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daffd08e-cc13-4e36-a14f-1e8f7c9260ac
Land use mapping series
dataset
Rodney Borregoacevedo
Senior Scientist
rodney.borregoacevedo@des.qld.gov.au
Department of Environment and Science
20230116
ArcGIS Metadata
1.0
Esri File Geodatabase
1
Rodney Borregoacevedo
Senior Scientist
rodney.borregoacevedo@des.qld.gov.au
Land use mapping - 2021 - Great Barrier Reef Catchment
1.0
Esri File Geodatabase
Land use mapping - 2021 - Great Barrier Reef NRM regions
Land use mapping - 2021 - Great Barrier Reef NRM regions
2022-06-30T00:00:00
2022-12-20T00:00:00
2022-12-20T00:28:28
1
Dan Tindall - Science Leader
Department of Environment and Science
Science Leader
<DIV STYLE="text-align:Left;"><DIV><P><SPAN>The Great Barrier Reef (GBR) land use mapping 2021 dataset is a digital land use map for the GBR catchment area. The dataset encompasses the 2021 update of land use mapping series for the six GBR Natural Resource Management (NRM) regions: Cape York, Wet Tropics, Burdekin, Mackay Whitsundays, Fitzroy, and Burnett Mary. The assignment of land use classes to the dataset was based on image interpretation of multi-temporal Landsat, Sentinel, Earth-i and high spatial resolution SPOT (French: Satellite Pour l’Observation de la Terre) and Pléiades satellite imagery, as well as ancillary datasets containing land use information and field observations. Attribution of land use classes is based on a modified Australian Land Use Management Classification (ALUMC) Schema, Version 8 (October 2016).</SPAN></P></DIV></DIV>
The GBR land use mapping 2021 dataset represents primary, secondary, and specific tertiary land use levels aligned with a modified Australian Land Use and Management Classification (ALUMC) Schema, Version 8. This land use dataset was created to support the Paddock to Reef Monitoring Program (P2R) modelling team for the purpose of modelling the nutrients and sediments within the GBR region and their impact on water quality for the Reef 2050 Water Quality Improvement Plan.
State of Queensland (Department of Environment and Science), December 2022.
LAND USE CLASSES
AGRICULTURE
AGRICULTURE-Crops
AGRICULTURE-Horticulture
AGRICULTURE-Irrigation
AGRICULTURE-Livestock
ENERGY
ENERGY-Coal
ENERGY-Electricity
ENERGY-Petroleum
ENERGY-Use
FISHERIES-Aquaculture
FORESTS
FORESTS-Agriforestry
FORESTS-Natural
FORESTS-Plantation
HUMAN-ENVIRONMENT-Housing
HUMAN-ENVIRONMENT-Planning
HUMAN-ENVIRONMENT-Structures-and-Facilities
INDUSTRY-Manufacturing
INDUSTRY-Mining
INDUSTRY-Other
INDUSTRY-Primary
INDUSTRY-Service
LAND
LAND-Cadastre
LAND-Geography
LAND-Use
MARINE-Estuaries
TRANSPORTATION
TRANSPORTATION-Land
VEGETATION
WASTE
WASTE-Liquid
WASTE-Sewage
WASTE-Solid
WATER-Lakes
WATER-Rivers
WATER-Surface
WATER-Wetlands
LAND USE CLASSES
AGRICULTURE
AGRICULTURE-Crops
AGRICULTURE-Horticulture
AGRICULTURE-Irrigation
AGRICULTURE-Livestock
ENERGY
ENERGY-Coal
ENERGY-Electricity
ENERGY-Petroleum
ENERGY-Use
FISHERIES-Aquaculture
FORESTS
FORESTS-Agriforestry
FORESTS-Natural
FORESTS-Plantation
HUMAN-ENVIRONMENT-Housing
HUMAN-ENVIRONMENT-Planning
HUMAN-ENVIRONMENT-Structures-and-Facilities
INDUSTRY-Manufacturing
INDUSTRY-Mining
INDUSTRY-Other
INDUSTRY-Primary
INDUSTRY-Service
LAND
LAND-Cadastre
LAND-Geography
LAND-Use
MARINE-Estuaries
TRANSPORTATION
TRANSPORTATION-Land
VEGETATION
WASTE
WASTE-Liquid
WASTE-Sewage
WASTE-Solid
WATER-Lakes
WATER-Rivers
WATER-Surface
WATER-Wetlands
OFFICIAL
State of Queensland (Department of Environment and Science), December 2022.
Creative Commons Attribution 4.0 International
150
The GBR land use mapping 2021 dataset was produced within the Queensland Land Use Mapping Program (QLUMP) by the Queensland Department of Environment and Science. The dataset comprises an ESRI vector geodatabase. The mapping scale is approximately 1:50,000. The layer is a polygon dataset with each feature having attributes describing land use according to a modified ALUMC. Five primary classes were identified in order of increasing levels of land use intensity (i.e. degree of modification from the natural state). Water was included separately as a sixth primary class, in accordance with ALUMC. ABARES (2011) specifies that under ALUMC’s three-level hierarchical structure of primary, secondary and tertiary, the minimum attribution level for Queensland land use mapping 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). For this dataset, modifications of the ALUMC Version 8 were applied as follows: 1) Attribution was performed to specific tertiary levels for sugar and tree fruits, 2) tertiary codes 542 = "Rural residential with agriculture", 543 = " Rural residential without agriculture", 544 = "Remote communities" and 545 = "Farm building/infrastructure" were all aggregated to code 540, 3) tertiary classes within “Manufacturing and Industrial”, “Services”, “Utilities”, “Water treatment and disposal” and “Transport and Communications” were all mapped as secondary level code 500, 4) tertiary classes for “Nature Conservation”, “Managed Resource Protection” and “Other Minimal Use” were all mapped as primary level code 100 “Conservation and Natural Environment”. Where information was available, commodity level classification of banana, avocado, macadamia and mango orchards (dryland and irrigated) was included. The dataset is an update for published land use datasets for Burdekin 2016, Burnett Mary 2017, Cape York 2013, Fitzroy 2017, Mackay Whitsundays 2016 and Wet Tropics 2015. Due to the use of a modified ALUMC in the GBR land use 2021 dataset, there are limitations in comparing the 2021 dataset with previously published land use datasets. Comparative analyses should be applied where the class aggregation is equivalent between the datasets being compared. 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/
References
Australian Bureau of Agricultural and Resource Economics and Sciences, 2011, Guidelines for land use mapping in Australia: principles, procedures and definitions, fourth edition, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra
Esri ArcGIS 12.9.4.32739
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Rodney Borrego Acedevo
Department of Environment and Science
Senior Scientist
Schema or standard: https://www.agriculture.gov.au/abares/aclump/land-use/alum-classification
Completeness Omission: Completeness of coverage: All spatial and attribute data are complete for the entire dataset. Completeness of classification: Land use features were classified from a range of source data of varying scales and completeness. The mapping is nominally 1:50,000 with a minimum mapping unit of 2 hectares and a minimum width of 50 metres for linear features in accordance with ABARES (2011). Mapping from satellite imagery was generally undertaken to the smallest discrete unit able to be visually interpreted using the visual cues of colour, texture, scale, context, pattern, and size. Ancillary information was used to inform the mapping where available. These data may be at a range of scales and completeness.
Conceptual Consistency: The data is to a standard determined as fit for the purpose for which it was collected.
Positional Accuracy: Polygons were derived from spatial datasets from a range of scales. 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.
Quantitative Attribute Accuracy: The “grazing native vegetation” class (2.1.0) occurs on a range of pasture types including native and exotic as well as mixtures of both. Areas mapped as grazing may include road reserves, cleared and uncleared land adjacent to rivers and streams as well as land immediately adjacent to or between cropped paddocks. The “Other minimal use” (1.3.0) and “Residual native cover” (1.3.3) classes may also be confused with the grazing class as the appearance of these can be highly variable and classification may therefore not be consistent. Areas mapped as cropping and irrigated cropping include features such as tracks and drainage lines which are below the minimum mapping unit(s). “Rural residential with agriculture” (5.4.0) areas may include properties on the fringes of suburban settlements, hobby farms and subdivisions. This class may also be misclassified as “Grazing native vegetation” (2.1.0) and “Other minimal use” (1.3.0), especially on larger properties extending up to 20 hectares. The distinction between dryland and irrigated cropping (3.3.0 and 4.3.0) may be confused in some cases. Proximity to water sources (watercourse or dam) and expert knowledge was used to confirm areas of irrigation. Some areas mapped as irrigated cropping were identified as being irrigated on a periodic basis in time-series imagery and may not necessarily have been irrigated in 2021. The Queensland Herbarium's wetlands dataset (Version 5, 2019-06-14) 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 classes can lead to confusion as they may be present in imagery of one date and either absent or of differing extent in imagery of earlier or later dates. For this reason, only wetlands, lakes and dams that were commonly or permanently inundated and larger than 2 hectares and/or greater than 50m linear width were included in the dataset. The land use dataset was an interpretation of the primary land use for a given year. However, the intention was to distinguish between an actual land use change and change due to management within a land use class (e.g. a crop rotation). For example, an area that was usually cropped, but was not used for that particular purpose in the year of interest, was still mapped as cropping.
Completeness Commission: Due to scale and information sources used for the mapping and assignment of attributes/classes, this dataset may have commission errors included in some classes. Commission errors were not formally assessed. Expert user review provided some opportunity for refinement of the draft prior to finalising this dataset.
Previous land use datasets available for the six GBR NRM regions formed the basis of the GBR land use mapping 2021 dataset. The dataset was generated from visual interpretation of multi-temporal Landsat, Sentinel, Earth-i and high spatial resolution SPOT and Pléiades satellite imagery, as well as ancillary datasets containing land use information, and field observations from the Wet Tropics region. Ancillary datasets included the Digital Cadastral Database (DCDB), Estates, Nature Refuge, Plantation, Queensland Herbarium’s Wetlands, Recreation Land, Intensive Husbandry and Mining, available from the Queensland Spatial Information Resources (SIR) database. Mapping was completed by overlaying existing land use datasets on imagery and manually modifying land use classes aligned with a modified ALUMC. To ensure the completeness of the data, the topology rules to fix errors of "Must Not Overlap" and "Must Not Have Gaps" were applied to the GBR land use mapping 2021 dataset. Users, including modellers from the Paddock to Reef program (Department of Environment and Science) and from the Department of Agriculture and Fisheries, Queensland Fire and Emergency Services and Department of Transport and Main Roads, provided spatially explicit and evidence-based feedback via a web map application. They placed markers for areas that required changes or further consideration. The final GBR land use mapping 2021 dataset incorporated refinements from the users’ feedback.
Sensor: https://esriaustralia.com.au/arcgis-pro
Sensor: https://www.airbus.com/en
Sensor: https://sentinel.esa.int/web/sentinel/missions/sentinel-2
Sensor: https://www.usgs.gov/landsat-missions
Sensor: https://earthi.space/
Repsonsible party: Remote Sensing Sciences, Department of Environment and Science
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