Soil-Landscape Relationships in Kedah - A Study in Soil Genesis and Classification

The genesis, classification and soil-landscape relationships of three major landscapes and fourteen soils in Kedah were studied. The BRIS soils are sandy, structureless, loose, highly leached and have a very low water holding retention and cation exchange capacity (CEC). Soils on the youngest and the oldest ridges are Entisols. Spodosols are found in the middle ridges. Podzolisation is also discussed. The marine coastal plain soils are poorly drained, clayey and have a high CEC (> 20 cmol(+)kg-l soil) due to the presence 2: 1 clay minerals. Soil formation is strongly influenced by the activities of man, parent materials, physiography, climate and sea water. The plough layer and plough sole are formed by the continual addition of organic matter and wet ploughing. Desalination is accomplished by bunding and by flushing the soils with fresh irrigation water. The influence of climate is manifested by deep vertical cracks and gypsum crystals during the dry season. Profile development improves towards the hinterland due to progressively higher physiographic positions, lower ground water table, higher leaching intensity and improved drainage. Three of the soils are Entisols (Fluvaquents) because the organic carbon at 1.25 m is greater than 0.2% although they are more appropriately classified as Inceptisols (Tropaquepts) due changes in the soil colour and the structures. A Vertisol was identified. Soils of the pediplains and the P surfaces are characterised by a lateritic layer. Lateritic soils on P surfaces in eastern Kedah have larger and more angular boulders and gravels. The gravels on the pediplain proper are finer and more rounded due to more cycles of pedimentation. Etchplains and remnants of pediplains (R.O.P.) are absent in the P/P3 surfaces and are uncommon in the pediplains. Lateritic soils in northern Kedah are Ultisols and in southern and eastern Kedah, Oxisols; this being due to differences in the parent materials and climate

Random Forests Applied as a Soil Spatial Predictive Model in Arid Utah

Initial soil surveys are incomplete for large tracts of public land in the western USA. Digital soil mapping offers a quantitative approach as an alternative to traditional soil mapping. I sought to predict soil classes across an arid to semiarid watershed of western Utah by applying random forests (RF) and using environmental covariates derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and digital elevation models (DEM). Random forests are similar to classification and regression trees (CART). However, RF is doubly random. Many (e.g., 500) weak trees are grown (trained) independently because each tree is trained with a new randomly selected bootstrap sample, and a random subset of variables is used to split each node. To train and validate the RF trees, 561 soil descriptions were made in the field. An additional 111 points were added by case-based reasoning using aerial photo interpretation. As RF makes classification decisions from the mode of many independently grown trees, model uncertainty can be derived. The overall out of the bag (OOB) error was lower without weighting of classes; weighting increased the overall OOB error and the resulting output did not reflect soil-landscape relationships observed in the field. The final RF model had an OOB error of 55.2% and predicted soils on landforms consistent with soil-landscape relationships. The OOB error for individual classes typically decreased with increasing class size. In addition to the final classification, I determined the second and third most likely classification, model confidence, and the hypothetical extent of individual classes. Pixels that had high possibility of belonging to multiple soil classes were aggregated using a minimum confidence value based on limiting soil features, which is an effective and objective method of determining membership in soil map unit associations and complexes mapped at the 1:24,000 scale. Variables derived from both DEM and Landsat 7 ETM+ sources were important for predicting soil classes based on Gini and standard measures of variable importance and OOB errors from groves grown with exclusively DEM- or Landsat-derived data. Random forests was a powerful predictor of soil classes and produced outputs that facilitated further understanding of soil-landscape relationships

Soil landscape relationships on restored hillslopes on the Des Moines Lobe in central Iowa

Increased awareness of the economic and environmental values of prairie and wetland ecosystems has resulted in numerous areas of cultivated land being converted back to native ecosystems. Little information exists documenting the changes in soils and hydrology on these ecosystems. This dissertation includes studies of the hydrology, the sources of soil spatial variability, and the amount of carbon in restored prairie-wetland complexes in central Iowa. Four general groups were developed to describe soil hydromorphology along the restored prairie-wetland hillslopes. Soil morphology was correlated with hydrology for all groups on the restored hillslopes. Mean and shallowest water table depths varied for all groups. Group I included Clarion and Nicollet soils on upland prairie summits with redoximorphic features restricted to Bg and Cg horizons. Group II soils included Delft and Webster soils on upland prairie-backslopes. Group III soils included Canisteo and Delft soils on wet prairie or sedge wetland-footslopes. In general, group II and III soils had thicker A horizons than group I soils with low chroma mottles or pore linings and Bg horizons with low chroma mottles, high chroma mottles or a combination of both. Group IV soils included dominantly Okoboji soils in closed pond depressions with sola having a mixture of low chroma mottles, high chroma mottles, and low/high chroma pore linings. Soil depth explained most of the systematic variability in available K and P and total and organic C in upland prairies and wetland ecosystems. Sources of variability differed among prairie and wetland ecosystems for coarse silt, clay, bulk density, extractable cations, CEC, and pH. Highest soil microbial biomass C amounts and variability was in the upland prairie-backslopes. Slope position explained 52% of the total systematic variability in organic C. Soil depth explained for 74% of the total systematic variability in microbial biomass C. Our studies conclude (i) restored hillslopes are producing significant amounts of microbial biomass and organic C; (ii) longer term monitoring of water tables is required to better understand soil hydromorphic relationships; (iii) spatial relationships and variability attributable to site, transect, vegetation, slope position, and depth should be considered when assessing restored hillslopes

Spatial disaggregation of multi-component soil map units using legacy data and a tree-based algorithm in southern Brazil

Soil surveys often contain multi-component map units comprising two or more soil classes, whose spatial distribution within the map unit is not represented. Digital Soil Mapping tools supported by information from soil surveys make it possible to predict where these classes are located. The aim of this study was to develop a methodology to increase the detail of conventional soil maps by means of spatial disaggregation of multi-component map units and to predict the spatial location of the derived soil classes. Three digital maps of terrain variables - slope, landforms, and topographic wetness index - were correlated with the soil map and 72 georeferenced profiles from the Porto Alegre soil survey. Explicit rules that expressed regional soil-landscape relationships were formulated based on the resulting combinations. These rules were used to select typical areas of occurrence of each soil class and to train a decision tree model to predict the occurrence of individualized soil classes. Validation of the soil map predictions was conducted by comparison with available soil profiles. The soil map produced showed high agreement (80.5 % accuracy) with the soil classes observed in the soil profiles; Ultisols and Lithic Udorthents were predicted with greater accuracy. The soil variables selected in this study were suitable to represent the soil-landscape relationships, suggesting potential use in future studies. This approach developed a more detailed soil map relevant to current demands for soil information and has potential to be replicated in other areas in which data availability is similar

Soil-landscape relationships in a toposequence developed from basaltic parent material

Variations in soil attributes depend on the soil position in the landscape and drainage, erosion, and deposition processes. This study aimed to evaluate the soil physical and chemical properties, in a toposequence developed from basaltic parent material, in Batatais, São Paulo State, Brazil. The area presents a flatter topography and altitude ranging from 740 m to 610 m, in a basalt-dominated region. The experiment was carried out along a transect of 3,000 m from the top downwards. The geomorphic surfaces were identified and delimited according to topographic and stratigraphic criteria, based on detailed field investigations. Samples were collected along the representative side of profiles, for each geomorphic surface (GS) of the toposequence (GS I = top; GS II = hillside and transport foothill; GS III = shoulder and deposition foothill), totaling 142 samples. In addition, trenches were opened in the slope segments of the mapped geomorphic surfaces. The samples were analyzed for bulk density, texture, exchange bases (Ca2+, K+, and Mg2+), sum of bases, cation exchange capacity, base saturation, pH (water and KCl), SiO2, Al2O3, Fe2O3 (H2SO4 attack), free Fe oxides extracted with dithionite-citrate-bicarbonate, and poorly crystallized Fe extracted with ammonium oxalate. The results showed that soils developed from basaltic parent material presented physical and chemical attributes tied to the relief shapes. The use of multivariate statistical techniques made possible to identify three different environments, which are equivalent to the three geomorphic surfaces

A soil-landscape model for Mahurangi Forest, Northland, New Zealand

Exotic plantation forestry is an important land use of both economic and environmental significance in Northland and elsewhere in New Zealand. It is therefore of considerable importance that forestlands be managed sustainably by employing approaches such as site-specific management. The establishment of site-specific forest management practices requires information regarding the distribution of key soil properties (Turvey and Poutsma, 1980). Quantitative modelling to predict key soil properties from landscape features may be an effective approach to mapping forestlands. A study investigating the efficacy of such an approach is being conducted within Mahurangi Forest, Northland, New Zealand. As a pilot to the study, a detailed qualitative soil-landscape model was developed in order to gain a greater understanding of the soil-landscape relationships and soil pattern of the area. The qualitative soil-landscape model developed in the pilot study is presented here

Comparing three approaches of spatial disaggregation of legacy soil maps based on 1 DSMART algorithm

Enhancing the spatial resolution of pedological information is a great challenge in the field of Digital Soil 34 Mapping (DSM). Several techniques have emerged to disaggregate conventional soil maps initially 35 available at coarser spatial resolution than required for solving environmental and agricultural issues. At the 36 regional level, polygon maps represent soil cover as a tessellation of polygons defining Soil Map Units 37 (SMU), where each SMU can include one or several Soil Type Units (STU) with given proportions derived 38 from expert knowledge. Such polygon maps can be disaggregated at finer spatial resolution by machine 39 learning algorithms using the Disaggregation and Harmonisation of Soil Map Units Through Resampled 40 Classification Trees (DSMART) algorithm. This study aimed to compare three approaches of spatial 41 disaggregation of legacy soil maps based on DSMART decision trees to test the hypothesis that the 42 disaggregation of soil landscape distribution rules may improve the accuracy of the resulting soil maps. 43 Overall, two modified DSMART algorithm (DSMART with extra soil profiles, DSMART with soil 44 landscape relationships) and the original DSMART algorithm were tested. The quality of disaggregated soil 45 maps at 50 m resolution was assessed over a large study area (6,775 km2) using an external validation based 46 on independent 135 soil profiles selected by probability sampling, 755 legacy soil profiles and existing 47 detailed 1:25,000 soil maps. Pairwise comparisons were also performed, using Shannon entropy measure, 48 to spatially locate differences between disaggregated maps. The main results show that adding soil landscape 49 relationships in the disaggregation process enhances the performance of prediction of soil type distribution. 50 Considering the three most probable STU and using 135 independent soil profiles, the overall accuracy 51 measures are: 19.8 % for DSMART with expert rules against 18.1 % for the original DSMART and 16.9 % 52 for DSMART with extra soil profiles. These measures were almost twofold higher when validated using 53 3x3 windows. They achieved 28.5% for DSMART with soil landscape relationships, 25.3% and 21% for 54 original DSMART and DSMART with extra soil observations, respectively. In general, adding soil 55 landscape relationships as well as extra soil observations constraints the model to predict a specific STU 56 that can occur in specific environmental conditions. Thus, including global soil landscape expert rules in 57 the DSMART algorithm is crucial to obtain consistent soil maps with clear internal disaggregation of SMU 58 across the landscape