浏览全部资源
扫码关注微信
Published： 07 January 2021 ，
扫 描 看 全 文
刘涵，宫鹏.2021.21世纪逐日无缝数据立方体构建方法及逐年逐季节土地覆盖和土地利用动态制图—中国智慧遥感制图iMap （China） 1.0.遥感学报，25（1）： 126-147
Liu H and Gong P. 2021. 21st century daily seamless data cube reconstruction and seasonal to annual land cover and land use dynamics mapping-iMap （China） 1.0. National Remote Sensing Bulletin， 25（1）：126-147
粮食安全、高质量人居环境建设、生物多样性保护、星球健康等社会可持续发展目标和对地球系统的理解、模拟与管理都迫切需要多尺度、长时序、辐射和几何精度高且一致性强的遥感观测数据集和针对用户需求的、信息主题灵活的制图产品。但是由于技术和成本限制，传统的遥感卫星难以提供同时具有高空间分辨率、高时间频率和高质量的观测数据。现有的制图和反演方案多是针对于单一传感器系列，难以充分挖掘和联合利用多源异构遥感大数据的信息潜力，造成观测时段和分辨率有限、时空一致性和可比性较差。因此，遥感领域迫切需要新的技术范式。本文基于前沿的云计算、人工智能、虚拟星座、时空融合重建等技术，针对现有遥感大数据特别是国产卫星数据，提出一套智慧遥感制图（iMap）框架。该框架从用户需求出发、问题驱动，能够大大改善当前遥感数据产品难以满足农林管理、国情监测、生态环境保护、防灾减灾、城市建设等用户的多样化、高精度地表监测需求的现状。在该框架的指导下，基于亚马逊云计算（AWS）高性能、高弹性、可扩展的分布式计算资源，搭建了在线实时、自动化、无服务器、端到端的遥感大数据生产链和并行制图系统，并生产了首套21世纪中国全境逐日无缝数据立方体（SDC）及逐年逐季节土地覆盖和土地利用制图产品。逐日SDC综合利用Landsat和MODIS卫星数据构建虚拟星座，并通过多源时空数据融合重建技术研制得到无云无缝、高精度的反射率产品，作为分析就绪数据（ARD），为高精度定量遥感反演和制图打下根基。基于这一SDC，完成了逐年逐季节地表制图。逐年平均精度超过80%。在制图过程中，基于多套多层土地覆盖和土地利用分类体系，运用有限样本稳定分类理论，迁移使用全季节普适样本库，采用自动机器学习（AutoML）策略集成优化多种分类器，并结合时空一致性变化检测和后处理技术。这两套制图产品证明了本文提出的智慧遥感制图框架的可行性和有效性。未来将进一步完善和发展该框架，以开放和灵活的理念，为促进中国遥感进一步发展提供新的思路。
Sustainable development goals such as food security
high-quality habitat construction
biodiversity conservation
planetary health
and the understanding
modeling
and management of the Earth system urgently require multi-scale
long time series
high-accuracy
and consistent remote sensing observation datasets and mapping products with flexible classification systems to meet user needs. However
due to technical and cost constraints
it is difficult for conventional remote sensing satellites to provide observations with high spatial resolution
high temporal frequency
and high quality at the same time. The existing mapping and inversion schemes are mostly for a single sensor
making it difficult to fully exploit and jointly utilize the information potential of multi-source heterogeneous remote sensing big data
resulting in limited observation periods and resolutions
low spatial and temporal consistency and comparability. Therefore
new technical paradigms are urgently needed in the field of remote sensing. In this paper
based on advanced technologies
including cloud computing
artificial intelligence
virtual constellation
spatio-temporal fusion reconstruction
an intelligent mapping framework is proposed for remote sensing big data. The framework is user-driven and problem-driven
which can significantly improve the current situation that remote sensing data products can hardly meet users’ diversified and high-precision surface monitoring needs in agriculture and forestry management
national monitoring
ecological environment protection
disaster prevention and mitigation
urban planning
etc. Under this framework’s guidance
we built an online real-time
automated
serverless
end-to-end remote sensing big data production chain and parallel mapping system based on Amazon Web Services (AWS) high-performance
elastic
and scalable distributed computing resources. We produced the first set of 21st century daily Seamless Data Cube (SDC) and seasonal to annual land cover and land use mapping products of China. Integrating Landsat and MODIS satellite as a virtual constellation
through multi-source spatio-temporal data fusion and reconstruction technology
the daily SDC
cloud-free
high-precision reflectance product
is developed. As Analysis Ready Data (ARD)
it lays the foundation for high-precision quantitative remote sensing inversion and mapping. Based on SDC
we developed the seasonal to annual mapping product with multiple multi-level land cover and land use classification systems
whose mean annual accuracy exceeds 80%. The main mapping pipeline includes migrating the all-season sample set based on stable classification theory with limited samples
optimizing and ensembling multiple classifiers by Automatic Machine Learning (AutoML) strategies
and using change detection and post-processing techniques to achieve consistency. The two sets of products demonstrate the feasibility and effectiveness of the intelligent remote sensing mapping framework proposed in this paper. We will continue to improve and develop the framework with an open and flexible concept to provide new ideas to promote remote sensing development in China.
无缝数据立方体逐日数据逐季节制图云计算智慧遥感制图长时序变化监测人工智能时空大数据
seamless data cubedailyseasonal mappingcloud computingintelligent remote sensing mappinglong time serieschange monitoringartificial intelligencespatio-temporal big data
Bai Y, Wong C P, Jiang B, Hughes A C, Wang M and Wang Q. 2018. Developing China’s Ecological Redline Policy using ecosystem services assessments for land use planning. Nature Communications， 9(1): 3034
Bartholome E and Belward A S. 2005. GLC2000: a new approach to global land cover mapping from Earth observation data. International Journal of Remote Sensing， 26(9): 1959-1977
Baumann P, Mazzetti P, Ungar J, Barbera R, Barboni D, Beccati A, Bigagli L, Boldrini E, Bruno R, Calanducci A, Campalani P, Clements O, Dumitru A, Grant M, Herzig P, Kakaletris G, Laxton J, Koltsida P, Lipskoch K, Mahdiraji A R, Mantovani S, Merticariu V, Messina A, Misev D, Natali S, Nativi S, Oosthoek J, Pappalardo M, Passmore J, Rossi A P, Rundo F, Sen M, Sorbera V, Sullivan D, Torrisi M, Trovato L, Veratelli M G and Wagner S. 2016. Big Data Analytics for Earth Sciences: the EarthServer approach. International Journal of Digital Earth, 9(1): 3-29
Baumann P, Rossi A P, Bell B, Clements O, Evans B, Hoenig H, Hogan P, Kakaletris G, Koltsida P and Mantovani S. 2018. Fostering cross-disciplinary earth science through datacube analytics. Earth Observation Open Science and Innovation, Springer, Cham: 91-119
Benabdelkader S and Melgani F. 2008. Contextual spatiospectral postreconstruction of cloud-contaminated images. IEEE Geoscience and remote sensing letters, 5(2): 204-208
Beusch L, Gudmundsson L and Seneviratne S I. 2020. Crossbreeding CMIP6 Earth System Models With an Emulator for Regionally Optimized Land Temperature Projections. Geophysical Research Letters, 47(15): e2019GL086812
Brooks E B, Wynne R H, Thomas V A, Blinn C E and Coulston J W。2014. On-the-Fly Massively Multitemporal Change Detection Using Statistical Quality Control Charts and Landsat Data. IEEE Transactions on Geoscience and Remote Sensing， 52(6): 3316-3332.
Brown J F, Tollerud H J, Barber C P, Zhou Q, Dwyer J L, Vogelmann J E, Loveland T R, Woodcock C E, Stehman S V, Zhu Z, Pengra B W, Smith K, Horton J A, Xian G, Auch R F, Sohl T L, Sayler K L, Gallant A L, Zelenak D, Reker R R and Rover J. 2020. Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach. Remote Sensing of Environment, 238: 111356
Bullock E L, Woodcock C E and Holden C E. 2020. Improved change monitoring using an ensemble of time series algorithms. Remote Sensing of Environment, 238: 111165
Chen J, Chen J, Liao A, Cao X, Chen L, Chen X, He C, Han G, Peng S and Lu M. 2015. Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103: 7-27
Chen J, Jönsson P, Tamura M, Gu Z, Matsushita B and Eklundh L. 2004. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote sensing of Environment, 91(3-4): 332-344
Chen J, Zhu X, Vogelmann J E, Gao F and Jin S. 2011. A simple and effective method for filling gaps in Landsat ETM+ SLC-off images. Remote sensing of environment, 115(4): 1053-1064
Cheng Q, Shen H, Zhang L, Yuan Q and Zeng C. 2014. Cloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model. ISPRS journal of photogrammetry and remote sensing, 92: 54-68
Crowther T W, Glick H B, Covey K R, Bettigole C, Maynard D S, Thomas S M, Smith J R, Hintler G, Duguid M C, Amatulli G, Tuanmu M N, Jetz W, Salas C, Stam C, Piotto D, Tavani R, Green S, Bruce G, Williams S J, Wiser S K, Huber M O, Hengeveld G M, Nabuurs G J, Tikhonova E, Borchardt P, Li C F, Powrie L W, Fischer M, Hemp A, Homeier J, Cho P, Vibrans A C, Umunay P M, Piao S L, Rowe C W, Ashton M S, Crane P R and Bradford M A.2015. Mapping tree density at a global scale. Nature, 525(7568): 201-205
Dorogush A V, Ershov V and Gulin A. 2018. CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363
Dwyer J L, Roy D P, Sauer B, Jenkerson C B, Zhang H K and Lymburner L. 2018. Analysis ready data: Enabling analysis of the Landsat archive. Remote Sensing, 10(9): 1363
Egorov A V, Roy D P, Zhang H K, Li Z, Yan L and Huang H. 2019. Landsat 4, 5 and 7 (1982 to 2017) Analysis Ready Data (ARD) Observation Coverage over the Conterminous United States and Implications for Terrestrial Monitoring. Remote Sensing, 11(4)
Eilers P H C. 2003. A Perfect Smoother. Analytical Chemistry, 75(14): 3631-3636
Erickson N, Mueller J, Shirkov A, Zhang H, Larroy P, Li M and Smola A. 2020. AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data. arXiv preprint arXiv:2003.06505
Feddema J J, Oleson K W, Bonan G B, Mearns L O, Buja L E, Meehl G A and Washington W M. 2005. The importance of land-cover change in simulating future climates. Science, 310(5754): 1674-1678
Foley J A, DeFries R, Asner G P, Barford C, Bonan G, Carpenter S R, Chapin F S, Coe M T, Daily G C and Gibbs H K. 2005. Global consequences of land use. science, 309(5734): 570-574
Foody G M. 2010. Assessing the accuracy of land cover change with imperfect ground reference data. Remote Sensing of Environment, 114(10): 2271-2285
Friedl M.A, Sulla-Menashe D, Tan B, Schneider A, Ramankutty N, Sibley A and Huang X. 2010. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114(1): 168-182
Gao F, Masek J, Schwaller M and Hall F. 2006. On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote sensing, 44(8): 2207-2218
Giuliani G, Chatenoux B, Bono A De, Rodila D, Richard J P, Allenbach K, Dao H and Peduzzi P. 2017. Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD). Big Earth Data, 1(1-2): 100-117
Gómez C, White J C and Wulder M A. 2016. Optical remotely sensed time series data for land cover classification: A review. ISPRS Journal of Photogrammetry and Remote Sensing. 116: 55-72
Gong P, Li X, Wang J, Bai Y, Chen B, Hu T, Liu X, Xu B, Yang J, Zhang W and Zhou Y. 2020. Annual maps of global artificial impervious area (GAIA) between 1985 and 2018. Remote Sensing of Environment, 236: 111510
Gong P, Li X and Zhang W. 2019. 40-Year (1978—2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing. Science Bulletin, 64(11): 756-763
Gong P, Liu H, Zhang M, Li C, Wang J, Huang H, Clinton N, Ji L, Li W, Bai Y, Chen B, Xu B, Zhu Z, Yuan C, Suen H P, Guo J, Xu N, Li W, Zhao Y, Yang J, Yu C, Wang X, Fu H, Yu L, Dronova I, Hui F, Cheng X, Shi X, Xiao F, Liu Q and Song L. 2019. Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Science Bulletin, 64(6): 370-373
Gong P, Wang J, Yu L, Zhao Y, Zhao Y, Liang L, Niu Z, Huang X, Fu H and Liu S. 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
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D and Moore R. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment. 202: 18-27
Hamilton S E and Casey D. 2016. Creation of a high spatio-temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC-21). Global Ecology and Biogeography, 25(6): 729-738
Hansen M C, DeFries R S, Townshend J R G and Sohlberg R. 2000. Global land cover classification at 1 km spatial resolution using a classification tree approach. International journal of remote sensing, 21(6-7): 1331-1364
Hansen M C and Loveland T R. 2012. A review of large area monitoring of land cover change using Landsat data. Remote sensing of Environment, 122: 66-74
Hansen M C, Potapov P V, Moore R, Hancher M, Turubanova S A, Tyukavina A, Thau D, Stehman S V, Goetz S J, Loveland T R, Kommareddy A, Egorov A, Chini L, Justice C O and Townshend J R G. 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, 342(6160): 850
He C, Liu Z, Gou S, Zhang Q, Zhang J and Xu L. 2018. Detecting global urban expansion over the last three decades using a fully convolutional network. Environmental Research Letters
He K, Zhang X, Ren S and Sun J. 2016. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition
He Z, Liu H, Wang Y and Hu J. 2017. Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification. Remote Sensing, 9(10)
Homer C, Dewitz J, Fry J, Coan M, Hossain N, Larson C, Herold N, McKerrow A, VanDriel J N and Wickham J. 2007. Completion of the 2001 national land cover database for the counterminous United States. Photogrammetric engineering and remote sensing, 73(4): 337
Homer C, Dewitz J, Jin S, Xian G, Costello C, Danielson P, Gass L, Funk M, Wickham J, Stehman S, Auch R and Riitters K. 2020. Conterminous United States land cover change patterns 2001—2016 from the 2016 National Land Cover Database. ISPRS Journal of Photogrammetry and Remote Sensing, 162: 184-199
Homer C, Dewitz J, Yang L, Jin S, Danielson P, Xian G, Coulston J, Herold N, Wickham J and Megown K. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-representing a decade of land cover change information. Photogrammetric Engineering & Remote Sensing, 81(5): 345-354
Homer C, Huang C, Yang L, Wylie B and Coan M. 2004. Development of a 2001 national land-cover database for the United States. Photogrammetric Engineering & Remote Sensing, 70(7): 829-840
Hu S, Niu Z, Chen Y, Li L and Zhang H. 2017. Global wetlands: Potential distribution, wetland loss, and status. Science of The Total Environment, 586: 319-327
Huang H, Wang J, Liu C, Liang L, Li C and Gong P. 2020. The migration of training samples towards dynamic global land cover mapping. ISPRS Journal of Photogrammetry and Remote Sensing, 161: 27-36
Ji L, Gong P, Wang J, Shi J and Zhu Z. 2018. Construction of the 500 m Resolution Daily Global Surface Water Change Database (2001—2016). Water Resources Research, 54(12): 10, 270-210, 292
Julien Y and Sobrino J A. 2010. Comparison of cloud-reconstruction methods for time series of composite NDVI data. Remote Sensing of Environment, 114(3): 618-625
Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q and Liu T Y. 2017. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems
Kennedy R E, Yang Z and Cohen W B. 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—Temporal segmentation algorithms. Remote Sensing of Environment, 114(12): 2897-2910
Killough B. 2018. Overview of the open data cube initiative. IGARSS 2018—2018 IEEE International Geoscience and Remote Sensing Symposium, IEEE
Killough B. 2019. The Impact of Analysis Ready Data in the Africa Regional Data Cube. IGARSS 2019—2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE
Kong D, Zhang Y, Gu X and Wang D. 2019. A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 155: 13-24
Lanaras C, Bioucas-Dias J, Galliani S, Baltsavias E and Schindler K. 2018. Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network. ISPRS Journal of Photogrammetry and Remote Sensing, 146: 305-319
Lewis A, Lymburner L, Purss M B J, Brooke B, Evans B, Ip A, Dekker A G, Irons J R, Minchin S, Mueller N, Oliver S, Roberts D, Ryan B, Thankappan M, Woodcock R and Wyborn L. 2016. Rapid, high-resolution detection of environmental change over continental scales from satellite data - the Earth Observation Data Cube. International Journal of Digital Earth, 9(1): 106-111
Lewis A, Oliver S, Lymburner L, Evans B, Wyborn L, Mueller N, Raevksi G, Hooke J, Woodcock R and Sixsmith J. 2017. The Australian geoscience data cube—Foundations and lessons learned. Remote Sensing of Environment, 202: 276-292
Li C, Gong P, Wang J, Zhu Z, Biging G S, Yuan C, Hu T, Zhang H, Wang Q, Li X, Liu X, Xu Y, Guo J, Liu C, Hackman K O, Zhang M, Cheng Y, Yu L, Yang J, Huang H and Clinton N. 2017. The first all-season sample set for mapping global land cover with Landsat-8 data. Science Bulletin, 62(7): 508-515
Lim B, Son S, Kim H, Nah S and Lee K M. 2017. Enhanced deep residual networks for single image super-resolution. Proceedings of the IEEE conference on computer vision and pattern recognition workshops
Liu D and Cai S. 2012. A spatial-temporal modeling approach to reconstructing land-cover change trajectories from multi-temporal satellite imagery. Annals of the Association of American Geographers 102(6): 1329-1347
Liu H, Gong P, Wang J, Clinton N, Bai Y and Liang S. 2020. Annual Dynamics of Global Land Cover and its Long-term Changes from 1982 to 2015. Earth System Science Data, 12(2): 1217-1243
Liu H, He L and Li J. 2017. Remote sensing image classification based on convolutional neural networks with two-fold sparse regularization. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Liu H, Li J, He L and Wang Y. 2019. Superpixel-Guided Layer-Wise Embedding CNN for Remote Sensing Image Classification. Remote Sensing, 11(2)
Liu J, Liu M, Tian H, Zhuang D, Zhang Z, Zhang W, Tang X and Deng X. 2005. Spatial and temporal patterns of China’s cropland during 1990—2000: an analysis based on Landsat TM data. Remote sensing of Environment, 98(4): 442-456
Liu X, Hu G, Chen Y, Li X, Xu X, Li S, Pei F and Wang S. 2018. High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. Remote sensing of environment, 209: 227-239
Liu X, Huang Y, Xu X, Li X, Li X, Ciais P, Lin P, Gong K, Ziegler A D, Chen A, Gong P, Chen J, Hu G, Chen Y, Wang S, Wu Q, Huang K, Estes L and Zeng Z. 2020. High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. Nature Sustainability. 3(7): 564-570
Loveland T R and Dwyer J L. 2012. Landsat: Building a strong future. Remote Sensing of Environment, 122: 22-29
Loveland T R, Reed B C, Brown J F, Ohlen D O, Zhu Z, Yang L and Merchant J W. 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
Ma C, Rao Y, Cheng Y, Chen C, Lu J and Zhou J. 2020. Structure-Preserving Super Resolution with Gradient Guidance. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
Mao D, He X, Wang Z, Tian Y, Xiang H, Yu H, Man W, Jia M, Ren C and Zheng H. 2019. Diverse policies leading to contrasting impacts on land cover and ecosystem services in Northeast China. Journal of Cleaner Production, 240: 117961
Masek J G, Vermote E F, Saleous N E, Wolfe R, Hall F G, Huemmrich K F, Feng G, Kutler J and Teng-Kui L. 2006. A Landsat surface reflectance dataset for North America, 1990—2000. IEEE Geoscience and Remote Sensing Letter,s 3(1): 68-72
Melgani F. 2006. Contextual reconstruction of cloud-contaminated multitemporal multispectral images. IEEE Transactions on Geoscience and Remote Sensing, 44(2): 442-455
Miller J D and Thode A E. 2007. Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sensing of Environment, 109(1): 66-80
Murray N J, Phinn S R, DeWitt M, Ferrari R, Johnston R, Lyons M B, Clinton N, Thau D and Fuller R A. 2019. The global distribution and trajectory of tidal flats. Nature, 565(7738): 222
Pekel J F, Cottam A, Gorelick N and Belward A S. 2016. High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633): 418
Peng J, Hu X, Wang X, Meersmans J, Liu Y and Qiu S. 2019. Simulating the impact of Grain-for-Green Programme on ecosystem services trade-offs in Northwestern Yunnan, China. Ecosystem Services, 39: 100998
Pengra B W, Stehman S V, Horton J A, Dockter D J, Schroeder T A, Yang Z, Cohen W B, Healey S P and Loveland T R. 2020. Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program. Remote Sensing of Environment, 238: 111261
Pesaresi M, Huadong G, Blaes X, Ehrlich D, Ferri S, Gueguen L, Halkia M, Kauffmann M, Kemper T, Lu L, Marin-Herrera M A, Ouzounis G K, Scavazzon M, Soille P, Syrris V and Zanchetta L. 2013. A Global Human Settlement Layer From Optical HR/VHR RS Data: Concept and First Results. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(5): 2102-2131
Pickens A H, Hansen M C, Hancher M, Stehman S V, Tyukavina A, Potapov P, Marroquin B and Sherani Z. 2020. Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series. Remote Sensing of Environment, 243: 111792
Qiu S, Lin Y, Shang R, Zhang J, Ma L and Zhu Z. 2019. Making Landsat Time Series Consistent: Evaluating and Improving Landsat Analysis Ready Data. Remote Sensing, 11(1)
Qiu S, Zhu Z and He B. 2019. Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4—8 and Sentinel-2 imagery. Remote Sensing of Environment, 231: 111205
Qiu T, Song C, Zhang Y, Liu H and Vose J M. 2020. Urbanization and climate change jointly shift land surface phenology in the northern mid-latitude large cities. Remote Sensing of Environment. 236: 111477
Rakwatin P, Takeuchi W and Yasuoka Y. 2008. Restoration of Aqua MODIS band 6 using histogram matching and local least squares fitting. IEEE Transactions on Geoscience and Remote Sensing, 47(2): 613-627
Ren S, He K, Girshick R and Sun J. 2016. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE transactions on pattern analysis and machine intelligence, 39(6): 1137-1149
Rizvi S R, Killough B, Cherry A and Gowda S. 2018. Lessons learned and cost analysis of hosting a full stack Open data cube (ODC) application on the amazon web services (AWS). IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, IEEE
Roerink G, Menenti M and Verhoef W. 2000. Reconstructing cloudfree NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing, 21(9): 1911-1917
Roy D P, Ju J, Kline K, Scaramuzza P L, Kovalskyy V, Hansen M, Loveland T R, Vermote E and Zhang C. 2010. Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States. Remote Sensing of Environment, 114(1): 35-49
Roy D P, Kovalskyy V, Zhang H K, Vermote E F, Yan L, Kumar S S and Egorov A. 2016. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sensing of Environment, 185: 57-70
Schaaf C B, Gao F, Strahler A H, Lucht W, Li X, Tsang T, Strugnell N C, Zhang X, Jin Y, Muller J P, Lewis P, Barnsley M, Hobson P, Disney M, Roberts G, Dunderdale M, Doll C, d’Entremont R P, Hu B, Liang S, Privette J L and Roy D. 2002. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sensing of Environment, 83(1): 135-148
Shen H, Li X, Cheng Q, Zeng C, Yang G, Li H and Zhang L. 2015. Missing Information Reconstruction of Remote Sensing Data: A Technical Review. IEEE Geoscience and Remote Sensing Magazine, 3(3): 61-85
Shen H, Zeng C and Zhang L. 2010. Recovering reflectance of AQUA MODIS band 6 based on within-class local fitting. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(1): 185-192
Shimada M, Itoh T, Motooka T, Watanabe M, Shiraishi T, Thapa R and Lucas R. 2014. New global forest/non-forest maps from ALOS PALSAR data (2007—2010). Remote Sensing of environment, 155: 13-31
Simonyan K and Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
Soenen S A, Peddle D R and Coburn C A. 2005. SCS+C: a modified Sun-canopy-sensor topographic correction in forested terrain. IEEE Transactions on Geoscience and Remote Sensing, 43(9): 2148-2159
Song B, Li J, Mura M D, Li P, Plaza A, Bioucas-Dias J M, Benediktsson J A and Chanussot J. 2014. Remotely Sensed Image Classification Using Sparse Representations of Morphological Attribute Profiles. IEEE Transactions on Geoscience and Remote Sensing, 52(8): 5122-5136
Song X P, Hansen M C, Stehman S V, Potapov P V, Tyukavina A, Vermote E F and Townshend J R. 2018. Global land change from 1982 to 2016. Nature, 560(7720): 639-643
Sulla-Menashe D, Gray J M, Abercrombie S P and Friedl M A. 2019. Hierarchical mapping of annual global land cover 2001 to present: The MODIS Collection 6 Land Cover product. Remote sensing of environment, 222: 183-194
Sy S and Quesada B. 2020. Anthropogenic land cover change impact on climate extremes during the 21st century. Environmental Research Letters, 15(3): 034002
Townshend J R and Justice C. 1986. Analysis of the dynamics of African vegetation using the normalized difference vegetation index. International Journal of Remote Sensing, 7(11): 1435-1445
Turner B L, Lambin E F and Reenberg A. 2007. The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences, 104(52): 20666-20671
Verbesselt J, Hyndman R, Newnham G and Culvenor D. 2010. Detecting trend and seasonal changes in satellite image time series. Remote sensing of Environment, 114(1): 106-115
Verbesselt J, Zeileis A and Herold M. 2012. Near real-time disturbance detection using satellite image time series. Remote Sensing of Environment, 123: 98-108
Vermote E, Justice C, Claverie M and Franch B. 2016. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment, 185: 46-56
Vogelmann J E, Howard S M, Yang L, Larson C R, Wylie B K and Van Driel N. 2001. Completion of the 1990s National Land Cover Data Set for the conterminous United States from Landsat Thematic Mapper data and ancillary data sources. Photogrammetric Engineering and Remote Sensing, 67(6)
Wang H, Zhao X, Zhang X, Wu D and Du X. 2019. Long Time Series Land Cover Classification in China from 1982 to 2015 Based on Bi-LSTM Deep Learning. Remote Sensing, 11(14)
Wang J, Zhao Y, Li C, Yu L, Liu D and Gong P. 2015. Mapping global land cover in 2001 and 2010 with spatial-temporal consistency at 250 m resolution. ISPRS Journal of Photogrammetry and Remote Sensing， 103: 38-47
Wang L, Qu J J, Xiong X, Hao X, Xie Y and Che N. 2006. A new method for retrieving band 6 of Aqua MODIS. IEEE Geoscience and Remote Sensing Letters, 3(2): 267-270
Wood E F, Roundy J K, Troy T J, Van Beek L P H, Bierkens M F P, Blyth E, de Roo A, Döll P, Ek M and Famiglietti J. 2011. Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water. Water Resources Research, 47(5)
Woodcock C E, Allen R, Anderson M, Belward A, Bindschadler R, Cohen W, Gao F, Goward S N, Helder D and Helmer E. 2008. Free access to Landsat imagery. Science, 320(5879): 1011-1011
Woodcock C E, Loveland T R, Herold M and Bauer M E. 2020. Transitioning from change detection to monitoring with remote sensing: A paradigm shift. Remote Sensing of Environment, 238: 111558
Wulder M A, Coops N C, Roy D P, White J C and Hermosilla T. 2018. Land cover 2.0. International Journal of Remote Sensing, 39(12): 4254-4284
Wulder M A, Loveland T R, Roy D P, Crawford C J, Masek J G, Woodcock C E, Allen R G, Anderson M C, Belward A S, Cohen W B, Dwyer J, Erb A, Gao F, Griffiths P, Helder D, Hermosilla T, Hipple J D, Hostert P, Hughes M J, Huntington J, Johnson D M, Kennedy R, Kilic A, Li Z, Lymburner L, McCorkel J, Pahlevan N, Scambos T A, Schaaf C, Schott J R, Sheng Y, Storey J, Vermote E, Vogelmann J, White J C, Wynne R H and Zhu Z. 2019. Current status of Landsat program, science, and applications. Remote Sensing of Environment, 225: 127-147
Wulder M A, Masek J G, Cohen W B, Loveland T R and Woodcock C E. 2012. Opening the archive: How free data has enabled the science and monitoring promise of Landsat. Remote Sensing of Environment, 122: 2-10
Xian G, Homer C and Fry J. 2009. Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods. Remote Sensing of Environment, 113(6): 1133-1147
Xu H. 2006. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International journal of remote sensing, 27(14): 3025-3033
Xu X, Liu J, Zhang S, Li R, Yan C and Wu S. 2018. China’s Multi-Period Land Use Land Cover Remote Sensing Monitoring Data Set (CNLUCC). Resource and Environment Data Cloud Platform: Beijing, China
Yan L and Roy D P. 2020. Spatially and temporally complete Landsat reflectance time series modelling: The fill-and-fit approach. Remote Sensing of Environment, 241: 111718
Yang J, Gong P, Fu R, Zhang M, Chen J, Liang S, Xu B, Shi J and Dickinson R. 2013. The role of satellite remote sensing in climate change studies. Nature Climate Change, 3(10): 875-883
Yang L, Jin S, Danielson P, Homer C, Gass L, Bender S M, Case A, Costello C, Dewitz J and Fry J. 2018. A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies. ISPRS journal of photogrammetry and remote sensing, 146: 108-123
Yu L, Liang L, Wang J, Zhao Y, Cheng Q, Hu L, Liu S, Yu L, Wang X, Zhu P, Li X, Xu Y, Li C, Fu W, Li X, Li W, Liu C, Cong N, Zhang H, Sun F, Bi X, Xin Q, Li D, Yan D, Zhu Z, Goodchild M F and Gong P. 2014. Meta-discoveries from a synthesis of satellite-based land-cover mapping research. International Journal of Remote Sensing, 35(13): 4573-4588
Zeng C, Shen H and Zhang L. 2013. Recovering missing pixels for Landsat ETM+ SLC-off imagery using multi-temporal regression analysis and a regularization method. Remote Sensing of Environment, 131: 182-194
Zha Y, Gao J and Ni S. 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
Zhang C, Li W and Travis D. 2007. Gaps‐fill of SLC‐off Landsat ETM+ satellite image using a geostatistical approach. International Journal of Remote Sensing, 28(22): 5103-5122
Zhang H K and Roy D P. 2017. Using the 500m MODIS land cover product to derive a consistent continental scale 30 m Landsat land cover classification. Remote Sensing of Environment, 197: 15-34
Zhang J. 2010. Multi-source remote sensing data fusion: status and trends. International Journal of Image and Data Fusion 1(1): 5-24
Zhang Z, Wang X, Zhao X, Liu B, Yi L, Zuo L, Wen Q, Liu F, Xu J and Hu S. 2014. A 2010 update of National Land Use/Cover Database of China at 1∶100000 scale using medium spatial resolution satellite images. Remote Sensing of Environment, 149: 142-154
Zhao Y, Gong P, Yu L, Hu L, Li X, Li C, Zhang H, Zheng Y, Wang J, Zhao Y, Cheng Q, Liu C, Liu S and Wang X. 2014. Towards a common validation sample set for global land-cover mapping. International Journal of Remote Sensing, 35(13): 4795-4814
Zhu X, Chen J, Gao F, Chen X and Masek J G. 2010. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sensing of Environment, 114(11): 2610-2623
Zhu X, Gao F, Liu D and Chen J. 2012. A Modified Neighborhood Similar Pixel Interpolator Approach for Removing Thick Clouds in Landsat Images. IEEE Geoscience and Remote Sensing Letters, 9(3): 521-525
Zhu Z and Woodcock C E. 2014. Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change. Remote Sensing of Environment, 152: 217-234
Zhu Z and Woodcock C E. 2014. Continuous change detection and classification of land cover using all available Landsat data. Remote sensing of Environment, 144: 152-171
Zhu Z, Zhang J, Yang Z, Aljaddani A H, Cohen W B, Qiu S and Zhou C. 2020. Continuous monitoring of land disturbance based on Landsat time series. Remote Sensing of Environment, 238: 111116
Fu W，Chen Y L，Shi M W，Zhang X D，Xiao H and Gong P. 2014. The spatio temporal changes of surface cover color in China revealed by satellite observations. Journal of Remote Sensing，18(1):154-179
付薇, 陈焱磊, 施楣梧, 张旭东, 肖红和宫鹏. 2014. 卫星动态观测数据揭示中国地表色调分布及时序变化特征. 遥感学报, 18(1): 154-179 [DOI:10.11834/jrs.20132323http://dx.doi.org/10.11834/jrs.20132323]
Gong P, Zhang W, Yu L, Li C C, Wang J, Liang L, Li X C, Ji L Y and Bai Y Q. 2016. New research paradigm forglobal land cover mapping. Journal of Remote Sensing, 20(5): 1002-1016
宫鹏, 张伟, 俞乐, 李丛丛, 王杰, 梁璐, 李雪草, 计璐艳和白玉琪. 2016. 全球地表覆盖制图研究新范式. 遥感学报, 20(5): 1002-1016 [DOI:10.11834/jrs.20166138http://dx.doi.org/10.11834/jrs.20166138]
Liu F, Yan H M, Liu J Y, Xiao X M, Qin Y W. 2016. Spatial pattern of land use intensity in China in 2000. Acta geographica sciences, 2016, 71(7): 1130-1143
刘芳, 闫慧敏, 刘纪远, 肖向明和秦元伟 .2016. 21世纪初中国土地利用强度的空间分布格局. 地理学报, 71(7): 1130-1143. [DOI:10.11821/dlxb201607004http://dx.doi.org/10.11821/dlxb201607004]
Liu H, He Land Li J.2017.Advances in Deep learning and it's applications in image processing .ZTE Technology Journal,23(04): 1-6.
刘涵, 贺霖 和 李军. 2017. 深度学习进展及其在图像处理领域的应用. 中兴通讯技术, 23(04): 1-6
Liu J Y, Kuang W H, Zhang Z X, Xu X L, Qin Y W, Ning J, Zhou W C, Zhang S W, Li R D, Yan C Z, Wu S X, Shi X Z, Jiang N, Yu D S, Pan X Z, Chi W F. Spatiotemporal characteristics, patterns and causes of land use changes in China since the late 1980.2014.Acta Geographica Sinica, 69(1): 3-14
刘纪远, 匡文慧, 张增祥, 徐新良, 秦元伟, 宁佳, 周万村, 张树文, 李仁东, 颜长珍, 吴世新, 史学正, 江南, 于东升, 潘贤章和迟文峰 .2014. “20世纪80年代末以来中国土地利用变化的基本特征与空间格局.”地理学报 69(1): 3-14
相关文章
相关作者
相关机构
京公网安备11010802024621