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

Dobos, Endre; Michéli, Erika; Baumgardner, M. F.; Biehl, L. L.; Helt, Todd

doi:10.1016/s0016-7061(00)00046-x

Cited by 151 publications

(84 citation statements)

References 23 publications

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“…The number of depressions was 78 times higher in DEM-ASTER and 25 times higher in DEM-TM than in DEM-TS, indicating a depression with a size of two pixels only. The causes of these errors in the DEM may be associated with the interpolation methods used to obtain the models (Dobos et al, 2000). For Hengl et al (2004), the high number of spurious depressions in DEMs obtained by remote sensors is common, due to gross errors arising from the sensor data collection, particularly in the case of ASTER.…”

Section: Resultsmentioning

confidence: 99%

Assessment of Digital Elevation Model for Digital Soil Mapping in a Watershed with Gently Undulating Topography

Moura-Bueno

Dalmolin

Caten

et al. 2016

Rev. Bras. Ciênc. Solo

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ABSTRACT:Terrain attributes (TAs) derived from digital elevation models (DEMs) are frequently used in digital soil mapping (DSM) as auxiliary covariates in the construction of prediction models. The DEMs and information extracted from it may be limited with regard to the spatial resolution and error magnitude, and can differ in the behavior of terrain features. The objective of this study was to evaluate the quality and limitations of free DEM data and to evaluate a topographic survey (TS) underlying the choice of a more appropriate model, for use in DSMs at a scale of 1:10,000. The study was conducted in an area of 937 ha in the watershed of Lajeado Giruá, in southern Brazil. The DEMs: DEM-TS, DEM-Topographic Map (TM), DEM-ASTER, DEM-SRTM, and DEM-TOPODATA were evaluated with regard to the precision elevation by statistical tests based on field reference points, the root mean square error (RMSE), identification of the number and size of spurious depressions, and the application of the Brazilian Cartographic Accuracy Standards Law (BCASL) to define the scale of each DEM. In addition, the TA derived from each DEM was compared with the TA from DEM-TS, considered to be terrain reality. The results showed that the elevation data of DEM-TS had the best quality (RMSE = 1.93 m), followed by DEM-SRTM (RMSE = 5.95 m), DEM-Topographic Map (RMSE = 8.28 m), DEM-TOPODATA (RMSE = 9.78 m) and DEM-ASTER (RMSE = 15.57 m). The DEM-TS was well-represented at a 1:10,000 scale, while the DEM-Topographic Map and DEM-SRTM fitted 1:50,000, the DEM-TOPODATA 1:50,000 and the DEM-ASTER a 1:100,000 scale. The results of DEM-SRTM and DEM-TOPODATA were closest to terrain reality (DEM-TS) and had the lowest number of spurious depressions and RMSE values for each evaluated attribute, but were inadequate for not fitting detailed scales compatible with small areas. The techniques for the acquisition of elevation data of each DEM and mainly the flat to gently undulating topography were factors that influenced the results. For a DSM at a scale of 1:10,000 in similar areas, the most appropriate model is DEM-TS.

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Section: Resultsmentioning

confidence: 99%

Assessment of Digital Elevation Model for Digital Soil Mapping in a Watershed with Gently Undulating Topography

Moura-Bueno

Dalmolin

Caten

et al. 2016

Rev. Bras. Ciênc. Solo

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“…Ongoing research in digital soil mapping has demonstrated that reasonably accurate soil maps can be produced using quantitative predictive models. Digital soil mapping may also expedite soil survey (Lagacherie and Holmes, 1997;Dobos et al, 2000;Zhu, 2000;Moran and Bui, 2002;McBratney et al, 2003;Scull et al, 2003;Cole, 2004;Shi et al, 2004;Henderson et al, 2005;Saunders, 2005;Scull et al, 2005;Cole and Boettinger, 2007;Saunders and Boettinger, 2007;Brungard, 2009).…”

Section: Introductionmentioning

confidence: 99%

Random Forests Applied as a Soil Spatial Predictive Model in Arid Utah

Stum

2010

Digital Soil Mapping

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Initial soil surveys are incomplete for large tracts of public land in the western USA. Digital soil mapping offers a quantitative approach as an alternative to traditional soil mapping. I sought to predict soil classes across an arid to semiarid watershed of western Utah by applying random forests (RF) and using environmental covariates derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and digital elevation models (DEM). Random forests are similar to classification and regression trees (CART).However, RF is doubly random. Many (e.g., 500) weak trees are grown (trained) independently because each tree is trained with a new randomly selected bootstrap sample, and a random subset of variables is used to split each node. To train and validate the RF trees, 561 soil descriptions were made in the field. An additional 111 points were added by case-based reasoning using aerial photo interpretation. As RF makes classification decisions from the mode of many independently grown trees, model iii uncertainty can be derived. The overall out of the bag (OOB) error was lower without weighting of classes; weighting increased the overall OOB error and the resulting output did not reflect soil-landscape relationships observed in the field. The final RF model had an OOB error of 55.2% and predicted soils on landforms consistent with soil-landscape relationships. The OOB error for individual classes typically decreased with increasing class size. In addition to the final classification, I determined the second and third most likely classification, model confidence, and the hypothetical extent of individual classes.Pixels that had high possibility of belonging to multiple soil classes were aggregated using a minimum confidence value based on limiting soil features, which is an effective and objective method of determining membership in soil map unit associations and complexes mapped at the 1:24,000 scale. Variables derived from both DEM and Landsat 7 ETM+ sources were important for predicting soil classes based on Gini and standard measures of variable importance and OOB errors from groves grown with exclusively DEM-or Landsat-derived data. Random forests was a powerful predictor of soil classes and produced outputs that facilitated further understanding of soil-landscape relationships.(147 pages) iv ACKNOWLEDGMENTS

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“…From this, primary (slope and plan curvature) and secondary (compound topographic index (CTI) and potential drainage density (PDD)) terrain features were calculated according to Moore et al (1993), Dobos et al (2000), Shary et al (2002), Gessler et al (1995), and McBratney et al (2003).…”

Section: Derivation Of Terrain Featuresmentioning

confidence: 99%

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

Cited by 151 publications

References 23 publications

Assessment of Digital Elevation Model for Digital Soil Mapping in a Watershed with Gently Undulating Topography

Assessment of Digital Elevation Model for Digital Soil Mapping in a Watershed with Gently Undulating Topography

Random Forests Applied as a Soil Spatial Predictive Model in Arid Utah

Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils

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