Land Cover Classification using Google Earth Engine and Random Forest Classifier - The Role of Image Composition

[1]  Joanne C. White,et al.  Land cover 2.0 , 2018 .

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

[3]  Franz Makeschin,et al.  A multi-criteria approach for an integrated land-cover-based assessment of ecosystem services provision to support landscape planning , 2012 .

[4]  Chris Bacon,et al.  Using Google Earth Engine to Map Complex Shade-Grown Coffee Landscapes in Northern Nicaragua , 2018, Remote. Sens..

[5]  A. Huete,et al.  A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[6]  Masoud Mahdianpari,et al.  Google Earth Engine for geo-big data applications: A meta-analysis and systematic review , 2020 .

[7]  Michael A. Wulder,et al.  Landsat continuity: Issues and opportunities for land cover monitoring , 2008 .

[8]  Ryan N. Engstrom,et al.  Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine , 2018, Remote. Sens..

[9]  Laurence Hubert-Moy,et al.  Evaluation of Sentinel-2 time-series for mapping floodplain grassland plant communities , 2019, Remote Sensing of Environment.

[10]  Andrea Nascetti,et al.  Monitoring of Urbanization and Analysis of Environmental Impact in Stockholm with Sentinel-2A and SPOT-5 Multispectral Data , 2019, Remote. Sens..

[11]  Daniel R. Richards,et al.  Global Changes in Urban Vegetation Cover , 2019, Remote. Sens..

[12]  Bardan Ghimire,et al.  An Evaluation of Bagging, Boosting, and Random Forests for Land-Cover Classification in Cape Cod, Massachusetts, USA , 2012 .

[13]  David Morin,et al.  Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series , 2017, Remote. Sens..

[14]  P. Beckschäfer.  Obtaining rubber plantation age information from very dense Landsat TM & ETM + time series data and pixel-based image compositing , 2017 .

[15]  M. Fernández-Giménez,et al.  Cross-boundary and cross-level dynamics increase vulnerability to severe winter disasters (dzud) in Mongolia , 2012 .

[16]  Victor F. Rodriguez-Galiano,et al.  Evaluation of different machine learning methods for land cover mapping of a Mediterranean area using multi-seasonal Landsat images and Digital Terrain Models , 2014, Int. J. Digit. Earth.

[17]  Martin Kappas,et al.  Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery , 2017, Sensors.

[18]  Abdulhakim M. Abdi,et al.  Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data , 2019, GIScience & Remote Sensing.

[19]  Onisimo Mutanga,et al.  Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers , 2014 .

[20]  R. Reid,et al.  Mongolian rangelands at a tipping point? Biomass and cover are stable but composition shifts and richness declines after 20 years of grazing and increasing temperatures. , 2015 .

[21]  Lukas W. Lehnert,et al.  Monitoring Oil Exploitation Infrastructure and Dirt Roads with Object-Based Image Analysis and Random Forest in the Eastern Mongolian Steppe , 2020, Remote. Sens..

[22]  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..

[23]  Onisimo Mutanga,et al.  Remote Sensing of Above-Ground Biomass , 2017, Remote. Sens..

[24]  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..

[25]  M. Herold,et al.  Revisiting land cover observation to address the needs of the climate modeling community , 2011 .

[26]  Xingfa Gu,et al.  Land cover classification using Landsat 8 Operational Land Imager data in Beijing, China , 2014 .

[27]  David P. Roy,et al.  Wetland mapping in the Congo Basin using optical and radar remotely sensed data and derived topographical indices , 2010 .

[28]  Koreen Millard,et al.  On the Importance of Training Data Sample Selection in Random Forest Image Classification: A Case Study in Peatland Ecosystem Mapping , 2015, Remote. Sens..

[29]  Weimin Huang,et al.  Canadian Wetland Inventory using Google Earth Engine: The First Map and Preliminary Results , 2019, Remote. Sens..

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

[31]  Run Wang,et al.  Mapping US Urban Extents from MODIS Data Using One-Class Classification Method , 2015, Remote. Sens..

[32]  L. Disperati,et al.  Assessment of land-use and land-cover changes from 1965 to 2014 in Tam Giang-Cau Hai Lagoon, central Vietnam , 2015 .

[33]  Joanne C. White,et al.  Disturbance-Informed Annual Land Cover Classification Maps of Canada's Forested Ecosystems for a 29-Year Landsat Time Series , 2018 .

[34]  C. C. Lautenbacher.  The Global Earth Observation System of Systems: Science Serving Society , 2006 .

[35]  P. Reich,et al.  Diversity and Productivity in a Long-Term Grassland Experiment , 2001, Science.

[36]  J. Hill,et al.  Phenology-adaptive pixel-based compositing using optical earth observation imagery , 2017 .

[37]  R. G. Oderwald,et al.  Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. , 1983 .

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

[39]  Patrick Hostert,et al.  A Pixel-Based Landsat Compositing Algorithm for Large Area Land Cover Mapping , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[40]  M. Mahdianpari,et al.  Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery , 2017 .

[41]  Jon Atli Benediktsson,et al.  Hyperspectral Image Classification With Rotation Random Forest Via KPCA , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[42]  C. Woodcock,et al.  Continuous change detection and classification of land cover using all available Landsat data , 2014 .

[43]  F. Woodward,et al.  Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate , 2010, Science.

[44]  Benjamin Smith,et al.  Importance of vegetation dynamics for future terrestrial carbon cycling , 2015 .

[45]  R. Reid,et al.  Exploring linked ecological and cultural tipping points in Mongolia , 2017 .

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

[47]  S. K. McFeeters.  The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .

[48]  S. McNaughton,et al.  Grazing as an Optimization Process: Grass-Ungulate Relationships in the Serengeti , 1979, The American Naturalist.

[49]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[50]  Przemyslaw Kupidura,et al.  The Comparison of Different Methods of Texture Analysis for Their Efficacy for Land Use Classification in Satellite Imagery , 2019, Remote. Sens..

[51]  Cornelius Senf,et al.  Mapping land cover in complex Mediterranean landscapes using Landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery , 2015 .

[52]  A. Huete,et al.  A feedback based modification of the NDVI to minimize canopy background and atmospheric noise , 1995 .

[53]  C. Everson,et al.  Grasslands-more important for ecosystem services than you might think , 2019, Ecosphere.

[54]  Huadong Guo,et al.  Earth observation satellite sensors for biodiversity monitoring: potentials and bottlenecks , 2014 .

[55]  Timothy A. Warner,et al.  Implementation of machine-learning classification in remote sensing: an applied review , 2018 .

[56]  Timothy A. Warner,et al.  Large-Area, High Spatial Resolution Land Cover Mapping Using Random Forests, GEOBIA, and NAIP Orthophotography: Findings and Recommendations , 2019, Remote. Sens..

[57]  M. Herold,et al.  Fusing Landsat and SAR time series to detect deforestation in the tropics , 2015 .

[58]  D. Roy,et al.  A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin , 2008 .

[59]  Doreen S. Boyd,et al.  Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach , 2016, Remote. Sens..

[60]  R. B. Jackson,et al.  A Large and Persistent Carbon Sink in the World’s Forests , 2011, Science.

[61]  Xiaoping Liu,et al.  Land-cover mapping using Random Forest classification and incorporating NDVI time-series and texture: a case study of central Shandong , 2018, International Journal of Remote Sensing.

[62]  Zhe Zhu,et al.  Toward near real-time monitoring of forest disturbance by fusion of MODIS and Landsat data , 2013 .

[63]  Alexis J. Comber,et al.  Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data , 2014 .

[64]  A. Ducharne,et al.  The impact of global land-cover change on the terrestrial water cycle , 2013 .

[65]  L.L.F. Janssen,et al.  Accuracy assessment of satellite derived land - cover data : a review , 1994 .

[66]  T. Boucher,et al.  Measuring the Impacts of Community-based Grasslands Management in Mongolia's Gobi , 2012, PloS one.

[67]  D. Tilman,et al.  Productivity and sustainability influenced by biodiversity in grassland ecosystems , 1996, Nature.

[68]  Eric Pottier,et al.  Evaluation of Using Sentinel-1 and -2 Time-Series to Identify Winter Land Use in Agricultural Landscapes , 2018, Remote. Sens..

[69]  C. Woodcock,et al.  Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data , 2012 .

[70]  Yuan Gao,et al.  Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine , 2019, Remote. Sens..

[71]  G. Birth,et al.  Measuring the Color of Growing Turf with a Reflectance Spectrophotometer1 , 1968 .

[72]  Francisco Alonso-Sarría,et al.  Modification of the random forest algorithm to avoid statistical dependence problems when classifying remote sensing imagery , 2017, Comput. Geosci..

[73]  S. Seneviratne,et al.  Climate extremes and the carbon cycle , 2013, Nature.

[74]  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.

[75]  Jozef Syktus,et al.  Land use and land cover change impacts on the regional climate of non-Amazonian South America: a review. , 2015 .

[76]  P. C. Smits,et al.  QUALITY ASSESSMENT OF IMAGE CLASSIFICATION ALGORITHMS FOR LAND-COVER MAPPING , 1999 .

[77]  Stefan Dech,et al.  A semi-automated approach for the generation of a new land use and land cover product for Germany based on Landsat time-series and Lucas in-situ data , 2017 .

[78]  R. Reid,et al.  Dynamics and Resilience of Rangelands and Pastoral Peoples Around the Globe , 2014 .

[79]  Lola Fatoyinbo,et al.  Cloud-computing and machine learning in support of country-level land cover and ecosystem extent mapping in Liberia and Gabon , 2020, PloS one.

[80]  R. Naiman,et al.  Large African herbivores decrease herbaceous plant biomass while increasing plant species richness in a semi-arid savanna toposequence , 2008 .

[81]  Rasmus Fensholt,et al.  Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery , 2014 .

[82]  Gabriela Augusto,et al.  Ecosystem services and biodiversity trends in Mozambique as a consequence of land cover change , 2017 .

[83]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

[84]  Xavier Blaes,et al.  Estimating smallholder crops production at village level from Sentinel-2 time series in Mali's cotton belt , 2018, Remote Sensing of Environment.

[85]  G. Foody.  Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy , 2004 .

[86]  David P. Roy,et al.  The global Landsat archive: Status, consolidation, and direction , 2016 .

[87]  G. Foody.  Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification , 2020, Remote Sensing of Environment.

[88]  Lizhe Wang,et al.  A Comparison of Machine Learning Algorithms for Mapping of Complex Surface-Mined and Agricultural Landscapes Using ZiYuan-3 Stereo Satellite Imagery , 2016, Remote. Sens..

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

[90]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .