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Pierre-Antoine Rouby authored0e8b8c5c
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<meta name="author" content="Jean-Matthieu Monnet" />
<meta name="date" content="2022-07-19" />
<title>R workflow for field plot coregistration with ALS data</title>
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) 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<div id="header">
<h1 class="title toc-ignore">R workflow for field plot coregistration
with ALS data</h1>
<h4 class="author">Jean-Matthieu Monnet</h4>
<h4 class="date">2022-07-19</h4>
</div>
<hr />
<p>This workflow compares a canopy height model (CHM) derived from
airborne laser scanning (ALS) data with tree positions inventoried in
the field, and then proposes a translation in plot position for better
matching. The method is described in <span class="citation">Monnet and
Mermin (2014)</span>. Here it is exemplified with circular plots, but it
can be applied to any shape of field plots. The workflow is based on
functions from <code>R</code> packages <a href="https://cran.r-project.org/package=lidaRtRee">lidaRtRee</a>
(tested with version 4.0.3) and <a href="https://cran.r-project.org/package=lidR">lidR</a> (tested with
version 4.0.1). Example data were acquired in the forest of Lac des
Rouges Truites (Jura, France).</p>
<p>Licence: GNU GPLv3 / <a href="https://gitlab.irstea.fr/jean-matthieu.monnet/lidartree_tutorials/-/blob/master/R/coregistration.Rmd">Source
page</a></p>
<p>Many thanks to Pascal Obstétar for checking code and improvement
suggestions.</p>
<div id="material" class="section level2">
<h2>Material</h2>
<div id="field-data" class="section level3">
<h3>Field data</h3>
<p>The study area is a part of the forest of Lac des Rouges Truites. 44
plots have been inventoried, 15 are available for testing. Plots have a
14.10 m radius. A data.frame <code>p</code> contains the positions of
the center of plots. Attributes are:</p>
<ul>
<li><code>placette</code>: plot id</li>
421422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451452453454455456457458459460461462463464465466467468469470471472473474475476477478479480481482483484485486487488489490
<li><code>Xtheo</code> and <code>Ytheo</code>: XY coordinates initially
sampled when preparing the field inventory</li>
<li><code>XGPS</code> and <code>YGPS</code>: XY coordinates recorded in
the field with a GNSS receiver during the field inventory</li>
<li><code>Prec</code>: GNSS precision in meter specified by the
receiver</li>
<li><code>dist</code>: horizontal distance between the sample and
recorded coordinates.</li>
</ul>
<pre class="r"><code># load plot coordinates (data.frame with lines corresponding to the las objects)
load(file = "../data/coregistration/plotsCoregistration.rda")
head(p, n = 3L)</code></pre>
<pre><code>## placette Xtheo Ytheo XGPS YGPS ZGPS Prec dist
## 1 6 930422.7 6615047 930423.1 6615046 1087.803 2.1 1.502383
## 2 12 930521.4 6615046 930517.3 6615047 1082.799 1.6 4.161719
## 3 22 930720.9 6615249 930722.1 6615245 1089.593 0.3 3.980429</code></pre>
<p>On each plot, five trees which were considered suitable for
coregistration (vertical trunk, high and peak-shaped crown, well
separated from neighboring trees) have been positioned relatively to the
plot center. From the XY coordinates recorded by the GNSS receiver and
the relative coordinates (slope, distance, azimuth), the XY coordinates
of those trees are computed. Data.frame <code>ap</code> contains the
following attributes:</p>
<ul>
<li><code>plac</code>: plot id</li>
<li><code>n</code>: tree id</li>
<li><code>dia</code>: tree diameter in cm</li>
<li><code>distR</code>: slope distance from the plot center to tree
center, in m</li>
<li><code>azimutG</code>: azimuth from the plot center to the tree
center, in grades</li>
<li><code>pente.</code>: slope from plot center to tree center, in
grades</li>
<li><code>XYZGPS</code>: coordinates of the plot center</li>
<li><code>xyz</code>: coordinates of the tree center</li>
<li><code>d</code>: horizontal distance between plot center and tree in
m</li>
</ul>
<pre class="r"><code># load inventoried trees (data.frame with plot id info )
load(file = "../data/coregistration/treesCoregistration.rda")
head(ap, n = 3L)</code></pre>
<pre><code>## plac n ess dia distR azimutG pente. Rem XGPS YGPS ZGPS x
## 1 6 223 ABAL 54 9.02 9 -29 930423.1 6615046 1087.803 930424.2
## 2 6 224 ABAL 48 9.18 304 52 930423.1 6615046 1087.803 930415.0
## 3 6 225 ABAL 45 7.91 214 52 930423.1 6615046 1087.803 930421.6
## y z d
## 1 6615054 1085.291 8.663071
## 2 6615046 1092.038 8.144650
## 3 6615039 1091.452 7.017885</code></pre>
</div>
<div id="als-data" class="section level3">
<h3>ALS data</h3>
<p>Airborne laser scanning data on the study area is part of a campaign
acquired in 2016 with an airborne RIEGL LMS Q680i sensor. Acquisition
was funded by the Région Franche-Comté.</p>
<p>ALS data over the plots is provided as a list of LAS objects in
<code>rda</code> file.</p>
<pre class="r"><code># load point cloud over reference plots (list of las objects)
load(file = "../data/coregistration/lasCoregistration.rda")</code></pre>
<p>Display point cloud of plot 1.</p>
<pre class="r"><code># plot point cloud
lidR::plot(las[[1]])</code></pre>
<p>The code to extract LAS objects from a directory containing the LAS
files is (code corresponding to parameters in the next paragraph has to
be run beforehand):</p>
<pre class="r"><code># create catalog of LAS files
cata <- lidR::readALSLAScatalog("/directory_with_classified_laz_files/")
# "/media/reseau/lessem/ProjetsCommuns/Lidar/data/Franche-Comte/norm.laz/"
# set coordinate system
lidR::projection(cata) <- 2154
491492493494495496497498499500501502503504505506507508509510511512513514515516517518519520521522523524525526527528529530531532533534535536537538539540541542543544545546547548549550551552553554555556557558559560
# option to read only xyzc attributes (coordinates, intensity, echo order and classification) from files
# lidR::opt_select(cata) <- "xyzc"
# extract LAS data on plot extent
las <- lidR::clip_circle(cata, p$XGPS, p$YGPS, p_radius + b_size + 5)
# normalize heights if point cloud are not already normalized
las <- lapply(las, function(x) {
lidR::normalize_height(x, lidR::tin())
})
# save as rda file for later use
save(las, file = "../data/coregistration/lasCoregistration.rda")</code></pre>
</div>
</div>
<div id="parameters" class="section level2">
<h2>Parameters</h2>
<p>Several parameters have to be specified for optimal results.</p>
<pre class="r"><code># vegetation height threshold for removal of high values
hmax <- 50
# field plot radius for computation of pseudo-CHM on the inventory area
p_radius <- 14.105
# raster resolution for image processing
r_res <- 0.5
# maximum distance around initial position to search for coregistration
b_size <- 5
# increment step to search for coregistration (should be a multiple of resolution)
s_step <- 0.5</code></pre>
</div>
<div id="coregistration-of-one-plot" class="section level2">
<h2>Coregistration of one plot</h2>
<div id="canopy-height-model" class="section level3">
<h3>Canopy height model</h3>
<p>The first step is to compute the canopy height model from the ALS
data, and remove artefacts by thresholding extreme values and applying a
median filter.</p>
<pre class="r"><code># Choose a plot as example
i <- 10
# compute canopy height model
chm <- lidR::rasterize_canopy(las[[i]], res = r_res, algorithm = lidR::p2r())
# apply threshold to canopy height model (CHM)
chm[chm > hmax] <- hmax
# fill NA values with 0
chm[is.na(chm)] <- 0
# apply median filter (3x3 window) to CHM
chmfilt <- lidaRtRee::dem_filtering(chm, "Median", 3, sigmap = 0)$non_linear_image
# plot canopy height model
par(mfrow = c(1, 2))
terra::plot(chm, main = "Raw canopy height model")
terra::plot(chmfilt, main = "Filtered canopy height model")</code></pre>
<p><img 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width="864" style="display: block; margin: auto;" /></p>
</div>
<div id="plot-mask-from-tree-inventory" class="section level3">
<h3>Plot mask from tree inventory</h3>
<p>The trees corresponding to the plot are extracted, and a plot mask is
computed from the plot center and radius.</p>
<pre class="r"><code># plot centre
centre <- p[i, c("XGPS", "YGPS")]
# extract plot trees
trees <- ap[ap$plac == p$placette[i], ]
# create raster with plot extent
r <- lidaRtRee::circle2Raster(centre$XGPS, centre$YGPS, p_radius, resolution = r_res)
# keep only trees with diameter information
trees <- trees[!is.na(trees[, "dia"]), ]
# create plot mask
r_mask <- lidaRtRee::raster_xy_mask(c(centre$XGPS, centre$YGPS),
p_radius, r, binary = T)
# replace 0 by NA
r_mask[r_mask == 0] <- NA
# specify projection
terra::crs(r_mask) <- "epsg:2154"
# display plot mask
terra::plot(r_mask, main = "Plot mask and tree positions", legend = FALSE)
561562563564565566567568569570571572573574575576577578579580581582583584585586587588589590591592593594595596597598599600601602603604605606607608609610611612613614615616617618619620621622623624625626627628629630
# add tree positions
points(trees[, c("x", "y")], cex = trees[, "dia"] / 40)
# add plot center
points(centre, pch = 3)
legend("topleft", c("Trees", "Plot center"), pch = c(1, 3))</code></pre>
<p><img 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" width="432" style="display: block; margin: auto;" /></p>
</div>
<div id="compute-correlation-and-identify-optimal-translation" class="section level3">
<h3>Compute correlation and identify optimal translation</h3>
<p>First the function computes the correlation for different possible
translations of the plot center inside the buffer specified by the user,
and outputs an image of correlation values between the ALS CHM and the
pseudo CHM. The pseudo CHM is a height model where at the location of
inventoried trees pixels are attributed the value corresponding to tree
size (e.g. height or diameter). Second the correlation image is analyzed
to identify which translation yields the highest correlation. The
function outputs a list which first element is the correlation image,
and the second one the corresponding statistics.</p>
<pre class="r"><code># compute correlation image
coreg <- lidaRtRee::coregistration(chmfilt, trees[, c("x", "y", "dia")],
mask = r_mask,
buffer = b_size, step = s_step, dm = 2, plot = FALSE
)
# correlation image statistics
round(coreg$local_max, 2)</code></pre>
<pre><code>## correlation dx1 dy1 dx2 dy2 ratiomax1max2 rmedloc1 rmedloc2 rquanta rquantb
## 1 0.06 0.5 -2.5 2.5 4 3.96 1.07 1.2 1.49 8.6</code></pre>
<p>The maximum of the correlation image is located at (0.5, -2.5), given
by attributes <code>dx1</code> and <code>dy1</code>.</p>
<pre class="r"><code>par(mfrow = c(1, 3))
terra::plot(chmfilt, main = "Initial tree positions and CHM")
# display initial tree positions
graphics::points(trees$x, trees$y, cex = trees$dia / 40)
# display correlation image
terra::plot(coreg$correlation_raster,
main = "Correlation image",
col = cm.colors(16)
)
# plot local maximum
graphics::points(coreg$local_max$dx1, coreg$local_max$dy1, pch = 4)
abline(h = 0, lty = 2)
abline(v = 0, lty = 2)
legend("topleft", "Maximum", pch = 4)
#
terra::plot(chmfilt, main = "Coregistered tree positions and CHM")
# display coregistered tree positions
graphics::points(trees$x + coreg$local_max$dx1, trees$y + coreg$local_max$dy1,
cex = trees$dia / 40, col = "red"
)
# display initial plot center
graphics::points(p[i, c("XGPS", "YGPS")], pch = 3)
# display coregistered plot center
graphics::points(p$XGPS[i] + coreg$local_max$dx1, p$YGPS[i] + coreg$local_max$dy1,
pch = 3,
col = "red"
)
graphics::legend("topleft", c("Initial center", "Coregistered"),
pch = 3,
col = c("black", "red")
)</code></pre>
<p><img 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syHO3r06O+///76668LIXJycvz9/c+ePXvp0qUWLVoUatm3b183N7dly5b5+flNmjSp2N40GS+Vat68eQkJCVOmTFm0aJH0DuclS5ZMmzZtzpw5GzZsEEJER0dHRUX17ds3KipKev3gX3/9FRwcPHv27C5duvj7+2tydsWejuY9f/bZZ6dPny76LQEAoDkKYAAAFMPCVMi0TFbnVEwkqM4SEhJ2797t4eEhhDh79uy+ffvkcrkQwtbWdvbs2SEhIaqN161bt2bNGtU1SUlJ2h7xxYsXI0eOlKpfeXl5Y8eOleYDK/MpmJub9+rV6/vvv09JSXF2dhZC7Nq1Kz8/v9j3H7Zr127NmjUDBgxQrnFzc3NxcVF9TZAQ4ttvv/X19R05cuTIkSPj4uIiIyObNm2qPoyZM2dKNSohRHBwsBCiadOmUo1KCFGrVi1/f/99+/alp6c7OTlpdYIV1zMAAKggzs7Oy5cvv3fvnurKhISEAwcO+Pn5denSRXW9nZ3dG2+8UZ7DTZgwQaoPCSEsLCx69+59/vz5Bw8elK20o+F4SY3nz59HRkbWrVt34cKFyhlMw8LCYmNjnz59Ki3Onz/fyspq48aNysm3GjZs+OWXXw4YMGDTpk2qZSptz07znkeOHEn1C0AlIP9j3CiAAQAAVFE+Pj7Xr1+X3vpSv3795ORk5aaHDx86ODioNj506FBCQkJ5DvfixQvlvF/btm0bP378H3/8ERgYWM4a2DvvvLNp06aYmJgxY8YIIXbs2GFnZ1cotSTp2LFjx44dFQrFrVu3bt26dfPmzcOHD//555+2traqzTw8PFasWBEaGvrxxx8HBwervlyxJA0aNFB+tra2FkI0adJEtYG0sgwqrmcAAFBxPvjgg0JrIiMjDxw48Le//W3hwoW6Pdabb76pumhjY1Oe3jQcL6lx7dq1nJycLl26qD5Ab2JicvjwYelzfn5+YmKim5vbpk2bVHeUpjU9d+6c6kqtzk6rnstZdwQAQBhcASwtLS0+Pv7hw4ePHj2ytyT11rUAACAASURBVLd3c3Nr165doewPAACAcZg6deqIESPS0tIGDhw4Z86c6dOn29vbN2/ePD4+/sMPPxwxYoRq4/Xr10+dOlV1zfTp03/99VcNj6Va/ZLm/Tp48GBQUFD5a2DdunWztbXdtm3bmDFjnj17duDAgXfeecfCwqJoy9zc3M8//3z16tWPHz+WyWRubm5t2rRxdXXNzMws1HLw4MEzZsy4f//+8OHDNZmTo2gbXb2QsOJ6RnlIdcdyZhgBAHpkTPkfR0dHHfam+XipJLdu3RJCuLu7l9QgKSkpJyfn9u3bRcuEQoiMjAzVRa3OTquey3MDFgAAEoMpgGVnZ0+aNCkqKio3N9fR0bFGjRoZGRlPnjwxNTUdNmzYihUr1E/8AAAAYHAGDhyoUCg+//zzmTNnSoWWTp06CSHs7e3Hjx9faJoHuVxeaKZ0e3t7DQ+Ul5fXt2/fAwcOuLm5HTp0SHqGqXbt2soaWNeuXePi4jTvUJWlpWVISMi2bdvS0tJ++eWXnJwc1Zf2qAoNDd26devw4cPHjBnTqlUrqYbxxhtvXLlypVDLefPm3b9/39LScvr06W+//XbZAtNWoaQMqrKIiIiQkJCgoCB9BwIA0Br5H/U0Hy+VRCp9paSkFFpfUFCgUChMTU1dXV1NTU07d+68b98+3QavVc/K1zMCAFBmBnMtmTZt2i+//LJly5Znz54lJydfv349OTk5IyNj165dR44cmTJlir4DBAAA0L1BgwZdvHjx8uXL+/fv//7773ft2nX8+PGkpKT58+dr8uSThlJSUgpVvyRSDax58+aXLl06f/58mfsfMGBAXl7erl27duzYYW1t3b1796JtXrx4ERsb27Jlyw0bNvztb39TvjlQOReF0smTJ+fNm9e7d++1a9cmJSVNnjy5zIGp9/LlS+VnhUJx6tSpCjoQdM7T03PIkCEkzgDAEJH/UUPz8ZIaTZo0kV54mJ+fr7r+tddec3FxUSgUFhYWDRo0SEhIeP78uWqDuLi4iRMn7t27t8zxV1zPAAAUy2D+Jty+ffvq1at79eplZWWlXCmXy7t16xYVFbVz5049xgYAAFChmjZtGhQU9Pe//713795t27bV+bRS7u7up0+fTkhIKDR/lRCidu3av/32W1xcXMeOHcvcf48ePaytraOiovbs2dO9e/di4zcxMcnLy3vy5MmLFy+kNXl5efPnz//zzz/z8/MVCoW08vnz56GhoTVr1vz6669DQ0NDQkI2btwYGxtb5tiK5ezsLISIiYlRrlmyZMnly5d1exQAAFAU+R/x6l04qjQcL6lXs2bN995779q1a3PmzFHusnLlyps3b3bv3l26xSosLCwtLW3o0KHKStW9e/cGDhy4YsUKDw+P8pyObnsGAEA9g3kFolwuz8rKKnbTixcv5HJ5JccDAABgTFq3bl3Sppo1a7Zr1648nUtPfe3YsUMI0b9//2LbyOXy0NDQ9evXN2zYsEuXLiYmJnFxcUKIgICAkydPvvfee5988om3t/fkyZOvX7/+3Xffubq6CiHWrFnj4+MzduzYdu3a6XCCjUGDBi1fvnzmzJlHjx6V7lM+fvy4l5fX7du3dXUIAABQLH3lf2rXri2EqFWrVgX1ryFbW1shRHR0tEwmCw0N9ff3V92q4Xip1KPMnz8/Li5uwYIF0dHRLVu2vH379qlTp5ydnRctWiQ1GDNmzK5du3bv3l23bt2AgIC0tLRTp04VFBQsWLDA19e3PKejq54BANCEwTwBNnz48NGjR0s3pOTm5gohcnNz79y5s27dusGDB4eGhuo7QAAAAJTonXfeEUJYWFiEhISU1GbVqlWffvqpra3t9u3bL1y4MGDAgPPnz3/11Ve+vr7R0dFJSUm7d+9et25d7969//GPf0i7eHh4LFmy5MGDBxMnTtRhtAEBAT/99FO7du3i4uJWrFhx4sSJf/7znzNmzNDhIQAAQLH0lf8ZPHjwsWPHZs6cWUH9a6hZs2bTpk2Ty+UbNmy4f/9+0Qaljpc0OYqLi8uZM2emT59uZ2e3Z8+ep0+fjhkz5sKFC25ublIDU1PTPXv2REZG+vr6njhx4tatW507d96zZ094eHg5T0dXPQMAoAmZhs9H611BQcH8+fNXrVr18OFDIYSlpaX0ALWrq+uECRNmzZqlfvevvvpq8uTJkyZNWrZsWWWECwDQN3t7+6dPn6anp9esWVOrHR0dHZ88eVJjg5DZaHfEjJFCkSFSU1OdnJy02xOoGP3799+5cyfjn3J68OCBXC7X4eNlRbm4uKSkpCQnJ0uvXgSAMivn+CftozQHKwetdqz1Za3HWY8Z/0CHyP+gzBhTAdUT+R+oYTCvQDQxMZk9e/ZHH3109erVBw8epKamOjo6urq6+vr6mpkZzFkAAADAsChvhYYBuXDhQmxsrHTLub5jAQBoh/wPAADQFYMZOvTr169z584TJkxo3rx58+bN9R0OAAAAgCpq3rx50dHRrVu37tGjh75jAQBoh/wPAADQFYOZAywmJmbevHkdOnRITEzUdywAAAAAqi5pzpicnBx9BwIA0Br5HwAAoCsG8wSYEGLt2rV3795t3759nz59Zs2a1aBBA31HBAAwWpYmQmaq3S6ZQhjGvJoAAABAFUb+BwBQacj/GDeDeQJMCGFqajphwoSLFy9aWFg0b968X79+27dvz87O1ndcAAAAAAAA0A3yPwAAQCcMqQAmcXNzW7NmzaVLl+rUqTNmzBgHB4eOHTuGh4frOy4AAAAAAAyZnRA1tfxPpu+YYbzI/wAAgHIyvAKYpF69esuXL3/w4MH333/v6+sbExOj74gAAAAAAACgS+R/AABAmRnSHGBFWVhY9O3bt2/fvvoOBAAAAAAAABWC/A8AACgDg3kCbNu2bX5+fvqOAgAAAAAAABVFb/mfq1dFp07iyJHy9/To0aMZM2Z069bNzc3N0dHxjTfeCAsLS05OLn/POjR69GiZTHb16lV9B6KpXbt2yWSyJUuW6DsQAIAhMZgnwAYMGCCEuHv3blxcXHJycmZmprOzs4eHR2BgoLW1tb6jAwAAAFBVSH8g2NjY6DsQAIDW9Jb/2b9fHDkiduwQHTuWp5v//ve/77//fnJyspmZma+vb3Z29oULF06fPr1u3brY2NhOnTrpKNxKpVAocnJyTE1NzcwMJpEIAIAwoAJYTk7Oe++9t2XLFisrq8zMTFNTU1dX1+TkZBsbm6lTp86ePVvfAQIAAACoEiIiIkJCQoKCgvQdCABAa3rL/ygU//d/y+rcuXN9+/aVyWSRkZGjRo2ysrISQmRkZCxcuHDBggWDBw8+f/68q6urTuItp/Dw8Pfff9/Ly0uTxvHx8W3btp02bdqiRYsqOjAAAHTIYF6BGBYW9scff1y4cCEjI+PWrVutW7f+7LPPMjMz165du3z58mXLluk7QAAAAABVgqen55AhQ0xMDOaPHQCAkkHnfyZOnJifnx8TE/PBBx9I1S8hRI0aNebPnz9u3LiUlJRvv/1WvxEqNWjQICAgwNLSUt+BAABQgQzmb8LY2NilS5c2a9ZMCOHl5RUREbF48WK5XD5w4MDVq1dv2LBB3wECAAAAAACgXAw3/7N169a4uLjOnTv37Nmz6NYxY8YIIbZv317m/jMyMsoeXBVg6PEDAAyRwRTAcnJyzM3NlYuWlpbK6UNr1qx58+ZNPcUFAAAAAAAA3TDc/I9U3Prggw+K3dqqVSuFQpGQkKBck5eXt2DBgvbt29esWdPb23vAgAFnz55V3WX69Okymezx48dRUVF169bt0aOHctPmzZuDg4Nr167t7OwcHBy8d+/eQofLzs6eOXNmy5YtnZycevbsefjw4YiICJlMdvXqVanBuHHjVBeFEAcOHOjevbuHh4etra2vr++CBQuysrKEEG+//Xbbtm2FEIsXL5bJZJs3b9YwjPLEn5qaOnbsWB8fH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width="1152" style="display: block; margin: auto;" /></p>
<pre class="r"><code># export as pdf
# dev.copy2pdf(file = paste("Coregistration_", p$placette[i], ".pdf", sep = ""))</code></pre>
</div>
</div>
<div id="batch-processing" class="section level2">
<h2>Batch processing</h2>
<p>The following code processes several plots using multi-core
computing. It is based on packages <code>future</code> and
<code>future.apply</code> for parallel computing.</p>
631632633634635636637638639640641642643644645646647648649650651652653654655656657658659660661662663664665666667668669670671672673674675676677678679680681682683684685686687688689690691692693694695696697698699700
<pre class="r"><code># specify to use two parallel sessions
future::plan("multisession", workers = 2L)
# remove warning when using random numbers in parallel sessions
options(future.rng.onMisuse = "ignore")</code></pre>
<div id="chms-computation" class="section level3">
<h3>CHMs computation</h3>
<p>First CHMs are calculated for each point cloud contained in the
list.</p>
<pre class="r"><code>l_chm <- future.apply::future_lapply(
as.list(1:length(las)),
FUN = function(i) {
# compute CHM
chm <- lidR::rasterize_canopy(las[[i]], res = r_res, algorithm = lidR::p2r(), pkg = "terra")
# apply threshold to canopy height model (CHM)
chm[chm > hmax] <- hmax
# fill NA values with 0
chm[is.na(chm)] <- 0
# apply median filter (3x3 window) to CHM
chmfilt <-
lidaRtRee::dem_filtering(chm, "Median", 3, sigmap = 0)[[1]]
return(terra::wrap(chmfilt))
}
)</code></pre>
</div>
<div id="trees-lists-extraction-and-plot-masks-computation" class="section level3">
<h3>Trees lists extraction and plot masks computation</h3>
<pre class="r"><code>l_field <- future.apply::future_lapply(
as.list(1:length(las)),
FUN = function(i) {
# plot centre
centre <- p[i, c("XGPS", "YGPS")]
# extract plot trees
trees <- ap[ap$plac == p$placette[i],]
# create raster with plot extent
r <-
lidaRtRee::circle2Raster(centre$XGPS, centre$YGPS, p_radius, resolution = r_res)
# keep only trees with diameter information
trees <- trees[!is.na(trees[, "dia"]),]
# create plot mask
r_mask <- lidaRtRee::raster_xy_mask(rbind(c(centre$XGPS, centre$YGPS),
c(centre$XGPS, centre$YGPS)),
c(p_radius, p_radius), r,
binary = T)
# replace 0 by NA
r_mask[r_mask == 0] <- NA
# specify projection
terra::crs(r_mask) <- "epsg:2154"
return(list(trees = trees[, c("x", "y", "dia")], r_mask = terra::wrap(r_mask)))
}
)</code></pre>
</div>
<div id="correlation-images-and-corresponding-statistics-computation" class="section level3">
<h3>Correlation images and corresponding statistics computation</h3>
<p>Then the correlation image and corresponding statistics are computed
for each plot.</p>
<pre class="r"><code>l_coreg <- future.apply::future_lapply(
as.list(1:length(las)),
FUN = function(i) {
dummy <- lidaRtRee::coregistration(
terra::rast(l_chm[[i]]),
l_field[[i]]$trees,
mask = terra::rast(l_field[[i]]$r_mask),
buffer = b_size,
step = s_step,
dm = 2,
plot = FALSE
)
# wrap raster
dummy$correlation_raster <- terra::wrap(dummy$correlation_raster)
# output
701702703704705706707708709710711712713714715716717718719720721722723724725726727728729730731732733734735736737738739740741742743744745746747748749750751752753754755756757758759760761762763764765766767768769770
return(dummy)
}
)</code></pre>
<p>Finally results for all plots are combined in a single
data.frame.</p>
<pre class="r"><code>translations <- future.apply::future_lapply(
as.list(1:length(las)),
FUN = function(i) {
# create data.frame with coregistration results and new plot coordinates
data.frame(
plotid = p$placette[i],
X_cor = p$XGPS[i] + l_coreg[[i]]$local_max$dx1,
Y_cor = p$YGPS[i] + l_coreg[[i]]$local_max$dy1,
l_coreg[[i]]$local_max
)
}
)
# bind in single data.frame
translations <- do.call(rbind, translations)
# remove row.names
row.names(translations) <- NULL
#
round(head(translations, n = 3L), 2)</code></pre>
<pre><code>## plotid X_cor Y_cor correlation dx1 dy1 dx2 dy2 ratiomax1max2 rmedloc1
## 1 6 930422.1 6615047 0.06 -1.0 1 2 -4.5 4.77 1.07
## 2 12 930516.8 6615045 0.07 -0.5 -2 -4 3.0 2.59 1.13
## 3 22 930721.1 6615247 0.06 -1.0 2 0 -5.0 4.64 1.09
## rmedloc2 rquanta rquantb
## 1 1.31 1.71 12.74
## 2 1.51 2.62 4.77
## 3 1.36 1.88 8.30</code></pre>
</div>
<div id="export-graphics-as-pdf-files" class="section level3">
<h3>Export graphics as pdf files</h3>
<pre class="r"><code># unwrap raster files
l_chm <- lapply(l_chm, terra::rast)
# l_coreg <- lapply(l_coreg, function(x) list(correlation_raster = terra::rast(x$correlation_raster), local_max = x$local_max))
# l_field <- lapply(l_field, function(x) list(trees = x$trees, r_mask = terra::rast(x$r_mask)))
#
for (i in 1:length(las))
{
# CHM
pdf(file = paste("Coregistration_", p$placette[i], ".pdf", sep = ""))
terra::plot(l_chm[[i]], asp = 1)
# display initial tree positions
graphics::points(l_field[[i]]$trees$x, l_field[[i]]$trees$y,
cex = l_field[[i]]$trees$dia / 40
)
# display coregistered tree positions
graphics::points(l_field[[i]]$trees$x + l_coreg[[i]]$local_max$dx1,
l_field[[i]]$trees$y + l_coreg[[i]]$local_max$dy1,
cex = l_field[[i]]$trees$dia / 40, col = "red"
)
# display initial plot center
graphics::points(p[i, c("XGPS", "YGPS")], pch = 3)
# display coregistered plot center
graphics::points(p$XGPS[i] + l_coreg[[i]]$local_max$dx1,
p$YGPS[i] + l_coreg[[i]]$local_max$dy1,
pch = 3, col = "red"
)
graphics::legend("topleft", c("Initial", "Coregistered"),
pch = 15,
col = c("black", "red")
)
# export as pdf
dev.off()
}</code></pre>
</div>
<div id="add-coregistered-plot-positions-and-update-tree-coordinates" class="section level3">
<h3>Add coregistered plot positions and update tree coordinates</h3>
771772773774775776777778779780781782783784785786787788789790791792793794795796797798799800801802803804805806807808809810811812813814815816817818819820821822823824825826827828829830831832833834835836837838839840
<p>Optimized plot center positions are added to the original data.</p>
<pre class="r"><code>p <- base::merge(p, translations[, c("plotid", "X_cor", "Y_cor")],
by.x = "placette",
by.y = "plotid"
)
round(head(p, n = 3L), 2)</code></pre>
<pre><code>## placette Xtheo Ytheo XGPS YGPS ZGPS Prec dist X_cor Y_cor
## 1 6 930422.7 6615047 930423.1 6615046 1087.80 2.1 1.50 930422.1 6615047
## 2 12 930521.4 6615046 930517.3 6615047 1082.80 1.6 4.16 930516.8 6615045
## 3 22 930720.9 6615249 930722.1 6615245 1089.59 0.3 3.98 930721.1 6615247</code></pre>
<p>Tree positions are corrected to account for new center position.</p>
<pre class="r"><code># add plot center coregistered coordinates to trees data.frame
ap <- base::merge(ap, p[, c("placette", "X_cor", "Y_cor")], by.x = "plac", by.y = "placette")
# compute new tree coordinates from coregistered plot center
dummy <- lidaRtRee::polar2Projected(
ap$X_cor, ap$Y_cor, ap$ZGPS, ap$azimutG / 200 * pi,
ap$distR, atan(ap$pente. / 100), 1.55 / 180 * pi,
-2.2 / 180 * pi, 0
)
ap$X_cor <- dummy$x
ap$Y_cor <- dummy$y
# save new table
# write.table(round(p.cor,3),file="coregistered_plots.csv", row.names=F,sep=";")</code></pre>
</div>
<div id="analysis-of-gps-errors" class="section level3">
<h3>Analysis of GPS errors</h3>
<p>Graphics on the difference between initial and corrected positions
are displayed. The mean difference is -0.85 m in the X axis and -0.95 m
in the Y axis. <code>p.values</code> of a t.test are respectively 0.03
and 0.1. Mean absolute distance is 2.1 m with a standard deviation of
0.8 m.</p>
<pre class="r"><code>par(mfrow = c(1, 2))
# plot position difference with additionnal jitter to visualize same points
plot(jitter(p$X_cor - p$XGPS), jitter(p$Y_cor - p$YGPS),
asp = 1, col = "black",
main = "Corrected-Initial position", xlab = "X difference", ylab = "Y difference"
)
abline(v = 0, lty = 2)
abline(h = 0, lty = 2)
# Distance between initial and corrected
hist(sqrt((p$X_cor - p$XGPS)^2 + (p$Y_cor - p$YGPS)^2),
main = "Histogram of distances",
xlab = "Absolute distance corrected-initial", ylab = "Number of plots"
)</code></pre>
<p><img src="data:image/png;base64,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" 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<div id="references" class="section level2 unnumbered">
<h2 class="unnumbered">References</h2>
<div id="refs" class="references csl-bib-body hanging-indent">
<div id="ref-Monnet2014" class="csl-entry">
Monnet, Jean-Matthieu, and Éric Mermin. 2014. <span>“Cross-Correlation
of Diameter Measures for the Co-Registration of Forest Inventory Plots
with Airborne Laser Scanning Data.”</span> <em>Forests</em> 5 (9):
2307–26. <a href="https://doi.org/10.3390/f5092307">https://doi.org/10.3390/f5092307</a>.
</div>
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