A novel linear spectral unmixing-based method for tree decline monitoring by fusing UAV-RGB and optical space-borne data

ABSTRACT

Remote sensing-assisted monitoring of forest health entails methods that can provide up-to-date and accurate information on decline and mortality of individual trees, while maintaining time and cost efficiency. However, the trade-off of applying consumer-grade UAV-RGB data as the most affordable and accessible data source at the catchment level is constrained by its poor spectral information content. We developed a method based on the fusion of UAV-RGB data with space-borne Sentinel-2 Multispectral Instrument (MSI) at the level of tree crowns, with the specific target of supporting studies on semi-arid tree decline. We applied linear spectral unmixing (Spectral Unmixing-Based data Fusion method, LSUBF) by considering a limited number of endmembers and calculating the abundances (fractional covers) from the UAV data, and evaluated the results by high-resolution MSI space-borne data including SPOT-6 (1.5 m spatial resolution) and PlanetScope (3 m spatial resolution). This method suggested an increase in the coefficient of determination of the applied generalized additive model for decline severity estimation at tree crown level from 0.61 to 0.69, while it was improved from 0.70 to 0.91 when fitting a non-parametric random forest model. The results of sensitivity analysis demonstrated that the additional spectral information obtained from the proposed method results in higher accuracy in estimating decline severity. We suggest this method as a cost-effective alternative to monitor periodical tree decline, in particular across semi-arid ecosystems.

KEYWORDS:

• Tree decline
• data fusion
• linear spectral unmixing
• UAV-RGB
• Sentinel-2
• Semi-arid vegetation

Acknowledgements

The authors are grateful to field crews in three provinces of Kermanshah, Chaharmahal-and-Bakhtiari and Fars who collected the field data. This research was conducted within the Research Lab “Remote Sensing for Ecology and Ecosystem Conservation (RSEEC)” of the KNTU (Link: https://www.researchgate.net/lab/Research-Lab-Remote-Sensing-for-Ecology-and-Ecosystem-Conservation-RSEEC-Hooman-Latifi). The field data were provided via “The National Zagros Forest Monitoring Plan” (project No. 01-01-09-047-97012) by Iran´s Research Institute of Forests and Rangelands (RIFR). The PlanetScope data was provided by Elham Shafeian, a VIRS student at the University of British Columbia. The SPOT-6 data was provided via the Project No. PP0089126 submitted to the European Space Agency.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data Availability statement

The dataset generated and analysed during the current study are available from the corresponding author on reasonable request.

Declaration

The authors have no financial or proprietary interests in any material discussed in this article.

Supplementary Material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/01431161.2024.2305630.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

The work was supported by the Research Institute of Forests and Rangelands [. 01-01-09-047-97012].