def calculate_bestfit_LAD_profile(subplot_coordinate_file,
LAI_file,
las_file,
Plot_name,
minimum_height=0):
subplot_polygons, subplot_labels = aux.load_boundaries(
subplot_coordinate_file)
field_LAI = aux.load_field_LAI(LAI_file)
lidar_pts = lidar.load_lidar_data(las_file)
n_subplots = subplot_polygons[Plot_name].shape[0]
max_height = 80
layer_thickness = 1
n_layers = np.ceil(max_height / layer_thickness)
subplot_lidar_profiles = np.zeros((n_subplots, n_layers))
n_ground_returns = np.zeros(n_subplots)
subplot_LAI = np.zeros(n_subplots)
for i in range(0, n_subplots):
print "Subplot: ", subplot_labels[Plot_name][i]
sp_pts = lidar.filter_lidar_data_by_polygon(
lidar_pts, subplot_polygons[Plot_name][i, :, :])
heights, subplot_lidar_profiles[
i, :], n_ground_returns[i] = bin_returns(sp_pts, max_height,
layer_thickness)
subplot_LAI[i] = field_LAI['LAI'][np.all(
(field_LAI['Subplot']
== subplot_labels[Plot_name][i], field_LAI['Plot'] == Plot_name),
axis=0)]
kmin = 0.20
kmax = 5.
kinc = 0.005
misfit, ks, best_k_LAD_profiles, best_k = minimise_misfit_for_k(
kmin, kmax, kinc, subplot_LAI, subplot_lidar_profiles,
n_ground_returns, layer_thickness, minimum_height)
return heights, best_k_LAD_profiles
MacArthurHorn_LAD = {}
radiative_LAD = {}
radiative_DTM_LAD = {}
inventory_LAD = {}
lidar_profiles = {}
lidar_profiles_adjusted = {}
MacArthurHorn_LAI = {}
radiative_LAI = {}
radiative_DTM_LAI = {}
inventory_LAI = {}
Hemisfer_LAI = {}
# load coordinates and lidar points for target areas
subplot_polygons, subplot_labels = aux.load_boundaries(subplot_coordinate_file)
all_lidar_pts = lidar.load_lidar_data(las_file)
# load field data and retrieve allometric relationships
field_data = field.load_crown_survey_data(field_file)
a, b, CF, r_sq, p, H, D = field.retrieve_crown_allometry(allometry_file)
a_ht, b_ht, CF_ht, a_A, b_A, CF_A = field.calculate_allometric_equations_from_survey(
field_data)
# load LAI estimates from hemiphotos
field_LAI = aux.load_field_LAI(LAI_file)
# loop through all plots to be analysed
for pp in range(0, N_plots):
print Plots[pp]
Plot_name = Plots[pp]
# clip LiDAR point cloud to plot level (this makes subsequent processing much faster)