Yunshan Meng

Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China

The Lushan earthquake with magnitude Ms 7.0 (Mw 6.6, USGS) in Sichuan Province, China, triggered a large number of landslides, which seriously aggravated the earthquake’s destructive consequences. This paper mainly focuses on the methodology of the urgent landslide susceptibility assessment right after the earthquake. The detailed landslide inventory (including 5,688 landslides) is prepared by means of urgent post-earthquake landslide field survey, landslide remote sensing interpretation of multi-source remote sensing images including high-resolution unmanned aerial vehicle images and historical landslide archives. Ten remarkable causative factors for landslide occurrence have been selected to conduct the landslide susceptibility assessment, including earthquake intensity, landslide density and slope gradient. An integration assessment approach is developed to facilitate the effective urgent post-earthquake landslide susceptibility assessment using three methods: factor sensitivity analysis, analytical hierarchy process and factor-weighted overlay. Such integration can effectively reduce the subjectivity and uncertainty resulting from using single method. The validation evaluation using the area under curve suggests the landslide susceptibility assessment results have satisfactory accuracy, and the suggested methodology is effective for the urgent post-earthquake landslide susceptibility assessment. The study results reveal that earthquake intensity and slope gradient are the two most important causative factors for post-earthquake landslide occurrence in the Lushan earthquake-impacted area. The dominant slope gradient and slope aspect with relatively higher landslide frequency are 45°–50° and south-east direction, respectively. The intense earthquake impact increased the dominant slope gradient of landslide spatial distribution, and the thrust campaign of seismogenic fault with strike NE–SW made south-east direction as the dominant slope aspect of the landslide spatial distribution. The locations with very high and high landslide susceptibility are mainly distributed in the regions with higher earthquake intensity and adverse terrain conditions, such as Shuangshi town and Longmen town of Lushan county and Muping town of Baoxing county. The study results are expected to provide a beneficial reference for the landslide prevention and infrastructure reconstruction after the Lushan earthquake.

Post-earthquake rainfall-triggered slope stability analysis in the Lushan area

The “4.20” Lushan earthquake in Sichuan province, China has induced a large amount of geological hazards and produced abundant loose materials which are prone to post-earthquake rainfall-triggered landslides. A detailed landslide inventory was acquired through post-earthquake emergent field investigation and high resolution remote sensing interpretation. The rainfall analysis was conducted using historical rainfall records during the period from 1951 to 2010. Results indicate that the average annual rainfall distribution is heterogeneous and the largest average annual rainfall occurs in Yucheng district. The Stability Index MAPping (SINMAP) model was adopted to assess and analyze the post-earthquake slope stability under different rainfall scenarios (light rainfall, moderate rainfall, heavy rainfall, and rainstorm). The model parameters were calibrated to reflect the significant influence of strong earthquakes on geological settings. The slope stability maps triggered by different rainfall scenarios were produced at a regional scale. The effect of different rainfall conditions on the slope stability is discussed. The expanding trend of the unstable area was quantitatively assessed with the different critical rainfall intensity. They provide a new insight into the spatial distribution and characteristics of post-earthquake rainfall-triggered landslides in the Lushan seismic area. An increase of rainfall intensity results in a significant increase of unstable area. The heterogeneous distribution of slope instability is strongly correlated with the distribution of earthquake intensity in spite of different rainfall conditions. The results suggest that the both seismic intensity and rainfall are two crucial factors for post-earthquake slope stability. This study provides important references for landslide prevention and mitigation in the Lushan area after earthquake.

An Improved DINEOF Algorithm for Filling Missing Values in Spatio-Temporal Sea Surface Temperature Data.

In this study, an improved Data INterpolating Empirical Orthogonal Functions (DINEOF) algorithm for determination of missing values in a spatio-temporal dataset is presented. Compared with the ordinary DINEOF algorithm, the iterative reconstruction procedure until convergence based on every fixed EOF to determine the optimal EOF mode is not necessary and the convergence criterion is only reached once in the improved DINEOF algorithm. Moreover, in the ordinary DINEOF algorithm, after optimal EOF mode determination, the initial matrix with missing data will be iteratively reconstructed based on the optimal EOF mode until the reconstruction is convergent. However, the optimal EOF mode may be not the best EOF for some reconstructed matrices generated in the intermediate steps. Hence, instead of using asingle EOF to fill in the missing data, in the improved algorithm, the optimal EOFs for reconstruction are variable (because the optimal EOFs are variable, the improved algorithm is called VE-DINEOF algorithm in this study). To validate the accuracy of the VE-DINEOF algorithm, a sea surface temperature (SST) data set is reconstructed by using the DINEOF, I-DINEOF (proposed in 2015) and VE-DINEOF algorithms. Four parameters (Pearson correlation coefficient, signal-to-noise ratio, root-mean-square error, and mean absolute difference) are used as a measure of reconstructed accuracy. Compared with the DINEOF and I-DINEOF algorithms, the VE-DINEOF algorithm can significantly enhance the accuracy of reconstruction and shorten the computational time.

Application of the Model of Universal Gravity to Oceanic Front Detection Near the Kuroshio Front

Oceanic front is a narrow transitional zone that the penetration of sea is obviously different between two or more waters there,and it plays an important role in the national production,national defense,marine and weather.Based on the modified theory of universal gravity,sea surface temperature(SST) data near the Kuroshio front are used for front detection.The theory of universal gravity assumes that each image pixel is a celestial body with a mass represented by its value.According to the law of universal gravity,the forces of the pixels in the 3×3 neighbourhood exerted on the central pixels can be calculated.Because fronts are susceptible to the noise and intense of fronts are commonly low,a modified method are proposed to solve these problems in this article.This method firstly eliminates the pixels that values equal to 0.Then in order to decrease the reliance on the brightness level of original data,a normalization step is applied to each 3×3 neighbourhood and next based on image enhancement function,each normalized 3×3 area can be enhanced.Finally,the theory of universal gravity is applied to enhanced data for front detection.The algorithm was tested and compared with conventional methods using in the fronts detection such as Sobel,Jensen-Shannon.The results show that compared to conventional methods in some areas,the proposed algorithm can decrease noise while not cause fronts discontinuous.

Characteristics of Surface Deformation Detected by X-band SAR Interferometry over Sichuan-Tibet Grid Connection Project Area, China

The Sichuan-Tibet grid connection project is a national key project implemented in accordance with the developmental needs of Tibet and the living requirements of 700 thousand local residents. It is the first grid project with special high voltage that passes through eastern margin of the Tibetan Plateau. The ground deformation due to widely distributed landslides and debris flow in this area is the major concern to the safety of the project. The multi-temporal interferometry technique is applied to retrieve the surface deformation information using high resolution X-band SAR imagery. The time series of surface deformation is obtained through the sequential high spatial and temporal resolution TerraSAR images (20 scenes of X-band TerraSAR SLC images acquired from 5 January 2014 to 12 December 2014). The results have been correlated with the permafrost activities and intensive precipitation. They show that the study area is prone to slow to moderate ground motion with the range of −30 to +30 mm/year. Seasonal movement is observed due to the freeze-thaw cycle effect and intensive precipitation weather condition. Typical region analysis suggests that the deformation rate tends to increase dramatically during the late spring and late autumn while slightly during the winter time. The correlations of surface deformations with these two main trigger factors were further discussed. The deformation curves of persistent scatterers in the study area showing the distinct seasonal characteristics coincide well with the effect of freeze-thaw cycle and intensive precipitation. The movement occurring at late spring is dominated by the freeze-thaw cycle which is a common phenomenon in such a high-elevated area as the Tibetan Plateau. Intensive precipitation plays more important role in triggering landsides in the summer season. The combining effect of both factors results in fast slope movement in May. The results also suggest that the movement often occur at the middle to toe part of the slope where the combining effect of freeze-thaw cycle and precipitation plays an important role. Therefore the majority of transmission towers are not threatened significantly by geological hazards since they are located on the higher elevation which is beyond the boundary of slope movement. The comparison between field observations and the persistent scatterers interferometry (PSI) results reveals good agreement in obvious deformation accumulations. High uncertainty still exists due to issue of SAR imagery quality and the persistent scatterers interferometry technique. Nevertheless, this study provides an insight into understanding the characteristics of ground movement trend in the complicated eastern Tibet area.

Reconstruction of Satellite-Derived Sea Surface Temperature Data Based on an Improved DINEOF Algorithm

An improved data interpolating empirical orthogonal function (I-DINEOF) algorithm was proposed in this study. Compared with the ordinary DINEOF algorithm, in the I-DINEOF algorithm, the existing data are not necessary to be selected for cross-validation and the initial matrix is directly used for reconstruction. Instead of using single EOF to reconstruct the whole spatio-temporal matrix, the initial matrix is divided into several subareas and each subarea is reconstructed by the most suitable EOF. To validate the accuracy of the I-DINEOF algorithm, a real sea surface temperature (SST) data set and three synthetic data sets with different missing data percentage are reconstructed by using the DINEOF and I-DINEOF algorithms. Four parameters (Pearson correlation coefficient, signal-to-noise ratio, root-mean-square error, and mean absolute difference) are used as a measure of reconstructed accuracy. Compared with the DINEOF algorithm, the I-DINEOF algorithm is less affected by the missing data and can significantly enhance the accuracy of reconstruction.

Application of a sea surface temperature front composite algorithm in the Bohai, Yellow, and East China Seas

The oceanic front is a narrow zone in which water properties change abruptly within a short distance. The sea surface temperature (SST) front is an important type of oceanic front, which plays a signifi cant role in many fi elds including fi sheries, the military, and industry. Satellite-derived SST images have been used widely for front detection, although these data are susceptible to infl uence by many objective factors such as clouds, which can cause missing data and a reduction in front detection accuracy. However, front detection in a single SST image cannot fully refl ect its temporal variability and therefore, the long-term mean frequency of occurrence of SST fronts and their gradients are often used to analyze the variations of fronts over time. In this paper, an SST front composite algorithm is proposed that exploits the frontal average gradient and frequency more eff ectively. Through experiments based on MODIS Terra and Aqua data, we verifi ed that fronts could be distinguished better by using the proposed algorithm. Additionally through its use, we analyzed the monthly variations of fronts in the Bohai, Yellow, and East China Seas, based on Terra data from 2000 to 2013.

A model of sea surface temperature front detection based on a threshold interval

A model (Bayesian oceanic front detection, BOFD) of sea surface temperature (SST) front detection in satellite- derived SST images based on a threshold interval is presented, to be used in different applications such as climatic and environmental studies or fisheries. The model first computes the SST gradient by using a Sobel algorithm template. On the basis of the gradient value, the threshold interval is determined by a gradient cumulative histogram. According to this threshold interval, front candidates can be acquired and prior probability and likelihood can be calculated. Whether or not the candidates are front points can be determined by using the Bayesian decision theory. The model is evaluated on the Advanced Very High-Resolution Radiometer images of part of the Kuroshio front region. Results are compared with those obtained by using several SST front detection methods proposed in the literature. This comparison shows that the BOFD not only suppresses noise and small-scale fronts, but also retains continuous fronts.

An Enhanced Linear Spatio-Temporal Fusion Method for Blending Landsat and MODIS Data to Synthesize Landsat-Like Imagery

Landsat and MODIS data have been widely utilized in many remote sensing applications, however, the trade-off between the spatial resolution and temporal frequency has limited their capacities in monitoring detailed spatio-temporal dynamics. Spatio-temporal fusion methods based on a linear model that considers the differences between fine- and coarse-spatial-resolution images as linear can effectively solve this trade-off problem, yet the existing linear fusion methods either regard the coefficients of the linear model as constants or have adopted regression methods to calculate the coefficients, both of which may introduce some errors in the fusion process. In this paper, we proposed an enhanced linear spatio-temporal fusion method (ELSTFM) to improve the data fusion accuracy. In the ELSTFM, it is not necessary to calculate the slope of the linear model, and the intercept, which can be deemed as the residual caused by systematic biases, is calculated based on spectral unmixing theory. Additionally, spectrally similar pixels in a given fine-spatial-resolution pixel’s neighborhood and their corresponding weights were used in the proposed method to mitigate block effects. Landsat-7/ETM+ and 8-day composite MODIS reflectance data covering two study sites with heterogeneous and homogenous landscapes were selected to validate the proposed method. Compared to three other typical spatio-temporal fusion methods visually and quantitatively, the predicted images obtained from ELSTFM could acquire better results for the two selected study sites. Furthermore, the resampling methods used to resample MODIS to the same spatial resolution of Landsat could slightly, but did not significantly influence the fusion accuracy, and the distributions of slopes of different bands for the two study sites could all be deemed as normal distributions with a mean value close to 1. The performance of ELSTFM depends on the accuracy of residual calculation at fine-resolution and large landscape changes may influence the fusion accuracy.

An enhanced spatial and temporal adaptive reflectance fusion model based on optimal window

In this paper, we introduce an enhanced spatial and temporal adaptive reflectance fusion model based on optimal sub-window size. The sub-window size can affect the accuracy of fusion and instead of using the fixed sub-window size in the original STARFM algorithm, the proposed algorithm uses the density of similar pixels to determine the optimal sub-window size. Compared with the original STARFM algorithm, the proposed algorithm can enhance the accuracy of fusion.