Landcover Change in Queensland, 1991-1995: June 1999 report
CAUTION! The maps and tables in this report should be interpreted with careful reference to the methods section.
Summary of results
- The statewide annual average clearing rate for the 1991-95 period is 289 000 ha/year. This figure supersedes all previous 1991-95 change figures produced by SLATS. Previous preliminary and interim estimates were based on the best possible information at the time. The change in these figures is a result of improved estimates due to completion of ground truthing, land cover mapping (which provides a mask of woody vegetation before clearing), consistency checks with 1995-97 change analysis, final interpretation and checking by independent operators.
- Approximately 54 per cent of clearing is occurring on leasehold land, 44 per cent on freehold land and the remaining 2 per cent on crown land.
- Approximately 92 per cent of woody vegetation change is clearing of woody vegetation to pasture.
- Jericho shire is the local government area with the highest clearing rate. It contains 14 per cent of the 1991-95 State clearing total.
- The brigalow belt biogeographic region contains 52 per cent of the 1991-95 State clearing total. The other regions with significant clearing are the mulga lands (22 per cent) and the desert uplands (12 per cent).
The Statewide Landcover and Tree Study (SLATS) is a major vegetation monitoring initiative of the Queensland Department of Natural Resources (DNR). SLATS is gathering 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 and 1997, 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 remnant bushland studies using 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 data may be less than the size of the vegetation to be mapped.
A project advisory committee was established to monitor SLATS progress; the committee provides input with regard to overall direction and methods and provides advice on the release of project results. The committee consists of representatives of DNR, Department of Primary Industries (DPI), Environment Protection Agency (EPA), Queensland Conservation Council, Cattlemenâs Union, United Graziers Association, Queensland Canegrowers, Local Government Association, academia, Land and Water Resources Research and Development Corporation and Queensland Grain Growers Association.
An analysis of 1991-95 woody vegetation change has been completed for the entire State and is the subject of this report. The 1995-97 change analysis is nearing completion and will be presented in a separate report. A sample of scenes has been processed for 1988-91 vegetation change with approximately 12 per cent of the State complete. Detailed baseline landcover mapping which discriminates areas of trees from, pasture, crop, water, settlement, etc is being done using 1991 imagery. This has been completed for approximately 70 per cent of the State, which includes all areas affected by clearing in the 1991-95 period.
This document reports woody vegetation cover change for the period 1991-95 and supersedes all previous 1991-95 change figures. There were two previous SLATS 1991-95 change results; the preliminary 308,000 ha/year ± 25 per cent reported in 1996, then the 262,000 ha/year ± 10 per cent which was published in the Interim Report in 1997 (QDNR, 1997). Both these results were based on the best possible information at the time. The final figure has now converged to 289 000 ha/year which is within the range quoted on both previous figures.
The change in these figures is a result of improved estimates due to completion of ground truthing, land cover mapping (which provides a mask of woody vegetation before clearing), consistency checks with 1995-97 change analysis, final interpretation and checking by independent operators. There will continue to be minor updates to the 1991-95 vegetation change data as 1988-91 change analysis and consistency checks are completed. The land cover mapping has been a very time consuming task and has delayed the ability to report final 1991-95 figures. Now that it is completed within the major clearing areas, new change analysis (e.g.1997-99) can be completed much faster.
A separate report covering land cover change in south east Queensland (QDNR,1999) over the period 1988-97 has been published. This also includes information on 1991-95 vegetation change and provides a more detailed breakdown of clearing by catchments within south east Queensland.
Definition of woody vegetation
There are many definitions of what constitutes 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). It is clear in pages 5.14 and 5.25 of the the Intergovernmental Panel on Climate Change guidelines (IPCC,1996) that where possible, all changes in woody biomass stocks resulting from human interventions should be measured and included in the 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 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 5 per cent foliage cover or greater.
Landsat TM satellite imagery was purchased from the Australian Centre for Remote Sensing (ACRES). In most cases 1991 and 1995 imagery was used but when cloud-free 1991 and 1995 imagery was not available, 1990 or 1994 imagery was purchased. Approximately 7 per cent of the State is covered with 1990 imagery and 15 per cent of the State with 1994 imagery. The remainder is 1991 and 1995 imagery. The 1990 and 1994 data are referred to throughout the report as 1991 and 1995 imagery. Winter imagery was chosen to maximise discrimination between grasses and the woody component of the vegetation.
Imagery must undergo several pre-processing steps prior to use in change detection and mapping procedures. The imagery is first corrected for variation in solar zenith angle and then standardised for atmospheric variations between dates by radiometrically registering all dates to a reference year. Corrections are applied to improve scene to scene matching along each path and an across-scene correction is also applied (Collett et al., 1998).
Geometric correction involves registration of all scenes to the 1991 reference year. Semi-automated image correlation methods are 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. Preliminary transformations are generated using ground control points identified on 1:100 000 and 1:250 000 scale topographic maps. The accuracy of these points is upgraded in the field using differential global positioning systems (Fugro and Garmin type, with sub 10m accuracy) to maintain a constant spatial accuracy of at least 1:100 000 scale. (Kuhnell et al., 1998).
Woody vegetation change detection
The vegetation change has been mapped using classification procedures similar to those described in 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.
Each scene was ground truthed;
- to verify vegetation change classification accuracy;
- to measure differential GPS control points;
- to collect vegetation site data for calibration and validation purposes;
- by liaising with local DNR officers and landholders.
A detailed baseline landcover survey was done using 1991 imagery and vegetation site data. This involved 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).
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 an independent operator.
Both clearing and regrowth of woody vegetation were classified. Regrowth is more difficult to measure due both to slow rates of change (relative to clearing) and to 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 1991-95 period.
The proportion of clearing which is re-clearing of previously cleared areas (regrowth clearing) is unknown. Regrowth clearing is best mapped using a sequence of imagery and identifying cleared-woody-cleared transitions. Historical Landsat Multi Spectral Scanner (MSS) imagery (1972, 1980, 1984) has been acquired for the entire State. This imagery and the more recent Landsat TM imagery will be analysed to determine the proportion of clearing which is regrowth control and to provide estimates of historical clearing rates.
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 shows that on average a fire removes less than 2m2/ha basal area. Hence it is not common for fire to change the landcover from woody to non-woody in single event. There were a few cases of very hot fire resulting in land cover change and these areas were assigned a replacement landcover class of pasture.
A significant amount of natural tree death occurred in the area surrounding Charters Towers due to a prolonged drought in this area. It is quite common for trees to lose foliage during dry spells then recover. The area where this happens is far greater than where tree death is occurring so it is quite difficult to distinguish tree death from normal variations in the foliage cover using remote sensing alone. Considerable field work and additional imagery is required to map areas affected by natural tree death. While canopy loss is evident in the 1995 imagery, the majority of actual tree death was believed to occur after the 1995 imagery. Hence this change is not included in the statistics in this report. An assessment of this natural tree death will be provided in a report on 1995-97 vegetation change.
Replacement land cover
Every patch which was 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 converted to urban development some time later. These factors will cause the interpretation accuracy for replacement class to be lower than the accuracy for identification of woody vegetation change.
|Replacement landcover or land use||Description|
|Pasture||Cleared to pasture. Includes clearing for grazing, rural residential, future urban land use, native forestry on private land, privately owned plantations cleared to 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 urban development|
Compiling statewide data sets
Large seamless mosaics of vegetation change, landcover and vegetation cover data layers were created by joining together the 58 scenes that contained some vegetation change. Scenes were overlapped with preference to the newer final date in the overlap (e.g 1991-95 used in preference to 1991-94). The size of the digital file for each mosaic was approximately 7 Gigabytes. In order to calculate annual tree clearing rates, a vector geographic information system (GIS) layer containing the extent and dates (Julian date) of mosaiced scenes was created. The mosaic raster of cleared areas was intersected with one kilometre resolution GIS overlays depicting date, tenure type, 30'x30' grid cell, soil types and structural vegetation classifications to generate tabular statistics. A spatial resolution of one kilometre resolution was used as it was appropriate given the scale of most input data and was more efficient to compute than full 30m resolution. The tabular statistics show slightly different State clearing totals which is due to the different scales of the GIS overlays used.
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.
Accuracy of interpretation
The traditional form of accuracy assessment is an assessment against 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 its use is not a viable option and other options need to be considered. A formal accuracy assessment is proposed as part of the national Remote Sensing of Agricultural Land Cover Change project (Barson, 1996) and will use independent methods rather than independent data. DNR was a partner in this project so this will be done with the 1991-95 change data that DNR supplied to this project. It is expected to be completed by December 1999.
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 the vegetation change. After analysing these data, it has been considered that the error term on the current statewide clearing figure is approximately ±8 per cent at a 95 per cent 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. Another measure of the accuracy is how much the change analysis is modified as a result of field work and subsequent interpretation checks. A recent field trip in southern Queensland to check six 1995-97 change scenes showed a maximum variation of 2.6% on a single scene and a variation of 1% on the six scene total.
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 operators 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, the analysis is biased so that too much change is mapped. Following fieldwork, the incorrect areas are rejected. This ensures that very little change is missed and our fieldwork confirms this is the case.
Hence it seems the largest source of variation is not mis-classicication of change but determining the extent (area) of change at a clearing location. The woody vegetation mask (FPC layer) 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 this FPC layer is 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. In the future, laser profiling techniques may provide a further means of calibrating and validating the woody FPC layer. There has been an initial investigation of these laser altimetry methods by Tickle et al. (1998) and a detailed two year testing program is now underway.
Statewide assessment of woody vegetation change
The average annual clearing rate over the period 1991 to 1995 was calculated to be 289,000 hectares per year or 0.16 per cent of the land area of Queensland per year. A previous study (Danaher et al., 1992) has shown that the area of wooded vegetation in Queensland is 76 million hecatres. 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 0.48 per cent of this area per year. In total, the annual clearing figure represents a clearing area of 54km x 54km or approximately an area the size of a 1:100,000 map sheet per year. If a woody vegetation definition of 12 per cent FPC (approximately 20 per cent crown cover) were used, the annual average clearing rate from 1991 to 1995 would be 276,000 hectares per year. A spatial view of where the clearing is occurring within Queensland is shown in Figure 1. It shows 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 1991 woody vegetation cleared each year over the 1991-95 period, as in Figure 2. This map was created by using the 1991 baseline landcover mapping (Kuhnel et al,1998) produced by SLATS and combining this with the 1991-95 clearing data. A map of 1991 woody vegetation by 30° x 30° grid cell is shown in Figure 3. This map is not yet complete for the entire State but is complete for all the 1991-95 clearing areas. Please take care when interpreting the results in Figures 2 and 3 as some of the differences in per centage of 1991 woody vegetation are related to the original type of vegetation cover, not clearing. For example there are significant areas of natural grassland which were never woody.
The replacement landcover after clearing is summarised in Table 2. It shows that the majority of clearing is for conversion to pasture for grazing.
The 1991-95 woody vegetation change has also been grouped by tenure in Table 3. This information has ben derived through a combination of the woody change data with the Digital Cadastral Data Base and the Tenures Administration System. Figure 4 is a map of the four broad tenure classes used in Table 3. The area of predominantly freehold tenure is also shown on the maps in Figures 1 to 3 using shading.
Figure 1: Queensland average annual clearing rate (1991-1995)
Figure 2: Queensland per centage of 1991 woody vegetation cleared (1991-1995)
Figure 3: Queensland woody vegetation cover (1991).
|Replacement cover||Clearing rate (km2/year)||% of state clearing total|
|Tenure||Rate of woody vegetation change (km2/year)||% Tenure type area cleared per year (1991- 1995)||% of total clearing in QLD per year (1991-1995)|
(Commonwealth lands, mining tenure, main roads, water, railways, ports, action pending etc)
(State forest, timber reserves, national parks)
Figure 4: Land tenures in Queensland, 1997
Woody vegetation change by foliage cover and basal area
The 1991-95 woody vegetation change data was 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 foliage cover (Figure 5). The highest frequency of clearing occurs at an FPC value of 24 per cent. Development of the FPC layer is described by Kuhnell et al (1998).
Figure 5: 1991-95 clearing by foliage projective cover (FPC)
A woody basal area layer was derived from the foliage cover (FPC) layer using relationships between FPC and basal area based on site measurements. This relationship only holds for mature vegetation, so areas of young regrowth identified using 1988 imagery were set to a nominal basal area of 1m2/ha. The basal area layer will continue to be improved using historical Landsat TM and MSS analyses to identify previously cleared vegetation. Kuhnell et al (1998) describe the development of the basal area data. Figure 6 shows the distribution of clearing in relation to basal area. The highest frequency of clearing occurs at the value 9m2/ha. An example of a typical woodland of basal area 9m2/ha and average foliage projective cover 24 per cent is shown in Figure 7.
Figure 6: 1991-95 clearing by basal area
Figure 7: Eucalypt woodland basal area of approximately 9m2/ha.
The 1991-95 clearing raster was intersected with the foliage cover classes from the 1:5 000 000 Carnahan Present Vegetation Map (Carnahan, 1988) to produce Table 4. All vegetation classes which included some clearing have been included in this table. In the National and State 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 are added into 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 in Queensland are due to this problem.
|Carnahan vegetation class||Description||Clearing rate (km2/year)||% of total clearing|
|F3||Other herbaceous plants 30-70% foliage cover||0.37||0.01|
|F4||Other herbaceous plants >70% foliage cover||13.90||0.43|
|G2||Tussocky or Tufted Grasses 10-30% foliage cover||40.91||1.42|
|G3||Tussocky or Tufted Grasses 30-70% foliage cover||333.60||11.58|
|G4||Tussocky or Tufted Grasses >70% foliage cover||20.18||0.70|
|L1||Low trees (<10m) <10% foliage cover||339.28||11.77|
|L2||Low trees (<10m) 10-30% foliage cover||339.24||11.77|
|L3||Low trees (<10m) 30-70% foliage cover||9.20||0.32|
|L4||Low trees (<10m) >70% foliage cover||0.31||0.01|
|M1||Medium trees (10-30m) <10% foliage cover||684.15||23.74|
|M2||Medium trees (10-30m) 10-30% foliage cover||828.64||28.75|
|M3||Medium trees (10-30m) 30-70% foliage cover||250.24||8.68|
|M4||Medium trees (10-30m) >70% foliage cover||7.93||0.28|
|S1||Tall shrubs (>2m) <10% foliage cover||8.40||0.29|
|S2||Tall shrubs(>2m) 10-30% foliage cover||4.79||0.17|
|T3||Tall trees (>30m) 30-70% foliage cover||0.04||0.00|
|Z1||Low shrubs (<2m) <10% foliage cover||0.73||0.03|
Woody vegetation change by soil type
A table of clearing by soil type (Table 5) was created by intersecting the 1991-95 clearing areas with data from the 1:2,000,000 Atlas of Australian Soils.
|Dominant soil type (Northcote)||Clearing rate (km2/year)||% of total clearing|
Barson, M.M. (1996) Remote Sensing of Agricultural Land Cover for the Australian Continent, In: Proceedings of the 8th Australasian Remote Sensing Conference, Canberra 25-29 March 1996, Vol. 2, pp 122-127.
Carnahan, J.A. (1988) Map of Australia Present Vegetation, Australian Surveying and Land Information Group, Canberra.
Collett, L., Goulevitch, B and Danaher, T.J. (1998) SLATS Radiometric Correction: A Semi Automated Multi Stage Process for the Standardisation of Temporal and Spatial Radiometric Differences, In: Proceedings of the 9th Australasian Remote Sensing and Photogrammetry Conference, Sydney, Australia, July 1998.
Danaher, T., Carter, J.O., Brook, K.D. and Dudgeon, G. (1992) Broadscale Vegetation Mapping Using NOAA AVHRR Imagery. In: ΐroceedings of the Sixth Australasian Remote Sensing Conference, 2-6 November 1992, Wellington, New Zealand.⠬ Vol. 3, pp 128-137.
Intergovernmental Panel on Climate Change (1996) Greenhouse Gas Inventory Reference Manual, Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories, Volume 3.
Kuhnell, C., Goulevitch, B., Danaher, T. and Harris, D. (1998) Mapping Woody Vegetation Cover over the State of Queensland using Landsat TM Imagery. In: Proceedings of the 9th Australasian Remote Sensing and Photogrammetry Conference, Sydney, Australia, July 1998.
Paudyal, D., Kuhnell,C. & Danaher, T. (1997) Detecting Change in Woody Vegetation in Queensland using Landsat TM Imagery, In proceedings, In: Proceedings of the North Australasian Remote Sensing and GIS Conference (NARGIS), Cairns, April 28-30,1997.
Queensland Department of Natural Resources (1997) Interim Report - The Statewide Landcover and Trees Study (SLATS), October 1997. pp. 37.
Queensland Department of Natural Resources (1999) Land Cover Change in South East Queensland 1988-1997, A Statewide Landcover and Trees Study (SLATS) report, April 1999. pp. 44.
Tickle, P., Witte, C., Danaher, T. and Jones, K. (1998). The Application of Large-Scale Video and Laser Altimetry to Forest Inventory, In: Proceedings of the 9th Australasian Remote Sensing and Photogrammetry Conference, Sydney, Australia, July 1998.
Last updated 1 June 1999