2022
DOI: 10.3390/rs14092068
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Multisite and Multitemporal Grassland Yield Estimation Using UAV-Borne Hyperspectral Data

Abstract: Grassland ecosystems can be hotspots of biodiversity and act as carbon sinks while at the same time providing the basis of forage production for ruminants in dairy and meat production. Annual grassland dry matter yield (DMY) is one of the most important agronomic parameters reflecting differences in usage intensity such as number of harvests and fertilization. Current methods for grassland DMY estimation are labor-intensive and prone to error due to small sample size. With the advent of unmanned aerial vehicle… Show more

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Cited by 11 publications
(7 citation statements)
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“…In recent years, significant progress has been made in grassland monitoring research based on digital cameras and multispectral data. However, the focus has mainly been on aspects such as fractional vegetation cover, above-ground biomass, vegetation moisture content, average community height, and the impact of different grazing intensities on grassland biomass [ 14 , 22 , 29 ]. However, research on the identification of degraded grassland vegetation communities is still in its early stages.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…In recent years, significant progress has been made in grassland monitoring research based on digital cameras and multispectral data. However, the focus has mainly been on aspects such as fractional vegetation cover, above-ground biomass, vegetation moisture content, average community height, and the impact of different grazing intensities on grassland biomass [ 14 , 22 , 29 ]. However, research on the identification of degraded grassland vegetation communities is still in its early stages.…”
Section: Discussionmentioning
confidence: 99%
“…However, this approach heavily relies on manual feature extraction, requiring extensive expert knowledge and parameter settings. In contrast to machine learning feature engineering methods [ 21 , 22 ], deep learning has the advantage of extracting intrinsic deep features from images, which is beneficial for resolving the classification problem of desert grassland degradation indicators. In order to better evaluate the performance of 2D convolution and 3D convolution in convolutional neural networks, this study builds upon a constructed simplified 2D ResNet model (DGRNet) and further establishes a 3D_ResNet model (3D_DGRNet).…”
Section: Methodsmentioning
confidence: 99%
“…Finally, the best prediction performance was achieved by integrating machine models, with an R 2 of 0.854. Wengert et al [76] collected multispectral-image data of grasslands in different seasons by using drones, analyzed characteristic bands and vegetation indices, and evaluated the model performance of four machine learning algorithms and finally found that the model based on the CBR algorithm had the best prediction performance, with high prediction accuracy and robustness. Pranga et al [77] fused the structure and spectral data of drones to predict ryegrass yield and extracted canopy height and vegetation index information collected by sensors, and the model performance of PLSR, RF, and SVM machine learning algorithms for predicting dry matter DMY were evaluated; they found that the prediction accuracy based on multi-channel fusion was higher, and that the RF algorithm had the best prediction performance, with a maximum error of no more than 308 kg ha −1 .…”
Section: • Yield Calculation Of Economic Cropsmentioning
confidence: 99%
“…Additionally, multispectral UAVs have also gained importance in commercial agriculture, where vegetation indices (VIs) are used for crop health assessment and yield estimation [18][19][20]. Some recent studies of natural habitats have combined a species identification methodology with yield estimations to assess the vegetation's aboveground biomass (AGB) using multi-or hyperspectral UAVs [21,22]. The measurement of the AGB of invasive species can play a central role in the planning and execution of vegetation management campaigns.…”
Section: Introductionmentioning
confidence: 99%