vignettes/fd_data_mgmt.Rmd
fd_data_mgmt.RmdAt the core of FoRTE is the collection of heterogeneous data, from many different instruments, requiring multiple different approaches, to measure different environmental variables. Below we outline the collection and preparation procedures for all data products in fortedata.
For each data product listed includes a Data Preparation section that includes:
CST data are located in the /data/lidar on Google Drive. The raw data from the PCL instrument is in the form of a two column .csv file containing raw distance from instrument values (i.e. lidar pulse returns) and intensity values for each measured transect. These data are processed externally using forestr in R, with the output for each transect containing several .csv files: 1) a two row .csv containing a head of CST metrics and a second row containing values for those metrics; 2) an adjusted leaf area/vegetation area hit grid matrix; 3) a three column, x, z, VAI file; 4) summary matrix file containing x, z, and column specific values (NEED TO ADJUST); and 5) a hit grid plot of VAI.
The first of these files, the two row CST metrics file(s) for each transect are collated into one file where each row represents each transect–the source canopy_structural_traits.csv file in /inst/extdata/ from which fd_canopy_structure draws.
#set random seed
cst <- read_csv_file("canopy_structural_traits.csv")
# show the top of
str(cst)
## tibble[,53] [195 x 53] (S3: tbl_df/tbl/data.frame)
## $ subplot_id : chr [1:195] "C01E" "C01E" "C01W" "C01W" ...
## $ year : int [1:195] 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 ...
## $ mean.height.mean : num [1:195] 9.75 10.3 9.57 8.05 9.61 ...
## $ height.2 : num [1:195] 4.87 4.37 4.31 4.3 5.97 ...
## $ mean.height.median: num [1:195] 8.89 9.52 10.32 7.6 7.7 ...
## $ mean.height.var : num [1:195] 23.7 19.1 18.6 18.5 35.6 ...
## $ mean.height.rms : num [1:195] 10.9 11.2 10.5 9.1 11.3 ...
## $ transect.length : int [1:195] 40 40 40 40 40 40 40 20 40 10 ...
## $ can.max.ht : num [1:195] 22.6 23.3 21.5 21.1 25.3 ...
## $ moch : num [1:195] 14.7 15.2 13.8 13.1 16.7 ...
## $ can.max.ht.median : num [1:195] 16 17 14.9 13.9 20.1 ...
## $ vai.mean : num [1:195] 5.98 6.17 6.27 5.68 5.93 ...
## $ vai.sd : num [1:195] 2.81 2.61 2.45 2.69 2.31 ...
## $ vai.median : num [1:195] 7.72 7.71 7.82 7.2 6.56 ...
## $ vai.column.max : int [1:195] 8 8 8 8 8 8 8 8 8 8 ...
## $ vai.max.ht.mean : num [1:195] 10.18 9.93 9.3 7.72 9.82 ...
## $ vai.max.ht.sd : num [1:195] 6.43 5.67 5.5 5.64 7.83 ...
## $ vai.max.ht.median : num [1:195] 11 10 8 5.5 8 9 10 9 4 4 ...
## $ vai.max : num [1:195] 7 7.43 8 6.6 7 ...
## $ vai.mean.peak : num [1:195] 2.95 3.13 3.6 3.19 3.03 ...
## $ vai.peak.sd : num [1:195] 1.85 1.92 2.11 1.76 1.62 ...
## $ vai.peak.median : num [1:195] 2.86 3.21 3.44 3.42 2.96 ...
## $ deep.gaps : int [1:195] 2 0 1 0 0 0 0 0 0 0 ...
## $ deep.gap.fraction : num [1:195] 0.05 0 0.025 0 0 0 0 0 0 0 ...
## $ porosity : num [1:195] 0.696 0.669 0.733 0.714 0.716 ...
## $ std.std : num [1:195] 264 115 205 260 793 ...
## $ mean.std : num [1:195] 11.89 8.29 10.73 9.95 18.44 ...
## $ rugosity : num [1:195] 11.06 6.82 9.46 12.69 21.29 ...
## $ top.rugosity : num [1:195] 5.79 6.07 5.46 4.92 7.46 ...
## $ mean.return.ht : num [1:195] 7.1 7.98 7.53 5.54 7.18 ...
## $ sd.return.ht : num [1:195] 4.1 4.55 4.9 4.46 6.05 ...
## $ median.ht : num [1:195] 6.67 6.84 6.2 3.28 5.53 ...
## $ sky.fraction : num [1:195] 18.72 11.9 8.75 14.02 9.85 ...
## $ cover.fraction : num [1:195] 81.3 88.1 91.3 86 90.1 ...
## $ max.ht : num [1:195] 22.6 23.3 21.5 21.5 26.7 ...
## $ scan.density : num [1:195] 2962 2638 2324 2992 2244 ...
## $ rumple : num [1:195] 6.2 5 5.2 4.33 5.15 ...
## $ clumping.index : num [1:195] 0.93 0.929 0.944 0.875 0.9 ...
## $ enl : num [1:195] 18.6 19.2 18 15 18.3 ...
## $ fhd : num [1:195] 2.83 2.81 2.85 2.5 2.71 ...
## $ gini : num [1:195] 1.82 1.62 1.68 1.38 1.53 ...
## $ mean.intensity : num [1:195] 55 57 56.3 50 51.1 ...
## $ median.intensity : int [1:195] 57 59 58 49 52 54 54 57 47 45 ...
## $ sd.intensity : num [1:195] 14 13.7 13.3 12.4 12.9 ...
## $ max.intensity : num [1:195] 100 108 104 94 94 ...
## $ min.intensity : num [1:195] 6 6 0.612 6 6 6 6 6 3 2 ...
## $ skew.intensity : num [1:195] -0.22053 -0.50636 -0.3968 0.00147 -0.3191 ...
## $ kurtosis.intensity: num [1:195] 2.93 3.41 3.52 3.14 2.98 ...
## $ p10 : num [1:195] 2.06 2.91 1.96 1.76 2.23 ...
## $ p25 : num [1:195] 3.33 3.94 3.11 2.36 2.88 ...
## $ p50 : num [1:195] 6.67 6.84 6.2 3.28 5.53 ...
## $ p75 : num [1:195] 10.3 11.21 11.46 9.59 8.06 ...
## $ p90 : num [1:195] 12.5 14.2 14.8 12.2 19.9 ...#General Data Format and Preparation
This section summarizes general formatting and preparation guidelines for all data shared and uploaded to fortedata.
forte_table_metadata.csv (if not already listed)
# call forte_table_metadata
fd_metadata()
## # A tibble: 240 x 5
## table field description class units
## <chr> <chr> <chr> <chr> <chr>
## 1 fd_inven~ subplot_~ <NA> chara~ <NA>
## 2 fd_inven~ tag Tree tag ID number integ~ <NA>
## 3 fd_inven~ species Species code from the USDA Plants Database chara~ <NA>
## 4 fd_inven~ dbh_cm Bole diameter at 1.37 m numer~ cm
## 5 fd_inven~ health_s~ Live (L), moribund (M), or dead (D) chara~ <NA>
## 6 fd_inven~ canopy_s~ Overstory dominant (OD), overstory submissi~ chara~ <NA>
## 7 fd_inven~ date Date of measurement date <NA>
## 8 fd_inven~ notes <NA> chara~ <NA>
## 9 fd_inven~ replicate (from plots table) chara~ <NA>
## 10 fd_inven~ plot (from plots table) integ~ <NA>
## # ... with 230 more rows