Producing a Gap-free Landsat Time Series for the Taita Hills, Southeastern Kenya

Tang, Zhipeng; Adhikari, Hari; Pellikka, Petri; Heiskanen, Janne

doi:10.1109/igarss39084.2020.9324671

Cited by 2 publications

(4 citation statements)

References 9 publications

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“…In general, a higher accurate gap-filling method (MOPSTM) produced more accurate predictions. This study only used 1 year of the Landsat time series to model TCC because MOPSTM gap-filling method is proposed to fill gaps over a 1-year period (Tang et al, 2020(Tang et al, , 2021 although the method can also use STMs calculated over several years for imputation of missing values. It will be more challenging to examine gap-filling effects for a longer period (Brandt et al, 2018) as few gap-filling methods have been suggested to deliver good performance in a long time series.…”

Section: Different Gap-filling Methods and Future Perspectivesmentioning

confidence: 99%

“…TCC modelling based on remote sensing data, an essential measurement in forest management, vegetation growth cycle monitoring, and disease prevention, has received strong interest in research on mountainous tropical areas (Wang et al, 2005;Anchang et al, 2020;Tang et al, 2020). Measurement of TCC using wall-to-wall airborne lidar is comparable to that using field data (Korhonen et al, 2011) but can provide a large size of samples and enables random samples to be used in inaccessible terrain (Adhikari et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Different Gap-filling Methods and Future Perspectivesmentioning

confidence: 99%

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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“…Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM) was developed to recover large-area gaps in Landsat images [18,50]. MOPSTM uses a k-Nearest Neighbor (k-NN) machine-learning method to predict missing observations based on the valid pixels in the target image and statistical spectral-temporal metrics (STMs) computed for a 1-year period as feature space [50]. MOPSTM may be sensitive to the time period.…”

Section: Hybrid Methodsmentioning

confidence: 99%

“…Observations with low temporal consistency often have small weights and thus have little impact on the weighted STMs. For example, pixels that undergo large LULC changes are more likely to have very small spectral similarity and a very large temporal distance given that large LULC changes are not typically observed over a short period [50]. Reducing the effects of the low-temporalconsistency observations helps improve the quality of STMs.…”

Section: Comparisons With the Other Gap-filling Methodsmentioning

confidence: 99%

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

et al. 2021

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The number of Landsat time-series applications has grown substantially because of its approximately 50-year history and relatively high spatial resolution for observing long term changes in the Earth’s surface. However, missing observations (i.e., gaps) caused by clouds and cloud shadows, orbit and sensing geometry, and sensor issues have broadly limited the development of Landsat time-series applications. Due to the large area and temporal and spatial irregularity of time-series gaps, it is difficult to find an efficient and highly precise method to fill them. The Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM) method has been proposed and delivered good performance in filling large-area gaps of single-date Landsat images. However, it can be less practical for a time series longer than one year due to the lack of mechanics that exclude dissimilar data in time series (e.g., different phenology or changes in land cover). To solve this problem, this study proposes a new gap-filling method, Spectral Temporal Information for Missing Data Reconstruction (STIMDR), and examines its performance in Landsat reflectance time series. Two groups of experiments, including 2000 × 2000 pixel Landsat single-date images and Landsat time series acquired from four sites (Kenya, Finland, Germany, and China), were performed to test the new method. We simulated artificial gaps to evaluate predicted pixel values with real observations. Quantitative and qualitative evaluations of gap-filled images through comparisons with other state-of-the-art methods confirmed the more robust and accurate performance of the proposed method. In addition, the proposed method was also able to fill gaps contaminated by extreme cloud cover for a period (e.g., winter in high-latitude areas). A down-stream task of random forest supervised classification through both gap-filled simulated datasets and the original valid datasets verified that STIMDR-generated products are relevant to the user community for land cover applications.

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Producing a Gap-free Landsat Time Series for the Taita Hills, Southeastern Kenya

Cited by 2 publications

References 9 publications

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

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

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

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