Timber Volume Calculator

Description

This model calculates individual tree volumes based on LiDAR derived Canopy Height Module (CHM).

Jupyter nbviewr

Workflow

The main Workflow steps:

1. Delineate individual Tree Crowns using Local Maxima and Watershed segmentation algorithm.
2. Extract explanatory variables (Tree Height & Crown Area) for each tree.
3. Predict DBH using Regression models – from "R.J. Hall et al., 1985: A Comparison of Existing Models for DBH estimation from Large-scale Photos"
4. Estimate Volumes using BC forests 1976 volumes equations.

Required packages

The following packages need to be installed on your Python Env to run the this code:

• Numpy
• Scipy
• Pandas
• Osgeo (gdal and ogr)
• Scikit-image

import os
import sys

from osgeo import gdal,ogr,osr

import math
import numpy as np
import pandas as pd
from scipy import ndimage as ndi

from skimage.feature import peak_local_max
from skimage.morphology import watershed
from skimage.measure import regionprops,regionprops_table

import matplotlib.pyplot as plt
%matplotlib inline 

Calculate CHM

A Canopy Height Model (CHM) is produced by subtracting the DTM from the DSM:
CHM = DSM - DTM.
DTM = Digital Terrain Model. This is a Bare Earth model with surface features not included.
DSM = Digital Surface Model. This is a model representing features elevated above the "Bare Earth.

PDAL pipeline examples are provided above to calculate DTM and DSM from LiDAR Point Cloud (LAZ or LAS). They can be run in Command Line:

pdal 00_pipeline_create_DSM.json
pdal 00_pipeline_create_DSM.json

GDAL calc can then be used to calculate the CHM:

gdal_calc.py -A dtm.tif -B dsm.tif --calc="B-A" --outfile chm.tif

Contributions

All contributions are welcome.