Compare tree height measured from the ground to a Lidar-based Canopy Height Model

This data tutorial provides instruction on working with two different NEON data products to estimate tree height:

• DP3.30015.001, Ecosystem structure, aka Canopy Height Model (CHM)
• DP1.10098.001, Woody plant vegetation structure

The CHM data are derived from the Lidar point cloud data collected by the remote sensing platform. The vegetation structure data are collected by by field staff on the ground. We will be using data from the Wind River Experimental Forest NEON field site located in Washington state. The predominant vegetation there are tall evergreen conifers.

If you are coming to this exercise after following tutorials on data download and formatting, and therefore already have the needed data, skip ahead to section 4.

Start by installing and loading packages (if necessary) and setting options. One of the packages we'll be using, geoNEON, is only available via GitHub, so it's installed using the devtools package. The other packages can be installed directly from CRAN.

Installation can be run once, then periodically to get package updates.

install.packages("neonUtilities")
install.packages("sp")
install.packages("raster")
install.packages("devtools")
devtools::install_github("NEONScience/NEON-geolocation/geoNEON")

Now load packages. This needs to be done every time you run code. We'll also set a working directory for data downloads.

library(sp)
library(raster)
library(neonUtilities)
library(geoNEON)

options(stringsAsFactors=F)

# set working directory
# adapt directory path for your system
wd <- "~/data"
setwd(wd)

2. Vegetation structure data

Download the vegetation structure data using the loadByProduct() function in the neonUtilities package. Inputs needed to the function are:

• dpID: data product ID; woody vegetation structure = DP1.10098.001
• site: 4-letter site code; Wind River = WREF
• package: basic or expanded; we'll download basic here
• check.size: should this function prompt the user with an estimated download size? Set to FALSE here for ease of processing as a script, but good to leave as default True when downloading a dataset for the first time.

Refer to the tutorial for the neonUtilities package for more details if desired.

veglist <- loadByProduct(dpID="DP1.10098.001", 
                         site="WREF", 
                         package="basic", 
                         check.size = FALSE)

Use the getLocTOS() function in the geoNEON package to get precise locations for the tagged plants. Refer to the package documentation for more details.

vegmap <- getLocTOS(veglist$vst_mappingandtagging, 
                          "vst_mappingandtagging")

Merge the mapped locations of individuals (the vst_mappingandtagging table) with the annual measurements of height, diameter, etc (the vst_apparentindividual table). The two tables join on individualID, the identifier for each tagged plant, but we'll include namedLocation, domainID, siteID, and plotID in the list of variables to merge on, to avoid ending up with duplicates of each of those columns. Refer to the variables table and to the Data Product User Guide for Woody plant vegetation structure for more information about the contents of each data table.

veg <- merge(veglist$vst_apparentindividual, vegmap, 
             by=c("individualID","namedLocation",
                  "domainID","siteID","plotID"))

Let's see what the data look like! Make a stem map of the plants in plot WREF_075. Note that the circles argument of the symbols() function expects a radius, but stemDiameter is just that, a diameter, so we will need to divide by two. And stemDiameter is in centimeters, but the mapping scale is in meters, so we also need to divide by 100 to get the scale right.

symbols(veg$adjEasting[which(veg$plotID=="WREF_075")], 
        veg$adjNorthing[which(veg$plotID=="WREF_075")], 
        circles=veg$stemDiameter[which(veg$plotID=="WREF_075")]/100/2, 
        inches=F, xlab="Easting", ylab="Northing")

And now overlay the estimated uncertainty in the location of each stem, in blue:

symbols(veg$adjEasting[which(veg$plotID=="WREF_075")], 
        veg$adjNorthing[which(veg$plotID=="WREF_075")], 
        circles=veg$stemDiameter[which(veg$plotID=="WREF_075")]/100/2, 
        inches=F, xlab="Easting", ylab="Northing")
symbols(veg$adjEasting[which(veg$plotID=="WREF_075")], 
        veg$adjNorthing[which(veg$plotID=="WREF_075")], 
        circles=veg$adjCoordinateUncertainty[which(veg$plotID=="WREF_075")], 
        inches=F, add=T, fg="lightblue")

3. Canopy height model data

Now we'll download the CHM tile corresponding to plot WREF_075. Several other plots are also covered by this tile. We could download all tiles that contain vegetation structure plots, but in this exercise we're sticking to one tile to limit download size and processing time.

The tileByAOP() function in the neonUtilities package allows for download of remote sensing tiles based on easting and northing coordinates, so we'll give it the coordinates of plot WREF_075 and the data product ID, DP3.30015.001.

The download will include several metadata files as well as the data tile. Load the data tile into the environment using the raster package.

byTileAOP(dpID="DP3.30015.001", site="WREF", year="2017", 
          easting=veg$adjEasting[which(veg$plotID=="WREF_075")], 
          northing=veg$adjNorthing[which(veg$plotID=="WREF_075")],
          check.size = FALSE, savepath=wd)

chm <- raster(paste0(wd, "/DP3.30015.001/2017/FullSite/D16/2017_WREF_1/L3/DiscreteLidar/CanopyHeightModelGtif/NEON_D16_WREF_DP3_580000_5075000_CHM.tif"))

Let's view the tile.

plot(chm, col=topo.colors(5))

4. Comparing the two datasets

Now we have the heights of individual trees measured from the ground, and the height of the top surface of the canopy, measured from the air. There are many different ways to make a comparison between these two datasets! This section will walk through three different approaches.

First, subset the vegetation structure data to only the individuals that fall within this tile, using the extent() function from the raster package.

This step isn't strictly necessary, but it will make the processing faster.

vegsub <- veg[which(veg$adjEasting >= extent(chm)[1] &
                      veg$adjEasting <= extent(chm)[2] &
                      veg$adjNorthing >= extent(chm)[3] & 
                      veg$adjNorthing <= extent(chm)[4]),]

Starting with a very simple first pass: use the extract() function from the raster package to get the CHM value matching the coordinates of each mapped plant. Include a buffer equal to the uncertainty in the plant's location, and extract the highest CHM value within the buffer. Then make a scatter plot of each tree's height vs. the CHM value at its location.

bufferCHM <- extract(chm, 
                     cbind(vegsub$adjEasting,
                           vegsub$adjNorthing),
                     buffer=vegsub$adjCoordinateUncertainty, 
                     fun=max)

plot(bufferCHM~vegsub$height, pch=20, xlab="Height", 
     ylab="Canopy height model")
lines(c(0,50), c(0,50), col="grey")

How strong is the correlation between the ground and lidar measurements?

cor(bufferCHM, vegsub$height, use="complete")

## [1] 0.3650552

There are a lot of points clustered on the 1-1 line, but there is also a cloud of points above the line, where the measured height is lower than the canopy height model at the same coordinates. This makes sense, because we made no attempt to filter out the understory. There are likely many plants measured in the vegetation structure data that are not at the top of the canopy, and the CHM sees only the top surface of the canopy.

How to exclude understory plants from this analysis? Again, there are many possible approaches. We'll try out two, one map-centric and one tree-centric.

Starting with the map-centric approach: select a pixel size, and aggregate both the vegetation structure data and the CHM data to find the tallest point in each pixel. Let's try this with 10m pixels.

Start by rounding the coordinates of the vegetation structure data, to create 10m bins. Use floor() instead of round() so each tree ends up in the pixel with the same numbering as the raster pixels (the rasters/pixels are numbered by their southwest corners).

easting10 <- 10*floor(vegsub$adjEasting/10)
northing10 <- 10*floor(vegsub$adjNorthing/10)
vegsub <- cbind(vegsub, easting10, northing10)

Use the aggregate() function to get the tallest tree in each 10m bin.

vegbin <- stats::aggregate(vegsub, by=list(vegsub$easting10, vegsub$northing10), FUN=max)

To get the CHM values for the 10m bins, use the raster package version of the aggregate() function. Let's take a look at the lower-resolution image we get as a result.

CHM10 <- raster::aggregate(chm, fact=10, fun=max)
plot(CHM10, col=topo.colors(5))

Use the extract() function again to get the values from each pixel. We don't need a buffer this time, since we've put both datasets onto the same grid. But our grids are numbered by the corners, so add 5 to each tree coordinate to make sure it's in the correct pixel.

vegbin$easting10 <- vegbin$easting10+5
vegbin$northing10 <- vegbin$northing10+5
binCHM <- extract(CHM10, cbind(vegbin$easting10, 
                               vegbin$northing10))
plot(binCHM~vegbin$height, pch=20, 
     xlab="Height", ylab="Canopy height model")
lines(c(0,50), c(0,50), col="grey")

cor(binCHM, vegbin$height, use="complete")

## [1] 0.3565511

The understory points are thinned out substantially, but so are the rest. We've lost a lot of data by going to a lower resolution.

Let's try and see if we can identify the tallest trees by another approach, using the trees as the starting point instead of map area. Start by sorting the veg structure data by height.

vegsub <- vegsub[order(vegsub$height, decreasing=T),]

Now, for each tree, let's estimate which nearby trees might be beneath its canopy, and discard those points. To do this:

1. Calculate the distance of each tree from the target tree.
2. Pick a reasonable estimate for canopy size, and discard shorter trees within that radius. The radius I used is 0.3 times the height, based on some rudimentary googling about Douglas fir allometry. It could definitely be improved on!
3. Iterate over all trees.

We'll use a simple for loop to do this:

vegfil <- vegsub
for(i in 1:nrow(vegsub)) {
    if(is.na(vegfil$height[i]))
        next
    dist <- sqrt((vegsub$adjEasting[i]-vegsub$adjEasting)^2 + 
                (vegsub$adjNorthing[i]-vegsub$adjNorthing)^2)
    vegfil$height[which(dist<0.3*vegsub$height[i] & 
                        vegsub$height<vegsub$height[i])] <- NA
}

vegfil <- vegfil[which(!is.na(vegfil$height)),]

Now extract the raster values, as above. Let's also increase the buffer size a bit, to better account for the uncertainty in the Lidar data as well as the uncertainty in the ground locations.

filterCHM <- extract(chm, cbind(vegfil$adjEasting, vegfil$adjNorthing),
                         buffer=vegfil$adjCoordinateUncertainty+1, fun=max)
plot(filterCHM~vegfil$height, pch=20, 
     xlab="Height", ylab="Canopy height model")
lines(c(0,50), c(0,50), col="grey")

cor(filterCHM,vegfil$height)

## [1] 0.7273229

This is quite a bit better! There are still several understory points we failed to exclude, but we were able to filter out most of the understory without losing so many overstory points.

Let's try one last thing. The plantStatus field in the veg structure data indicates whether a plant is dead, broken, or otherwise damaged. In theory, a dead or broken tree can still be the tallest thing around, but it's less likely, and it's also less likely to get a good Lidar return. Exclude all trees that aren't alive:

vegfil <- vegfil[which(vegfil$plantStatus=="Live"),]
filterCHM <- extract(chm, cbind(vegfil$adjEasting, vegfil$adjNorthing),
                         buffer=vegfil$adjCoordinateUncertainty+1, fun=max)
plot(filterCHM~vegfil$height, pch=20, 
     xlab="Height", ylab="Canopy height model")
lines(c(0,50), c(0,50), col="grey")

cor(filterCHM,vegfil$height)

## [1] 0.8135262

Nice!

One final note: however we slice the data, there is a noticeable bias even in the strongly correlated values. The CHM heights are generally a bit shorter than the ground-based estimates of tree height. There are two biases in the CHM data that contribute to this. (1) Lidar returns from short-statured vegetation are difficult to distinguish from the ground, so the "ground" estimated by Lidar is generally a bit higher than the true ground surface, and (2) the height estimate from Lidar represents the highest return, but the highest return may slightly miss the actual tallest point on a given tree. This is especially likely to happen with conifers, which are the top-of-canopy trees at Wind River.