[3944] | 1 | #!/usr/bin/env python3 |
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| 2 | # -*- coding: utf-8 -*- |
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| 3 | #--------------------------------------------------------------------------------# |
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| 4 | # This file is part of the PALM model system. |
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| 5 | # |
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| 6 | # PALM is free software: you can redistribute it and/or modify it under the terms |
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| 7 | # of the GNU General Public License as published by the Free Software Foundation, |
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| 8 | # either version 3 of the License, or (at your option) any later version. |
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| 9 | # |
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| 10 | # PALM is distributed in the hope that it will be useful, but WITHOUT ANY |
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| 11 | # WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR |
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| 12 | # A PARTICULAR PURPOSE. See the GNU General Public License for more details. |
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| 13 | # |
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| 14 | # You should have received a copy of the GNU General Public License along with |
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| 15 | # PALM. If not, see <http://www.gnu.org/licenses/>. |
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| 16 | # |
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[4481] | 17 | # Copyright 1997-2020 Leibniz Universitaet Hannover |
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[3944] | 18 | #--------------------------------------------------------------------------------# |
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| 19 | # |
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| 20 | # Current revisions: |
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| 21 | # ----------------- |
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| 22 | # |
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[4023] | 23 | # |
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[3944] | 24 | # Former revisions: |
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| 25 | # ----------------- |
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| 26 | # $Id: palm_csd_canopy_generator.py 3773 2019-03-01 08:56:57Z maronga $ |
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[4023] | 27 | # Added support for tree shape |
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| 28 | # |
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| 29 | # 3773 2019-03-01 08:56:57Z maronga |
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[3944] | 30 | # Bugfix: conversion to integer required for indexing |
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| 31 | # |
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| 32 | # 3668 2019-01-14 12:49:24Z maronga |
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| 33 | # Various improvements and bugfixes |
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| 34 | # |
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| 35 | # 3629 2018-12-13 12:18:54Z maronga |
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| 36 | # Initial revision |
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| 37 | # |
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| 38 | # Description: |
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| 39 | # ------------ |
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| 40 | # Canopy generator routines for creating 3D leaf and basal area densities for |
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| 41 | # single trees and tree patches |
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| 42 | # |
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| 43 | # @Author Bjoern Maronga (maronga@muk.uni-hannover.de) |
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| 44 | #------------------------------------------------------------------------------# |
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| 45 | import numpy as np |
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| 46 | import math |
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| 47 | import scipy.integrate as integrate |
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| 48 | |
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| 49 | def generate_single_tree_lad(x,y,dz,max_tree_height,max_patch_height,tree_type,tree_height,tree_dia,trunk_dia,tree_lai,season,fill): |
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| 50 | |
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| 51 | |
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| 52 | # Step 1: Create arrays for storing the data |
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| 53 | max_canopy_height = max( max_tree_height,max_patch_height ) |
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| 54 | |
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| 55 | zlad = np.arange(0,math.floor(max_canopy_height/dz)*dz+2*dz,dz) |
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| 56 | zlad[1:] = zlad[1:] - 0.5 * dz |
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| 57 | |
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| 58 | lad = np.ones((len(zlad),len(y),len(x))) |
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| 59 | lad[:,:,:] = fill |
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| 60 | |
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| 61 | bad = np.ones((len(zlad),len(y),len(x))) |
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| 62 | bad[:,:,:] = fill |
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| 63 | |
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| 64 | ids = np.ones((len(zlad),len(y),len(x))) |
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| 65 | ids[:,:,:] = fill |
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| 66 | |
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| 67 | |
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| 68 | # Calculating the number of trees in the arrays and a boolean array storing the location of trees which is used for convenience in the following loop environment |
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| 69 | number_of_trees_array = np.where( (tree_type.flatten() != fill) | (tree_dia.flatten() != fill) | (trunk_dia.flatten() != fill),1.0,fill) |
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| 70 | number_of_trees = len( number_of_trees_array[number_of_trees_array == 1.0] ) |
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| 71 | dx = x[1] - x[0] |
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| 72 | |
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| 73 | valid_pixels = np.where( (tree_type[:,:] != fill) | (tree_dia[:,:] != fill) | (trunk_dia[:,:] != fill),True,False) |
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| 74 | |
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| 75 | # For each tree, create a small 3d array containing the LAD field for the individual tree |
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| 76 | print("Start generating " + str(number_of_trees) + " trees...") |
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| 77 | tree_id_counter = 0 |
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| 78 | if (number_of_trees > 0): |
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| 79 | for i in range(0,len(x)-1): |
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| 80 | for j in range(0,len(y)-1): |
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| 81 | if valid_pixels[j,i]: |
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| 82 | tree_id_counter = tree_id_counter + 1 |
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| 83 | # print(" Processing tree No " + str(tree_id_counter) + " ...", end="") |
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[4021] | 84 | lad_loc, bad_loc, x_loc, y_loc, z_loc, status = process_single_tree(dx,dz,tree_type[j,i],fill,tree_height[j,i],tree_lai[j,i],tree_dia[j,i],trunk_dia[j,i],season) |
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[3944] | 85 | if ( np.any(lad_loc) != fill ): |
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| 86 | |
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| 87 | # Calculate the position of the local 3d tree array within the full domain in order to achieve correct mapping and cutting off at the edges of the full domain |
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| 88 | lad_loc_nx = int(len(x_loc) / 2) |
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| 89 | lad_loc_ny = int(len(y_loc) / 2) |
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| 90 | lad_loc_nz = int(len(z_loc)) |
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| 91 | |
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| 92 | odd_x = int(len(x_loc) % 2) |
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| 93 | odd_y = int(len(y_loc) % 2) |
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| 94 | |
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| 95 | ind_l_x = max(0,(i-lad_loc_nx)) |
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| 96 | ind_l_y = max(0,(j-lad_loc_ny)) |
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| 97 | ind_r_x = min(len(x)-1,i+lad_loc_nx-1+odd_x) |
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| 98 | ind_r_y = min(len(y)-1,j+lad_loc_ny-1+odd_y) |
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| 99 | |
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| 100 | out_l_x = ind_l_x - (i-lad_loc_nx) |
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| 101 | out_l_y = ind_l_y - (j-lad_loc_ny) |
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| 102 | out_r_x = len(x_loc)-1 + ind_r_x - (i+lad_loc_nx-1+odd_x) |
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| 103 | out_r_y = len(y_loc)-1 + ind_r_y - (j+lad_loc_ny-1+odd_y) |
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| 104 | |
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| 105 | |
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| 106 | lad[0:lad_loc_nz,ind_l_y:ind_r_y+1,ind_l_x:ind_r_x+1] = np.where(lad_loc[0:lad_loc_nz,out_l_y:out_r_y+1,out_l_x:out_r_x+1] != fill,lad_loc[0:lad_loc_nz,out_l_y:out_r_y+1,out_l_x:out_r_x+1],lad[0:lad_loc_nz,ind_l_y:ind_r_y+1,ind_l_x:ind_r_x+1]) |
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| 107 | bad[0:lad_loc_nz,ind_l_y:ind_r_y+1,ind_l_x:ind_r_x+1] = np.where(bad_loc[0:lad_loc_nz,out_l_y:out_r_y+1,out_l_x:out_r_x+1] != fill,bad_loc[0:lad_loc_nz,out_l_y:out_r_y+1,out_l_x:out_r_x+1],bad[0:lad_loc_nz,ind_l_y:ind_r_y+1,ind_l_x:ind_r_x+1]) |
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| 108 | ids[0:lad_loc_nz,ind_l_y:ind_r_y+1,ind_l_x:ind_r_x+1] = np.where(lad_loc[0:lad_loc_nz,out_l_y:out_r_y+1,out_l_x:out_r_x+1] != fill,tree_id_counter,ids[0:lad_loc_nz,ind_l_y:ind_r_y+1,ind_l_x:ind_r_x+1]) |
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| 109 | |
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| 110 | |
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| 111 | # if ( status == 0 ): |
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| 112 | # status_char = " ok." |
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| 113 | # else: |
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| 114 | # status_char = " skipped." |
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| 115 | # print(status_char) |
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| 116 | |
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| 117 | del lad_loc, x_loc, y_loc, z_loc, status |
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| 118 | return lad, bad, ids, zlad |
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| 119 | |
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| 120 | |
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[4021] | 121 | def process_single_tree(dx,dz,tree_type,tree_shape,tree_height,tree_lai,tree_dia,trunk_dia,season): |
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[3944] | 122 | |
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| 123 | # Set some parameters |
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| 124 | sphere_extinction = 0.6 |
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| 125 | cone_extinction = 0.2 |
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| 126 | ml_n_low = 0.5 |
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| 127 | ml_n_high = 6.0 |
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| 128 | |
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| 129 | # Populate look up table for tree species and their properties |
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| 130 | # #1 Tree shapes were manually lookep up. |
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| 131 | # #2 Crown h/w ratio - missing |
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| 132 | # #3 Crown diameter based on Berlin tree statistics |
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| 133 | # #4 Tree height based on Berlin tree statistics |
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| 134 | # #5 Tree LAI summer - missing |
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| 135 | # #6 Tree LAI winter - missing |
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| 136 | # #7 Height of lad maximum - missing |
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| 137 | # #8 Ratio LAD/BAD - missing |
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| 138 | # #9 Trunk diameter at breast height from Berlin |
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| 139 | default_trees = [] |
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| 140 | #1 #2 #3 #4 #5 #6 #7 #8 #9 |
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| 141 | default_trees.append(tree("Default", 1.0, 1.0, 4.0, 12.0, 3.0, 0.8, 0.6, 0.025, 0.35)) |
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| 142 | default_trees.append(tree("Abies", 3.0, 1.0, 4.0, 12.0, 3.0, 0.8, 0.6, 0.025, 0.80)) |
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| 143 | default_trees.append(tree("Acer", 1.0, 1.0, 7.0, 12.0, 3.0, 0.8, 0.6, 0.025, 0.80)) |
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| 144 | default_trees.append(tree("Aesculus", 1.0, 1.0, 7.0, 12.0, 3.0, 0.8, 0.6, 0.025, 1.00)) |
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| 145 | default_trees.append(tree("Ailanthus", 1.0, 1.0, 8.5, 13.5, 3.0, 0.8, 0.6, 0.025, 1.30)) |
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| 146 | default_trees.append(tree("Alnus", 3.0, 1.0, 6.0, 16.0, 3.0, 0.8, 0.6, 0.025, 1.20)) |
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| 147 | default_trees.append(tree("Amelanchier", 1.0, 1.0, 3.0, 4.0, 3.0, 0.8, 0.6, 0.025, 1.20)) |
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| 148 | default_trees.append(tree("Betula", 1.0, 1.0, 6.0, 14.0, 3.0, 0.8, 0.6, 0.025, 0.30)) |
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| 149 | default_trees.append(tree("Buxus", 1.0, 1.0, 4.0, 4.0, 3.0, 0.8, 0.6, 0.025, 0.90)) |
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| 150 | default_trees.append(tree("Calocedrus", 3.0, 1.0, 5.0, 10.0, 3.0, 0.8, 0.6, 0.025, 0.50)) |
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| 151 | default_trees.append(tree("Caragana", 1.0, 1.0, 3.5, 6.0, 3.0, 0.8, 0.6, 0.025, 0.90)) |
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| 152 | default_trees.append(tree("Carpinus", 1.0, 1.0, 6.0, 10.0, 3.0, 0.8, 0.6, 0.025, 0.70)) |
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| 153 | default_trees.append(tree("Carya", 1.0, 1.0, 5.0, 17.0, 3.0, 0.8, 0.6, 0.025, 0.80)) |
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| 154 | default_trees.append(tree("Castanea", 1.0, 1.0, 4.5, 7.0, 3.0, 0.8, 0.6, 0.025, 0.80)) |
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| 155 | default_trees.append(tree("Catalpa", 1.0, 1.0, 5.5, 6.5, 3.0, 0.8, 0.6, 0.025, 0.70)) |
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| 156 | default_trees.append(tree("Cedrus", 1.0, 1.0, 8.0, 13.0, 3.0, 0.8, 0.6, 0.025, 0.80)) |
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| 157 | default_trees.append(tree("Celtis", 1.0, 1.0, 6.0, 9.0, 3.0, 0.8, 0.6, 0.025, 0.80)) |
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| 158 | default_trees.append(tree("Cercidiphyllum", 1.0, 1.0, 3.0, 6.5, 3.0, 0.8, 0.6, 0.025, 0.80)) |
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| 159 | default_trees.append(tree("Cercis", 1.0, 1.0, 2.5, 7.5, 3.0, 0.8, 0.6, 0.025, 0.90)) |
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| 160 | default_trees.append(tree("Chamaecyparis", 5.0, 1.0, 3.5, 9.0, 3.0, 0.8, 0.6, 0.025, 0.70)) |
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| 161 | default_trees.append(tree("Cladrastis", 1.0, 1.0, 5.0, 10.0, 3.0, 0.8, 0.6, 0.025, 0.80)) |
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| 162 | default_trees.append(tree("Cornus", 1.0, 1.0, 4.5, 6.5, 3.0, 0.8, 0.6, 0.025, 1.20)) |
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| 163 | default_trees.append(tree("Corylus", 1.0, 1.0, 5.0, 9.0, 3.0, 0.8, 0.6, 0.025, 0.40)) |
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| 164 | default_trees.append(tree("Cotinus", 1.0, 1.0, 4.0, 4.0, 3.0, 0.8, 0.6, 0.025, 0.70)) |
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| 165 | default_trees.append(tree("Crataegus", 3.0, 1.0, 3.5, 6.0, 3.0, 0.8, 0.6, 0.025, 1.40)) |
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| 166 | default_trees.append(tree("Cryptomeria", 3.0, 1.0, 5.0, 10.0, 3.0, 0.8, 0.6, 0.025, 0.50)) |
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| 167 | default_trees.append(tree("Cupressocyparis", 3.0, 1.0, 3.0, 8.0, 3.0, 0.8, 0.6, 0.025, 0.40)) |
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| 168 | default_trees.append(tree("Cupressus", 3.0, 1.0, 5.0, 7.0, 3.0, 0.8, 0.6, 0.025, 0.40)) |
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| 169 | default_trees.append(tree("Cydonia", 1.0, 1.0, 2.0, 3.0, 3.0, 0.8, 0.6, 0.025, 0.90)) |
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| 170 | default_trees.append(tree("Davidia", 1.0, 1.0,10.0, 14.0, 3.0, 0.8, 0.6, 0.025, 0.40)) |
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| 171 | default_trees.append(tree("Elaeagnus", 1.0, 1.0, 6.5, 6.0, 3.0, 0.8, 0.6, 0.025, 1.20)) |
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| 172 | default_trees.append(tree("Euodia", 1.0, 1.0, 4.5, 6.0, 3.0, 0.8, 0.6, 0.025, 0.90)) |
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| 173 | default_trees.append(tree("Euonymus", 1.0, 1.0, 4.5, 6.0, 3.0, 0.8, 0.6, 0.025, 0.60)) |
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| 174 | default_trees.append(tree("Fagus", 1.0, 1.0,10.0, 12.5, 3.0, 0.8, 0.6, 0.025, 0.50)) |
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| 175 | default_trees.append(tree("Fraxinus", 1.0, 1.0, 5.5, 10.5, 3.0, 0.8, 0.6, 0.025, 1.60)) |
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| 176 | default_trees.append(tree("Ginkgo", 3.0, 1.0, 4.0, 8.5, 3.0, 0.8, 0.6, 0.025, 0.80)) |
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| 177 | default_trees.append(tree("Gleditsia", 1.0, 1.0, 6.5, 10.5, 3.0, 0.8, 0.6, 0.025, 0.60)) |
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| 178 | default_trees.append(tree("Gymnocladus", 1.0, 1.0, 5.5, 10.0, 3.0, 0.8, 0.6, 0.025, 0.80)) |
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| 179 | default_trees.append(tree("Hippophae", 1.0, 1.0, 9.5, 8.5, 3.0, 0.8, 0.6, 0.025, 0.80)) |
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| 180 | default_trees.append(tree("Ilex", 1.0, 1.0, 4.0, 7.5, 3.0, 0.8, 0.6, 0.025, 0.80)) |
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| 181 | default_trees.append(tree("Juglans", 1.0, 1.0, 7.0, 9.0, 3.0, 0.8, 0.6, 0.025, 0.50)) |
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| 182 | default_trees.append(tree("Juniperus", 5.0, 1.0, 3.0, 7.0, 3.0, 0.8, 0.6, 0.025, 0.90)) |
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| 183 | default_trees.append(tree("Koelreuteria", 1.0, 1.0, 3.5, 5.5, 3.0, 0.8, 0.6, 0.025, 0.50)) |
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| 184 | default_trees.append(tree("Laburnum", 1.0, 1.0, 3.0, 6.0, 3.0, 0.8, 0.6, 0.025, 0.60)) |
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| 185 | default_trees.append(tree("Larix", 3.0, 1.0, 7.0, 16.5, 3.0, 0.8, 0.6, 0.025, 0.60)) |
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| 186 | default_trees.append(tree("Ligustrum", 1.0, 1.0, 3.0, 6.0, 3.0, 0.8, 0.6, 0.025, 1.10)) |
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| 187 | default_trees.append(tree("Liquidambar", 3.0, 1.0, 3.0, 7.0, 3.0, 0.8, 0.6, 0.025, 0.30)) |
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| 188 | default_trees.append(tree("Liriodendron", 3.0, 1.0, 4.5, 9.5, 3.0, 0.8, 0.6, 0.025, 0.50)) |
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| 189 | default_trees.append(tree("Lonicera", 1.0, 1.0, 7.0, 9.0, 3.0, 0.8, 0.6, 0.025, 0.70)) |
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| 190 | default_trees.append(tree("Magnolia", 1.0, 1.0, 3.0, 5.0, 3.0, 0.8, 0.6, 0.025, 0.60)) |
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| 191 | default_trees.append(tree("Malus", 1.0, 1.0, 4.5, 5.0, 3.0, 0.8, 0.6, 0.025, 0.30)) |
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| 192 | default_trees.append(tree("Metasequoia", 5.0, 1.0, 4.5, 12.0, 3.0, 0.8, 0.6, 0.025, 0.50)) |
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| 193 | default_trees.append(tree("Morus", 1.0, 1.0, 7.5, 11.5, 3.0, 0.8, 0.6, 0.025, 1.00)) |
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| 194 | default_trees.append(tree("Ostrya", 1.0, 1.0, 2.0, 6.0, 3.0, 0.8, 0.6, 0.025, 1.00)) |
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| 195 | default_trees.append(tree("Parrotia", 1.0, 1.0, 7.0, 7.0, 3.0, 0.8, 0.6, 0.025, 0.30)) |
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| 196 | default_trees.append(tree("Paulownia", 1.0, 1.0, 4.0, 8.0, 3.0, 0.8, 0.6, 0.025, 0.40)) |
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| 197 | default_trees.append(tree("Phellodendron", 1.0, 1.0,13.5, 13.5, 3.0, 0.8, 0.6, 0.025, 0.50)) |
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| 198 | default_trees.append(tree("Picea", 3.0, 1.0, 3.0, 13.0, 3.0, 0.8, 0.6, 0.025, 0.90)) |
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| 199 | default_trees.append(tree("Pinus", 3.0, 1.0, 6.0, 16.0, 3.0, 0.8, 0.6, 0.025, 0.80)) |
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| 200 | default_trees.append(tree("Platanus", 1.0, 1.0,10.0, 14.5, 3.0, 0.8, 0.6, 0.025, 1.10)) |
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| 201 | default_trees.append(tree("Populus", 1.0, 1.0, 9.0, 20.0, 3.0, 0.8, 0.6, 0.025, 1.40)) |
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| 202 | default_trees.append(tree("Prunus", 1.0, 1.0, 5.0, 7.0, 3.0, 0.8, 0.6, 0.025, 1.60)) |
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| 203 | default_trees.append(tree("Pseudotsuga", 3.0, 1.0, 6.0, 17.5, 3.0, 0.8, 0.6, 0.025, 0.70)) |
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| 204 | default_trees.append(tree("Ptelea", 1.0, 1.0, 5.0, 4.0, 3.0, 0.8, 0.6, 0.025, 1.10)) |
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| 205 | default_trees.append(tree("Pterocaria", 1.0, 1.0,10.0, 12.0, 3.0, 0.8, 0.6, 0.025, 0.50)) |
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| 206 | default_trees.append(tree("Pterocarya", 1.0, 1.0,11.5, 14.5, 3.0, 0.8, 0.6, 0.025, 1.60)) |
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| 207 | default_trees.append(tree("Pyrus", 3.0, 1.0, 3.0, 6.0, 3.0, 0.8, 0.6, 0.025, 1.80)) |
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| 208 | default_trees.append(tree("Quercus", 1.0, 1.0, 8.0, 14.0, 3.1, 0.1, 0.6, 0.025, 0.40)) # |
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| 209 | default_trees.append(tree("Rhamnus", 1.0, 1.0, 4.5, 4.5, 3.0, 0.8, 0.6, 0.025, 1.30)) |
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| 210 | default_trees.append(tree("Rhus", 1.0, 1.0, 7.0, 5.5, 3.0, 0.8, 0.6, 0.025, 0.50)) |
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| 211 | default_trees.append(tree("Robinia", 1.0, 1.0, 4.5, 13.5, 3.0, 0.8, 0.6, 0.025, 0.50)) |
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| 212 | default_trees.append(tree("Salix", 1.0, 1.0, 7.0, 14.0, 3.0, 0.8, 0.6, 0.025, 1.10)) |
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| 213 | default_trees.append(tree("Sambucus", 1.0, 1.0, 8.0, 6.0, 3.0, 0.8, 0.6, 0.025, 1.40)) |
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| 214 | default_trees.append(tree("Sasa", 1.0, 1.0,10.0, 25.0, 3.0, 0.8, 0.6, 0.025, 0.60)) |
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| 215 | default_trees.append(tree("Sequoiadendron", 5.0, 1.0, 5.5, 10.5, 3.0, 0.8, 0.6, 0.025, 1.60)) |
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| 216 | default_trees.append(tree("Sophora", 1.0, 1.0, 7.5, 10.0, 3.0, 0.8, 0.6, 0.025, 1.40)) |
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| 217 | default_trees.append(tree("Sorbus", 1.0, 1.0, 4.0, 7.0, 3.0, 0.8, 0.6, 0.025, 1.10)) |
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| 218 | default_trees.append(tree("Syringa", 1.0, 1.0, 4.5, 5.0, 3.0, 0.8, 0.6, 0.025, 0.60)) |
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| 219 | default_trees.append(tree("Tamarix", 1.0, 1.0, 6.0, 7.0, 3.0, 0.8, 0.6, 0.025, 0.50)) |
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| 220 | default_trees.append(tree("Taxodium", 5.0, 1.0, 6.0, 16.5, 3.0, 0.8, 0.6, 0.025, 0.60)) |
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| 221 | default_trees.append(tree("Taxus", 2.0, 1.0, 5.0, 7.5, 3.0, 0.8, 0.6, 0.025, 1.50)) |
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| 222 | default_trees.append(tree("Thuja", 3.0, 1.0, 3.5, 9.0, 3.0, 0.8, 0.6, 0.025, 0.70)) |
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| 223 | default_trees.append(tree("Tilia", 3.0, 1.0, 7.0, 12.5, 3.0, 0.8, 0.6, 0.025, 0.70)) |
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| 224 | default_trees.append(tree("Tsuga", 3.0, 1.0, 6.0, 10.5, 3.0, 0.8, 0.6, 0.025, 1.10)) |
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| 225 | default_trees.append(tree("Ulmus", 1.0, 1.0, 7.5, 14.0, 3.0, 0.8, 0.6, 0.025, 0.80)) |
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| 226 | default_trees.append(tree("Zelkova", 1.0, 1.0, 4.0, 5.5, 3.0, 0.8, 0.6, 0.025, 1.20)) |
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| 227 | default_trees.append(tree("Zenobia", 1.0, 1.0, 5.0, 5.0, 3.0, 0.8, 0.6, 0.025, 0.40)) |
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| 228 | |
---|
| 229 | # Define fill values |
---|
| 230 | fillvalues = { |
---|
| 231 | "tree_data": float(-9999.0), |
---|
| 232 | } |
---|
| 233 | |
---|
| 234 | |
---|
| 235 | # Check for missing data in the input and set default values if needed |
---|
| 236 | if ( tree_type == fillvalues["tree_data"] ): |
---|
| 237 | tree_type = int(0) |
---|
| 238 | else: |
---|
| 239 | tree_type = int(tree_type) |
---|
| 240 | |
---|
[4021] | 241 | if ( tree_shape == fillvalues["tree_data"] ): |
---|
| 242 | tree_shape = default_trees[tree_type].shape |
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| 243 | |
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[3944] | 244 | if ( tree_height == fillvalues["tree_data"] ): |
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| 245 | tree_height = default_trees[tree_type].height |
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| 246 | |
---|
| 247 | if ( tree_lai == fillvalues["tree_data"] ): |
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| 248 | if (season == "summer"): |
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| 249 | tree_lai = default_trees[tree_type].lai_summer |
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| 250 | |
---|
| 251 | else: |
---|
| 252 | tree_lai = default_trees[tree_type].lai_winter |
---|
| 253 | |
---|
| 254 | if ( tree_dia == fillvalues["tree_data"] ): |
---|
| 255 | tree_dia = default_trees[tree_type].diameter |
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| 256 | |
---|
| 257 | if ( trunk_dia == fillvalues["tree_data"] ): |
---|
| 258 | trunk_dia = default_trees[tree_type].dbh |
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| 259 | |
---|
| 260 | |
---|
| 261 | # Assign values that are not defined as user input from lookup table |
---|
| 262 | tree_ratio = default_trees[tree_type].ratio |
---|
| 263 | lad_max_height = default_trees[tree_type].lad_max_height |
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| 264 | bad_scale = default_trees[tree_type].bad_scale |
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| 265 | |
---|
| 266 | #print("Tree input parameters:") |
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| 267 | #print("----------------------") |
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| 268 | #print("type: " + str(default_trees[tree_type].species) ) |
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| 269 | #print("height: " + str(tree_height)) |
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| 270 | #print("lai: " + str(tree_lai)) |
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| 271 | #print("crown diameter: " + str(tree_dia)) |
---|
| 272 | #print("trunk diameter: " + str(trunk_dia)) |
---|
| 273 | #print("shape: " + str(tree_shape)) |
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| 274 | #print("height/width: " + str(tree_ratio)) |
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| 275 | |
---|
| 276 | # Calculate crown height and height of the crown center |
---|
| 277 | crown_height = tree_ratio * tree_dia |
---|
| 278 | if ( crown_height > tree_height ): |
---|
| 279 | crown_height = tree_height |
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| 280 | |
---|
| 281 | crown_center = tree_height - crown_height * 0.5 |
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| 282 | |
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| 283 | |
---|
| 284 | # Calculate height of maximum LAD |
---|
| 285 | z_lad_max = lad_max_height * tree_height |
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| 286 | |
---|
| 287 | # Calculate the maximum LAD after Lalic and Mihailovic (2004) |
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| 288 | lad_max_part_1 = integrate.quad(lambda z: ( ( tree_height - z_lad_max ) / ( tree_height - z ) ) ** (ml_n_high) * np.exp( ml_n_high * (1.0 - ( tree_height - z_lad_max ) / ( tree_height - z ) ) ), 0.0, z_lad_max) |
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| 289 | lad_max_part_2 = integrate.quad(lambda z: ( ( tree_height - z_lad_max ) / ( tree_height - z ) ) ** (ml_n_low) * np.exp( ml_n_low * (1.0 - ( tree_height - z_lad_max ) / ( tree_height - z ) ) ), z_lad_max, tree_height) |
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| 290 | |
---|
| 291 | lad_max = tree_lai / (lad_max_part_1[0] + lad_max_part_2[0]) |
---|
| 292 | |
---|
| 293 | |
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| 294 | # Define position of tree and its output domain |
---|
| 295 | nx = int(tree_dia / dx) + 2 |
---|
| 296 | nz = int(tree_height / dz) + 2 |
---|
| 297 | |
---|
| 298 | # Add one grid point if diameter is an odd value |
---|
| 299 | if ( (tree_dia % 2.0) != 0.0 ): |
---|
| 300 | nx = nx + 1 |
---|
| 301 | |
---|
| 302 | |
---|
| 303 | # Create local domain of the tree's LAD |
---|
| 304 | x = np.arange(0,nx*dx,dx) |
---|
| 305 | x[:] = x[:] - 0.5 * dx |
---|
| 306 | y = x |
---|
| 307 | |
---|
| 308 | z = np.arange(0,nz*dz,dz) |
---|
| 309 | z[1:] = z[1:] - 0.5 * dz |
---|
| 310 | |
---|
| 311 | # Define center of the tree position inside the local LAD domain |
---|
| 312 | tree_location_x = x[int(nx/2)] |
---|
| 313 | tree_location_y = y[int(nx/2)] |
---|
| 314 | |
---|
| 315 | |
---|
| 316 | # Calculate LAD profile after Lalic and Mihailovic (2004). Will be later used for normalization |
---|
| 317 | lad_profile = np.arange(0,nz,1.0) |
---|
| 318 | lad_profile[:] = 0.0 |
---|
| 319 | |
---|
| 320 | for k in range(1,nz-1): |
---|
| 321 | if ( (z[k] > 0.0) & (z[k] < z_lad_max) ): |
---|
| 322 | n = ml_n_high |
---|
| 323 | else: |
---|
| 324 | n = ml_n_low |
---|
| 325 | |
---|
| 326 | lad_profile[k] = lad_max * ( ( tree_height - z_lad_max ) / ( tree_height - z[k] ) ) ** (n) * np.exp( n * (1.0 - ( tree_height - z_lad_max ) / ( tree_height - z[k] ) ) ) |
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| 327 | |
---|
| 328 | # Create lad array and populate according to the specific tree shape. This is still experimental |
---|
| 329 | lad_loc = np.ones((nz,nx,nx)) |
---|
| 330 | lad_loc[:,:,:] = fillvalues["tree_data"] |
---|
| 331 | bad_loc = np.copy(lad_loc) |
---|
| 332 | |
---|
| 333 | |
---|
| 334 | # For very small trees, no LAD is calculated |
---|
| 335 | if ( tree_height <= (0.5*dz) ): |
---|
| 336 | print(" Shallow tree found. Action: ignore.") |
---|
| 337 | return lad_loc, bad_loc, x, y, z, 1 |
---|
| 338 | |
---|
| 339 | |
---|
| 340 | # Branch for spheres and ellipsoids. A symmetric LAD sphere is created assuming an LAD extinction towards the center of the tree, representing the effect of sunlight extinction and thus |
---|
| 341 | # less leaf mass inside the tree crown. Extinction coefficients are experimental. |
---|
| 342 | if ( tree_shape == 1 ): |
---|
| 343 | for i in range(0,nx-1): |
---|
| 344 | for j in range(0,nx-1): |
---|
| 345 | for k in range(0,nz): |
---|
| 346 | r_test = np.sqrt( (x[i] - tree_location_x)**(2)/(tree_dia * 0.5)**(2) + (y[j] - tree_location_y)**(2)/(tree_dia * 0.5)**2 + (z[k] - crown_center)**(2)/(crown_height * 0.5)**(2)) |
---|
| 347 | if ( r_test <= 1.0 ): |
---|
| 348 | lad_loc[k,j,i] = lad_max * np.exp( - sphere_extinction * (1.0 - r_test) ) |
---|
| 349 | else: |
---|
| 350 | lad_loc[k,j,i] = fillvalues["tree_data"] |
---|
| 351 | |
---|
| 352 | if ( np.any( lad_loc[:,j,i] != fillvalues["tree_data"]) ): |
---|
| 353 | lad_loc[0,j,i] = 0.0 |
---|
| 354 | |
---|
| 355 | |
---|
| 356 | # Branch for cylinder shapes |
---|
| 357 | if ( tree_shape == 2 ): |
---|
| 358 | k_min = int((crown_center - crown_height * 0.5) / dz) |
---|
| 359 | k_max = int((crown_center + crown_height * 0.5) / dz) |
---|
| 360 | for i in range(0,nx-1): |
---|
| 361 | for j in range(0,nx-1): |
---|
| 362 | for k in range(k_min,k_max): |
---|
| 363 | r_test = np.sqrt( (x[i] - tree_location_x)**(2)/(tree_dia * 0.5)**(2) + (y[j] - tree_location_y)**(2)/(tree_dia * 0.5)**(2)) |
---|
| 364 | if ( r_test <= 1.0 ): |
---|
| 365 | r_test3 = np.sqrt( (z[k] - crown_center)**(2)/(crown_height * 0.5)**(2)) |
---|
| 366 | lad_loc[k,j,i] = lad_max * np.exp ( - sphere_extinction * (1.0 - max(r_test,r_test3) ) ) |
---|
| 367 | else: |
---|
| 368 | lad_loc[k,j,i] = fillvalues["tree_data"] |
---|
| 369 | |
---|
| 370 | if ( np.any( lad_loc[:,j,i] != fillvalues["tree_data"]) ): |
---|
| 371 | lad_loc[0,j,i] = 0.0 |
---|
| 372 | |
---|
| 373 | # Branch for cone shapes |
---|
| 374 | if ( tree_shape == 3 ): |
---|
| 375 | k_min = int((crown_center - crown_height * 0.5) / dz) |
---|
| 376 | k_max = int((crown_center + crown_height * 0.5) / dz) |
---|
| 377 | for i in range(0,nx-1): |
---|
| 378 | for j in range(0,nx-1): |
---|
| 379 | for k in range(k_min,k_max): |
---|
| 380 | k_rel = k - k_min |
---|
| 381 | r_test = (x[i] - tree_location_x)**(2) + (y[j] - tree_location_y)**(2) - ( (tree_dia * 0.5)**(2) / crown_height**(2) ) * ( z[k_rel] - crown_height)**(2) |
---|
| 382 | if ( r_test <= 0.0 ): |
---|
| 383 | r_test2 = np.sqrt( (x[i] - tree_location_x)**(2)/(tree_dia * 0.5)**(2) + (y[j] - tree_location_y)**(2)/(tree_dia * 0.5)**(2)) |
---|
| 384 | r_test3 = np.sqrt( (z[k] - crown_center)**(2)/(crown_height * 0.5)**(2)) |
---|
| 385 | lad_loc[k,j,i] = lad_max * np.exp ( - cone_extinction * (1.0 - max((r_test+1.0),r_test2,r_test3)) ) |
---|
| 386 | else: |
---|
| 387 | lad_loc[k,j,i] = fillvalues["tree_data"] |
---|
| 388 | |
---|
| 389 | if ( np.any( lad_loc[:,j,i] != fillvalues["tree_data"]) ): |
---|
| 390 | lad_loc[0,j,i] = 0.0 |
---|
| 391 | |
---|
| 392 | # Branch for inverted cone shapes. TODO: what is r_test2 and r_test3 used for? Debugging needed! |
---|
| 393 | if ( tree_shape == 4 ): |
---|
| 394 | k_min = int((crown_center - crown_height * 0.5) / dz) |
---|
| 395 | k_max = int((crown_center + crown_height * 0.5) / dz) |
---|
| 396 | for i in range(0,nx-1): |
---|
| 397 | for j in range(0,nx-1): |
---|
| 398 | for k in range(k_min,k_max): |
---|
| 399 | k_rel = k_max - k |
---|
| 400 | r_test = (x[i] - tree_location_x)**(2) + (y[j] - tree_location_y)**(2) - ( (tree_dia * 0.5)**(2) / crown_height**(2) ) * ( z[k_rel] - crown_height)**(2) |
---|
| 401 | if ( r_test <= 0.0 ): |
---|
| 402 | r_test2 = np.sqrt( (x[i] - tree_location_x)**(2)/(tree_dia * 0.5)**(2) + (y[j] - tree_location_y)**(2)/(tree_dia * 0.5)**(2)) |
---|
| 403 | r_test3 = np.sqrt( (z[k] - crown_center)**(2)/(crown_height * 0.5)**(2)) |
---|
| 404 | lad_loc[k,j,i] = lad_max * np.exp ( - cone_extinction * ( - r_test ) ) |
---|
| 405 | else: |
---|
| 406 | lad_loc[k,j,i] = fillvalues["tree_data"] |
---|
| 407 | |
---|
| 408 | if ( np.any( lad_loc[:,j,i] != fillvalues["tree_data"]) ): |
---|
| 409 | lad_loc[0,j,i] = 0.0 |
---|
| 410 | |
---|
| 411 | |
---|
| 412 | # Branch for paraboloid shapes |
---|
| 413 | if ( tree_shape == 5 ): |
---|
| 414 | k_min = int((crown_center - crown_height * 0.5) / dz) |
---|
| 415 | k_max = int((crown_center + crown_height * 0.5) / dz) |
---|
| 416 | for i in range(0,nx-1): |
---|
| 417 | for j in range(0,nx-1): |
---|
| 418 | for k in range(k_min,k_max): |
---|
| 419 | k_rel = k - k_min |
---|
| 420 | r_test = ((x[i] - tree_location_x)**(2) + (y[j] - tree_location_y)**(2)) * crown_height / (tree_dia * 0.5)**(2) - z[k_rel] |
---|
| 421 | if ( r_test <= 0.0 ): |
---|
| 422 | lad_loc[k,j,i] = lad_max * np.exp ( - cone_extinction * (- r_test) ) |
---|
| 423 | else: |
---|
| 424 | lad_loc[k,j,i] = fillvalues["tree_data"] |
---|
| 425 | |
---|
| 426 | if ( np.any( lad_loc[:,j,i] != fillvalues["tree_data"]) ): |
---|
| 427 | lad_loc[0,j,i] = 0.0 |
---|
| 428 | |
---|
| 429 | |
---|
| 430 | |
---|
| 431 | # Branch for inverted paraboloid shapes |
---|
| 432 | if ( tree_shape == 6 ): |
---|
| 433 | k_min = int((crown_center - crown_height * 0.5) / dz) |
---|
| 434 | k_max = int((crown_center + crown_height * 0.5) / dz) |
---|
| 435 | for i in range(0,nx-1): |
---|
| 436 | for j in range(0,nx-1): |
---|
| 437 | for k in range(k_min,k_max): |
---|
| 438 | k_rel = k_max - k |
---|
| 439 | r_test = ((x[i] - tree_location_x)**(2) + (y[j] - tree_location_y)**(2)) * crown_height / (tree_dia * 0.5)**(2) - z[k_rel] |
---|
| 440 | if ( r_test <= 0.0 ): |
---|
| 441 | lad_loc[k,j,i] = lad_max * np.exp ( - cone_extinction * (- r_test) ) |
---|
| 442 | else: |
---|
| 443 | lad_loc[k,j,i] = fillvalues["tree_data"] |
---|
| 444 | |
---|
| 445 | if ( np.any( lad_loc[:,j,i] != fillvalues["tree_data"]) ): |
---|
| 446 | lad_loc[0,j,i] = 0.0 |
---|
| 447 | |
---|
| 448 | |
---|
| 449 | # Normalize the LAD profile so that the vertically integrated Lalic and Mihailovic (2004) is reproduced by the LAD array. Deactivated for now. |
---|
| 450 | #for i in range(0,nx-1): |
---|
| 451 | #for j in range(0,nx-1): |
---|
| 452 | #lad_clean = np.where(lad_loc[:,j,i] == fillvalues["tree_data"],0.0,lad_loc[:,j,i]) |
---|
| 453 | #lai_from_int = integrate.simps (lad_clean, z) |
---|
| 454 | #print(lai_from_int) |
---|
| 455 | #for k in range(0,nz): |
---|
| 456 | #if ( np.any(lad_loc[k,j,i] > 0.0) ): |
---|
| 457 | #lad_loc[k,j,i] = np.where((lad_loc[k,j,i] != fillvalues["tree_data"]),lad_loc[k,j,i] / lai_from_int * lad_profile[k],lad_loc[k,j,i]) |
---|
| 458 | |
---|
| 459 | |
---|
[4021] | 460 | # Create BAD array and populate. TODO: revise as low LAD inside the foliage does not result in low BAD values. |
---|
| 461 | bad_loc = np.where(lad_loc != fillvalues["tree_data"],(1.0 - lad_loc)*0.1,lad_loc) |
---|
[3944] | 462 | |
---|
| 463 | |
---|
| 464 | # Overwrite grid cells that are occupied by the tree trunk |
---|
| 465 | radius = trunk_dia * 0.5 |
---|
| 466 | for i in range(0,nx-1): |
---|
| 467 | for j in range(0,nx-1): |
---|
| 468 | for k in range(0,nz): |
---|
| 469 | if ( z[k] <= crown_center ): |
---|
| 470 | r_test = np.sqrt( (x[i] - tree_location_x)**(2) + (y[j] - tree_location_y)**(2) ) |
---|
| 471 | if ( r_test <= radius ): |
---|
| 472 | bad_loc[k,j,i] = 1.0 |
---|
| 473 | if ( (r_test == 0.0) & (trunk_dia <= dx) ): |
---|
| 474 | bad_loc[k,j,i] = radius**(2) * 3.14159265359 |
---|
| 475 | |
---|
| 476 | return lad_loc, bad_loc, x, y, z, 0 |
---|
| 477 | |
---|
| 478 | |
---|
| 479 | def process_patch(dz,patch_height,max_height_lad,patch_lai,alpha,beta): |
---|
| 480 | |
---|
| 481 | # Define fill values |
---|
| 482 | fillvalues = { |
---|
| 483 | "tree_data": float(-9999.0), |
---|
| 484 | "pch_index": int(-9999), |
---|
| 485 | } |
---|
| 486 | |
---|
| 487 | phdz = patch_height[:,:] / dz |
---|
| 488 | pch_index = np.where( (patch_height[:,:] != fillvalues["tree_data"]),phdz.astype(int)+1,int(-1)) |
---|
| 489 | pch_index = np.where( (pch_index[:,:] == 0) ,fillvalues["pch_index"],pch_index[:,:]) |
---|
| 490 | pch_index = np.where( (pch_index[:,:] == -1) ,0,pch_index[:,:]) |
---|
| 491 | |
---|
| 492 | max_canopy_height = max(max(patch_height.flatten()),max_height_lad) |
---|
| 493 | |
---|
| 494 | z = np.arange(0,math.floor(max_canopy_height/dz)*dz+2*dz,dz) |
---|
| 495 | |
---|
| 496 | z[1:] = z[1:] - 0.5 * dz |
---|
| 497 | |
---|
| 498 | nz = len(z) |
---|
| 499 | ny = len(patch_height[:,0]) |
---|
| 500 | nx = len(patch_height[0,:]) |
---|
| 501 | |
---|
| 502 | pre_lad = np.ones((nz)) |
---|
| 503 | pre_lad[:] = 0.0 |
---|
| 504 | lad_loc = np.ones( (nz,ny,nx) ) |
---|
| 505 | lad_loc[:,:,:] = fillvalues["tree_data"] |
---|
| 506 | |
---|
| 507 | for i in range(0,nx-1): |
---|
| 508 | for j in range(0,ny-1): |
---|
| 509 | int_bpdf = 0.0 |
---|
| 510 | if ( (patch_height[j,i] != fillvalues["tree_data"]) & (patch_height[j,i] >= (0.5*dz)) ): |
---|
| 511 | for k in range(1,pch_index[j,i]): |
---|
| 512 | int_bpdf = int_bpdf + ( ( ( z[k] / patch_height[j,i] )**( alpha - 1 ) ) * ( ( 1.0 - ( z[k] / patch_height[j,i] ) )**(beta - 1 ) ) * ( dz / patch_height[j,i] ) ) |
---|
| 513 | |
---|
| 514 | for k in range(1,pch_index[j,i]): |
---|
| 515 | pre_lad[k] = patch_lai[j,i] * ( ( ( dz*k / patch_height[j,i] )**( alpha - 1.0 ) ) * ( ( 1.0 - ( dz*k / patch_height[j,i] ) )**(beta - 1.0 ) ) / int_bpdf ) / patch_height[j,i] |
---|
| 516 | |
---|
| 517 | lad_loc[0,j,i] = pre_lad[0] |
---|
| 518 | |
---|
| 519 | for k in range(0,pch_index[j,i]): |
---|
| 520 | lad_loc[k,j,i] = 0.5 * ( pre_lad[k-1] + pre_lad[k] ) |
---|
| 521 | |
---|
| 522 | return lad_loc, nz, 0 |
---|
| 523 | |
---|
| 524 | |
---|
| 525 | # CLASS TREE |
---|
| 526 | # |
---|
| 527 | # Default tree geometrical parameters: |
---|
| 528 | # |
---|
| 529 | # species: name of the tree type |
---|
| 530 | # |
---|
| 531 | # shape: defines the general shape of the tree and can be one of the following types: |
---|
| 532 | # 1.0 sphere or ellipsoid |
---|
| 533 | # 2.0 cylinder |
---|
| 534 | # 3.0 cone |
---|
| 535 | # 4.0 inverted cone |
---|
| 536 | # 5.0 paraboloid (rounded cone) |
---|
| 537 | # 6.0 inverted paraboloid (invertes rounded cone) |
---|
| 538 | # |
---|
| 539 | # ratio: ratio of maximum crown height to the maximum crown diameter |
---|
| 540 | # diameter: default crown diameter (m) |
---|
| 541 | # height: default total height of the tree including trunk (m) |
---|
| 542 | # lai_summer: default leaf area index fully leafed |
---|
| 543 | # lai_winter: default winter-teim leaf area index |
---|
| 544 | # lad_max: default maximum leaf area density (m2/m3) |
---|
| 545 | # lad_max_height: default height where the leaf area density is maximum relative to total tree height |
---|
| 546 | # bad_scale: ratio of basal area in the crown area to the leaf area |
---|
| 547 | # dbh: default trunk diameter at breast height (1.4 m) (m) |
---|
| 548 | # |
---|
| 549 | class tree: |
---|
| 550 | def __init__(self, species, shape, ratio, diameter, height, lai_summer, lai_winter, lad_max_height, bad_scale, dbh): |
---|
| 551 | self.species = species |
---|
| 552 | self.shape = shape |
---|
| 553 | self.ratio = ratio |
---|
| 554 | self.diameter = diameter |
---|
| 555 | self.height = height |
---|
| 556 | self.lai_summer = lai_summer |
---|
| 557 | self.lai_winter = lai_winter |
---|
| 558 | self.lad_max_height = lad_max_height |
---|
| 559 | self.bad_scale = bad_scale |
---|
| 560 | self.dbh = dbh |
---|