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Many wetlands around the world that occur at the base of watersheds are under threat from land‐use change, hydrological alteration, nutrient pollution, and invasive species. A relevant measure of whether the ecological character of these ecosystems has changed is the species diversity of wetland‐dependent waterbirds, especially those of conservation value. Here, we evaluate the potential mechanisms controlling variability over time and space in avian species diversity of the wetlands in the Palo Verde National Park, a Ramsar Site of international importance in Costa Rica. To do so, we assessed the relative importance of several key wetland condition metrics (i.e., surface water depth, wetland extent, and vegetation greenness), and temporal fluctuations in these metrics, in predicting the abundance of five waterbirds of high conservation value as well as overall waterbird diversity over a 9‐yr period. Generalized additive models revealed that mean NDVI, an indicator of vegetation greenness, combined with a metric used to evaluate temporal fluctuations in the wetland extent best predicted four of the five waterbird species of high conservation value as well as overall waterbird species richness and diversity. Black‐bellied Whistling‐ducks, which account for over one‐half of all waterbird individuals, and all waterbird species together were better predicted by including surface water depth along with wetland extent and its fluctuations. Our calibrated species distribution model confidently quantified monthly averages of the predicted total waterbird abundances in seven of the 10 sub‐wetlands making up the Ramsar Site and confirmed that the biophysical diversity of this entire wetland system is important to supporting waterbird populations both as a seasonal refuge and more permanently. This work further suggests that optimizing the timing and location of ongoing efforts to reduce invasive vegetation cover may be key to avian conservation by increasing waterbird habitat. [ABSTRACT FROM AUTHOR]

Historical wetland hydrology data are instrumental to support the design of wetland management and restoration strategies but are rarely available. In this study, we tested the capabilities and limitations of a simple methodological framework based on publicly available MODIS Land Reflectance Products to estimate wetland soil surface saturation and inundation spatiotemporal dynamics. Using supervised learning and high-resolution groundwater and surface water elevation data, the framework searches for spectral algorithms, referred to as the wet/dry wetland status classifier (WSC) and the continuous wetland dynamics identifier (WDI), that best predict upper soil layer wetness status in the study wetland. We used Google Earth Engine (GEE) for fast access and processing of the full range of MODIS data. The capabilities of GEE also enabled us to readily conduct a comparative assessment of the MODIS 8-day composite and daily collections and test various pixel-level quality filters to select reliable data at the highest possible temporal resolution. We tested the framework on the internationally-recognized Ramsar site Palo Verde National Park wetland in Costa Rica, and we obtained good results (overall prediction accuracy of 86.6% and kappa coefficient of 0.7 for the WSC; r2 of 0.71 for the WDI). High-resolution water level data allowed us to assess the challenges, promises and limitations of using MODIS products for wetland hydrology applications. We then applied the WSC and WDI to map the 2000-2016 sub-weekly wetland hydroperiod at 500m resolution, achieving a temporal resolution rarely matched in remote sensing for wetland studies. The analysis of the end-products, combined with the field water elevation data, provided new insights into the drivers controlling the spatiotemporal dynamics of hydroperiod within the Palo Verde wetland and did not reveal any significant temporal trends. The WSC and WDI framework developed here can be useful for reconstructing long-term hydroperiod variability and uncovering its drivers for other wetland systems globally. • A systematic framework for wetland soil saturation and flooding detection is proposed. • The framework is tested with high temporal resolution water elevation data. • Hydric status diagnostic mismatch when working with multiple sensors is explained. • Opportunities and limitations of using the full range of MODIS data is presented. • New insights into Palo Verde wetland hydroperiod history and drivers is gained. [ABSTRACT FROM AUTHOR]

One great advantage of optical hyperspectral remote sensing from unmanned aerial systems (UAS) compared to satellite missions is the possibility to fly and collect data below clouds. The most typical scenario is flying below intermittent clouds and under turbulent conditions, which causes tilting of the platform. This study aims to advance hyperspectral imaging from UAS in most weather conditions by addressing two challenges: (i) the radiometric and spectral calibrations of miniaturized hyperspectral sensors; and (ii) tilting effects on measured downwelling irradiance. We developed a novel method to correct the downwelling irradiance data for tilting effects. It uses a hybrid approach of minimizing measured irradiance variations for constant irradiance periods and spectral unmixing, to calculate the spectral diffuse irradiance fraction for all irradiance measurements within a flight. It only requires the platform's attitude data and a standard incoming light sensor. We demonstrated the method at the Palo Verde National Park wetlands in Costa Rica, a highly biodiverse area. Our results showed that the downwelling irradiance correction method reduced systematic shifts caused by a change in flight direction of the UAS, by 87% and achieving a deviation of 2.78% relative to a on ground reference in terms of broadband irradiance. High frequency (< 3 s) irradiance variations caused by high-frequency tilting movements of the UAS were reduced by up to 71%. Our complete spectral and radiometric calibration and irradiance correction can significantly remove typical striped illumination artifacts in the surface reflectance-factor map product. The possibility of collecting precise hyperspectral reflectance-factor data from UAS under varying cloud cover makes it more operational for environmental monitoring or precision agriculture applications, being an important step in advancing hyperspectral imaging from UAS. • Unmanned Aerial System for hyperspectral remote sensing under most sky conditions. • Novel method to correct downwelling irradiance for sensor tilt under all conditions. • Correction method is easy to deploy, it only requires standard sensors. • Spectral and radiometric sensor calibration for accurate reflectance mapping. • Step towards reliable and repeatable hyperspectral environmental monitoring. [ABSTRACT FROM AUTHOR]