Goals, progresses, and issues on phenocam leaf phenology analysis.
- One goal is to overlap and fuse the leaf phenology trajectory from mulitple sources of data. Especially, to overlap leaf cover time series from BCI drone data (https://www.mdpi.com/2072-4292/11/13/1534) with phenocam-driven vegetation index time series.
- The other goal is to gather phenocam data from all tropical region, to study interannual varibility of leaf productivity in a vegetation demography context.
Current and possible research grant sources related to phenocam.
- LTER (discontiued, but many grants lasts until 2022-2023). https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=7671
- Macrosystems and NEON-enabled science (recently changed) https://www.nsf.gov/pubs/2019/nsf19538/nsf19538.htm
- Phenocam NSF grant abstract (tenuare July 2022) https://www.nsf.gov/awardsearch/showAward?AWD_ID=1702697
- xROI by Bijan, postdoc at the Richardson lab. https://github.com/bnasr/xROI
- Hawaii phenocam. http://www.hippnet.hawaii.edu/
- Working with Phenocam Images, by treystaf. https://github.com/treystaff/PhenoAnalysis/wiki/Working-with-PhenoCam-Images
Jul 5 2019
- tower shadow lies on the trees in the morning earlier than 10am, but photos early in the morning appears to be in crisper quality.
- Download early morning images and compare (6am -8am)
- Photos after 16:00 gets direct sunlight. Retrieve photos before 16:00.
Jul 5 2019
- BCI2 - Inactive, however the best image qualities. Therefore download the data. Actually, use all three camera data. Jul 6 2019
- Check other Phenocam data, e.g. Lopes 2016, Leaf flush drives dry season green-up of the Central Amazon. (Cannot find this data from phenocam network.)