Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery

[摘要] Plant phenology regulates ecosystem services at local and global scales andis a sensitive indicator of global change. Estimates of phenophasetransition dates, such as the start of spring or end of fall, can bederived from sensor-based time series, but must be interpreted in terms ofbiologically relevant events. We use the PhenoCam archive of digital repeatphotography to implement a consistent protocol for visual assessment ofcanopy phenology at 13 temperate deciduous forest sites throughout easternNorth America, and to perform digital image analysis for time-series-basedestimation of phenophase transition dates. We then compare these results toremote sensing metrics of phenophase transition dates derived from theModerate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very HighResolution Radiometer (AVHRR) sensors. We present a new type of curve fitthat uses a generalized sigmoid function to estimate phenology dates, and wequantify the statistical uncertainty of phenophase transition datesestimated using this method. Results show that the generalized sigmoidprovides estimates of dates with less statistical uncertainty than othercurve-fitting methods. Additionally, we find that dates derived fromanalysis of high-frequency PhenoCam imagery have smaller uncertainties thansatellite remote sensing metrics of phenology, and that dates derived fromthe remotely sensed enhanced vegetation index (EVI) have smaller uncertaintythan those derived from the normalized difference vegetation index (NDVI).Near-surface time-series estimates for the start of spring are found toclosely match estimates derived from visual assessment of leaf-out, as wellas satellite remote-sensing-derived estimates of the start of spring.However late spring and fall phenology metrics exhibit larger differencesbetween near-surface and remote scales. Differences in late spring phenologybetween near-surface and remote scales are found to correlate with alandscape metric of deciduous forest cover. These results quantify theeffect of landscape heterogeneity when aggregating to the coarser spatialscales of remote sensing, and demonstrate the importance of accurate curvefitting and vegetation index selection when analyzing and interpretingphenology time series.

[学科分类] 地球化学与岩石