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

Moura-Bueno, Jean Michel; Dalmolin, Ricardo Simão Diniz; Caten, Alexandre ten; Ruiz, Luis Fernando Chimelo; Ramos, Priscila Vogelei; Dotto, André Carnieletto

doi:10.1590/18069657rbcs20150022

Cited by 12 publications

(13 citation statements)

References 22 publications

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“…The definition of the respective parameter settings is based on the expert's experience and expertise. These results are in line with results of [39] and [41], who showed that DEM with a low variation in elevation values generated more accurate prediction models of soil classes (units) with a higher number of homogeneous relief units.…”

Section: Resultssupporting

confidence: 92%

Analysis of Digital Elevation Model and LNDSAT Data Using Geographic Information System for Soil Mapping in Urban Areas

Mohamed¹

2017

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This study applies digital analysis methods of topographic data derived from digital elevation models (DEMs) and Landsat remotely sensed spectral data using GIS tools to evaluate the quality and limitations of the morphometric parameters (terrain attributes: TAs). This aims to check its suitability for digital soil mapping (DSM) and survey in urban areas at the target scale 1:50,000. This scale represents the standard scale level for compiling soil inventories within all German states. The study is conducted on an urban area of 112.68 km 2 in the southwest part of the state of Berlin in Germany. These relief units are the basis for determining the soil mapping units at the scale of 1:50,000. The generated preliminary soil map was compared to soil maps made using traditional soil survey methods. For the mainly natural soils, the equivalence area is 94.91%, and for the anthropogenic soils, the equivalence area is 95.34%. The proposed methodology is adequate for preliminary mapping of soil units based on the digital derivation of TAs. Landsat scenes are spatially explicit, physical representations of environmental covariates on the land surface. The free DEM-ASTER in combination with Landsat OLI images is found to be the appropriate model to represent the terrain surface and derive the TAs for environmental modeling and fitting of derivation the relief units and their topography features. However, the 30 m spatial resolution and the fairly coarse spectral resolution of DEMs and Landsat images limit their utility for digital soil mapping at this scale in urban areas with little topographic variation.

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Section: Resultssupporting

confidence: 92%

Analysis of Digital Elevation Model and LNDSAT Data Using Geographic Information System for Soil Mapping in Urban Areas

Mohamed¹

2017

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“…When all cartographic data (hydrography and contour lines) and accurate available ground points are used in conjunction with hydrologically consistent algorithms, the ability of the resulting DEM in accurately represent morphology of the modeled terrainsurface (Tables 3, 4 and Figure 4) is better. This corroborates some literature results (Chagas et al, 2010;Moura-Bueno et al, 2016;Neumann;Roig;Souza, 2012;Pinheiro et al, 2012). Moreover, the finer the DEM spatial resolution the higher the accuracy of elevation values.…”

Section: Dem Characteristics and Quality For Dsmsupporting

confidence: 91%

“…Sheet 22S45_ZN was used in this study. A set of 10 quality criteria (Table 2) were used to assess the quality of three DEMs in each spatial resolutions ( Figure 3) (Baltensweileret al, 2017;Cavazzi et al, 2013;Chagas et al, 2010;Moura-Bueno et al, 2016;Neumann;Roig;Souza, 2012;Penížek et al, 2016;Pinheiro et al, 2012;Thompson;Bell;Butler, 2001). Descriptive statistics are minimum, maximum, mean, and standard deviation of the elevation.…”

Section: Elevation Datamentioning

confidence: 99%

“…There are several strategies to select a single satellite image and DEM for a DSM project. In the case of DEMs, a popular strategy is to assess its quality (Baltensweiler et al, 2017;Cavazzi et al, 2013;Chagas et al, 2010;Moura-Bueno et al, 2016;Neumann;Roig;Souza, 2012;Penížek et al, 2016;Pinheiro et al, 2012). In general, the DEM´s quality is defined by combining various indicators and measures, presumably related to the capacity of a DEM and derived attributes to represent terrain surface features.…”

Section: Introductionmentioning

confidence: 99%

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Digital elevation model quality on digital soil mapping prediction accuracy

2018

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Digital elevation models (DEM) used in digital soil mapping (DSM) are commonly selected based on measures and indicators (quality criteria) that are thought to reflect how well a given DEM represents the terrain surface. The hypothesis is that the more accurate a DEM, the more accurate will be the DSM predictions. The objective of this study was to assess different criteria to identify the DEM that delivers the most accurate DSM predictions. A set of 10 criteria were used to evaluate the quality of nine DEMs constructed with different data sources, processing routines and three resolutions (5, 20, and 30 m). Multinomial logistic regression models were calibrated using 157 soil observations and terrain attributes derived from each DEM. Soil class predictions were validated using leave-one-out cross-validation. Results showed that, for each resolution, the quality criteria are useful to identify the DEM that more accurately represents the terrain surface. However, for all three resolutions, the most accurate DEM did not produce the most accurate DSM predictions. With the 20-m resolution DEMs, DSM predictions were five percentage points less accurate when using the more accurate DEM. The 5-m resolution was the most accurate DEM overall and resulted in DSM predictions with 44% accuracy; this value was equal to that obtained with two coarser resolution, lower accuracy DEMs. Thus, identifying the truly best DEM for DSM requires assessment of the accuracy of DSM predictions using some form of external validation, because not necessarily the most accurate DEM will produce the best DSM predictions.

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“…It is as these in recent years, there have witnessed a significant increase in the use of geotechnological tools (remote sensing [RS] and GIS) to study soil erosive processes. The principle objectives of many of these contributions are: (a) the multi-temporal and spatial modeling of erosive processes (Zeng et al, 2013); (b) the analysis of the effects of the changes in vegetal cover and land use (Lech-hab et al, 2015); (c) the quantification of soil loss (Parveen and Kumar, 2012;Farhan et al, 2013;Amsalu and Mengaw, 2014;Lahlaoi et al, 2015;Ganasri and Ramesh, 2016;Tahiri et al, 2016); (d) the zoning of the hazard and risk control (Prasannakumar et al, 2012;Gaatib and Larabi, 2014;Uddin et al, 2014;Chirala et al, 2015); and (e) soil mapping and classification (Alves et al, 2015;Rizzo et al, 2015;Moura-Bueno et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Soil erosion risk zoning in the Ecuadorian coastal region using geo-technological tools

2019

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The activation of erosive processes on the slopes of the drainage catchments in the Ecuadorian coastal region represents a serious environmental problem that results in a loss of soil resources with the consequent reduction in agricultural productivity, an increasing lack of areas with vegetation, and decreased water quality. These issues increase, among other things, the probability of flooding. The purpose of this research is to zone soil erosion risk in the Manabí province, which is located on the Ecuadorian coast. The methodology is supported by the employment of Geographic Information Technologies (GIT) in order to access and process information of interest, such as: precipitation data with which to calculate the R Factor of the Universal Soil Loss Equation (USLE); topography, which was employed to create a slope map from the Digital Elevation Model (DEM) ASTER; vegetation cover, by applying the Standard Difference Vegetation Index (NDVI); land use, through the interpretation of orthophotos and a field survey; and infiltration capacity, obtained by considering the texture of the soil. The factors were combined by means a weighted relationship (map algebra) and exploiting the potential of software for the design of Geographic Information Systems (GIS). The results indicate the presence of soils with little or without vegetal protection, in areas with steep slopes, moderate rainfall and extensive farming activities. These characteristics determine conditions of high soil erodability, and propitiate the triggering of erosive processes. This scenario shows the need for autonomous governments to implement policies, strategies and actions so as to promote the strengthening of institutional and community capacities that will promote environmental sustainability.

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

Cited by 12 publications

References 22 publications

Analysis of Digital Elevation Model and LNDSAT Data Using Geographic Information System for Soil Mapping in Urban Areas

Analysis of Digital Elevation Model and LNDSAT Data Using Geographic Information System for Soil Mapping in Urban Areas

Digital elevation model quality on digital soil mapping prediction accuracy

Soil erosion risk zoning in the Ecuadorian coastal region using geo-technological tools

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