East Kimberley remotely sensed datasets

Full description

This compilation data release is a selection of remotely sensed imagery used in the Exploring for the Future (EFTF) East Kimberley Groundwater Project.
Datasets include:
• Mosaic 5 m digital elevation model (DEM) with shaded relief
• Normalised Difference Vegetation Index (NDVI) percentiles
• Tasselled Cap exceedance summaries
• Normalised Difference Moisture Index (NDMI)
• Normalised Difference Wetness Index (NDWI)

The 5m spatial resolution digital elevation model with associated shaded relief image were derived from the East Kimberley 2017 LiDAR survey (Geoscience Australia, 2019b).

The Normalised Difference Vegetation Index (NDVI) percentiles include 20th, 50th, and 80th for dry seasons (April to October) 1987 to 2018 and were derived from the Landsat 5,7 and 8 data stored in Digital Earth Australia (see Geoscience Australia, 2019a). Tasselled Cap Exceedance Summary include brightness, greenness and wetness as a composite image and were also derived from the Landsat data. These surface reflectance products can be used to highlight vegetation characteristics such as wetness and greenness, and land cover.

The Normalised Difference Moisture Index (NDMI) and Normalised Difference Water Index (NDWI) were derived from the Sentinel-2 satellite imagery. These datasets have been classified and visually enhanced to detect vegetation moisture stress or water-logging and show distribution of moisture. For example, positive NDWI values indicate waterlogged areas while waterbodies typically correspond with values greater than 0.2. Waterlogged areas also correspond to NDMI values of 0.2 to 0.4.

Geoscience Australia, 2019a. Earth Observation Archive. Geoscience Australia, Canberra. http://dx.doi.org/10.4225/25/57D9DCA3910CD

Geoscience Australia, 2019b. Kimberley East - LiDAR data. Geoscience Australia, Canberra. C7FDA017-80B2-4F98-8147-4D3E4DF595A2 https://pid.geoscience.gov.au/dataset/ga/129985

Lineage

Maintenance and Update Frequency: notPlanned

Statement: Lineage statements are included for each unique dataset or group of datasets. The 5m spatial resolution digital elevation model (DEM) mosaic raster was resampled from orthometric 1m mosaic DEM using the cubic convolution value assigning method in ArcGIS. The shaded relief raster represents ground surface illuminated at 45 degree altitude with North West azimuth, and 10 times vertical exaggeration at 5m spatial resolution. LiDAR data were acquired by AAM Pty Ltd in June 2017 using Optech Galaxy LiDAR unit. Authors acknowledge the tremendous work of the Geoscience Australia Elevation team who carried out post processing, classification, production, quality assurance and delivery of all released LiDAR data products (see Geoscience Australia, 2019). In particular, the authors thank Graham Hammond, Kevin Kennedy, Jonathan Weales, Grahaem Chiles, Robert Kay, Shane Crossman, and Simon Costelloe. The Normalised Difference Vegetation Index (NDVI) was calculated for each Landsat surface reflectance observation within the period of interest (April to October 1987-2018). Percentiles - 20th, 50th and 80th were calculated for each pixel. Tasselled Cap brightness, greenness and wetness coefficients (see Crist, 1985) were calculated for period 1987 to 2018. Brightness, greenness and wetness exceedance was identified where the respective coefficient exceeded a particular threshold. The mean and standard deviation statistics of brightness, greenness and wetness were calculated on pixel basis for the available time series. Authors acknowledge innovative work of the Geoscience Australia Digital Earth Australia team for providing support and access to Data Cube Collection of imagery. In particular, authors thank Vanessa Newey, Bex Dunn, and Leo Lymburner for providing a script and guidance on calculating Tasselled Cap summary statistics (see https://github.com/opendatacube/datacube-stats/blob/master/datacube_stats/statistics/uncategorized.py). A weighted geometric median statistic was applied to Sentinel-2 data to derive a median reflectance from the period of 2015 to 2018 (Roberts et al., 2017). The Normalised Difference Moisture Index (NDMI; Gao, 1996) and Normalised Difference Water Index (NDWI; McFeeters, 1996) were calculated from the median reflectance. Crist, E. P. (1985) A TM tasselled cap equivalent transformation for reflectance factor data. Remote Sensing of Environment 17(3), 301–306 Gao, B.C., (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote sensing of environment, 58(3), 257-266. Lewis, A., Oliver, S., Lymburner, L., Evans, B., Wyborn, L., Mueller, N., Raevksi, G., Hooke, J., Woodcock, R., Sixsmith, J., Wu, W., Tan, P., Li, F., Killough, B., Minchin, S., Roberts, D., Ayers, D., Bala, B., Dwyer, J., Dekker, A., Dhu, T., Hicks, A., Ip, A., Purss, M., Richards, C., Sagar, S., Trenham, C., Wang, P. & Wang, L.-W. The Australian Geoscience Data Cube — Foundations and lessons learned. Remote Sensing of Environment. McFeeters, S.K., (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International journal of remote sensing, 17(7), 1425-1432. Roberts, D., Mueller, N., & McIntyre, A. (2017). High-dimensional pixel composites from earth observation time series. IEEE Transactions on Geoscience and Remote Sensing, 55(11), 6254-6264. Roberts, D., Dunn, B., Mueller, N., (2018). Open Data Cube Products Using High-Dimensional Statistics of Time Series. International Geoscience and Remote Sensing Symposium. Valencia, Spain: IEEE Geoscience and Remote Sensing Society.