Mapping Robinia Pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial, and Textural Information Extracted from IKONOS Imagery and Random Forest Classifier

Mapping Robinia Pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial, and Textural Information Extracted from IKONOS Imagery and Random Forest Classifier

The textural and spatial information extracted from very high resolution (VHR) remote sensing imagery provides complementary information for applications in which the spectral information is not sufficient for identification of spectrally similar landscape features. In this study grey-level co-occur...

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Bibliographic Details
Main Authors: Hong Wang, Yu Zhao, Ruiliang Pu, Zhenzhen Zhang
Format: Article
Language:English
Published: MDPI AG 2015-07-01
Series:Remote Sensing
Subjects:
GLCM
Getis statistic
random forest
forest health condition
Robinia pseudoacacia
Online Access:http://www.mdpi.com/2072-4292/7/7/9020

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