By Topic

Automatic Updating of an Object-Based Tropical Forest Cover Classification and Change Assessment

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
Rasi, R. ; Joint Res. Centre of the Eur. Comm., Inst. for Environ. & Sustainability, Ispra, Italy ; Beuchle, R. ; Bodart, C. ; Vollmar, M.
more authors

The TREES-3 project of the European Commission's Joint Research Centre is producing estimates of tropical forest cover changes for two time periods: 1990-2000 and 2000-2010. This paper presents the method developed for the automatic change detection and classification of year 2010 imagery integrating the existing segmentation and classification results of the period 1990-2000. The year 2010 imagery is processed in three automatic steps: segmentation, change detection and object spectral classification. The validated maps of forest cover for the years 1990 and 2000 are integrated as thematic input layer into the image segmentation and classification process for the year 2010 data. An object-based change detection technique is applied using Tasseled Cap components and spectral Euclidean distances. Objects detected as changed are classified in two steps: parametric classification based on membership functions and change vector analysis for the remaining unclassified objects. All objects identified as `unchanged' are used as training areas for parametric classification and spectral signatures are extracted from 2010 imagery. The change vectors are defined according to the validated land cover classification of the year 2000. The segmentation approach was tested on 568 sample units spread over Brazil. The segmentation results for year 2010 demonstrated consistency with the segmentation of imagery for the period 1990-2000. The resulting overall accuracy of the automatic classification was calculated for the 281 sample units of the Brazilian Amazon biome and for 201 sample units of three more complex Brazilian biomes, Caatinga, Cerrado and Pantanal, at 92% and 91% respectively.

Published in:

Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of  (Volume:6 ,  Issue: 1 )