以位于云贵高原至广西丘陵倾斜面上的云南省富宁县为研究区，提出了适合研究区地形特点的地貌形态分类指标体系；基于 SRTM DEM 90 m 分辨率的地形数据，用均值变点分析法，确定8像元×8像元（0．5184 km^2）的格网为该县地形起伏...以位于云贵高原至广西丘陵倾斜面上的云南省富宁县为研究区，提出了适合研究区地形特点的地貌形态分类指标体系；基于 SRTM DEM 90 m 分辨率的地形数据，用均值变点分析法，确定8像元×8像元（0．5184 km^2）的格网为该县地形起伏度的最佳统计单元，据此提取了该县地形起伏度（0～707 m）；最后，叠加分析了该县绝对海拔和地形起伏度数据，得到12种基本地貌形态，并得出结论：小起伏较低山、小起伏中山是该县最主要的地貌形态。展开更多
PU （phase unwrapping） is the key step and important problem in DEM （digital elevation model） extraction and the measurement of surface deformation of InSAR （Interferometric synthetic aperture radar）. The CKFPUA ...PU （phase unwrapping） is the key step and important problem in DEM （digital elevation model） extraction and the measurement of surface deformation of InSAR （Interferometric synthetic aperture radar）. The CKFPUA （conventional Kalman filter phase unwrapping algorithm） can obtain reliable results in the flat terrain areas, but it caused error transmission not making the accurate inversion of surface deformation information in the steep terrain. Considering this situation, so it needs to introduce topographic information for guiding phase unwrapping. Here the 90 m resolution DEM data have been used and it is obtained by SRTM （shuttle radar topography mission） measured jointly by NASA （National Aeronautics and Space Administration） and NIMA （National Imaging Mapping Agency） of U.S. Department of Defense. This paper presents a SD-KFPUA （Kalman filter phase unwrapping algorithm） based on SRTM DEM. With SRTM DEM directing InSAR image to implement phase unwrapping, the speed and accuracy are improved. By analyzing with the conventional Kalman filter phase unwrapping algorithms, it is shown that the proposed method can achieve good results in particular to improve unwrapping accuracy in the low coherence region.展开更多
Groundwater resources provide most of the domestic water supply in rural Zimbabwe and support poverty reduction through irrigation facilities. Most agricultural and environmental plans need water table depth analysis ...Groundwater resources provide most of the domestic water supply in rural Zimbabwe and support poverty reduction through irrigation facilities. Most agricultural and environmental plans need water table depth analysis as an input in designing best management strategies. There are limited direct measurements of groundwater levels in Zimbabwe due to high costs and the limited human expertise. The study is aimed at coming up with a proof of concept that altitude of rivers as determined by an SRTM digital elevation model can be used to estimate the levels of groundwater in parts of Mutirikwi and Runde sub catchments of southern Zimbabwe. The study also maps the groundwater levels of the area as determined by river altitude from the digital elevation model. Firstly, the groundwater levels for nine boreholes are measured. Secondly, the altitude of a river bed nearest to each borehole site is extracted from a digital elevation model. Finally, the Spearman’s correlation coefficient is used to determine the nature and strength of the relationship between the two variables. Linear regression analysis was also used to obtain the predictive equation of the relationship and its coefficient of determination. After the relationship between groundwater and river altitude is established, 9 new random points of river altitude are generated across the study area interpolated using kriging interpolation to give the estimated altitude of river altitude. The altitude of groundwater is then determined by running the predictive equation Y = 0.8736 * X + 0.852 obtained from regression analysis. The depth to groundwater level of area is obtained by subtracting the determined groundwater altitude from the SRTM DEM. The results show strong positive and statistically significant (ρ = 0.000, α = 0.01) correlation coefficient of 0.971 between measured groundwater levels and altitude of rivers. The regression model shows a coefficient of determination (r2) of 0.975. The research therefore determines that altitude of rivers and use of geostatistics can produce physically plausible estimates of groundwater levels in the study area.展开更多
文摘以位于云贵高原至广西丘陵倾斜面上的云南省富宁县为研究区，提出了适合研究区地形特点的地貌形态分类指标体系；基于 SRTM DEM 90 m 分辨率的地形数据，用均值变点分析法，确定8像元×8像元（0．5184 km^2）的格网为该县地形起伏度的最佳统计单元，据此提取了该县地形起伏度（0～707 m）；最后，叠加分析了该县绝对海拔和地形起伏度数据，得到12种基本地貌形态，并得出结论：小起伏较低山、小起伏中山是该县最主要的地貌形态。
基金Acknowledgments The research is supported by the National Science Foundation of China （40874001） and National 863 plans projects of China （2009AA12Z147）. The authors would like to express thanks to ESA （European Space Agency） for providing ENVISAT satellite data.
文摘PU （phase unwrapping） is the key step and important problem in DEM （digital elevation model） extraction and the measurement of surface deformation of InSAR （Interferometric synthetic aperture radar）. The CKFPUA （conventional Kalman filter phase unwrapping algorithm） can obtain reliable results in the flat terrain areas, but it caused error transmission not making the accurate inversion of surface deformation information in the steep terrain. Considering this situation, so it needs to introduce topographic information for guiding phase unwrapping. Here the 90 m resolution DEM data have been used and it is obtained by SRTM （shuttle radar topography mission） measured jointly by NASA （National Aeronautics and Space Administration） and NIMA （National Imaging Mapping Agency） of U.S. Department of Defense. This paper presents a SD-KFPUA （Kalman filter phase unwrapping algorithm） based on SRTM DEM. With SRTM DEM directing InSAR image to implement phase unwrapping, the speed and accuracy are improved. By analyzing with the conventional Kalman filter phase unwrapping algorithms, it is shown that the proposed method can achieve good results in particular to improve unwrapping accuracy in the low coherence region.
文摘Groundwater resources provide most of the domestic water supply in rural Zimbabwe and support poverty reduction through irrigation facilities. Most agricultural and environmental plans need water table depth analysis as an input in designing best management strategies. There are limited direct measurements of groundwater levels in Zimbabwe due to high costs and the limited human expertise. The study is aimed at coming up with a proof of concept that altitude of rivers as determined by an SRTM digital elevation model can be used to estimate the levels of groundwater in parts of Mutirikwi and Runde sub catchments of southern Zimbabwe. The study also maps the groundwater levels of the area as determined by river altitude from the digital elevation model. Firstly, the groundwater levels for nine boreholes are measured. Secondly, the altitude of a river bed nearest to each borehole site is extracted from a digital elevation model. Finally, the Spearman’s correlation coefficient is used to determine the nature and strength of the relationship between the two variables. Linear regression analysis was also used to obtain the predictive equation of the relationship and its coefficient of determination. After the relationship between groundwater and river altitude is established, 9 new random points of river altitude are generated across the study area interpolated using kriging interpolation to give the estimated altitude of river altitude. The altitude of groundwater is then determined by running the predictive equation Y = 0.8736 * X + 0.852 obtained from regression analysis. The depth to groundwater level of area is obtained by subtracting the determined groundwater altitude from the SRTM DEM. The results show strong positive and statistically significant (ρ = 0.000, α = 0.01) correlation coefficient of 0.971 between measured groundwater levels and altitude of rivers. The regression model shows a coefficient of determination (r2) of 0.975. The research therefore determines that altitude of rivers and use of geostatistics can produce physically plausible estimates of groundwater levels in the study area.
