Evaluation of temporal compositing algorithms for annual land cover classification using Landsat time series data
In this paper, four widely used temporal compositing algorithms, i.e. median, maximum NDVI, medoid, and weighted scoring-based algorithms, were evaluated for annual land cover classification using monthly Landsat time series data. Four study areas located in California, Texas, Kansas, and Minnesota,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17538947.2023.2230958 |
| _version_ | 1797678424348688384 |
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| author | Xichen Meng Shuai Xie Lin Sun Liangyun Liu Yilong Han |
| author_facet | Xichen Meng Shuai Xie Lin Sun Liangyun Liu Yilong Han |
| author_sort | Xichen Meng |
| collection | DOAJ |
| description | In this paper, four widely used temporal compositing algorithms, i.e. median, maximum NDVI, medoid, and weighted scoring-based algorithms, were evaluated for annual land cover classification using monthly Landsat time series data. Four study areas located in California, Texas, Kansas, and Minnesota, USA were selected for image compositing and land cover classification. Results indicated that images composited using weighted scoring-based algorithms have the best spatial fidelity compared to other three algorithms. In addition, the weighted scoring-based algorithms have superior classification accuracy, followed by median, maximum NDVI, and medoid in descending order. However, the median algorithm has a significant advantage in computational efficiency which was ∼70 times that of weighted scoring-based algorithms, and with overall classification accuracy just slightly lower (∼0.13% on average) than weighted scoring-based algorithms. Therefore, we recommended the weighted scoring-based compositing algorithms for small area land cover mapping, and median compositing algorithm for the land cover mapping of large area considering the balance between computational complexity and classification accuracy. The findings of this study provide insights into the performance difference between various compositing algorithms, and have potential uses for the selection of pixel-based image compositing technique adopted for land cover mapping based on Landsat time series data. |
| first_indexed | 2024-03-11T22:59:35Z |
| format | Article |
| id | doaj.art-5c7f93e8fc664498a3abc715513567aa |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T22:59:35Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-5c7f93e8fc664498a3abc715513567aa2023-09-21T15:09:03ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552023-12-011612574259810.1080/17538947.2023.22309582230958Evaluation of temporal compositing algorithms for annual land cover classification using Landsat time series dataXichen Meng0Shuai Xie1Lin Sun2Liangyun Liu3Yilong Han4Shandong University of Science and TechnologyShandong University of Science and TechnologyShandong University of Science and TechnologyAerospace Information Research Institute, Chinese Academy of SciencesShandong University of Science and TechnologyIn this paper, four widely used temporal compositing algorithms, i.e. median, maximum NDVI, medoid, and weighted scoring-based algorithms, were evaluated for annual land cover classification using monthly Landsat time series data. Four study areas located in California, Texas, Kansas, and Minnesota, USA were selected for image compositing and land cover classification. Results indicated that images composited using weighted scoring-based algorithms have the best spatial fidelity compared to other three algorithms. In addition, the weighted scoring-based algorithms have superior classification accuracy, followed by median, maximum NDVI, and medoid in descending order. However, the median algorithm has a significant advantage in computational efficiency which was ∼70 times that of weighted scoring-based algorithms, and with overall classification accuracy just slightly lower (∼0.13% on average) than weighted scoring-based algorithms. Therefore, we recommended the weighted scoring-based compositing algorithms for small area land cover mapping, and median compositing algorithm for the land cover mapping of large area considering the balance between computational complexity and classification accuracy. The findings of this study provide insights into the performance difference between various compositing algorithms, and have potential uses for the selection of pixel-based image compositing technique adopted for land cover mapping based on Landsat time series data.http://dx.doi.org/10.1080/17538947.2023.2230958temporal compositingspatial fidelitytime seriesland cover classificationlandsat |
| spellingShingle | Xichen Meng Shuai Xie Lin Sun Liangyun Liu Yilong Han Evaluation of temporal compositing algorithms for annual land cover classification using Landsat time series data International Journal of Digital Earth temporal compositing spatial fidelity time series land cover classification landsat |
| title | Evaluation of temporal compositing algorithms for annual land cover classification using Landsat time series data |
| title_full | Evaluation of temporal compositing algorithms for annual land cover classification using Landsat time series data |
| title_fullStr | Evaluation of temporal compositing algorithms for annual land cover classification using Landsat time series data |
| title_full_unstemmed | Evaluation of temporal compositing algorithms for annual land cover classification using Landsat time series data |
| title_short | Evaluation of temporal compositing algorithms for annual land cover classification using Landsat time series data |
| title_sort | evaluation of temporal compositing algorithms for annual land cover classification using landsat time series data |
| topic | temporal compositing spatial fidelity time series land cover classification landsat |
| url | http://dx.doi.org/10.1080/17538947.2023.2230958 |
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