| A discussion recently arose on "what should be considered as the starting point for measurement of diameter breast height, that is at what point does the stump height / tree stem start". Seems simple doesn't it; "high side of the tree at ground level" Well this forest type had a range of buttressing, exposed roots, was mostly on flat ground with spot and line cultivation evident. We were intrigued in the lack of clarity here that sparked this debate, so to offer some clarification and to help guide standards, let's look over the guidelines available to us in this area.
Figure 1 – DBH Height measurement examples.
Firstly a look at the trees in question (Figure 1). Red mark-up shows correct / accepted technique: locating highest ground soil level immediately around tree base (after removing needles / leaves), see Figure 20 diagram D below. Yellow lines show the incorrect method of selecting the general ground surrounding ground level thus ignoring the spot mounding effect of solid soil being present higher on the tree than the surrounding ground level. A simple practical application of this guideline is "can you put a chainsaw through the stump at this point without clogging the chain with soil".
Guidance on the Assessment of Diameter Breast Height
Interpine developed a comprehensive guide for the placement of breast height for the Ministry for the Environment, LUCAS Carbon Measurement Planted Forest Measurement Manual (Herries et al, 2010), which has also been used in the Emissions Trading Scheme Forest Measurement Approach (ETS FMA) manuals published by the Ministry of Agriculture and Forestry in New Zealand (MAF, 2011), Figure 20 below. These are published documents and serve a basis for some clarification, specifically diagrams C,D.
Figure 20 – Standard Points of Diameter Breast Height Assessment (extracted from Herries et al, 2010)
Guide to Figure 20:
(A) 1.4m above ground level, 90 degrees to the tree axis. (B) leaning tree, 1.4m on the inside of the lean. (C) sloping ground, taken on the high side. (D) uneven ground, taken on the higher side. (E) large swelling, two diameters are taken at equal distances from breast height, record averaged diameter. Record the bottom height in DBH height column and the other actual measurement point as a comment in data file. (F) small swelling, single diameter taken if moving <+/-15cm from breast height (ensure the height of the diameter is recorded). (G) forked below breast height, two diameter are taken each being considered a separate tree. (H) fork at breast height, single diameter taken where practical below fork (ensure the height of the diameter is recorded). (I) butt flaring or buttressing at breast height, single diameter taken where practical above; (J), (K), (L) small bent and crooked stems showing breast height measured 1.4m in a straight line from the base of the tree. (M), (N) are down live trees with tree-form branches growing vertical from main bole: when a down live tree, touching the ground, has vertical (less than 45 degrees from vertical) tree-like branches coming off the main bole, first determine whether or not the pith of the main bole (averaged along the first log of the tree) is above (M) or below the duff layer (N). If the general pith line of the main bole is above the duff layer, use the same forking rules specified for a forked tree as shown in (M). If the general pith line of main tree bole is below the duff layer, ignore the main bole, and treat each tree-like branch as a separate tree; take DBH and length measurements from the ground, not necessarily from the top of the down bole. However, if the top of the main tree bole curves out of the ground towards a vertical angle, treat that portion of that top as an individual tree originating where the pith leaves the duff layer, as shown by (N).
Literature Review of Breast Height Measurement
A quick review of literature shows the ruling above to be consistent. Of interest is the use of root collar (RC) / point of germination (POG) or high side soil / ground level whichever is greater (Figure 2 below), which deals with trees growing on substrates other than soil (rocks, mounds, swamp species etc.), is useful and could be added to our general definition in the future, although is not often seem in New Zealand or Australia forest plantations.
Figure 2 – Root Crown / Point of Germination (POG) Assessment (BCFS 2010 and USFS 2010). Showing high side or root crown / POG whichever is higher.
References:
BCFS, 2010. Cruise Manual for Timber on Crown Lands of British Columbia. British Columbia Forest Service, Canada.
Brack C, 1999. Standard Point of Tree Bole for Measurement. Australian National University, Australia
Hamilton, 1996. Forest Mensuration. Forestry Commission, HMSO, United Kingdom.
Herries D; Paul T; Beets P; Chikono C; Thompson R; Searles N, 2010. LUCAS Planted Forest Data Collection Manual, Ministry for the Environment, New Zealand.
Herries D; Hill B; Crawley D, 2007. PlotSafe Overlapping Feature Cruising Forest Inventory Procedures, CNI Regional YTGEN User Group, Rotorua, New Zealand.
MAF, 2011. A Guide to the Field Measurement Approach for the Forestry in the Emissions Trading Scheme, Ministry of Agriculture and Forestry, New Zealand.
USFS, 2010. Forest Inventory and Analysis, National Core Field Guide. Northern Research Station Forest Service, US Department of Agriculture.
WOD, 2012. Specialty Expressions: Stump Height (http://www.websters-online-dictionary.org/definitions/stump+height), Websters Online Dictionary.
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| The use of recreational grade GPS (for example Garmin 60csx or Garmin 62S) is now standard practice for the location of forest inventory plots in New Zealand and Australia. We recently reviewed a few questions regarding the accuracy and precision of these GPS units, with many in the industry not understanding the measure of precision displayed to the user.
If the GPS displays an Accuracy of 10m, does this mean it is 10m from the actual / absolute location?
MOST DEFINITELY NOT !! Firstly it is a measure of precision and in no way indicates accuracy. GPS's display to the user an estimate of position error (EPE). An EPE is displayed on most recreational GPS devices, although there is no general specification on what this should display between manufacturers or in some cases models from the same supplier, so an EPE from a Garmin device cannot be directly compared to a Trimble or Magellan unit. So don't be tricked by the marketing material that one unit is more accurate than another just off a comparison of EPE. Also the EPE displayed on any type of GPS is not guaranteed to be the maximum position error.
So an EPE is a statistical level of confidence generated for each position. A wide range of factors can influence this including multipath, number of satellites, SNR, PDOP, satellites elevations and period of time GPS unit is switched on prior to recording and how long you stay in one location to record the position (let's not go into all these today).
Although Garmin's calculation of EPE is propriety, most regard Garmin GPS to use an EPE based of 50% circular error probable (CEP) (*1). This means that 50% of all measurements calculated are within a radius of 10m. On the other hand, 50 % of all measured positions are outside of this radius ! So there is an equal probability that the error is greater than 10m.
Commonly it seems wise to double the EPE presented on your Garmin device to give you a higher confidence of your position precision (*1,*2).
For those interested, 50% CEP can be calculated on the higher grade GPS units from additional information: EPE = HDOP * URA (1-sigma) * 0.73 gives a measure of the 50% confidence circle, i.e., 50% of your position fixes would fall within and 50% would fall outside (*5).
A good way to visualise this is to plot each individual calculated position (in this case I'm using a TerraSync connected to a recreational grade SiRFstarIII GPS in a Trimble Nomad (similar to the Garmin GPS Chip). As shown by the dimension arrow the location moved by some 30m in just 1 minute, yet the EPE shown in the top of the screen is calculated at 14m, and of course the GPS physically did not move and had been operating for more than 10mins at this location prior to recording. TerraSync EPE is based on 68% precision estimate (1sd) (*6).
Figure 1: Example of a Recreational Grade GPS plotting last 60 positions calculated (brown dots), showing displacement of some 30m, without physical movement of the GPS unit.
So remember your GPS's EPE readout is just a quantity used to characterize the performance of the GPS, derived from the statistical analysis of the measurements of position being calculated.
It is NOT an indication that the given position readout is within "EPE" distance of the absolute/actual position.
Definitions:
- HDOP - Horizontal Dilution of Precision,
- URA - User Range Accuracy is a quantity that is transmitted in the navigation message that is the predicted (not measured) statistical ranging accuracy. Since it is defined for SPS (Standard Positioning Service), it includes SA.
References
- Understanding GPS Error Measures (http://gpsinformation.net/main/errors.htm)
- How Accurate if a Garmin GPS (http://www.bluepeak.net/blog/2010/10/20/how-accurate-is-a-garmin-gps/)
- Accuracy Values by Garmin Recievers (http://www.kowoma.de/en/gps/accuracy.htm)
- PDOP vs EPE (http://forums.groundspeak.com/GC/index.php?showtopic=97584)
- GPS, What is EPE ?, US National Park Service (http://www.nps.gov/gis/gps/WhatisEPE.html)
- Trimble Office Help V4
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| History of Stem / Tree Taper Equation Development
Taper equations can be used to provide predictions of inside bark diameter at any point along the tree stem and stem volume based on widely recorded tree dimensions. Equations that can accurately predict tree diameter at any point of the stem based on total tree height and diameter at breast height have long been the focus of research (Bi 2000).
Beginning with the earliest taper equations by Hójer (1903) increasingly complex methodologies have been have been developed stimulated by improvements in computational capacity. Kozak (2004) reported on a history of taper function research at the University of British Columbia that dates back to 1956. The intensity and longevity of research into taper functions has led to an extensive array of different approaches and a considerable body of literature.
In New Zealand a compatible polynomial approach (Goulding and Murray 1976, Gordon 1983) is most commonly used. The compatible polynomial approach, along with many other taper functions, fails to account for differences in stem form between trees and so lacks the flexibility to describe changes in stem form resulting from site specific or silvicultural factors (Bi and Long 2001). A large degree of local bias over some portions of the stem may also exist despite a low global bias (Bi 2000); this has implications for log bucking algorithms which rely on accurate stem diameters. To negate these weaknesses a series of polynomial equations can be developed to reflect a wide range of geographical and stand conditions or a more flexible approach is required.
The Variable-Form Taper Equation
Kozac (1988) introduced an approach which uses a single continuous function as the base with an exponent that changes along the stem to describe the changing stem form.
Following on from this work Bi (2000) developed a variable-form taper model;
where the base function is constructed from a trigonometric volume-ratio equation following the geometry of a tree stem and the exponent includes variables that depict changes along the stem and differences in stem form associated with changes in tree size.
This function was found to be stable, flexible in its suitability for species and trees where stem form is variable, and an accurate predictor of stem taper. This model was fitted to 25 species of Australian native trees (Bi 2000) and later to Pinus radiata in New South Wales (Bi and Long 2001) where it was found to have greater prediction accuracy than nine individual site-specific taper functions currently in use. This superior performance is a result of the flexibility of functions of this type in depicting change not only along the stem but between trees of different sizes which is beyond the capacity of polynomial taper equations.
This approach was later extended to South Australia where the model was fitted to a large South Australian data set (See here for more details) further underlining its flexibility. With an adequate dataset this model could be fitted to New Zealand conditions to provide a solution with greater prediction accuracy capable of predicting stem form across the range of growing conditions and management types. This may be particularly relevant in the light of potentially changing management objectives and harvesting systems in the light of forest expansion, carbon forestry, and increased mechanisation.
Figure 1 - Derived stem profiles from the use of a Variable Form Taper Equation, (Source: Bi, H 2000 Figure 3)
The trigonometric variable-form taper function is publicly available and has been implemented in YTGEN with species specific coefficients available for Pinus radiata and several other species. If you have the latest release of YTGEN installed you will find these installed in sample methods file in this location:
C:\ProgramData\Silmetra\YTGen\samples\method_files\methods.ytm.
Interpine have used the function commercially in Australia and trialled its use for research purposes in New Zealand with promising results.
For more information on taper functions or any other aspect forest resource assessment please contact us.
References
Bi, H. 2000. Trigonometric Variable-Form Taper Equations for Australian Eucalypts. Forest Science 46 (3) 397-409
Bi, H. and Long, Y. 2001. Flexible taper equations for site specific management of Pinus radiata in New South Wales. Forest Ecology and Management 148 79-91
Goulding, C. J. and Murray, J. C. 1976. New Zealand Journal of Forestry Sciences 5 (3) 313-22
Gordon, A. 1983. Comparison of Compatible Polynomial Taper equations. New Zealand Journal of Forestry Science 13 (2) 146-55
Hojer, A. 1903. Growth of Scots pine and Norway spruce. Stockholm, Bilaga till. Loven, F. A. om vara barrskorar.
Kozak, A. 1988. A variable-exponent taper equation. Can. J. For. Res. 18 1363-1368
Kozak,A 2004. My Last Words on Taper Functions. The Forestry Chronicle 80 (4) |
| Interpine is working closely with Halgof Sweden in 2012 to review the cost effectiveness of deployment of a range of Haglof forest mensuration equipment in NZ and Australia. Based in Northern Sweden, Haglof has been developing and building professional measurement equipment for the forest industry world-wide for many years.
Many foresters around the world have become very familiar with the Haglof Vertex, which has become the industry standard worldwide for measuring accurate heights, distance and horizontal distance in the field. With its ultra-sonic measuring system, the Vertex is perfect to use in the forest to provide accurate readings in even the thickest undergrowth.
Interpine on behalf of the NZ and Australian forest industry will be conducting a range of field trials and work studies around forest inventory measurement techniques, looking at optimisations in both productivity and accuracy using some of the newest technology available from Haglof Sweden. This will also include some integration with our software products like PlotSafe.
If you would like to keep in touch with developments feel free to contact us. |
| A freely available publication and data collection template is now available to help forest owners and forest inventory providers with electronically capturing carbon forest inventory data for the MAF Emission Trading Scheme (ETS) Forest Measurement Approach (FMA).
The manual is a free download from the www.interpine.co.nz website, with only free online registration required to gain access.
This provides the New Zealand forest industry with the first way to capture all the ETS FMA data electronically in one place. Data can be captured in PlotSafe forest inventory software in the field using Windows mobile field computers, or in the office using the Windows desktop version of PlotSafe. PlotSafe being the most widely used dedicated forest inventory software in New Zealand and Australia, current licensed users can use this freely available template "FMA11" to collect ETS FMA data immediately without any additional costs.
While the guide outlines the collection of ETS FMA data within PlotSafe it also covers practical tips for implementation and measurement of survey plots that compliments the information provided in the MAF ETS FMA Guide. Topics covered include:
FMA11 PlotSafe Template for ETS FMA Data Collection
- Data entry structure.
- Plot, subplot and subsample naming.
- Additional data fields available for the full complement of data collection from shrubs, small trees, entry of all stand record datasets, entry of PSP standard fields or stem cruising using overlapping features.
- Known limitations of FMA11
- Conversion of data to MAF XML standards
Practical Tips for Implementation and Collection of ETS FMA Data
- Background learning
- Locating plots, including topics on relocating plots, locating plot centres with vertex equipment, witness tree measurements.
- Measurement impact and tree marking
- Plot photos
- Stand history observations
- Dead and wind-thrown/toppled tree measurement
- Use of sub-plot and sub-samples
- Definition of multiple stems at DBH height
Quality Assurance Audit System
- Plot grading and auditing system built around industry standards
Contracted Inventory Specifications Template
- A useful question and specification template to ensure ETS FMA inventory is deployed consistently and instructions are well understood by forest owners, inventory contractors and survey field teams.
If you already have a login for the website you can follow this link to download the guide and PlotSafe templates.
Just complete our Feedback form for a free login to the website or to request further information about this guide and the PlotSafe templates.
INTENT: This field manual has been produced by Interpine Forestry Ltd (Interpine) as a reference guide for the use of PlotSafe forest inventory software for the collection of New Zealand Emissions Trading Scheme Field Measurement Approach Inventory. The data collection procedures are based on the Ministry of Agriculture and Forestry Forest Measurement Approach (FMA) standards and guidelines referenced below. This guide is intended for public release to current users of PlotSafe software, and assumes a level of familiarity with PlotSafe and its terminology. For more information on PlotSafe see the provided help documentation by pressing <F1> from within the software. |
| The New Zealand ForestTech 2011 conference was held recently in Rotorua. Interpine's David Herries presented to the delegates a review of a standardised data collection system for electronic collection, storage and export of carbon forest inventory measurement data collected for the ETS FMA (emissions trading scheme forest measurement approach) into formats compliant with the MAF XML information standards.
An overview of the Forest Measurement Approach was looked at, allowing delegates to realise the simplicity of the FMA carbon forest inventory, but still understand the importance of correct work scope definition to ensure cost-effective implementation of their FMA measurement programmes.
Some practical advice was also covered around pre-deployment specifications for field staff, field plot placement considerations which should be completed before crews travel into the field, stand record observations and photographs, quality assurance considerations and expected productivity of field crews.
The main focus of the presentation was to overview the release of a freely available template for the data collection of FMA data in the field using PlotSafe handheld data collection software. This allows collection of all the required data by using PlotSafe on a Windows Mobile device in the field or in the office using a Windows computer. Data is then downloaded to Interpine and as a service can easily be converted into the MAF compliant data formats. Note that some participants may want to also provide a bulk upload a file of all the stand record information, rather than typing this directly into the PlotSafe field template.
For more information download the presentation below, or contact us.
Presentation:  The National Forest Inventory Standards for Carbon Measurement.pdf
Figure 1 – showing the template structure and layout for collecting in PlotSafe
Figure 2 – Data handling and processing for storage, secondary validation and export to FMA compliant data formats.
Figure 2 – Practical tips covered around plot movement around CAA boundaries. |
| ForestTech 2011 has held in late 2011 in both Australia and New Zealand. Interpine's Jonathan Dash and Hamish Marshall presented a discussion on Interpine's involvement in a range of trial work relating to LiDAR analysis. A quick overview of use and role of LiDAR in forest inventory systems was covered. Then a review of some of the practical decisions / advice we ourselves have worked through while planning and interrogating LiDAR datasets, including sample design, installing ground control plots, allocation of plot size and dealing with edge plots. Finally a brief summary example was given on its use in improving accuracy or reducing cost of traditional rule based sample design inventory carried out in the eastern BOP of New Zealand.
Acknowledgements are given to Future Forest Research, PF Olsen Ltd, Ministry for the Environment, Susana Gonzalex-Aracil (Interpine Post-Grad Research Intern), and Outline Imagery for this assistance during some of the LiDAR projects over the last few years, and also in the ability to share some of the results with the people attending the ForestTech 2011 conference.
If you would like more information on any of this work, please feel free to review the presentation below or contact us.
Conference Presentation:The effectiveness of LiDAR based inventory systems - v3.pdf
Figure 1 – Deriving Relationships: Intergrating LiDAR and Ground Plots
Figure 2 – New ways to look at forest variables in a continuous manner rather than the normal discrete strata variables.
Figure 3 – Sample design considerations, systematic random grid and model based hybrid.
Figure 4 – Edge plot modelling for LiDAR populations |
| Existing popular methods such as the "plot mirage method" (Schmid-Haas, 1969) for eliminating bias due to boundary overlap suffer some disadvantages in practical use;
having to establish a duplicate plot outside the forest edge which might not always be possible (water body, cliff, or private land boundaries), needing to assume the forest edge as straight, time consuming setup of duplicate plot centres and boundaries, corners, or coincidence with more than one forest boundary inside the plot may mean placing more than one mirage plot centre resulting in some trees needing to be counted up to four times, while others are only counted once.
These problems often result in erroneous use in practice with common things heard from practitioners in the field.
- "just too hard to train correctly – just pull the plot into the stand"
- "if the plot falls on an edge just ignore it (remove from sample)"
- "don't even try corners mirages, just pull it back on one side of the edge"
- "just do full mirages (half circle everything counted twice), save's time having to set up second mirage"
- "move the plot till you find a straight forest edge to do a mirage"
Interpine for several years now have been using for some types of forest inventories a method for edge correction called the "walk through method" (introduced in collaboration with Brian Rawley). This is based on the inclusion areas for single trees, rather than a mirage of the entire plot. The method is very easy to apply in the field and does not require linear forest edges, so is commonly deployed where inventories are likely to experience irregular boundaries or where there are small mapped gaps.
The walk through method is really a simplification of the boundary reflection or mirage method, motivated by a simple insight by Ducey et al 2004. In the boundary reflection method, the boundary is reflected through the tree (or object), and the position of the reflected boundary is assessed relative to the plot centre. This is geometrically equivalent to reflecting the plot centre through the tree (or object) itself and comparing its position with the boundary. In practice this is much simpler to implement, because it requires reflecting only a single tree or object, not an entire section of boundary across the plot.
Walkthrough Method Implementation
- Establish the plot centre, measure and record all trees that fall inside the plot and are also inside the boundary of the stand that is being measured.
- Then for each tree in the plot that is closer to the boundary than it is to the centre of the plot, record that tree twice.
To decide whether a tree is closer to the boundary than to the plot centre measure the distance (x metres) from the plot centre to the tree. Continue along the same bearing for x metres past the tree. If after walking x metres past the tree you are outside the stand that is being measured then the tree is counted twice. If you are inside the stand then it is counted only once.
Example 1 Straight line boundary through plot
Example 2 Straight line boundary away from plot
Example 3 Complex boundary
It does not matter how many times you cross the boundary. It is whether the end of the line is inside the stand that you are measuring or outside it that counts. It is only necessary to walk the full line if it is not obvious whether the end falls inside the stand being measured or not.
IMPORTANT NOTE: You MUST consider any boundary that is less than one plot radius from the edge of the plot (Example 2 and Example 3 for other side of mapped gap)). Keep an eye on the map for boundaries. This is not like a mirage plot where you only consider boundaries that pass through the plot.
Further Reading and References: A Walkthrough Solution to the Boundary Overlap Problem (Ducey et al. 2004), Diagrams have been kindly provided by Brian Rawley.
A Walkthrough Solution to the Forest Boundary Problem.pdf |
| This is an introductory course learning how to manipulate and process LiDAR datasets, with a specific focus on forestry derived outputs, such as terrain and vegetation surfaces, vegetation related metrics, down to extracting plot and tree level data.
The course covers the topics with hands-on labs and presentations. Held at computer facility at the Waiariki Institute of Technology in Rotorua, participates work with forestry specific LiDAR derived datasets using the software such as FurgoViewer and FUSION. FUSION is one of the forest industry's leading LiDAR tools for analysis, and is available free from USDA Forest Service. FUSION allows 3D terrain and canopy surface models and LIDAR data to be fused with more traditional 2D imagery (e.g., orthophotographs, topographic maps, satellite imagery, GIS shapefiles). The course will focus on getting started with LiDAR data, visualising data, creating surfaces models of terrain or vegetation, single tree or plot extraction, and calculation of LiDAR forest metrics, there will be something to learn for all those interested in taking the next step in utilising their own LiDAR datasets.
There is also a brief opportunity to look at the tools available in GRASS GIS and ESRI ArcGIS Spatial Analyst for handling LiDAR datasets.
Course Outline:
Understanding LiDAR Use, Collection and Application
- Overview of LiDAR
- LiDAR Data Collection Parameters
- Considerations for LiDAR Survey Collection
- Review Derived Products
- Direct Application of LiDAR in Forestry
Hands-on LiDAR Analysis Lab
Getting started with LiDAR data
Getting started with FUSION
- Understanding LiDAR data viewer
- File types
- Digital terrain models / bare earth filters
- Simple Measurements in FUSION
Create Surfaces with FUSION
- Terrain / vegetation surfaces
- Extracting Plot Subsets with FUSION
- Calculate LiDAR Forest Vegetation Related Metrics
Overview of Additional LiDAR Analysis Tools
- GRASS GIS
- ESRI ArcGIS Spatial Analyst
Date: Tuesday 29th November 2011
Location: Waiariki Institute of Technology, O Block (Forestry Building)
Time: 9-5pm
Course Costs: $350 per person
Enrolment: hamish.marshall@interpine.co.nz 07 345 7573 ext 704
Interpine - Forestry Orientated LiDAR Course - 29 Nov 2011.pdf |
| Improving log inventory is a key area where the New Zealand forestry industry could significant improve its supply chain performance. Although the process of counting logs seems relatively simple; in reality it is a difficult and labour intensive job. This is particularly significant to the New Zealand log export industry which is required to count and barcode every log (excluding pulp) that is exported. The fluctuating nature of export markets means that automated methods of counting logs hold significant potential.
Hamish Marshall recently presented at the SilviLaser 2011 conference in Tasmania, looking at the accuracy of log counts for logs in pile/stacks using 3-dimensional (3D) point cloud data obtained from a ground based LiDAR scanner. In the past there have been a number of attempts to develop an automatic log counting system, the majority of these have used 2-dimensional photographic images. It was hypothesised that using 3D point data would overcome some of the problems that these approaches have encountered in the past. The validation study carried out on the algorithm showed that logs can be accurately counted and log diameters can be measured. Further work would be required to develop the algorithm into a commercial product and to determine the most cost effective hardware required to collect the 3-dimensional data required by the algorithm.
SilviLaser 2011 brought together research scientists and practitioners from around the world to share their experience in the development and application of LiDAR for forest assessment and inventory and strengthen and develop new linkages between researchers, data providers and product end-users.
This project was conducted through Interpine's Research and Development division working alongside Scion Research and Future Forest Research.
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