Articles | Volume 19, issue 10
https://doi.org/10.5194/bg-19-2699-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-19-2699-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Jun 2022
Research article |
| 01 Jun 2022
| 01 Jun 2022
Estimating dry biomass and plant nitrogen concentration in pre-Alpine grasslands with low-cost UAS-borne multispectral data – a comparison of sensors, algorithms, and predictor sets
Anne Schucknecht
CORRESPONDING AUTHOR
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT),
82467 Garmisch-Partenkirchen, Germany
Bumsuk Seo
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT),
82467 Garmisch-Partenkirchen, Germany
Alexander Krämer
WWL Umweltplanung und Geoinformatik GbR, 79189 Bad Krozingen, Germany
Sarah Asam
German Remote Sensing Data Center, German Aerospace Center,
82234 Wessling, Germany
Clement Atzberger
Institute of Geomatics, University of Natural Resources and Life
Sciences (BOKU), 1190 Vienna, Austria
Ralf Kiese
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT),
82467 Garmisch-Partenkirchen, Germany
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Cited
18 citations as recorded by crossref.
- UAV Hyperspectral Characterization of Vegetation Using Entropy-Based Active Sampling for Partial Least Square Regression Models D. Amitrano et al. 10.3390/app13084812
- Pixels to pasture: Using machine learning and multispectral remote sensing to predict biomass and nutrient quality in tropical grasslands M. Zwick et al. 10.1016/j.rsase.2024.101282
- Predictive Modeling of Above-Ground Biomass in Brachiaria Pastures from Satellite and UAV Imagery Using Machine Learning Approaches C. Alvarez-Mendoza et al. 10.3390/rs14225870
- Exploring the Potential of Multi-Temporal Crop Canopy Models and Vegetation Indices from Pleiades Imagery for Yield Estimation D. Dimov & P. Noack 10.3390/rs15163990
- Detecting vineyard plants stress in situ using deep learning M. Cándido-Mireles et al. 10.1016/j.compag.2023.107837
- Prediction of pasture yield using machine learning-based optical sensing: a systematic review C. Stumpe et al. 10.1007/s11119-023-10079-9
- Estimating aboveground biomass of grassland in central Asia mountainous areas using unmanned aerial vehicle vegetation indices and image textures – A case study of typical grassland in Tajikistan T. Pan et al. 10.1016/j.indic.2024.100345
- Opportunities for Adaptation to Climate Change of Extensively Grazed Pastures in the Central Apennines (Italy) E. Bellini et al. 10.3390/land12020351
- Enhancement of quality and quantity of woody biomass produced in forests using machine learning algorithms W. Peng & O. Karimi Sadaghiani 10.1016/j.biombioe.2023.106884
- A framework for national-scale predictions of forage dry mass in Senegal: UAVs as an intermediate step between field measurements and Sentinel-2 images M. Nungi-Pambu et al. 10.1080/01431161.2023.2290992
- An Overview of Using Unmanned Aerial System Mounted Sensors to Measure Plant Above-Ground Biomass A. Bazrafkan et al. 10.3390/rs15143543
- A Review of Estimation Methods for Aboveground Biomass in Grasslands Using UAV C. Bazzo et al. 10.3390/rs15030639
- Prediction of Needle Physiological Traits Using UAV Imagery for Breeding Selection of Slash Pine X. Niu et al. 10.34133/plantphenomics.0028
- Supplement to the Article: Influence of spatial and temporal variability on the performance of machine learning-based pasture biomass prediction C. Stumpe et al. 10.2139/ssrn.4617637
- Machine learning for sustainable reutilization of waste materials as energy sources – a comprehensive review W. Peng & O. Karimi Sadaghiani 10.1080/15435075.2023.2255647
- The role of remote sensing in tropical grassland nutrient estimation: a review A. Arogoundade et al. 10.1007/s10661-023-11562-6
- Multisite and Multitemporal Grassland Yield Estimation Using UAV-Borne Hyperspectral Data M. Wengert et al. 10.3390/rs14092068
- Quantification of Grassland Biomass and Nitrogen Content through UAV Hyperspectral Imagery—Active Sample Selection for Model Transfer M. Franceschini et al. 10.3390/drones6030073
16 citations as recorded by crossref.
- UAV Hyperspectral Characterization of Vegetation Using Entropy-Based Active Sampling for Partial Least Square Regression Models D. Amitrano et al. 10.3390/app13084812
- Pixels to pasture: Using machine learning and multispectral remote sensing to predict biomass and nutrient quality in tropical grasslands M. Zwick et al. 10.1016/j.rsase.2024.101282
- Predictive Modeling of Above-Ground Biomass in Brachiaria Pastures from Satellite and UAV Imagery Using Machine Learning Approaches C. Alvarez-Mendoza et al. 10.3390/rs14225870
- Exploring the Potential of Multi-Temporal Crop Canopy Models and Vegetation Indices from Pleiades Imagery for Yield Estimation D. Dimov & P. Noack 10.3390/rs15163990
- Detecting vineyard plants stress in situ using deep learning M. Cándido-Mireles et al. 10.1016/j.compag.2023.107837
- Prediction of pasture yield using machine learning-based optical sensing: a systematic review C. Stumpe et al. 10.1007/s11119-023-10079-9
- Estimating aboveground biomass of grassland in central Asia mountainous areas using unmanned aerial vehicle vegetation indices and image textures – A case study of typical grassland in Tajikistan T. Pan et al. 10.1016/j.indic.2024.100345
- Opportunities for Adaptation to Climate Change of Extensively Grazed Pastures in the Central Apennines (Italy) E. Bellini et al. 10.3390/land12020351
- Enhancement of quality and quantity of woody biomass produced in forests using machine learning algorithms W. Peng & O. Karimi Sadaghiani 10.1016/j.biombioe.2023.106884
- A framework for national-scale predictions of forage dry mass in Senegal: UAVs as an intermediate step between field measurements and Sentinel-2 images M. Nungi-Pambu et al. 10.1080/01431161.2023.2290992
- An Overview of Using Unmanned Aerial System Mounted Sensors to Measure Plant Above-Ground Biomass A. Bazrafkan et al. 10.3390/rs15143543
- A Review of Estimation Methods for Aboveground Biomass in Grasslands Using UAV C. Bazzo et al. 10.3390/rs15030639
- Prediction of Needle Physiological Traits Using UAV Imagery for Breeding Selection of Slash Pine X. Niu et al. 10.34133/plantphenomics.0028
- Supplement to the Article: Influence of spatial and temporal variability on the performance of machine learning-based pasture biomass prediction C. Stumpe et al. 10.2139/ssrn.4617637
- Machine learning for sustainable reutilization of waste materials as energy sources – a comprehensive review W. Peng & O. Karimi Sadaghiani 10.1080/15435075.2023.2255647
- The role of remote sensing in tropical grassland nutrient estimation: a review A. Arogoundade et al. 10.1007/s10661-023-11562-6
2 citations as recorded by crossref.
- Multisite and Multitemporal Grassland Yield Estimation Using UAV-Borne Hyperspectral Data M. Wengert et al. 10.3390/rs14092068
- Quantification of Grassland Biomass and Nitrogen Content through UAV Hyperspectral Imagery—Active Sample Selection for Model Transfer M. Franceschini et al. 10.3390/drones6030073
Latest update: 13 Dec 2024
Short summary
Actual maps of grassland traits could improve local farm management and support environmental assessments. We developed, assessed, and applied models to estimate dry biomass and plant nitrogen (N) concentration in pre-Alpine grasslands with drone-based multispectral data and canopy height information. Our results indicate that machine learning algorithms are able to estimate both parameters but reach a better level of performance for biomass.
Actual maps of grassland traits could improve local farm management and support environmental...
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