Skip to main content

Temporal dynamics of above ground biomass of Kaimoor Wildlife Sanctuary, Uttar Pradesh, India: conjunctive use of field and Landsat data

Proceedings of the Indian National Science Academy volume 87, pages 499–513 (2021)Cite this article

Abstract

The present study assesses the temporal dynamics of above ground biomass (AGB) in Kaimoor Wildlife Sanctuary (KWS), Uttar Pradesh, India using series of Landsat data for the years 1989, 2000, 2010 and 2018. The satellite images were preprocessed for surface reflectance, and subsequently we computed Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI). A forest density map was generated by slicing the computed NDVI and this was used as a basis for sampling strategy. A total of 30 sampling locations were randomly identified. Plot size of 30 m × 30 m was established. Tree species were enumerated at each plot and the girth at breast height and tree height measurements were recorded for the year 2018. Tree and plot level AGB (i.e. AGBF, t ha−1) was computed by multiplying the tree volume and specific gravity of wood. AGB (i.e. AGBP) prediction models were developed as linear regression equations for the year 2018 by assessing the vegetation indices and the AGBF. The significant AGBP models (R2 = 0.94; P = 0.0001) were applied for all study years after the data correction among the Landsat sensor series. The AGBP was over estimated (22.67 t ha−1; 7.75%) compared to AGBF (t ha−1). Moreover, EVI (R2 = 0.90) was found to be a better predictor for AGB compared to NDVI (R2 = 0.69) or SAVI (R2 = 0.77). The AGBP of KWS ranged between 289 ± 36 (in 1989) and 292 ± 40 (in 2018) with an average decadal positive change of 1.06%.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

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

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

Data availability

The satellite data used in this analysis is available on USGS website. The secondary data used is available on www.vindhyabachao.org.

References

  • Baboo, B., Sagar, R., Bargali, S.S., Verma, H.: Tree species composition, regeneration and diversity of an Indian dry tropical forest protected area. Trop. Ecol. 58(2), 409–423 (2017)

    Google Scholar 

  • Baccini, A., Laporte, N., Goetz, S., Sun, M., Dong, H.: A first map of tropical Africa’s above-ground biomass derived from satellite imagery. Environ. Res. Lett. 3, 045011 (2008)

    Article  Google Scholar 

  • Baccini, A., Goetz, S., Walker, W., Laporte, N., Sun, M., Sulla-Menashe, D., Hackler, J., Beck, P., Dubayah, R., Friedl, M.: Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Change 2, 182–185 (2012)

    CAS  Article  Google Scholar 

  • Brown, S.: Estimating Biomass and Biomass Change in Tropical Forests, a Primer (FAO). Forestry paper-134. FAO. United Nations Rome (1997)

  • Brown, S.L., Schroeder, P.E.: Spatial patterns of aboveground production and mortality of woody biomass for eastern US forests. Ecol. Appl. 9(3), 968–980 (1999)

    Google Scholar 

  • Carreiras, J.M.B., Pereira, J.M.C., Pereira, J.S.: Estimation of tree canopy cover in evergreen oak woodlands using remote sensing. For. Ecol. Manage. 223, 45–53 (2006)

    Article  Google Scholar 

  • Champion, H.G., Seth, S.K.: A Revised Survey of the Forest Types of India, p. 200p. Government of India Publications, Delhi (1968)

    Google Scholar 

  • Chaturvedi, A.N., Khanna, L.S.: Forest Mensuration. International Book Distributors, Dehradun (1982)

    Google Scholar 

  • Chenge, I.B., Osho, J.S.A.: Mapping tree aboveground biomass and carbon in Omo Forest Reserve Nigeria using Landsat 8 OLI data. Southern For (2018). https://doi.org/10.2989/20702620.2018.1463150

    Article  Google Scholar 

  • Choudhary, R.S.: Taxa of family Fabaceae: a potential of Local Medicinal values in Vindhya Region, Uttar Pradesh, India. Int. J. Pharm. Biol Sci. 1, 4 (2010)

    Google Scholar 

  • Dube, T., Mutanga, O.: Evaluating the utility of the medium spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa. ISPRS J. Photogramm. Remote Sens. 101, 36–46 (2015)

    Article  Google Scholar 

  • Egglestan, H.S., Buendia, L., Miwa, K., Ngara, T., Tanabe, K.: IPCC Guidelines for National Greenhouse Gas Inventories, Volume IV Agriculture, Forestry and Other Land Use. Institute of Global Environment Strategies, Harayana (2006). https://doi.org/10.1088/1755-1315/144/1/012064

    Book  Google Scholar 

  • Foody, G.M., Boyd, D.S., Cutler, M.E.J.: Predictive relations of tropical forest biomass from Landset TM data and their transferability between regions. Remote Sens. Environ. 85, 463–474 (2003)

    Article  Google Scholar 

  • Galidaki, G., Zianis, D., Gitas, I., Radoglou, K., Karathanassi, M., Strati, T., Woodhouse, I., Mallinis, G.: Vegetation biomass estimation with remote sensing: focus on forest and other wooded land over the Mediterranean ecosystem. Int. J. Remote Sens. 38(7), 1940–1966 (2017). https://doi.org/10.1080/01431161.2016.1266113

    Article  Google Scholar 

  • Gibbs, H.K., Brown, S., Niles, J.O., Foley, J.A.: Monitoring and estimating tropical forest carbon stocks: Making REDD a reality. Environ. Res. Lett. 2, 045023 (2007). https://doi.org/10.1088/1748-9326/2/4/045023

    CAS  Article  Google Scholar 

  • Gizachew, B., Solberg, S., Naesset, E., Gobakken, T., Bollandsas, O.M., Breidenbach, J., Zahabu, E., Mauya, E.W.: Mapping and estimating the total living biomass and carbon in low-biomass woodlands using Landsat 8 CDR data. Carbon Balance Manage. 11, 1–14 (2016)

    Article  Google Scholar 

  • Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice, C.O., Townshend, J.R.G.: High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013)

    CAS  Article  Google Scholar 

  • Huete, A.R.: A Soil Adjusted Vegetation Index (SAVI). Remote Sens Environ. 25, 295–309 (1988). https://doi.org/10.1016/0034-4257(88)90106-X

    Article  Google Scholar 

  • Huete, A.R., Didan, K., Miurat, T., Rodriguez, E.P., Gao, X., Ferreira, L.L.: Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ. 83, 195–213 (2002)

    Article  Google Scholar 

  • Imran, A.B., Ahmed, S.: Potential of Landsat 8 spectral indices to estimate forest biomass. Int. J. Hum Capital Urban Manage. 3(4), 303–314 (2018)

    Google Scholar 

  • IPCC: Climate Change Mitigation. In: Metz, B., Davidson, O.R., Bosch, P.R., Dave, R., Meyer, L.A. (eds). Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, New York

  • Ismail, I., Sohail, M., Gilani, H., Ali, A., Hussain, K., Hussain Karky, B.S., Qamer, F.M., Qazi, W., Ning, W., Kotru, R.: Forest inventory and analysis in Gilgit-Baltistan: a contribution towards developing a forest inventory for all Pakistan. Int. J. Clim. Change Strategies Manage. 10(4), 616–631 (2018)

    Article  Google Scholar 

  • Karlson, M., Ostwald, M., Reese, H., Sanou, J., Tankoano, B., Mattsson, E.: Mapping tree canopy cover and aboveground biomass in Sudano-Sahelian woodlands using Landsat 8 and random forest. Remote Sens. 7, 10017–10041 (2015)

    Article  Google Scholar 

  • Karmacharya, S.B., Singh, K.P.: Biomass and net production of Teak plantations in a dry tropical region in India. For. Ecol. Manage. 55, 233–247 (1992)

    Article  Google Scholar 

  • Kotchenova, S.Y., Vermote, E.F., Matarrese, R., Klemm, F.J., Jr.: Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part i: Path radiance. Appl. Opt. 45(26), 6762–6774 (2006)

    Article  Google Scholar 

  • Kumar, N.R.: Forest cover, stand volume and biomass assessment in Dudhwa National Park using satellite remote sensing data (optical and EnviSat ASAR). Dissertation, Andhra University, India (2007)

  • Kumar, L., Matunga, O.: Remote sensing of above ground biomass. Remote Sens. 9(9), 935 (2017). https://doi.org/10.3390/rs9090935

    Article  Google Scholar 

  • Lazaridou, M.A., Karagianni, A.C.: Landsat 8 Multispectral And Pansharpened Imagery Processing on the Study of Civil Engineering Issues. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Volume XLI-B8, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic (2016)

  • Lehtonen, A., Makipaa, R., Heikkinen, J., Sievanen, R., Liski, J.: Biomass expansion factors for scots Pine, Norway Spruce and Birch according to stand age for boreal forests. For. Ecol. Manage. 88, 211–214 (2004)

    Article  Google Scholar 

  • Lu, D.: Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon. Int. J. Remote Sens. 26, 2509–2525 (2005)

    Article  Google Scholar 

  • Lu, D.: The potential and challenge of remote sensing-based biomass estimation. Int. J. Remote Sens. 27, 1297–1328 (2006). https://doi.org/10.1080/01431160500486732

    Article  Google Scholar 

  • Mancino, G., Nole, A., Ripullone, F., Ferrara, A.: Landsat TM imagery and NDVI differencing to detect vegetation change: assessing natural forest expansion in Basilicata, southern Italy. iForest Biogeosci. For. 7(2), 75–84 (2014). https://doi.org/10.3832/ifor0909-007

    Article  Google Scholar 

  • Mancino, G., Ferrara, A., Padula, A., Nole, A.: Cross-comparison between Landsat 8 (OLI) and Landsat 7 (ETM+) derived vegetation indices in a mediterranean environment. Remote Sens. 12, 291 (2020). https://doi.org/10.3390/rs12020291

    Article  Google Scholar 

  • Mbow, C., Verstraete, M.M., Sambou, B., Diaw, A.T., Neufeldt, H.: Allometric models for aboveground biomass in dry Savanna trees of the Sudan and Sudan Guinean ecosystems of Southern Senegal. J. For. Res. 19, 340–347 (2013). https://doi.org/10.1007/s10310-013-0414-1

    CAS  Article  Google Scholar 

  • Meena, A., Bidalia, A., Hanief, M., Dinakaran, J., Rao, K.S.: Assessment of above- and belowground carbon pools in a semi-arid forest ecosystem of Delhi. India. Ecol Process. 8, 8 (2019). https://doi.org/10.1186/s13717-019-0163-y

    Article  Google Scholar 

  • Mishra, N., Haqque, M.O., Leigh, L., Aaron, D., Markham, Helder D., B, : Radiometric Cross Calibration of Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+). Remote Sens. 6(12), 2619–2638 (2014). https://doi.org/10.3390/rs61212619

    Article  Google Scholar 

  • Navar, J.: Allometric equations for tree species and carbon stocks for forests of northwestern Mexico. For. Ecol. Manage. 257(2), 427–434 (2009)

    Article  Google Scholar 

  • Negi, S.S.: Handbook of National Parks, Wildlife sanctuaries and biosphere reserves of India. Indus Publishing, Brand (2002)

    Google Scholar 

  • Nelson, R.F., Kimes, D.S., Salas, W.A., Routhier, M.: Secondary forest age and tropical forest biomass estimation using Thematic Mapper imagery. Bioscience 50, 419–431 (2000)

    Article  Google Scholar 

  • NRSA: IRS‐1D User Handbook, Hyderabad, India. Department of Space, Government of India (1997)

  • Padmakumar, B., Sreekanth, N.P., Shanthiprabha, V., Paul, J., Sreedharan, K., Augustine, T., Jayasooryan, K.K., Rameshan, M., Mohan, M., Ramasamy, E.V., Thomas, A.P.: Tree biomass and carbon density estimation in the tropical dry forest of Southern Western Ghats. India. Iforest. 11, 534–541 (2018). https://doi.org/10.3832/ifor2190-011

    Article  Google Scholar 

  • Pan, Y., Birdsey, R.A., Fang, J., Houghton, R., Kauppi, P.E., Kurz, W.A., Phillips, O.L., Shividenko, A., Lewis, S.L., Canadell, J.G., Ciais, P., Jackson, R.B., Packala, S.W., McGuire, A.D., Piao, S., Rautianen, A., Sitch, S., Hayes, D.: A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011)

    CAS  Article  Google Scholar 

  • Pant, D.N., Das, K.K., Roy, P.S.: Mapping of Tropical Dry Deciduous Forest and landuse in part of Vindhyan Range using Satellite Remote Sensing. J. Ind. Soc. Rem. Sens. 20, 1 (1992)

    Article  Google Scholar 

  • Paolini, L., Grings, F., Sobrino, J.A., Muñoz, J.C.J., Karszenbaum, H.: Radiometric correction effects in Landsat multi-date/multi-sensor change detection studies. Int. J. Remote Sens. 27, 685–704 (2006). https://doi.org/10.1080/01431160500183057

    Article  Google Scholar 

  • Peterson, H., Holm, S., Stahl, G., Alger, D., Fridman, J., Lehtonen, A., Lundstrom, A., Makipaa, R.: Individual tree biomass equations or biomass expansion factors for assessment of carbon stock changes in living biomass—a comparative study. For. Ecol. Manage. 270, 78–84 (2012)

    Article  Google Scholar 

  • Powell, S., Cohenc, W., Healey, S., Kennedy, R., Moisen, G., Pierce, K.: Quantification of life above ground forest biomass dynamics with Landsat Time series and field inventory data: a comparison of empirical modelling approaches. Remote Sens. Environ. 114, 1053–1068 (2010)

    Article  Google Scholar 

  • Ranjitsinh, M.K., Jhala, Y.V.: Assessing the Potential for Reintroducing the Cheetah in India. Report: 1-180. NOIDA, Dehradun, India, Wildlife Institute of India, Wildlife Trust of India (2010)

  • Ravan, R.P.S.: Biomass estimation using satellite remote sensing data—an investigation on possible approaches for natural forest. J. Biosci. 21(4), 535–561 (1996)

    Article  Google Scholar 

  • Rodger, A.S.: The carbon cycle and global forest ecosystem. Water Air Soil Pollut. 70, 295–307 (1993)

    Article  Google Scholar 

  • Rodgers, W.A., Panwar, H.S., Mathur, V.B.: Biogeographic Classification of India in Wildlife Protected Area Network in India: A Review. Executive Summary Wildlife Institute of India, Dehradun (2000)

    Google Scholar 

  • Rouse, J.W., Haas, R.H., Scheel, J.A., Deering, D.W.: Monitoring Vegetation Systems in the Great Plains with ERTS. In: Proceedings, 3rd Earth Resource Technology Satellite (ERTS) Symposium, vol. 1, pp. 48–62 (1974)

  • Saatchi, S.S., Harris, N.L., Brown, S., Lefsky, M., Mitchard, E.T.A., Salas, W., Buermann, Z.B.R., Lewis, S.L., Hagen, S.: Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl. Acad. Sci. 108, 9899–9904 (2011)

    CAS  Article  Google Scholar 

  • Shaoqing, Z., Xu, L. The comparative study of three methods of remote sensing image change detection. In: The International Archives of Photogrammetry Remote Sensing and Spatial Information Science. XXXVII, Part B7, Beijing (2008)

  • Singh, T.P., Singh, S., Roy, P.S., Rao, B.S.P.: Vegetation mapping and characterization in West Siang District of Arunachal Pradesh, India—a satellite remote sensing-based approach. Curr. Sci. 83, 1221–1230 (2002)

    Google Scholar 

  • Sinha, D., Chowdhary, R. xWildlife Inventory and Proposal for Sloth Bear Conservation Reserve in Marihan-Sukrit- Chunar Landscape of Mirzapur Division, U.P. (2008)

  • Suharidiman, A., Tampubolon, B.A., Sumaryono, M. Examining spectral properties of Landsat 8 OLI, for predicting above ground carbon of Labaman forest, Berau. In: IOP Conference Series: Earth and Environmental Science, Volume 144, 1st International Conference on Tropical Studies and Its Application (ICTROPS), Samarinda, East Kalimantan, Indonesia (2017)

  • Tahoor, A., Musavi, A., Khan, A.J.: Biomass extraction impact on vegetation community structure in Kaimoor Wildlife Sanctuary, Uttar Pradesh. India. Trop. Plant Res. 3(1), 142–152 (2016)

    Google Scholar 

  • Tajdar, A., Hashim, M., Nagpal, A., Gaur, S.: Assessment of forest biomass in Gorakhpur district of Uttar Pradesh. Haya Saudi J. Life Sci. (SJLS) 3(7), 524–528 (2018). https://doi.org/10.21276/haya.2018.3

    Article  Google Scholar 

  • Teillet, P.M., Staenz, K., William, D.J.: Effects of spectral, spatial, and radiometric characteristics on remote sensing vegetation indices of forested regions. Remote Sens. Environ. 61, 139–149 (1997)

    Article  Google Scholar 

  • The State of Forest Report: Government of India, Ministry of forests and Climate, Forest Survey of India. Dehradun (2013)

  • The state of forest report: Forest Survey of India and Ministry of Environment, Forests and climate change. Government of India. Dehradun (2019)

  • Verma, N.: The study of land use/land cover changes around Singrauli coal fields India using remote sensing. J. Indian Soc Remote Sens. 22, 1 (1994)

    Article  Google Scholar 

  • Vermote, E.F., El Saleous, N., Justice, C.O., Kaufman, Y.J., Privette, J.L., Remer, L., Roger, J.C., Tanre, D.: Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces: background, operational algorithm and validation. NASA Publications (1997) https://digitalcommons.unl.edu/nasapub/31

  • Zhang, H., Song, T., Wang, K., Yang, H., Yue, Y., Zeng, Z., Peng, W., Zeng, F.: Influences of stand characteristics and environmental factors on forest biomass and root–shoot allocation in southwest China. Ecol. Eng. 91, 7–15 (2016). https://doi.org/10.1016/j.ecoleng.2016.01.040

    Article  Google Scholar 

  • Zhang, X., Chen, X., Tian, M., Fan, Y., Ma, J., Xing, D.: An evaluation model for aboveground biomass based on hyperspectral data from field and TM8 in Khorchin grassland, China. PLoS ONE (2020). https://doi.org/10.1371/journal.pone.0223934

    Article  Google Scholar 

  • Zhao, P., Lu, D., Wang, G., Liu, L., Li, D., Zhu, J., Yu, S.: Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data. Int. J. Appl. Earth Obs. Geoinf. 53, 1–15 (2016). https://doi.org/10.1016/j.jag.2016.08.007

    Article  Google Scholar 

  • Zheng, D., Rademacher, J., Chen, J., Crow, T., Bresee, M., Moine, J.L., Ryu, S.R.: Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sens. Environ. 93, 402–411 (2004)

    Article  Google Scholar 

  • Zhu, X.L., Liu, D.S.: Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series. ISPRS J. Photogramm Remote Sens. 102, 222–231 (2015)

    Article  Google Scholar 

Download references

Acknowledgements

We thank the USGS (https://earthexplorer.usgs.gov/) for providing Landsat data free-of-cost. We are also grateful to the forest authorities of KWS for permitting forest inventory and data collection.

Funding

No funding in any form has been received by any of the authors.

Author information

Authors and Affiliations

  1. Vindhyan Ecology and Natural History Foundation, 36/30, Shivpuri Colony, Mirzapur, Uttar Pradesh, 231001, India

    Laxmi Goparaju, Firoz Ahmad & Debadityo Sinha

  2. Precision Agriculture Research Chair, Deanship of Scientific Research, King Saud University, Riyadh, 11451, Saudi Arabia

    Rangaswamy Madugundu

  3. Ecosystem Analysis Laboratory, Department of Botany, Banaras Hindu University, Varanasi, Uttar Pradesh, 221005, India

    Jamuna Sharan Singh

Authors
  1. Laxmi Goparaju
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rangaswamy Madugundu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Firoz Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Debadityo Sinha
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jamuna Sharan Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

LG proposed, analyzed the data and prepared a draft, RM contributed in the analysis of field data and preparation of models from remote sensing data. FA helped in collection and preprocessing of satellite data and provided critical analysis for the study and DS provided field related inputs, JSS provided critical analysis and suggestions for the development of the manuscript.

Corresponding author

Correspondence to Laxmi Goparaju.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Goparaju, L., Madugundu, R., Ahmad, F. et al. Temporal dynamics of above ground biomass of Kaimoor Wildlife Sanctuary, Uttar Pradesh, India: conjunctive use of field and Landsat data. Proc.Indian Natl. Sci. Acad. 87, 499–513 (2021). https://doi.org/10.1007/s43538-021-00046-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s43538-021-00046-1

Keywords

  • Normalized Difference Vegetation Index (NDVI)
  • Enhanced Vegetation Index (EVI)
  • Soil Adjusted Vegetation Index (SAVI)
  • Biomass
  • Kaimoor
  • Tropical forests
  • Regression