With the fast urbanization process, how does the vegetation environment change in one of the most economically developed metropolis, Shanghai in East China? To answer this question, there is a pressing demand to explore the non-stationary relationship between socio-economic conditions and vegetation across Shanghai. In this study, environmental data on vegetation cover, the Normalized Difference Vegetation Index (NDVI) derived from MODIS imagery in 2003 were integrated with socio-economic data to reflect the city’s vegetative conditions at the census block group level. To explore regional variations in the relationship of vegetation and socio-economic conditions, Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models were applied to characterize mean NDVI against three independent socio-economic variables, an urban land use ratio, Gross Domestic Product (GDP) and population density. The study results show that a considerable distinctive spatial variation exists in the relationship for each model. The GWR model has superior effects and higher precision than the OLS model at the census block group scale. So, it is more suitable to account for local effects and geographical variations. This study also indicates that unreasonable excessive urbanization, together with non-sustainable economic development, has a negative influence of vegetation vigor for some neighborhoods in Shanghai.
The main objective of this study was to retrieve rice yield and biomass fromRadarsat-2 SAR data with artificial neuralnetwork (ANN).For this purpose, a practical scheme for estimating rice yield from Radarsat-2 data is established, which demonstrates that Radarsat-2 data can serve asan important data source for monitoring rice system and estimating rice yield.The ANN was composed of the rice backscattering coefficients extracted from multi-temporal Radarsat-2 images and rice canopy parameters (i.e. height, moisture content and biomass) observed from the fields, and then it was applied to simulate the correlation betweenthese two parts. The rice yield and biomass onAugust 21 and September14 were retrieved based on the trained network, respectively. Compared with the measured data, the retrieved rice yield and biomassonAug.21 and Sept.14 were quite accurate.Our results suggested thatRadarsat-2SGX images can be usedto estimate rice yield regionally, and neural network method is feasible with respects to the estimation of rice yield and biomass.