Simulating Alpine Vegetation Net Primary Productivity by Remote Sensing in Qinghai Province,China

Primary productivity of ecosystem is important indicator about ecological assessment. Remote sensing technology has been used to monitor net primary productivity （NPP） of ecological system for several years. In this paper, the remotely sensed NPP simulation model of alpine vegetation in Qinghai Province of Tibet Plateau was set up based on the theory of light use efficiency. Firstly a new approach based on mixed pixels and Support Vector Machine （SVM） algorithm were used to correct simulated NPP values derived from Moderate Resolution Imaging Spectroradiometer （MODIS） data. Finally, spatial distribution and monthly variation characteristics of NPP in Qinghai Province detail. The result showed in 2006 were analyzed in that NPP of vegetation in Qinghai Province in 2006 ranged from o to 422 gC/m2/a and the average NPP was 151 gC/m2/a. NPP gradually increased from northwest to southeast. NPP of different vegetation types were obviously different. The average NPP of broad-leaved forest was the largest （314 gC/m2/a）, and sparse shrub was the smallest （101 gC/m2/a）. NPP in Qinghai Province significantly changed with seasonal variation. The accumulation of NPP was primarily in the period （from April to September） with better moist and heat conditions. In July, the average NPP of vegetation reached the maximum value （43 gC/m2）. In our model, the advantage of traditional LUE models was adopted, and our study fully considered typicalcharacteristics of alpine vegetation light use efficiency and environmental factors in the study area. Alpine vegetation is the most important ecological resource of Tibet Plateau, exactly monitoring its NPP value by remote sensing is an effective protection measure.

Potential promoted productivity and spatial patterns of medium-and low-yield cropland land in China

With a continuously increasing population and better food consumption levels, im- proving the efficiency of arable land use and increasing its productivity have become funda- mental strategies to meet the growing food security needs in China. A spatial distribution map of medium- and low-yield cropland is necessary to implement plans for cropland improvement In this study, we developed a new method to identify high-, medium-, and low-yield cropland from Moderate Resolution Imaging Spectroradiometer （MODIS） data at a spatial resolution of 500 m. The method could be used to reflect the regional heterogeneity of cropland productiv- ity because the classification standard was based on the regionalization of cropping systems in China. The results showed that the proportion of high-, medium-, and low-yield cropland in China was 21%, 39%, and 40%, respectively. About 75% of the low-yield cropland was lo- cated in hilly and mountainous areas, and about 53% of the high-yield cropland was located in plain areas. The five provinces with the largest area of high-yield cropland were all located in the Huang-Huai-Hai region, and the area amounted to 42% of the national high-yield cropland area. Meanwhile, the proportion of high-yield cropland was lower than 15% in Hei- Iongjiang, Sichuan, and Inner Mongolia, which had the largest area allocated to cropland in China. If all the medium-yield cropland could be improved to the productive level of high-yield cropland and the low-yield cropland could be improved to the level of medium-yield cropland, the total productivity of the land would increase 19% and 24%, respectively.