Based on moderate resolution remote sensing images,forest vegetation type monitoring was performed to provide technical support for achievement monitoring of the natural forest protection project area and forest vegetation mapping.Based on Landsat8 OLI images in growing and non-growing seasons,random forest and time dimension correction methods were applied to forest vegetation type monitoring in Wangqing Forestry Bureau.Based on confusion matrix and recall ratio,factors leading to classification confusion were analyzed.The results showed that:1) Overall accuracy of forest vegetation type monitoring was 86.41%,and kappa coefficient was 0.82,illustrating a better classification effect.2) Among various forest vegetation types,deciduous broadleaved forest land had high classification accuracy,with producer's and user's accuracy of over 90%,respectively.Deciduous coniferous forest land had relatively high producer's accuracy of 86.96%.Evergreen coniferous forest land and coniferous and broadleaved mixed forest land had relatively low producer's and user's accuracies of average 75.19%.Classification confusion between evergreen coniferous forest,deciduous coniferous forest,deciduous broadleaved forest,and coniferous and broadleaved mixed forest frequently occurred as the result of mixed proportion,forest canopy closure and forest age.3) Forest coverage of Wangqing Forestry Bureau was 96.64%.The area proportion of deciduous broadleaved forest land was the largest,that of coniferous and broadleaved mixed forest land was the secondly largest,and that of deciduous coniferous forest land,evergreen coniferous forest land,shrubland and other forest land was small.The analysis showed that multitemporal and phenological information of moderate resolution remote sensing images was effective in obtaining forest vegetation types of the natural forest protection project area.