Assessing Canopy Phenological Variations and Gross Primary Productivity in A Savanna Ecosystem in Yuanjiang, Yunnan Province of Southwest China
Authors:   CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Xishuangbanna 666303, China, Myo S. T. Z. ,   University of Chinese Academy of Sciences, Beijing 100049, China,   Myanma Timber Enterprise, Ministry of Natural Resources and Environmental Conservation, Yangon 11012, Myanmar, Zhang Y. P. ,   CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Xishuangbanna 666303, China,   Center of Plant Ecology, Core Botanical Gardens, Chinese Academy of Sciences, Xishuangbanna 666303, China, Song Q. H. ,   CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Xishuangbanna 666303, China,   Center of Plant Ecology, Core Botanical Gardens, Chinese Academy of Sciences, Xishuangbanna 666303, China, Chen A. G. ,   CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Xishuangbanna 666303, China,   Yuanjiang Savanna Ecosystem Research Station, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Yuanjiang 653300, China, Yang D. X. ,   CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Xishuangbanna 666303, China,   Yuanjiang Savanna Ecosystem Research Station, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Yuanjiang 653300, China, Zhou L. G. ,   CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Xishuangbanna 666303, China, Lin Y. X. ,   CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Xishuangbanna 666303, China, Phyo Z. ,   CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Xishuangbanna 666303, China,   University of Chinese Academy of Sciences, Beijing 100049, China,   Myanma Timber Enterprise, Ministry of Natural Resources and Environmental Conservation, Yangon 11012, Myanmar, Fei X. H. ,   College of Resources and Environmental Engineering, Guizhou University, Guiyang 550025, China, Liang N. S. ,   Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Ibaraki 305-8506, Japan
Published: September 2023

Vegetation phenology is an important indicator of environmental change and strongly connected to forest ecosystem productivity change. This study aimed to analyse the pattern of phenological variations derived from digital imagery for the interpretation of ecosystem productivity. For 2014, 2015 and 2016, the seasonal phenological development of savanna was analysed by using towerbased imagery from a digital camera. The green excess index (GEI) was the best at representing the phenological transition dates (PTDs) and useful for investigating the gross primary production (GPP) in the savanna ecosystem. There was a significant correlation between the monthly pattern of the strength of green (Sgreen), green excess index (GEI) and vegetation contrast index (VCI) and GPP throughout the year. Additionally, the annual pattern of colour indices had significant relationship (p < 0.05) with GPP but this was not seasonal. The air temperature (air T) and soil temperature (soil T) were strongly significantly correlated (p < 0.001) with the start of growing season (SGS) and caused the advance in green-up and the timing of the start of the growing season in 2014 and 2016. The short growing season length (GSL) had an impact on the productivity. The colour indices from the digital camera images not only provided the phenological pattern of a forest canopy but also revealed the forest ecosystem productivity by showing the response to environmental factors. Our results indicate that daily continuous digital camera images might be useful for ecologists to use as a tool for future prediction of the long-term phenological modelling.