Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine

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

[2]  Liu Hui,et al.  Integrating Multiple Textural Features for Remote Sensing Image Change Detection , 2017 .

[3]  Benjamin Bechtel,et al.  Global transferability of local climate zone models , 2019, Urban Climate.

[4]  Rudi Goossens,et al.  Monitoring land use/land cover change using multi-temporal Landsat satellite images in an arid environment: a case study of El-Arish, Egypt , 2013, Arabian Journal of Geosciences.

[5]  Thomas Blaschke,et al.  Evaluation of Feature Selection Methods for Object-Based Land Cover Mapping of Unmanned Aerial Vehicle Imagery Using Random Forest and Support Vector Machine Classifiers , 2017, ISPRS Int. J. Geo Inf..

[6]  Yang Hu,et al.  Land Cover Changes and Their Driving Mechanisms in Central Asia from 2001 to 2017 Supported by Google Earth Engine , 2019, Remote. Sens..

[7]  Xiao Xiang Zhu,et al.  Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network , 2019, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

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

[9]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[10]  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.

[11]  Xiaoping Liu,et al.  High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform , 2018 .

[12]  Zhe Zhu,et al.  Cloud detection algorithm comparison and validation for operational Landsat data products , 2017 .

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

[14]  Timothy R. Oke,et al.  Evaluation of the ‘local climate zone’ scheme using temperature observations and model simulations , 2014 .

[15]  Hui Liu,et al.  Assessing and Improving the Accuracy of GlobeLand30 Data for Urban Area Delineation by Combining Multisource Remote Sensing Data , 2016, IEEE Geoscience and Remote Sensing Letters.

[16]  Mariana Belgiu,et al.  Random forest in remote sensing: A review of applications and future directions , 2016 .

[17]  Luis Carrasco,et al.  Evaluating Combinations of Temporally Aggregated Sentinel-1, Sentinel-2 and Landsat 8 for Land Cover Mapping with Google Earth Engine , 2019, Remote. Sens..

[18]  Xiao Xiang Zhu,et al.  Data Fusion and Remote Sensing: An ever-growing relationship , 2016, IEEE Geoscience and Remote Sensing Magazine.

[19]  Giorgos Mountrakis,et al.  A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research , 2016 .

[20]  Thomas Blaschke,et al.  A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[21]  Rajashree Kotharkar,et al.  Evaluating urban heat island in the critical local climate zones of an Indian city , 2018 .

[22]  Peng Jiang,et al.  Assessing Impacts of Urban Form on Landscape Structure of Urban Green Spaces in China Using Landsat Images Based on Google Earth Engine , 2018, Remote. Sens..

[23]  Xiaofeng Li,et al.  Improved land cover mapping using random forests combined with landsat thematic mapper imagery and ancillary geographic data. , 2010 .

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

[25]  Alemayehu Midekisa,et al.  Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing , 2017, PloS one.

[26]  T. Oke,et al.  Local Climate Zones for Urban Temperature Studies , 2012 .

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

[28]  Justin Braaten,et al.  Automated cloud and cloud shadow identification in Landsat MSS imagery for temperate ecosystems , 2015 .

[29]  Martha C. Anderson,et al.  Landsat-8: Science and Product Vision for Terrestrial Global Change Research , 2014 .

[30]  Peijun Du,et al.  A review of supervised object-based land-cover image classification , 2017 .

[31]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[32]  Sérgio Freire,et al.  Unveiling 25 Years of Planetary Urbanization with Remote Sensing: Perspectives from the Global Human Settlement Layer , 2018, Remote. Sens..

[33]  R. DeFries,et al.  Effects of Land Cover Conversion on Surface Climate , 2002 .

[34]  Annemarie Schneider,et al.  Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach , 2012 .

[35]  Lilik Budi Prasetyo,et al.  Random Forest Classification for Mangrove Land Cover Mapping Using Landsat 5 TM and Alos Palsar Imageries , 2015 .

[36]  David A. Seal,et al.  The Shuttle Radar Topography Mission , 2007 .

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

[38]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[39]  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 .

[40]  Yuqi Bai,et al.  Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine , 2017 .

[41]  Ryutaro Tateishi,et al.  Production of global land cover data – GLCNMO , 2011, Int. J. Digit. Earth.

[42]  Edzer Pebesma,et al.  Using Google Earth Engine to detect land cover change: Singapore as a use case , 2018 .

[43]  Niklaus E. Zimmermann,et al.  Impacts of land cover and climate data selection on understanding terrestrial carbon dynamics and the CO 2 airborne fraction , 2011 .

[44]  C. Elvidge,et al.  Why VIIRS data are superior to DMSP for mapping nighttime lights , 2013 .