Analytical dynamic orbit improvement for the evaluation of geodetic-geodynamic satellite data

Summary Given an analytical solution (solution of the equations of motion using analytical integration techniques) an integration of the variational equations is not necessary but simply a differentiation of the analytical solution. Based on a high-precision analytical solution explicit expressions for the coefficients of the observation equations to improve state variables and force model parameters have been derived.The definition of suitable unknowns for a pure dynamic orbital improvement can be based then on an analysis of the information content of the data at hand by using those expressions.Regarding the Fourier series structure of orbital perturbations as well as the special properties of crossover difference data, the proposed technique is particularly favourable for an evaluation of Remote Sensing Data such as altimeter, SST and gradiometer data from satellites in repeating orbits.After a general discussion of the observation equations for crossover data our present results using GEOSAT altimeter data are discussed as one example for applications. Some short remarks about further applications are given.     

数字化地图印刷一种随机挂网计算模型

基于彩色图像像素3刺激值与相应网点面积率之间的α幂修正的Neugebauer方程,建立由常规的调幅网为基础的调频挂网计算模型,以此实现分色胶片随机挂网     

Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records

Understanding the spatiotemporal dynamics of urban population is crucial for addressing a wide range of urban planning and management issues. Aggregated geospatial big data have been widely used to quantitatively estimate population distribution at fine spatial scales over a given time period. However, it is still a challenge to estimate population density at a fine temporal resolution over a large geographical space, mainly due to the temporal asynchrony of population movement and the challenges to acquiring a complete individual movement record. In this article, we propose a method to estimate hourly population density by examining the time‐series individual trajectories, which were reconstructed from call detail records using BP neural networks. We first used BP neural networks to predict the positions of mobile phone users at an hourly interval and then estimated the hourly population density using log‐linear regression at the cell tower level. The estimated population density is linearly correlated with population census data at the sub‐district level. Trajectory clustering results show five distinct diurnal dynamic patterns of population movement in the study area, revealing spatially explicit characteristics of the diurnal commuting flows, though the driving forces of the flows need further investigation.     

Extraction of Alteration Mineral Information from Moderate Remote Sensing Images Using MPS Method

Although alteration minerals related to metallogenesis is very important in mineral exploration, information of alteration mineral is weakly expressed in remote sensing imagery, which is often subject to interfering noise and sometimes limited in spectral and spatial resolutions. Because of easy access, moderate images are the main sources of alteration mineral information. Therefore, it is very important to develop alteration mineral information extraction methods from remote sensing images. In this paper, a combined method based on Mask, principal component analysis (PCA) and support vector machine method (SVM) was used to extract alteration mineral information from Enhanced thematic mapper plus remote sensing data with limited spectral and spatial resolutions. First, a mask image of the remote sensing imagery was created to remove interference information such as vegetation, shadow and water. Then, PCA was employed to collect sample data relating to iron, argillic, and carbonatization alteration. Finally, SVM was used to deal with alteration anomaly and build a feature extraction model of high accuracy. The Mask-PCA-SVM model is used to extract alteration mineral information from remote sensing images of Hatu area, Xinjiang Uygur Autonomous Regions, China. The results show that the new methods proposed in this paper can coincide well with known deposits occurrences, rate reached 86.51%. While, the consistent rate with known deposits of the ratio model, PCA model and Spectral angle mapper model were only 3.37, 65.08 and 69.05% respectively. This suggests that the proposed model can find the actual distribution of mineral deposits more effectively by reducing interference to a greater degree.     

A Stochastic Approach to Estimate Distribution of Built-Up Area in Regions with Thick Tree Cover

Buildings and other human-made constructions have been accepted as an indicator of human habitation and are identified as built-up area. Identification of built-up area in a region and its subsequent measurement is a key step in many fields of studies like urban planning, environmental studies, and population demography. Remote sensing techniques utilising medium resolution images (e.g. LISS III, Landsat) are extensively used for the extraction of the built-up area as high-resolution images are expensive, and its processing is difficult. Extraction of built land use from medium resolution images poses a challenge in regions like Western-Ghats, North-East regions of India, and countries in tropical region, due to the thick evergreen tree cover. The spectral signature of individual houses with a small footprint are easily overpowered by the overlapping tree canopy in a medium resolution image when the buildings are not clustered. Kerala is a typical case for this scenario. The research presented here proposes a stochastic-dasymetric process to aid in the built-up area recognition process by taking Kerala as a case study. The method utilises a set of ancillary information to derive a probability surface. The ancillary information used here includes distance from road junctions, distance from road network, population density, built-up space visible in the LISS III image, the population of the region, and the household size. The methodology employs logistic regression and Monte Carlo simulation in two sub processes. The algorithm estimates the built-up area expected in the region and distributes the estimated built-up area among pixels according to the probability estimated from the ancillary information. The output of the algorithm has two components. The first component is an example scenario of the built-up area distribution. The second component is a probability surface, where the value of each pixel denotes the probability of that pixel to have a significant built-up area within it. The algorithm is validated for regions in Kerala and found to be significant. The model correctly predicted the built-up pixel count count over a validation grid of 900 m in 95.2% of the cases. The algorithm is implemented using Python and ArcGIS.     

Analysis of the Seasonal Variation of CO<Subscript>2</Subscript> Concentration in China Based on GOSAT Satellite Data

Carbon dioxide (CO₂) is one of the major gases that contribute to the global warming. Therefore, studying the distribution of CO₂ can help people understand the carbon cycle. Based on the GOSAT retrieved CO₂ products, the temporal and spatial distribution and seasonal variation of CO₂ concentration were analyzed from 2011 to 2015. CO₂ concentration has obvious seasonal variation. It was low in summer, and was high in spring, and the annual increase was about 2 ppm. Nevertheless, the annual growth rate of CO₂ concentration in summer was higher than that in spring, it was 0.5425% in summer and was 0.46% in spring. CO₂ concentration was low in the northwest and was high in the southeast. The growth rate of CO₂ was 2.8 ppm in the northwest and was 3.42 ppm in the southeast. More human’s activities made CO₂ concentration higher in the southeast than that in other regions.     

Accuracy Assessment of Digital Elevation Model Generated by SAR Stereoscopic Technique Using COSMO-Skymed Data

Radargrammetry technique using the stereoscopic synthetic aperture radar (SAR) images is used for the generation of a digital elevation model (DEM) of a region requires only the amplitude images. SAR stereoscopic technique is analogous to the stereo-photogrammetric technique where the optical stereoscopic images are used for DEM generation. While the advantages of the SAR images are their indifference to atmospheric transparency and solar illumination conditions, the side-looking geometry of the SAR increases the complexity in the SAR stereo analysis. The availability of high spatial and temporal resolution SAR data in recent years has facilitated generation of high-resolution DEM with greater vertical accuracy using radargrammetric technique. In the present study, attempt has been made to generate the DEM of Dehra Dun region, India, from the COSMO-Skymed X-band SAR data-pair acquired at 8 days interval through the radargrammetry technique. Here, radargrammetric orientation approach has been adopted to generate the DEM and various issues and processing steps with the radargrammetry technique have been discussed. The DEM was validated with ground measured elevation values using a differential global positioning system and the root-mean-square error of the DEM was found as 7.3 m. The DEM was compared with the reference DEM of the study area generated from the Cartosat-1 stereo data with a model accuracy of 4 m.     

Study on Sampling Rule and Simplification of LiDAR Point Cloud Based on Terrain Complexity

It is difficult to obtain digital elevation model (DEM) in the mountainous regions. As an emerging technology, Light Detection and Ranging (LiDAR) is an enabling technology. However, the amount of points obtained by LiDAR is huge. When processing LiDAR point cloud, huge data will lead to a rapid decline in data processing speed, so it is necessary to thin LiDAR point cloud. In this paper, a new terrain sampling rule had been built based on the integrated terrain complexity, and then based on the rule a LiDAR point cloud simplification method, which was referred as to TCthin, had been proposed. The TCthin method was evaluated by experiments in which XUthin and Lasthin were selected as the TCthin’s comparative methods. The TCthin’s simplification degree was estimated by the simplification rate value, and the TCthin’s simplification quality was evaluated by Root Mean Square Deviation. The experimental results show that the TCthin method can thin LiDAR point cloud effectively and improve the simplification quality, and at 5 m, 10 m, 30 m scale levels, the TCthin method has a good applicability in the areas with different terrain complexity. This study has theoretical and practical value in sampling theory, thinning LiDAR point cloud, building high-precision DEM and so on.     

Sea-Level Change Over the China Sea and Its Vicinity Derived from 25-Year T/P Series Altimeter Data

The characteristics of sea-level change in the China Sea and its vicinity are studied by combining TOPEX/Poseidon (T/P), Jason-1, Jason-2, and Jason-3 altimeter data. First, the sea-surface height is computed by using monthly data via collinear adjustment, regional selection, and crossover adjustment. The sea-level anomaly (SLA) from October 1992 to July 2017 is calculated based on the difference that is obtained by the value derived from the inverse distance weighting method to interpolate the CNES_CLS15 model value at a normal point. By analyzing the satellite data at the same time in orbit, three mean bias groups over the China Sea and its vicinity are obtained: the difference between T/P and Jason-1 is ??11.76 cm, the difference between Jason-1 and Jason-2 is 9.6 cm, and the difference between Jason-2 and Jason-3 is 2.42 cm. To establish an SLA series for 25 years in the study area, the SLAs are corrected. Mean rate of sea-level rise of the Bohai Sea, Yellow Sea, East China Sea, and South China Sea of 4.87 mm/a, 2.68 mm/a, 2.88 mm/a, and 4.67 mm/a, respectively, is found by analyzing the series of SLAs.