Monitoring vegetation dynamics in Qinling–Daba mountains during 2001–2023 using an improved two-leaf model and remote-sensing datasets
Mountains are critical in terrestrial ecosystems, understanding vegetation dynamics is essential in the context of global climate change. This study employed an improved two-leaf light use efficiency (LUE) model to simulate gross primary productivity (GPP), integrating the leaf area index (LAI) and...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2538238 |
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| Summary: | Mountains are critical in terrestrial ecosystems, understanding vegetation dynamics is essential in the context of global climate change. This study employed an improved two-leaf light use efficiency (LUE) model to simulate gross primary productivity (GPP), integrating the leaf area index (LAI) and kernel normalized difference vegetation index (kNDVI). We analyzed the vegetation dynamics and inter-relationships between these indices in the Qinling – Daba Mountains during 2001–2023. The results indicate the following: The root mean square error of the improved model decreased by 19.3%, significantly enhancing model accuracy. Throughout the study period, the three vegetation indices demonstrated a high degree of consistency, exhibiting a general upward trend. Notably, 77.5% of the area experienced synchronous increases, primarily concentrated in the Qinling – Daba Mountains, while only 2.2% of the area showed synchronous decreases, scattered across the area. LAI exhibited a stronger correlation and had a greater influence on GPP than kNDVI, indicating that the vegetation structure is more critical in mountain productivity than greenness. However, in specific regions, such as the Hubei Province, the influence of kNDVI was more significant. In addition, factors such as elevation, precipitation, and temperature were found to be important drivers of vegetation changes. |
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| ISSN: | 1753-8947 1753-8955 |