Application of remote sensing by the New Zealand forest ...

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Application of remote sensing by the New Zealand forest industry Aaron Gunn

Transcript of Application of remote sensing by the New Zealand forest ...

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