气候变化领域集成服务门户(CCPortal): A keystone species, European aspen (Populus tremula L.), in boreal forests: Ecological role, knowledge needs and mapping using remote sensing

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DOI	10.1016/j.foreco.2020.118008
	A keystone species, European aspen (Populus tremula L.), in boreal forests: Ecological role, knowledge needs and mapping using remote sensing
	Kivinen S.; Koivisto E.; Keski-Saari S.; Poikolainen L.; Tanhuanpää T.; Kuzmin A.; Viinikka A.; Heikkinen R.K.; Pykälä J.; Virkkala R.; Vihervaara P.; Kumpula T.
发表日期	2020
ISSN	0378-1127
卷号	462
英文摘要	European aspen (Populus tremula L.) is a keystone species in boreal forests that are dominated by coniferous tree species. Both living and dead aspen trees contribute significantly to the species diversity of forest landscapes. Thus, spatial and temporal continuity of aspen is a prerequisite for the long-term persistence of viable populations of numerous aspen-associated species. In this review, we collate existing knowledge on the ecological role of European aspen, assess the knowledge needs for aspen occurrence patterns and dynamics in boreal forests and discuss the potential of different remote sensing techniques in mapping aspen at various spatiotemporal scales. The role of aspen as a key ecological feature has received significant attention, and studies have recognised the negative effects of modern forest management methods and heavy browsing on aspen occurrence and regeneration. However, the spatial knowledge of occurrence, abundance and temporal dynamics of aspen is scarce and incomprehensive. The remote sensing studies reviewed here highlight particularly the potential of three-dimensional data derived from airborne laser scanning or photogrammetric point clouds and airborne imaging spectroscopy in mapping European aspen, quaking aspen (Populus tremuloides Michx.) and other Populus species. In addition to tree species discrimination, these methods can provide information on biophysical, biochemical properties and even genetic diversity of aspen trees. Major obstacles in aspen detection using remote sensing are the low proportion and scattered occurrence of European aspen in boreal forests and the overlap of spectral and/or structural properties of European aspen and quaking aspen with some other tree species. Furthermore, the suitability of remote sensing data for aspen mapping and monitoring depends on the geographical coverage of data, the availability of multitemporal data and the costs of data acquisition. Our review highlights that integration of ecological knowledge with spatiotemporal information acquired by remote sensing is key to understanding the current and future distribution patterns of aspen-related biodiversity. © 2020 Elsevier B.V.
关键词	Arches Biodiversity Data acquisition Ecology Mapping Reforestation Airborne Laser scanning Biochemical properties Boreal forests European aspen Populus tremula L Remote sensing techniques Spatiotemporal information Three-dimensional data Remote sensing airborne survey biodiversity boreal forest coniferous forest ecological approach forest management keystone species knowledge mapping method photogrammetry population viability analysis remote sensing species diversity Biochemical Properties Biodiversity Data Processing Ecology Mapping Reforestation Coniferales Populus Populus tremula Populus tremuloides
语种	英语
来源机构	Forest Ecology and Management
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/132923
推荐引用方式 GB/T 7714	Kivinen S.,Koivisto E.,Keski-Saari S.,et al. A keystone species, European aspen (Populus tremula L.), in boreal forests: Ecological role, knowledge needs and mapping using remote sensing[J]. Forest Ecology and Management,2020,462.
APA	Kivinen S..,Koivisto E..,Keski-Saari S..,Poikolainen L..,Tanhuanpää T..,...&Kumpula T..(2020).A keystone species, European aspen (Populus tremula L.), in boreal forests: Ecological role, knowledge needs and mapping using remote sensing.,462.
MLA	Kivinen S.,et al."A keystone species, European aspen (Populus tremula L.), in boreal forests: Ecological role, knowledge needs and mapping using remote sensing".462(2020).