Description
Data files and R script used to analyse vegetation phenology from digital cameras (phenocam) and satellite data across four seasonal dry tropical vegetation sites in Brazil. The use of digital cameras to monitor vegetation phenology (phenocams) has become increasingly common as a means of ground-truthing estimates of land surface phenology from Earth observation data. Whilst the relationship between phenocam and Earth Observation-derived indices of land surface phenology has been examined across many temperate land cover types, our understanding of these relationships across the seasonally dry tropics is limited. Here we examined phenological time-series derived from coarse-scale MODIS and fine-scale phenocam data across four seasonally dry tropical sites in Brazil, to determine their correlation, as well as how phenological metrics derived from these time series differed. While MODIS-derived vegetation indices showed seasonal patterns, we found a poor correlation with vegetation indices from phenocams at sites with a high proportion of evergreen vegetation and a poor correlation of MODIS indices with specific vegetation components. The high spatial and temporal resolution of phenocams allowed us to demonstrate differences in phenological metrics among different components of the vegetation which were obscured in the coarser MODIS data. This study highlights the potential of phenocam data to improve our understanding of complex vegetation leaf phenology and its drivers within mixed tree-shrub-grass systems in the seasonally dry tropics. This could help improve the representation of the savanna, grass, and shrubland biomes within terrestrial biosphere models, and lead to better predictions of the impact of climate change on carbon dynamics via shifting vegetation phenology.
Data Citation
Koolen, S. P., Godlee, J., Alberton, B., Ramos, D., Moura, M. S. B. de ., Cerdeira Morellato, L. P., & Dexter, K. (2025). Dataset: Coexistence of trees, shrubs and grasses creates a complex pic-ture of land surface phenology in tropical dry ecosystems [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15616374
| Date made available | 6 Jun 2025 |
|---|---|
| Publisher | Zenodo |
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