Computing procedures for mapping soil features at sub-catchment scale

GE Rinder; E Fritsch; RW Fitzpatrick

doi:10.1071/sr9940909

Computing procedures for mapping soil features at sub-catchment scale

Soil Research ◽

10.1071/sr9940909 ◽

1994 ◽

Vol 32 (5) ◽

pp. 908 ◽

Cited By ~ 1

Author(s):

GE Rinder ◽

E Fritsch ◽

RW Fitzpatrick

Keyword(s):

Soil Maps ◽

Catchment Scale ◽

Black And White ◽

Soil Features ◽

Adobe Illustrator ◽

Soil Landscape ◽

Apple Macintosh Computer ◽

Soil Components ◽

Detailed Computer ◽

Landscape Units

Procedures for detailed mapping of a large number of soil features in small landscape units displayed in either vertical cross section (i.e. soil toposequences) or horizontal plane (i.e. soil maps) are presented. Rom hand coloured drawings that display the soil-landscape features, an Apple Macintosh Computer, with Adobe Illustrator and Adobe Patterns and Textures programs, is used to produce high quality coloured maps ready for reproduction in any form (e.g. posters, publications, slides or overhead transparencies). The first step is to produce the detailed computer map with all soil features included. This detailed computer map is made up of the following three layers or bases: (i) a black linework and lettering base, (ii) a polygon base filled with black and white textures, and (iii) a polygon base filled with colours. The detailed computer map, saved as the master file, is very easily modified to generate more simplified and thematic maps by: (i) grouping soil features into larger soil components in order to display soil-landscapes in a more simplified form, (ii) deleting certain soil-landscape components in order to highlight specific soil features, and (iii) adding newly aquired information (e.g. chemical and hydrological data) to previous versions.

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

10.5194/soil-2019-36 ◽

2019 ◽

Cited By ~ 1

Author(s):

Yosra Ellili ◽

Brendan Philip Malone ◽

Didier Michot ◽

Budiman Minasny ◽

Sébastien Vincent ◽

...

Keyword(s):

Spatial Resolution ◽

Soil Type ◽

Expert Knowledge ◽

External Validation ◽

Machine Learning Algorithms ◽

Soil Profiles ◽

Soil Maps ◽

Soil Map ◽

Soil Landscape ◽

Spatial Disaggregation

Abstract. Enhancing the spatial resolution of pedological information is a great challenge in the field of Digital Soil Mapping (DSM). Several techniques have emerged to disaggregate conventional soil maps initially available at coarser spatial resolution than required for solving environmental and agricultural issues. At the regional level, polygon maps represent soil cover as a tessellation of polygons defining Soil Map Units (SMU), where each SMU can include one or several Soil Type Units (STU) with given proportions derived from expert knowledge. Such polygon maps can be disaggregated at finer spatial resolution by machine learning algorithms using the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm. This study aimed to compare three approaches of spatial disaggregation of legacy soil maps based on DSMART decision trees to test the hypothesis that the disaggregation of soil landscape distribution rules may improve the accuracy of the resulting soil maps. Overall, two modified DSMART algorithm (DSMART with extra soil profiles, DSMART with soil landscape relationships) and the original DSMART algorithm were tested. The quality of disaggregated soil maps at 50 m resolution was assessed over a large study area (6775 km2) using an external validation based on independent 135 soil profiles selected by probability sampling, 755 legacy soil profiles and existing detailed 1 : 25 000 soil maps. Pairwise comparisons were also performed, using Shannon entropy measure, to spatially locate differences between disaggregated maps. The main results show that adding soil landscape relationships in the disaggregation process enhances the performance of prediction of soil type distribution. Considering the three most probable STU and using 135 independent soil profiles, the overall accuracy measures are: 19.8 % for DSMART with expert rules against 18.1 % for the original DSMART and 16.9 % for DSMART with extra soil profiles. These measures were almost twofold higher when validated using 3 × 3 windows. They achieved 28.5 % for DSMART with soil landscape relationships, 25.3 % and 21 % for original DSMART and DSMART with extra soil observations, respectively. In general, adding soil landscape relationships as well as extra soil observations constraints the model to predict a specific STU that can occur in specific environmental conditions. Thus, including global soil landscape expert rules in the DSMART algorithm is crucial to obtain consistent soil maps with clear internal disaggregation of SMU across the landscape.

Inductively mapping expert-derived soil-landscape units within dambo wetland catenae using multispectral and topographic data

Geoderma ◽

10.1016/j.geoderma.2009.01.013 ◽

2009 ◽

Vol 150 (1-2) ◽

pp. 72-84 ◽

Cited By ~ 29

Author(s):

Matthew K. Hansen ◽

David J. Brown ◽

Philip E. Dennison ◽

Scott A. Graves ◽

Ross S. Bricklemyer

Keyword(s):

Topographic Data ◽

Soil Landscape ◽

Landscape Units

SOIL MAPPING ACCURACY FROM BLACK AND WHITE, COLOR, AND INFRARED AERIAL PHOTOGRAPHY

Canadian Journal of Soil Science ◽

10.4141/cjss71-061 ◽

1971 ◽

Vol 51 (3) ◽

pp. 461-469 ◽

Cited By ~ 1

Author(s):

K. W. G. VALENTINE ◽

T. M. LORD ◽

W. WATT ◽

A. L. BEDWANY

Keyword(s):

Aerial Photography ◽

Soil Mapping ◽

Photographic Film ◽

Soil Maps ◽

Mapping Accuracy ◽

Color Film ◽

Black And White ◽

White Film ◽

Soil Surveys ◽

Terrain Features

The accuracy obtainable from four types of aerial photographic film in the mapping and description of soil and terrain features was measured. Black and white film gave a soil mapping accuracy of 72% and was just as good as the color or infrared films for the description of specific terrain features in mountain lands. The accuracy of the soil map in the mountain lands and the description of terrain features in an alluvial valley increased to over 80% with the color film. Infrared film, both color and black and white, gave slightly more accurate soil maps in the valley. The use of a film like Kodak Special Ektachrome MS Aerographic Type SO-151 is recommended for future soil surveys. Black and white prints and color prints and transparencies can all be obtained from the same roll of this film type.

Representation of soils in the landscapes maps

Visnyk of the Lviv University. Series Geography ◽

10.30970/vgg.2011.39.2169 ◽

2011 ◽

pp. 113-121

Author(s):

M. Grodzynskyi

Keyword(s):

Key Words ◽

Soil Science ◽

Soil Series ◽

Landscape Mapping ◽

Soil Landscape ◽

Different Types ◽

Vegetation Water ◽

Landscape Units ◽

Landscape Map ◽

Over Time

Series (succession sequences) of soils that change each other over time and within the landscape units are proper objects for landscape mapping. The soil series give an idea of both retrospective state of a soil before its anthropogenic transformations and of tendencies of soil development in landscape complexes of various types. The names of soils as they are appeared in soil nomenclature of Soil science should not be duplicated in the legends of landscape maps. "Landscape" names for soils have to stress on their features and attributes that are of primary importance for vegetation, water, thermal and other ecological regimes of landscapes. The "landscape" names for different types of Albeluvisols and Phaeozems of Ukraine are suggested. Key words: soil, landscape, landscape map, landscape science.

Using a fuzzy logic approach to reveal soil-landscape relationships produced by digital soil maps in the humid tropical region of East Java, Indonesia

Geoderma Regional ◽

10.1016/j.geodrs.2021.e00468 ◽

2021 ◽

pp. e00468

Author(s):

D. Cahyana ◽

B. Barus ◽

Darmawan ◽

B. Mulyanto ◽

Y. Sulaeman ◽

...

Keyword(s):

Fuzzy Logic ◽

Tropical Region ◽

Soil Maps ◽

Soil Landscape ◽

Fuzzy Logic Approach ◽

Logic Approach ◽

Digital Soil Maps

The driving role of microhabitats in soil ecology: rebuilding artificial 3D soil-like nanostructured microhabitats for experimental reproducible works

10.5194/egusphere-egu21-12421 ◽

2021 ◽

Author(s):

Fabrizio De Cesare ◽

Elena Di Mattia ◽

Antonella Macagnano

Keyword(s):

Soil Ph ◽

Model Systems ◽

Natural Soil ◽

Phenotypic Traits ◽

Local Concentration ◽

Bacterial Populations ◽

Soil Features ◽

Soil Mesocosms ◽

Soil Ecosystems ◽

Soil Components

Soil ecosystems are composed of microhabitats that often differ in composition and ecological strategies at the microscale. Besides, the assumption that soil organism behaviour at the ecosystem level is similar to that at microscale may drive unexpected findings. Soil pH at microsites either can differ significantly from whole soil pH. Moreover, the large porosity measured in the whole soil can contrast with water, nutrient, air and waste flow limitations and dramatic constraints to microbial mobility and access to food, when analysed at the microscale, consequent to local pore geometry, connectivity and tortuosity. Incidentally, soil microorganisms, which are present in billions of individuals per gram of soil, have micrometre sizes and prevalently interact with the other soil components at the nano-to-microscale. They colonise soil microhabitat based on the local concentration and composition of air, nutrients and materials. Finally, different organic materials and minerals in the soil induce distinct interactions at microsites, generating diverse organo-mineral associations and different microbial populations.&#160;The study of soil microhabitats can enable comprehending how the microsites' dynamics can drive to ecosystems' macroscale behaviours. However, the study of soil microhabitats in real conditions, even when investigated in soil mesocosms and microcosms, can be challenging or require complicated and expensive instrumentations to achieve such outcomes.&#160;The rebuilding of soil microhabitats in model systems can help study the microhabitats' mutual interactions at the microscale. However, it is impossible to reproduce any possible combination of soil components to replicate the multitude of microhabitats existing in natural soil ecosystems. Then, approximations are necessary.&#160;The present study proposes to recreate an artificial model 3D soil-like microhabitat resulting from the aggregation of the major classes of soil components (mineral particles, organic polymeric components, and microorganisms) in nano- to macro-architectures to study organo-mineral-microbe interactions at the microscale, and enable reproducible works. Electrospinning/electrospraying technologies were chosen for their extreme versatility in creating self-standing 3D complex, porous and functional structures and their proven capacity to permit microbes to grow on the resulting composite fibrous frameworks.Bacteria strains of&#160;Pseudomonas fluorescens&#160;and&#160;Burkholderia terricola, typical microbial species populating the rhizosphere soils, will be utilised as microhabitat microbial components for generating a simplified microbiome in the 3D soil-like nanostructures. At first instance, we intended to use microscopy (e.g. SEM, TEM, confocal) as the tool of choice to investigate over time the spatial distribution of bacterial populations throughout the artificial nanostructured soil microhabitat here reproduced, the release of EPS by the bacterial populations and possible interactions. The proposed 3D soil-like nanostructures are supposed to provide the possibility of investigating the microbial lifestyle in microhabitats at different scales, from nm to mm, then linking microbial phenotypic traits to specific soil features.

Use of Soil Maps and Surveys to Interpret Soil-Landform Assemblages and Soil-Landscape Evolution

Geopedology ◽

10.1007/978-3-319-19159-1_15 ◽

2016 ◽

pp. 251-264

Author(s):

R. J. Schaetzl ◽

B. A. Miller

Keyword(s):

Landscape Evolution ◽

Soil Maps ◽

Soil Landscape

Development of class pedotransfer functions for integrating water retention properties into Portuguese soil maps

Soil Research ◽

10.1071/sr12347 ◽

2013 ◽

Vol 51 (4) ◽

pp. 262 ◽

Cited By ~ 10

Author(s):

T. B. Ramos ◽

M. C. Gonçalves ◽

D. Brito ◽

J. C. Martins ◽

L. S. Pereira

Keyword(s):

Water Balance ◽

Bulk Density ◽

Water Retention ◽

Swat Model ◽

Pedotransfer Functions ◽

Soil Horizon ◽

Soil Maps ◽

Catchment Scale ◽

Small Catchment ◽

Water Retention Properties

Hydrological modellers have recently been challenged to improve watershed models by better integrating soil information into model applications. Reliable soil hydraulic information is thus necessary for better describing the water balance components at the catchment scale. Frequently, that information does not exist. This study presents a set of class-pedotransfer functions (PTFs) for estimating the water retention properties of Portuguese soils. The class-PTFs were established from a dataset containing 697 soil horizons/layers, by averaging values of total porosity and volumetric water contents at –0.25, –1, –3.2, –6.3, –10, –33, –100, –250, and –1500 kPa matric potentials after grouping data by soil texture class, soil horizon, and bulk density. Fitted retention curves using the van Genuchten model were also obtained for every class-PTF. The root mean square error varied between 0.039 and 0.057 cm3/cm3, with smaller values found when using the 12 texture classes of the International Soil Science Society (ISSS) system rather than the five texture classes of FAO, and when bulk density was also considered. The class-PTFs were then integrated into Portuguese soil maps and its usage was demonstrated by deriving maps of available water capacity to be used for modelling the water balance in a small catchment area with the SWAT model. The model successfully simulated the reservoir inflow when using the derived maps, but the results did not vary much whether using coarser or finer description of the catchment soils. Nonetheless, the class-PTFs contributed to a better soil characterisation than when using coarse-scaled information. The approach followed here was simple, inexpensive, and feasible for modellers with few resources but interested in considering the spatial variability of soil retention properties at large scales and in advancing hydrologic modelling in Portugal.

Quantitative soil - landscape models for the Haldon and Hurunui soil sets, New Zealand

Soil Research ◽

10.1071/sr04074 ◽

2005 ◽

Vol 43 (2) ◽

pp. 127 ◽

Cited By ~ 9

Author(s):

Jochen Schmidt ◽

Phil Tonkin ◽

Allan Hewitt

Keyword(s):

New Zealand ◽

Soil Properties ◽

Soil Survey ◽

Fuzzy Classification ◽

Aerial Photographs ◽

Soil Profiles ◽

Land Resource ◽

Landscape Models ◽

Soil Landscape ◽

Landscape Units

Limited resources and large areas of steeplands with limited field access forced soil and land resource surveyors in New Zealand often to develop generalised models of soil–landscape relationships and to use these to produce soil maps by manual interpretation of aerial photographs and field survey. This method is subjective and non-reproducible. Recent studies showed the utility of digital information and analysis to complement manual soil survey. The study presents quantitative soil–landscape models for the Hurunui and Haldon soil sets (New Zealand), developed from conceptual soil–landscape models. Spatial modelling techniques, including terrain analysis and fuzzy classification, are applied to compute membership maps of landform components for the study areas. The membership maps can be used to derive a ‘hard’ classification of land components and uncertainty maps. A soil taxonomic model is developed based on field data (soil profiles), which attaches dominant soil profiles and soil properties, including their uncertainties, to the defined land components. The method presented in this study is proposed as a potential technique for modelling land components of steepland areas in New Zealand, in which the spatial soil variation is dominantly controlled by landform properties. A soil map was developed that includes the uncertainty in the fundamental definitions of landscape units and the variability of soil properties within landscape units.

Spatial terroir characterization and grape composition in the Southern Côtes-du-Rhône vineyard (Nyons-Valreas Basin)

OENO One ◽

10.20870/oeno-one.1998.32.4.1043 ◽

1998 ◽

Vol 32 (4) ◽

pp. 169 ◽

Cited By ~ 2

Author(s):

Emmanuelle Vaudour ◽

Michel-Claude Girard ◽

L.-M. Bremond ◽

L. Lurton

Keyword(s):

Agricultural Land ◽

Sugar Content ◽

Satellite Image ◽

Titratable Acidity ◽

Large Area ◽

Land Form ◽

Soil Landscape ◽

Using Data ◽

Elevation Model ◽

Landscape Units

In order for the characterization of terroir in vineyard situations to benefit both viticultural and wine making practices, it is necessary to consider the spatial aspect of the vineyard environment. An exploratory approach at characterising terroir in the Nyons-Valreas Basin (figure 1) considers both the spatial analysis and frequency analysis of the harvest. Data gathered from stereoscopic aerial photographic examination, satellite image processing, land surveys, and the Digital Elevation Model are combined and structured within a Geographic Information System along with the existing soil and geological data (figure 2). The result is a comprehensive soils model applicable to a relatively large area (11,340 ha). The Nyons-Valreas Basin is a neogene and quaternary sedimentary basin, and the soils found there are described by 21 soil landscape units which integrate 15 variables (table I). The area examined is considered to be representative of the surrounding regional diversity. The variables used in characterising terroir include soil types, geomorphology, lithology, stratigraphy, vegetation, land form, and land use. The various viticultural terroirs are regarded as parts of agricultural land consistent with both soil landscapes and harvest/wine responses. Multivariate clustering of the soil landscape units indicates that there exists 7 distinct viticultural terroirs, essentially on the basis of geomorphology and soils (figures 3 and 4, table II). Four distinct terroirs were compared (figures 5 and 6) using data gathered from 14 sites over the course of 15 vintages (1982-1996). Grenache is the grape variety planted at each site, and the variables measured at harvest (pH, sugar content, titratable acidity, the weight of 200 berries, and the sugar/acidity ratio) appear to significantly discriminate the sites examined according to the terroir modeling performed (tables III, IV and V).