多尺度遥感数据融合的荒漠净初级生产力估算

Estimation of Net Primary Productivity of Desert Ecosystem Based on Multi-scale Remote Sensing Data Fusion

以新疆准噶尔盆地东部五彩湾地区为研究区,利用气象数据和多尺度遥感数据,通过改进的CASA模型对2010年研究区域的净初级生产力(NPP)进行估算,并对NPP分布进行分析。结果表明:1将TM数据和MODIS NDVI数据融合,生成高时间分辨率的TM影像,对于NPP的高空间分辨率的研究提供数据支持。2 2010年该研究区植被NPP为6.571 78 Mt·a-1,总体分布趋势由西北向东南递减,且随季节变化的趋势也非常明显,在6—8月NPP占全年的57.35%,12月、1月、2月植被NPP值极低,可以忽略为0。3利用地面实测点验证NPP模型估算值,平均相对误差为0.167,精度达到83.3%,NPP实测值与估算值的相关系数为0.952并在0.01水平上显著。4该模型在数据获取方面具有极大的优越性,仅利用遥感数据和地面气象数据就可以对干旱区植被NPP进行估算,为NPP的估算提供了较为便捷的方法。

Net Primary Productivity （NPP） refers to the amount of the organic dry matter accumulation of the green plants per unit time and per unit area, is the rest of organic matter amount produced by photosynthesis after deducting the autotrophic breathing. In recent years, the NPP has gradually become a hotspot of research on re- sources and environment in arid areas, accurate estimating the spatial and temporal variations of NPP is of great significance to understand climate change and carbon cycle in arid areas. Taking Wucaiwan area in eastern junggar basin in Xinjiang as the research area, this paper estimated the NPP value of the study area during 2010 through the improved CASA model using meteorological data and multi-scale remote sensing data, and analyzed its spatial and temporal distribution. The results of this paper showed that ： ① NPP can be estimated just using remote sensing da- ta and ground meteorological data. It showed strongly superiority for this model and its application can be en- hanced. ② The MODIS NDVI and TM fuse together to produce high-time-resolution TM data which contributes to the study on high-spatial-resolution NPP data. ③ The results showed that the total annual NPP in the study area was 6. 571 78 Mt · a^-1 in 2010. The spatial changes of NPP were reasonable, and it decreased from northwest to southeast. Moreover, seasonal variations of NPP were also large. It was about 57.35% of the total annual NPP in three month of June, July and August, which shows that the vegetation grew vigorously. However, the NPP value was very low, which was almost zero, in December, January, February, because vegetation almost stopped grow- ing. ④ Using the ground measured NPP data to verify the estimated NPP data by CASA model, the average relative error is about 0. 167, so the average verification precision is up to 83.3%. The correlation coefficient between NPP observed data and NPP estimated data is 0.952 which is significant at the 0.01 level.