Conventional Soil Map Research Articles

Soil depth spatial prediction by fuzzy soil-landscape model

Soil depth is a soil property that influences land use, land suitability, and earth surface processes. This article presents a simple method for predicting soil depth by constructing a membership function based on fuzzy C-means. This paper incorporates the soil type map, the land use map, and one type of DEM data to construct a soil-landscape model for soil depth prediction. It compares a fuzzy C-means classifier that includes expert judgment with a conditional autoregressive (CAR) model. Prediction efficiency was evaluated in the Three Gorges area of China using the root-mean-square error (RMSE) and the agreement coefficient (AC) of predictions at validation points. The prediction stability of soil depth values from the fuzzy model is close to the regression model; the AC value indicates a better agreement by the fuzzy C-means method (0.428) than when using the regression model (0.420). The purposive sampling approach was provided by our method by the centroid where the fuzzy membership value is above 0.85, which improves the efficiency of the field sampling. The expansibility of our method is limited as the typical centroid sample location is dependent on the study area. The fuzzy membership value must be recalculated to provide a new typical centroid for field sample when enlarging the study area. The results indicate that the soil-landscape model constructed by the fuzzy membership value with fuzzy C-means method and the conventional soil map provides better quality soil depth spatial information on soil depth.

Soil Lead Spatial Prediction Using a Fuzzy Soil-landscape Model

Soil lead pollution does great harm to the environment. The relation between soil lead and environmental covariates, however, is complicated. A fuzzy logic approach with expert knowledge has proven to be successful in mapping spatial variation of soil lead. The reasons are the limited availability of expert knowledge and the ignorance of soil type and land use of the non-soil area when predicting soil lead in current fuzzy clustering methodology. This paper incorporates the soil type map, the land use map and the DEM data to construct a soil\'96\'6 landscape model for soil lead prediction. It compares a fuzzy C-means classifier that includes expert judgment with conditional regressive model. Prediction efficiency was evaluated in the Three Gorges area of China using the root mean square error (RMSE) and the agreement coefficient (AC) of predictions at validation points. The result indicates that the soil-landscape model constructed by the fuzzy membership functions with fuzzy c-means method and the conventional soil map were able to produce good quality soil lead spatial information.

Data mining methods applied to map soil units on tropical hillslopes in Rio de Janeiro, Brazil

A soil survey is the main source of soil spatial information and is very useful in supporting land use decisions, especially for agronomic purposes. However, the continuity of soil surveying in Brazil has been compromised due the minimal amount of government investment in the sector. The goal of this study was to evaluate the performance of three data mining methods—decision trees (DT), random forest (RF), and artificial neural networks (ANN)—with R software to predict soil map units from the Guapi-Macacu watershed in Rio de Janeiro State, Brazil. Terrain attributes derived from a digital elevation model (DEM), remote sensing data (Landsat 5), and a geology map were used as predictors. Evaluation of the models' performance was based on statistical indices obtained from a confusion matrix by using validation samples. Furthermore, a comparison was performed to analyze the agreement between the maps created from the models and from a conventional soil map (legacy data); sampling locations were not used as input data in the training process. The results indicated better performance from the RF model compared with the DT and ANN models. The accuracy was calculated through validation samples indicating values in the Kappa index corresponding to 0.76 (RF), 0.72 (ANN) and 0.66 (DT). A comparison between the maps obtained from the models and the conventional soil map exhibited better agreement with the RF model (68.7%), followed by the ANN (62.8%), and DT (62.3%) models. The classification accuracy, obtained through comparisons with sampling locations, exhibited correct classification of 57.6% in the RF model, 56.8% in the DT, and 56.1% in the ANN model; the conventional soil map had 69.7% correct classification. In general, most of the confusion between soil map units was observed in Haplic Gleysols, Haplic Acrisols (Clayic) and Fluvisols. In terms of covariates importance, in general, the terrain attributes derived from the DEM had more influence as predictor covariates than the indices obtained from the Landsat data. The application of data mining can improve the methods applied to soil map units, thereby contributing to increasing information related to soil-landscape relationships and enhancing the products of soil surveys.

Comparing the efficiency of digital and conventional soil mapping to predict soil types in a semi-arid region in Iran

The efficiency of different digital and conventional soil mapping approaches to produce categorical maps of soil types is determined by cost, sample size, accuracy and the selected taxonomic level. The efficiency of digital and conventional soil mapping approaches was examined in the semi-arid region of Borujen, central Iran. This research aimed to (i) compare two digital soil mapping approaches including Multinomial logistic regression and random forest, with the conventional soil mapping approach at four soil taxonomic levels (order, suborder, great group and subgroup levels), (ii) validate the predicted soil maps by the same validation data set to determine the best method for producing the soil maps, and (iii) select the best soil taxonomic level by different approaches at three sample sizes (100, 80, and 60 point observations), in two scenarios with and without a geomorphology map as a spatial covariate. In most predicted maps, using both digital soil mapping approaches, the best results were obtained using the combination of terrain attributes and the geomorphology map, although differences between the scenarios with and without the geomorphology map were not significant. Employing the geomorphology map increased map purity and the Kappa index, and led to a decrease in the ‘noisiness’ of soil maps. Multinomial logistic regression had better performance at higher taxonomic levels (order and suborder levels); however, random forest showed better performance at lower taxonomic levels (great group and subgroup levels). Multinomial logistic regression was less sensitive than random forest to a decrease in the number of training observations. The conventional soil mapping method produced a map with larger minimum polygon size because of traditional cartographic criteria used to make the geological map 1:100,000 (on which the conventional soil mapping map was largely based). Likewise, conventional soil mapping map had also a larger average polygon size that resulted in a lower level of detail. Multinomial logistic regression at the order level (map purity of 0.80), random forest at the suborder (map purity of 0.72) and great group level (map purity of 0.60), and conventional soil mapping at the subgroup level (map purity of 0.48) produced the most accurate maps in the study area. The multinomial logistic regression method was identified as the most effective approach based on a combined index of map purity, map information content, and map production cost. The combined index also showed that smaller sample size led to a preference for the order level, while a larger sample size led to a preference for the great group level.

Geotechnologies and Soil Mapping for Delimitation of Management Zones as an Approach to Precision Viticulture

Data of the physical and chemical properties of soils from three vineyards located in Vale dos Vinhedos, Bento Gonçalves, Rio Grande do Sul state, in southern Brazil, were processed. Soil mapping was performed by means of four profiles and the digital elevation model in detailed scale. Then, superficial soils (0–20 cm) were sampled according to a grid pattern. Analysis of variance (ANOVA), kriging, and unsupervised classification methods were applied on physical and chemical data of superficial soils sampled according to grid pattern. This study aimed to compare both methods, the conventional soil mapping and the map produced with superficial soil sampling, about their potential for definition of the management zones, as an approach for precision agriculture. Maps elaborated by conventional soil mapping overlapped partially with the maps derived from superficial sampling, probably due to the specific methodological differences of each case. Anyway, both methods are complementary because of the focus on vertical variability and horizontal variability, respectively. In that sense, slope appears as significant edaphic parameter, due to its control on water circulation in the profile of soil.

Disaggregating conventional soil maps with limited descriptive data: A knowledge-based approach in Serra Gaúcha, Brazil

Conventional soil surveys remain as a major source of information about soils and their properties, but are no longer suited for new demands requiring finer soil data. Spatial disaggregation has been proposed as a way to enrich the resolution of such inventories, in order to better fit the needs for each application. In general, this involves the allocation of individual soil types or their properties within map units based on rules developed from contextual information stored in digital soil geographical databases. However, many countries do not have such soil data infrastructures, thus requiring another strategy for spatially distinguishing individual soils within the polygons of conventional soil maps. Focusing this reality, we developed a disaggregation exercise in a study area located in the wine production zone Serra Gaúcha, Northeast of State Rio Grande do Sul, southern Brazil. We proposed a knowledge-based approach to go around the scarcity of descriptive information, using a heuristic selection of typical sites for each map unit component, based on the survey report and on field observations. A subset of these typical locations was then employed to build a decision tree and to predict individual soil types in the whole study area, using a set of 21 environmental covariates. The spatial detail in predicted maps was considerably improved in relation to the original, while number, proportions and extents of predicted soil types per map unit agreed with the correspondent in the survey report at rates above 70%. Total extent of each soil type was close to the computed from the original, and results have revealed probable inconsistencies in informed proportions of two similar soils. In comparison to a set of 233 independent pedons, the predicted soil types matched that of the validation points better than each component of the original map units. Furthermore, when applying a framework for assessing soil suitability for viticulture, the predicted maps appeared much more coherent with current land use than when using the original polygons. We concluded that the proposed method is a promising option for disaggregating conventional soil maps with poor descriptive data, and that estimates made by experienced soil surveyors could easily fill in contextual information gaps.

Soil Mapping in Chongwe, Zambia by Digital Analysis of Landsat Data

<p>Designing a methodology for mapping and studying soils in a quick and inexpensive way is critical especially in developing countries like Zambia, which lack detailed soil surveys. Therefore, this study was conducted to determine the potential of Landsat 7 ETM+ data (Enhanced Thematic Mapper plus) in mapping soils in Chongwe, a semi-arid region in Zambia. In addition, the study attempted to establish how accurate spectral soil maps produced by digital analysis of Landsat data can be and how such maps compared with field observation data. Also, in situations where there was poor agreement between Landsat data and field observation data, possible causes of such discrepancies where determined.</p><p>A soil inventory of the Chongwe region of Zambia was prepared using computer-aided digital analysis of two Landsat 7 ETM+ satellite images acquired in the dry and rainy seasons to investigate the hypothesis that there is a relationship between Landsat spectral reflectance and certain soil types and that this relationship can be used to map soils with reasonable accuracy.</p><p>The study revealed that digital analysis of Landsat 7 ETM images has the capacity to map and delineate soil patterns with reasonable accuracy, especially when acquired during the dry season when there are long periods of cloud free skies, low soil moisture and minimal vegetation cover. The overall agreement between the Landsat classification and reference data was 72%, indicating a definite relationship between Landsat imagery and soil types.</p><p>In terms of soilscape boundary delineation, the Landsat derived map was had a higher level of agreement with field observations than the conventional soil map. In addition, the study showed that overall, upland areas have a better agreement with Landsat spectral data compared to lowland areas, probably due to the diverse origin of sediments and low spatial extent of most landforms in lowland areas.</p>

Legacy soil maps as a covariate in digital soil mapping: A case study from Northern Iran

Digital soil mapping (DSM) can be used for updating soil surveys. Legacy soil survey maps are often used as a covariate for updating soil surveys because such soil survey maps are logically assumed to contain significant information about the spatial distribution of soil classes. In the present study the usefulness of including conventional soil survey maps as a DSM covariate was investigated. Random forest and multinomial logistic regression models were built using two different covariate sets: covariate set 1 included the legacy soil survey, covariate set 2 excluded the soil survey. Soil Great Groups, Subgroups, and Series taxonomic classes were modeled using both models and covariate sets for an area of ~85,000ha in Golestan Province, northern Iran. Overall model accuracy, the Kappa statistic, and individual covariate importances were used to assess the influence of including the legacy soil survey.Including the conventional soil map as covariate generally increased model accuracy, but the improvement in model accuracy was surprisingly small at all taxonomic levels. This may be due to soil change or the mapping scale of the legacy soil survey. Random forests was found to be more accurate than multinomial logistic regression at all taxonomic levels. Multinomial logistic regression models at the soil Series level were less accurate than the legacy soil survey.

Digital soil mapping at local scale using a multi-depth Vis–NIR spectral library and terrain attributes

Conventional soil mapping is costly and time consuming. Therefore, the development of quick, cheap, but accurate methods is required. Several studies highlight the importance of developing regional soil spectral libraries for digital soil mapping, but few studies report on the use of these libraries to aid digital mapping of soil types. This study aims to produce a digital soil map using as training set Visible and Near Infra-Red (Vis–NIR) spectra from local soil samples, a regional spectral library and terrain attributes. The soils were sampled in 162 locations on a 270-ha farm in the municipality of Piracicaba, São Paulo, Brazil. Spectra from topsoil and subsoil were measured in laboratory (400–2500nm) and arranged as multi-depth spectra. Information was summarized by principal component analysis. Regression tree models were calibrated to predict principal components (PC) scores based on terrain attributes. After calibration, the models were applied to the entire study site, resulting in PC score maps. Fuzzy c-means and PC maps were used to define the soil mapping units (MU). Based on fuzzy centroids, representative samples (RS) were defined to the MU. Munsell soil color and soil order were predicted from soil spectra and used to characterize the MU. The regression tree model had a good fit for PC1, with an r2 of 0.92, and a satisfactory r2 for PC3, PC4, and PC5, respectively 0.58, 0.66 and 0.53. The fuzzy clustering defined seven MU. The R2 for Munsell color predictions were 0.94 (hue), 0.96 (value) and 0.73 (chroma). Soil order had good agreement in validation, with kappa coefficient of 0.41. The methodology indicates the potential of Vis–NIR spectra to improve soil mapping campaigns and consequently provides a product similar to a conventional soil map.

Knowledge-based soil type classification using terrain segmentation

Soil information covering regional, continental, or even global scales is needed for modelling, prediction, or estimation of environmental risks, crop yield estimation, carbon stock estimation, or research on climate change. This study aims to evaluate the extent to which geographic object-based image analysis and expert-knowledge, using digital maps of climate, topography, vegetation, and geology as soil covariates (GEOBIA approach), might model and reproduce a conventional soil map at a scale 1:1000000 in the south-west of Romania. The environmental variables were segmented with a region-growing algorithm, the resulting objects being subsequently classified into soil types using expert-knowledge fuzzy classification rules. To assess the geographical support of classification for the modelling of a conventional soil map, we quantitatively evaluated a pixel-based soil map produced using the same expert-knowledge classification rules, as an alternative to an object-based approach. To evaluate the source of soil information, we quantitatively assessed the map of the World Reference Base soil groups produced by the data-driven global soil information system, SoilGrids, as an alternative to expert-knowledge rules. The digital soil maps were quantitatively compared with the conventional soil map. Evidence was provided that the similarity of soil types with the conventional soil map was higher when the modelling was conducted through GEOBIA approach (general similarity of 65% and fuzzy kappa index of 0.58) than the pixel-based approach and SoilGrids. Furthermore, the results showed that the SoilGrids map achieved higher similarity to conventional soil map than the pixel-based soil map. When tested in another area, without modification to the knowledge-based methodologies, the same conclusions could be drawn, although the two maps recorded lower similarity values. The overall reduction in similarity values is explained by a high variability of some soil types under different environmental conditions.

COMPARAÇÃO DE ESQUEMAS DE AMOSTRAGEM PARA TREINAMENTO DE MODELOS PREDITORES NO MAPEAMENTO DIGITAL DE CLASSES DE SOLOS

Os modelos preditores usados no mapeamento digital de solos (MDS) precisam ser treinados com dados que captem ao máximo a variação dos atributos do terreno e dos solos, a fim de gerar correlações adequadas entre as variáveis ambientais e a ocorrência dos solos. Para avaliar a acurácia desses modelos, tem sido constatado o uso de diferentes métodos de avaliação da acurácia no MDS. Os objetivos deste estudo foram comparar o uso de três esquemas de amostragem para treinar algoritmo de árvore de classificação (CART) e avaliar a capacidade de predição dos modelos gerados por meio de quatro métodos. Foram utilizados os esquemas de amostragem: aleatório simples; proporcional à área de cada unidade de mapeamento de solos (UM); e estratificado pelo número de UM. Os métodos de avaliação testados foram: aparente, divisão percentual, validação cruzada com 10 subconjuntos e reamostragem com sete conjuntos de dados independentes. As acurácias dos modelos estimadas pelos métodos foram comparadas com as acurácias mensuradas obtidas pela comparação dos mapas gerados, a partir de cada esquema de amostragem, com o mapa convencional de solos na escala 1:50.000. Os esquemas de amostragem influenciaram na quantidade de UMs preditas e na acurácia dos modelos e dos mapas gerados. Os esquemas de amostragem proporcional e estratificada resultaram mapas digitais menos acurados, e a acurácia dos modelos variou conforme o método de avaliação empregado. A amostragem aleatória resultou no mapa digital mais acurado e apresentou valores da acurácia semelhantes para todos os métodos de avaliação testados.

Large-area spatial disaggregation of a mosaic of conventional soil maps: evaluation over Western Australia

Conventional soil maps may be the best available source for spatial soil information in data-limited areas, including individual soil properties. Spatial disaggregation of these maps, or mapping the unmapped soil components, offers potential for transforming them into spatially referenced soil class distributions. We used an automated, iterative classification tree approach to spatially disaggregate a patchwork of soil surveys covering Western Australia (2.5 × 106 km2) to produce raster surfaces of soil class occurrence. The resulting rasters capture the broad spatial patterns of dominant soils and harmonise soil class designations across most survey boundaries. More than 43 000 archived profiles were used to evaluate the accuracy of the rasters. In 20% of cases, the predicted soil class with the highest probability matched that recorded for the profile; when any of the three highest probability soil classes predicted were considered correct, the global accuracy was 40%. The accuracy increased to 71% when the rasters were reassembled to represent a higher level in the soil classification system. The predicted surfaces retained features related to the mapping intensity of the original surveys and generally had higher prediction accuracy of profile soil class where the surface geochemistry was more homogeneous. The best indicator of prediction accuracy was how common the profile soil class was in the original mapping (94% variance explained); profile observations collected during soil survey may be biased towards rare soils, making them less suitable for validation or modelling directly from point data.

A Pedological Tale IV: Conversations with Turtle, Bear, and Eagle

A Pedological Tale IV: Conversations with Turtle, Bear, and Eagle

Operationalizing digital soil mapping for nationwide updating of the 1:50,000 soil map of the Netherlands

This paper presents a pedometric approach to updating the Dutch 1:50,000 national soil map for the peatlands, and illustrates this approach for a 187,525ha area in the northern peatlands. This is the first time that digital soil mapping replaces conventional soil mapping in a nationwide, government-funded soil survey program in the Netherlands. Soil classes were updated indirectly through mapping two quantitative diagnostic soil properties: the thickness and starting depth of the peat layer. From these, five major soil groups could be constructed. Because the point data were zero-inflated, a two-step simulation approach was implemented. First, peat presence/absence indicators were simulated from probabilities of peat occurrence that were predicted with a generalized linear model. Second, conditional peat thickness values were simulated from kriging with external drift predictions. The indicator and peat thickness simulations were combined to obtain simulations of the unconditional peat thickness. A similar approach was followed for the starting depth. From the simulated soil properties, probability distributions of soil groups were derived. These groups were refined with information on (static) soil properties derived from the 1:50,000 map to obtain soil classes according to the 1:50,000 legend. The updated raster map was then incorporated in the 1:50,000 polygon map. The prediction models were calibrated with legacy point data, that were updated for peat thickness before being used, in addition to a set of newly acquired point data. The uncertainty associated to the updated peat thickness values in the legacy dataset was quantified and accounted for by the prediction models. The peat thickness map and a map with three soil orders were validated with independent probability sample data. The overall purity of the soil order map was 66% for both subareas. For subarea 1 this was a 12% purity improvement compared to the current 1:50,000 map, for subarea 2 this was 3%. For subarea 1, the mean absolute error of the predicted peat thickness was 23.5cm, and the R2 is 0.50. For subarea 2 these accuracy measures were 30.9cm and 0.65. We conclude that nationwide updating the 1:50,000 map with pedometric techniques is feasible. In order to increase the value and usability of the legacy point data as well as the large set of newly acquired field observations and the updated 1:50,000 map, we recommend installation of a soil monitoring network in the Dutch peatlands.

Solum depth spatial prediction comparing conventional with knowledge-based digital soil mapping approaches

Solum depth and its spatial distribution play an important role in different types of environmental studies. Several approaches have been used for fitting quantitative relationships between soil properties and their environment in order to predict them spatially. This work aimed to present the steps required for solum depth spatial prediction from knowledge-based digital soil mapping, comparing the prediction to the conventional soil mapping approach through field validation, in a watershed located at Mantiqueira Range region, in the state of Minas Gerais, Brazil. Conventional soil mapping had aerial photo-interpretation as a basis. The knowledge-based digital soil mapping applied fuzzy logic and similarity vectors in an expert system. The knowledge-based digital soil mapping approach showed the advantages over the conventional soil mapping approach by applying the field expert-knowledge in order to enhance the quality of final results, predicting solum depth with suited accuracy in a continuous way, making the soil-landscape relationship explicit.

Cadastral-level soil mapping in basaltic terrain using Cartosat-1-derived products

Lack of topographic details in cadastral maps and limited terrain information in coarse-resolution digital elevation models (DEMs) are limitations in understanding the soil–environment relationship, identification of soil patterns, and their boundaries. The present study describes the technique of cadastral-level soil mapping using Cartosat-1-derived products. A high-resolution DEM with a posting of 10 m generated from a Cartosat-1 stereo pair was used to derive terrain attributes. Based on erosional and depositional processes, five major landforms, namely plateau top, escarpment, pediment (erosional), alluvial plain, and narrow valley (depositional), have been delineated using 3D perspective viewing of the landscape and 10 m contours generated from the DEM. The pediments and alluvial plain were further divided into upper and lower pediments and alluvial plain based on elevation. A detailed slope map has been generated from the DEM and reclassified into seven slope classes, namely nearly level (0–1%), very gently sloping (1–3%), gently sloping (3–5%), moderately sloping (5–10%), strongly sloping (10–15%), moderately steep to steep (15–25%), and steep to very steep (25–50%). Five land-use/land-cover classes, namely single crop, double crop, wasteland with and without scrub, and degraded forest have been delineated using Cartosat-1-sharpened IRS-P6 LISS-IV data. The landforms, slope, and land-use/land-cover maps were integrated in a GIS framework and 45 precise physiography–land-use (PLU) units were derived. Nine soil series were identified in major landforms. Soils of plateau top, escarpment, and pediment associated with a low weathering front are very shallow to shallow in depth, have moderate to severe erosion, slight stoniness, and clay texture, whereas soils of alluvial plain, developed in Deccan basaltic alluvium, are deep to very deep (>150 cm), fine textured with shrink–swell potential, and calcareous in nature. Different phases of soil series were identified based on tone, texture, and pattern variations in the satellite image. A detailed soil map has been developed from the PLU map using soil series information and augur observations through the PLU–soil relationship. The combined use of the high-resolution DEM and satellite data has immensely helped in understanding the soil-forming environment, identification of soil patterns, and their boundaries for precise and faster mapping compared to conventional soil mapping.

Semi-Automated Disaggregation of a Conventional Soil Map Using Knowledge Driven Data Mining and Random Forests in the Sonoran Desert, USA

Conventional soil maps (CSM) have provided baseline soil information for land use planning for over 100 years. Although CSMhave been widely used, they are not suitable to meet growing demands for high resolution soil information at field scales. The authors present a repeatable method to disaggregate CSM data into ~30-meter resolution rasterized soil class maps that include continuous representation of probabilistic map uncertainty. Methods include training set creation for original CSM component soil classes from soil-landscape descriptions within the original survey database. Training sets are used to build a random forest predictive model utilizing environmental covariate maps derived from ASTER satellite imagery and the United States Geological Survey (USGS) National Elevation Dataset. Results showed agreement at 70 percent of independent field validation sites and equivalent accuracy between original CSM map units and the disaggregated map. Uncertainty of predictions was mapped by relating prediction frequencies of the random forest model and success rates at validation sites.

Creating a conceptual hydrological soil response map for the Stevenson Hamilton Research Supersite, Kruger National Park, South Africa

The soil water regime is a defining ecosystem service, directly influencing vegetation and animal distribution. Therefore the understanding of hydrological processes is a vital building block in managing natural ecosystems. Soils contain morphological indicators of the water flow paths and rates in the soil profile, which are expressed as ‘conceptual hydrological soil responses’ (CHSR’s). CHSR’s can greatly aid in the understanding of hydrology within a landscape and catchment. Therefore a soil map could improve hydrological assessments by providing both the position and area of CHSR’s. Conventional soil mapping is a tedious process, which limits the application of soil maps in hydrological studies. The use of a digital soil mapping (DSM) approach to soil mapping can speed up the mapping process and thereby extend soil map use in the field of hydrology. This research uses an expert-knowledge DSM approach to create a soil map for Stevenson Hamilton Research Supersite within the Kruger National Park, South Africa. One hundred and thirteen soil observations were made in the 4 001 ha area. Fifty-four of these observations were pre-determined by smart sampling and conditioned Latin hypercube sampling. These observations were used to determine soil distribution rules, from which the soil map was created in SoLIM. The map was validated by the remaining 59 observations. The soil map achieved an overall accuracy of 73%. The soil map units were converted to conceptual hydrological soil response units (CHSRUs), providing the size and position of the CHSRUs. Such input could potentially be used in hydrological modelling of the site.Keywords: Digital soil mapping, terrain analysis, ecosystem services, conceptual hydrological soil responses, SoLIM

Qualitative Land Suitability Evaluation for Main Irrigated Crops in the Shahrekord Plain, Iran: A Geostatistical Approach Compared with Conventional Method

The purity of soil map units and their quality for various uses like land suitability evaluation are always questioned. The main objective of this study was to compare the precision of qualitative land suitability classification based on geostatistical and conventional soil mapping methods for main irrigated crops in the Shahrekord Plain, Central Iran. A regular grid sampling method consisting 104 sample points was designed and soil samples were collected. Ordinary kriged maps were achieved for all studied soil attributes after physico-chemical analyses. Afterward, to combine kriged maps and ecological requirements of the studied crops, a script was designed in ILWIS 3.4 software and consequently, kriged qualitative land suitability maps were generated. Conventional qualitative land suitability was also mapped based on the representative pedon analysis in each soil map unit. Finally, comparison of the conventional and kriged maps was carried out using the statistical method, error matrix. The results showed that the overall accuracies of wheat, sugar beet, potato and alfalfa maps were 39.8%, 24.3%, 18.7% and 18.6% at subclass category, respectively, whereas these values increased to 80.9%, 82.3%, 23.7% and 82.3% at class level, respectively. Hence, it can be stated that thanks to the relative facility of conventional soil survey compared with geostatistical methods, this method can be expressed as a preferable way for handling a usual land suitability evaluation design; but using soil map units as land suitability delineations may lead to unsatisfactory results in estimation of quantity and type of existing limitations.

Constructing a soil class map of Denmark based on the FAO legend using digital techniques

Soil mapping in Denmark has a long history and a series of soil maps based on conventional mapping approaches have been produced. In this study, a national soil map of Denmark was constructed based on the FAO–Unesco Revised Legend 1990 using digital soil mapping techniques, existing soil profile observations and environmental data. This map was developed using soil-landscape models generated with a decision tree-based digital soil mapping technique. As input variables in the model, more than 1170 soil profile data and 17 environmental variables including geology, land use, landscape type, area of wetlands, digital elevation model and its derivatives were compiled. The predicted map showed that Podzols and Luvisols were the most frequent soil groups, covering almost two-thirds of the area of Denmark. Geographically, Podzols occupied a major portion of western Denmark, where the soils have developed on sandy parent material, whereas eastern Denmark mostly contained Luvisols developed on loamy basal till. The occurrence of the predicted soil groups was assigned using several variables, of the most important was clay content in the topsoil and subsoil, elevation, geology and landscape type. The overall prediction accuracy based on a 20% hold-back validation data was 60%, but increased to 76% when prediction accuracy of similar soil groups was considered. Podzoluvisols and Alisols were among the weakly predicted groups (<48% prediction confidence), whereas Podzols and Luvisols had the highest accuracy of prediction (>70%). Overall, the average prediction uncertainty was less than 34%. Compared to the existing conventional soil map, the new map showed promising predictions. Validation of the predicted map with different techniques (point validation, prediction confidence analysis, and map-to-map comparison) confirmed that the output is reliable and can be used in various soil and environmental studies without major difficulties. This study also verified the importance of GlobalSoilMap products and a priori pedological information that improved prediction performance and quality of the new FAO soil map of Denmark.