Abstract. We compile a data set of forest surveys from expeditions to the north-east of the Russian Federation, in Krasnoyarsk Krai, the Republic of Sakha (Yakutia) and the Chukotka Autonomous Okrug (59-73° N, 97-169° E). The region is characterized by permafrost soils, and forests dominated by larch (Larix gmelinii RUPR., Larix cajanderi MAYR). Our dataset consists of a plot data base describing 226 georeferenced vegetation survey sites, and of a tree data base with information about all trees on these plots. The tree data base contains information on height, species and vitality of 40,289 trees. A subset of the trees was subject to a more detailed inventory, recording stem diameter at base and at breast height, crown diameter and height of the beginning of the crown. We recorded heights up to 28.5 m (median = 2.5 m) and stand densities up to 120,000 trees per ha (median = 1197 ha⁻¹), both values tending to be higher in the more southerly areas. Observed taxa include Larix MILL., Pinus L., Picea A.DIETR., Abies MILL., Salix L., Betula L., Populus L., Alnus MILL. and Ulmus L.. In this study, we present the forest inventory data aggregated per site. Additionally, we connect it with different remote sensing data products to find out how accurately forest structure can be predicted from such products. Allometries were calculated to obtain the diameter from height measurements for every species group. For Larix, the most frequent of ten species groups, allometries depend also on the stand density, as denser stands are characterized by thinner trees, relative to height. The remote sensing products used to compare against the inventory data include climate, forest biomass, canopy height, and forest loss or disturbance. We find that the forest metrics measured in the field can only be reconstructed from the remote sensing data to a limited extent, as they depend on local properties. This illustrates the need for ground inventories like those data we present here. The data can be used for studying the forest structure of north-eastern Siberia, and for the calibration and validation of remotely sensed data.

中文翻译：

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西伯利亚东北部沿气候梯度的森林结构和单棵树库存

摘要。我们汇编了俄罗斯联邦东北部、克拉斯诺亚尔斯克边疆区、萨哈共和国（雅库特）和楚科奇自治区（59-73° N，97-169° E）的森林调查数据集。该地区的特点是多年冻土土壤和以落叶松（Larix gmelinii ）为主的森林RUPR.，落叶松 cajanderi MAYR）。我们的数据集包括一个描述 226 个地理参考植被调查点的地块数据库，以及一个包含这些地块上所有树木信息的树木数据库。树数据库包含有关 40,289 棵树的高度、种类和活力的信息。对一部分树木进行更详细的清单，记录基部和胸高处的茎直径、树冠直径和树冠起点的高度。我们记录的高度高达 28.5 m（中位数 = 2.5 m），林分密度高达每公顷 120,000 棵树（中位数 = 1197 ha ^-1），这两个值在更偏南的地区往往更高。观察到的分类群包括Larix MILL.、Pinus L.、Picea A.DIETR.、AbiesMILL.、Salix L.、Betula L.、Populus L.、Alnus MILL。和Ulmus L.。在这项研究中，我们展示了每个站点汇总的森林清单数据。此外，我们将其与不同的遥感数据产品联系起来，以了解从这些产品中预测森林结构的准确程度。计算异速生长以从每个物种组的高度测量中获得直径。对于落叶松，最常见的十个物种群，异速生长也取决于林分密度，因为相对于高度，更密集的林分的特点是树木更薄。用于与清单数据进行比较的遥感产品包括气候、森林生物量、冠层高度以及森林损失或干扰。我们发现，实地测量的森林指标只能在有限的范围内从遥感数据中重建，因为它们取决于当地的属性。这说明了对地面库存的需求，例如我们在此提供的数据。该数据可用于研究西伯利亚东北部的森林结构，并可用于遥感数据的校准和验证。