Using airborne lidar to characterize North European terrestrial high‐dark‐diversity habitats

IF 3.9 2区 环境科学与生态学 Q1 ECOLOGY Remote Sensing in Ecology and Conservation Pub Date : 2022-10-21 DOI:10.1002/rse2.314
J. Moeslund, K. K. Clausen, L. Dalby, Camilla Fløjgaard, M. Pärtel, N. Pfeifer, M. Hollaus, Ane Kirstine Brunbjerg
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引用次数: 2

Abstract

A key aspect of nature conservation is knowledge of which aspects of nature to conserve or restore to favor the characteristic diversity of plants in a given area. Here, we used a large plant dataset with >40 000 plots combined with airborne laser scanning (lidar) data to reveal the local characteristics of habitats having a high plant dark diversity—that is, absence of suitable species—at national extent (>43 000 km2). Such habitats have potential for reaching high realized diversity levels and hence are important in a conservation context. We calculated 10 different lidar based metrics (both terrain and vegetation structure) and combined these with seven different field‐based measures (soil chemistry and species indicators). We then used Integrated Nested Laplace Approximation for modelling plant dark diversity across 33 North European habitat types (open landscapes and forests) selected by the European communities to be important. In open habitat types high‐dark‐diversity habitats had relatively low pH, high nitrogen content, tall homogenous vegetation, and overall relatively homogenous terrains (high terrain openness) although with a rather high degree of local microtopographical variations. High‐dark‐diversity habitats in forests had relatively tall vegetation, few natural‐forest indicators, low potential solar radiation input and a low cover of small woody plants. Our results highlight important vegetation, terrain‐ and soil‐related factors that managers and policymakers should be aware of in conservation and restoration projects to ensure a natural plant diversity, for example low nutrient loads, natural microtopography and possibly also open forests with old‐growth elements such as dead wood and rot attacks.

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利用机载激光雷达表征北欧陆地高暗多样性栖息地
自然保护的一个关键方面是了解要保护或恢复自然的哪些方面,以利于特定地区植物的特征多样性。在这里,我们使用了一个大于40的大型植物数据集 000个地块与机载激光扫描(lidar)数据相结合,揭示了在全国范围内具有高植物暗多样性(即缺乏合适物种)的栖息地的当地特征(>43 000 平方公里)。这种栖息地有可能达到已实现的高多样性水平,因此在保护环境中很重要。我们计算了10种不同的基于激光雷达的指标(地形和植被结构),并将其与7种不同的实地指标(土壤化学和物种指标)相结合。然后,我们使用集成嵌套拉普拉斯近似对欧洲社区选择的33种北欧栖息地类型(开阔景观和森林)的植物暗多样性进行建模。在开放栖息地类型中,高暗多样性栖息地具有相对较低的pH值、高氮含量、高大同质的植被和总体相对同质的地形(高地形开放性),尽管局部微观地形变化程度相当高。森林中的高暗多样性栖息地植被相对较高,自然森林指标较少,潜在的太阳辐射输入较低,小型木本植物覆盖率较低。我们的研究结果强调了管理者和政策制定者在保护和恢复项目中应该意识到的重要植被、地形和土壤相关因素,以确保自然植物多样性,例如低营养负荷、自然微地形,以及可能存在枯木和腐病等老生长元素的开放森林。
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来源期刊
Remote Sensing in Ecology and Conservation
Remote Sensing in Ecology and Conservation Earth and Planetary Sciences-Computers in Earth Sciences
CiteScore
9.80
自引率
5.50%
发文量
69
审稿时长
18 weeks
期刊介绍: emote Sensing in Ecology and Conservation provides a forum for rapid, peer-reviewed publication of novel, multidisciplinary research at the interface between remote sensing science and ecology and conservation. The journal prioritizes findings that advance the scientific basis of ecology and conservation, promoting the development of remote-sensing based methods relevant to the management of land use and biological systems at all levels, from populations and species to ecosystems and biomes. The journal defines remote sensing in its broadest sense, including data acquisition by hand-held and fixed ground-based sensors, such as camera traps and acoustic recorders, and sensors on airplanes and satellites. The intended journal’s audience includes ecologists, conservation scientists, policy makers, managers of terrestrial and aquatic systems, remote sensing scientists, and students. Remote Sensing in Ecology and Conservation is a fully open access journal from Wiley and the Zoological Society of London. Remote sensing has enormous potential as to provide information on the state of, and pressures on, biological diversity and ecosystem services, at multiple spatial and temporal scales. This new publication provides a forum for multidisciplinary research in remote sensing science, ecological research and conservation science.
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