Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley

By Juliane Bendig, K. Yu, Helge Aasen, Andreas Bolten, Simon Bennertz, Janis Broscheit, M. Gnyp, Georg Bareth. International Journal of Applied Earth Observation and Geoinformation, 2015

1,170 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/2334c0eae8519ae8b6deb36285f30d2bfb15d7b4

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Citations in the corpus

Predicting cover crop biomass by lightweight UAS-based RGB and NIR photography: an applied photogrammetric approach (2017, methodology): “This is in accordance with studies performed for grassland (Gillan et al. 2014), buckwheat (Murakami et al. 2012) and barley (Bendig et al. 2014, 2015).”
Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry (2017, background): “In this context, UAS have become suitable to assess crops’ conditions by gathering huge amounts of raw data that require further processing to enable a wide range of applications, such as water status [2], vigor assessment [3], biomass estimation [4] and disease monitoring [5].”
Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment (2017, background): “The combination of vegetation indices and UAS-based canopy surface models have been shown to estimate biomass in barley and corn more accurately than vegetation indices alone.(15,16) The results from these studies suggest that crop height metrics can be used as a complement to NDVI and potentially a”
Monitoring cotton (Gossypium hirsutum L.) germination using ultrahigh-resolution UAS images (2018, methodology): “A 3D first-surface-return point cloud model is usually used to estimate plant height (Bendig et al. 2015; Chu et al. 2016; Zarco-Tejada et al. 2014) or 3D plantation dimensionality (Dı́az-Varela et al. 2015; Gatziolis et al. 2015; Torres-Sánchez et al. 2015), while a georeferenced 2D orthomosaic…”
Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives (2017, methodology): “Themain adoptedmethods for crop phenotyping by UAV remote sensing include image analysis, physical modelmethod, empirical statistic method and advanced data analysis method, such as machine learning (Liang et al., 2013; Bendig et al., 2015).”
Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows (2018, neutral): “, in forests [72,73] and agriculture [211,212].”
High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture (2019, methodology): “An octocopter with an RGB camera was used at an altitude of 50 m to estimate biomass and height of barley plants [37].”
Assessment of a canopy height model (CHM) in a vineyard using UAV-based multispectral imaging (2017, background): “Crop heights, generally in the form of CHM, are the most common remotely sensed 3D vegetation measurements (Mathews and Jensen 2013; Bendig et al. 2015; Geipel, Link, and Claupein 2014; Kalisperakis et al. 2015).”