Spatiotemporal Continuous Impervious Surface Mapping by Fusion of Landsat Time Series Data and Google Earth Imagery
The monitoring of impervious surfaces in urban areas using remote sensing with fine spatial and temporal resolutions is crucial for monitoring urban development and environmental changes in urban areas. Spatiotemporal super-resolution mapping (STSRM) fuses fine-spatial-coarse-temporal remote sensing...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-06-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/12/2409 |
| _version_ | 1797529529140379648 |
|---|---|
| author | Rui Chen Xiaodong Li Yihang Zhang Pu Zhou Yalan Wang Lingfei Shi Lai Jiang Feng Ling Yun Du |
| author_facet | Rui Chen Xiaodong Li Yihang Zhang Pu Zhou Yalan Wang Lingfei Shi Lai Jiang Feng Ling Yun Du |
| author_sort | Rui Chen |
| collection | DOAJ |
| description | The monitoring of impervious surfaces in urban areas using remote sensing with fine spatial and temporal resolutions is crucial for monitoring urban development and environmental changes in urban areas. Spatiotemporal super-resolution mapping (STSRM) fuses fine-spatial-coarse-temporal remote sensing data with coarse-spatial-fine-temporal data, allowing for urban impervious surface mapping at both fine-spatial and fine-temporal resolutions. The STSRM involves two main steps: unmixing the coarse-spatial-fine-temporal remote sensing data to class fraction images, and downscaling the fraction images to sub-pixel land cover maps. Yet, challenges exist in each step when applying STSRM in mapping impervious surfaces. First, the impervious surfaces have high spectral variability (i.e., high intra-class and low inter-class variability), which impacts the accurate extraction of sub-pixel scale impervious surface fractions. Second, downscaling the fraction images to sub-pixel land cover maps is an ill-posed problem and would bring great uncertainty and error in the predictions. This paper proposed a new Spatiotemporal Continuous Impervious Surface Mapping (STCISM) method to deal with these challenges in fusing Landsat and Google Earth imagery. The STCISM used the Multiple Endmember Spectral Mixture Analysis and the Fisher Discriminant Analysis to minimize the within-class variability and maximize the between-class variability to reduce the spectral unmixing uncertainty. In addition, the STCISM adopted a new temporal consistency check model to incorporate temporal contextual information to reduce the uncertainty in the time-series impervious surface prediction maps. Unlike the traditional temporal consistency check model that assumed the impervious-to-pervious conversion is unlikely to happen, the new model allowed the bidirectional conversions between pervious and impervious surfaces. The temporal consistency check was used as a post-procession method to correct the errors in the prediction maps. The proposed STCISM method was used to predict time-series impervious surface maps at 5 m resolution of Google Earth image at the Landsat frequency. The results showed that the proposed STCISM outperformed the STSRM model without using the temporal consistency check and the STSRM model using the temporal consistency check based on the unidirectional pervious-to-impervious surface conversion rule. |
| first_indexed | 2024-03-10T10:15:08Z |
| format | Article |
| id | doaj.art-e8d73d7a3d954738bf11e47266470560 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T10:15:08Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e8d73d7a3d954738bf11e472664705602023-11-22T00:53:31ZengMDPI AGRemote Sensing2072-42922021-06-011312240910.3390/rs13122409Spatiotemporal Continuous Impervious Surface Mapping by Fusion of Landsat Time Series Data and Google Earth ImageryRui Chen0Xiaodong Li1Yihang Zhang2Pu Zhou3Yalan Wang4Lingfei Shi5Lai Jiang6Feng Ling7Yun Du8Key Laboratory for Environment and Disaster Monitoring and Evaluation of Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaKey Laboratory for Environment and Disaster Monitoring and Evaluation of Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaKey Laboratory for Environment and Disaster Monitoring and Evaluation of Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaKey Laboratory for Environment and Disaster Monitoring and Evaluation of Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaKey Laboratory for Environment and Disaster Monitoring and Evaluation of Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaHenan Agricultural University School of Resources and Environment, Zhengzhou 450002, ChinaHubei Water Resources Research Institute, Hubei Water Resources and Hydropower Science and Technology Promotion Center, Wuhan 430070, ChinaKey Laboratory for Environment and Disaster Monitoring and Evaluation of Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaKey Laboratory for Environment and Disaster Monitoring and Evaluation of Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaThe monitoring of impervious surfaces in urban areas using remote sensing with fine spatial and temporal resolutions is crucial for monitoring urban development and environmental changes in urban areas. Spatiotemporal super-resolution mapping (STSRM) fuses fine-spatial-coarse-temporal remote sensing data with coarse-spatial-fine-temporal data, allowing for urban impervious surface mapping at both fine-spatial and fine-temporal resolutions. The STSRM involves two main steps: unmixing the coarse-spatial-fine-temporal remote sensing data to class fraction images, and downscaling the fraction images to sub-pixel land cover maps. Yet, challenges exist in each step when applying STSRM in mapping impervious surfaces. First, the impervious surfaces have high spectral variability (i.e., high intra-class and low inter-class variability), which impacts the accurate extraction of sub-pixel scale impervious surface fractions. Second, downscaling the fraction images to sub-pixel land cover maps is an ill-posed problem and would bring great uncertainty and error in the predictions. This paper proposed a new Spatiotemporal Continuous Impervious Surface Mapping (STCISM) method to deal with these challenges in fusing Landsat and Google Earth imagery. The STCISM used the Multiple Endmember Spectral Mixture Analysis and the Fisher Discriminant Analysis to minimize the within-class variability and maximize the between-class variability to reduce the spectral unmixing uncertainty. In addition, the STCISM adopted a new temporal consistency check model to incorporate temporal contextual information to reduce the uncertainty in the time-series impervious surface prediction maps. Unlike the traditional temporal consistency check model that assumed the impervious-to-pervious conversion is unlikely to happen, the new model allowed the bidirectional conversions between pervious and impervious surfaces. The temporal consistency check was used as a post-procession method to correct the errors in the prediction maps. The proposed STCISM method was used to predict time-series impervious surface maps at 5 m resolution of Google Earth image at the Landsat frequency. The results showed that the proposed STCISM outperformed the STSRM model without using the temporal consistency check and the STSRM model using the temporal consistency check based on the unidirectional pervious-to-impervious surface conversion rule.https://www.mdpi.com/2072-4292/13/12/2409Impervious surfaceLandsatspectral variabilitytime seriestemporal consistency |
| spellingShingle | Rui Chen Xiaodong Li Yihang Zhang Pu Zhou Yalan Wang Lingfei Shi Lai Jiang Feng Ling Yun Du Spatiotemporal Continuous Impervious Surface Mapping by Fusion of Landsat Time Series Data and Google Earth Imagery Remote Sensing Impervious surface Landsat spectral variability time series temporal consistency |
| title | Spatiotemporal Continuous Impervious Surface Mapping by Fusion of Landsat Time Series Data and Google Earth Imagery |
| title_full | Spatiotemporal Continuous Impervious Surface Mapping by Fusion of Landsat Time Series Data and Google Earth Imagery |
| title_fullStr | Spatiotemporal Continuous Impervious Surface Mapping by Fusion of Landsat Time Series Data and Google Earth Imagery |
| title_full_unstemmed | Spatiotemporal Continuous Impervious Surface Mapping by Fusion of Landsat Time Series Data and Google Earth Imagery |
| title_short | Spatiotemporal Continuous Impervious Surface Mapping by Fusion of Landsat Time Series Data and Google Earth Imagery |
| title_sort | spatiotemporal continuous impervious surface mapping by fusion of landsat time series data and google earth imagery |
| topic | Impervious surface Landsat spectral variability time series temporal consistency |
| url | https://www.mdpi.com/2072-4292/13/12/2409 |
| work_keys_str_mv | AT ruichen spatiotemporalcontinuousimpervioussurfacemappingbyfusionoflandsattimeseriesdataandgoogleearthimagery AT xiaodongli spatiotemporalcontinuousimpervioussurfacemappingbyfusionoflandsattimeseriesdataandgoogleearthimagery AT yihangzhang spatiotemporalcontinuousimpervioussurfacemappingbyfusionoflandsattimeseriesdataandgoogleearthimagery AT puzhou spatiotemporalcontinuousimpervioussurfacemappingbyfusionoflandsattimeseriesdataandgoogleearthimagery AT yalanwang spatiotemporalcontinuousimpervioussurfacemappingbyfusionoflandsattimeseriesdataandgoogleearthimagery AT lingfeishi spatiotemporalcontinuousimpervioussurfacemappingbyfusionoflandsattimeseriesdataandgoogleearthimagery AT laijiang spatiotemporalcontinuousimpervioussurfacemappingbyfusionoflandsattimeseriesdataandgoogleearthimagery AT fengling spatiotemporalcontinuousimpervioussurfacemappingbyfusionoflandsattimeseriesdataandgoogleearthimagery AT yundu spatiotemporalcontinuousimpervioussurfacemappingbyfusionoflandsattimeseriesdataandgoogleearthimagery |