Skip to main content
SpringerLink
Log in

Biomass estimation using satellite remote sensing data—An investigation on possible approaches for natural forest

  • Published:
Journal of Biosciences Aims and scope Submit manuscript

Abstract

Vegetation type and its biomass are considered important components affecting biosphere-atmosphere interactions. The measurements of biomass per unit area and productivity have been set as one of the goals for International Geosphere-Biosphere Programme (IGBP). Ground assessment of biomass, however, has been found insufficient to present spatial extent of the biomass. The present study suggests approaches for using satellite remote sensing data for regional biomass mapping in Madhav National Park (MP). The stratified random sampling in the homogeneous vegetation strata mapped using satellite remote sensing has been effectively utilized to extrapolate the sample point biomass observations in the first approach.

In the second approach attempt has been to develop empirical models with satellite measured spectral response and biomass. The results indicate that there is significant relationships with spectral responses. These relationships have seasonal dependency in varying phonological conditions. The relationships are strongest in visible bands and middle infrared bands. However, spectral biomass models developed using middle infrared bands would be more reliable as compared to the visible bands as the later spectral regions are less sensitive to atmospheric changes

It was observed that brightness and wetness parameters show very strong relationship with the biomass values. Multiple regression equations using brightness and wetness isolates have been used to predict biomass values. The model used has correlation coefficient of 0.77. Per cent error between observed and predicted biomass was 10.5%. The biomass estimated for the entire national park using stratified and spectral response modelling approaches were compared and showed similarity with the difference of only 4.69%. The results indicate that satellite remote sensing data provide capability of biomass estimation

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.

Similar content being viewed by others

References

  • Ardo J 1992 Volume quantification of coniferous forest compartments using spectral radiance recorded by Landsat Thematic Mapper;Int. J. Remote Sensing 13 1779–1786

    Article  Google Scholar 

  • Alchrona E 1988The impact of climate and man on land transformation in Central Sudan Medolelande from Lunds Universities Geografiska Institutionez, Avhandlingar 103 (Lund: Lund University Press)

    Google Scholar 

  • Bolin B 1977 Changes of land biota and their importance for Carbon cycle;Science 196 613–615

    Article  CAS  Google Scholar 

  • Champion H G and Seth S K 1968A revised survey of forest types of India (New Delhi: Govt. Publication)

    Google Scholar 

  • Chavez P S Jr 1988 An improved dark object subtraction technique for atmospheric scattering correction of multi-spectral data.Remote Sensing Environ. 24 459–479

    Article  Google Scholar 

  • Curcio J A 1961 Evaluation of atmospheric aerosol particle size distribution from scattering measurement in the visible and infrared;J. Opt. Soc. Am,51 548–551

    Article  Google Scholar 

  • Curran P J 1981 Multispectral remote sensing for estimation of biomass and productivity; inPlants and daylight spectrum (ed.) H Smith (New York: Academic Press) pp 65–69

    Google Scholar 

  • Esser G 1984The significance of biospheric carbon pools and fluxes for the atmospheric CO2: A proposal mode structure in progress in Biometeorology (eds) H Lieth, Swets and Zeithinger B V Lisse Vol. 3, pp 253–294

  • Franklin J 1986 Thematic Mapper analysis of coniferous structure and composition;Int. J. Remote Sensing 7 1287–1301

    Article  Google Scholar 

  • Franklin J and Strahler A H 1988 Thematic mapper analysis of coniferous forest structure and composition;Int. J. Remote Sensing 26 809–825

    Google Scholar 

  • Franklin J and Hiernaux PHY 1991 Estimating foliage and woody biomass in Sahelian and Sudanian woodlands using a remote sensing model;Int. J. Remote Sensing 12 1387–1404

    Article  Google Scholar 

  • Goward S N and Dye D G 1987 Evaluation of North American net primary productivity with satellite data:Adv. Space Res. 7 165–174

    Article  Google Scholar 

  • Graetz R D, Pech R P and Devis A W 1988 The assessment and monitoring of sparsely vegetated rangelands using calibrated Landsat data;Int. J. Remote Sensing 9 237–251

    Article  Google Scholar 

  • Horler D N H and Ahern R J 1986 Forestry information content of thematic mapper data;Int. J. Remote Sensing 7 405–428

    Article  Google Scholar 

  • Kira T and Ogawa H 1971 Assessment of primary production in tropical forests;Proc. Productivity of Forest Ecosystems, Brussels-1969, UNESCO Publication

  • Kirchner F 1984 Influence of different land uses on some parameters of the energy and water balance;Prog. Biometeorol. 3 65–74

    Google Scholar 

  • Ovington J D 1968 Some factors affecting nutrient distribution within ecosystems; inFunctioning of terrestrial ecosystems of the primary production level (ed.) F E Eckardt Proc. Copen. Symp., UNESCO, Paris, pp 95–105

    Google Scholar 

  • Richardson A J and Wiegand C L 1977 Distinguishing vegetation from soil background information;Photogramm. Eng. Remote Sensing 43 1541–1552

    Google Scholar 

  • Ripple W J 1985 A synoptic reflectance characteristics grass vegetation;Photogramm. Eng. Remote Sensing 51 1911–1921

    Google Scholar 

  • Ripple W J, Wang S, Isaacson D L and Pairre D P 1991A preliminary comparison of Landsat Thematic Mapper and SPOT-1 HRV multispectral data for estimating coniferous forest volume, IJRS, Vol. 12, No. 9, pp 1971-1991

  • Rodgers W A and Panwar S H 1988Biogeographic classification of India (Dehra Dun: New Forest)

    Google Scholar 

  • Roy P S and Kumar S 1986 Advanced Very High Resolution Radiometer (AVHRR) satellite data for vegetation monitoring;Proc. International Seminar on Photogrammetry and Remote Sensing for developing countries, New Delhi 11–14 March, 1986

  • Roy P S 1989 Spectral reflectance characteristics of vegetation and their use in estimating productive potential;Proc. Indian Acad. Sci. (Plant Sci.) 99 59–81

    Google Scholar 

  • Roy P S, Ravan S A, Singh I J and Singh S 1993 Approach for terrestrial biomass estimation using satellite remote sensing. I. Monoculture plantation in Tarai region of Uttar Pradesh;Proc. Global Change Studies,Scientific Report, ISRO-GBP-SR-42-92

  • Roy P S, Ranganath B K, Diwakar P G, Vohra T P S, Bhan S K, Singh I J and Pandian V C 1991 Tropical forest type mapping and monitoring using remote sensing;Int. J. Remote Sensing 12 2205–2225

    Article  Google Scholar 

  • Roy P S, Saxena K G and Kamat D S 1986 Biomass estimation through remote sensing;II RS Tech. Rep 1–78

  • Sabins P F Jr 1978Remote sensing principles and interpretation (San Franacisco: Freeman)

    Google Scholar 

  • Sader S A, Waide R B, Lawrence W T and Joyce A T 1989 Tropical forest biomass and successional age class relationship to a vegetation index derived from Landsat TM data;Remote sensing Environ. 28 143–156

    Article  Google Scholar 

  • Sellers P J 1985 Canopy reflectance, photosynthesis and transpiration;Int. J. Remote Sensing 6 1335–1372

    Article  Google Scholar 

  • Shirish A Ravan 1994Ecological analysis of vegetation from satellite remote sensing at Madhav National Park, Shivpuri (M.P.), Ph.D. thesis, H N B Garhwal Univ., Srinagar Garhwal

    Google Scholar 

  • Shirish A Raven, Roy P S and Sharma C M 1995 Space remote sensing for spatial vegetation characterizeation;J. Biosci. 20 427–438

    Article  Google Scholar 

  • Shute D A and West N E 1982 Two basic methodological choices in wildland vegetation inventories: their consequences and implications;J. Appl. Ecol. 19 249

    Article  Google Scholar 

  • Siegal B S, Gillespie A R and Skaley J E 1980Remote sensing in geology (New York: Wielcy)

    Google Scholar 

  • Singh K P and Misra R 1979 Structure and functioning of natural, modified and silvicultural ecosystems of Eastern Uttar Pradesh;Final Technical Report (1975–78).MAB Research project, Banaras Hindu University, Varanasi

    Google Scholar 

  • Slatter P N, Doyle F J, Fritz N L and Welch R 1883Photographic systems for remote sensing, American Society of Photogrammetry, Second Edition of Manual of Remote sensing, Vol. 1, Chap. 6, pp 231–291

  • Spanner M A, Pierce I L, Peterson D L and Running S W 1990 Remote sensing of temperate coniferous forest leaf area index: the influence of canopy closure, underst orey vegetation and background reflectance;Int. J. Remote Sensing 11 195–211

    Article  Google Scholar 

  • Tiwari A K 1992 Componentwise biomass model for trees: a nonharvest technique;Indian Forester 118 404–410

    Google Scholar 

  • Tucker C J and Sellers P J 1986 Satellite remote sensing of primary production;Int. J. Remote Sensing 7 1395–1411

    Article  Google Scholar 

  • Tucker C J, Vanpraet C, Boerwinkle E and Gaston A 1983 Satellite remote sensing of total dry matter accumulation in the Senegalese Sahel;Remote Sensing Environ. 13 461–474

    Article  Google Scholar 

  • Tucker C J Vanpraet C L, Sharman M J and Van Ittersum G 1985 Satellite remote sensing of total herbaceous biomass production in the Senegalese Sahel, 1980–1984;Remote Sensing Environ. 17 233–249

    Article  Google Scholar 

  • Turner R E, Malila W A and Nalepha R F 1971 Importance of atmospheric scattering in remote sensing;Proc. of Seventh International Symposium of Remote Sensing of Environment, Ann Arbor, MI, pp 1651-1697

  • Unni N V M, Roy P S and Parthasarathy V 1986 Evaluation of Landsat and Airborne multispectral data and aerial photograph for mapping forest feature and phenomena in a part of the Godavari Basin;Int. J. Remote Sensing 6 419–431

    Article  Google Scholar 

  • Warrick R A, Shugart H H, Antonvsky M J, Terrant J R and Tucker C J 1986 The effects of increased CO2 and climatic changes on terrestrial ecosystem, Global perspectives, aims and issues; inThe greenhouse effect, climatic change and ecosystems, (Scope 29) (eds) W Bolin and D Jagger (New York: John Wiley) pp 363–392

    Google Scholar 

  • Whittaker R H 1966 Forest dimensions and production in the Great Smoky Mountains;Ecology 47 103–121

    Article  Google Scholar 

  • Whittaker R H and Woodwell G M 1971 Measurement of NPP of forests; inProductivity of forest ecosystems (ed.) P Duvigneaud (Paris: UNESCO) pp 159–175

    Google Scholar 

  • Whittingham C P 1974The mechanism of photosynthesis (London: Edward Arnold)

    Google Scholar 

  • Woodwell 1983 The blue planet: of whole and parts and Man;Ecol. Stud. Anal. Synthesis 44 2–10

    Google Scholar 

  • Young H E, Strand L and Allenbberger R 1964 Weight of wood substance for components of seven tree species;TAPPI 48 466–469

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Forestry and Ecology Division, Indian Institute of Remote Sensing, 4, Kalidas Road, 248 001, Dehra Dun, India

    P. S. Roy & Shirish A Ravan

  2. Remote Sensing/GIS Analyst, World Wide Fund for Nature, 172 B, Lodi Estate, Max Mueller Marg, 110003, New Delhi, India

    Shirish A Ravan

Authors
  1. P. S. Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shirish A Ravan
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Roy, P.S., Ravan, S.A. Biomass estimation using satellite remote sensing data—An investigation on possible approaches for natural forest. J. Biosci. 21, 535–561 (1996). https://doi.org/10.1007/BF02703218

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02703218

Keywords

Search

Navigation