气候变化领域集成服务门户(CCPortal): Deep-learning based high-resolution mapping shows woody vegetation densification in greater Maasai Mara ecosystem

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DOI	10.1016/j.rse.2020.111953
	Deep-learning based high-resolution mapping shows woody vegetation densification in greater Maasai Mara ecosystem
	Li W.; Buitenwerf R.; Munk M.; Bøcher P.K.; Svenning J.-C.
发表日期	2020
ISSN	00344257
卷号	247
英文摘要	The Greater Maasai Mara Ecosystem (GMME) in Kenya is an iconic savanna ecosystem of high importance as natural and cultural heritage, notably by including the largest remaining seasonal migration of African ungulates and the semi-nomadic pastoralist Maasai culture. Comprehensive mapping of vegetation distribution and dynamics in GMME is important for understanding ecosystem changes across time and space since recent reports suggest dramatic declines in wildlife populations alongside troubling reports of grassland conversion to cropland and habitat fragmentation due to increasing small-holder fencing. Here, we present the first comprehensive vegetation map of GMME at high (10-m) spatial resolution. The map consists of nine key vegetation cover types (VCTs), which were derived in a two-step process integrating data from high-resolution WorldView-3 images (1.2-m) and Sentinel-2 images using a deep-learning workflow. We evaluate the role of anthropogenic, topographic, and climatic factors in affecting the fractional cover of the identified VCTs in 2017 and their MODIS-derived browning/greening rates in the preceding 17 years at 250-m resolution. Results show that most VCTs showed a preceding greening trend in the protected land. In contrast, the semi- and unprotected land showed a general preceding greening trend in the woody-dominated cover types, while they exhibited browning trends in grass-dominated cover types. These results suggest that woody vegetation densification may be happening across much of the GMME, alongside vegetation declines within the non-woody covers in the semi- and unprotected lands. Greening and potential woody densification in GMME is positively correlated with mean annual precipitation and negatively correlated with anthropogenic pressure. Increasing woody densification across the entire GMME in the future would replace high-quality grass cover and pose a risk to the maintenance of the region's rich savanna megafauna, thus pointing to a need for further investigation using alternative data sources. The increasing availability of high-resolution remote sensing and efficient approaches for vegetation mapping will play a crucial role in monitoring conservation effectiveness as well as ecosystem dynamics due to pressures such as climate change. © 2020 Elsevier Inc.
英文关键词	Deep-learning; Maasai Mara; Savanna ecosystem; Savanna vegetation classification; Sentinel-2; Vegetation fractional cover; Woody densification; WorldView-3
语种	英语
scopus关键词	Climate change; Densification; Ecosystems; Mapping; Remote sensing; Vegetation; Anthropogenic pressures; Conservation effectiveness; Habitat fragmentation; High resolution remote sensing; High-resolution mapping; Mean annual precipitation; Vegetation distribution; Wildlife populations; Deep learning; climate change; ecosystem dynamics; grassland; habitat fragmentation; remote sensing; satellite imagery; savanna; vegetation cover; vegetation mapping; vegetation type; wild population; woody plant; WorldView; Kenya; Masai Mara; Narok; Ungulata
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179229
作者单位	Center for Biodiversity Dynamics in a Changing World (BIOCHANGE), Aarhus University, Ny Munkegade 114, Aarhus C, 8000, Denmark; Section for Ecoinformatics and Biodiversity, Department of Biology, Aarhus University, Ny Munkegade 114, Aarhus C, 8000, Denmark; State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China
推荐引用方式 GB/T 7714	Li W.,Buitenwerf R.,Munk M.,et al. Deep-learning based high-resolution mapping shows woody vegetation densification in greater Maasai Mara ecosystem[J],2020,247.
APA	Li W.,Buitenwerf R.,Munk M.,Bøcher P.K.,&Svenning J.-C..(2020).Deep-learning based high-resolution mapping shows woody vegetation densification in greater Maasai Mara ecosystem.Remote Sensing of Environment,247.
MLA	Li W.,et al."Deep-learning based high-resolution mapping shows woody vegetation densification in greater Maasai Mara ecosystem".Remote Sensing of Environment 247(2020).