MULTI-TEMPORAL CROP SURFACE MODELS COMBINED WITH THE RGB VEGETATION INDEX FROM UAV-BASED IMAGES FOR FORAGE MONITORING IN GRASSLAND

41 citations as recorded by crossref.

1. Usability of smartphone-based RGB vegetation indices for steppe rangeland inventory and monitoring O. İeri 10.1007/s11119-024-10195-0
2. Using digital photography to monitor changes in biocrusts and ground cover in a savanna rangeland . Than Myint Swe et al. 10.1071/RJ22019
3. Assessing the Temporal and Spatial Variability of Coffee Plantation Using RPA-Based RGB Imaging M. Martello et al. 10.3390/drones6100267
4. RGB Indices and Canopy Height Modelling for Mapping Tidal Marsh Biomass from a Small Unmanned Aerial System G. Morgan et al. 10.3390/rs13173406
5. Quick Aboveground Carbon Stock Estimation of Densely Planted Shrubs by Using Point Cloud Derived from Unmanned Aerial Vehicle X. Zhang 10.3390/rs11242914
6. Unmanned aerial vehicle for the assessment of woody and herbaceous phytomass in Sahelian savanna M. Bossoukpe et al. 10.19182/remvt.36802
7. Intra-Season Crop Height Variability at Commercial Farm Scales Using a Fixed-Wing UAV M. Ziliani et al. 10.3390/rs10122007
8. Unmanned aerial vehicle outputs and associated field measurements of the herbaceous and tree layers of the Senegalese savannah S. Taugourdeau et al. 10.1111/aje.13123
9. Assessing the Impact of Clearing and Grazing on Fuel Management in a Mediterranean Oak Forest through Unmanned Aerial Vehicle Multispectral Data L. Pádua et al. 10.3390/drones8080364
10. Estimating potato aboveground biomass using unmanned aerial vehicle RGB imagery and analyzing its relationship with tuber biomass Y. Ye et al. 10.1016/j.fcr.2024.109657
11. Utilizing Spectral, Structural and Textural Features for Estimating Oat Above-Ground Biomass Using UAV-Based Multispectral Data and Machine Learning R. Dhakal et al. 10.3390/s23249708
12. Can Unmanned Aerial Vehicle Images Be Used to Estimate Forage Production Parameters in Agroforestry Systems in the Caatinga? W. Santos et al. 10.3390/app14114896
13. Impacts of Abrupt Terrain Changes and Grass Cover on Vertical Accuracy of UAS-SfM Derived Elevation Models A. Thomas et al. 10.1080/23754931.2020.1782254
14. A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems L. G. Poley & G. J. McDermid 10.3390/rs12071052
15. Unoccupied-Aerial-Systems-Based Biophysical Analysis of Montmorency Cherry Orchards: A Comparative Study G. Morgan & L. Stevenson 10.3390/drones8090494
16. Combining a Standardized Growth Class Assessment, UAV Sensor Data, GIS Processing, and Machine Learning Classification to Derive a Correlation with the Vigour and Canopy Volume of Grapevines R. Dillner et al. 10.3390/s25020431
17. Dronesourcing: a modular, expandable multi-sensor UAV platform for combined, real-time environmental monitoring A. Tripolitsiotis et al. 10.1080/01431161.2017.1287975
18. Combining spectrum, thermal, and texture features using machine learning algorithms for wheat nitrogen nutrient index estimation and model transferability analysis S. Zhang et al. 10.1016/j.compag.2024.109022
19. Multispectral Image Determination of Water Content in Aquilaria sinensis Based on Machine Learning P. Wang et al. 10.3390/f14061144
20. Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application G. Jang et al. 10.3390/rs12060998
21. An unmanned aerial vehicle-based cotton nitrogen nutrition index estimation model utilizing feature selection and machine learning C. Xiao et al. 10.1016/j.compag.2025.110798
22. Systematic Mapping Study on Remote Sensing in Agriculture J. García-Berná et al. 10.3390/app10103456
23. Sustainability Trait Modeling of Field-Grown Switchgrass (Panicum virgatum) Using UAV-Based Imagery Y. Xu et al. 10.3390/plants10122726
24. Combining multiple UAV-Based indicators for wheat yield estimation, a case study from Germany S. Khodjaev et al. 10.1080/22797254.2023.2294121
25. Temporal resolution trumps spectral resolution in UAV-based monitoring of cereal senescence dynamics F. Tschurr et al. 10.1186/s13007-024-01308-x
26. Quantifying Aboveground Biomass of Shrubs Using Spectral and Structural Metrics Derived from UAS Imagery L. Poley et al. 10.3390/rs12142199
27. Automatic Identification of Shrub-Encroached Grassland in the Mongolian Plateau Based on UAS Remote Sensing Y. Dong et al. 10.3390/rs11131623
28. Analysis of Multi-Temporal Multispectral Aerial Photography Data to Detect the Boundaries of Historical Anthropogenic Impact A. Shaura et al. 10.15622/ia.21.2.8
29. Extraction of Broad-Leaved Tree Crown Based on UAV Visible Images and OBIA-RF Model: A Case Study for Chinese Olive Trees K. Yang et al. 10.3390/rs14102469
30. Prediction of Biomass and N Fixation of Legume–Grass Mixtures Using Sensor Fusion E. Grüner et al. 10.3389/fpls.2020.603921
31. Estimation of nitrogen nutrition index in rice from UAV RGB images coupled with machine learning algorithms Z. Qiu et al. 10.1016/j.compag.2021.106421
32. A Review of Estimation Methods for Aboveground Biomass in Grasslands Using UAV C. Bazzo et al. 10.3390/rs15030639
33. Estimating Black Oat Biomass Using Digital Surface Models and a Vegetation Index Derived from RGB-Based Aerial Images L. Trevisan et al. 10.3390/rs15051363
34. Estimation of Grassland Canopy Height and Aboveground Biomass at the Quadrat Scale Using Unmanned Aerial Vehicle H. Zhang et al. 10.3390/rs10060851
35. Detection of necrotic foliage in a young Eucalyptus pellita plantation using unmanned aerial vehicle RGB photography – a demonstration of concept M. Dell et al. 10.1080/00049158.2019.1621588
36. Quality Assessment of Photogrammetric Methods—A Workflow for Reproducible UAS Orthomosaics M. Ludwig et al. 10.3390/rs12223831
37. Estimation of Biophysical Parameters of Forage Cactus Under Different Agricultural Systems Through Vegetation Indices and Machine Learning Using RGB Images Acquired with Unmanned Aerial Vehicles G. Silva et al. 10.3390/agriculture14122166
38. Evaluation of Aboveground Nitrogen Content of Winter Wheat Using Digital Imagery of Unmanned Aerial Vehicles B. Yang et al. 10.3390/s19204416
39. Predictive Production Models for Mountain Meadows: A Review A. Jarne et al. 10.3390/agronomy14040830
40. Prediction of Cereal Rye Cover Crop Biomass and Nutrient Accumulation Using Multi-Temporal Unmanned Aerial Vehicle Based Visible-Spectrum Vegetation Indices R. Roth et al. 10.3390/rs15030580
41. Combining UAV multispectral imagery and ecological factors to estimate leaf nitrogen and grain protein content of wheat Z. Fu et al. 10.1016/j.eja.2021.126405