University of Twente Student Theses


Remote Sensing of Vegetation Chlorophyll Content for Climate Modelling

Ningthoujam, Ramesh Kumar (2009) Remote Sensing of Vegetation Chlorophyll Content for Climate Modelling.

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Abstract:In the coming decades, there is prediction for an increasing greenhouse gases by unlimited degree Celsius. Thus, affecting many natural systems including the functioning of terrestrial key ecosystem within the biosphere. To understand such effects of exchange between the systems, the establishment of the relationship between the state of natural vegetation (productivity) and associated climate anomaly would be an advantage. In this study, the possibility of estimating the different phenological variables using MERIS MTCI time- series data, its relation with temperature anomaly and comparing with a process model, PnET for a period of 3 years in the UK was investigated. The spring and autumn anomalies were detected for 2003, 2004 and 2005 by taking the difference of the three years against the Long- Term Average (1961- 1990). Therefore, eight positive and two negative anomalies were identified in different years. The natural vegetation phenology variables (i.e.., start, end, length of the growing season and net productivity) were estimated for the “needle- leaved evergreen forest” and “mosaic of forest with either Shrub land or grassland” in these anomaly zones using MTCI. On the other hand, using the PnET model, the productivity of these two vegetation types was derived and comparison was made between the two output and their relationship with the anomaly during the study period. The results showed that the “needle- leaved evergreen forest” does exhibit a low integrated MTCI value ranging from 20- 80 (unit less) and the magnitude vary with different years. The year 2005 was recorded to have the maximum MTCI value than in 2004 and in 2003 in conjunction with the anomaly. This suggests that there is an earlier onset of greenness in the spring and a delayed end of the season, thus a higher MTCI value in 2005 and 2004 than in 2003. Such relationship reveals that the spring and autumn anomalies of 2004 and 2005 are responsible for such a result. Moreover, the model output (NPP foliage) was also showing a strong positive relationship with the temperature anomaly in these two years, thus supporting the findings. Even the model derived NPP foliage and the integrated MTCI also show similar positive relationship, statistically (r2 = 0.65). All these results showed similar observations that in 2004 and 2005, a higher net primary productivity was observed (in both Integrated MTCI and PnET Model) within “needle- leaved evergreen forest”, for which the earlier start of onset of greenness and delayed end of season during the spring and autumn anomaly but with varying magnitude. In conclusion, short- term temperature anomaly was found to have a positive relationship with the vegetation productivity as similar with those of long- term studies. Further, investigation within the “mosaic of forest with either Shrub land or grassland” was also carried out to see any different responses by different vegetation types. But, the result was not that much significant. However, there is a need for improvement (in the model parameter, phenology detection technique) for better accuracy and validate with the ground data. Key Words: Vegetation Phenology, Productivity, Temperature Anomaly, MTCI, PnET model.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
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