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Phenology of different types of vegetation and their response to climate change in the Qilian Mountains,China

Journal of Mountain Science 2024年第2期21卷 511-525页

作者： ZHAO Kaixin LI Xuemei ZHANG Zhengrong LIU Xinyu faculty of geomatics Lanzhou Jiaotong UniversityLanzhou 730070China National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring Lanzhou 730070China Gansu Provincial Engineering Laboratory for National Geographic State Monitoring Lanzhou 730070China

The Qilian Mountains(QM)possess a delicate vegetation ecosystem,amplifying the evident response of vegetation phenology to climate change.The relationship between changes in vegetation growth and climate remains complex.To this end,we used MODIS NDVI data to extract the phenological parameters of the vegetation including meadow(MDW),grassland(GSD),and alpine vegetation(ALV))in the QM from 2002 to 2021.Then,we employed path analysis to reveal the direct and indirect impacts of seasonal climate change on vegetation phenology.Additionally,we decomposed the vegetation phenology in a time series using the trigonometric seasonality,Box-Cox transformation,ARMA errors,and Trend Seasonal components model(TBATS).The findings showed a distinct pattern in the vegetation phenology of the QM,characterized by a progressive shift towards an earlier start of the growing season(SOS),a delayed end of the growing season(EOS),and an extended length of the growing season(LOS).The growth cycle of MDW,GSD,and ALV in the QM species is clearly defined.The SOS for MDW and GSD occurred earlier,mainly between late April and August,while the SOS for ALVs occurred between mid-May and mid-August,a one-month delay compared to the other vegetation.The EOS in MDW and GSD were concentrated between late August and April and early September and early January,respectively.Vegetation phenology exhibits distinct responses to seasonal temperature and precipitation patterns.The advancement and delay of SOS were mainly influenced by the direct effect of spring temperatures and precipitation,which affected 19.59%and 22.17%of the study area,respectively.The advancement and delay of EOS were mainly influenced by the direct effect of fall temperatures and precipitation,which affected 30.18%and 21.17%of the area,respectively.On the contrary,the direct effects of temperature and precipitation in summer and winter on vegetation phenology seem less noticeable and were mainly influenced by indirect effects.The indirect effect of winter precipitation is the main fac

关键词： Vegetation phenology Time series decomposition Path Analysis Climate change

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Weight Analysis of Impact Factors of Interbedded Anti-Inclined Slopes Block-Flexure Toppling Based on Support Vector Regression

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Journal of Earth Science 2024年第2期35卷 568-582页

作者： Bocheng Zhang Huiming Tang Yibing Ning Kun Fang Ding Xia Department of Engineering Geology and Geotechnical Engineering Faculty of EngineeringChina University of GeosciencesWuhan430074China Badong National Observation and Research Station of Geohazards China University of GeosciencesWuhan430074China College of Geological Engineering and geomatics Chang’an UniversityXi’an710054China

Block-flexure toppling failure is frequently encountered in interbedded anti-inclined rock(IAR)slopes,and seriously threatens the construction of hydropower infrastructure.In this study,we first investigated the Lean Reservoir area’s geological setting and the Linda landslide’s characteristics.Then,uniform design and random design were used to design 110 training datasets and 31 testing datasets,respectively.Afterwards,the toppling response was obtained by using the discrete element code.Finally,support vector regression was used to obtain the influence weights of 21 impact factors.The results show that the influence weight of the slope angle and rock formation dip angle on the toppling deformation among tertiary impact factors is 25.96%and 17.28%,respectively,which are much greater than the other 19 impact factors within the research range.For the primary impact factors,the influence weight is sorted from large to small as slope geometry parameters,joints parameters,and rock mechanics parameters.Joints parameters,especially the geometric parameters,cannot be ignored when evaluating the stability of IAR slopes.Through numerical simulation,it was qualitatively determined that failure surfaces of slopes were controlled by cross joints and that the rocks in the slope toe play a role in preventing slope deformation.

关键词： interbedded anti-inclined slopes block-flexure toppling impact factors numerical simulation support vector regression engineering geology

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Extensive identification of landslide boundaries using remote sensing images and deep learning method

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China Geology 2024年第2期7卷 277-290页

作者： Chang-dong Li Peng-fei Feng Xi-hui Jiang Shuang Zhang Jie Meng Bing-chen Li faculty of Engineering China University of GeoscienceWuhan 430074China Badong National Observation and Research Station of Geohazards China University of GeosciencesWuhan 430074China School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China College of Geology Engineering and geomatics Chang’an UniversityXi’an 710054China

The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue.It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response.Therefore,the Skip Connection DeepLab neural network(SCDnn),a deep learning model based on 770 optical remote sensing images of landslide,is proposed to improve the accuracy of landslide boundary detection.The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features.SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block(ASPC)with a coding structure that reduces model complexity.The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8and 0.9;while 52 images with MIoU values exceeding 0.9,which exceeds the identification accuracy of existing techniques.This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future inve stigations and applications in related domains.

关键词： Geohazard Landslide boundary detection Remote sensing image Deep learning model Steep slope Large annual rainfall Human settlements Infrastructure Agricultural land Eastern Tibetan Plateau Geological hazards survey engineering

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Integrating Shipborne Images with Multichannel Deep Learning for Landslide Detection

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Journal of Earth Science 2024年第1期35卷 296-300页

作者： Pengfei Feng Changdong Li Shuang Zhang Jie Meng Jingjing Long School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China faculty of Engineering China University of GeosciencesWuhan 430074China Department of Civil Badong National Observation and Research Station of Geohazards China University of GeosciencesWuhan 430074China College of Geology Engineering and geomatics Changan UniversityXi'an 710054China Department of Geosciences University of OsloOslo 0316Norway

0 INTRODUCTION.Landslide disasters in recession zones along riverbanks impose significant global social,economic,and ecological damage.These areas are particularly susceptible to landslides.They inflict significant harm due to fluctuating water levels(Meng et al.,2023;Dai et al.,2021;Li et al.,2021).This,in turn,endangers the safety and accessibility of roads and waterways adjacent to rivers,and hydropower station and reservoir security(Yan et al.,2022;Guo et al.,2020;Tang et al.,2017).

关键词： reservoir Landslide Ship

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From spatio-temporal landslide susceptibility to landslide risk forecast

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Geoscience Frontiers 2024年第2期15卷 220-235页

作者： Tengfei Wang Ashok Dahal Zhice Fang Cees van Westen Kunlong Yin Luigi Lombardo faculty of Engineering China University of GeosciencesWuhan 430074China faculty of Geo-Information Science and Earth Observation(ITC) University of TwenteEnschedeAE 7500Netherlands Institute of Geophysics and geomatics China University of GeosciencesWuhan 430074China

The literature on landslide susceptibility is rich with examples that span a wide range of topics.However,the component that pertains to the extension of the susceptibility framework toward space–time modeling is largely unexplored.This statement holds true,particularly in the context of landslide risk,where few scientific contributions investigate risk dynamics in space and time.This manuscript proposes a modeling protocol where a dynamic landslide susceptibility is obtained via a binomial Generalized Additive Model whose inventories span nine years(from 2013 to 2021).For the analyses,the data cube is organized with a mapping unit consisting of 26,333 slope units repeated over an annual temporal unit,resulting in a total of 236,997 units.This phase already includes several interesting modeling experiments that have rarely appeared in the landslide literature(e.g.,variable interaction plots).However,the main innovative effort is in the subsequent phase of the protocol we propose,as we used climate projections of the main trigger(rainfall)to obtain future estimates of yearly susceptibility patterns.These estimates are then combined with projections of urban settlements and associated populations to create a dynamic risk model,assuming vulnerability=1.Overall,this manuscript presents a unique example of such a modeling routine and offers a potential standard for administrations to make informed decisions regarding future urban development.

关键词： Space-time statistics Dynamic landslide susceptibility Landslide risk Future projections

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A CMIP6-based assessment of regional climate change in the Chinese Tianshan Mountains

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Journal of Arid Land 2024年第2期16卷 195-219页

作者： LIU Xinyu LI Xuemei ZHANG Zhengrong ZHAO Kaixin LI Lanhai faculty of geomatics Lanzhou Jiaotong UniversityLanzhou 730070China National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring Lanzhou 730070China Gansu Provincial Engineering Laboratory for National Geographic State Monitoring Lanzhou 730070China State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqi 830011China Research Center for Ecology and Environment of Central Asia Chinese Academy of SciencesUrumqi 830011China

Climate warming profoundly affects hydrological changes,agricultural production,and human society.Arid and semi-arid areas of China are currently displaying a marked trend of warming and wetting.The Chinese Tianshan Mountains(CTM)have a high climate sensitivity,rendering the region particularly vulnerable to the effects of climate warming.In this study,we used monthly average temperature and monthly precipitation data from the CN05.1 gridded dataset(1961-2014)and 24 global climate models(GCMs)of the Coupled Model Intercomparison Project Phase 6(CMIP6)to assess the applicability of the CMIP6 GCMs in the CTM at the regional scale.Based on this,we conducted a systematic review of the interannual trends,dry-wet transitions(based on the standardized precipitation index(SPI)),and spatial distribution patterns of climate change in the CTM during 1961-2014.We further projected future temperature and precipitation changes over three terms(near-term(2021-2040),mid-term(2041-2060),and long-term(2081-2100))relative to the historical period(1961-2014)under four shared socio-economic pathway(SSP)scenarios(i.e.,SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5).It was found that the CTM had experienced significant warming and wetting from 1961 to 2014,and will also experience warming in the future(2021-2100).Substantial warming in 1997 was captured by both the CN05.1 derived from interpolating meteorological station data and the multi-model ensemble(MME)from the CMIP6 GCMs.The MME simulation results indicated an apparent wetting in 2008,which occurred later than the wetting observed from the CN05.1 in 1989.The GCMs generally underestimated spring temperature and overestimated both winter temperature and spring precipitation in the CTM.Warming and wetting are more rapid in the northern part of the CTM.By the end of the 21st century,all the four SSP scenarios project warmer and wetter conditions in the CTM with multiple dry-wet transitions.However,the rise in precipitation fails to counterbalance the drought induced by escalating temperatu

关键词： climate change Coupled Model Intercomparison Project Phase 6(CMIP6) global climate models(GCMs) shared socio-economic pathway(SSP)scenarios standardized precipitation index(SPI) Chinese Tianshan Mountains

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An analysis of air pollution associated with the 2023 sand and dust storms over China:Aerosol properties and PM_(10) variability

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Geoscience Frontiers 2024年第2期15卷 155-168页

作者： Mikalai Filonchyk Michael P.Peterson Lifeng Zhang Haowen Yan faculty of geomatics Lanzhou Jiaotong UniversityLanzhou 730070China Gansu Provincial Engineering Laboratory for National Geographic State Monitoring Lanzhou 730070China Department of Geography/Geology University of Nebraska OmahaOmahaNE 68182USA

Every spring,a large part of China is confronted with sand and dust storms(SDS)–mainly originating in the Gobi(including Chinese and Mongolian Gobi)and Taklamakan deserts.In March-April 2023,most of northern,northwestern and northeastern China was struck by three sandstorms that affected an area with more than 500 million people.In this study,aerosol optical,microphysical and radiative properties were studied during these SDS events using an integrated approach that combines satellite,terrestrial and re-analysis data.The results showed that dusty conditions were observed in most areas north of the Yangtze River(Chang Jiang)with daily average PM_(10)concentrations exceeding 1000 lg/m^(3) in many cities.VIIRS aerosol optical depth(AOD)at 550 nm during three SDS events exceeded a value of 1 throughout nearly the entire northern part of the country.The AERONET data obtained from the AOE_Baotou site showed a significant increase in total AOD and a corresponding decrease in AE during the SDS.The single scattering albedo(SSA),asymmetry parameter(ASY),real refractive index(RRI)and imaginary refractive index(IRI)values indicate an abundance of scattering coarse-mode particles.Aerosol radiative forcing(ARF)at top of the atmosphere and at the earth’s surface was nearly always negative during the period and ranged from48.5 to+2.7 Wm^(-2)and from180.8 to66.6 Wm^(-2),resulting in high positive ARF values at ATM(from+63.8 to+132.3 Wm^(-2)).Each of these affects the heating of the atmosphere and cooling on the earth’s surface.The atmospheric heating rates ranged from 1.8 to 3.7 K day1.The formation of these SDS mainly resulted from the passage of cold fronts associated with low pressure systems in the Gobi and Taklamakan deserts,creating conditions for dust to rise into the atmosphere and move further downwind.

关键词： Dust storm PM_(10) Aerosol optical depth Radiative forcing VIIRS AERONET

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Drivers,Trends,and Patterns of Changing Vegetation-greenness in Nansha Islands,China from 2016 to 2022

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Chinese Geographical Science 2024年第4期34卷 662-673页

作者： TANG Jiasheng FU Dongjie SU Fenzhen YU Hao WANG Xinhui faculty of geomatics Lanzhou Jiaotong UniversityLanzhou 730070China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Collaborative Innovation Center of South China Sea Studies Nanjing 210023China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049China Beijing North-Star Technology Development Co. LtdBeijing 100120China

Changes in vegetation status generally also represents changes in the ecological health of islands and reefs(IRs).However,studies are limited of drivers and trends of vegetation change of Nansha Islands,China and how they relate to climate change and human activities.To resolve this limitation,we studied changes to the Normalized Difference Vegetation Index(NDVI)vegetation-greenness index for 22 IRs of Nansha Islands during normal and extreme conditions.Trends of vegetation greenness were analyzed using Sen's slope and Mann-Kendall test at two spatial scales(pixel and island),and driving factor analyses were performed by time-lagged partial correlation analyses.These were related to impacts from human activities and climatic factors under normal(temperature,precipitation,radiation,and Normalized Difference Built-up Index(NDBI))and extreme conditions(wind speed and latitude of IRs)from 2016 to 2022.Results showed:1)among the 22 IRs,NDVI increased/decreased significantly in 15/4 IRs,respectively.Huayang Reef had the highest NDVI change-rate(0.48%/mon),and Zhongye Island had the lowest(–0.29%/mon).Local spatial patterns were in one of two forms:dotted-form,and degradation in banded-form.2)Under normal conditions,human activities(characterized by NDBI)had higher impacts on vegetation-greenness than other factors.3)Under extreme conditions,wind speed(R^(2)=0.2337,P<0.05)and latitude(R^(2)=0.2769,P<0.05)provided limited explanation for changes from typhoon events.Our results provide scientific support for the sustainable development of Nansha Islands and the United Nations‘Ocean Decade’initiative.

关键词： island and reefs(IRs) Normalized Difference Vegetation Index(NDVI) vegetation-greenness change-rate Sen's slope Nansha Islands China

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A SCR method for uncertainty estimation in geodesy non-linear error propagation: Comparisons and applications

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Geodesy and Geodynamics 2022年第4期13卷 311-320页

作者： Chuanyi Zou Hao Ding Leyang Wang Department of Geophysics School of Geodesy and GeomaticsWuhan UniversityWuhan 430079China Hubei Luojia Laboratory Wuhan 430079China faculty of geomatics East China University of TechnologyNanchang 330013China

We review three derivative-free methods developed for uncertainty estimation of non-linear error propagation, namely, MC(Monte Carlo), SUT(scaled unscented transformation), and SI(sterling interpolation). In order to avoid preset parameters like as these three methods need, we introduce a new method to uncertainty estimation for the first time, namely, SCR(spherical cubature rule), which is no need for setting parameters. By theoretical derivation, we prove that the precision of uncertainty obtained by SCR can reach second-order. We conduct four synthetic experiments, for the first two experiments, the results obtained by SCR are consistent with the other three methods with optimal setting parameters, but SCR is easier to operate than other three methods, which verifies the superiority of SCR in calculating the uncertainty. For the third experiment, real-time calculation is required, so the MC is hardly feasible. For the forth experiment, the SCR is applied to the inversion of seismic fault parameter which is a common problem in geophysics, and we study the sensitivity of surface displacements to fault parameters with errors. Our results show that the uncertainty of the surface displacements is the magnitude of ±10 mm when the fault length contains a variance of 0.01 km^(2).

关键词： SCR method Uncertainty estimation Non-linear error propagation Inversion of seismic fault parameter

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Medical image translation using an edge-guided generative adversarial network with global-to-local feature fusion

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The Journal of Biomedical Research 2022年第6期36卷 409-422页

作者： Hamed Amini Amirkolaee Hamid Amini Amirkolaee School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehran ***Iran Civil and geomatics Engineering faculty Tafresh State UniversityTafresh ***Iran

In this paper,we propose a framework based deep learning for medical image translation using paired and unpaired training data.Initially,a deep neural network with an encoder-decoder structure is proposed for image-to-image translation using paired training data.A multi-scale context aggregation approach is then used to extract various features from different levels of encoding,which are used during the corresponding network decoding stage.At this point,we further propose an edge-guided generative adversarial network for image-to-image translation based on unpaired training data.An edge constraint loss function is used to improve network performance in tissue boundaries.To analyze framework performance,we conducted five different medical image translation tasks.The assessment demonstrates that the proposed deep learning framework brings significant improvement beyond state-of-the-arts.

关键词： edge-guided generative adversarial network global to local medical image translation magnetic resonance imaging computed tomography

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