Phenocam
Observations from Phenocam¶
Download phenocam observations using phenocamR
To use, install phenocamR in your springtime environment:
install.packages("phenocamr")
Browsing sites¶
Phenocam data is available at various sites. You can browse them visually on the phenocam website. Alternatively, a list is available with all phenocam sites. This list can be loaded in springtime.
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from springtime.datasets.phenocam import list_sites
phenocam_sites = list_sites()
phenocam_sites.query('site=="harvard"')
from springtime.datasets.phenocam import list_sites
phenocam_sites = list_sites()
phenocam_sites.query('site=="harvard"')
Out[2]:
| site | elev | contact1 | contact2 | date_start | date_end | nimage | tzoffset | active | infrared | ... | MAP_worldclim | dominant_species | primary_veg_type | secondary_veg_type | koeppen_geiger | ecoregion | wwf_biome | landcover_igbp | site_acknowledgements | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 226 | harvard | 340.0 | Andrew Richardson <andrew DOT richardson AT na... | Bill Munger <jwmunger AT seas DOT harvard DOT ... | 2008-04-04 | 2024-01-08 | 190650 | -5.0 | True | N | ... | 1139.0 | Quercus rubra, Acer rubrum, Pinus strobus | DB | EN | Dfb | 5.0 | 4.0 | 5.0 | The Harvard EMS site is supported is an AmeriF... | POINT (-72.17150 42.53780) |
1 rows × 34 columns
As you can see, the listing contains information on the data availability, as well as additional info such as the climate zones of each site, the dominant species, and the exact latitude/longitude coordinates.
Note that there are also many non-exact matches.
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harvard_like = phenocam_sites.query('site.str.contains("harvard")')
print(len(harvard_like), "site names containing harvard")
harvard_like["site"].values
harvard_like = phenocam_sites.query('site.str.contains("harvard")')
print(len(harvard_like), "site names containing harvard")
harvard_like["site"].values
12 site names containing harvard
Out[3]:
array(['harvard', 'harvardbarn', 'harvardbarn2', 'harvardblo',
'harvardchill', 'harvardems2', 'harvardfarmnorth',
'harvardfarmsouth', 'harvardgarden', 'harvardhemlock',
'harvardhemlock2', 'harvardlph'], dtype=object)
Following the convention of phenocamR, springtime looks for exact site matches by appending a dollar sign after the site name: "harvard$".
Regions of interest¶
Phenocam data consists of series of pictures taken at a fixed site but different times. Depending on the setup of the system, the picture may contain one or multiple types of vegetation and other objects. To derive plant phenometrics from such pictures, each site can define multiple regions of interest (ROIs). For example, this harvard site ROI crops off the sky and a beam of the instrumentation.
All ROIs for all sites can be listed using the list_rois function.
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from springtime.datasets.phenocam import list_rois
phenocam_rois = list_rois()
phenocam_rois.query('site=="harvard"')
from springtime.datasets.phenocam import list_rois
phenocam_rois = list_rois()
phenocam_rois.query('site=="harvard"')
Out[4]:
| site | veg_type | roi_id_number | description | first_date | last_date | site_years | missing_data_pct | geometry | |
|---|---|---|---|---|---|---|---|---|---|
| 333 | harvard | DB | 1 | Deciduous trees in foreground | 2008-04-04 | 2024-01-08 | 15.7 | 0.0 | POINT (-72.17150 42.53780) |
| 334 | harvard | DB | 1000 | Deciduous trees in foreground | 2008-04-04 | 2024-01-08 | 15.7 | 0.0 | POINT (-72.17150 42.53780) |
Retrieving site data¶
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from springtime.datasets import Phenocam
# Use $ in site name to get an exact match
dataset = Phenocam(site="harvard$", years=(2010, 2015))
print(dataset)
from springtime.datasets import Phenocam
# Use $ in site name to get an exact match
dataset = Phenocam(site="harvard$", years=(2010, 2015))
print(dataset)
Phenocam( dataset='phenocam', years=YearRange(start=2010, end=2015), veg_type=None, frequency='3', variables=[], site='harvard$', roi_id=None, area=None )
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df = dataset.raw_load()
df.head()
df = dataset.raw_load()
df.head()
INFO:/home/peter/phenology/springtime/src/springtime/datasets/phenocam.py:Looking for data INFO:/home/peter/phenology/springtime/src/springtime/datasets/phenocam.py:Found /home/peter/.cache/springtime/phenocam/harvard_DB_0001_3day.csv INFO:/home/peter/phenology/springtime/src/springtime/datasets/phenocam.py:Found /home/peter/.cache/springtime/phenocam/harvard_DB_1000_3day.csv
Out[6]:
| site | roi_id_number | veg_type | date | year | doy | image_count | midday_filename | midday_r | midday_g | ... | smooth_rcc_90 | smooth_ci_gcc_mean | smooth_ci_gcc_50 | smooth_ci_gcc_75 | smooth_ci_gcc_90 | smooth_ci_rcc_mean | smooth_ci_rcc_50 | smooth_ci_rcc_75 | smooth_ci_rcc_90 | int_flag | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | harvard | 0001 | DB | 2008-01-05 | 2008 | 5 | NaN | NaN | NaN | NaN | ... | 0.31592 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 1.0 |
| 1 | harvard | 0001 | DB | 2008-01-06 | 2008 | 6 | NaN | NaN | NaN | NaN | ... | 0.31552 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 1.0 |
| 2 | harvard | 0001 | DB | 2008-01-07 | 2008 | 7 | NaN | NaN | NaN | NaN | ... | 0.31514 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 1.0 |
| 3 | harvard | 0001 | DB | 2008-01-08 | 2008 | 8 | NaN | NaN | NaN | NaN | ... | 0.31476 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 1.0 |
| 4 | harvard | 0001 | DB | 2008-01-09 | 2008 | 9 | NaN | NaN | NaN | NaN | ... | 0.31441 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 1.0 |
5 rows × 52 columns
Notice that the filenames don't contain the requested year range: phenocam time series are downloaded in full. As per the springtime conventional, raw_load remains true to the data on disk. In the load method, we filter out the requested year range. Additionally, we convert to a pandas geodataframe.
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df = dataset.load()
df.head()
df = dataset.load()
df.head()
INFO:/home/peter/phenology/springtime/src/springtime/datasets/phenocam.py:Looking for data INFO:/home/peter/phenology/springtime/src/springtime/datasets/phenocam.py:Found /home/peter/.cache/springtime/phenocam/harvard_DB_0001_3day.csv INFO:/home/peter/phenology/springtime/src/springtime/datasets/phenocam.py:Found /home/peter/.cache/springtime/phenocam/harvard_DB_1000_3day.csv
Out[7]:
| datetime | geometry | roi_id_number | veg_type | midday_r | midday_g | midday_b | midday_gcc | midday_rcc | r_mean | ... | smooth_rcc_90 | smooth_ci_gcc_mean | smooth_ci_gcc_50 | smooth_ci_gcc_75 | smooth_ci_gcc_90 | smooth_ci_rcc_mean | smooth_ci_rcc_50 | smooth_ci_rcc_75 | smooth_ci_rcc_90 | int_flag | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2010-01-01 | POINT (-72.17150 42.53780) | 0001 | DB | NaN | NaN | NaN | NaN | NaN | NaN | ... | 0.31163 | 0.00306 | 0.00305 | 0.00318 | 0.00370 | 0.00721 | 0.00795 | 0.00699 | 0.00660 | NaN |
| 1 | 2010-01-02 | POINT (-72.17150 42.53780) | 0001 | DB | 52.19172 | 71.35788 | 75.82381 | 0.35791 | 0.26178 | 48.39274 | ... | 0.31155 | 0.00301 | 0.00300 | 0.00313 | 0.00364 | 0.00709 | 0.00782 | 0.00688 | 0.00650 | NaN |
| 2 | 2010-01-03 | POINT (-72.17150 42.53780) | 0001 | DB | NaN | NaN | NaN | NaN | NaN | NaN | ... | 0.31160 | 0.00297 | 0.00296 | 0.00308 | 0.00359 | 0.00699 | 0.00771 | 0.00678 | 0.00640 | NaN |
| 3 | 2010-01-04 | POINT (-72.17150 42.53780) | 0001 | DB | NaN | NaN | NaN | NaN | NaN | NaN | ... | 0.31175 | 0.00295 | 0.00294 | 0.00307 | 0.00357 | 0.00696 | 0.00767 | 0.00674 | 0.00637 | NaN |
| 4 | 2010-01-05 | POINT (-72.17150 42.53780) | 0001 | DB | 54.79733 | 68.07477 | 65.62895 | 0.36114 | 0.29070 | 58.15591 | ... | 0.31197 | 0.00297 | 0.00297 | 0.00309 | 0.00360 | 0.00701 | 0.00773 | 0.00680 | 0.00642 | NaN |
5 rows × 48 columns
Search data in bounding box¶
Instead of loading a single site, we can also search for sites within given coordinate bounds.
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harvard = {"name": "harvard", "bbox": [-73, 42, -72, 43]}
dataset = Phenocam(area=harvard, years=[2019, 2020])
print(dataset)
harvard = {"name": "harvard", "bbox": [-73, 42, -72, 43]}
dataset = Phenocam(area=harvard, years=[2019, 2020])
print(dataset)
Phenocam( dataset='phenocam', years=YearRange(start=2019, end=2020), veg_type=None, frequency='3', variables=[], site=None, roi_id=None, area=NamedArea(name='harvard', bbox=BoundingBox(xmin=-73.0, ymin=42.0, xmax=-72.0, ymax=43.0)) )
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dataset.load()
df = dataset.load()
df.head()
dataset.load()
df = dataset.load()
df.head()
INFO:/home/peter/phenology/springtime/src/springtime/datasets/phenocam.py:Looking for data INFO:/home/peter/phenology/springtime/src/springtime/datasets/phenocam.py:Found /home/peter/.cache/springtime/phenocam/bbc1_DB_1000_3day.csv INFO:/home/peter/phenology/springtime/src/springtime/datasets/phenocam.py:Found /home/peter/.cache/springtime/phenocam/bbc2_DB_1000_3day.csv INFO:/home/peter/phenology/springtime/src/springtime/datasets/phenocam.py:Found /home/peter/.cache/springtime/phenocam/harvard_DB_0001_3day.csv INFO:/home/peter/phenology/springtime/src/springtime/datasets/phenocam.py:Found /home/peter/.cache/springtime/phenocam/harvard_DB_1000_3day.csv INFO:/home/peter/phenology/springtime/src/springtime/datasets/phenocam.py:Found /home/peter/.cache/springtime/phenocam/harvardbarn_DB_1000_3day.csv INFO:/home/peter/phenology/springtime/src/springtime/datasets/phenocam.py:Found /home/peter/.cache/springtime/phenocam/harvardbarn_EN_1000_3day.csv INFO:/home/peter/phenology/springtime/src/springtime/datasets/phenocam.py:Found /home/peter/.cache/springtime/phenocam/harvardbarn2_DB_1000_3day.csv INFO:/home/peter/phenology/springtime/src/springtime/datasets/phenocam.py:Found /home/peter/.cache/springtime/phenocam/harvardbarn2_DB_2000_3day.csv INFO:/home/peter/phenology/springtime/src/springtime/datasets/phenocam.py:Found /home/peter/.cache/springtime/phenocam/harvardbarn2_EN_1000_3day.csv INFO:/home/peter/phenology/springtime/src/springtime/datasets/phenocam.py:Found /home/peter/.cache/springtime/phenocam/harvardbarn2_EN_2000_3day.csv INFO:/home/peter/phenology/springtime/src/springtime/datasets/phenocam.py:Found /home/peter/.cache/springtime/phenocam/harvardblo_UN_1000_3day.csv INFO:/home/peter/phenology/springtime/src/springtime/datasets/phenocam.py:Found /home/peter/.cache/springtime/phenocam/harvardblo_UN_2000_3day.csv INFO:/home/peter/phenology/springtime/src/springtime/datasets/phenocam.py:Found 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Out[9]:
| datetime | geometry | roi_id_number | veg_type | midday_r | midday_g | midday_b | midday_gcc | midday_rcc | r_mean | ... | smooth_rcc_90 | smooth_ci_gcc_mean | smooth_ci_gcc_50 | smooth_ci_gcc_75 | smooth_ci_gcc_90 | smooth_ci_rcc_mean | smooth_ci_rcc_50 | smooth_ci_rcc_75 | smooth_ci_rcc_90 | int_flag | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2019-01-01 | POINT (-72.17436 42.53508) | 1000 | DB | NaN | NaN | NaN | NaN | NaN | NaN | ... | 0.33691 | 0.00290 | 0.00310 | 0.00320 | 0.00321 | 0.00903 | 0.00921 | 0.00947 | 0.00925 | NaN |
| 1 | 2019-01-02 | POINT (-72.17436 42.53508) | 1000 | DB | 84.86659 | 85.59192 | 85.59225 | 0.33428 | 0.33144 | 75.33211 | ... | 0.33514 | 0.00282 | 0.00301 | 0.00311 | 0.00312 | 0.00877 | 0.00895 | 0.00921 | 0.00899 | NaN |
| 2 | 2019-01-03 | POINT (-72.17436 42.53508) | 1000 | DB | NaN | NaN | NaN | NaN | NaN | NaN | ... | 0.33330 | 0.00283 | 0.00302 | 0.00313 | 0.00313 | 0.00881 | 0.00899 | 0.00924 | 0.00903 | NaN |
| 3 | 2019-01-04 | POINT (-72.17436 42.53508) | 1000 | DB | NaN | NaN | NaN | NaN | NaN | NaN | ... | 0.33180 | 0.00293 | 0.00313 | 0.00324 | 0.00324 | 0.00912 | 0.00931 | 0.00957 | 0.00935 | NaN |
| 4 | 2019-01-05 | POINT (-72.17436 42.53508) | 1000 | DB | 60.30106 | 67.32647 | 78.02750 | 0.32738 | 0.29321 | 70.06766 | ... | 0.33101 | 0.00301 | 0.00321 | 0.00332 | 0.00332 | 0.00935 | 0.00955 | 0.00982 | 0.00959 | NaN |
5 rows × 48 columns
Export as recipe¶
As always, we can export the dataset as recipe for sharing and reproducibility.
In [10]:
Copied!
print(dataset.to_recipe())
print(dataset.to_recipe())
dataset: phenocam years: - 2019 - 2020 frequency: '3' variables: [] area: name: harvard bbox: - -73.0 - 42.0 - -72.0 - 43.0
TODO¶
We are already excited about working with phenocam data, but in order to combine it with other springtime datasets, we need to do a bit more work.
- Extract relevant variables / infer events
- Pivot dataframe such that it has one row per year/site.