人工园林大面积种植区植被净初级生产力时空变化研究

云南西南地区自然资源丰富，近年来，随着社会经济的快速发展，橡胶、茶园和桉树等人工园林大规模种植主要占用了天然林和耕地，土地利用结构和空间布局发生了很大变化。为了探讨土地利用/覆被变化对植被净初级生产力（NPP）的影响，以人工园林大面积种植区西盟县为研究区，运用综合模型、CASA模型和MODIS产品（BIOME-BGC模型），对西盟县2000年、2005年、2010年和2015年各地类NPP的时间变化和空间分布特征进行分析。结果表明：（1）2000~2015年，CASA模型模拟的西盟县NPP呈先减少后增加的趋势，MODIS模型模拟的NPP值变化趋势与CASA模型一致，综合模型则呈现逐年增加的趋势；（2）西盟县NPP的空间分布形态从西至东均呈“低值-高值-低值-高值-低值”（“M”）变化，与西盟县水热资源的分布特征和土地利用/覆被变化有关，西盟县NPP空间变化主要集中于人工园林种植区；（3）3种模型中，CASA模型和MODIS产品（BIOME-BGC模型）比综合模型更适用于地形气候复杂多样的山区NPP的模拟计算，其中CASA模型空间精度更高。

Study on Spatio-temporal Variation of Net Primary Productivity in a Region With Large-area Artificial Garden

Southwest of Yunnan is wealthy in natural resources.With the rapid development of social economy in recent years,artificial-garden,including rubber,tea and eucalyptus were planted on a large scale,which mainly occupied natural forests and arable land.It resulted in changes of land-use structure and space distribution.In order to discuss the effect of land use/cover change on the net primary productivity of vegetation,the paper analyzed temporal variation and spatial distribution of NPP using comprehensive model,CASA model and MODIS products(BIOME-BGC model) of 2000,2005,2010 and 2015 in Ximeng County.The results showed that:(1) the NPP of Ximeng County was affected by meteorological change,vegetation types and growth status,and human activities,the values of both BIOME-BGC model and CASA model decreased firstly and then increased,whereas the values of comprehensive model kept increasing during 2000 to 2015;(2) the spatial distribution pattern of vegetation NPP change present as "low-high-low-high-low" type ("M") from west to east in Ximeng County.It was related to the distribution characteristics of hydro-thermal resources land use/cover change,the spatial distribution change of NPP in Ximeng county was mainly concentrated in artificial-garden planting area;(3) In the three NPP models,the CASA model and the MODIS(BIOME-BGC) model were more suitable for the simulation of NPP in mountainous terrain with complex terrain and climate,and the CASA model was more accurate.