279
Views
0
CrossRef citations to date
0
Altmetric
Research Article

A phenological knowledge transfer-based fine grained land cover change sample collection method: a case study of coastal wetlands

, , , &
Article: 2310090 | Received 26 Sep 2023, Accepted 20 Jan 2024, Published online: 30 Jan 2024

References

  • Baeza, S., and J. M. Paruelo. 2020. “Land use/Land Cover Change (2000–2014) in the Rio de la Plata Grasslands: An Analysis Based on MODIS NDVI Time Series.” Remote Sensing 12 (3): 381–322. https://doi.org/10.3390/rs12030381.
  • Blickensdörfer, L., M. Schwieder, D. Pflugmacher, C. Nendel, S. Erasmi, and P. Hostert. 2022. “Mapping of Crop Types and Crop Sequences with Combined Time Series of Sentinel-1, Sentinel-2 and Landsat 8 Data for Germany.” Remote Sensing of Environment 269: 112831. https://doi.org/10.1016/j.rse.2021.112831.
  • Dawson, G., J. Landy, M. Tsamados, A. S. Komarov, S. Howell, H. Heorton, and T. Krumpen. 2022. “A 10-Year Record of Arctic Summer Sea ice Freeboard from CryoSat-2.” Remote Sensing of Environment 268: 112744. https://doi.org/10.1016/j.rse.2021.112744.
  • Fleischmann, A. S., F. Papa, A. Fassoni-Andrade, J. M. Melack, S. Wongchuig, R. C. D. Paiva, S. K. Hamilton, et al. 2022. “How Much Inundation Occurs in the Amazon River Basin?” Remote Sensing of Environment 278: 113099. https://doi.org/10.1016/j.rse.2022.113099.
  • Guan, X., C. Huang, G. Liu, X. Meng, and Q. Liu. 2016. “Mapping Rice Cropping Systems in Vietnam Using an NDVI-Based Time-Series Similarity Measurement Based on DTW Distance.” Remote Sensing 8 (1): 19–44. https://doi.org/10.3390/rs8010019.
  • Himalaya, K., A. Solanki, V. Gupta, and M. Joshi. 2022. “Application of Machine Learning Algorithms in Landslide Susceptibility Mapping, Kali Valley, Kumaun Himalaya, India.” Geocarto International 37 (0): 16846–16871. https://doi.org/10.1080/10106049.2022.2120546.
  • Hong, D., L. Gao, J. Yao, B. Zhang, A. Plaza, and J. Chanussot. 2021. “Graph Convolutional Networks for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 59 (7): 5966–5978. https://doi.org/10.1109/TGRS.2020.3015157.
  • Hong, D., L. Gao, N. Yokoya, J. Yao, J. Chanussot, Q. Du, and B. Zhang. 2021. “More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification.” IEEE Transactions on Geoscience and Remote Sensing 59 (5): 4340–4354. https://doi.org/10.1109/TGRS.2020.3016820.
  • Hong, D., N. Yokoya, J. Chanussot, and X. X. Zhu. 2019. “An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing.” IEEE Transactions on Image Processing 28 (4): 1923–1938. https://doi.org/10.1109/TIP.2018.2878958.
  • Hu, Y., Y. Dong, and Batunacun. 2018. “An Automatic Approach for Land-Change Detection and Land Updates Based on Integrated NDVI Timing Analysis and the CVAPS Method with GEE Support.” ISPRS Journal of Photogrammetry and Remote Sensing 146 (February): 347–359. https://doi.org/10.1016/j.isprsjprs.2018.10.008.
  • Huang, J., Y. Liu, M. Wang, Y. Zheng, J. Wang, and D. Ming. 2019. “Change Detection of High Spatial Resolution Images Based on Region-Line Primitive Association Analysis and Evidence Fusion.” Remote Sensing 11 (21): 2484–2423. https://doi.org/10.3390/rs11212484.
  • Huang, H., J. Wang, C. Liu, L. Liang, C. Li, and P. Gong. 2020. “The Migration of Training Samples Towards Dynamic Global Land Cover Mapping.” ISPRS Journal of Photogrammetry and Remote Sensing 161: 27–36. https://doi.org/10.1016/j.isprsjprs.2020.01.010.
  • Huang, F., X. Xia, Y. Huang, S. Lv, Q. Chen, Y. Pan, and X. Zhu. 2022. “Comparison of Winter Wheat Extraction Methods Based on Different Time Series of Vegetation Indices in the Northeastern Margin of the Qinghai-Tibet Plateau : A Case Study of Minhe, China.” Remote Sensing 14: 343.
  • Hubert-Moy, L., E. Fabre, and S. Rapinel. 2020. “Contribution of SPOT-7 Multi-Temporal Imagery for Mapping Wetland Vegetation.” European Journal of Remote Sensing 53 (1): 201–210. https://doi.org/10.1080/22797254.2020.1795727.
  • Jin, S., Y. Liu, S. Fagherazzi, H. Mi, G. Qiao, W. Xu, C. Sun, Y. Liu, B. Zhao, and C. G. Fichot. 2021. “River Body Extraction from Sentinel-2A/B MSI Images Based on an Adaptive Multi-Scale Region Growth Method.” Remote Sensing of Environment 255 (July 2020): 112297. https://doi.org/10.1016/j.rse.2021.112297.
  • Kamilaris, A., and F. X. Prenafeta-Boldú. 2018. “Deep Learning in Agriculture: A Survey.” Computers and Electronics in Agriculture 147 (2): 70–90. https://doi.org/10.1016/j.compag.2018.02.016.
  • Li, J., X. Huang, and X. Chang. 2020. “A Label-Noise Robust Active Learning Sample Collection Method for Multi-Temporal Urban Land-Cover Classification and Change Analysis.” ISPRS Journal of Photogrammetry and Remote Sensing 163 (January): 1–17. https://doi.org/10.1016/j.isprsjprs.2020.02.022.
  • Li, Z., Z. Wang, X. Liu, Y. Zhu, K. Wang, and T. Zhang. 2022. “Classification and Evolutionary Analysis of Yellow River Delta Wetlands Using Decision Tree Based on Time Series SAR Backscattering Coefficient and Coherence.” Frontiers in Marine Science 9: 940342. https://doi.org/10.3389/fmars.2022.940342.
  • Li, C., G. Xian, Q. Zhou, and B. W. Pengra. 2021. “A Novel Automatic Phenology Learning (APL) Method of Training Sample Selection Using Multiple Datasets for Time-Series Land Cover Mapping.” Remote Sensing of Environment 266 (August): 112670. https://doi.org/10.1016/j.rse.2021.112670.
  • Liang, J., and D. Liu. 2020. “A Local Thresholding Approach to Flood Water Delineation Using Sentinel-1 SAR Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 159 (2): 53–62. https://doi.org/10.1016/j.isprsjprs.2019.10.017.
  • Lin, C., P. Du, A. Samat, E. Li, X. Wang, and J. Xia. 2019. “Automatic Updating of Land Cover Maps in Rapidly Urbanizing Regions by Relational Knowledge Transferring from Globeland30.” Remote Sensing 11 (12): 1397. https://doi.org/10.3390/rs11121397.
  • Liu, L., X. Xiao, Y. Qin, J. Wang, X. Xu, Y. Hu, and Z. Qiao. 2020. “Mapping Cropping Intensity in China Using Time Series Landsat and Sentinel-2 Images and Google Earth Engine.” Remote Sensing of Environment 239 (December 2019): 111624. https://doi.org/10.1016/j.rse.2019.111624.
  • Liu, G., Y. Yuan, Y. Zhang, Y. Dong, and X. Li. 2022. “Style Transformation-Based Spatial-Spectral Feature Learning for Unsupervised Change Detection.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–15. https://doi.org/10.1109/TGRS.2020.3026099.
  • Lu, Y., and L. Wang. 2021. “How to Automate Timely Large-Scale Mangrove Mapping with Remote Sensing.” Remote Sensing of Environment 264 (June): 112584. https://doi.org/10.1016/j.rse.2021.112584.
  • Lv, Z., S. Member, H. Huang, W. Sun, S. Member, and M. Jia. 2023. “Iterative Training Sample Augmentation for Enhancing Land Cover Change Detection Performance with Deep Learning Neural Network.” IEEE Transactions on Neural Networks and Learning Systems: 1–14. https://doi.org/10.1109/TNNLS.2023.3282935.
  • Lv, Z., X. Yang, X. Zhang, and J. A. Benediktsson. 2022. “Object-Based Sorted-Histogram Similarity Measurement for Detecting Land Cover Change with VHR Remote Sensing Images.” IEEE Geoscience and Remote Sensing Letters 19: 1–5. https://doi.org/10.1109/LGRS.2022.3163169.
  • Rapinel, S., E. Fabre, S. Dufour, D. Arvor, C. Mony, and L. Hubert-Moy. 2019. “Mapping Potential, Existing and Efficient Wetlands Using Free Remote Sensing Data.” Journal of Environmental Management 247: 829–839. https://doi.org/10.1016/j.jenvman.2019.06.098.
  • Rapinel, S., L. Panhelleux, G. Gayet, R. Vanacker, B. Lemercier, B. Laroche, F. Chambaud, A. Guelmami, and L. Hubert-Moy. 2023. “National Wetland Mapping Using Remote-Sensing-Derived Environmental Variables, Archive Field Data, and Artificial Intelligence.” Heliyon 9 (2): e13482. https://doi.org/10.1016/j.heliyon.2023.e13482.
  • Saha, S., F. Bovolo, and L. Bruzzone. 2021. “Building Change Detection in VHR SAR Images via Unsupervised Deep Transcoding.” IEEE Transactions on Geoscience and Remote Sensing 59 (3): 1917–1929. https://doi.org/10.1109/TGRS.2020.3000296.
  • Shen, Q., J. Huang, M. Wang, S. Tao, and R. Yang. 2022. “Semantic Feature-Constrained Multitask Siamese Network for Building Change Detection in High-Spatial-Resolution Remote Sensing Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 189 (May): 78–94. https://doi.org/10.1016/j.isprsjprs.2022.05.001.
  • Shi, S., Y. Zhong, J. Zhao, P. Lv, Y. Liu, and L. Zhang. 2022. “Land-Use/Land-Cover Change Detection Based on Class-Prior Object-Oriented Conditional Random Field Framework for High Spatial Resolution Remote Sensing Imagery.” IEEE Transactions on Geoscience and Remote Sensing 60 (Dc): 1–16. https://doi.org/10.1109/TGRS.2020.3034373.
  • Shu, Q., J. Pan, Z. Zhang, and M. Wang. 2022. “MTCNet: Multitask Consistency Network with Single Temporal Supervision for Semi-Supervised Building Change Detection.” International Journal of Applied Earth Observation and Geoinformation 115 (September): 103110. https://doi.org/10.1016/j.jag.2022.103110.
  • Su, T., and S. Zhang. 2021. “Object-Based Crop Classification in Hetao Plain Using Random Forest.” Earth Science Informatics 14 (1): 119–131. https://doi.org/10.1007/s12145-020-00531-z.
  • Tong, X., M. Brandt, P. Hiernaux, S. Herrmann, L. V. Rasmussen, K. Rasmussen, F. Tian, T. Tagesson, W. Zhang, and R. Fensholt. 2020. “The Forgotten Land Use Class: Mapping of Fallow Fields Across the Sahel Using Sentinel-2.” Remote Sensing of Environment 239 (December 2019): 111598. https://doi.org/10.1016/j.rse.2019.111598.
  • Wang, M., D. Mao, X. Xiao, K. Song, M. Jia, C. Ren, and Z. Wang. 2023. “Interannual Changes of Coastal Aquaculture Ponds in China at 10-m Spatial Resolution During 2016–2021.” Remote Sensing of Environment 284 (August 2022): 113347. https://doi.org/10.1016/j.rse.2022.113347.
  • Wu, Y., T. Hong, L. Meng, L. Xiao, Y. Li, and X. Bi. 2023. “Identification of Wetland Conservation and Restoration Priorities in Regions of Oil Extraction in the Yellow River Delta Using Circuit Theory Modelling.” Ecological Indicators 154: 110621. https://doi.org/10.1016/j.ecolind.2023.110621.
  • Xiao, T., Y. Liu, Y. Huang, M. Li, and G. Yang. 2023. “Enhancing Multiscale Representations with Transformer for Remote Sensing Image Semantic Segmentation.” IEEE Transactions on Geoscience and Remote Sensing 61: 1–16. https://doi.org/10.1109/TGRS.2023.3256064.
  • Xing, H., J. Niu, Y. Feng, D. Hou, Y. Wang, and Z. Wang. 2023. “A Coastal Wetlands Mapping Approach of Yellow River Delta with a Hierarchical Classification and Optimal Feature Selection Framework.” Catena 223 (December 2022): 106897. https://doi.org/10.1016/j.catena.2022.106897.
  • Xu, S., Y. Liao, X. Yan, and G. Zhang. 2020. “Change Detection in SAR Images Based on Iterative Otsu.” European Journal of Remote Sensing 53 (1): 331–339. https://doi.org/10.1080/22797254.2020.1852606.
  • Xu, J., Y. Tang, J. Xu, S. Shu, B. Yu, J. Wu, and Y. Huang. 2022. “Impact of Snow Cover Phenology on the Vegetation Green-Up Date on the Tibetan Plateau.” Remote Sensing 14: 3909. https://doi.org/10.3390/rs14163909
  • Xu, D., C. Wang, J. Chen, M. Shen, B. Shen, R. Yan, Z. Li, et al. 2021. “The Superiority of the Normalized Difference Phenology Index (NDPI) for Estimating Grassland Aboveground Fresh Biomass.” Remote Sensing of Environment 264: 112578. https://doi.org/10.1016/j.rse.2021.112578
  • Yan, J., L. Wang, W. Song, Y. Chen, X. Chen, and Z. Deng. 2019. “A Time-Series Classification Approach Based on Change Detection for Rapid Land Cover Mapping.” ISPRS Journal of Photogrammetry and Remote Sensing 158 (June 2020): 249–262. https://doi.org/10.1016/j.isprsjprs.2019.10.003.
  • Yan, J., J. Zhu, S. Zhao, and F. Su. 2023. “Coastal Wetland Degradation and Ecosystem Service Value Change in the Yellow River Delta, China.” Global Ecology and Conservation 44: e02501. https://doi.org/10.1016/j.gecco.2023.e02501.
  • Zhang, Y., F. Ling, X. Wang, G. M. Foody, D. S. Boyd, X. Li, Y. Du, and P. M. Atkinson. 2021. “Tracking Small-Scale Tropical Forest Disturbances: Fusing the Landsat and Sentinel-2 Data Record.” Remote Sensing of Environment 261 (May): 112470. https://doi.org/10.1016/j.rse.2021.112470.
  • Zhang, X., G. Wang, B. Xue, M. Zhang, and Z. Tan. 2021. “Dynamic Landscapes and the Driving Forces in the Yellow River Delta Wetland Region in the Past Four Decades.” Science of The Total Environment 787: 147644. https://doi.org/10.1016/j.scitotenv.2021.147644.
  • Zhang, B., S. Wdowinski, D. Gann, S. H. Hong, and J. Sah. 2022. “Spatiotemporal Variations of Wetland Backscatter: The Role of Water Depth and Vegetation Characteristics in Sentinel-1 Dual-Polarization SAR Observations.” Remote Sensing of Environment 270: 112864. https://doi.org/10.1016/j.rse.2021.112864.
  • Zhang, X., X. Xiao, S. Qiu, X. Xu, X. Wang, Q. Chang, J. Wu, and B. Li. 2022. “Quantifying Latitudinal Variation in Land Surface Phenology of Spartina Alterniflora Saltmarshes Across Coastal Wetlands in China by Landsat 7/8 and Sentinel-2 Images.” Remote Sensing of Environment 269: 112810. https://doi.org/10.1016/j.rse.2021.112810.
  • Zhang, B., G. Xu, H. Yu, H. Wang, H. Pei, and W. Hong. 2023. “Array 3-D SAR Tomography Using Robust Gridless Compressed Sensing.” IEEE Transactions on Geoscience and Remote Sensing 61: 1–13. https://doi.org/10.1109/TGRS.2023.3259980.
  • Zhao, J., and L. Itti. 2018. “shapeDTW: Shape Dynamic Time Warping.” Pattern Recognition 74: 171–184. https://doi.org/10.1016/j.patcog.2017.09.020.
  • Zhong, C., C. Wang, H. Li, W. Chen, and Y. Hou. 2018. “Mapping Inter-Annual Land Cover Variations Automatically Based on a Novel Sample Transfer Method.” Remote Sensing 10 (9), 1457, https://doi.org/10.3390/rs10091457.
  • Zhou, K. 2022. “Wetland Landscape Pattern Evolution and Prediction in the Yellow River Delta.” Applied Water Science 12 (8): 190. https://doi.org/10.1007/s13201-022-01711-6.
  • Zhou, Y., J. Wang, J. Ding, B. Liu, N. Weng, and H. Xiao. 2023. “SIGNet: A Siamese Graph Convolutional Network for Multi-Class Urban Change Detection.” Remote Sensing 15: 2464. https://doi.org/10.3390/rs15092464
  • Zhu, L., Z. Guo, H. Xing, and W. Sun. 2023. “A Coupled Temporal-Spectral-Spatial Multidimensional Information Change Detection Framework Method: A Case of the 1990–2020 Tianjin, China.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 16: 5741–5758. https://doi.org/10.1109/JSTARS.2023.3288218.
  • Zhu, L., X. Jiang, L. Zhao, H. Qu, and W. Sun. 2023. “A Temporal-Spectral Value and Shape Change Detection Method Integrating Thematic Index Information and Spectral Band Information.” Environmental Science and Pollution Research 30: 47408–47421. https://doi.org/10.1007/s11356-023-25685-3.
  • Zhu, L., W. Sun, J. Wu, and D. Fan. 2023. “Spatiotemporal Distribution of Carbon Sink Indicators—NPP and Its Driving Analysis in Ordos City, China.” Applied Sciences 13 (11): 6457. https://doi.org/10.3390/app13116457
  • Zhu, L., W. Sun, Q. Zhang, C. Wang, and Z. Guo. 2023. “Fine-grained Agricultural and Pastoral Information Extraction Using Sentinel-1 and Sentinel-2 Intra-Year Time Series in Jingyang District, Deyang City.” Advances in Space Research 72 (9): 4031–4047. https://doi.org/10.1016/j.asr.2023.07.061.
  • Zhu, Q., Y. Wang, J. Liu, X. Li, H. Pan, and M. Jia. 2021. “Tracking Historical Wetland Changes in the China Side of the Amur River Basin Based on Landsat Imagery and Training Samples Migration.” Remote Sensing 13 (11). https://doi.org/10.3390/rs13112161.
  • Zhu, L., H. Xing, and D. Hou. 2022. “Analysis of carbon emissions from land cover change during 2000 to 2020 in Shandong Province, China.” Scientific Reports 12 (1): 1–12. https://doi.org/10.1038/s41598-021-99269-x.
  • Zhu, L., H. Xing, L. Zhao, H. Qu, and W. Sun. 2023. “A Change Type Determination Method Based on Knowledge of Spectral Changes in Land Cover Types.” Earth Science Informatics 16: 1265–1279. https://doi.org/10.1007/s12145-023-00968-y
  • Zhu, Z., J. Zhang, Z. Yang, A. H. Aljaddani, W. B. Cohen, S. Qiu, and C. Zhou. 2020. “Continuous monitoring of land disturbance based on Landsat time series.” Remote Sensing of Environment 238 (March): 111116. https://doi.org/10.1016/j.rse.2019.03.009.