University of Twente Student Theses


Tea bush biomass assessment through polarimetric decomposition and semi-empirical modelling

Banerjee, Abhishek (2012) Tea bush biomass assessment through polarimetric decomposition and semi-empirical modelling.

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Abstract:Advances in remote sensing techniques lead the natural resource scientists, technologists and managers to apply in many important fronts like biomass estimation of vegetation (short and long height trees of forests), agricultural crops, and now of the important beverage the tea. The biomass of the forest has been an important parameter regarding global carbon stock modelling. The climate change and the global warming greatly influence the natural vegetation. The above ground biomass is also essential for carbon budget assessment and ecosystem productivity. The conventional remote sensing techniques are not yet capable of estimating the biomass much accurately, particularly, of low height vegetation with dense canopy structure such as tea-bushes. A continuous effort is being made at a faster pace to replace the traditional methods of biomass estimation with the polarimetric SAR technique. Especially, this is because of the SAR having higher penetration capability. Biomass estimation in the present scenario still remains a challenging task in inaccessible areas and low height vegetation. Bushes are quite different in structure as compared to the trees. They have low height and the canopy structure is often dense and thick. Optical imagery is often not capable of assessing the bushes and/or the bush parameters, which on the other hand is possible to some extent by the SAR data. Microwave backscatter is sensitive to vegetation features. Earlier attempts to estimate the biomass of the forests show appreciable results with SAR backscatter data. This prompted in the present study, to make an attempt to estimate the above ground biomass of the tea-bushes utilizing the concept of semi-empirical model together with the SAR data. Available literatures show that not much work has been done on the North-Eastern parts of India and, especially, on the biomass estimation of the tea-bushes; in spite of the fact that tea is one of the major beverages of the globe and India occupies the first position in the world, not only as the tea producer and consumer but also as the exporter of tea. The tea variety which India produces is one of its own kinds and has much demand all over the places in the world. However, tea-bush biomass estimation not only helps in identifying the growth and production of tea but also as carbon stock modelling. That is why, it appeals to work in the North-Eastern parts of India, the region of Assam, which has the largest and best tea estates in India. The present study deals with the model based approach of the water cloud model to retrieve the above ground biomass of the tea-bushes. The model was integrated with the field measured data. The study area which covers the Sarusarai tea estate (Lat: 26.29o - 26.95o N and Long: 94.03o - 94.47o E) was visited for the collection of ground truth and tea inventory. The field survey revealed that there are lots of variations in the physical state of the tea-bushes due to the age and the year of plantations of tea. ALOS PALSAR data was used in the area under study. The SAR data was decomposed to retrieve the surface and volume scattering information. The model was trained with the help of the SAR data and the field measured data. The training provided the backscatter from ground, the two way transmissivity. The trained model was implemented to estimate the above ground biomass of the tea-bushes in the tea estates. The results show that the model gives better values for the biomass estimation. The validation of the model against ground measured data shows the capability of the model is good. The performance of the validated model is that it yielded the above ground tea-biomass with low root mean square error. The correlation was appreciably reasonable which assures the possibility and capability of the model for future routine use in the estimation of biomass of tea estates. Thus, the encouraging results out of the present study for tea-bushes indicate that there is definitely a high hope of estimating the above ground biomass and the bush parameters using the semi-empirical modelling approach utilizing polarimetric SAR data and in-situ measurements. Keywords: SAR data, tea, tea-bushes, backscatter, above ground biomass, bush volume, polarimetry.
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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