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Klosterman, Stephen

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Klosterman

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Stephen

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Klosterman, Stephen
Author's Publications: search.filters.isAuthorOfPublication.46af024f-2423-4813-93a6-27de6e1b65a9

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    Transitions in high-Arctic vegetation growth patterns and ecosystem productivity tracked with automated cameras from 2000 to 2013
    (Springer Netherlands, 2017) Westergaard-Nielsen, Andreas; Lund, Magnus; Pedersen, Stine Højlund; Schmidt, Niels Martin; Klosterman, Stephen; Abermann, Jakob; Hansen, Birger Ulf
    Climate-induced changes in vegetation phenology at northern latitudes are still poorly understood. Continued monitoring and research are therefore needed to improve the understanding of abiotic drivers. Here we used 14 years of time lapse imagery and climate data from high-Arctic Northeast Greenland to assess the seasonal response of a dwarf shrub heath, grassland, and fen, to inter-annual variation in snow-cover, soil moisture, and air and soil temperatures. A late snow melt and start of growing season is counterbalanced by a fast greenup and a tendency to higher peak greenness values. Snow water equivalents and soil moisture explained up to 77 % of growing season duration and senescence phase, highlighting that water availability is a prominent driver in the heath site, rather than temperatures. We found a significant advance in the start of spring by 10 days and in the end of fall by 11 days, resulting in an unchanged growing season length. Vegetation greenness, derived from the imagery, was correlated to primary productivity, showing that the imagery holds valuable information on vegetation productivity. Electronic supplementary material The online version of this article (doi:10.1007/s13280-016-0864-8) contains supplementary material, which is available to authorized users.
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    Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery
    (Copernicus GmbH, 2014) Klosterman, Stephen; Hufkens, Koen; Gray, J. M.; Melaas, E.; Sonnentag, O.; Lavine, I.; Mitchell, L.; Norman, R.; Friedl, M. A.; Richardson, Andrew
    Plant phenology regulates ecosystem services at local and global scales and is a sensitive indicator of global change. Estimates of phenophase transition dates, such as the start of spring or end of autumn, can be derived from sensor-based time series data at the near-surface and remote scales, but must be interpreted in terms of biologically relevant events. We use the PhenoCam archive of digital repeat photography to implement a consistent protocol for visual assessment of canopy phenology at 13 temperate deciduous forest sites throughout eastern North America, as well as to perform digital image analysis for time series-based estimates of phenology dates. We then compare these near-surface results to remote sensing metrics of phenology at the landscape scale, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors. We present a new type of curve fit, using a generalized sigmoid, to estimate phenology dates. We quantify the statistical uncertainty of phenophase transition dates estimated using this method and show that the generalized sigmoid results in less statistical uncertainty than other curve-fitting methods. Additionally, we find that dates derived from analysis of high-frequency PhenoCam imagery have smaller uncertainties than remote sensing metrics of phenology, and that dates derived from the remotely-sensed enhanced vegetation index (EVI) have smaller uncertainty than those derived from the normalized difference vegetation index (NDVI). Near-surface time series estimates for the start of spring are found to closely match visual assessment of leaf out, as well as remote sensing-derived estimates of the start of spring. However late spring and autumn phenology exhibit larger differences between near-surface and remote scales. Differences in late spring phenology between near-surface and remote scales are found to correlate with a landscape metric of deciduous forest cover. These results quantify the effect of landscape heterogeneity when aggregating to the coarser spatial scales of remote sensing, and demonstrate the importance of accurate curve fitting and vegetation index selection when analyzing and interpreting phenology time series.
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    Tracking forest phenology and seasonal physiology using digital repeat photography: a critical assessment
    (Ecological Society of America, 2014) Keenan, Trevor; Darby, B.; Felts, Libby; Sonnentag, O.; Friedl, M. A.; Hufkens, K.; O'keefe, John; Klosterman, Stephen; Munger, J.; Toomey, M.; Richardson, Andrew
    Digital repeat photography is becoming widely used for near-surface remote sensing of vegetation. Canopy greenness, which has been used extensively for phenological applications, can be readily quantified from camera images. Important questions remain, however, as to whether the observed changes in canopy greenness are directly related to changes in leaf-level traits, changes in canopy structure, or some combination thereof. We investigated relationships between canopy greenness and various metrics of canopy structure and function, using five years (2008–2012) of automated digital imagery, ground observations of phenological transitions, leaf area index (LAI) measurements, and eddy covariance estimates of gross ecosystem photosynthesis from the Harvard Forest, a temperate deciduous forest in the northeastern United States. Additionally, we sampled canopy sunlit leaves on a weekly basis throughout the growing season of 2011. We measured physiological and morphological traits including leaf size, mass (wet/dry), nitrogen content, chlorophyll fluorescence, and spectral reflectance and characterized individual leaf color with flatbed scanner imagery. Our results show that observed spring and autumn phenological transition dates are well captured by information extracted from digital repeat photography. However, spring development of both LAI and the measured physiological and morphological traits are shown to lag behind spring increases in canopy greenness, which rises very quickly to its maximum value before leaves are even half their final size. Based on the hypothesis that changes in canopy greenness represent the aggregate effect of changes in both leaf-level properties (specifically, leaf color) and changes in canopy structure (specifically, LAI), we developed a two end-member mixing model. With just a single free parameter, the model was able to reproduce the observed seasonal trajectory of canopy greenness. This analysis shows that canopy greenness is relatively insensitive to changes in LAI at high LAI levels, which we further demonstrate by assessing the impact of an ice storm on both LAI and canopy greenness. Our study provides new insights into the mechanisms driving seasonal changes in canopy greenness retrieved from digital camera imagery. The nonlinear relationship between canopy greenness and canopy LAI has important implications both for phenological research applications and for assessing responses of vegetation to disturbances.
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    Greenness indices from digital cameras predict the timing and seasonal dynamics of canopy-scale photosynthesis
    (Ecological Society of America, 2014) Toomey, Michael; Friedl, Mark; Frolking, Steve; Hufkens, Koen; Klosterman, Stephen; Sonnentag, Oliver; Baldocchi, Dennis; Bernacchi, Carl; Biraud, Sebastien; Bohrer, Gil; Brzostek, Edward; Burns, Sean P.; Coursolle, Carole; Hollinger, David Y.; Margolis, Hank A.; McCaughey, Harry; Monson, Russell K.; Munger, J.; Pallardy, Stephen; Phillips, Richard P.; Torn, Margaret; Wharton, Sonia; Zeri, Marcello; Richardson, Andrew
    The proliferation of digital cameras co-located with eddy covariance instrumentation provides new opportunities to better understand the relationship between canopy phenology and the seasonality of canopy photosynthesis. In this paper we analyze the abilities and limitations of canopy color metrics measured by digital repeat photography to track seasonal canopy development and photosynthesis, determine phenological transition dates, and estimate intra-annual and interannual variability in canopy photosynthesis. We used 59 site-years of camera imagery and net ecosystem exchange measurements from 17 towers spanning three plant functional types (deciduous broadleaf forest, evergreen needle leaf forest and grassland/crops) to derive color indices and estimate gross primary productivity (GPP). GPP was strongly correlated with greenness derived from camera imagery in all three plant functional types. Specifically, the beginning of the photosynthetic period in deciduous broadleaf forest and grassland/crops and the end of the photosynthetic period in grassland/crops were both correlated with changes in greenness; changes in redness were correlated with the end of the photosynthetic period in deciduous broadleaf forest. However, it was not possible to accurately identify the beginning or ending of the photosynthetic period using camera greenness in evergreen needle leaf forest. At deciduous broadleaf sites, anomalies in integrated greenness and total GPP were significantly correlated up to 60 days after the mean onset date for the start of spring. More generally, results from this work demonstrate that digital repeat photography can be used to quantify both the duration of the photosynthetically active period as well as total GPP in deciduous broadleaf forest and grassland/crops, but that new and different approaches are required before comparable results can be achieved in evergreen needleleaf forest.