Comparison between detailed digital and conventional soil maps of an area with complex geology

Filho, Osmar Bazaglia; Rizzo, Rodnei; Lepsch, I. F.; Prado, Hélio do; Gomes, Felipe Haenel; Mazza, Jairo Antônio; Demattê, José Alexandre Melo

doi:10.1590/s0100-06832013000500003

Cited by 48 publications

(32 citation statements)

References 23 publications

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“…Another important factor is that accuracy and effectiveness of surveys conducted conventionally are contingent upon the ability of the pedologist. Indeed Bazaglia Filho et al (2013) showed differences in maps developed between five pedologists, which show the importance of increasing accuracy through geotechnologies, as indicated in the present study.…”

Section: General Characteristics Of the Digital Soil Map (Dsm) And Cosupporting

confidence: 66%

Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils

Alves

Demattê

Barros

2015

Rev. Bras. Ciênc. Solo

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in recent years, geotechnologies as remote and proximal sensing and attributes derived from digital terrain elevation models indicated to be very useful for the description of soil variability. however, these information sources are rarely used together. therefore, a methodology for assessing and specialize soil classes using the information obtained from remote/proximal sensing, Gis and technical knowledge has been applied and evaluated. two areas of study, in the state of são Paulo, Brazil, totaling approximately 28.000 ha were used for this work. first, in an area (area 1), conventional pedological mapping was done and from the soil classes found patterns were obtained with the following information: a) spectral information (forms of features and absorption intensity of spectral curves with 350 wavelengths -2,500 nm) of soil samples collected at specific points in the area (according to each soil type); b) obtaining equations for determining chemical and physical properties of the soil from the relationship between the results obtained in the laboratory by the conventional method, the levels of chemical and physical attributes with the spectral data; c) supervised classification of Landsat TM 5 images, in order to detect changes in the size of the soil particles (soil texture); d) relationship between classes relief soils and attributes. subsequently, the obtained patterns were applied in area 2 obtain pedological classification of soils, but in GIS (ArcGIS). finally, we developed a conventional pedological mapping in area 2 to which was compared with a digital map, ie the one obtained only with pre certain standards. the proposed methodology had a 79 % accuracy in the first categorical level of Soil Classification System, 60 % accuracy in the second category level and became less useful in the categorical level 3 (37 % accuracy).

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Section: General Characteristics Of the Digital Soil Map (Dsm) And Cosupporting

confidence: 66%

Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils

Alves

Demattê

Barros

2015

Rev. Bras. Ciênc. Solo

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“…(Cipra, 1973) Digital application of environmental similarity method Legros andBonneric (1979) 1980s Soil geostatistics Hajrasuliha et al (1980), Burgess and Webster (1980) Geographical information system Burrough (1986 Pedometrics: nonlinear geostatistics, fuzzy clustering, uncertainty analysis McBratney and Gruijter (1992), Odeh et al (1992b) Digital terrain modelling Gallant and Wilson (1996) Environmental correlation & cloprt model Moore et al (1993), McKenzie and Ryan (1999) Mapping using reference area Lagacherie et al (1995) Mapping using expert knowledge and expert system Skidmore et al (1996), Zhu et al (1997) Use of data mining tools Pachepsky et al (1996) (2014) Digital soil mapping has transformed soil survey and cartography more than anything else in recent times, and it is being tested and routinely used in soil mapping programmes around the world. Several studies have shown that digital soil mapping produces comparable or more accurate maps compared to conventional mapping (Bazaglia Filho et al, 2013;Collard et al, 2014). Lorenzetti et al (2015) compared 1:5,000,000 map of Italian soil regions and digital soil mapping at 1 km grid resolution in predicting the WRB reference soil groups (RSGs).…”

Section: Digital Soil Mappingmentioning

confidence: 99%

Digital soil mapping: A brief history and some lessons

2016

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“…Bazaglia Filho et al [9] also reached a range from 12% to 35% agreement between the traditional and digital soil map in an area of tropical soils. In that occasion, only terrain attributes derived from relif were used for soil classes prediction.…”

Section: Supervised Classification and Digital Soil Mapsmentioning

confidence: 82%

“…There is an indication that the results vary according to some parameters such as prediction co-variables, the scale, the complexity of the area and the statistical methods [9,65]. Although [34] have reached 91% of agreement for Oxisol class in their study, Alfisol and Ultisol classes just reached 46% of concordance between conventional and digital maps.…”

Section: Supervised Classification and Digital Soil Mapsmentioning

confidence: 91%

“…On the other hand, several subjectivities come from pedologists, and conventional soil maps are not exempt of error. Filho et al [9] demonstrated that five pedologists developed different soil maps in the same area, indicating the necessity to insert some technology to diminish this variation. In many cases, the soil mappers faces complex problems and must make empirical decisions based on their knowledge to decide soil limits or classification.…”

Section: Introductionmentioning

confidence: 99%

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Remote Sensing from Ground to Space Platforms Associated with Terrain Attributes as a Hybrid Strategy on the Development of a Pedological Map

Demattê¹,

López

Rizzo

et al. 2016

Remote Sensing

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There is a consensus about the necessity to achieve a quick soil spatial information with few human resources. Remote/proximal sensing and pedotransference are methods that can be integrated into this approach. On the other hand, there is still a lack of strategies indicating on how to put this in practice, especially in the tropics. Thus, the objective of this work was to suggest a strategy for the spatial prediction of soil classes by using soil spectroscopy from ground laboratory spectra to space images platform, as associated with terrain attributes and spectral libraries. The study area is located in São Paulo State, Brazil, which was covered by a regular grid (one per ha), with 473 boreholes collected at top and undersurface. All soil samples were analyzed in laboratory (granulometry and chemical), and scanned with a VIS-NIR-SWIR (400-2500 nm) spectroradiometer. We developed two traditional pedological maps with different detail levels for comparison: TFS-1 regarding orders and subgroups; and TFS-2 with additional information such as color, iron and fertility. Afterwards, we performed a digital soil map, generated by models, which used the following information: (i) predicted soil attributes from undersurface layer (diagnostic horizon), obtained by using a local spectral library; (ii) spectral reflectance of a bare soil surface obtained by Landsat image; and (iii) derivative of terrain attributes. Thus, the digital map was generated by a combination of three variables: remote sensing (Landsat data), proximal sensing (laboratory spectroscopy) and relief. Landsat image with bare soil was used as a first observation of surface. This strategy assisted on the location of topossequences to achieve soil variation in the area. Afterwards, spectral undersurface information from these locations was used to modelize soil attributes quantification (156 samples). The model was used to quantify samples in the entire area by spectra (other 317 samples). Since the surface was bare soil, it was sampled by image spectroscopy. Indeed, topsoil spectral laboratory information presented great similarity with satellite spectra. We observed angle variation on spectra from clayey to sandy soils as differentiated by intensity. Soil lines between bands 3/4 and 5/7 were helpful on the link between laboratory and satellite data. The spectral models of soil attributes (i.e., clay, sand, and iron) presented a high predictive performance (R 2 0.71 to 0.90) with low error. The spatial prediction of these attributes also presented a high performance (validations with R 2 > 0.78). The models increased spatial resolution of soil weathering information using a known spectral library. Elevation (altitude) improved mapping due to correlation with soil attributes (i.e., clay, iron and chemistry). We observed a close relationship between soil weathering index map and laboratory spectra + image spectra + relief parameters. The color composite of the 5R, 4G and 3B had great performance on the detection of soils along topossequences, since...

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Comparison between detailed digital and conventional soil maps of an area with complex geology

Cited by 48 publications

References 23 publications

Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils

Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils

Digital soil mapping: A brief history and some lessons

Remote Sensing from Ground to Space Platforms Associated with Terrain Attributes as a Hybrid Strategy on the Development of a Pedological Map

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