Soil organic carbon concentrations and stocks on Barro Colorado Island — Digital soil mapping using Random Forests analysis

Grimm, Rosina; Behrens, Thorsten; Maerker, Michael; Elsenbeer, Helmut

doi:10.1016/j.geoderma.2008.05.008

Cited by 575 publications

(345 citation statements)

References 78 publications

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“…O padrão para ntree definido no sistema é de 500. Embora resultados mais estáveis possam ser alcançados com um número maior (Grimm et al, 2008), testes preliminares mostraram que o aumento do ntree não melhora o desempenho do modelo; assim, optou-se por utilizar valores de 70, 120 e 500, conforme os testes preliminares da performance dos modelos. Para o valor de "nodesize", utilizou-se o padrão de cinco para cada nó terminal.…”

Section: Methodsunclassified

Regressão linear múltipla e modelo Random Forest para estimar a densidade do solo em áreas montanhosas

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Chagas

et al. 2016

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi o desenvolvimento de modelos com diferentes conjuntos de dados, para estimar a densidade de solos de regiões tropicais montanhosas, a partir de atributos de solos comumente encontrados nas análises de perfis de solos descritos nos levantamentos regionais. O conjunto total de dados compõe-se de 163 amostras e foi dividido em seis grupamentos, dos quais três com 73 amostras, com o máximo de 32 covariáveis, e três com 163 amostras, com o máximo de 18 covariáveis. Testaram-se modelos de regressão linear múltipla (RLM) e randomForest (RF). A menor incerteza entre os modelos foi alcançada pelo RLM2, com R 2 de 0,56, 13 covariáveis e 73 amostras. Nos grupamentos com 163 amostras, os melhores modelos foram os RF, com R 2 médio de 0,48. A raiz quadrada da média do erro ao quadrado variou entre 0,09 e 0,14. As covariáveis mais importantes no modelo RF foram: carbono orgânico, hidrogênio, areia fina e grossa, saturação por bases e capacidade de troca catiônica. Pelo método "stepwise regression", as variáveis mais importantes foram: a relação silte/argila; areia grossa e fina; carbono orgânico; saturação por bases; e potássio.Termos para indexação: estoque de carbono, funções de pedotransferência, modelos dirigidos pelos dados, stepwise. Multiple linear regression and Random Forest model to estimate soil bulk density in mountainous regionsAbstract -The objective of this work was the development of models with different sets of data for estimating soil bulk density in tropical mountainous regions, from soil attributes commonly found in the analyses of soil profiles described in regional surveys. The complete dataset is composed of 163 samples and it was divided into six groups, of which three groups have 73 samples and the maximum of 32 covariables, and three have 163 samples and the maximum of 18 covariables. The linear regression (RLM) and randomForest (RF) models were tested. The lowest uncertainty between the models was achieved by RLM2, with R 2 of 0.56, 13 covariables, and 73 samples. Considering the groups with 163 samples, the best models were the RFs with mean R 2 of 0.48. The root mean squared error ranged between 0.09 and 0.14. The most important covariables in the RF model were: organic carbon, hydrogen, fine and coarse sand, base saturation, and cation exchange capacity. By the stepwise backward regression, the main covariables were: silt and clay relation; fine and coarse sand; organic carbon; base saturation; and potassium.

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Section: Methodsunclassified

Regressão linear múltipla e modelo Random Forest para estimar a densidade do solo em áreas montanhosas

Carvalho

Filho

Chagas

et al. 2016

Pesq. agropec. bras.

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“…Random forest (RF; Breiman 2001) is increasingly used in a range of applications including digital soil mapping (Grimm et al 2008), forest biomass mapping (Baccini et al 2012), species distribution modeling (Evans and Cushman 2009) and others given its often superior performance compared to other methods (Evans et al 2011). RF is also gaining prominence in land-use classification (e.g., Aide et al 2013;Grinand et al 2013), where it outperforms classification and regression trees (CART; RodriguezGaliano et al 2012) and maximum likelihood classifiers (Schneider 2012).…”

Section: Introductionmentioning

confidence: 99%

Multiple-scale prediction of forest loss risk across Borneo

et al. 2017

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Context The forests of Borneo have among the highest biodiversity and also the highest forest loss rates on the planet. Objectives Our objectives were to: (1) compare multiple modelling approaches, (2) evaluate the utility of landscape composition and configuration as predictors, (3) assess the influence of the ratio of forest loss and persistence points in the training sample, (4) identify the multiple-scale drivers of recent forest loss and (5) predict future forest loss risk across Borneo. Methods We compared random forest machine learning and logistic regression in a multi-scale approach to model forest loss risk between 2000 and 2010 as a function of topographical variables and landscape structure, and applied the highest performing model to predict the spatial pattern of forest loss risk between 2010 and 2020. We utilized a naïve model as a null comparison and used the total operating characteristic AUC to assess model performance. Results Our analysis produced five main results. We found that: (1) random forest consistently outperformed logistic regression and the naïve model; (2) including landscape structure variables substantially improved predictions; (3) a ratio of occurrence to nonoccurrence points in the training dataset that does not match the actual ratio in the landscape biases the predictions of both random forest and logistic regression; (4) forest loss risk differed between the three nations that comprise Borneo, with patterns in Kalimantan highly related to distance from the edge of the previous frontier of forest loss, while Malaysian Borneo showed a more diffuse pattern related to the structure of the landscape; (5) we predicted continuing 123Landscape Ecol (2017) 32:1581-1598 DOI 10.1007 very high rates of forest loss in the 2010-2020 period, and produced maps of the expected risk of forest loss across the full extent of Borneo. Conclusions These results confirm that multiplescale modelling using landscape metrics as predictors in a random forest modelling framework is a powerful approach to landscape change modelling. There is immense immanent risk to Borneo's forests, with clear spatial patterns of risk related to topography and landscape structure that differ between the three nations that comprise Borneo.

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“…However, despite of classifying the variables according to their importance to the model (Archer;Kimes, 2008), this method does not generate a final equation of the model, as opposed to SMLR. Therefore, it is sometimes referred to as a black-box method (Grimm et al, 2008), although some works have pointed out that this method is robust and provides better results than other methods for both spatial and non-spatial predictions (Hengl et al, 2015;Lies;Glaser;Huwe, 2012;Souza et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Multiple linear regression and random forest to predict and map soil properties using data from portable X-ray fluorescence spectrometer (pXRF)

et al. 2017

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Determination of soil properties helps in the correct management of soil fertility. The portable X-ray fluorescence spectrometer (pXRF) has been recently adopted to determine total chemical element contents in soils, allowing soil property inferences. However, these studies are still scarce in Brazil and other countries. The objectives of this work were to predict soil properties using pXRF data, comparing stepwise multiple linear regression (SMLR) and random forest (RF) methods, as well as mapping and validating soil properties. 120 soil samples were collected at three depths and submitted to laboratory analyses. pXRF was used in the samples and total element contents were determined. From pXRF data, SMLR and RF were used to predict soil laboratory results, reflecting soil properties, and the models were validated. The best method was used to spatialize soil properties. Using SMLR, models had high values of R² (≥0.8), however the highest accuracy was obtained in RF modeling. Exchangeable Ca, Al, Mg, potential and effective cation exchange capacity, soil organic matter, pH, and base saturation had adequate adjustment and accurate predictions with RF. Eight out of the 10 soil properties predicted by RF using pXRF data had CaO as the most important variable helping predictions, followed by P 2 O 5 , Zn and Cr. Maps generated using RF from pXRF data had high accuracy for six soil properties, reaching R 2 up to 0.83. pXRF in association with RF can be used to predict soil properties with high accuracy at low cost and time, besides providing variables aiding digital soil mapping.Index terms: Soil analyses; spatial prediction; proximal sensor. RESUMOA determinação de atributos do solo auxilia no correto manejo da sua fertilidade. O equipamento portátil de fluorescência de raios-X (pXRF) foi recentemente adotado para determinar o teor total de elementos químicos em solos, permitindo inferências sobre atributos do solo. No entanto, esses estudos ainda são escassos no Brasil e em outros países. Os objetivos deste trabalho foram prever atributos do solo a partir de dados do pXRF, comparando-se os métodos de regressão linear múltipla stepwise (SMLR) e de random forest (RF), além de mapear e validar atributos do solo. 120 amostras de solo foram coletadas em três profundidades e submetidas a análises laboratoriais. Utilizou-se o pXRF para leitura das amostras e determinou-se o teor total de elementos. A partir dos dados do pXRF, foram utilizadas SMLR e RF para predizer resultados laboratoriais, que refletem atributos do solo, e os modelos foram validados. O melhor método foi utilizado para espacializar os atributos do solo. Utilizando SMLR, os modelos apresentaram valores elevados de R² (≥0,8), porém maior acurácia foi obtida na modelagem com RF. A capacidade de troca de cátions potencial e efetiva, matéria orgânica do solo, pH, saturação por bases e teores trocáveis de Ca, Al e Mg apresentaram ajustes adequados e predições acuradas com RF. Dos dez atributos do solo preditos por RF a partir de dados do pXRF, sete apr...

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Soil organic carbon concentrations and stocks on Barro Colorado Island — Digital soil mapping using Random Forests analysis

Cited by 575 publications

References 78 publications

Regressão linear múltipla e modelo Random Forest para estimar a densidade do solo em áreas montanhosas

Regressão linear múltipla e modelo Random Forest para estimar a densidade do solo em áreas montanhosas

Multiple-scale prediction of forest loss risk across Borneo

Multiple linear regression and random forest to predict and map soil properties using data from portable X-ray fluorescence spectrometer (pXRF)

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