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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Land cover map production for Brazilian Amazon using NDVI SPOT VEGETATION time series

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Author(s):
Rodrigues, A. [1] ; Marcal, A. R. S. [2, 3] ; Furlan, D. [4] ; Ballester, M. V. [4] ; Cunha, M. [1]
Total Authors: 5
Affiliation:
[1] Univ Porto, Fac Ciencias, CICGE, P-4100 Oporto - Portugal
[2] Univ Porto, Fac Ciencias, Dept Matemat, P-4100 Oporto - Portugal
[3] Univ Porto, INESC TEC, P-4100 Oporto - Portugal
[4] Ctr Energia Nucl Agr CENA, Piracicaba - Brazil
Total Affiliations: 4
Document type: Journal article
Source: CANADIAN JOURNAL OF REMOTE SENSING; v. 39, n. 4, p. 277-289, AUG 2013.
Web of Science Citations: 3
Abstract

Earth Observation Satellite (EOS) data have a great potential for land cover mapping, which is mostly based on high resolution images. However, in tropical areas the use of these images is seriously limited due to the presence of clouds. This paper evaluates the ability of temporal-based image classification methods to produce land cover maps in tropical regions. A new approach is proposed for land cover classification and updating based exclusively on temporal series data, illustrated with a practical test using SPOT VEGETATION satellite images from 1999 to 2011 for Rondonia (Amazon), Brazil. Using the GLC2000 as reference, a Normalized Difference Vegetation Index (NDVI) time series of 15 distinct land cover classes (LCC) were created. Two classifiers were used (Euclidean Distance and Dynamic Time Warping) to produce maps of land cover changes for 1999-2011. Due to the difficulties in discriminating 15 LCC in the Amazon region, a hierarchical aggregation was performed by joining the initial classes gradually up to four broad classes. The land cover changes in the 1999-2011 period were evaluated using criteria based on the classification results for the individual years. The comparison with reference data showed consistent results, proving that this approach is able to produce accurate land cover maps using exclusively temporal series EOS data. (AU)

FAPESP's process: 10/02228-0 - Effect of change of use and soil cover in the water and energy balance of river basin JI-PARANPÁ (ro) multitemporal data using remote sensing
Grantee:Deise Nunes Furlan
Support type: Scholarships in Brazil - Doctorate