Object-oriented forest classification based on combination of HJ-1 CCD and MODIS-NDVI data

As the world's largest terrestrial ecosystem, forest is very important to human living and environment sustainable development. Therefore, grasping the status and changes of forest resources are of significance. But classification of sub-category information of forest vegetation has always been difficult for remote sensing, because of the impact of complex terrain, irregular distributed vegetation, and the similar spectral information of different forest types. In this study, Eastern Jilin was chosen as the study area, where approximately 80% of the land is covered with forest vegetation, and the sub-category of forest vegetation contained broadleaved deciduous forest, deciduous coniferous forest, evergreen coniferous forest, mixed broadleaf-conifer forest, and deciduous shrub. The classification method operated in this study (based on object-oriented method combining HJ-1 CCD data and MODIS-NDVI data) could also be used in classifying vegetation in other regions, but the parameters in this study is regional adoptive.