Open Access
E3S Web Conf.
Volume 96, 2019
2018 6th International Conference on Environment Pollution and Prevention (ICEPP 2018)
Article Number 02002
Number of page(s) 7
Section Ecosystem and Solid Waste Management
Published online 28 May 2019
  1. W. Walker, A. Baccini, M. Nepstad, N. Horning, D. Knight, E. Braun and A. Bausch. Field Guide for Forest Biomass and Carbon Estimation, Version 1.0. Woods Hole Research Center, Massachusetts, EE.UU. (2011) [Google Scholar]
  2. FAO. Evaluación de los recursos forestales mundiales. Roma: FAO-Montes, (2015) Available: [Google Scholar]
  3. E. A. Grigorets and L. I. Permitina. Using Russian remote sensing data for studying the dynamic of ecological and resource potential recovery of forests area after the impact of forest fires. Universitet, Krasnoyarsk, Russia, (2016). Available: (In Russian) [Google Scholar]
  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 estimates of boreal and temperate forest woody biomass: carbon pools, sources, and sinks. Remote Sensing of Env. 84. pp. 393-410, (2003) [CrossRef] [Google Scholar]
  5. J. Rouse, R. Hass, J. Schell and D. Deering. Monitoring vegetation systems in Great Plains with ERTS. 3rd ERTS-1 Symp. Washington, EE.UU. pp 309–317, (1974) [Google Scholar]
  6. A. S. Cherepanov and E. G. Druzhinina. Spectral properties of vegetation and vegetative indices. Geomatika 3, pp. 28-32, (2009). (In Russian) [Google Scholar]
  7. R. Lasaponara and N. Masini. Satellite Remote Sensing: A New Tool for Archaeology. Springer Science and Business Media. pp 26-27, (2012) [Google Scholar]
  8. N. Tilly, H. Aasen and G. Bareth. Fusion of plant height and Vegetation Indices for the estimation of Barley biomass. Remote Sensing 7, (2015) [Google Scholar]
  9. W. Cielsa.Cambio Climático Bosques Y Ordenación Forestal. Una Visión de Conjunto FAO Montes 126. Roma, Italia, (1995) [Google Scholar]
  10. S. Cartaya, S. Zurita, E. Rodríguez and V. Montalvo. Comprobación del NDVI en imágenes RAPIDEYE para determinar cobertura vegetal y usos de la tierra en la provincia de Manabí. Revista San Gregorio, 7. 75-92, (2015) [Google Scholar]
  11. A. M. Tarko and T. Van Lang. Calculation of the Role of Indochina Countries in Global Warming and its Consequences in the World. J. of Sci, and Tech, 51. pp. 1-10, (2012) [Google Scholar]
  12. J. Anaya, E. Chuvieco and A. Palacios. Estimación de biomasa aérea en Colombia a partir de imágenes MODIS. Revista de Teledetección, 30. pp. 5-22, (2008) [Google Scholar]
  13. C. Meneses. El índice normalizado diferencial de la vegetación como indicador de la degradación del bosque. Unasylva, 62, (2011) [Google Scholar]
  14. J. Campo, J. García, A. Navarrete and C. Siebe. Almacenes y dinámica del carbono orgánico en ecosistemas forestales tropicales de México. Terra Latinoamericana, 34. pp. 31-38, (2016) [Google Scholar]
  15. R. Norbya, et al. Forest response to elevated CO2 is conserved across a broad range of productivity. PNAS, 102. pp. 18052–56, (2005) [CrossRef] [Google Scholar]
  16. A. I. Kurbatova, A. M. Tarko, E. V. Kozlova. Impact of Global Climate Change on Ecosystem Functions of African Countries. Arid Ecosystems, 7. pp. 217–223. (2017) [CrossRef] [Google Scholar]
  17. S. Paulick, C. Dislich, J. Homeier and R. Fischer. The carbon fluxes in different successional stages: modelling the dynamics of tropical montane forests in South Ecuador. Forest Ecosystems, 4. (2017) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.