Data

Fractional cover - MODIS, CSIRO algorithm

Commonwealth Scientific and Industrial Research Organisation
Guerschman, Juan
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=http://hdl.handle.net/102.100.100/42094?index=1&rft.title=Fractional cover - MODIS, CSIRO algorithm&rft.identifier=http://hdl.handle.net/102.100.100/42094?index=1&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=Vegetation Fractional Cover represents the exposed proportion of Photosynthetic Vegetation (PV), Non-Photosynthetic Vegetation (NPV) and Bare Soil (BS) within each pixel. The sum of the three fractions is 100% (+/- 3%) and shown in Red/Green/Blue colors. In forested canopies the photosynthetic or non-photosynthetic portions of trees may obscure those of the grass layer and/or bare soil. This product is derived from the MODIS Nadir BRDF-Adjusted Reflectance product (MCD43A4) collection 6 and has 500 meters spatial resolution. \n\nA suite of derivative products are also produced including monthly fractional cover, total vegetation cover (PV+NPV), and anomaly of total cover against the time series. \nMonthly: The monthly product is aggregated from the 8-day composites using the medoid method.\nAnomaly: represents the difference between total vegetation cover (PV+NPV) in a given month and the mean total vegetation cover for that month in all years available, expressed in units of cover. For example, if the mean vegetation cover in January (2001-current year) was 40% and the vegetation cover for the pixel in January 2018 was 30%, the anomaly for the pixel in Jan 2018 would be -10%. \nDecile: represents the ranking (in ten value intervals) for the total vegetation cover in a given month in relation to the vegetation cover in that month for all years in the time-series.\n\nMODIS fractional cover has been validated for Australia. \nLineage: Version 3.0: Fractional cover was derived using a linear unmixing methodology (Guerschman et al. 2015). The method uses all 7 MODIS bands and adds log transforms and band interaction terms to account for non-linearities in the spectral mixing. A cross-validation step was also included to select the optimal number of singular values to avoid over-fitting. The calibration and validation steps used 1171 field observations across Australia. Overall, the model fitted and applied to MCD43A4 fractional cover has a root mean square error (RMSE) of 12.9%, 18.1% and 16.6% for the PV, NPV and BS fractions respectively (percentage cover)\n\nVersion 3.1: (v310) Same as Version 3.0 with the following modifications: 1- input data changed to MODIS Collection 6.0 (MCD43A4.006) (see source). 2- calibration dataset expanded to include ~3022 field measurement sites accross Australia. 3- overall accuracy improved to an RMSE of 11.3%, 16.1% and 14.7% for the PV, NPV and BS fractions respectively.\n\nMonthly vegetation cover is calculated from the 8-day composites using a medoid method as described in Gill et al. \n\nMonthly anomalies show the difference between total vegetation cover (PV+NPV) in a given month and the mean total vegetation cover for that month in all years available\\nMonthly deciles show the decile (ranking) for the total vegetation cover in a given month in relation to the vegetation cover in that month for all years in the time series.\n&rft.creator=Guerschman, Juan &rft.date=2019&rft.edition=v2&rft.relation=http://doi.org/10.1080/2150704X.2018.1465611&rft.relation=http://dx.doi.org/10.1016/j.rse.2015.01.021&rft.coverage=westlimit=-180.0; southlimit=-60.0; eastlimit=180.0; northlimit=70.0; projection=WGS84&rft_rights=Creative Commons Attribution 4.0 International Licence https://creativecommons.org/licenses/by/4.0/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO 2014.&rft_subject=Vegetation cover&rft_subject=vegetation area fraction&rft_subject=MODIS&rft_subject=fractional cover&rft_subject=ground cover&rft_subject=total cover&rft_subject=Agricultural land management&rft_subject=Agriculture, land and farm management&rft_subject=AGRICULTURAL, VETERINARY AND FOOD SCIENCES&rft_subject=Landscape ecology&rft_subject=Ecological applications&rft_subject=ENVIRONMENTAL SCIENCES&rft_subject=Ecological applications not elsewhere classified&rft_subject=Conservation and biodiversity&rft_subject=Environmental management&rft_subject=Environmental management&rft_subject=Natural resource management&rft_subject=Land capability and soil productivity&rft_subject=Soil sciences&rft.type=dataset&rft.language=English Access the data

Licence & Rights:

Open Licence view details
CC-BY

Creative Commons Attribution 4.0 International Licence
https://creativecommons.org/licenses/by/4.0/

Data is accessible online and may be reused in accordance with licence conditions

All Rights (including copyright) CSIRO 2014.

Access:

Open view details

Accessible for free

Contact Information



Brief description

Vegetation Fractional Cover represents the exposed proportion of Photosynthetic Vegetation (PV), Non-Photosynthetic Vegetation (NPV) and Bare Soil (BS) within each pixel. The sum of the three fractions is 100% (+/- 3%) and shown in Red/Green/Blue colors. In forested canopies the photosynthetic or non-photosynthetic portions of trees may obscure those of the grass layer and/or bare soil. This product is derived from the MODIS Nadir BRDF-Adjusted Reflectance product (MCD43A4) collection 6 and has 500 meters spatial resolution.

A suite of derivative products are also produced including monthly fractional cover, total vegetation cover (PV+NPV), and anomaly of total cover against the time series.
Monthly: The monthly product is aggregated from the 8-day composites using the medoid method.
Anomaly: represents the difference between total vegetation cover (PV+NPV) in a given month and the mean total vegetation cover for that month in all years available, expressed in units of cover. For example, if the mean vegetation cover in January (2001-current year) was 40% and the vegetation cover for the pixel in January 2018 was 30%, the anomaly for the pixel in Jan 2018 would be -10%.
Decile: represents the ranking (in ten value intervals) for the total vegetation cover in a given month in relation to the vegetation cover in that month for all years in the time-series.

MODIS fractional cover has been validated for Australia.
Lineage: Version 3.0: Fractional cover was derived using a linear unmixing methodology (Guerschman et al. 2015). The method uses all 7 MODIS bands and adds log transforms and band interaction terms to account for non-linearities in the spectral mixing. A cross-validation step was also included to select the optimal number of singular values to avoid over-fitting. The calibration and validation steps used 1171 field observations across Australia. Overall, the model fitted and applied to MCD43A4 fractional cover has a root mean square error (RMSE) of 12.9%, 18.1% and 16.6% for the PV, NPV and BS fractions respectively (percentage cover)

Version 3.1: ("v310") Same as Version 3.0 with the following modifications: 1- input data changed to MODIS Collection 6.0 (MCD43A4.006) (see source). 2- calibration dataset expanded to include ~3022 field measurement sites accross Australia. 3- overall accuracy improved to an RMSE of 11.3%, 16.1% and 14.7% for the PV, NPV and BS fractions respectively.

Monthly vegetation cover is calculated from the 8-day composites using a medoid method as described in Gill et al.

Monthly anomalies show the difference between total vegetation cover (PV+NPV) in a given month and the mean total vegetation cover for that month in all years available\
Monthly deciles show the decile (ranking) for the total vegetation cover in a given month in relation to the vegetation cover in that month for all years in the time series.

Available: 2019-11-11

Data time period: 2001-01-01 to 2019-11-11

This dataset is part of a larger collection

180,70 180,-60 0,-60 -180,-60 -180,70 0,70 180,70

0,5