Gómez, C., White, J. C. & Wulder, M. A. Optical remotely sensed time series data for land cover classification: A review. ISPRS J. Photogramm. Remote. Sens. 116, 55–72 (2016).
Google Scholar
Dale, V. H. The relationship between land-use change and climate change. Ecol. Appl. 7, 753–769 (1997).
Google Scholar
Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Chang. 3, 52–58 (2013).
Google Scholar
Houghton, R. A. et al. Carbon emissions from land use and land-cover change. Biogeosciences 9, 5125–5142 (2012).
Google Scholar
Lambin, E. F. et al. The causes of land-use and land-cover change: Moving beyond the myths. Glob. Environ. Chang. 11, 261–269 (2001).
Google Scholar
Song, X. P. et al. Global land change from 1982 to 2016. Nature 560, 639–643 (2018).
Google Scholar
Grimm, N. B. et al. Global change and the ecology of cities. Science 319, 756–760 (2008).
Google Scholar
Utkin, L. V. et al. A weighted random survival forest. Knowl. Based Syst. 177, 136–144 (2019).
Google Scholar
Gašparović, M., Zrinjski, M. & Gudelj, M. Automatic cost-effective method for land cover classification (ALCC). Comput. Environ. Urban Syst. 76, 1–10 (2019).
Google Scholar
Hu, Y., Dong, Y. & Batunacun. ,. An automatic approach for land-change detection and land updates based on integrated NDVI timing analysis and the CVAPS method with GEE support. ISPRS J. Photogramm. Remote Sens. 146, 347–359 (2018).
Google Scholar
ESA. Sentinel-2 Mission Requirements Document. Earth (2007).
Main-Knorn, M. et al. Sen2Cor for Sentinel-2. In 3 (2017). https://doi.org/10.1117/12.2278218.
Gong, P. 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. Sci. Bull. 64, 370–373 (2019).
Google Scholar
Homer, C., Huang, C., Yang, L., Wylie, B. & Coan, M. Development of a 2001 National Land-Cover Database for the United States. Photogramm. Eng. Remote. Sens. 70, 829–840 (2004).
Google Scholar
Fry, J. A. et al. Completion of the 2006 national land cover database for the conterminous united states. Photogramm. Eng. Remote Sens. 77, 858–864 (2011).
Homer, C. et al. Completion of the 2011 national land cover database for the conterminous United States—Representing a decade of land cover change information. Photogramm. Eng. Remote Sens. 81, 345–354 (2015).
Yang, L. et al. A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies. ISPRS J. Photogramm. Remote Sens. 146, 108–123 (2018).
Google Scholar
Chen, J. et al. Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS J. Photogramm. Remote Sens. 103, 7–27 (2015).
Google Scholar
Hoang, T. T., Truong, V. T., Hayashi, M., Tadono, T. & Nasahara, K. N. New JAXA high-resolution land use/land cover map for Vietnam aiming for natural forest and plantation forest monitoring. Remote Sens. 12, 2707 (2020).
Phan, D. C., Trung, T. H., Nasahara, K. N. & Tadono, T. JAXA high-resolution land use/land cover map for Central Vietnam in 2007 and 2017. Remote Sens. 10, 1406 (2018).
Google Scholar
Nemani, R. Nasa earth exchange: Next generation earth science collaborative. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XXXVIII–8/, 17–17 (2012).
Mugiraneza, T., Nascetti, A. & Ban, Y. Continuous monitoring of urban land cover change trajectories with landsat time series and landtrendr-google earth engine cloud computing. Remote Sens. 12, 2883 (2020).
Jin, Y., Liu, X., Yao, J., Zhang, X. & Zhang, H. Mapping the annual dynamics of cultivated land in typical area of the Middle-lower Yangtze plain using long time-series of Landsat images based on Google Earth Engine. Int. J. Remote Sens. 41, 1625–1644 (2020).
Google Scholar
Hu, Y. & Hu, Y. Detecting forest disturbance and recovery in Primorsky Krai, Russia, using annual landsat time series and multi-source land cover products. Remote Sens. 12, 129 (2020).
Google Scholar
Miettinen, J., Shi, C. & Liew, S. C. 2015 Land cover map of Southeast Asia at 250 m spatial resolution. Remote Sens. Lett. 7, 701–710 (2016).
Google Scholar
Ghorbanian, A. et al. Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples. ISPRS J. Photogramm. Remote Sens. 167, 276–288 (2020).
Google Scholar
Huang, H. et al. The migration of training samples towards dynamic global land cover mapping. ISPRS J. Photogramm. Remote Sens. 161, 27–36 (2020).
Google Scholar
Bagan, H. & Yamagata, Y. Land-cover change analysis in 50 global cities by using a combination of Landsat data and analysis of grid cells. Environ. Res. Lett. 9, 064015 (2014).
Radoux, J. et al. Automated training sample extraction for global land cover mapping. Remote Sens. 6, 3965–3987 (2014).
Google Scholar
Tran, H., Tran, T. & Kervyn, M. Dynamics of land cover/land use changes in the Mekong Delta, 1973–2011: A Remote sensing analysis of the Tran Van Thoi District, Ca Mau Province, Vietnam. Remote Sens. 7, 2899–2925 (2015).
Google Scholar
Chi, V. K. et al. Land transitions in Northwest Vietnam: An integrated analysis of biophysical and socio-cultural factors. Hum. Ecol. 41, 37–50 (2013).
Google Scholar
Villamor, G. B., Catacutan, D. C., Truong, V. A. T. & Thi, L. D. Tree-cover transition in Northern Vietnam from a gender-specific land-use preferences perspective. Land Use Policy 61, 53–62 (2017).
Google Scholar
Truong, V. T. et al. JAXA annual forest cover maps for Vietnam during 2015–2018 Using ALOS-2/PALSAR-2 and auxiliary data. Remote Sens. 11, 2412 (2019).
Google Scholar
And, R. D. of A. Vietnam’S Modified Submission on Refreence Levels for Redd+ Results Based Payments Under Unfccc. https://redd.unfccc.int/files/vietnam_frl_modified__submission_final_for_posting.pdf (2016).
Xu, X., Jain, A. K. & Calvin, K. V. Quantifying the biophysical and socioeconomic drivers of changes in forest and agricultural land in South and Southeast Asia. Glob. Chang. Biol. 25, 2137–2151 (2019).
Google Scholar
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science (80- ) 342, 850–853 (2013).
Google Scholar
Di Gregorio, A., and Jansen, L. J. M. Land Cover Classification System (LCCS): Classification Concepts and User Manual. Fao http://www.fao.org/3/x0596e/x0596e00.htm (2000).
Van Thinh, T., Phan, D. C., Nasahara, K. N. & Tadono, T. How does land use/land cover map’s accuracy depend on number of classification classes? Sci. Online Lett. Atmos. 15, 28–31 (2019).
Talukdar, S. et al. Land-use land-cover classification by machine learning classifiers for satellite observations—a review. Remote Sens. 12, 1135 (2020).
Google Scholar
Song, S., Gong, W., Zhu, B. & Huang, X. Wavelength selection and spectral discrimination for paddy rice, with laboratory measurements of hyperspectral leaf reflectance. ISPRS J. Photogramm. Remote Sens. 66, 672–682 (2011).
Google Scholar
Szantoi, Z., Smith, S. E., Strona, G., Koh, L. P. & Wich, S. A. Mapping orangutan habitat and agricultural areas using Landsat OLI imagery augmented with unmanned aircraft system aerial photography. Int. J. Remote Sens. 38, 2231–2245 (2017).
Google Scholar
Hill, R. A. & Thomson, A. G. Mapping woodland species composition and structure using airborne spectral and LiDAR data. Int. J. Remote Sens. 26, 3763–3779 (2005).
Google Scholar
Kontgis, C., Schneider, A. & Ozdogan, M. Mapping rice paddy extent and intensification in the Vietnamese Mekong River Delta with dense time stacks of Landsat data. Remote Sens. Environ. 169, 255–269 (2015).
Google Scholar
Kontgis, C. et al. Monitoring peri-urbanization in the greater Ho Chi Minh City metropolitan area. Appl. Geogr. 53, 377–388 (2014).
Google Scholar
D’Amour, C. B. et al. Future urban land expansion and implications for global croplands. Proc. Natl. Acad. Sci. USA 114, 8939–8944 (2017).
Google Scholar
Ha, T. V., Tuohy, M., Irwin, M. & Tuan, P. V. Monitoring and mapping rural urbanization and land use changes using Landsat data in the northeast subtropical region of Vietnam. Egypt. J. Remote Sens. Sp. Sci. 23, 11–19 (2020).
Nhan, T. Q., Van Ly, L. T. & Tan, L. V. How much do rice farmers earn from their crops? Evidence from a rice-exporting country. J. Agric. Stud. 8, 302 (2020).
Truong, T. D. & Do, L. H. Mangrove forests and aquaculture in the Mekong river delta. Land Use Policy 73, 20–28 (2018).
Google Scholar
Lam-Dao, N., Pham-Bach, V., Nguyen-Thanh, M., Pham-Thi, M.-T. & Hoang-Phi, P. Change detection of land use and riverbank in Mekong Delta, Vietnam using time series remotely sensed data. J. Resour. Ecol. 2, 370–374 (2011).
Ha, T. T. P., van Dijk, H. & Visser, L. Impacts of changes in mangrove forest management practices on forest accessibility and livelihood: A case study in mangrove-shrimp farming system in Ca Mau Province, Mekong Delta, Vietnam. Land Use Policy 36, 89–101 (2014).
Google Scholar
Le, T. N., Bregt, A. K., van Halsema, G. E., Hellegers, P. J. G. J. & Nguyen, L. D. Interplay between land-use dynamics and changes in hydrological regime in the Vietnamese Mekong Delta. Land Use Policy 73, 269–280 (2018).
Google Scholar
Khuc, Q. V., Tran, B. Q., Meyfroidt, P. & Paschke, M. W. Drivers of deforestation and forest degradation in Vietnam: An exploratory analysis at the national level. For. Policy Econ. 90, 128–141 (2018).
Google Scholar
Meyfroidt, P., Vu, T. P. & Hoang, V. A. Trajectories of deforestation, coffee expansion and displacement of shifting cultivation in the Central Highlands of Vietnam. Glob. Environ. Chang. 23, 1187–1198 (2013).
Google Scholar
Ngo-Duc, T., Kieu, C., Thatcher, M., Nguyen-Le, D. & Phan-Van, T. Climate projections for Vietnam based on regional climate models. Clim. Res. 60, 199–213 (2014).
Google Scholar
Lindesay, J. A. et al. International geosphere-biosphere programme/international global atmospheric chemistry SAFARI-92 field experiment: Background and overview. J. Geophys. Res. Atmos. 101, 23521–23530 (1996).
Google Scholar
Klemas, V. V., Dobson, J. E., Ferguson, R. L. & Haddad, K. D. A coastal land cover classification system for the NOAA Coastwatch Change Analysis Project. J. Coast. Res. 9, 862–872 (1993).
Saah, D. et al. Primitives as building blocks for constructing land cover maps. Int. J. Appl. Earth Obs. Geoinf. 85, 101979 (2020).
Google Scholar
Keys, R. G. Cubic convolution interpolation for digital image processing. IEEE Trans. Acoust. 29, 1153–1160 (1981).
Google Scholar
Farr, T. G. et al. The shuttle radar topography mission. Rev. Geophys. 45, RG2004 (2007).
Filipponi, F. Sentinel-1 GRD preprocessing workflow. Proceedings 18, 11 (2019).
Google Scholar
Soenen, S. A., Peddle, D. R. & Coburn, C. A. SCS+C: A modified sun-canopy-sensor topographic correction in forested terrain. IEEE Trans. Geosci. Remote Sens. 43, 2148–2159 (2005).
Google Scholar
Saleous, N. & Kutler, J. LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2 Summary: Data Citation: Model Product Description: Oak Ridge National Laboratory Distributed Active Archive Center http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1146 (2012) 10.3334/ORNLDAAC/1146.
Vermote, E., Justice, C., Claverie, M. & Franch, B. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sens. Environ. 185, 46–56 (2016).
Google Scholar
Foga, S. et al. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sens. Environ. 194, 379–390 (2017).
Google Scholar
Louis, J. et al. Sentinel-2 Global Surface Reflectance Level-2a Product Generated with Sen2Cor. In International Geoscience and Remote Sensing Symposium (IGARSS) 8522–8525 (2019). https://doi.org/10.1109/IGARSS.2019.8898540.
Roy, D. P. et al. A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance. Remote Sens. Environ. 176, 255–271 (2016).
Google Scholar
Roy, D. P. et al. Examination of Sentinel-2A multi-spectral instrument (MSI) reflectance anisotropy and the suitability of a general method to normalize MSI reflectance to nadir BRDF adjusted reflectance. Remote Sens. Environ. 199, 25–38 (2017).
Google Scholar
Lee, J. S., Ainsworth, T. L., Wang, Y. & Chen, K. S. Polarimetric SAR speckle filtering and the extended sigma filter. IEEE Trans. Geosci. Remote Sens. 53, 1150–1160 (2015).
Google Scholar
Roy, D. P. et al. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sens. Environ. 185, 57–70 (2016).
Google Scholar
Claverie, M. et al. The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sens. Environ. 219, 145–161 (2018).
Google Scholar
Flood, N. Seasonal composite landsat TM/ETM+ Images using the medoid (a multi-dimensional median). Remote Sens. 5, 6481–6500 (2013).
Google Scholar
Kaufman, Y. J. & Tanré, D. Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS. IEEE Trans. Geosci. Remote Sens. 30, 261–270 (1992).
Google Scholar
Saah, D. et al. Land cover mapping in data scarce environments: Challenges and opportunities. Front. Environ. Sci. 7, 150 (2019).
Google Scholar
Li, C., Wang, J., Wang, L., Hu, L. & Gong, P. Comparison of classification algorithms and training sample sizes in urban land classification with landsat thematic mapper imagery. Remote Sens. 6, 964–983 (2014).
Google Scholar
Richards, J. A. Remote sensing digital image analysis: An introduction. Remote Sensing Digital Image Analysis: An Introduction vol. 9783642300 (2013).
Kruse, F. A. et al. The spectral image processing system (SIPS)-interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ. 44, 145–163 (1993).
Google Scholar
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Google Scholar
He, Y., Lee, E. & Warner, T. A. A time series of annual land use and land cover maps of China from 1982 to 2013 generated using AVHRR GIMMS NDVI3g data. Remote Sens. Environ. 199, 201–217 (2017).
Google Scholar
Zhao, H. & Chen, X. Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+. In International Geoscience and Remote Sensing Symposium (IGARSS) vol. 3 1666–1668 (IEEE, 2005).
Baloloy, A. B., Blanco, A. C., Raymund Rhommel, R. R. C. & Nadaoka, K. Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping. ISPRS J. Photogramm. Remote Sens. 166, 95–117 (2020).
Google Scholar
García, M. J. L. & Caselles, V. Mapping burns and natural reforestation using thematic mapper data. Geocarto Int. 6, 31–37 (1991).
Google Scholar
Wright, C. & Gallant, A. Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data. Remote Sens. Environ. 107, 582–605 (2007).
Google Scholar
Hutchinson, C. F. Techniques for combining Landsat and ancillary data for digital classification improvement. Photogramm. Eng. Remote Sens. 48, 123–130 (1982).
Tadono, T. et al. Precise global DEM generation by ALOS PRISM. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. II–4, 71–76 (2014).
Google Scholar
Jokar Arsanjani, J., Zipf, A., Mooney, P. & Helbich, M. OpenStreetMap in GIScience. OpenStreetMap in GIScience: Experiences, Research, Applications (2015). https://doi.org/10.1007/978-3-319-14280-7.
Open Development Mekong. OpenDevelopmentMekong. East-West Management Institute https://data.opendevelopmentmekong.net/organization/6f37a27d-2790-4b9a-8570-a36cb1d8108f?res_format=KML (2015).
Truong, V. T. et al. JAXA annual forest cover maps for Vietnam during 2015–2018 Using ALOS-2/PALSAR-2 and auxiliary data. Remote Sens. 11 (2019).
Olofsson, P. et al. Good practices for estimating area and assessing accuracy of land change. Remote Sens. Environ. 148, 42–57 (2014).
Google Scholar
Congalton, R. G. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37, 35–46 (1991).
Google Scholar
Schmidt, M. The Sankey diagram in energy and material flow management—part II: Methodology and current applications. J. Ind. Ecol. 12, 173–185 (2008).
Google Scholar
Zha, Y., Gao, J. & Ni, S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int. J. Remote Sens. 24, 583–594 (2003).
Google Scholar
Lacaux, J. P., Tourre, Y. M., Vignolles, C., Ndione, J. A. & Lafaye, M. Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal. Remote Sens. Environ. 106, 66–74 (2007).
Google Scholar
Tucker, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8, 127–150 (1979).
Google Scholar
McFeeters, S. K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 17, 1425–1432 (1996).
Google Scholar
Huete, A. R. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25, 295–309 (1988).
Google Scholar
Penuelas, J., Baret, F. & Filella, I. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica 31, 221–230 (1995).
Google Scholar
Jayamanna, S., Kawamura, M. & Tsujiko, Y. Relation between social and environmental conditions in colombo, sri lanka and the urban index estimated by satellite remote sensing data. Int. Arch. Photogram. Remote Sens. 31, 321–326 (1996).
Shen, L. & Li, C. Water body extraction from Landsat ETM+ imagery using adaboost algorithm. In 2010 18th International Conference on Geoinformatics, Geoinformatics 2010 (2010). https://doi.org/10.1109/GEOINFORMATICS.2010.5567762.
Perry, C. R. & Lautenschlager, L. F. Functional equivalence of spectral vegetation indices. Remote Sens. Environ. 14, 169–182 (1984).
Google Scholar
As-syakur, A. R., Adnyana, I. W. S., Arthana, I. W. & Nuarsa, I. W. Enhanced built-UP and bareness index (EBBI) for mapping built-UP and bare land in an urban area. Remote Sens. 4, 2957–2970 (2012).
Google Scholar
Liu, H. Q. & Huete, A. Feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Trans. Geosci. Remote Sens. 33, 457–465 (1995).
Google Scholar
Gitelson, A. A., Viña, A., Ciganda, V., Rundquist, D. C. & Arkebauer, T. J. Remote estimation of canopy chlorophyll content in crops. Geophys. Res. Lett. 32, 1–4 (2005).
Google Scholar
Source: Ecology - nature.com
