ABSTRACT The transition zone between the boreal forest and Arctic tundra, the forest-tundra ecotone (FTE), is an area of high ecological and climatological significance. Despite its importance, a globally consistent high spatial resolution mapping is lacking. Accurate mapping of the FTE requires the use of satellite remote sensing data. Here we use the Landsat Vegetation Continuous Fields (VCF) product and reference point data to derive the location and characteristics of the FTE. An image texture-based supervised classification scheme is developed based on a study area in Central Eurasia to statistically exploit the spatial patterns of the transition zone. Texture statistics for the VCF image are derived from the grey-level co-occurrence matrix (GLCM) based on which the study area is classified into forest, tundra, and FTEs. Adaptive parameterization is implemented to achieve optimal classification performance in the study area. This method is further applied to six additional study areas around the circumarctic region to test its adaptability. In all study areas, this method achieves better FTE delineation results than previously reported methods, showing better classification accuracies (average of 0.826) and more realistic and complete representation of the FTE as shown by visual examination. This shows the universal applicability of the method and it is potential to be used to achieve more detailed and accurate circumarctic mapping of the FTE, which could serve as the basis of time series analysis of FTE positions, eventually contributing to a better understanding of the inter-relations between climate change and shifts in sub-arctic vegetation.

中文翻译：

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使用基于纹理的卫星图像分类划分森林-苔原交错带

摘要 北方森林和北极苔原之间的过渡带，森林-苔原交错带（FTE），是一个具有高度生态和气候意义的地区。尽管它很重要，但缺乏全球一致的高空间分辨率映射。FTE 的准确映射需要使用卫星遥感数据。在这里，我们使用 Landsat 植被连续场 (VCF) 产品和参考点数据来推导出 FTE 的位置和特征。基于欧亚大陆中部的一个研究区开发了一种基于图像纹理的监督分类方案，以统计利用过渡区的空间模式。VCF 图像的纹理统计来自灰度共生矩阵 (GLCM)，基于该矩阵将研究区域分为森林、苔原和 FTE。实施自适应参数化以实现研究区域的最佳分类性能。该方法进一步应用于环绕北极地区的六个额外研究区域，以测试其适应性。在所有研究领域，该方法比以前报告的方法获得了更好的 FTE 描绘结果，显示出更好的分类准确度（平均为 0.826）和更真实和更完整的 FTE 表示，如视觉检查所示。这显示了该方法的普遍适用性，并且有可能用于实现更详细和准确的 FTE 环北极映射，这可以作为 FTE 位置的时间序列分析的基础，最终有助于更好地理解相互之间的关系。 - 气候变化与亚北极植被变化之间的关系。