Fuzzy logic and differences between interpretive soil maps

Improving digital soil mapping in Bogor, Indonesia using parent material information

For developing countries, detailed soil resource data and maps are essential in land-use planning. Unfortunately, obtaining detailed soil data for mapping is expensive. Detailed soil studies and mapping in developing countries often use the grid method. In addition to being time-consuming, the grid method needs a lot of sample points and surveyors. Geomorphometry can be a less expensive alternative for detailed soil mapping. Geomorphometry uses computationally measured terrain characteristics to describe other hard-to-measure terrain and soil properties. In our study, landform arrangements and slopes were analyzed together to create a map of soil pH. Bompon watershed, Indonesia, was used as a case study. Soil mapping units with potentially similar soil pH were created based on a classification system of the two geomorphometric parameters. Soil samples were taken from each of the units. The samples' soil pH was measured and compared to the geomorphometric predicted result. Regression tests were performed to see the significance of geomorphometric parameters on soil pH conditions. Regression tests show that the results of p value of the four soil layers are 0.046, 0.019, 0.037, and 0.047, respectively, on a 5% confidence level. According to the test result, landform arrangements and slopes can indicate soil pH conditions in Bompon. Our estimate suggests that our geomorphometric method is cheaper than the grid method by a factor of seven. The ability to use geomorphometric parameters to describe other soil properties could enable a cheap and fast production of detailed soil maps for developing countries.

Soil Taxonomy of Croatia

Automated soil inference under fuzzy logic

Soil mapping, classification, and pedologic modeling: History and future directions

Geological and terrain attributes for predicting soil classes using pixel- and geographic object-based image analysis in the Brazilian Cerrado

There is an ever-growing need for soil maps, since detailed soil information is directly related to agricultural activities, urbanization and environmental protection. However, there is a lack of large-scale soil maps in developing tropical countries such as Brazil. Albeit there are soil maps for small areas, large regions usually have undetailed maps. Considering the importance of finding low-cost alternatives to overcome the lack of detailed soil information, the main objective of this work was to manually create a local soil map and extrapolate it to similar larger areas that lack detailed soil information. The Anhumas River Basin, in the municipality of Itajubá, southeast Brazil, was manually mapped and this map was used to predict soils distribution for the entire municipality. First, the prediction model was tested in the same basin and provided sufficient results, achieving 67% global accuracy and 0.62 Kappa coefficient. Second, the resulting map was used together with the soil map of the larger José Pereira Basin to map the entire municipality, achieving 54% global accuracy and 0.40 Kappa coefficient. Low resolution parent material information was found to confuse models; maps showed better results when this variable was removed. The Minas Gerais soil map presents general mapping units only for the Acrisol class and its associations with other soil classes in the area. The soil map predicted by this work identified more soil classes. Mapping representative areas and extrapolating these maps to larger similar areas constitute a promising alternative to overcome the lack of detailed soil maps.

To remain relevant in a changing world, soil survey and soil science need to look beyond the traditional to urban applications. Urban and suburban areas generally have the highest population densities and land values and arguably the greatest need for the ecosystem services that soil can provide. The urban environment poses a distinctive set of challenges for soil scientists. The description, classification, and mapping of soils in human-altered and human-transported materials is the charge of the field soil scientist, extending above and beyond the urban setting. Environmental professionals, planners, educators, and students in urban areas are often unfamiliar with soil survey and soil science in general. This paper discusses a successful approach to establishing a soil survey or soil science program in the urban community. For over 20 years, the United States Department of Agriculture Natural Resources Conservation Service (USDA-NRCS) and the New York City Soil and Water Conservation District (NYCSWCD) have conducted a soil survey program in New York City, producing multiple soil surveys at different scales, culminating in a 1:12000 scale survey posted on the Web Soil Survey site in 2014. The multifaceted program has also included onsite investigations, research projects, and an education and outreach component including lectures, training sessions, and workshops, as well as volunteer and internship opportunities. New and innovative approaches in soil survey methods, product design and delivery, and outreach have been used, as appropriate, to best fit the needs of the urban environment. The NYC Soil Survey program has built a succession of partnerships with government agencies, colleges, universities, and community and commercial groups, resulting in a variety of products and services to meet local soil science needs. Over time, the program has successfully been able to raise awareness of soil science in the urban environment, establish a USDA-NRCS presence in the City, and provide useful soil information to an underserved segment of the population. Making the local connection, listening to local needs, offering a multifaceted and wide array of soil science-related services, exploring innovative approaches, and creating partnerships have shown to be successful components of establishing a soil survey program in the urban environment.

Detailed soil maps are an essential tool for water resource, land management and agricultural planning. However, due to different soil processes and variation in geology, soil textural properties vary in space at different scales challenging the development of accurate soil maps. Despite the advances in sampling and efforts to produce accurate maps, uncertainties remain at many scales. Therefore, by implementing error evaluation methods such as comparing predictions and observations, it is possible to understand the performance of digital soil maps. The aim of this work was to obtain the uncertainties of soil categories from a Danish national soil map (AC map) based on measured contents of soil texture and soil organic matter. The AC maps describes the horizon characteristics (soil texture, soil organic matter, soil horizon depths and bulk density) at three depths corresponding to the A (0-30 cm), B (30-70 cm), and C (70-120 cm) horizon. In addition, it combines information on soil classification (soil texture), geology at a depth of ca. 1.5 meters as well as the national geological region. The map has a spatial resolution of 250 meters in the A and B horizons and 500 meters in the C horizon. The analysis is based on 38,000 textural points for the A horizon, 7,000 points for the B horizon, and almost 1,700 points for the C horizon. Considering mean and medians of the content of clay and organic matter together with the observed data, the AC map was validated. The uncertainties of the AC map, statistical correlation coefficient (R2), root mean square error (RMSE), and the Nash-Sutcliffe efficiency (NSE) were used. The results showed that the AC map has an acceptable performance when predicting the clay content in the A horizon (R2 = 0.97, RMSE = 1.15%, and NSE = 0.91), B horizon (R2 = 0.95, RMSE = 1.85%, and NSE= 0.8), and C horizon (R2 = 0.74, RMSE = 5.32%, and NSE = 0.41, respectively. The performance of the prediction of organic matter content in the A horizon (R2 = 0.84, RMSE = 0.39%, and NSE = 0.65) and B horizon (R2 = 0.66, RMSE = 0.44%, and NSE = 0.32) horizons was acceptable as well. Also, the result of validation showed that the highest residual errors of AC maps for clay content and organic matter content has been related loamy (>15% clay) and peat soils. In conclusion, the AC maps, with its optimal accuracy especially in the A and B horizons, can be a suitable tool for use at variable scales in the analysis of crop growth and nitrate leaching modelling studies.Keywords: Soil texture, digital soil maps, soil variability, soil organic matter, clay, soil properties

Two soil mapping methodologies at different scales applied in the same area were compared in order to investigate the potential of their combined use to achieve an integrated and more accurate soil description for sustainable land use management. The two methodologies represent the main types of soil mapping systems used and still applied in soil surveys in Greece. Diomedes Botanical Garden (DBG) (Athens, Greece) was used as a study area because past cartographic data of soil survey were available. The older soil survey data were obtained via the conventional methodology extensively used over time since the beginnings of soil mapping in Greece (1977). The second mapping methodology constitutes the current soil mapping system in Greece recently used for compilation of the national soil map. The obtained cartographic and soil data resulting from the application of the two methodologies were analyzed and compared using appropriate geospatial techniques. Even though the two mapping methodologies have been performed at different mapping scales, using partially different mapping symbols and different soil classification systems, the description of the soils based on the cartographic symbols of the two methodologies presented an agreement of 63.7% while the soil classification by the two taxonomic systems namely Soil Taxonomy and World Reference Base for Soil Resources had an average coincidence of 69.5%.

Mapping Soils under Water

History of soil geography in the context of scale

Soil maps of The Netherlands

Abstract. In the last few decades, the sizable effort that has been devoted to the mechanistic, quantitative description of soil processes has been justified on the grounds that theories and models help us understand how soils function, and also predict how, e.g., they are likely to adjust in the future to environmental change. The argument, familiar to physicists, that theories uniquely determine what should be measured has rarely if ever been invoked in the soil science literature. On the contrary, to enable the classification and mapping of soil, enormous amounts of “theory-free” data have been and continue to be amassed by soil scientists. In this general context, the key objective of the present Forum article is to argue that the accumulation of more “theory-free” data, in particular to allow the application of artificial intelligence methods, is not sensible at this stage, and that the development of improved theories of soil processes is crucial, to provide guidance about the type of measurements that should be performed. Hopefully, this Forum article will stimulate a debate on this issue, and will lead to a much needed intensification of theoretical research and modelling in soil science.

Abstract: The objective of this work was to disaggregate the polygons of physiographic map units in order to individualize the soil classes in each one, representing them as simple soil map units and generating a more detailed soil map than the original one, making these data more useful for future reference. A physiographic map, on a 1:25,000 scale, of the Tarumãzinho watershed, located in the municipality of Águas Frias, in the state of Santa Catarina, Brazil, was used. For disaggregation, three geomorphometric parameters were applied: slope and landforms, both derived from the digital terrain model; and an elevation map. The boundaries of the physiographic units and the elevation, slope, and landform maps were subjected to cross tabulation to identify the existing combinations between the soil classes of each physiographic unit. Based on these combinations, rules were established to select typical areas of occurrence of each soil type in order to train a decision tree model to predict the occurrence of soil classes. The model was trained using the Weka software and was validated with a set of georeferenced soil profiles. Disaggregation enables the individualization and spatialization of soil classes and is useful in producing detailed soil maps.