Analyze height for regions in a geojson shapefile using a canopy height model (CHM)

Vectorize approach to height estimation per region in a shapefile using a canopy height model. Calculated from geo.subtract_dsm, a canopy height model is estimated by subtracting a bare-ground DSM from a DSM with plants included.

plantcv.geospatial.analyze.chm(dsm, geojson, bins=10, label=None)

returns Histogram of average height values per plot.

  • Parameters:

    • dsm - DSM image object representing a canopy height model (CHM) from geo.subtract_dsm
    • bins - Number of height bins to calculate for the per-plot height distribution
    • geojson - Path to the shapefile/GeoJSON containing the plot boundaries. Can be Polygon or MultiPolygon geometry.
    • label - Optional label parameter, modifies the variable name of observations recorded. Can be a prefix, or list (default = pcv.params.sample_label)

  • Context:

    • This function will utilize the geojson's ID attribute for Outputs labels if available.
    • Output data stored: Data ('height_mean', 'height_std', and binned height frequencies) automatically get stored to the Outputs class when this function is run. These data can be accessed during a workflow (example below). For more detail about data output see Summary of Output Observations.

  • Example use:

import plantcv.geospatial as gcv
import plantcv.plantcv as pcv

# Read in dsm as geotif
dsm_bareground = gcv.read.geotif(filename="./data/timepoint_0.tif", bands=[0])
dsm_withplants = gcv.read.geotif(filename="./data/timepoint_1.tif", bands=[0])

# Calculate Canopy Height Model
chm = gcv.subtract_dsm(dsm_withplants, dsm_bareground)

# Analyze coverage for each region in the geojson
dist = gcv.analyze.chm(dsm=chm,
                           geojson="./shapefiles/experimental_plots.geojson",
                           bins=25,
                           label="plot")

# To access individual observation values:
print(pcv.outputs.observations["plot_0"]["height_mean"]["value"])

Screenshot

Source Code: Here