SpringerLink
Log in

Vegetation biomass and carbon stocks in the Parnaíba River Delta, NE Brazil

  • Original Paper
  • Published:
Wetlands Ecology and Management Aims and scope Submit manuscript

Abstract

Coastal ecosystems are considered environments with great potential for carbon storage. Given the difficulties in quantifying biomass, allometric equations and remote sensing have become fundamental tools in the studies of quantification of vegetation biomass and carbon stocks. Thus, the objective of this study is to quantify and estimate the spatial distribution of vegetation biomass and to quantify the carbon stock of the Parnaíba River Delta vegetation. The study was carried out in part of the Parnaíba River Delta Environmental Protection Area and in the Parnaíba River Delta Marine Extractive Reserve, in NE Brazil, in five spots within distinct vegetation types: psammophile pioneer vegetation, dune subevergreen vegetation, mangrove evergreen vegetation, floodplain vegetation and vegetation associated with carnaubals. At 26 collection points, 10 × 20 m plots were marked, in which the diameter at breast height and height of all individuals were measured. The collected data were used in allometric equations for vegetation biomass estimates and these values were converted into carbon stocks. The spatial distribution of aboveground vegetation biomass (AGB) was also estimated by remote sensing, where we extracted and selected spectral variables obtained from Landsat-8 OLI sensor images, on three different dates. Prediction models were calculated by multiple linear regression analysis. It was observed that the mangrove evergreen vegetation obtained higher vegetation biomass and carbon stock than the others. The models obtained through remote sensing that provided the best estimates of AGB were those of November 12th, 2016 (EAM = 6.84; RMSE = 47.89 Mg ha−1; R2 = 0.72) and November 28th, 2016 (EAM = 9.63; RMSE = 34.67 Mg ha−1; R2 = 0.58).

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Source INMET (2018)

Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Alba E et al (2017) Uso de imagens de média resolução espacial para o monitoramento de dosséis de Eucalyptus grandis. Sci Agraria 18:1–8. https://doi.org/10.5380/rsa.v18i4.51944

    Article  Google Scholar 

  • Alongi DM (2014) Carbon cycling and storage in mangrove forests. Ann Rev Mar Sci 6(1):195–219

    Article  Google Scholar 

  • Barsi JA et al (2014) The spectral response of the landsat-8 operational land imager. Remote Sens 6:10232–10251. https://doi.org/10.3390/rs61010232

    Article  Google Scholar 

  • Barrachina M, Cristóbal H, Tulla AF (2015) Estimating above-ground biomass on mountain meadows and pastures through remote sensing. Int J Appl Earth Obs Geoinf 38:184–192. https://doi.org/10.1016/j.jag.2014.12.002

    Article  Google Scholar 

  • Bokhorst S et al (2017) Dwarf shrub and grass vegetation resistant to long-term experimental warming while microarthropod abundance declines on the Falkland Islands. Aust Ecol 42:984–994. https://doi.org/10.1111/aec.12527

    Article  Google Scholar 

  • Brazil (1996) Decreto de 28 de Agosto de 1996. Dispõe sobre a criação da Área de Proteção Ambiental Delta do Parnaíba, nos Estados do Piauí, Maranhão, e Ceará, e dá outras providências, 1996.

  • Brazil (2000) Lei nº 9.985, de 18 de julho de 2000. Regulamenta o art.225, §1º, incisos I, II, III e IV da Constituição Federal, institui o Sistema Nacional de Unidades de Conservação da Natureza e dá outras providências

  • Brazil (2000b) Decreto de 16 de novembro de 2000. Cria a Reserva Extrativista Marinha do Delta do Parnaíba, no Município de Ilha Grande de Santa Isabel, Estado do Piauí, e nos Municípios de Araióses e Água Doce, Estado do Maranhão, e dá outras providências. Disponível em: https://www.planalto.gov.br/ccivil_03/DNN/DNN9084.htm , acesso em 28 de março de 2019

  • Brown S, Gillespie AJR, Lugo AEL (1989) Biomass estimation methods for tropical forests with applications to forest inventory data. For Sci 35:881–902. https://doi.org/10.1093/forestscience/35.4.881

    Article  Google Scholar 

  • Cabral LJRS (2018) Levantamento pedológico da planície do Delta do Parnaíba, PI. Dissertação (Mestrado em Geografia), Centro de ciências Humanas e Letras, Universidade Federal do Piauí, Teresina, 2018

  • Carneiro ACO et al (2014) Potencial energético da madeira de Eucalyptus sp. em função da idade e de diferentes materiais genéticos. Rev Árvore 38:375–381. https://doi.org/10.1590/S0100-67622014000200019

    Article  Google Scholar 

  • Chambers JQ et al (2001) Tree damage, allometric relationships, and above-ground net primary production in central Amazon forest. For Ecol Manag 152:73–84

    Article  Google Scholar 

  • Comley BWT, Mcguinness KA (2005) Above- and below-ground biomass, and allometry, of four common northern Australian mangroves. Aust J Bot 53:431–436

    Article  Google Scholar 

  • Cusack M et al (2018) Organic carbon sequestration and storage in vegetated coastal habitats along the western coast of the Arabian Gulf. Environ Res Lett 13(7):074007

    Article  Google Scholar 

  • de Paula Filho FJ, Marins RV, Lacerda LD de (2015) Natural and anthropogenic emissions of N and P to the Parnaíba River Delta in NE Brazil. Estuar Coast Shelf Sci 166:34–44

    Article  Google Scholar 

  • Duncan C et al (2018) Satellite remote sensing to monitor mangrove forest resilience and resistance to sea level rise. Methods Ecol Evol 9:1837–1852. https://doi.org/10.1111/2041-210X.12923

    Article  Google Scholar 

  • Fatoyinbo T et al (2018) Estimating mangrove aboveground biomass from airborne LiDAR data: a case study from the Zambezi River delta. Environ Res Lett 13(2):025012

    Article  Google Scholar 

  • Fidelis A, Lyra MFS, Pivello VR (2012) Above- and below-ground biomass and carbondynamics in Brazilian Cerrado wet grasslands. J Veg Sci 24:356–364. https://doi.org/10.1111/j.1654-1103.2012.01465

    Article  Google Scholar 

  • Fonseca ILA, Mochel FR (2016) Fitomassa aérea de um manguezal no estuário do rio dos cachorros, São Luís, Maranhão, Brasil. Boletim Lab Hidrobiol 26:17–25

    Google Scholar 

  • Frangi JL, Lugo AE (1985) Ecosystem dynamics of a subtropical floodplain forest. Ecolog Monogr 55:351–369. https://doi.org/10.2307/1942582

    Article  Google Scholar 

  • Fromard F et al (1998) Structure, above-ground biomass and dynamics of mangrove ecosystems: new data from French Guiana. Oecologia 115:39–53. https://doi.org/10.1007/s004420050

    Article  CAS  PubMed  Google Scholar 

  • Fernandes AG et al (1996) IV-Componentes biológicos: Vegetação. CEPRO, Macrozoneamento Costeiro do Estado do Piauí: relatório geo-ambiental e sócio-econômico. Teresina, Fundação CEPRO, pp 43–72

    Google Scholar 

  • Frank D et al (2015) Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts. Glob Chang Biol 21(8):2861–2880

    Article  Google Scholar 

  • Fundacion Solar (2010) Elementos técnicos para inventários de carbono em uso del solo. Guatemala

  • Gao B (1996) A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58:257–266. https://doi.org/10.1016/S0034-4257(96)00067-3

    Article  Google Scholar 

  • Gitelson AA, Kaufman YJ, Merzlyak MN (1996) Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens Environ 58:289–298. https://doi.org/10.1016/S0034-4257(96)00072-7

    Article  Google Scholar 

  • Guzzi A (2012) Biodiversidade do Delta do Parnaíba: litoral piauiense. EDUFPI, Parnaíba

    Google Scholar 

  • Hickey SM et al (2018) Spatial complexities in aboveground carbon stocks of a semi-arid mangrove community: a remote sensing height-biomass-carbon approach. Estuar Coast Shelf Sci 200:194–201. https://doi.org/10.1016/j.ecss.2017.11.004

    Article  CAS  Google Scholar 

  • Huete AR (1988) A soil adjusted vegetation index (SAVI). Remote Sens Environ 25:295–309. https://doi.org/10.1016/0034-4257(88)90106-X

    Article  Google Scholar 

  • Huete AR et al (1997) A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sens Environ 59:440–451. https://doi.org/10.1016/S0034-4257(96)00112-5

    Article  Google Scholar 

  • Intergovernmental Panel on Climate Change (IPCC) (2007) Climate change 2007: the scientific basis summary for policymakers. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Intergovernmental Panel on Climate Change (IPCC) (2014) Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. In: Wetlands, Hiraishi T, Krug T, Tanabe K, Srivastava N, Baasansuren J, Fukuda M, Troxler TG (eds) IPCC, Switzerland

  • Kamruzzaman Md et al (2017) Species composition, biomass, and net primary productivity of mangrove forest in Okukubi River, Okinawa Island, Japan. Reg Stud Mar Sci 12:19–27

    Article  Google Scholar 

  • Kauffman JB, Donato DC (2012) Protocols for the measurement, monitoring, and reporting of structure, biomass and carbon stocks in mangrove forests. CIFOR Working Paper 86. Center for International Forest Research, Indonesia

  • Kauffman JB, Bhomia RK (2017) Ecosystem carbon stocks of mangroves across broad environmental gradients in West-Central Africa: global and regional comparisons. PLoS ONE 7(12):1–17. https://doi.org/10.1371/journal.pone.0187749

    Article  CAS  Google Scholar 

  • Kauffman JB et al (2018) Carbon stocks of mangroves and salt marshes of the Amazon region. Brazil Biol Lett 14:1–4. https://doi.org/10.1098/rsbl.2018.0208

    Article  CAS  Google Scholar 

  • Komiyama A, Poungparn S, Kato S (2005) Common allometric equations for estimating the tree weight of mangroves. J Trop Ecol 21:471–477. https://doi.org/10.1017/S0266467405002476

    Article  Google Scholar 

  • Lima Júnior C et al (2014) Estimation of “caatinga” woody biomass using allometric equations and vegetation index. For Sci 42:289–298

    Google Scholar 

  • Lu D, Batistella M, Moran E (2005) Satellite estimation of aboveground biomass and impacts of forest stand structure. Photogr Eng Remote Sens 71:967–974

    Article  Google Scholar 

  • Macdicken K (1997) A guide to monitoring carbon storage in forestry and agroforestry projects. Institute for Agricultural Development, Winrock International, Arlington

    Google Scholar 

  • Maia GN (2004) Caatinga: árvores e arbustos e suas utilidades. Leitura & Arte, São Paulo

    Google Scholar 

  • Naidoo G (2016) The mangroves of South Africa: an ecophysiological review. S Afr J Bot 107:101–113. https://doi.org/10.1016/j.sajb.2016.04.014

    Article  Google Scholar 

  • Nakai ES (2016) Quantificação da biomassa e estoque de carbono em diferentes coberturas vegetais por meio de sensoriamento remoto. Piracicaba, Escola Superior de Agricultura “Luiz de Queiroz”, Tese (Doutorado em Ciências)

  • Naessens W, Maere T, Nopens I (2012) Critical review of membrane bioreactor models. Part 1: Biokinetic and filtration models. Biores Technol 122:95–106. https://doi.org/10.1016/j.biortech.2012.05.070

    Article  CAS  Google Scholar 

  • Neubauer SC, Megonigal JP (2019) Correction to: moving beyond global warming potentials to quantify the climatic role of ecosystems. Ecosystems 22(8):1931–1932

    Article  CAS  Google Scholar 

  • Nguyen HT et al (2015) Growth responses of the mangrove Avicennia marina to salinity: development and function of shoot hydraulic systems require saline conditions. Ann Bot. https://doi.org/10.1093/aob/mcu257

    Article  PubMed  PubMed Central  Google Scholar 

  • Pavani BF et al (2018) Estimating and valuing the carbon release in scenarios of land-use and climate changes in a Brazilian coastal area. J Environ Manag 226:416–427. https://doi.org/10.1016/j.jenvman.2018.08.059

    Article  Google Scholar 

  • Pearson RL, Miller LD (1972) Remote map** of standing crop biomass for estimation of the productivity of shortgrass prairie, Pawnee National Grasslands, Colorado. In: Proceedings of the 8th international symposium on remote sensing of the environment, Ann Arbor, MI, 2, 1355–1379

  • Perry CR, Lautenschlager LF (1984) Functional equivalence of spectral vegetation indices. Remote Sens Environ 14:169–182. https://doi.org/10.1016/0034-4257(84)90013-0

    Article  Google Scholar 

  • Pinto LM et al (2016) Sequestro de carbono atmosférico no bosque do manguezal da APA da Serra do Guararú, Guarujá-SP. UNISANTA Biosci 5:51–57

    Google Scholar 

  • Ray R et al (2013) Improved model calculation of atmospheric CO2 increment in affecting carbon stock of tropical mangrove forest. Tellus 65:1–11. https://doi.org/10.3402/tellusb.v65i0.18981

    Article  CAS  Google Scholar 

  • Ray R et al (2011) Carbon sequestration and annual increase of carbon stock in a mangrove forest. Atmos Environ 45:5016–5024. https://doi.org/10.1016/j.atmosenv.2011.04.074

    Article  CAS  Google Scholar 

  • R Core Team (2017) R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Disponível em: https://www.R-project.org/

  • Risdiyanto I, Fakhrul M (2017) Examination of multi-spectral radiance of the Landsat 8 satellite data for estimating biomass carbon stock at wetland ecosystem. Preprints 1:1–14. https://doi.org/10.20944/preprints201704.0020

    Article  Google Scholar 

  • Rocha AES, Miranda IS (2012) Cobertura vegetal, biomassa aérea e teor de proteína do estrato herbáceo de ambiente savânico no município de Maracanã, Pará, Brasil. Rev Bras Biociênc 10:513–520

    Google Scholar 

  • Rouse JW et al (1974) Monitoring the vernal advancement of retrogradation (greenwave effect) of natural vegetation. NASA/GSFC, Type III, Final Report, Greenbelt, MD

  • Saintilan N et al (2014) Mangrove expansion and salt marsh decline at mangrove poleward limits. Glob Change Biol 20:147–157. https://doi.org/10.1111/gcb.12341

    Article  Google Scholar 

  • Saenger P, Snedaker SC (1993) Pantropical trends in mangrove above-ground biomass and annual litterfall. Oecologia 96:293–299

    Article  Google Scholar 

  • Santos HVS et al (2017) Allometric models for estimating the aboveground biomass of the mangrove Rhizophora mangle. Bras J Oceanogr 65:44–53

    Article  Google Scholar 

  • Santos RC et al (2016) Estoques de volume, biomassa e carbono na madeira de espécies da Caatinga em Caicó, RN. Pesqui Florest Bras 36:1–7. https://doi.org/10.1590/s1679-87592017127006501

    Article  Google Scholar 

  • Silva AGA da et al (2019) Coastline change and offshore suspended sediment dynamics in a naturally develo** delta (Parnaíba Delta, NE Brazil). Mar Geol 410:1–15

    Article  Google Scholar 

  • Simard M et al (2018) Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. Nat Geosci 12(1):40–45. https://doi.org/10.1038/s41561-018-0279-1

    Article  CAS  Google Scholar 

  • Tang W et al (2016) The assessment of mangrove biomass and carbon in West Africa: a spatially explicit analytical framework. Wetlands Ecol Manag 24:153–171. https://doi.org/10.1007/s11273-015-9474-7

    Article  CAS  Google Scholar 

  • Vermote E et al (2016) Preliminary analysis of the performance of the landsat 8/oli land surface reflectance product. Remote Sens Environ 185:46–56. https://doi.org/10.1016/j.rse.2016.04.008

    Article  PubMed  Google Scholar 

  • Wang G et al (2013) Ecosystem carbon stocks of mangrove forest in Yingluo Bay, Guangdong Province of South China. For Ecol Manag 310:539–546. https://doi.org/10.1016/j.foreco.2013.08.045

    Article  Google Scholar 

  • Xu H (2006) Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sens 27:3025–3033. https://doi.org/10.1080/01431160600589179

    Article  Google Scholar 

  • Zhang C et al (2018) Quantification of sawgrass marsh aboveground biomass in the coastal Everglades using object-based ensemble analysis and Landsat data. Remote Sens Environ 204:366–379

    Article  Google Scholar 

Download references

Acknowledgements

The authors especially thank the Conselho Nacional de Desenvolvimento Científico e Tecnológico – CNPq for Scholarship PQ2 (301254/2017-6; G.S. Valladares), and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES for Scholarship (M.G.T. Portela).

Author information

Authors and Affiliations

  1. Department of Geography, Federal University of Piauí (UFPI), Teresina, Brazil

    Mirya Grazielle Torres Portela, Giovana Mira de Espindola, Gustavo Souza Valladares, João Victor Alves Amorim & Jéssica Cristina Oliveira Frota

Authors
  1. Mirya Grazielle Torres Portela
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Giovana Mira de Espindola
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gustavo Souza Valladares
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. João Victor Alves Amorim
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jéssica Cristina Oliveira Frota
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giovana Mira de Espindola.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Portela, M.G.T., de Espindola, G.M., Valladares, G.S. et al. Vegetation biomass and carbon stocks in the Parnaíba River Delta, NE Brazil. Wetlands Ecol Manage 28, 607–622 (2020). https://doi.org/10.1007/s11273-020-09735-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11273-020-09735-y

Keywords

Search

Navigation