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

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 factor affecting the advance or delay of SOS,and the area proportions were 16.29%and 23.42%,respectively.The indirect effects of fall temperatures and precipitation were the main factors affecting the delay and advancement of EOS,respectively,with an area share of 15.80%and 21.60%.This study provides valuable insight into the relationship between vegetation phenology and climate change,which can be of great practical value for the ecological protection of the Qinghai-Tibetan Plateau as well as for the development of GSD ecological animal husbandry in the QM alpine pastoral area.

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

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 temperature in the future,so the nature of the drought in the CTM will not change at all.Additionally,the projected summer precipitation shows negative correlation with the radiative forcing.This study holds practical implications for the awareness of climate change and subsequent research in the CTM.

Defining the We-map Reference Frames and Providing Mathematical Expressions for Transformation Relations

We-map is an interactive mobile map that can be easily communicated and applied on personal electronic devices,such as personal computers and mobile phones.Therefore,the study of direction systems and coordinate systems is critical,and exploring reference frames is essential in direction and coordinate systems.Despite its significance,existing research on We-map lacks specific solutions for the exploration of reference frames is indispensable for the establishment of accurate direction and coordinate systems.In this paper,we endeavor to address this gap by elucidating the significance of We-map reference frames,defining them with mathematical constraints,summarizing their nature and characteristics,deriving their transformation relationships and representing them through mathematical formulars and equations.Our work contributes to the fundamental theory of We-map and provides valuable systems and support for the mathematical foundation of We-map,map production,and platform development.Ultimately,this research serves to advance the development of We-map.

Quantitative distinction of the relative actions of climate change and human activities on vegetation evolution in the Yellow River Basin of China during 1981-2019

Under the combined influence of climate change and human activities,vegetation ecosystem has undergone profound changes.It can be seen that there are obvious differences in the evolution patterns and driving mechanisms of vegetation ecosystem in different historical periods.Therefore,it is urgent to identify and reveal the dominant factors and their contribution rates in the vegetation change cycle.Based on the data of climate elements(sunshine hours,precipitation and temperature),human activities(population intensity and GDP intensity)and other natural factors(altitude,slope and aspect),this study explored the spatial and temporal evolution patterns of vegetation NDVI in the Yellow River Basin of China from 1989 to 2019 through a residual method,a trend analysis,and a gravity center model,and quantitatively distinguished the relative actions of climate change and human activities on vegetation evolution based on Geodetector model.The results showed that the spatial distribution of vegetation NDVI in the Yellow River Basin showed a decreasing trend from southeast to northwest.During 1981-2019,the temporal variation of vegetation NDVI showed an overall increasing trend.The gravity centers of average vegetation NDVI during the study period was distributed in Zhenyuan County,Gansu Province,and the center moved northeastwards from 1981 to 2019.During 1981-2000 and 2001-2019,the proportion of vegetation restoration areas promoted by the combined action of climate change and human activities was the largest.During the study period(1981-2019),the dominant factors influencing vegetation NDVI shifted from natural factors to human activities.These results could provide decision support for the protection and restoration of vegetation ecosystem in the Yellow River Basin.

Spatiotemporal Interaction Between Rural Settlements and Cultivated Land in Karst Mountainous Area,China

Settlements and cultivated land are important production and living spaces in promoting rural revitalization.However,few studies have explored the relationship between rural settlements and cultivated land from spatiotemporal interaction perspective.This paper analyzed the spatiotemporal conversion and the interactive mechanism between rural settlements and cultivated land in a karst mountainous area(Qixingguan District of Guizhou Province)with fragile ecological environment in China during 2009–2018.The results showed that the expansion of rural settlements and the loss of cultivated land coexisted in Qixingguan District.Only 2.68%of the new cultivated land was reclaimed from rural settlements,whereas 85.45%of the new rural settlements occupied cultivated land.Six spatial expansion modes of rural settlements when occupying cultivated land were identified.Among these six modes,the area of the edge-expansion&along traffic roads(EA)mode accounted for 52.75%.The occupation by rural settlements made the cultivated land landscape more fragmented.The area ratio index of cultivated land to rural settlements(ARICR)of Qixingguan District averaged 18.75in 2009 and 17.21 in 2018,respectively.The ARICR reduced in all township administrative regions.Cultivated land with suitable slope condition for farming or without rocky desertification was more likely to be occupied by rural settlements.The probability of cultivated land occupied by rural settlements increased with the decrease of the distance to traffic roads,towns,and old rural settlements.The better the economic and social development of the township administrative regions,the more the ARICR decreased,while the richer the agricultural resources and the better the rural development of the township administrative regions,the less the ARICR decreased.The optimal reconstruction path of rural settlements,the comprehensive conservation path of cultivated land and the urban-rural integration development path in karst mountainous area were proposed.The findings would contribute to our understanding of the spatiotemporal interaction between rural settlements and cultivated land,and would provide a theoretical basis for promoting the coordinated development of rural man-land relationship and rural revitalization in karst areas.

Environmental factors influencing snowfall and snowfall prediction in the Tianshan Mountains, Northwest China

Snowfall is one of the dominant water resources in the mountainous regions and is closely related to the development of the local ecosystem and economy. Snowfall predication plays a critical role in understanding hydrological processes and forecasting natural disasters in the Tianshan Mountains, where meteorological stations are limited. Based on climatic, geographical and topographic variables at 27 meteorological stations during the cold season(October to April) from 1980 to 2015 in the Tianshan Mountains located in Xinjiang of Northwest China, we explored the potential influence of these variables on snowfall and predicted snowfall using two methods: multiple linear regression(MLR) model(a conventional measuring method) and random forest(RF) model(a non-parametric and non-linear machine learning algorithm). We identified the primary influencing factors of snowfall by ranking the importance of eight selected predictor variables based on the relative contribution of each variable in the two models. Model simulations were compared using different performance indices and the results showed that the RF model performed better than the MLR model, with a much higher R^2 value(R^2=0.74; R^2, coefficient of determination) and a lower bias error(RSR=0.51; RSR, the ratio of root mean square error to standard deviation of observed dataset). This indicates that the non-linear trend is more applicable for explaining the relationship between the selected predictor variables and snowfall. Relative humidity, temperature and longitude were identified as three of the most important variables influencing snowfall and snowfall prediction in both models, while elevation, aspect and latitude were of secondary importance, followed by slope and wind speed. These results will be beneficial to understand hydrological modeling and improve management and prediction of water resources in the Tianshan Mountains.

Formula for calculating spatial similarity degrees between point clouds on multi-scale maps taking map scale change as the only independent variable

The degree of spatial similarity plays an important role in map generalization, yet there has been no quantitative research into it. To fill this gap, this study first defines map scale change and spatial similarity degree/relation in multi-scale map spaces and then proposes a model for calculating the degree of spatial similarity between a point cloud at one scale and its gener- alized counterpart at another scale. After validation, the new model features 16 points with map scale change as the x coordinate and the degree of spatial similarity as the y coordinate. Finally, using an application for curve fitting, the model achieves an empirical formula that can calculate the degree of spatial similarity using map scale change as the sole independent variable, and vice versa. This formula can be used to automate algorithms for point feature generalization and to determine when to terminate them during the generalization.

Performance and uncertainty analysis of a short-term climate reconstruction based on multi-source data in the Tianshan Mountains region,China

Short-term climate reconstruction,i.e.,the reproduction of short-term(several decades)historical climatic time series based on the relationship between observed data and available longer-term reference data in a certain area,can extend the length of climatic time series and offset the shortage of observations.This can be used to assess regional climate change over a much longer time scale.Based on monthly grid climate data from a Coupled Model Inter-comparison Project phase 5(CMIP5)dataset for the period of 1850–2000,the Climatic Research Unit(CRU)dataset for the period of 1901–2000 and the observed data from 53 meteorological stations located in the Tianshan Mountains region(TMR)of China during the period of 1961–2011,we calibrated and validated monthly average temperature(MAT)and monthly accumulated precipitation(MAP)in the TMR using the delta,physical scaling(SP)and artificial neural network(ANN)methods.Performance and uncertainty during the calibration(1971–1999)and verification(1961–1970)periods were assessed and compared using traditional performance indices and a revised set pair analysis(RSPA)method.The calibration and verification processes were subjected to various sources of uncertainty due to the influence of different reconstructed variables,different data sources,and/or different methods used.According to traditional performance indices,both the CRU and CMIP5 datasets resulted in satisfactory calibrated and verified MAT time series at 53 meteorological stations and MAP time series at 20 meteorological stations using the delta and SP methods for the period of 1961–1999.However,the results differed from those obtained by the RSPA method.This showed that the CRU dataset produced a low degree of uncertainty(positive connection degree)during the calibration and verification of MAT using the delta and SP methods compared to the CMIP5 dataset.Overall,the calibrated and verified MAP had a high degree of uncertainty(negative connection degree)regardless of the dataset or reconstruction method used.Therefore,the reconstructed time series of MAT for the period of 1850(or 1901)–1960 based on the CRU and CMIP5 datasets using the delta and SP methods could be used for further study.The results of this study will be useful for short-term(several decades)regional climate reconstruction and longer-term(100 a or more)assessments of regional climate change.

Evaluation of ERA-Interim Monthly Temperature Data over the Tibetan Plateau

In this study, surface air temperature from 75 meteorological stations above 3000 m on the Tibetan Plateau are applied for evaluation of the European Centre for Medium-Range Weather Forecasts(ECMWF) third-generation reanalysis product ERA-Interim in the period of 1979-2010. High correlations ranging from 0.973 to 0.999 indicate that ERA-Interim could capture the annual cycle very well. However, an average root-meansquare error(rmse) of 3.7°C for all stations reveals that ERA-Interim could not be applied directly for the individual sites. The biases can be mainly attributed to the altitude differences between ERA-Interim grid points and stations. An elevation correction method based on monthly lapse rates is limited to reduce the bias for all stations. Generally, ERA-Interim captured the Plateau-Wide annual and seasonal climatologies very well. The spatial variance is highly related to the topographic features of the TP. The temperature increases significantly(10°C- 15°C) from the western to the eastern Tibetan Plateau for all seasons, in particular during winter and summer. A significant warming trend(0.49°C/decade) is found over the entire Tibetan Plateau using station time series from 1979-2010. ERA-Interim captures the annual warming trend with an increase rate of 0.33°C /decade very well. The observation data and ERA-Interim data both showed the largest warming trends in winter with values of 0.67°C/decade and 0.41°C/decade, respectively. We conclude that in general ERA-Interim captures the temperature trends very well and ERA-Interim is reliable for climate change investigation over the Tibetan Plateau under the premise of cautious interpretation.

Alpine vegetation responses to snow phenology in the Chinese Tianshan mountainous region

Investigating the interrelation between snow and vegetation is essential to explain the response of alpine ecosystems to climate change.Based on the MOD10 A1 daily cloud-free snow product and MOD13 A1 NDVI(normalized difference vegetation index)data,this study analysed the spatial and temporal patterns of snow phenology including snow onset date,snow end date,snow cover days,and vegetation phenology including the start of growing season,the end of growing season and the length of growing season in the Chinese Tianshan Mountainous Region(CTMR)from 2002 to 2018,and then investigated the snow phenological effects on the vegetation phenology among different ecogeographic zones and diverse vegetation types.The results indicated that snow onset date was earlier at higher elevations and later at lower elevations,while snow end date showed opposite spatial distribution characteristics.The end of growing season occurred later on the northwest slope of the CTMR and the Yili Valley.The earliest end of growing season was in the middle part of the CTMR.A long growing season was mainly distributed along the northern slope and the Yili Valley,while a short growing season was concentrated in the middle part of the CTMR.The response of vegetation phenology to changes in snow phenology varied among vegetation types and ecogeographic zones.The effect of snow phenology on vegetation phenology was more significant in IID5(Yili Valley)than in the other ecogeographic zones.A negative correlation was observed between the start of growing season and snow end date in nearly 54.78%of the study area,while a positive correlation was observed between the start of growing season and the snow end date in 66.85%of the study area.The sensitivity of vegetation phenology to changes in snow cover varied among different vegetation types.Snow onset date had the greatest effect on the start of growing season in the four vegetation cover types(alpine meadows,alpine steppes,shrubs,and alpine sparse vegetation),whereas the snow cover days had the least impact.Snow end date had the greatest impact on the end of growing season in the alpine steppes and shrub areas.The study results are helpful for understanding the vegetation dynamics under ongoing climate change,and can benefit vegetation management and pasture development in the CTMR.

Quantitatively determine the dominant driving factors of the spatial–temporal changes of vegetation NPP in the Hengduan Mountain area during 2000-2015

The Hengduan mountain area,located in the upper reaches of the Yangtze River of China,is an important ecological barrier that significantly impacts the climate and ecological environment of the surrounding region and western China as a whole.This paper introduces the gravity center model used to analyze the spatial-temporal variation patterns of vegetation Net Primary Productivity(NPP)from 2000 to 2015,which were determined by the use of MOD17 A3 NPP products.Additionally,the dominant driving factors of the spatial–temporal changes of vegetation NPP of the Hengduan Mountain area were quantitatively determined with a geographical detector over 2000-2015.The results revealed that:(1)From 2000 to 2015,there was an increasing trend of vegetation NPP in the Hengduan mountain area.Throughout the whole study region,the vegetation NPP with a mean value of 611.37 gC·m^(-2)·a^(-1) indicated a decreasing trend from southeast to northwest in terms of spatial distribution.(2)The gravity centers of vegetation NPP in 2000-2015 were mainly concentrated in Zhongdian County.During the study period,the gravity center of vegetation NPP moved northward,which indicated that the increment and increasing rate of vegetation NPP in the northern parts were greater than that of the southern areas.(3)The vegetation NPP showed a moderately positive correlation with temperature,accumulated temperature(>10℃),and sunshine,while there was an overall negative relationship between NPP and precipitation.(4)The dominant factors and interactive dominant factors changed in different subregions over different segments of the study period.The dominant factors of most sub-regions in Hengduan mountain were natural factors,and the climate change factors played an increasingly greater role over the 16 years of the study period.

Trends in aerosol optical properties over Eastern Europe based on MODIS-Aqua

This study provides characteristics of aerosol columnar properties,measured over ten countries in Eastern Europe from 2002 to 2019.Aerosol optical depth(AOD)and Angstrom exponent(AE)were obtained with the Moderate Resolution Imaging Spectroradiometer(MODIS)Collection 6.1 merged Dark Target and Deep Blue aerosol product.The product is validated using ground-based Aerosol Robotic Network(AERONET)situated at Minsk,Belsk,Moldova and Kyiv.The results showed that 76.15%of retrieved AOD data are within the expected error.It was established that 64.2%of AOD points are between 0 and 0.2 and 79.3%of all AE points are over 1.Mean AOD values in the region vary from 0.130±0.04(Moldova)to 0.193±0.03(Czech Republic)with mean value in the region 0.162±0.05.Seasonal mean AOD(AE)values were at the maximum during the summer from 0.231±0.05(1.482±0.09 in winter)to minimum 0.087±0.04 during the winter(1.363±0.17 in summer).Gradual AOD reduction is observed in all countries with annual trend from-0.0050(Belarus)to-0.0029(Russia).Finally,the relationship between AOD and AE was studied to classify various aerosol types and showed seasonal non-uniformity of their contribution depending on variation in sources.The entire region is under significant impact of various aerosol types,including clean continental(CC),mixed(MX)and anthropogenic/burning(AB)aerosols types that are at 59.77%,24.72%,and 12.97%respectively.These results form an important basis for further regional studies of air quality and distribution of sources of pollution.

Projected change in precipitation forms in the Chinese Tianshan Mountains based on the Back Propagation Neural Network Model

In the context of global warming,precipitation forms are likely to transform from snowfall to rainfall with a more pronounced trend.The change in precipitation forms will inevitably affect the processes of regional runoff generation and confluence as well as the annual distribution of runoff.Most researchers used precipitation data from the CMIP5 model directly to study future precipitation trends without distinguishing between snowfall and rainfall.CMIP5 models have been proven to have better performance in simulating temperature but poorer performance in simulating precipitation.To overcome the above limitations,this paper used a Back Propagation Neural Network(BNN)to predict the rainfall-to-precipitation ratio(RPR)in months experiencing freezing-thawing transitions(FTTs).We utilized the meteorological(air pressure,air temperature,evaporation,relative humidity,wind speed,sunshine hours,surface temperature),topographic(altitude,slope,aspect)and geographic(longitude,latitude)data from 28 meteorological stations in the Chinese Tianshan Mountains region(CTMR)from 1961 to 2018 to calculate the RPR and constructed an index system of impact factors.Based on the BNN,decision-making trial and evaluation laboratory method(BP-DEMATEL),the key factors driving the transformation of the RPR in the CTMR were identified.We found that temperature was the only key factor affecting the transformation of the RPR in the BP-DEMATEL model.Considering the relationship between temperature and the RPR,the future temperature under different representative concentration pathways(RCPs)(RCP2.6/RCP4.5/RCP8.5)provided by 21 CMIP5 models and the meteorological factors from meteorological stations were input into the BNN model to acquire the future RPR from 2011 to 2100.The results showed that under the three scenarios,the RPR in the number of months experiencing FTTs during 2011-2100 will be higher than that in the historical period(1981-2010)in the CTMR.Furthermore,in terms of spatial variation,the RPR values on the south slope will be larger than those on the north slope under the three emission scenarios.Moreover,the RPR values exhibited different variation characteristics under different emission scenarios.Under the low-emission scenario(RCP2.6),as time passed,the RPR values changed slightly at more stations.Under the mediumemission scenario(RCP4.5),the RPR increased in the whole CTMR and stabilized on the north slope by the end of this century.Under the high-emission scenario(RCP8.5),the RPR values increased significantly through the 21 st century in the whole CTMR.This study may help to provide a scientific management basis for agricultural production and hydrology.

Exploration and Application of Ubiquitous Mapping in Engineering Practice Courses in the Intelligence Age

In the current era of digital surveying and mapping to intelligent surveying and mapping,ubiquitous surveying and mapping has brought many opportunities and challenges to college engineering course teaching.With the development of ubiquitous surveying and mapping,college engineering practice courses urgently need to respond to ubiquitous surveying and mapping.The research aims to integrate the development of ubiquitous surveying and mapping into the teaching of engineering practice courses in colleges,including promoting Android,Brower/Server(B/S),and Client/Server(C/S)to build a platform for practice courses.This also incorporates real development cases in measurement data processing such as gravity field refinement.In this way,the teaching level of engineering practice courses in colleges can be improved,and new ideas can be put forward for cultivating surveying and mapping talents in the new era in colleges.Finally,it can also provide new ideas for the organization of surveying and mapping practice courses under the background of the pandemic.

The IGN MEMS Accelerographs Record the First Accelerograms in Different Locations in the Lorca City

A recent earthquake (13 December 2018, Mag. 3.5) has allowed the recording 11 accelerograms from a network microelectromechanical systems (MEMS) low-cost accelerograph Silex. The National Geographic Institute of Spain (IGN) has built this kind of equipment and has installed in the Lorca City. The epicenter of the earthquake is only approximately 6 km from Lorca. This is important because an earthquake hit this city on 11th May, 2011. This earthquake caused nine deaths, more than 400 injured people and a loss estimated about $1 billion. We have also got records from two commercial accelerograhs GeoSig GMSPlus and GeoSig GSR-18 in two different stations in Lorca and we have been able to compare these signals with signals from Silex. We have studied carefully the records from places where peak accelerations are noticeably higher than in other locations. We have seen that the waveforms of these records have different features and we think that the effect sites are very important. Finally we have also compared the peak acceleration with macroseismic intensity obtained from questionnaries. We have checked there is no clear correlation between instrumental acceleration and macroseismic intensity at least for earthquake with small magnitudes.

Coseismic deformation of the 2020 Yutian MW 6.4 earthquake from Sentinel-1A and the slip inversion

A remarkable earthquake struck Yutian,China on June 26th,2020.Here,we use Sentinel-1 images to investigate the deformation induced by this event.We invert the InSAR observations using a two-step approach:a nonlinear inversion to constrain fault geometries with uniform slip based on the rectangular plane dislocation in an elastic half-space,followed by a linear inversion to retrieve the slip distribution on the fault plane.The results show that the maximum LOS displacement is 22.6 cm,and the fault accessed to the ruptured characteristics of normal faults with the minor left-lateral strike-slip component.The fault model indicates a 210strike.The main rupture zone concentrates in the depth of 5-15 km,and the fault slip peaks at 0.89 m at the depth of 9 km.Then,we calculate the variation of the static Coulomb stress based on the optimal fault model,the results suggest that the Coulomb stress of the Altyn Tagh fault and other neighboring faults has increased and more attention should be paid to possible seismic risks.

Analyzing the Causes of Spatial Differentiation of the Traditional Villages in Gansu Province, Western China

The study and protection for traditional villages are very important for us to protect Chinese historical and cultural heritage. Data show that under the condition of rapid urbanization. The number and coverage of traditional villages in western China are decreasing. It is impossible to effectively protect a large number of rural settlements at the bottom of China’s traditional settlement system. Therefore, it is necessary to explore the spatial survival status of traditional villages and protect them comprehensively and extensively. Using the digital elevation model (DEM) data of traditional villages in Gansu Province, China, published by the Ministry of Housing and Urban Development and the attribute data obtained by the Statistics Bureau of Gansu Province, China, the nuclear density, the Mulan indices, the correlations between the heights and the centers are calculated and used to study the spatial different characteristics of the villages, and a number of results have been achieved: 1) In spatial differentiation, the spatial agglomeration of the villages is obvious and different, which can be seen by the distribution of the villages from along the upper reaches of the Yellow River to the southeast, and the distribution of prefecture-level cities is related to the landforms. 2) In vertical spatial distribution, the span of the altitude data is large. Among the villages, the Zagana Village in Diebu County of Gannan City is the highest and the Zhengjiashe Village in Bingkou Town of Longnan City is the lowest. With the increase of altitude, the number of traditional villages generally shows a tendency of decrease. 3) The spatial differentiation of the traditional villages has a clear normal distribution with the elevation, and the spatial differentiation is low, showing a distinct gourd-like structure;the eastern and southern regions are more concentrated, while the northern and western regions are less concentrated. 4) The spatial differences of the traditional villages have the characteristics of regional differences, which are weakly related to the distance between the central cities and occur mainly in the Longnan mountain regions, the Loess Plateau in the middle of Longzhong and the Gannan plateau. The results of this study are a useful support in protection of traditional villages in provincial scale. It helps to enhance the integrity and systematicness of the protection of the spatial distribution of traditionally villages. The Chinese government had put forward the “Poverty alleviation strategy” to help Gansu Province to get out of the villages’ trouble. Viewed from this angle, the research for effective patterns of traditional villages’ protection and exploitation plays a crucial role in the development of China’s “Poverty Alleviation Strategy”.

深海危情

人类对大鲨鱼的肆意捕杀破坏了整个海洋生态的平衡,造成海洋食物链一连串的反应……请尊重我们的自然,请保护地球的有限资源!一项新的研究表明,由于过度捕捞,北大西洋中的大鲨鱼数量急剧减少,这一情况已经打破了整个海洋生态系统的平衡。现在,扇贝、蛤和牡蛎都在为此付出代价。研究者们说,小型的鲨鱼、鳐和鱼通常是大鲨鱼口中的食物,如今它们的数量变得十分巨大,正在大肆破坏水生贝类资源。

The Research Application of 3D Laser Scanning Technology in the Deformation Detection of Large Cylindrical Oil Tank

In order to ensure the safety in using a large cylindrical storage tank,it is necessary to regularly detect its defonnatioii.The traditional total station method has high accuracy in determining the deformation,however,it has a low measxirement efficiency.Long-term observation means,there are more risks in the petrochemical plant,therefore,this paper proposes the usage of the 3D laser scanner,replacing the traditional total station to determine the defbnnation of a large cylindrical storage tank.The Matlab program,is compiled to calculate the point cloud data,while the tank deformation is analyzed from two different points which are,the local concave convex degree and the ovality degree.It is concluded that,the difference between the data obtained by 3D laser scanning,and total station is within the range of oil tank deformation limit,therefore,3D laser scanner can be used for oil tank deformation detection.

Parameter Method Data Processing for CPⅢ Precise Trigonometric Leveling Network

In view of the limitation of the difference method,the adjustment model of CPⅢprecise trigonometric leveling control network based on the parameter method was proposed in the present paper.The experiment results show that this model has a simple algorithm and high data utilization,avoids the negative influences caused by the correlation among the data acquired from the difference method and its accuracy is improved compared with the difference method.In addition,the strict weight of CPⅢprecise trigonometric leveling control network was also discussed in this paper.The results demonstrate that the ranging error of trigonometric leveling can be neglected when the vertical angle is less than 3 degrees.The accuracy of CPⅢprecise trigonometric leveling control network has not changed significantly before and after strict weight.