Journal of Remote Sensing / 2021 / Article / Tab 7

Review Article

Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects

Table 7

Summary of the reported accuracies of the 30 m global forest products.

StudyRegionAccuracy description

Chen et al. [14]GlobalThe producer’s and user’s accuracies of the forest class of GlobeLand30 are 92.4% and 84.1%, respectively.
Gong et al. [12]GlobalThe producer’s and user’s accuracies of the forest class of FROM_GLC are 76.5% and 80.5%, respectively.
Validation data link: http://data.ess.tsinghua.edu.cn/data/temp/GlobalLandCoverValidationSampleSet_v1.xlsx
Zhang et al. [16]GlobalGLC_FCS30, GlobeLand30, and FROM_GLC had the producer’s accuracies of 94.0%, 92.6%, and 74.9%, respectively, and user’s accuracies of 90.4%, 90.5%, and 77.1%, respectively, for the forest land-cover type.
Validation data link: 10.5281/zenodo.3551994
Kang et al. [39]IndonesiaGLC_FCS30, GlobeLand30, and FROM_GLC had the producer’s accuracies of 82.96, 56.72%, and 58.15%, respectively, and user’s accuracies of 83.21%, 95.05%, and 96.25%, respectively, for the forest land-cover type.
Gao et al. [45]European UnionGLC_FCS30, GlobeLand30, and FROM_GLC had the producer’s accuracies of 92.86%, 93.60%, and 81.59%, respectively, and user’s accuracies of 92.14%, 97.36%, and 74.91%, respectively, for the forest land-cover type.
Validation data link: https://ec.europa.eu/eurostat/web/lucas/data/lucas-grid
Yang et al. [111]The Loess Plateau, ChinaThe forest class in GlobeLand30, FROM_GLC, GLADForest, and GFCC30TC had an overall accuracy of , , , and , respectively. The producer’s and user’s accuracies of these four forest products were and , and , and , and and , respectively.
Moreno-Sanchez et al. [54]MexicoThe accuracy of the forest class of GlobeLand30 is higher in tropical forests than in temperate forests (around 90% compared with around 77%).
Tsendbazar et al. [55]AfricaGlobeLand30-2010 had a producer’s accuracy of and a user’s accuracy of using 15,252 validation samples for the forest land-cover type.
Xu et al. [35]AfricaThe producer’s and user’s accuracies of the forest class of the second version of the African land-cover mapping in FROM_GLC are 82.4% and 72.6%, respectively.
Song et al. [57]Maryland, United StatesThe overall accuracy, producer’s accuracy, and user’s accuracy of GFCC30TC were 82%, 80%, and 91%, respectively, and of GLADForest were 81%, 86%, and 85%, respectively, for Maryland, United States.
Townshend et al. [109]GlobalThe overall accuracy of GFCC30TC is above or near 90%; the average user’s accuracy and producer’s accuracy for persisting forests are 92.5% and 95.4%, respectively. For persisting nonforest, forest loss, and forest gain, the corresponding accuracies are 94.8% and 91.1%, 87.1% and 91.1%, and 84.7% and 81.5%, respectively.
Sexton et al. [31]GlobalThe overall accuracy of the static forest-cover layers in GFCC30TC was 91%, and the overall accuracy of forest-cover change was >88%—these are among the highest accuracies reported for recent global forest- and land-cover products.
Hadi et al. [56]FinlandGFCC30TC underestimated the high-canopy cover forest () and overestimated the low-canopy cover forest () in boreal forests, giving an of 0.53 and a bias of –2.1% for the comparison with the field-measured data.
Qin et al. [58]South AmericaThe user’s and producer’s accuracies of GFCC30TC were and , respectively, and of TCC-2010 were and , respectively.
Zhang et al. [112]ChinaTCC-2000 had an overall accuracy of 94.5%, a producer’s accuracy of 82.13%, and a user’s accuracy of 89.26%.
Pengra et al. [113]South AmericaTCC-2010 had an overall accuracy of , a producer’s accuracy of , and a user’s accuracy of .
Jia et al. [59]KyrgyzstanTCC-2010 and GlobeLand30-2010 had the overall accuracies of 94.50% and 88.27%, kappa coefficients of 0.80 and 0.65, producer’s accuracies of 75.71% and 79.50%, and user’s accuracies of 94.42% and 66.07%, respectively, using 9923 validation points.
Hansen et al. [30]GlobalThe GLADForest product was validated in terms of forest losses and gains using 1500 points. The results showed that it had an overall accuracy of and the producer’s and user’s accuracies of and , respectively, for forest losses; it had an overall accuracy of and the producer’s and user’s accuracies of and , respectively, for forest gains.
Arjasakusuma et al. [60]Central Kalimantan Province, IndonesiaThe overall accuracy of GLADForest is 66%–56%
Cunningham et al. [61]Costa RicaThe overall accuracy of GLADForest is , and the producer’s and user’s accuracies are 79% and 81%, respectively, based on 1154 validation points.
Zhang et al. [32]GlobalGFC30 had an overall accuracy of 90.94%, a producer’s accuracy of 93.95%, and a user’s accuracy of 87.12%. By continent, the PA and UA were 90.43% and 85.43% in Africa, 92.65% and 89.05% in Asia, 94.95% and 95.66% in Europe, 92.19% and 78.86% in North America, 93.95% and 89.00% in Oceania, and 96.33% and 89.51% in South America, respectively.