Generating Maps

from rforge import Raster
from rforge import composite, index, slope, aspect, distance, height, fuel

Importing Layer Data

The initial step in every algorithm is data loading. Here, we utilize the container classes provided by Raster Forge.

file_path = 'file/path/example.tif'

raster = Raster(scale=1)
layer_dict = [
    {'name': 'red', 'id': 1},
    {'name': 'blue', 'id': 2},
    {'name': 'green', 'id': 3},
    {'name': 'nir', 'id': 4},
    {'name': 'dsm', 'id': 5},
    {'name': 'dtm', 'id': 6},
    {'name': 'alpha', 'id': 7},
]
raster.import_layers(file_path, layer_dict)

Creating Composites

Here we employ the composite function to generate a true color composite:

layers = [raster.layers['red'], raster.layers['green'], raster.layers['blue']]
gamma = [1, 1, 1]

composite_layer = composite(layers=layers, alpha=raster.layers['alpha'], gamma=gamma)
raster.add_layer(layer=composite_layer, name='rgb')

In the above example, the result is returned as a Layer and added to the Raster collection. However, it is also possible to directly receive just a NumPy N-dimentional array.

composite_array = composite(
    layers=layers, alpha=raster.layers['alpha'], gamma=gamma, as_array=True
)

Generating Multipsectral Index

The index fuction draws on arguments sourced from the Spyndex package. Specifically, it utilizes the same indices identifiers and the parameter dictionary structure. The :py:meth:index <rforge.library.processes.index> function facilitates index thresholding, enabling the retention of only the pertinent data, while also offering the capability to convert the resultant data into a binary mask for subsequent processing.

parameters_dict = {'N': raster.layers['nir'], 'R': raster.layers['red']}

• Simple

index_layer = index(
    index_id='NDVI', parameters=parameters_dict, alpha=raster.layers['alpha']
)
raster.add_layer(layer=index_layer, name='ndvi')

• Only Vegetation Data

vegetation_layer = index(
    index_id='NDVI',
    parameters=parameters_dict,
    alpha=raster.layers['alpha'],
    thresholds=[0, 1],
)
raster.add_layer(layer=vegetation_layer, name='vegetation')

• Vegetation Mask (Binary)

vegetation_binary_mask_layer = index(
    index_id='NDVI',
    parameters=parameters_dict,
    alpha=raster.layers['alpha'],
    thresholds=[0.25, 0.75],
    binarize=True,
)
raster.add_layer(layer=vegetation_binary_mask_layer, name='vegetation mask')

Other Processes

slope_layer = slope(
    dem=raster.layers['dtm'], units='degrees', alpha=raster.layers['alpha']
)
raster.add_layer(layer=slope_layer, name='slope')

aspect_layer = aspect(
    dem=raster.layers['dtm'], units='degrees', alpha=raster.layers['alpha']
)
raster.add_layer(layer=aspect_layer, name='aspect')

distance_layer = distance(
    layer=raster.layers['vegetation mask'], alpha=raster.layers['alpha']
)
raster.add_layer(layer=distance_layer, name='distance')

height_layer = height(
    dtm=raster.layers['dtm'], dsm=raster.layers['dsm'], alpha=raster.layers['alpha']
)
raster.add_layer(layer=height_layer, name='canopy height')

fuel_layer = fuel(
    coverage=raster.layers['vegetation'],
    height=raster.layers['canopy height'],
    alpha=raster.layers['alpha'],
)
raster.add_layer(layer=height_layer, name='canopy height')