Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data
Wetland ecosystems have experienced dramatic challenges in the past few decades due to natural and human factors. Wetland maps are essential for the conservation and management of terrestrial ecosystems. This study is to obtain an accurate wetland map using an object-based stacked generalization (St...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-10-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0303243420300118 |
| _version_ | 1811328899860135936 |
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| author | Yaotong Cai Xinyu Li Meng Zhang Hui Lin |
| author_facet | Yaotong Cai Xinyu Li Meng Zhang Hui Lin |
| author_sort | Yaotong Cai |
| collection | DOAJ |
| description | Wetland ecosystems have experienced dramatic challenges in the past few decades due to natural and human factors. Wetland maps are essential for the conservation and management of terrestrial ecosystems. This study is to obtain an accurate wetland map using an object-based stacked generalization (Stacking) method on the basis of multi-temporal Sentinel-1 and Sentinel-2 data. Firstly, the Robust Adaptive Spatial Temporal Fusion Model (RASTFM) is used to get time series Sentinel-2 NDVI, from which the vegetation phenology variables are derived by the threshold method. Subsequently, both vertical transmit-vertical receive (VV) and vertical transmit-horizontal receive (VH) polarization backscatters (σ0 VV, σ0 VH) are obtained using the time series Sentinel-1 images. Speckle noise inherent in SAR data, resulting in over-segmentation or under-segmentation, can affect image segmentation and degrade the accuracies of wetland classification. Therefore, we segment Sentinel-2 multispectral images to delineate meaningful objects in this study. Then, in order to reduce data redundancy and computation time, we analyze the optimal feature combination using the Sentinel-2 multispectral images, Sentinel-2 NDVI time series, phenological variables and other vegetation index derived from Sentinel-2 multispectral images, as well as time series Sentinel-1 backscatters at the object level. Finally, the stacked generalization algorithm is utilized to extract the wetland information based on the optimal feature combination in the Dongting Lake wetland. The overall accuracy and Kappa coefficient of the object-based stacked generalization method are 92.46% and 0.92, which are 3.88% and 0.04 higher than that using the pixel-based method. Moreover, the object-based stacked generalization algorithm is superior to single classifiers in classifying vegetation of high heterogeneity areas. |
| first_indexed | 2024-04-13T15:34:24Z |
| format | Article |
| id | doaj.art-653b48ebd0494aa2b788934469db961b |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-13T15:34:24Z |
| publishDate | 2020-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-653b48ebd0494aa2b788934469db961b2022-12-22T02:41:19ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322020-10-0192102164Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR dataYaotong Cai0Xinyu Li1Meng Zhang2Hui Lin3Research Center of Forestry Remote Sensing & Information Engineering Central South University of Forestry & Technology, Changsha 410004, Hunan province, PR China; Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Changsha 410004, Hunan province, PR China; Key Laboratory of State Forestry & Grassland Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, Hunan province, PR ChinaResearch Center of Forestry Remote Sensing & Information Engineering Central South University of Forestry & Technology, Changsha 410004, Hunan province, PR China; Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Changsha 410004, Hunan province, PR China; Key Laboratory of State Forestry & Grassland Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, Hunan province, PR China; School of Information Science and Engineering, Hunan First Normal University, Changsha 410205, PR ChinaResearch Center of Forestry Remote Sensing & Information Engineering Central South University of Forestry & Technology, Changsha 410004, Hunan province, PR China; Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Changsha 410004, Hunan province, PR China; Key Laboratory of State Forestry & Grassland Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, Hunan province, PR China; Corresponding authors at: Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry & Technology, Changsha 410004, Hunan province, PR China.Research Center of Forestry Remote Sensing & Information Engineering Central South University of Forestry & Technology, Changsha 410004, Hunan province, PR China; Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Changsha 410004, Hunan province, PR China; Key Laboratory of State Forestry & Grassland Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, Hunan province, PR China; Corresponding authors at: Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry & Technology, Changsha 410004, Hunan province, PR China.Wetland ecosystems have experienced dramatic challenges in the past few decades due to natural and human factors. Wetland maps are essential for the conservation and management of terrestrial ecosystems. This study is to obtain an accurate wetland map using an object-based stacked generalization (Stacking) method on the basis of multi-temporal Sentinel-1 and Sentinel-2 data. Firstly, the Robust Adaptive Spatial Temporal Fusion Model (RASTFM) is used to get time series Sentinel-2 NDVI, from which the vegetation phenology variables are derived by the threshold method. Subsequently, both vertical transmit-vertical receive (VV) and vertical transmit-horizontal receive (VH) polarization backscatters (σ0 VV, σ0 VH) are obtained using the time series Sentinel-1 images. Speckle noise inherent in SAR data, resulting in over-segmentation or under-segmentation, can affect image segmentation and degrade the accuracies of wetland classification. Therefore, we segment Sentinel-2 multispectral images to delineate meaningful objects in this study. Then, in order to reduce data redundancy and computation time, we analyze the optimal feature combination using the Sentinel-2 multispectral images, Sentinel-2 NDVI time series, phenological variables and other vegetation index derived from Sentinel-2 multispectral images, as well as time series Sentinel-1 backscatters at the object level. Finally, the stacked generalization algorithm is utilized to extract the wetland information based on the optimal feature combination in the Dongting Lake wetland. The overall accuracy and Kappa coefficient of the object-based stacked generalization method are 92.46% and 0.92, which are 3.88% and 0.04 higher than that using the pixel-based method. Moreover, the object-based stacked generalization algorithm is superior to single classifiers in classifying vegetation of high heterogeneity areas.http://www.sciencedirect.com/science/article/pii/S0303243420300118WetlandClassificationSentinel-1/2Multi-TemporalObject-BasedStacked generalization |
| spellingShingle | Yaotong Cai Xinyu Li Meng Zhang Hui Lin Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data International Journal of Applied Earth Observations and Geoinformation Wetland Classification Sentinel-1/2 Multi-Temporal Object-Based Stacked generalization |
| title | Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data |
| title_full | Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data |
| title_fullStr | Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data |
| title_full_unstemmed | Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data |
| title_short | Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data |
| title_sort | mapping wetland using the object based stacked generalization method based on multi temporal optical and sar data |
| topic | Wetland Classification Sentinel-1/2 Multi-Temporal Object-Based Stacked generalization |
| url | http://www.sciencedirect.com/science/article/pii/S0303243420300118 |
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