NSW inundation count dataset: all dates

This raster dataset covers all of NSW and is a raw count of inundated pixel observations from all available Landsat acquisitions from mid 1984 to mid 2016. The dataset was produced by applying a water index to each Landsat scene using the technique developed by Fisher and Danaher (2016). Water indexed images were classified into inundated and not inundated classes using a threshold value of -10. Masking of cloud, cloud shadow and other erroneous pixels resulting from sensor anomalies was undertaken using the F-mask technique (Zhu and Woodcock 2012) and these pixels were allocated a 'no data' value . The classified images (with pixels allocated to 'inundated' or 'not inundated' or 'no data' classes) were then stacked and the number of inundated observations were counted for each pixel in available Landsat scenes. Known commission errors include areas of terrain shadow, building shadow especially in urban areas, and tall dense forest such as some pine plantations. Known omission errors include areas of greater vegetation cover. Potential users should note that inundated observations are only recorded for cloud free observation times and locations, thus inundation events on cloudy days may not have been detected.

Data and Resources

Metadata Summary What is metadata?

Field Value
Data Type Raster
Metadata Date 03/04/2017
Date Created 03/04/2017
Edition 1.0
Purpose spatial modelling - inundation, surface water, wetlands, biodiversity
License Creative Commons Attribution 4.0
Update Frequency There are no plans to update the data
Keywords inundation,surface water,wetlands
Field of Research earth sciences,environmental sciences

Dataset extent

Geospatial Topic Environment
Language English
Temporal Coverage From 01/06/1984 - 01/06/2016
Datum GDA94 Geographic (Lat\Long)
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Attribution Department of Planning, Industry and Environment asserts the right to be attributed as author of the original material in the following manner: "© State Government of NSW and Department of Planning, Industry and Environment 2017"