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Forest biomass estimation using remote sensing and field inventory: a case study of Tripura, India

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Abstract

Forests are the potential source for managing carbon sequestration, regulating climate variations and balancing universal carbon equilibrium between sources and sinks. Further, assessment of biomass, carbon stock, and its spatial distribution is prerequisite for monitoring the health of forest ecosystem. Moreover, vegetation field inventories are valuable source of data for estimating aboveground biomass (AGB), density, and the carbon stored in biomass of forest vegetation. In view of the importance of biomass, the present study makes an attempt to estimate temporal AGB of Tripura State, India, using Moderate Resolution Imaging Spectroradiometer (MODIS), normalized difference vegetation index (NDVI), leaf area index (LAI) and the field inventory data through geospatial techniques. A model was developed for establishing the relationship between biomass, LAI, and NDVI in the selected study site. The study also aimed to improve method for quantifying and verifying inventory-based biomass stock estimation. The results demonstrate the correlation value obtained between LAI and NDVI were 0.87 and 0.53 for the years 2011 and 2014, respectively. The correlation value between estimated AGB with LAI were found as 0.66 and 0.69, while with NDVI, the values were obtained as 0.64 and 0.94 for the years 2011 and 2014, respectively. The regression model of measured biomass with MODIS NDVI and LAI was developed for the data obtained during the period 2011–2014. The developed model was used to estimate the spatial distribution of biomass and its relationship between LAI and NDVI. The R2 values obtained were 0.832 for estimated and the measured AGB during the training and 0.826 for the validation. The results indicate that the methodology adopted in this study can help in selecting best fit model for analyzing relationship between AGB and NDVI/LAI and for estimating biomass using allometric equation at various spatial scales. The developed output thematic map showed an average biomass distribution of 32–94 Mg ha−1. The highest biomass values (72–95 Mg ha −1) was confined to the dense region of the forest while the lowest biomass values (32–46 Mg ha−1) was identified in the outer regions of the study site.

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References

  • Anaya, J. A., Chuvieco, E., & Palacios-Orueta, A. (2009). Aboveground biomass assessment in Colombia: a remote sensing approach. Forest Ecology and Management, 257(4), 1237–1246.

    Google Scholar 

  • Andersen, H.-E., Reutebuch, S. E., & McGaughey, R. J. (2006). A rigorous assessment of tree height measurements obtained using airborne Lidar and conventional field methods. Canadian Journal of Remote Sensing, 32(5), 355–366.

    Google Scholar 

  • Baccini, A., Friedl, M., Woodcock, C., & Warbington, R. (2004). Forest biomass estimation over regional scales using multisource data. Geophysical Research Letters, 31(10).

  • Baccini, A., Laporte, N., Goetz, S., Sun, M., & Dong, H. (2008). A first map of tropical Africa’s above-ground biomass derived from satellite imagery. Environmental Research Letters, 3(4), 045011.

    Google Scholar 

  • Baishya, R., Barik, S. K., & Upadhaya, K. (2009). Distribution pattern of aboveground biomass in natural and plantation forests of humid tropics in northeast India. Tropical Ecology, 50(2), 295.

    Google Scholar 

  • Bhattacharyya, T., Sarkar, D., Pal, D., Mandal, C., Baruah, U., Telpande, B., & Vaidya, P. (2010). Soil information system for resource management—Tripura as a case study. Current Science, 1208–1217.

  • Bhattacharyya, A., Mehrotra, N., & Shah, S. K. (2011). Holocene vegetation and climate of south Tripura based on palynological analysis. Journal of the Geological Society of India, 77(6), 521–526.

    Google Scholar 

  • Bombelli, A., Avitabile, V., BelelliMarchesini, L., Balzter, H., Bernoux, M., Hall, R., Henry, M., Law, B., Manlay, R., & Marklund, L. (2009). Assessment of the status of the development of the standards for the terrestrial essential climate variables: biomass. Rome: Food and Agriculture Organization-Global Terrestrial Observation System.

    Google Scholar 

  • Bora, N., Nath, A. J., & Das, A. K. (2013). Aboveground biomass and carbon stocks of tree species in tropical forests of Cachar District, Assam, northeast India. International Journal of Ecology and Environmental Sciences, 39(2), 97–106.

    Google Scholar 

  • Brown, S. (1997). Estimating biomass and biomass change of tropical forests: a primer, 134. Rome: Food & Agriculture Org.

    Google Scholar 

  • Brown, S., Hall, C. A., Knabe, W., Raich, J., Trexler, M. C., & Woomer, P. (1993). Tropical forests: their past, present, and potential future role in the terrestrial carbon budget. Water, Air, and Soil Pollution, 70(1–4), 71–94.

    CAS  Google Scholar 

  • Brown, S. L., Schroeder, P., & Kern, J. S. (1999). Spatial distribution of biomass in forests of the eastern USA. Forest Ecology and Management, 123(1), 81–90.

    Google Scholar 

  • Campbell, J. B., & Wynne, R. H. (2011). Introduction to remote sensing. New York: Guilford Press.

    Google Scholar 

  • Chacko, V. (1965). A manual on sampling techniques for forest surveys, New Delhi, India.

  • Champion, S. H., & Seth, S. K. (1968). A revised survey of the forest types of India. A revised survey of the forest types of India (pp. 1–404). New Delhi: Manager of Publications.

  • Chandra, R. B. (2017). Climate status of Tripura: a tight-rope walking. International Journal of Advance Research and Development, 2(5), 72–84.

    Google Scholar 

  • Chhabra, A., Palria, S., & Dadhwal, V. (2002). Growing stock-based forest biomass estimate for India. Biomass and Bioenergy, 22(3), 187–194.

    Google Scholar 

  • Choudhary, B. K., Majumdar, K., & Datta, B. K. (2017). Tree Diversity and Ecosystem Carbon Stock Patterns Along Selected Land Use, Land Cover Systems in Tripura, Northeastern India. International Journal of Ecology and Environmental Sciences, 42(5), 91–106.

  • Deb, D. B. (2016). The flora of Tripura state (pp. 776). Calcutta: Botanical Suvey of India. Available Online: http://hdl.handle.net/10603/160118.

  • Deb, D., Deb, S., Debbarma, J., & Datta, B. (2016). Tree species richness and carbon stock in Tripura University Campus, Northeast India. Journal of Biodiversity Management & Forestry, 5(4), 1–7.

    Google Scholar 

  • Dixon, R. K., Solomon, A., Brown, S., Houghton, R., Trexier, M., & Wisniewski, J. (1994). Carbon pools and flux of global forest ecosystems. Science, 263(5144), 185–190.

    CAS  Google Scholar 

  • Duchemin, B., Hadria, R., Erraki, S., Boulet, G., Maisongrande, P., Chehbouni, A., Escadafal, R., Ezzahar, J., Hoedjes, J., & Kharrou, M. (2006). Monitoring wheat phenology and irrigation in Central Morocco: on the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices. Agricultural Water Management, 79(1), 1–27.

    Google Scholar 

  • FSI. (2017). Carbon Stock in India’s Forests. Dehradun: Indian State of Forest Report (ISFR), Ministry of Environment Forests and climate change.

  • Gao, T., Xu, B., Yang, X., Jin, Y., Ma, H., Li, J., & Yu, H. (2013). Using MODIS time series data to estimate aboveground biomass and its spatio-temporal variation in Inner Mongolia’s grassland between 2001 and 2011. International Journal of Remote Sensing, 34(21), 7796–7810.

    Google Scholar 

  • Gautam, N., & Chennaiah, G. C. (1985). Land-use and land-cover mapping and change detection in Tripura using satellite LANDSAT data. International Journal of Remote Sensing, 6(3–4), 517–528.

    Google Scholar 

  • Gitelson, A. A., Wardlow, B. D., Keydan, G. P., & Leavitt, B. (2007). An evaluation of MODIS 250-m data for green LAI estimation in crops. Geophysical Research Letters, 34(20).

  • Gupta, A. K. (2000). Shifting cultivation and conservation of biological diversity in Tripura, Northeast India. Human Ecology, 28(4), 605–629.

    Google Scholar 

  • Gupta, G., Singh, J., Pandey, P. C., Tomar, V., Rani, M., & Kumar, P. (2014). Geospatial strategy for estimation of soil organic carbon in tropical wildlife reserve, Remote Sensing Applications in Environmental Research (pp. 69–83). Berlin: Springer.

    Google Scholar 

  • Houghton, R. (2005). Aboveground forest biomass and the global carbon balance. Global Change Biology, 11(6), 945–958.

    Google Scholar 

  • Houghton, R., Butman, D., Bunn, A. G., Krankina, O., Schlesinger, P., & Stone, T. (2007). Mapping Russian forest biomass with data from satellites and forest inventories. Environmental Research Letters, 2(4), 045032.

    Google Scholar 

  • Hyyppä, J., Hyyppä, H., Inkinen, M., Engdahl, M., Linko, S., & Zhu, Y.-H. (2000). Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, 128(1–2), 109–120.

    Google Scholar 

  • India Biodiversity Portal. (2018). Species. In: I. B. Portal (Editor), https://globalspecies.org/ntaxa/1259199. Accessed 22 Jan 2019.

  • Jhajharia, D., Yadav, B. K., Maske, S., Chattopadhyay, S., & Kar, A. K. (2012). Identification of trends in rainfall, rainy days and 24 h maximum rainfall over subtropical Assam in Northeast India. Comptes Rendus Geoscience, 344(1), 1–13.

    Google Scholar 

  • Kerr, J. T., & Ostrovsky, M. (2003). From space to species: ecological applications for remote sensing. Trends in Ecology & Evolution, 18(6), 299–305.

    Google Scholar 

  • Ketterings, Q. M., Coe, R., van Noordwijk, M., & Palm, C. A. (2001). Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests. Forest Ecology and Management, 146(1–3), 199–209.

    Google Scholar 

  • Klein, L., Milburn, R., Praderas, C., & Taaheri, A. (2006). A tool for conversion of earth observing system data products to GIS compatible formats and for the provision of post-processing functionality, Earth Science Satellite Remote Sensing (pp. 178–189). Berlin: Springer.

    Google Scholar 

  • Kshirsagar, R., & Upadhyay, S. (2009). Free radical scavenging activity screening of medicinal plants from Tripura, Northeast India. Natural Product Radiance, 8(2), 117–122.

    Google Scholar 

  • Kumar, P., Rani, M., Pandey, P., Majumdar, A., & Nathawat, M. (2010). Monitoring of deforestation and forest degradation using remote sensing and GIS: a case study of Ranchi in Jharkhand (India). Report and Opinion, 2(4), 14–20.

    Google Scholar 

  • Kumar, P., Sharma, L. K., Pandey, P. C., Sinha, S., & Nathawat, M. S. (2013). Geospatial strategy for tropical forest-wildlife reserve biomass estimation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(2), 917–923.

    Google Scholar 

  • Kumar, P., Pandey, P. C., Kumar, V., Singh, B. K., Tomar, V., & Rani, M. (2015). Efficient recognition of forest species biodiversity by inventory-based geospatial approach using LISS IV sensor. IEEE Sensors Journal, 15(3), 1884–1891.

    Google Scholar 

  • Kumar, P., Pandey, P. C., Singh, B., Katiyar, S., Mandal, V., Rani, M., Tomar, V., & Patairiya, S. (2016). Estimation of accumulated soil organic carbon stock in tropical forest using geospatial strategy. The Egyptian Journal of Remote Sensing and Space Science, 19(1), 109–123.

    Google Scholar 

  • Le Toan, T., Quegan, S., Davidson, M., Balzter, H., Paillou, P., Papathanassiou, K., Plummer, S., Rocca, F., Saatchi, S., & Shugart, H. (2011). The BIOMASS mission: mapping global forest biomass to better understand the terrestrial carbon cycle. Remote Sensing of Environment, 115(11), 2850–2860.

    Google Scholar 

  • Lu, D. (2006). The potential and challenge of remote sensing-based biomass estimation. International Journal of Remote Sensing, 27(7), 1297–1328.

    Google Scholar 

  • Lu, D., Mausel, P., Brondizio, E., & Moran, E. (2002). Above-ground biomass estimation of successional and mature forests using TM images in the Amazon Basin, Advances in spatial data handling (pp. 183–196). Berlin: Springer.

    Google Scholar 

  • Lu, D., Chen, Q., Wang, G., Liu, L., Li, G., & Moran, E. (2016). A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. International Journal of Digital Earth, 9(1), 63–105.

    Google Scholar 

  • Luckman, A., Baker, J., Kuplich, T. M., Yanasse, C. D. C. F., & Frery, A. C. (1997). A study of the relationship between radar backscatter and regenerating tropical forest biomass for spaceborne SAR instruments. Remote Sensing of Environment, 60(1), 1–13.

    Google Scholar 

  • Majumdar, K., Shankar, U., & Datta, B. K. (2012). Tree species diversity and stand structure along major community types in lowland primary and secondary moist deciduous forests in Tripura, Northeast India. Journal of Forestry Research, 23(4), 553–568.

    CAS  Google Scholar 

  • Majumdar, K., Choudhary, B. K., & Datta, B. K. (2016). Aboveground woody biomass, carbon stocks potential in selected tropical forest patches of Tripura, Northeast India. Open Journal of Ecology, 6(10), 598–612.

    Google Scholar 

  • Malhi, Y., & Grace, J. (2000). Tropical forests and atmospheric carbon dioxide. Trends in Ecology & Evolution, 15(8), 332–337.

    CAS  Google Scholar 

  • Mani, S., & Parthasarathy, N. (2007). Above-ground biomass estimation in ten tropical dry evergreen forest sites of peninsular India. Biomass and Bioenergy, 31(5), 284–290.

    Google Scholar 

  • Marley, S., Moore, M., & Clark, B. (2003). Building cost-effective remote data storage capabilities for NASA’s EOSDIS, mass storage systems and technologies, 2003 (MSST 2003). Proceedings. 20th IEEE/11th NASA Goddard Conference on. IEEE (pp. 28–39).

  • Meier, E. (2019). Wood Data Base. Available online: https://www.wood-database.com/ceylon-ebony/. Accessed 15 Feb 2019.

  • Muukkonen, P., & Heiskanen, J. (2007). Biomass estimation over a large area based on standwise forest inventory data and ASTER and MODIS satellite data: a possibility to verify carbon inventories. Remote Sensing of Environment, 107(4), 617–624.

    Google Scholar 

  • Myneni, R., Knyazikhin, Y., Glassy, J., Votava, P., & Shabanov, N. (2003). User’s guide: FPAR, LAI (ESDT: MOD15A2) 8-day composite NASA MODIS land algorithm. Terra, 1–17.

  • Nath, A. J., & Das, A. K. (2011). Carbon storage and sequestration in bamboo-based smallholder homegardens of Barak Valley, Assam. Current Science, 229–233.

  • Pan, Y., Birdsey, R. A., Fang, J., Houghton, R., Kauppi, P. E., Kurz, W. A., Phillips, O. L., Shvidenko, A., Lewis, S. L., & Canadell, J. G. (2011). A large and persistent carbon sink in the world’s forests. Science, 1201609.

  • Pandey, P. C., Tate, N. J., & Balzter, H. (2014). Mapping tree species in coastal Portugal using statistically segmented principal component analysis and other methods. IEEE Sensors Journal, 14(12), 4434–4441.

    Google Scholar 

  • Pandey, P. C., Anand, A., & Srivastava, P. K. (2019a). Spatial distribution of mangrove forest species and biomass assessment using field inventory and earth observation hyperspectral data. Biodiversity and Conservation, 28(8–9), 2143–2162. https://doi.org/10.1007/s10531-019-01698-8.

  • Pandey, P. C., Koutsias, N., Petropoulos, G. P., Srivastava, P. K., & Ben-Dor, Eyal.(2019b) Land use/land cover in view of earth observation: data sources, input dimensions, and classifiers—a review of the state of the art. Geocarto International, 1–32. https://doi.org/10.1080/10106049.2019.1629647.

  • Phillips, O. L., Malhi, Y., Higuchi, N., Laurance, W. F., Núnez, P. V., Vásquez, R. M., Laurance, S. G., Ferreira, L. V., Stern, M., & Brown, S. (1998). Changes in the carbon balance of tropical forests: evidence from long-term plots. Science, 282(5388), 439–442.

    CAS  Google Scholar 

  • Rani, M., Kumar, P., Pandey, P. C., Srivastava, P. K., Chaudhary, B., Tomar, V., & Mandal, V. P. (2018). Multi-temporal NDVI and surface temperature analysis for Urban Heat Island inbuilt surrounding of sub-humid region: a case study of two geographical regions. Remote Sensing Applications: Society and Environment, 10, 163–172.

    Google Scholar 

  • Rastogi, N., & Rastogi, A. (2007). Phytosociological analysis of the restored sal (Shorea robusta) plantations and natural sal forest of Tripura. Indian Journal of Forestry, 30(4), 377–385.

    Google Scholar 

  • Roy, P., & Ravan, S. A. (1996). Biomass estimation using satellite remote sensing data—an investigation on possible approaches for natural forest. Journal of Biosciences, 21(4), 535–561.

    Google Scholar 

  • Sader, S. A. (1987). Forest biomass, canopy structure, and species composition relationships with multipolarization L-band synthetic aperture radar data. Photogrammetric Engineering and Remote Sensing, 53, 193–202.

    Google Scholar 

  • Smith, S., & Brown, J. (2009). Assessment of the status of the development of the standards for the Terrestrial Essential Climate Variables-T7-Permafrost and seasonally frozen ground. International Permafrost Association (IPA). Global Terrestrial Observing System Rome. Available Online: http://library.arcticportal.org/668/.

  • Sodhi, N. S., Koh, L. P., Brook, B. W., & Ng, P. K. (2004). Southeast Asian biodiversity: an impending disaster. Trends in Ecology & Evolution, 19(12), 654–660.

    Google Scholar 

  • TFD. (2018). Forest resources of Tripura, Tripura Forest Department. Tripura: Tripura Forest Department.

    Google Scholar 

  • Thakur, D., Yadav, A., Gogoi, B., & Bora, T. (2007). Isolation and screening of Streptomyces in soil of protected forest areas from the states of Assam and Tripura, India, for antimicrobial metabolites. Journal de Mycologie Médicale, 17(4), 242–249.

    Google Scholar 

  • Thokchom, A., & Yadava, P. S. (2013). Biomass and carbon stock assessment in the sub-tropical forests of Manipur, North-East India. International Journal of Ecology and Environmental Sciences, 39(2), 107–113.

    Google Scholar 

  • Tomar, V., Kumar, P., Rani, M., Gupta, G., & Singh, J. (2013). A satellite-based biodiversity dynamics capability in tropical forest. Electronic Journal of Geotechnical Engineering, 18, 1171–1180.

    Google Scholar 

  • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150.

    Google Scholar 

  • Vashum, K. T., & Jayakumar, S. (2012). Methods to estimate above-ground biomass and carbon stock in natural forests-a review. Journal of Ecosystem and Ecography, 2(4), 1–7.

    Google Scholar 

  • Waring, R., & Pitman, G. (1985). Modifying lodgepole pine stands to change susceptibility to mountain pine beetle attack. Ecology, 66(3), 889–897.

    Google Scholar 

  • White, J. W., Andrade-Sanchez, P., Gore, M. A., Bronson, K. F., Coffelt, T. A., Conley, M. M., Feldmann, K. A., French, A. N., Heun, J. T., & Hunsaker, D. J. (2012). Field-based phenomics for plant genetics research. Field Crops Research, 133, 101–112.

    Google Scholar 

  • Yin, G., Zhang, Y., Sun, Y., Wang, T., Zeng, Z., & Piao, S. (2015). MODIS based estimation of forest aboveground biomass in China. PLoS One, 10(6), e0130143.

    Google Scholar 

  • Zhang, Y., Xu, M., Chen, H., & Adams, J. (2009). Global pattern of NPP to GPP ratio derived from MODIS data: effects of ecosystem type, geographical location and climate. Global Ecology and Biogeography, 18(3), 280–290.

    Google Scholar 

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Acknowledgments

Many thanks are extended to the Goddard Space Flight Center NASA and Land Processes Distribution Active Archive Center (LP DAAC USGS) for providing the MODIS data of the case study area, free of charge. We also thank staff of Tripura Forest Department (TFD) for their help and contents.

Funding

This study was financially supported by the SERB, Department of Science and Technology, India (NPDF/2016/002487).

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Authors and Affiliations

  1. Center for Environmental Sciences & Engineering, Shiv Nadar University, Gautam Buddha Nagar, Greater Noida, Uttar Pradesh, 201314, India

    Prem Chandra Pandey

  2. Institute of Environment and Sustainable Development and DST Mahamana Centre for Excellence in Climate Change Research, Banaras Hindu University, Varanasi, India

    Prem Chandra Pandey & Prashant K. Srivastava

  3. Department of Remote Sensing and GIS, Kumaun University, Almora, India

    Tilok Chetri & Pavan Kumar

  4. Department of Environmental Science, Women’s College, Agartala, Agartala, Tripura, India

    Bal Krishan Choudhary

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  1. Prem Chandra Pandey
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Pandey, P.C., Srivastava, P.K., Chetri, T. et al. Forest biomass estimation using remote sensing and field inventory: a case study of Tripura, India. Environ Monit Assess 191, 593 (2019). https://doi.org/10.1007/s10661-019-7730-7

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