Seasonal trend analysis of image time series

Eastman, J. Ronald; Sangermano, Florencia; Ghimire, Bardan; Zhu, Hao; Chen, Hao; Neeti, Neeti; Cai, Yongming; Oliveira, Elia Machado de; Crema, Stefano

doi:10.1080/01431160902755338

Cited by 118 publications

(42 citation statements)

References 7 publications

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“…STA is a procedure based on harmonic analysis of each year in a time series, which extracts a set of shape parameters for the seasonal curves. STA begins by performing a harmonic regression using each image, and then uses Kendall analysis of the amplitude and phases produced by the regression (Eastman et al, 2009). Using this analysis, it is possible to determine the magnitude (i.e., amplitude) and timing (i.e., phase) of LST cycles over the course of each year.…”

Section: Lst Time Series Analysismentioning

confidence: 99%

“…The TS operator is a non-linear trend metric, which indicates the median slope angle of all pairs of observations, providing a robust measure of trend (Eastman et al, 2009). A contextual MannKendall significance method facilitates spatially relevant determination of monotonic trend significance (Neeti et al, 2011).…”

Section: Lst Time Series Analysismentioning

confidence: 99%

“…The effect of UTC change on LST was investigated using three types of trend analysis: OLS regression between UTC and summer-only LST; monotonic trend analysis of all LST dates; and Seasonal Trend Analysis (STA) (Eastman et al, 2009) of all LST dates. Monotonic trend analysis relied on the Theil-Sen median slope operator (TS), which is capable of determining the trend slope of noisy data, providing a robust trend measurement (Huth and Pokorná, 2004).…”

Section: Lst Time Series Analysismentioning

confidence: 99%

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Effects of urban tree canopy loss on land surface temperature magnitude and timing

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et al. 2017

ISPRS Journal of Photogrammetry and Remote Sensing

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Section: Lst Time Series Analysismentioning

confidence: 99%

Section: Lst Time Series Analysismentioning

confidence: 99%

Section: Lst Time Series Analysismentioning

confidence: 99%

See 1 more Smart Citation

Effects of urban tree canopy loss on land surface temperature magnitude and timing

Elmes

Rogan

Williams

et al. 2017

ISPRS Journal of Photogrammetry and Remote Sensing

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“…The per-pixel magnitude and direction of linear trends in the calculated 34-year Vgreenup, Dgreenup and Agreenup time series were evaluated by applying a robust nonparametric median trend test (Theil-Sen, TS), which is resistant to outliers and recommended for assessing the rate of change in noisy or short time series of satellite vegetation growth data (De Beurs & Henebry, 2005;Eastman et al, 2009;Hirsch & Slack, 1984). TS is calculated from the linear regression slope between every pairwise combination of time as the independent variable and Vgreenup, Dgreenup, and Agreenup as the dependent variable and then finding the median value.…”

Section: Analysesmentioning

confidence: 99%

Acceleration of global vegetation greenup from combined effects of climate change and human land management

Wang

Tian

Wang

et al. 2018

Global Change Biology

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Global warming and human land management have greatly influenced vegetation growth through both changes in spring phenology and photosynthetic primary production. This will presumably impact the velocity of vegetation greenup (Vgreenup, the daily rate of changes in vegetation productivity during greenup period), yet little is currently known about the spatio-temporal patterns of Vgreenup of global vegetation. Here, we define Vgreenup as the ratio of the amplitude of greenup (Agreenup) to the duration of greenup (Dgreenup) and derive global Vgreenup from 34-year satellite leaf area index (LAI) observations to study spatio-temporal dynamics of Vgreenup at the global, hemispheric, and ecosystem scales. We find that 19.9% of the pixels analyzed (n = 1,175,453) experienced significant trends toward higher greenup rates by an average of 0.018 m m day for 1982-2015 as compared to 8.6% of pixels with significant negative trends (p < 0.05). Global distribution and dynamics of Vgreenup show high spatial heterogeneity and ecosystem-specific patterns, which is primarily determined by the high spatial variation in Agreenup, while the temporal dynamics of Vgreenup are directly controlled by both changes in Dgreenup and Agreenup. Areas with the largest Vgreenup and largest positive trends are both observed in deciduous and mixed forests as compared to nonforest ecosystems showing both lower Vgreenup and trends. For nonforest ecosystems, human-managed ecosystems (e.g., rangelands and rainfed croplands) exhibited higher Vgreenup and positive trends than those of natural counterparts, suggesting strong imprints of human land management on terrestrial ecosystem functioning. Globally, warming has accelerated Vgreenup in temperature-constrained high latitude forest ecosystems and arctic regions, but decelerated Vgreenup in temperate and arid/semiarid areas. These results suggest that the combined effects of climate change and human land management have greatly accelerated global vegetation greenup, with important implications for changes in terrestrial ecosystem functioning and global carbon cycling.

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“…The predictors were derived from NDVI using harmonic regression (Eastman et al, 2009) on an annual basis from 1983 to 2012. Linear harmonic regression estimates the amplitude (maximum) and phase (timing) of a fitted time series, but unless higher-order harmonics are introduced, linear harmonic regression is too rigid to account for outliers and multimodal regimes commonly found in the tropics.…”

Section: Remote Sensing Predictorsmentioning

confidence: 99%

Continuous and consistent land use/cover change estimates using socio-ecological data

et al. 2017

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Abstract.A growing body of research shows the importance of land use/cover change (LULCC) on modifying the Earth system. Land surface models are used to stimulate land-atmosphere dynamics at the macroscale, but model bias and uncertainty remain that need to be addressed before the importance of LULCC is fully realized. In this study, we propose a method of improving LULCC estimates for land surface modeling exercises. The method is driven by projectable socio-ecological geospatial predictors available seamlessly across sub-Saharan Africa and yielded continuous (annual) estimates of LULCC at 5 km × 5 km spatial resolution. The method was developed with 2252 sample area frames of 5 km × 5 km consisting of the proportion of several land cover types in Kenya over multiple years. Forty-three socio-ecological predictors were evaluated for model development. Machine learning was used for data reduction, and simple (functional) relationships defined by generalized additive models were constructed on a subset of the highest-ranked predictors (p ≤ 10) to estimate LULCC. The predictors explained 62 and 65 % of the variance in the proportion of agriculture and natural vegetation, respectively, but were less successful at estimating more descriptive land cover types. In each case, population density on an annual basis was the highest-ranked predictor. The approach was compared to a commonly used remote sensing classification procedure, given the wide use of such techniques for macroscale LULCC detection, and outperformed it for each land cover type. The approach was used to demonstrate significant trends in expanding (declining) agricultural (natural vegetation) land cover in Kenya from 1983 to 2012, with the largest increases (declines) occurring in densely populated high agricultural production zones. Future work should address the improvement (development) of existing (new) geospatial predictors and issues of model scalability and transferability.Published by Copernicus Publications on behalf of the European Geosciences Union.

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Seasonal trend analysis of image time series

Cited by 118 publications

References 7 publications

Effects of urban tree canopy loss on land surface temperature magnitude and timing

Effects of urban tree canopy loss on land surface temperature magnitude and timing

Acceleration of global vegetation greenup from combined effects of climate change and human land management

Continuous and consistent land use/cover change estimates using socio-ecological data

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