Evaluating medium resolution satellite data for monitoring seasonal vegetation dynamics
Quantitative monitoring of vegetation change over time is essential in understanding the
environmental processes of which are important in climate change and global warming
models, because vegetation change is an indicator of environmental variability. However,
obtaining such information has been a challenge especially for vegetation phenology due to
the lack of appropriate methods for quantitative assessment. There is therefore a need to
derive methods to quantitatively characterize vegetation dynamics in order to monitor the
effect of climate change on the biosphere and as inputs to global change models. The aim of
this research was to test the relationships between ground-based measurement of leaf area
index (LAI) and vegetation indices (VI) derived from satellite remote sensing instruments to
quantitatively monitor vegetation dynamics in a broadleaf and coniferous forest in the UK.
This research has four key hypotheses. First, phenological changes (which is the timing of
recurring biological events in plants) in broadleaf and coniferous forest canopies may be
characterized using ground-based measurement of LAI, because LAI is good proxy for
vegetation phenology. Second, cloud cover frequency in the UK leads to a requirement for
higher temporal resolution remote sensing data to monitor changes in vegetation phenology.
Third, data from the Disaster Monitoring Constellation (DMC) satellites provides a
sufficiently high temporal resolution for monitoring vegetation phenology in the UK. Fourth,
vegetation indices derived from atmospherically corrected DMC data may be used to monitor
vegetation phenology in the UK.
Analysis of Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution
Imaging Spectroradiometer (MODIS) cloud mask showed that the average of number of cloud
free days at the UK test sites in the year 2005 was five days per month with a minimum of
one cloud free day per month implying that high temporal resolution satellites like the DMC
will be appropriate for monitoring vegetation change. Nine DMC satellite images were
acquired over 2005/2006 for the study sites plus one coincident Landsat ETM+ in 2005. Four
vegetation indices (VI) were derived from the satellite data sets and were related to LAI/PAI.
PAI is the plant area index defined as the total surface area of both photosynthetic and nonphotosynthetic
part of plant per unit ground area. A regression model was used to predict
LAI/PAI and the root mean square error (RMSE) was determined for both sites. The RMSE
of the observed and predicted LAI values show that the levels of errors at Risley Moss were
0.51 for LAI, 0.52 for overstorey PAI and 0.8 for total canopy while PAI was 1.1 for
Charter's Moss. Therefore, the DMC and one Landsat ETM+ data set related to LAI/PAI can
adequately retrieve biophysical parameter in the deciduous woodland. However, in the
coniferous canopy the numbers of observations was fewer and the measurement errors larger
leading to a requirement for more data in order to establish statistically significant and
ecologically useful relationships. Improvements in the accuracy of ground-based LAI/PAI
measurements, radiometric and atmospheric correction of satellite data are expected to
increase the accuracy of such LAI/PAI estimates in future.
Ogunbadewa, E. Evaluating medium resolution satellite data for monitoring seasonal vegetation dynamics. (Thesis). Salford : University of Salford
|Deposit Date||Oct 3, 2012|
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