Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine
暂无分享,去创建一个
Xiao Xiang Zhu | Michael Schmitt | Chunping Qiu | Lei Ma | Qingyu Li | Lei Ma | Xiaoxiang Zhu | M. Schmitt | Qingyu Li | C. Qiu
[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 .