We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts delayed and continuous effects. This study expands on this by mapping the seasonal characterization of NPP and climate variables from space using geographic information system (GIS) technology at the pixel level. Our approach was developed in southeastern China using moderate-resolution imaging spectroradiometer (MODIS) data. The results showed that air temperature,precipitation and sunshine percentage contributed significantly to seasonal variation of NPP. In the northern portion of the study area,a significant positive 32-d lagged correlation was observed between seasonal variation of NPP and climate (P<0.01),and the influences of changing climate on NPP lasted for 48 d or 64 d. In central southeastern China,NPP showed 16-d,48-d,and 96-d lagged correlation with air temperature,precipitation,and sunshine percentage,respectively (P<0.01); the influences of air temperature and precipitation on NPP lasted for 48 d or 64 d,while sunshine influence on NPP only persisted for 16 d. Due to complex topography and vegetation distribution in the southern part of the study region,the spatial patterns of vegetation-climate relationship became complicated and diversiform,especially for precipitation influences on NPP. In the northern part of the study area,all vegetation NPP had an almost similar response to seasonal variation of air temperature except for broad crops. The impacts of seasonal variation of precipitation and sunshine on broad and cereal crop NPP were slightly different from other vegetation NPP.
Dai-liang PENGJing-feng HUANGAlfredo R. HUETETai-ming YANGPing GAOYan-chun CHENHui CHENJun LIZhan-yu LIU
Tea （Camellia sinensis） is one of the most valuable cash crops in southern China; however, the planting distribution of tea crops is not optimal and the production and cultivation regions of tea crops are restricted by law and custom. In order to evaluate the suitability of tea crops in Zhejiang Province, the annual mean temperature, the annual accumulated temperature above 10 ℃, the frequency of extremely low temperature below -13 ℃, the mean humidity from April to October, slope, aspect, altitude, soil type, and soil texture were selected from climate, topography, and soil factors as factors for land ecological evaluation by the Delphi method based on the ecological characteristics of tea crops. These nine factors were quantitatively analyzed using a geographic information system （GIS）. The grey relational analysis （GRA） was combined with the analytic hierarchy process （AHP） to address the uncertainties during the process of evaluating the traditional land ecological suitability, and a modified land ecological suitability evaluation （LESE） model was built. Based on the land-use map of Zhejiang Province, the regions that were completely unsuitable for tea cultivation in the province were eliminated and then the spatial distribution of the ecological suitability of tea crops was generated using the modified LESE model and GIS. The results demonstrated that the highly, moderately, and non-suitable regions for the cultivation of tea crops in Zhejiang Province were 27552.66, 42 724.64, and 26507.97 km2, and accounted for 28.47%, 44.14%, and 27.39% of the total evaluation area, respectively. Validation of the method showed a high degree of coincidence with the current planting distribution of tea crops in Zhejiang Province. The modified LESE model combined with GIS could be useful in quickly and accurately evaluating the land ecological suitability of tea crops, providing a scientific basis for the rational distribution of tea crops and acting as a reference to land policy makers and land use
LI BoZHANG FengZHANG Li-WenHUANG Jing-FengJIN Zhi-FengD.K.GUPTA
