Huili Gong

Subregional-scale groundwater depletion detected by GRACE for both shallow and deep aquifers in North China Plain

This study explores the capability of Gravity Recovery and Climate Experiment (GRACE) to detect heterogeneous groundwater storage (GWS) variations in two subregions of the North China Plain: the Piedmont Plain (PP, ~54,000 km2, mainly exploiting shallow groundwater) and East Central Plain (ECP, ~86,000 km2, mainly exploiting deep groundwater). Results show that the GWS anomalies estimated from GRACE data (2003–2013) agree well with those estimated from in situ observations (2005–2010) for both PP (R2 = 0.91) and ECP (R2 = 0.75). The shallow GWS (2003–2013) in PP declines faster (−46.5 ± 6.8 mm/yr) than the deep GWS in ECP (−16.9 ± 1.9 mm/yr). However, the shallow GWS in PP recovered more quickly especially during the 2008–2011 drought period. Despite its lower magnitude, the GRACE-derived GWS depletion in ECP reveals the overexploitation of deep GWS. This study demonstrated that the heterogeneous GWS variations can potentially be detected by GRACE at the subregional scale smaller than the typical GRACE footprint (200,000 km2).

A new method of virtual reality based on Unity3D

In the area of geographic information system, there are always two methods to get 3D virtual reality, one is to use a 2D professional platform such as ArcGIS software to get the virtual reality by secondary development, the other is to use a 3D or 2.5D software as a platform for development, such as the Skyline software. In this paper, we will use a different platform, Unity3D, which is usually treated as game development software, as a virtual reality development platform. Firstly, the hierarchical approach of geographic information system is adopted in the study area, and the area is divided into four layers: Terrain Layer, Building Layer, Transport Layer, Vegetation Layer, additionally, the raw data of Terrain Layer is obtained by GPS measurements. Secondly, all the geographical entities which related to different layers are converted to 3D model by AutoCAD and 3dsMax software. Thirdly, the 3D models are imported into the Unity3D, and programming with Javascript language in Visual Programming Language Editor in order to achieve Gameobjects and Scenes. Lastly, the Scenes are integrated and published on the network. The attribute data of study area is stored by MySQL which is connected with Unity game platform by external interface. Visitors can download the ActiveX control to browse the study area, the scene is keep on updating 60 times per second, the viewer will subconsciously input and immediately immersed in the virtual scene for spontaneous exploration and observation. With a full range of personalized mode of operation, the users can choose their own way to browse and participate in the virtual reality, and give full play to their imagination according to their own wishes without affecting the others by using the designated keys on the keyboard.

Imaging Land Subsidence Induced by Groundwater Extraction in Beijing (China) Using Satellite Radar Interferometry

Beijing is one of the most water-stressed cities in the world. Due to over-exploitation of groundwater, the Beijing region has been suffering from land subsidence since 1935. In this study, the Small Baseline InSAR technique has been employed to process Envisat ASAR images acquired between 2003 and 2010 and TerraSAR-X stripmap images collected from 2010 to 2011 to investigate land subsidence in the Beijing region. The maximum subsidence is seen in the eastern part of Beijing with a rate greater than 100 mm/year. Comparisons between InSAR and GPS derived subsidence rates show an RMS difference of 2.94 mm/year with a mean of 2.41 ± 1.84 mm/year. In addition, a high correlation was observed between InSAR subsidence rate maps derived from two different datasets (i.e., Envisat and TerraSAR-X). These demonstrate once again that InSAR is a powerful tool for monitoring land subsidence. InSAR derived subsidence rate maps have allowed for a comprehensive spatio-temporal analysis to identify the main triggering factors of land subsidence. Some interesting relationships in terms of land subsidence were found with groundwater level, active faults, accumulated soft soil thickness and different aquifer types. Furthermore, a relationship with the distances to pumping wells was also recognized in this work.

Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images

Urban tree species mapping is an important prerequisite to understanding the value of urban vegetation in ecological services. In this study, we explored the potential of bi-temporal WorldView-2 (WV2, acquired on 14 September 2012) and WorldView-3 images (WV3, acquired on 18 October 2014) for identifying five dominant urban tree species with the object-based Support Vector Machine (SVM) and Random Forest (RF) methods. Two study areas in Beijing, China, Capital Normal University (CNU) and Beijing Normal University (BNU), representing the typical urban environment, were evaluated. Three classification schemes—classification based solely on WV2; WV3; and bi-temporal WV2 and WV3 images—were examined. Our study showed that the single-date image did not produce satisfying classification results as both producer and user accuracies of tree species were relatively low (44.7%–82.5%), whereas those derived from bi-temporal images were on average 10.7% higher. In addition, the overall accuracy increased substantially (9.7%–20.2% for the CNU area and 4.7%–12% for BNU). A thorough analysis concluded that near-infrared 2, red-edge and green bands are always more important than the other bands to classification, and spectral features always contribute more than textural features. Our results also showed that the scattered distribution of trees and a more complex surrounding environment reduced classification accuracy. Comparisons between SVM and RF classifiers suggested that SVM is more effective for urban tree species classification as it outperforms RF when working with a smaller amount and imbalanced distribution of samples.

Sensitivity Analysis of Vegetation Reflectance to Biochemical and Biophysical Variables at Leaf, Canopy, and Regional Scales

The objective of this paper is to investigate the sensitivity of reflectance to the variation in biochemical and biophysical variables at leaf, canopy, and regional scales using a modeling approach. The results show that, at the leaf scale, the variations in chlorophyll a+b content, the leaf structure parameter, and the water content dominate the reflectance variance in the visible light (VIS), near infrared (NIR), and short-wave infrared (SWIR) regions, respectively. At the canopy scale, the sensitivity of reflectance to variation in the leaf structure parameter is very slight. For sparse foliage cover (leaf area index ), LAI is the most important variable to the canopy reflectance. As LAI increases, the sensitivity of reflectance to variation in LAI is reduced to a very low value. Moreover, chlorophyll a+b, dry matter, and water content control the variation of canopy reflectance in the VIS, NIR, and SWIR regions, respectively. At the regional scale, the sensitivity of reflectance to variation in vegetation variables is highly influenced by the mixed pixels. Thirty-six vegetation indices (VIs) are chosen in this paper to illustrate the scale dependence of the estimation accuracy of vegetation variables. The results show that the relationships between the VIs and the variables highly depend on the observation scale. For chlorophyll a+b content estimation, transformed chlorophyll absorption in reflectance index (TCARI), Blue Green pigment Index, leaf chlorophyll index (LCI), modified Normalized Difference (mND705), and Plant Biochemical Index at the leaf scale and canopy scale of and TCARI at the canopy scale of are highly related. The correlation between the indices and chlorophyll content in the regional scale is, however, much lower. For water content estimation, disease water stress index (DSWI), leaf water vegetation index 2 (LWVI_2), moisture stress index (MSI), normalized difference infrared index (NDII), normalized difference water index (NDWI), hyperspectral perpendicular vegetation index (RVI), SWIR water stress index (SIWSI), SR water index (SRWI), and water index (WI) are good choices at the leaf scale and canopy scale of , while at the canopy scale of and the regional scale, the correlation between the indices and water content is very low. For LAI estimation, VIs, including the Greenness Index, simple ratio (SR), Normalized Difference VI, modified soil-adjusted vegetation index (MSAVI), modified triangular vegetation index 1 (MTVI1), modified triangular vegetation index 2 (MTVI2), optimized soil-adjusted vegetation index (OSAVI), modified chlorophyll absorption ratio index 1 (MCARI1), modified chlorophyll absorption ratio index 2 (MCARI2), Enhanced VI, LAI Determining Index, renormalized difference vegetation index (RDVI), Spectral Polygon VI, Wide Dynamic Range VI, and triangular vegetation index (TVI), have high correlation with LAI at the canopy scale of while a low correlation at the canopy scale of and the regional scale.

Spatial correlation between land subsidence and urbanization in Beijing, China

The large-scale construction of buildings, extensive road and rail networks, and increased traffic flow associated with urbanization has the potential to cause land subsidence. Land subsidence caused by urbanization is an increasingly significant problem in Beijing, China; therefore, it is important to investigate the relationship between urbanization and land subsidence. Landsat TM images covering the Beijing plain were used to acquire spatial changes information of built-up areas by calculating an index-based built-up index (IBI). We used ENVISAT Advanced Synthetic Aperture Radar data acquired from 2003 to 2009 and persistent scatterers for SAR interferometry (PSI) technology to estimate land subsidence. Geographic information systems spatial analysis method was used to identify the relationship between the settlement rate and the IBI value for three different sampling units. The result showed that it was a positive correlation between construction density and land subsidence; for land subsidence, the effect from the combination of high-density building clusters and extensive transportation networks was more significant than the presence of buildings alone. However, there may be a delay between the completion of building construction and the development of land subsidence.

Topographic Correction of ZY-3 Satellite Images and Its Effects on Estimation of Shrub Leaf Biomass in Mountainous Areas

The availability of ZY-3 satellite data provides additional potential for surveying, mapping, and quantitative studies. Topographic correction, which eliminates the terrain effect caused by the topographic relief, is one of the fundamental steps in data preprocessing for quantitative analysis of vegetation. In this paper, we rectified ZY-3 satellite data using five commonly used topographic correction models and investigate their impact on the regression estimation of shrub forest leaf biomass obtained from sample plots in the study area. All the corrections were assessed by means of: (1) visual inspection (2) reduction of the standard deviation (SD) at different terrain slopes (3) correlation analysis of different correction results. Best results were obtained from the Minnaert+SCS correction, based on the non-Lambertian reflection assumption. Additional analysis showed that the coefficient correlation of the biomass fitting result was improved after the Minnaert+SCS correction, as well as the fitting precision. The R2 has increased by 0.113 to reach 0.869, while the SD (standard deviation) of the biomass dropped by 21.2%. Therefore, based on the facts, we conclude that in the region with large topographic relief, the topographical correction is essential to the estimation of the biomass.

Comprehensive analysis and artificial intelligent simulation of land subsidence of Beijing, China

Mechanism and modeling of the land subsidence are complex because of the complicate geological background in Beijing, China. This paper analyzed the spatial relationship between land subsidence and three factors, including the change of groundwater level, the thickness of compressible sediments and the building area by using remote sensing and GIS tools in the upper-middle part of alluvial-proluvial plain fan of the Chaobai River in Beijing. Based on the spatial analysis of the land subsidence and three factors, there exist significant non-linear relationship between the vertical displacement and three factors. The Back Propagation Neural Network (BPN) model combined with Genetic Algorithm (GA) was used to simulate regional distribution of the land subsidence. Results showed that at field scale, the groundwater level and land subsidence showed a significant linear relationship. However, at regional scale, the spatial distribution of groundwater depletion funnel did not overlap with the land subsidence funnel. As to the factor of compressible strata, the places with the biggest compressible strata thickness did not have the largest vertical displacement. The distributions of building area and land subsidence have no obvious spatial relationships. The BPN-GA model simulation results illustrated that the accuracy of the trained model during fifty years is acceptable with an error of 51% of verification data less than 20 mm and the average of the absolute error about 32 mm. The BPN model could be utilized to simulate the general distribution of land subsidence in the study area. Overall, this work contributes to better understand the complex relationship between the land subsidence and three influencing factors. And the distribution of the land subsidence can be simulated by the trained BPN-GA model with the limited available dada and acceptable accuracy.

Statistic inversion of multi-zone transition probability models for aquifer characterization in alluvial fans

Abstract Understanding the heterogeneity arising from the complex architecture of sedimentary sequences in alluvial fans is challenging. This paper develops a statistical inverse framework in a multi-zone transition probability approach for characterizing the heterogeneity in alluvial fans. An analytical solution of the transition probability matrix is used to define the statistical relationships among different hydrofacies and their mean lengths, integral scales, and volumetric proportions. A statistical inversion is conducted to identify the multi-zone transition probability models and estimate the optimal statistical parameters using the modified Gauss–Newton–Levenberg–Marquardt method. The Jacobian matrix is computed by the sensitivity equation method, which results in an accurate inverse solution with quantification of parameter uncertainty. We use the Chaobai River alluvial fan in the Beijing Plain, China, as an example for elucidating the methodology of alluvial fan characterization. The alluvial fan is divided into three sediment zones. In each zone, the explicit mathematical formulations of the transition probability models are constructed with optimized different integral scales and volumetric proportions. The hydrofacies distributions in the three zones are simulated sequentially by the multi-zone transition probability-based indicator simulations. The result of this study provides the heterogeneous structure of the alluvial fan for further study of flow and transport simulations.

Characterization and causes of land subsidence in Beijing, China

ABSTRACT Long-term overexploitation of groundwater is the primary factor causing regional land subsidence in the Beijing plain area, China. Currently, large subsidence funnels exist, one each in southern and northern Beijing. We adopted the multi-temporal interferometric synthetic aperture radar (MT-InSAR) method, incorporating both persistent scatterer (PS) and small baseline (SB) approaches on 47 Envisat Advanced Synthetic Aperture Radar (ASAR) single look complex (SLC) images to map land subsidence in the Beijing plain area. The temporal and spatial variations of land subsidence and its seasonal variation were explained by the MT-InSAR results. Then, the InSAR results were combined with the dynamic monitoring of groundwater level, extensometer measurements, and hydrogeological data; the characterization and causes of land subsidence were analysed with Geographic Information System (GIS) spatial analysis methods. The results show the following. 1) Land subsidence developed rapidly in the Beijing plain area from 2003 to 2010, with obviously uneven settlement; settlement rates exceeded 100 mm year−1 in some areas. Seasonal variation in settlement rates may be affected by changes in the precipitation rates and the exploitation of groundwater. 2) The contribution of different aquifer systems to land subsidence varies. The variation in the groundwater level in the second confined aquifer, at a depth of 100–180 m, has the greatest impact on land subsidence. 3) The settlement is centred in the lower part of the Wenyu–Chaobai and Yongding alluvial fan areas, where the compressible layer is more than 100 m thick. Meanwhile, land subsidence forms a structural feature with larger differences in the deformation gradient on both sides of faults.