气候变化领域集成服务门户(CCPortal): Towards national-scale characterization of grassland use intensity from integrated Sentinel-2 and Landsat time series

CCPortal

DOI	10.1016/j.rse.2019.03.017
	Towards national-scale characterization of grassland use intensity from integrated Sentinel-2 and Landsat time series
	Griffiths P.; Nendel C.; Pickert J.; Hostert P.
发表日期	2020
ISSN	00344257
卷号	238
英文摘要	The increased availability of systematically acquired high spatial and temporal resolution optical imagery improves the characterization of dynamic land surface processes such as agriculture. The use of time series phenology can help overcome limitations of conventional classification-based mapping approaches encountered when, for example, attempting to characterize grassland use intensity. In Europe, permanent grasslands account for more than one third of all agricultural land and a considerable share of the EU Common Agricultural Policy (CAP) budget is devoted to grasslands. The frequency and timing of mowing events is an important proxy for grassland use intensity and methods that allow characterizing grassland use intensity at the parcel level and over large areas are urgently needed. Here we present a novel algorithm that allows detecting and quantifying the number and timing of mowing events in central European grasslands. The algorithm utilizes all imagery acquired by Sentinel-2 MSI and Landsat-8 OLI for one entire year as available from the NASA Harmonized Landsat-Sentinel dataset. Cloud-free observations from both sensors are first synthesized through compositing at 10-day interval. Machine learning algorithms are then used to derive a grassland stratum. The intra-annual growing season profiles of NDVI values are subsequently assessed and compared to an idealized growing season trajectory. Residuals between the idealized trajectory and a polynomial model fit to the observed NDVI values are then evaluated to detect potential mowing events. We demonstrate and evaluate the performance of our algorithm and utilize its large area analysis capabilities by mapping the frequency and timing of grassland mowing events in 2016 on the national-scale across Germany. Our results suggest that 25% of the grassland area is not used for mowing. Validation results however suggest a relatively high omission error of the algorithm for areas that only experienced a single mowing event. The date ranges of detected mowing events compare overall well to a sample of interpreted time series points and to farm level reports on mowing dates. The mapped mowing patterns depict typical management regimes across Germany. Overall, our results exemplify the value of multi-sensor time series applications for characterizing land use intensity across large areas. © 2019 Elsevier Inc.
英文关键词	Agriculture; CAP; Compositing; Europe; Grassland; Land use intensity; Phenology; Time series analysis
语种	英语
scopus关键词	Agriculture; Biology; Budget control; Image enhancement; Land use; Learning algorithms; Machine learning; Mapping; NASA; Analysis capabilities; Common agricultural policy; Compositing; Europe; Grassland; Phenology; Scale characterization; Spatial and temporal resolutions; Time series analysis; agricultural land; algorithm; Common Agricultural Policy; grassland; growing season; land use; Landsat; NDVI; satellite imagery; Sentinel; time series analysis; trajectory; Germany
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/179483
作者单位	Geography Department, Humboldt Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany; European Space Agency - Earth Observation, Science, Applications & Climate Department, Frascati, Roma, Italy; Leibniz Centre for Agricultural Landscape Research, Eberswalder Straße 84, Müncheberg, 15374, Germany; Integrative Research Institute on Transformations of Human-Environment Systems (IRI THESys), Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany
推荐引用方式 GB/T 7714	Griffiths P.,Nendel C.,Pickert J.,et al. Towards national-scale characterization of grassland use intensity from integrated Sentinel-2 and Landsat time series[J],2020,238.
APA	Griffiths P.,Nendel C.,Pickert J.,&Hostert P..(2020).Towards national-scale characterization of grassland use intensity from integrated Sentinel-2 and Landsat time series.Remote Sensing of Environment,238.
MLA	Griffiths P.,et al."Towards national-scale characterization of grassland use intensity from integrated Sentinel-2 and Landsat time series".Remote Sensing of Environment 238(2020).