Climate models predict that precipitation patterns in tropical dry forests (TDFs) will change, with an overall reduction in rainfall amount and intensification of dry intervals, leading to greater susceptibility to drought. In this paper, we explore the effect of drought on phenology and carbon dynamics of a secondary TDF located in the Santa Rosa National Park (SRNP), Costa Rica. Through the use of optical sensors and an eddy covariance flux tower, seasonal phenology and carbon fluxes were monitored over a four-year period (2013)(2014)(2015)(2016). Over this time frame, annual precipitation varied considerably. Total precipitation amounts for the 2013-2016 seasons equaled 1591.8 mm (+14.4 mm SD), 1112.9 mm (+9.9 mm SD), 600.8 mm (+7.6 mm SD), and 1762.2 mm (+13.9 mm SD), respectively. The 2014 and 2015 (ENSO) seasonal precipitation amounts represent a 30% and 63% reduction in precipitation, respectively, and were designated as drought seasons. Phenology was affected by precipitation patterns and availability. The onset of green-up was closely associated with pre-seasonal rains. Drought events lead to seasonal NDVI minimums and changes in phenologic cycle length. Carbon fluxes, assimilation, and photosynthetic light use efficiency were negatively affected by drought. Seasonal minimums in photosynthetic rates and light use efficiency were observed during drought events, and gross primary productivity was reduced by 13% and 42% during drought seasons 2014 and 2015, respectively. However, all four growth seasons were net carbon sinks. Results from this study contribute towards a deeper understanding of the impact of drought on TDF phenology and carbon dynamics.

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The fields of tropical biology and conservation face significant transformations due to rapid technological developments in remote sensing. Other fields (e.g. Archeology) are experiencing this momentous change even more rapidly. In this article, we review some of the challenges that the fields of tropical biology and conservation face during the first quarter of the twenty‐first century from the perspective of various remote sensing technologies, and discuss the transformations that they may bring to these disciplines. In addition, we review two emerging technologies driving paradigm changes in the nexus of ecology, remote sensing, and analytics: near‐surface remote sensing and Wireless Sensor Networks. These two technologies, arising from the eScience paradigm, offer unique opportunities to integrate field observations at hyper‐temporal and spatial resolutions that were not possible as recently as 5 years ago.

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Commercially available autonomous photochemical reflectance index (PRI) sensors are a new development in the remote sensing field that offer novel opportunities for a deeper exploration of vegetation physiology dynamics. In this study, we evaluated the reliability of autonomous PRI sensors (SRS-PRI) developed by METER Group Inc. as proxies of light use efficiency (LUE) in an aspen (Populus tremuloides) forest stand. Before comparisons between PRI and LUE measurements were made, the optical SRS-PRI sensor pairs required calibrations to resolve diurnal and seasonal patterns properly. An offline diurnal calibration procedure was shown to account for variable sky conditions and diurnal illumination changes affecting sensor response. Eddy covariance measurements provided seasonal gross primary productivity (GPP) measures as well as apparent canopy quantum yield dynamics (α). LUE was derived from the ratio of GPP to absorbed photosynthetically active radiation (APAR). Corrected PRI values were derived after diurnal and midday cross-calibration of the sensor’s 532 nm and 570 nm fore-optics, and closely related to both LUE (R2 = 0.62, p < 0.05) and α (R2 = 0.72, p < 0.05). A LUE model derived from corrected PRI values showed good correlation to measured GPP (R2 = 0.77, p < 0.05), with an accuracy comparable to results obtained from an α driven LUE model (R2 = 0.79, p < 0.05). The automated PRI sensors proved to be suitable proxies of light use efficiency. The onset of continuous PRI sensors signifies new opportunities for explicitly examining the cause of changing PRI, LUE, and productivity over time and space. As such, this technology represents great value for the flux, remote sensing and modeling community.