Advanced search
Publication Cover
GIScience & Remote Sensing Volume 61, 2024 - Issue 1
Submit an article Journal homepage
313
Views
0
CrossRef citations to date
0
Altmetric
Method

Automatic training sample collection utilizing multi-source land cover products and time-series Sentinel-2 images

Yanzhao Wanga Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing, China;b College of Resources Environment and Tourism, Capital Normal University, Beijing, China;c State Key Laboratory of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing, China;d Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Beijing, ChinaORCID Iconhttps://orcid.org/0000-0002-3975-5449View further author information
ORCID Icon,
Yonghua Suna Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing, China;b College of Resources Environment and Tourism, Capital Normal University, Beijing, China;c State Key Laboratory of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing, China;d Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Beijing, ChinaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0001-8582-3062View further author information
ORCID Icon,
Xuyue Caoa Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing, China;b College of Resources Environment and Tourism, Capital Normal University, Beijing, China;c State Key Laboratory of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing, China;d Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Beijing, ChinaView further author information
,
Yihan Wanga Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing, China;b College of Resources Environment and Tourism, Capital Normal University, Beijing, China;c State Key Laboratory of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing, China;d Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Beijing, ChinaView further author information
,
Wangkuan Zhanga Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing, China;b College of Resources Environment and Tourism, Capital Normal University, Beijing, China;c State Key Laboratory of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing, China;d Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Beijing, ChinaView further author information
,
Xinglu Chenga Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing, China;b College of Resources Environment and Tourism, Capital Normal University, Beijing, China;c State Key Laboratory of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing, China;d Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Beijing, ChinaView further author information
,
Ruozeng Wanga Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing, China;b College of Resources Environment and Tourism, Capital Normal University, Beijing, China;c State Key Laboratory of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing, China;d Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Beijing, ChinaView further author information
&
Jinkun Zonga Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing, China;b College of Resources Environment and Tourism, Capital Normal University, Beijing, China;c State Key Laboratory of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing, China;d Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Beijing, ChinaView further author information
 show all
Article: 2352957 | Received 29 Nov 2023, Accepted 06 May 2024, Published online: 14 May 2024

References

  • Aggarwal, C. C., and S. Sathe. 2015. “Theoretical Foundations and Algorithms for Outlier Ensembles.” ACM SIGKDD Explorations Newsletter 17 (1): 24–25. https://doi.org/10.1145/2830544.2830549.
  • Baig, M. F., M. R. U. Mustafa, I. Baig, H. B. Takaijudin, and M. T. Zeshan. 2022. “Assessment of Land Use Land Cover Changes and Future Predictions Using CA-ANN Simulation for Selangor, Malaysia.” Water 14 (3): 402. https://doi.org/10.3390/w14030402.
  • Bontemps, S., M. Herold, L. Kooistra, A. Van Groenestijn, A. Hartley, O. Arino, I. Moreau, and P. Defourny. 2012. “Revisiting Land Cover Observation to Address the Needs of the Climate Modeling Community.” Biogeosciences 9 (6): 2145–2157. https://doi.org/10.5194/bg-9-2145-2012.
  • Breunig, M. M., H.-P. Kriegel, R. T. Ng, and J. Sander. 2000. “LOF: Identifying Density-Based Local Outliers.” Proceedings of the 2000 ACM SIGMOD international conference on Management of data, Dallas, Texas, USA, 93–104.
  • Britto, A. S., Jr, R. Sabourin, and L. E. Oliveira. 2014. “Dynamic Selection of Classifiers—A Comprehensive Review.” Pattern Recognition 47 (11): 3665–3680. https://doi.org/10.1016/j.patcog.2014.05.003.
  • Brown, C. F., S. P. Brumby, B. Guzder-Williams, T. Birch, S. B. Hyde, J. Mazzariello, W. Czerwinski, et al. 2022. “Dynamic World, Near Real-Time Global 10 M Land Use Land Cover Mapping.” Scientific Data 9 (1). https://doi.org/10.1038/s41597-022-01307-4.
  • Calderón-Loor, M., M. Hadjikakou, and B. A. Bryan. 2021. “High-Resolution Wall-To-Wall Land-Cover Mapping and Land Change Assessment for Australia from 1985 to 2015.” Remote Sensing of Environment 252:112148. https://doi.org/10.1016/j.rse.2020.112148.
  • Cao, Q., D. Yu, M. Georgescu, Z. Han, and J. Wu. 2015. “Impacts of Land Use and Land Cover Change on Regional Climate: A Case Study in the Agro-Pastoral Transitional Zone of China.” Environmental Research Letters 10 (12): 124025. https://doi.org/10.1088/1748-9326/10/12/124025.
  • Chandola, V., A. Banerjee, and V. Kumar. 2009. “Anomaly Detection: A Survey.” ACM Computing Surveys (CSUR) 41 (3): 1–58. https://doi.org/10.1145/1541880.1541882.
  • Chen, J., S. Li, H. Wu, and X. Chen. 2017. “Towards a Collaborative Global Land Cover Information Service.” International Journal of Digital Earth 10 (4): 356–370. https://doi.org/10.1080/17538947.2016.1267268.
  • Chen, Z., B. Yu, Y. Zhou, H. Liu, C. Yang, K. Shi, and J. Wu. 2019. “Mapping Global Urban Areas from 2000 to 2012 Using Time-Series Nighttime Light Data and MODIS Products.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (4): 1143–1153. https://doi.org/10.1109/JSTARS.2019.2900457.
  • Colditz, R. R., M. Schmidt, C. Conrad, M. C. Hansen, and S. Dech. 2011. “Land Cover Classification with Coarse Spatial Resolution Data to Derive Continuous and Discrete Maps for Complex Regions.” Remote Sensing of Environment 115 (12): 3264–3275. https://doi.org/10.1016/j.rse.2011.07.010.
  • Congalton, R. G., J. Gu, K. Yadav, P. Thenkabail, and M. Ozdogan. 2014. “Global Land Cover Mapping: A Review and Uncertainty Analysis.” Remote Sensing 6 (12): 12070–12093. https://doi.org/10.3390/rs61212070.
  • De Maesschalck, R., D. Jouan-Rimbaud, and D. L. Massart. 2000. “The Mahalanobis Distance.” Chemometrics and Intelligent Laboratory Systems 50 (1): 1–18. https://doi.org/10.1016/S0169-7439(99)00047-7.
  • Diek, S., F. Fornallaz, M. E. Schaepman, and R. de Jong. 2017. “Barest Pixel Composite for Agricultural Areas Using Landsat Time Series.” Remote Sensing 9 (12): 1245. https://doi.org/10.3390/rs9121245.
  • Di Gregorio, A., and L. J. M. Jansen. 2000. Land Cover Classification System(ICCS): Classification Concepts and User Manual. Rome: Food and Agriculture Organization of the United Nations.
  • Drusch, M., U. Del Bello, S. Carlier, O. Colin, V. Fernandez, F. Gascon, B. Hoersch, et al. 2012. “Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services.” Remote Sensing of Environment 120:25–36. https://doi.org/10.1016/j.rse.2011.11.026.
  • Estabrooks, A., T. Jo, and N. Japkowicz. 2004. “A Multiple Resampling Method for Learning from Imbalanced Data Sets.” Computational Intelligence 20 (1): 18–36. https://doi.org/10.1111/j.0824-7935.2004.t01-1-00228.x.
  • Foody, G. M., and A. Mathur. 2006. “The Use of Small Training Sets Containing Mixed Pixels for Accurate Hard Image Classification: Training on Mixed Spectral Responses for Classification by a SVM.” Remote Sensing of Environment 103 (2): 179–189. https://doi.org/10.1016/j.rse.2006.04.001.
  • Freeman, E. A., G. G. Moisen, and T. S. Frescino. 2012. “Evaluating Effectiveness of Down-Sampling for Stratified Designs and Unbalanced Prevalence in Random Forest Models of Tree Species Distributions in Nevada.” Ecological Modelling 233:1–10. https://doi.org/10.1016/j.ecolmodel.2012.03.007.
  • Friedl, M. A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, and X. Huang. 2010. “MODIS Collection 5 Global Land Cover: Algorithm Refinements and Characterization of New Datasets.” Remote Sensing of Environment 114 (1): 168–182. https://doi.org/10.1016/j.rse.2009.08.016.
  • Gao, B. C. 1996. “NDWI—A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space.” Remote Sensing of Environment 58 (3): 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3.
  • Giri, C., B. Pengra, J. Long, and T. R. Loveland. 2013. “Next Generation of Global Land Cover Characterization, Mapping, and Monitoring.” International Journal of Applied Earth Observation and Geoinformation 25:30–37. https://doi.org/10.1016/j.jag.2013.03.005.
  • Gong, P., J. Wang, L. Yu, Y. Zhao, Y. Zhao, L. Liang, Z. Niu, X. Huang, H. Fu, and S. Liu. 2013. “Finer Resolution Observation and Monitoring of Global Land Cover: First Mapping Results with Landsat TM and ETM+ Data.” International Journal of Remote Sensing 34 (7): 2607–2654. https://doi.org/10.1080/01431161.2012.748992.
  • Han, S., X. Hu, H. Huang, M. Jiang, and Y. Zhao. 2022. “Adbench: Anomaly Detection Benchmark.” Advances in Neural Information Processing Systems 35:32142–32159. https://doi.org/10.2139/ssrn.4266498.
  • Hansen, M. C., and B. Reed. 2000. “A Comparison of the IGBP DISCover and University of Maryland 1 Km Global Land Cover Products.” International Journal of Remote Sensing 21 (6–7): 1365–1373. https://doi.org/10.1080/014311600210218.
  • Hao, X., Y. Qiu, G. Jia, M. Menenti, J. Ma, and Z. Jiang. 2023. “Evaluation of Global Land Use–Land Cover Data Products in Guangxi, China.” Remote Sensing 15 (5): 1291. https://doi.org/10.3390/rs15051291.
  • Hermosilla, T., M. A. Wulder, J. C. White, and N. C. Coops. 2022. “Land Cover Classification in an Era of Big and Open Data: Optimizing Localized Implementation and Training Data Selection to Improve Mapping Outcomes.” Remote Sensing of Environment 268:112780. https://doi.org/10.1016/j.rse.2021.112780.
  • 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 & Remote Sensing 146:347–359. https://doi.org/10.1016/j.isprsjprs.2018.10.008.
  • Huete, A. R., H. Q. Liu, K. Batchily, and W. vanLeeuwen. 1997. “A Comparison of Vegetation Indices Global Set of TM Images for EOS-MODIS.” Remote Sensing of Environment 59 (3): 440–451. https://doi.org/10.1016/S0034-4257(96)00112-5.
  • Jin, Q., E. Xu, and X. Zhang. 2022. “A Fusion Method for Multi-Source Land Cover Products Based on Superpixels and Statistical Extraction for Enhancing Resolution and Improving Accuracy.” Remote Sensing 14 (7): 1676. https://doi.org/10.3390/rs14071676.
  • Kang, J., X. Yang, Z. Wang, H. Cheng, J. Wang, H. Tang, Y. Li, Z. Bian, and Z. Bai. 2022. “Comparison of Three ten Meter Land Cover Products in a Drought Region: A Case Study in Northwestern China.” The Land 11 (3): 427. https://doi.org/10.3390/land11030427.
  • Karra, K., C. Kontgis, Z. Statman-Weil, J.C. Mazzariello, M. Mathis, and S.P. Brumby. 2021. “Global Land Use/Land Cover With Sentinel 2 and Deep Learning.” In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 4704–4707.
  • Kayet, N., K. Pathak, A. Chakrabarty, and S. Sahoo. 2016. “Spatial Impact of Land Use/Land Cover Change on Surface Temperature Distribution in Saranda Forest, Jharkhand.” Modeling Earth Systems and Environment 2 (3): 1–10. https://doi.org/10.1007/s40808-016-0159-x.
  • Kim, H., H. Kim, H. Moon, and H. Ahn. 2011. “A Weight-Adjusted Voting Algorithm for Ensembles of Classifiers.” Journal of the Korean Statistical Society 40 (4): 437–449. https://doi.org/10.1016/j.jkss.2011.03.002.
  • Kim, D., S. Lee, and J. Lee. 2020. “An Ensemble-Based Approach to Anomaly Detection in Marine Engine Sensor Streams for Efficient Condition Monitoring and Analysis.” Sensors 20 (24): 7285. https://doi.org/10.3390/s20247285.
  • Kinoshita, T., K. Iwao, and Y. Yamagata. 2014. “Creation of a Global Land Cover and a Probability Map Through a New Map Integration Method.” International Journal of Applied Earth Observation and Geoinformation 28:70–77. https://doi.org/10.1016/j.jag.2013.10.006.
  • Li, M., B. Chen, C. Webster, P. Gong, and B. Xu. 2022. “The Land-Sea Interface Mapping: China’s Coastal Land Covers at 10 M for 2020.” Science Bulletin 67 (17): 1750–1754. https://doi.org/10.1016/j.scib.2022.07.012.
  • Li, J., J. Li, C. Wang, F. J. Verbeek, T. Schultz, and H. Liu. 2023. “Outlier Detection Using Iterative Adaptive Mini-Minimum Spanning Tree Generation with Applications on Medical Data.” Frontiers in Physiology 14. https://doi.org/10.3389/fphys.2023.1233341.
  • Ling, C. X., and C. Li. 1998. “Data Mining for Direct Marketing: Problems and Solutions.” Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, New York, USA, 73–79.
  • Liu, X., X. Li, and X. Zhang. 2010. “Determining Class Proportions within a Pixel Using a New Mixed-Label Analysis Method.” IEEE Transactions on Geoscience and Remote Sensing 48 (4): 1882–1891. https://doi.org/10.1109/TGRS.2009.2033178.
  • Liu, L., X. Zhang, Y. Gao, X. Chen, X. Shuai, and J. Mi. 2021. “Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects.” Journal of Remote Sensing 2021. https://doi.org/10.34133/2021/5289697.
  • Liu, L., T. Zhao, and X. Zhang. 2023. “A Novel Stratified Random Sampling Global Validation Dataset in 2020—Srs_val Dataset.” https://doi.org/10.5281/zenodo.7724983.
  • Li, Y., Q. Zhang, X. Liu, Z. Tan, and J. Yao. 2019. “The Role of a Seasonal Lake Groups in the Complex Poyang Lake-Floodplain System (China): Insights into Hydrological Behaviors.” Journal of Hydrology 578:124055. https://doi.org/10.1016/j.jhydrol.2019.124055.
  • Loveland, T. R., and Belward, A. S. 1997. “The IGBP-DIS Global 1km Land Cover Data Set, DISCover: First Results.” International Journal of Remote Sensing 18 (15): 3289–3295. https://doi.org/10.1080/014311697217099.
  • Loveland, T. R., B. C. Reed, J. F. Brown, D. O. Ohlen, Z. Zhu, L. Yang, and J. W. Merchant. 2000. “Development of a Global Land Cover Characteristics Database and IGBP DISCover from 1 Km AVHRR Data.” International Journal of Remote Sensing 21 (6–7): 1303–1330. https://doi.org/10.1080/014311600210191.
  • Ma, Y., J. Niu, B. Xu, X. Song, W. Huang, and G. Sun. “Identification Method for Users-Transformer Relationship in Station Area Based on Local Selective Combination in Parallel Outlier Ensembles Algorithm.”
  • Mellor, A., S. Boukir, A. Haywood, and S. Jones. 2015. “Exploring Issues of Training Data Imbalance and Mislabelling on Random Forest Performance for Large Area Land Cover Classification Using the Ensemble Margin.” ISPRS Journal of Photogrammetry and Remote Sensing 105:155–168. https://doi.org/10.1016/j.isprsjprs.2015.03.014.
  • Nassif, A. B., M. A. Talib, Q. Nasir, and F. M. Dakalbab. 2021. “Machine Learning for Anomaly Detection: A Systematic Review.” IEEE Access 9:78658–78700. https://doi.org/10.1109/ACCESS.2021.3083060.
  • Ouyang, B., Y. Song, Y. Li, G. Sant, and M. Bauchy. 2021. “EBOD: An Ensemble-Based Outlier Detection Algorithm for Noisy Datasets.” Knowledge-Based Systems 231:107400. https://doi.org/10.1016/j.knosys.2021.107400.
  • Pelletier, C., S. Valero, J. Inglada, N. Champion, C. Marais Sicre, and G. Dedieu. 2017. “Effect of Training Class Label Noise on Classification Performances for Land Cover Mapping with Satellite Image Time Series.” Remote Sensing 9 (2): 173. https://doi.org/10.3390/rs9020173.
  • Pérez-Hoyos, A., F. J. García-Haro, and J. San-Miguel-Ayanz. 2012. “A Methodology to Generate a Synergetic Land-Cover Map by Fusion of Different Land-Cover Products.” International Journal of Applied Earth Observation and Geoinformation 19:72–87. https://doi.org/10.1016/j.jag.2012.04.011.
  • Phiri, D., M. Simwanda, S. Salekin, V. R. Nyirenda, Y. Murayama, and M. Ranagalage. 2020. “Sentinel-2 Data for Land Cover/Use Mapping: A Review.” Remote Sensing 12 (14): 2291. https://doi.org/10.3390/rs12142291.
  • Proverbio, M., N. J. Bertola, and I. F. C. Smith. 2018. “Outlier-Detection Methodology for Structural Identification Using Sparse Static Measurements.” Sensors 18 (6): 1702. https://doi.org/10.3390/s18061702.
  • Radoux, J., C. Lamarche, E. Van Bogaert, S. Bontemps, C. Brockmann, and P. Defourny. 2014. “Automated Training Sample Extraction for Global Land Cover Mapping.” Remote Sensing 6 (5): 3965–3987. https://doi.org/10.3390/rs6053965.
  • Ran, Y., X. Li, L. Lu, and Z. Li. 2012. “Large-Scale Land Cover Mapping with the Integration of Multi-Source Information Based on the Dempster–Shafer Theory.” International Journal of Geographical Information Science 26 (1): 169–191. https://doi.org/10.1080/13658816.2011.577745.
  • Ren, K., H. Yang, Y. Zhao, W. Chen, M. Xue, H. Miao, S. Huang, and J. Liu. 2019. “A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification.” IEEE Transactions on Neural Networks and Learning Systems 30 (10): 3072–3083. https://doi.org/10.1109/TNNLS.2018.2870666.
  • Rodriguez-Galiano, V. F., B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez. 2012. “An Assessment of the Effectiveness of a Random Forest Classifier for Land-Cover Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 67:93–104. https://doi.org/10.1016/j.isprsjprs.2011.11.002.
  • Schepaschenko, D., I. McCallum, A. Shvidenko, S.Fritz, F. Kraxner, and M. Obersteiner. 2011. “A New Hybrid Land Cover Dataset for Russia: a Methodology for Integrating Statistics, Remote Sensing and in situ Information.” Journal of Land Use Science 6 (4): 245–259. https://doi.org/10.1080/1747423X.2010.511681.
  • Schubert, E., R. Wojdanowski, A. Zimek, and H.-P. Kriegel. 2012. “On Evaluation of Outlier Rankings and Outlier Scores.” Proceedings of the 2012 SIAM International Conference on Data Mining (SDM), Anaheim, California, USA, 1047–1058.
  • Shirahata, L. M., K. Iizuka, A. Yusupujiang, F. R. Rinawan, R. Bhattarai, and X. Dong. 2017. “Production of Global Land Cover Data–GLCNMO2013.” Journal of Geography & Geology 9. https://doi.org/10.5539/jgg.v9n3p1.
  • Tucker, C. J. 1979. “RED and PHOTOGRAPHIC INFRARED LINEAR COMBINATIONS for MONITORING VEGETATION.” Remote Sensing of Environment 8 (2): 127–150. https://doi.org/10.1016/0034-4257(79)90013-0.
  • Venter, Z. S., D. N. Barton, T. Chakraborty, T. Simensen, and G. Singh. 2022. “Global 10 M Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover.” Remote Sensing 14 (16): 4101. https://doi.org/10.3390/rs14164101.
  • Wang, Z., and G. Mountrakis. 2023. “Accuracy Assessment of Eleven Medium Resolution Global and Regional Land Cover Land Use Products: A Case Study Over the Conterminous United States.” Remote Sensing 15 (12): 3186. https://doi.org/10.3390/rs15123186.
  • Wang, J., X. Yang, Z. Wang, H. Cheng, J. Kang, H. Tang, Y. Li, Z. Bian, and Z. Bai. 2022. “Consistency Analysis and Accuracy Assessment of Three Global Ten-Meter Land Cover Products in Rocky Desertification Region—A Case Study of Southwest China.” ISPRS International Journal of Geo-Information 11 (3): 202. https://doi.org/10.3390/ijgi11030202.
  • Wang, H., H. Yan, Y. Hu, Y. Xi, and Y. Yang. 2022. “Consistency and Accuracy of Four High-Resolution LULC Datasets—Indochina Peninsula Case Study.” The Land 11 (5): 758. https://doi.org/10.3390/land11050758.
  • Wen, Y., X. Li, H. Mu, L. Zhong, H. Chen, Y. Zeng, S. Miao, et al. 2022. “Mapping Corn Dynamics Using Limited but Representative Samples with Adaptive Strategies.” Isprs Journal of Photogrammetry and Remote Sensing 190:252–266. https://doi.org/10.1016/j.isprsjprs.2022.06.012.
  • Yang, J., and X. Huang. 2021. “The 30 M Annual Land Cover Dataset and Its Dynamics in China from 1990 to 2019.” Earth System Science Data 13 (8): 3907–3925. https://doi.org/10.5194/essd-13-3907-2021.
  • Yang, H., S. Li, J. Chen, X. Zhang, and S. Xu. 2017. “The Standardization and Harmonization of Land Cover Classification Systems Towards Harmonized Datasets: A Review.” Isprs International Journal of Geo-Information 6 (5): 154. https://doi.org/10.3390/ijgi6050154.
  • Yin, Y., R. Xia, Y. Chen, R. Jia, N. Zhong, C. Yan, Q. Hu, X. Li, and H. Zhang. 2023. “Non-Steady State Fluctuations in Water Levels Exacerbate Long-Term and Seasonal Degradation of Water Quality in River-Connected Lakes.” Water Research 242:120247. https://doi.org/10.1016/j.watres.2023.120247.
  • Zanaga, D., R. Van De Kerchove, W. De Keersmaecker, N. Souverijns, C. Brockmann, R. Quast, J. Wevers, et al. 2021. “ESA WorldCover 10 m 2020 v100.” https://doi.org/10.5281/zenodo.5571936.
  • Zha, Y., J. Gao, and S. Ni. 2003. “Use of Normalized Difference Built-Up Index in Automatically Mapping Urban Areas from TM Imagery.” International Journal of Remote Sensing 24 (3): 583–594. https://doi.org/10.1080/01431160304987.
  • Zhang, J., Z. Li, K. Nai, Y. Gu, and A. Sallam. 2019. “DELR: A Double-Level Ensemble Learning Method for Unsupervised Anomaly Detection.” Knowledge-Based Systems 181:104783. https://doi.org/10.1016/j.knosys.2019.05.026.
  • Zhang, X., L. Liu, X. Chen, Y. Gao, S. Xie, and J. Mi. 2021. “GLC_FCS30: Global Land-Cover Product with Fine Classification System at 30m Using Time-Series Landsat Imagery.” Earth System Science Data 13 (6): 2753–2776. https://doi.org/10.5194/essd-13-2753-2021.
  • Zhang, H. K., and D. P. Roy. 2017. “Using the 500 M MODIS Land Cover Product to Derive a Consistent Continental Scale 30 M Landsat Land Cover Classification.” Remote Sensing of Environment 197:15–34. https://doi.org/10.1016/j.rse.2017.05.024.
  • Zhang, C., H. Zhang, and S. Tian. 2023. “Phenology-Assisted Supervised Paddy Rice Mapping with the Landsat Imagery on Google Earth Engine: Experiments in Heilongjiang Province of China from 1990 to 2020.” Computers and Electronics in Agriculture 212:108105. https://doi.org/10.1016/j.compag.2023.108105.
  • Zhao, Y., Z. Nasrullah, M. K. Hryniewicki, and Z. Li. 2019. “LSCP: Locally Selective Combination in Parallel Outlier Ensembles.” Proceedings of the 2019 SIAM International Conference on Data Mining(SDM), Calgary, Alberta, Canada, 585–593.
  • Zhao, T., X. Zhang, Y. Gao, J. Mi, W. Liu, J. Wang, M. Jiang, and L. Liu. 2023. “Assessing the Accuracy and Consistency of Six Fine-Resolution Global Land Cover Products Using a Novel Stratified Random Sampling Validation Dataset.” Remote Sensing 15 (9): 2285. https://doi.org/10.3390/rs15092285.
  • Zhu, L., G. Jin, X. Zhang, R. Shi, Y. La, and C. Li. 2021. “Integrating Global Land Cover Products to Refine GlobeLand30 Forest Types: A Case Study of Conterminous United States (CONUS).” International Journal of Remote Sensing 42 (6): 2105–2130. https://doi.org/10.1080/01431161.2020.1851797.
  • Zimek, A., R. J. Campello, and J. Sander. 2014. “Ensembles for Unsupervised Outlier Detection: Challenges and Research Questions a Position Paper.” ACM SIGKDD Explorations Newsletter 15 (1): 11–22. https://doi.org/10.1145/2594473.2594476.
  • Zuo, Z., Z. Li, P. Cheng, and J. Zhao. 2023. “A Novel Subspace Outlier Detection Method by Entropy-Based Clustering Algorithm.” Scientific Reports 13 (1). https://doi.org/10.1038/s41598-023-42261-4.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.