Browsing by Subject "DECIDUOUS BROADLEAF FOREST"

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  • Wingate, L.; Ogee, J.; Cremonese, E.; Filippa, G.; Mizunuma, T.; Migliavacca, M.; Moisy, C.; Wilkinson, M.; Moureaux, C.; Wohlfahrt, G.; Hammerle, A.; Hoertnagl, L.; Gimeno, C.; Porcar-Castell, A.; Galvagno, M.; Nakaji, T.; Morison, J.; Kolle, O.; Knohl, A.; Kutsch, W.; Kolari, P.; Nikinmaa, E.; Ibrom, A.; Gielen, B.; Eugster, W.; Balzarolo, M.; Papale, D.; Klumpp, K.; Koestner, B.; Gruenwald, T.; Joffre, R.; Ourcival, J. -M.; Hellstrom, M.; Lindroth, A.; George, C.; Longdoz, B.; Genty, B.; Levula, J.; Heinesch, B.; Sprintsin, M.; Yakir, D.; Manise, T.; Guyon, D.; Ahrends, H.; Plaza-Aguilar, A.; Guan, J. H.; Grace, J. (2015)
    Plant phenological development is orchestrated through subtle changes in photoperiod, temperature, soil moisture and nutrient availability. Presently, the exact timing of plant development stages and their response to climate and management practices are crudely represented in land surface models. As visual observations of phenology are laborious, there is a need to supplement long-term observations with automated techniques such as those provided by digital repeat photography at high temporal and spatial resolution. We present the first synthesis from a growing observational network of digital cameras installed on towers across Europe above deciduous and evergreen forests, grasslands and croplands, where vegetation and atmosphere CO2 fluxes are measured continuously. Using colour indices from digital images and using piecewise regression analysis of time series, we explored whether key changes in canopy phenology could be detected automatically across different land use types in the network. The piecewise regression approach could capture the start and end of the growing season, in addition to identifying striking changes in colour signals caused by flowering and management practices such as mowing. Exploring the dates of green-up and senescence of deciduous forests extracted by the piecewise regression approach against dates estimated from visual observations, we found that these phenological events could be detected adequately (RMSE <8 and 11 days for leaf out and leaf fall, respectively). We also investigated whether the seasonal patterns of red, green and blue colour fractions derived from digital images could be modelled mechanistically using the PROSAIL model parameterised with information of seasonal changes in canopy leaf area and leaf chlorophyll and carotenoid concentrations. From a model sensitivity analysis we found that variations in colour fractions, and in particular the late spring 'green hump' observed repeatedly in deciduous broadleaf canopies across the network, are essentially dominated by changes in the respective pigment concentrations. Using the model we were able to explain why this spring maximum in green signal is often observed out of phase with the maximum period of canopy photosynthesis in ecosystems across Europe. Coupling such quasi-continuous digital records of canopy colours with co-located CO2 flux measurements will improve our understanding of how changes in growing season length are likely to shape the capacity of European ecosystems to sequester CO2 in the future.
  • Wang, Siyu; Lu, Xinchen; Cheng, Xiao; Li, Xianglan; Peichl, Matthias; Mammarella, Ivan (2018)
    Recent efforts have been made to monitor the seasonal metrics of plant canopy variations globally from space, using optical remote sensing. However, phenological estimations based on vegetation indices (VIs) in high-latitude regions such as the pan-Arctic remain challenging and are rarely validated. Nevertheless, pan-Arctic ecosystems are vulnerable and also crucial in the context of climate change. We reported the limitations and challenges of using MODerate-resolution Imaging Spectroradiometer (MODIS) measurements, a widely exploited set of satellite measurements, to estimate phenological transition dates in pan-Arctic regions. Four indices including normalized vegetation difference index (NDVI), enhanced vegetation index (EVI), phenology index (PI), plant phenological index (PPI) and a MODIS Land Cover Dynamics Product MCD12Q2, were evaluated and compared against eddy covariance (EC) estimates at 11 flux sites of 102 site-years during the period from 2000 to 2014. All the indices were influenced by snow cover and soil moisture during the transition dates. While relationships existed between VI-based and EC-estimated phenological transition dates, the R-2 values were generally low (0.01-0.68). Among the VIs, PPI-estimated metrics showed an inter-annual pattern that was mostly closely related to the EC-based estimations. Thus, further studies are needed to develop region-specific indices to provide more reliable estimates of phenological transition dates.
  • Peltoniemi, Mikko; Aurela, Mika; Bottcher, Kristin; Kolari, Pasi; Loehr, John; Karhu, Jouni; Linkosalmi, Maiju; Tanis, Cemal Melih; Tuovinen, Juha-Pekka; Arslan, Ali Nadir (2018)
    In recent years, monitoring of the status of ecosystems using low-cost web (IP) or time lapse cameras has received wide interest. With broad spatial coverage and high temporal resolution, networked cameras can provide information about snow cover and vegetation status, serve as ground truths to Earth observations and be useful for gap-filling of cloudy areas in Earth observation time series. Networked cameras can also play an important role in supplementing laborious phenological field surveys and citizen science projects, which also suffer from observer-dependent observation bias. We established a network of digital surveillance cameras for automated monitoring of phenological activity of vegetation and snow cover in the boreal ecosystems of Finland. Cameras were mounted at 14 sites, each site having 1-3 cameras. Here, we document the network, basic camera information and access to images in the permanent data repository (http://www.zenodo.org/communities/phenology_camera/). Individual DOI-referenced image time series consist of half-hourly images collected between 2014 and 2016 (https://doi.org/10.5281/zenodo.1066862). Additionally, we present an example of a colour index time series derived from images from two contrasting sites.