B051-0014
Novel integration of high resolution satellites with drone flights improves monitoring of tree-crown scale autumn leaf phenology
Thursday, 10 December 2020
Poster
Wu Shengbiao1, Jing Wang1, Zhengbing Yan1, Guangqin Song1, Yang Chen1, Qin Ma2, Meifeng Deng2, Yuntao Wu2, Yingyi Zhao1, Zhengfei Guo1, Xiangtao Xu3, Xi Yang4, Yanjun Su2, Lingli Liu2,5 and Jin Wu1,6, (1)The University of Hong Kong, School of Biological Sciences, Hong Kong, Hong Kong, (2)Institute of Botany, Chinese Academy of Sciences, State Key Laboratory of Vegetation and Environmental Change, Beijing, China, (3)Cornell University, Ecology and Evolutionary Biology, Ithaca, NY, United States, (4)University of Virginia, Environmental Sciences, Charlottesville, VA, United States, (5)University of Chinese Academy of Sciences, Beijing, China, (6)Chinese University of Hong Kong, State Key Laboratory of Agrobiotechnology, Hong Kong, Hong Kong
Abstract:
Autumn leaf phenology signals the end of leaf growing season and shows large inter-crown variability in response to global climate change, which strongly regulates the carbon, water, and nutrient cycles from individual tree-crowns up to ecosystems. However, critical challenges remain with the monitoring of tree-crown scale autumn leaf phenology over large spatial coverage due to the lack of spatially explicit information of individual tree-crowns and high-quality and high-resolution time-series observations. Traditional field and proximate remote sensing methods (e.g. field observations, phenocam measurements and UAV flights) are the commonly-used means to monitor crown-scale leaf phenology, but are constrained to a very limited footprint and time span. Satellite remote sensing might provide another alternative solution, but most satellites remain too coarse spatial resolution to resolve individual tree-crowns.
To address the above challenges, we integrated local drone surveys that enable segmentation of each individual tree-crown with high-resolution PlanetScope satellite measurements of 3-m resolution and near-daily revisit cycle that enable autumn leaf phenology monitoring. To test this integrated method, we used a temperate forest in Northeast China as an example, as all the relevant data are available there. Our results show that the proposed drone-PlanetScope integration enabled to capture large inter-crown variation in leaf autumn phenology (i.e. ~ 30 days difference in leaf fall date across different species). The crown-scale phenology derived from PlanetScope also agreed well with local phenocam measurements (R2=0.81). These findings demonstrate large spatial heterogeneity in crown-scale autumn leaf phenology within a temperate forest, suggesting the importance of using high-resolution satellites to advance crown-scale phenology studies over large geographical areas.
