Applying MERIS time series and dynamic time warping for delineating areas with similar temporal behaviour in the northern Baltic Sea

Suominen, Tapio

doi:10.1016/j.ecolind.2018.08.023

Cited by 6 publications

(14 citation statements)

References 41 publications

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“…Accordingly, instead of utilizing biogeochemical data products, we concentrate on analyzing reflectance data and thereby avoid the potential error sources identified in bio-geochemical products and consequent artificial spatial patterns. We use the same daily MERIS instrument normalized remote sensing reflectance R rs (λ) data as in Suominen [38], the wavelengths λ being 510 nm and 620 nm in this study. The rationale for selecting these wavelengths is further justified in Section 3.2.…”

Section: R Rs (λ) Time Seriesmentioning

confidence: 99%

“…Descriptions of DTW, constraints, and step patterns are given e.g. by Giorgino [75], Keogh and Ratanamahatana [76], Petitjean et al [44] and Suominen [38]. The workflow presented in Sections 2 and 3 is summarized in Figure 4.…”

Section: Description Of Dynamic Time Warping Dtwmentioning

confidence: 99%

“…This approach has been applied earlier by Suominen [38] and by Mantas et al [39]. For this research we develop computational methods to perform k-means clustering of large amounts of time series efficiently, enabling the use of the presented method up to a global scale.…”

Section: Introductionmentioning

confidence: 99%

“…The datasets can be noisy and include spatial and temporal gaps and temporal shifts, or the time series may have varying lengths. Dynamic time warping (DTW) [42] is widely applied in land use classifications and studies of terrestrial cycles [19,24,43], and in recent years it has been used in the aquatic realm by Suominen [38] and Mantas et al [39]. A crucial step in our approach is the forming of a prototype of a set of time series.…”

Section: Introductionmentioning

confidence: 99%

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Partition of Marine Environment Dynamics According to Remote Sensing Reflectance and Relations of Dynamics to Physical Factors

et al. 2021

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Seawaters exhibit various types of cyclic and trend-like temporal alterations in their biological, physical, and chemical processes. Surface water dynamics may vary, for instance, when the timings, durations, or amplitudes of seasonal developments of water properties alter between years and locations. We introduce a workflow using remote sensing to identify surface waters undergoing similar dynamics. The method, called ocean surface dynamics partitioning, classifies pixels based on their temporal change patterns instead of their properties at successive time snapshots. We apply an efficient parallel computing method to calculate Dynamic Time Warping (DTW) time series distances of large datasets of Earth Observation MERIS-instrument reflectance data Rrs(510 nm) and Rrs(620 nm), and produce a matrix of time series distances between 12,252 locations/time series in the Baltic Sea, for both wavelengths. We define cluster prototypes by hierarchical clustering of distance matrices and use them as initial prototypes for an iterative process of partitional clustering in order to identify areas that have similar reflectance dynamics. Lastly, we compute distances from the time series of the reflectance data to selected physical factors (wind, precipitation, and changes in sea surface temperature) obtained from Copernicus data archives. The workflow is reproducible and capable of managing large datasets in reasonable computation times and identifying areas of distinctive dynamics. The results show spatially coherent and logical areas without a priori information about the locations of the satellite image time series. The alignments of the reflectance time series vs. the observational time series of the physical environment clarify the causalities behind the cluster formation. We conclude that following the changes in an aquatic realm by biogeochemical observations at certain temporal intervals alone is not sufficient to identify environmental shifts. We foresee that the changes in dynamics are a sensitive measure of environmental threats and therefore they will be important to follow in the future.

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Section: R Rs (λ) Time Seriesmentioning

confidence: 99%

Section: Description Of Dynamic Time Warping Dtwmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Partition of Marine Environment Dynamics According to Remote Sensing Reflectance and Relations of Dynamics to Physical Factors

et al. 2021

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“…DTW evaluates the extent to which one time-series has to be reshaped in order to match another reference time-series. The method is implemented in the R packages 'dtw' (Giorgino 2009) and 'Tsdist' (Mori et al 2016), and has been used to align remote sensing data with different temporal resolutions (Baumann et al 2017), cluster areas with similar temporal dynamics (Suominen 2018), and to correlate stratigraphical data from different sediment cores (Trauth et al 2018).…”

Section: Introductionmentioning

confidence: 99%

distantia: an open‐source toolset to quantify dissimilarity between multivariate ecological time‐series

Benito

Birks

2020

Ecography

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There is a large array of methods to extract knowledge and perform ecological forecasting from ecological time-series. However, in spite of its importance for data-mining, pattern-matching and ecological synthesis, methods to assess their similarity are scarce. We introduce distantia (v1.0.1), an R package providing general toolset to quantify dissimilarity between ecological time-series, independently of their regularity and number of samples. The functions in distantia provide the means to compute dissimilarity scores by time and by shape and assess their significance, evaluate the partial contribution of each variable to dissimilarity, and align or combine sequences by similarity. We evaluate the sensitivity of the dissimilarity metrics implemented in distantia, describe its structure and functionality, and showcase its applications with two examples. Particularly, we evaluate how geographic factors drive the dissimilarity between nine pollen sequences dated to the Last Interglacial, and compare the temporal dynamics of climate and enhanced vegetation index of three stands across the range of the European beech. We expect this package may enhance the capabilities of researchers from different fields to explore dissimilarity patterns between multivariate ecological time-series, and aid in generating and testing new hypotheses on why the temporal dynamics of complex-systems changes over space and time.

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Land cover mapping based on time‐series MODIS‐NDVI using a dynamic time warping approach: A casestudy of the agricultural pastoral ecotone of northern China

et al. 2020

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To curb land degradation, a series of ecological restoration projects have been carried out since 1999, leading to dramatic land cover change (LCC) in the agricultural pastoral ecotone of northern China (APENC). To date, there is still lack of timely and accurate land cover (LC) information for management and assessment actions. This paper presents a LC mapping scheme to map annual LC information based on dynamic time warping (DTW) approach and time‐series MODIS‐NDVI product for the APENC. The DTW approach was optimized firstly based on the change of vegetation phenology phase. Next, typical reference time‐series curves set of different LC types were established and pixel‐wise similarity was examined to identify the LC type. Before that, the reconstruction of time‐series MODIS‐NDVI was performed using harmonic analysis of time‐series and reconstruction accuracy was evaluated quantitatively at a regional scale. Results showed that the gap error was relatively large for the high vegetation cover areas while it might be ignored for the moderate and low vegetation cover areas. The overall accuracy of LC mapping in year 2010 (LC‐2010) was 77.89% and kappa coefficient reached 0.71, which suggested that LC mapping scheme could extract LC information with high accuracy. The producer's accuracy ranged between 69.83% (grasslands) and 100% (water body), while the user's accuracy changed from 58% (built‐up) to 86% (forest). The overall spatial agreement between LC‐2010 and GlobaLand30‐2010 was about 64.44%. The total area of forests, croplands, water body and built‐up exhibited an increasing trend while the contrary trend were found for the grasslands and bare land during the 2001–2017. These LC maps are valuable for research the land degradation investigation and monitoring, benefit evaluation of ecological restoration projects and effect of LCC on surface hydrothermal process in APENC.

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Applying MERIS time series and dynamic time warping for delineating areas with similar temporal behaviour in the northern Baltic Sea

Cited by 6 publications

References 41 publications

Partition of Marine Environment Dynamics According to Remote Sensing Reflectance and Relations of Dynamics to Physical Factors

Partition of Marine Environment Dynamics According to Remote Sensing Reflectance and Relations of Dynamics to Physical Factors

distantia: an open‐source toolset to quantify dissimilarity between multivariate ecological time‐series

Land cover mapping based on time‐series MODIS‐NDVI using a dynamic time warping approach: A casestudy of the agricultural pastoral ecotone of northern China

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