Assessing Drought-Induced Tree Mortality in Open Mediterranean Forests Integrating Landsat Time Series, Spectral Unmixing, and UAS Validation.
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| Title: | Assessing Drought-Induced Tree Mortality in Open Mediterranean Forests Integrating Landsat Time Series, Spectral Unmixing, and UAS Validation. |
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| Authors: | Raunak, Alma1,2 (AUTHOR), Huesca, Margarita1,2 (AUTHOR) m.huescamartinez@utwente.nl, Nyktas, Panagiotis1,3 (AUTHOR), Paris, Claudia1 (AUTHOR) |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 5, p792. 23p. |
| Subjects: | Tree mortality, Remote sensing, Abiotic stress, Normalized difference vegetation index, Forests & forestry, Drone aircraft, Forest monitoring |
| Geographic Terms: | Crete (Greece), Greece, Southern Europe |
| Abstract: | Highlights: What are the main findings? Spectral Unmixing in LandTrendr effectively detects drought-induced tree loss. Spectral Unmixing has the potential to improve three mortality detection at subpixel level. What is the implication of the main finding? UAS and satellite data integration enables early detection of tree mortality. UAS imagery provides a robust reference for tree mortality assessments. Drought-induced tree mortality is a growing threat to Mediterranean ecosystems, which host high biodiversity but face increasing water stress under climate change. Detecting mortality over large areas with satellite data remains challenging due to open canopies and mixed pixels that obscure vegetation signals. This study evaluates the performance of two widely used vegetation indices—the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)—alongside a novel application of Spectral Unmixing derived vegetation cover Spectral Unmixing (SU) within the LandTrendr algorithm to track tree mortality in southwest Crete, Greece. High-resolution Unmanned Aerial System (UAS) imagery was used to validate satellite observations, demonstrating strong agreement with field data ( R 2 = 0.95) and confirming its suitability as reference data. LandTrendr applied to NDVI, NDWI, and SU detected major mortality events between 1995 and 2008, with SU identifying the largest affected area. While NDVI and NDWI achieved higher accuracy in distinguishing unaffected plots, SU performed best at detecting mortality. Regression analysis revealed a limited ability of all approaches to quantify mortality magnitude, though SU improved when high-mortality plots were excluded. Overall, NDVI effectively tracked canopy changes, NDWI provided early warnings of drought stress, and SU reduced soil interference to better capture mortality patterns. By integrating satellite time series with UAS validation, this study demonstrates a scalable approach for detecting forest decline and offers actionable insights to guide Mediterranean forest management under increasing drought pressure. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Spectral Unmixing in LandTrendr effectively detects drought-induced tree loss. Spectral Unmixing has the potential to improve three mortality detection at subpixel level. What is the implication of the main finding? UAS and satellite data integration enables early detection of tree mortality. UAS imagery provides a robust reference for tree mortality assessments. Drought-induced tree mortality is a growing threat to Mediterranean ecosystems, which host high biodiversity but face increasing water stress under climate change. Detecting mortality over large areas with satellite data remains challenging due to open canopies and mixed pixels that obscure vegetation signals. This study evaluates the performance of two widely used vegetation indices—the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)—alongside a novel application of Spectral Unmixing derived vegetation cover Spectral Unmixing (SU) within the LandTrendr algorithm to track tree mortality in southwest Crete, Greece. High-resolution Unmanned Aerial System (UAS) imagery was used to validate satellite observations, demonstrating strong agreement with field data ( R 2 = 0.95) and confirming its suitability as reference data. LandTrendr applied to NDVI, NDWI, and SU detected major mortality events between 1995 and 2008, with SU identifying the largest affected area. While NDVI and NDWI achieved higher accuracy in distinguishing unaffected plots, SU performed best at detecting mortality. Regression analysis revealed a limited ability of all approaches to quantify mortality magnitude, though SU improved when high-mortality plots were excluded. Overall, NDVI effectively tracked canopy changes, NDWI provided early warnings of drought stress, and SU reduced soil interference to better capture mortality patterns. By integrating satellite time series with UAS validation, this study demonstrates a scalable approach for detecting forest decline and offers actionable insights to guide Mediterranean forest management under increasing drought pressure. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18050792 |
