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• W420162890 abstract "Quantitative monitoring of vegetation change over time is essential in understanding theenvironmental processes of which are important in climate change and global warmingmodels, because vegetation change is an indicator of environmental variability. However,obtaining such information has been a challenge especially for vegetation phenology due tothe lack of appropriate methods for quantitative assessment. There is therefore a need toderive methods to quantitatively characterize vegetation dynamics in order to monitor theeffect of climate change on the biosphere and as inputs to global change models. The aim ofthis research was to test the relationships between ground-based measurement of leaf areaindex (LAI) and vegetation indices (VI) derived from satellite remote sensing instruments toquantitatively 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 ofrecurring biological events in plants) in broadleaf and coniferous forest canopies may becharacterized using ground-based measurement of LAI, because LAI is good proxy forvegetation phenology. Second, cloud cover frequency in the UK leads to a requirement forhigher temporal resolution remote sensing data to monitor changes in vegetation phenology.Third, data from the Disaster Monitoring Constellation (DMC) satellites provides asufficiently high temporal resolution for monitoring vegetation phenology in the UK. Fourth,vegetation indices derived from atmospherically corrected DMC data may be used to monitorvegetation phenology in the UK.Analysis of Advanced Very High Resolution Radiometer (AVHRR) and Moderate ResolutionImaging Spectroradiometer (MODIS) cloud mask showed that the average of number of cloudfree days at the UK test sites in the year 2005 was five days per month with a minimum ofone cloud free day per month implying that high temporal resolution satellites like the DMCwill be appropriate for monitoring vegetation change. Nine DMC satellite images wereacquired over 2005/2006 for the study sites plus one coincident Landsat ETM+ in 2005. Fourvegetation 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 nonphotosyntheticpart of plant per unit ground area. A regression model was used to predictLAI/PAI and the root mean square error (RMSE) was determined for both sites. The RMSEof the observed and predicted LAI values show that the levels of errors at Risley Moss were0.51 for LAI, 0.52 for overstorey PAI and 0.8 for total canopy while PAI was 1.1 forCharter's Moss. Therefore, the DMC and one Landsat ETM+ data set related to LAI/PAI canadequately retrieve biophysical parameter in the deciduous woodland. However, in theconiferous canopy the numbers of observations was fewer and the measurement errors largerleading to a requirement for more data in order to establish statistically significant andecologically useful relationships. Improvements in the accuracy of ground-based LAI/PAImeasurements, radiometric and atmospheric correction of satellite data are expected toincrease the accuracy of such LAI/PAI estimates in future." @default.
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• W420162890 date "2009-01-01" @default.
• W420162890 modified "2023-09-25" @default.
• W420162890 title "Evaluating medium resolution satellite data for monitoring seasonal vegetation dynamics" @default.
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