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Assessing suitable and critical habitat for wood bison ( Bison bison athabascae) using remote sensing and geographic information systems.

机译:使用遥感和地理信息系统评估木野牛(Bison野牛athabascae)的合适且关键的栖息地。

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Wood bison (Bison bison athabascae) are listed by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) as threatened. The Species at Risk Act (SARA) requires that listed species have their critical habitat identified in a recovery or action plan. Defining critical habitat requires that the species-habitat relationship for wood bison be clearly understood at several spatial scales that would be applicable to the management and conservation of this species. To create the most accurate picture of critical habitat a very accurate map of land cover was created. I explored a technique of image segmentation using ecological regions as a means of explaining the spectral variance in remote sensing imagery to increase classification. Results show a significant increase in classification accuracy (alpha = 0.05, one-tailed) over two-stage approaches (Z = 2.49, Z crit = 1.65 p=0.0063). Resource use was assessed by examining a series of models established a priori using logistic regression. The resultant models were compared by assessing the Aikake Information Criteria (AIC) scores and the final models was assessed by using k-fold cross validation and out-of sample validation with data for a separate study area. I found that a good model of resource use could be created using predictors of use that included measures of habitat type as well as several measures of landscape physiognomy, Contrast Weighted Edge Density (CWED), Patch Density (PD), and Contagion.
机译:伍德野牛(Bison bison athabascae)被加拿大濒危野生生物状况委员会(COSEWIC)列为受威胁物种。 《濒危物种法》(SARA)要求在恢复或行动计划中确定所列物种的关键栖息地。定义关键栖息地要求在几个空间尺度上清楚地了解木材野牛的物种-栖息地关系,这将适用于该物种的管理和保护。为了生成最准确的关键栖息地图,创建了非常准确的土地覆盖图。我探索了一种使用生态区域的图像分割技术,以解释遥感影像中的光谱差异以增加分类。结果显示,与两阶段方法(Z = 2.49,Z crit = 1.65 p = 0.0063)相比,分类准确性显着提高(α= 0.05,单尾)。通过检查使用逻辑回归建立的先验模型来评估资源使用。通过评估Aikake信息标准(AICake)信息评分比较所得模型,并通过k倍交叉验证和样本外验证以及单独研究区域的数据来评估最终模型。我发现,可以使用使用预测因子(包括栖息地类型的度量以及景观地貌,对比度加权边缘密度(CWED),斑块密度(PD)和传染性)的几种度量来创建一个良好的资源使用模型。

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