Prediction of landslides using ASTER imagery and data mining models

The aim of this study was to identify landslide-related factors using only remotely sensed data and to present landslide susceptibility maps using a geographic information system, data-mining models, an artificial neural network (ANN), and an adaptive neuro-fuzzy interface system (ANFIS). Landslide-related factors were identified in Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. The slope, aspect, and curvature of topographic features were calculated from a digital elevation model that was made using the ASTER imagery. Lineaments, land-cover, and normalized difference vegetative index layers were also extracted from the imagery. Landslide-susceptible areas were analyzed and mapped based on occurrence factors using the ANN and ANFIS. The generalized bell-shaped built-in membership function of the ANFIS was applied to landslide susceptibility mapping. Analytical results were validated using landslide test location data. In the validation results, the ANN model showed 80.42% prediction accuracy and the ANFIS model showed 86.55% prediction accuracy. These results suggest that the ANFIS model has a better performance than does the ANN in predicting landslide susceptibility.     

Estimation of stress and its use in evaluation of landslide prone regions using remote sensing data

Recently, use of remote sensing data for determining the orientation of stress has been demonstrated. The present study deals with the estimation of stress pattern in the part of the Himalayan region which shows the ongoing neo-tectonic activities. The study area falls into a tectonically active zone of the Central-Himalaya, with a complex geotectonic set-up confined by a number of faults. Efforts have been made to evaluate the technique as a fast algorithm for quick and time limited analysis of linear feature from which the orientation of the lineaments are estimated by using remote sensing data. Further, the estimation of stress and the lineament analysis have been used in mapping of landslide prone areas. Terrain information such as land cover, geology, lineament, faults, mega faults, geomorphology and drainage has been derived from the satellite imageries, and the existing thematic information has been updated to enable the quantification of landslide causative parameters. Spatial and temporal multi-layered information have been used for landslides hazard susceptibility analysis. The qualitative hazard analysis has been carried out using the map overlying techniques in GIS environment along the central part of Himalayan region. It has been observed that the high potential zones have been found to have very high lineament density, moderate to low drainage density and high slope areas of the terrain. On the basis of the geological and morphological analysis, it is further suggested that the combined impacts of the crushed nature of bed rock (due to the neo-tectonic activities), heavy rainfall and lack of vegetation cover cause persistent recurrence of landslides along this region. The role of earthquake on induction of landslides will be presented.     

RS-GIS based morphometrical and geological multi-criteria approach to the landslide susceptibility mapping in Gish River Basin,West Bengal,India

Darjeeling Himalaya is one of the several mountainous areas of India which is often suffered from landslide hazards. In this paper, a multi criteria evaluation is applied using 16 morphometric indicators, geology and lineaments to identify the areas vulnerable in respect to drainage and relief conditions. As both drainage and relief parameters exert strong influences on landslide intensity, both the diversity maps are integrated for final landslide susceptibility mapping. The obtained results show that 20.17?sq.?km (7.61%) area within the basin is highly susceptible for landslides, where average drainage density is 3.78?km/sq.?km, relative relief is greater than 408?m and slope is greater than 12°. The validation result shows that very high landslide susceptible zone is associated with very high frequency of landslide occurrence. Beside this, ROC curve also suggests good predicted rate (86.60%) for the model. So, the proposed method can be applied for predicting landslide susceptible zone.     

Field-based landcover classification using TerraSAR-X texture analysis

The present study aims to evaluate the field-based approach for the classification of landcover using high-resolution SAR data. TerraSAR-X (TSX) strip mode imagery, coupled with digital ortho-photos (DOPs) with 20 cm spatial resolution was used for landcover classification and parcel mapping respectively. Different filtering and analysis techniques were applied to extract textural information from the TSX image in order to assess the enhancement of the classification accuracy. Several attributes of parcels were derived from the available TSX images in order to define the most suitable parameters discriminating between different landcover types. Then, these attributes were further statistically analysed in order to define separability and thresholds between different landcover types. The results showed that textural analysis resulted in high classification accuracy. Hence, this paper confirms that integrated landcover classification using the textural information of TerraSAR-X has a high potential for landcover mapping.     

Integration of Landsat-8 and Sentinel-1 dataset to extract geological lineaments in complex formations of Tepal mountain area,Shahrood, north Iran

Recently, the detection and extraction of geological lineaments have become an essential analytical technique to find relationships between the characteristics and occurrence of hydrogeology, and tectonic studies. The use of remote sensing, with the progressive development of image enhancement techniques, provides an opportunity to produce more reliable and comprehensive lineament maps. In this paper, semi-automatic approach based on Landsat 8 and Sentinel 1 radar data is proposed for lineaments extraction and validation. The combined method of linear filtering and automatic line module ensures a high degree of accuracy resulting in a lineament map. Based on identified lineaments, Sentinel1 is more capable of detecting edges than Landsat8, but the primary orientation lineaments extracted from Landsat8 and Sentinel1 were different. So, by combining band6 of Landsat8, and VV and VH polarization of Sentinel1, the area lineaments were extracted with high accuracy. Rose diagram showed the extracted lineaments' orientation is in good compliance with the region's existing faults. Also, the formations' lineament length density has good consistent with the density of the faults in the geological map.     

Object-based rice mapping using time-series and phenological data

Remote sensing techniques are often used in mapping rice, but high quality time-series remote sensing data are difficult to obtain due to the cloudy weather of rice growing areas and long satellite revisit interval. As such, rice mapping is usually based on mono-temporal Landsat TM/ETM+ data, which have large uncertainties due to the spectral similarity of different vegetation types. Moreover, conventional pixel-based classification method is unable to meet the required accuracy for rice mapping. Therefore, this study proposes a new strategy for mapping rice in cloud-prone areas using fused data of Landsat-8 OLI time-series and phenological parameters, based on the object-based method. We determine the critical growth stages of paddy rice from observed phenological data and MODIS-NDVI time-series data. The spatial and temporal adaptive reflectance fusion model (STARFM) is used to blend the MODIS and Landsat data to obtain a multi-temporal Landsat-like dataset for classification. Finally, an object-oriented algorithm is used to extract rice paddies from the Landsat-like, time-series dataset. The validation experiments show that the proposed method can provide high accuracy rice mapping, with an overall accuracy of 92.38% and a kappa coefficient of 0.85.     

基于蚁群算法的卫星磁力矩器反馈参数设计

卫星姿态控制系统参数设计中,引入不确定性的部件误差模型可以提高系统模型的精度。针对传统优化算法处理随机因素的能力不强的情况,提出了基于蚁群算法的参数优化设计方法;并将该方法应用在某型号偏置动量轮小卫星的磁力矩器反馈控制参数设计中。与Powell方法对比,蚁群算法的寻优过程有明显的收敛趋势,这表明蚁群算法相对于传统优化算法鲁棒性好、处理随机因素能力强,可以应用在考虑随机因素的参数优化设计中。     

GDP spatialization in Ningbo City based on NPP/VIIRS night-time light and auxiliary data using random forest regression

Accurate spatial distribution information on gross domestic product (GDP) is of great importance for the analysis of economic development, industrial distribution and urbanization processes. Traditional administrative unit-based GDP statistics cannot depict the detailed spatial differences in GDP within each administrative unit. This paper presents a study of GDP spatialization in Ningbo City, China based on National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) night-time light (NTL) data and town-level GDP statistical data. The Landsat image, land cover, road network and topographic data were also employed as auxiliary data to derive independent variables for GDP modelling. Multivariate linear regression (MLR) and random forest (RF) regression were used to estimate GDP at the town scale and were assessed by cross-validation. The results show that the RF model achieved significantly higher accuracy, with a mean absolute error (MAE) of 109.46 million China Yuan (CNY)·km⁻² and a determinate coefficient (R² = 0.77) than the MLR model (MAE = 161.8 million CNY·km⁻², R² = 0.59). Meanwhile, by comparing with the estimated GDP data at the county level, the town-level estimated data showed a better performance in mapping GDP distribution (MAE decreased from 115.1 million CNY·km⁻² to 74.8 million CNY·km⁻²). Among all of the independent variables, NTL, land surface temperature (T_s) and plot ratio (PR) showed higher impacts on the GDP estimation accuracy than the other variables. The GDP density map generated by the RF model depicted the detailed spatial distribution of the economy in Ningbo City. By interpreting the spatial distribution of the GDP, we found that the GDP of Ningbo was high in the northeast and low in the southwest and formed continuous clusters in the north. In addition, the GDP of Ningbo also gradually decreased from the urban centre to its surrounding areas. The produced GDP map provides a good reference for the future urban planning and socio-economic development strategies.     

Drag and energy accommodation coefficients during sunspot maximum

Conditions appropriate to gas-surface interactions on satellite surfaces in orbit have not been successfully duplicated in the laboratory. However, measurements by pressure gauges and mass spectrometers in orbit have revealed enough of the basic physical chemistry that realistic theoretical models of the gas-surface interaction can now be used to calculate physical drag coefficients. The dependence of these drag coefficients on conditions in space can be inferred by comparing the physical drag coefficient of a satellite with a drag coefficient fitted to its observed orbital decay. This study takes advantage of recent data on spheres and attitude stabilized satellites to compare physical drag coefficients with the histories of the orbital decay of several satellites during the recent sunspot maximum. The orbital decay was obtained by fitting, in a least squares sense, the semi-major axis decay inferred from the historical two-line elements acquired by the US Space Surveillance Network. All the principal orbital perturbations were included, namely geopotential harmonics up to the 16th degree and order, third body attraction of the Moon and the Sun, direct solar radiation pressure (with eclipses), and aerodynamic drag, using the Jacchia-Bowman 2006 (JB2006) model to describe the atmospheric density. After adjusting for density model bias, a comparison of the fitted drag coefficient with the physical drag coefficient has yielded values for the energy accommodation coefficient as well as for the physical drag coefficient as a function of altitude during solar maximum conditions. The results are consistent with the altitude and solar cycle variation of atomic oxygen, which is known to be adsorbed on satellite surfaces, affecting both the energy accommodation and angular distribution of the reemitted molecules.     

Automatic lineament extraction in a heavily vegetated region using Landsat Enhanced Thematic Mapper (ETM+) imagery

Lineament extraction from satellite remotely sensed data has been one of the widely used applications of remote sensing in geology. In fact, recent advances in digital image processing allow such lineament extraction to be accomplished in semi-automatic to fully automatic approaches. However, satellite remotely sensed data acquired in heavily vegetated regions such as tropical rainforest, are vulnerable to higher inherent noise levels attributed to the resultant effects of scattering by clouds and adjacency effects of highly inhomogeneous vegetation cover within the pixel dimension. In this study, we examined the effects of noise levels to lineament extraction using a fully automatic approach, consisting of a combination of edge-line detection algorithms. Ancillary information from a digitized topographic map and image classification was used to discriminate between cultural and natural lineaments from the extracted lineaments. Adapting the combination of edge detection and a line-linking algorithm, we have found the optimal parameters for automatic lineament extraction of such complex areas using Enhanced Thematic Mapper (ETM+) data. A noise level of 30% is the maximum threshold before artifacts are generated. It is therefore concluded that the combination of edge-based and line-linking digital image processing operations with the priori local optimal parameters is crucial in lineament feature extraction in heavily vegetated regions.     

Satellite ocean colour algorithm for Prochlorococcus, Synechococcus, and picoeukaryotes concentration retrieval in the South China Sea

An algorithm for retrieval of surface waters cell concentrations (in cell/ml) for three picophytoplankton components, Prochlorococcus (Pro), Synechococcus (Syn), and picoeukaryotes (Peuk) in the South China Sea (SCS), from ocean colour satellite data was developed and tested. Level 3 merged multisensor Ocean Colour Climate Change Initiative satellite data is used. Training is performed using in situ data on abundances of the three phytoplankton components. Several predictors derived from satellite reflectance data were tested. The regression form that assures the highest accuracy of the algorithm was chosen based on cross-validation (CV). According to the CV on test data subset, the algorithm performance is characterized by the r value 0.89, 0.72, and 0.73 and MAPD 38, 71 and 51% for Peuk, Pro, and Syn respectively. This is one of the few studies aimed at the Peuk, Pro, and Syn distribution research in the northern SCS using ocean colour satellite data. This is the only research providing algorithm with accuracy estimates of the Peuk, Pro, and Syn concentrations retrieval from the ocean colour data. Analysis of the developed algorithm allows us to conclude that both mechanisms (specific spectral features caused by pigments composition and spectrum features sensitive to general primary productivity, e.g. band ratios in 443–510?nm range and spectrum absolute values) are important for getting accurate information on the picophytoplankton composition.     

Estimation of differential code biases with Jason-2/3 onboard GPS observations

With the continuous deployment of Low Earth Orbit (LEO) satellites, the estimation of differential code biases (DCBs) based on GNSS observations from LEO has gained increasing attention. Previous studies on LEO-based DCB estimation are usually using the spherical symmetry ionosphere assumption (SSIA), in which a uniform electron density is assumed in a thick shell. In this study, we propose an approach (named the SHLEO method) to simultaneously estimate the satellite and LEO onboard receiver DCBs by modeling the distribution of the global plasmaspheric total electron content (PTEC) above the satellite orbit with a spherical harmonic (SH) function. Compared to the commonly used SSIA method, the SHLEO model improves the GPS satellite DCB estimation accuracy by 13.46% and the stability by 22.34%, respectively. Compared to the GPS satellite DCBs estimated based on the Jason-3-only observations, the accuracy and monthly stability of the satellite DCBs can be improved by 14.42% and 26.8% when both Jason-2 and Jason-3 onboard observations are jointly processed. Compared with the Jason-2 solutions, the GPS satellite DCB estimates based on the fusion of Jason-2 and Jason-3 observations have an improved consistency of better than 18.26% and 9.71% with the products provided by the Center for Orbit Determination in Europe (CODE) and Chinese Academy of Sciences (CAS). Taking the DCB products provided by the German Aerospace Center (DLR) as references, there is no improvement in accuracy of the GPS satellite DCB estimates based on the fusion of Jason-2 and Jason-3 observations than the Jason-2 solutions alone. A periodic variation is found in the time series of both the Jason-3 and Jason-2 onboard receiver DCB estimates. Preliminary analysis of the PTEC distribution based on the estimated SH coefficients are also presented.     

Modeling satellite drag coefficients with response surfaces

Satellite drag coefficients are a major source of uncertainty in predicting the drag force on satellites in low Earth orbit. Among other things, accurately predicting the orbit requires detailed knowledge of the satellite drag coefficient. Computational methods are an important tool in computing the drag coefficient but are too intensive for real-time and predictive applications. Therefore, analytic or empirical models that can accurately predict drag coefficients are desired. This work uses response surfaces to model drag coefficients. The response surface methodology is validated by developing a response surface model for the drag coefficient of a sphere where the closed-form solution is known. The response surface model performs well in predicting the drag coefficient of a sphere with a root mean square percentage error less than 0.3% over the entire parameter space. For more complex geometries, such as the GRACE satellite, the Hubble Space Telescope, and the International Space Station, the model errors are only slightly larger at about 0.9%, 0.6%, and 1.0%, respectively.     

Drag coefficient modeling for grace using Direct Simulation Monte Carlo

Drag coefficient is a major source of uncertainty in predicting the orbit of a satellite in low Earth orbit (LEO). Computational methods like the Test Particle Monte Carlo (TPMC) and Direct Simulation Monte Carlo (DSMC) are important tools in accurately computing physical drag coefficients. However, the methods are computationally expensive and cannot be employed real time. Therefore, modeling of the physical drag coefficient is required. This work presents a technique of developing parameterized drag coefficients models using the DSMC method. The technique is validated by developing a model for the Gravity Recovery and Climate Experiment (GRACE) satellite. Results show that drag coefficients computed using the developed model for GRACE agree to within 1% with those computed using DSMC.     

Gravity field models from kinematic orbits of CHAMP,GRACE and GOCE satellites

The aim of our work is to generate Earth’s gravity field models from GPS positions of low Earth orbiters. Our inversion method is based on Newton’s second law, which relates the observed acceleration of the satellite with forces acting on it. The observed acceleration is obtained as numerical second derivative of kinematic positions. Observation equations are formulated using the gradient of the spherical harmonic expansion of the geopotential. Other forces are either modelled (lunisolar perturbations, tides) or provided by onboard measurements (nongravitational perturbations). From this linear regression model the geopotential harmonic coefficients are obtained.     

一种基于小波相关滤波的无线电干涉测量处理方法

针对深空探测中干涉测量微弱信号相关处理问题,提出了一种基于小波滤波的无线电干涉测量方法。首先,在小波相关滤波算法分析的基础上,提出了基于移位相关、逆序处理以及最高层小波系数阈值处理的改进算法;其次,分析并构建了深空探测宽带信号模型,并给出了基于小波相关滤波的无线电测量方案;最后通过蒙特卡洛仿真和实测数据处理证明小波相关滤波可以改善无线电干涉测量精度,其中同步卫星群时延估计精度改善约10%。     

基于遗传算法的航空发动机部件特性修正

研究了航空发动机部件特性修正技术,提出了一种基于变适应度函数的模型优化算法,以达到减小总体建模误差,提高模型精度的目的.在稳态模型的基础上,对引气系数、总压恢复系数和各部件的特性进行了修正,使修正后的模型输出与实验数据相一致.采用改进遗传算法,对交叉率和变异率进行了非线性自适应调整,并根据误差大小调整适应度加权系数,避免算法陷入局部最优,同时减小最大建模误差.仿真结果表明,修正后各实验参数平均误差从2.420 8%减小到0.321 7%,模型满足稳态误差小于2%的要求.      

基于会切场推力器无拖曳系统建模及参数优化

随着卫星重力测量技术的突破性进展,对航天器试验环境要求也在不断提高,航天器受到的残余扰动必须尽可能减小。作为中国将来重力场测量卫星备选主推力器的会切场推力器,其推力器的控制精度直接决定了测量的准确性。文章首先通过PID方法设计了位移模式下的无拖曳控制器,该控制器在预估阻力系数、参考质量与卫星本体的位移差、速度差等性能方面有良好的表现,在应对卫星运行时的突发情况时表现出很强的稳定性。但PID参数没有达到最优解,在此基础上对于该模型的控制精度进行优化,用遗传算法对PID控制的参数进行筛选。结果分析表明,会切场推力器的控制精度有所改善,NTW方向上的速度和位移误差均减小;推力阻力和显著减少;控制精度提高,更好地满足使用需求。     

Analysis of GNSS clock prediction performance with different interrupt intervals and application to real-time kinematic precise point positioning

Continuous and timely real-time satellite orbit and clock products are mandatory for real-time precise point positioning (RT-PPP). Real-time high-precision satellite orbit and clock products should be predicted within a short time in case of communication delay or connection breakdown in practical applications. For prediction, historical data describing the characteristics of the real-time orbit and clock can be used as the basis for performing the prediction. When historical data are scarce, it is difficult for many existing models to perform precise predictions. In this paper, a linear regression model is used to predict clock products. Seven-day GeoForschungsZentrum (GFZ) final clock products sampled at 30 s are used to analyze the characteristics of GNSS clocks. It is shown that the linear regression model can be used as the prediction model for the satellite clock products. In addition, the accuracy of the clock prediction for different satellites are analyzed using historical data with different periods (such as 2 and 10 epochs). Experimental results show that the accuracy of the clock with the linear regression prediction model using historical data with 10 epochs is 1.0 ns within 900 s. This is higher accuracy than that achieved using historical data of 2 epochs. Finally, the performance analysis for real-time kinematic precise point positioning (PPP) is provided using GFZ final clock prediction results and state space representation (SSR) clock prediction results when communication delay or connection breakdown occur. Experimental results show that the positioning accuracy without prediction is better than that with prediction in general, whether using the final clock product or the SSR clock product. For the final clock product, the positioning accuracy in the north (N), east (E), and up (U) directions is better than 10.0 cm with all visible GNSS satellites with prediction. In comparison, the 3D positioning accuracy of N, E, and U directions with visible GNSS satellites whose prediction accuracy is better than 0.1 ns using historical data of 10 epochs is improved from 15.0 cm to 7.0 cm. For the SSR clock product, the positioning accuracy of N, E, and U directions is better than 12.0 cm with visible GNSS satellites with prediction. In comparison, the 3D positioning accuracy of N, E, and U directions with visible GNSS satellites whose prediction accuracy is better than 0.1 ns using historical data of 10 epochs is improved from 12.0 cm to 9.0 cm.     

全极化散射计极化失真误差校正方法

全极化散射计是一种新型微波散射计,可以同时测量目标的同极化和交叉极化散射系数.一方面能够解决传统散射计在海面风场反演中的风向模糊问题,提高海面风场测量精度,另一方面交叉极化散射系数可以有效提高海面风速测量范围,解决台风灾害监测和预报问题.利用全极化散射计数据进行高精度海面风场反演,首先需要解决全极化散射计系统极化失真误差导致无法准确测量目标不同极化散射系数的问题.本文基于全极化散射计信号模型推导了极化失真误差影响下极化散射系数模型,并对极化失真误差影响进行了定量分析.在极化散射系数模型基础上,通过引入海面散射特性假设和极化定标技术推导了极化散射系数校正模型.基于RADARSAT-2获取的不同区域海面全极化散射系数数据仿真误差影响下极化散射系数并利用该模型进行校正,结果表明该方法可以有效校正极化失真误差,显著提高极化散射系数测量精度.