mosaic images. From this, using the vector editing procedure, the region's hydrographic network was drawn, as well as the roads and main access routes. To do this, the "micro-watersheds" thematic class was created, along with its categories: boundary; hydrography; riparian forest and roads. Once the vector editing is complete, Spring allows you to calculate the perimeter and areas of the polygons for each class created, and the software will automatically report the measurements of each vectorised class. As a result, the operator now has a source of 28 numerical information on the main elements that characterise the region. In the case of this study, as well as being located largely within the municipality of Itaqui, the focus of this research, the chosen area also comprises a riverside portion of the province of Corrientes in Argentina, where the urban areas of the departments of Alvear and La Cruz can be seen (Figure 4.1). As a result of this editing work, it was possible to assess that the study area has a total area of 387 Km2 , with 14.9 Km2 of water surface, 7.6 Km2 of riparian forest and 37.6 Km2 of urban areas. Figure 4.1. Map characterising the study area. Looking at the layout of the irrigation dams, it's clear that the integration of urban activities with rural farming activities is one of the main reasons for this. he outstanding characteristic of these micro-basins is that they are privileged areas in terms of water resources, as they have a large river like the Uruguay and smaller tributaries like the Cambaí Stream, Chocolate Stream, Sanga Olaria and Arroio da Cruz. Clearly visualising the water connections allows managers to better understand the implications of using water resources. It is also worth remembering that the region 29 lies on top of the Guarani Aquifer, which is the largest transboundary freshwater source in the world. Extensive livestock farming, pig breeding in technologised farms and irrigated rice production are all part of the region's productive arrangements, and it is possible to include any facilities deemed relevant on the map by vector editing, preferably in specific information layers for this purpose. This information can be important for discovering, for example, point sources of pollution that may be occurring in the region. Another important piece of information to consider has to do with soil characteristics. To draw up this map, general surveys can be used as a first approximation, such as the one carried out by the RADAM Brazil project, on a scale of 1/250000, for the state of Rio Grande do Sul; this material can be found on the website <http://www.fzb.rs.gov.br/novidades/images/07_solos_unidades_150.pdf>. Using vector editing with the support of the aforementioned map, polygons were created for each type of soil. As a result, we have the map in FIGURE 4.2, where we can see that the predominant soil is Luvissolos (74%), followed by Chernossolo (16%), Planossolo (4%), Cambissolo (2%), Gleissolo (0.9%), with 3.1% remaining in urban areas, which in Figure 4.2 are represented as rocky outcrops. These results were obtained using the vector layer measurement tool. 30 Figure 4.2. Soil classification map of the study area. It is clear from the information presented that the study area has abundant amounts of water, whether through dams, artificial dams, natural reservoirs, rivers, streams or groundwater, as well as having characteristics and types of soil suitable for agricultural production. With these two characteristics combined, the high production potential of this region is clear. The study area is being used for various purposes, such as animal watering, for cattle herds, which are characteristic of the region, as well as being used for agriculture, specifically irrigated rice, which has become its core. There is also an important potential for tourism associated with fishing, water sports and contact with nature, due to the important presence of riparian forest in the region. 4.2 Land use Using free images from the TM sensor of the Landsat5 satellite, processed using remote sensing techniques, it was possible to create maps that record the land use situation in the region on the date the satellite passed over. Each pixel of the image contains information related to the position of the point and the radiation characteristics 31 (reflectance) of the terrestrial targets imaged by the sensor. However, the raw image is difficult to interpret for a non-specialised information user. For this reason, a procedure called "principal component analysis" was applied, which produces a "highlight" of the characteristics of each target (Figure 4.3). This analysis is available in the Spring application and can be generated automatically. The "supervised classification by regions" procedure was then carried out to transform the original image into a map that can be easily interpreted by most users. This result is shown in Figure 4.4 as a thematic map. Figure 4.1. Landsat5 satellite image of bands 5,4,3 in RGB composition, before and after processing using principal component analysis. In addition to the visual potential, the procedure allows for numerical assessments of land use in the study area, since the programme issues a report with the areas occupied by each class chosen in the classification. Using this potential, a comparison was made between the land use results obtained from the 1991 and 2011 images. These data correspond to the 1990 and 2010 harvests and serve to provide an indication of trends in the region, which exemplifies a typical situation for obtaining information for land management. Figure 4.4 shows the growth in crop areas over the 20-year period. The area was divided into six classes: a) rice crops; b) fallow area; c) native grassland; d) riparian forest; e) urbanised area; and f) bodies of water. Three classes 32 grew: rice plantations increased by 14%, riparian forest by 11% and urbanised areas by 3%. The other classes decreased by 1%, water bodies by 8%, fallow land by 8% and native grassland by 20%, as shown in Table 4.1. Table 4 1Comparisons of the classes analysed between 1991 and 2011. 1991 2011 1991 2011 COMPARISON (2011-1991) Km2 % Km2 % BODIES OF WATER 44 39 11 10 -5 -1 RICE FARMING 50 105 13 27 55 14 URBANISED AREA 18 31 5 8 13 3 POWDER AREA 119 89 31 23 -30 -8 NATIVE FIELD 126 50 32 13 -76 -20 CILIARY FOREST 30 73 8 19 43 11 TOTAL 388 387 100 100 -1 0 This information confirms the fact that the Western Frontier of Rio Grande do Sul, especially the municipality of Itaqui, where the study area is located, is characterised by the cultivation of large areas of irrigated rice, where the system of cultivation with terraced fields predominates. Irrigation in the vast majority of crops is poorly planned, although the water is controlled. Flooding occurs from higher levels, with the water being conveyed by gravity, maintaining a water table through slabs built with a difference in level of between 5 and 10 cm. These characteristics provide ample room for improvement in water management systems that can lead to more efficient use of water. This requires information such as that presented here, which can be obtained in an economically viable way and presented in the short term after the satellite passes over. An aspect as important as the volume of water required by rice irrigated by flooding the soil is the sum of the water needed to saturate the soil, form a sheet, compensate for evapotranspiration and replace losses through percolation and lateral flow. When calculating a crop's water needs, losses in the irrigation channels must also be included. Thus, the amount of water used depends mainly on climatic conditions, crop management, the physical characteristics of the soil, the dimensions and lining of the channels, the crop cycle, the location of the source and the depth of the water table. All of these factors can be represented and included in mathematical models, which will make it possible to forecast requirements in an efficient and reliable way. These models need to be fed with input data on land use such as those presented here. 33 Another aspect that can be analysed on the basis of this information is the availability of natural resources and the suitability of areas, in order to support discussions on the management and optimisation of natural resources, and their effect on production and land productivity. With a history of land use in the region, managers can make decisions about encouraging certain activities in preference to others. For comparison purposes, we will use data obtained from the IBGE Automatic Recovery System (SIDRA), which shows the growth in areas planted with irrigated rice, as well as the increase in productivity in the municipality of Itaqui (Table 4.2). In 20 years, from 1990 to 2010, there was an increase of 15,550 hectares in the area planted, which corresponds to a 31% increase, as well as 236,250 tonnes more produced in this period. The data obtained by remote sensing for the micro-watersheds under study reflect a 14 per cent increase in these watersheds, which confirms the increase in crops even in the vicinity of the urban area. With information like this, crop forecasts can be prepared for municipal managers and farmers. Table 4 2 - History of rice-growing areas in 1990 and 2010. PLANTED AREA AREA HARVESTED QUANTITY PRODUCED YEAR HECTARE TONS 1990 50000 50000 255000 2010 65550 62240 420742 Finally, the information on the map in Figure 4 can make a significant contribution to interpreting the results of physicochemical and microbiological analyses of surface water that are part of an environmental monitoring system in the region, as will be discussed in the next section. It is also important to consider that the data presented is associated with a certain amount of uncertainty, as all procedures result in some degree of error, both instrumental and due to operator manipulation. However, when working in comparative terms, with several sampling points, the data presented becomes more relevant, since the correlations between the variables make it possible to detect gross errors or real changes in the measured values. With regard to images, they also have some degree of uncertainty that can be calculated using error propagation procedures. A good practice is for those images taken as a reference from the database to be 34 processed by the same operator in order to maintain greater control over the procedures carried out. 20-YEAR LAND USE HISTORY Figure 2.4. Land use in 1991 and 2011. 4.3 Results of integrating limnological data and GIS The integration of limnological data with remote sensing products and the creation of a GIS and relational database enabled a better understanding of the effects of anthropogenic activities on the aquatic ecosystems analysed. The results obtained from the statistical, physicochemical and microbiological analyses of the water will be presented below, as well as an example of how to use and consult the Spring relational database. 4.3.1 Relational database The results of the limnological analyses integrated with the GIS made it possible to create a geo-relational database. This database facilitates consultation and understanding of the dynamics of water quality and identifies potentially polluting points. Figure 4.5 shows the query to this database, where we have the table with the name of each point and the value of each analysis carried out. Just click on the point to be 35 analysed and it will be selected in green, or if any value in the table catches your eye, just click on the corresponding line in the table and the point will be geographically located on the map and also selected in green. This query was made on top of the thematic map referring to Chloride data for the month of November 2011, but it is possible to make queries with images of the area as shown in Figure 4.6. To check variables that you want to establish a standard for, for example, values that do not comply with the CONAMA resolution. In the case of chlorides, the established limits are up to 250 mg/L. So all you have to do is select the attribute query in the relational database, where you have to indicate the established parameters, as shown in Figure 4.7. After applying the query, it can be seen that for the month of November 2011 only the Hipódromo point had chloride concentrations above the 250mg/L established by legislation. This type of query can be applied to all months and all variables, but in this work the aim is to present the structure of a reliable GIS, which is why various water quality analyses are presented in the form of correlations. This model can easily be passed on to municipal public bodies and rural producers who are interested in analysing the environment, thus adding value to the products produced and detecting weak points in the system for efficient decision-making. 36 Figure 4 3Consultation of the geo-relational database - chloride map. Figure 4 4Consultation of the geo-relational database - image November 2011. 43 CHAPTER 6 REFERENCES APPA, AWWA, WPCF, Métodos Normalizados para elAnálisis de Aguas Potables y Residuales, Editorial Diaz de Santos, 1a . Ed, 1992 BLASCHKE, T; KUX, H. Remote Sensing and Advanced Geographic Information Systems. 2007. BRAZIL. DECREE No 97.632, OF 10 APRIL 1989. Available at: <https://www.planalto.gov.br/ccivil_03/decreto/1980-1989/d97632.htm>. 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