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Suitability evaluation of habitat for ardeidae waterfowls based on logistic regression model

机译:基于逻辑回归模型的Ardeidae水禽栖息地的适用性评价

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Wetland ecological systems have recently suffered varying degrees of damage, significantly threatening wetland waterfowls and their living spaces. Considering Mai Po-Deep Bay Wetland as an example, the current study analyzed 14 independent variables that are closely related to ardeidae waterfowls. The actual data on ardeidae waterfowls in January 2003 were used as induced variables in a logistic regression model. Nine variable factors, including land use, normalized difference vegetation index, gradient, rainfall, TM4 vein, TM3 vein, road density, road distance, and habitat density, were obtained via screening. The precision of the model reached 0.743 via Nagelkerke R2 inspection with better fitting. The model result was used for the fast clustering for suitability classification of habitat. Classification result shows a good agreement with the actual data on ardeidae waterfowls within the same period, and the precision reached 77.4%. Finally, all variable factor data in January 2009 were collected to perform time-scale inspection on the regression equation. Moreover, the fitting precision with actual data on ardeidae waterfowls within the same period reached 75.8%. Therefore, the proposed model has better universality.
机译:湿地生态系统最近遭受了不同程度的损害,显着威胁湿地水禽及其生活空间。考虑到Mai Po-Deep Bay Wetland作为一个例子,目前的研究分析了与Ardeidae水禽密切相关的14个独立变量。 2003年1月Ardeidae Waterfls的实际数据被用作逻辑回归模型中的诱导变量。通过筛选获得九个可变因素,包括土地利用,归一化差异植被指数,梯度,降雨,TM4静脉,TM3静脉,道路密度,道路距离和栖息地密度。通过Nagelkerke R2检查,该模型的精度达到0.743,具有更好的拟合。模型结果用于栖息地适用性分类的快速聚类。分类结果表明,与同一时期内的Ardeidae水禽的实际数据表明了良好的一致性,精度达到77.4%。最后,收集了2009年1月的所有可变因子数据,以对回归方程进行时尺度检查。此外,在同一时期内的Ardeidae水禽的实际数据的配合精度达到75.8%。因此,所提出的模型具有更好的普遍性。

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