Production of global daily seamless data cubes and quantification of global land cover change from 1985 to 2020 - iMap World 1.0

Abstract Longer time high-resolution, high-frequency, consistent, and more detailed land cover data are urgently needed in order to achieve sustainable development goals on food security, high-quality habitat construction, biodiversity conservation and planetary health, and for the understanding, simulation and management of the Earth system. However, due to technological constraints, it is difficult to provide simultaneously high spatial resolution, high temporal frequency, and high quality observation data. Existing mapping solutions are limited by traditional remotely sensed data, that have shorter observation periods, poor spatio-temporal consistency and comparability. Therefore, a new mapping paradigm is needed. This paper develops a framework for intelligent mapping (iMap) of land cover based on state-of-the-art technologies such as cloud computing, artificial intelligence, virtual constellations, and spatio-temporal reconstruction and fusion. Under this framework, we built an automated, serverless, end-to-end data production chain and parallel mapping system based on Amazon Web Services (AWS) and produced the first 30 m global daily seamless data cubes (SDC), and annual to seasonal land cover maps for 1985–2020. The SDC was produced through a multi-source spatio-temporal data reconstruction and fusion workflow based on Landsat, MODIS, and AVHRR virtual constellations. Independent validation results show that the relative mean error of the SDC is less than 2.14%. As analysis ready data (ARD), it can lay a foundation for high-precision quantitative remote sensing information extraction. From this SDC, we produced 36-year long, 30 m resolution global land cover map data set by combining strategies of sample migration, machine learning, and spatio-temporal adjustment. The average overall accuracy of our annual land cover maps over multiple periods of time is 80% for level 1 classification and over 73% for level 2 classification (29 and 33 classes). Based on an objective validation sample consisting of FLUXNET sites, our map accuracy is 10% higher than that of existing global land cover datasets including Globeland30. Our results show that the average global land cover change rate is 0.36%/yr. Global forest decreased by 1.47 million km2 from 38.44 million km2, cropland increased by 0.84 million km2 from 12.49 million km2 and impervious surface increased by 0.48 million km2 from 0.57 million km2 during 1985– 2020.

[1]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[2]  Robert E. Wolfe,et al.  A Landsat surface reflectance dataset for North America, 1990-2000 , 2006, IEEE Geoscience and Remote Sensing Letters.

[3]  Nikos Komodakis,et al.  Cloud-Gan: Cloud Removal for Sentinel-2 Imagery Using a Cyclic Consistent Generative Adversarial Networks , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[4]  Chaoqing Yu,et al.  Identifying patterns and hotspots of global land cover transitions using the ESA CCI Land Cover dataset , 2018, Remote Sensing Letters.

[5]  Xiaolin Zhu,et al.  An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions , 2010 .

[6]  B. Scanlon,et al.  Ground water and climate change , 2013 .

[7]  Martin Herold,et al.  Transitioning from change detection to monitoring with remote sensing: A paradigm shift , 2020 .

[8]  J. Benediktsson,et al.  Remotely Sensed Image Classification Using Sparse Representations of Morphological Attribute Profiles , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Le Yu,et al.  Mapping global land cover in 2001 and 2010 with spatial-temporal consistency at 250 m resolution , 2015 .

[10]  Yuyu Zhou,et al.  Annual maps of global artificial impervious area (GAIA) between 1985 and 2018 , 2020 .

[11]  Hanqiu Xu.  Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .

[12]  Xiaolin Zhu,et al.  Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions , 2018, Remote. Sens..

[13]  David P. Roy,et al.  Analysis Ready Data: Enabling Analysis of the Landsat Archive , 2018, Remote. Sens..

[14]  Weidong Li,et al.  Gaps‐fill of SLC‐off Landsat ETM+ satellite image using a geostatistical approach , 2007 .

[15]  Jian Peng,et al.  STAIR: A generic and fully-automated method to fuse multiple sources of optical satellite data to generate a high-resolution, daily and cloud-/gap-free surface reflectance product , 2018, Remote Sensing of Environment.

[16]  Bin Chen,et al.  Comparison of Spatiotemporal Fusion Models: A Review , 2015, Remote. Sens..

[17]  Joanne C. White,et al.  A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS , 2009 .

[18]  S. Carpenter,et al.  Global Consequences of Land Use , 2005, Science.

[19]  G. Meehl,et al.  The Importance of Land-Cover Change in Simulating Future Climates , 2005, Science.

[20]  Ben Evans,et al.  The Australian Geoscience Data Cube - foundations and lessons learned , 2017 .

[21]  D. Roy,et al.  Spatially and temporally complete Landsat reflectance time series modelling: The fill-and-fit approach , 2020 .

[22]  Jin Chen,et al.  A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .

[23]  Chao Dong,et al.  Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[24]  Liangpei Zhang,et al.  Recovering Reflectance of AQUA MODIS Band 6 Based on Within-Class Local Fitting , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[25]  Chao Zeng,et al.  Missing Data Reconstruction in Remote Sensing Image With a Unified Spatial–Temporal–Spectral Deep Convolutional Neural Network , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Jin Chen,et al.  Global land cover mapping at 30 m resolution: A POK-based operational approach , 2015 .

[27]  Liming Zhou,et al.  Afforestation in China cools local land surface temperature , 2014, Proceedings of the National Academy of Sciences.

[28]  Craig A. Coburn,et al.  SCS+C: a modified Sun-canopy-sensor topographic correction in forested terrain , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[29]  A. Taylor,et al.  Widespread Increase of Tree Mortality Rates in the Western United States , 2009, Science.

[30]  Zhifeng Liu,et al.  Detecting global urban expansion over the last three decades using a fully convolutional network , 2019, Environmental Research Letters.

[31]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[32]  Feng Gao,et al.  A Modified Neighborhood Similar Pixel Interpolator Approach for Removing Thick Clouds in Landsat Images , 2012, IEEE Geoscience and Remote Sensing Letters.

[33]  Bin Chen,et al.  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. , 2019, Science bulletin.

[34]  Hankui K. Zhang,et al.  Using the 500 m MODIS Land Cover Product to Derive a Consistent Continental Scale 30 m Landsat Land Cover Classification , 2017 .

[35]  Marinos Kavouras,et al.  An overview of 21 global and 43 regional land-cover mapping products , 2015 .

[36]  D. Roy,et al.  Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States , 2010 .

[37]  Wenzhong Shi,et al.  FSDAF 2.0: Improving the performance of retrieving land cover changes and preserving spatial details , 2020 .

[38]  T. A. Black,et al.  Spatial and temporal variations in global soil respiration and their relationships with climate and land cover , 2020, Science Advances.

[39]  J. van Vliet,et al.  Direct and indirect loss of natural area from urban expansion , 2019 .

[40]  Feng Gao,et al.  A simple and effective method for filling gaps in Landsat ETM+ SLC-off images , 2011 .

[41]  D. Roy,et al.  Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data , 2008 .

[42]  Peter Potapov,et al.  Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping , 2020, Remote. Sens..

[43]  Luis Guanter,et al.  Multitemporal Unmixing of Medium-Spatial-Resolution Satellite Images: A Case Study Using MERIS Images for Land-Cover Mapping , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[44]  Xia Li,et al.  High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015 , 2020, Nature Sustainability.

[45]  Hankui K. Zhang,et al.  Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. , 2016, Remote sensing of environment.

[46]  R. Cowling,et al.  Ecosystem Services, Land-Cover Change, and Stakeholders: Finding a Sustainable Foothold for a Semiarid Biodiversity Hotspot , 2009 .

[47]  Masashi Matsuoka,et al.  Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[48]  Hongzhang Xu,et al.  Deep learning in environmental remote sensing: Achievements and challenges , 2020, Remote Sensing of Environment.

[49]  Brian D. Killough,et al.  Lessons Learned and Cost Analysis of Hosting a Full Stack Open Data Cube (ODC) Application on the Amazon Web Services (AWS) , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[50]  Ranga B. Myneni,et al.  Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems , 2004 .

[51]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[52]  Liangpei Zhang,et al.  An Integrated Framework for the Spatio–Temporal–Spectral Fusion of Remote Sensing Images , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[53]  Jie Zhou,et al.  Structure-Preserving Super Resolution With Gradient Guidance , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[54]  P. Gong,et al.  Construction of the 500‐m Resolution Daily Global Surface Water Change Database (2001–2016) , 2018, Water Resources Research.

[55]  Dieter Oertel,et al.  Unmixing-based multisensor multiresolution image fusion , 1999, IEEE Trans. Geosci. Remote. Sens..

[56]  J. Townshend,et al.  Global land change from 1982 to 2016 , 2018, Nature.

[57]  Brian Killough,et al.  The Impact of Analysis Ready Data in the Africa Regional Data Cube , 2019, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium.

[58]  Giles M. Foody,et al.  SFSDAF: An enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion , 2020, Remote Sensing of Environment.

[59]  Tie-Yan Liu,et al.  LightGBM: A Highly Efficient Gradient Boosting Decision Tree , 2017, NIPS.

[60]  J. Pekel,et al.  High-resolution mapping of global surface water and its long-term changes , 2016, Nature.

[61]  M. Hansen,et al.  Eastern Europe's forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive , 2015 .

[62]  Huadong Guo,et al.  A Global Human Settlement Layer From Optical HR/VHR RS Data: Concept and First Results , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[63]  N. C. Strugnell,et al.  First operational BRDF, albedo nadir reflectance products from MODIS , 2002 .

[64]  Yang Bai,et al.  Developing China’s Ecological Redline Policy using ecosystem services assessments for land use planning , 2018, Nature Communications.

[65]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[66]  M. Hansen,et al.  Near doubling of Brazil’s intensive row crop area since 2000 , 2018, Proceedings of the National Academy of Sciences.

[67]  L. P. Koh,et al.  Is oil palm agriculture really destroying tropical biodiversity? , 2008 .

[68]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[69]  Qiang Zhou,et al.  Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach , 2020, Remote Sensing of Environment.

[70]  R. Dickinson,et al.  The role of satellite remote sensing in climate change studies , 2013 .

[71]  F. Gao,et al.  An algorithm for the retrieval of 30-m snow-free albedo from Landsat surface reflectance and MODIS BRDF , 2011 .

[72]  D. Roy,et al.  The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally , 2008 .

[73]  J. Randerson,et al.  The Impact of Boreal Forest Fire on Climate Warming , 2006, Science.

[74]  R. Pielke.  Land Use and Climate Change , 2005, Science.

[75]  Dimitar Misev,et al.  Fostering Cross-Disciplinary Earth Science Through Datacube Analytics , 2018 .

[76]  Michael A. Lefsky,et al.  A flexible spatiotemporal method for fusing satellite images with different resolutions , 2016 .

[77]  David Thau,et al.  The global distribution and trajectory of tidal flats , 2018, Nature.

[78]  Chao Zeng,et al.  Recovering missing pixels for Landsat ETM + SLC-off imagery using multi-temporal regression analysis and a regularization method , 2013 .

[79]  Liangpei Zhang,et al.  Cloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model , 2014 .

[80]  M. Lathuillière,et al.  The origin, supply chain, and deforestation risk of Brazil’s beef exports , 2020, Proceedings of the National Academy of Sciences.

[81]  Hankui K. Zhang,et al.  A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance , 2016 .

[82]  Bo Huang,et al.  Spatiotemporal Reflectance Fusion via Sparse Representation , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[83]  Yuqi Bai,et al.  Annual dynamics of global land cover and its long-term changes from 1982 to 2015 , 2020, Earth System Science Data.

[84]  Hankui K. Zhang,et al.  Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data , 2013 .

[85]  Enrico Magli,et al.  DeepSUM: Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[86]  F. Achard,et al.  A Synthesis of Information on Rapid Land-cover Change for the Period 1981–2000 , 2005 .

[87]  Robert E. Wolfe,et al.  A 30+ year AVHRR Land Surface Reflectance Climate Data Record and its application to wheat yield monitoring , 2017, Remote. Sens..

[88]  Zhe Zhu,et al.  Making Landsat Time Series Consistent: Evaluating and Improving Landsat Analysis Ready Data , 2018, Remote. Sens..

[89]  Daniel Rueckert,et al.  Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[90]  F. Creutzig,et al.  Future urban land expansion and implications for global croplands , 2016, Proceedings of the National Academy of Sciences.

[91]  Gang Yang,et al.  Missing Information Reconstruction of Remote Sensing Data: A Technical Review , 2015, IEEE Geoscience and Remote Sensing Magazine.

[92]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[93]  Zhe Zhu,et al.  Object-based cloud and cloud shadow detection in Landsat imagery , 2012 .

[94]  Xiao Xiang Zhu,et al.  Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion , 2020, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[95]  Lei Deng,et al.  Effects of national ecological restoration projects on carbon sequestration in China from 2001 to 2010 , 2018, Proceedings of the National Academy of Sciences.

[96]  John Shalf,et al.  Performance Analysis of High Performance Computing Applications on the Amazon Web Services Cloud , 2010, 2010 IEEE Second International Conference on Cloud Computing Technology and Science.

[97]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[98]  Huabing Huang,et al.  The migration of training samples towards dynamic global land cover mapping , 2020 .

[99]  M. Claverie,et al.  Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. , 2016, Remote sensing of environment.

[100]  Denisa Rodila,et al.  Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD) , 2017 .

[101]  Thomas Hilker,et al.  An Improved Image Fusion Approach Based on Enhanced Spatial and Temporal the Adaptive Reflectance Fusion Model , 2013, Remote. Sens..

[102]  Qingshan Liu,et al.  Spatiotemporal Satellite Image Fusion Using Deep Convolutional Neural Networks , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[103]  Jay Gao,et al.  Use of normalized difference built-up index in automatically mapping urban areas from TM imagery , 2003 .

[104]  Sen Guo,et al.  Global Drivers of Russian Timber Harvest , 2016 .

[105]  C. Woodcock,et al.  Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images , 2015 .

[106]  Jun Yang,et al.  The first all-season sample set for mapping global land cover with Landsat-8 data. , 2017, Science bulletin.

[107]  Shengjie Hu,et al.  Global wetlands: Potential distribution, wetland loss, and status. , 2017, The Science of the total environment.

[108]  Yiwen Wang,et al.  Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification , 2017, Remote. Sens..

[109]  S. P. Abercrombie,et al.  Hierarchical mapping of annual global land cover 2001 to present: The MODIS Collection 6 Land Cover product , 2019, Remote Sensing of Environment.

[110]  Monidipa Das,et al.  A Deep-Learning-Based Forecasting Ensemble to Predict Missing Data for Remote Sensing Analysis , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[111]  Adam Lewis,et al.  Rapid, high-resolution detection of environmental change over continental scales from satellite data – the Earth Observation Data Cube , 2016, Int. J. Digit. Earth.

[112]  Zhe Zhu,et al.  Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change , 2014 .

[113]  Konrad Schindler,et al.  Super-Resolution of Sentinel-2 Images: Learning a Globally Applicable Deep Neural Network , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[114]  Le Yu,et al.  Towards a common validation sample set for global land-cover mapping , 2014 .

[115]  Maggi Kelly,et al.  A spatial–temporal approach to monitoring forest disease spread using multi-temporal high spatial resolution imagery , 2006 .

[116]  Takeo Tadono,et al.  CEOS Analysis Ready Data for Land (CARD4L) Overview , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[117]  David Frantz,et al.  FORCE - Landsat + Sentinel-2 Analysis Ready Data and Beyond , 2019, Remote. Sens..

[118]  Desheng Liu,et al.  A Spatial-Temporal Modeling Approach to Reconstructing Land-Cover Change Trajectories from Multi-temporal Satellite Imagery , 2012 .

[119]  Mathew R. Schwaller,et al.  On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[120]  M. Hansen,et al.  Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series , 2020 .

[121]  Carl H. Key,et al.  Landscape Assessment (LA) , 2006 .

[122]  C. Willmott.  Some Comments on the Evaluation of Model Performance , 1982 .

[123]  R. Lucas,et al.  New global forest/non-forest maps from ALOS PALSAR data (2007–2010) , 2014 .

[124]  Yongqiang Zhang,et al.  A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[125]  Damien Sulla-Menashe,et al.  MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .

[126]  Hang Zhang,et al.  AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data , 2020, ArXiv.

[127]  Guang Xu,et al.  Generating a series of land covers by assimilating the existing land cover maps , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[128]  Naoto Yokoya,et al.  Nonlocal Tensor Completion for Multitemporal Remotely Sensed Images’ Inpainting , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[129]  Xinghua Li,et al.  Cloud removal in remote sensing images using nonnegative matrix factorization and error correction , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[130]  Yong Wang,et al.  An empirical and radiative transfer model based algorithm to remove thin clouds in visible bands , 2016 .

[131]  Anna Veronika Dorogush,et al.  CatBoost: gradient boosting with categorical features support , 2018, ArXiv.

[132]  K. Seto,et al.  Urban land teleconnections and sustainability , 2012, Proceedings of the National Academy of Sciences.

[133]  Gregory Giuliani,et al.  Earth Observation Open Science: Enhancing Reproducible Science Using Data Cubes , 2019, Data.

[134]  Christopher E. Holden,et al.  Generating synthetic Landsat images based on all available Landsat data: Predicting Landsat surface reflectance at any given time , 2015 .

[135]  Emma Izquierdo-Verdiguier,et al.  Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud , 2020, Remote sensing of environment.

[136]  E. Lambin,et al.  Dynamics of Land-Use and Land-Cover Change in Tropical Regions , 2003 .