Multi-scale monitoring of rice aboveground biomass by combining spectral and textural information from UAV hyperspectral images

Tianyue Xu, Fumin Wang, Zhou Shi, Yuxin Miao

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Selecting the appropriate unmanned aerial vehicle flight height is beneficial for increasing the monitoring efficiency. We firstly used an unmanned aerial vehicle to explore the scale effect on monitoring rice aboveground biomass. The results confirmed the feasibility of using vegetation indices and textures from hyperspectral images to improve the estimations at different spatial resolutions. The monitoring accuracy of combining vegetation indices and textures was the highest, and exhibited a decreasing trend as the spatial resolution decreased with the greatest accuracy appearing at 13 cm. Two new concepts were proposed: “appropriate monitoring scale domain” to define the range of spatial resolution where the monitoring accuracy was less affected by scale effect, and “appropriate monitoring scale threshold” to define the spatial resolution where accuracy dropped noticeably. The appropriate monitoring scale domains varied at different growth stages and the appropriate monitoring scale thresholds of using vegetation indices and textures were lower than those using textures: 39 cm, 52 cm, and 65 cm at the pre-heading, post-heading, and entire growth stages, respectively when using textures, and 52 cm, 65 cm, and 78 cm at the corresponding growth stages when combining vegetation indices and textures. In terms of aboveground biomass level, growth stage and error value, the relatively lower aboveground biomass levels, earlier growth stages of the multi-temporal models, and overestimations were more likely to yield notable accuracy changes when the spatial resolution converted to lower level on both sides of appropriate monitoring scale threshold. Vegetation indices containing red-edge or near-infrared bands were effective for estimation. Yellow/green band textures and vegetation indices containing green bands with near-infrared/red-edge bands also obtained inspiring performances. MEA was indispensable in estimation while more diverse textures were incorporated into the models of the entire growth stages and models established at lower spatial resolutions. These findings are essential for understanding the scale effect in estimating rice aboveground biomass, facilitating efficient monitoring at field scale.

Original languageEnglish (US)
Article number103655
JournalInternational Journal of Applied Earth Observation and Geoinformation
Volume127
DOIs
StatePublished - Mar 2024

Bibliographical note

Publisher Copyright:
© 2024 The Author(s)

Keywords

  • Data fusion
  • Grey level co-occurrence matrix
  • Machine learning
  • Observation scale
  • Scaling
  • Time-series

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