Small-scale Soil Map Research Articles

Soil orders and their areas on the updated soil map of the Russian Federation, 1 : 2.5 M scale

An analysis of the soil cover of Russia as presented on the soil map on a scale of 1 : 2.5 M with the use of a new substantive-genetic soil classification system has been performed at the level of soil orders. The high level of classification-based generalization makes it possible to assess the most general patterns of soil geography and soil resources and to identify changes that have occurred as a result of renaming of each polygon on the map with the use of the new classification. The areas occupied by soil orders have been calculated. In total, there are 24 soil orders on the new map, including 21 orders of natural soils and 3 orders (agrozems, turfzems, stratozems) of anthropogenically transformed soils. Soils of the orders of agro-abrazems, chernozems, and turbozems are not presented on the map. As on most small-scale soil maps of Russia, the zonal regularities of the soil cover in the East European Plain and high lithogenic mosaicity in Central and Eastern Siberia are clearly seen. The new map includes soil orders that were absent on the initial map: cryozems, cryometamorphic and hydrometamorphic soils, lithozems, cryoabrazems, cryoturbozems, urbostratozems, and organo-accumulative soils. Soils characteristic of humid conditions predominate: Al-Fe-humus soils (Podzols) (319.2 M ha, or 19% of the land fund of Russia), gley soils (Gleysols) (223.9 M ha, 13%), texture-differentiated soils (Luvisols and Regosols) (190.8 M ha, 11%), and peat soils (Histosols) (143.5 M ha, 8%) and occupy more than a half of the territory of Russia. The area of humus-accumulative soils most suitable for arable use is 103.6 M ha (6%). Considerable areas are occupied by soils of the orders of cryozems (Turbic Cryosols) (111.4 M ha), iron-metamorphic soils (Chromic Cambisols) (92.7 M ha), structure-metamorphic soils (Cambisols) (47.3 M ha), pale-metamorphic soils (Cambic Cryosols) (12.8 M ha), hydrometamorphic soils (Calcic Gleysols) (4.3 M ha), and cryometamorphic soils (Cambisols Gelic) (3.4 M ha), which corresponds to the vast continental territory of Russia with balanced moisture conditions. Separate place belongs to the soils with strict limitations for use (lithosols (Leptosols), weakly developed soils (Regosols, Nudilithic Leptosols)) but playing important biospheric functions and requiring special protection.

Possible futures of soil-mapping in France

The need for soil data has largely increased worldwide given the growing general concern about the maintenance and recovery of ecosystem resources and services. The development of digital soil mapping (DSM) is often seen as a means for answering this demand. In France, the national soil mapping strategy has been defined in the early 1990s within the Soil Inventory, Management and Conservation Programme (IGCS) and based on conventional soil mapping approaches. Now, small-scale soil map coverage of France has been almost achieved, soil data needs have evolved and DSM approaches have matured. The question therefore arises of what should be the future soil mapping strategy in France so as to foster soil mapping, better answer end-users needs and raise societal concern about soils. To answer this issue, we present in this paper a prospective analysis of the French national soil mapping strategy, which included i) a survey of the needs and difficulties expressed by producers, managers and users of soil data and ii) a foresight study of potential future scenarios for the development of soil mapping that takes advantage of DSM approaches. The survey indicated that soil information needs are high in terms of soil attributes, spatial resolution and accuracy and go beyond the data and maps presently available for France. The survey also showed that DSM methods remain little known outside the academic sector. The foresight study led to two main outputs. The first is to propose two complementary spatial sampling strategies for new data acquisition: i) upgrading the density of observed soil profiles to homogenize the accuracy of 1:250,000 soil maps for France and ii) improving the knowledge of local soil distribution patterns in the French regions by developing detailed mapping of reference areas, representative of the local soil patterns. The second output is a set of four possible scenarios for the development of soil mapping that differ according to the expected level of concern about soils that may exist in France in the future. The comparison of the scenarios led to several recommendations for favouring soil mapping, acquisition of new soil data and dissemination of soil knowledge. The recommendations include awareness raising about soil mapping and its potential for answering many environmental challenges, capacity building of soil surveyors and soil data users for DSM approaches, and improved quality assessment of soil maps to guide users and stimulate new investments of map producers. This certainly involves renewed public support before market development of soil mapping activities can take place and become the main support for soil mapping in France.

Soil cover patterns in the forest-steppe and steppe zones of the East European Plain

Abstract The soil cover of the forest-steppe and steppe zones of the East European Plain is characterized by diverse soil combinations revealed during large-scale and detailed soil mapping against the background of a traditional zonal sequence of dominant automorphic soils alternating from the north to the south and clearly displayed on small-scale soil maps. The composition, configuration and functioning of particular soil cover patterns are determined by the soil forming factors acting within a given area. The elementary soil areas (detailed scale) and elementary soil cover patterns maps (large scale) of the Central Russian, Kalach, and Volga Uplands are created by both traditional and digital soil mapping methods. Low-contrasting soil combinations with the background Haplic Chernozems (Loamic or Clayic, Pachic) alternating with zooturbated Haplic Chernozems (Loamic or Clayic, Pachic) on convex elements of the microtopography and Luvic Chernozems (Loamic or Clayic, Pachic) on concave elements of the microtopography prevails under conditions of thick clay loamy parent materials and free drainage. Under conditions of shallow embedding by low-permeable clayey sediments, the soil cover includes Chernozems or Chernic Phaeozems with stagnic features in some part of the soil profile or even Mollic Stagnosols. The presence of shrink-swell clays of different ages leads to the formation of Bathyvertic Chernozems, Vertic Chernozems, Vertic Chernic Phaeozems and/or Pellic Vertisols. The presence of soluble salts in the parent material leads to the development of solonetzic soil complexes consisting of Protosodic or Sodic Chernozems and different types of Solonetzes.

Soil cover patterns and land resources in the south of the Selenga mountainous region

Soil cover patterns within the Kyakhta area of pine stands and the Kudarinsk area of dry steppes in the south of the Selenga mountainous region are characterized. The groups of soil combinations are shown on the map developed on a scale of 1: 500000. The areas of particular soils composing the combinations have been calculated. Thus, this small-scale soil map generally preserves information reflected on large- and medium-scale soil maps.

Quantification of soil properties based on external information by means of fuzzy-set theory

en

The Use of DEM and Satellite Data for Regional Scale Soil Databases

New, quantitative methods and data sources for characterizing small scale soil resources have been demonstrated. AVHRR and coarse spatial resolution DEM were designed for mapping large areas of the world quickly and cost effectively. The method combines digital elevation data, “ground truth” information, including the soil taxonomic class for measured soil locations, and a time series of satellite images to form a digital soil database. The results show that using ancillary information such as AVHRR data and DEM derivatives from the national to continental level surveys is among the most promising tools for geographers and soil surveyors. The AVHRR data is often used for land cover studies but its usefulness in soil studies has not yet been proven. This study is a representative example of the usefulness of AVHRR data in characterizing the soil-forming environment and delineating soil patterns, particularly when integrated with other data for describing the soil landscape, such as the DEM, slope, curvature and PDD. The predictive power of AVHRR and similar low spatial resolution satellite data sources could be further improved with the development of soil sensitive filters. Mention should be made of the potential improvement of the products derived from these data sources with the use of better quality data provided by satellites that have been launched recently. Neither the AVHRR nor the DEM-derivatives show high correlation with the soil classes, but both represent a great portion of the environmental variability. In general, the more uncorrelated information is extracted from DEM and AVHRR, the better explanation of the spatial soil variability is achieved with an integrated use of them. The images of AVHRR time series show a relatively low correlation, thus each of the new dates adds much potential information on the soils. The studies also highlighted the great help of surface vegetation in soil remote sensing, as indicated by the high R² value of Band 1 and NDVI. The importance of the short-term weather history of the study area was also demonstrated. Terrain information and terrain variables were primarily developed for large scale local studies. Small scale mapping of large regions presents different issues, like over-generalization and over-smoothing of the soil information. The terrain features with smaller extents are dissolved into a larger neighborhood. As a smoother terrain map is created, a lot of detail is lost and less variability is observable. Many of the terrain attributes are useless with this approach. Elevation, slope, relief intensity, potential drainage density and the curvature variables are the most informative digital variables for characterizing the soil-landscape in small scale inventories. The resulting soil databases will have all the advantages of quantitatively derived databases, including consistency, homogeneity, and reduced data generalization and edge-matching problems. Although the results from the above procedures are believed to be accurate enough to serve as a basis for global and regional studies, they should be checked and further revised by local and regional experts to ensure quality. Research should continue on improving the procedures, augmenting the pedon data with new field sampling, and incorporating new image and DEM data sources. One of the most important results of these studies is the demonstration of the usefulness of these data sources for small scale soil mapping and the overall validity and representatitivity of the AVHRR-terrain/soil correlation within the temperate region of the world. Further studies will need to be performed to test the use of AVHRR and terrain data for other climate zones of the World, where potential problems, like continuous cloud cover, may occur.

A regional scale soil mapping approach using integrated AVHRR and DEM data

There is an increasing need for reasonably accurate small-scale soil databases. The compilation of a continental or global-scale soil database requires a lot of spatially and thematically accurate soil data. The aim of this study was to test a method for small-scale soil mapping in Italy using Advanced Very High Resolution Radiometer (AVHRR) and digital elevation data. This method was employed in an earlier study in Hungary for a much smaller area and a significantly different soil-forming environment. An integrated, 45-layer AVHRR-terrain database was used for the study, including a digital elevation model (DEM), slope, curvature, aspect, potential drainage density, and the five bands of AVHRR data for eight different dates. The data were processed using the Discriminant Analysis Feature Extraction (DAFE) function, which is based on a canonical analysis procedure. Two types of images (basic and transformed) were classified using the maximum likelihood classifier. Two training sets were chosen that have identical geographic coverage, but differ in the level of soil classification. One set was based on the soil units (SU) of the FAO-revised legend, while the other set represented major soil groupings (MSG). The best 10, 15, 20, 25, 30, 35, 40 and 45 layers were selected using the Bhattachryya feature selection method and were then classified. The results of the different sets were compared. The performance of the purely AVHRR and purely terrain-data-based images, respectively, were also interpreted. The results indicate that the terrain descriptors alone are not sufficient for soil classification. However, the feature selection algorithms always selected the DEM and its derivatives among the first ones, highlighting their importance for soil-landscape characterization. When using AVHRR data alone, test class performances of 49.8 percent (MSG) and 48.6 percent (SU) were achieved. Integration of terrain data into the AVHRR database produced relatively small improvements (4.6 and 2.8 percent). The best test class performances were achieved when all available channels were used for the classification, namely 51.4 for the FAO's SUs and 54.4 for the MSGs on the basic image, and 51.7 and 54.4 respectively on the DAFE-transformed images. The most informative AVHRR bands were found to be from the spring period (April-May), while the most abundant bands were the visible-red (band 1) and bands 3 and 4.

Use of combined digital elevation model and satellite radiometric data for regional soil mapping

Previous reports demonstrated that data from air- and spaceborne sensors are appropriate for delineation of soil patterns. Also, many attempts have been made to use digital elevation model (DEM) for deriving soil information. However, little is known about the potential use of low spatial resolution satellite and digital elevation data in small-scale soil mapping. A case study was conducted to assess the use of integrated terrain and Advanced Very High Resolution Radiometer (AVHRR) databases for small-scale soil pattern delineation. The main objective was to test the effect of the addition of terrain descriptor data to the AVHRR data set on the classification results. Two database were used for the study. The first one was purely AVHRR data and contained the five basic AVHRR channels and the normalized difference vegetation index (NDVI) of five different dates, while in the second database the AVHRR data was complemented with a DEM, a curvature, a slope, an aspect and the potential drainage density layers. The performance of these two databases when employed to derive soil information was compared. These databases were then further processed using the Discriminant Analysis Feature Extraction (DAFE) function (which is based on a canonical analysis procedure), and were then classified using the Fisher linear discriminant, and the ECHO spectral–spatial classifiers. Based on the results, it was concluded that the two reflective bands, the middle infrared, the two thermal bands and the NDVI provided a relatively wide range of detectable soil information. The use of single images or small dimensional AVHRR data sets (less then 10 layers) does not result in acceptable performances, while the use of multispectral and multitemporal databases improved the classification performance very significantly. However, the purely AVHRR-based model could not always delineate soil variations related to terrain differences, and resulted in an overall classification performance of 49.1%. Digital elevation and terrain descriptor data were essential in the model for achieving acceptable results. In the second part of the study an integrated AVHRR-terrain database was used, where five terrain layers were added to the 30 AVHRR channels. Two different spatial resolutions were compared, 500 m and 1 km, respectively. The use of elevation, slope, aspect and curvature as differentiating criteria often lead to a satisfactory result in terrain characterization, particularly in large-scale mapping. However, with those variables extracted from DEM of physiographically complex areas, e.g., — where plain areas and mountainous/hilly regions occur together in the same study — often lose their ability to delineate soil variations of the level lands. Beyond these terrain descriptors we implemented a new function, called potential drainage density (PDD) to improve the performance of the model on the plain areas. The classification accuracy of the integrated AVHRR-terrain database was improved significantly over the case when only AVHRR data was in the model. The classification performances of the three different resolution images were 87.3% for the 500-m resolution image and 70.1% for the 1-km resolution image.

Extracting raster GIS data from scanned thematic maps

One of the problems which continues to trouble practical applications of GIS is the time and cost of capturing data from paper maps. Even where the data are required in raster form the complexity of most maps means that the data are first digitised in vector form, and then rasterised, a slow and error‐prone process. This paper describes an alternative approach based upon readily available hardware and software in which the map is scanned using Spot Colour Scanning, and then processed using a specially designed majority filter and editing with a painting package. Tests on a small scale soil map produce results which are about 80% as accurate as digitising, but take far less time to produce. An analysis of the sources of error indicates that many of these are due to the difficulty of scanning such a complex document as a map, and suggests that more sophisticated image processing algorithms may be needed to classify the original scanned image before the application of the filter algorithm.

Colour additive viewing techniques for small-scale soil mapping in an area of Karimnagar district, Andhra Pradesh, India

Abstract Multi-spectral imagery from Landsat relating to the Karimnagar district of Andhra Pradesh, in South India was analysed using colour additive viewing techniques. The results were correlated with an existing soil association map prepared from aerial photo-interpretation with adequate ground truth. The four landscapes and their subdivisions that were identified on the soil map also could be identified on the Landsat map. An overall classification performance of 83·9, 82·4, 80·9 and 95·7 percent respectively has been achieved for rugged (R), undulating hummocky (U), very gently sloping (V) and river alluvium (A) landscapes. The best class performance was in the order of 95·7, 97·6, 81.8 and 95·7 percent and that of the lowest class performance of 50·5, 57·3, 70·1 and 95·7 percent for R, U, V and A landscapes, respectively. The overall combined performance of 85·7 percent has been achieved with respect to soilscape boundary delineations for all the four landscapes. The Student's t-test of significance revealed that for units R4, U4 and V2 the differences were significant and the rest were found to be nonsignificant. This study has demonstrated the usefulness of colour additive viewing techniques in the analysis of Landsat-MSS data for small-scale soil mapping and the same could be used for the preparation of small-scale soil maps of the States and the country.

Soil resource mapping using IRS-1A-LISS II digital data—A case study of Kandi area adjacent to Chandigarh-India

Abstract Soil survey provides information on soils, their spatial distribution and their areal extent for proper land use planning and agro-technology transfer. Remote sensing has emerged as a potential modern tool which can be utilised as a cost eiTective means of small-scale soil mapping. In the present study, different soilscape units (physiography-cum-soil association units) were identified following a supervised classification based on a maximum likelihood classifier using IRS (Indian Remote Sensing Satellite) 1A-LISS-II digital data. Six soilscape units, namely hills, valleys, moderate and severe eroded piedmonts, alluvial plain and river terraces were identified and delineated digitally. The overall classification accuracy of the digital soil map is 92 per cent. Major soils (subgroup levels) encountered in various soilscape units are-Typic/Lithic Ustorthents; Typic/Udic Ustochrepts; Typic Haplustalfs; Typic Ustipsamments and Typic Ustifluvents. The pedogenetic clues for the development of these soi...

Computer-captured expert knowledge to evaluate possibilities for injection of slurry from animal manure in the Netherlands

Surface application of slurry from animal manure substantially contributes to acid deposition in the Netherlands because of volatilization of ammonia. A qualitative physical land evaluation procedure was used to explore the possibilities for slurry injection. Expert knowledge, characteristic for qualitative land evaluation methods, was captured into the Automated Land Evaluation System (ALES) and successfully linked to a geographical information system (GIS) in which a small-scale soil map was stored. Decision trees to assess the physical suitability for slurry injection were developed within ALES. Maps are presented showing the physical suitability of the individual soil units and of agro-statistical regions. The latter were obtained by aggregation. A table is provided with the relative area for each suitability class of the agro-statistical regions. In a number of major Dutch grassland regions less than 40% of the area turned out to be suited for slurry injection, which was judged by the Dutch government to be insufficient for successful incorporation of injection in environmental legislation. A combined use of small-scale soil maps, computer-captured expert knowledge and GIS proved to be very useful in exploring land use options in a reconnaissance stage. Quick results are obtained which reduce the number of possible land use options within a short time. Thus, efforts can be concentrated on promising land use options, probably requiring application of quantitative evaluation methods.

Utility of satellite data for land evaluation in land use planning for a part of Northern Karnataka

The study reported herein deals with the utility of satellite remote sensing techniques for land evaluation for agricultural land use planning. False colour composite of Landsat imagery in the scale of 1:250,000 was visually interpreted for physiography that formed the base for mapping soil and land resources in the field. The small-scale soil map thus prepared has thirteen map units with association of soil families. Soil and land resource units shown on these small-scale maps were evaluated for their suitability for growing sorghum crop by matching the relevant land qualities against the land requirements for sorghum. The land evaluation carried out for growing sorghum crop in the study area revealed that about 38.6 per cent is highly suitable (S1), 31.5 per cent moderately suitable (S2) and 24.5 per cent marginally suitable (S3). An area of about 5.4 per cent is not suitable, of which 3.0 per cent is currently not suitable (N1) and 2.4 per cent permanently not suitable for growing sorghum crop.

Relative utility of Landsat-TM and MSS data small scale soil mapping

The Landsat MSS and TM data in the form of false colour composite (FCC) prints at 1∶250,000 scale over parts of Mirzapur (U.P.) and Rohtas district of (Bihar) were interpreted monoscopically in concert with the collateral data and limited field check for soilscape boundary delineation. The study has revealed that at the mapping scale, except for improved image contrast and capturing features of relatively smaller dimensions, no additional advantage has been noticed with TM data over MSS data with respect to exhibition of soilscape boundaries. However, the capability of TM data to withstand enlargement upto 1∶50,000 which is not feasible with MSS data is an additional feature from soil mapping viewpoint.

Visual interpretation of black and white enlargements of landsat imagery for the preparation of a small scale soil map of a part of karimnagar district, andhra pradesh

Individual bands of black and white photographic enlargements of Landsat imagery were interpreted visually using tone, texture, drainage pattern and morphological expressions of the relief features. The results have been related to the existing soil map prepared by API techniques with limited field check. Interpretation map of band 5 gave maximum number of delineations which have relevance to the soil association boundaries of the API map followed by band 4, and bands 6 and 7 helped in delineating water bodies and hills. A composite map prepared by superimposition of the interpretation maps of all the four bands was compared with the existing API map. 12 categories could be delineated on the Landsat composite map as against 13 of the API map. The overall classification performance achieved was in the order of 41.2, 47.1, 93.3 and 30.4 percent respectively for R, U, V and A landscapes and the combined overall performance for all the landscapes was 53%. The classification performance results could be improved by delineating the landscape units at a higher level such as hills, interfluves and toe slopes instead of hills, inselbergs, foot slopes, interfluves and toe slopes.

The utility of LANDSAT imagery as an integral part of the data base for small-scale soil mapping

Abstract A soil map of parts of the Bundelkhand region and its adjoining areas in Uttar Pradesh (northern India), covering an area of 41 000 km2, was prepared following a collative approach that used visual interpretation of LANDSAT imagery in conjunction with lithological, topographical and other collateral data and information from field work. The soil map was found to be better than the conventional map in terms of soil landscape boundary delineation. The study has demonstrated the usefulness of LANDSAT imagery for small-scale soil mapping. An overall accuracy of 93-3 per cent with respect to soil landscape boundary delineation has been achieved.

Relative utility of landsat mss and mkf‐6m data for small scale soil mapping

Abstract In this study, an attempt has been made to evaluate Landsat MSS imagery and MKF‐6M space photographs over part of Dharwar district of Kamataka, Southem India for small scale soil mapping. Dharwar schists, gneiss and quartzite comprise broad lithological units whereas hills, ridge and pediplain are major physiographic units. False colour composite prints as well as black and white images of these data were interpreted visually in conjunction with lithological, topographical and available soil information, and limited field check for preparing soil maps. The results reveal that while no remarkable improvement in the abstraction level of soils over Landsat imagery could be made on MKF‐6M photographs, further divisions within undulating upland which are associated with different soil taxa could be delineated. Besides, MKF‐6M data provided better contrast amongst various landscape units. It could be attributed to better spatial and radiometric resolution of MKF‐6M photographs than Landsat MSS.

Delineation of soil associations for a part of tamil nadu by digital processing of landsat multispectral data

The utility of Landsat multispectral data for small scale soil mapping has been demonstrated in this study. The scene used for the study has path-row number 153-054 of Landsat-1 dated 26th February, 1973 covering parts of Ramnad, Tirunelveli and Kanyakumari districts of Tamil Nadu. Associations of sub groups have been delineated on 1 : 250,000 scale using computer-aided multispectrcl data analysis system (M-DAS). Soil map prepared using the computer has been found to be camparable with the soil map prepared by conventional methods at the same scale. Apart from the soil associations, other land use/land cover classes like water bodies forest/scrub/hiliy areas, crcps etc. were also categorised in the colour coded soil map.

Comparison of small scale soil maps prepared by conventional methods and air photo-interpretation procedure

A small scale soil map for a part of Barnagar Tehsil prepared by the adoption of a systematic air photo-interpretation procedure has been compared with the soil map for the same area resulting from a previous reconnaissance soil survey using toposheets as base maps. Tt has been shown that the soil boundaries of the photo-interpretation map are both more accurate and natural in shape; delineation of gullied lands has been achieved more accurately by the A.P.I. procedure; soil erosion could be mapped more consistently and accurately through photo-interpretation. Additionally. the photo-internretation procedure achieves accurate delineation of distinctive facets of land forms because of the advantages arising from the viewing of a stereo model of the landscape; this paves the way for easier and better soil mapping in view of the correlation between landscape elements and soil patterns. Apart from the increased accuracy and diminished costs the photo-interpretation procedure will be helpful in modernising previously prepared reconnaissance soil maps.

An approach to use of aerial photos in small scale soil mapping based on experience of Jodhpur district, Rajasthan

Photocharacteristics of some of the dominant soils were already known from experience within and outside the survey area. Using these known photocharacteristics an interpretation was carried out enabling a coverage of nearly 65% of the total area of the district. Simultaneously, the remaining 35% area split into various sized patches was photo-analysed and the characteristics noted. Thereafter field traversing was taken to carry out checking of the already interpreted area and interpretation of photo-analytical units and a few uncertain areas. This method of partial interpretation-cum-analysis in the first stage itself was found fully workable for the present area.