Spectral Temporal Information for Missing Data Reconstruction (STIMDR) of Landsat Reflectance Time Series

Tang, Zhipeng; Amatulli, Giuseppe; Pellikka, Petri; Heiskanen, Janne

doi:10.3390/rs14010172

Cited by 10 publications

(8 citation statements)

References 78 publications

(100 reference statements)

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“…Notably, the selection and further screening of similar pixels are mainly determined by the information of the reference image, which means that when there is a large difference between the land cover type of the reference image and the target image or when there is a sudden change in the land cover type within a short time, the cloud-covered region in the cloudy image cannot be predicted accurately. Therefore, as in most previously proposed multi-temporal gap-filling methods [75][76][77], SAIW is suitable for situations where the time intervals are relatively short or land cover changes are not obvious.…”

Section: Discussionmentioning

confidence: 99%

A Methodological Approach for Gap Filling of WFV Gaofen-1 Images from Spatial Autocorrelation and Enhanced Weighting

Chen,

Yu,

Zhang

et al. 2024

Atmosphere

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Clouds and cloud shadow cover cause missing data in some images captured by the Gaofen-1 Wide Field of View (GF-1 WFV) cameras, limiting the extraction and analysis of the image information and further applications. Therefore, this study proposes a methodology to fill GF-1 WFV images using the spatial autocorrelation and improved weighting (SAIW) method. Specifically, the search window size is adaptively determined using Getis-Ord Gi* as a metric. The spatial and spectral weights of the pixels are computed using the Chebyshev distance and spectral angle mapper to better filter the suitable similar pixels. Each missing pixel is predicted using linear regression with similar pixels on the reference image and the corresponding similar pixel located in the non-missing region of the cloudy image. Simulation experiments showed that the average correlation coefficient of the proposed method in this study is 0.966 in heterogeneous areas, 0.983 in homogeneous farmland, and 0.948 in complex urban areas. It suggests that SAIW can reduce the spread of errors in the gap-filling process to significantly improve the accuracy of the filling results and can produce satisfactory qualitative and quantitative fill results in a wide range of typical land cover types and has extensive application potential.

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Section: Discussionmentioning

confidence: 99%

A Methodological Approach for Gap Filling of WFV Gaofen-1 Images from Spatial Autocorrelation and Enhanced Weighting

Chen,

Yu,

Zhang

et al. 2024

Atmosphere

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“…In papers [11,12], the authors made predictions for a time series with incomplete observations using the spectral-temporal metrics method (Missing Observation Prediction based on Spectral-Temporal Metrics, MOPSTM), yielding promising outcomes. The use of supervised random forest classification problems in this work using artificial gap filling of simulated datasets may not be applicable for measured empirical data like ours, there may be a loss of valid data.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Visual identification of some regularities in packet network traffic

Mirzakulova,

Ibrayeva,

Kuanova

et al. 2024

EEJET

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Modern heterogeneous packet networks generate network traffic with a complex structure. In this article, the object of study is a time series. The total number of User Datagram Protocol (UDP) packets has reached 250242. According to analysts, the growth trend of traffic, including real-time applications, will continue and the volume of data will grow, which may lead to the formation of packet queues when processed by network devices. In this case, there may be losses in case of long queues. To solve this problem, a power spectrum assessment was carried out. The AR maximum entropy estimator has been shown to be more sensitive than the auxiliary Fourier estimator. Accounting for non-stationarity by spectral methods is possible only through estimation in a sliding time window. Nine diagrams of spectral-temporal analysis of the original series, its increments, and the mixed series of increments were obtained: with default parameters, with small and large windows. Diagrams related to the original series reflect the dynamics of changes in data transmission intensity in the network; they show higher temporal resolution, indicating the presence of high-frequency components (noise) and the presence of low-frequency components (trend). Diagrams with increments describe signals of periodic components; changing the length of the window did not reflect the presence of noise or trend signs. Diagrams with mixed increments show that frequency components are uniformly distributed. The uniqueness of this work lies in the real measured data, and a distinctive feature of the obtained results is the visual examination of the complex traffic structure, allowing for the resolution of the investigated problem. Practical application of the results obtained can be applied in Quality of Service (QoS) management, resource planning, and network performance optimization

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“…We then filled the gaps with pixels from an extension of the study area using MOPSTM (Tang et al, 2021). Because the comparison was between gap-filled and actual pixels, we used the more accurate MOPSTM-gap-filled results (gap-filling performance comparisons of MOPSTM and Steffen spline can be seen in Tang et al (2022)).…”

Section: Filling Simulated Gaps In Single-date Imagesmentioning

confidence: 99%

“…To explore how the selection of gap-filling method affects TCC modelling results, we included a popular temporal-based method, Steffen spline interpolation, for comparison. We selected Steffen spline interpolation because it demonstrated the best performance among several temporal interpolation methods in Tang et al (2022). The study area encompassed a large Afromontane landscape in Taita Hills, Kenya with seasonally persistent cloud cover and a bimodal rainfall pattern, which poses a challenge for acquiring cloudfree images and applying time series approaches.…”

Section: Introductionmentioning

confidence: 99%

Impact of Preprocessing on Tree Canopy Cover Modelling: Does Gap-Filling of Landsat Time Series Improve Modelling Accuracy?

Tang

Adhikari

Pellikka

et al. 2022

Front. Remote Sens.

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Preprocessing of Landsat images is a double-edged sword, transforming the raw data into a useful format but potentially introducing unwanted values with unnecessary steps. Through recovering missing data of satellite images in time series analysis, gap-filling is an important, highly developed, preprocessing procedure, but its necessity and effects in numerous Landsat applications, such as tree canopy cover (TCC) modelling, are rarely examined. We address this barrier by providing a quantitative comparison of TCC modelling using predictor variables derived from Landsat time series that included gap-filling versus those that did not include gap-filling and evaluating the effects that gap-filling has on modelling TCC. With 1-year Landsat time series from a tropical region located in Taita Hills, Kenya, and a reference TCC map in 0–100 scales derived from airborne laser scanning data, we designed comparable random forest modelling experiments to address the following questions: 1) Does gap-filling improve TCC modelling based on time series predictor variables including the seasonal composites (SC), spectral-temporal metrics (STMs), and harmonic regression (HR) coefficients? 2) What is the difference in TCC modelling between using gap-filled pixels and using valid (actual or cloud-free) pixels? Two gap-filling methods, one temporal-based method (Steffen spline interpolation) and one hybrid method (MOPSTM) have been examined. We show that gap-filled predictors derived from the Landsat time series delivered better performance on average than non-gap-filled predictors with the average of median RMSE values for Steffen-filled and MOPSTM-filled SC’s being 17.09 and 16.57 respectively, while for non-gap-filled predictors, it was 17.21. MOPSTM-filled SC is 3.7% better than non-gap-filled SC on RMSE, and Steffen-filled SC is 0.7% better than non-gap-filled SC on RMSE. The positive effects of gap-filling may be reduced when there are sufficient high-quality valid observations to generate a seasonal composite. The single-date experiment suggests that gap-filled data (e.g. RMSE of 16.99, 17.71, 16.24, and 17.85 with 100% gap-filled pixels as training and test datasets for four seasons) may deliver no worse performance than valid data (e.g. RMSE of 15.46, 17.07, 16.31, and 18.14 with 100% valid pixels as training and test datasets for four seasons). Thus, we conclude that gap-filling has a positive effect on the accuracy of TCC modelling, which justifies its inclusion in image preprocessing workflows.

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Spectral Temporal Information for Missing Data Reconstruction (STIMDR) of Landsat Reflectance Time Series

Cited by 10 publications

References 78 publications

A Methodological Approach for Gap Filling of WFV Gaofen-1 Images from Spatial Autocorrelation and Enhanced Weighting

A Methodological Approach for Gap Filling of WFV Gaofen-1 Images from Spatial Autocorrelation and Enhanced Weighting

Visual identification of some regularities in packet network traffic

Impact of Preprocessing on Tree Canopy Cover Modelling: Does Gap-Filling of Landsat Time Series Improve Modelling Accuracy?

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