气候变化领域集成服务门户(CCPortal): Continuous monitoring of land disturbance based on Landsat time series

CCPortal

DOI	10.1016/j.rse.2019.03.009
	Continuous monitoring of land disturbance based on Landsat time series
	Zhu Z.; Zhang J.; Yang Z.; Aljaddani A.H.; Cohen W.B.; Qiu S.; Zhou C.
发表日期	2020
ISSN	00344257
卷号	238
英文摘要	We developed a new algorithm for COntinuous monitoring of Land Disturbance (COLD) using Landsat time series. COLD can detect many kinds of land disturbance continuously as new images are collected and provide historical land disturbance maps retrospectively. To better detect land disturbance, we tested different kinds of input data and explored many time series analysis techniques. We have several major observations as follows. First, time series of surface reflectance provides much better detection results than time series of Top-Of-Atmosphere (TOA) reflectance, and with some adjustments to the temporal density, time series from Landsat Analysis Ready Data (ARD) is better than it is from the same Landsat scene. Second, the combined use of spectral bands is always better than using a single spectral band or index, and if all the essential spectral bands have been employed, the inclusion of other indices does not further improve the algorithm performance. Third, the remaining outliers in the time series can be removed based on their deviation from model predicted values based on probability-based thresholds derived from normal or chi-squared distributions. Fourth, model initialization is pivotal for monitoring land disturbance, and a good initialization stability test can influence algorithm performance substantially. Fifth, time series model estimation with eight coefficients model, updated for every single observation, based on all available clear observations achieves the best result. Sixth, a change probability of 0.99 (chi-squared distribution) with six consecutive anomaly observations and a mean included angle < 45° to confirm a change provide the best results, and the combined use of temporally-adjusted Root Mean Square Error (RMSE) and minimum RMSE is recommended. Finally, spectral changes (or “breaks”) contributed from vegetation regrowth should be excluded from land disturbance maps. The COLD algorithm was developed and calibrated based on all these lessons learned above. The accuracy assessment shows that COLD results were accurate for detecting land disturbance, with an omission error of 27% and a commission error of 28%. © 2019 Elsevier Inc.
英文关键词	Change detection; COLD; Land disturbance; Landsat; Near real-time; Time series
语种	英语
scopus关键词	Errors; Mean square error; Monitoring; Probability distributions; Reflection; Time series; Algorithm performance; Change detection; Chi-squared distribution; COLD; Continuous monitoring; LANDSAT; Near-real time; Root mean square errors; Time series analysis; accuracy assessment; algorithm; detection method; real time; satellite data; time series; time series analysis
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/179484
作者单位	Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT 06269, United States; Department of Geosciences, Texas Tech University, MS 1053, Science Building 125, Lubbock, TX 79409, United States; USDA Forest Service, RMRS Research Station, 507 25th Street, Ogden, UT 84401, United States; Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR 97331, United States; Pacific Northwest Research Station, U.S. Forest Service, Corvallis, OR 97331, United States
推荐引用方式 GB/T 7714	Zhu Z.,Zhang J.,Yang Z.,et al. Continuous monitoring of land disturbance based on Landsat time series[J],2020,238.
APA	Zhu Z..,Zhang J..,Yang Z..,Aljaddani A.H..,Cohen W.B..,...&Zhou C..(2020).Continuous monitoring of land disturbance based on Landsat time series.Remote Sensing of Environment,238.
MLA	Zhu Z.,et al."Continuous monitoring of land disturbance based on Landsat time series".Remote Sensing of Environment 238(2020).