The impact of dataset selection on land degradation assessment.

Burrell, A., J.P. Evans and Y. Liu
ISPRS Journal of Photogrammetry and Remote Sensing, 146, 22-37, doi:10.1016/j.isprsjprs.2018.08.017, 2018.


Accurate quantification of land degradation is a global need, particularly in the world’s dryland areas. However, there is a well-documented lack of field data and long-term observational studies for most of these regions. Remotely sensed data offers the only long-term vegetation record that can be used for land degradation as- sessment at a national, continental or global scale. Both the rainfall and vegetation datasets used for land de- gradation assessment contain errors and uncertainties, but little work has been done to understand how this may impact results. This study uses the recently developed Time Series Segmented RESidual TREND (TSS-RESTREND) method applied to six rainfall and two vegetation datasets to assess the impact of dataset selection on the estimates of dryland degradation over Australia. Large differences in the data and methods used to produce the precipitation datasets did not significantly impact results with the estimate of average change varying by < 4% and a single dataset being sufficient to capture the direction of change in > 95% of regions. On the other hand, the vegetation dataset selection had a much greater impact. Calibration errors in the Global Inventory Monitoring and Modeling System Version 3 NDVI (GIMMSv3.0g) dataset caused significant errors in the trends over some of Australia’s dryland regions. Though identified over Australia, the problematic calibra- tion in the GIMMSv3.0g dataset may have effected dryland NDVI values globally. These errors have been ad- dressed in the updated GIMMSv3.1g which is strongly recommended for use in future studies. Our analysis suggests that using an ensemble composed of multiple runs performed using different datasets allows for the identification of errors that cannot be detected using only a single run or with the data quality flags of the input datasets. A multi-run ensemble made using different input datasets provides more comprehensive quantification of uncertainty and errors in space and time.

Key Figure

Figure 6. (a) Mean of the total change (TCmean) of the G2015 ensemble. Regions with no significant change are are masked in white. (b) The difference between the G2015 TCmean per year and the matched G3.1 TCmean per year. Regions with absolute rate difference < 0.00025 are masked in white. The dark grey are pixels with less than 85% valid data points and are masked in all figures.

UNSW    This page is maintained by Jason Evans | Last updated 23 January 2018