Estimation of above-ground forest biomass in amazonia with neural networks and remote sensing

IEEE Latin America Transactions

Volumn 7, Issue 1, 2009, Pages 27-32

  Almeida, Arthur Da Costa ^a   Barros, Paulo Luiz Contente ^b   Monteiro, José Humberto Araujo ^a   Da Rocha, Brigida Ramati Pereira ^a  

^a FEDERAL UNIVERSITY OF PARÁ   (Brazil)

^b UNIVERSIDADE FEDERAL RURAL DA AMAZÔNIA   (Brazil)

Author keywords

Biomass estimation; Neural networks; Remote sensing

Indexed keywords

AMAZON REGION; AMAZONIA; ARTIFICIAL NEURAL NETWORK; BIOMASS ESTIMATION; FOREST BIOMASS; FOREST INVENTORY; GEOGRAPHICAL INFORMATION SYSTEM; INTEGRATED METHODOLOGY;

BIOMASS; ESTIMATION; GEOGRAPHIC INFORMATION SYSTEMS; REMOTE SENSING;

NEURAL NETWORKS;

EID: 77957900870     PISSN: 15480992     EISSN: None     Source Type: Journal    
DOI: 10.1109/TLA.2009.5173462     Document Type: Conference Paper

References (20)

1. I. M. O. Silva, Estudo da Sustentabilidade de Energias Renováveis no Marajó. Tese de Doutorado. UFPA, Belém, 2003.
2. P. Bettinger, R. Hayashi. "Estimation of Aboveground Biomass with Remotely Sensed Imagery: A Brief Literature Review". Center for Forest Business. Research Note 25. University of Georgia, 2006.
3. W. Nishida; L. C. Bastos; "Classificação de Imagens de Sensoriamento Remoto Utilizando uma Rede Neural Artificial com Função de Base Radial". Anais do IX Simpósio Brasileiro de Sensoriamento Remoto. Santos-Sp, 1998.
4. A. Venturieri. J. R. Santos; "Técnicas de Classificação de Imagens para Análise de Cobertura Vegetal", In: ASSAD, E.D; SANO, E.E; Sistemas de Informações Geográficas: Aplicações na Agricultura. Embrapa. Brasília, 1998.
5. J. R. Jensen; F. Qiu; M. Ji; "Predictive Modeling of Coniferous Forest Age Using Statistical and Artificial Neural Networks Approaches to Remote Sensor Data". International Journal of Remote Sensing. 20, 1999.
6. F. Frate; D. Solimini; "On the Retrieval of Forest Biomass from SAR Data by Neural Networks". Proc. IEEE, Ed. IEEE, 2003.
7. F. Frate; D. Solimini; "On Neural Networks Algorithms for Retrieving Forest Biomass from SAR Data". IEEE Transactions on Geoscience and Remote Sensing. Vol. 42, 2004.
8. S. S. Saatchi; R. A. Houghton; R. C. S. Alvala;J. V. Soares;Y. Yo; "Distribution of Aboveground Live Biomass in the Amazon Basin". Global Change Biology, 2006.
9. D. Lu; P. Mausel; E. Brondizio; E, Moran. "Above-ground Biomass Estimation of Successional and Mature Forests Using TM Images in the Amazon Basin". Symposium on Geospatial Theory, Processing and Applications. Ottawa, Canada, 2002.
10. D. Lu; "The Potential and Challenge of Remote-sensing Based Biomass Estimation". International Journal of Remote Sensing,. 27, 2006.
11. S. Haykin, Redes Neurais, Princípios e Prática. Ed. Bookman, Porto Alegre, 2001.
12. M. Egmont-Petersen, D. Ridder, H. Handels,. "Image processing with neural networks - a review". Pattern Recognition, vol 35, pp 2279-2301, Ed. Pergamon, 2002.
13. T. Kohonen, "The Self Organizing Map". Proceedings IEEE, vol 78, no 9, pp 1464-1480, Ed. IEEE,1990..
14. T. Poggio, F. Girosi, "Networks for approximation and learning". Proceedings IEEE, vol 78, no 9, pp 1481-1497,Ed. IEEE, 1990.
15. A. Conci, E. O. Nunes, "Texture Segmentation and Edge Location of Regions in Multiband Images", IEEE Latin America Transactions, Vol. 5, No. 3, pp. 185-192, June 2007.
16. C.M Bishop; Neural Networks for Pattern Recognition. Oxford University Press. Oxford, 1995.
17. G. Câmara, R.C. M Freitas, "SPRING: Integrating remote sensing and GIS by object-oriented data modelling". Journal of Computer &Graphics. 20(3) 395-403, 1996.
18. C. L. Sousa Uso de Imagens-índice e de Imagens- proporção para avaliar quantidade de madeira em povoamentos de pinus spp. (Dissertação de Mestrado).INPE, 1997.
19. D. Zhieng, J. Rademacher, J. Chen, T. Crow, M. Bresee, J. Le Moine, S. Ryu, Estimating aboveground biomass using Landsat 7 ETM data across a managed landscape in northern Wisconsin, USA. Remote Sensing of Environment, v.3,3, pp 402-411, Ed. Elsevier, 2004.
20. S. S. Panda, V. Garg, I. Chaubey. Artificial Neural Networks Application in Lake Water Quality Estimation Using Satellite Imagery. Journal of Environment Informatics. V. 4, 2004.