Analyze spectral index signatures for regions in a geojson shapefile using zonalstats

Vectorized approach to spectral index analysis per region in a shapefile.

plantcv.geospatial.analyze.spectral_index(img, geojson, index, mask=None, percentiles=None, label=None, distance=20)

returns Debug image with regions drawn on the input index.

  • Parameters:

    • img - GEO image object, likely read in with gcv.read_geotif
    • geojson - Path to the shapefile/GeoJSON containing the plot boundaries. Can be Polygon or MultiPolygon geometry.
    • index - spectral index to calculate and analyze. Must be an available index from PlantCV; see full list here.
    • mask - binary mask indicating which pixels should be used to calculate statistics. Defaults to None.
    • percentiles - Iterable of numeric percentiles [0-100]. 0 and 100 are automatically included (default = None, where range(0, 101, 25) is used)
    • label - Optional label parameter, modifies the variable name of observations recorded. Can be a prefix, or list (default = pcv.params.sample_label)
    • distance - Amount of flexibility (in nanometers) regarding the bands used to calculate an index.

  • Context:

    • This function will utilize the geojson's ID (or FID) attribute for Outputs labels if available and label=None.
    • Providing a binary mask where you have segmented plants is useful if you do not want to average a spectral index over both plant and soil values, such as for individual plants or when canopy coverage is not complete.
    • Output data stored: Data (index minimum, maximum, mean, median, standard deviation, percentile_25, and percentile_75) automatically gets 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 multiband image as geotif
img = gcv.read.geotif(filename="./data/EX_8_DAP_46_2021_Casselton_YT_06-22_5band.tif", bands="b,g,r,RE,NIR")

# calculate NDVI for example
bounds = gcv.analyze.spectral_index(img=img,
                           geojson="./Shapefiles/shapefile.shx",
                           index = "ndvi"
                           label=None)

# To access individual observation values:
print(pcv.outputs.observations['default_1']['mean_index_ndvi']['value'])

Screenshot

Source Code: Here