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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Predicting Vascular Plant Richness in a Heterogeneous Wetland Using Spectral and Textural Features and a Random Forest Algorithm
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Predicting Vascular Plant Richness in a Heterogeneous Wetland Using Spectral and Textural Features and a Random Forest Algorithm

机译：利用光谱和纹理特征以及随机森林算法预测非均质湿地中维管束植物的丰度

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摘要

A method to predict vascular plant richness using spectral and textural variables in a heterogeneous wetland is presented. Plant richness was measured at 44 sampling plots in a 16-ha anthropogenic peatland. Several spectral indices, first-order statistics (median and standard deviation), and second-order statistics [metrics of a gray-level co-occurrence matrix (GLCM)] were extracted from a Landsat 8 Operational Land Imager image and a Pleiades 1B image. We selected the most important variables for predicting richness using recursive feature elimination and then built a model using random forest regression. The final model was based on only two textural variables obtained from the GLCM and derived from the Landsat 8 image. An accurate predictive capability was reported ( ; ), highlighting the possibility of obtaining parsimonious models using textural variables. In addition, the results showed that the mid-resolution Landsat 8 image provided better predictors of richness than the high-resolution Pleiades image. This is the first study to generate a model for plant richness in a wetland ecosystem.
机译：提出了一种在异质湿地中使用光谱和纹理变量预测维管植物丰富度的方法。在一个16公顷的人为泥炭地上的44个采样点上测量了植物的丰富度。从Landsat 8 Operational Land Imager图像和Pleiades 1B图像中提取了几个光谱指数，一阶统计量（中值和标准差）和二阶统计量（灰度共生矩阵的度量）。 。我们选择了最重要的变量，以使用递归特征消除来预测丰富度，然后使用随机森林回归建立模型。最终模型仅基于从GLCM获得并从Landsat 8图像得出的两个纹理变量。报告了准确的预测能力（；），突显了使用纹理变量获得简约模型的可能性。此外，结果表明，与高分辨率P宿星图像相比，中分辨率Landsat 8图像提供了更好的富集预测指标。这是第一个为湿地生态系统中的植物丰富度建立模型的研究。

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