Mapping health levels of Robinia pseudoacacia forests in the Yellow River delta,China, using IKONOS and Landsat 8 OLI imagery |
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| Authors: | Hong Wang Ruiliang Pu Qi Zhu Liliang Ren Zhenzhen Zhang |
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| Affiliation: | 1. State Key Laboratory of Hydrology–Water Resources and Hydraulic Engineering, Department of Geographical Information Science, School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, People’s Republic of China;2. School of Geosciences, University of South Florida, Tampa, FL 33620, USAhongwang@hhu.edu.cn;4. School of Geosciences, University of South Florida, Tampa, FL 33620, USA;5. Department of Computer Science and Technology, College of Internet of Things Engineering, Hohai University, Changzhou 213022, People’s Republic of China |
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| Abstract: | The largest artificial Robinia pseudoacacia forests in the Yellow River delta of China have been infected by dieback diseases. Over the past several decades, this has caused a large amount of mortality of Robinia pseudoacacia forests in this area. Timely and accurate information on the health levels of the forests is crucial to improving local ecological and economic conditions. Remote sensing has been demonstrated to be a useful tool to map forest diseases over a large area. In this study, IKONOS and Landsat 8 Operational Land Imager (OLI) sensor data were collected for comparing their capability of accurately mapping health levels of the artificial forests. There were three health levels (i.e. healthy, medium dieback, and severe dieback) based on explicit tree crown symptoms. After the IKONOS and OLI images were preprocessed, both spatial and spectral features were extracted from the IKONOS and OLI imagery, and a maximum likelihood classification method was used to identify and map health levels of Robinia pseudoacacia forests. The experimental results indicate that the IKONOS sensor has greater potential for identifying and mapping forest health levels. Furthermore, texture features, especially texture variance, derived from the IKONOS panchromatic band, contributed greatly to the accuracy of classification results, achieving an overall accuracy (OA) of 96% for the IKONOS sensor and an OA of 88% for the OLI 2, which used both OLI spectral and IKONOS spatial features, compared with an OA of 74% for the OLI sensor alone. Our results indicate that the texture features extracted from high resolution imagery can improve the classification accuracy of health levels of planted forests with a regular spatial pattern. Our experimental results also demonstrate that classification of an image with a spatial resolution similar to, or finer than, tree crown diameter outperforms that of relatively coarse resolution imagery for differentiating living tree crowns and understorey dense green grass. |
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