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Predicting bathymetry based on vertical gravity gradient anomaly and analyses for various influential factors

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Geodesy and Geodynamics 2024年第4期15卷 386-396页

作者： Huan Xu Jinhai Yu Yanyan Zeng Qiuyu Wang Yuwei Tian Zhongmiao Sun College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijingChina Beijing Institute of Surveying and Mapping BeijingChina National Key laboratory of geoinformation Engineering Xi'an 710054China

The prediction of bathymetry has advanced significantly with the development of satellite ***,the majority of its data originate from marine gravity *** this study,based on the expression of vertical gravity gradient(VGG)of a rectangular prism,the governing equations for determining sea depths to invert *** governing equation is solved by linearization through an iterative process,and numerical simulations verify its algorithm and its *** also study the processing methods of different interference *** regularization method improves the stability of the inversion process for errors.A piecewise bilinear interpolation function roughly replaces the low-frequency error,and numerical simulations show that the accuracy can be improved by 41.2%after this *** variable ocean crust density,simulation simulations verify that the root-mean-square(RMS)error of prediction is approximately 5 m for the sea depth of 6 km if density is chosen as the average ***,two test regions in the South China Sea are predicted and compared with ship soundings data,RMS errors of predictions are 71.1 m and 91.4 m,respectively.

关键词： Rectangular prism Vertical gravity gradient Bathymetry Numerical simulation Prediction error

Parameter-driven Level of Detail Derivation Method for Semantic Building Facade Model

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Journal of Geodesy and geoinformation Science 2024年

作者： Yuefeng Wang Wei Jiao College of Geomatics and geoinformation Guilin University of Technology College of Environment and Resources Guangxi Normal University Guangxi Key laboratory of Spatial Information and Geomatics Guilin University of Technology

The relentless progress in the research of geographic spatial data models and their application scenarios is propelling an unprecedented rich Level of Detail (LoD) in realistic 3D representation and smart cities. This pursuit of rich details not only adds complexity to entity models but also poses significant computational challenges for model visualization and 3D GIS. This paper introduces a novel method for deriving multi-LOD models， which can enhance the efficiency of spatial computing in complex 3D building models. Firstly， we extract multiple facades from a 3D building model (LoD3) and convert them into individual semantic facade models. Through the utilization of the developed facade layout graph， each semantic facade model is then transformed into a parametric model. Furthermore， we explore the specification of geometric and semantic details in building facades and define three different LODs for facades， offering a unique expression. Finally， an innovative heuristic method is introduced to simplify the parameterized facade. Through rigorous experimentation and evaluation， the effectiveness of the proposed parameterization methodology in capturing complex geometric details， semantic richness， and topological relationships of 3D building models is demonstrated.

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Impact of climate and human activity on NDVI of various vegetation types in the Three-River Source Region, China

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Journal of Arid Land 2024年第8期16卷 1080-1097页

作者： LU Qing KANG Haili ZHANG Fuqing XIA Yuanping YAN Bing research Center of Resource and Environment Economics East China University of TechnologyNanchang 330013China Jiangxi Key laboratory of Watershed Ecological Process and Information East China University of TechnologyNanchang 330013China School of Surveying and geoinformation Engineering East China University of TechnologyNanchang 330013China Nanchang Key laboratory of Landscape Process and Territorial Spatial Ecological Restoration East China University of TechnologyNanchang 330013China Institute of Energy research Jiangxi Academy of SciencesNanchang 330096China

The Three-River Source Region(TRSR)in China holds a vital position and exhibits an irreplaceable strategic importance in ecological preservation at the national *** the basis of an in-depth study of the vegetation evolution in the TRSR from 2000 to 2022,we conducted a detailed analysis of the feedback mechanism of vegetation growth to climate change and human activity for different vegetation *** the growing season,the spatiotemporal variations of normalized difference vegetation index(NDVI)for different vegetation types in the TRSR were analyzed using the Moderate Resolution Imaging Spectroradiometer(MODIS)-NDVI data and meteorological data from 2000 to *** addition,the response characteristics of vegetation to temperature,precipitation,and human activity were assessed using trend analysis,partial correlation analysis,and residual *** indicated that,after in-depth research,from 2000 to 2022,the TRSR's average NDVI during the growing season was *** preliminary ranking of the average NDVI for different vegetation types was as follows:shrubland(0.5762)>forest(0.5443)>meadow(0.4219)>highland vegetation(0.2223)>steppe(0.2159).The NDVI during the growing season exhibited a fluctuating growth trend,with an average growth rate of 0.0018/10a(P<0.01).Notably,forests displayed a significant development trend throughout the growing season,possessing the fastest rate of change in NDVI(0.0028/10a).Moreover,the upward trends in NDVI for forests and steppes exhibited extensive spatial distributions,with significant increases accounting for 95.23%and 93.80%,*** sensitivity to precipitation was significantly enhanced in other vegetation types other than highland *** contrast,steppes,meadows,and highland vegetation demonstrated relatively high vulnerability to temperature fluctuations.A further detailed analysis revealed that climate change had a significant positive impact on the TRSR from 2000 to 2022,particularly in its

关键词： growing season normalized difference vegetation index(NDVI) highland vegetation trend analysis partial correlation analysis residual analysis contribution rate

Slip distribution inversion of seismic sub-fault dip iteration using gradient based optimizer algorithm

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Geodesy and Geodynamics 2024年第2期15卷 114-121页

作者： Leyang Wang Han Li Ming Pang Key laboratory of Mine Environmental Monitoring and Improving Around Poyang Lake of Ministry of Natural Resources East China University of TechnologyNanchang 330013China School of Surveying and geoinformation Engineering East China University of TechnologyNanchang 330013China College of Resources and Environment Shandong Agricultural UniversityTaian 271018China

This paper considers setting different dips for different sub-faults to fit the actual rupture situation based on the fault rupture of the 2013 Lushan M_(S)7.0 ***,combined with the coseismic GNSS data of the Lushan earthquake,the source parameters and sliding distribution of the Lushan earthquake fault are ***,we use the gradient based optimizer(GBO)in nonlinear inversion to obtain the source parameters of this seismic *** inversion results indicate that the strike of the fault is 206.52°,the dip is 44.10°,the length is 21.92 km,and the depth is 12.79 *** refine the sliding distribution of the seismic fault,the seismic fault is divided into 3×3 ***,we fix the central sub-fault dip of 44.10°;the dip of other sub-faults is obtained by *** that,the model is further divided into a fault layer model composed of 23×19 sub fault slices,and using the Matlab fitting function is used to fit the dip of the 23×19 sub ***,the Lushan seismic fault plane is established as a shovel structure with steep upper and gentle lower,steep south and gentle *** slip distribution inversion results indicate that the depth of the slip peak is 13 km,the corresponding maximum slip momentum is 0.67 m,the seismic moment is 1.10×10^(19)N·m and the corresponding moment magnitude is *** results above are consistent with the research results of seismology.

关键词： Gradient basedoptimizer(GBO) Lushan earthquake Non-planefault model Slid distribution

Mapping Air Quality Using Remote Sensing Technology: A Case Study of Nairobi County

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Open Journal of Air Pollution 2024年第1期13卷 1-22页

作者： Quinto Juma Meltus Faith Njoki Karanja Geospatial Engineering Department The University of Nairobi (UoN) Nairobi Kenya data Management Modeling and Geoinformation Unit (DMMG) International Centre of Insect Physiology and Ecology (ICIPE) Nairobi Kenya

Nairobi County experiences rapid industrialization and urbanization that contributes to the deteriorating state of air quality, posing a potential health risk to its growing population. Currently, in Nairobi County, most air quality monitoring stations use low-cost, inaccurate monitors prone to defects. The study’s objective was to map Nairobi County’s air quality using freely available remotely sensed imagery. The Air Pollution Index (API) formula was used to characterize the air quality from cloud-free Landsat satellite images i.e., Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI from Google Earth Engine. The API values were computed based on vegetation indices namely NDVI, TVI, DVI, and the SWIR1 and NIR bands on the QGIS platform. Qualitative accuracy assessment was done using sample points drawn from residential, industrial, green spaces, and traffic hotspot categories, based on a passive-random sampling technique. In this study, Landsat 5 API imagery for 2010 provided a reliable representation of local conditions but indicated significant pollution in green spaces, with recorded values ranging from -143 to 334. The study found that Landsat 7 API imagery in 2002 showed expected results with the range of values being -55 to 287, while Landsat 8 indicated high pollution levels in Nairobi. The results emphasized the importance of air quality factors in API calibration and the unmatched spatial coverage of satellite observations over ground-based monitoring techniques. The study recommends the recalibration of the API formula for characteristic regions, exploring newer satellite sensors like those onboard Landsat 9 and Sentinel 2, and involving key stakeholders in a discourse to develop a suitable Kenyan air quality index.

关键词： Air Quality Air Pollution Index (API) Satellite Imagery Vegetation Indices Nairobi County

Modeling urban redevelopment:A novel approach using time-series remote sensing data and machine learning

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Geography and Sustainability 2024年第2期5卷 211-219页

作者： Li Lin Liping Di Chen Zhang Liying Guo Haoteng Zhao Didarul Islam Hui Li Ziao Liu Gavin Middleton Center for Spatial Information Science and Systems George Mason UniversityFairfaxVA22030USA Department of Geography and geoinformation Science George Mason UniversityFairfaxVA22030USA

Accurate mapping and timely monitoring of urban redevelopment are pivotal for urban studies and decisionmakers to foster sustainable urban *** mapping methods heavily depend on field surveys and subjective questionnaires,yielding less objective,reliable,and timely *** advancements in Geographic Information Systems(GIS)and remote-sensing technologies have improved the identification and mapping of urban redevelopment through quantitative analysis using satellite-based ***,challenges persist,particularly concerning accuracy and significant temporal *** study introduces a novel approach to modeling urban redevelopment,leveraging machine learning algorithms and remote-sensing *** methodology can facilitate the accurate and timely identification of urban redevelopment *** study’s machine learning model can analyze time-series remote-sensing data to identify spatio-temporal and spectral patterns related to urban *** model is thoroughly evaluated,and the results indicate that it can accurately capture the time-series patterns of urban *** research’s findings are useful for evaluating urban demographic and economic changes,informing policymaking and urban planning,and contributing to sustainable urban *** model can also serve as a foundation for future research on early-stage urban redevelopment detection and evaluation of the causes and impacts of urban redevelopment.

关键词： Urban redevelopment Urban sustainability Remote sensing Time-series analysis Machine learning

The GNSS PWV retrieval using non-observation meteorological parameters based on ERA5 and its relation with precipitation

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Geodesy and Geodynamics 2024年第3期15卷 302-313页

作者： Weifeng Yang Zhiping Chen Kaiyun Lv Pengfei Xia Tieding Lu School of Surveying and geoinformation Engineering East China University of TechnologyNanchang 330013China Key laboratory of Mine Environmental Monitoring and Improving around Poyang Lake of Ministry of Natural Resources East China University of TechnologyNanchang 330013China State Key laboratory of Geodesy and Earth's Dynamics Innovation Academy for Precision Measurement Science and TechnologyChinese Academy of SciencesWuhan 430077China Jiangxi Province Engineering research Center of Surveying Mapping and Geographic InformationNanchang 330025China School of Geodesy and Geomatics Wuhan UniversityWuhan 430079China

The pressure and temperature significantly influence precipitable water vapor(PWV) retrieval. Global Navigation Satellite System(GNSS) PWV retrieval is limited because the GNSS stations lack meteorological sensors. First, this article evaluated the accuracy of pressure and temperature in 68 radiosonde stations in China based on ERA5 Reanalysis data from 2015 to 2019 and compared them with GPT3model. Then, the accuracy of pressure and temperature calculated by ERA5 were estimated in 5 representative IGS stations in China. And the PWV calculated by these meteorological parameters from ERA5(ERA5-PWV) were analyzed. Finally, the relation between ERA5-PWV and precipitation was deeply explored using wavelet coherence analysis in IGS stations. These results indicate that the accuracy of pressure and temperature of ERA5 is better than the GPT3 model. In radiosonde stations, the mean BIAS and MAE of pressure and temperature in ERA5 are-0.41/1.15 hpa and-0.97/2.12 K. And the mean RMSEs are 1.35 hpa and 2.87 K, which improve 74.77% and 40.58% compared with GPT3 model. The errors of pressure and temperature of ERA5 are smaller than the GPT3 model in bjfs, hksl and wuh2, and the accuracy of ERA5-PWV is improved by 18.77% compared with the GPT3 model. In addition, there is a significant positive correlation between ERA5-PWV and precipitation. And precipitation is always associated with the sharp rise of ERA5-PWV, which provides important references for rainfall prediction.

关键词： ERA5 GNSS Precipitable water vapor Precipitation Wavelet coherence analysis

An improved typhoon monitoring model based on precipitable water vapor and pressure

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Geodesy and Geodynamics 2024年第3期15卷 276-290页

作者： Junyu Li Haojie Li Lilong Liu Jiaqing Chen Yibin Yao Mingyun Hu Liangke Huang Fade Chen Tengxu Zhang Lv Zhou College of Geomatics and geoinformation Guilin University of TechnologyGuilin 541006China Guangxi Key laboratory of Spatial Information and Geomatics Guilin 541006China Qinzhou Institute of Surveying and Mapping for Housing and Urban-Rural Development Co. Ltd.Qinzhou 535000China School of Geodesy and Geomatics Wuhan UniversityWuhan 430079China School of Resources and Environmental Science and Engineering Hubei University of Science and TechnologyXianning 437100China

The potential of monitoring the movement of typhoons using the precipitable water vapor(PWV) has been confirmed. However, monitoring the movement of typhoon is focused on PWV, making it difficult to describe the movement of a typhoon in detail minutely and resulting in insufficient accuracy. Hence,based on PWV and meteorological data, we propose an improved typhoon monitoring mode. First, the European Centre for Medium-Range Weather Forecasts Reanalysis 5-derived PWV(ERA5-PWV) and the Global Navigation Satellite System-derived PWV(GNSS-PWV) were compared with the reference radiosonde PWV(RS-PWV). Then, using the PWV and atmospheric parameters derived from ERA5, we discussed the anomalous variations of PWV, pressure(P), precipitation, and wind speed during different typhoons. Finally, we compiled a list of critical factors related to typhoon movement, PWV and P. We developed an improved multi-factor typhoon monitoring mode(IMTM) with different models(i.e.,IMTM-I and IMTM-II) in different cases with a higher density of GNSS observation or only Numerical Weather Prediction(NWP) data. The IMTM was evaluated through the reference movement speeds of HATO and Mangkhut from the China Meteorological Observatory Typhoon Network(CMOTN). The results show that the root mean square(RMS) of the IMTM-I is 1.26 km/h based on ERA5-P and ERA5-PWV,and the absolute bias values are mostly within 2 km/h. Compared with the models considering the single factor ERA5-P/ERA5-PWV, the RMS of the IMTM-I is improved by 26.3% and 38.5%, respectively. The IMTM-II model manifests a residual of only 0.35 km/h. Compared with the single-factor model based on GNSS-PWV/P, the residual of the IMTM-II model is reduced by 90.8% and 84.1%, respectively. These results propose that the typhoon movement monitoring approach combining PWV and P has evident advantages over the single-factor model and is expected to supplement traditional typhoon monitoring.

关键词： Typhoon GNSS/ERA5 PWV Pressure Monitoring Improved model

A GIS-based analytical framework for evaluating the effect of COVID-19 on the restaurant industry with big data

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big Earth data 2023年第1期7卷 37-58页

作者： Siqin Wang Ruomei Wang Xiao Huang Zhenlong Li Shuming Bao School of Earth and Environmental Sciences University of QueenslandBrisbaneQueenslandAustralia Graduate School of Interdisciplinary Information Studies University of TokyoTokyoJapan Department of Geosciences University of ArkansasAnn ArborMichiganUSA geoinformation and big data research laboratory Department of GeographyUniversity of South CarolinaSouth CarolinaUSA China data Institute MichiganUSA

COVID-19 cripples the restaurant industry as a crucial socioeconomic sector that contributes immensely to the global ***,what the current literature less explored is to quantify the effect of COVID-19 on restaurant visitation and revenue at different spatial scales,as well as its relationship with the neighborhood character-istics of customers’*** on the Point of Interest(POI)measures derived from SafeGraph data providing mobility records of 45 million cell phone users in the US,our study takes Lower Manhattan,New York City,as the pilot study,and aims to examine 1)the change of restaurant visitations and revenue in the period prior to and after the COVID-19 outbreak,2)the areas where restaurant customers live,and 3)the association between the neighborhood characteristics of these areas and lost *** doing so,we provide a geographic information system-based analytical frame-work integrating the big data mining,web crawling techniques,and spatial-economic *** analytical framework can be implemented to estimate the broader effect of COVID-19 on other industries and can be augmented in a financially monitoring manner in response to future pandemics or public emergencies.

关键词： COVID-19 pandemic effect restaurant visitation human mobility New York City