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Tea is one of the beverages in Kenya. It is the number one foreign exchange earner. Recently, it has been found that the tea industry in Kenya is losing its ground. This is mainly because of wrong production mix, inability to compete with other tea producing countries due to high cost of production, poor organization of small holder farmers, and poor quality control at the processing level.
Remote sensing and GIS technologies have been efficiently used for monitoring several annual crops like rice, wheat, etc. Therefore, developing an approach for monitoring tea plantations using remote sensing and GIS has become a pressing need. The lack of previous studies in monitoring tea using remote sensing provided the idea to develop an approach that can aid in monitoring the changes of tea plantations cover and help in taking effective measures when the need arises. This study attempts to evaluate the changes of the tea plantations cover that have occurred since 2016 up to date in Mathira constituency. It involved the processing of the remotely sensed imagery of Sentinel 2A using SNAP tool through Supervised Classification and testing of the Random Forest Classifier. Further analysis of the data was done which included the generation of vegetation indices such as NDVI, MNDVI and OSAVI so as to determine the trends of the health of tea plantations cover. In addition, these vegetation indices were correlated with LAI to determine the which index can best be used in monitoring the trends of the tea canopy. |
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