MDPI Books
Go to cart
Reprint
Download Flyer

Crops and Vegetation Monitoring with Remote/Proximal Sensing

Edited by
November 2023
290 pages
  • ISBN978-3-0365-9446-0 (Hardback)
  • ISBN978-3-0365-9447-7 (PDF)
https://doi.org/10.3390/books978-3-0365-9447-7
Free download (PDF)

This book is a reprint of the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing that was published in

Engineering
Environmental & Earth Sciences
Summary

Remote sensing is a powerful technique for characterizing and monitoring crop or vegetation properties at reasonable temporal and spatial resolutions. Remote sensing uses airborne and spaceborne platforms to collect various imageries and is widely applied for the vegetation monitoring of local- or large-scale interest concerning the effect of geophysical and climate parameters.

The Special Issue highlights vegetation monitoring using remote sensing data acquired from satellite or unmanned aerial vehicle platforms. In addition to the optical data, thermal data is utilized to estimate crop yield or production, orchard water status, chlorophyll content, forest diversity mapping, or vegetation phenology.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
rice and wheat; nitrogen remote sensing; quantitative retrieval; research prospect; vegetation phenology; snow cover; vegetation index; SOS; Tibetan Plateau; remote sensing; forest diversity; GEDI LiDAR; Sentinel-2; machine Learning; yield forecasting; remote sensing; logistic model; normalization method; crop canopy temperature; maize; broadband vegetation indices; chlorophyll content; leaf angle distribution; Sentinel-2; WorldView-2; RapidEye; GaoFen-6; random forest; land evaluation; soil; biomass; Hungary; gross primary productivity; soil health; soil quality; coastal marsh; continuum removal; hyperspectral; spectral signatures; unmanned aerial vehicle (UAV); vegetation species discrimination; second derivative transformation; canopy temperature; crop water status index; accuracy assessment; peach orchard; stem water potential; backscatter; gradient boosting; machine learning; NDVI; precision agriculture; forest stock volume; NDVIRE; Sentinel-2; random forest; Helan mountains; convolutional neural networks (CNNs); remote sensing; unmanned aerial vehicles (UAVs); semi-natural grasslands; plant communities; time series; reconstruction algorithm; smoothing; optical remote sensing; cropping intensity; temporal mixture analysis; endmember; unmixing; time series images