Accuracy assessment of seven global land cover datasets over China

Yang, Yongke; Xiao, Pengfeng; Xia, Feng; Li, Haixing

doi:10.1016/j.isprsjprs.2017.01.016

Cited by 288 publications

(187 citation statements)

References 69 publications

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“…The overall accuracy results at the continental level (Table 5, Appendix A) are generally consistent with previous studies carried out in Africa [42] and China [58], where GlobeLand30 is the dataset with the highest overall accuracy. Indeed, GlobeLand30 shows the highest overall accuracy values (around 80%) for the three continents.…”

Section: Accuracy Assessmentssupporting

confidence: 89%

Comparison of Global Land Cover Datasets for Cropland Monitoring

et al. 2017

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Accurate and reliable information on the spatial distribution of major crops is needed for detecting possible production deficits with the aim of preventing food security crises and anticipating response planning. In this paper, we compared some of the most widely used global land cover datasets to examine their comparative advantages for cropland monitoring. (1) highlighting discrepancies in the extent and spatial distribution of cropland, (2) comparing the areas with FAO agricultural statistics at the country level, and (3) providing accuracy assessment through freely available reference datasets. Recommendations for crop monitoring at the country level are based on a priority ranking derived from the results obtained from analyses 2 and 3. Our results revealed that cropland information varies substantially among the analyzed land cover datasets. FAO-GLCshare and Globeland30 generally provided adequate results to monitor cropland areas, whereas LC-CCI2010 and GLC2000 are less unsuitable due to large overestimations in the former and out of date information and low accuracy in the latter. The recently launched LC-CCI datasets (i.e., LC-CCI2015) show a higher potential for cropland monitoring uses than the previous version (i.e., LC-CCI2010).

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Section: Accuracy Assessmentssupporting

confidence: 89%

Comparison of Global Land Cover Datasets for Cropland Monitoring

et al. 2017

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“…These buffers gained more validation pixels with 30 m resolution, which improved the accuracy assessment [39]. For example, if the size of the ROIs are too small, than the inherent location errors of the satellite images can affect the validation results and produce more uncertainty in landscape heterogeneity [40,41]. Finally, the ROIs were put into the Google Earth and interpreted referring to the very high resolution images from around 2010.…”

Section: Validation Data Collected By Google Earth Imagery and Field mentioning

confidence: 99%

Accuracy Assessment and Inter-Comparison of Eight Medium Resolution Forest Products on the Loess Plateau, China

Yang

Dong

Liu

et al. 2017

IJGI

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Abstract:Forests play an important role in maintaining ecosystem services, especially in ecologically fragile areas such as the Loess Plateau (LP) in China. However, there is still great uncertainty in the spatial extent and distribution of forests in such a fragmented region. In order to examine the advantages and disadvantages of existing forest mapping products, we conducted a thorough accuracy assessment on the eight recent, medium resolution (30-50 m) products by using the LP in 2010 as the region of interest. These mapping products include Landsat and/or PALSAR images, including the forest products from GlobeLand30, FROM-GLC, Hansen, ChinaCover, NLCD-China, GLCF VCF, OU-FDL, and JAXA. The same validation data were used to assess and rank the accuracy of each product. Additionally, the spatial consistency of the different forest products and their dependence on the terrain were analyzed. The results showed that the overall accuracies of the eight forest products on the LP in 2010 were between 0.93 ± 0.003 and 0.97 ± 0.002 with a 95% confidence interval, and GlobeLand30 presented the highest overall accuracy (0.97 ± 0.002). Among them, the PALSAR-based products (OU-FDL and JAXA) indicated relatively high accuracies, while the six Landsat-based products showed a large diversity in the accuracy. According to the eight products, the total estimated forest area of the LP varied from 7.627 ± 0.077 to 10.196 ± 0.1 million ha with a 95% confidence interval. We also found that the consistency in the spatial distribution of forests between these maps: (1) increased substantially with increasing elevation until 2000m, but then decreased at higher elevations, and (2) showed mild variation along increasing slope, but had a slight rate of increase. Our findings implied that future forest mapping studies should consider topographical attributes such as elevation and slope in their final products. Our results are fundamental in guiding future applications of these existing forest maps.

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“…At the country/region level, a satisfactory overall accuracy was estimated as 82.4% for China [31], 80% for Italy [29], 77.90% for all of Iran [14], 80.1% for Nepal [32], and 89.7% for Kyiv Oblast, Ukraine [33]. Yang et al [31] evaluated the accuracy of seven land cover products over China, namely, International Geosphere-Biosphere Program Data and Information System's land cover dataset (IGBP DISCover), The University of Maryland land cover dataset (UMD), Global Land Cover 2000 dataset (GLC2000) from the European Commission's Joint Research Center (JRC), Moderate Resolution Imaging Spectroradiometer (MODIS) land cover products MOD12Q1 and MCD12Q1, Global Map-Global LC (GLCNMO) dataset from the International Steering Committee for Global Mapping, Climate Change Initiative land cover dataset (CCI-LC) from European Space Agency (ESA), and GlobeLand30; they found that GlobeLand30-2010 has the highest overall accuracy (82.4%). Arsanjani et al [28] reported that GlobeLand30 has high agreements with CORINE (92.52%), Urban Atlas (85.43%), OpenStreetMap (74.24%), and ATKIS (85.23%) in Germany.…”

Section: Accuracy Analysismentioning

confidence: 98%

Analysis and Applications of GlobeLand30: A Review

Chen

Cao

Peng

et al. 2017

IJGI

164

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GlobeLand30, donated to the United Nations by China in September 2014, is the first wall-to-wall 30 m global land cover (GLC) data product. GlobeLand30 is widely used by scientists and users around the world. This paper provides a review of the analysis and applications of GlobeLand30 based on its data-downloading statistics and published studies. An average accuracy of 80% for full classes or one single class is achieved by third-party researchers from more than 10 countries through sample-based validation or comparison with existing data. GlobeLand30 has users from more than 120 countries on five continents, and from all five Social Benefit Areas. The significance of GlobeLand30 is demonstrated by a number of published papers dealing with land-cover status and change analysis, cause-and-consequence analysis, and the environmental parameterization of Earth system models. Accordingly, scientific data sharing in the field of geosciences and Earth observation is promoted, and fine-resolution GLC mapping and applications worldwide are stimulated. The future development of GlobeLand30, including comprehensive validation, continuous updating, and monitoring of sustainable development goals, is also discussed.

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Accuracy assessment of seven global land cover datasets over China

Cited by 288 publications

References 69 publications

Comparison of Global Land Cover Datasets for Cropland Monitoring

Comparison of Global Land Cover Datasets for Cropland Monitoring

Accuracy Assessment and Inter-Comparison of Eight Medium Resolution Forest Products on the Loess Plateau, China

Analysis and Applications of GlobeLand30: A Review

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