Abstract
Can social sensing detect the spatio-temporal variability of autumn phenology? We analyzed data published on the Twitter social media website through the text mining of non-geotagged tweets regarding a forested, mountainous region in Japan. We were able to map the spatial characteristic of tweets regarding peak leaf coloring along an altitudinal gradient and found that text mining of tweets is a useful approach to the in situ collection of autumn phenology information at multiple locations over a broad spatial scale. Potential uncertainties in this approach were examined and compared to other online research sources and methods, including Google Trends and information on widely available websites and live camera images. Finally, we suggest ways to reduce the uncertainties identified within our approach and to create better integration between text mining of tweets and other online research data sources and methods.
Highlights
Can the spatio-temporal variability of autumn phenology—leaf coloring and leaf fall—be detected by “social sensing” through analysis of data published on social media such as Twitter? Accurate in situ observation of spatio-temporal variability of autumn phenology at multiple locations over a broad spatial scale is an important but challenging task
The agency records observations on 41 plant phenology items and 24 animal phenology items. These observations enable us to evaluate the spatio-temporal variability of the first date of various kinds of flowering and appearances of animals throughout a year (Japan Meteorological Agency, 2021)
Autumn phenology items are limited to the flowering of cluster-amaryllis, Japanese pampas grass, and Japanese bush clover; the leaf fall of ginkgo; and the leaf coloring of ginkgo and maple
Summary
Can the spatio-temporal variability of autumn phenology—leaf coloring and leaf fall—be detected by “social sensing” through analysis of data published on social media such as Twitter? Accurate in situ observation of spatio-temporal variability of autumn phenology at multiple locations over a broad spatial scale is an important but challenging task. The triggering of leaf-coloring and leaf-fall ( known as leaf senescence) is affected by numerous factors such as air temperature, photoperiod, precipitation, solar radiation, and leaf longevity (Park et al, 2017; Xie et al, 2018a,b). Solving this problem for deciduous forests, is instrumental to achieving accurate evaluation of the spatio-temporal variability of ecosystem functions and services and to establishing relationships between animal and plant phenology on the one hand and biodiversity on the other hand, and will contribute to the important social task of collecting
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