Bản đồ Sử dụng Đất đai và Lớp phủ Đất dựa trên Hình ảnh Vệ tinh Sentinel-2, Landsat-8 và Google Earth Engine: So sánh hai phương pháp ghép hỗn hợp

Esfandeh, S., Danehkar, A., Salmanmahiny, A., Sadeghi, S.M.M., and Marcu, M.V. (2021). Climate Change Risk of Urban Growth and Land Use/Land Cover Conversion: An In-Depth Review of the Recent Research in Iran. Sustainability, 14.

Yao, 2022, Classifying Land-Use Patterns by Integrating Time-Series Electricity Data and High-Spatial Resolution Remote Sensing Imagery, Int. J. Appl. Earth Observ. Geoinf., 106, 102664

Qian, 2022, An Integration Method to Improve the Quality of Global Land Cover, Adv. Space Res., 69, 1427, 10.1016/j.asr.2021.11.002

Schulz, 2021, Land Use Mapping Using Sentinel-1 and Sentinel-2 Time Series in a Heterogeneous Landscape in Niger, Sahel, ISPRS J. Photogramm. Remote Sens., 178, 97, 10.1016/j.isprsjprs.2021.06.005

Viana, C.M., Girão, I., and Rocha, J. (2019). Long-Term Satellite Image Time-series for land use/land cover change detection using refined open source data in a rural region. Remote Sens., 11.

Praticò, S., Solano, F., Di Fazio, S., and Modica, G. (2021). Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation. Remote Sens., 13.

Sobhani, P., Esmaeilzadeh, H., Barghjelveh, S., Sadeghi, S.M.M., and Marcu, M.V. (2021). Habitat Integrity in Protected Areas Threatened by LULC Changes and Fragmentation: A Case Study in Tehran Province, Iran. Land, 11.

Steinhausen, 2018, Combining Sentinel-1 and Sentinel-2 Data for Improved Land Use and Land Cover Mapping of Monsoon Regions, Int. J. Appl. Earth Observ. Geoinf., 73, 595

Kupidura, P. (2019). The Comparison of Different Methods of Texture Analysis for Their Efficacy for Land Use Classification in Satellite Imagery. Remote Sens., 11.

Wang, 2022, Mapping Mangrove Species Using Combined UAV-LiDAR and Sentinel-2 Data: Feature Selection and Point Density Effects, Adv. Space Res., 69, 1494, 10.1016/j.asr.2021.11.020

Ienco, 2019, Combining Sentinel-1 and Sentinel-2 Satellite Image Time Series for Land Cover Mapping Via a Multi-Source Deep Learning Architecture, ISPRS J. Photogramm. Remote Sens., 158, 11, 10.1016/j.isprsjprs.2019.09.016

Sun, B., Zhang, Y., Zhou, Q., and Zhang, X. (2022). Effectiveness of Semi-Supervised Learning and Multi-Source Data in Detailed Urban Landuse Mapping with a Few Labeled Samples. Remote Sens., 14.

Clerici, 2017, Fusion of Sentinel-1A and Sentinel-2A Data for Land Cover Mapping: A Case Study in the Lower Magdalena Region, Colombia, J. Maps, 13, 718, 10.1080/17445647.2017.1372316

Sudmanns, 2020, Assessing Global Sentinel-2 Coverage Dynamics and Data Availability for Operational Earth Observation (EO) Applications Using the EO-Compass, Int. J. Digital Earth, 13, 768, 10.1080/17538947.2019.1572799

Wulder, 2016, The Global Landsat Archive: Status, Consolidation, and Direction, Remote Sens. Environ., 185, 271, 10.1016/j.rse.2015.11.032

Gorelick, 2017, Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone, Remote Sens. Environ., 202, 18, 10.1016/j.rse.2017.06.031

Guo, J., Huang, C., and Hou, J. (2022). A Scalable Computing Resources System for Remote Sensing Big Data Processing Using GeoPySpark Based on Spark on K8s. Remote Sens., 14.

Kempeneers, P., Kliment, T., Marletta, L., and Soille, P. (2022). Parallel Processing Strategies for Geospatial Data in a Cloud Computing Infrastructure. Remote Sens., 14.

Cooper, 2021, Combining Simulated Hyperspectral EnMAP and Landsat Time Series for Forest Aboveground Biomass Mapping, Int. J. Appl. Earth Observ. Geoinf., 98, 102307

Xu, 2022, Time Series Analysis for Global Land Cover Change Monitoring: A Comparison Across Sensors, Remote Sens. Environ., 271, 112905, 10.1016/j.rse.2022.112905

Lary, 2016, Machine Learning in Geosciences and Remote Sensing, Geosci. Front., 7, 3, 10.1016/j.gsf.2015.07.003

Maxwell, 2018, Implementation of Machine-Learning Classification in Remote Sensing: An Applied Review, Int. J. Remote Sens., 39, 2784, 10.1080/01431161.2018.1433343

Kwan, C., Ayhan, B., Budavari, B., Lu, Y., Perez, D., Li, J., Bernabe, S., and Plaza, A. (2020). Deep Learning for Land Cover Classification Using Only a Few Bands. Remote Sens., 12.

Naushad, R., Kaur, T., and Ghaderpour, E. (2021). Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study. Sensors, 21.

Sefrin, O., Riese, F.M., and Keller, S. (2020). Deep Learning for Land Cover Change Detection. Remote Sens., 13.

Frantz, D. (2019). FORCE—Landsat+ Sentinel-2 Analysis Ready Data and Beyond. Remote Sens., 11.

Tassi, A., and Vizzari, M. (2020). Object-Oriented LULC Classification in Google Earth Engine Combining SNICc, GLCM, and Machine Learning Algorithms. Remote Sens., 12.

Kumar, L., and Mutanga, O. (2018). Google Earth Engine Applications Since Inception: Usage, Trends, and Potential. Remote Sens., 10.

Carrasco, L., O’Neil, A.W., Morton, R.D., and Rowland, C.S. (2019). Evaluating Combinations of Temporally Aggregated Sentinel-1, Sentinel-2 and Landsat 8 for Land Cover Mapping with Google Earth Engine. Remote Sens., 11.

Htitiou, A., Boudhar, A., Chehbouni, A., and Benabdelouahab, T. (2021). National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine. Remote Sens., 13.

Liang, S., Gong, Z., Wang, Y., Zhao, J., and Zhao, W. (2022). Accurate Monitoring of Submerged Aquatic Vegetation in a Macrophytic Lake Using Time-Series Sentinel-2 Images. Remote Sens., 14.

Zhou, 2022, A Novel Regression Method for Harmonic Analysis of Time Series, ISPRS J. Photogramm. Remote Sens., 185, 48, 10.1016/j.isprsjprs.2022.01.006

Luo, 2022, Regional Soil Organic Matter Mapping Models Based on the Optimal Time Window, Feature Selection Algorithm and Google Earth Engine, Soil Tillage Res., 219, 105325, 10.1016/j.still.2022.105325

Okujeni, 2018, Generalizing Machine Learning Regression Models Using Multi-Site Spectral Libraries for Mapping Vegetation-Impervious-Soil Fractions Across Multiple Cities, Remote Sens. Environ., 216, 482, 10.1016/j.rse.2018.07.011

Kowalski, 2022, Quantifying Drought Effects in Central European Grasslands Through Regression-Based Unmixing of Intra-Annual Sentinel-2 Time Series, Remote Sens. Environ., 268, 112781, 10.1016/j.rse.2021.112781

Liu, 2020, Mapping Cropping Intensity in China Using Time Series Landsat and Sentinel-2 Images and Google Earth Engine, Remote Sens. Environ., 239, 111624, 10.1016/j.rse.2019.111624

Hermosilla, 2018, Disturbance-Informed Annual Land Cover Classification Maps of Canada’s Forested Ecosystems for a 29-Year Landsat Time Series, Can. J. Remote Sens., 44, 67, 10.1080/07038992.2018.1437719

Yuan, 2022, SITS-Former: A Pre-Trained Spatio-Spectral-Temporal Representation Model for Sentinel-2 Time Series Classification, Int. J. Appl. Earth Observ. Geoinf., 106, 102651

Pflugmacher, 2019, Mapping Pan-European Land Cover Using Landsat Spectral-Temporal Metrics and the European LUCAS Survey, Remote Sens. Environ., 221, 583, 10.1016/j.rse.2018.12.001

White, 2022, Mapping, Validating, and Interpreting Spatio-Temporal Trends in Post-Disturbance Forest Recovery, Remote Sens. Environ., 271, 112904, 10.1016/j.rse.2022.112904

Salinero-Delgado, M., Estévez, J., Pipia, L., Belda, S., Berger, K., Paredes Gómez, V., and Verrelst, J. (2021). Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression. Remote Sens., 14.

Azzari, 2017, Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring, Remote Sens. Environ., 202, 6474, 10.1016/j.rse.2017.05.025

Hemmerling, 2021, Mapping Temperate Forest Tree Species Using Dense Sentinel-2 Time Series, Remote Sens. Environ., 267, 112743, 10.1016/j.rse.2021.112743

Xie, S., Liu, L., Zhang, X., Yang, J., Chen, X., and Gao, Y. (2019). Automatic Land-Cover Mapping Using Landsat Time-Series Data Based on Google Earth Engine. Remote Sens., 11.

Lan, H., Stewart, K., Sha, Z., Xie, Y., and Chang, S. (2022). Data Gap Filling Using Cloud-Based Distributed Markov Chain Cellular Automata Framework for Land Use and Land Cover Change Analysis: Inner Mongolia as a Case Study. Remote Sens., 14.

Stromann, O., Nascetti, A., Yousif, O., and Ban, Y. (2019). Dimensionality Reduction and Feature Selection for Object-Based Land Cover Classification Based on Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine. Remote Sens., 12.

Masolele, 2021, Spatial and Temporal Deep Learning Methods for Deriving Land-Use Following Deforestation: A Pan-Tropical Case Study Using Landsat Time Series, Remote Sens. Environ., 264, 112600, 10.1016/j.rse.2021.112600

Santos, L.A., Ferreira, K., Picoli, M., Camara, G., Zurita-Milla, R., and Augustijn, E.-W. (2021). Identifying Spatiotemporal Patterns in Land Use and Cover Samples from Satellite Image Time Series. Remote Sens., 13.

Thonfeld, F., Steinbach, S., Muro, J., and Kirimi, F. (2020). Long-Term Land Use/Land Cover Change Assessment of the Kilombero Catchment in Tanzania Using Random Forest Classification and Robust Change Vector Analysis. Remote Sens., 12.

Daei, 2021, The Effectiveness of Urban Trees in Reducing Airborne Particulate Matter by Dry Deposition in Tehran, Iran, Env. Mon. Assess., 193, 842, 10.1007/s10661-021-09616-8

Hidalgo, 2021, Dimensionality Reduction of Hyperspectral Images of Vegetation and Crops Based on Self-Organized Maps, Inf. Process. Agricul., 8, 310

Tu, 1998, A Fast Two-stage Classification Method for High-Dimensional Remote Sensing Data, IEEE Trans. Geosci. Remote Sens., 36, 182, 10.1109/36.655328

McCarty, D.A., Kim, H.W., and Lee, H.K. (2020). Evaluation of Light Gradient Boosted Machine Learning Technique in Large Scale Land Use and Land Cover Classification. Environments, 7.

Li, 2020, Integrating Google Earth Imagery with Landsat Data to Improve 30-m Resolution Land Cover Mapping, Remote Sens. Environ., 237, 111563, 10.1016/j.rse.2019.111563

Yang, 2017, Open Land-Use Map: A Regional Land-Use Mapping Strategy for Incorporating OpenStreetMap with Earth Observations, Geo-Spatial Inf. Sci., 20, 269, 10.1080/10095020.2017.1371385

Colditz, 2015, An Evaluation of Different Training Sample Allocation Schemes for Discrete and Continuous Land Cover Classification Using Decision Tree-Based Algorithms, Remote Sens., 7, 9655, 10.3390/rs70809655

Thanh Noi, P., and Kappas, M. (2017). Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for and Cover Classification Using Sentinel-2 Imagery. Sensors, 18.

Li, 2022, An Automatic Cloud Detection Model for Sentinel-2 Imagery Based on Google Earth Engine, Remote Sens. Lett., 13, 196, 10.1080/2150704X.2021.1988753

Tsai, Y.H., Stow, D., Chen, H.L., Lewison, R., An, L., and Shi, L. (2018). Mapping Vegetation and Land Use Types in Fanjingshan National Nature Reserve Using Google Earth Engine. Remote Sens., 10.

Xu, 2018, Tracking Annual Cropland Changes from 1984 to 2016 Using Time-Series Landsat Images with a Change-Detection and Post-Classification Approach: Experiments from Three Sites in Africa, Remote Sens. Environ., 218, 13, 10.1016/j.rse.2018.09.008

Gong, 2013, Finer Resolution Observation and Monitoring of Global Land Cover: First Mapping Results with Landsat TM and ETM+ data, Int. J. Remote Sens., 34, 2607, 10.1080/01431161.2012.748992

Nasiri, V., Darvishsefat, A.A., Arefi, H., Griess, V.C., Sadeghi, S.M.M., and Borz, S.A. (2022). Modeling Forest Canopy Cover: A Synergistic Use of Sentinel-2, Aerial Photogrammetry Data, and Machine Learning. Remote Sens., 14.

Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (2021, December 17). Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation. Prog. Rep. RSC 1978–1. Available online: https://core.ac.uk/download/pdf/42887948.pdf.

Kulkarni, 2021, NDBI Based Prediction of Land Use Land Cover Change, J. Indian Soc. Remote Sens., 49, 2523, 10.1007/s12524-021-01411-9

Zha, 2003, Use of Normalized Difference Built-Up Index in Automatically Mapping Urban Areas from TM Imagery, Int. J. Remote Sens., 24, 583, 10.1080/01431160304987

Buschmann, 1993, In Vivo Spectroscopy and Internal Optics of Leaves as Basis for Remote Sensing of Vegetation, Int. J. Remote Sens., 14, 711, 10.1080/01431169308904370

Gitelson, 1998, Remote Sensing of Chlorophyll Concentration in Higher Plant Leaves, Adv. Space Res., 22, 689, 10.1016/S0273-1177(97)01133-2

Brandt, 2006, Land Use–Land Cover Conversion, Regeneration and Degradation in the High Elevation Bolivian Andes, Landsc. Ecol., 21, 607, 10.1007/s10980-005-4120-z

Breiman, 2001, Random Forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324

Sheykhmousa, 2020, Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 13, 6308, 10.1109/JSTARS.2020.3026724

O’Hara, R., Zimmermann, J., and Green, S. (2022). A Multimodality Test Outperforms Three Machine Learning Classifiers for Identifying and Mapping Paddocks Using Time Series Satellite Imagery. Geocarto Int.

Pelletier, 2016, Assessing the Robustness of Random Forests to Map Land Cover with High Resolution Satellite Image Time Series Over Large Areas, Remote Sens. Environ., 187, 156, 10.1016/j.rse.2016.10.010

Le Bris, A., Chehata, N., Briottet, X., and Paparoditis, N. (2015, January 26–31). A random forest class memberships based wrapper band selection criterion: Application to hyperspectral. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.

Abdullah, A.Y.M., Masrur, A., Adnan, M.S.G., Baky, M., Al, A., Hassan, Q.K., and Dewan, A. (2019). Spatio-Temporal Patterns of Land Use/Land Cover Change in the Heterogeneous Coastal Region of Bangladesh Between 1990 and 2017. Remote Sens., 11.

Piao, Y., Jeong, S., Park, S., and Lee, D. (2021). Analysis of Land Use and Land Cover Change Using Time-Series Data and Random Forest in North Korea. Remote Sens., 13.

Hurskainen, 2019, Auxiliary Datasets Improve Accuracy of Object-Based Land Use/Land Cover Classification in Heterogeneous Savanna Landscapes, Remote Sens. Environ., 233, 111354, 10.1016/j.rse.2019.111354

Jain, 2016, Monitoring Land Use Change and Its Drivers in Delhi, India Using Multi-Temporal Satellite Data, Model. Earth Syst. Environ., 2, 19, 10.1007/s40808-016-0075-0

Elmahdy, S., Mohamed, M., and Ali, T. (2020). Land Use/Land Cover Changes Impact on Groundwater Level and Quality in the Northern Part of the United Arab Emirates. Remote Sens., 12.

Ha, N.T., Manley-Harris, M., Pham, T.D., and Hawes, I. (2020). A Comparative Assessment of Ensemble-Based Machine Learning and Maximum Likelihood Methods for Mapping Seagrass Using Sentinel-2 Imagery in Tauranga Harbor, New Zealand. Remote Sens., 12.

Liu, C., Li, W., Zhu, G., Zhou, H., Yan, H., and Xue, P. (2020). Land Use/Land Cover Changes and Their Driving Factors in the Northeastern Tibetan Plateau Based on Geographical Detectors and Google Earth Engine: A Case Study in Gannan Prefecture. Remote Sens., 12.

Denize, J., Hubert-Moy, L., Betbeder, J., Corgne, S., Baudry, J., and Pottier, E. (2018). Evaluation of Using Sentinel-1 and-2 Time-Series to Identify Winter Land Use in Agricultural Landscapes. Remote Sens., 11.

Venter, Z.S., and Sydenham, M.A. (2021). Continental-Scale Land Cover Mapping at 10 m Resolution Over Europe (ELC10). Remote Sens., 13.

Rufin, P., Frantz, D., Ernst, S., Rabe, A., Griffiths, P., Özdoğan, M., and Hostert, P. (2019). Mapping Cropping Practices on a National Scale Using Intra-Annual Landsat Time Series Binning. Remote Sens., 11.

Phan, T.N., Kuch, V., and Lehnert, L.W. (2020). Land Cover Classification Using Google Earth Engine and Random Forest Classifier—The Role of Image Composition. Remote Sens., 12.

Forkuor, 2018, Landsat-8 vs. Sentinel-2: Examining the Added Value of Sentinel-2′s Red-Edge Bands to Land-Use and Land-Cover Mapping in Burkina Faso, GIScience Remote Sens., 55, 331, 10.1080/15481603.2017.1370169

Immitzer, M., Vuolo, F., and Atzberger, C. (2016). First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe. Remote Sens., 8.

Ghayour, L., Neshat, A., Paryani, S., Shahabi, H., Shirzadi, A., Chen, W., Al-Ansari, N., Geertsema, M., Pourmehdi Amiri, M., and Gholamnia, M. (2021). Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison Between Machine Learning Algorithms. Remote Sens., 13.

Zhao, 2022, ASI: An Artificial Surface Index for Landsat 8 Imagery, Int. J. Appl. Earth Obs. Geoinf., 107, 102703

Ettehadi Osgouei, P., Kaya, S., Sertel, E., and Alganci, U. (2019). Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery. Remote Sens., 11.

Salmon, 2015, Global Rain-Fed, Irrigated, and Paddy Croplands: A New High Resolution Map Derived from Remote Sensing, Crop Inventories and Climate Data, Int. J. Appl. Earth Observ. Geoinf., 38, 321

Petrushevsky, N., Manzoni, M., and Monti-Guarnieri, A. (2021). Fast Urban Land Cover Mapping Exploiting Sentinel-1 and Sentinel-2 Data. Remote Sens., 14.

Mercier, 2020, Evaluation of Sentinel-1 & 2 Time Series for Predicting Wheat and Rapeseed Phenological Stages, ISPRS J. Photogramm. Remote Sens., 163, 231, 10.1016/j.isprsjprs.2020.03.009

Makinde, 2020, Land Cover Mapping Using Sentinel-1 SAR and Landsat 8 Imageries of Lagos State for 2017, Environ. Sci. Pollut. Res., 27, 66, 10.1007/s11356-019-05589-x

Modica, 2022, Integrated Use of Sentinel-1 and Sentinel-2 Data and Open-Source Machine Learning Algorithms for Land Cover Mapping in a Mediterranean Region, Eur. J. Remote Sens., 55, 52, 10.1080/22797254.2021.2018667

Ofori-Ampofo, S., Pelletier, C., and Lang, S. (2021). Crop Type Mapping from Optical and Radar Time Series Using Attention-Based Deep Learning. Remote Sens., 13.