Continuous monitoring of land disturbance based on Landsat time series

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 tes...

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Bibliographic Details
Published in:Remote sensing of environment 2020-03, Vol.238, p.111116, Article 111116
Main Authors: Zhu, Zhe, Zhang, Junxue, Yang, Zhiqiang, Aljaddani, Amal H., Cohen, Warren B., Qiu, Shi, Zhou, Congliang
Format: Article
Language:English
Subjects:
Algorithms
Band spectra
Change detection
Chi-square test
COLD
Disturbance
Image detection
Land disturbance
Landsat
Landsat satellites
Monitoring
Near real-time
Outliers (statistics)
Reflectance
Regrowth
Remote sensing
Root-mean-square errors
Spectral bands
Stability tests
Time series
Vegetation regrowth
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Summary: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 
ISSN:0034-4257
1879-0704
DOI:10.1016/j.rse.2019.03.009