Surface pollen and stomata of 61 samples collected in a study area ranging from tropical seasonal rainforest to oak forest(Quercus spinosa) in the Yulong Snow Mountain region in Yunnan,China,are used to distinguish vegetation communities.The results show that tropical seasonal rainforest(and mountain rainforest),south subtropical evergreen broad-leaved forest,and Quercus shrub are distinguished effectively from other vegetation types by analysis of surface pollen.The south subtropical evergreen broad-leaved forest,Pinus kesiya forest and evergreen broadleaf forest are distinguished effectively from other types of vegetation by pollen analysis.However,P.yunnanensis forest is not distinguished from other vegetation types,and P.armandii,P.densata forest and temperate deciduous conifer mixed forest are not distinguished.The over-representation of Pinus pollen is the main reason that these vegetation communities are not distinguished from each other.Conifer stomata analysis is an effective tool for identifying and distinguishing different types of coniferous forest,and this method performs well even with a small number of sampling points.
SHEN HuaDongLI ChunHaiWAN HeWenTONG GuoBangLIU JinSongDAN Johnson
MODIS 1B影像中存在横向和纵向条带,基于均衡化曲线补偿技术,提出了一种快速检测和去除条带的算法,给出了条带检测判定参数,即:如果A帧和B帧的10组传感器校正系数C_i值分别位于0的两侧,且ABS(C_i)>0.008,则存在宽条带;如果10组传感器的校正系数MAX(C_i)>0.05时,则存在单行条带。去除宽条带时,需以当前列为首列,从其后连续的100列影像中提取各传感器相对当前列的校正系数。结果表明,条带检测模型能有效识别单行条带和宽条带,多行条带需目视识别,单行条带和宽条带去除效果明显好于NASA网站公布数据,多行条带虽未彻底去除,但处理后的影像质量得到明显改善。
