Integrating Airborne Hyperspectral, Topographic, and Soil Data for Estimating Pasture Quality Using Recursive Feature Elimination with Random Forest Regression
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Gabor Kereszturi | Rajasheker R. Pullanagari | Ian Yule | I. Yule | G. Kereszturi | R. R. Pullanagari
[1] Ian J. Yule,et al. Mapping of macro and micro nutrients of mixed pastures using airborne AisaFENIX hyperspectral imagery , 2016 .
[2] Rasmus Bro,et al. Variable selection in regression—a tutorial , 2010 .
[3] L. Kumar,et al. Estimating and mapping grass phosphorus concentration in an African savanna using hyperspectral image data , 2007 .
[4] Elizabeth M. Middleton,et al. Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Reflectance and Hyperion/EO-1 Data , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[5] Balázs Deák,et al. Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery , 2015, Remote. Sens..
[6] Onisimo Mutanga,et al. Forage quality of savannas - Simultaneously mapping foliar protein and polyphenols for trees and grass using hyperspectral imagery , 2010 .
[7] R. Kokaly,et al. Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies , 2009 .
[8] Onisimo Mutanga,et al. High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm , 2012, Int. J. Appl. Earth Obs. Geoinformation.
[9] Zou Xiaobo,et al. Variables selection methods in near-infrared spectroscopy. , 2010, Analytica chimica acta.
[10] Jan G. P. W. Clevers,et al. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review , 2015 .
[11] R. Richter,et al. Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: Atmospheric/topographic correction , 2002 .
[12] Georg Bareth,et al. Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages , 2010, Precision Agriculture.
[13] C. Furlanello,et al. Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products , 2006 .
[14] Simon D. Jones,et al. Hyperspectral determination of feed quality constituents in temperate pastures: Effect of processing methods on predictive relationships from partial least squares regression , 2012, Int. J. Appl. Earth Obs. Geoinformation.
[15] Marco Heurich,et al. Canopy foliar nitrogen retrieved from airborne hyperspectral imagery by correcting for canopy structure effects , 2017, Int. J. Appl. Earth Obs. Geoinformation.
[16] A. Skidmore,et al. Integrating imaging spectroscopy and neural networks to map grass quality in the Kruger National Park, South Africa , 2004 .
[17] R. Pullanagari,et al. Quantification of dead vegetation fraction in mixed pastures using AisaFENIX imaging spectroscopy data , 2017, Int. J. Appl. Earth Obs. Geoinformation.
[18] Ian J. Yule,et al. Developing variable rate application technology: Economic impact for farm owners and topdressing operators , 2007 .
[19] P. Curran. Remote sensing of foliar chemistry , 1989 .
[20] K. Soder,et al. Effect of soil type and fertilization level on mineral concentration of pasture: potential relationships to ruminant performance and health. , 2003, Journal of animal science.
[21] Andrew K. Skidmore,et al. Dry season mapping of savanna forage quality, using the hyperspectral Carnegie Airborne Observatory sensor , 2011 .
[22] Mohamed Medhat Gaber,et al. Random forests: from early developments to recent advancements , 2014 .
[23] M. Cho,et al. Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data , 2013 .
[24] V. Kakani,et al. Estimation of bioenergy crop yield and N status by hyperspectral canopy reflectance and partial least square regression , 2017, Precision Agriculture.
[25] M. P. Tuohy,et al. Seasonal prediction of in situ pasture macronutrients in New Zealand pastoral systems using hyperspectral data , 2013 .
[26] Matthias Rothmund,et al. Precision agriculture on grassland : Applications, perspectives and constraints , 2008 .
[27] P. Burrough,et al. Principles of geographical information systems , 1998 .
[28] Yoshio Inoue,et al. Estimating forage biomass and quality in a mixed sown pasture based on partial least squares regression with waveband selection , 2008 .
[29] Raymond F. Kokaly,et al. Investigating a Physical Basis for Spectroscopic Estimates of Leaf Nitrogen Concentration , 2001 .
[30] Elfatih M. Abdel-Rahman,et al. Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data , 2013 .
[31] A. Skidmore,et al. Narrow band vegetation indices overcome the saturation problem in biomass estimation , 2004 .
[32] John R. Miller,et al. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .
[33] M. P. Tuohy,et al. Multi-spectral radiometry to estimate pasture quality components , 2012, Precision Agriculture.
[34] Michael Wachendorf,et al. Development of canopy reflectance models to predict forage quality of legume-grass mixtures. , 2009 .
[35] Hao Zhou,et al. Structure damage detection based on random forest recursive feature elimination , 2014 .
[36] Ian J. Yule,et al. Developing variable rate application technology: Scenario development and agronomic evaluation , 2007 .
[37] A. Skidmore,et al. Red edge shift and biochemical content in grass canopies , 2007 .
[38] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[39] D. I. Gray,et al. Controlling pasture quality on hill country-key decisions and techniques , 2015 .
[40] J. Glover,et al. Milk production from pasture , 1961, The Journal of Agricultural Science.
[41] F. Scrimgeour,et al. Pathways ahead for New Zealand hill country farming , 2016 .
[42] Aixia Guo,et al. Gene Selection for Cancer Classification using Support Vector Machines , 2014 .
[43] M. P. Tuohy,et al. In-field hyperspectral proximal sensing for estimating quality parameters of mixed pasture , 2011, Precision Agriculture.
[44] J. Pereira,et al. Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest , 2012 .
[45] John G. Hodgson,et al. New zealand pasture and crop science , 1999 .
[46] F. Kırkpınar,et al. Chemical Composition, In vivo Digestibility and Metabolizable Energy Values of Caramba (Lolium multiflorum cv. caramba) Fresh, Silage and Hay , 2015, Asian-Australasian journal of animal sciences.