A critical review of exploring the recent trends and technological advancements in forest biomass estimation

Authors

DOI:

https://doi.org/10.14719/pst.6695

Keywords:

Carbon, Challenges , Climate change, Forest Biomass estimation, Remote sensing

Abstract

Biomass estimation is pivotal in understanding and managing global carbon stocks, offering vital insights into climate change and environmental ecology. It serves as a critical tool for evaluating carbon sequestration potential, a natural mechanism for regulating atmospheric carbon dioxide levels. Accurate estimation of forest biomass not only aids in quantifying carbon stocks but also provides a basis for sustainable forest management, conservation efforts, and policymaking to mitigate climate change impacts. This article provides a comprehensive review of various biomass estimation methods, including ground-based measurements, remote sensing technologies, and hybrid approaches. Each method's strengths, limitations, and practical applications are critically examined, highlighting their suitability for different spatial scales and ecological contexts. Traditional methods, while precise at small scales, are often labour-intensive and limited in coverage. In contrast, remote sensing technologies such as LiDAR, RADAR, and hyperspectral imaging have revolutionized biomass estimation by enabling large-scale and high-resolution assessments. Additionally, recent advancements in machine learning, data fusion, and satellite-based monitoring systems are transforming the field, offering unprecedented accuracy and efficiency. By presenting these trends and innovations, this article provides valuable insights for researchers, practitioners, and policymakers, emphasizing the importance of integrating advanced technologies into biomass estimation for sustainable development and climate resilience.

Downloads

References

Houghton RA. Aboveground Forest biomass and the global carbon balance. Glob Chang Biol. 2005;11(6):945-58. https://doi.org/10.1111/j.1365-2486.2005.00955.x

Sedjo R, Sohngen B. Carbon sequestration in forests and soils. Annu Rev Resour Econ. 2012;4(1):127-44. https://doi.org/10.1146/annurev-resource-083110-115941

Chave J, Réjou?Méchain M, Búrquez A, Chidumayo E, Colgan MS, Delitti WB, Duque A, Eid T, Fearnside PM, Goodman RC, Henry M. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob Chang Biol. 2014;20(10):3177-90.. https://doi.org/10.1111/gcb.12629

Montes N, Gauquelin T, Badri W, Bertaudiere V, Zaoui EH. A non-destructive method for estimating above-ground forest biomass in threatened woodlands. For Ecol Manage. 2000;130(1-3):37-46. https://doi.org/10.1016/S0378-1127(99)00188-7

Qureshi A, Badola R, Hussain SA. A review of protocols used for assessment of carbon stock in forested landscapes. Environ Sci Policy. 2012;16:81-9. https://doi.org/10.1016/j.envsci.2011.11.001

Frolking S, Palace MW, Clark DB, Chambers JQ, Shugart HH, Hurtt GC. Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure. J Geophys Res Biogeosci. 2009;114(G2):1-27. https://doi.org/10.1029/2008JG000911

Shepardson DP, Niyogi D, Choi S, Charusombat U. Students’ conceptions about the greenhouse effect, global warming, and climate change. Clim Change. 2011;104(3):481-507. http://dx.doi.org/10.1007/s10584-012-0472-y

Kipkemboi K, Odhiambo KO, Odwori PO. Adoption of tree nursery practices as strategic enterprise at millenium villages project, Siaya County, Kenya. Afr Environ Review J. 2019;3(2):26-34. https://doi.org/10.2200/aerj.v3i2.88

Negassa MD, Mallie DT, Gemeda DO. Forest cover change detection using Geographic Information Systems and remote sensing techniques: a spatio-temporal study on Komto Protected Forest priority area, East Wollega Zone, Ethiopia. Environ Syst Res. 2020;9:1-4. https://doi.org/10.3390/su8101071

Henry M, Picard N, Trotta C, Manlay R, Valentini R, Bernoux M, Saint André L. Estimating tree biomass of sub-Saharan African forests: a review of available allometric equations. Silva Fennica. 2011; 45(38):477-569.

Brahma B, Nath AJ, Deb C, Sileshi GW, Sahoo UK, Das AK. A critical review of forest biomass estimation equations in India. Trees For People. 2021;5:100098. https://doi.org/10.1016/j.tfp.2021.100098

Houghton RA, Hall F, Goetz SJ. Importance of biomass in the global carbon cycle. Journal of Geophysical Research: Biogeosciences. 2009;114(G2). https://doi.org/10.1029/2009JG000935

Wang G, Dai Y, Yang H, Xiong Q, Wang K, Zhou J, Li Y, Wang S. A review of recent advances in biomass pyrolysis. Energy fuels. 2020;34(12):15557-78.https://doi.org/10.1021/acs.energyfuels.0c03107

Eggleston HS, Buendia L, Miwa K, Ngara T, Tanabe K. 2006 IPCC guidelines for national greenhouse gas inventories.

Davidson EA, Janssens IA. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature. 2006;440(7081):165-73. https://doi.org/10.1038/nature04514

Gibbs HK, Brown S, Niles JO, Foley JA. Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ Res Lett. 2007;2(4):045023. https://doi.org/10.1088/1748-9326/2/4/045023

Koch B. Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment. ISPRS J Photogramm Remote Sens. 2010;65(6):581-90. https://doi.org/10.1016/j.isprsjprs.2010.09.001

Mutanga O, Adam E, Cho MA. High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. Int J Appl Earth Obs Geoinf. 2012;18:399-406. https://doi.org/10.1016/j.jag.2012.03.012

Seidel D, Fleck S, Leuschner C, Hammett T. Review of ground-based methods to measure the distribution of biomass in forest canopies. Ann For Sci. 2011;68:225-44.https://doi.org/10.1007/s13595-011-0040-z

Bortolot ZJ, Wynne RH. Estimating forest biomass using small footprint LiDAR data: An individual tree-based approach that incorporates training data. ISPRS Int J Geoinf. 2005;59(6):342-60. https://doi.org/10.1016/j.isprsjprs.2005.07.001

Temesgen H, Affleck D, Poudel K, Gray A, Sessions J. A review of the challenges and opportunities in estimating above ground forest biomass using tree-level models. Scand J For Res. 2015;30(4):326-35. https://doi.org/10.1080/02827581.2015.1012114

Lu Z, Im J, Rhee J, Hodgson M. Building type classification using spatial and landscape attributes derived from LiDAR remote sensing data. Landsc Urban Plan. 2014 ;130:134-48. https://doi.org/10.1016/j.landurbplan.2014.07.005.

Ji L, Wylie BK, Nossov DR, Peterson B, Waldrop MP, McFarland JW, Rover J, Hollingsworth TN. Estimating aboveground biomass in interior Alaska with Landsat data and field measurements. Int J Appl Earth Obs Geoinf. 2012;18:451-61. https://doi.org/10.1016/j.jag.2012.03.019

Picard N, Saint-André L, Henry M. Manual for building tree volume and biomass allometric equations: from field measurement to prediction.

Ravindranath NH, Ostwald M. Methods for estimating above-ground biomass. Carbon inventory methods handbook for greenhouse gas inventory, carbon mitigation and roundwood production projects. 2008:113-47. https://doi.org/10.1007/978-1-4020-6547-7_10

Návar J. Allometric equations for tree species and carbon stocks for forests of northwestern Mexico. For Ecol Manage. 2009;257(2):427-34. https://doi.org/10.1016/j.foreco.2008.09.028

Brown S, Gillespie AJ, Lugo AE. Biomass estimation methods for tropical forests with applications to forest inventory data. Forest sci. 1989;35(4):881-902. https://doi.org/10.1093/forestscience/35.4.881

Aboal JR, Arévalo JR, Fernández Á. Allometric relationships of different tree species and stand above ground biomass in the Gomera laurel forest (Canary Islands). FLORA. 2005;200(3):264-74. https://doi.org/10.1016/j.flora.2004.11.001

Ravindranath NH, Ostwald M. Carbon inventory methods: handbook for greenhouse gas inventory, carbon mitigation and roundwood production projects. Springer Science & Business Media; 2007 Dec 3.https://doi.org/10.1007/978-1-4020-6547-7

Simhadri K, Bariki SK, Swamy AV. A study on sequestration of carbon by trees in Eastern Ghats Lambasingi, Chinthapalli Mandal, Visakhapatnam District, Andhra Pradesh, India.

Samalca I. Estimation of forest biomass and its error: A case in Kalimantan, Indonesia. ITC. 2007.

Avery TE, Burkhart HE. Forest measurements. Waveland Press; 2015 May 18.

ÖZÇEL?K R, Eraslan T. Two-stage sampling to estimate individual tree biomass. Turk J Agric For. 2012;36(3):389-98. https://doi.org/10.3906/tar-1103-45

Chave J, Condit R, Aguilar S, Hernandez A, Lao S, Perez R. Error propagation and scaling for tropical forest biomass estimates. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences. 2004;359(1443):409-20. https://doi.org/10.1098/rstb.2003.1425

Chave J, Andalo C, Brown S, Cairns MA, Chambers JQ, Eamus D, Fölster H, Fromard F, Higuchi N, Kira T, Lescure JP. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia. 2005;145:87-99. https://doi.org/10.1007/s00442-005-0100-x

Jenkins JC, Chojnacky DC, Heath LS, Birdsey RA. National-scale biomass estimators for United States tree species. Forest sci. 2003;49(1):12-35. https://doi.org/10.1093/forestscience/49.1.12

St?Onge B, Hu Y, Vega C. Mapping the height and above?ground biomass of a mixed forest using lidar and stereo Ikonos images. Int J Remote Sens. 2008;29(5):1277-94. https://doi.org/10.1080/01431160701736505

Pandey R, Rawat GS, Kishwan J. Changes in distribution of carbon in various forest types of India from 1995–2005. Silva Lusitana. 2011;19(1):41-54.

Chavan B, Rasal G. Total sequestered carbon stock of Mangifera indica. JEES. 2012;2(1):37-49.

Hangarge LM, Kulkarni DK, Gaikwad VB, Mahajan DM, Chaudhari N. Carbon sequestration potential of tree species in somjaichi rai (sacred grove) at nandghur village, in bhor region of pune district, Maharashtra state, India. Ann Biol Res. 2012;3(7):3426-9.

Pattnayak¹ Su, Sahu S, KumaR¹ M, Dhal¹ NK. Carbon stock estimation in different forest lands: A review. Carbon. 2016 Jun:11.

Lodhiyal N, Lodhiyal LS. Biomass and net primary productivity of Bhabar Shisham forests in central Himalaya, India. For Ecol Manage. 2003;176(1-3):217-35. https://doi.org/10.1016/S0378-1127(02)00267-0

Nelson BW, Mesquita R, Pereira JL, De Souza SG, Batista GT, Couto LB. Allometric regressions for improved estimate of secondary forest biomass in the central Amazon. For Ecol Manage. 1999;117(1-3):149-67. https://doi.org/10.1016/S0378-1127(98)00475-7

Xiao CW, Ceulemans R. Allometric relationships for below-and aboveground biomass of young Scots pines. For Ecol Manage. 2004;203(1-3):177-86. https://doi.org/10.1016/j.foreco.2004.07.062

Návar J. Biomass component equations for Latin American species and groups of species. Ann For Sci. 2009;66(2):1-21. https://doi.org/10.1051/forest/2009001

Vashum KT, Jayakumar S. Methods to estimate above-ground biomass and carbon stock in natural forests-a review. J Ecosyst Ecography. 2012;2(4):1-7. https://doi.org/10.4172/2157-7625.1000116

Dubayah RO, Drake JB. Lidar remote sensing for forestry. J For. 2000;98(6):44-6. https://doi.org/10.1093/jof/98.6.44

Lu D. The potential and challenge of remote sensing?based biomass estimation. Int J Remote Sens. 2006;27(7):1297-328. https://doi.org/10.1080/01431160500486732

Tsitsi B. Remote sensing of aboveground forest biomass: A review. Trop Ecol. 2016;57:125-32.

Aricak B, Bulut A, Altunel AO, Sakici OE. Estimating above-ground carbon biomass using satellite image reflection values: a case study in camyazi forest directorate, Turkey. Šumarski list. 2015;139(7-8):369-76.

Liu S, Wang X, Liu M, Zhu J. Towards better analysis of machine learning models: A visual analytics perspective. Vis Inform. 2017;1(1):48-56. https://doi.org/10.1016/j.visinf.2017.01.006

Sun G, Ranson KJ, Guo Z, Zhang Z, Montesano P, Kimes D. Forest biomass mapping from lidar and radar synergies. Remote sens Environ. 2011;115(11):2906-16. https://doi.org/10.1016/j.rse.2011.03.021

Urbazaev M, Thiel C, Cremer F, Dubayah R, Migliavacca M, Reichstein M, Schmullius C. Estimation of forest aboveground biomass and uncertainties by integration of field measurements, airborne LiDAR, and SAR and optical satellite data in Mexico. Carbon Balance Manag. 2018;13:1-20.https://doi.org/10.1186/s13021-018-0093-5

Kumar L, Mutanga O. Remote sensing of above-ground biomass. Remote Sensing. 2017;9(9):935. https://doi.org/10.3390/rs9090935

Tian X, Li J, Zhang F, Zhang H, Jiang M. Forest Aboveground Biomass Estimation Using Multisource Remote Sensing Data and Deep Learning Algorithms: A Case Study over Hangzhou Area in China. Remote Sens. 2024;16(6):1074. https://doi.org/10.3390/rs16061074

Smith J, Johnson L, Lee K. Utilization of C-band SAR for biomass estimation: A review. Remote Sens Environ. 2018; 210:133-145. https://doi.org/10.1016/j.rse.2018.03.024

Sarker ML, Nichol J, Iz HB, Ahmad BB, Rahman AA. Forest biomass estimation using texture measurements of high-resolution dual-polarization C-band SAR data. IEEE Transactions on Geoscience and Remote Sensing. 2012 Nov 15;51(6):3371-84. https://doi.org/10.1109/TGRS.2012.2219872

Patenaude G, Milne R, Dawson TP. Synthesis of remote sensing approaches for forest carbon estimation: reporting to the Kyoto Protocol. Environ Sci Policy. 2005;8(2):161-78. https://doi.org/10.1016/j.envsci.2004.12.010

Brown S, Gaston G. Use of forest inventories and geographic information systems to estimate biomass density of tropical forests: application to tropical Africa. Environ Monit Assess.1995;38:157-68. https://doi.org/10.1007/BF00546760

Chen L, Ren C, Bao G, Zhang B, Wang Z, Liu M, Man W, Liu J. Improved object-based estimation of forest aboveground biomass by integrating LiDAR data from GEDI and ICESat-2 with multi-sensor images in a heterogeneous mountainous region. Remote Sensing. 2022 Jun 7;14(12):2743. https://doi.org/10.3390/rs14122743

Smith J, Brown P, Johnson L. Utilizing GEDI LiDAR data for comprehensive forest structure and biomass analysis in temperate ecosystems. ISPRS J Photogramm Remote Sens. 2022;183:352-66. https://doi.org/10.1016/j.isprsjprs.2022.01.012

Kumar L, Sinha P, Taylor S, Alqurashi AF. Review of the use of remote sensing for biomass estimation to support renewable energy generation. J Appl Remote Sens.2015;9(1):097696-. https://doi.org/10.1117/1.JRS.9.097696

Gonzalez P, Asner GP, Battles JJ, Lefsky MA, Waring KM, Palace M. Forest carbon densities and uncertainties from Lidar, QuickBird, and field measurements in California. Remote Sens Environ. 2010;114(7):1561-75. https://doi.org/10.1016/j.rse.2010.02.011

Chen B, Tu Y, Song Y, Theobald DM, Zhang T, Ren Z, Li X, Yang J, Wang J, Wang X, Gong P. Mapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America. ISPRS Journal of Photogrammetry and Remote Sensing. 2021 Aug 1;178:203-18. https://doi.org/10.1016/j.isprsjprs.2021.06.010

Oehmcke S, Höfle B, Rutzinger M. Predicting wood volume and aboveground biomass from airborne LiDAR point clouds using deep learning systems. ISPRS J Photogramm Remote Sens. 2021;178:257-71. https://doi.org/10.1016/j.isprsjprs.2021.06.010

Morin D, Boissier O, Fayolle A. High-resolution mapping of forest height and biomass across metropolitan France using multi-sensor satellite imagery and GEDI LiDAR data. Remote Sens Environ. 2023;294:113661. https://doi.org/10.1016/j.rse.2023.113661

Dong Y, Li Y, Zhang X. Comparison of remote sensing-based forest biomass mapping approaches using new forest inventory plots in northeastern and southwestern China. Forests. 2024;15(1):125. https://doi.org/10.3390/f15010125

May PB, Finley AO. Calibrating satellite maps with field data for improved predictions of forest biomass. Environmetrics. 2024:e2892. https://doi.org/10.1002/env.2892

Ni W, Ranson KJ, Zhang Z, Sun G. Features of point clouds synthesized from multi-view ALOS/PRISM data and comparisons with LiDAR data in forested areas. Remote Sens Environ. 2014;149:47-57. https://doi.org/10.1016/j.rse.2014.04.001

Li D, Wang C, Hu Y, Liu S. General review on remote sensing-based biomass estimation. Geomatics and Information Science of Wuhan University. 2012;37(6):631-35.

Sinha S, Jeganathan C, Sharma LK, Nathawat MS. A review of radar remote sensing for biomass estimation. Int J Environ Sci Technol. 2015;12(5):1779-92. https://doi.org/10.1007/s13762-015-0750-0

Nath AJ, Tiwari BK, Sileshi GW, Sahoo UK, Brahma B, Deb S, Devi NB, Das AK, Reang D, Chaturvedi SS, Tripathi OP. Allometric models for estimation of forest biomass in North East India. Forests. 2019;10(2):103. https://doi.org/10.3390/f10020103.

Giri K, Pandey R, Jayaraj RS, Nainamalai R, Ashutosh S. Regression equations for estimating tree volume and biomass of important timber species in Meghalaya, India. Curr Sci. 2019;116(1):75-81.https://doi.org/10.18520/cs/v116/i1/75-81

Tumuluru, J. S. (Ed.). (2017). Biomass volume estimation and valorization for energy. BoD–Books on Demand. https://doi.org/10.5772/62678

Klinge H, Rodrigues WA, Brunig E, Fittkau EJ. Biomass and structure in a central Amazonian rain forest. InTropical ecological systems: trends in terrestrial and aquatic research 1975 (pp. 115-122). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-88533-4_9

Vann DR, Palmiotto PA, Strimbeck GR. Allometric equations for two South American conifers: test of a non-destructive method. For Ecol Manage. 1998;106(2-3):55-71. https://doi.org/10.1016/S0378-1127(97)00215-6

Overman JP, Witte HJ, Saldarriaga JG. Evaluation of regression models for above-ground biomass determination in Amazon rainforest. J Trop Ecol. 1994;10(2):207-18. https://doi.org/10.1017/S0266467400007859

Brown S, Lugo AE. Biomass of tropical forests: a new estimate based on forest volumes. Science. 1984;223(4642):1290-3. https://doi.org/10.1126/science.223.4642.1290

Brown S, Iverson LR, Lugo AE. Land-use and biomass changes of forests in Peninsular Malaysia from 1972 to 1982: a GIS approach. Springer New York; 1994.https://doi.org/10.1007/978-1-4613-8363-5_4

Foody GM, Boyd DS, Cutler ME. Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. Remote Sens Environ. 2003;85(4):463-74. https://doi.org/10.1016/S0034-4257(03)00039-7

Lu D, Batistella M, Moran E. Satellite estimation of aboveground biomass and impacts of forest stand structure. Photogramm Eng Remote Sensing. 2005;71(8):967-74. https://doi.org/10.14358/PERS.71.8.967

Lu D. Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon. Int J Remote Sens. 2005;26(12):2509-25. https://doi.org/10.1080/01431160500142145

Lu D, Chen Q, Wang G, Moran E, Batistella M, Zhang M, Vaglio Laurin G, Saah D. Aboveground forest biomass estimation with Landsat and LiDAR data and uncertainty analysis of the estimates. Int J For Res. 2012;2012(1):436537. https://doi.org/10.1155/2012/436537

Sarker ML, Nichol J, Ahmad B, Busu I, Rahman AA. Potential of texture measurements of two-date dual polarization PALSAR data for the improvement of forest biomass estimation. ISPRS J Photogramm Remote Sens. 2012;69:146-66. https://doi.org/10.1016/j.isprsjprs.2012.03.002

Popescu SC, Zhao K, Neuenschwander A, Lin C. Satellite lidar vs. small footprint airborne lidar: Comparing the accuracy of aboveground biomass estimates and forest structure metrics at footprint level. Remote Sens Environ. 2011;115(11):2786-97. https://doi.org/10.1016/j.rse.2011.01.026

Tsui OW, Coops NC, Wulder MA, Marshall PL. Integrating airborne LiDAR and space-borne radar via multivariate kriging to estimate above-ground biomass. Remote Sens Environ. 2013;139:340-52. https://doi.org/10.1016/j.rse.2013.08.012

Montesano PM, Cook BD, Sun G, Simard M, Nelson RF, Ranson KJ, Zhang Z, Luthcke S. Achieving accuracy requirements for forest biomass mapping: A spaceborne data fusion method for estimating forest biomass and LiDAR sampling error. Remote Sens Environ 2013;130:153-70. https://doi.org/10.1016/j.rse.2012.11.016

Laurin GV, Chen Q, Lindsell JA, Coomes DA, Del Frate F, Guerriero L, Pirotti F, Valentini R. Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data. ISPRS J Photogramm Remote Sens. 2014;89:49-58. https://doi.org/10.1016/j.isprsjprs.2014.01.001

Tiwari AK, Singh JS. Mapping forest biomass in India through aerial photographs and nondestructive field sampling. Appl Geogr. 1984;4(2):151-65. https://doi.org/10.1016/0143-6228(84)90019-5

Thenkabail PS, Stucky N, Griscom BW, Ashton MS, Diels J, Van der Meer B, Enclona E. Biomass estimations and carbon stock calculations in the oil palm plantations of African derived savannas using IKONOS data. Int J Remote Sens. 2004;25(23):5447-72. https://doi.org/10.1080/01431160412331291279

Roy PS, Ravan SA. Biomass estimation using satellite remote sensing data—an investigation on possible approaches for natural forest. J Biosci . 1996;21:535-61. https://doi.org/10.1007/BF02703218

Barbosa PM, Stroppiana D, Grégoire JM, Cardoso Pereira JM. An assessment of vegetation fire in Africa (1981–1991): Burned areas, burned biomass, and atmospheric emissions. Global Biogeochem Cycles. 1999;13(4):933-50. https://doi.org/10.1029/1999GB900042

Santos JR, Lacruz MP, Araujo LS, Keil M. Savanna and tropical rainforest biomass estimation and spatialization using JERS-1 data. Int J Remote Sens. 2002;23(7):1217-29. https://doi.org/10.1080/01431160110092867

Brown S. Estimating biomass and biomass change of tropical forests: a primer. Food & Agriculture Org. 1997;143.

Published

31-01-2025

How to Cite

1.
Kabinesh V, Suwethaasri D, Baranidharan K, Ravi R, Tilak M, Kalpana M, Ragunath K, Vennila S, Hemalatha P, Vijayabhama M, Bargavi S, Eniya A. A critical review of exploring the recent trends and technological advancements in forest biomass estimation. Plant Sci. Today [Internet]. 2025 Jan. 31 [cited 2025 Mar. 31];12(sp1). Available from: https://horizonepublishing.com/journals/index.php/PST/article/view/6695

Most read articles by the same author(s)

1 2 > >> 

Similar Articles

You may also start an advanced similarity search for this article.