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

Transcription

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

2 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

3 History LiDAR Cluster Group NZ LiDAR Cluster Group established 2011 Capture specifications Data storage Capture collaborations Processing Products required

4 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

5 Blakely Pacific LiDAR Project 9,000ha of Douglas-fir forest In-sufficient inventory information Sites established between 1995 & 2003 Thinning operations looming!

6 Tree Height Model

7 Program results to date Provided immediate identification of high productive sites 18% of project now completed Improved operational efficiencies & cost savings

8 Additional Benefits Contour dataset Digital Elevation Model

9 Remote Sensing Achievements - NZ Forestry Digital Elevation Models LiDAR based Forest Inventory System Individual tree identification HarvestNav Application Rapid Eye/SatTools - EVI

10 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.

11 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 conditions User defined inputs Outputs Crop age (years) 28 Predicted percent ground returns (%) Crop stocking (stems/ha) 500 Pulse density required (points/m2) 0.9 Noncrop stocking (stems/ha) 0 Stand slope (degrees) 20

12 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!!

13 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

14 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

15 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

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17 knn Case Studies Tairua Eastern BOP Kaingaroa

18 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

19 Validation suggests excellent performance TRV MTH BA Sph

20 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

21 Individual Tree Identification

22 Individual Tree Identification

23 HarvestNav Is an application that runs on a tablet computer and displays and informs operators about the surrounding terrain

24 HarvestNav Field Trials

25 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

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

27 RapidEye & Enhanced Vegetation Index (EVI) Detection of the Crop using

28 RapidEye & Enhanced Vegetation Index (EVI) Detection of Harvest Area

29 Satellite Imagery for Disease Detection Spray plot locations coloured by mean needle drop % (no disease) (disease expressed)

30 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.

31 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