Journal
REMOTE SENSING
Volume 12, Issue 18, Pages -Publisher
MDPI
DOI: 10.3390/rs12182888
Keywords
remote sensing; temporal smoothing; google earth engine; machine learning; random forest; land cover
Categories
Funding
- ICIMOD
- government of Afghanistan
- government of Australia
- government of Austria
- government of Bangladesh
- government of Bhutan
- government of China
- government of India
- government of Myanmar
- government of Nepal
- government of Norway
- government of Pakistan
- government of Switzerland
- government of United Kingdom
- NASA
- USAID
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Time series land cover data statistics often fluctuate abruptly due to seasonal impact and other noise in the input image. Temporal smoothing techniques are used to reduce the noise in time series data used in land cover mapping. The effects of smoothing may vary based on the smoothing method and land cover category. In this study, we compared the performance of Fourier transformation smoothing, Whittaker smoother and Linear-Fit averaging smoother on Landsat 5, 7 and 8 based yearly composites to classify land cover in Province No. 1 of Nepal. The performance of each smoother was tested based on whether it was applied on image composites or on land cover primitives generated using the random forest machine learning method. The land cover data used in the study was from the years 2000 to 2018. Probability distribution was examined to check the quality of primitives and accuracy of the final land cover maps were accessed. The best results were found for the Whittaker smoothing for stable classes and Fourier smoothing for other classes. The results also show that classification using a properly selected smoothing algorithm outperforms a classification based on its unsmoothed data set. The final land cover generated by combining the best results obtained from different smoothing approaches increased our overall land cover map accuracy from 79.18% to 83.44%. This study shows that smoothing can result in a substantial increase in the quality of the results and that the smoothing approach should be carefully considered for each land cover class.
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