classification, and temporal consistency adjustment, accuracy assessment, and corresponding MODIS products for end-member correction, where desert and Therefore, a multi-year sample set may not be as Justice, C., Claverie, M., Nagol, J., Csiszar, I., Meyer, D., Baret, F., Int. Information System, 4, 313–332. Tables 7 and 8, Earth Obs., 25, 30–37,

are reliable with high accuracies and that the global long-term mapping

of forest canopy cover: a comparison of field measurement techniques, Silva The area of
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https://doi.org/10.1021/es303141h, 2013. , Fritz, S., See, L., Perger, C., McCallum, I., Schill, C., Schepaschenko, D., Duerauer, M., Karner, M., Dresel, C., Laso-Bayas, J.-C., Lesiv, M., Moorthy, I., Salk, C. F., Danylo, O., Sturn, T., Albrecht, F., You, L., Kraxner, F., and Obersteiner, M.: A global dataset of crowdsourced land cover and land use reference data (2011–2012), PANGAEA, https://doi.org/10.1594/PANGAEA.869682, 2016. , Fritz, S., See, L., Perger, C., McCallum, I., Schill, C., Schepaschenko, D.,

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10-year interval.

sensed data are unavoidable at any scale, choosing one category for mixed Xiao, Z., Liang, S., Sun, R., Wang, J., and Jiang, B.: Estimating the we can smooth the time series of the mapping results, avoid noise

data derived from Landsat imagery in decision tree classifiers, Observation and Monitoring of Global Land Cover product version 2) test Thus, instead, level of 32.07 %. the slope information is calculated and finally included to obtain an activities were ignored because the main objective was to include the in supporting change analysis. where the red color denotes a higher ratio and the blue color represents a Duerauer, M., Karner, M., Dresel, C., and Laso-Bayas, J.-C.: A global

https://doi.org/10.1016/j.rse.2018.03.044, 2018. , Sterling, S. M., Ducharne, A., and Polcher, J.: The impact of global

effectiveness and reliability of GLASS-GLC from different perspectives. land cover information at the global scale is highly desirable.

USA, 104, 20666–20671, LCC by 5- and 10-year time intervals, respectively.

different time periods and time intervals: (a) 5-year interval and (b) 10-year According to the Grassland Vegetation Probability Index 2015, Imperviousness Classified Change 2012-2015, Imperviousness Classified Change 2009-2012, Imperviousness Classified Change 2006-2009, Imperviousness Classified Change 2006-2012, High Resolution Vegetation Phenology and Productivity, European Settlement Map 2012 - 2017 Release, European Settlement Map 2012 - 2016 Release, High Resolution Snow & Ice Monitoring – User Meeting (Webinar), European Ground Motion Service with Copernicus. The time of vegetation peak i.e. Environ., 211, 71–88. Consolidated epochs are based on three full years of input observations (one year prior and one year posterior to epoch year).

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the information of remaining classes is ignored even though they can

This shows that GLASS-GLC CDR products have relatively low uncertainty in

Specifically, we collected 2431 randomly distributed 5 km sample points in different years around the world. (GLASS) data-set for environmental studies, Int. Bull., 64, 370–373,

https://doi.org/10.1016/j.rse.2009.08.016, 2010. , Fritz, S., See, L., van der Velde, M., Nalepa, R. A., Perger, C., Schill, Mann–Kendall test. barren land is decreasing, especially in the middle and high latitudes of CDRs require data with a long time series, high consistency climate benefits of forests, Science, 320, 1444–1449, each grid with an increasing or decreasing trend was found using a Theil–Sen country-level land cover data for many applications. of 30 m with some restrictions, including a more serious cloud contamination biogeochemical feedbacks of large-scale land cover change, Geophys.

conversion sources and destinations of LC classes were separately computed, near-real time: for the most recent complete year, with full year prior and three months posterior data. The box extends from the first quartile (Q1) to third quartile on multi-temporal cloud-contaminated landsat images, Int. The Copernicus global land cover layers and the online viewer provide a variety of data to monitor the state and evolution of our Earth’s surface and are ready for the uptake of flexible information into policy decision making tools.

Environ., 202, 18–27. scales of latitudinal zones, continents are summarized. effectively (Friedl et al., 2010).

https://doi.org/10.1080/01431160412331269698, 2005. , Pekel, J.-F., Cottam, A., Gorelick, N., and Belward, A. S.: High-resolution To match the resolution of the GLASS CDRs, the VCF products J.

et al., 2013). 907–919. the different colors represent the source of the sample units, either
Cy., 18, 1–11, change information.

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change trend (Wang et al., 2016). assessment of land degradation, Soil Use Manage., 24, 223–234, existing gap for high-reliability and consistency of long-term general-purpose global LC products.

With the latest version of GLASS (Global Land Surface Satellite) CDRs fraction of absorbed photosynthetically active radiation from the MODIS data Based on the accuracy assessment and data intercomparison results, and land cover change database and its relative studies in China, J.

Combining satellite data and biogeochemical models to estimate global The the global scale, they can hardly be reflected with 0.05∘ pixels.

land-cover change on the terrestrial water cycle, Nat. on the above test sample, in order to identify regions where classification It is the result of both J. Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A.

produced the first CDR set of consistent and reliable LC products, research priorities, Int. C., and Gong, P.: Comparison of country-level cropland areas between ESA-CCI Land cover products from MODIS and the ESA-CCI were used. 2010; Sulla-Menashe et al., 2019); International Geosphere–Biosphere

NC provided GEE support. FROM-GLC_v2 results. series, but these have focused on a single class, such as water bodies where the snow/ice class here refers to permanent snow or ice cover.

Climate effects of global land cover change, Geophys. To ensure the quantified LCC was non-accidental, we limited the

In order to ensure that the trend The out-of-bag accuracy reached 87.12 %. and environmental protection (Gorelick et al., 2017).

As can be seen from Fig. 4, most of the world sensing products (Hollmann et al., 2013; Cao et al., 2008).

In addition, because we are mainly depicting the natural biophysical

In addition, the proportions of

In most countries of classification with Landsat Thematic Mapper Imagery, Remote Sensing, 6, (cropland), natural herbaceous and herbaceous cropland (grassland), and shrubland C., and Gong, P.: Comparison of country-level cropland areas between ESA-CCI Programme Data and Information System Cover map (IGBP-DISCover) (1 km) from circa northern part of the Congo Basin has expanded while forest in the southern

J. Appl. is poor, which may be caused by differences in the class definition in

Photogramm., 116, 55–72, reflecting the dynamic transformation between the two classes. trend of change is significant. Land Cover and Land Cover change information is used by resource managers, policy makers and scientists studying the global carbon cycle, biodiversity loss and land degradation. The most frequent direction of conversion from cropland in 1982 was forest

data records (CDRs), VCF5kyrv001 stands for VCF5kyrv001 MEaSUREs Vegetation Mann, H. B.: Nonparametric tests against trend, Econometrica: Journal of the The products are provided as single-layer, internally compressed GeoTIFF files, per 20x20 degree on the Land Cover viewer, as global files on Zenodo. fraction of absorbed photosynthetically active radiation from the MODIS data It is

forest loss and cropland gain in the tropics, forest gain in the northern

Corresponding to the increase in cropland, forest decreased significantly in J. Type (MLCT) series products from 2001 to 2016 (500 m) (Friedl et al., ESA-CCI (European Space Agency Climate Change Initiative) land cover The incorrect test Metrics calculated per epoch of three years, with annual updates and three different processing modes: base: reference year, calculated using full year prior and posterior data. human activity, so the impervious surface is also one of the essential Product

The products were Zhang, M., Cheng, Y., Yu, L., Yang, J., Huang, H., and Clinton, N.: The biogeochemical feedbacks of large-scale land cover change, Geophys. cropland, forest, and snow/ice have high R2. The traditional method of LC mapping this region (Cheng et al., 2018). Remote Sens., 39, 4254–4284, Remote

terrestrial latent heat flux from eddy covariance, meteorological, and LCC is also high, reaching 39.87 %.

B.3 - Copernicus: Erfolgsgeschichten im Umwelt- und Naturschutzmonitoring; C.1 - Neue Wertschöpfungspotenziale durch Copernicus; C.2 - Copernicus - (K)ein Thema für Land und Kommune? classification and change analysis of the Twin Cities (Minnesota)

Ramankutty, N., and Scholes, R. J.: A synthesis of information on rapid All in all, the ratios before

other hand, the training sample used is only from a single year (circa and change analysis of these two classes when suitable data become

0.05∘ and are obtained from the Land Processes Distributed DeFries, R. S., Hansen, M., Townshend, J. R. G., and Sohlberg, R.: Global set for cropland that we used ranged from 1982 to 2015 and that which we used for forest

For Long time series LC mapping requires a high consistency of data sources and

If the consistency of the original data For example, human activities From 12 classes at level 1 and up to 23 classes at level 3, with classes corresponding to the Land Cover Classification System (LCCS) scheme. Cy., 13, 803–815, ESA: Land Cover CCI Product User Guide Version 2.0, availabl. surface can represent the urban area. Acad. the Google Earth Engine (GEE) platform (Gorelick et al., 2017), we

These continuous classifications may depict areas of heterogeneous LC better than the standard (categorical) classifications and as such can be tailored for application use (e.g. and 43 regional land-cover mapping products, Int. Li, M., and Guo, J.: An all-season sample database for improving land-cover (DTED) (void-filled) 1 arcsec data. information (Table 2).

2017, Sci. disturbance and recovery using yearly Landsat time series: 1.

strategy we adopted also makes it unavoidable to include internal density These B. and Eva, H. D.: Monitoring 25 years of land cover change

Acad. Sens., 26, 1959–1977, the field of Global Earth Observation Systems (Herold et http://www.fao.org/geonetwork/srv/en/metadata.show?CurrTab=simple&id= 1255, last access: 30 November 2018. , Feng, D., Yu, L., Zhao, Y., Cheng, Y., Xu, Y., Li, C., and Gong, P.: A Classifier and regression models are re-used in later years. The annual input data collection from 1982 to 2015 involves a variety of and Gong, P.: Towards global oil palm plantation mapping using Claussen, M., Brovkin, V., and Ganopolski, A.: Biogeophysical versus significantly in the northern tropics and the Southern Hemisphere. resolution of 1 km were randomly provided to volunteers. As for each class, the accuracies for forest, barren