xjy11 / forestr

forestr R package

forestr

The forestr package calculates forest structure and canopy structure metrics from multiple data forms, including two-dimensional portable canopy LiDAR (PCL) raw data files and from certain processed three-dimensional terrestrial LiDAR scanner (TLS) data forms.

Installation

To install version 1.0.1 which is on CRAN

install_packages("forestr")

To install the development version

devtools::install_github("atkinsjeff/forestr")

Usage

To process raw PCL data, which comes in .csv form as two columns: column one is a string of numbers that represent return distance in meters, and column two is a string of integers that represent return intensity.

You can run an example data set that is included, a 40 m forest transect from Ordway-Swisher Biological Station in Hawthorn, FL.

The process_pcl function writes data to an output folder that is created in the working directory.

require(forestr)

process_pcl(osbs)

The output includes:

1. an output.csv file that contains 24 canopy structral complexity (CSC) metrics including rumple, canopy rugosity, and max canopy height. These metrics are defined in Hardiman et al. 2013 and in Atkins et al. (In review at Methods in Ecology and Evolution.

2. an output_hit_matrix.csv file that is a file that contains the adjusted VAI by x and z position in the canopy.

3. a summary_matrix.csv file that gives the mean height, max heights, VAI and variance metrics by each columnar position, or x position along the transect.

Plotting

The forestr package also produces hit grids--vegetation area index (VAI) for by 1 squared meter bins on the x and z axis through the canopy.

Worked Example

Let's do a complete run using data from a red pine plantation in Michigan. These data are saved in the data folder as red_pine.rda in the master GitHub directory and can be accessed as red_pine and processed as such where we will look at all of the optional parameters:

red_pine <- "https://raw.githubusercontent.com/atkinsjeff/forestr/master/data-raw/red_pine_plain1.CSV"
forestr::process_pcl(red_pine, user_height = 1.05, marker.spacing = 10, max.vai = 8, pavd = TRUE, hist = TRUE )
#> how many in base df have NA
#> [1] 3446
#> Transect Length
#> [1] 30
#> Table of sky hits
#> 
#> FALSE  TRUE 
#> 14113  3446
#> RAW LiDAR metrics -- WARNING
#> Mean Return Height (m) of raw data
#> [1] 16.05084
#> Standard Deviation of raw  Canopy Height returns-- meanStd in old code
#> [1] 2.530479
#> Max Measured Canopy Height (m)
#> [1] 21.491
#> Scan Density
#> [1] 585.3
#> OPENNESS AND COVER METRICS
#> Sky Fraction (%)
#> [1] 19.62974
#> Cover Fraction (%)
#> [1] 80.37026
#> Rumple
#> [1] 1.8
#> Mean Gap Fraction ---as error check should be same as porosity
#> [1] 0.6848485
#> now we replace the 0's with 1's so when we take the ln they = 0
#> Clumping Index
#> [1] 0.5480317
#> Transect Length (m)
#> [1] 30
#> HEIGHT METRICS
#> Mean Leaf Height (H) - plot mean of column mean leaf height
#> [1] 16.47294
#> Height2 (H[2]) - standard deviation of column mean leaf height
#> [1] 1.658249
#> Mean Leaf Height variance (H[var]) - variance of column mean leaf height
#> [1] 2.749791
#> Root Mean Square Mean Leaf Height (H[rms]) - the root mean square or quadratic mean of column mean leaf height for the transect
#> [1] 16.55342
#> Max canopy height (m)
#> [1] 21.491
#> Mean Outer Canopy Height (m) or MOCH
#> [1] 18.96943
#> AREA AND DENSITY METRICS
#> Mean VAI - mean VAI for entire transect
#> [1] 4.166451
#> Mean Height of VAI[max] - modeEl
#> [1] 16.03333
#> Mode 2- The standard deviation of VAImax or MaxEl
#> [1] 2.722617
#> Maximum VAI for entire transect -- max el!
#> [1] 4.939786
#> Mean Peak VAI for entire transect
#> [1] 1.862675
#> CANOPY AND OPENNESS METRICS (cont.)
#> Deep Gaps
#> [1] 0
#> Deep Gap Fraction (0-1)
#> [1] 0
#> ARRANGEMENT METRICS
#> Canopy porosity
#> [1] 0.6848485
#> Square of leaf height variance (stdStd from old script)
#> [1] 19.35462
#> Mean Standard deviation of leaf heights -- meanStd
#> [1] 3.632249
#> Canopy Rugosity
#> [1] 2.482215
#> Surface Rugosity--TopRugosity
#> [1] 0.944426
#> Effective Number of Layers:
#> [1] 9.15834
#> [1] "red_pine_plain1_output"
#> [1] "./output/"
#> No. of NA values in hit matrix
#> [1] 452

Running the following command produces the following output to the console: