气候变化领域集成服务门户(CCPortal): Three decades of variability in California's giant kelp forests from the Landsat satellites

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DOI	10.1016/j.rse.2018.06.039
	Three decades of variability in California's giant kelp forests from the Landsat satellites
	Bell T.W.; Allen J.G.; Cavanaugh K.C.; Siegel D.A.
发表日期	2020
ISSN	00344257
卷号	238
英文摘要	Global, repeat satellite imagery serves as an essential tool to understand ecological patterns and processes over diverse temporal and spatial scales. Recently, the use of spaceborne imagery has become indispensable for monitoring giant kelp, a globally distributed foundation species that displays variable seasonal and interannual dynamics. In order to develop and maintain a continuous and spatially expansive time series, we describe a fully automated protocol to classify giant kelp canopy biomass across three Landsat sensors. This required correcting kelp canopy estimates to account for changes in the spectral response functions between the three sensors by simulating data using hyperspectral imagery. Combining multiple sensors also necessitated the use of an extended (15 year) time series of diver estimated kelp biomass to validate each sensor. We also describe a novel gap filling technique using known spatial scales of kelp biomass synchrony to correct for missing data due to the Enhanced Thematic Mapper Plus scan line corrector failure. These developments have led to a publicly available 34-year, seasonal time series of kelp canopy biomass across ~1500 km of California coastline. We then use this time series to examine the role of temporal and spatial scale on the detection of long-term biomass trends. We found that kelp canopy biomass trends are associated with trends in low frequency marine climate oscillations, like the North Pacific Gyre Oscillation. Long-term (~20 year) increases in the state of the North Pacific Gyre Oscillation have led to a cooler, nutrient-rich environment that benefits the growth of giant kelp across a large portion of the kelp biomass time series, however recent warming events have led to weak, or nonexistent trends over the length of the time series. The cyclical nature of these low frequency marine climate oscillations complicates the detection of trends that may be associated with anthropogenic climate change. A longer and continuous time series is needed to analyze long-term canopy biomass trends outside of natural low frequency climate variability for giant kelp ecosystems along the coast of California, if we are to make accurate assessments of the impacts of climate change. © 2018 Elsevier Inc.
英文关键词	Automated classification; Climate change; Coastal ecology; Gap filling; Long-term trends; Low frequency climate oscillations; Merged sensors; Multiple scales; North Pacific Gyre Oscillation; Tides
语种	英语
scopus关键词	Biomass; Continuous time systems; Ecosystems; Satellite imagery; Spectroscopy; Tides; Time series; Automated classification; Climate oscillation; Gap filling; Long-term trend; Multiple scale; North Pacific; Climate change; airborne sensing; automation; canopy; classification; gyre; kelp forest; Landsat; satellite imagery; sensor; tidal cycle; time series analysis; California; Pacific Ocean; Pacific Ocean (North); United States; Macrocystis pyrifera
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/179493
作者单位	Earth Research Institute, University of California, Santa Barbara, CA 93016, United States; Dept. of Geography, University of California, Los Angeles, CA 90095, United States; Dept. of Geography, University of California, Santa Barbara, CA 93016, United States
推荐引用方式 GB/T 7714	Bell T.W.,Allen J.G.,Cavanaugh K.C.,et al. Three decades of variability in California's giant kelp forests from the Landsat satellites[J],2020,238.
APA	Bell T.W.,Allen J.G.,Cavanaugh K.C.,&Siegel D.A..(2020).Three decades of variability in California's giant kelp forests from the Landsat satellites.Remote Sensing of Environment,238.
MLA	Bell T.W.,et al."Three decades of variability in California's giant kelp forests from the Landsat satellites".Remote Sensing of Environment 238(2020).