Reprint

UAS-Remote Sensing Methods for Mapping, Monitoring and Modeling Crops

Edited by
April 2021
174 pages
  • ISBN978-3-0365-0526-8 (Hardback)
  • ISBN978-3-0365-0527-5 (PDF)

This book is a reprint of the Special Issue UAS-Remote Sensing Methods for Mapping, Monitoring and Modeling Crops that was published in

Engineering
Environmental & Earth Sciences
Summary
The advances in unmanned aerial vehicle (UAV) platforms and onboard sensors in the past few years have greatly increased our ability to monitor and map crops. The ability to register images at ultrahigh spatial resolution at any moment has made remote sensing techniques increasingly useful in crop management. These technologies have revolutionized the way in which remote sensing is applied in precision agriculture, allowing for decision-making in a matter of days instead of weeks. However, it is still necessary to continue research to improve and maximize the potential of UAV remote sensing in agriculture. This Special Issue of Remote Sensing includes different applications of UAV remote sensing for crop management, covering RGB, multispectral, hyperspectral and light detection and ranging (LiDAR) sensor applications aboard UAVs. The papers reveal innovative techniques involving image analysis and cloud points. However, it should be emphasized that this Special Issue is a small sample of UAV applications in agriculture and that there is much more to investigate.
Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
vegetable mapping; multi-temporal UAV; recurrent convolutional neural network; attention mechanism; UAV; crop row; precision agriculture; DEM; RGB; vegetation indices; damage detection; winter crop; UAV; multispectral imagery; vegetation indices; agricultural insurance; technical roads; grapevine detection; precision viticulture; 3D vineyard structure; photogrammetry; UAV-borne LiDAR scanning system; grasshopper optimization algorithm; GPS/INS integrated navigation; point cloud up-sampling network (PU-net); clustering segmentation; 3-dimensional reconstruction; olive tree; UAV; hyperspectral; classification; irrigation technique; PLS; wavelength selection; high-throughput phenotyping; hyperspectral data; LiDAR; biomass prediction; RGB; multispectral; hyperspectral; LiDAR; agriculture