Abstract In this paper we present the activities performed at the Microwaves and Radar Institute of the German Aerospace Center (DLR) to derive global forest/non-forest classification mosaics from interferometric synthetic aperture radar (InSAR) data acquired by the TanDEM-X mission. The data have been collected between 2011 and 2016 in bistatic stripmap single polarization (HH) mode, with the main goal of generating a consistent, timely, and highly accurate 3D representation of the global terrain’s surface (digital elevation model, DEM). The global data set of quicklook images, which represent a spatially averaged version of the original full resolution data at a ground independent pixel spacing of 50 m × 50 m, was used as input, in order to limit the computational burden. For classification purposes, several observables, systematically provided by the TanDEM-X system, can be exploited, such as the calibrated amplitude, the digital elevation model (DEM), and the interferometric coherence. Among the several factors contributing to a coherence degradation in InSAR data, the so-called volume correlation factor quantifies the coherence loss due to volume scattering phenomena, which typically occur in presence of vegetation. This quantity is directly derived from the interferometric coherence and used as main indicator for the identification of vegetated areas. For this purpose, a fuzzy multi-clustering classification approach, which takes into account the geometry and acquisition configuration, is applied to each acquired scene separately. A certain variability of the interferometric coherence at X band was observed among different forest types, mainly due to changes in forest structure, density, and tree height, which led to an adjustment of the algorithm settings depending on the considered type of forest. The identification of additional information layers, such as urban settlements or water areas, is also discussed, and the procedure for mosaicking of overlapping acquisitions (two at global scale, up to ten over mountainous terrain, forests, and desert regions) to improve the classification accuracy is detailed. The resulting global forest/non-forest map was validated using external reference information as well as with other existing classification maps and an overall agreement was observed that often exceeds 90%. Finally, examples for high-resolution (at 12 m × 12 m) forest maps and potentials for deforestation monitoring over selected regions are presented as well, demonstrating the unique capabilities offered by the TanDEM-X bistatic system for a broad range of geoinformation services and scientific applications. The global TanDEM-X forest/non-forest map presented in this paper will be made available to the scientific community for free download and usage.

中文翻译：

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来自 TanDEM-X 干涉 SAR 数据的全球森林/非森林地图

摘要 在本文中，我们介绍了德国航空航天中心 (DLR) 微波和雷达研究所开展的活动，以从 TanDEM-X 任务获取的干涉合成孔径雷达 (InSAR) 数据中导出全球森林/非森林分类镶嵌图。这些数据是在 2011 年至 2016 年间以双基地条形图单极化 (HH) 模式收集的，主要目标是生成一致、及时且高度准确的全球地形表面 3D 表示（数字高程模型，DEM）。quicklook 图像的全局数据集表示原始全分辨率数据的空间平均版本，地面独立像素间距为 50 m × 50 m，用作输入，以限制计算负担。出于分类目的，有几个观察值，由 TanDEM-X 系统系统地提供，可以被利用，例如校准幅度、数字高程模型 (DEM) 和干涉相干。在导致 InSAR 数据相干性退化的几个因素中，所谓的体积相关因子量化了由于体积散射现象引起的相干性损失，这通常发生在植被存在的情况下。该数量直接来自干涉相干性，并用作识别植被区域的主要指标。为此，将考虑几何和采集配置的模糊多聚类分类方法分别应用于每个采集的场景。在不同森林类型之间观察到 X 波段干涉相干的一定变化，主要是由于森林结构、密度和树高的变化，这导致根据所考虑的森林类型调整算法设置。还讨论了其他信息层的识别，例如城市住区或水域，以及重叠采集的镶嵌程序（全球范围内的两个，山区、森林和沙漠地区最多十个）以改进分类准确性很详细。由此产生的全球森林/非森林地图使用外部参考信息以及其他现有分类地图进行了验证，观察到的总体一致性通常超过 90%。最后，还提供了高分辨率（12 m × 12 m）森林地图的示例和选定区域森林砍伐监测的潜力，展示了 TanDEM-X 双基地系统为广泛的地理信息服务和科学应用提供的独特功能。本文中介绍的全球 TanDEM-X 森林/非森林地图将提供给科学界免费下载和使用。