Digital soil mapping, downscaling and updating conventional soil maps using GIS, RS, statistics and auxiliary data

Spatial distribution of soil types and soil properties in the landscape are important in many environmental researches. Conventional soil surveys are not designed to provide the high-resolution soil information required in environmental modelling and site-specific farm management. The objectives of this study were to investigate the relationship between soil development, soil evolution in the landscape, updating legacy soil maps and pedodiversity in an arid and semi-arid region. The application of Digital Soil Mapping (DSM) techniques was investigated with a particular focus to predict soil taxonomic classes and spatial distribution of soil types by soil observations and covariate sets representative of s,c,o,r,p,a,n factors. In the first study, focus is on establishing relationships between pedodiversity and landform evolution in a 86,000 ha region in Borujen, Chaharmahal-Va-Bakhtiari Province, Central Iran. From an overview study, we could conclude that landform evolution was mainly affected by topography and its components. A second study compares various DSM-methods and a conventional soil mapping approach for soil class maps in terms of accuracy, information value and cost in central Iran. Also, the effects of different sample sizes were investigated. Our results demonstrated that in most predicted maps, in DSM approaches, the best results were obtained using the combination of terrain attributes and the geomorphology map. Furthermore, results showed that the conventional soil mapping approach was not as effective as DSM approach. In the third study, different models of the DSM approach were compared to predict the spatial distribution of some important soil properties such as clay content, soil organic carbon and calcium carbonate content. Among all studied models, the terrain attribute “elevation” is the most important variable to predict soil properties. Random forest had promising performance to predict soil organic carbon. But results revealed that all models could not predict the spatial distributions of clay content properly. The minimum area of land that can be legibly delineated in a traditional (printed) map is highly dependent upon mapping scale. For example, this area at a mapping scale of 1:24,000 is about 2.3 ha but at a mapping scale of 1:1,000,000 it is about 1000 ha. A mapping scale of 1:1,000,000 is just too coarse to show a fine-scale pattern or soil type with any degree of legibility, but finer-scale soil maps are more expensive and time-consuming to produce. Thus, spatial variation is often unavoidably obscured. The fourth study of this dissertation focuses on downscaling and updating soil map methods. Thus, the objectives were to apply supervised and unsupervised disaggregation approaches to disaggregate soil polygons of conventional soil map at a scale of 1: 1,000,000 in the selected area. Therefore, soil subgroups and great groups were selected because it is a basic taxonomic level in regional and national soil maps in Iran. In general, we conclude that DSM approach and also disaggregation approach are capable to predict soil types and properties, produce and update legacy soil maps. However, still a number of challenges need to be evaluated e.g. influence of expert knowledge on CSM approach, resolution of ancillary data, georeferenced legacy soil samples data to validate disaggregated soil maps

Developing land management units using Geospatial technologies: An agricultural application

This research develops a methodology for determining farm scale land managementunits (LMUs) using soil sampling data, high resolution digital multi-spectral imagery (DMSI) and a digital elevation model (DEM). The LMUs are zones within a paddock suitable for precision agriculture which are managed according to their productive capabilities. Soil sampling and analysis are crucial in depicting landscape characteristics, but costly. Data based on DMSI and DEM is available cheaply and at high resolution.The design and implementation of a two-stage methodology using a spatiallyweighted multivariate classification, for delineating LMUs is described. Utilising data on physical and chemical soil properties collected at 250 sampling locations within a 1780ha farm in Western Australia, the methodology initially classifies sampling points into LMUs based on a spatially weighted similarity matrix. The second stage delineates higher resolution LMU boundaries using DMSI and topographic variables derived from a DEM on a 10m grid across the study area. The method groups sample points and pixels with respect to their characteristics and their spatial relationships, thus forming contiguous, homogenous LMUs that can be adopted in precision agricultural applications. The methodology combines readily available and relatively cheap high resolution data sets with soil properties sampled at low resolution. This minimises cost while still forming LMUs at high resolution.The allocation of pixels to LMUs based on their DMSI and topographic variables has been verified. Yield differences between the LMUs have also been analysed. The results indicate the potential of the approach for precision agriculture and the importance of continued research in this area

A semi-automated approach for GIS based generation of topographic attributes for landform classification

This paper presents LANDFORM, a customized GIS application for semi-automated classification of landform elements, based on landscape parameters. Using custom commands, topographic attributes like curvature or elevation percentile were derived from a Digital Elevation Model (DEM) and used as thresholds for the classification of Crests, Flats, Depressions and Simple Slopes. With a new method, Simple Slopes were further subdivided in Upper, Mid and Lower Slopes at significant breakpoints along slope profiles. The paper discusses the results of a fuzzy set algorithm that was used to compare the similarity between the map generated by LANDFORM and the visual photo- interpretation conducted by a soil expert over the same area. The classification results can be used in applications related to precision agriculture, land degradation studies, and spatial modelling applications where landform is identified as an influential factor in the processes under study

Classification and use of landform information to increase the accuracy of land condition monitoring in Western Australian pastoral rangelands

The aim of this research was to develop land unit scale data to assist land condition monitoring projects in pastoral rangelands in Western Australia. Landforms are a major components of land units and methods were explored to include landforms as a variable in land unit predictive modelling. Three land unit prediction models were tested, a Binary Weighted Overlay (BWO), a Fuzzy Weighted Overlay (FWO) and a Positive Weights of Evidence (PWofE) model

Creating Initial Digital Soil Properties Map Of Afghanistan

Afghanistan is a country with a population of more than 31 million people and is located in south central Asia. The total arable land in the country is 12%, 5% is irrigated and the remaining 7% is rainfed. Due to the lack of available soil information, poor farming practices and land management planning severely affect the yield of agriculture products. In order to ensure sustainable agriculture and prevent land degradation problems, understanding spatial variability of soil is crucial. The overall objective of this research study was to use digital soil mapping techniques to identify the soil resources and generate a spatially explicit soil map of a 8,358,160 ha pilot study area. The specific objective is to develop a version 1 map of the six Northern provinces of Afghanistan. Several techniques such as artificial neural networks, multiple regression analysis, hybrid geostatisitcal approaches are developed to create digital soil maps. However, most of these procedures required large amounts of data to create digital soil maps at a useful resolution. Countries like Afghanistan have limited available data and it is difficult to develop the map based on the aforementioned procedures. For this research, we utilized a knowledge based approach utilizing fuzzy logic to create a version 1 map with limited point data. The fuzzy logic maps are developed based on five soil forming factors; therefore soil knowledge and soil landscape relationship is required. From the ecoregion map of the study area, we assumed that climate, organisms and time were constant and geology and topography were the deriving factors of soil formation. Therefore, the fuzzy property map of the study area was developed from geology and geomorphon composition. In ordered to capture the variability of the soil, we used those terrain attributes which have close relationship with water redistribution. geomorphon was used to classify the landforms of the study area. As a part of the fuzzy process, membership curves are required to define the soil similarity vectors. Traditionally, the membership curves are manually defined by the soil scientists based on their tacit knowledge of the soil and landscape. Even though, the manual method adequately predicts soil properties, it is time consuming and limits the application of fuzzy logic. In order to make fuzzy logic an easy and time effective approach for developing functional property maps, it is essential to use the Automatic Landform Inference Mapping (ALIM) model to automatically generate the accurate membership functions. Purdue University developed ALIM model was used for this research to define the membership functions. To generate the membership functions, ALIM model combines the digital elevation model derived terrain attributes to the soil classes. The determined membership values and soil property values were then assigned to the Zhu (1997), equation to predict the soil property maps of the pilot area. The overall results showed that predicted properties generally followed the landscape patterns but in some cases, they did not. The accuracy test of Normalized Root Mean Square Prediction Error (RMSPEr) also showed that the model prediction was insignificant. Several factors such as few data points, inaccurate coordinate location of the data points and low 90 m resolution DEM were assumed to be the reason for inaccurate assessment. Overall, the methods did produce a spatially explicit map that will be useful for the next map version. More data and a higher resolution DEM is necessary for improving the soil property predictions of the pilot area