Uplands represent unique landscapes that provide a range of vital benefits to society, but are under increasing pressure from the management needs of a diverse number of stakeholders (e.g. farmers, conservationists, foresters, government agencies and recreational users). Mapping the spatial distribution of upland vegetation could benefit management and conservation programmes and allow for the impacts of environmental change (natural and anthropogenic) in these areas to be reliably estimated. The aim of this study was to evaluate the use of medium spatial resolution optical and radar satellite data, together with ancillary soil and topographic data, for identifying and mapping upland vegetation using the Random Forests (RF) algorithm. Intensive field survey data collected at three study sites in Ireland as part of the National Parks and Wildlife Service (NPWS) funded survey of upland habitats was used in the calibration and validation of different RF models. Eight different datasets were analysed for each site to compare the change in classification accuracy depending on the input variables. The overall accuracy values varied from 59.8% to 94.3% across the three study locations and the inclusion of ancillary datasets containing information on the soil and elevation further improved the classification accuracies (between 5 and 27%, depending on the input classification dataset). The classification results were consistent across the three different study areas, confirming the applicability of the approach under different environmental contexts.

中文翻译：

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使用随机森林和光学和雷达卫星数据绘制高地植被图。

高地代表着独特的景观，可为社会带来一系列重大利益，但受到来自众多利益相关者（例如，农民，保护主义者，森林人，政府机构和娱乐使用者）的管理需求的压力越来越大。绘制高地植被的空间分布图可以使管理和保护计划受益，并可以可靠地估算这些地区环境变化（自然和人为）的影响。这项研究的目的是评估使用中空分辨率光学和雷达卫星数据以及辅助土壤和地形数据，使用随机森林（RF）算法识别和绘制高地植被的情况。作为国家公园和野生动物服务局（NPWS）资助的高地栖息地调查的一部分，在爱尔兰的三个研究地点收集的密集野外调查数据被用于不同RF模型的校准和验证。针对每个站点分析了八个不同的数据集，以根据输入变量比较分类准确性的变化。在这三个研究地点中，总体准确度值从59.8％到94.3％不等，并且包含有关土壤和海拔高度信息的辅助数据集进一步提高了分类精度（介于5％到27％之间，具体取决于输入的分类数据集）。在三个不同的研究领域中，分类结果是一致的，证实了该方法在不同环境下的适用性。