Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches
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{"title"=>"Improving the spatial prediction of soil organic carbon stocks in a complex tropical mountain landscape by methodological specifications in machine learning approaches", "type"=>"journal", "authors"=>[{"first_name"=>"Mareike", "last_name"=>"Ließ", "scopus_author_id"=>"35769417900"}, {"first_name"=>"Johannes", "last_name"=>"Schmidt", "scopus_author_id"=>"57189212970"}, {"first_name"=>"Bruno", "last_name"=>"Glaser", "scopus_author_id"=>"7103024868"}], "year"=>2016, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "pmid"=>"27128736", "scopus"=>"2-s2.0-84966269208", "pui"=>"610305347", "doi"=>"10.1371/journal.pone.0153673", "sgr"=>"84966269208"}, "id"=>"38120462-bab6-37ac-84fa-cb983abc2735", "abstract"=>"Tropical forests are significant carbon sinks and their soils' carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms-including the model tuning and predictor selection-were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models' predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction.", "link"=>"http://www.mendeley.com/research/improving-spatial-prediction-soil-organic-carbon-stocks-complex-tropical-mountain-landscape-methodol-1", "reader_count"=>40, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Researcher"=>9, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>7, "Student > Master"=>9, "Other"=>2, "Student > Bachelor"=>2, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Researcher"=>9, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>7, "Student > Master"=>9, "Other"=>2, "Student > Bachelor"=>2, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>6, "Engineering"=>3, "Environmental Science"=>14, "Mathematics"=>1, "Agricultural and Biological Sciences"=>11, "Computer Science"=>2, "Earth and Planetary Sciences"=>3}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>11}, "Computer Science"=>{"Computer Science"=>2}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>6}, "Environmental Science"=>{"Environmental Science"=>14}}, "reader_count_by_country"=>{"Brazil"=>1, "Italy"=>1, "Australia"=>1}, "group_count"=>2}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/5039815"], "description"=>"<p>RMSE boxplots of repeated cross-validation (a<sub>1</sub>-e<sub>1</sub>) and development of the mean RMSE of rpeated cross-validation during the predictor selection process (a<sub>2</sub>-e<sub>2</sub>). a) RF, b) ANN, c) MARS, d) BRT, e) SVM. In a<sub>1</sub>-e<sub>1</sub>: “all” refers to the all-predictor model, “1”refers to the 10bestPR model, “2” to the sFS model, and “3” to the 3stepFS model. In a<sub>2</sub>-e<sub>2</sub>: The star refers to the mean RMSE of the best individual predictor model of step 1, black points refer to added predictors and the resulting mean RMSE during step 2, and grey points refer to the mean RMSE after step 3. The dashed line represents the mean RMSE of the all-predictor model.</p>", "links"=>[], "tags"=>["predictor selection strategies", "Machine Learning Approaches Tropical forests", "multivariate adaptive regression splines", "mountain forest landscape", "support vector machines", "regression tree algorithm", "Complex Tropical Mountain Landscape", "SOC stocks", "GIS search radius", "Soil Organic Carbon Stocks", "model"], "article_id"=>3209989, "categories"=>["Environmental Sciences not elsewhere classified", "Chemical Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Plant Biology"], "users"=>["Mareike Ließ", "Johannes Schmidt", "Bruno Glaser"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0153673.g004", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Improving_the_Spatial_Prediction_of_Soil_Organic_Carbon_Stocks_in_a_Complex_Tropical_Mountain_Landscape_by_Methodological_Specifications_in_Machine_Learning_Approaches_-_Fig_4/3209989", "title"=>"Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches - Fig 4", "pos_in_sequence"=>4, "defined_type"=>1, "published_date"=>"2016-04-29 07:50:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/5039887"], "description"=>"<p>Selected predictors in order of importance.</p>", "links"=>[], "tags"=>["predictor selection strategies", "Machine Learning Approaches Tropical forests", "multivariate adaptive regression splines", "mountain forest landscape", "support vector machines", "regression tree algorithm", "Complex Tropical Mountain Landscape", "SOC stocks", "GIS search radius", "Soil Organic Carbon Stocks", "model"], "article_id"=>3210055, "categories"=>["Environmental Sciences not elsewhere classified", "Chemical Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Plant Biology"], "users"=>["Mareike Ließ", "Johannes Schmidt", "Bruno Glaser"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0153673.t003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Selected_predictors_in_order_of_importance_/3210055", "title"=>"Selected predictors in order of importance.", "pos_in_sequence"=>8, "defined_type"=>3, "published_date"=>"2016-04-29 07:50:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/5039782"], "description"=>"<p>Each part of the stacked bar plots refers to the carbon stock of a layer of 10 cm with exception of the lowermost layer, here indicated by black colour.</p>", "links"=>[], "tags"=>["predictor selection strategies", "Machine Learning Approaches Tropical forests", "multivariate adaptive regression splines", "mountain forest landscape", "support vector machines", "regression tree algorithm", "Complex Tropical Mountain Landscape", "SOC stocks", "GIS search radius", "Soil Organic Carbon Stocks", "model"], "article_id"=>3209959, "categories"=>["Environmental Sciences not elsewhere classified", "Chemical Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Plant Biology"], "users"=>["Mareike Ließ", "Johannes Schmidt", "Bruno Glaser"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0153673.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Carbon_stocks_of_the_organic_layer_in_each_of_the_sampled_profiles_at_the_upper_middle_and_lower_part_of_the_transects_M1_to_M20_/3209959", "title"=>"Carbon stocks of the organic layer in each of the sampled profiles at the upper,middle and lower part of the transects M1 to M20.", "pos_in_sequence"=>2, "defined_type"=>1, "published_date"=>"2016-04-29 07:50:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/5039860"], "description"=>"<p>Predictors derived from the landsat image.</p>", "links"=>[], "tags"=>["predictor selection strategies", "Machine Learning Approaches Tropical forests", "multivariate adaptive regression splines", "mountain forest landscape", "support vector machines", "regression tree algorithm", "Complex Tropical Mountain Landscape", "SOC stocks", "GIS search radius", "Soil Organic Carbon Stocks", "model"], "article_id"=>3210034, "categories"=>["Environmental Sciences not elsewhere classified", "Chemical Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Plant Biology"], "users"=>["Mareike Ließ", "Johannes Schmidt", "Bruno Glaser"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0153673.t001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Predictors_derived_from_the_landsat_image_/3210034", "title"=>"Predictors derived from the landsat image.", "pos_in_sequence"=>6, "defined_type"=>3, "published_date"=>"2016-04-29 07:50:56"}
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  • {"files"=>["https://ndownloader.figshare.com/files/5039896"], "description"=>"<p>Overview of selected predictors.</p>", "links"=>[], "tags"=>["predictor selection strategies", "Machine Learning Approaches Tropical forests", "multivariate adaptive regression splines", "mountain forest landscape", "support vector machines", "regression tree algorithm", "Complex Tropical Mountain Landscape", "SOC stocks", "GIS search radius", "Soil Organic Carbon Stocks", "model"], "article_id"=>3210070, "categories"=>["Environmental Sciences not elsewhere classified", "Chemical Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Plant Biology"], "users"=>["Mareike Ließ", "Johannes Schmidt", "Bruno Glaser"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0153673.t004", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Overview_of_selected_predictors_/3210070", "title"=>"Overview of selected predictors.", "pos_in_sequence"=>9, "defined_type"=>3, "published_date"=>"2016-04-29 08:24:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/5039803"], "description"=>"<p>Forward selection procedure for predictor subset selection.</p>", "links"=>[], "tags"=>["predictor selection strategies", "Machine Learning Approaches Tropical forests", "multivariate adaptive regression splines", "mountain forest landscape", "support vector machines", "regression tree algorithm", "Complex Tropical Mountain Landscape", "SOC stocks", "GIS search radius", "Soil Organic Carbon Stocks", "model"], "article_id"=>3209977, "categories"=>["Environmental Sciences not elsewhere classified", "Chemical Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Plant Biology"], "users"=>["Mareike Ließ", "Johannes Schmidt", "Bruno Glaser"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0153673.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Forward_selection_procedure_for_predictor_subset_selection_/3209977", "title"=>"Forward selection procedure for predictor subset selection.", "pos_in_sequence"=>3, "defined_type"=>1, "published_date"=>"2016-04-29 07:50:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/5039836"], "description"=>"<p>a) Median prediction value, b) Interquartile range (overlaid hillshading with light source from north).</p>", "links"=>[], "tags"=>["predictor selection strategies", "Machine Learning Approaches Tropical forests", "multivariate adaptive regression splines", "mountain forest landscape", "support vector machines", "regression tree algorithm", "Complex Tropical Mountain Landscape", "SOC stocks", "GIS search radius", "Soil Organic Carbon Stocks", "model"], "article_id"=>3210013, "categories"=>["Environmental Sciences not elsewhere classified", "Chemical Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Plant Biology"], "users"=>["Mareike Ließ", "Johannes Schmidt", "Bruno Glaser"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0153673.g005", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/SOC_stock_prediction_with_best_model_BRT_3stepFS_/3210013", "title"=>"SOC stock prediction with best model BRT 3stepFS.", "pos_in_sequence"=>5, "defined_type"=>1, "published_date"=>"2016-04-29 07:50:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/5039872"], "description"=>"<p>Predictors obtained from DEM.</p>", "links"=>[], "tags"=>["predictor selection strategies", "Machine Learning Approaches Tropical forests", "multivariate adaptive regression splines", "mountain forest landscape", "support vector machines", "regression tree algorithm", "Complex Tropical Mountain Landscape", "SOC stocks", "GIS search radius", "Soil Organic Carbon Stocks", "model"], "article_id"=>3210046, "categories"=>["Environmental Sciences not elsewhere classified", "Chemical Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Plant Biology"], "users"=>["Mareike Ließ", "Johannes Schmidt", "Bruno Glaser"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0153673.t002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Predictors_obtained_from_DEM_/3210046", "title"=>"Predictors obtained from DEM.", "pos_in_sequence"=>7, "defined_type"=>3, "published_date"=>"2016-04-29 07:50:56"}

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Relative Metric

{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}
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