Burnt forest recovery is normally monitored with a time-series analysis of satellite data because of its proficiency for large observation areas. Traditional methods, such as linear correlation plotting, have been proven to be effective, as forest recovery naturally increases with time. However, these methods are complicated and time consuming when increasing the number of observed parameters. In this work, we present a random forest variable importance (RF-VIMP) scheme called multilevel RF-VIMP to compare and assess the relationship between 36 spectral indices (parameters) of burnt boreal forest recovery in the Great Xing'an Mountain, China. Six Landsat images were acquired in the same month 0, 1, 4, 14, 16, and 20 years after a fire, and 39,380 fixed-location samples were then extracted to calculate the effectiveness of the 36 parameters. Consequently, the proposed method was applied to find correlations between the forest recovery indices. The experiment showed that the proposed method is suitable for explaining the efficacy of those spectral indices in terms of discrimination and trend analysis, and for showing the satellite data and forest succession dynamics when applied in a time series. The results suggest that the tasseled cap transformation wetness, brightness, and the shortwave infrared bands (both 1 and 2) perform better than other indices for both classification and monitoring.
Abstract:Andrews first proposed an equation to visualize the structures within data in 1972. Since then, this equation has been used for data transformation and visualization in a wide variety of fields. However, it has yet to be applied to satellite image data. The effect of unwanted, or impure, pixels occurring in these data varies with their distribution in the image; the effect is greater if impurity pixels are included in a classifier's training set. Andrews' curves enable the interpreter to select outlier or impurity data that can be grouped into a new category for classification. This study overcomes the above-mentioned problem and illustrates the novelty of applying Andrews' plots to satellite image data, and proposes a robust method for classifying the plots that combines Dempster-Shafer theory with fuzzy set theory. In addition, we present an example, obtained from real satellite images, to demonstrate the application of the proposed classification method. The accuracy and robustness of the proposed method are investigated for different training set sizes and crop types, and are compared with the results of two traditional classification methods. We find that outlier data are easily eliminated by examining Andrews' curves and that the proposed method significantly outperforms traditional methods when considering the classification accuracy.
Plasma biochemical profiles were studied in 112 mature (3 to 5-year-old) healthy cattle comprised of 61 Thai indigenous and 51 Simmental x Brahman crossbred male and cyclic female cattle at Nongkwang (Central Thailand) Livestock Research and Breeding Center, Thailand. Data were analysed for the effect of breed and sex. The results showed that the plasma glucose and gamma-glutamyl transferase (GGT) in the two breeds were significantly (P < 0.05) different. Furthermore, the urea, creatinine, albumin, total protein, aspartate amino transferase (AST), alanine amino transferase (ALT) and alkaline phosphatase (ALP) levels in Thai indigenous were significantly (P < 0.01) higher than in crossbred cattle. However, creatine kinase did not significantly differ in crossbred and indigenous animals. A sex difference was found in glucose level with male Thai indigenous having significantly higher levels (P < 0.05) than the other three groups. Plasma urea concentration in male crossbred cattle was lower than in the other groups (P < 0.05). Female crossbred cattle had significantly (P < 0.05) lower plasma creatinine levels than the other animals. Furthermore, levels of albumin in male and total protein in female crossbred were the lowest (P < 0.05) among the groups. The AST, ALT, ALP and GGT levels were significantly (P < 0.05) different between male and female. Female crossbred cattle had the lowest (P < 0.05) AST and GGT levels, whereas lowest (P < 0.05) ALT and ALP concentration was determined in male individuals of these breeds.
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