Reconnaissance Soil Map Research Articles

Classification in hydrologic groups of soil of Uruguay

Dominant soils of the mapping units of the Reconnaissance Soil Map of Uruguay were tentatively classified in hydrologic groups (GH) following the procedure developed by the U.S. Soil Conservation Service (SCS). Since information on the relationship between precipitation and runoff is not available yet, the classification is mostly based on soil morphology: horizons sequence, their texture and structure, soil matrix color, mottling and presence of Fe-Mn concretions as indications of redoximorphic phenomena. Estimates of soil permeability and drainage on the basis of profile morphology and the knowledge of the hydric regime support the classification in GH. The results show that only very few soils are included in Group A, mainly Arenosols (Psamments) or very gravelly soils. Group B (24 profiles) includes mainly moderately well to well drained Subeutric Brunosols (Udolls). Group C (68 profiles) includes Subeutric and Eutric Brunosols (Udolls) and Argisols (Udolls and Udalfs), mostly moderately well to somewhat poorly drained, with a fine textured subsoil. Group D (63 profiles) includes all Vertisols, Planosols (Albolls and Aqualfs), Gleysols (mostly Aquolls and Udolls) and solonetzic soils (Aqualfs), most Litosols (Lithic Orthents and Udolls) and a few Brunosols (Udolls); drainage range from somewhat excessive to poor. the classification achieved is razonably consistent with available information on infiltration rate and hydraulic conductivity measurements for some soils. The comparison with known soils of USA, classified in GH with a more scientific basis, has not shown any evident contradiction between the classification criteria used both in US and Uruguay to assign each soil to the correct GH.

Disaggregation of the 1:100,000 Reconnaissance soil map of the Busia Area, Kenya using a soil landscape rule-based approach

A 1:100,000 soil map of the Busia area in western Kenya published in 1991 is the main source of soil information for the area, but the map is outdated and unsuitable for new emerging demands. We disaggregated the Reconnaissance Soil Map of the Busia Area (quarter sheet No. 101) into individual soil classes to produce a soil class map that may better meet current needs. A soil landscape rule-based model was used to disaggregate the soil map units by exploiting information in the map legend and the map unit descriptions. These descriptions were used to generate rules that were applied to a fuzzy soil class map generated from a parent material map and a K-means cluster map generated from six terrain attributes, namely, multiresolution ridgetop flatness (MRRTF), multiresolution valley bottom flatness (MRVBF), topographic wetness index (TWI), topographic position index (TPI), planform curvature, and profile curvature. The result was a soil class map with a spatial resolution of 30 m with an overall accuracy of 58% and a Kappa coefficient of 0.54. The soil landscape rule-based approach provides an opportunity to disaggregate traditional soil maps in cases where there is no adequate dataset. The drawback with this approach, however, is that it relies heavily on the resolution of the DEM and the type of information contained within the soil survey report describing the soil classes within the soil map units.

Refining a reconnaissance soil map by calibrating regression models with data from the same map (Normandy, France)

Reconnaissance soil maps at 1:250,000 scale are the most detailed source of soil information for large parts of France. For many environmental applications, however, the level of detail and accuracy of these maps is insufficient. Funds are lacking to refine and update these maps by traditional soil survey. In this study we investigated the merit of digital soil mapping to refine and improve the 1:250,000 reconnaissance soil map of a 1580km2 area in Haute-Normandie, France. The soil map was produced in 1988 and distinguishes nine soil class units. The approach taken was to predict soil class from a large number of environmental covariates using regression techniques. The covariates used include DEM derivatives, geology and land cover maps. Because very few soil point observations were available within the area, we calibrated the regression model by sampling the soil map on a grid. We calibrated three models: classification tree (CT), multinomial logistic regression (MLR) and random forests (RF), and used these models to predict the nine soil classes across the study area. The new and original maps were validated with field data from 123 locations selected with a stratified simple random sampling design. For MLR, the estimate of the overall purity was 65.9%, while that of the reconnaissance map was 55.5%. The difference between the purity estimates of these maps was statistically significant (p=0.014). The significant improvement over the existing soil map is remarkable because the regression model was calibrated with the existing soil map and uses no additional soil observations.

Soil Erosion Prediction Using GIS and Remote Sensing on Manjunto Watershed Bengkulu, Indonesia

Soil Erosion Prediction Using GIS and Remote Sensing on Manjunto Watershed Bengkulu, Indonesia

Small scale digital soil mapping in Southeastern Kenya

Digital soil mapping techniques appear to be an interesting alternative for traditional soil survey techniques. However, most applications deal with (semi-)detailed soil surveys where soil variability is determined by a limited number of soil forming factors. The question that remains is whether digital soil mapping techniques are equally suitable for exploratory or reconnaissance soil surveys in more extensive areas with limited data availability. We applied digital soil mapping in a 13,500 km2 study area in Kenya with the main aim to create a reconnaissance soil map to assess clay and soil organic carbon contents in terraced maize fields. Soil spatial variability prediction was based on environmental correlation using the concepts of the soil forming factors equation. During field work, 95 composite soil samples were collected. Auxiliary spatially exhaustive data provided insight on the spatial variation of climate, land cover, topography and parent material. The final digital soil maps were elaborated using regression kriging. The variance explained by the regression kriging models was estimated as 13% and 37% for soil organic carbon and clay respectively. These results were confirmed by cross-validation and provide a significant improvement compared to the existing soil survey.

Comparison of soil maps with different scales and details belonging to the same area

Two different soil maps prepared by different institutes at scales of 1:200 000 and 1:25 000 covering identical areas were compared to determine the accuracy of reconnaissance. These soil maps are widely used in land resources assessment studies in Turkey. For this purpose, the soil maps were digitised and performed a data set. Then the map layers were compared by using GIS technology in order to assess the soil properties and land characteristics. The reconnaissance soil map at the scale of 1:200 000 has the highest accuracy for the slope due to the fact that topographic maps have been used as basic maps for the field studies. The accuracy of other properties in descending order is as follows; slope > depth > salinity > texture > drainage > top soil texture. In addition, physiographic and topographic patterns of soils also affect the accuracy of maps. The reconnaissance soil map was found to be less accurate in flood plains where the slope does not affect other soil properties.

Soil distribution in the McMurdo Dry Valleys, Antarctica

The McMurdo Dry Valleys (MDVs) are the largest ice-free area (ca. 6692 km 2) in Antarctica. Here we present a reconnaissance (scale = 1:2 million) soil map of the MDVs. The soil map units are subgroups as identified in the U.S. Department of Agriculture Soil Taxonomy. The dominant soil subgroups in the MDVs are Typic Anhyorthels (43%), Typic Haploturbels (36%), and Typic Anhyturbels (14%). Soils of the MDVs represent an evolutionary sequence that include Glacic Haploturbels/Anhyturbels on Holocene surfaces, Typic Haploturbels/Anhyturbels on late Quaternary surfaces, Typic Anhyorthels on late to mid-Quaternary surfaces, Salic Anhyorthels on mid-to early Quaternary surfaces, and Petrosalic/Petrogypsic/Petronitric Anhyorthels on Pliocene and older surfaces. Soils on silt-rich tills of Pliocene and older age generally are Typic or Salic Anhyorthels; they feature less weathering than younger soils because (i) they are derived from quartzose materials largely devoid of weatherable minerals and (ii) they have been subject to considerable wind erosion.

Comparison of soil variability in a detailed and a reconnaissance soil map in central Iran

Quality of soil maps is a prerequisite for rational land use and soil management. To assess the accuracy of the existing reconnaissance soil map (1:50,000) of the Chaharmahal Bakhtiari province, Iran, a second-order soil survey was conducted. Using aerial photo interpretation, profile description, and field observations, a detailed soil map (1:20,000) was prepared. Then, 85 pedons were described and samples were taken for laboratory analyses. The location of pedons was determined with a Global Positioning System (GPS) and then marked on both soil maps using ILWIS software. Properties of the surface horizons including percentage of gravel, clay, sand, silt, CaCO 3 equivalent, organic matter, and the thickness of A horizon were determined. Although detailed soil map units were more homogenous, purity of map units at family and series levels was lower than those expected in American Soil Survey Manual. Taxonomic purity at series level was 18.75% to 33.3% and 0% to 1.3% for detailed and reconnaissance soil maps, respectively. With respect to similar soils, interpretive purity could improve to 75% in detailed soil map. For all the surface soil properties except organic matter, the detailed soil map had within-map unit variances that were smaller than the total variance, whereas reconnaissance soil map variance remained high. Soil classification in both the detailed and the reconnaissance mapping units accounted for 5% to 45% and 1.1% to 27% of the total variance for these properties. Sampling demand to predict mean values was much larger in the reconnaissance soil map for most of the topsoil properties. Laboratory studies showed that the detailed soil map units in similar physiographic units could have different soils and vice versa. It appears that the definition of mapping units based on guidelines by Soil Survey Manual is rather optimistic and does not have sufficient reliability for using in sustainable agriculture.

Classification of Soils Supporting Mangroves in the Sultan Qaboos Qurm Nature Reserve (Oman)

Eight soli profiles iii the Qunn Nature Reserve (Muscat, Oman) were described and classified according to SoilTaxonomy.A reconnaissance soil map of the Reserve was prepared which includes the following soil types; Typic Torrifluvents in relatively drier areas; Typic Aquisalids on an upper marine terrace; AquicTorrifluvents on medium terraces; Typic Fluvaquents on the lower terrace and Typic Psammaquents on a coastal sand bar. Mangrove tree (Avicennia marina) growth appeared better on the Typic Fluvaquents than on other soils. Mangrove growth performance in relation to soil type is briefly discussed. وصفت ثمانية مقاطع من التربة في محمية القرم الطبيعية ( قرم السلطان قابوس ) حسب قانون تصنيف التربة ، ورسمت خارطة لهذه المنطقة متضمنة لهذه المقاطع وأهم أنواعها كما يلي :

How good is a reconnaissance soil map for agronomic purposes?

Abstract. Information about the soil fertility status in irrigated ricelands at regional scales (1:50 000–1:250 000) is commonly not contained in classical soil maps. To assess the agronomic suitability of two different reconnaissance soil maps, we conducted a detailed soil survey in the Nueva Ecija province, Philippines. Soil samples were collected from 384 farmers' fields, and soil properties were measured for topsoil and subsoil samples. For most soil properties, a soil map made in 1940 (1:125 000) had within‐map unit variances that were smaller than the total variance, whereas a new soil map of 1992 (1:50 000) did not significantly reduce the within‐class variance. In both soil maps, classification into mapping units accounted for 0–40% of the variance of 14 agronomically important soil properties and large within‐map unit variabilities were found. Underlying strategies of classical soil survey supported the partition of variance for relatively stable soil properties, such as soil texture, CEC, and organic matter. If reconnaissance soil maps are used in quantitative land evaluation studies, existing maps require upgrading by adding quantitative information about relevant soil properties and their within‐map unit variability The sampling demand for upgrading a reconnaissance soil map was large, but pedotransfer functions can be used as cost‐saving tools. Measures of soil nutrient status were highly variable within all mapping units and differences among farmers were much greater than the differences between soil types. Therefore, nutrient management in the study region should be based on individual field or farm recommendations rather than on soil‐map based recommendations.

Reconnaissance Soil Mapping of a Small Watershed Using Electromagnetic Induction and Global Positioning System Techniques

Reconnaissance Soil Mapping of a Small Watershed Using Electromagnetic Induction and Global Positioning System Techniques

Quantification of soil textural fractions of Bas-Zaïre using soil map polygons and/or point observations

Reconnaissance soil surveys typically produce qualitative choropleth maps and descriptions of sampled soil profiles. These can be converted to quantitative information directly. Alternatively quantitative predictors can be made by interpolating between the point observations, or the two can be combined. Three methods to predict the sand and clay content of Bas-Zaïre using a reconnaissance soil map and 151 soil profiles are compared. A cross-validation showed that the combination method produced the most precise predictions with an acceptably small bias. This result was obtained only after verifying and correcting the soil surveyor's estimations of the soil map prediction variances and stratifying the area to acquire a relative pooled within-stratum variogram.

An assessment of the success of a geomorphologically based reconnaissance soil survey in eastern Zambia

The success of a 1:100,000 scale reconnaissance soil survey of the Mtetezi River area in eastern Zambia is assessed using independent profile data obtained from six grid-based detailed pilot surveys undertaken prior to reconnaissance soil mapping. The quality of the soil map is first determined by measuring the success with which the map and its accompanying legend and report estimate correctly the proportions of different soil classes within mapping units (Estimating Success). Secondly, an evaluation is made of the ability of the map to predict correctly the kind of soil occurring at any point within the mapped area (Survey Success). Estimating Success and Survey Success are calculated on both an “unweighted” and a “weighted” basis, the latter compensating for impurities within mapping units based on measurements of mapping consistency. Estimating Success scores are 73% (unweighted) and 78% (weighted), and Survey Success scores are 38% (unweighted) and 22% (weighted). These compare favourably with scores reported in other known studies. The relatively low Survey Success scores reflect the fact that many of the reconnaissance map units are soil associations. Survey Success is shown to vary across the geomorphic units used as a basis for the reconnaissance soil map. It is suggested that all soil surveys should include an estimate of their success and that the methodology described in this paper provides a simple cost-effective method suitable for this purpose.

Colour infrared aerial photographs as data base for reconnaissance soil mapping and land suitability grouping

Colour infrared and panchromatic B & W aerial photographs at 1∶25,000 scale over part of Anantapur district of Andhra Pradesh were interpreted stereoscopically for delineating soilscape units. Soils and land properties were evaluated for suitability of the land for agriculture i.e. paddy and wheat. The results indicate that the relative case in delineating physiographic units offered by colour infrared air-photos does not commensurate with their cost. In the study area, 19.57 and 14.78 per cent area have been found to be suitable for paddy and wheat, respectively.

Photo-interpretation of boulder clay areas as an aid to engineering geological studies

Summary The over-all view of the environment, the exploitation of vertical scale exaggeration and the use of film sensitive to soil moisture content, enables an experienced photo-interpreter to produce information that can supplement engineering geology studies made directly at the site. Reconnaissance soil mapping can be carried out in glacial areas by geomorphological methods supported by drainage, gully, vegetal and tonal characteristics, together with rapid field checks in selected characteristic areas. Numerical values are given for the proportion of buried bedrock features which have been detected in test areas covered with boulder clay, as well as for changes in detectability with variables such as the depth of superficial cover, the type of land use, and air photograph quality. The criteria for the identification of buried features are discussed, as well as the detection of landslides, subsidence, drainage and flooding problems, and the choice of airphoto-graph scale, materials and processes for engineering geological studies.

Reconnaissance Soil Survey in Northern Surinam

By aerial photography and numerous field observations a map with soil landscapes of the northern part of Surinam on a scale of approximately 1: 600,000 was constructed. On this map three main landscapes were distinguished: the landscapes with young soils from sedimentary parent materials on young coastal plain, old soils from sedimentary materials or old coastal plain and soils from residual parent materials. The last group included soil landscapes on schists, on granite and on subgreywacke and all lateritic or laterite-like soils. Near Paramaribo part of the young and the old coastal plain was examined more closely and the results were shown on a reconnaissance soil association map on a scale 1:100,000. Methods were discussed and ways were considered of using aerial photographs for soil mapping in humid tropical areas covered by dense forests. With expensive and therefore limited fieldwork the relation between soil and aerial photographic image needed to be determined. Individual soils had to be grouped into soil mapping units recognizable on aerial photographs, thus making it possible to follow their boundaries on photographs and to plot them directly. Real physiographic soil maps 1:100,000 could be made with mapping units distinguished on a reconnaissance soil map but with boundaries as detailed as from a detailed soil map.