DOI: 10.1016/j.jag.2016.09.005
Scopus记录号: 2-s2.0-85018195485
论文题名: Phenology from Landsat when data is scarce: Using MODIS and Dynamic Time-Warping to combine multi-year Landsat imagery to derive annual phenology curves
作者: Baumann M ; , Ozdogan M ; , Richardson A ; D ; , Radeloff V ; C
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2017
卷: 54 起始页码: 72
结束页码: 83
语种: 英语
英文关键词: Dynamic time warping
; EVI
; Green-leaf phenology
; Landsat
; MODIS
; PhenoCam
Scopus关键词: growing season
; Landsat
; leaf
; MODIS
; Monte Carlo analysis
; phenology
; remote sensing
; spatial resolution
; vegetation index
; United States
英文摘要: Green-leaf phenology describes the development of vegetation throughout a growing season and greatly affects the interaction between climate and the biosphere. Remote sensing is a valuable tool to characterize phenology over large areas but doing at fine- to medium resolution (e.g., with Landsat data) is difficult because of low numbers of cloud-free images in a single year. One way to overcome data availability limitations is to merge multi-year imagery into one time series, but this requires accounting for phenological differences among years. Here we present a new approach that employed a time series of a MODIS vegetation index data to quantify interannual differences in phenology, and Dynamic Time Warping (DTW) to re-align multi-year Landsat images to a common phenology that eliminates year-to-year phenological differences. This allowed us to estimate annual phenology curves from Landsat between 2002 and 2012 from which we extracted key phenological dates in a Monte-Carlo simulation design, including green-up (GU), start-of-season (SoS), maturity (Mat), senescence (Sen), end-of-season (EoS) and dormancy (Dorm). We tested our approach in eight locations across the United States that represented forests of different types and without signs of recent forest disturbance. We compared Landsat-based phenological transition dates to those derived from MODIS and ground-based camera data from the PhenoCam-network. The Landsat and MODIS comparison showed strong agreement. Dates of green-up, start-of-season and maturity were highly correlated (r 0.86-0.95), as were senescence and end-of-season dates (r > 0.85) and dormancy (r > 0.75). Agreement between the Landsat and PhenoCam was generally lower, but correlation coefficients still exceeded 0.8 for all dates. In addition, because of the high data density in the new Landsat time series, the confidence intervals of the estimated keydates were substantially lower than in case of MODIS and PhenoCam. Our study thus suggests that by exploiting multi-year Landsat imagery and calibrating it with MODIS data it is possible to describe green-leaf phenology at much finer spatial resolution than previously possible, highlighting the potential for fine scale phenology maps using the rich Landsat data archive over large areas. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79941
Appears in Collections: 气候变化事实与影响
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作者单位: Geography Department, Humboldt-University Berlin, Unter den Linden 6, Berlin, Germany; Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI, United States; Department of Organismic and Evolutionary Biology, Harvard University, 22 Divinity Avenue, Cambridge, MA, United States; SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI, United States
Recommended Citation:
Baumann M,, Ozdogan M,, Richardson A,et al. Phenology from Landsat when data is scarce: Using MODIS and Dynamic Time-Warping to combine multi-year Landsat imagery to derive annual phenology curves[J]. International Journal of Applied Earth Observation and Geoinformation,2017-01-01,54