Using ground observations of a digital camera in the VIS-NIR range for quantifying the phenology of Mediterranean woody species |
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| Institution: | 1. Department of Environmental Geochemical Cycle Research, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Kanazawa-ku, Yokohama 236-0001, Japan;2. Faculty of Agriculture and Agricultural Science Programme, Kochi University, Nankoku-shi, Kochi 783-8502, Japan;3. United Graduate School of Agricultural Sciences, Ehime University, Matsuyama 790-8566, Japan;4. Research Institute for Humanity and Nature, Motoyama, Kamigamo, Kita-ku, Kyoto 603-8047, Japan;5. Graduate School of Human and Environmental Studies, Kyoto University, Yoshida Nihonmatsu-cho, Sakyo-ku, Kyoto 606-8501, Japan;1. The Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, China;2. University of the Chinese Academy of Sciences, Beijing 100049, China;3. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China;4. Center for Advanced Land Management Information Technologies, School of Natural Resources, University of Nebraska–Lincoln, Lincoln, NE 68583, USA;5. Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, AB T6G 2E3, Canada;6. Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada;7. Department of Biology, University of Toronto at Mississauga, Mississauga, ON L5L 1C6, Canada;8. Graduate Program in Ecology and Evolutionary Biology, University Toronto, Toronto, ON M5S 3B2, Canada;9. Geospatial Sciences Center of Excellence GSCE, Department Geography, South Dakota State University, 1021 Medary Ave,Wecota Hall 506B, Brookings, SD 57007, USA;10. School of Earth, Environment and Society, McMaster University, 1280 Main St West, Hamilton, ON L8S 4L8, Canada;11. University of British Columbia, 136-2357 Main Mall, Vancouver, BC V6T 1Z4, Canada |
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| Abstract: | The spectral reflectance of most plant species is quite similar, and thus the feasibility of identifying most plant species based on single date multispectral data is very low. Seasonal phenological patterns of plant species may enable to face the challenge of using remote sensing for mapping plant species at the individual level. We used a consumer-grade digital camera with near infra-red capabilities in order to extract and quantify vegetation phenological information in four East Mediterranean sites. After illumination corrections and other noise reduction steps, the phenological patterns of 1839 individuals representing 12 common species were analyzed, including evergreen trees, winter deciduous trees, semi-deciduous summer shrubs and annual herbaceous patches. Five vegetation indices were used to describe the phenology: relative green and red (green\red chromatic coordinate), excess green (ExG), normalized difference vegetation index (NDVI) and green-red vegetation index (GRVI). We found significant differences between the phenology of the various species, and defined the main phenological groups using agglomerative hierarchical clustering. Differences between species and sites regarding the start of season (SOS), maximum of season (MOS) and end of season (EOS) were displayed in detail, using ExG values, as this index was found to have the lowest percentage of outliers. An additional visible band spectral index (relative red) was found as useful for characterizing seasonal phenology, and had the lowest correlation with the other four vegetation indices, which are more sensitive to greenness. We used a linear mixed model in order to evaluate the influences of various factors on the phenology, and found that unlike the significant effect of species and individuals on SOS, MOS and EOS, the sites' location did not have a direct significant effect on the timing of phenological events. In conclusion, the relative advantage of the proposed methodology is the exploitation of representative temporal information that is collected with accessible and simple devices, for the subsequent determination of optimal temporal acquisition of images by overhead sensors, for vegetation mapping over larger areas. |
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| Keywords: | Remote sensing Tree species Classification Phenology Digital camera Mediterranean |
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