A cost-effective monitoring method using digital time-lapse cameras for detecting temporal and spatial variations of snowmelt and vegetation phenology in alpine ecosystems |
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| Institution: | 1. School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849, USA;2. Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA;3. U.S. Geological Survey, Alabama Cooperative Fish and Wildlife Research Unit, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36845, USA;1. Norwegian Institute for Nature Research (NINA), FRAM – High North Research Centre for Climate and the Environment, PO Box 6606, Langnes, NO-9296 Tromsø, Norway;2. Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Sheffield S10 2TN, UK;3. Norwegian Meteorological Institute, NO-0313 Oslo, Norway;4. Northern Research Institute – Tromsø, Science Park, NO-9294 Tromsø, Norway;5. Norwegian Polar Institute, FRAM – High North Research Centre for Climate and the Environment, PO Box 6606, Langnes, NO-9296 Tromsø, Norway;6. Department of Ecological Science, VU University Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands;7. Institute of Geodesy and Cartography, 02-679 Warsaw, Poland;1. National Snow and Ice Data Center, University of Colorado, Boulder CO 80309-0449, USA;2. Earth Research Institute, University of California, Santa Barbara CA 93106-3060, USA;3. Bren School of Environmental Science & Management, University of California, Santa Barbara CA 93106-5131, USA;1. Ecoclimatology, Department of Ecology and Ecosystem Management, Technical University of Munich, 85354 Freising, Germany;2. School of Biological, Earth and Environmental Sciences, University College Cork, T12K8AF Cork, Ireland;3. Department of Geography, University College Cork, T12K8AF Cork, Ireland;4. German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany;5. Institute for Advanced Study, Technical University of Munich, 85748 Garching, Germany;1. Faculty of Education and Integrated Arts and Sciences, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan;2. Department of Environmental Geochemical Cycle Research, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 3173-25 Showa-machi, Kanazawa-ku, Yokohama 236-0001, Japan |
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| Abstract: | Alpine ecosystems are particularly vulnerable to the effects of climate change. Although long-term and detailed monitoring is required to conserve alpine ecosystems, field surveillance and satellite remote sensing have difficulties in providing wide coverage or frequent observation in mountain areas. In this study, a new method for monitoring alpine ecosystems by digital cameras was developed in order to detect both snow-cover areas and vegetation phenology at the plant community or species level. We used images from cameras that have been installed at mountain lodges in the northern Japanese Alps (at elevations around 2350–3100 m). Red, green, and blue (RGB) digital numbers were derived from each pixel within the images. The snow-cover and snow-free pixels were statistically classified by analysis of variance of gray-level histograms. A flexible threshold was determined for each image to maximize the between-class variance. The temporal variations of the snowmelt rate showed site-specific characteristics and yearly variations. The snowmelt times reflected the local microtopography and differed among the habitats of various functional types of vegetation (i.e., evergreen dwarf pine, deciduous shrubs, evergreen Sasa, tall forbs, and snowbed plants). In addition, the vegetation phenology was quantified by using a vegetation index (green ratio) calculated from RGB digital numbers. An increase in the green ratio indicated the start of the growing period following snowmelt and a decrease indicated leaf senescence. By using pixel-based analysis of the temporal variations of the green ratio, local distributions of the start and end dates and length of the growing period were illustrated at the plant species level for the first time. The distribution of the start of the growing period strongly corresponded to the snowmelt gradient, whereas the end of the growing period was related to the vegetation type. Our results suggest that the length of the growing period mainly corresponded to the snowmelt gradient in relation to the local microtopography. Thus, commercially available digital time-lapse cameras enabled us to clarify the snow–vegetation relationships and the growing period at high temporal and spatial resolutions. This monitoring method should greatly improve our understanding of alpine ecosystems and help to assess the influence of future climate change. |
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