| Texto completo | |
| Autor(es): |
Mello, Marcio Pupin
[1, 2, 3]
;
Risso, Joel
[4]
;
Atzberger, Clement
[1]
;
Aplin, Paul
[5]
;
Pebesma, Edzer
[3]
;
Oliveira Vieira, Carlos Antonio
[6]
;
Theodor Rudorff, Bernardo Friedrich
[4]
Número total de Autores: 7
|
| Afiliação do(s) autor(es): | [1] Univ Nat Resources & Life Sci BOKU, Inst Surveying Remote Sensing & Land Informat IVF, A-1190 Vienna - Austria
[2] Natl Inst Space Res INPE, DSR, BR-12227010 Sao Jose Dos Campos, SP - Brazil
[3] Univ Munster, Inst Geoinformat Ifgi, D-48151 Munster - Germany
[4] Agrosatelite Appl Geotechnol, BR-88032005 Florianopolis, SC - Brazil
[5] Univ Nottingham, Sch Geog, Nottingham NG7 2RD - England
[6] Univ Fed Santa Catarina, Dept Geosci, BR-88040900 Florianopolis, SC - Brazil
Número total de Afiliações: 6
|
| Tipo de documento: | Artigo Científico |
| Fonte: | REMOTE SENSING; v. 5, n. 11, p. 5999-6025, NOV 2013. |
| Citações Web of Science: | 5 |
| Resumo | |
This paper describes the basis functioning and implementation of a computer-aided Bayesian Network (BN) method that is able to incorporate experts' knowledge for the benefit of remote sensing applications and other raster data analyses: Bayesian Network for Raster Data (BayNeRD). Using a case study of soybean mapping in Mato Grosso State, Brazil, BayNeRD was tested to evaluate its capability to support the understanding of a complex phenomenon through plausible reasoning based on data observation. Observations made upon Crop Enhanced Index (CEI) values for the current and previous crop years, soil type, terrain slope, and distance to the nearest road and water body were used to calculate the probability of soybean presence for the entire Mato Grosso State, showing strong adherence to the official data. CEI values were the most influencial variables in the calculated probability of soybean presence, stating the potential of remote sensing as a source of data. Moreover, the overall accuracy of over 91% confirmed the high accuracy of the thematic map derived from the calculated probability values. BayNeRD allows the expert to model the relationship among several observed variables, outputs variable importance information, handles incomplete and disparate forms of data, and offers a basis for plausible reasoning from observations. The BayNeRD algorithm has been implemented in R software and can be found on the internet. (AU) | |
| Processo FAPESP: | 08/56252-0 - Environmental and socioeconomic impacts associated with the production and consumption of sugarcane ethanol in south central Brazil |
| Beneficiário: | Evlyn Márcia Leão de Moraes Novo |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa BIOEN - Temático |