THE RESEARCH ON MAXIMUM TURBIDITY OF YANGTZE RIVER ESTUARY AND URBAN GROWTH OF SHANGHAI USING TM CCT DATA

Based on the research on the diffusion of suspended sediments discharged outside of Yangtze River estuary and the landuse of Shanghai using Landsat MSS images in several years, the authors analysed the characteristics of TM CCT data of Shanghai scene, pointed out concrete range of maximum turbidity and growth of urban boundary of Shanghai through the information extraction.The feature vector combination method is used in the research process. The result is getting nice.

Dynamics of land use change in a mining area:a case study of Nadowli District,Ghana

In a developing country like Ghana, the study of land use and land cover change(LULCC) based on satellite imageries still remains a challenge due to cost, resolution and availability with less skilled man power. Existing researches are skewed towards the southerly part of Ghana thereby leaving the Northern sectors uncovered. The maximum likelihood classification(MLC) algorithm was employed for the LULCC between 2000 and 2014 in Nadowli: an area characterized by an upsurge in mining in the Northern belt of Ghana. A spatial-social approach was utilized combining both satellite imagery and socio economic data. Land use transition matrix, land use integrated index/degree indices was used to depict the characters of the change. A semi structured interview, pair wise ranking and key informant interviews were used to correlate the socio economic impact of the different LULC. Overall changes in the landscape showed an increase in bare ground by 19.22%, open savannah by 16.8% whereas closed savanna decreased by 50%. Land use change matrix showed increasing trends of bare ground at the expense of vegetation. The integrated land use index highlighted the bare ground and built up areas rising with a decreasing closed vegetation woodlot. Large farm size are shrinking whiles majority of the people view mining as the main socio economic activity affecting the environment and the reduction in vegetation. This study therefore provides a strategic guide and a baseline data for land use policy actors in the Northern belt of Ghana. This will aid in developing models for future land use change implications in surrounding areas where mining is on the rise.

Real time remaining useful life prediction based on nonlinear Wiener based degradation processes with measurement errors

Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item's individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.

Estimation of Terrestrial Net Primary Productivity in China from Fengyun-3D Satellite Data

Currently,the satellite data used to estimate terrestrial net primary productivity(NPP)in China are predominantly from foreign satellites,and very few studies have based their estimates on data from China’s Fengyun satellites.Moreover,despite their importance,the influence of land cover types and the normalized difference vegetation index(NDVI)on NPP estimation has not been clarified.This study employs the Carnegie–Ames–Stanford approach(CASA)model to compute the fraction of absorbed photosynthetically active radiation and the maximum light use efficiency suitable for the main vegetation types in China in accordance with the finer resolution observation and monitoring-global land cover(FROM-GLC)classification product.Then,the NPP is estimated from the Fengyun-3D(FY-3D)data and compared with the Moderate Resolution Imaging Spectroradiometer(MODIS)NPP product.The FY-3D NPP is also validated with existing research results and historical field-measured NPP data.In addition,the effects of land cover types and the NDVI on NPP estimation are analyzed.The results show that the CASA model and the FY-3D satellite data estimate an average NPP of 441.2 g C m^(−2) yr^(−1) in 2019 for China’s terrestrial vegetation,while the total NPP is 3.19 Pg C yr^(−1).Compared with the MODIS NPP,the FY-3D NPP is overestimated in areas of low vegetation productivity and is underestimated in high-productivity areas.These discrepancies are largely due to the differences between the FY-3D NDVI and MODIS NDVI.Compared with historical field-measured data,the FY-3D NPP estimation results outperformed the MODIS NPP results,although the deviation between the FY-3D NPP estimate and the in-situ measurement was large and may exceed 20%at the pixel scale.The land cover types and the NDVI significantly affected the spatial distribution of NPP and accounted for NPP deviations of 17.0%and 18.1%,respectively.Additionally,the total deviation resulting from the two factors reached 29.5%.These results show that accurate NDVI products and land cover types are important prerequisites for NPP estimation.