The main purpose of this study is to derive a method suitable for producing a
land cover map of southern hemisphere Africa at a spatial resolution of 1 km.
Daily SPOT-Vegetation images from the year 2000 were used to build a dataset
of monthly composite images. The composites were used in the development of
two different classifiers obtained through the induction of classification trees.
The selection of image data for training the classifiers and for accuracy
assessment was supported by maps at several different scales, expert knowledge,
Landsat Thematic Mapper (TM) imagery, and a preliminary unsupervised
classification of the monthly image composites. One classification is based on the
construction and application of a single tree classifier, and a second classification
relies on the construction and application of an ensemble of tree classifiers using
bootstrap aggregation (bagging). Classification accuracy was assessed using a
validation dataset. The ensemble of trees produced better results than the single
tree classifier. The advantages and limitations of the methods used are discussed,
and suggestions for future work presented.
CABRAL A.I.R;
VASCONCELOS M.J.P.;
PEREIRA J.M.C.;
MARTINS E.;
BARTHOLOME' Etienne;
2006-06-01
TAYLOR & FRANCIS LTD
JRC33507
https://publications.jrc.ec.europa.eu/repository/handle/JRC33507,
10.1080/01431160500307409,
| Name | Country | City | Type |
|---|
This document is only visible at the Commission level.
You are not authorized to publish or distribute it outside the European Commission.
This is a public document. You can share this publication.
Datasets
| ID | Title | Public URL |
|---|
Dataset collections
| ID | Acronym | Title | Public URL |
|---|
Scripts / source codes
| Description | Public URL |
|---|
Additional supporting files
| File name | Description | File type |
|---|