Land Cover Change Alters the Seasonality of Photosynthetic Activity and Transpiration in Tropical Forests of the Amazon

Abstract:

Land cover change has the potential to influence the hydrological cycle at the local, regional and global scale. The southeastern Amazon has seen some of the highest rates of land cover change. In this region, vegetation transpiration is one of the most important pathways of water recycling from the land surface to the atmosphere. Tropical forests not only have high photosynthesis and transpiration rates, but they also have a distinct response to climate seasonality; deforestation in the region could dramatically alter these process rates. In this study, we aimed to determine the effect of land cover change on photosynthetic activity and transpiration in tropical forests using remote sensing data. We tested differences in photosynthetic activity and transpiration between regions with high and low land cover change during the wet and the dry seasons. We used Solar Induced Fluorescence (SIF) data from the GOME-2 sensor and transpiration data from the Global Land Evaporation Amsterdam Model (GLEAM) for the period 2007-2015.

We found distinct seasonal changes in photosynthetic activity and transpiration due to land cover change. During the dry season, photosynthetic activity and transpiration decreased with land cover change. In contrast, during the wet season, photosynthetic activity increased with land cover change, while no change was observed in transpiration. The amplitude of the cycle of photosynthetic activity and transpiration increased in both the wet and the dry season. Most of these effects intensified with increasing extents of land cover change. We hypothesize that these changes in photosynthetic activity and transpiration result from the changes in vegetation cover. These findings can inform models looking at large-scale changes in the water cycle and climate in this region. Agreement between our results and previous field- and modeling-based studies demonstrates the potential to monitor changes in vegetation seasonality using remote sensing data.