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CC BY V4.0, Open Access
Journal article
A New Remote Sensing Index for Forest Dryness Monitoring Using Multi-Spectral Satellite Imagery
Forests, Vol.15(6), 915
2024
Abstract
Canopy water content is a fundamental indicator for assessing the level of plant water stress. The correlation between changes in water content and the spectral reflectance of plant leaves at near-infrared (NIR) and short-wave infrared (SWIR) wavelengths forms the foundation for developing a new remote sensing index, the Infrared Canopy Dryness Index (ICDI), to monitor forest dryness that can be used to predict the consequences of water stress. This study introduces the index, that uses spectral reflectance analysis at near-infrared wavelengths, encapsulated by the Normalized Difference Infrared Index (NDII), in conjunction with specific canopy conditions as depicted by the widely recognized Normalized Difference Vegetation Index (NDVI). Development of the ICDI commenced with the construction of an NDII/NDVI feature space, inspired by a conceptual trapezoid model. This feature space was then parameterized, and the spatial region indicative of water stress conditions, referred to as the dry edge, was identified based on the analysis of 10,000 random observations. The ICDI was produced from the combination of the vertical distance (i.e., under consistent NDVI conditions) from an examined observation to the dry edge. Comparisons between data from drought-affected and non-drought-affected control plots in the Australian Northern Jarrah Forest affirmed that the ICDI effectively depicted forest dryness. Moreover, the index was able to detect incipient water stress several months before damage from an extended drought and heatwave. Using freely available satellite data, the index has potential for broad application in monitoring the onset of forest dryness. This will require validation of the ICDI in diverse forest systems to quantify the efficacy of the index.
Details
- Title
- A New Remote Sensing Index for Forest Dryness Monitoring Using Multi-Spectral Satellite Imagery
- Authors/Creators
- Thai Son Le - Murdoch UniversityBernard DellRichard Harper - Murdoch University, Centre for Crop and Food Innovation
- Publication Details
- Forests, Vol.15(6), 915
- Publisher
- MDPI
- Identifiers
- 991005680461807891
- Copyright
- © 2024 by the authors.
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.169 Remote Sensing
- 4.169.91 Vegetation Mapping
- Web Of Science research areas
- Forestry
- ESI research areas
- Plant & Animal Science