暂无分享,去创建一个
Thomas Nauss | Christoph Reudenbach | Stephan Wöllauer | Hanna Meyer | C. Reudenbach | T. Nauss | H. Meyer | Stephan Wöllauer
[1] Fabio Terribile,et al. High-resolution space–time rainfall analysis using integrated ANN inference systems , 2010 .
[2] Fan Yang,et al. Precise estimation of soil organic carbon stocks in the northeast Tibetan Plateau , 2016, Scientific Reports.
[3] B. McGill,et al. Testing the predictive performance of distribution models , 2013 .
[4] Louise Willemen,et al. Machine Learning Using Hyperspectral Data Inaccurately Predicts Plant Traits Under Spatial Dependency , 2018, Remote. Sens..
[5] Lin Wang,et al. Mapping Annual Precipitation across Mainland China in the Period 2001-2010 from TRMM3B43 Product Using Spatial Downscaling Approach , 2015, Remote. Sens..
[6] T. Behrens,et al. Spatial modelling with Euclidean distance fields and machine learning , 2018, European Journal of Soil Science.
[7] Morteza Sadeghi,et al. A statistical framework for estimating air temperature using MODIS land surface temperature data , 2017 .
[8] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[9] Lukas Gudmundsson,et al. Towards observation-based gridded runoff estimates for Europe , 2014 .
[10] Tim Appelhans,et al. Evaluating machine learning approaches for the interpolation of monthly air temperature at Mt. Kilimanjaro, Tanzania , 2015 .
[11] Lei Deng,et al. Prediction of aboveground grassland biomass on the Loess Plateau, China, using a random forest algorithm , 2017, Scientific Reports.
[12] G. Groom,et al. Spatial application of Random Forest models for fine-scale coastal vegetation classification using object based analysis of aerial orthophoto and DEM data , 2015, Int. J. Appl. Earth Obs. Geoinformation.
[13] Carsten F. Dormann,et al. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure , 2017 .
[14] Jane Elith,et al. blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models , 2018, bioRxiv.
[15] Pierre Roudier,et al. Mapping Daily Air Temperature for Antarctica Based on MODIS LST , 2016, Remote. Sens..
[16] Tomislav Hengl,et al. Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation , 2018, Environ. Model. Softw..
[17] Falk Huettmann,et al. Predictions from machine learning ensembles: marine bird distribution and density on Canada’s Pacific coast , 2017 .
[18] Catherine Linard,et al. Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling , 2019, Geocarto International.
[19] Max Kuhn,et al. caret: Classification and Regression Training , 2015 .
[20] Jin Li,et al. Application of machine learning methods to spatial interpolation of environmental variables , 2011, Environ. Model. Softw..
[21] Matthew J. Cracknell,et al. Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information , 2014, Comput. Geosci..
[22] Lukas W. Lehnert,et al. From local spectral measurements to maps of vegetation cover and biomass on the Qinghai-Tibet-Plateau: Do we need hyperspectral information? , 2017, Int. J. Appl. Earth Obs. Geoinformation.
[23] Vincent Bretagnolle,et al. Spatial leave‐one‐out cross‐validation for variable selection in the presence of spatial autocorrelation , 2014 .
[24] Aniruddha Ghosh,et al. A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery , 2014, Int. J. Appl. Earth Obs. Geoinformation.
[25] Jacob Cohen. A Coefficient of Agreement for Nominal Scales , 1960 .
[26] George Alan Blackburn,et al. How well does random forest analysis model deforestation and forest fragmentation in the Brazilian Atlantic forest? , 2017, Environmental and Ecological Statistics.
[27] Luca Montanarella,et al. Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy , 2013, PloS one.
[28] Tomislav Hengl,et al. Spatio-temporal interpolation of soil water, temperature, and electrical conductivity in 3D + T: The Cook Agronomy Farm data set , 2015 .
[29] Marvin N. Wright,et al. Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables , 2018, PeerJ.
[30] Mikhail Kanevski,et al. Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping , 2013, Mathematical Geosciences.
[31] Shashi Shekhar,et al. Transdisciplinary Foundations of Geospatial Data Science , 2017, ISPRS Int. J. Geo Inf..
[32] Thomas Nauss,et al. Revealing the potential of spectral and textural predictor variables in a neural network-based rainfall retrieval technique , 2017 .
[33] Roberta E. Martin,et al. A Tale of Two “Forests”: Random Forest Machine Learning Aids Tropical Forest Carbon Mapping , 2014, PloS one.
[34] Jukka Heikkonen,et al. Estimating the prediction performance of spatial models via spatial k-fold cross validation , 2017, Int. J. Geogr. Inf. Sci..
[35] Pierre-Alain Danis,et al. Identification of ecological thresholds from variations in phytoplankton communities among lakes: contribution to the definition of environmental standards , 2016, Environmental Monitoring and Assessment.
[36] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[37] Andreas Huth,et al. Using airborne LiDAR to assess spatial heterogeneity in forest structure on Mount Kilimanjaro , 2017, Landscape Ecology.
[38] Francisco Alonso-Sarría,et al. Modification of the random forest algorithm to avoid statistical dependence problems when classifying remote sensing imagery , 2017, Comput. Geosci..
[39] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[40] M. Alfò,et al. Evaluating the effects of climate change on tree species abundance and distribution in the Italian peninsula , 2011 .
[41] Julian D. Olden,et al. Assessing transferability of ecological models: an underappreciated aspect of statistical validation , 2012 .
[42] Eric S Walsh,et al. A Random Forest approach to predict the spatial distribution of sediment pollution in an estuarine system , 2017, PloS one.
[43] Yaping Yang,et al. A Comparison of Different Regression Algorithms for Downscaling Monthly Satellite-Based Precipitation over North China , 2016, Remote. Sens..
[44] Alexander Brenning,et al. Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data , 2019, Ecological Modelling.
[45] Amir Hossein Alavi,et al. Machine learning in geosciences and remote sensing , 2016 .
[46] Max Kuhn,et al. Applied Predictive Modeling , 2013 .
[47] A. Brenning. Spatial prediction models for landslide hazards: review, comparison and evaluation , 2005 .
[48] Craig S. T. Daughtry,et al. A visible band index for remote sensing leaf chlorophyll content at the canopy scale , 2013, Int. J. Appl. Earth Obs. Geoinformation.
[49] Thomas C. Edwards,et al. Machine learning for predicting soil classes in three semi-arid landscapes , 2015 .