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Product pages » Crop-mapping - Shapefiles, DES algorithm, Queensland Coverage

Crop-mapping - Shapefiles, DES algorithm, Queensland Coverage

Last modified by Matt Pringle on 2018/08/17 10:49

Link to the dataSummer_2017.png

DescriptorData linkLayer name
Dataset digital object identifier (DOI)
 
GeoNetwork recordNot Available Yet

QLD Shapefiles - THREDDShttp://qld.auscover.org.au/thredds/catalog/auscover/landsat/crop_frequency/qld/crop_type/catalog.html

QLD Shapefiles - FTP accessftp://qld.auscover.org.au/landsat/crop_frequency/qld/crop_type
Forage reporthttps://www.longpaddock.qld.gov.au/forage/cropfrequency.phpcrop frequency report 

Data licence and Access rights

ItemDetail
RightsCopyright 2010-2015. Rights owned by the Queensland Department of Environment and Science (DES).
LicenceCreative Common Attribution (CC-BY) 3.0
AccessWhile every care is taken to ensure the accuracy of this information, DES 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.
 CitationPringle, M.J., Schmidt, M., and Tindall, D.R. (2018): Multi-decade, multi-sensor time-series modelling---based on geostatistical concepts---to predict broad groups of crops. Remote Sensing of Environment, 216, 183--200.

Alternate title

Crop-mapping---Western Queensland cropping zone 1987-2018, derived from Landsat imagery, Sentinel-2 imagery and MODIS MOD13Q1 imagery .

This dataset shows the broad groups of crops grown in the western zone of Queensland's Strategic Cropping Land, for the winter and summer growing-seasons, from 1987 to the current year. The winter growing-season is predefined as June to October, and the summer growing-season is November to May. The basis of the maps is imagery from the (when available) Landsat-5 TM, Landsat-7 ETM+, Landsat-8 OLI, and Sentinel-2(A,B) satellites; MODIS MOD13Q1 imagery was used as a backup in the case of large, temporal data gaps. Clusters of temporally 'alike' pixels, termed 'image segments', were identified in the imagery for each growing season, and served as an approximation of field boundaries. Per-image-segment phenological information, derived from the satellite imagery, was then combined with a tiered, tree-based statistical classifier, using >7000 field observations as training data, and >2500 independent observations for validation. The prediction of the classification model is stored in the attribute table of the shapefile (field 'GROUP'), along with the probability of the prediction (field 'P_GROUP'). The dataset supersedes a former crop-mapping effort (Schmidt et al., 2016).

The statistical classifier predicts these groups in winter:

  • Cereal crop (e.g. wheat, barley, oats);
  • Pulse crop (e.g. chickpea); and
  • Bare soil.

The groups predicted in summer are:

  • Coarse-grain & Pulse crops (e.g. sorghum, maize, mungbean);
  • Cotton crop; and
  • Bare soil.

The 'Bare soil' group is to be defined as 'displays in Landsat imagery as obviously bare soil for at least two months during a growing-season, with no obvious crop grown'.

A number of ancillary datasets have been used to mask out certain features that are not related to the prediction of actively growing crops: - Nature conservation, Mining, Transport infrastructure, Waste and Disposal, Manufacturing and industrial infrastructure, Stock routes, Townships, Forestry and plantation areas have been masked using the nearest contemporary Queensland Land Use Mapping Program (QLUMP) dataset.

For further information please read the paper published in Remote Sensing of Environment.

Validation status

The overall classification accuracies over all growing seasons are:

-  summer: 'tau_p' statistic = 0.80, 'kappa' statistic = 0.78;

- winter: 'tau_p' statistic = 0.86, 'kappa' statistic = 0.86.

Limitations:

The largest sources of error were that ‘Bare soil’ is likely to be under-represented, and that, during winter, ‘Pulse' can be misclassified as ‘Cereal’. Other errors may occur in the data which are due to unknown effects.

Pastures: these may be incorrectly classified as cropping, particularly in wet years or in seasons when pastures green-up rapidly and at similar times to actively growing crops.

Tree Crops: horticulture is excluded from the crop mapping based on the QLUMP mapping.

Land-in-transition: formerly-cropped land may still appear as cropped if the vegetation greens-up during the growing period, e.g. weeds or redundant or abandoned crops and crop residue are dominating the ground cover.

Failed crop: a failed crop can only be detected if it reached a certain level of greenness, which may vary between region, seasons and years.

Clearing: some recent vegetation clearing and conversion to agriculture may not be included in the mapping but are expected to be mapped in future updates.

Water: vegetated areas around watercourses and dams may be classified as crop due to the strong greening-up phase after rainfall events. Some areas of shallow water or water with emergent or floating vegetation may be incorrectly classified as cropped, due to seasonal patterns in greenness that may be similar to crops.

Topographic effects: areas with steep slopes (i.e. a slope of greater than 10%) are excluded. Inaccuracies in the DEM used for identifying these areas may result in some areas being included.

Spatial and Temporal extents

ItemDetail
Spatial resolution (metres)Created on a 30-m grid, then vectorised.
Spatial coverage (degrees)North:-20.3; South:-29.2; West:146.8; East:152.5
Temporal resolutionSeasonally (winter and summer)
Temporal coverage1987-ongoing
Sensor & platformLandsat-5 TM, Landsat-7 ETM+, Landsat-8 OLI, Sentinel-2(A,B) MSI, MODIS MOD13Q1
ItemDetail
Spatial representation typeVector
Spatial reference systemAustralian Albers. EPSG:3577

Point of contact

ItemDetail
NameMatthew Pringle
OrganisationQLD Department of Environment and Science
PositionA/Environmental Statistician
ailmatthew.pringle@des.qld.gov.au
Rolepoint of contact
AddressRemote Sensing Centre, DES, EcoSciences Precinct
Telephone+61 7 3170 5680
URLhttps://www.qld.gov.au/environment/land/vegetation/mapping/crops

Credit

Landsat imagery was obtained from the US Geological Survey. Modified-Copernicus-Sentinel-2 imagery was obtained from the European Space Agency. MODIS MOD13Q1 imagery was obtained from the LP DAAC Data Pool.

Filenaming convention

Filenames follow a simple convention:

ACTIVECROP_<season><year>_X.shp

Example:

ACTIVECROP_WINTER1987_X.shp

In the current growing-season, an interim map is produced that represents a mid-season forecast. In this case, the naming convention is:

ACTIVECROP_<season><year>_X_vInterim.shp

The interim map will be replaced at the season's end.

Keywords

ThesauriKeyword
GCMDAgriculture
CFvegetation_area_fraction
FoRDIVISION 07 AGRICULTURAL AND VETERINARY SCIENCES >> GROUP 0703 CROP AND PASTURE PRODUCTION

There are three main thesauri that AusCover recommends:

  1. Global Change Master Directory (http://gcmd.nasa.gov)
  2. Climate and Forecast (CF) convention standard names (http://cfconventions.org/standard-names.html).
  3. Fields of Research codes (http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E?opendocument).

Data quality

Horizontal Positional Accuracy

All the data described here has been generated on the basis of Landsat TM, ETM+ and OLI data, which has a spatial resolution of approximately 30 m. Sentinel-2 and MODIS imagery were re-sampled to the same grid. The imagery is rectified using control points measured with a differential GPS ensuring a maximum root mean square (RMS) error of 20 m at these control points. However, it is possible that errors up to ±50 m occur between these control points. The imagery has been corrected for height displacement using a 3" digital elevation model (DEM) based on the National Aeronautics and Space Administration (NASA), Shuttle Radar Topography Mission (SRTM). It is not recommended that these data sets be used at scales more detailed than 1:100,000.

Vertical Positional Accuracy

All the data described here has been generated from the analysis of Landsat TM, ETM+ and OLI data, which has a spatial resolution of approximately 30 m. The imagery is rectified using control points measured with a differential GPS ensuring a maximum root mean square (RMS) error of 20 m at these control points. However, it is possible that errors up to ±50 m occur between these control points. The imagery has been corrected for height displacement using a 3" digital elevation model (DEM) based on the National Aeronautics and Space Administration (NASA), Shuttle Radar Topography Mission (SRTM). It is not recommended that these data sets be used at scales more detailed than 1:100,000.

References and further reading:

ItemDetail or link
Publication Pringle, M.J., Schmidt, M., and Tindall, D.R. (2018): Multi-decade, multi-sensor time-series modelling---based on geostatistical concepts---to predict broad groups of crops. Remote Sensing of Environment, 216, 183--200.
PublicationSchmidt, M., Pringle, M., Devadas, R., Denham, J., and Tindall, D. (2016). A framework for large-area mapping of past and present cropping activity using seasonal Landsat images and time series metrics. Remote Sensing, 8, 312.

Metadata history

DateDetail
2015-04-30Metadata creation date
2015-05-19Added DOI and minor format changes
2015-05-22Minor changes
Tags:
Created by Bec Trevithick on 2017/11/30 12:38

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