Accuracy of interpolation techniques for the derivation of digital elevation models in relation to landform types and data density

Chaplot, Vincent; Darboux, Frédéric; Bourennane, Hocine; Leguédois, Sophie; Silvera, Norbert; Phachomphon, Konngkeo

doi:10.1016/j.geomorph.2005.12.010

Cited by 345 publications

(255 citation statements)

References 47 publications

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“…In some situations, there is a need of a brief data pre-processing before the execution of the functions, particularly in DEMs presenting a strong microrelief expression, such as the high resolution ones (CHAPLOT et al, 2006). In these instances, the need for pretreatment comes from the fact that tiny relief imperfections could be considered as saddle points, which has no practical sense.…”

Section: Separating the Hills By A Hydrological Conceptmentioning

confidence: 99%

Automated Mapping of Permanent Preservation Areas on Hilltops

Oliveira

Filho

2016

CERNE

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Permanent Preservation Areas (PPAs) on hilltops are among the many areas protected by the New Forest Code in Brazil. Mapping of these involves difficult interpretation and application of the Law, as well a complex task of translating it in map algebra. This paper aims to present, in detail, a methodological model for delimitation of PPAs on hilltops, according to the Brazilian new Forest Code (NFC, Law 12,651/2012). The model was developed in Model Builder for ArcGIS 10.2, and is able to map the PPAs in any digital terrain model. However, field validations are required to verify its efficiency. There is need for legal standardization of criteria that may cause subjectivity in delimitation. The organization of these data on a large scale is very important, as example, to the Rural Environmental Registry, which provides georeferencing of all rural properties and its protected areas in Brazil. MAPEAMENTO AUTOMATIZADO DE ÁREAS DE PRESERVAÇÃO PERMANENTE EM TOPO DE MORROSRESUMO: As Áreas de Preservação Permanente em topos de morros estão entre as diversas áeras protegidas pelo novo Código Florestal (NCF) brasileiro. O mapeamento das mesmas envolve dificuldades técnicas de interpretação da Lei, bem como a complexa tarefa de traduzi-la em álgebra de mapas. O presente trabalho apresenta, em detalhe, um modelo metodológico para delimitação de APPs em topos de morros, de acordo com o NCF (Lei 12.651/2012). O modelo foi construído em ambiente Model Builder para ArcGIS 10.2 e é capaz de mapear as APPs em qualquer modelo digital de terreno. No entanto, validações em campo são necessárias para verificar a eficiência do modelo. Também, é necessária a regulamentação de critérios que causam subjetividade na delimitação. A organização desses dados em larga escala é importante, por exemplo, para o Cadastro Ambiental Rural (CAR), que prevê o georreferenciamento de todas as propriedades rurais e suas áreas protegidas, no Brasil.

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Section: Separating the Hills By A Hydrological Conceptmentioning

confidence: 99%

Automated Mapping of Permanent Preservation Areas on Hilltops

Oliveira

Filho

2016

CERNE

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“…Each grid cell value is the predicted or interpolated elevation at the center or corner of the cell. Digital elevation models are the more commonly used in geographic information systems (GIS) environments because primary derivations, such as slope and aspect, can be easily calculated (Chaplot et al, 2006). Very useful secondary derivations, such as compound topographic index (CTI), specific catchment area, and stream networks can also be derived.…”

Section: Environmental Covariatesmentioning

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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“…There were only few differences between the interpolation techniques for high sampling densities while kriging performed best for low sampling densities. For the three mountainous sites located in Laos where high variation of altitude existed, IDW performed better than the other interpolation methods (Chaplot et al, 2006 In order to produce a continuous river surface, cross-sectional data points are interpolated by different interpolation techniques. These techniques often do not account of spatial trends in river bathymetry thus producing inaccurate interpolated surfaces (Merwade, 2009).…”

Section: Errors Due To Interpolation and Sampling Techniquementioning

confidence: 99%

Investigating the Role of DEM Resolution and Accuracy on Flood Inundation Mapping

Saksena

2015

World Environmental and Water Resources Congress 2015

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Accuracy of interpolation techniques for the derivation of digital elevation models in relation to landform types and data density

Cited by 345 publications

References 47 publications

Automated Mapping of Permanent Preservation Areas on Hilltops

Automated Mapping of Permanent Preservation Areas on Hilltops

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

Investigating the Role of DEM Resolution and Accuracy on Flood Inundation Mapping

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