General Actions:
Figure 1: Fractional cover monthly median composite for January 2014
Descriptor | Data link | Layer name |
---|---|---|
Persistent URL | http://www.auscover.org.au/purl/modis-fractional-cover-monthly-oeh | |
GeoNetwork record | ||
Image files | ftp://qld.auscover.org.au/modis/fractional_cover/monthly/ | |
Subsetting Tool (Experimental 'Clip and Ship') | http://vegcover.com/chopper/ | Monthly MODIS Fractional Cover |
Geoserver | http://tern-auscover.science.uq.edu.au/geoserver/derm/wms?service=WMS&version=1.1.0&request=GetMap&layers=aus_modis_monthly_fractional_cover&time=2010-01 | derm:aus_modis_monthly_fractional_cover |
Timeseries Tool (experimental) | http://vegcover.com/past/ |
Item | Detail |
---|---|
Rights | Copyright 2013-2014 NSW Office of Environment and Heritage. Rights owned by the NSW Office of Environment and Heritage. Rights licensed subject to Creative Commons Attribution (CC BY). |
Licence | Creative Commons Attribution 3.0 License, http://creativecommons.org/licenses/by/3.0. |
Access | These data can be freely downloaded and used subject to the CC BY licence. Attribution and citation is required as described at http://www.auscover.org.au/citation. We ask that you send us citations and copies of publications arising from work that use these data. |
Each image is a composite of all MODIS fractional cover images for the month. The input images are version 2.2 or version 3.0.1 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.
These data are used in support of the NSW DustWatch project, http://www.environment.nsw.gov.au/dustwatch.
Item | Detail |
---|---|
Spatial resolution (metres) | 500 m |
Spatial coverage (degrees) | 110.000000 to 155.001329 E, -10.000000 to -45.000512 N |
Temporal resolution | Monthly |
Temporal coverage | 2000-03 to ongoing |
Sensor & platform | MODIS Terra&Aqua |
Item | Detail |
---|---|
Spatial representation type | grid |
Spatial reference system | WGS 84 |
Item | Detail |
---|---|
Name | Tony Gill |
Organisation | NSW Office of Environment and Heritage |
Position | Remote Sensing Scientist |
Tony.Gill@environment.nsw.gov.au | |
Role | pointOfContact |
Address | |
Telephone | |
URL |
We thank CSIRO for making the fractional cover data publicly available.
Thesauri | Keyword |
---|---|
GCMD | EARTH SCIENCE > BIOSPHERE > VEGETATION > VEGETATION COVER |
CF | vegetation_area_fraction |
FoR | Environmental Sciences > Ecological Applications = 0501 |
There are three main thesauri that AusCover recommends:
Fractional cover data quality is subject to the data quality data quality constraints of the MODIS Fractional Cover product.
Potential data quality issues arising from the medoid method are described in Flood et al 2013.
Validation of the fractional cover data is subject to the validation performed for the MODIS Fractional Cover product.
Validation of the medoid method is described in Flood et al 2013.
Item | Product link |
---|---|
MODIS Fractional Cover | Fractional cover - MODIS, CSIRO Land and Water algorithm, Australia coverage |
MODIS Fractional Cover metrics | Fractional cover metrics - MODIS, ABARES algorithm, Australia coverage |
Landsat Seasonal Fractional Cover | Fractional cover - Landsat, Joint Remote Sensing Research Program algorithm, Australia coverage |
Item | Detail or link |
---|---|
Publication | Gill, T., Heidenreich, S., Guerschman, J P. (2014). MODIS Monthly Fractional Cover: Product Creation and Distribution. Joint Remote Sensing Research Program Publication Series. Available at: http://www.gpem.uq.edu.au/cser-web/docs/JRSRP_Publication_Series/gillt.2014.modis_monthly_cover.pdf |
Publication | Juan Pablo Guerschman, Michael J. Hill, Luigi J. Renzullo, Damian J. Barrett, Alan S. Marks, Elizabeth J. Botha (2009). Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors Remote Sensing of Environment, Volume 113, Issue 5, pp. 928-945. http://dx.doi.org/10.1016/j.rse.2009.01.006 |
Publication | Flood (2013), Seasonal composite Landsat TM/ETM+ images using the medoid (a multi-dimensional median), Remote Sensing, 5(12), pp. 6481-6500, http://dx.doi.org/10.3390/rs5126481 |
Validation report | Guerschman, JP, Oyarzabal, M, Malthus, TJ, McVicar, TM, Byrne, G, Randall, LA and Stewart, JB (2012), Validation of the MODIS-based vegetation fractional cover product, CSIRO Land and Water Science Report, Canberra, April 2012, available at http://www.clw.csiro.au/publications/science/2012/SAF-MODIS-fractional-cover.pdf |
Updated algorithm information can be found in the README file, ftp://tern-auscover.science.uq.edu.au/aus_modis_monthly_fractional_cover/README.
Three products are provided for each version. These are identified by a three-character product 'stage' code in the file name.
For version 2.2 the stages are:
For version 3.0.1 the stage codes are:
For ba2, bb1, ba4 and bb3 (monthly and gap-filled composites) the following Band labels apply:
The medoid method of Flood (2013) was used to create the composites. The medoid has two steps. For each grid cell:
This method has the advantage that the output pixel is one of the input pixels. This is important for the fractional cover as the sum of the percent covers is normally 100, and we wish to retain this in the composites. We compute a medoid where there is at least one non-null pixel from the input images.
For each month number below, the day of year (doy) images listed were used to create the composites:
Month number | Input DoY list | Notes |
---|---|---|
1 | 361 001 009 017 | Day 361 of the year before |
2 | 025 033 041 049 | |
3 | 057 065 073 | |
4 | 081 089 097 105 | |
5 | 113 121 129 137 | |
6 | 145 153 161 169 | |
7 | 177 185 193 201 | |
8 | 209 217 225 233 | |
9 | 241 249 257 | |
10 | 265 273 281 289 | |
11 | 297 305 313 321 | |
12 | 329 337 345 353 |
The monthly composites may contain missing values. This is likely for cloudy areas where there are no valid observations within the month. Depending on the application, it may be desirable to have images without data gaps.
The gap-fill algorithm fills null values with a resampled pixel at a coarser resolution. The resolution of the resampled image is made progressively coarser until all pixels are filled. The resampled pixel sizes are the original resolution multiplied by a power of 2. For example, if res is the original pixel size then the resampled pixel sizes are: res*21, res*22, res*23, ..., res*2n.
While this method is simple, it has the disadvantage that the sum-to-one constraint is lost, i.e. the sum of the percent covers may no longer be 100 where a pixel has been filled.
The gap-filled code image identifies those pixels in the gap-filled product that had to be filled. A non-zero pixel value represents a filled pixel. The value itself is the resolution of the resampled image used to fill the pixel, expressed as the power of 2 used. For example if the resolution was res*24, then the code value is 4. A value of zero represents a pixel that was not filled. The null value (ocean pixels) are set to 255.
Version label | Detail |
---|---|
1.0 | Initial release |
Date | Detail |
---|---|
2014-07-01 | Metadata creation date |
2014-07-30 | Added version 3.0.1 based composites to the server and updated the abstract. Filled out the Algorithm section with details from the README. |