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PANGAEA.
Data Publisher for Earth & Environmental Science

Sabaghy, Sabah; Abuzar, Mohammad; Crawford, Douglas L; McAllister, Andy; Sheffield, Kathryn (2024): Victoria Land Cover Map: Random Forest Classification Using Sentinel-2 Imagery from April 2021 - March 2022 [dataset]. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.973963 (DOI registration in progress)

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Abstract:
The land cover mapping of Victoria, Australia, for 2021/22 was conducted using Sentinel-2 satellite imagery and the random forest machine learning algorithm. This map represents the entire Victoria at a spatial resolution of 20 meters, considerably improving earlier versions that previously were developed using coarser-resolution MODIS imagery. The map follows the FAO Land Cover Classification System to maintain consistency and accuracy in defining land cover types. Sentinel-2 data from April 2020 to March 2021 have been collected to capture seasonal variation relevant to a wide range of applications, from agricultural management to environmental monitoring, including climate change modelling. The data collection was done based on the usage of Sentinel-2 Level-1C orthorectified reflectance data that were further processed with the intention of deriving temporal aggregates of both spectral bands and vegetation indices. These pre-processed data then formed the basis of training a random forest classifier, calibrated with a blend of field data and desktop-derived samples from trustworthy sources. The land cover map has been rigorously validated, achieving an overall accuracy of 86%. This dataset could serve as a base tool in policy formulation, research, and land management applications to enable informed decisions on agricultural policy-making, climate resilience initiatives, and sustainable land use practices.
Keyword(s):
Land cover mapping; machine learning; remote sensing
Supplement to:
Sabaghy, Sabah; Abuzar, Mohammad; Crawford, Douglas L; McAllister, Andy; Sheffield, Kathryn (submitted): Remote sensing for land cover mapping across Victoria, Australia – a machine learning application. Scientific Data
Coverage:
Median Latitude: -36.560000 * Median Longitude: 145.390000 * South-bound Latitude: -39.270000 * West-bound Longitude: 140.630000 * North-bound Latitude: -33.850000 * East-bound Longitude: 150.150000
Event(s):
Victoria_Land_Cover * Latitude Start: -33.850000 * Longitude Start: 140.630000 * Latitude End: -39.270000 * Longitude End: 150.150000 * Method/Device: Sentinel -2 MSI (MultiSpectral Instrument, Level-1C) * Comment: Random Forest Classification from April 2021 - March 2022
Parameter(s):
#NameShort NameUnitPrincipal InvestigatorMethod/DeviceComment
1Binary ObjectBinarySabaghy, SabahMultiSpectral Instrument (MSI), Sentinel, 2A
2Binary Object (File Size)Binary (Size)BytesSabaghy, SabahMultiSpectral Instrument (MSI), Sentinel, 2A
3Binary Object (Media Type)Binary (Type)Sabaghy, SabahMultiSpectral Instrument (MSI), Sentinel, 2A
4Binary Object (MD5 Hash)Binary (Hash)Sabaghy, SabahMultiSpectral Instrument (MSI), Sentinel, 2A
5File contentContentSabaghy, Sabah
Status:
Curation Level: Enhanced curation (CurationLevelC)
Size:
4 data points

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