Few studies exist on the relationships among tropical dry forest phenology, as observed with space borne remote sensing, and ecosystem attributes like forest structure or species traits. We know that multiseason satellite imagery is useful for distinguishing among tropical forest types (Helmer et al. 2012), yet for dry tropical forests that include many drought deciduous species, we might also expect that seasonal patterns in spectral measures of canopy greenness should correlate with field measurements of forest attributes.
One reason for the lack of studies on the relationships among space borne characterization of tropical forest phenology and ecosystem attributes is that persistent cloud cover in satellite imagery means that only imagery collected daily or every other day collect enough clear observations to track changes in forest greenness. So far, most of the imagery available with such frequent coverage has a coarse spatial resolution, with pixel sizes of 250 m to 1 km, which is a range in spatial scales that is too coarse to relate to most field studies.
We tested whether a new algorithm (Zhu et al. In prep.) that maps remotely sensed forest phenology metrics at the finer spatial resolution of Landsat multispectral data, which is 30 m, could help explain variation in tropical forest ecosystem attributes. We evaluated whether such phenology information contributed to explaining variation in forest structure along a gradient from tropical dry to moist and wet forest in Puerto Rico (Gwenzi et al. In prep), as well as spatial variation in leaf area index and intraspecific variation in leaf characteristics along a moisture gradient within the tropical dry forest on Mona Island of Puerto Rico (Zhu et al. In prep.).
We found that seasonal spectral metrics at the spatial scale of Landsat imagery explained substantial variation in forest structure and other ecosystem attributes, but that additional remotely sensed data can improve the precision of such estimates.
Helmer, E.H., et al. "Detailed maps of tropical forest types are within reach: Forest tree communities for Trinidad and Tobago mapped with multiseason Landsat and multiseason fine-resolution imagery." Forest Ecology and Management279 (2012): 147-166.
Zhu, X., et al. Reconstructing seasonal Landsat time-series to detect tropical forest phenology in Mona Island, Puerto Rico. In prep.
Gwenzi, D., et al. Landsat-scale phenology and variation of tropical forest structure from dry to wet ecological zones in Puerto Rico. In prep.