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首页> 美国卫生研究院文献>Ecology and Evolution >Performances of machine learning algorithms for mapping fractional cover of an invasive plant species in a dryland ecosystem
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Performances of machine learning algorithms for mapping fractional cover of an invasive plant species in a dryland ecosystem

机译：机器学习算法在旱地生态系统中入侵植物的分数覆盖图绘制中的性能

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摘要

In recent years, an increasing number of distribution maps of invasive alien plant species (IAPS) have been published using different machine learning algorithms (MLAs). However, for designing spatially explicit management strategies, distribution maps should include information on the local cover/abundance of the IAPS. This study compares the performances of five MLAs: gradient boosting machine in two different implementations, random forest, support vector machine and deep learning neural network, one ensemble model and a generalized linear model; thereby identifying the best‐performing ones in mapping the fractional cover/abundance and distribution of IPAS, in this case called Prosopis juliflora (SW. DC.). Field level Prosopis cover and spatial datasets of seventeen biophysical and anthropogenic variables were collected, processed, and used to train and validate the algorithms so as to generate fractional cover maps of Prosopis in the dryland ecosystem of the Afar Region, Ethiopia. Out of the seven tested algorithms, random forest performed the best with an accuracy of 92% and sensitivity and specificity >0.89. The next best‐performing algorithms were the ensemble model and gradient boosting machine with an accuracy of 89% and 88%, respectively. The other tested algorithms achieved comparably low performances. The strong explanatory variables for Prosopis distributions in all models were NDVI, elevation, distance to villages and distance to rivers; rainfall, temperature, near‐infrared and red reflectance, whereas topographic variables, except for elevation, did not contribute much to the current distribution of Prosopis. According to the random forest model, a total of 1.173 million ha (12.33% of the study region) was found to be invaded by Prosopis to varying degrees of cover. Our findings demonstrate that MLAs can be successfully used to develop fractional cover maps of plant species, particularly IAPS so as to design targeted and spatially explicit management strategies.
机译：近年来，使用不同的机器学习算法（MLA）发布了越来越多的外来入侵植物物种（IAPS）分布图。但是，为了设计空间明确的管理策略，分布图应包括有关IAPS的本地覆盖/数量的信息。本研究比较了五个MLA的性能：梯度提升机在两种不同的实现方式中，即随机森林，支持向量机和深度学习神经网络，一个集成模型和一个广义线性模型；从而在绘制IPAS的覆盖率/丰度和分布比例图（本例中称为Prosopis juliflora（SW。DC。））时找出表现最佳的方案。收集，处理并处理了田野级别的Prosopis覆盖和17个生物物理和人为变量的空间数据集，用于训练和验证算法，从而在埃塞俄比亚Afar地区的旱地生态系统中生成Prosopis的局部覆盖图。在这七个经过测试的算法中，随机森林以92％的准确度，灵敏度和特异性> 0.89表现最佳。接下来的最佳算法是集成模型和梯度提升机，其准确度分别为89％和88％。其他经过测试的算法的性能相对较低。在所有模型中，对Prosopis分布的有力解释变量是NDVI，海拔，到村庄的距离和到河的距离。降雨，温度，近红外和红色反射率，而地形变量（海拔除外）对当前Prosopis的分布没有太大影响。根据随机森林模型，发现Prosopis共入侵了11.73百万公顷（占研究区域的12.33％），覆盖程度不同。我们的发现表明，MLA可成功用于开发植物物种的局部覆盖图，尤其是IAPS，从而设计目标明确且在空间上明确的管理策略。

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