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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Wang, H.; Zhao, Y.; Pu, R.; Zhang, Z. (2016)
Publisher: Copernicus Publications
Languages: English
Types: Article
Subjects: TA1-2040, T, TA1501-1820, Applied optics. Photonics, Engineering (General). Civil engineering (General), Technology
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. The 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 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 (4) an OA of 96.9% created with the combined 16 spectral (four), spatial (four), and textural (eight) features. 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) IKONOS NIR band was more powerful than visible bands in quantifying varying degree of forest crown dieback.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Abdel-Rahman, E.M.; Mutanga, O.; Adam, E.; Ismail, R., 2014.
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    • Pu, R.; Cheng, J., 2015. Mapping forest leaf area index using reflectance and textural information derived from WorldView-2 imagery in a mixed natural forest area in Florida, US.
    • International Journal of Applied Earth Observation, 42, pp.11- 23.
    • Schomaker, M.E.; Zarnoch, S.J.; Bechtold, W.A.; Latelle, D.J.; Burkman, W.G.; Cox, S.M., 2007. Crown Condition Classification: a Guide to Data Collection and Analysis. USDA Forest Service, Fort Collins, CO, USA.
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    • Treits, P.; Hwarth, P., 2000. High spatial resolution remote sensing data for forest ecosystem classification: an examination of spatial scale. Remote Sensing of Environment, 76, pp.268- 289.
    • Wang, H.; Pu, R.; Zhu, Q.; Ren, L., 2015a. Mapping Health Levels of Robinia pseudoacacia Forests in the Yellow River Delta, China, Using IKONOS and Landsat 8 OLI Imagery.
    • International Journal of Remote Sensing, 36, pp.1114-1135.
    • Wang, H.; Zhao, Y., Pu, R.; Zhang, Z.Z., 2015b. Mapping Robinia pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial and Textural Information Extracted from IKONOS Imagery and Random Forest Classifier. Remote Sensing, 7, pp.9020-9044.
    • Waske, B.; van der Linden, S.; Oldenburg, C.; Jakimow, B.; Rabe, A.; Hostert, P., 2012. ImageRF- A user-oriented implementation for remote sensing image analysis with Random Forests. Environmental Modelling Software, 35, pp.192-193.
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  • Discovered through pilot similarity algorithms. Send us your feedback.