干旱区生态环境知识资源中心(Arid): Revisiting the Past: Replicability of a Historic Long-Term Vegetation Dynamics Assessment in the Era of Big Data Analytics

Arid

DOI	10.3390/rs14030597
	Revisiting the Past: Replicability of a Historic Long-Term Vegetation Dynamics Assessment in the Era of Big Data Analytics
	Frantz, David; Hostert, Patrick; Rufin, Philippe; Ernst, Stefan; Roeder, Achim; van der Linden, Sebastian
通讯作者	Frantz, D (corresponding author)，Humboldt Univ, Dept Geog, Unter Linden 6, D-10099 Berlin, Germany. ; Frantz, D (corresponding author)，Trier Univ, Earth Observat & Climate Proc, D-54286 Trier, Germany.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:3
英文摘要	Open and analysis-ready data, as well as methodological and technical advancements have resulted in an unprecedented capability for observing the Earth's land surfaces. Over 10 years ago, Landsat time series analyses were inevitably limited to a few expensive images from carefully selected acquisition dates. Yet, such a static selection may have introduced uncertainties when spatial or inter-annual variability in seasonal vegetation growth were large. As seminal pre-open-data-era papers are still heavily cited, variations of their workflows are still widely used, too. Thus, here we quantitatively assessed the level of agreement between an approach using carefully selected images and a state-of-the-art analysis that uses all available images. We reproduced a representative case study from the year 2003 that for the first time used annual Landsat time series to assess long-term vegetation dynamics in a semi-arid Mediterranean ecosystem in Crete, Greece. We replicated this assessment using all available data paired with a time series method based on land surface phenology metrics. Results differed fundamentally because the volatile timing of statically selected images relative to the phenological cycle introduced systematic uncertainty. We further applied lessons learned to arrive at a more nuanced and information-enriched vegetation dynamics description by decomposing vegetation cover into woody and herbaceous components, followed by a syndrome-based classification of change and trend parameters. This allowed for a more reliable interpretation of vegetation changes and even permitted us to disentangle certain land-use change processes with opposite trajectories in the vegetation components that were not observable when solely analyzing total vegetation cover. The long-term budget of net cover change revealed that vegetation cover of both components has increased at large and that this process was mainly driven by gradual processes. We conclude that study designs based on static image selection strategies should be critically evaluated in the light of current data availability, analytical capabilities, and with regards to the ecosystem under investigation. We recommend using all available data and taking advantage of phenology-based approaches that remove the selection bias and hence reduce uncertainties in results.
英文关键词	Crete Mediterranean Landsat Big Data time series long-term land degradation phenology reproducibility replicability semi-arid vegetation decomposition
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000754849500001
WOS关键词	SPECTRAL MIXTURE ANALYSIS ; LAND-SURFACE PHENOLOGY ; TIME-SERIES ; SPATIAL-RESOLUTION ; REGIONAL-SCALE ; SATELLITE DATA ; TREND ANALYSIS ; CLOUD SHADOW ; FOREST COVER ; REFLECTANCE
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376391
作者单位	[Frantz, David; Hostert, Patrick; Rufin, Philippe; Ernst, Stefan] Humboldt Univ, Dept Geog, Unter Linden 6, D-10099 Berlin, Germany; [Frantz, David; Roeder, Achim] Trier Univ, Earth Observat & Climate Proc, D-54286 Trier, Germany; [Hostert, Patrick; Rufin, Philippe] Humboldt Univ, Integrat Res Inst Transformat Human Environm Syst, Unter Linden 6, D-10099 Berlin, Germany; [Rufin, Philippe] Catholic Univ Louvain, Earth & Life Inst, Pl Pasteur 3, B-1348 Louvain La Neuve, Belgium; [van der Linden, Sebastian] Univ Greifswald, Inst Geog & Geol, Friedrich Ludwig Jahn Str 16, D-17489 Greifswald, Germany
推荐引用方式 GB/T 7714	Frantz, David,Hostert, Patrick,Rufin, Philippe,et al. Revisiting the Past: Replicability of a Historic Long-Term Vegetation Dynamics Assessment in the Era of Big Data Analytics[J],2022,14(3).
APA	Frantz, David,Hostert, Patrick,Rufin, Philippe,Ernst, Stefan,Roeder, Achim,&van der Linden, Sebastian.(2022).Revisiting the Past: Replicability of a Historic Long-Term Vegetation Dynamics Assessment in the Era of Big Data Analytics.REMOTE SENSING,14(3).
MLA	Frantz, David,et al."Revisiting the Past: Replicability of a Historic Long-Term Vegetation Dynamics Assessment in the Era of Big Data Analytics".REMOTE SENSING 14.3(2022).