Land subsidence over oilfields in the Yellow River Delta

Subsidence in river deltas is a complex process that has both natural and human causes. Increasing human activities like aquaculture and petroleum extraction are affecting the Yellow River delta, and one consequence is subsidence. The purpose of this study is to measure the surface displacements in the Yellow River delta region and to investigate the corresponding subsidence source. In this paper, the Stanford Method for Persistent Scatterers (StaMPS) package was employed to process Envisat ASAR images collected between 2007 and 2010. Consistent results between two descending tracks show subsidence with a mean rate up to 30 mm/yr in the radar line of sight direction in Gudao Town (oilfield), Gudong oilfield and Xianhe Town of the delta, each of which is within the delta, and also show that subsidence is not uniform across the delta. Field investigation shows a connection between areas of non-uniform subsidence and of petroleum extraction. In a 9 km2 area of the Gudao Oilfield, a poroelastic disk reservoir model is used to model the InSAR derived displacements. In general, good fits between InSAR observations and modeled displacements are seen. The subsidence observed in the vicinity of the oilfield is thus suggested to be caused by fluid extraction

Framework to Create Cloud-Free Remote Sensing Data Using Passenger Aircraft as the Platform

Cloud removal in optical remote sensing imagery is essential for many Earth observation applications.Due to the inherent imaging geometry features in satellite remote sensing, it is impossible to observe the ground under the clouds directly; therefore, cloud removal algorithms are always not perfect owing to the loss of ground truth. Passenger aircraft have the advantages of short visitation frequency and low cost. Additionally, because passenger aircraft fly at lower altitudes compared to satellites, they can observe the ground under the clouds at an oblique viewing angle. In this study, we examine the possibility of creating cloud-free remote sensing data by stacking multi-angle images captured by passenger aircraft. To accomplish this, a processing framework is proposed, which includes four main steps: 1) multi-angle image acquisition from passenger aircraft, 2) cloud detection based on deep learning semantic segmentation models, 3) cloud removal by image stacking, and 4) image quality enhancement via haze removal. This method is intended to remove cloud contamination without the requirements of reference images and pre-determination of cloud types. The proposed method was tested in multiple case studies, wherein the resultant cloud- and haze-free orthophotos were visualized and quantitatively analyzed in various land cover type scenes. The results of the case studies demonstrated that the proposed method could generate high quality, cloud-free orthophotos. Therefore, we conclude that this framework has great potential for creating cloud-free remote sensing images when the cloud removal of satellite imagery is difficult or inaccurate

A Novel Effective Indicator of Weighted Inter-City Human Mobility Networks to Estimate Economic Development

Estimation of economic development in advance is benefit to test the validity of economic policy or to take timely remedial measures for economic recession. Due to the inevitable connections between human mobility and economic status, estimation of economic trend in advance from easily observable big data in human mobility has the superiority of authenticity, timeliness, and convenience. However, high-precision quantitative relations between human mobility and economic growth remain an outstanding question. To this issue, we firstly analyzed and compared the general patterns of human mobility and economic development; then, a novel, simple, and effective hybrid human mobility indicator ( H H M I i ) of weighted human mobility networks was proposed to quantitatively estimate economic growth. H H M I i contained two parts, that is, the interaction volumes of a given city with all participation cities and only top hub cities, respectively. This implied that the economic growth of a city is affected by not only its own strength, but also the cooperation with hub cities. Several empirical experiments demonstrated that the proposed H H M I i had an exceedingly high estimation ability of economic growth, especially for the tertiary industry. Compared with other complex network indicators, H H M I i had a distinct advantage and its best accuracy reached 0.9543. These results can provide policy-making supports for inter-city sustainable coordinated development

Disaster Chain Analysis of Landfill Landslide: Scenario Simulation and Chain-Cutting Modeling

Landfill landslide is a man-made event that occurs when poorly managed garbage mounds at landfills collapse. It has become common in recent decades due to the rising waste volumes in cities. Normally, it is a complex process involving many disaster-causing factors and composed by many sequential sub-events. However, most current studies treat the landslide as a single and independent event and cannot give a full picture of the disaster. We propose a disaster chain analysis framework for landfill landslide in terms of scenario simulation and chain-cutting modeling. Each stage of the landfill landslide is modeled by taking advantage of various advanced techniques, e.g., remote sensing, 3DGIS, non-Newtonian fluid model, central finite difference scheme, and agent-base steering model. The 2015 Shenzhen “1220” landslide was firstly reviewed to summarize the general disaster chain model for landfill landslide. Guided by this model, we then proposed the specific steps for landfill landslide disaster chain analysis and applied them to another undergoing landfill, i.e., Xinwuwei landfill in Shenzhen, China. The scenario simulation in this landfill provides suggestions on potential hazardous risks and some applicable treatments. Through chain-cutting modeling, we further validated the effectiveness and feasibility of these treatments. The most optimized solution is subsequently deduced, which can provide support for disaster prevention and mitigation for this landfill

An Improved Method for Power-Line Reconstruction from Point Cloud Data

This paper presents a robust algorithm to reconstruct power-lines using ALS technology. Point cloud data are automatically classified into five target classes before reconstruction. In order to improve upon the defaults of only using the local shape properties of a single power-line span in traditional methods, the distribution properties of power-line group between two neighbor pylons and contextual information of related pylon objects are used to improve the reconstruction results. First, the distribution properties of power-line sets are detected using a similarity detection method. Based on the probability of neighbor points belonging to the same span, a RANSAC rule based algorithm is then introduced to reconstruct power-lines through two important advancements: reliable initial parameters fitting and efficient candidate sample detection. Our experiments indicate that the proposed method is effective for reconstruction of power-lines from complex scenarios

Time-Series Analysis on Persistent Scatter-Interferometric Synthetic Aperture Radar (PS-InSAR) Derived Displacements of the Hong Kong–Zhuhai–Macao Bridge (HZMB) from Sentinel-1A Observations

The synthetic aperture radar interferometry (InSAR) technique has been applied in monitoring the deformation of infrastructures, such as bridges, highways, railways and subways. Persistent scatterer (PS)-InSAR is one of the InSAR techniques, which utilises persistent scatterers to derive long-term displacements. This study applied time-series methods to post-process the PS-InSAR-derived time-series displacements with the use of 86 Sentinel-1A acquisitions spanning from 6 January 2018 to 27 November 2020. Empirical mode decomposition (EMD) and seasonal and trend decomposition using loess (STL) were combined to estimate the seasonal component of the total time-series displacements. Then, a temperature correlation map was generated by correlating the seasonal component with the temperature variation. Results show that the thermal expansion phenomenon is pronounced on the buildings of the Zhuhai–Macao Passenger Terminal as well as the bridge and road connecting to the Hong Kong International Airport (HKIA), while it is less obviously observed at the main Hong Kong-Zhuhai-Macao Bridge (HZMB). In addition, sudden changes between subsidence and uplift can be detected through the p-values derived by applying the augmented Dickey-Fuller (ADF) test to the residual signals after removing the linear and seasonal components from the original ones

A Stochastic Geometry Method for Pylon Reconstruction from Airborne LiDAR Data

Object detection and reconstruction from remotely sensed data are active research topic in photogrammetric and remote sensing communities. Power engineering device monitoring by detecting key objects is important for power safety. In this paper, we introduce a novel method for the reconstruction of self-supporting pylons widely used in high voltage power-line systems from airborne LiDAR data. Our work constructs pylons from a library of 3D parametric models, which are represented using polyhedrons based on stochastic geometry. Firstly, laser points of pylons are extracted from the dataset using an automatic classification method. An energy function made up of two terms is then defined: the first term measures the adequacy of the objects with respect to the data, and the second term has the ability to favor or penalize certain configurations based on prior knowledge. Finally, estimation is undertaken by minimizing the energy using simulated annealing. We use a Markov Chain Monte Carlo sampler, leading to an optimal configuration of objects. Two main contributions of this paper are: (1) building a framework for automatic pylon reconstruction; and (2) efficient global optimization. The pylons can be precisely reconstructed through energy optimization. Experiments producing convincing results validated the proposed method using a dataset of complex structure

Analysis of Surface Air Temperature Change in Macao During the Period 1901–2007

Change related to climate in Macao was studied on the basis of daily temperature observations over the period 1901–2007. The result shows that annual mean surface air temperature in Macao as a whole rose with a warming rate of about 0.066 °C per 10 years in the recent 107 years. The most evident warming occurred in spring and winter. The interdecadal variations of the seasonal mean temperature in summer and winter appeared as a series of waves with a time scale of about 30 years and 60 years, respectively. The annual mean minimum temperature increased about twice as fast as the annual mean maximum temperature, resulting in a broad decline in the annual mean diurnal range. The interdecadal variations of annual mean maximum temperature are obviously different from those of annual mean minimum temperature. It appears that the increase in the annual mean maximum temperature in the recent 20 years may be part of slow climate fluctuations with a periodicity of about 60 years, whereas that in the annual mean minimum temperature appears to be the continuation of a long-term warming trend. Fong, S., C .Wu, A. Wang, et al., 2010: Analysis of surface air temperature change in Macao during the period 1901–2007. Adv. Clim. Change Res., 1, doi: 10.3724/SP.J.1248.2010.00084