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Remote Sens. 2015, 7(7), 9020-9044; doi:10.3390/rs70709020

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

1
School of Earth Sciences and Engineering, Hohai University, No. 1 Xikang Road, Nanjing 210098, China
2
School of Geosciences, University of South Florida, 4202 E. Fowler Avenue, NES 107, Tampa, FL 33620, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Parth Sarathi Roy and Prasad Thenkabail
Received: 22 April 2015 / Revised: 7 July 2015 / Accepted: 9 July 2015 / Published: 16 July 2015
View Full-Text   |   Download PDF [3483 KB, uploaded 16 July 2015]   |  

Abstract

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-occurrence matrix (GLCM) textures and a local statistical analysis Getis statistic (Gi), computed from IKONOS multispectral (MS) imagery acquired from the Yellow River Delta in China, along with a random forest (RF) classifier, were used to discriminate Robina pseudoacacia tree health levels. Specifically, eight GLCM texture features (mean, variance, homogeneity, dissimilarity, contrast, entropy, angular second moment, and correlation) were first calculated from IKONOS NIR band (Band 4) to determine an optimal window size (13 × 13) and an optimal direction (45°). Then, the optimal window size and direction were applied to the three other IKONOS MS bands (blue, green, and red) for calculating the eight GLCM textures. Next, an optimal distance value (5) and an optimal neighborhood rule (Queen’s case) were determined for calculating the four Gi features from the four IKONOS MS bands. Finally, different RF classification results of the three forest health conditions were created: (1) an overall accuracy (OA) of 79.5% produced using the four MS band reflectances only; (2) an OA of 97.1% created with the eight GLCM features calculated from IKONOS Band 4 with the optimal window size of 13 × 13 and direction 45°; (3) an OA of 93.3% created with the all 32 GLCM features calculated from the four IKONOS MS bands with a window size of 13 × 13 and direction of 45°; (4) an OA of 94.0% created using the four Gi features calculated from the four IKONOS MS bands with the optimal distance value of 5 and Queen’s neighborhood rule; and (5) an OA of 96.9% created with the combined 16 spectral (four), spatial (four), and textural (eight) features. The most important feature ranked by RF classifier was GLCM texture mean calculated from Band 4, followed by Gi feature calculated from Band 4. The experimental results demonstrate that (a) both textural and spatial information was more useful than spectral information in determining the Robina pseudoacacia forest health conditions; and (b) the IKONOS NIR band was more powerful than visible bands in quantifying varying degrees of forest crown dieback. View Full-Text
Keywords: GLCM; Getis statistic; random forest; forest health condition; Robinia pseudoacacia GLCM; Getis statistic; random forest; forest health condition; Robinia pseudoacacia
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Wang, H.; Zhao, Y.; Pu, R.; Zhang, Z. Mapping Robinia Pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial, and Textural Information Extracted from IKONOS Imagery and Random Forest Classifier. Remote Sens. 2015, 7, 9020-9044.

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