Effect of forest harvesting on soil phys

Project No. PN99.805

© 2003 Forest & Wood Products Research & Development Corporation All rights reserved.

Publication: Effect of forest harvesting on soil physical properties: Developing and evaluating meaningful soil indicators of sustainable forest management in southeastern Australia

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Project no: PN99.805

Researchers:

S.T. Lacey, M.A. Rab., R.J. McCormack

CSIRO Forestry and Forest Products

PO Box E4008, Kingston ACT 2604 Tel: 02 6281 8312 Fax: 02 6281 8312

Forest and Wood Products Research and Development Corporation

PO Box 69, World Trade Centre, Victoria 8005 Phone: 03 9614 7544 Fax: 03 9614 6822 Email: info@fwprdc.org.au Web: www.fwprdc.org.au

Effect of forest harvesting on soil physical properties:

Developing and evaluating meaningful soil indicators of sustainable forest management

in southeastern Australia

Prepared for the

Forest & Wood Products Research & Development Corporation

by

S.T. Lacey M.A. Rab

R.J. McCormack

The FWPRDC is jointly funded by the Australian forest and wood products industry

and the Australian Government.

Preface

The Australian Government Wood and Paper Industry Strategy (1995) and forest industry provided funding for research projects on assessing the value and applicability in Australian forests of particular indicators from the Montreal Process. The Forest and Wood Products Research and Development Corporation managed these projects and the key outputs were reports to the FWPRDC from 1999-2003, when all projects were concluded. This report, PN99.805, is part of a series, which includes those from: PN97.104, PN97.601 & PN99.801, PN98.801, PN98.803, PN98.806, PN99.802, PN99.803, PN99.804, PN99.805, PN99.807, PN99.807 Part A, PN99.807 Part B, PN99.808, PN99.808 Part A, PN99.808 Part B, PN99.808 Part C, PN99.808 Part D, PN99.808 Part E1, PN99.808 Part E2, PN99.808 Part E3, PN99.808 Part E4, PN99.808 Part E5, PN99.809, PN99.810, PN99.811, PN99.812, PN99.814, PN99.814A.

The key findings of these projects were summarised in FWPRDC Project PN03.815 Synthesis and Integration of the Outputs from Research and Development Projects on Montreal Indicators Supported under the Wood and Paper Industry Strategy (WAPIS) and Industry Funding. This report should be considered in conjunction with the report of PN03.815.

SUMMARY

General This study was conducted to collect and analyse strategic data relating to soil physical property

change from forest harvesting in southeastern Australia. A major aim was to obtain and analyse data that could provide links between degree of soil change and degree of individual and stand- level growth impacts. Several approaches to quantifying disturbance and soil change were implemented on common sites to evaluate alternative approaches. These baseline studies also included the innovative technique of mapping logging machine movements by GPS throughout the harvesting operation to gain a better understanding of the links between traffic intensity, disturbance, and soil property changes.

The studies were undertaken as two major groups: a set of three investigations conducted in dry sclerophyll Eucalyptus sieberi forest in NSW and a set of three studies undertaken in moist E. regnans forest in Victoria..

The overall aims of the study were as follows: To assess the capability and suitability of the interim soil physical indicator (4.1e) to characterise

the forest soil environment and the changes created by forest harvesting; Establish relationships between changes in soil physical properties post-harvest and subsequent

forest growth at long-term and retrospective research sites; Monitor temporal changes in soil physical properties post-harvest at long-term sites; Evaluate different methods of post-harvest indicator assessment to devise cost-effective procedures

for monitoring soil physical indicators under operational forest management; and Develop guidelines for selecting and characterising a network of reference sites suitable for long-

term monitoring of soil physical and, potentially, soil carbon indicators.

NSW Studies

There were two major studies: One was a logging study in which we characterised the effects of logging on soil physical properties. In the other study, we measured tree regeneration and soil strength in relation to prior soil disturbance in a coupe that had been logged some nine years beforehand. For the purposes of this summary, we will refer to these as the logging study and the regeneration study. Both studies sites were located in Yambulla State Forest where the soils are infertile with a coarse sandy pale texture supporting dry sclerophyll forest.

For the logging study, soils were sampled for bulk density and aeration porosity on a broad grid prior to logging. During logging, forwarding machines were tracked by automatically recording GPS equipment to develop a map of traffic intensity. After logging, two alternative survey methods were used to assess the extent of soil disturbance in a series of disturbance classes corresponding to operational category (snig tracks, access tracks, etc) or disturbance severity. Soils were sampled for bulk density, aeration porosity ands soil strength following one of the sampling patterns.

In the regeneration study, vegetation plots were established in a coupe that had been logged in June 1990, had aerial photographs taken, and those photographs interpreted for soil disturbance classes. Plots were of two types: rectangular plots subtending track type disturbance features plus adjacent forest, and circular plots that were located in areas of broader, generalised disturbance. Some 104 plots across eight disturbance class were assessed for properties relating to eucalypt regrowth (height, DBHOB, total number of trees< 10 cm DBH) and properties of potentially interacting vegetation. Soil strength was also measured at each plot and a range of site characteristics, mainly terrain attributes, determined for each plot.

The results of the logging studies were as follows:

About 20% of the coupe was occupied by snig tracks. Heavily disturbed classes such as major snig tracks, access tracks, log landings and additional sub-soil disturbed areas accounted for between 5 to 10% of the coupe.

The survey method based on a grid with 50 m randomly oriented transects (grid point-intercept method) and method based on regularly spaced transects across the coupe gave produced comparable results with a few differences between individual disturbance classes.

Soil physical property change was relatively low. Only the most heavily disturbed classes showed evidence of significant increase in bulk density or soil strength or reduction in aeration porosity: access tracks, log landings, and major snig tracks.

The matching of trafficking intensity (number of passes) with disturbance classes and soil physical properties did not reveal any clear relationships. Major snig tracks were partly associated with high numbers of passes, but a significant proportion of these tracks also had relatively few passes. Soil physical property change was less clearly related to the number of passes, and this probably reflected the low overall level of soil physical property change. Nevertheless, the machine tracking approach shows promise and is worth pursuing for further refinement of the technology.

When soil sample data were evaluated with reference to the interim indicator 4.1e, we found that only one sample location exceeded the criterion of a more than 20% increase in bulk density in the 0-300 mm depth. When examined for 0-100 mm depth, this increased to 21, or 12%, of samples. Very few samples fell below the 10% critical value for aeration porosity that is adopted in the interim indicator. We also evaluated soil strength measurements in the context of an indicator (although there is no proposed criterion) and found a much higher proportion exceeding a 20% increase. If examined in the same manner as aeration porosity, i.e. with reference to an absolute critical value (we adopted 2.5 MPa), only 10 samples exceeded the value.

The results of the regeneration study were as follows. The access road, landing snig tracks (i.e., snig tracks closest to landings), and major snig tracks had

significantly higher soil strength to around 150 mm than the least disturbed classes. Statistically significant differences in tree volume between disturbance classes were few as data

were variable. Nevertheless, the range in means was large, and the evidence for reduced tree growth on access tracks and, to a lesser extent, landing snig tracks and major snig tracks was strong. There was a tendency for growth to be less on the track disturbance classes than the general disturbance area classes. Variability (coefficient of variation) was also greater for track classes.

There was some evidence, though not compelling, for an edge effect for track disturbance classes. (Edge effect is a documented phenomenon whereby trees adjacent tracks grow larger than those in adjacent areas that would appear to have similar levels of soil disturbance.)

For disturbance class level data, there was a clear relationship between tree volume per hectare and soil strength. At plot level the relationship was not apparent.

A significant relationship between tree growth and aspect was found, but not for any other of the many explanatory variables that we measured or extracted from digital elevation model datasets. The aspect effect was relatively evenly distributed through the various disturbance classes and was of no assistance in elucidating differences between disturbance classes.

Victorian Studies

There were three separate studies undertaken. All dealt with logging of wet sclerophyll forests dominated by Eucalyptus regnans in the Victorian Central Highlands. Each study is presented separately in scientific paper format in Part II of this report. The first study analysed factors influencing the extent of soil disturbance on 20 clearfelled coupes. The second examined soil physical properties and regrowth response to disturbance in a coupe that had been logged ten years previously and assessed for disturbance and regeneration since the post logging stage. The third study was a detailed comparison of alternative survey methods for recording disturbance, taking soil samples, and assessing change according to the interim indicator 4.1e.

Each of the studies in Part II is prefaced by a summary, so the reader is asked to examine these rather than repeat them here. A very brief account of the major points of each study are as follows.

Study 1: The extent of the operational categories across the logged area appeared to be independent of the site and operational factors recorded, but there was a strong relationship between the volume of timber extracted per unit area and the degree and extent of soil profile disturbance.

Study 2: Primary snig tracks and subsoil disturbed areas still had significantly higher bulk density and lower organic matter and aeration porosity than undisturbed areas. Tree volumes were lower on primary snig tracks, and, though with less compelling evidence, on secondary snig tracks and minor and moderately disturbed areas.

Study 3: Some differences were found between the three transect methods applied. However, estimates of the various disturbance class were relatively insensitive to sample size indicating that less intensive sampling may be adequate.

Recommendations On the basis of the combined results, our recommendations for an indicator that can be cost

effectively applied on an operational basis to ground-based native forest logging is as follows. The framework for monitoring for indicator 4.1e for ground-based native forest logging should be

based on simple mapping of disturbance categories with interpretation underpinned by scientifically established relationships between disturbance class, soil type, and logging conditions (soil moisture). Note that at present, there is insufficient data to define the effect of moisture status at the time of logging to incorporate it in the framework, but the potential exists if the relationship is better defined in future.

Integration of high resolution hand-held GPS equipment with hand-held computers supporting GIS functionality provides a highly cost-effective platform for efficient data acquisition. It may be necessary to perform a supplementary transect-quadrat survey with three or four transects with the purpose of identifying S2 and S3 level disturbance not connected with the track network.

Data should be reported as “the area and percent of the net logged area occupied by detrimental disturbance”. Detrimental disturbance would be defined as follows: For operations on high-fertility forest soils, all areas of unrehabilitated log landings, log truck access tracks, primary snig tracks, secondary snig tracks and additional areas of S3 disturbance. For operations on low-fertility forest soils, all areas of unrehabilitated log landings, log truck access tracks, primary snig tracks, and any additional areas of S3 level disturbance. For sites of intermediate or indeterminate fertility status the high-fertility criterion should be applied until a substantive basis to alter it is obtained.

For sustainability reporting purposes, data would be reported in aggregated format (as detrimental disturbance). However, data should be stored as collected and, wherever possible, in GIS compatible format. This will enable re-classification of detrimental disturbance as future research results and refinements of the relationships dictate. It will also permit the spatial data to be directly integrated with forest management information systems.

It is suggested that a target of 20% of the coupes in a forest management area should be randomly selected and assessed in the manner outlined. This is a preliminary recommendation, but in general, the figure should be adjusted up or down as required consistent with a reasonable imposition on forest management labour costs. (However it should not fall below 10% whilst 100% would be ideal, especially for the management utility of the survey data.)

With respect to messages for forest managers, the most important finding from these studies was that heavy soil disturbance appears to be reducing, or at least potentially reducing, regeneration vigour and, ultimately, timber volumes, in the Victorian Central Highlands. This requires further validation, but evidence would suggest that managers should take this threat seriously and begin to seek methods for reducing disturbance, such as pre-planning snig track locations.

If the framework for measuring indicator 4.1e that we recommend is adopted, future research would need to continue to define the relationship between trafficking patterns and soil disturbance If the framework for measuring indicator 4.1e that we recommend is adopted, future research would need to continue to define the relationship between trafficking patterns and soil disturbance

GENERAL INTRODUCTION

Native forest harvesting, by its nature, requires heavy machinery to transport logs from stump to landing and to load trucks. Sometimes these machines are adapted earthmoving machines such as bulldozers and excavators, whilst purpose-built, rubber-tyred skidders are also employed widely for their higher work rate. In all cases, the machines are large and impose high ground pressures that cause soil disturbance and compaction. In addition, sloping terrain often results in a significant proportion of the extraction network being made up of formed tracks that involve major topsoil disturbance or displacement. Forest managers know that this heavy disturbance has the potential to degrade soil quality, chiefly relating to soil physical properties, but also to chemical fertility through topsoil displacement (Lacey, 1993).

Australian scientists have now conducted a number of studies into this aspect of harvesting with results clearly demonstrating the decline in soil quality (e.g., Jakobsen, 1983; Incerti et al., 1987; Williamson, 1990; Rab, 1994, 1996, Lacey et al., 1994). The proportion of the harvested area affected, however, is subject to variation, especially between major logging systems (Lacey, 1993). Considerable doubt also persists over the degree and extent of soil change needed to cause a change to ecosystem function or commercial productivity. Typically, a rather small proportion of a harvested unit will display evidence of large, and presumably deleterious, change. This area can range from 5 to 15% of the area. On the other hand, a significantly larger area can show evidence of moderate, but statistically significant change; change that may or may not affect productivity. This portion of the harvested area can be as much as 50 to 70% (Rab, 1996) and would clearly be of concern if it were sufficient to slow individual tree growth. However, measurements of the effects of soil physical change on the regenerating stand from Australian native forests are scarce and limited in scope (e.g., Jakobsen, 1983; King et al., 1993).

The challenge to develop indicators of “the area and percent of forested land with significant compaction or change in soil physical properties” (see preface) clearly lies with the biological effects of these changes and their spatial and temporal patterns. How much change is required to cause a measurable reduction in individual tree growth (i.e. what is significant)? What percentage area needs to be so affected to constitute a stand-level reduction (the basis of an indicator)? There is unlikely to be one answer to each of these questions because factors such as inherent soil fertility (itself a complex of properties), climate, and forest type are all likely to influence critical values. It is essential, therefore, to obtain data oriented towards answering these questions replicated across contrasting environments.

In addition to simply knowing what level of changes are “significant”, it would also be useful to gain a better understanding of the relationship between machine operation and the resultant changes in soil properties. Such relationships could provide innovative strategies to minimise impacts, document indicators of change, or both.

This study was conducted to collect and analyse strategic data relating to soil property change from forest harvesting in southeastern Australia. A major aim was to obtain and analyse data that could provide some links between degree of soil change and degree of individual and stand-level growth impacts. However, we also set out to conduct a detailed study of the soil disturbance process during logging to elucidate machine-soil relationships and provide sites with well-characterised baseline conditions for long-term monitoring. Several approaches to quantifying disturbance and soil change were implemented on common sites to provide indications of the more accurate and/or easily applied approaches amongst varying methodologies. These baseline studies also included the innovative technique of mapping logging machine movements by GPS throughout the harvesting operation. This machine tracking was done to gain a better understanding of the links between traffic intensity, disturbance, and soil property changes.

The studies fall into two major groups: a set of three investigations conducted in dry sclerophyll Eucalyptus sieberi forest in NSW and a set of three studies undertaken in moist E. regnans forest in Victoria. Consequently, this report is divided into two major sections corresponding with the respective groups of studies. The Victorian studies are presented as three stand-alone papers. The The studies fall into two major groups: a set of three investigations conducted in dry sclerophyll Eucalyptus sieberi forest in NSW and a set of three studies undertaken in moist E. regnans forest in Victoria. Consequently, this report is divided into two major sections corresponding with the respective groups of studies. The Victorian studies are presented as three stand-alone papers. The

The overall aims of the study were as follows: To assess the capability and suitability of the interim soil physical indicator (4.1e) to characterise

the forest soil environment and the changes created by forest harvesting; Establish relationships between changes in soil physical properties post-harvest and subsequent

forest growth at long-term and retrospective research sites; Monitor temporal changes in soil physical properties post-harvest at long-term sites; Evaluate different methods of post-harvest indicator assessment to devise cost-effective procedures

for monitoring soil physical indicators under operational forest management; and Develop guidelines for selecting and characterising a network of reference sites suitable for long-

term monitoring of soil physical and, potentially, soil carbon indicators.

PART I EFFECT OF FOREST HARVESTING ON SOIL PHYSICAL PROPERTIES AND REGENERATION IN SOUTH EASTERN NEW SOUTH WALES

by Stephen Lacey, Jagrutee Parekh

State Forests of NSW, Research and Development Division, PO Box 100, Beecroft,

NSW, 2119

and Bob McCormack

CSIRO, F orestry and F orest Products, PO Box E 4008, Kingston, ACT 2604

1.0 Introduction

In New South Wales, most native forest logging falls into one of two types. The most widespread type is selective logging of trees suitable for use as sawlogs and poles. The other is called integrated harvesting, where not only sawlogs and poles are removed, but also trees that are not suited to sawlogs or poles, but which can be chipped for use in paper production. Integrated logging is concentrated in, though not totally restricted to, far southeastern NSW where pulp timber is directed to the Daishowa chip mill and woodchip export facility, which is located south of Eden. Sawlogs have been processed in Bombala since the closure of the Eden sawmill in 1998.

Of the two types of logging, the southern integrated logging operations are by far the more intensive and cause a considerably greater amount of soil disturbance within the logged area. Early environmental concerns about the effects of this higher logging intensity led to a number of studies on ecological and hydrological effects. Logging methods have changed continually over time in response to research or through the adoption of precautionary principles.

In the late 1980s, continuing need to explore the effects of integrated logging led to research on disturbance, soil physical properties and erosion (Lacey et al. 1994). This research, covering several soil types, found that logging disturbance caused changes to soil physical properties through compaction and displacement. It further showed that these changes were most pronounced and potentially detrimental to subsequent plant growth on temporary internal roads (access tracks), log landings (or log dumps) and, to a lesser extent, major snig tracks. Evidence of change was apparent on less severely disturbed snig tracks or disturbed areas, but the likelihood of negative effects in the subsequent rotation was not considered high. It was concluded that the key to whether the effects of soil physical change lowered site productivity in the long term was whether detrimental changes over a significant area were cumulative from rotation to rotation (Lacey et al. 1994). Without a better understanding of the effects of individual soil physical property values on tree growth, the impacts of disturbance could not be confidently predicted. The management recommendation from this research, which is still applicable, is that provided areas such as access tracks, log dumps and major snig tracks are re-used in successive rotations, then the chance of cumulative damage could

be minimised. With the launch of the Montreal Process for Criteria and Indicators for Sustainable Forest

Management, there was a need to re-visit the question of sustainability of operations with respect to soil disturbance. Criterion 4.1e relates to the “area and percent of forest land with significant compaction or changes in soil physical properties resulting from human activities.” These simple words raise numerous scientific questions: Which soil physical properties should we examine? What is significant change? What cost-efficient measurement methods could we adopt that would allow us to measure and report such an indicator with an acceptable level of precision? Indeed, what is an acceptable level of precision?

Questions such as these had occupied the minds of forest soil scientists in the USA since more than

20 years ago. The US National Forest Management Act, 1976 required that logging "will not produce substantial and permanent impairment of the productivity of the land." (This included soil physical properties.) Faced with the same questions and uncertainties raised by the more recent sustainability concept, forest soil scientists suggested a criterion that no logging operation should cause more than a 20% increase in bulk density over more than 20% of the logged area. This was adopted and a statistically based method of quantifying the changes was proposed (Howes et al., 1983; Hazard and Geist, 1984) and the criterion was widely applied in the United States.

As a first response to Criterion 4.1e, it was proposed that an interim indicator be adopted and tested: the area and percent of soil with a more than 20% increase in bulk density to a depth of 30 cm

(MIG, 1998). The research presented within this report was conceived to evaluate this proposed indicator and, if necessary, propose alternatives.

For the NSW situation, we felt that because of the differences in logging intensity, and proportions of area affected, that the highest priority was to examine integrated logging. This aim was aided by the fact that there was pre-existing research (Lacey et al., 1994). Stemming from this earlier research, some aerial photographs existed that had been interpreted for soil disturbance classes (although the data were never published other than in an environmental impact statement). These photographs offered a chance to examine the links between disturbance, soil physical properties and tree growth in 8-year-old regeneration. We also saw a need to make detailed measurements of a new logging operation to apply the indicator, trial alternative survey methods of gathering the necessary data, and to apply new machine-tracking technology. The machine tracking, using low- maintenance GPS equipment, was designed to provide some understanding of the link between the logging operation itself and soil disturbance and physical property changes. The potential for automation with this technology also presented the possibility of an extremely efficient method of gathering data for reporting an appropriately phrased indicator.

So with this background, the project was conceived with the following specific aims: To quantify soil disturbance and associated soil physical property change in dry sclerophyll forest

on adamellite-derived soil; To carefully characterise logging traffic, by the use of skidder-mounted GPS, and relate it to soil

disturbance and change; To apply alternative measurement techniques in characterising disturbance and apply the interim

indicator to gain insights into the identification of appropriate, cost effective approaches to reporting the proposed indicator;

To quantify tree growth response to various classes of disturbance in regenerating dry sclerophyll forest on adamellite-derived soil.

2.0 Methods

2.1 General Overview

The study involved two major components, each centered on a different logging coupe. One component was a logging study in which we characterised the effects of logging on soil physical properties. We first measured pre-harvest soil properties, then recorded all harvesting machine movements during logging, and finally, at the completion of logging, re-characterised the resulting disturbance and soil property changes. The coupe examined was Coupe 1 of Compartment 551, Yambulla State Forest (Figure 1). In the other component of the study, we measured tree regeneration and soil strength in relation to prior soil disturbance in a coupe that had been logged some nine years before measurements were made. The coupe was formerly Coupe 5 of Compartment 394, Yambulla State Forest (Figure 1).

2.2 Study Sites

Both coupes were located within Yambulla State Forest in southeastern New South Wales. Figure 1 shows the general and more precise location of the compartments. The compartments were similar in terms of soil and vegetation, but differed in topography. Compartment 394 had only small areas Both coupes were located within Yambulla State Forest in southeastern New South Wales. Figure 1 shows the general and more precise location of the compartments. The compartments were similar in terms of soil and vegetation, but differed in topography. Compartment 394 had only small areas

The soil is classified as a coarse sandy yellow podzolic using the Great Soil Group classification (Stace et al. 1968). Under the new Australian Soil Classification the soil can represent a number of soil orders, mainly Tenosols and Kandosols, but also Kurosols, Podosols, and Hydrosols (Isbell, 1996). This suggests a highly variable soil landscape, but it is mainly the B horizon which varies in depth, texture, and pedological organisation. It is notable that the soil is also quite uniform in terms of the ubiquitous dominance of the coarse sand fraction (Ryan, 1993). The soil is derived from quartz-rich Devonian adamellite, which exists as a large pluton underlying the area known as the Wallagaraugh adamellite. The soil is characterised by its high content of coarse sand and small, gravel-sized quartz grains. Slope position has a strong influence on soil genesis and depth ranges from zero at rock outcrops to several meters on footslopes, but is more commonly 50 to 100 cm deep on the hillslopes and crests.

Ryan (1993) gave a detailed account of the soil landscape model: a toposequence consisting of upslope residual or degrading zones and lower slope aggrading zones reflecting a strong transportational influence on profile development. Whilst the coarse quartz grains are found throughout the profile, clay content increases in the B-horizon. On hillslopes, the B-horizon still tends to be very sandy, and the increased clay content of the <2-mm fraction, whilst sufficient to push the classification into the “podsolic” category, is not enough to prevent the in-situ soil from being predominantly coarse sandy in nature. In the aggrading zones, the profile is considerably more clayey, especially in the B2 horizon.

The soils are low in overall fertility. They are strongly acid, relatively high in exchangeable aluminium, and low in total phosphorus and exchangeable cations (Ryan, 1993).

Vegetation, consistent with the low soil fertility, is dry sclerophyll forest with a relatively simple structure consisting of overstorey, shrub layer and ground cover. The overstorey is dominated by silvertop ash (Eucalyptus sieberi muell.). This species is present in percentages from around 50% ranging up to almost pure stands, depending mainly on landscape position. Other overstorey species include E. cypellocarpa, E. globoidea, E. obliqua and E. muelleriana. Common species of the shrub layer are Acacia terminalis, A. stricta, A. mearnsii, Daviesia latifolia, Allocasuarina littoralis, Cassinia trinerva and Leucopogan lanceolata. Ground cover species include bracken fern (Pteridium esculentum), tussock grasses (Poa meionectes, Chionochloa pallida), sedge (Ghania radula ), creepers (eg. Viola hederacea), and erect forbs (Gonocarpus tetragyna). Many of the ground covers and shrubs tend to be opportunistic colonisers, present in low density until the overstorey is removed by fire or logging, after which they quickly increase in abundance. The overstorey species are also relatively fire resistant with most having thick bark to protect epicormic buds.

2.3 Soil sampling equipment

In both studies, the same types of soil sampling equipment were used, and these are described here for reference.

2.3.1 Penetration resistance

We measured penetration resistance with a Rimik CP20 recording penetrometer fitted with a standard cone of 13-mm base-diameter, 25-mm length and 30º cone angle. This provided profiles of penetration resistance to a depth of 400 mm in 25-mm increments. The data could be downloaded from the penetrometer to a personal computer for summary and analysis.

2.3.2 Disturbed bulk density

The term “disturbed” bulk density is used to signify that samples of known volume were obtained for weighing and measurement of bulk density, but that they were not kept in-tact, and were simply pushed out of the sampling ring into a plastic jar for transportation. These cores were obtained using a thin walled steel cylinder, 40 mm long and 56 mm in diameter, driven into the soil in a cast- stainless steel housing (Figure 4A). Samples were sealed and weighed on the day they were collected and again after oven drying at 104ºC to determine water content and bulk density. All samples were passed through a 2-mm sieve.

2.3.3 Undisturbed bulk density and aeration porosity

Undisturbed bulk density cores were, as the name suggests, kept in-tact following sampling so that laboratory measurements could be made on physical properties that are affected by sample disturbance: principally air-filled porosity.

These cores were taken using purpose built equipment consisting of a mild steel, three-legged tubular core guide (Figure 4B), and PVC plastic cylinders that were driven into the soil through the guide by a driving head. After placing the guide on the soil surface, a pedestal slightly large than the core diameter was formed by excavation (Figure 4C). The PVC sampling cylinder was then driven into the pedestal. The purpose of the pedestal was to remove external pressure on the cylinder and, hence, to minimise compaction of the core. The guide was removed and the sampling cylinder containing the soil core carefully lifted off the soil (Figure 4D). The core was then trimmed and contained at both ends by thin plywood, taped and placed in a plastic bag for transport. Weighing was undertaken on the day of collection.

The moisture content at 100 cm suction on sand-substrate suction tables was determined. Using a particle density of 2.51 g cm -3 (measured from samples), the air-filled porosity, or aeration porosity, of the cores was calculated.

Figure 1. Location of the study area within southeastern Australia and the location of the two study sites within the study area.

Figure 2. Topography of compartment 551, Coupe 1.

Figure 3. Topography of Compartment 394, Coupe 5.

C)

A)

D)

E)

B)

Figure 4. Coring equipment used to take A), disturbed cores, and

B) undisturbed cores. Undisturbed cores were obtained by excavating a pedestal with the core guide in place (C), driving the sampling ring into the pedestal through the guide (D), clearing excess soil and then removing the intact core for trimming and packing (E).

2.4 Logging Study

2.4.1 Pre-logging measurement

Prior to the commencement of harvesting, soil samples were taken to obtain an independent sample of the soil properties of interest across the site. The main purpose of performing a pre-harvest sampling is to preserve the principal of random sampling: that all sample locations should have an equal probability of being selected. Any undisturbed samples obtained after logging are compromised to some extent by the fact that much of the area has been disturbed, and so not all locations are available for sampling. The sample may include, therefore, inherent or random bias.

The pre-harvest samples were obtained by randomly locating and orienting a grid over the coupe. The grid was sized to yield 45 grid intersects using the equation adapted from Hazard and Geist (1984):

A * 10 , 000

(1) Where G = grid spacing (m), A = coupe area (ha), and n = number of grid intersects. Due to

irregularities in the coupe boundary, we were ultimately able to select 32 sample locations with the calculated grid size of 110 m.

At each grid point intersect, a bulk density sample was obtained from each of the depths 0-10 cm, 10-20 cm and 20-30 cm. The disturbed core method was used at 15 sampling locations, whilst the undisturbed core method was used at 17 locations. Sometimes sampling conditions prevented the full range of depths from being sampled. Figure 5 shows the pre-harvest sampling grid.

Figure 5. Pre-harvest sampling locations, compartment 551.

2.4.2 Logging machine tracking and traffic intensity measurement

The following description of methods for logging machine tracking includes work performed for the studies reported in both Part I (NSW studies) and Part II (Victorian studies, specifically Study

C) of this report 1 . Further details on the GPS equipment and methods are included as Appendix 1.

GPS Tracking Methods The objective of the GPS tracking was to collect complete travel path data for the skidding

machines used during logging. These data were used to explore relationships, if any, between

1 Due to certain complications with the Victorian data and time limitations, no data could be included in this report.

traffic intensity disturbance and soil physical property change. Detailed mapping of major coupe features such as logged area and transect locations was also undertaken manually by high-precision GPS as part of the tracking study.

Collection of travel path data from skidding machines was achieved using GPS receivers and recorders temporarily mounted in the machine cabs. GPS recording is now common in forestry studies (Thompson 1998, Veal 2001), although there are still caveats in respect to usefulness under tree canopies, or adjacent to tall forest edges (Gandeseca et. al., 2001).

Data Collection Two types of GPS receiver were used in the experiments. The NSW study relied primarily on a

Sokkia Spectrum GPS unit. The Victorian study used a purpose built experimental forest machinery data logger constructed at CSIRO FFP (details in Appendix 1). During the last period of the NSW trial, data were collected using a second CSIRO FFP forest machine data logger because the trained GPS technician was unable to be present to operate the Sokkia unit. With both types of GPS recorder, data were recorded at five-second intervals and data from remote base stations were used to differentially correct the position estimates (the base station was at Canberra for the NSW study and Melbourne for the Victorian study). There were several periods for which differential processing was not possible, due to equipment failure in both the field and at the base stations. For these periods, data accuracy was reduced and traffic patterns had to be analysed manually, as described below.

Importing GPS Data The GPS data files contained fields for latitude, longitude and height for each point. A typical

stream file for a single day of data collection could contain five thousand or more points. There were some “gaps” in the GPS data where the satellite signal was lost due to poor satellite position, too few satellites, or lost signal due to topography and canopy cover. To account for these gaps in the GPS data an ArcView GIS program was used to filter the data during the importing stage. The program flagged missing points and evaluated the distance travelled in each observation interval. Errors such as those induced by multi-path signal reception cause major jumps in reported position. By setting a maximum plausible travel distance for the five-second interval, observations could be processed to identify and flag occurrences of implausible jumps. These were typically followed in the next observation by a return jump to the correct position. Data were corrected after visual inspection, by deleting the erroneous point and assuming the travel path was a straight line between the point before and the point after the jump.

Map Production Traffic intensity maps were produced in two ways. The preferred method used an automated tool

set developed and installed in the ArcView GIS. The back up method relied on visual inspection of the GIS data on screen, counting and manual recording of traffic path. This was used when automated processing was not possible.

In the automated method, a composite GIS file containing all snig tracks on the site was assembled first. From this, a simplified schematic of the snig track network was drawn as an overlay layer in the GIS, identifying the track sections and junctions. Next, a line was drawn on the overlay layer approximately perpendicular to the direction of the track at each point where a traffic count was desired. These counting lines were usually placed midway between junctions. Figure 6 shows a section of track, associated data and counter bars. The automated computer routine was programmed to count the number of GPS track records that intersect each of these counting lines and report the results.

For parts of the data set that could not be processed automatically due to poor data quality, counting was done by individually tracing the machine path from the ArcView records, and recording passes manually on the simplified snig track map. Automated processing was possible for about two- For parts of the data set that could not be processed automatically due to poor data quality, counting was done by individually tracing the machine path from the ArcView records, and recording passes manually on the simplified snig track map. Automated processing was possible for about two-

The statistic evaluated in this study was the number of machine passes along the snig track. At the level of accuracy achieved, it is not possible to have confidence about the accuracy of the actual number of wheel passes over a point. Analysis was, therefore, also undertaken to condense the data to broad traffic classes (eg. 1-10, 11-25, 25+ passes).

Log Landing

Figure 6. A portion of the snig track network with counter bars and number of passes labelled.

2.4.3 Post logging measurement

Disturbance classification and traffic intensity classes Soil disturbance by the logging operation was quantified by several alternative methods. Two of

these represented alternative methods of obtaining essentially the same data for comparative purposes. The line intercept method (Figure 7) used randomly located transects. Their orientation was subjectively chosen to be approximately normal to the dominant direction of snigging (see Appendix 2) and the distance between them chosen to give around ten transects (100 m; eight transects fell within the coupe). Ground condition classes were recorded at 15-m spacings commencing at a randomly selected distance between 0 and 15 m from the transect start.

The grid point-intercept method used a randomly located and oriented grid with randomly oriented 50-m transects arising from each grid intercept. The grid spacing was 100 m, providing 38 grid intersects within the coupe. Ground condition classes were recorded along the 50-m transects as in the line-intercept method, except at a 10 m spacing. The locations of both sets of transects, along with a GPS-mapped layout of the harvested area, are shown in Figure 7.

Both the line intercept and grid-point intercept surveys were applied to less than the entire coupe area, and were, for the most part, restricted to the logged area. This was to avoid the need to Both the line intercept and grid-point intercept surveys were applied to less than the entire coupe area, and were, for the most part, restricted to the logged area. This was to avoid the need to

The areas surveyed were slightly different for the two methods. The grid-point intercept transects were re-oriented by 180 degrees if the random bearing resulted in the transect being entirely within non-harvested forest. Hence, adjustments to transect locations could be made in response to irregularities in the harvested area so that transects were primarily located within harvested area. The main exception to this was an non-harvested area within a tongue of filter strip in the south- west corner of the coupe where a transect fell within the non-harvested area and was surveyed (see Figure 7). When the line-intercept transects passed through such irregularities, the non-harvested areas were recorded. The proportion of the coupe surveyed was estimated from GPS data and was used to scale all disturbance category assessments according to the following formula;

( Ps × As ) + ( Pu Au ) P (%)

= × 100 (2)

Where Ps = proportion of category in surveyed area, As = surveyed area (ha), Pu = proportion of category in non-surveyed area, Au = coupe area not surveyed, A = total coupe area. Surveys conducted with GPS provided close estimates of the coupe area surveyed and not surveyed. These were 28 and 27.1 ha for the grid-point intercept survey, and 37.4 and 17.7 ha for the line intercept survey, respectively. Based on field observations, the non-surveyed part of the coupe was regarded as 100% undisturbed.

In both surveys, five types of ground condition were recorded simultaneously using the classification method of Rab (1998) as shown in Table 1. Operational categories are a description of how the land was used or disturbed during logging. The disturbance categories consist of three different descriptor categories: a subjective disturbance severity classifier; type of soil mixing or removal; and dominant horizon exposed, which might also be thought of as a surrogate for depth of disturbance. In the line intercept survey, snig tracks were not placed into sub-categories.

Subsequent soil sampling was based on the grid point-intercept method since the use of more frequent shorter transects gave better control over sample point relocation in repeated visits. The layout also made sampling logistically more efficient.

S # Grid-point

Drainage

intercept sampling site

Contours Disturbed area

Line intercept

Coupe

sampling point

0 100 200 Metres

RDD GI S SFN SW August 2001

Figure 7. Line intercept and grid point intercept transect locations for post-harvest assessment in the logging study of compartment 551. The snig track network, mapped by hand-held GPS, is also shown.

Table 1. Ground condition classification system used at compartment 551, Coupe 1, Yambulla State Forest (after Rab, 1999).

A: Operational categories:

Unharvested Area (UA)

Areas of retained forest or other vegetation

Harvested Area (HA)

General logging area within which trees are felled

Firebreak (FB) Perimeter boundary (not present at cmpt 551, but used in Victorian operations) Snig Tracks (ST)

Tracks created by towing or winching logs to the landing. Further divided into minor, moderate and major under the grid point-intercept method.

Log Landings (LL) Area where logs are snigged for sorting and loaded for transportation Access Roads (AR)

Temporary roads used during the harvesting operation

B: Soil disturbance categories: Degree of soil profiledisturbance

Type of mixing/ removal

Dominant Horizon

Undisturbed (S0)

Forest intact (FI)

O1

Understorey intact (UI)

O1

O1 Lightly disturbed(S1)

Litter layer intact (LI)

Litter layer disturbed (LD)

O2

O2 Moderately disturbed (S2)

Litter layer partially removed (LR)

Litter completely removed and topsoil

exposed (TE)

Litter mixed with topsoil (LM)

A Topsoil disturbed (TD) 1 A

Topsoil mixed with subsoil (TM)

Topsoil partially removed (TP)

Severely disturbed (S3)

Topsoil completely removed and subsoil

exposed (SE)

Topsoil mixed with subsoil (SM)

B Subsoil disturbed (SD) 2 B

Subsoil mixed with parent material (SC)

Subsoil partially removed (SR)

Subsoil removed and parent material exposed (PE)

C. Soil and slash piling categories:

Soil piling (SP)

Soil piled at a height >0.3 m

Soil and slash piling (SS)

Soil and slash piled at height >0.3 m

Slash and/or bark piling (SB)

Slash and/or bark piling at height >0.3 m

1 Topsoil consists of A 1 ,A 2 and A 3 horizons except where A 2 is conspicuously bleached whereby A 2 and A 3 are regarded as subsoil. 2 Subsoil includes B 1 and B 2 horizons and conspicuously bleached A 2 horizon (and any other A-horizon below the A 2 ).

Note: Fire intensity categories presented in Rab (1998) were not used as this study excluded the effects of post-logging burning.

The GPS machine tracking data were also used as a disturbance classification (number of skidder passes or traffic intensity class). We compared the traffic intensity classes with the visual classification wherever the two types of data could be spatially related.

Soil Sampling Soil sampling was carried out following disturbance class assessment. Disturbed bulk density and