Bamboo trees are very common in almost every household of rural areas in Assam. Monitoring their expansion is useful for various environmentalists. Bamboo is in the path of becoming a great replacement for plastics. Hence, its preservation and monitoring are very important. Most of the existing works of tree identification are based on machine learning models, Convolutional Neural Network, UNet and Fully Convolutional deep learning models. Specific tree detection of coconut and palm trees has also been done using satellite images. Very few works have mapped bamboo regions using Sentinel products. Landsat and WorldView are the repeatedly used data for bamboo mapping and classification. The aim of this study is to provide a view to the ability of Sentinel imagery and vegetation indices for monitoring of Bambusa Tulda (Jati bamboo) during winter season. The study was carried out in Dimoria Development Block of Assam, India. The bamboo classification techniques using satellite products have been vigorously compared in this work. We have used Normalized Difference Vegetation Index, Stress Index and Bamboo Index to extract the features of Bambusa Tulda. The results show that Bamboo Index and Stress Index improves the bamboo classification result

Satellite Imagery, Bamboo mapping, Sentinel, NDVI, BI, SI, Tree identification

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