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首页> 外文期刊>PLoS One >Estimation of woody plant species diversity during a dry season in a savanna environment using the spectral and textural information derived from WorldView-2 imagery
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Estimation of woody plant species diversity during a dry season in a savanna environment using the spectral and textural information derived from WorldView-2 imagery

机译：估算粮草环境中的旱季植物物种多样性，使用来自WorldView-2图像的谱和纹理信息

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Remote sensing techniques are useful in the monitoring of woody plant species diversity in different environments including in savanna vegetation types. However, the performance of satellite imagery in assessing woody plant species diversity in dry seasons has been understudied. This study aimed to assess the performance of multiple Gray Level Co-occurrence Matrices (GLCM) derived from individual bands of WorldView-2 satellite imagery to quantify woody plant species diversity in a savanna environment during the dry season. Woody plant species were counted in 220 plots (20 m radius) and subsequently converted to a continuous scale of the Shannon species diversity index. The index regressed against the GLCMs using the all-possible-subsets regression approach that builds competing models to choose from. Entropy GLCM yielded the best overall accuracy (adjusted R 2 : 0.41?0.46; Root Mean Square Error (RMSE): 0.60?0.58) in estimating species diversity. The effect of the number of predicting bands on species diversity estimation was also explored. Accuracy generally increased when three–five bands were used in models but stabilised or gradually decreased as more than five bands were used. Despite the peak accuracies achieved with three–five bands, performances still fared well for models that used fewer bands, showing the relevance of few bands for species diversity estimation. We also assessed the effect of GLCM window size (3×3, 5×5 and 7×7) on species diversity estimation and generally found inconsistent conclusions. These findings demonstrate the capability of GLCMs combined with high spatial resolution imagery in estimating woody plants species diversity in a savanna environment during the dry period. It is important to test the performance of species diversity estimation of similar environmental set-ups using widely available moderate-resolution imagery.
机译：遥感技术可用于监测不同环境中的木质植物物种多样性，包括在大草原植被类型中。然而，已经解读了卫星图像在评估木质植物物种在干燥季节的多样性中的性能。本研究旨在评估来自WorldView-2卫星图像的各个频段的多灰度共生矩阵（GLCM）的性能，以量化在旱季的大草原环境中的木质植物物种多样性。木质植物物种被计算在220个地块（半径20米）中，随后转换为Shannon物种的连续规模。使用构建竞争模型的所有可能的亚群回归方法来对GLCMS回归到GLCMS。熵GLCM产生了最佳的整体精度（调整后的R 2：0.41？0.46;根均线误差（RMSE）：0.60？0.58）在估计物种多样性。还探讨了预测乐队数量对物种分集估计的影响。当在模型中使用三维带时，精度通常会增加，但随着超过五个带稳定或逐渐降低。尽管使用三维频段实现的峰值准确性，但对于使用较少频段的模型，表演仍然非常好，呈现出几种频段的相关性多样性估计的相关性。我们还评估了GLCM窗口大小（3×3,5×5和7×7）对物种的多样性估计的影响，并且通常发现结论不一致。这些研究结果表明，在干燥期间，在粮草环境中估算木质植物物种多样性时，GLCMS结合了高空间分辨率图像。使用广泛可用的适度分辨率图像测试类似环境组织的物种多样性估计的性能非常重要。

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