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.
Highlights
Recent cases of increased tree mortality and die-offs triggered by drought and/or high temperature have been documented [1,2,3]
Since the textural and local spatial information has the potential to improving the accuracy of class designation by minimizing intra-class variation [30,53], the overall objective of this study is to assess whether the grey-level co-occurrence matrix (GLCM) and Getis statistic (Gi) features extracted from IKONOS
With the fixed 13 × 13 window size, we further tested the effects of four directions (0°, 45°, 90°, 135°) on the random forest (RF) classification result with the eight GLCM features extracted from IKONOS
Summary
Recent cases of increased tree mortality and die-offs triggered by drought and/or high temperature have been documented [1,2,3]. Based on the decline spiral model [5,6], drought can operate as a trigger that may lead to mortality of trees that are already under stress (due to causes such as old age, poor site condition, and air pollution). Stress in forests displays a variety of symptoms, some of which may be detected by remote sensing [8,9]. Plant symptoms detectable by remote sensing may be attributed to an increase in red reflectance due to lowered chlorophyll absorption, a decrease in near infrared (NIR) reflectance due to reduced cell vigor, or shifting of a red edge position that may be invoked by different types of stress (e.g., water or nitrogen deficit) [10] or by natural development of plants (e.g., phenology changes) [11]
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