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Coupled phenocam - satellite surveillance of changing seasonally dry environments in the Southern Hemisphere


Phenology has achieved a position of high relevance in environmental research due to its applications to monitor, detect and attribute shifts in phenological timing to climate change. The paucity of long-term phenology data, the high diversity of species and the absence of a marked dormant season have hampered our ability to relate increasing warming temperatures to temporal shifts in tropical and South Hemisphere phenology. Leaf phenology has significant implications to ecosystem dynamics and productivity as well as to carbon cycles. Tropical and temperate grasslands are essential contributors to global biodiversity and productivity, yet there is still a lack of studies exploring the landscape phenology and the spatial-temporal dynamics within these ecosystems. We propose to integrate methodologies that encompass spatial (satellites) and temporal (phenocameras) approach to monitor variations of leaf phenology to investigate (i) the temporal dynamics of ecosystem productivity; (ii) the associated hydroclimatic factors constraining leafing exchange strategies and; (iii) ecological forecasting of climate change impacts in those biomes at local and regional scales. This project aims to (i) utilize and compare greenness measures derived from phenocams and satellite sensors for the analysis and upscaling of grasslands and seasonally dry tropical/temperate wooded biomes; and (ii) to detect geographic shifts and trends in vegetation phenology across Southern Hemisphere biomes (Australia-Brazil). The proposed exchange project is both well-connected to ongoing research proposal as well as projects under evaluation by FAPESP (#2019/11835-2) and will certainly result in a long-term fruitful collaborative research. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
WANG, JING; SONG, GUANGQIN; LIDDELL, MICHAEL; MORELLATO, PATRICIA; LEE, CALVIN K. F.; YANG, DEDI; ALBERTON, BRUNA; DETTO, MATTEO; MA, XUANLONG; ZHAO, YINGYI; et al. An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites. REMOTE SENSING OF ENVIRONMENT, v. 286, p. 16-pg., . (14/00215-0, 13/50155-0, 19/16191-6, 16/01413-5, 19/11835-2)

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