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CC BY V4.0, Open Access
Journal article
Application of remote sensing in detecting and monitoring water stress in forests
Remote sensing (Basel, Switzerland), Vol.15(13), 3360
2023
Abstract
In the context of climate change, the occurrence of water stress in forest ecosystems, which are solely dependent on precipitation, has exhibited a rising trend, even among species that are typically regarded as drought-tolerant. Remote sensing techniques offer an efficient, comprehensive, and timely approach for monitoring forests at local and regional scales. These techniques also enable the development of diverse indicators of plant water status, which can play a critical role in evaluating forest water stress. This review aims to provide an overview of remote sensing applications for monitoring water stress in forests and reveal the potential of remote sensing and geographic information system applications in monitoring water stress for effective forest resource management. It examines the principles and significance of utilizing remote sensing technologies to detect forest stress caused by water deficit. In addition, by a quantitative assessment of remote sensing applications of studies in refereed publications, the review highlights the overall trends and the value of the widely used approach of utilizing visible and near-infrared reflectance data from satellite imagery, in conjunction with classical vegetation indices. Promising areas for future research include the utilization of more adaptable platforms and higher-resolution spectral data, the development of novel remote sensing indices with enhanced sensitivity to forest water stress, and the implementation of modelling techniques for early detection and prediction of stress.
Details
- Title
- Application of remote sensing in detecting and monitoring water stress in forests
- Authors/Creators
- Thai Son Le - Murdoch UniversityRichard Harper - Murdoch University, Centre for Crop and Food InnovationBernard Dell
- Publication Details
- Remote sensing (Basel, Switzerland), Vol.15(13), 3360
- Publisher
- MDPI
- Identifiers
- 991005593752407891
- Copyright
- © 2023 by the authors.
- Murdoch Affiliation
- School of Agricultural Sciences
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
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- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.169 Remote Sensing
- 4.169.91 Vegetation Mapping
- Web Of Science research areas
- Environmental Sciences
- Geosciences, Multidisciplinary
- Imaging Science & Photographic Technology
- Remote Sensing
- ESI research areas
- Geosciences