Application of remote sensing by the New Zealand forest industry

Aaron Gunn

Presentation Overview

• History – LiDAR Cluster Group

• Personal Experience - Blakely Pacific Ltd (silvicultural scheduling)

• Remote Sensing achievements in NZ Plantation Forestry

Digital Elevation Models

LiDAR based Forest Inventory System - kNN

Individual tree identification

Satellite Imagery - Rapid Eye

History – LiDAR Cluster Group

NZ LiDAR Cluster Group

established 2011

• Capture specifications

• Data storage

• Capture collaborations

• Processing

• Products required

History – LiDAR Cluster Group

Scion/FFR research

• Developed standards for LiDAR capture in a NZ forestry environment

• Provided insights to:

Processing software options

LiDAR terminology

• Provided assistance to forestry companies

Blakely Pacific LiDAR Project

• 9,000ha of Douglas-fir

forest

• In-sufficient inventory

information

• Sites established

between 1995 & 2003

• Thinning operations

looming!

Tree Height

Model

Program results to date

• Provided immediate

identification of high

productive sites

• 18% of project now completed

• Improved operational

efficiencies & cost savings

Additional

Benefits

• Contour

dataset

• Digital

Elevation

Model

Remote Sensing Achievements - NZ

Forestry

• Digital Elevation Models

• LiDAR based Forest Inventory System

• Individual tree identification

• HarvestNav Application

• Rapid Eye/SatTools - EVI

Digital Elevation Model (DEM)

• Especially valuable during the harvesting and

road planning stage for steep-land sites.

• Is the base for above ground LiDAR point

cloud sampling.

Optimal Point Density - DEM

• Minimum ground return density for a DEM =

0.2 ground returns per m²

• Spreadsheet developed to determine DEM capture specifications

Determining minimum LiDAR pulse density for an accurate DEM, under forested conditionsUser defined inputs Outputs

Crop age (years) 28 Predicted percent ground returns (%) 21.51

Crop stocking (stems/ha) 500 Pulse density required (points/m2) 0.9

Noncrop stocking (stems/ha) 0

Stand slope (degrees) 20

Optimal Point Density - CHM (capture over Douglas-fir forest)

Initial LiDAR capture:

Minimum pulse density for acquisition is 2-3

pulses/m2

Subsequent LiDAR capture:

Once an accurate DEM is available - key metrics

of interest could be predicted from a capture

specification of 0.2 pulses/m2!!

LiDAR and Forest Inventory -

Background

LiDAR does not measure recoverable volume or

replace existing methods.

We still need:

• Plots measured by trained

professionals

• Yield modelling software

• Tree and plot biometric functions

LiDAR and Forest Inventory -

Background

Aerial LiDAR provides auxiliary information that can be useful for

forest inventory

Fewer plots = $ saving

Productivity Surfaces = better resolution information

Estimates for AOI:

• stands

• felling coupes

LiDAR and Forest Inventory -

Background

A LiDAR based inventory

system must provide:

• Yield tables including log

product estimates

• Sampling error for AOIs

• Use current software and

models

Tairua

Kaingaroa

Eastern BOP

kNN Case Studies

Kaingaroa – Case Study

FFR funded project to investigate LiDAR

inventory methods

Kaingaroa – 4000ha trial area

213 plots ground plots installed

Yields and sampling error for 102 stands

Independent validation dataset

Validation suggests excellent

performance

TRV MTH

BA Sph

Key conclusions– kNN technique

• Provides a robust and practical solution for using LiDAR

data for forest inventory.

• Is suitable to replace some components of current forest

inventory practices.

• Can extrapolate a small number of ground plots to many

stands using existing software & biometric functions

• Provides accurate results and precision benefits at the

stand level

Individual Tree Identification

Individual Tree Identification

HarvestNav

Is an application that runs on a tablet computer

and displays and informs operators about the

surrounding terrain

HarvestNav – Field Trials

HarvestNav

• Operators comfortable with technology

• GPS (on the tablet) reception in cab seems

excellent

• Appears to be an effective way of

communicating harvest planning information to

operators

• Future advancements planned…

Satellite Imagery

Table 1: Selected satellite sensors and their characteristics. 1Prices are based on images available in the archive and are correct as at September 2011 2Red, green and blue; NIR - near infrared : Pan - Panchromatic

RapidEye & Enhanced Vegetation Index (EVI)

Detection of the Crop using

RapidEye & Enhanced Vegetation Index (EVI)

Detection of Harvest Area

Satellite Imagery for

Disease Detection

Spray plot locations coloured by mean needle drop %

2011-01-02 (no disease) 2011-09-02 (disease expressed)

Key Highlights

• LiDAR Cluster Group – assisted with the early

uptake of LiDAR technology and the format of

having all interested parties at the table was very

beneficial

• LiDAR & Blakely Pacific – Now feel

comfortable using this technology.

Key Highlights

Remote Sensing Achievements - NZ Forestry

• Confidence in LiDAR capture specifications

• Proven method for LiDAR based inventory

• We can count trees using LiDAR

• We have a tablet based on-board navigation system that utilises LiDAR derived DEMs

• Satellite imagery option that allows the calculation of an EVI