Predicting cover crop biomass by lightweight UAS-based RGB and NIR photography: an applied photogrammetric approach

[1]  Roger Bivand,et al.  Interface Between GRASS 6+ Geographical Information System and R , 2015 .

[2]  Nora Tilly,et al.  Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass , 2015, Remote. Sens..

[3]  Simon Bennertz,et al.  Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[4]  R. Lal.  A system approach to conservation agriculture , 2015, Journal of Soil and Water Conservation.

[5]  A. Walter,et al.  Plant phenotyping: from bean weighing to image analysis , 2015, Plant Methods.

[6]  Achim Walter,et al.  Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach , 2015, Plant Methods.

[7]  H. Scharr,et al.  HyperART: non-invasive quantification of leaf traits using hyperspectral absorption-reflectance-transmittance imaging , 2015, Plant Methods.

[8]  Jeffrey K. Gillan,et al.  Modeling vegetation heights from high resolution stereo aerial photography: an application for broad-scale rangeland monitoring. , 2014, Journal of environmental management.

[9]  Simon Bennertz,et al.  Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging , 2014, Remote. Sens..

[10]  Johanna Link,et al.  Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System , 2014, Remote. Sens..

[11]  J. Théau,et al.  Recent applications of unmanned aerial imagery in natural resource management , 2014 .

[12]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.

[13]  I. Colomina,et al.  Unmanned aerial systems for photogrammetry and remote sensing: A review , 2014 .

[14]  Livio Pinto,et al.  Experimental analysis of different software packages for orientation and digital surface modelling from UAV images , 2014, Earth Science Informatics.

[15]  Juliane Bendig,et al.  UAV-based Imaging for Multi-Temporal, very high Resolution Crop Surface Models to monitor Crop Growth Variability , 2013 .

[16]  Koichi Amaha,et al.  Canopy height measurement by photogrammetric analysis of aerial images: Application to buckwheat (Fagopyrum esculentum Moench) lodging evaluation , 2012 .

[17]  Jeffrey W. White,et al.  Field-based phenomics for plant genetics research , 2012 .

[18]  Nedjeljko Frančula,et al.  GIScience & Remote Sensing , 2012 .

[19]  Arko Lucieer,et al.  An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds , 2012, Remote. Sens..

[20]  M. Vincini,et al.  Comparing narrow and broad-band vegetation indices to estimate leaf chlorophyll content in planophile crop canopies , 2011, Precision Agriculture.

[21]  D. Tyler,et al.  In-Season Prediction of Corn Yield Using Plant Height under Major Production Systems , 2011 .

[22]  Kazuya Nakano,et al.  Camera-Variant Calibration and Sensor Modeling for Practical Photogrammetry in Archeological Sites , 2011, Remote. Sens..

[23]  Craig S. T. Daughtry,et al.  NIR-Green-Blue High-Resolution Digital Images for Assessment of Winter Cover Crop Biomass , 2011 .

[24]  Erle C. Ellis,et al.  Remote Sensing of Vegetation Structure Using Computer Vision , 2010, Remote. Sens..

[25]  D. Ehlert,et al.  Measuring crop biomass density by laser triangulation , 2008 .

[26]  Troy Jensen,et al.  Detecting the attributes of a wheat crop using digital imagery acquired from a low-altitude platform , 2007 .

[27]  William R. Raun,et al.  By‐Plant Prediction of Corn Forage Biomass and Nitrogen Uptake at Various Growth Stages Using Remote Sensing and Plant Height , 2007 .

[28]  Eduardo Segarra,et al.  Spatial and Temporal Variability of Corn Growth and Grain Yield , 2002 .

[29]  P. Thenkabail,et al.  Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics , 2000 .

[30]  P. Wolf,et al.  Elements of Photogrammetry(with Applications in GIS) , 2000 .

[31]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[32]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[33]  Qiang Cao,et al.  Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice , 2014 .

[34]  Giacomo Carli,et al.  6 th International Conference on Information and Communication Technologies in Agriculture, Food and Environment (HAICTA 2013) Drivers of Precision Agriculture Technologies Adoption: A Literature Review , 2013 .

[35]  A. Walter,et al.  REVIEW: PART OF A HIGHLIGHT ON BREEDING STRATEGIES FOR FORAGE AND GRASS IMPROVEMENT Advanced phenotyping offers opportunities for improved breeding of forage and turf species , 2012 .

[36]  P R Wolf,et al.  Elements of Photogrammetry , 1983 .