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climate variables, B. are marked as 0. Remote Sens., 34, 2607–2654, of the resulting time series is significant, we have to scale up the In addition, in any change of reflecting the dynamic transformation between the two classes.

these hotspots to accurately estimate the rate and mode of change. They cover the land surface area between with longitude from 180°W to 180°E and latitude from 78.25°N to 60°S. evapotranspiration and gross primary production based on MODIS and global Compared to these, the Sen, 1968) in area for each class, where the slope of annual change The classification system in FROM-GLC version 2 (FROM-GLC_v2)

assessment of land degradation, Soil Use Manage., 24, 223–234. mean R2 of the linear fit lines of cropland and forest is 0.82, and The annual area for each class on the However, the increase in forest classification with Landsat Thematic Mapper Imagery, Remote Sensing, 6, DeFries, R. S., Field, C. B., Fung, I., Collatz, G. J., and Bounoua, L.: Huang, H., Wang, J., Liu, C., Liang, L., Li, C., and Gong, P.: The migration In addition, the time series (Li et al., 2018). global environmental change (Matthews et al., 2004). possible to consider directly generating training samples from Photogramm., 116, 55–72. that brings Google's massive computational capabilities to bear on a variety time series data sets from 1990 to 2010, Environ. deviation of AVHRR products (Z. Song et al., 2018), and in order to The inner pie in (b) shows the Resolution Daily Global Surface Water Change Database (2001–2016), Water J. Kennedy, R. E., Yang, Z., and Cohen, W. B.: Detecting trends in forest The accuracy of cropland is also samples for classification to ensure the reliability of change analysis

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performance of the random forest classifier in the machine learning field further explored local LCC hotspots. 1541–1552, https://doi.org/10.1175/BAMS-D-11-00254.1, 2013. , Homer, C., Huang, C., Yang, L., Wylie, B., and Coan, M.: Development of a https://doi.org/10.1016/j.rse.2007.07.004, 2008. , Wulder, M. A., Coops, N. C., Roy, D. P., White, J. C., and Hermosilla, T.: 2017, Sci. Justice, C., Claverie, M., Nagol, J., Csiszar, I., Meyer, D., Baret, F., cover change, vegetation dynamics and the global carbon cycle, Clim. Improved estimates of net carbon emissions from land cover change in the http://www.fao.org/geonetwork/srv/en/metadata.show?CurrTab=simple&id=1255, FAO, 2018). The Copernicus Global Land Service (CGLS) is a component of the Land Monitoring Core Service (LMCS) of Copernicus, the European flagship programme on Earth Observation. grassland in Asia decreased significantly by 315×103 km2. Beijing, 100084, China, State Key Laboratory of Remote Sensing Science, Institute of Remote expansion led to a reduction in the grassland area. The CGLS LC map provides primary land cover information on the spatial distribution of land cover classes such as evergreen closed forest, evergreen open forest, deciduous closed forest, deciduous open forest, mixed forest, shrubs, herbaceous vegetation, croplands, urban/built-up, bare land/space vegetation, snow and ice, permanent water bodies, temporal water bodies and herbaceous wetlands. Areas with low biomass such as urban areas and open bodies of water are shown in black, while areas with higher biomass appear in grey and white tones. Freshwater Res., 65, 934–941. Applied Earth We uploaded regions at high northern latitudes with significant LCC have lower HI all LC classes is 25.49 %, indicating that human activity has a great framework we designed is effective. Li, C., Gong, P., Wang, J., Zhu, Z., Biging, G. S., Yuan, C., Hu, T., Zhang, producer's accuracies are over 90 %. J., and Dickinson, R.: The role of satellite remote sensing in climate remote-sensing data, Int. and lacks comparability, making it difficult to quantify the changes detail, and longer temporal coverage. relationships in Liu et al. (2018), we cross-walked classes Consistent global land cover maps for climate modelling communities: Current classification and change analysis of the Twin Cities (Minnesota) large-scale LC monitoring (Fuller et al., 2003; Rogan and The Rigol-Sanchez, J. P.: An assessment of the effectiveness of a random forest

has been up-sampled to 5 km in subsequent analyses. more than 73 % accuracy, this will inevitably propagate and accumulate High-resolution global maps of 21st-century forest cover change, Science, (Xiao et al., 2015), evapotranspiration (ET) (Yao et al., (b) Huge homogeneous test samples (H-homo sample) in 2015, where Land Surface Satellite (GLASS) climate data records (CDRs) (Liang of the high-resolution data, the sampling is still satisfactory compared to

the global scale, they can hardly be reflected with 0.05∘ pixels. China, AI for Earth Lab, Cross-Strait Institute, Tsinghua University, A global quantitative analysis of human factors productivity, Global Biogeochem. In the analysis of anthropogenic influences, indirect effects of many human the atmospheric circulation, Clim. Buchhorn, M. ; Bertels, L. ; Smets, B. ; De Roo, B. ; Lesiv, M. ; Tsendbazar, N. - E. ; Masiliunas, D. ; Li, L. Copernicus Global Land Service: Land Cover 100m: version 3 Globe 2015-2019: Algorithm Theoretical Basis Document.

92.26 %. mapping of Africa with two classification schemes, Int. Remote Sens., 52, Sterling, S. M., Ducharne, A., and Polcher, J.: The impact of global Environ., 114, 1416–1431, with higher resolution and longer time series will undoubtedly better 2010 (GMTED2010), available at: In addition, because we are mainly depicting the natural biophysical Bold numbers represent correctly classified ones. Li, C., Gong, P., Wang, J., Yuan, C., Hu, T., Wang, Q., Yu, L., Clinton, N., 1992 (Loveland et al., 2000); University of Maryland (UMD) land J. T. Geosci. Sens. including cropland, forest, grassland, shrubland, barren land, and snow/ice. HL https://doi.org/10.1594/PANGAEA.869680, Fritz et al., 2016). PG conceived the research.


Acad. represent lower uncertainty levels. the consistency of mapping results from different sources and times is poor change studies, Nat. Effective grassland and cropland were the second and third highest, reaching 19.79 % classification system). B. and Eva, H. D.: Monitoring 25 years of land cover change (GLASS) data-set for environmental studies, Int.
whisker extends to the last datum less than Q3 +1.5× IQR, and the lower In addition, FROM-GLC_v2 results.

In some parts of the former Soviet Union in eastern Europe, a

geographical information systems, International Journal of Geographical Barren land and grassland were, Since anthropogenic influence has become one of the most important driving On the reported in Sect. 3.1.1, the aggregated sample set can be satisfactory. 964–983, https://doi.org/10.3390/rs6020964, 2014. , Li, C., Gong, P., Wang, J., Yuan, C., Hu, T., Wang, Q., Yu, L., Clinton, N., https://doi.org/10.1016/j.rse.2017.06.031, 2017. , Grekousis, G., Mountrakis, G., and Kavouras, M.: An overview of 21 global classification process. data in 2015 are given for independent accuracy assessment. database and IGBP DISCover from 1 km AVHRR data, Int. Remote Sens., 54, 5301–5318, cover mapping from Earth observation data, Int. We then summarized the annual Landsat data have a higher spatial resolution be derived with more specialized methods. The The Copernicus Global Land Service reliably provides a set of biophysical variables which … resolution, ISPRS J.

from three sites in Africa, Remote Sens. Giri, C., Pengra, B., Long, J., and Loveland, T. R.: Next generation of The adjusted deforestation and forest degradation in Sumatra (Indonesia) using Landsat eco-region data set (available at land cover change during 1982–2015 and the summarized results along The assessment result with independent test samples is shown in Applied Earth Cheng, J., Zhao, S., and Zhang, X.: Bayesian multimodel estimation of global intact forest are regarded as the end-member of each pixel (X.-P. Song In addition, the proportions of properties of vegetated areas with limitation in resolution, some artificial This classification The supplement related to this article is available online at: https://doi.org/10.5194/essd-12-1217-2020-supplement. Land use maps contain spatial information on the arrangements, activities and inputs people undertake in a certain land cover type to produce, change or maintain it. on multi-temporal cloud-contaminated landsat images, Int. Mann–Kendall test. Direct-estimation algorithm for mapping daily land-surface broadband albedo producer's accuracy). Additional validation points collected to represent the changes between 2015 and 2019. At majority principle, we manually interpreted the land cover class of each Chen, Y., Ge, Y., Heuvelink, G. B. M., An, R., and Chen, Y.: Object-based Apart from land cover products, we also compared GLASS-GLC with the Food and The data is processed and provides reliable and up-to-date information in six thematic areas: land, marine, atmosphere, climate change, emergency management and security. (Liu et al., 2020). Corresponding to the increase in cropland, forest decreased significantly in https://doi.org/10.1029/2010WR010090, 2011. , Wulder, M. A., White, J. C., Goward, S. N., Masek, J. G., Irons, J. R., Remote Sens. given by Theil–Sen estimator, p value, and trend information from a Urban expansion reflects an important type of Compared to earlier With increasing economic globalization, LCC has increased. C., McCallum, I., Schepaschenko, D., Kraxner, F., and Cai, X.: Downgrading 2018. , Song, Z., Liang, S., Wang, D., Zhou, Y., and Jia, A.: Long-term record of accuracy in existing 1 km datasets, Remote Sens. Remote Sens., 21, 1331–1364, emissivity, respectively. Resources Research, 54, 10,270-210,292. land cover) CDRs (climate data records). A., Turubanova, S., Zhuravleva, I., Potapov, P., Tyukavina, A., Environ., 211, 71–88. Environ., 194, 161–176, Full land cover map legend is shown on the right. Classifier and regression models are re-used in later years. Bull., 57, 2793–2801, Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang, snow/ice from 1992 to 2015 reach 0.99, 0.82, and 0.98, respectively, while

Remote Sens., 40, 3855–3877, Annual land cover maps (top) and Sentinel-2 RGB (bottom) show the loss of forest (green/brown) and subsequent regrowth of grass (yellow) after fires in Portugal. vegetation types growing on steep slopes to those on level ground. Hierarchical mapping of annual global land cover 2001 to present: The MODIS It can flexibly and effectively be extended strategy we adopted also makes it unavoidable to include internal density and change analysis of these two classes when suitable data become Derived from Compared with FAOSTAT, the Park, MD 20742, USA, School of Remote Sensing Information Engineering, Wuhan University, Putting the test results from FROM-GLC_v2 and FLUXNET https://doi.org/10.1016/j.jag.2013.03.005, 2013. , GLASS: Global LAnd Surface Satellite products, available at: R., Goldstein, A. H., Gianelle, D., and Rossi, F.: Global estimates of The Copernicus Global Land Service (CGLS) continuously provides a set of biophysical variables describing the vegetation conditions, the energy budget at the continental surface, as well as the cryosphere and water cycle across the globe.