Abstract:2000.The remote sensing and GIS methods were used to find the changes temporally and spatially. The result indicates: the forests were fallen in a large area, from 49.46% to 39.03% of total land area. Simultaneously, the croplands were increased rapidly from 26.02% to 37.42%. The conversion of forests and croplands were the main activities of landuse. Oppositely, Urbanization resulted in the decrease of the croplands in Southeast China during this period. In order to predict the landuse in 2015 and 2030 in this region, the CA-Markov model was taken. The predicting result indicates: From 2000 to 2015, 2000 to 2030, the croplands would increase 2.53% and 2.85% respectively, which account that the croplands exploitation reached a peak, only a small area of land can be used in croplands. (Turner et al., 1994). Recently, China is susceptible to land Land use/cover change is one of important factors that resulted in global population pressure on the land. Land use /cover changes during the past Markov INTRODUCTION
Due to many factors in the physical properties of the ground surface, the corresponding interferometric coherence values change dynamically over time. Among these factors, the roles of the vegetation and its temporal variation have not yet been revealed so far. In this paper, synthetic aperture radar (Sentinel-1) data and optical remote sensing (Landsat TM) images over four whole seasons are employed to reveal the relationship between the interferometric coherence and the normalized difference vegetation index (NDVI) at five sites that have ground deformation due to mining in Henan province, China. The result showed: (1) As for the village area with few vegetation cover, the related coherence values are significantly higher than that in the farm land area with high densities of vegetation in the spring and summer, which indicates that the subsidence by mining in few vegetation cover area is easier to be monitored; (2) Linear regression coefficients (R 2) between the interfereometric coherence values and the NDVI values is 0.62, which indicate the interferometric coherence values and the nDVi values change reversely in both farm land and village areas over the year. it suggests months between November and March with lower NDVI value are more suitable for deformation detecting. Therefore, the interfereometric coherence values can be used to detect the density of vegetation, while NDVI values can be reference for elucidating when the traditional differential interferometric synthetic aperture radar (DInSAR) could be effectively used. DInSAR leverages the phase difference between two correlated synthetic aperture radar (SAR) images to accurately detect large scale surface displacements and is widely used for mine deformation monitoring 1-4. However, as the technique suffers from a number of limitations, including spatial decorrelation, thermal noise, Doppler centroid shift, and temporal decorrelation, it is not appropriate in certain situations 5-7. Some research address the limitations of traditional DInSAR disturbed by the agricultural activities, especially the high density of crop vegetation, in the test of the polarimetric InSAR (POLInSAR) technique for its ability to increase interferometric coherence 8,9. Therefore, it is necessary to elucidate the deformation monitoring conditions under which the traditional DInSAR can be effectively used. The coherence is also taken as the main parameter in target classification 10,11 , forest change detection 12-14 , and lake study 15,16. The extent of temporal changes in the scatterers is a key factor affecting interferometric coherence 11,17. In DInSAR deformation measurements, the interferometric coherence is used for selecting the stable scatterers to achieve better accuracy 5. Compared with other scatterers, vegetation has a larger impact on SAR image coherence. In the seasons when vegetation is growing, the temporal decorrelation phenomena is
Abstract. The existing GPS tracking networks established primarily for surveying, geodesy and navigation purposes may also be used for meteorology studies. This research uses hourly surface temperature and pressure (T & P) observations from Australia for GPS Precipitable Water Vapor (PWV) estimation. The paper outlines the basic meteorological data requirements, and presents experimental results to show the comparison between interpolated and observed T and P values, and agreement between GPS-PWV estimates, using surface meteorological data and radiosonde PWV results. Data analysis of 36 data points in the Victoria region has demonstrated that the Ordinary Kriging method is preferable to pressure interpolation, resulting in an overall standard deviation of 0.40 mbar in pressure or 0.15mm in PWV estimation. We use the interpolated T and P measurements for four Australian IGS GPS sites to estimate GPS-PWV and compare against the radiosonde PWV results for the closely located radiosonde observations. 195 comparisons from all the sites have shown that GPS-PWV estimates agree with the Rad-PWV solutions at an average mean difference of -0.604 mm and RMS of 1.74mm for the tested stations. This agreement level is considered very reasonable. The experimental study shows a possible way to develop GPS meteorology and applications with the existing meteorological data network. This could save significant costs in installation of GPS-Met sensors.
A new land surface clustering algorithm is developed to retrieve soil moisture (SM) using the Global Navigation Satellite System reflectometry (GNSS-R) technique. Data from the BuFeng-1 (BF-1) twin satellites A/B, a pilot mission for the Chinese GNSS-R constellation, is used for SM retrieval. The core concept of the algorithm is to cluster global land areas into different types according to the land properties and calculate the SM type by type, based on the linear relationship between equivalent specular reflectivity (ESR) and SM. The global comparison between the results and SM product from the SMAP mission shows the correlation coefficient (R) is 0.82, and unbiased root mean square error (ubRMSE) is 0.070 cm 3 .cm -3 . The results also show good agreement compared with in situ SM measurements with the mean ubRMSE of 0.036 cm 3 .cm -3 . This study proves that the global SM can be retrieved successfully from the BF-1 mission with the land surface clustering algorithm. By taking full advantage of the similarity of land surface physical properties in different regions, the algorithm provides a practical approach for global SM retrieval using spaceborne GNSS-R data.
Land subsidence is a global geological disaster that seriously affects the safety of surface and underground buildings/structures and even leads to loss of life and property. The large-scale and continuous long-time coverage of Interferometric Synthetic Aperture Radar (InSAR) time series analysis techniques provide data and a basis for the development of methods for the investigation and evolution mechanism study of regional land subsidence. Based on the 108 SAR data of Sentinel-1 from April 2017 to December 2020, this study used Persistent Scatterer InSAR (PS-InSAR) technology to monitor the land subsidence in Qingdao. In addition, detailed analysis and discussion of land subsidence combined with the local land types and subway construction were carried out. From the entire area to the local scale, the deformation analysis was carried out in the two dimensions of time and space. The results reveal that the rate of surface deformation in Qingdao from 2017 to 2020 was mainly −34.48 to 5.77 mm/a and that the cumulative deformation was mainly −126.10 to 30.18 mm. The subsidence areas were mainly distributed in coastal areas (along the coasts of Jiaozhou Bay and the Yellow Sea) and inland areas (northeast Laixi City and central Pingdu City). In addition, it was found that obvious land subsidence occurred near the Health Center Station of Metro Line 8, a logistics company in Qingdao, and near several high-rise residential areas and business office buildings. It is necessary for the relevant departments to take timely action to prevent and mitigate subsidence-related disasters in these areas.
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