@InProceedings{BeuchleRaBoVoSeAc:2012:UpObPa,
author = "Beuchle, Ren{\'e} and Raši, Rastislav and Bodart, Catherine and
Vollmar, Michael and Seliger, R. and Achard, Fr{\'e}d{\'e}ric",
title = "Updating an object-based pan-tropical forest cover change
assessment by automatic change detection and classification",
booktitle = "Proceedings...",
year = "2012",
editor = "Feitosa, Raul Queiroz and Costa, Gilson Alexandre Ostwald Pedro da
and Almeida, Cl{\'a}udia Maria de and Fonseca, Leila Maria Garcia
and Kux, Hermann Johann Heinrich",
pages = "332--337",
organization = "International Conference on Geographic Object-Based Image
Analysis, 4. (GEOBIA).",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
keywords = "Global Forestry, Landsat, Object-based Change Detection,
Classification, Segmentation, Sampling.",
abstract = "The TREES-3 project of the European Commission\‟s Joint
Research Centre is producing estimates of tropical forest cover
changes during the period 1990 to 2010. Three reference years are
considered: 1990, 2000 and 2010. This paper presents the method
developed for the automatic processing of year 2010 with the
assessment of performance of this method. The processing of
imagery of year 2010 includes automatic segmentation, change
detection and object spectral classification. The validated maps
of forest cover changes for the period 1990-2000 are used as
thematic input layer into the segmentation and classification
process of the year 2010 images. Object-based change detection
(OBCD) technique is applied using Tasselled Cap (TCap) parameters
and spectral Euclidian Distances (ED). Objects detected as changed
are classified by change vector analysis. The segmentation
approach was tested on a subsample of 568 sample units over
Brazil. The segmentation results for year 2010 are consistent with
segmentation of imagery for the period 1990-2000, the segmentation
statistics (number of objects, average objects size, average
number of objects per sample site) remain stable between the two
approaches. A two-step method of (a) change detection and (b)
classification of changed objects was developed on basis of
thresholding TCap variance and Euclidian Distance. The approach
was tested over 281 sample units in the Brazilian biome of the
Amazon, for which validated land cover information for the year
2010 was already available. The resulting overall accuracy of
classification for the 281 sample units was 92.2%.",
conference-location = "Rio de Janeiro",
conference-year = "May 7-9, 2012",
isbn = "978-85-17-00059-1",
language = "en",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP8W/3BT7KBE",
url = "http://urlib.net/ibi/8JMKD3MGP8W/3BT7KBE",
targetfile = "095.pdf",
type = "Forest Analysis",
urlaccessdate = "27 abr. 2024"
}