Modified: 2014-07-10 10:26:03
Soil sensors were installed at two meadows on the eastern edge of YNP. The first site is located in Inyo National Forest right outside the eastern border of Yosemite. The second site located in Dana Meadows within YNP. The location of the two study meadows is shown in Map 1.
## [1] "http://maps.google.com/maps/api/staticmap?center=37.925361,-119.279854&zoom=12&size=640x640&maptype=terrain&format=png32&sensor=true&markers=size:small|col:red|char:A|37.955324,-119.285925|size:small|col:red|char:|37.900357,-119.25587"
## [1] "http://maps.google.com/maps/api/staticmap?center=37.955512,-119.286317&zoom=20&size=640x640&maptype=satellite&format=png32&sensor=true&markers=color:red|label:K|37.955324,-119.285925&markers=color:red|label:A|37.955795,-119.286435&markers=color:red|label:G|37.955445,-119.286314&markers=color:red|label:M|37.955544,-119.286244"
## [1] "http://maps.google.com/maps/api/staticmap?center=37.897539,-119.254&zoom=17&size=640x640&maptype=satellite&format=png32&sensor=true&markers=color:red|label:K|37.955324,-119.285925&markers=color:red|label:A|37.955795,-119.286435&markers=color:red|label:G|37.955445,-119.286314&markers=color:red|label:M|37.955544,-119.286244"
Ther are three data loggers installed in the first meadow. Their approximate locations are shown on the map below: Aniaml (A) in the dry portion of the meadow, etc
| Code | Name | P1 | P2 | P3 | P4 | P5 |
|---|---|---|---|---|---|---|
| A | Animal | W1, T1 | W2, T2 | W3, T3 | P1, T4 | P2, T5 |
| G | Gonzo | W1, T1 | W2, T2 | W3, T3 | P1, T4 | P2, T5 |
| M | MissPig | W1, T1 | W2, T2 | W3, T3 | P1, T4 | P2, T5 |
| K | Fozzy | W1, T1 | W2, T2 | W3, T3 | W4, T4 | W5, T5 |
| K | Kermit | W1, T1 | W2, T2 | W3, T3 | W4, T4 | W5, T5 |
Describe data logger locations and purpose (tephra)
Logger names
The datalogger files are downloaded incrementally and saved as “AnimalYYYYMMDD.csv”, “GonzoYYYYMMDD.csv”. The datalogger files are opened by searching in the current directory for the file names that contain the phrases “Animal”, “Gonzo” etc.
loggerfiles1 = list.files(path = ".", pattern = logger1)
loggerfiles2 = list.files(path = ".", pattern = logger2)
loggerfiles3 = list.files(path = ".", pattern = logger3)
loggerfiles4 = list.files(path = ".", pattern = logger4)
loggerfiles5 = list.files(path = ".", pattern = logger5)
loggerfiles6 = list.files(path = ".", pattern = logger6)
loggerfiles7 = list.files(path = ".", pattern = logger7)
loggerfiles8 = list.files(path = ".", pattern = logger8)
loggerfiles9 = list.files(path = ".", pattern = logger9)
The following files were found in the directory reaveals that there are 21 files:
## [1] "Animal20111023.csv" "Animal20111103.csv" "Animal20120614.csv" "Animal20120706.csv"
## [5] "Animal20120718.csv" "Animal20120727.csv" "Animal20120803.csv" "Animal20120807.csv"
## [9] "Animal20120819.csv" "Animal20120826.csv" "Animal20120907.csv" "Animal20120921.csv"
## [13] "Animal20121005.csv" "Animal20121019.csv" "Animal20130619.csv" "Animal20130627.csv"
## [17] "Animal20130711.csv" "Animal20130725.csv" "Animal20130809.csv" "Animal20130918.csv"
## [21] "Animal20131105.csv"
## [1] "Gonzo20120727.csv" "Gonzo20120803.csv" "Gonzo20120807.csv" "Gonzo20120819.csv"
## [5] "Gonzo20120826.csv" "Gonzo20120921.csv" "Gonzo20121019.csv" "Gonzo20130619.csv"
## [9] "Gonzo20130627.csv" "Gonzo20130711.csv" "Gonzo20130725.csv" "Gonzo20131004.csv"
## [13] "Gonzo20131105.csv"
## [1] "misspig20120616.csv" "misspig20120707.csv" "misspig20120718.csv" "misspig20120727.csv"
## [5] "misspig20120803.csv" "misspig20120807.csv" "misspig20120819.csv" "misspig20120826.csv"
## [9] "misspig20120921.csv" "misspig20121019.csv" "misspig20130619.csv" "misspig20130627.csv"
## [13] "misspig20130711.csv" "misspig20130725.csv" "misspig20130809.csv" "misspig20130918.csv"
## [17] "misspig20131004.csv" "misspig20131105.csv"
## [1] "Fozzy20120707.csv" "Fozzy20120719.csv" "Fozzy20120727.csv" "Fozzy20120803.csv"
## [5] "Fozzy20120807.csv" "Fozzy20120819.csv" "Fozzy20120907.csv" "Fozzy20120921.csv"
## [9] "Fozzy20121019.csv" "Fozzy20130619.csv" "Fozzy20130627.csv" "Fozzy20130711.csv"
## [13] "Fozzy20130725.csv" "Fozzy20130809.csv" "Fozzy20130918.csv" "Fozzy20131004.csv"
## [17] "Fozzy20131105.csv"
## [1] "Kermit20120707.csv" "Kermit20120719.csv" "Kermit20120727.csv" "Kermit20120803.csv"
## [5] "Kermit20120807.csv" "Kermit20120819.csv" "Kermit20120907.csv" "Kermit20120921.csv"
## [9] "Kermit20121019.csv" "Kermit20130619.csv" "Kermit20130627.csv" "Kermit20130711.csv"
## [13] "Kermit20130725.csv" "Kermit20130809.csv" "Kermit20130918.csv" "Kermit20131004.csv"
## [17] "Kermit20131105.csv"
## [1] "Ernie01.csv" "Ernie02.csv" "Ernie03.csv" "Ernie04.csv"
## [1] "Bert20120727.csv" "Bert20120826.csv" "Bert20120917.csv" "Bert20121019.csv"
## [1] "Dorothy20120717.csv" "Dorothy20120801.csv" "Dorothy20120826.csv" "Dorothy20121019.csv"
## [5] "Dorothy20130513.csv" "Dorothy20130815.csv" "Dorothy20131105.csv"
## [1] "Elmo01.csv" "Elmo02.csv" "Elmo03.csv" "Elmo04.csv"
These files are read sequentially to dataframes named all.datai, where i denotes the loggernumber. Each datalogger is connected to five sensors and the data columns are arranged by sensor number. The 5TM sensors record water content (W) and temperature while MPS sensors record water potential (P) and temperature (T). The first column is a timestamp of data collection.
Animal
## Time W1 T1 W2 T2 W3 T3 P1 P2
## 1 2011/10/23 15:00:00 0.378 9.7 0.352 6.6 0.374 6.1 NA NA
## 2 2011/10/23 15:30:00 0.379 10.1 0.354 6.8 0.374 6.2 NA NA
## 3 2011/10/23 16:00:00 0.379 10.3 0.353 7.1 0.374 6.2 NA NA
## 4 2011/10/23 16:30:00 0.379 10.4 0.354 7.3 0.374 6.3 NA NA
## 5 2011/10/23 17:00:00 0.379 10.2 0.354 7.4 0.374 6.4 NA NA
## 6 2011/10/23 17:30:00 0.379 9.9 0.354 7.6 0.374 6.4 NA NA
## 7 2011/10/23 18:00:00 0.379 9.5 0.353 7.6 0.374 6.5 NA NA
## 8 2011/10/23 18:30:00 0.379 9.0 0.353 7.6 0.374 6.5 NA NA
## 9 2011/10/23 19:00:00 0.379 8.6 0.353 7.5 0.373 6.5 NA NA
## 10 2011/10/23 19:30:00 0.379 8.2 0.353 7.4 0.374 6.6 NA NA
Gonzo
## Time W1 T1 W2 T2 W3 T3 P1 PT1 P2 PT2
## 1 2012/07/27 13:30:00 0.311 10.9 0.326 12.7 <NA> <NA> <NA> <NA> <NA> <NA>
## 2 2012/07/27 14:00:00 -0.004 26.5 -0.012 34.1 <NA> <NA> <NA> <NA> <NA> <NA>
## 3 2012/07/27 14:30:00 0.295 18.3 0.004 31.5 <NA> <NA> <NA> <NA> <NA> <NA>
## 4 2012/07/27 15:00:00 0.693 -39.4 0.002 23.2 <NA> <NA> -5.3 15.8 <NA> <NA>
## 5 2012/07/27 15:30:00 <NA> <NA> 0.299 15.3 0.3 14.5 -5.3 15.5 -5.3 12.5
## 6 2012/07/27 16:00:00 <NA> <NA> 0.299 14.9 0.298 11.6 -0.1 0 -5.3 12.2
## 7 2012/07/27 16:30:00 <NA> <NA> <NA> <NA> 0.299 11.5 <NA> <NA> -5.3 12
## 8 2012/07/27 17:00:00 <NA> <NA> <NA> <NA> 0.299 11.4 <NA> <NA> -5.3 11.9
## 9 2012/07/27 17:30:00 <NA> <NA> <NA> <NA> 0.298 11.3 <NA> <NA> -5.3 11.8
## 10 2012/07/27 18:00:00 <NA> <NA> <NA> <NA> 0.298 11.2 <NA> <NA> -5.3 11.7
misspig
## Time W1 T1 W2 T2 W3 T3 P1 PT1 P2 PT2
## 1 2012/06/16 15:30:00 1.011 3.1 0.539 0.9 0.253 0.7 -4.7 1.7 -6.0 1.1
## 2 2012/06/16 16:00:00 1.011 3.2 0.541 1 0.255 0.8 -4.7 1.8 -6.0 1.3
## 3 2012/06/16 16:30:00 1.011 2.7 0.548 1.1 0.259 0.8 -4.7 1.9 -5.9 1.4
## 4 2012/06/16 17:00:00 1.011 1.9 0.555 1.1 0.262 0.7 -4.7 1.9 -5.8 1.1
## 5 2012/06/16 17:30:00 1.011 1.7 0.56 0.9 0.264 0.7 -4.7 1.6 -5.8 1.0
## 6 2012/06/16 18:00:00 1.011 1.8 0.564 0.9 0.266 0.6 -4.7 1.5 -5.8 0.9
## 7 2012/06/16 18:30:00 1.011 1.9 0.567 0.8 0.269 0.6 -4.7 1.4 -5.7 0.9
## 8 2012/06/16 19:00:00 1.011 2.0 0.57 0.7 0.270 0.6 -4.7 1.2 -5.7 0.9
## 9 2012/06/16 19:30:00 1.011 2.0 0.573 0.7 0.272 0.6 -4.7 1.2 -5.7 0.9
## 10 2012/06/16 20:00:00 1.011 2.1 0.576 0.7 0.273 0.6 -4.7 1.1 -5.7 0.8
Fozzy
## Time W1 T1 W2 T2 W3 T3 W4 T4 W5 T5
## 1 2012/07/07 15:00:00 NA NA NA NA NA NA NA NA NA NA
## 2 2012/07/07 15:30:00 -26.4 17.5 -138.3 19.5 -39.1 18.1 -11.4 15.6 -69.0 13.8
## 3 2012/07/07 16:00:00 -12.3 16.0 -37.7 14.6 -13.1 13.1 -7.4 12.1 -14.5 11.5
## 4 2012/07/07 16:30:00 -10.4 16.1 -25.3 14.3 -10.4 12.6 -7.1 11.7 -10.7 11.1
## 5 2012/07/07 17:00:00 -10.1 16.2 -16.9 14.4 -9.1 12.6 -7.0 11.6 -9.0 11.0
## 6 2012/07/07 17:30:00 -9.9 16.2 -13.4 14.3 -8.2 12.5 -6.9 11.6 -8.4 11.0
## 7 2012/07/07 18:00:00 -9.6 16.0 -12.0 14.3 -7.7 12.6 -6.9 11.6 -8.1 11.0
## 8 2012/07/07 18:30:00 -9.2 15.9 -11.3 14.3 -7.3 12.6 -6.8 11.6 -7.9 10.9
## 9 2012/07/07 19:00:00 -8.9 15.6 -10.9 14.2 -7.1 12.5 -6.8 11.5 -7.7 10.9
## 10 2012/07/07 19:30:00 -8.7 15.3 -10.6 13.9 -6.9 12.5 -6.8 11.5 -7.6 10.9
Kermit
## Time P1 T1 P2 T2 P3 T3 P4 T4 P5 T5
## 1 2012/07/07 14:30:00 0.278 18.2 0.385 14.1 0.333 11.9 NA NA NA NA
## 2 2012/07/07 15:00:00 0.250 19.9 0.315 16.6 0.262 14.9 0.325 14.4 0.328 13.2
## 3 2012/07/07 15:30:00 0.254 19.0 0.321 15.9 0.269 14.3 0.327 12.6 0.296 11.8
## 4 2012/07/07 16:00:00 0.248 17.3 0.316 14.3 0.270 12.9 0.329 12.3 0.290 11.6
## 5 2012/07/07 16:30:00 0.248 17.3 0.315 14.1 0.270 12.7 0.329 12.1 0.289 11.5
## 6 2012/07/07 17:00:00 0.248 17.5 0.314 14.2 0.271 12.6 0.329 12.0 0.288 11.5
## 7 2012/07/07 17:30:00 0.249 17.5 0.314 14.2 0.271 12.6 0.329 12.0 0.287 11.5
## 8 2012/07/07 18:00:00 0.249 17.4 0.314 14.2 0.272 12.6 0.329 11.9 0.287 11.5
## 9 2012/07/07 18:30:00 0.249 17.3 0.314 14.2 0.272 12.6 0.329 11.9 0.286 11.4
## 10 2012/07/07 19:00:00 0.248 17.1 0.313 14.2 0.273 12.6 0.329 11.9 0.285 11.4
Ernie
## Time P1 T1 P2 T2 P3 T3 P4 T4 P5 T5
## 1 2012/07/27 10:30:00 0.417 23.0 0.343 20.5 0.281 19.8 0.360 18.4 0.269 16.4
## 2 2012/07/27 11:00:00 0.388 14.9 0.393 14.4 0.281 14 0.364 13.9 0.268 12.8
## 3 2012/07/27 11:30:00 0.385 12.9 0.395 12.3 0.283 12.4 0.367 12.6 0.27 11.9
## 4 2012/07/27 12:00:00 0.382 13.1 0.394 12.1 0.283 12.2 0.367 12.2 0.269 11.6
## 5 2012/07/27 12:30:00 0.381 13.5 0.393 12.1 0.282 12.1 0.367 12.0 0.269 11.4
## 6 2012/07/27 13:00:00 0.379 14.1 0.392 12.2 0.282 12 0.366 11.9 0.269 11.3
## 7 2012/07/27 13:30:00 0.377 14.7 0.392 12.3 0.282 11.9 0.366 11.8 0.269 11.2
## 8 2012/07/27 14:00:00 0.375 15.2 0.391 12.5 0.282 11.9 0.366 11.7 0.269 11.1
## 9 2012/07/27 14:30:00 0.372 15.7 0.391 12.7 0.282 11.9 0.366 11.6 0.269 11.1
## 10 2012/07/27 15:00:00 0.370 16.0 0.391 12.9 0.282 11.9 0.366 11.6 0.269 11
Bert
## Time P1 T1 P2 T2 P3 T3 P4 T4 P5 T5
## 1 2012/07/27 10:30:00 -65.7 27.3 -31.3 26.6 -17.6 20.3 -19.5 19.7 -16.6 14.9
## 2 2012/07/27 11:00:00 -28.9 19.4 -21.2 18.5 -16.7 16.1 -17.1 15.4 -14.0 13.3
## 3 2012/07/27 11:30:00 -20.3 15 -18.5 13.7 -16.6 13.5 -15 13.7 -13.7 12.5
## 4 2012/07/27 12:00:00 -18.6 15.3 -18.1 13.4 -16.6 13 -14.8 13.1 -13.7 12.1
## 5 2012/07/27 12:30:00 -18.2 16 -17.8 13.4 -16.6 12.7 -14.7 12.7 -13.7 11.9
## 6 2012/07/27 13:00:00 -18.1 16.8 -17.7 13.5 -16.6 12.5 -14.7 12.4 -13.7 11.7
## 7 2012/07/27 13:30:00 -18.1 17.5 -17.6 13.6 -16.6 12.4 -14.7 12.2 -13.7 11.5
## 8 2012/07/27 14:00:00 -18.2 18.2 -17.6 13.9 -16.7 12.3 -14.7 12.1 -13.8 11.4
## 9 2012/07/27 14:30:00 -18.3 18.7 -17.5 14.2 -16.6 12.3 -14.7 12 -13.8 11.4
## 10 2012/07/27 15:00:00 -18.4 19.1 -17.4 14.5 -16.6 12.3 -14.7 11.9 -13.8 11.3
Dorothy
## Time W1 T1 W2 T2 W3 T3 W4 T4 W5 T5
## 1 2012/07/27 11:30:00 0.141 19.1 0.250 17.6 0.190 16.8 0.152 17.0 0.134 16.3
## 2 2012/07/27 12:00:00 0.140 15.8 0.244 14.9 0.185 15.3 0.141 14.9 0.136 13.8
## 3 2012/07/27 12:30:00 0.139 16.2 0.243 14.4 0.184 14.7 0.140 14.6 0.136 13.2
## 4 2012/07/27 13:00:00 0.139 17.3 0.242 14.6 0.184 14.4 0.139 14.4 0.136 12.9
## 5 2012/07/27 13:30:00 0.138 18.6 0.242 14.9 0.184 14.2 0.139 14.2 0.136 12.8
## 6 2012/07/27 14:00:00 0.138 19.7 0.242 15.3 0.184 14.0 0.139 14.0 0.136 12.7
## 7 2012/07/27 14:30:00 0.137 20.6 0.243 15.8 0.184 14.0 0.139 13.8 0.136 12.5
## 8 2012/07/27 15:00:00 0.136 21.4 0.243 16.2 0.184 13.9 0.139 13.7 0.136 12.4
## 9 2012/07/27 15:30:00 0.136 22.0 0.243 16.7 0.184 13.9 0.139 13.6 0.136 12.3
## 10 2012/07/27 16:00:00 0.135 22.4 0.243 17.1 0.184 14.0 0.139 13.5 0.136 12.3
Elmo
## Time P1 T1 P2 T2 P3 T3 P4 T4 P5 T5
## 1 2012/07/27 11:30:00 -1090 32.9 -970.1 28.3 -12377.3 23.3 -100006.0 23.0 -100006.0 17.7
## 2 2012/07/27 12:00:00 -1090 32.9 -279.8 18.7 -576.3 18.7 -68679.3 17.3 -100006.0 16.2
## 3 2012/07/27 12:30:00 -1090 32.9 -175.4 16.3 -222.2 16.1 -4415.0 15.3 -100006.0 15.0
## 4 2012/07/27 13:00:00 -1090 32.9 -140.0 15.7 -157.2 15.3 -1348.1 14.7 -50202.6 14.5
## 5 2012/07/27 13:30:00 -1090 32.9 -116.8 15.5 -128.5 14.9 -770.1 14.4 -7427.2 14.1
## 6 2012/07/27 14:00:00 -1090 32.9 -99.8 15.5 -109.1 14.7 -533.2 14.2 -2961.6 13.9
## 7 2012/07/27 14:30:00 -1090 32.9 -86.7 15.6 -94.7 14.5 -402.9 14.1 -1693.7 13.6
## 8 2012/07/27 15:00:00 -1090 32.9 -76.8 15.8 -83.2 14.5 -325.1 13.9 -1165.5 13.4
## 9 2012/07/27 15:30:00 -1090 32.9 -69.1 16.0 -74.4 14.5 -274.6 13.9 -892.5 13.3
## 10 2012/07/27 16:00:00 -1090 32.9 -62.2 16.3 -67.5 14.5 -239.7 13.7 -718.8 13.2
| Data Logger | Records | Start Time | End Time |
|---|---|---|---|
| Animal | 44053 | 2011/10/23 15:00:00 | 2014/06/30 08:30:00 |
| Gonzo | 29698 | 2012/07/27 13:30:00 | 2014/06/30 10:00:00 |
| misspig | 34396 | 2012/06/16 15:30:00 | 2014/06/30 08:30:00 |
| Fozzy | 33493 | 2012/07/07 15:00:00 | 2014/06/30 08:30:00 |
| Kermit | 33496 | 2012/07/07 14:30:00 | 2014/06/30 09:00:00 |
| Ernie | 13921 | 2012/07/27 10:30:00 | 2013/05/13 10:30:00 |
| Bert | 13921 | 2012/07/27 10:30:00 | 2013/05/13 10:30:00 |
| Dorothy | 32203 | 2012/07/27 11:30:00 | 2014/06/30 12:00:00 |
| Elmo | 12376 | 2012/07/27 11:30:00 | 2013/05/13 11:00:00 |
The time stamps must be recognized as R date-time data. We also add a new column of numeric time stamp as seconds since Jan 01, 1970.
all.data1$Time = as.numeric(as.POSIXlt(all.data1$Time))
all.data1$t = as.POSIXlt(all.data1$Time, origin = "1970-01-01")
all.data2$Time = as.numeric(as.POSIXlt(all.data2$Time))
all.data2$t = as.POSIXlt(all.data2$Time, origin = "1970-01-01")
all.data3$Time = as.numeric(as.POSIXlt(all.data3$Time))
all.data3$t = as.POSIXlt(all.data3$Time, origin = "1970-01-01")
all.data4$Time = as.numeric(as.POSIXlt(all.data4$Time))
all.data4$t = as.POSIXlt(all.data4$Time, origin = "1970-01-01")
all.data5$Time = as.numeric(as.POSIXlt(all.data5$Time))
all.data5$t = as.POSIXlt(all.data5$Time, origin = "1970-01-01")
## Warning: NAs introduced by coercion
## Warning: NAs introduced by coercion
## pdf
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