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A Machine Learning and Cross-Validation Approach for the Discrimination of Vegetation Physiognomic Types Using

Ram C Sharma¹, Keitarou Hara¹, Hidetake Hirayama²

¹Department of Informatics, Tokyo University of Information Sciences, 4-1 Onaridai, Wakaba-ku, Chiba 265-8501, Japan.

|July 12, 2017

Summary

This summary is machine-generated.

Machine learning classifiers effectively discriminate vegetation types using satellite data. Random Forests achieved the highest accuracy, highlighting the importance of input features and ground truth data size for vegetation mapping.

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Area of Science:

Earth and Space Sciences
Computer and Information Sciences

Background:

Accurate vegetation physiognomic mapping is crucial for environmental monitoring.
Satellite time-series surface reflectance data offer rich information for vegetation classification.

Purpose of the Study:

To evaluate machine learning classifiers for discriminating six vegetation physiognomic classes.
To assess the impact of input features and ground truth data size on classification accuracy.

Main Methods:

Utilized time-series satellite surface reflectance data to extract rich features.
Applied and evaluated multiple supervised machine learning classifiers.
Employed 10-fold cross-validation for performance assessment.

Main Results:

Random Forests classifier achieved the highest accuracy (0.81) and kappa coefficient (0.78).
Classification accuracy showed high sensitivity to the selection of input features and the size of ground truth data.
Performance variation across different classifiers and parameters was minimal.

Conclusions:

Machine learning, particularly Random Forests, is effective for vegetation physiognomic classification using satellite data.
Feature selection and sufficient ground truth data are critical for optimizing classification performance.
The findings support improved vegetation physiognomic mapping in Japan.