tree_index = np.all(
(census_plot
== plot[i], np.any(
(tree_tag == tree[i], alt_tag == tree[i]), axis=0)),
axis=0)
tree_index_alt = np.all(
(field_data['plot'] == plot[i], field_data['tag'] == tree[i]), axis=0)
# first case scenario - we have located tree in canopy map
if tree_index_alt.sum() == 1:
subplot[i] = field_data['subplot'][tree_index_alt]
tree_xy = np.asarray([
field_data['Xfield'][tree_index_alt] + plot_origin[plot[i]][0],
field_data['Yfield'][tree_index_alt] + plot_origin[plot[i]][1]
])
tree_pts = lidar.filter_lidar_data_by_neighbourhood(
plot_pts[plot[i]], tree_xy, radius)
if tree_pts.size > 0:
heights, first_return_profile, n_ground_returns = LAD.bin_returns(
tree_pts, max_height, layer_thickness)
LAD_tree = LAD.estimate_LAD_MacArthurHorn(first_return_profile,
n_ground_returns,
layer_thickness, 1.)
I_tree = clim.estimate_canopy_light_transmittance(
LAD_tree, heights, k)
"""
# subplot based profile
subplot_poly = subplot_polygons[plot[i]][subplot_labels[plot[i]] == subplot[i],:,:][0]
sp_pts = lidar.filter_lidar_data_by_polygon(plot_pts[plot[i]],subplot_poly)
heights_sp,first_return_profile_sp,n_ground_returns_sp = LAD.bin_returns(sp_pts, max_height,layer_thickness)
LAD_sp = LAD.estimate_LAD_MacArthurHorn(first_return_profile_sp, n_ground_returns_sp, layer_thickness, 1.)
I_sp=clim.estimate_canopy_light_transmittance(LAD_sp,heights_sp,k)
weights = results_iter['weights'] / np.sum(
np.isfinite(
results_iter['raster_values'][0].
values) * results_iter['weights'])
cover_mc[ii] = np.sum(
(results_iter['raster_values']
[0].values >= gap_ht) * weights)
quantiles_mc[ii] = st.weighted_quantiles(
results_iter['raster_values']
[0].values, results_iter['weights'],
quantiles)
# now get canopy cover directly from LiDAR point cloud
pts_sub = lidar_tools.filter_lidar_data_by_neighbourhood(
lidar_pts, [
subplot['geometry']['coordinates']
[0] + xerr, subplot['geometry']
['coordinates'][1] + yerr
], radius)
point_heights = np.zeros(pts_sub.shape[0])
for idx in range(0, point_heights.size):
dist, pixel_id = dem_trees[0].query(
pts_sub[idx, :2], k=1)
point_heights[idx] = pts_sub[
idx, 2] - ZZ[pixel_id]
point_weights = 1 / pts_sub[:, 7]
canopy_mask = point_heights >= gap_ht
cover_fraction_from_pointcloud_mc[
ii] = np.sum(
point_weights[canopy_mask] /