Landcover Change in Queensland, 1997-1999: September 2000 report

Issued September 2000 (last revision 27 September 2000)

Resource Sciences and Knowledge
Queensland Department of Natural Resources
Natural Sciences Precinct
80 Meiers Road, Indooroopilly, 4068

CAUTION! The maps and tables in this report should be interpreted with careful reference to the methods section.

Summary of results

Background

The Statewide Landcover and Trees Study (SLATS) is a major vegetation monitoring initiative of the Queensland Department of Natural Resources (DNR). SLATS gathers accurate vegetation cover and cover change information for vegetation management planning and for greenhouse gas inventory purposes. Landsat Thematic Mapper (TM) satellite imagery is being used to compare the vegetation cover between 1988, 1991, 1995, 1997 and 1999, and to provide baseline landcover mapping over the entire State of Queensland. The Landsat TM imagery has a resolution of 30 metres, enabling most areas of vegetation change (one hectare or greater) to be detected. Typically, it is used to produce maps at a scale of 1:100 000 or smaller. This study provides a consistent data set covering the entire State at a medium resolution, but it is not intended to be a substitute for high resolution studies of patchy remnant bushland which would conventionally use aerial photography. Landsat TM satellite imagery should be used with caution when mapping narrow vegetation corridors, such as riparian vegetation, as the resolution of the imagery may be less than the size of the vegetation to be mapped.

An analysis of 1997-99 woody vegetation change has been completed for the entire State and is the subject of this report. Imagery for the 1991-95 and 1995-97 periods have previously been analysed for vegetation change and reported (DNR, 1999B, 1999C). A sample of scenes has been processed for 1988-91 vegetation change with approximately 12 per cent of the State completed. Detailed baseline landcover mapping which discriminates areas of trees from pasture, crop, water, settlement areas etc. is being done using 1991 imagery. This mapping has been completed for approximately 85 per cent of the State and will be finalised later in 2000. When completed it will form the first medium resolution map of woody vegetation cover for the entire State of Queensland.

Land clearing contributes to a significant proportion of Queensland's and Australia's total greenhouse gas emissions. Remotely sensed estimates of rates of land cover change reported by SLATS form the basis for estimating emissions of greenhouse gases associated with clearing of forests and woodlands in Queensland for the National Greenhouse Gas Inventory (NGGI).

This Inventory is compiled as part of Australia's reporting obligations as a signatory to the United Nations Framework Convention on Climate Change (UNFCCC), but if the Kyoto Protocol which was proposed in 1997 is ratified by the international community, Australia will also have to monitor and report all changes in carbon stocks on lands subject to human-induced afforestation, reforestation and deforestation activities since 1990. The time series of rates of land cover change from SLATS will be the basis of monitoring and verifying areas of land in Queensland to be included in accounting for the Kyoto Protocol.

A project advisory committee was established to provide feedback from a wide range of stakeholders and assist with communication to industry and the wider community. The committee provides input with regard to overall direction and methods, and assists in the dissemination of project results. The committee consists of representatives of DNR, Department of Primary Industries (DPI), Environment Protection Agency (EPA), Queensland Conservation Council, Brisbane Region Environment Council, Wildlife Preservation Society of Queensland, Agforce, Queensland Canegrowers, Local Government Association, and academia.

Methods

Definition of Woody Vegetation

There are many definitions of what constitutes a forest or woody vegetation. A common definition used by foresters is 20 per cent crown cover which equates to approximately 12 per cent foliage projective cover (FPC) (NFI, 1998). It is clear in pages 5.14 and 5.25 of the Intergovernmental Panel on Climate Change guidelines (IPCC, 1996) that where possible, all changes in woody biomass stocks resulting from human activity should be measured and included in a national inventory. Hence, we have mapped vegetation change for all perennial woody plants of all sizes which can be distinguished with Landsat TM imagery.

The statistics for vegetation change and woody vegetation cover quoted in this report include all woody vegetation. This includes remaining areas of native vegetation, disturbed areas of native vegetation, regrowth, plantations of native and exotic species and domestic woody vegetation. In higher rainfall coastal areas, woody vegetation of 12 per cent FPC or greater can be detected with a high level of confidence but more sparse vegetation is difficult to distinguish from pastures. However, there is very little woody vegetation of less than 12 per cent FPC in coastal areas due to fast vegetation growth rates. In the lower rainfall areas, it is possible to detect clearing of vegetation with an FPC of five per cent or greater.

Imagery selection and pre-processing

Landsat TM satellite imagery was purchased from the Australian Centre for Remote Sensing (ACRES). In most cases, 1997 and 1999 imagery was used. The 1999 satellite imagery used was acquired over the period July to December 1999. However, the five December satellite scenes used were over areas with high clearing rates and account for approximately 40 per cent of the State clearing rate.

There were a few areas where it was not possible to acquire sufficiently cloud-free 1999 or 2000 imagery. For the Sarina and St Lawrence scenes previous rates (1995-98) were used. For Cooktown and Kalinga, 1995-97 rates were used, although there is little clearing in either of these scenes. There are another five Cape York scenes which were not available. Previous analysis has shown that little or no clearing occurs in these areas and hence no allowance for clearing was included. Figure 1 shows the images used in this study.

Where possible, dry season imagery was chosen to maximise discrimination between grasses and the woody component of the vegetation. However, there was considerable rain in the winter and spring of 1999 so some of the imagery contained green pasture and this made interpretation more difficult.

Prior to use in change detection and mapping procedures, the imagery was first radiometrically corrected for variation in solar zenith angle. Geometric correction involves registration of all scenes to the 1991 reference year. Semi-automated image correlation methods are being used to register imagery to better than 0.4 pixels root mean square error. The majority of subsequent processing uses these registered images. To enable rectification of change detection classifications, vegetation mapping and other products, scene to map transformations are calculated. These transformations are generated using ground control points measured in the field with differential global positioning systems (Fugro and Garmin type, with sub 10 m accuracy) to maintain a constant spatial accuracy of at least 1:100 000 scale (Kuhnell et al., 1998).

Figure 1: Era of imagery used in 1997-1999 analysis

Woody vegetation change detection

Vegetation change has been mapped using classification procedures similar to those described by Paudyal et al. (1997). A semi-automated computer classification was used to identify areas of change and a sample of these areas was then field checked. Field checking was done for 44 of a total of 78 scenes and these encompassed 97 per cent of the overall change in the State. The primary purpose of the field checks was to verify the 1997-99 change analysis. However, the opportunity was also used to gather statistics on the method of change and the amount of coarse woody debris remaining after clearing. These statistics were also recorded for a sample of change areas from the 1991-95 and 1995-97 change analysis. These data have not yet been analysed or reported.

A detailed baseline landcover survey using 1991 imagery and vegetation site data is also being completed. This involves the development of woody/non-woody masks, foliage projective cover (overstorey and shrub) and tree basal area layers. A full description of field methods and landcover mapping methods is given by Kuhnell et al. (1998). It is complete for approximately 85 per cent of the State, including all the clearing areas.

To determine vegetation change, the woody vegetation mask was used in conjunction with the change classification to isolate changes in woody vegetation and produce a final change classification. Maps of vegetation change were then edited using visual interpretation techniques aided by data gathered in the field. Finally, the change classification for each scene was checked by two independent operators.

Both clearing and regrowth of woody vegetation were classified. Regrowth is more difficult to measure due to slow rates of change (relative to clearing) and the low initial density of some regrowth stands. The "new woody regrowth" figures included in the tables are regrowth generation, i.e. areas which have changed from non-woody to woody within the 1997-99 period. The "new woody regrowth" rates for the 1997-99 period are considerably less than for previous periods. This is mostly due to the wet conditions which preceded some of the 1999 imagery and made it more difficult to discriminate regrowth from green pasture.

Areas affected by fire have not generally been mapped as change. While fires can remove a significant proportion of the woody vegetation foliage it is usually a temporary effect, and in most cases the foliage recovers quickly. SLATS site data show that on average a fire removes less than 2 m2/ha basal area. Hence it is not common for fire to change the landcover from woody to non-woody in a single event.

In the 1995-97 SLATS study (DNR, 1999C), significant areas of natural tree death were reported. For the 1997-99 period, no instances of natural tree death were observed.

Replacement landcover

Every patch identified in the change analysis as clearing was assigned one of the replacement landcover classes in Table 1. The assignment of these classes is primarily based on visual interpretation. State forest boundaries are used to re-code some of the native forest clearing which would otherwise appear as clearing to pasture. In areas where there are many different forms of land use, it is sometimes difficult to interpret the correct replacement class. Sometimes land is cleared to pasture then later converted to urban development. These factors will cause the interpretation accuracy for replacement class to be lower than the accuracy for identification of woody vegetation change.

Table 1: Replacement landcover classes for 1997-1999 woody vegetation change
Replacement landcover or land use Description
Pasture Cleared for pasture; includes clearing for grazing, rural residential, future urban land use, native forestry on private land and privately owned plantations cleared for pasture (i.e. not replanted as plantations)
Crops Cleared for growing crops
Forest State forest clearing including plantation and native forest: cleared private plantations which are replanted
Mining Cleared for mining
Infrastructure Cleared for roads, railways, water storage
Settlement Cleared for current urban development

Compilation of statewide data sets

Large, seamless mosaics of vegetation change, landcover and vegetation cover were created by joining together the 78 scenes used in this analysis. Scenes were overlapped with preference for the more recent final date in the overlap (e.g. 1997-99 used in preference to 1995-98). In order to calculate annual tree clearing rates, a vector geographic information system (GIS) layer containing the extent and dates of mosaiced scenes was created. The mosaic raster of cleared areas was intersected with GIS overlays depicting date, tenure type, 30' x 30' grid cell, soil type etc in order to generate tabular statistics. In most cases a spatial resolution of one kilometre was used as it was appropriate given the scale of most input data and it was more efficient to compute than at the full 30m resolution. However the foliage projective cover and basal area data were intersected at 30m resolution and regional ecosystems mapping at 100m resolution. The tabular statistics show slightly different State clearing totals which are due to the different scales of the GIS overlays used.

It is important to note that all statistics were generated based on an Albers equal-area projection, so clearing rates for different regions could be compared; and all the vegetation change statistics in this report have been converted to annual rates to account for the variation in scene dates. The units of clearing rate used in the tables are km2/year. These can be converted to ha/year by multiplying by 100.

Accuracy of interpretation

The traditional form of accuracy assessment uses an independent data source of higher resolution, but this is not always possible. While some change areas on each scene are checked in the field, access and cost limit the extent of this checking to a representative sample only. The aerial photography coverage available does not usually align with the same dates of the satellite imagery, so in most cases it is not a viable option and other alternatives need to be considered.

An initial accuracy assessment based on scene overlaps has been done. The north-south and east-west overlaps of the satellite scenes provide two measures of vegetation change. After analysing these data for the 1991-95 and 1995-97 change periods, it has been considered that the error term on the Statewide clearing figures are approximately ± 8 per cent at a 95 percent confidence interval.

In fact, the error term should be better than this, as the east-west scene overlaps used in this assessment contain some real change, due to path date differences.

A formal accuracy assessment of Landsat TM vegetation change analysis was done as part of the national Remote Sensing of Agricultural Land Cover Change project (Barson et al., 2000). It used independent methods rather than independent data to assess the accuracy of the land cover change analysis provided to BRS during this project. DNR was a partner in this project and was responsible for providing the 1991-95 change data for Queensland. The accuracy assessment showed that a high proportion of the individual sub-sample results were not significantly different at the 95 percent confidence level from the DNR estimates of change for the scene and therefore no Queensland scenes required re-processing for BRS. The 1995-97 and 1997-99 change analyses were done with similar methods and the same team so similar accuracy could be expected.

A considerable amount of work has gone into ensuring the quality of the change analysis. The satellite imagery was selected at dates which maximise discrimination between the grass and woody layers. Fully documented procedures have been developed to analyse the data and these are available to SLATS scientists on the project Intranet. Many of the procedures have been scripted with error traps and log files to avoid errors and allow errors to be traced. The change detection method used offers the advantage of automated and visual methods combined with independent checks. Initially, a preliminary change analysis is done then it is field checked. Following fieldwork, the incorrect areas are rejected and if necessary areas of change are added. When these changes are complete the analysis is then checked by another two team members to ensure a high level of accuracy and consistency.

Hence, it seems the largest source of variation is not misclassification of change but determining the extent (area) of change at a clearing location. The woody vegetation mask is very important for determining the area of vegetation change, as it delineates how much woody vegetation existed before clearing. Considerable effort has gone into ensuring that the woody vegetation mask and FPC layer are well calibrated to ground vegetation measurements. These ground vegetation measurements and the radiometric correction methods used ensure the FPC layer is well matched across scene boundaries. This process ensures a high level of accuracy and consistency in the change area measurements.

Statewide assessment of woody vegetation change

Woody vegetation change by 30'x30' grid cell

The average annual clearing rate over the period 1997-99 was calculated to be 425 000 hectares per year or 0.24 per cent of the land area of Queensland per year. A previous study (Danaher et al., 1992) has estimated that the area of wooded vegetation in Queensland is 76 million hectares. Of this, approximately 60 million hectares of vegetation resources occur in agro-ecological zones where some land development could occur. Hence, the annual clearing rate is 0.71 percent of this area per year. In total, the annual clearing figure represents a clearing area of 65 km x 65 km. If a woody vegetation definition of 12 per cent FPC or approximately 20 per cent crown cover were used, the annual average clearing rate for 1997-99 would be 354 000 hectares per year. In calculating this figure it is assumed that all the young regrowth clearing identified (i.e. not mapped as woody in the 1991 woody cover mapping) was less than 20 per cent crown cover.

A spatial view of where clearing is occurring within Queensland is shown in Figure 2 representing the rate of clearing (km2/year) per 30' x 30' (latitude/longitude) grid cell. These 30' x 30' cells are the same size as a 1:100 000 map sheet.

The area of change can also be shown as a proportion of the remaining woody vegetation cover cleared each year over the 1997-99 period, as in Figure 3. This map was created by using the 1991 baseline landcover mapping (Kuhnell et al., 1998) produced by SLATS, updating it with the 1991-95 and 1995-97 vegetation change mapping and combining this with the 1997-99 clearing data. A map of 1997 woody vegetation by 30' x 30' grid cell is shown in Figure 4. This map is not yet complete for the entire State but is complete for all the major clearing areas. Please take care when interpreting the results in Figures 3 and 4 as some of the differences in percentage of 1991 woody vegetation are related to the original type of vegetation cover, not clearing. For example, there are significant areas of natural grassland in Queensland, such as the extensive Mitchell Grass plains which never contained woody vegetation such as trees and shrubs.

Figure 2: Average annual clearing rate (1997-1999)

Figure 3: Percentage of 1997 woody vegetation cleared (1997-1999)

Figure 4: Queensland woody vegetation cover (1997)

Woody vegetation change by conservation status

The 1997-99 clearing rate data were cross tabulated with a map of conservation status which was derived from the Herbarium's regional ecosystem mapping as of May 2000 to produce preliminary figures of clearing by conservation status. These figures will be subject to revision as the Queensland Herbarium updates the regional ecosystem mapping using the 1997-99 change analysis and 1999 Landsat imagery. This regional ecosystem mapping contained 92 per cent of the State total clearing for 1997-99. As the mapping did not cover all the clearing areas, only a percentage breakdown of clearing within the mapped area was calculated (Table 2).

In many cases, clearing of trees for grazing is not permanent and trees regrow from lignotubers or seed. This analysis reveals that two thirds of clearing is clearing of remnant vegetation and one third is clearing of vegetation mapped as regrowth. The Herbarium consider that vegetation is regrowth if it is less than 70 per cent height or less than 50 per cent cover of the dominant stratum, relative to the normal height and cover of that stratum (Boulter et al., 2000).

Table 2: Percentage of clearing within mapped area by conservation status
Mapped conservation status (current May 2000) % of clearing within the area of completed regional ecosystem mapping
Endangered dominant 5.7
Endangered sub-dominant 3.7
Of concern dominant 18.6
Of concern sub-dominant 6.0
Not of concern 33.5
Not remnant 32.5
Total 100.0

Woody vegetation change by land tenure and land use

The 1997-99 woody vegetation change rates have been grouped by tenure in Table 3. Figure 5 is a map of the four broad tenure classes used in Table 3. Table 4 shows the clearing rate by tenure for the 1991-95, 1995-97 and 1997-99 periods. Figure 6 shows the trend in clearing over all SLATS measurement periods. The information in these tables was derived by combining the woody vegetation change data with the State as Digital Cadastral Data Base and Tenures Administration System data. The area of predominantly freehold tenure is also shown in Figures 2 to 4 using hatching.

Table 3: 1997-1999 woody vegetation change by tenure type
Tenure Rate of woody vegetation change (km2/year) % Tenure type area cleared per year % of total clearing in QLD
Type Area (km2) New woody regrowth Clearing
Pasture Crop Forest Mining Infra-structure Settle-ment Total cleared
Leasehold 1 184 435

25.15

 1496.2

54.32

 6.79 11.97 54.65 0.71 1624.64 0.14 38.18
Freehold 424 641

44.11

 2102.09

335.03

 13.51 8.38 34.89 13.85 2507.75 0.59 58.94
Other tenures[1] 23 292 0.26 16.05 13.35 0.03 0.28 0.71 0.08 30.49 0.13 0.72
Other Reserves[2]  114 017 26.95 23.65 5.27 53.68 0.21 9.06 0.23 92.10 0.08 2.16
State Totals 1 746 385

96.47

 3637.98

407.97

 74.0 20.83 99.32 14.87 4254.98 0.24 100.00

The replacement landcover after clearing for the periods 1991-95, 1995-97 and 1997-99 is summarised in Table 5. It shows that the majority of clearing is for conversion to pasture for grazing with conversion to crop the second largest in area. The trend in clearing rate by replacement land cover class for all SLATS measurement periods is shown in figure 7.

Figure 5: Land tenures in Queensland (1997)

Table 4: Current and previous clearing rates by land tenure

Tenure Clearing rate 1991-95 Clearing rate 1995-97 Clearing rate 1997-99
Rate (km2/year) % of State clearing total Rate (km2/year) % of State clearing total Rate (km2/year) % of State clearing total

Freehold

 1260 44 1945 57 2508

58.9

Leasehold

 1550 53.5 1361 40 1625 38.2

Other Tenures

 14 0.5 29 1 30 0.7

Other Reserves

 59 2 69 2 92

2.1
Total 2883 100.0 3404 100.0 4255 100.0

Figure 6: Trend in clearing rate by tenure type

Table 5: Current and previous clearing rates by replacement land cover
Replacement cover 

Clearing rate 1991-95

 Clearing rate 1995-97

Clearing Rate 1997-99
Clearing rate
(km2/year)		 % of State clearing total Clearing rate
(km2/year)		 % of State clearing total Clearing rate
(km2/year)		 % of State clearing total
Pasture 2653 91.8 2926 86.0 3638 85.5
Crop 125 4.4 294 8.6 408 9.6
Forest 48 1.7 50 1.5 74 1.7
Mining 15 0.5 27 0.8 21 0.5
Infrastructure 24 0.8 82 2.4 99 2.3
Settlement 23 0.8 25 0.7 15 0.4
Total 2888 100.0 3404 100.0 4255 100.0

Figure 7: Trend in clearing rate by replacement land cover

Woody vegetation change by foliage cover and basal area

The woody vegetation change data for all SLATS measurement periods were cross-tabulated with the 1991 woody vegetation cover (FPC) layer which was generated from the Landsat TM imagery, to produce a histogram showing clearing in relation to woody vegetation cover (Figure 8). The highest frequency of clearing occurs at a FPC value of 24 per cent for 1991-95, at 28 per cent for 1995-97 and at 29 per cent for the 1997-99 period. There are areas of young regrowth which were cleared in the 1995-97 and 1997-99 periods but were not mapped as woody vegetation in 1991 and could not be included in the histogram. The rates of young regrowth clearing were 344 km2/year for 1995-97 and 642km2/year for 1997-99. Absence of this young regrowth clearing from the histogram in Figure 8 might partly be responsible for the increasing peak frequency and distortion of the left hand side of the curve. It is intended to develop a method for assigning an FPC value to regrowth for future change analyses. Development of the FPC layer is described by Kuhnell et al. (1998).

Figure 8: Clearing by woody vegetation cover (FPC)

The SLATS woody vegetation cover data were divided into the four foliage over classes used by Carnahan (1988) and intersected with the 1997-99 clearing rate data. These clearing rates by woody vegetation cover data are given in table 6.

Woody vegetation cover % (overstorey and shrub FPC) 1997-99 Clearing rate (km2/year) % of State clearing total
<10 668 15.7
10-29 1836 43.1
30-69 1723 40.5
70-100 28 0.7
Total 4255 100.0

A tree basal area layer was derived from the woody vegetation cover (FPC) layer using relationships between FPC and basal area based on site measurements. This relationship only holds for mature vegetation, so areas which are known to be young regrowth were not included. These young regrowth areas account for approximately 16 per cent of all clearing in the 1997-99 period.

The tree basal area layer will continue to be improved using historical Landsat TM and MSS analyses (possibly as far back as the 1970s) to identify previously cleared vegetation. Kuhnell et al. (1998) describe the development of the basal area data. Figure 9 shows the frequency distribution of clearing rate in relation to basal area. The highest frequency of clearing occurs at the values of 9 m2/ha for the 1991-95 change, of 10 m2/ha for the 1995-97 change and 11 m2/ha for the 1997-99 change. An example of typical woodland of basal area 11 m2/ha is shown in Figure
10.

Figure 9: Clearing by tree basal area

The 1991-95, 1995-97 and 1997-99 vegetation change mosaics were intersected with the foliage cover classes from the 1:5 000 000 Carnahan Present Vegetation Map (Carnahan, 1988) to produce Table 7. All vegetation classes which included some clearing have been included in this table. In the National and State greenhouse gas inventories, clearing is grouped into three vegetation types which are combinations of the Carnahan classification.

The three groups are tropical and temperate closed forests (T4,M4,L4,T3), dense woodlands and open forests (M3,L3), and open woodland (M2,L2). In Queensland, the only additional classes which have significant clearing were added to the open woodland class. The Carnahan classes when mapped are geographically large and represent the dominant vegetation formation but may include significant areas of other vegetation types. Apparent anomalies, such as no clearing in T4 (trees > 30m height and 70 per cent foliage cover) in Queensland, are due to this problem.

Figure 10: Eucalypt woodland basal area of approximately 11 m2/ha

Table 7: Clearing rates by Carnahan present vegetation class
Carnahan Vegetation Class Description Clearing rate 1991-95 Clearing rate 1995-97 Clearing rate 1997-99
Clearing rate (km2/year) % of State clearing total Clearing rate (km2/year) % of State clearing total Clearing rate (km2/year) % of State clearing total
F3 Other herbaceous plants 30-70% foliage cover 0.4 0.0 0.5 0.0 0.2 0.0
F4 Other herbaceous plants >70% foliage cover 13.9 0.4 37.9 1.1 29.1 0.7
G2 Tussocky or tufted grasses 10-30% foliage cover 40.9 1.4 76.7 2.2 47.5 1.1
G3 Tussocky or tufted grasses 30-70% foliage cover 333.6 11.6 445.1 13.0 498.2 11.7
G4 Tussocky or tufted grasses >70% foliage cover 20.2 0.7 20.8 0.6 17.1 0.4
L1 Low trees (<10m) <10% foliage cover 339.3 11.8 192.8 5.6 251.5 5.9
L2 Low trees (<10m) 10-30% foliage cover 339.2 11.8 255.7 7.5 422.6 9.9
L3 Low trees (<10m) 30-70% foliage cover 9.2 0.3 10.0 0.3 6.6 0.2
L4 Low trees (<10m) >70% foliage cover 0.3 0.0 0.2 0.0 0.3 0.0
M1 Medium trees (10-30m) <10% foliage cover 684.2 23.7 1028.6 30.1 1290.5 30.3
M2 Medium trees (10-30m) 10-30% foliage cover 828.6 28.8 1092.2 32.0 1394.1 32.7
M3 Medium trees (10-30m) 30-70% foliage cover 250.2 8.7 233.8 6.8 279.9 6.6
M4 Medium trees (10-30m) >70% foliage cover 7.9 0.3 6.9 0.2 4.8 0.1
S1 Tall shrubs (>2m) <10% foliage cover 8.4 0.3 12.9 0.4 14.7 0.3
S2 Tall shrubs(>2m) 10-30% foliage cover 4.8 0.2 2.8 0.1 3.8 0.1
T3 Tall trees (>30m) 30-70% foliage cover 0.0 0.0 0.0 0.0 0.0 0.0
Z1 Low shrubs (<2m) <10% foliage cover 0.7 0.0 0.3 0.0 0.0 0.0
Z3 Low shrubs (<2m) 30-70% foliage cover 0.0 0.0 0.0 0.0 0.3 0.0
Total    2881.9 100.0 3418.3 100.0 4261.2 100.0

Woody vegetation change by biomass

Data from many studies on eucalypt, acacia and rainforest sites were assembled and the relationships between stand basal area and biomass calculated. A power function (Figure 11) explained much of the variation in the biomass data. The data are more scattered at higher biomass values and some data (mainly from Victoria) were omitted as they fell well outside the main trend line. The scatter at the high biomass end of the relationship is of little concern as there is little clearing or no clearing of these high biomass communities in Queensland. Fortunately, Queensland communities are well represented due to work done on above and below ground biomass for SLATS by Dr Bill Burrows (DPI).

Figure 11: Estimating above ground live biomass from stand basal area

Quality below-ground biomass data are scarce and the relationship is much weaker (Figure 12) and supported only by one point at the high end. Nevertheless the root-to-tops ratio is similar to that found in many overseas stands of comparable biomass. Two data points (plotted) were omitted from the equation as these data points were from sand islands and are unrepresentative of communities being cleared.

These equations were applied to pre-clearing live stand basal area as mapped by SLATS to estimate live above and below ground (root) biomass cleared. It is estimated that approximately 32 Mt of dry biomass or 16 Mt of carbon were cleared each year during the 1997-99 period. Although this calculation still needs further refinement, it is not inconsistent with that obtained using the National Greenhouse Gas Inventory methodology (NGGIC, 1998). At this stage no account is made for dead timber cleared or of the lower biomass found in regrowth communities. While the biomass cleared will eventually decay, the rate of release of CO2 depends to a great extent on clearing method and post clearing management of woody debris. The approach used in this calculation will enhance the estimation of carbon stocks at a regional scale.

Figure 12: Estimation of below ground biomass from above ground biomass

Woody vegetation change by soil type

A table showing clearing rate by soil type was created by intersecting the 1991-95, 1995-97 and 1997-99 clearing areas with data from the 1:2,000,000 Atlas of Australian Soils. The complete table of clearing rate by soil principal profile form was too large to include in this publication so a summary table (Table 8 ) was included.

Table 8: Clearing rates by Atlas of Australian Soils type
Dominant soil type (Northcote) Clearing rate 1991-95 Clearing rate 1995-97 Clearing rate 1997-99
Clearing rate
(km2/year)		 % of State clearing total Clearing rate
(km2/year)		 % of State clearing total Clearing rate
(km2/year)		 % of State clearing total
Duplex 737 25.44 1123 32.63 1343.31 31.40
Gradational 1272 43.90 1299 37.74 1736.80 40.6
Organic 1 0.04 1 0.02 1.71 0.04
Uniform 877 30.62 1019 29.60 1193.61 27.96
Total 2887 100.00 3442 100.00 4275.43 100.00

[1] Includes Commonwealth lands, mining, main roads, railways, ports, action pending etc

[2] Includes State forest, timber reserves and national parks

Last updated 1 September 2000

Statewide Landcover and Trees Study