• editor.aipublications@gmail.com
  • Track Your Paper
  • Contact Us
  • ISSN: 2456-8791

International Journal Of Forest, Animal And Fisheries Research(IJFAF)

Mapping of Wood Carbon Stocks in the Classified Forest of Wari-Maro in Benin Center (West Africa)


Download | Downloads : 11 | Total View : 1719

DOI: 10.22161/ijfaf.2.2.3

Journal : International Journal Of Forest, Animal And Fisheries Research(IJFAF)


The Emissions Reducing program related to Deforestation and Forest Degradation (Redd +) calls for the development of approaches to quantify and spatialize forest carbon in order to design more appropriate forest management policies. The mapping of carbon stocks was done in the Wari-Maro Forest Reserve. To achieve this, forest inventory data (in situ) and remotely sensed data (Landsat 8 image) were used to construct a wood carbon stock forecasting model. Simple linear regression was used to test the correlation between these two variables. In situ surveys indicate that 64% of carbon stocks are contributed by forest formations, 32.72% are provided by savannah formations and 3.27% are from anthropogenic formations. The quantitative relationship between NDVI and carbon in situ shows a very good correlation with a high coefficient of determination R² = 91%. The carbon map generated from the model identified fronts of deforestation through their low carbon content. This remote sensing approach indicates that forest formations sequester 60% of forest carbon. The savannah formations reserve 33%, the anthropic formations bring only 6% of the stocks. Mapping has further captured the spatial variability among land use types, thus providing arguments to fully meet the objectives of Redd +.

Cartography, Classified Forest, Linear Regression, Surveyed in situ, Carbon Stocks, Wari-Maro.

[1] J.-L. Dupouey, G. Pignard, V. Badeau, A. Thimonier, J.-F. Dhôte, G. Nepveu, L. Bergès, L. Augusto, S. Belkacem, C. Nys. Carbon stores and fluxes in French forests. Comptes rendus de l’Académie d’Agriculture de France, 85 : 1999, pp 293-310.
[2] P. G. Asner, J. Mascaro, C. H. Muller-Landau, G. Vieilledent, R. Vaudry. A universal airborne LiDAR approach for tropical forest carbon mapping. Œcologia, 168 (4): 2012, pp 1147-1160.
[3] M.J. Metzger, M.D.A. Rounsevell, L. Acosta-Michlik, R. Leemans, D. Schroter. The vulnerability of ecosystem services to land use change. Agriculture, Ecosystems and Environment, 114: 2006, pp 69-85.
[4] J. Dong, R.K. Kaufmann, R.B. Myneni, C.J. Tucker, P.E. Kauppi, J. Liski, W. Buermann, V. Alexeyev and M.K. Hughes. Remote Sensing estimâtes of boréal and temperate forest woody biomass: carbon pools, sources and sinks. Remote Sensing of Environment, vol. 84, 2003, pp 393-410.
[5] S. Le Clec’h, J. Oszwald, N. Jégou, S. Dufour, P.-A. Cornillon, I. Miranda, L. Gonzaga, M. Grimaldi, V. Gond, X. Arnauld de Sartre. Cartographier le carbone stocké dans la végétation : perspectives pour la spatialisation d’un service écosystémique. Bois et forêts des tropiques 316 (2), 2013, pp 35-48.
[6] C. Grinand. Développement d’une méthode de spatialisation des stocks de carbone dans le sol à l’échelle régionale : Application à un projet REDD à Madagascar. Rapport final du projet SILAT, 2010, p 47.
[7] S. Brown, Estimating biomass and biomass changes of tropical forests: A primer, FAO Forestry Paper, Rome, Italy, 1997.
[8] IPCC, Second Assessment Climate Change 1995. A report of the intergovernmental panel on climate change, 1995, p 73.
[9] P., Migolet, L., Coulibaly, H.G., Adegbidi, E., Hervet, Utilization of neural networks for the estimation of aboveground forest biomass from Ikonos satellite image and multi-source geo-scientific data. IGARSS 2007: pp 4339-4342.
[10] FAO, Proceedings of Expert Meeting on Harmonizing Forest-related Definitions for Use by Various Stakeholders. FAO, Rome, Italie, 2002. Disponible auprès de: Http: //www.fao.org/forestry/fop/fopw/Climate/doc/Y3431E.pdf.
[11] CIRAD, Dossier de séquestration du carbone, 2002.
[12] M.E. Jakubauskas, and K.P. Price, Empirical relationships between structural and spectral factors of Yellowstone lodgepole pine forests. Photogrammetric Engineering & Remote Sensing, vol. 63, no. 12, 1997, pp. 1375-1381.
[13] D.L. Peterson, W.E. Westman, N.J. Stephenson, Y.G. Ambrosia, J.A. Brass, and M.A. Spanner, Analysis of forest structure using Thematic Mapper Simulator data. IEEE Transactions on Geoscience and Remote Sensing, vol. 24, no. 1, 1986, pp. 113-121.
[14] J. Dong, R.K. Kaufmann, R.B. Myneni, C.J. Tucker, P.E. Kauppi, J. Liski, W. Buermann, V. Alexeyev, and M.K. Hughes, Remote Sensing estimâtes of boreal and temperate forest woody biomass: carbon pools, sources and sinks. Remote Sensing of Environment, vol. 84, 2003, pp. 393-410.
[15] J. Franklin, Thematic mapper analysis of coniferous forest structure and composition. International Journal of Remote Sensing, vol. 7, no. 10, 1986, pp. 1287-1301.
[16] Centre de Suivi Ecologique (CSE), Suivi de la production végétale 2017, Ministère de l’environnement et du développement durable (MEDD), Sénégal, 2017, p 29.
[17] S. Labrecque, Cartographie de la biomasse forestière à l'aide des données d'inventaire forestier et des images TM de Landsat. Mémoire présenté pour l'obtention du grade de Maître ès sciences (M.Sc.) en géographie, cheminement Télédétection, Département de géographie et télédétection Faculté des lettres et sciences humaines Université de Sherbrooke, 2004, p 101.
[18] M.A. Lefsky, W.B. Cohen, and T.A. Spies, An évaluation of altemate remote sensing products for forestry inventory, monitoring and mapping of Douglas-fir forests in eastem Oregon. Canadian Journal of Forest Research, vol. 31, 2001 pp. 78-81.
[19] C.B. Puhr, and D.N.M. Donoghue, Remote sensing of upland conifer plantations using Landsat TM data: a case study from Galloway, south-west Scotland. International Journal of Remote Sensing, vol. 21, no. 4, 2000, pp. 633-646.
[20] W. B. Cohen and T.A. Spies, Estimating structural attributes of Douglas-fir/Westem hemlock forest stands from Landsat and Spot imagery. Remote Sensing of Environment, vol. 41, no. 1, 1992, pp. 1-17.
[21] W.B. Cohen, T.K. Maiersperger, S.T. Gower, and O.P. Tumer, An improved strategy for régression of biophysical variables and Landsat ETM+ data. Remote Sensing of Environment, vol. 84, 2003, pp. 561-571.
[22] O. Diallo, M. Rasmussen, K.S. Sawadogo, L. Traore, Document technique pour le suivi environnemental du Programme National De Gestion des Terroirs.
[23] K.S.; Sawadogo, Z. Koudougou, D. Kissou, S.Tiemtore, J. BEOGO, Suivi des ressources Pastorales, Campagne 1993. Rapport Annuel, 1994, p 53.
[24] J. Chave, C. Andalo, S. Brown, M. Cairns, J. Chambers, D. Eamus, H. Fölster, F. Fromard, N. Higuchi, T. Kira, J.-P Lescure, B.W. Nelson, H. Ogawa, H. Puig, B. Riera, and T. Yamakura, « Tree allometry and improved estimation of carbon stocks and balance in tropical forests », Oecologia,145, 2005 p. 87–99.
[25] A.B. McBratney, M.L. Mendonça, B. Minasny, on digital Soil Mapping. Geoderma 117, 2003, pp 3–52.
[26] A.C. Adomou, Vegetation patterns and environmental gradients in Benin: Implicatations for biogeography and conservation. PhD thesis, Wageningen University of Wageningen, 2005, p 150.
[27] Y. Malhi & J.Wright, Spatial patterns and recent trends in the climate of tropical rainforest regions. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences. 359: 2004, pp 311-329.
[28] S.A. Vieira, L.F. Alves, M. Aidar, L.S. Araujo, T. Baker, J.L.F. Batista, M.C. Campos, P.B. Camargo, J. Chave, W.B.C. Delitti, N. Higuchi, E. Honorio, C.A. Joly, M. Keller, L.A. Martinelli, E.A. Di Mattos, T. Metzker, O. Phillips, F.A.M. Dos Santos, M.T. Shimabukuro, M. Silveira & S.E. Trumbore, Estimation of biomass and carbon stocks: the case of the Atlantic Forest. Biota Neotropica, 8(2), 2008, pp 21-29.
[29] S. Brown and A.E. Lugo, Aboveground biomass estimates for tropical moist forests of the brazilian amazon. Interciercia 17 (1), 1992, pp 8-18.
[30] M. Elias & C. Potvin, Assessing inter- and intra-specific variation in trunk carbon concentration for 32 neotropical tree species. Canadian Journal of Forest Research, 33(6), 2003, pp 1039-1045.
[31] Y.I. Moumouni, Dynamique du couvert forestier et évaluation des stocks de carbone dans la Forêt Classée de Wari-Maro au Bénin. Mémoire de DEA en Géosciences de l’Environnement et Gestion de l’Espace, EDP/FLASH/UAC Bénin, 2016, p 99.
[32] Y.I. Moumouni, O. Arouna, N.T. Issaka, I.T. Imorou, S. Zakari, M. Djaouga, Estimation de la variabilité de la biomasse aérienne ligneuse en forêt tropicale sèche: cas de la forêt classée de Wari-maro au Centre-Bénin. Revue de Géographie du Laboratoire Leïdi, Université Gaston Berger, Saint Louis (Sénégal), N°17, 2017, pp 38-56.