GIS as a Support to Soil Mapping in Southern Brazil
9.3 Results and Discussion
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mapping units drawn on the field were scanned and georeferenced based on an UTM grid. Then the limits of the mapping units were digitized manually on-screen, us-ing the georeferenced field map sheets as a basis, producus-ing vector line features (See Fig. 9.3).
Fig. 9.3 The topographic map sheet resulting from the field work and the mapping units after digitized (See also Plate 10 in the Colour Plate Section)
Vector lines extraction was performed in a continuous way, alternating the back-drops but capturing the limits of the mapping units in a unique layer without the map sheet’s divisions. After the topologic structuring of the limits of mapping units, soil polygons were built and associated with a set of attributes, such as their area (in hectares), order, suborder, group and subgroup, acronym of the mapping unit, among other features. The objective of this procedure was to guarantee the consis-tence of the attributes and the spatial contiguity of the polygons among contiguous sheets, in order to generate a vector polygon file of soil types with the continuous coverage of the 20 map sheets.
The last step involved the preparation of the material for printing. The area of each of the 20 map sheets was clipped and used to generate a printing layout with the desired layers, including soil layer, complementary layers (hydrographic network, road system, and urbanized areas) and ancillary information (legend, map grid, text layer, etc.). In order to keep a regional context, the complete legend of the whole region was used in each sheet legend, turning grey the ones absent at the respective map sheet. In this printing layout the soil mapping units were colored using the colors defined by EMBRAPA (2006). Finally, in order to include relief information with an easier perception and comprehension than with simple contour lines, the colored soil polygons were combined with a DEM analytical hill shading.
9 GIS as a Support to Soil Mapping in Southern Brazil 109 mapping units were represented by 1,626 polygons divided in 7 soil groups: 24 Cambissolos (Inceptisols); 1 Gleissolos (Entisols); 1 Latossolos (Oxisols); 7 Chernossolos (Molisols); 15 Nitossolos (Alfisols); 8 Argissolos (Ultisols), and 5 Neossolos (Entisols).
The use of topographic maps and technologies like GPS and GIS in this study facilitated soil mapping in many aspects: support to field work activities, georefer-encing of sampling information, drawing of mapping units, soil data check-up and correction, maintenance of spatial consistency and storage of soil attributes, agility and uniformity at the generation of the printing layout.
The integration of field information with the data from the cartographic base in a GIS environment allowed the production of a continuous and georeferenced digitized soil map for the whole region, covering a surface that corresponds to 20 map sheets in scale 1:50,000 (See Fig. 9.4). The output is a vector polygon file that guarantees the consistence of the attributes and the spatial contiguity of the polygons among contiguous map sheets. Moreover, this file is associated with tables that contain the main attributes about the mapping units.
Fig. 9.4 Continuous georeferenced digitized soil map of the Serra Ga´ucha region (See also Plate 11 in the Colour Plate Section)
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Concerning the product for printing, the fusion of the soil polygon information with the hill shading derived from a DEM has a pleasant effect, showing the relief forms through the shading effect. The perception of the position of a given soil mapping unit on the relief is direct, making the interpretation and use of the map easier (See Fig. 9.5). The usual way of introducing topography into the maps is through contour lines, but in steep relief, however, contour lines may excessively congest informa-tion.
Fig. 9.5 Soil map with conventional soil information, analytical DEM hill shading and fusion of the hill shading with the conventional soil map (See also Plate 12 in the Colour Plate Section)
Printing of the final material was done in a fast and uniform way and presented excellent visualization. Moreover, it was done at the same time that the mapping process was concluded, which facilitated check-ups and corrections. The fusion of the soil map with the hill shading derived from a DEM resulted in a graphic material that highlights relief forms through the hill shading. The perception of the position of the mapping units in the landscape is direct, making the interpretation and use of the maps easier.
The final digitized soil map structured in GIS allows one to query physical and chemical characteristics of soils in a given place or to select places where soils have a set of desired characteristics. It also allows to quantify the surfaces and to cross soil information with other georeferenced data of the region. There-fore, the soil map presents a great potential of application for many purposes, like
9 GIS as a Support to Soil Mapping in Southern Brazil 111 zoning, diagnosis, suitability evaluation, among others. For instance, the product can be used as reference or as ancillary information for future studies on digital soil mapping, giving support to assess soil parameters and to validate modeling results.
Soil can be predicted through several approaches, for areas previously mapped, or for new areas. Among others, prediction can be done from soil attributes at the same location or at neighbouring locations from itself, from other soil attributes and from environmental attributes. In other words, real soil observations with a good density will ever be essential to develop, to evaluate or to fit the models (See also Chapter 17). Although conventional soil maps are essentially static prod-ucts, when structured in GIS they can provide useful information for analysis or interpretation aiming to soil prediction. This is true for soil polygons as well as for the field sample collection points, which data have higher confidence. So, we should not stop with traditional surveys, but improve methodologies attempting to optimize data collection procedures and to make results more consistent and easily available.