气候变化领域集成服务门户(CCPortal): Evaluating the temporal accuracy of grassland to cropland change detection using multitemporal image analysis

CCPortal

DOI	10.1016/j.rse.2021.112292
	Evaluating the temporal accuracy of grassland to cropland change detection using multitemporal image analysis
	Mardian J.; Berg A.; Daneshfar B.
发表日期	2021
ISSN	00344257
卷号	255
英文摘要	Grasslands are valuable carbon sinks in the effort to mitigate climate change. However, they are not well protected and are consequently being replaced by agricultural systems worldwide. Current monitoring efforts using remote sensing and ground-based methods are insufficient, and accordingly the mapping of grassland to cropland conversions must be improved to better document these changes in the Canadian Prairies. The purpose of this study is to evaluate different structural break methods and remote sensing datasets for their temporal accuracy in detecting grassland conversions in two Alberta study areas from 2010 to 2018. Breaks For Additive Seasonal and Trend (BFAST), BFAST Seasonal and Bayesian Estimator of Abrupt change, Seasonality and Trend (BEAST) methods were applied to evaluate their sensitivity to rangeland and pasture conversions. The best model was BFAST Seasonal, correctly predicting the year of change for 76% of rangelands and 66% of pastures. This demonstrates that seasonal models are effective in detecting interannual changes in vegetation composition amidst background noise from climate and management induced phenological changes. MODIS data outperformed Landsat, outlining the importance of high temporal resolution remote sensing data to successful change detection, even at the expense of higher spatial resolution. Overall, this study demonstrates that structural break methods are effective in identifying grassland to agriculture transitions and may be useful for the operational monitoring of grassland inventories in the future. © 2021
英文关键词	Agriculture; Breakpoints; Grasslands; Land cover change; Landsat; MODIS; Multitemporal image analysis; Soil carbon; Vegetation dynamics
语种	英语
scopus关键词	Agricultural robots; Agriculture; Climate models; Remote sensing; Agricultural system; Bayesian estimators; High temporal resolution; Multitemporal image analysis; Operational monitoring; Phenological changes; Remote sensing data; Vegetation composition; Climate change; Alberta
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/178964
作者单位	Department of Geography, Environment and Geomatics, University of Guelph, 50 Stone Road E, Guelph, ON N1G 2W1, Canada; AgroClimate, Geomatics and Earth Observation Division, Science and Technology Branch, Agriculture and Agri-Food Canada, 960 Carling Avenue, Ottawa, ON K1A 0C6, Canada
推荐引用方式 GB/T 7714	Mardian J.,Berg A.,Daneshfar B.. Evaluating the temporal accuracy of grassland to cropland change detection using multitemporal image analysis[J],2021,255.
APA	Mardian J.,Berg A.,&Daneshfar B..(2021).Evaluating the temporal accuracy of grassland to cropland change detection using multitemporal image analysis.Remote Sensing of Environment,255.
MLA	Mardian J.,et al."Evaluating the temporal accuracy of grassland to cropland change detection using multitemporal image analysis".Remote Sensing of Environment 255(2021).