Digital Soil Mapping Using Artificial Neural Networks and Terrain-Related Attributes

Bodaghabadi, Mohsen Bagheri; Martínez-Casasnovas, JoséAntonio; Salehi, Mona; Mohammadi, J.; Borujeni, Isa Esfandiarpoor; Toomanian, Norair; Gandomkar, Amir

doi:10.1016/s1002-0160(15)30038-2

Cited by 77 publications

(22 citation statements)

References 36 publications

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“…ANNs outperform traditional statistics in handling large datasets even when the input data are noisy with low levels of precision due to the ability to reduce bias by evenly distributing training data across classes [119]. Various researchers have employed ANNs for efficient prediction of quantitative soil chemical and hydrological properties [118,120,121] and adequate mapping categorical soil taxonomic classes [122][123][124][125][126][127][128][129] based on DTMs and environmental variables. Zhao et al [93] also tested the feasibility of using ANNs for soil drainage classification and found an accuracy of 52% between field observations and digital classification.…”

Section: New Emerging Methods To Predict Soil Propertiesmentioning

confidence: 99%

Application of Topographic Analyses for Mapping Spatial Patterns of Soil Properties

Li¹,

McCarty²

2019

Earth Observation and Geospatial Analyses [Working Title]

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Landscape topography is a key parameter impacting soil properties on the earth surface. Strong topographic controls on soil morphological, chemical, and physical properties have been reported. This chapter addressed applications of topographical information for mapping spatial patterns of soil properties in recent years. Objectives of this chapter are to provide an overview of (1) impacts of topographic heterogeneity on the spatial variability in soil properties and (2) commonly used topography-based models in soil science. A case study was provided to demonstrate the feasibility of applying topography-based models developed in field sites to predict soil property over a watershed scale. A large-scale soil property map can be obtained based on topographic information derived from high-resolution remotely sensed data, which would benefit studies in areas with limited data accesses or needed to extrapolate findings from representative sites to larger regions.

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Section: New Emerging Methods To Predict Soil Propertiesmentioning

confidence: 99%

Application of Topographic Analyses for Mapping Spatial Patterns of Soil Properties

Li¹,

McCarty²

2019

Earth Observation and Geospatial Analyses [Working Title]

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“…Para essa área todas as classes apresentaram acurácia do mapeador igual ou superior a 64%, sendo que as duas com maior número de perfis (LV e RR) apresentaram concordância superior a 90%. Na validação com os perfis de solos para ambas às áreas de estudo, os valores de AG obtidos podem ser considerados satisfatórios, uma vez que estão de acordo com outros estudos que utilizaram perfis de solos para predição de ocorrência dos solos no MDS (Häring et al, 2012;Bagheri Bodaghabadi et al, 2015;Vasques et al, 2015;Dias et al, 2016). Tabela 2.…”

Section: Resultsunclassified

Índice de área foliar residual como estratégia para manejo de pasto: estrutura do capim Tanzânia

Lemos

Ruggieri

Silva

et al. 2019

Agraria

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AGRONOMIA (AGRONOMY) RESUMO: Os estudos de mapeamento digital de classes de solos (MDS) têm utilizado mapas legados como principal fonte de informação para calibração dos modelos preditores. Entretanto, são necessárias novas abordagem com técnicas que permitam o uso de informações contidas em perfis de solos georreferenciados, permitindo a aplicação do MDS em áreas amostradas que não disponibilizem de mapas convencionais de solos. O objetivo deste estudo foi avaliar o desempenho na predição de ocorrência de solos de amostras coletadas em pixels de perfis de solos georreferenciados e em pixels coletados em buffers com raio de 50, 100, 150, 200 e 250 m dos perfis de solos nas bacias dos rios Lajeado Grande e Santo Cristo. As duas áreas possuem dados de levantamento de solos publicados em escala 1:50.000. Para predição da ocorrência das classes de solos foram utilizadas dez variáveis preditoras geradas a partir de um modelo digital de elevação com resolução espacial de 30 m. Para a predição foi utilizado o algoritmo Random Forest. Os mapas preditos foram avaliados quanto à exatidão em relação aos perfis de solos e quanto a reprodutibilidade dos mapas convencionais. A utilização dos pixels amostrais coletados nos buffers não alterou de forma expressiva a acurácia geral dos mapas preditos na bacia do rio Lajeado Grande, mas permitiu um ganho de 15,6% de exatidão na bacia do rio Santo Cristo. O uso apenas dos perfis georreferenciados resultou em mapas preditos com exatidão superior a 75% e concordância de reprodutibilidade igual ou superior a 67% em relação ao mapa convencional.

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“…The error matrix permits the calculation of a range of measures that describe the accuracy of one method with respect to the other. The overall accuracy (OA) (Congalton & Mead, ; Bagheri Bodaghabadi et al ., ) is the percentage of correctly classified or predicted areas with respect to the total number sampled.

OA = \frac{{false\sum}_{i = 1}^{n} X_{normalii}}{{false\sum}_{i = 1}^{n} \sum_{j = 1}^{n} X_{ij}}, {false\sum}_{i = 1}^{n} \sum_{j = 1}^{n} X_{ij} = n_{normaltot}

…”

Section: Methodsmentioning

confidence: 99%

Assessment of the FAO traditional land evaluation methods, A case study: Iranian Land Classification method

Bodaghabadi

Martínez‐Casasnovas

Khakili³

et al. 2015

Soil Use and Management

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Land evaluation is a critical step in land-use planning. Although many methods have been developed since the formulation of the FAO framework for land evaluation, several of the more traditional approaches still remain in widespread use but have not been adequately evaluated. Contrary to more recent land evaluation systems, which need considerable data, these systems only require basic soil and landscape information to provide a general view of land suitability for major types of land use. As the FAO initially presented its qualitative framework for land-use planning, based on two previous methods developed in Iran and Brazil, in this study we assessed the reliability and accuracy of a traditional land evaluation method used in Iran, called land classification for irrigation (LCI), comparing its results with several qualitative and quantitative methods and actual yield values. The results showed that, although simpler than more recently developed methods, LCI provided reliable land suitability classes and also showed good relationships both with other methods analysed and with actual yields. Comparisons between qualitative and quantitative methods produced similar results for common crops (a barley-alfalfa-wheat-fallow rotation). However, these methods performed differently for opportunist crops (such as alfalfa) that are more dependent on income and market conditions than on land characteristics. In this work, we also suggest that using the FAO method to indicate LCI subclasses could help users or managers to recognize limitations for land-use planning.

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Digital Soil Mapping Using Artificial Neural Networks and Terrain-Related Attributes

Cited by 77 publications

References 36 publications

Application of Topographic Analyses for Mapping Spatial Patterns of Soil Properties

Application of Topographic Analyses for Mapping Spatial Patterns of Soil Properties

Índice de área foliar residual como estratégia para manejo de pasto: estrutura do capim Tanzânia

Assessment of the FAO traditional land evaluation methods, A case study: Iranian Land Classification method

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