,
Rose Goueth [aut],
Wencong Li [aut],
Weijia Zhang [ctb],
Nicholas Horton [ctb],
Dominique Kelly [aut] ,
Albert Y. Kim [aut] | Title: | Retrieve Data from MacLeish Field Station |
|---|---|
| Description: | Download data from the Ada and Archibald MacLeish Field Station in Whately, MA. The Ada and Archibald MacLeish Field Station is a 260-acre patchwork of forest and farmland located in West Whately, MA that provides opportunities for faculty and students to pursue environmental research, outdoor education, and low-impact recreation (see <https://www.smith.edu/about-smith/sustainable-smith/macleish> for more information). This package contains weather data over several years, and spatial data on various man-made and natural structures. |
| Authors: | Benjamin S. Baumer [aut, cre] ,
Rose Goueth [aut],
Wencong Li [aut],
Weijia Zhang [ctb],
Nicholas Horton [ctb],
Dominique Kelly [aut] ,
Albert Y. Kim [aut] |
| Maintainer: | Benjamin S. Baumer <[email protected]> |
| License: | CC0 |
| Version: | 0.3.9 |
| Built: | 2024-06-24 03:48:54 UTC |
| Source: | https://github.com/beanumber/macleish |
Retrieve data from the Macleish Field Station weather monitors
## S3 method for class 'etl_macleish' etl_extract(obj, ...) ## S3 method for class 'etl_macleish' etl_transform(obj, ...) etl_transform_help(obj, ...)## S3 method for class 'etl_macleish' etl_extract(obj, ...) ## S3 method for class 'etl_macleish' etl_transform(obj, ...) etl_transform_help(obj, ...)
obj |
an |
... |
arguments passed to methods |
macleish <- etl("macleish") str(macleish) ## Not run: macleish %>% etl_extract() %>% etl_transform() %>% etl_load() whately <- macleish %>% tbl("whately") orchard <- macleish %>% tbl("orchard") whately %>% summarize(N = n(), avg_temp = mean(temperature)) orchard %>% summarize(N = n(), avg_temp = mean(temperature)) # check data types whately %>% glimpse() # if using SQLite, datetimes will get converted to integers whately <- whately %>% mutate(when_datetime = datetime(when, 'unixepoch')) whately %>% glimpse() # show the most recent data -- should be within the past hour whately %>% collect() %>% tail() # show that no time-shifting is happening if (require(ggplot2)) { macleish %>% tbl("whately") %>% collect() %>% mutate(when = lubridate::ymd_hms(when)) %>% filter(lubridate::year(when) == 2012 & month(when) == 12 & day(when) == 20) %>% ggplot(aes(x = when, y = temperature)) + geom_line() } ## End(Not run)macleish <- etl("macleish") str(macleish) ## Not run: macleish %>% etl_extract() %>% etl_transform() %>% etl_load() whately <- macleish %>% tbl("whately") orchard <- macleish %>% tbl("orchard") whately %>% summarize(N = n(), avg_temp = mean(temperature)) orchard %>% summarize(N = n(), avg_temp = mean(temperature)) # check data types whately %>% glimpse() # if using SQLite, datetimes will get converted to integers whately <- whately %>% mutate(when_datetime = datetime(when, 'unixepoch')) whately %>% glimpse() # show the most recent data -- should be within the past hour whately %>% collect() %>% tail() # show that no time-shifting is happening if (require(ggplot2)) { macleish %>% tbl("whately") %>% collect() %>% mutate(when = lubridate::ymd_hms(when)) %>% filter(lubridate::year(when) == 2012 & month(when) == 12 & day(when) == 20) %>% ggplot(aes(x = when, y = temperature)) + geom_line() } ## End(Not run)
Shapefiles from the MacLeish Field Station. The field station
itself is located at lat = 42.449167, lon = -72.679389. These
data contain information about various man-made and natural structures
surrounding the field station.
macleish_layersmacleish_layers
A list of sf::sf() objects, each providing a different layer.
Landmarks
Type of dominant tree in individual forests, as noted by Jesse Bellemare
local streams
Challenge courses on the property
Buildings at MacLeish
Wetland areas
the property boundary
research plots
soil deposits used by Amy Rhodes
Hiking trails
Two camp sites
30 foot elevation contours
Each of the sf::sf() objects are projected in
epsg:4326 for easy integration with Google Maps or
leaflet::leaflet() objects.
names(macleish_layers) macleish_layers[["buildings"]] if (require(sf)) { plot(macleish_layers[["buildings"]]) }names(macleish_layers) macleish_layers[["buildings"]] if (require(sf)) { plot(macleish_layers[["buildings"]]) }
Maple sap collection at MacLeish
maple_sapmaple_sap
the date of collection
how much sap was collected, in gallons
comments
who was there?
Retrieve elevation layers from MassGIS
mass_gis(layer = "contours250k") macleish_intersect(x)mass_gis(layer = "contours250k") macleish_intersect(x)
layer |
MassGIS layer name to import |
x |
an |
This function will download shapefiles from MassGIS, unzip them,
transform the projection to EPSG:4326, compute their intersection with the
boundary of the MacLeish property, and return the resulting
sf::sf() object.
Intersect a spatial layer with the MacLeish boundary layer
https://www.mass.gov/info-details/massgis-data-layers
## Not run: # have to download the shapefiles...could take a while... elevation <- mass_gis() macleish_elevation <- macleish_intersect(elevation) if (require(sf)) { plot(macleish_elevation) } dcr_trails <- mass_gis("dcrtrails") ## End(Not run)## Not run: # have to download the shapefiles...could take a while... elevation <- mass_gis() macleish_elevation <- macleish_intersect(elevation) if (require(sf)) { plot(macleish_elevation) } dcr_trails <- mass_gis("dcrtrails") ## End(Not run)
Phenocam contains over 70,000 images taken from MacLeish. Photos have been taken every 30 minutes since February 2017.
phenocam_image_url(when = NULL, ...) phenocam_read_day_urls(x = Sys.Date()) phenocam_read_monthly_midday_urls(x = Sys.Date()) phenocam_image_url_midday(x = Sys.Date()) phenocam_info() phenocam_download(...)phenocam_image_url(when = NULL, ...) phenocam_read_day_urls(x = Sys.Date()) phenocam_read_monthly_midday_urls(x = Sys.Date()) phenocam_image_url_midday(x = Sys.Date()) phenocam_info() phenocam_download(...)
when |
a string to be converted into a date-time |
... |
arguments passed to |
x |
a Date |
https://phenocam.nau.edu/webcam/sites/macleish/
phenocam_image_url() phenocam_image_url("2021-12-25 12:05:05") ## Not run: phenocam_read_day_urls() ## End(Not run) ## Not run: phenocam_read_monthly_midday_urls() ## End(Not run) ## Not run: phenocam_image_url_midday(Sys.Date() - 3) phenocam_image_url_midday(Sys.Date() - 365) ## End(Not run) ## Not run: phenocam_info() ## End(Not run) ## Not run: phenocam_download() df <- read_phenocam(file.path(tempdir(),"macleish_DB_1000_3day.csv")) print(str(df)) ## End(Not run)phenocam_image_url() phenocam_image_url("2021-12-25 12:05:05") ## Not run: phenocam_read_day_urls() ## End(Not run) ## Not run: phenocam_read_monthly_midday_urls() ## End(Not run) ## Not run: phenocam_image_url_midday(Sys.Date() - 3) phenocam_image_url_midday(Sys.Date() - 365) ## End(Not run) ## Not run: phenocam_info() ## End(Not run) ## Not run: phenocam_download() df <- read_phenocam(file.path(tempdir(),"macleish_DB_1000_3day.csv")) print(str(df)) ## End(Not run)
Data on change in tree diameter (in centimeters) for parasitic Hemlock Woolly Adelgid dominated areas on the Western side of MacLeish. Tree diameter was measured at 1.4 meters high above the ground.
tree_diameter1tree_diameter1
Module number that represents one of the 10 subplot modules that are 110 m and 20 x 50 m. There are five 10 x 10 modules along central 50 m axis.
Tag numbers used to identify each tree.
Tree species include Red Maple (Acer rubrum), Sweet Birch (Betula lenta), Paper Birch (Betula papyrifera), American Beech (Fagus grandifolia), American witch-hazel (Hamamelis virginiana), Eastern White Pine (Pinus strobus), Northern Red Oak (Quercus rubra), and Eastern Hemlock (Tsuga canadensis).
Stage of growth for each individual tree. The emergent position are the tallest trees, followed by canopy, subcanopy, and finally, sapling trees, which are the smallest trees.
Data collected from 2010-2015 in the fall semesters.
Notes collected on the wellbeing of tree species, including notes on death or poor health.
Tree diameter measured in centimeters and at 1.4 meters high above the ground.
Data obtained from Jesse Bellemare and Smith College students from BIO364-365 courses.
Data on change in tree diameter (in centimeters) for parasitic Hemlock Woolly Adelgid dominated areas on the Western side of MacLeish. Tree diameter was measured at 1.4 meters high above the ground.
tree_diameter2tree_diameter2
Module number that represents one of the 10 subplot modules that are 110 m and 20 x 50 m. There are five 10 x 10 modules along central 50 m axis.
Tag numbers used to identify each tree.
Tree species include Red Maple (Acer rubrum), Sweet Birch (Betula lenta), American Beech (Fagus grandifolia), Eastern White Pine (Pinus strobus), Northern Red Oak (Quercus rubra), and Eastern Hemlock (Tsuga canadensis).
Data collected from 2009-2012 in the fall semesters.
Notes collected on the wellbeing of tree species, including notes on death or poor health.
Tree diameter measured in centimeters and at 1.4 meters high above the ground.
Data obtained from Jesse Bellemare and Smith College students from BIO364-365 courses.
Weather data collected at the Macleish Field Station in Whately, MA during 2015.
whately_2015 orchard_2015whately_2015 orchard_2015
For both, a data frame (dplyr::tbl_df()) with roughly 52,560 rows and 8 or 9 variables.
The following variables are values that are found in either the whately_2015
or orchard_2015 data tables.
All variables are averaged over the 10 minute interval unless otherwise noted.
Timestamp for each measurement set in Eastern Standard Time.
average temperature, in Celsius
Wind speed, in meters per second
Wind direction, in degrees
How much water there is in the air, in millimeters
Atmospheric pressure, in millibars
Total rainfall, in millimeters
Amount of radiation coming from the sun, in Watts/meters^2. Solar measurement for Whately
Photosynthetically Active Radiation (sunlight between 400 and 700 nm), in average density of Watts/meters^2. One of two solar measurements for Orchard
Photosynthetically Active Radiation (sunlight between 400 and 700 nm), in average total over measurement period of Watts/meters^2. One of two solar measurements for Orchard
An object of class tbl_df (inherits from tbl, data.frame) with 52547 rows and 9 columns.
The Macleish Field Station is a remote outpost owned by Smith
College and used for field research. There are two weather stations on the
premises. One is called WhatelyMet and the other is OrchardMet.
The WhatelyMet station is located at (42.448470, -72.680553) and
the OrchardMet station is at (42.449653, -72.680315).
WhatelyMet is located at the end of Poplar Hill Road in Whately,
Massachusetts, USA. The meteorological instruments of WhatelyMet (except the
rain gauge) are mounted at the top of a tower 25.3 m tall, well above the
surrounding forest canopy. The tower is located on a local ridge at an
elevation 250.75m above sea level.
OrchardMet is located about 250 m north of the first tower in an open
field next to an apple orchard. Full canopy trees (~20 m tall) are within
30 m of this station. This station has a standard instrument configuration
with temperature, relative humidity, solar radiation, and barometric
pressure measured between 1.5 and 2.0 m above the ground. Wind speed and
direction are measured on a 10 m tall tower and precipitation is measured
on the ground. Ground temperature is measured at 15 and 30 cm below the
ground surface 2 m south of the tower. The tower is located 258.1 m above
sea level. Data collection at OrchardMet began on June 27th, 2014.
The variables shown above are weather data collected at WhatelyMet and
OrchardMet during 2015. Solar radiation is measured in two different ways:
see SlrW_Avgor the PAR variables for Photosynthetic Active Radiation.
Note that a loose wire resulted in erroneous temperature reading at OrchardMet in late November, 2015.
These data are recorded at https://www.smith.edu/about-smith/sustainable-smith/ceeds
## Not run: #' # loose wire anomalies if (require(dplyr) & require(ggplot2) & require(lubridate)) { orchard_2015 %>% filter(month(when) == 11) %>% ggplot(aes(x = when, y = temperature)) + geom_line() + geom_smooth() } ## End(Not run)## Not run: #' # loose wire anomalies if (require(dplyr) & require(ggplot2) & require(lubridate)) { orchard_2015 %>% filter(month(when) == 11) %>% ggplot(aes(x = when, y = temperature)) + geom_line() + geom_smooth() } ## End(Not run)