| Vegetation is generally considered as the plant communities covered on the surface of the earth, consist of crops, it is a natural link between soil, atmosphere and water, The dynamic changes of monitoring of the vegetation and the vegetation coverage was one of the hot spot of the ecological environment in the research direction. With the development of remote sensing technology, the remote sensing image was used widely in the crop condition monitoring and yield estimation. NDVI is one of the vegetation indexes that most commonly used; NDVI has a close relationship between the plant growth situation of the earth surface, productivity and other biological physical, biological chemical characteristics, crops growth, and development period.Remote sensing images (MODIS-NDVI) from 2003 to 2010 were collected and analyzed to investigate the vegetation changes in Chongqing. Research the seasonal changes, yearly changes and spatial changes of Normalized Difference Vegetation Index in Chongqing through the image mosaic, change the projection and maximum value composites. Discuss the estimating model of rice yield and corn yield based on MODIS-NDVI. The main results obtained in this work were summarized as follow:(1) Yearly changes of the vegetation coverage. Calculation the yearly change slop value through linear regression analysis, the slop value range is-0.0899-0.0450,negative said reduce,positive said increase, the yearly change has an obvious rising trend. The vegetation of 83.5%in the study area has a generally rising trend from 2003 to 2010,16.5%in the study area on the reduce trend.(2) Seasonal changes of NDVI. Significant seasonal changes of the NDVI were presented in the study area.NDVI in summer was highest with an average value of 0.825, and that in the winter was lowest with an average value of 0.561 due to the temperature restriction to the vegetation growth. NDVI in spring and autumn were 0.702 and 0.751.(3) Spatial distribution of NDVI. Use the significance analysis formula analysis the vegetation variations from 2003 to 2010,significant spatial differences were found in the study area,78.56%in the study area is significant and 13.98%in the study area is insignificant; NDVI has a higher percentage of very significant degradation and significant degradation in the construction land, especially the urban areas, such as Yubei, Jiangbei, Jiulongpo, Shapingba.; on the contrary, higher percentage of very significant increase and significant increase in Xiushan, Kaixian, Wanzhou, Yunyang, Zhongxian, Youyang counties where there are large area of forest. The change is no significant where there has a higher vegetation keeping degree in Rongchang, Dazu, Yongchuan, Bishan, Fuling, Tongliang, Tongnan.(4) The correlation between vegetation indices and the critical growth period of the crop.①Rice. Analyzing NDVI values in the jointing, booting stage, tasseling, milky mature and mature during the period of analysis of rice and rice yield, they have high correlation between the critical growth period and NDVI,and coefficient fits to a significant test of correlation.Coefficient R2 values range between 0.8193 to 0.9519.the highest correlation is the milky mature, and the lowest is the jointing, following by the milky mature> tasseling> booting stage> mature> jointing.②Corn. Analyzing the maize in the three leaves stage, seven leaves stage, jointing, tasseling, milky mature and mature during the period of NDVI values and the correlation between corn yield,they have high correlation, and correlation coefficients were passed a significant test of the correlation. Coefficient R2 values range between 0.9067-0.9307.The highest correlation is the milky mature, and the minimum of three leaves stage, following by the milky mature> tasseling> jointing> seven leaves stage> mature> three leaves stage.(5) the selection of composite yield estimation model based on NDVI.①Rice. Five growth periods of rice are composited differently, and composited of one variable, binary, ternary, quaternary and quinary rice yield estimation models based on NDVI totally 15. Through the equation fitting correlation coefficient 7 indexes and comprehensive comparison chosen better model. According to the three more optimal equations applied to 2010 year of the rice yield and the analysis of NDVI. Then choose the equation including the optimal composite equation for rice in every critical growth period.②Corn. Six growth periods of corn are composited differently, and composited of one variable, binary, ternary, quaternary, quinary and hexahydroxy corn yield estimation models based on NDVI totally 21. Through the equation fitting correlation coefficient 7 indexes and comprehensive comparison chosen better model. According to the three more optimal equations applied to 2010 year of the rice yield and the analysis of NDVI. Composite model 16 is the best equation, which includes the NDVI of corn yield estimation equation during growth period to heading date. |
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