Avaliação de cinco algoritmos de árvores de decisão e três tipos de modelos digitais de elevação para mapeamento digital de solos a nível semidetalhado na Bacia do Lageado Grande, RS, Brasil

Giasson, Élvio; Hartemink, Alfred E.; Tornquist, Carlos Gustavo; Teske, Rodrigo; Bagatini, Tatiane

doi:10.1590/s0103-84782013001100008

Cited by 19 publications

(21 citation statements)

References 11 publications

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“…The K value was 0.36, which is lower than the average value of 0.47 observed in similar studies conducted in Brazil (ten Caten et al, 2012). However, Giasson et al (2013) observed an average K of 0.37 in the Lajeado Grande watershed, which shows characteristics similar to the area of the present study. Likewise, Bagatini et al (2015) obtained a GA similar to those in the present study; in their study, depending on the sampling point density, GA varied between 60 and 76% in the Rio Santo Cristo watershed, and between 54% (lowest density, 0.1 points ha -1…”

Section: Resultssupporting

confidence: 86%

“…This GA behavior was similar to that observed in the Carajazinho chart, which belongs to the same physiographic region evaluated by Giasson et al (2013) and Bagatini et al (2015).…”

Section: Resultssupporting

confidence: 85%

“…According to Giasson et al (2013), among the legacy data are maps and reports generated without proper field validation. As there are no estimates on the uncertainty involved in these soil inventories, any eventual errors may be propagated further.…”

Section: Introductionmentioning

confidence: 99%

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Digital soil mapping and its implications in the extrapolation of soil-landscape relationships in detailed scale

Wolski

Dalmolin

Flores

et al. 2017

Pesq. agropec. bras.

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-The objective of this work was to test the extrapolation of soil-landscape relationships in a reference area (RA) to a topographic map (scale 1:50,000), using digital soil mapping (DSM), and to compare these results to those obtained in similar studies previously conducted in Brazil. A soil survey in a 10 km 2 RA, using conventional mapping techniques (scale 1:10,000), was made in order to map a 678 km 2 physiographically similar area (scale 1:50,000) using DSM. The decision tree technique was employed to build a predictive extrapolation model based on soil classes and eight terrain attributes in the RA. The validation of DSM by application of field observation points resulted in a 66.1% global accuracy and in 0.36 kappa index. The most representative soils in the area were correctly predicted, whereas the less representative and less frequent soils in the landscape (and consequently with reduced sampling) had their prediction compromised. The RA proportion, which equals 1.5% of the total area, is a limiting factor in the formulation of soil-landscape relationships to precisely represent the mapped area by DSM.

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Section: Resultssupporting

confidence: 86%

“…This GA behavior was similar to that observed in the Carajazinho chart, which belongs to the same physiographic region evaluated by Giasson et al (2013) and Bagatini et al (2015).…”

Section: Resultssupporting

confidence: 85%

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Digital soil mapping and its implications in the extrapolation of soil-landscape relationships in detailed scale

Wolski

Dalmolin

Flores

et al. 2017

Pesq. agropec. bras.

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“…They observed a high ratio between the elevation data and mapping units, and found that DEMs with a wide amplitude in elevation values generate decision tree models with less selected TA, reflected in the reduction of predicted soil classes. On the other hand, Giasson et al (2013) found small differences between the number of TA selected by the decision tree model and the Kappa index in a comparison of the DEMs ASTER (30 m), SRTM (30 m) and SRTM (90 m) for DSM at a scale of 1:50,000 in an area of 53,200 ha.…”

Section: Resultsmentioning

confidence: 87%

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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“…Em trabalho recente, Giasson et al (2013) relataram que a comparação entre três MDEs, com pixel de 30 e 90 m, utilizados para a predição de unidades de mapeamento de solos (UM), não foi conclusiva, pois a diferença na capacidade de predição dos modelos foi muito pequena. Ao utilizar análise wavelet e variáveis preditoras derivadas do MDE TOPODATA, ten Caten et al (2012) sugeriram que a resolução espacial a ser utilizada no MDS deve ser entre 32 e 40 m. Em estudo realizado por Cavazzi et al (2013), os autores demonstraram que, em áreas com relevo menos declivoso, os MDEs com menores resoluções espaciais (140 m) possibilitaram gerar modelos de predição de solos mais acurados.…”

Section: Introductionunclassified

Comparação do uso de modelos digitais de elevação em mapeamento digital de solos em Dois Irmãos, RS, Brasil

Teske

Giasson

Bagatini

2014

Rev. Bras. Ciênc. Solo

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Os modelos digitais de elevação (MDEs) são fontes fundamentais para correlacionar a ocorrência e distribuição de solos com a paisagem pelo mapeamento digital de solos (MDS). A influência dos tipos e das resoluções dos MDEs na capacidade de predição dos modelos preditores de classes de solo ainda é pouco estudada. Neste estudo, foram avaliados e comparados os efeitos de diferentes MDEs na predição de ocorrência de unidades de mapeamento de solo (UM). Foram correlacionados 12 atributos do terreno derivados de diferentes MDEs com a ocorrência de UM. Os MDEs utilizados foram os oriundos dos projetos SRTM v4.1, ASTER GDEM v2, TOPODATA e Brasil em Relevo, e os MDEs gerados a partir de curvas de nível na escala de 1:50.000, com resoluções de 30 e 90 m. Os modelos preditores foram treinados por árvore de decisão (Simple Cart) com dados amostrados em 4.280 pontos aleatórios contendo informações dos solos extraídos de um mapa convencional de solos na escala 1:20.000 e 12 atributos do terreno derivados de seis MDEs com tamanhos de pixel de 30 e 90 m. A validação dos modelos preditores de UM foi realizada com a totalidade dos dados da área. Os atributos do terreno que melhor explicaram a ocorrência das UM foram elevação, declividade, comprimento de fluxo e orientação das vertentes. Os MDEs com tamanho de pixel de 30 m geraram correlações solo-paisagem menos acuradas. Os modelos preditores mais acurados e com maior número de UM estimadas foram os gerados a partir dos MDEs com resolução espacial de 90 m (SRTM v4.1 e CN90), sendo esses os MDEs mais indicados para o MDS, quando predominarem relevos plano e suave ondulado.

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Avaliação de cinco algoritmos de árvores de decisão e três tipos de modelos digitais de elevação para mapeamento digital de solos a nível semidetalhado na Bacia do Lageado Grande, RS, Brasil

Cited by 19 publications

References 11 publications

Digital soil mapping and its implications in the extrapolation of soil-landscape relationships in detailed scale

Digital soil mapping and its implications in the extrapolation of soil-landscape relationships in detailed scale

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

Comparação do uso de modelos digitais de elevação em mapeamento digital de solos em Dois Irmãos, RS, Brasil

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