Skip to main content
SpringerLink
Log in

Bamboo Mapping Using Earth Observation Data: A Systematic Review

  • Review Article
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Abebe, S., Minale, A. S., & Teketay, D. (2021). Spatio-temporal bamboo forest dynamics in the lower beles River Basin, North-western Ethiopia (p. 100538). Society and Environment.

    Google Scholar 

  • Agrahari, D., Chaudhary, C. P., Singh, P., & Acharjee, M. (2020). Assessment of Research output on Bamboo in India: A Bibliometric Study. Library Philosophy and Practice, 4283.

  • Araujo, L. S., Sparoveka, G., dos Santosb, J. R., & Rodriguesa, R. R. (2008). High-resolution image to map bamboo-dominated gaps in the Atlantic Rain Forest, Brazil. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B4), 1287–1292.

    Google Scholar 

  • Bharadwaj, S. P., Subramanian, S., Manda, S., Ray, T., Mukherjee, P., & Rao, I. R. (2003). Bamboo livelihood development planning, monitoring and analysis through GIS and remote sensing. Journal of Bamboo and Rattan, 2(4), 453–461.

    Article  Google Scholar 

  • Bystriakova, N., Kapos, V., Lysenko, I., & Stapleton, C. M. A. (2003). Distribution and conservation status of forest bamboo biodiversity in the Asia-Pacific Region. Biodiversity and Conservation, 12(9), 1833–1841.

    Article  Google Scholar 

  • Cao, L., Coops, N. C., Sun, Y., Ruan, H., Wang, G., Dai, J., & She, G. (2019). Estimating canopy structure and biomass in bamboo forests using airborne LiDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 148, 114–129.

    Article  Google Scholar 

  • Chaves, P. P., Reategui Echeverri, N., Ruokolainen, K., Kalliola, R., Van doninck, J., Gomez Rivero, E., Zuquim, G., & Tuomisto, H. (2020). Using forestry inventories and satellite imagery to assess floristic variation in bamboo-dominated forests in Peruvian Amazonia. Journal of Vegetation Science, 32(1), e12938.

    Google Scholar 

  • Chen, Y., Li, L., Lu, D., & Li, D. (2019). Exploring bamboo forest aboveground biomass estimation using Sentinel-2 data. Remote Sensing, 11(1), 7.

    Article  Google Scholar 

  • Choong, M. K., Galgani, F., Dunn, A. G., & Tsafnat, G. (2014). Automatic evidence retrieval for systematic reviews. Journal of Medical Internet Research, 16(10), e223.

    Article  Google Scholar 

  • Das, D. J. (2012). Remote sensing and GIS application in mapping and estimation of bamboo biomass in Kolasib district, Mizoram: First step towards scientific resource management and sustainable development. International Journal of Innovative Research and Development, 1(7), 161–169.

    Google Scholar 

  • Das, G., Das, A. K., & Nandy, S. (2006). Nonlinear statistical model for culm growth of muli bamboo-Melocanna baccifera. International Journal of Ecology and Environmental Sciences, 32(2), 221–225.

    Google Scholar 

  • de Carvalho, A. L., Nelson, B. W., Bianchini, M. C., Plagnol, D., Kuplich, T. M., & Daly, D. C. (2013). Bamboo-dominated forests of the southwest Amazon: Detection, spatial extent, life cycle length and flowering waves. PLoS ONE, 8(1), e54852.

    Article  Google Scholar 

  • Dida, J. J. V., Araza, A. B., Eduarte, G. T., Umali, A. G. A., Malabrigo Jr, P. L., & Razal, R. A. (2021). Towards nationwide mapping of bamboo resources in the Philippines: Testing the pixel-based and fractional cover approaches. International Journal of Remote Sensing, 42(9), 3380–3404.

    Article  Google Scholar 

  • Du, H., Mao, F., Li, X., Zhou, G., Xu, X., Han, N., Sun, S., Gao, G., Cui, L., Li, Y., & Zhu, D. (2018). Mapping global bamboo forest distribution using multisource remote sensing data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(5), 1458–1471.

    Article  Google Scholar 

  • Dwivedi, A. K., Kumar, A., Baredar, P., & Prakash, O. (2019). Bamboo as a complementary crop to address climate change and livelihoods–Insights from India. Forest Policy and Economics, 102, 66–74.

    Article  Google Scholar 

  • FAO (2020). Global forest resources assessment 2020 main report. Food and Agriculture Organization of the United Nations, Rome

  • Fava, F., & Colombo, R. (2017). Remote Sensing-Based Assessment of the 2005–2011 Bamboo reproductive event in the Arakan Mountain range and its relation with wildfires. Remote Sensing, 9(1), 85.

    Article  Google Scholar 

  • FSI (2021). India State of Forest Report 2021. Forest Survey of India, Ministry of Environment, Forest and Climate Change, Government of India, Dehradun.

    Google Scholar 

  • Ghosh, A., & Joshi, P. K. (2014). A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery. International Journal of Applied Earth Observation and Geoinformation, 26, 298–311.

    Article  Google Scholar 

  • Goswami, J., Tajo, L., & Sarma, K. K. (2010). Bamboo resources mapping using satellite technology. Current Science, 99(5), 650–653.

    Google Scholar 

  • Greig, C., Robertson, C., & Lacerda, A. E. (2018). Spectral-temporal modelling of bamboo-dominated forest succession in the Atlantic Forest of Southern Brazil. Ecological Modelling, 384, 316–332.

    Article  Google Scholar 

  • Guan, F., & Fan, S. (2011). Study on monitoring the dynamic spatial-temporal change of bamboo resources in Shunchang based on remote sensing technology. In 2011 International Conference on Remote Sensing, Environment and Transportation Engineering (pp. 775–778). IEEE.

  • Han, N., Du, H., Zhou, G., Sun, X., Ge, H., & Xu, X. (2014). Object-based classification using SPOT-5 imagery for Moso bamboo forest mapping. International Journal of Remote Sensing, 35(3), 1126–1142.

    Article  Google Scholar 

  • Han, N., Du, H., Zhou, G., Xu, X., Cui, R., & Gu, C. (2013). Spatiotemporal heterogeneity of Moso bamboo aboveground carbon storage with Landsat Thematic Mapper images: A case study from Anji County China. International Journal of Remote Sensing, 34(14), 4917–4932.

    Article  Google Scholar 

  • Hasmadi, I., Nurul Atiqah, N., & Kamaruzaman, J. (2013). Sub-pixel technique of remotely sensed data for extracting bamboo areas in Temengor forest reserve, Perak, Malaysia. Pertanika Journal of Tropical Agriculture Science, 36, 221–230.

    Google Scholar 

  • INBAR. (1999). Socio-economic issues and constraints in the bamboo and rattan sectors, In INBAR’s Working Paper no. 23. International network for bamboo and rattan, Beijing, China.

  • INBAR. (2017). Bamboo and Rattan for inclusive and green development. International Network for Bamboo and Rattan, Beijing, China.

  • Jusoff, K. (2007). Mapping bamboo in Berangkat forest reserve, Kelantan, Malaysia using airborne hyperspectral imaging sensor. International Journal of Energy and Environment, 1(1), 1–6.

    Google Scholar 

  • Koizumi, K., Tanimoto, C., & Piao, C. (2003). Spread of Bamboo stands in the Kinki Region. In Proceedings of the KSRS Conference (pp. 441–443). The Korean Society of Remote Sensing.

  • Koutsos, T. M., Menexes, G. C., & Dordas, C. A. (2019). An efficient framework for conducting systematic literature reviews in agricultural sciences. Science of The Total Environment, 682, 106–117.

    Article  Google Scholar 

  • Kuehl, Y., & Yiping, L. (2012). Carbon off-setting with bamboo. In INBAR Working paper no. 71. International Network for Bamboo and Rattan, Beijing, China.

  • Kumar, R., Subramanian, S., & Duraisamy, J. (2010) Integrated advanced remote sensing GIS study for bamboo based livelihood analysis and rural development planning in Nhamatanda, Dondo districts of Sofala province, Mozambique.

  • Kumar, L., & Mutanga, O. (2018). Google Earth Engine applications since inception: Usage, trends, and potential. Remote Sensing, 10(10), 1509.

    Article  Google Scholar 

  • Kumar, R., Nandy, S., Agarwal, R., & Kushwaha, S. P. S. (2014). Forest cover dynamics analysis and prediction modeling using logistic regression model. Ecological Indicators, 45, 444–455.

    Article  Google Scholar 

  • Kumari, P. (2019). The Bambusoideae in India: An updated enumeration. Plantae Scientia, 1(06), 99–117.

    Article  Google Scholar 

  • Kushwaha, S. P. S., Nandy, S., Shah, M. A., Agarwal, R., & Mukhopadhyay, S. (2018). Forest cover monitoring and prediction in a Lesser Himalayan elephant landscape. Current Science, 115(3), 510–516.

    Article  Google Scholar 

  • Lallianthanga, R. K., & Sailo, R. L. (2012). Monitoring of bamboo flowering using satellite remote sensing and GIS techniques in Mizoram, India. Science Vision, 12(4), 147.

    Google Scholar 

  • Lessard, G., & Chouinard, A. (1980). Bamboo research in Asia: proceedings of a workshop held in Singapore, 28–30 May 1980. IDRC, Ottawa, ON, CA.

  • Li, L., Li, N., Lu, D., & Chen, Y. (2019). Mapping moso bamboo forest and its on-year and off-year distribution in a subtropical region using time-series Sentinel-2 and Landsat 8 data. Remote Sensing of Environment, 231, 111265.

    Article  Google Scholar 

  • Li, M., Li, C., Jiang, H., Fang, C., Yang, J., Zhu, Z., Shi, L., Liu, S., & Gong, P. (2016). Tracking bamboo dynamics in Zhejiang, China, using time-series of Landsat data from 1990 to 2014. International Journal of Remote Sensing, 37(7), 1714–1729.

    Article  Google Scholar 

  • Li, Y., Han, N., Li, X., Du, H., Mao, F., Cui, L., Liu, T., & Xing, L. (2018). Spatiotemporal estimation of bamboo forest aboveground carbon storage based on Landsat data in Zhejiang, China. Remote Sensing, 10(6), 898.

    Article  Google Scholar 

  • Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration. Journal of Clinical Epidemiology, 62(10), e1–e34.

    Article  Google Scholar 

  • Linderman, M., Liu, J., Qi, J., An, L., Ouyang, Z., Yang, J., & Tan, Y. (2004). Using artificial neural networks to map the spatial distribution of understorey bamboo from remote sensing data. International Journal of Remote Sensing, 25(9), 1685–1700.

    Article  Google Scholar 

  • Liu, W., Hui, C., Wang, F., Wang, M., & Liu, G. (2018b). Review of the Resources and Utilization of Bamboo in China. Bamboo–Current and Future Prospects, 174–198.

  • Liu, C., Xiong, T., Gong, P., & Qi, S. (2018). Improving large-scale moso bamboo mapping based on dense Landsat time series and auxiliary data: a case study in Fujian Province. China. Remote Sensing Letters, 9(1), 1–10.

    Article  Google Scholar 

  • Liu, G., Li, L., Gong, H., Jin, Q., Li, X., Song, R., Chen, Y., He, C., Huang, Y., & Yao, Y. (2017). Multisource remote sensing imagery fusion scheme based on bidimensional empirical mode decomposition (BEMD) and its application to the extraction of bamboo forest. Remote Sensing, 9(1), 19.

    Article  Google Scholar 

  • Lobovikov, M., Paudel, S., Ball, L., Piazza, M., Guardia, M., Ren, H., Russo, L., & Wu, J. (2007). World bamboo resources: a thematic study prepared in the framework of the global forest resources assessment 2005 (No. 18). Food & Agriculture Organization.

  • Van der Lugt, P., Thanglong, T., & King, C. (2018). Carbon sequestration and carbon emissions reduction through bamboo forests and products. In INBAR Working paper. International Network for Bamboo and Rattan, Beijing, China.

  • Menon, A. R. R. (1991). Remote sensing application in bamboo resources evaluation: a case study in Kerala. In Proceedings of the 4th International Bamboo Workshop, Chiangmai, Thailand, 27–30 November 1991

  • Mertens, B., Hua, L., Belcher, B., Ruiz-Pérez, M., Maoyi, F., & Xiaosheng, Y. (2008). Spatial patterns and processes of bamboo expansion in Southern China. Applied Geography, 28(1), 16–31.

    Article  Google Scholar 

  • Mishra, G., Giri, K., Panday, S., Kumar, R., & Bisht, N. S. (2014). Bamboo: Potential resource for eco-restoration of degraded lands. Journal of Biology and Earth Sciences, 4(2), 130–136.

    Google Scholar 

  • Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & Group, T. P. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med, 6(7), e1000097-6.

    Article  Google Scholar 

  • Nandy, S., Das, A. K., & Das, G. (2004). Phenology and culm growth of Melocanna baccifera (Roxb.) Kurz. in Barak Valley, North East India. Journal of Bamboo and Rattan, 3(1), 27–34.

    Article  Google Scholar 

  • Nandy, S., Ghosh, S., Kushwaha, S. P. S., & Kumar, A. S. (2019). Remote sensing-based forest biomass assessment in northwest Himalayan landscape. In R. R. Navalgund, A. S. Kumar, & S. Nandy (Eds.), Remote sensing of northwest himalayan ecosystems (pp. 285–311). Springer.

    Chapter  Google Scholar 

  • Nandy, S., & Kushwaha, S. P. S. (2011). Study on the utility of IRS 1D LISS-III data and the classification techniques for mapping of Sunderban mangroves. Journal of Coastal Conservation, 15(1), 123–137.

    Article  Google Scholar 

  • Nandy, S., Kushwaha, S. P. S., & Dadhwal, V. K. (2011). Forest degradation assessment in the upper catchment of the river Tons using remote sensing and GIS. Ecological Indicators, 11(2), 509–513.

    Article  Google Scholar 

  • Nandy, S., Srinet, R., & Padalia, H. (2021). Mapping forest height and aboveground biomass by integrating ICESat-2, Sentinel-1 and Sentinel-2 data using Random Forest algorithm in northwest Himalayan foothills of India. Geophysical Research Letters, 48(14), e2021GL093799.

    Article  Google Scholar 

  • Nath, A. J., Bhattacharjee, P., Nandy, S., & Das, A. K. (2011). Traditional utilization of village bamboos among the tea tribes of Barak Valley, northeast India. Bamboo Science and Culture, 24(1), 35–44.

    Google Scholar 

  • Nath, A. J., Das, G., & Das, A. K. (2009). Above ground standing biomass and carbon storage in village bamboos in North East India. Biomass and Bioenergy, 33(9), 1188–1196.

    Article  Google Scholar 

  • Nath, A. J., Lal, R., & Das, A. K. (2015). Managing woody bamboos for carbon farming and carbon trading. Global Ecology and Conservation, 3, 654–663.

    Article  Google Scholar 

  • Nath, A. J., Sileshi, G. W., & Das, A. K. (2018). Bamboo based family forests offer opportunities for biomass production and carbon farming in North East India. Land Use Policy, 75, 191–200.

    Article  Google Scholar 

  • Nath, A. J., Sileshi, G. W., & Das, A. K. (2020). Bamboo: Climate change adaptation and mitigation. CRC Press.

    Book  Google Scholar 

  • Nelson, B. W. & Bianchini, M. C. (2005). Complete life cycle of southwest Amazon bamboos (Guadua spp) detected with orbital optical sensors. In Anais XII Simpósio Brasileiro de Sensoriamento Remoto. INPE, Goiânia, Brasil, 1629–1636.

  • NESAC (2010). Bamboo resource mapping for six districts of Nagaland using remote sensing and GIS. North Eastern Space Applications Centre, Department of Space, Govt. of India, Meghalaya.

  • Nfornkah, B. N., Kaam, R., Zapfack, L., Tchamba, M., Djomo, C. C., Forje, W. G., Nkondjoua Dolanot, A. T., Tsewoue, M. R., Arnold, J. N., Zambou, J. C. G., & Okala, S. (2020). Spatial distribution and carbon storage of a native bamboo species in the high Guinea savannah of Cameroon: Oxytenanthera abyssinica (A. Rich.) Munro. International Journal of Environmental Studies, 78(3), 504–516.

    Article  Google Scholar 

  • Nirala, D. P., Nirbhay, A., & Phallo, K. (2017). A review on distribution of bamboos. Lifesciences Leaflets, 92, 70–78.

    Google Scholar 

  • Nonomura, A., Hozumia, S., & Masuda, T. (2010). Rule-based classification of ALOS/AVNIR-2 and PRISM data for bamboo distribution mapping. ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, 38(PART 8), 753–758.

    Google Scholar 

  • Ohrnberger, D. (1999). The bamboos of the world. Elsevier.

    Google Scholar 

  • Shang, Z., Zhou, G., Du, H., Xu, X., Shi, Y., Lü, Y., Zhou, Y., & Gu, C. (2013). Moso bamboo forest extraction and aboveground carbon storage estimation based on multi-source remotely sensed images. International Journal of Remote Sensing, 34(15), 5351–5368.

    Article  Google Scholar 

  • Shi, Y., Xu, X., Du, H., Zhou, G., Jin, W., & Zhou, Y. (2009). Remote sensing monitoring of a bamboo forest based on BP neural network. Frontiers of Forestry in China, 4(3), 363–367.

    Article  Google Scholar 

  • Sidhu, N., Pebesma, E., & Câmara, G. (2018). Using Google Earth Engine to detect land cover change: Singapore as a use case. European Journal of Remote Sensing, 51(1), 486–500.

    Article  Google Scholar 

  • Sileshi, G. W., & Nath, A. J. (2017). Carbon farming with bamboos in Africa: A call for action. A Discussion Paper. https://doi.org/10.13140/RG.2.2.34366.89926

    Article  Google Scholar 

  • Singh, A. K., Kala, S., Dubey, S. K., Rao, B. K., & Mishra, P. K. (2015). Bamboo based resource conservation – A viable technology for reclamation of Yamuna ravines. ICAR-IISWC Research Centre, Agra, India, 18pp.

  • Singnar, P., Das, M. C., Sileshi, G. W., Brahma, B., Nath, A. J., & Das, A. K. (2017). Allometric scaling, biomass accumulation and carbon stocks in different aged stands of thin-walled bamboos Schizostachyum dullooa, Pseudostachyum polymorphum and Melocanna baccifera. Forest Ecology and Management, 395, 81–91.

    Article  Google Scholar 

  • Srinet, R., Nandy, S., Watham, T., Padalia, H., Patel, N. R., & Chauhan, P. (2020b). Spatio-temporal variability of gross primary productivity in moist and dry deciduous plant functional types of northwest Himalayan foothills of India using temperature-greenness model. Geocarto International, 1–13.

  • Srinet, R., Nandy, S., Padalia, H., Ghosh, S., Watham, T., Patel, N. R., & Chauhan, P. (2020a). Mapping plant functional types in Northwest Himalayan foothills of India using random forest algorithm in Google Earth Engine. International Journal of Remote Sensing, 41(18), 7296–7309.

    Article  Google Scholar 

  • Tang, Y., Jing, L., Li, H., Liu, Q., Yan, Q., & Li, X. (2016). Bamboo classification using worldview-2 imagery of giant panda habitat in a large shaded area in Wolong, Sichuan province, China. Sensors, 16(11), 1957.

    Article  Google Scholar 

  • Tanigaki, Y., Harada, I., & Hara, K. A. (2010) Preliminary Study on the Method for Extracting Bamboo Groves in Chiba Prefecture, Japan Using Alos/Avnir-2 Data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, Volume XXXVIII, Part 8, Kyoto Japan 2010

  • Tuanmu, M. N., Viña, A., Bearer, S., Xu, W., Ouyang, Z., Zhang, H., & Liu, J. (2010). Mapping understory vegetation using phenological characteristics derived from remotely sensed data. Remote Sensing of Environment, 114(8), 1833–1844.

    Article  Google Scholar 

  • Van doninck, J., Westerholm, J., Ruokolainen, K., Tuomisto, H., & Kalliola, R. (2020). Dating flowering cycles of Amazonian bamboo-dominated forests by supervised Landsat time series segmentation. International Journal of Applied Earth Observation and Geoinformation, 93, 102196.

    Article  Google Scholar 

  • Varghese, A. O., Menon, A. R. R., Babu, P. S., Suraj, M. A., & Kumar, M. P. (1996). Remote sensing data utilisation in bamboo stock mapping. Journal of Non-Timber Forest Products, 3, 105–113.

    Google Scholar 

  • Venkatappa, M., Anantsuksomsri, S., Castillo, J. A., Smith, B., & Sasaki, N. (2020). Mapping the natural distribution of bamboo and related carbon stocks in the tropics using google earth engine, phenological behavior, landsat 8, and sentinel-2. Remote Sensing, 12(18), 3109.

    Article  Google Scholar 

  • Vorontsova, M. S., Clark, L.G., Dransfield, J., Govaerts, R., & Baker, W. J. (2016). World Checklist of Bamboos and Rattans. INBAR and the Board of Trustees of the Royal Botanic Gardens, Kew: Beijing, China.

  • Wang, M. H., & Ho, Y. S. (2012). A bibliometric analysis of global research on bamboo from 1992 to 2011. Archives of Environmental Science, 6, 68–79.

    Google Scholar 

  • Wang, T. J., Skidmore, A. K., & Toxopeus, A. G. (2009). Improved understorey bamboo cover mapping using a novel hybrid neural network and expert system. International Journal of Remote Sensing, 30(4), 965–981.

    Article  Google Scholar 

  • Ying, W., Jin, J., Jiang, H., Zhang, X., Lu, X., Chen, X., & Zhang, J. (2016). Satellite-based detection of bamboo expansion over the past 30 years in Mount Tianmushan, China. International Journal of Remote Sensing, 37(13), 2908–2922.

    Article  Google Scholar 

  • You, S., Zheng, Q., Lin, Y., Zhu, C., Li, C., Deng, J., & Wang, K. (2020). Specific bamboo forest extraction and long-term dynamics as revealed by Landsat time series stacks and Google Earth Engine. Remote Sensing, 12(18), 3095.

    Article  Google Scholar 

  • Yuen, J. Q., Fung, T., & Ziegler, A. D. (2017). Carbon stocks in bamboo ecosystems worldwide: Estimates and uncertainties. Forest Ecology and Management, 393, 113–138.

    Article  Google Scholar 

  • Zhang, M., Gong, P., Qi, S., Liu, C., & Xiong, T. (2019). Mapping bamboo with regional phenological characteristics derived from dense Landsat time series using Google Earth Engine. International Journal of Remote Sensing, 40(24), 9541–9555.

    Article  Google Scholar 

  • Zhao, Y., Feng, D., Jayaraman, D., Belay, D., Sebrala, H., Ngugi, J., Maina, E., Akombo, R., Otuoma, J., Mutyaba, J., Kissa, S., Qig, S., Assefab, F., Oduorb, M. N., Ndawulab, K. A., Lib, Y., & Gong, P. (2018). Bamboo mapping of Ethiopia, Kenya and Uganda for the year 2016 using multi-temporal Landsat imagery. International Journal of Applied Earth Observation and Geoinformation, 66, 116–125.

    Article  Google Scholar 

  • Zhou, B., Fu, M., Xie, J., Yang, X., & Li, Z. (2005). Ecological functions of bamboo forest: Research and application. Journal of Forestry Research, 16(2), 143–147.

    Article  Google Scholar 

  • Zhou, G., Meng, C., Jiang, P., & Xu, Q. (2011). Review of carbon fixation in bamboo forests in China. The Botanical Review, 77(3), 262.

    Article  Google Scholar 

Download references

Acknowledgements

The present review was carried out as a part of the project entitled “Carbon dynamics assessment in tropical forests of Northeast India using multi-sensor data” supported by ISRO-Climate & Atmospheric Programme (CAP). The authors wish to gratefully acknowledge Dr. S.K. Srivastav, Dean, and Dr. Prakash Chauhan, Director, Indian Institute of Remote Sensing (IIRS), ISRO, Dehradun, for their encouragement and support for this study. The first author would like to acknowledge the research grant received from the Department of Science and Technology INSPIRE programme (DST/INSPIRE Fellowship/2019/IF190154).

Author information

Authors and Affiliations

  1. Indian Institute of Remote Sensing, Indian Space Research Organisation, Department of Space, Government of India, Dehradun, 248001, India

    Muna Tamang, Subrata Nandy, Ritika Srinet & Hitendra Padalia

  2. Assam University, Silchar, Assam, 788011, India

    Ashesh Kumar Das

Authors
  1. Muna Tamang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Subrata Nandy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ritika Srinet
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ashesh Kumar Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hitendra Padalia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Subrata Nandy.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tamang, M., Nandy, S., Srinet, R. et al. Bamboo Mapping Using Earth Observation Data: A Systematic Review. J Indian Soc Remote Sens 50, 2055–2072 (2022). https://doi.org/10.1007/s12524-022-01600-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12524-022-01600-0

Keywords

Search

Navigation