The code used in these examples is taken from the following publication:
Michael J Mahoney, Lucas K Johnson, Eddie Bevilacqua & Colin M Beier (2022) Filtering ground noise from LiDAR returns produces inferior models of forest aboveground biomass in heterogenous landscapes, GIScience & Remote Sensing, 59:1, 1266-1280, DOI: 10.1080/15481603.2022.2103069
First, we load the required libraries and the source functions. The
lidar-metrics.R file contains the functions that are used to generate
the metrics.
suppressPackageStartupMessages({
library(lidR)
library(proceduralnames) # used in the source functions.
library(terra)
library(sf)
})
source("lidar-metrics.R")Then we load up some example data and of a point cloud of trees and create some fake plots to extract metrics from.
# load an example dataset from the lidR package
LASfile <- system.file("extdata", "MixedConifer.laz", package = "lidR")
trees <- readLAS(LASfile, filter = "-drop_z_below 0")
# plot(trees) to view the point cloud data in 3D
# make some fake plots:
tree_crs <- st_crs(trees)
trees_bbox <- lidR::st_bbox(trees) |>
sf::st_as_sfc()
trees_cent <- st_centroid(trees_bbox)
plot_max_ext <- (trees_bbox - trees_cent) * 0.75 + trees_cent
fake_plots <- sf::st_sample(x = plot_max_ext, size = 9, type = "regular") |>
sf::st_buffer(5) |>
sf::st_set_crs(tree_crs)
plot(fake_plots, col = "#cc4040c9")
The compute_pixel_metrics function is a wrapper around
lidR::pixel_metrics that generates 40 metrics from the point cloud
data and saves them to a raster file.
# This uses the `compute_pixel_metrics` which is a wrapper around
# `lidR::pixel_metrics`. It generates 40 rastersied metrics from the
# point cloud data.
gridded_metrics <- compute_pixel_metrics(
trees,
res = 2,
output_filename = "grid_metrics.tif"
)
gm <- terra::rast(gridded_metrics)
print(names(gm))## [1] "n" "zmean" "max" "min" "quad_mean"
## [6] "cv" "z_kurt" "z_skew" "L2" "L3"
## [11] "L4" "L_cv" "L_skew" "L_kurt" "h10"
## [16] "h20" "h30" "h40" "h50" "h60"
## [21] "h70" "h80" "h90" "h95" "h99"
## [26] "d10" "d20" "d30" "d40" "d50"
## [31] "d60" "d70" "d80" "d90" "cancov"
## [36] "quad_mean_c" "zmean_c" "cv_c" "hvol" "rpc1"
plot(gm[[1:12]], col = hcl.colors(100, "viridis"))
The compute_plot_metrics function is a wrapper around
lidR::plot_metrics that generates metrics for a set of polygons. The
rh_preds function is a reduced/modified version of the lidar_preds
function in the “lidar-metrics.R” script that can be used with
lidR::plot_metrics to generate relative height metrics at 1 percentile
intervals.
# now to extract metrics for these plots we use `compute`
plot_metrics_sf <- compute_plot_metrics(trees, geometry = fake_plots) |>
sf::st_as_sf()
print(plot_metrics_sf)## Simple feature collection with 9 features and 40 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: 481285.7 ymin: 3812930 xmax: 481340.7 ymax: 3812985
## Projected CRS: NAD83 / UTM zone 12N
## n zmean max min quad_mean cv z_kurt z_skew L2
## 1 371 12.667466 20.19 0.00 13.61539 0.3945663 0.4299871 -1.0716520 2.688853
## 2 347 14.064438 26.11 0.00 16.98074 0.6775131 -1.3412911 -0.4673196 5.300614
## 3 335 12.442448 26.04 0.00 14.96557 0.6693467 -1.4277621 -0.1542294 4.761612
## 4 340 6.692618 23.09 0.00 10.59899 1.2298392 -1.0007554 0.7875682 4.313862
## 5 360 10.472778 21.61 0.00 13.15030 0.7604602 -1.6261777 -0.3892487 4.316514
## 6 346 14.504538 24.23 0.01 16.15030 0.4904078 0.1414895 -1.2090949 3.631510
## 7 371 9.617358 20.81 0.00 11.89342 0.7285346 -1.4238382 -0.2706877 3.945884
## 8 362 13.947956 23.33 0.00 15.46067 0.4788576 -0.2143810 -0.8426465 3.675348
## 9 370 5.808514 20.60 0.00 8.74187 1.1262695 -1.5159070 0.4696203 3.478084
## L3 L4 L_cv L_skew L_kurt h10 h20 h30
## 1 -0.6891878 0.3233064 0.2122644 -0.25631295 0.12023954 6.110 8.070 11.010
## 2 -0.6972216 -0.3788791 0.3768806 -0.13153601 -0.07147834 0.070 0.142 9.450
## 3 -0.2350279 -0.2975110 0.3826909 -0.04935889 -0.06248115 0.084 4.008 6.690
## 4 1.4427196 -0.1879525 0.6445702 0.33443808 -0.04356944 0.020 0.040 0.080
## 5 -0.4546257 -0.5203100 0.4121651 -0.10532243 -0.12053941 0.050 0.110 0.245
## 6 -1.0914952 0.5223684 0.2503706 -0.30056236 0.14384330 0.115 11.270 14.805
## 7 -0.2309886 -0.2943429 0.4102877 -0.05853913 -0.07459492 0.060 0.130 2.410
## 8 -0.7285169 0.3344065 0.2635044 -0.19821711 0.09098636 0.161 9.622 11.860
## 9 0.8304321 -0.4197884 0.5987907 0.23876139 -0.12069530 0.040 0.080 0.110
## h40 h50 h60 h70 h80 h90 h95 h99 d10 d20
## 1 12.960 14.560 15.220 15.920 16.410 17.490 18.4200 19.7450 0.9326146 0.9245283
## 2 14.198 17.380 19.156 21.320 23.258 24.758 25.3250 26.0462 0.7319885 0.7291066
## 3 8.522 12.600 17.698 19.458 20.824 22.918 23.3830 24.8062 0.8268657 0.7641791
## 4 0.130 0.435 6.266 10.445 17.608 20.316 21.4435 22.8422 0.4647059 0.4441176
## 5 12.434 14.530 15.534 16.370 17.812 18.894 19.5245 20.7423 0.6500000 0.6500000
## 6 16.180 16.770 17.600 18.445 19.660 20.980 22.0275 23.5265 0.8294798 0.8294798
## 7 8.960 11.230 12.950 14.780 16.470 18.250 18.8700 20.1030 0.7008086 0.6927224
## 8 13.844 15.555 16.770 18.115 20.056 21.268 22.1290 22.9678 0.8839779 0.8563536
## 9 0.150 0.615 8.078 11.539 13.632 14.991 15.7455 17.3161 0.4621622 0.4567568
## d30 d40 d50 d60 d70 d80 d90
## 1 0.9029650 0.7978437 0.7331536 0.6657682 0.5336927 0.25067385 0.061994609
## 2 0.7233429 0.6570605 0.6224784 0.5475504 0.4466859 0.33429395 0.172910663
## 3 0.6507463 0.5462687 0.4895522 0.4507463 0.3731343 0.20000000 0.044776119
## 4 0.3823529 0.3323529 0.2735294 0.2558824 0.2205882 0.16470588 0.091176471
## 5 0.6388889 0.6222222 0.6111111 0.5777778 0.4333333 0.24166667 0.052777778
## 6 0.8265896 0.8121387 0.7947977 0.7109827 0.4653179 0.21965318 0.057803468
## 7 0.6819407 0.6226415 0.5363881 0.4393531 0.3234501 0.18059299 0.064690027
## 8 0.8397790 0.8093923 0.7071823 0.5883978 0.4392265 0.26243094 0.135359116
## 9 0.4108108 0.4000000 0.3594595 0.2729730 0.1567568 0.02432432 0.008108108
## cancov quad_mean_c zmean_c cv_c hvol rpc1
## 1 0.9326146 14.09865 13.57503 0.2808218 11.813863 1
## 2 0.7319885 19.84716 19.17008 0.2686463 10.295007 1
## 3 0.8268657 16.45787 15.02913 0.4470882 10.288233 1
## 4 0.4735294 15.59246 14.31159 0.4338278 3.169151 1
## 5 0.6527778 16.30961 16.03030 0.1878887 6.836397 1
## 6 0.8294798 17.73273 17.46540 0.1759392 12.031220 1
## 7 0.7035040 14.23243 13.69780 0.2826532 6.765851 1
## 8 0.8839779 16.44397 15.76797 0.2964050 12.329685 1
## 9 0.4621622 12.85575 12.37830 0.2812327 2.684475 1
## geometry
## 1 POLYGON ((481295.7 3812935,...
## 2 POLYGON ((481318.2 3812935,...
## 3 POLYGON ((481340.7 3812935,...
## 4 POLYGON ((481295.7 3812957,...
## 5 POLYGON ((481318.2 3812957,...
## 6 POLYGON ((481340.7 3812957,...
## 7 POLYGON ((481295.7 3812980,...
## 8 POLYGON ((481318.2 3812980,...
## 9 POLYGON ((481340.7 3812980,...
# or if you just want relative height metrics at 1 percentile intervals you
# can use:
rh_mets_sf <- lidR::plot_metrics(
trees,
func = rh_preds(Z),
geometry = fake_plots
) |>
sf::st_as_sf()
print(rh_mets_sf)## Simple feature collection with 9 features and 100 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: 481285.7 ymin: 3812930 xmax: 481340.7 ymax: 3812985
## Projected CRS: NAD83 / UTM zone 12N
## rh1 rh2 rh3 rh4 rh5 rh6 rh7 rh8 rh9 rh10 rh11
## 1 0.0270 0.0540 0.0710 0.1000 0.165 0.2520 3.1590 5.0420 5.6520 6.110 6.4200
## 2 0.0046 0.0100 0.0200 0.0284 0.030 0.0400 0.0400 0.0500 0.0600 0.070 0.0706
## 3 0.0034 0.0168 0.0304 0.0500 0.057 0.0600 0.0600 0.0700 0.0800 0.084 0.0900
## 4 0.0000 0.0100 0.0100 0.0100 0.010 0.0200 0.0200 0.0200 0.0200 0.020 0.0300
## 5 0.0100 0.0100 0.0100 0.0200 0.020 0.0300 0.0300 0.0300 0.0400 0.050 0.0500
## 6 0.0200 0.0300 0.0400 0.0500 0.070 0.0800 0.0815 0.0900 0.1005 0.115 0.1200
## 7 0.0100 0.0100 0.0200 0.0300 0.030 0.0400 0.0400 0.0500 0.0500 0.060 0.0600
## 8 0.0161 0.0200 0.0383 0.0500 0.060 0.0700 0.0900 0.1088 0.1249 0.161 0.1971
## 9 0.0000 0.0038 0.0100 0.0176 0.020 0.0214 0.0300 0.0352 0.0400 0.040 0.0500
## rh12 rh13 rh14 rh15 rh16 rh17 rh18 rh19 rh20 rh21
## 1 6.7240 7.0650 7.1380 7.1950 7.3060 7.3990 7.5080 7.7100 8.070 8.2470
## 2 0.0900 0.0900 0.1100 0.1100 0.1100 0.1200 0.1300 0.1374 0.142 0.1500
## 3 0.1000 0.1200 0.1300 0.1620 0.2188 0.3012 3.4632 3.8200 4.008 4.4278
## 4 0.0300 0.0307 0.0400 0.0400 0.0400 0.0400 0.0400 0.0400 0.040 0.0500
## 5 0.0600 0.0600 0.0700 0.0800 0.0900 0.0900 0.0900 0.1000 0.110 0.1200
## 6 0.1300 0.1300 0.1600 0.1700 0.1940 4.5880 7.8070 11.0515 11.270 12.1900
## 7 0.0700 0.0800 0.0880 0.1000 0.1000 0.1100 0.1160 0.1230 0.130 0.1400
## 8 3.5204 4.1179 4.4156 5.2465 7.0188 8.5374 8.6998 9.2040 9.622 9.8181
## 9 0.0500 0.0500 0.0500 0.0600 0.0600 0.0673 0.0700 0.0700 0.080 0.0800
## rh22 rh23 rh24 rh25 rh26 rh27 rh28 rh29 rh30 rh31
## 1 8.9600 9.1800 9.2900 9.425 9.7440 10.4660 10.7900 10.8790 11.010 11.2060
## 2 0.1700 0.1858 0.2104 0.270 0.5496 4.1458 8.0560 8.9982 9.450 9.6852
## 3 4.9244 5.1246 5.2532 5.300 5.6200 5.7390 5.8364 6.0962 6.690 7.1950
## 4 0.0500 0.0500 0.0600 0.060 0.0700 0.0700 0.0700 0.0700 0.080 0.0800
## 5 0.1300 0.1300 0.1400 0.155 0.1600 0.1800 0.1800 0.1922 0.245 0.3058
## 6 12.7030 12.9870 13.2620 13.355 13.5780 14.1205 14.3460 14.5640 14.805 15.0190
## 7 0.1500 0.1800 0.2000 0.225 0.2840 0.3400 0.5160 0.7720 2.410 5.8850
## 8 10.5484 10.6509 10.7840 10.950 11.0658 11.2935 11.5016 11.5700 11.860 11.9100
## 9 0.0900 0.0900 0.0900 0.090 0.0900 0.1000 0.1000 0.1100 0.110 0.1100
## rh32 rh33 rh34 rh35 rh36 rh37 rh38 rh39 rh40
## 1 11.8640 12.0560 12.2320 12.3500 12.4420 12.6260 12.8280 12.9030 12.960
## 2 9.9564 10.1472 10.3556 11.2390 12.0504 12.6136 13.2716 13.7758 14.198
## 3 7.5176 7.5900 7.6500 7.8070 8.0072 8.1848 8.2592 8.3700 8.522
## 4 0.0900 0.0900 0.1000 0.1065 0.1100 0.1100 0.1100 0.1200 0.130
## 5 0.3828 0.4482 0.7724 4.5715 6.2212 7.8750 9.3696 10.9368 12.434
## 6 15.0940 15.2270 15.3320 15.5075 15.7060 15.9060 16.0510 16.1120 16.180
## 7 6.4600 6.6630 7.3920 7.6700 7.7520 8.2670 8.3420 8.6870 8.960
## 8 11.9652 12.2130 12.4418 12.5970 12.6388 12.9757 13.3514 13.6737 13.844
## 9 0.1100 0.1200 0.1300 0.1300 0.1384 0.1400 0.1500 0.1500 0.150
## rh41 rh42 rh43 rh44 rh45 rh46 rh47 rh48 rh49
## 1 13.1600 13.2480 13.6400 13.7080 13.7750 13.9680 14.1580 14.2820 14.4990
## 2 14.8016 15.0728 15.3090 15.4544 15.5810 16.1240 16.3224 16.5064 16.8394
## 3 8.7100 8.8608 9.4082 9.6444 9.8240 10.7828 11.0598 11.5792 12.3196
## 4 0.1400 0.1500 0.1600 0.1616 0.1800 0.1994 0.2432 0.2972 0.3411
## 5 12.6795 12.9146 13.1337 13.3748 13.7530 13.9282 14.2392 14.3628 14.4373
## 6 16.2625 16.3490 16.3735 16.4000 16.5200 16.5870 16.6015 16.6520 16.7015
## 7 9.2770 9.4640 9.8330 9.8800 10.0800 10.3720 10.6610 10.7640 10.9930
## 8 13.9901 14.2062 14.4515 14.5384 14.7645 14.9018 14.9967 15.3396 15.4967
## 9 0.1600 0.1700 0.1700 0.1800 0.1900 0.2074 0.2529 0.4400 0.5124
## rh50 rh51 rh52 rh53 rh54 rh55 rh56 rh57 rh58
## 1 14.560 14.5800 14.6660 14.7130 14.8280 14.8600 14.8820 15.0150 15.1000
## 2 17.380 17.6038 17.6884 17.9114 18.0136 18.2120 18.4628 18.6344 18.7536
## 3 12.600 13.1156 14.0900 14.4910 14.5780 15.6240 16.2124 16.6676 16.7844
## 4 0.435 0.5389 0.6640 2.0468 2.6664 4.0170 4.8528 4.9969 5.5548
## 5 14.530 14.5809 14.6536 14.7054 14.7600 14.9525 15.0300 15.2567 15.3444
## 6 16.770 16.7900 16.8640 16.9585 17.0130 17.0575 17.1140 17.1925 17.3430
## 7 11.230 11.4340 11.7880 11.8570 12.0620 12.4150 12.4740 12.5820 12.7360
## 8 15.555 15.6444 15.7672 15.8099 15.9794 16.1300 16.3264 16.4585 16.6200
## 9 0.615 0.7057 0.7800 0.9427 3.3022 4.9065 5.0900 5.4698 5.8218
## rh59 rh60 rh61 rh62 rh63 rh64 rh65 rh66 rh67
## 1 15.1730 15.220 15.3670 15.4040 15.5240 15.5860 15.6450 15.7120 15.7590
## 2 18.8728 19.156 19.4590 20.0900 20.3468 20.5144 20.6250 20.7100 20.9828
## 3 17.3022 17.698 17.9118 17.9616 18.2952 18.4228 18.6840 18.7996 18.9756
## 4 6.0905 6.266 6.3900 7.0872 7.4614 7.8804 8.7775 8.8770 9.4069
## 5 15.4243 15.534 15.6097 15.7074 15.7500 15.7852 15.9000 15.9670 16.0465
## 6 17.4985 17.600 17.7435 17.7970 17.9070 17.9580 18.1025 18.1440 18.1975
## 7 12.8260 12.950 13.3300 13.4480 13.6600 14.1560 14.2550 14.4120 14.4870
## 8 16.7399 16.770 16.8526 17.0774 17.2486 17.3412 17.6065 17.6708 17.8961
## 9 6.7622 8.078 8.7871 9.2436 9.9847 10.2576 10.5000 10.5916 10.8046
## rh68 rh69 rh70 rh71 rh72 rh73 rh74 rh75 rh76
## 1 15.7760 15.8390 15.920 15.9810 16.0200 16.0620 16.1060 16.1500 16.2020
## 2 21.0512 21.2122 21.320 21.5428 21.8336 22.0132 22.2336 22.5500 22.6784
## 3 19.1324 19.3646 19.458 19.6140 19.8448 20.0046 20.2832 20.3700 20.4684
## 4 9.6988 10.0792 10.445 10.7721 11.2640 11.5894 12.4112 13.9725 14.5944
## 5 16.1560 16.2871 16.370 16.4589 16.6300 16.8835 17.0432 17.1925 17.3036
## 6 18.3100 18.3600 18.445 18.6570 18.7440 18.8170 18.9700 19.0525 19.2600
## 7 14.5700 14.6390 14.780 15.1100 15.3000 15.3610 15.4060 15.5050 15.6980
## 8 17.9644 18.0218 18.115 18.3231 18.4192 18.5177 18.7198 19.0750 19.1836
## 9 10.9500 11.3354 11.539 11.8493 12.1948 12.4511 12.6830 13.0250 13.2984
## rh77 rh78 rh79 rh80 rh81 rh82 rh83 rh84 rh85
## 1 16.2480 16.3160 16.3630 16.410 16.5050 16.5940 16.6510 16.8160 16.9500
## 2 22.8184 22.9104 23.0968 23.258 23.3000 23.4516 23.5254 23.7124 24.1210
## 3 20.6336 20.7212 20.7686 20.824 20.9416 21.2340 21.4462 21.6424 21.8070
## 4 15.2547 16.1236 16.3815 17.608 17.8518 18.1198 18.3737 18.7312 19.4215
## 5 17.3929 17.5300 17.6061 17.812 17.8995 17.9500 18.2461 18.3300 18.4700
## 6 19.3200 19.3540 19.4855 19.660 19.8845 19.9190 20.0470 20.0980 20.1450
## 7 15.8520 16.1020 16.3260 16.470 16.5320 16.6260 16.8090 17.0960 17.3900
## 8 19.3897 19.5370 19.7557 20.056 20.2600 20.4304 20.5578 20.6572 20.7355
## 9 13.4813 13.5182 13.5602 13.632 13.7900 13.9106 14.1081 14.3972 14.4830
## rh86 rh87 rh88 rh89 rh90 rh91 rh92 rh93 rh94
## 1 17.1140 17.2290 17.3340 17.4500 17.490 17.6360 17.7300 18.0600 18.2760
## 2 24.3112 24.3808 24.4948 24.6894 24.758 24.7900 24.8932 24.9278 25.1272
## 3 21.9772 22.0732 22.4676 22.6486 22.918 23.0094 23.1140 23.2186 23.3492
## 4 19.7832 19.9772 20.0732 20.1955 20.316 20.6615 21.0940 21.1454 21.2564
## 5 18.5322 18.5796 18.7392 18.8008 18.894 19.1169 19.2328 19.3574 19.3900
## 6 20.2700 20.3490 20.4360 20.6235 20.980 21.2450 21.4720 21.5585 21.7690
## 7 17.5400 17.7360 17.9440 18.1160 18.250 18.3810 18.5000 18.6470 18.7680
## 8 20.8592 21.0207 21.1200 21.2087 21.268 21.4404 21.6124 21.7646 21.8902
## 9 14.5600 14.6403 14.7460 14.8733 14.991 15.0200 15.2144 15.5368 15.6472
## rh95 rh96 rh97 rh98 rh99 rh100 geometry
## 1 18.4200 18.5340 18.7980 19.1340 19.7450 20.19 POLYGON ((481295.7 3812935,...
## 2 25.3250 25.4908 25.7244 25.9164 26.0462 26.11 POLYGON ((481318.2 3812935,...
## 3 23.3830 23.4956 23.6480 23.9740 24.8062 26.04 POLYGON ((481340.7 3812935,...
## 4 21.4435 21.9084 22.1630 22.7186 22.8422 23.09 POLYGON ((481295.7 3812957,...
## 5 19.5245 19.8820 20.0737 20.2300 20.7423 21.61 POLYGON ((481318.2 3812957,...
## 6 22.0275 22.3620 22.4530 22.9140 23.5265 24.23 POLYGON ((481340.7 3812957,...
## 7 18.8700 18.9360 19.0350 19.2120 20.1030 20.81 POLYGON ((481295.7 3812980,...
## 8 22.1290 22.2612 22.6168 22.6978 22.9678 23.33 POLYGON ((481318.2 3812980,...
## 9 15.7455 16.0068 16.1544 16.6420 17.3161 20.60 POLYGON ((481340.7 3812980,...
# the rh_preds function is just a reduced/modified version of the lidar_preds
# these examples should demonstrate how to build any custom functions that
# can be used with `lidR::plot_metrics` or `lidR::pixel_metrics`
plot(gm[[2]], col = hcl.colors(100, "viridis"))
plot(rh_mets_sf["rh99"], add = TRUE, key.pos = 1, axes = TRUE, axes = TRUE)