Senior Spatial Information Officer (Product Delivery)
Contact—organization name
Department of Natural Resources and Mines
Contact—position name
Client Outcomes (Product Delivery)
Rp Cnt Info
Cnt Address
Country
AU
Role
details available on request
Cit Resp Party
Contact—individual name
Data Custodian (Assistant D-G, Science Division)
Contact—organization name
Department of Environment and Science
Contact—position name
Assistant Director-General, Science Division
Rp Cnt Info
Cnt Address
Country
AU
Role
details available on request
Additional human language abstract/description
This dataset is a digital map of the most recent land use of Queensland. Land use is classified according to the Australian Land Use and Management Classification (ALUMC).
Resource purpose/summary
Indicates the most current primary use or management objective of the land for Queensland.
This material is licensed under a Creative Commons - Attribution 4.0 Australia licence. The Department requests attribution in the following manner: Copyright State of Queensland (Department of Environment and Science) 2018. Updated data available at http://qldspatial.information.qld.gov.au/catalogue/
This material is licensed under a Creative Commons - Attribution 4.0 Australia licence. The Department requests attribution in the following manner: Copyright State of Queensland (Department of Environment and Science) 2018. Updated data available at http://qldspatial.information.qld.gov.au/catalogue/
Res Const
Consts
Security use limitations
This material is licensed under a Creative Commons - Attribution 4.0 Australia licence. The Department requests attribution in the following manner: Copyright State of Queensland (Department of Environment and Science) 2019. Updated data available at http://qldspatial.information.qld.gov.au/catalogue/
Res Const
Consts
Security use limitations
The Australian Collaborative Land Use and Management Program (ACLUMP) publishes land use information (including location and extent) by assigning a land use code to features (subject to mapping scale limitations). Under version 8 of the Australian Land Use and Management (ALUM) Classification, land use can include commodity type and management practice information. Sources used to compile this information include remotely sensed data, state and national ancillary datasets, field observation and expert opinion. No personal or confidential information is collected as part of the land use mapping process nor contained within the land use datasets.
While every care is taken to ensure the accuracy of this information, Queensland Government makes no representations or warranties about its accuracy, reliability, completeness or suitability for any particular purpose and disclaims all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages (including indirect or consequential damage) and costs which might be incurred as a result of the information being inaccurate or incomplete in any way and for any reason.
The dataset is a product of the Queensland Land use Mapping Program (QLUMP) and was produced by the Queensland Government. The dataset comprises an ESRI vector geodatabase (10.5.6491) at a nominal scale of 1:50,000. The layer is a polygon dataset with each feature having attributes describing land use. Land use is classified according to the Australian Land Use and Management Classification (ALUMC) Version 8, October 2016. Five primary classes are identified in order of increasing levels of intervention or potential impact on the natural landscape. Water is included separately as a sixth primary class. Under the three-level hierarchical structure, the minimum attribution level for land use mapping in Queensland is secondary land use. Primary and secondary levels relate to land use (i.e. the principal use of the land in terms of the objectives of the land manager). The tertiary level includes data on commodities or vegetation, (e.g. crops such as cereals and oil seeds). Where required and possible, attribution is performed to tertiary level. QLUMP maps the land use classes of sugar and cotton consistently to tertiary level. Under ACLUMP version 8, we have also mapped all conservation, intensive animal husbandry, residential, services, utilities, and transport and communication land use classes to tertiary level. Since 2015, QLUMP includes commodity level classification of banana, avocado, macadamia and mango orchards (dryland and irrigated). Refer to the contact position for additional information regarding source data. Further information relating to land use mapping can be found at http://www.qld.gov.au/environment/land/vegetation/mapping/qlump/ and http://www.agriculture.gov.au/abares/aclump/land-use/.
Dq Info
Report
Data quality report, measure description
Completeness of coverage: All spatial and attribute data are complete for the entire dataset. Completeness of classification: Land use features were captured from a range of source data. Mapping from satellite imagery was generally undertaken to the smallest discrete unit able to be visually interpreted using the visual cues of colour, texture and pattern (approximately one hectare). Land use information from ancillary datasets was captured at the scale of the source data. The resulting land use dataset therefore contains features at a range of scales and resolutions. To promote consistency in the way land use features are handled and represented, project guidelines specify minimum data resolution standards appropriate to various mapping scales. At a scale of 1:50,000 the surface area of the smallest mapped feature is two hectares and minimum width for linear features is 50 metres. Land use classes were assigned according to the Australian Land Use and Management Classification Version 8, October 2016 (Australian Bureau of Agriculture and Resource Economics and Sciences, 2016).
Data quality report, evaluation method description
To ensure the completeness of the data, the topology rules of "Must Not Overlap" and "Must Not Have Gaps" are valid.
Report
Data quality report, measure description
Delineation of land use attributes was based on visual interpretation of multi-temporal Landsat, SPOT6 and SPOT7 satellite imagery, Earth-i imagery, Planet imagery, high-res ortho photography, scanned aerial photography as well as ancillary data sets containing land use information, field observations and personal communication with regional Queensland Government staff. Assignment of land use classes was based on ALUMC Version 8 (October 2016).
Data quality report, evaluation method description
An independent accuracy assessment of the completed dataset was undertaken using a modified version of the methods described in "Guidelines for land use mapping in Australia: principles, procedures and definitions", 4th edition, 2011 (ABARES).
Report
Data quality report, measure description
Land use polygons were derived from datasets with a range of scales, as well as on-screen hand digitising from imagery. The positional accuracy of source datasets was variable ranging from approximately 25 metres for information derived from 1:50,000 scale datasets, to 50 metres for information derived from 1:100,000 scale datasets. Hand digitising was undertaken to an error of approximately one millimetre (50 metres at map scale). Where there were inconsistencies between polygon boundaries defined by source datasets (vector coverages) and imagery, they were adjusted to conform to the imagery as this was considered to have higher positional accuracy.
Data quality report, evaluation method description
An independent accuracy assessment of the completed dataset was undertaken using a modified version of the methods described in "Guidelines for land use mapping in Australia: principles, procedures and definitions", 4th edition, 2011 (ABARES).
Report
Data quality report, measure description
Several classes were highlighted as being susceptible to misclassification and issues arose which increased uncertainty in others. Livestock grazing occurs on a range of pasture types including native and exotic as well as mixtures of both. Identifying and separating these using imagery, aerial photography and field observation is difficult and unreliable. Areas of pasture which appeared to be harvested for fodder or grazed often were mapped as Cropping (3.3.0). This may contribute to an over-estimation of cropping in the region. Other areas mapped as grazing include road reserves, cleared and uncleared land adjacent to rivers and streams as well as land immediately adjacent to or between cropped paddocks. Other minimal use (1.3.0) and Remnant native cover (1.3.3) may also be confused with this class. The appearance of these can be highly variable and classification may therefore not be consistent. The areas mapped as cropping and irrigated cropping include features such as tracks and drainage lines which are too small to be mapped separately at the scale of 1:50,000. Guidelines for mapping the class 'Intensive animal husbandry' (5.2.0) have changed in ALUMC Version 8. Only the infrastructure associated with intensive animal production should be classified as 5.2.0 (e.g. dairy sheds, 5.2.1). The surrounding pastures should generally mapped as 'Grazing modified pastures' (3.2.0) or 'Grazing irrigated modified pastures' (4.2.0). Rural residential with Agriculture (5.4.2) areas are a source of possible error. Properties on the fringes of suburban settlements, hobby farms and subdivisions in isolated localities with comparatively small lot sizes were mapped to this class. The use of QVAS (valuation information) was useful, based on whether or not the land owner was classified as a primary producer. This class may be misclassified with Grazing native vegetation (2.1.0) and Other Minimal Use (1.3.0), especially on larger properties. The distinction between dryland and irrigated cropping (3.3.0 and 4.3.0) was not always evident and it is likely there is some misclassification in these classes. Proximity to water sources (watercourse or dam) and local knowledge was used to confirm areas of irrigation as much as possible. Areas mapped as irrigated cropping are potentially only irrigated on a supplementary basis and may not have actually been irrigated in the current mapping year. The Queensland Herbarium's wetlands dataset provided the basis for mapping marsh/wetlands (6.5.0), lakes (6.1.0) and reservoir or dams (6.2.0). The ephemeral nature of many of these can lead to confusion insofar as they may be present in imagery of one date and either absent or of differing extent in imagery of subsequent or previous dates. For this reason, only wetlands, lakes and dams that were commonly or permanently inundated were included in the dataset. As a result, there is likely to be errors & omissions and some disagreement in the mapping of features such as farm dams, reservoirs, lakes, wetlands & other water features. Many water features whilst exceeding the minimum mappable area requirements, do not meet the criteria for linear or uniform features. The land use datasets are a snapshot of what was interpreted as the primary land use for a particular year. However, effort was given to distinguishing between an actual land use change and a rotation. For example, an area that is usually cropped, but is not used for that particular purpose in the year of interest, was still mapped as cropping even though no crop was present in that year. This was not considered an actual land use change, but rather a rotation, as the primary land use for that paddock would still be cropping. The revised land use mapping has been improved through the interpretation of the most suitable imagery available. On occasion this will be Landsat (30m), which raises some uncertainty in respect of accurately classifying the intensive land use classes. The minimum mapping unit (2ha) also contributes to the uncertainty through the aggregation of otherwise individual land use features, particularly at cadastral parcel level. These limitations may therefore lead to omission and commission errors in the classification of the intensive land use classes in earlier mapping products and the land use change products which are derived from them.
Data quality report, evaluation method description
An independent accuracy assessment of the completed dataset was undertaken using a modified version of the methods described in "Guidelines for land use mapping in Australia: principles, procedures and definitions", 4th edition, 2011 (ABARES).
Report
Data quality report, measure description
Several classes were highlighted as being susceptible to misclassification and issues arose which increased uncertainty in others. Livestock grazing occurs on a range of pasture types including native and exotic as well as mixtures of both. Identifying and separating these using imagery, aerial photography and field observation is difficult and unreliable. Areas of pasture which appeared to be harvested for fodder or grazed often were mapped as Cropping (3.3.0). This may contribute to an over-estimation of cropping in the region. Other areas mapped as grazing include road reserves, cleared and uncleared land adjacent to rivers and streams as well as land immediately adjacent to or between cropped paddocks. Other minimal use (1.3.0) and Remnant native cover (1.3.3) may also be confused with this class. The appearance of these can be highly variable and classification may therefore not be consistent. The areas mapped as cropping and irrigated cropping include features such as tracks and drainage lines which are too small to be mapped separately at the scale of 1:50,000. Guidelines for mapping the class 'Intensive animal husbandry' (5.2.0) have changed in ALUMC Version 8. Only the infrastructure associated with intensive animal production should be classified as 5.2.0 (e.g. dairy sheds, 5.2.1). The surrounding pastures should generally mapped as 'Grazing modified pastures' (3.2.0) or 'Grazing irrigated modified pastures' (4.2.0). Rural residential with Agriculture (5.4.2) areas are a source of possible error. Properties on the fringes of suburban settlements, hobby farms and subdivisions in isolated localities with comparatively small lot sizes were mapped to this class. The use of QVAS (valuation information) was useful, based on whether or not the land owner was classified as a primary producer. This class may be misclassified with Grazing native vegetation (2.1.0) and Other Minimal Use (1.3.0), especially on larger properties. The distinction between dryland and irrigated cropping (3.3.0 and 4.3.0) was not always evident and it is likely there is some misclassification in these classes. Proximity to water sources (watercourse or dam) and local knowledge was used to confirm areas of irrigation as much as possible. Areas mapped as irrigated cropping are potentially only irrigated on a supplementary basis and may not have actually been irrigated in the current mapping year. The Queensland Herbarium's wetlands dataset provided the basis for mapping marsh/wetlands (6.5.0), lakes (6.1.0) and reservoir or dams (6.2.0). The ephemeral nature of many of these can lead to confusion insofar as they may be present in imagery of one date and either absent or of differing extent in imagery of subsequent or previous dates. For this reason, only wetlands, lakes and dams that were commonly or permanently inundated were included in the dataset. As a result, there is likely to be errors & omissions and some disagreement in the mapping of features such as farm dams, reservoirs, lakes, wetlands & other water features. Many water features whilst exceeding the minimum mappable area requirements, do not meet the criteria for linear or uniform features. The land use datasets are a snapshot of what was interpreted as the primary land use for a particular year. However, effort was given to distinguishing between an actual land use change and a rotation. For example, an area that is usually cropped, but is not used for that particular purpose in the year of interest, was still mapped as cropping even though no crop was present in that year. This was not considered an actual land use change, but rather a rotation, as the primary land use for that paddock would still be cropping. The revised land use mapping has been improved through the interpretation of the most suitable imagery available. On occasion this will be Landsat (30m), which raises some uncertainty in respect of accurately classifying the intensive land use classes. The minimum mapping unit (2ha) also contributes to the uncertainty through the aggregation of otherwise individual land use features, particularly at cadastral parcel level. These limitations may therefore lead to omission and commission errors in the classification of the intensive land use classes in earlier mapping products and the land use change products which are derived from them.
Data quality report, evaluation method description
An independent accuracy assessment of the completed dataset was undertaken using a modified version of the methods described in "Guidelines for land use mapping in Australia: principles, procedures and definitions", 4th edition, 2011 (ABARES).
Data Lineage
Lineage statement, a general explanation
The land use features were identified using satellite image and ancillary data interpretation. The mapping is verified through field mapping and/or meeting with local officers or extension officers. Validation is conducted at the desktop using a stratified random sample of points.
Entity and Attribute Information
details available on request
This page should be cited as:
Department of Environment, Science and Innovation, Queensland (n.d.) Land use – spatial metadata, WetlandInfo website, accessed 8 May 2025. Available at: https://wetlandinfo.des.qld.gov.au/wetlands/facts-maps/get-mapping-help/metadata/land-use/
— Department of the Environment, Tourism, Science and Innovation