Digital Elevation Models

Florinsky, Igor V.

doi:10.1016/b978-0-12-385036-2.00003-1

Cited by 9 publications

(2 citation statements)

References 201 publications

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“…Over the last few decades, RS has gradually been applied in a more quantitative way to the interpretation of soil classes (Demattê et al, 2004;Mulder et al, 2011;Teng et al, 2018;Viscarra Rossel et al, 2016). Using digital elevation models, it is possible to extract several terrain attributes that can then be taken into consideration by a pedologist in a soil survey (Florinsky, 2012). In addition, the climate can contribute to a general understanding of the soil (Brevik et al, 2018) and can also assist in its quantification depending on the scale of the study.…”

Section: Introductionmentioning

confidence: 99%

Soil environment grouping system based on spectral, climate, and terrain data: a quantitative branch of soil series

Dotto

Demattê

Rossel

et al. 2020

SOIL

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Abstract. Soil classification has traditionally been developed by combining the interpretation of taxonomic rules that are related to soil information with the pedologist's tacit knowledge. Hence, a more quantitative approach is necessary to characterize soils with less subjectivity. The objective of this study was to develop a soil grouping system based on spectral, climate, and terrain variables with the aim of establishing a quantitative way of classifying soils. Spectral data were utilized to obtain information about the soil, and this information was complemented by climate and terrain variables in order to simulate the pedologist knowledge of soil–environment interactions. We used a data set of 2287 soil profiles from five Brazilian regions. The soil classes of World Reference Base (WRB) system were predicted using the three above-mentioned variables, and the results showed that they were able to correctly classify the soils with an overall accuracy of 88 %. To derive the new system, we applied the spectral, climatic, and terrain variables, which – using cluster analysis – defined eight groups; thus, these groups were not generated by the traditional taxonomic method but instead by grouping areas with similar characteristics expressed by the variables indicated. They were denominated as “soil environment groupings” (SEGs). The SEG system facilitated the identification of groups with equivalent characteristics using not only soil but also environmental variables for their distinction. Finally, the conceptual characteristics of the eight SEGs were described. The new system has been designed to incorporate applicable soil data for agricultural management, to require less interference from personal/subjective/empirical knowledge (which is an issue in traditional taxonomic systems), and to provide more reliable automated measurements using sensors.

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Section: Introductionmentioning

confidence: 99%

Soil environment grouping system based on spectral, climate, and terrain data: a quantitative branch of soil series

Dotto

Demattê

Rossel

et al. 2020

SOIL

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“…A DEM is a depiction of the physical topography of an area that consists of a watershed, horizontal curvatures, slope gradient, and other topographic characteristics [19]. DEM data are generated using various field, remote, and laboratory techniques such as conventional topographic surveys, kinematic GPS surveys, laser surveys, analog, radar techniques, and digitizing of contours [20]. DEM is available in many sources, such as SRTM, IFSAR, Alos Palsar, Indonesia's DEM data, and DEMNAS.…”

Section: Resultsmentioning

confidence: 99%

Modified geomorphic flood index (GFI) method on backwater problem in tidal tropical river for rapid flood assessment

Abusarif,

Nugroho,

Riawan

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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The Geomorphic Flood Index (GFI) model can show flood inundation in a watershed based on flood events. Flood inundation can occur because of the backwater effect in the river channel. The different water levels from the river and tide that occur downstream may temporarily disturb the water depth and flow velocity. Because of this disturbance, a backwater effect might occur. The case study is located at the Plumbon River in Semarang City, which has the potential for a backwater effect in the river downstream. Backwater is a hydraulic phenomenon that occurs in rivers and can be simulated using a hydrodynamic model, as it can show the relationship between river discharge and tides. The conventional GFI method does not consider hydraulic effects, such as backwater effects, as part of the calculation. Therefore, modifications will be inserted into the calculation of the hydraulic effect on the modelling of flood inundation. The main purpose of this study is to build a modified GFI method that includes backwater analysis in its modelling to predict the backwater effect that occurs downstream of rivers and its impact on inundation. This study will help rapidly predict flood inundation.

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Effect of landforms and vegetations on pedological variability and crop yield along the toposequence of Eastern Coastal Plain of Odisha, India

Ramasamy,

Manickam,

Padua

et al. 2024

J Coast Conserv

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Digital Elevation Models

Cited by 9 publications

References 201 publications

Soil environment grouping system based on spectral, climate, and terrain data: a quantitative branch of soil series

Soil environment grouping system based on spectral, climate, and terrain data: a quantitative branch of soil series

Modified geomorphic flood index (GFI) method on backwater problem in tidal tropical river for rapid flood assessment

Effect of landforms and vegetations on pedological variability and crop yield along the toposequence of Eastern Coastal Plain of Odisha, India

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