Soil Class Map Research Articles

Delineation and parameterization of banikdih watershed using remote sensing and avswat model

Delineation of Banikdih Agricultural watershed in Eastern India was carried out and various watershed parameters were extracted using Geographic Information System (GIS) and Remote Sensing. Digital Elevation Model (DEM) was developed with a contour interval of 10 m in the scale of 1:25000 using ARC/INFO modules. Sub watershed, drainage, slope, aspect, flow direction, soil series, soil texture, and soil class maps were independently generated and they were properly registered and integrated for analysis. The watershed was digitally delineated using AVSWAT model that couples hydrological model and GIS with appropriate threshold value of cell size. Subsequently, stream characteristics through the interface were generated. Indian Remote Sensing Satellite IRS-1D LISS-III data pertaining to the period of October 29, 1998 and October 23, 2000 was used to develop land use/land cover thematic map using ERDAS- 8.4 version image processing software. Eight major land use/land cover classes namely water body, lowland paddy, upland paddy, fallow land, upland crop (non-paddy crops), settlement, open mixed forest, and wasteland were segregated through digital image processing techniques using maximum likelihood algorithm. The information generated would be of immense help in hydrological modeling of watershed for prediction of runoff and sediment yield, thereby providing necessary inputs for developing suitable developmental management plans with sound scientific basis.

VS30mapping and soil classification for seismic site effect evaluation in Dinar region, SW Turkey

SUMMARY The Dinar earthquake (M S = 6.1) of 1995 October 1 killed 90 people and destroyed more than 4000 buildings. Despite the moderate size of the earthquake, the level of damage was extremely high, which led to many studies that were carried out in the region. The majority of these studies concluded that the main reasons for the damage were the construction errors and the poor soil conditions. However, at that time no appropriate soil condition map based on extended, high density measurements was available. Shear wave velocity is an important parameter for evaluating the dynamic behaviour of soil in the shallow subsurface. Thus site characterization in calculating seismic hazards is usually based on the near surface shear wave velocity values. The average shear wave velocity for the top 30 m of soil is referred to as V S 30 . For earthquake engineering design purposes, both the Uniform Building Code (UBC) and Eurocode 8 (EC8) codes use V S 30 to classify sites according to the soil type. The V s 30 values calculated by using multichannel analysis of surface waves (MASW) were used to create a new soil classification map of the Dinar region. Surface seismic measurements were carried out at 50 locations mostly in Dinar city and its surroundings. The dispersion data of the recorded Rayleigh waves were inverted using a Genetic Algorithm (GA) method to obtain shear wave velocity profiles of the investigated sites. Thus the derived V s 30 map of the Dinar region was transformed to the UBC and EC8 standards. Soil classification results show that most parts of the region, located in alluvial basin, have low shear wave velocity values. These values are within the range of 160–240 m s −1 and thus fall into the S D and S E categories according to the UBC and the C and D categories according to EC8. Within the region, some parts located on the hill zone and the transition zone have better soil conditions [corresponding to S C (UBC) and B (EC8) categories] and have comparatively high shear wave velocities in the range of 500–740 m s −1 and 350–450 m s −1 , respectively. V S 30 and soil classification maps were compared with the damage distribution associated with the earthquake. In possession of a detailed shear wave velocity map of Dinar City, in general, the results show that there is a correlation between the V S 30 values and the damage distribution of the region. In addition to the low V S 30 values, the likely causes of the damage were investigated, and it is observed that one of the major factors for high levels of damage is 3-D variations of geological structures.

APPLICATION OF FUZZY SOIL CLASSIFICATION IN VISUALIZING 3-D SOIL STRATA

The distribution of soil classes is an important factor in engineering design. In order to generate the soil class map, fuzzy soil classifications were developed to provide the means to characterize and quantify the soil classes, and then the spatial distribution of soil class can be interpolated by geostatistical method. For establishing continuous class soil maps, this paper present an index of fuzzy soil classification generated from principal component analysis (PCA). The ability of classification of the index is tested with a soil database which including 9,260 soil samples, and the accurate rate of simulation was 91.7%. There are two procedures presented to establish the 3-D soil class map. The first method is to integrate 2-D membership degree maps into 3-D maps; while the second method is to interpolate the spatial distribution of index of fuzzy soil classification directly by 3-D ordinary kriging. These procedures are illustrated by a demonstrated area.

Irrigation planning using geographic information system

PurposeThe purpose of this paper is to present a procedure to estimate the total irrigation water requirement for a command area of 2,500 hectares in an arid environment under various crops and soil types using GIS for data storage, analysis and visualization of results.Design/methodology/approachBani Al‐Harith agricultural area in Sana'a basin, Yemen was chosen for the study. ArcView GIS was used to plan for suitable crops and estimate the irrigation water requirements based on the farmer's preference and soil types. Using the available soil maps, the soil characteristics such as salinity, texture and suitable crop types were overlaid to produce the crop blocks map. The water balance equation was used to produce the crop water requirement map considering the crop coefficient for different crop stages. The total water demand for each irrigation block was calculated by summing the three components, namely percolation loss through the soil, maximum evapotranspiration of the crop and leaching requirement (LR) to maintain an acceptable salinity level.FindingsThe case study is an example of using GIS as an effective tool in irrigation planning. GIS facilities to acquire, store, analyze and display spatial data were used to produce the soil class map, soil profile map, crop map and water requirement map. The profile ECe values for the chosen crops is within the crop salinity tolerance for 100 percent yield except for blocks 4 and 5 where grape and coffee respectively are suggested to be grown. The profile ECe values are 18.37 dS/m in block 4 and 3.27 to 7.88 dS/m in block 5. The tolerance threshold of 100 percent yield for grape is 1.5 dS/m and for coffee is 3 to 6 dS/m. The salinity of the irrigation water was 2.08 dS/m. From the crop blocks map, the salinity tolerance level for 100 percent yield of onion for block 1 is 1.2 dS/m, tomato for block 2 is 2.5 dS/m, alfalfa for block 3 is 2 dS/m, grape for block 4 is 1.5/ dS/m, and the salinity tolerance level for 100 percent yield of coffee for block 5 is 5 dS/m. Leaching requirements were obtained by taking ECw value of 2.08 and ECe of 1.2, 2.5, 2, 1.5 and 5 for onion, tomato, alfalfa, grape and coffee respectively. The peak total water requirement occurred in May and was found to be 5,595 m3/ha, or 560 mm. The design irrigation water requirement for every block is shown in a map for easy visualization and manipulation to produce the best combination of soils, crops and water use.Research limitations/implicationsThis method of determining the total irrigation water requirement is dependent on the selected irrigation system and crops whether shallow‐rooted, deep‐rooted or tree crops. The use of water in agriculture should be judicious, precise and sustainable. Application of GIS can be a useful tool in irrigation management since it provides rapid access to underlying information of crop suitability. The designer can try out various combinations of crops, to suit the soils and available water.Practical implicationsThis methodology is useful for training irrigation engineers and water resource planners on the use of GIS technique to plan irrigation projects in arid areas.Originality/valueThis technique has never been applied to the study area.

Quantification and simulation of errors in categorical data for uncertainty analysis of soil acidification modelling

Simulation studies that use maps to generate georeferenced model input may be prone to errors in the definition and delineation of the map units. Our study aims at the estimation of errors in categorical data, i.e., a generalized soil and vegetation class map of the EU vs. a highly detailed soil/vegetation map of the Netherlands. From this, an error model evolves containing (i) an index of map purity and misclassified area fractions and (ii) indicator variograms describing the spatial autocorrelation structure of the degree of error at individual locations. Furthermore, we describe a method to evaluate the effect of these errors on the uncertainty of the outcome of the soil acidification model Simulation Model for Acidification's Regional Trends, version 2 (SMART2). This method involves the application of joint sequential simulation to produce equiprobable realisations of soil/vegetation maps. Results show that the errors in the EU-soil/vegetation map are considerable, because 69% of the area is misclassified when compared to highly detailed maps from the Netherlands. Simulated maps reproduced the error model for the dominant soil/vegetation map units well. Results of the uncertainty analyses show that errors in categorical data do have a pronounced influence on the uncertainty of SMART2 results. This influence was between 20% of the total variance for Al 3+concentrations and exceedance probabilities, and 40%–50% of the total variance for NO 3 − concentrations and exceedance probabilities.

Spacial prediction and mapping of continuous soil classes

Some problems associated with the use of discrete classes in soil mapping are discussed and a new approach to the production of soil class maps which attempts to circumvent such difficulties is presented. The method involves prediction of k+1 continuous classes resulting from a fuzzy k-means with extragrades grouping procedure, onto a fine grid. The prediction of individual class memberships is optimized by using ordinary kriging of log-ratio transformed memberships with a non-linear back transformation. The resulting k+1 rastered maps can be manipulated in various ways to produce isogram, choropleth or chorochromatic maps. In addition to potentially providing more soil information to the user this procedure also has implications in cartography and for geographic information systems. An example is given from a 6 km × 8 km area in the eastern part of the Netherlands.

Desmatamento por classe de declividade no município de Ibirubá, RS

The presente work aims at the evaluation of degree of deforestation, relating it to various classes of sloping in Ibirubá County, State of Rio Grande do Sul, Brazil.Four maps were drafted: one of them with the degree of slope and three with forest areas corresponding to the time periods of 1956, 1965 and 1975. The map of slope classes was based upon the soil classification map of the county and on categories slope obtained through photogrametry. The forest maps were done by using photogrametry of the three above dates.The results show that for the original forest area the deforestation started more intensively intensity in the class of 8 to 15% slope. In spite of the 2 to 8% class offering good topographc and agricultural conditions, the main forest reserves are found in this are and border on the back line of rural properties. Such reserves have gradually been used for agricultural activities especially in the period from 1965 to 1975. In the classes of slope less than 2% and more than 15% there was less deforestation due to the topographic conditions.In the original field area the deforestation went over all classes of slope more intensily where the topographical conditions favored the agricultural activity occupation, i.e., in the 2 to 8% and 8 to 15% slope classes.

Spare/repair parts provisioning recommendations: L. R. Murray and R. S. Morris. Proc. IEEE Reliability Symposium, Washington D.C., January 23–25, p. 224 (1979)

Diverse projects are being carried out worldwide focusing on development of more accurate soil maps and one of the most valuable sources of data are the existing soil maps. This work aimed to (i) compare two data mining tools, KnowledgeMiner and decision trees, to retrieve legacy soil data from a detailed soil map, (ii) to create and validate the predicted soil maps in the field with the objective to identify the best method for modeling and refining soil maps, (iii) extrapolating soils information to the surrounding similar areas and (iv) to assess the accuracy of this soil map. The study was carried out in Minas Gerais state, Southeastern Brazil. From a detailed soil map, information of 12 terrain attributes was retrieved from the entire polygon of each mapping unit of the map (MUP) and from a circular buffer around the sampled points (CBP). KnowledgeMiner and decision trees were employed to retrieve information per soil class and soil maps were created per method. A field validation of 20 samples was chosen by a cost-constrained conditioned Latin hypercube sampling scheme and the accuracy of all maps was assessed using a global index, Kappa index, and errors of omission and commission. The KnowledgeMiner MUP map had a greater accuracy than the other methods, being even more accurate than the original map, accounting for 80% of global index and a Kappa index of 0.6524. The information extracted by KnowledgeMiner provided rules for mapping the watershed surroundings with 70.97% of global index and a kappa index of 0.5586. Legacy soil data extracted by KnowledgeMiner from a detailed soil map and used to model soil class distribution outperformed decision trees, promoted improvements on the existing soil map, and allows for the creation of a low cost soil map for the surroundings of the study area.