Abstract In recent decades, mountainous areas that contain some of the best-preserved habitats worldwide are experiencing significant, rapid changes. Efficient monitoring of these areas is crucial for impact assessments, understanding the key processes underlying the changes, and development of measures that mitigate degradation. Remote sensing is an efficient, cost-effective means of monitoring landscapes. One of the main challenges in the development of remote sensing techniques is improving classification accuracy, which is complicated in mountainous areas because of the rugged topography. This study evaluated the 3 main steps in the supervised vegetation classification of a mountainous area in the Spanish Pyrenees using Landsat-5 Thematic Mapper imagery. The steps were (1) choosing the training data sampling type (expert supervised or random selection), (2) deciding whether to include ancillary data, and (3) selecting a classification algorithm. The combination (in order of importance) of randomly selected training data, ancillary data (topographic and vegetation index), and a random forest classifier improved classification accuracy significantly (4–11%) in the study area in the Spanish Pyrenees. The classification procedure includes important steps that improve classification accuracies; these are often ignored in standard vegetation classification protocols. Improved accuracy is vital to the study of landscape changes in highly sensitive mountain ecosystems.
Improving the Accuracy of Vegetation Classifications in Mountainous Areas
Maite Gartzia,C. Alados,F. Pérez-Cabello,C. G. Bueno
Published 2013 in Unknown venue
ABSTRACT
PUBLICATION RECORD
- Publication year
2013
- Venue
Unknown venue
- Publication date
2013-02-01
- Fields of study
Geology, Environmental Science
- Identifiers
- External record
- Source metadata
Semantic Scholar
CITATION MAP
EXTRACTION MAP
CLAIMS
CONCEPTS
- ancillary data
Supplementary topographic and vegetation index information used alongside spectral data in the classification process.
- classification accuracy
The statistical metric used to evaluate the performance of the vegetation mapping procedure.
- landsat-5 thematic mapper imagery
The multispectral satellite data source used for the supervised classification of vegetation.
- random forest classifier
The ensemble learning algorithm evaluated for its performance in supervised vegetation classification.
- random selection
A training data sampling method where points are chosen without expert supervision.
Aliases: randomly selected training data
- spanish pyrenees
The mountainous region in Spain where the vegetation classification study was conducted.
REFERENCES
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Showing 1-17 of 17 citing papers · Page 1 of 1