Phân loại lớp phủ đất bằng Google Earth Engine và Bộ phân loại rừng ngẫu nhiên—Vai trò của việc hợp thành hình ảnh

Herold, M. (2009). Assessment of the Status of the Development of the Standards for the Terrestrial Essential Climate Variables. Land. Land Cover, FAO.

Koschke, 2012, A multi-criteria approach for an integrated land-cover-based assessment of ecosystem services provision to support landscape planning, Ecol. Indic., 21, 54, 10.1016/j.ecolind.2011.12.010

Sterling, 2012, The impact of global land-cover change on the terrestrial water cycle, Nat. Clim. Chang., 3, 385, 10.1038/nclimate1690

Salazar, 2015, Land use and land cover change impacts on the regional climate of non-Amazonian South America: A review, Glob. Planet. Chang., 128, 103, 10.1016/j.gloplacha.2015.02.009

Niquisse, 2017, Ecosystem services and biodiversity trends in Mozambique as a consequence of land cover change, Int. J. Biodivers. Sci. Ecosyst. Serv. Manag., 13, 297, 10.1080/21513732.2017.1349836

Beer, 2010, Terrestrial gross carbon dioxide uptake: Global distribution and covariation with climate, Science, 329, 834, 10.1126/science.1184984

Pan, 2011, A large and persistent carbon sink in the world’s forests, Science, 333, 988, 10.1126/science.1201609

Reichstein, 2013, Climate extremes and the carbon cycle, Nature, 500, 287, 10.1038/nature12350

Ahlström, A., Xia, J., Arneth, A., Luo, Y., and Smith, B. (2015). Importance of vegetation dynamics for future terrestrial carbon cycling. Environ. Res. Lett., 10.

Bengtsson, 2019, Grasslands—more important for ecosystem services than you might think, Ecosphere, 10, e02582, 10.1002/ecs2.2582

Batkhishig, 2012, Cross-boundary and cross-level dynamics increase vulnerability to severe winter disasters (dzud) in Mongolia, Glob. Environ. Chang., 22, 836, 10.1016/j.gloenvcha.2012.07.001

Reid, 2014, Dynamics and Resilience of Rangelands and Pastoral Peoples around the Globe, Annu. Rev. Environ. Resour., 39, 217, 10.1146/annurev-environ-020713-163329

Khishigbayar, 2015, 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, J. Arid Environ., 115, 100, 10.1016/j.jaridenv.2015.01.007

Venable, 2017, Exploring linked ecological and cultural tipping points in Mongolia, Anthropocene, 17, 46, 10.1016/j.ancene.2017.01.003

Dashpurev, B., Bendix, J., and Lehnert, L. (2020). Monitoring Oil Exploitation Infrastructure and Dirt Roads with Object-Based Image Analysis and Random Forest in the Eastern Mongolian Steppe. Remote Sens., 12.

McNaughton, 1979, Grazing as an optimization process: Grass-ungulate relationships in the Serengeti, Am. Nat., 113, 691, 10.1086/283426

Tilman, 1996, Productivity and sustainability influenced by biodiversity in grassland ecosystems, Nature, 379, 718, 10.1038/379718a0

Tilman, 2001, Diversity and productivity in a long-term grassland experiment, Science, 294, 843, 10.1126/science.1060391

Jacobs, 2008, Large African herbivores decrease herbaceous plant biomass while increasing plant species richness in a semi-arid savanna toposequence, J. Arid Environ., 72, 891, 10.1016/j.jaridenv.2007.11.015

Leisher, C., Hess, S., Boucher, T.M., Beukering, P., and Sanjayan, M. (2012). Measuring the impacts of community-based grasslands management in Mongolia’s Gobi. PLoS ONE, 7.

Skole, D.S., Justice, C.O., Janetos, A., and Townshend, J.R.G. (1997). A land cover change monitoring program: A strategy for international effort. Mitigation and Adaptation Strategies for Global Change, Kluwer.

Lautenbacher, 2006, The Global Earth Observation System of Systems: Science Serving Society, Space Policy, 22, 8, 10.1016/j.spacepol.2005.12.004

Bontemps, 2012, Revisiting land cover observation to address the needs of the climate modeling community, Biogeosciences, 9, 2145, 10.5194/bg-9-2145-2012

Roy, 2014, Landsat-8: Science and product vision for terrestrial global change research, Remote Sens. Environ., 145, 154, 10.1016/j.rse.2014.02.001

Li, 2017, The first all-season sample set for mapping global land cover with Landsat-8 data, Sci. Bull., 7, 508, 10.1016/j.scib.2017.03.011

Wulder, 2016, The global Landsat archive: Status, consolidation and direction, Remote Sens. Environ., 185, 271, 10.1016/j.rse.2015.11.032

Disperati, 2015, Assessment of land-use and land-cover changes from 1965 to 2014 in Tam Giang-Cau Hai Lagoon, central Vietnam, Appl. Geogr., 58, 48, 10.1016/j.apgeog.2014.12.012

Reiche, 2015, Fusing Landsat and SAR time series to detect deforestation in the tropics, Remote Sens. Environ., 156, 276, 10.1016/j.rse.2014.10.001

Zhu, 2012, Assessment of spectral, polarimetric, temporal and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data, Remote Sens. Environ., 117, 72, 10.1016/j.rse.2011.07.020

Wan, 2015, Mapping US Urban Extents from MODIS Data Using One-Class Classification Method, Remote Sens., 7, 10143, 10.3390/rs70810143

Xin, 2013, Toward near real-time monitoring of forest disturbance by fusion of MODIS and Landsat data, Remote Sens. Environ., 135, 234, 10.1016/j.rse.2013.04.002

Thanh Noi, P., and Kappas, M. (2017). Comparison of Random Forest, k-Nearest Neighbor and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery. Sensors, 18.

Lambert, 2018, Estimating smallholder crops production at village level from Sentinel-2 time series in Mali’s cotton belt, Remote Sens. Environ., 216, 647, 10.1016/j.rse.2018.06.036

Rapinel, 2019, Evaluation of Sentinel-2 time-series for mapping floodplain grassland plant communities, Remote Sens. Environ., 223, 115, 10.1016/j.rse.2019.01.018

Furberg, D., Ban, Y., and Nascetti, A. (2019). Monitoring of Urbanization and Analysis of Environmental Impact in Stockholm with Sentinel-2A and SPOT-5 Multispectral Data. Remote Sens., 11.

Kuenzer, 2014, Earth observation satellite sensors for biodiversity monitoring: Potentials and bottlenecks, Inter. J. Remote Sens., 35, 6599, 10.1080/01431161.2014.964349

Mack, 2017, 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, Remote Sens. Lett., 8, 244, 10.1080/2150704X.2016.1249299

Wulder, 2008, Landsat continuity: Issues and opportunities for land cover monitoring, Remote Sens. Environ., 112, 955, 10.1016/j.rse.2007.07.004

Carrasco, L., O’Neil, A., Morton, R., and Rowland, C. (2019). Evaluating Combinations of Temporally Aggregated Sentinel-1, Sentinel-2 and Landsat 8 for Land Cover Mapping with Google Earth Engine. Remote Sens., 11.

Gorelick, 2017, Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18, 10.1016/j.rse.2017.06.031

Tamiminia, 2020, Google Earth Engine for geo-big data applications: A meta-analysis and systematic review, ISPRS J. Photogramm. Remote Sens., 164, 152, 10.1016/j.isprsjprs.2020.04.001

Kumar, L., and Mutanga, O. (2017). Remote Sensing of Above-Ground Biomass. Remote Sens., 9.

2017, Obtaining rubber plantation age information from very dense Landsat TM & ETM + time series data and pixel-based image compositing, Remote Sens. Environ., 196, 89, 10.1016/j.rse.2017.04.003

E Nyland, K., EGunn, G., IShiklomanov, N., NEngstrom, R., and AStreletskiy, D. (2018). Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine. Remote Sens., 10.

Xie, S., Liu, L., Zhang, X., Yang, J., Chen, X., and Gao, Y. (2019). Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine. Remote Sens., 11.

Hu, Y., and Hu, Y. (2019). Land Cover Changes and Their Driving Mechanisms in Central Asia from 2001 to 2017 Supported by Google Earth Engine. Remote Sens., 11.

Richards, D.R., and Belcher, R.N. (2019). Global Changes in Urban Vegetation Cover. Remote Sens., 12.

Griffiths, 2013, A Pixel-Based Landsat Compositing Algorithm for Large Area Land Cover Mapping, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 6, 2088, 10.1109/JSTARS.2012.2228167

Zhu, 2014, Continuous change detection and classification of land cover using all available Landsat data, Remote Sens. Environ., 144, 152, 10.1016/j.rse.2014.01.011

Hermosilla, 2018, Disturbance-Informed Annual Land Cover Classification Maps of Canada’s Forested Ecosystems for a 29-Year Landsat Time Series, Can. J. Remote Sens., 44, 67, 10.1080/07038992.2018.1437719

Roy, 2016, Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity, Remote Sens. Environ., 185, 57, 10.1016/j.rse.2015.12.024

Griffiths, 2013, Erratum: A pixel-based landsat compositing algorithm for large area land cover, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 6, 2088, 10.1109/JSTARS.2012.2228167

Denize, J., Hubert-Moy, L., Betbeder, J., Corgne, S., Baudry, J., and Pottier, E. (2018). Evaluation of Using Sentinel-1 and -2 Time-Series to Identify Winter Land Use in Agricultural Landscapes. Remote Sens., 11.

Kupidura, P. (2019). The Comparison of Different Methods of Texture Analysis for Their Efficacy for Land Use Classification in Satellite Imagery. Remote Sens., 11.

Li, X., Chen, W., Cheng, X., and Wang, L. (2016). A Comparison of Machine Learning Algorithms for Mapping of Complex Surface-Mined and Agricultural Landscapes Using ZiYuan-3 Stereo Satellite Imagery. Remote Sens., 8.

Jin, 2018, Land-cover mapping using Random Forest classification and incorporating NDVI time-series and texture: A case study of central Shandong, Inter. J. Remote Sens., 39, 8703, 10.1080/01431161.2018.1490976

(2020, June 15). Planet Satellite Imagery Products. Available online: https://www.planet.com.

Hansen, 2008, A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin, Remote Sens. Environ., 112, 2495, 10.1016/j.rse.2007.11.012

Bwangoy, 2010, Wetland mapping in the Congo Basin using optical and radar remotely sensed data and derived topographical indices, Remote Sens. Environ., 114, 73, 10.1016/j.rse.2009.08.004

De Sousa, C., Fatoyinbo, L., Neigh, C., Boucka, F., Angoue, V., and Larsen, T. (2020). Cloud-computing and machine learning in support of country-level land cover and ecosystem extent mapping in Liberia and Gabon. PLoS ONE, 15.

Millard, 2015, On the Importance of Training Data Sample Selection in Random Forest Image Classification: A Case Study in Peatland Ecosystem Mapping, Remote Sens., 7, 8489, 10.3390/rs70708489

2017, Modification of the random forest algorithm to avoid statistical dependence problems when classifying remote sensing imagery, Comput. Geosci., 103, 1, 10.1016/j.cageo.2017.02.012

Maxwell, A.E., Strager, M.P., Warner, T.A., Ramezan, C.A., Morgan, A.N., and Pauley, C.E. (2019). Large-Area, High Spatial Resolution Land Cover Mapping Using Random Forests, GEOBIA and NAIP Orthophotography: Findings and Recommendations. Remote Sens., 11.

Kelley, L.C., Pitcher, L., and Bacon, C. (2018). Using Google Earth Engine to Map Complex Shade-Grown Coffee Landscapes in Northern Nicaragua. Remote Sens., 10.

Teluguntla, 2018, 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, ISPRS J. Photogramm. Remote Sens., 144, 325, 10.1016/j.isprsjprs.2018.07.017

Amani, M., Mahdavi, S., Afshar, M., Brisco, B., Huang, W., Mohammad Javad Mirzadeh, S., White, L., Banks, S., Montgomery, J., and Hopkinson, C. (2019). Canadian Wetland Inventory using Google Earth Engine: The First Map and Preliminary Results. Remote Sens., 11.

Mahdianpari, 2017, Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band and TerraSAR-X imagery, ISPRS J. Photogramm. Remote Sens., 130, 13, 10.1016/j.isprsjprs.2017.05.010

Xia, 2017, Hyperspectral Image Classification With Rotation Random Forest Via KPCA, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 10, 1601, 10.1109/JSTARS.2016.2636877

2012, Evaluation of different machine learning methods for land cover mapping of a Mediterranean area using multi-seasonal Landsat images and Digital Terrain Models, Inter. J. Digital Earth, 7, 492

Mutanga, 2014, Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers, ISPRS J. Photogramm. Remote Sens., 88, 48, 10.1016/j.isprsjprs.2013.11.013

Comber, 2014, Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data, Remote Sens. Environ., 149, 118, 10.1016/j.rse.2014.04.010

Maxwell, 2018, Implementation of machine-learning classification in remote sensing: An applied review, Inter. J. Remote Sens., 39, 2784, 10.1080/01431161.2018.1433343

Ghimire, 2013, An Evaluation of Bagging, Boosting and Random Forests for Land-Cover Classification in Cape Cod, Massachusetts, USA, GISci. Remote Sens., 49, 623, 10.2747/1548-1603.49.5.623

Foody, G.M. (2020). Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification. Remote Sens. Environ., 239.

Foody, 2004, Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy, Photogramm. Eng. Remote Sens., 70, 627, 10.14358/PERS.70.5.627

Janssen, 1994, Accuracy assessment of satellite derived land-cover data: A review, Photogramm. Eng. Remote Sens., 60, 419

Congalton, 1983, Assessing Landsat classification accuracy using discrete multivariate-analysis statistical techniques, Photogramm. Eng. Remote Sens., 49, 1671

Smits, 1999, Quality assessment of image classification algorithms for land cover mapping: A review and a proposal for a cost based approach, Int. J. Remote Sens., 20, 1461, 10.1080/014311699212560

Agresti, A. (2007). An Introduction to Categorical Data Analysis, John Wiley & Sons.

Momeni, R., Aplin, P., and Boyd, D. (2016). Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach. Remote Sens., 8.

Senf, 2015, Mapping land cover in complex Mediterranean landscapes using Landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery, Remote Sens. Environ., 156, 527, 10.1016/j.rse.2014.10.018

Inglada, J., Vincent, A., Arias, M., Tardy, B., Morin, D., and Rodes, I. (2017). Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series. Remote Sens., 9.

Wulder, 2018, Land cover 2.0, Inter. J. Remote Sens., 39, 4254, 10.1080/01431161.2018.1452075

Frantz, 2017, Phenology-adaptive pixel-based compositing using optical earth observation imagery, Remote Sens. Environ., 190, 331, 10.1016/j.rse.2017.01.002

Abdi, 2020, Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data, GISci. Remote Sens., 57, 1, 10.1080/15481603.2019.1650447

Zha, 2003, Use of normalized difference built-up index in automatically mapping urban areas from TM imagery, Int. J. Remote Sens., 24, 583, 10.1080/01431160304987

Feyisa, 2014, Automated water extraction index: A new technique for surface water mapping using Landsat imagery, Remote Sens. Environ., 140, 23, 10.1016/j.rse.2013.08.029

Rouse, J., Hass, R., Schell, J., and Deering, D. (1973). Monitoring vegetation systems in the great plains with ERTS. Third ERTS Symposium, NASASP-351 I.

Liu, 1995, A feedback based modification of the NDV I to minimize canopy background and atmospheric noise, IEEE Trans. Geosci. Remote Sens., 33, 457, 10.1109/TGRS.1995.8746027

Huete, 1988, A Soil-Adjusted Vegetation Index (SAVI), Remote Sens. Environ., 25, 295, 10.1016/0034-4257(88)90106-X

Qi, 1994, A modified soil adjusted vegetation index, Remote Sens. Environ., 48, 119, 10.1016/0034-4257(94)90134-1

Gao, 1996, NDWI—A normalized difference water index for remote sensing of vegetation liquid water fromspace, Remote Sens. Environ., 58, 257, 10.1016/S0034-4257(96)00067-3

Xu, 2006, Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery, Int. J. Remote Sens., 27, 3025, 10.1080/01431160600589179

McFeeters, 1996, The use of the normalized difference water index (NDWI) in the delineation of open water features, Int. J. Remote Sens., 17, 1425, 10.1080/01431169608948714

Birth, 1968, Measuring the Color of Growing Turf with a Reflectance Spectrophotometer, Agron. J., 60, 640, 10.2134/agronj1968.00021962006000060016x

Jia, 2014, Land cover classification using Landsat 8 operational land imager data in Beijing, China, Geocarto Int., 29, 941, 10.1080/10106049.2014.894586