Note:This application has features which are not supported by Internet Explorer. It should still be functional using IE, but modern browsers such as Chrome and Firefox are recommended.
Please watch this short instructional video and read the instructions below. If you have any difficulty with any aspects of the application please contact us.
In the tools panel select the timeseries that interests you and date range and then press update. If you don't select a date range, the data will default to the start and end of the timeseries.
After selecting the timeseries, you can view the imagery in the map window by using the slider bar near the bottom of the screen. Depending on the demand on the server and the size of the map window, the imagery can take some time to display, so be patient. If you don't wish to view the imagery, you can make it transparent, using the transparency slider in the tools panel.
To obtain a report, you need to select a location on the map. The lowest button in the top left corner of the map is the marker button. Select this and then click on your location of interest. To move the marker, simply repeat the process. Once you have selected your location, click on the report page button.
Once you have selected a timeseries and location you should be able to access the report page. It can take up to 10-20 seconds for the report to be prepared and not all of the timeseries have a report associated with them. You can change the timeseries and date range of the report on the report page. From here you can email reports and save reports locally by right clicking on the image.
PaST is intended to be a simple tool to meet user's (landholders, extension officers and researchers) core needs. That is the visualisation and basic analysis of timeseries data for an area of interest.
The pasture seasonal timeseries (PaST) concept was the winning entry in the Inaugural NICTA Envirohack competition, held in November 2013. The video shown here is the concept pitch created on hack day.
PaST is developed on top of the extensive fractional cover timeseries products produced by Queensland DSITIA's Remote Sensing Centre and provided through the Queensland Government's Open Data initiative. We provide brief descriptions of each dataset below, but more Help, and the original products, can be obtained directly from the department.
Land cover fractions representing the proportions of green, non-green and bare cover retrieved by inverting multiple linear regression estimates and using synthetic endmembers in a constrained non-negative least squares unmixing model. The opening of the Landsat archive has provided an opportunity to composite imagery into representative seasonal images. The benefits of compositing in this manner are the creation of a regular time-series capturing seasonal variability, and the minimisation of missing data and contamination present in single date imagery (Flood, 2013).
Currently, this is an experimental product which has not been fully validated. The seasonal fractional ground cover product is derived directly from the seasonal fractional cover product, also produced by DSITIA's Remote Sensing Centre. The seasonal fractional cover product is a spatially explicit raster product, which predicts vegetation cover at medium resolution (30 m per-pixel) for each calendar season. However, the seasonal fractional cover product does not distinguish tree and mid-level woody foliage and branch cover from green and dry ground cover. As a result, in areas with even minimal tree cover (>15%), estimates of ground cover become uncertain. This product uses a related 'persistent green' product, derived from the fractional cover product, to adjust of the underlying spectral signature of the fractional cover image and creates a 'true' ground cover estimate for each season. The estimates of cover are restricted to areas of %lt60% woody vegetation.
Two fractional cover decile products, green cover and total cover, are currently produced. These products compare, at the per-pixel level, the level of cover for the specific season of interest against the long term cover for that same season. For each pixel all cover values over the entire time-series of seasonal images are classified into deciles. The cover value for the pixel in the season of interest is then classified according to the decile in which it falls. This is an excellent way of identifying areas of low or high cover, relative to what is normal at that location, at that time of year.
Each image is a composite of all MODIS fractional cover images for the month. The input images are version 2.2 of the CSIRO fractional cover product (Guerschman et.al. 2009, Guerschman et.al. 2012). The medoid method of Flood (2013) was used to create the composites.