REVIEW ARTICLE Bamboo Mapping Using Earth Observation Data: A Systematic Review Muna Tamang 1 • Subrata Nandy 1 • Ritika Srinet 1 • Ashesh Kumar Das 2 • Hitendra Padalia 1 Received: 2 March 2022 / Accepted: 11 August 2022 Ó Indian Society of Remote Sensing 2022 Abstract Bamboo is considered one of the world’s highest-yielding renewable natural resources. International Network of Bamboo and Rattan has identified that the effective utilization of bamboo resources can help in realizing at least six United Nations sustainable development goals. Therefore, the information about the spatial distribution and area under bamboo is essential for its better management and conservation. Hence, this paper systematically reviewed and compiled the published literature around the globe which rigorously focussed on mapping bamboo resources worldwide using remote sensing. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was adopted and a total of 46 papers published between 1991 and 2021 were evaluated based on the relevant criteria. It was observed that most of the studies on bamboo mapping were carried out using medium-resolution freely available satellite images. Around 47% of the studies utilized Landsat MSS, TM, ETM? & OLI data. The classification methods widely used for mapping bamboo were found to be the visual interpretation and maximum likelihood classifiers. However, after 2014 the studies emphasized more on using machine learning algorithms for accurate mapping of bamboo. In addition to that, the use of the Google Earth Engine cloud computing platform showed great potential for bamboo mapping by accessing a plethora of freely available datasets and classification algorithms. Spectral bands and vegetation indices were the most common variables used for bamboo mapping. The global overview highlighted that very little research on bamboo mapping has been carried out in bamboo-rich countries, except in China. This compilation will help in understanding the gaps related to the mapping and monitoring of this important natural resource worldwide. Keywords Bamboo Á Mapping Á PRISMA Á Remote sensing Á Review Á Spatial distribution Á Sustainability Introduction Bamboo, well known as the poor man’s timber or green gold, is one of the world’s highest-yielding renewable natural resources (Lessard & Chouinard, 1980; Nath et al., 2020; Singh et al., 2015). It belongs to the family Poaceae, a subfamily of Bambusoideae, and has 75–107 genera (Ohrnberger, 1999) with over 1642 known species of bamboo worldwide (Vorontsova et al., 2016). Bamboo is a widely distributed grass in the tropical, sub-tropical belts and mildly temperate regions between latitudes around 46° north and 47° south covering Asia, Africa, north, central and south America (FAO, 2020; Lobovikov et al., 2007). The total estimated bamboo resource in the world is 35.0 million ha, of which 24.9 million ha (71% of the total bamboo area) is in Asia (FAO, 2020). Most of the Asian forests have bamboo, with its significant presence in Northeast (NE) India through Burma to southern China, and through Sumatra to Borneo (Bystriakova et al., 2003). The total area under bamboo in the world increased by around 50% between 1990 and 2020, mostly because of the increases in China and India (FAO, 2020). India is a major bamboo-producing country covering 15 million ha (FSI, 2021) followed by China which covers 6.01 million ha (Liu et al., 2018b). In terms of bamboo diversity, China is among the richest countries in the world and alone has about 861 species belonging to 43 genera (Liu et al., 2018b). India has reported around 148 species of bamboo belonging to 33 genera and 6 different varieties (Kumari, 2019). Around 70% of the overall bamboo forest area in & Subrata Nandy subrato.nandy@gmail.com 1 Indian Institute of Remote Sensing, Indian Space Research Organisation, Department of Space, Government of India, Dehradun 248001, India 2 Assam University, Silchar, Assam 788011, India 123 Journal of the Indian Society of Remote Sensing https://doi.org/10.1007/s12524-022-01600-0