Detecting semi-arid forest decline using time series of Landsat data

ABSTRACT

Detecting forest decline is crucial for effective forest management in arid and semi-arid regions. Remote sensing using satellite image time series is useful for identifying reduced photosynthetic activity caused by defoliation. However, current studies face limitations in detecting forest decline in sparse semi-arid forests. In this study, three Landsat time-series-based approaches were used to distinguish non-declining and declining forest patches in the Zagros forests. The random forest was the most accurate approach, followed by anomaly detection and the Sen’s slope approach, with an overall accuracy of 0.75 (kappa = 0.50), 0.65 (kappa = 0.30), and 0.64 (kappa = 0.30), respectively. The classification results were unaffected by the Landsat acquisition times, indicating that rather, environmental variables may have contributed to the separation of declining and non-declining areas and not the remotely sensed spectral signal of the trees. We conclude that identifying declining forest patches in semi-arid regions using Landsat data is challenging. This difficulty arises from weak vegetation signals caused by limited canopy cover before a bright soil background, which makes it challenging to detect modest degradation signals. Additional environmental variables may be necessary to compensate for these limitations.

KEYWORDS:

• forest decline
• Landsat time series
• random forest
• anomaly
• Sen’s slope
• semi-arid

Acknowledgments

The German Academic Exchange Service (DAAD) is acknowledged by the first author for granting a Ph.D. scholarship. We would like to acknowledge the support received from the KIT-Publication Fund of the Karlsruhe Institute of Technology. Additionally, the Research Institute of Forests and Rangelands of Iran, the Agricultural and Natural Resource Research Center, and the Provincial Office of Natural Resources and Watershed Management of the Chaharmahal and Bakhtiari are appreciated for their aid in organizing site visits and assisting with fieldwork. Special thanks to Prof. Yaghoub Iranmanesh and the local foresters in the province for their valuable contributions. This study was conducted within the FORZA project at the Karlsruhe Institute of Technology and the K. N. Toosi University of Technology and was co-funded by the Iranian Ministry of Science, Research and Technology and the German National Space Agency on behalf of the German Federal Ministry of Education.

Disclosure statement

No known competing financial interests or personal relationships that could influence the study were declared by the authors.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/22797254.2023.2260549.

Authorship statement

Elham Shafeian: Conceptualization, Methodology, Fieldworks, Software, Formal analysis, Investigation, Visualization, Writing – original draft, Writing – review, and editing.

Fabian Ewald Fassnacht: Conceptualization, Methodology, Supervision, Software, Formal analysis, Visualization, Writing – review, and editing.

Hooman Latifi: Conceptualization, Fieldworks, Supervision, Writing – review and editing.

Additional information

Funding

This work was supported by the Deutscher Akademischer Austauschdienst; KIT-publication fund (library).