The spatial-temporal pattern and influencing factors of negative air ions in urban forests, Shanghai, China

Negative air ions are natural components of the air we breathe Forests are the main continuous natural source of negative air ions （NAI）. The spatio-temporal patterns of negative air ions were explored in Shanghai, based on monthly monitoring in 15 parks from March 2009 to February 2010. In each park, sampling sites were selected in forests and open spaces. The annual variation in negative air ion concentrations （NAIC） showed peak values from June to October and minimum values from December to January. NAIC were highest in summer and autumn, intermediate in spring, and lowest in winter. During spring and summer, NAIC in open spaces were significantly higher in rural areas than those in suburban areas. However, there were no significant differences in NAIC at forest sites among seasons. For open spaces, total suspended particles （TSP） were the dominant determining factor of NAIC in sum- mer, and air temperature and air humidity were the dominant determining factors of NAIC in spring, which were tightly correlated with Shanghai＇s ongoing urbanization and its impacts on the environment. R is suggested that urbanization could induce variation in NAIC along the urban-rural gradient, but that may not change the temporal variation pattern. Fur- thermore, the effects of urbanization on NAIC were limited in non-vegetated or less-vegetated sites, such as open spaces, but not in well-vegetated areas, such as urban forests. Therefore, we suggest that urban greening, especially urban forest, has significant resistance to theeffect of urbanization on NAIC.

Spatial-temporal pattern and formation mechanism of county urbanization on the Chinese Loess Plateau

Urbanization is a comprehensive and complex socioeconomic phenomenon that plays an influential role in promoting global socioeconomic development.The Loess Plateau region is an important part of the China’s ecological security pattern,and occupies an important position in the implementation of China’s new-type urbanization strategy and the realization of the urban dream.The characteristics of the staged changes and regional differentiation of urbanization in the area from 1990 to 2018 were studied with focus on regions and subregions by selecting 341 county-level administrative units on the Chinese Loess Plateau as the research area,and employing partition analysis and geographic detector methods.This revealed the formation mechanism of the spatial differentiation pattern of urbanization on the Loess Plateau.We found that the urbanization of the Loess Plateau,previously in a slow growth phase,entered the accelerated development phase,presenting a macro pattern of high rates of urbanization in central and eastern areas and low rates in western areas.The formation of the regional differentiation patterns of urbanization on the Loess Plateau were the combined results of natural geographical and socioeconomic factors.Among these factors,the interaction of any two factors had a stronger impact on regional urbanization patterns than a single factor,which was specifically manifested as nonlinear or bi-factor enhancement effects.The findings of this paper may provide a theoretical reference and scientific basis for the scientific promotion of healthy urbanization on the Chinese Loess Plateau and the ecologically fragile areas of developing countries around the world.

The Spatial-Temporal Distribution Patterns of Dyke Swarms in Central Asia and their Tectonic Significance: Case Studies in Eastern Tianshan and Western Junggar

Dykes are a special kind of intrusive rocks which were formed by deep magma intruded into the existing brittle fractures in the crust.Dykes swarms in different tectonic environments are very significant to re-construct the

Spatial-temporal analysis of wetland landscape pattern under the influence of artificial dykes in the Yellow River delta

The influence of anthropogenic activities,especially artificial dykes,on the coastal wetland landscape is now considered as a serious problem to the coastal ecosystem.It is important and necessary to analyze changes of coastal landscape pattern under the influence of artificial dykes for the protection and management of coastal wetland.Our study aimed to reveal the quantitative characteristics of the coastal wetland landscape and its spatial-temporal dynamics under the influence of artificial dykes in the Yellow River delta(YRD).It was analyzed by the methods of the statistical analysis of landscape structure,five selected landscape indices and the changes of spatial centroids of three typical wetland types,including reed marshes,tidal fiats and aquaculture-salt fields.The results showed that:(1)Reduction of wetland area,especially the degradation of natural wetlands,had been the principal problem since the dykes were constructed in the YRD.The dykes created conditions for the development of artificial wetlands.However,the new born artificial wetlands were still less than the vanished natural wetlands.(2)Compared with the open area,the building of artificial dykes significantly speeded up the changes of landscape patterns and the aggravation of the landscape fragmentation in the closed area.(3)The changes of area-weighted centroids of three typical wetland landscapes were greatly affected by dykes,and the movement of the centroid of the aquaculture-salt field was very sensitive to the dykes constructed in the corresponding period.

Spatial-temporal evolution of vegetation evapotranspiration in Hebei Province,China

Evapotranspiration （ET） is the sum of soil or water body evaporation and plant transpiration from the earth surface and ocean to the atmosphere, and thus plays a significant role in regulating carbon and water resource cycles. The time-series data set from the remote sensing MOLDS product （MOD16） was used to study the spatial-temporal evolution of vegetation evapotranspiration in salinized areas during 2000-2014 by analyzing the variability, spatial patterns and Mann-Kendall （MK） nonparametric trends for the time series. The results indicate that inter-annual and intra-annual variations of ET across various vegetated areas show seasonal changes, with the abnormal months identified. The Cultivated land displays a greater degree of spatial heterogeneity and the spatial pattern of ET in the area covered by broadleaved deciduous forests corresponds to a higher ET rate and increased water consumption. Awidespread decline of ET is observed only in cultivated areas. However, agricultural cultivation doesn＇t worsen water shortage and soil salinization problems in the region, and water shortage problems are worsening for other vegetated areas. This research provides a basis of reference for the reasonable allocation of water resources and restructuring of vegetation patterns in salinized areas.

Spatio-temporal Pattern Characteristics of Relationship Between Urbanization and Economic Development at County Level in China

The relationship between China’s urbanization and economic development(RCUED) is an important concern nationwide. As important actors in regional strategy and policy, county-level regions have played an increasingly significant role in the development of China’s social economy. However, the existing research on the RCUED lacks the fine depiction of the county-level administrative units.Using 2000 and 2010 census data and the statistical analysis method, we uncovered the evolution characteristics of China’s urbanization and economic development and conducted a quantitative identification for the RCUED with improved methods using the quadrant map approach. In addition, we investigated the spatial correlation effect of the RCUED using the spatial autocorrelation analysis method. The results were as follows: 1) In general, a high degree of matching exists between China’s urbanization and economic development at the county level at the significance level of 0.01. The correlation coefficients between China’s urbanization and economic development in2000 and 2010 were 0.608 and 0.603, respectively. 2) A significant regional difference exists in the RCUED at the county level. Based on a comparative analysis of 2276 county units in China in the two years, we found that county units can be categorized as under-urbanized, basic coordination and over-urbanized in various areas. No situation was observed where urbanization seriously lagged behind the economic development level, so the levels of urbanization and economic development appear to be basically coordinated,and the coordination state may be gradually optimized over time. 3) Over time, the spatial dependency of the RCUED has weakened and the spatial heterogeneity has increased. Northeast China has always been an area characterized by over-urbanization. The number of county units classified as under-urbanized has begun to decline in eastern coastal urban agglomeration areas, while counties rich in resources have transformed from having point-shaped over-urbanization to plane-shaped under-urbanization along the northern border,and the number of over-urbanized county units has increased in the middle reaches of the Yangtze River. 4)’Lag-lag’ type and ’advance-advance’ type accounted for 68% of all counties in China, and these counties were shown to have obvious spatial differentiation characteristics.

CLEAN:Frequent Pattern-Based Trajectory Compression and Computation on Road Networks

The volume of trajectory data has become tremendously huge in recent years. How to effectively and efficiently maintain and compute such trajectory data has become a challenging task. In this paper, we propose a trajectory spatial and temporal compression framework, namely CLEAN. The key of spatial compression is to mine meaningful trajectory frequent patterns on road network. By treating the mined patterns as dictionary items, the long trajectories have the chance to be encoded by shorter paths, thus leading to smaller space cost. And an error-bounded temporal compression is carefully designed on top of the identified spatial patterns for much low space cost. Meanwhile, the patterns are also utilized to improve the performance of two trajectory applications, range query and clustering, without decompression overhead. Extensive experiments on real trajectory datasets validate that CLEAN significantly outperforms existing state-of-art approaches in terms of spatial-temporal compression and trajectory applications.

Spatial Pattern and Development of Protected Areas in the North-south Transitional Zone of China

The north-south transitional zone in China mainly consists of the Qinling-Daba Mountains. It is the most important West-East geo-ecological space in China, containing protected areas vital for biodiversity conservation and ecological security of China. The protection and rational development of its natural habitat is of great significance to China’s ecological security and integration of protected areas based on mountain forest ecosystems on a global scale. In this study, five important types of protected areas in the transitional zone were selected, and their spatial patterns were analysed. Spatial analysis methods, such as kernel density estimation and accessibility analysis, were employed for both point and areal data, and focused on four aspects: land use scale, shift in the centre of gravity, spatial agglomeration, and accessibility. In addition, policy background and evolution of spatial and temporal characteristics of the protected area system in the transitional zone from 1963 to 2017 were also examined. We analysed the characteristics and geographical significance of the West-east corridor using the spatial pattern of the protected area system from the perspective of ecological and economic spaces. We focused on spatial shape, type intersection, and key areas to analyse the spatial overlap of the protected areas. Protected area establishment was divided into three stages: initial(1956–1980), rapid development(1981–2013), and national park transformation(2014–present). These stages reflected the change in the concept of ‘simple protection—sustainable use—integration and upgrade’ for protected areas of China. The spatial centre of gravity of the protection zone system was located in the west Qinling-Daba Mountains, and its high-density core exhibited a relatively stable N-shaped structure composed of four gathering areas. Affected by factors such as geographic environment and socio-economic development density, the average access time for protected areas was high(1.56 h);wetland parks and scenic areas are located closer to the city centre. As the West-east corridor in the transitional zone extends from west to east, there is a clear spatial dislocation between the development of protected areas and the intensity of human activities. During development, differentiated goal orientation should be adopted based on the idea of zoning and classified governance. With the advancement of the construction of protected areas, the spatial overlap of protected areas in the transition zone has become more prominent. At present, the spatially overlapped protected areas in the transitional zone remain prominent, with inclusion overlap being the most common, and forest parks exhibiting the highest probability of overlap with other protected areas, we should focus on in the integration process of the corridor-type ecological space based on the mountain forest ecosystem.

Spatial-temporal pattern changes of main agriculture natural disasters in China during 1990-2011

China is a disaster prone country, and a comprehensive understanding of change of disasters is very important for China＇s agricultural development. In this study, statistical tech niques and geographic information system tools are employed to quantify the main agricul ture disasters changes and effects on grain production in China during the period of 1990-2011. The results show that China＇s grain production was severely affected by disas ters including drought, flood, hail, frost and typhoon. The annual area covered by these dis asters reached up to 48.7x106 ha during the study period, which accounted for 44.8% of the total sown area, and about 55.1% of the per unit area grain yield change was caused by disasters. In addition, all of the disasters showed high variability, different changing trends, and spatial distribution. Drought, flood, and hail showed significantly decreasing trends, while frost and typhoon showed increasing trends. Drought and flood showed gradual changes and were distributed across the country, and disasters became more diversified from north to south. Drought was the dominated disaster type in northern China, while flood was the most important disaster type in the southern part. Hail was mainly observed in central and northern China, and frost was mainly distributed in southern China. Typhoon was greatly limited to the southeast coast. Furthermore, the resilience of grain production of each province was quite different, especially in several major grain producing areas, such as Shandong, Liaoning, Jilin and Jiangsu, where grain production was seriously affected by disasters. One reason for the difference of resilience of grain production was that grain production was marginalized in developed provinces when the economy underwent rapid development. For China＇s agricul tural development and grain security, we suggest that governments should place more em phasis on grain production, and invest more money in disaster prevention and mitigation, especially in the major grain producing provinces.

Spatial-temporal patterns of China＇s interprovincial migration, 1985-2010

Migration plays an increasing role in China＇s economy since mobility rose and economic restructuring has proceeded during the last three decades. Given the background of most studies focusing on migration in a particular period, there is a critical need to analyze the spatial-temporal patterns of migration. Using bicomponent trend mapping technique and interprovincial migration data during the periods 1985-1990, 1990-1995, 1995-2000, 2000- 2005, and 2005-2010 we analyze net-, in-, out-migration intensity, and their changes over time in this study. Strong spatial variations in migration intensity were found in China＇s interprovincial migration, and substantial increase in migration intensity was also detected in eastern China during 1985-2010. Eight key destinations are mostly located within the three rapidly growing economic zones of eastern China （Pearl River Delta, Yangtze River Delta and Beijing-Tianjin-Hebei Metropolitan Region）, and they are classified into three types： mature, emerging, and fluctuant origins, while most key origins are relatively undeveloped central and western provinces, which are exactly in accordance with China＇s economic development patterns. The results of bicomponent trend mapping indicate that, in a sense, the migration in the south was more active than the north over the last three decades. The result shows the new changing features of spatial-temporal patterns of China＇s interprovincial migration that Fan and Chen did not find out in their research. A series of social-economic changes including rural transformation, balanced regional development, and labor market changes should be paid more attention to explore China＇s future interprovincial migration.

Dynamics and controls of ecosystem multiserviceability across the Qingzang Plateau

Ecosystem multiserviceability(EMS),a comprehensive and significant ecological indicator,reflects the capacity of ecosystems to offer multiple services concurrently.Intensified climate change and human activity are continu-ously altering ecosystem functions,services,and EMSs.However,numerous studies have only focused on one or a few ecosystem services,rarely taking into account spatial-temporal distribution and drivers of EMS on behalf of different agencies.We calculated EMS including pastoralist(PA),environmental protection agency(EPA),bio-diversity conservation agency(BCA),and climate change mitigation agency(CCMA)using grassland production,habitat quality,water conservation,and carbon sequestration.Then,the effects of geographical features,climate factors,and human activities on spatial-temporal patterns of EMS were explored.The result indicated that EMS showed a decreasing tendency from the southeast to northwest on the Qingzang Plateau(QZP).Meanwhile,there were no obvious fluctuations in four simulated scenarios(PA,EPA,BCA and CCMA)among different vegetation types during 2000 to 2015.Notably,EMS of all simulated scenarios decreased in the alpine steppe ecosystem,but negligible changes were found in other ecosystems from 2015 to 2020.Moreover,the relative importance of precipitation in annual mean value(from 2000 to 2020)of PA,EPA,BCA and CCMA were 0.13,0.11,0.30 and 0.19,respectively.Overall,precipitation played the dominant role on the dynamics of EMS,followed by elevation and human footprint.Our findings highlighted that understanding the patterns and drivers of EMS could provide a reference for the regional management and maintenance of ecosystem stability on QZP.

AMOUNT OF INFORMATION FOR SPATIAL-TEMPORAL CORRELATION FILTER PATTERN RECOGNITION SYSTEM

In order to relate the design and analysis of an optical pattern recognition system with the structural parameters, only by introducing the prolate spheroidal wave function (PSWF) can the amount of information be computed. Combining the imaging wave function set {ψi(x)} and distorted wave function set {b_i(p)} and two integral equations they satisfy derives the expression of the amount of information. The design method of matched filter connected with its amount of information is studied, and their amounts of information belonging to different pattern recognition systems are illustrated. It can be seen that the difference of the amounts of information for various systems is obvious.

A spatial-temporal analysis of urban recreational business districts： A case study in Beijing, China

This study has revealed spatial-temporal changes in Recreational Business Dis- tricts （RBDs） in Beijing and examined the relationship between the location of urban RBDs and traffic conditions, resident and tourist density, scenic spots, and land prices. A more reasonable classification of urban RBDs （LSC, CPS, and ULA） is also proposed. Quantitative methods such as Gini Coefficient, Spatial Interpolation, Kernel Density Estimation, and Geographical Detector were employed to collect and analyze the data from three types of urban RBDs in Beijing in 1990, 2000, and 2014, respectively, and the spatial-temporal pat- terns as well as the distribution characteristics of urban RBDs were analyzed using ArcGIS software. It was concluded that （1） both the number and scale of urban RBDs in Beijing have been expanding and the trend for all types of urban RBDs in Beijing to be spatially agglom- erated is continuing; （2） the spatial-temporal evolution pattern of urban RBDs in Beijing is ＂single-core agglomeration-dual-core agglomeration-multi-core diffusion＂; and （3） urban RBDs were always located in areas with low traffic density, tourist attractions, high resident and tourist population density, and relatively high land valuations; these factors also affect the scale size of RBDs.

Impact of warm mesoscale eddy on tropical cyclone intensity

The spatial-temporal patterns of tropical cyclone(TC) intensity changes caused by the warm ocean mesoscale eddy(WOME) distribution are evaluated using two sets of idealized numerical experiments. The results show that the TC was intensified and weakened when a WOME was close to and far away from the TC center, respectively.The area where the WOME enhanced(weakened) TC intensity is called the inner(outer) area in this study.Amplitudes of the enhancement and weakening caused by the WOME in the inner and outer area decreased and increased over time, while the ranges of the inner and outer area diminished and expanded, respectively. The WOME in the inner area strengthened the secondary circulation of the TC, increased heat fluxes, strengthened the symmetry, and weakened the outer spiral rainband, which enhanced TC intensity. The effect was opposite if the WOME was in the outer area, and it weakened the TC intensity. The idealized simulation employed a stationary TC, and thus the results may only be applied to TCs with slow propagation. These findings can improve our understanding of the interactions between TC and the WOME and are helpful for improving TC intensity forecasting by considering the effect of the WOME in the outer areas.

Improving remote sensing-based net primary production estimation in the grazed land with defoliation formulation model

Remote sensing(RS) technologies provide robust techniques for quantifying net primary productivity(NPP) which is a key component of ecosystem production management. Applying RS, the confounding effects of carbon consumed by livestock grazing were neglected by previous studies, which created uncertainties and underestimation of NPP for the grazed lands. The grasslands in Xinjiang were selected as a case study to improve the RS based NPP estimation. A defoliation formulation model(DFM) based on RS is developed to evaluate the extent of underestimated NPP between 1982 and 2011. The estimates were then used to examine the spatiotemporal patterns of the calculated NPP. Results show that average annual underestimated NPP was 55.74 gC·m^(-2)yr^(-1) over the time period understudied, accounting for 29.06% of the total NPP for the Xinjiang grasslands. The spatial distribution of underestimated NPP is related to both grazing intensity and time. Data for the Xinjiang grasslands show that the average annual NPP was 179.41 gC·m^(-2)yr^(-1), the annual NPP with an increasing trend was observed at a rate of 1.04 gC·m^(-2)yr^(-1) between 1982 and 2011. The spatial distribution of NPP reveals distinct variations from high to low encompassing the geolocations of the Tianshan Mountains, northern and southern Xinjiang Province and corresponding with mid-mountain meadow, typical grassland, desert grassland, alpine meadow, and saline meadow grassland types. This study contributes to improving RS-based NPP estimations for grazed land and provides a more accurate data to support the scientific management of fragile grassland ecosystems in Xinjiang.

Travel time prediction with viscoelastic traffic model

Travel time through a ring road with a total length of 80 km has been predicted by a viscoelastic traffic model(VEM), which is developed in analogous to the non-Newtonian fluid flow. The VEM expresses a traffic pressure for the unfree flow case by space headway, ensuring that the pressure can be determined by the assumption that the relevant second critical sound speed is exactly equal to the disturbance propagation speed determined by the free flow speed and the braking distance measured by the average vehicular length. The VEM assumes that the sound speed for the free flow case depends on the traffic density in some specific aspects, which ensures that it is exactly identical to the free flow speed on an empty road. To make a comparison, the open Navier-Stokes type model developed by Zhang(ZHANG, H. M. Driver memory, traffic viscosity and a viscous vehicular traffic flow model. Transp. Res. Part B, 37, 27–41(2003)) is adopted to predict the travel time through the ring road for providing the counterpart results.When the traffic free flow speed is 80 km/h, the braking distance is supposed to be 45 m,with the jam density uniquely determined by the average length of vehicles l ≈ 5.8 m. To avoid possible singular points in travel time prediction, a distinguishing period for time averaging is pre-assigned to be 7.5 minutes. It is found that the travel time increases monotonically with the initial traffic density on the ring road. Without ramp effects, for the ring road with the initial density less than the second critical density, the travel time can be simply predicted by using the equilibrium speed. However, this simpler approach is unavailable for scenarios over the second critical.

Rainfall erosivity estimation over the Tibetan plateau based on high spatial-temporal resolution rainfall records

The Tibetan Plateau(TP)in China has been experiencing severe water erosion because of climate warming.The rapid development of weather station network provides an opportunity to improve our understanding of rainfall erosivity in the TP.In this study,1-min precipitation data obtained from 1226 weather stations during 2018–2019 were used to estimate rainfall erosivity,and subsequently the spatial-temporal patterns of rainfall erosivity in the TP were identified.The mean annual erosive rainfall was 295 mm,which accounted for 53%of the annual rainfall.An average of 14 erosive events occurred yearly per weather station,with the erosive events in the wet season being more likely to extend beyond midnight.In these cases,the precipitation amounts of the erosive events were found to be higher than those of the daily precipitations,which may result in implicit bias as the daily precipitation data were used for estimating the rainfall erosivity.The mean annual rainfall erosivity in the TP was 528 MJ mm·ha^(-1)·h^(-1),with a broader range of 0–3402 MJ mm·ha^(-1)·h^(-1),indicating a significant spatial variability.Regions with the highest mean annual rainfall erosivity were located in the forest zones,followed by steppe and desert zones.Finally,the precipitation phase records obtained from 140 weather stations showed that snowfall events slightly impacted the accuracy of rainfall erosivity calculation,but attention should be paid to the erosion process of snowmelt in the inner part of the TP.These results can be used as the reference data for soil erosion prediction in normal precipitation years.

A Deep Learning Modeling Framework to Capture Mixing Patterns in Reactive-Transport Systems

Prediction and control of chemical mixing are vital for many scientific areas such as subsurface reactive transport, climate modeling, combustion, epidemiology, and pharmacology. Due to the complex nature of mixing in heterogeneous andanisotropic media, the mathematical models related to this phenomenon are not analytically tractable. Numerical simulations often provide a viable route to predict chemical mixing accurately. However, contemporary modeling approaches for mixing cannot utilize available spatial-temporal data to improve the accuracy of the future prediction and can be compute-intensive, especially when the spatial domain is large andfor long-term temporal predictions. To address this knowledge gap, we will present inthis paper a deep learning (DL) modeling framework applied to predict the progress ofchemical mixing under fast bimolecular reactions. This framework uses convolutionalneural networks (CNN) for capturing spatial patterns and long short-term memory(LSTM) networks for forecasting temporal variations in mixing. By careful design ofthe framework—placement of non-negative constraint on the weights of the CNN andthe selection of activation function, the framework ensures non-negativity of the chemical species at all spatial points and for all times. Our DL-based framework is fast,accurate, and requires minimal data for training. The time needed to obtain a forecastusing the model is a fraction (≈ O(10−6)) of the time needed to obtain the result using a high-fidelity simulation. To achieve an error of 10% (measured using the infinitynorm) for capturing local-scale mixing features such as interfacial mixing, only 24%to 32% of the sequence data for model training is required. To achieve the same levelof accuracy for capturing global-scale mixing features, the sequence data required formodel training is 64% to 70% of the total spatial-temporal data. Hence, the proposedapproach—a fast and accurate way to forecast long-time spatial-temporal mixing patterns in heterogeneous and anisotropic media—will be a valuable tool for modelingreactive-transport in a wide range of applications.

A geographical and operational deep graph convolutional approach for flight delay prediction

Flight delay prediction has attracted great interest in civil aviation community due to its significant role in airline planning,flight scheduling,airport operation,and passenger service.Flight delay is affected by numerous factors and irregularly propagates in air transportation networks owing to flight connectivity,which brings critical challenges to accurate flight delay prediction.In recent years,Graph Convolutional Networks(GCNs)have become popular in flight delay prediction due to the advantage in extracting complicated relationships.However,most of the existing GCN-based methods have failed to effectively capture the spatial-temporal information in flight delay prediction.In this paper,a Geographical and Operational Graph Convolutional Network(GOGCN)is proposed for multi-airport flight delay prediction.The GOGCN is a GCN-based spatial-temporal model that improves node feature representation ability with geographical and operational spatial-temporal interactions in a graph.Specifically,an operational aggregator is designed to extract global operational information based on the graph structure,while a geographical aggregator is developed to capture the similar nature among spatially close airports.Extensive experiments on a real-world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods with a satisfying accuracy improvement.

Response of vegetation restoration to climate change and human activities in Shaanxi-Gansu- Ningxia Region

The ＂Grain for Green Project＂ initiated by the governments since 1999 were the dominant contributors to the vegetation restoration in the agro-pastoral transitional zone of northern China. Climate change and human activities are responsible for the improvement and degradation to a certain degree. In order to monitor the vegetation variations and clarify the causes of rehabilitation in the Shaanxi-Gansu-Ningxia Region, this paper, based on the MODIS-NDVI and climate data during the period of 2000-2009, analyzes the main charac- teristics, spatial-temporal distribution and reasons of vegetation restoration, using methods of linear regression, the Hurst Exponent, standard deviation and other methods. Results are shown as follows. （1） From 2000 to 2009, the NDVI of the study area was improved progres- sively, with a linear tendency being 0.032/10a, faster than the growth of the Three-North Shelter Forest Program （0.007/10a） from 1982 to 2006. （2） The vegetation restoration is characterized by two fast-growing periods, with an ＂S-shaped＂ increasing curve. （3） The largest proportion of the contribution to vegetation restoration was observed in the slightly improved area, followed by the moderate and the significantly improved area; the degraded area is distributed sporadically over southern part of Ningxia Hui Autonomous Region as well as eastern Dingbian of Shaanxi province, Huanxian and Zhengyuan of Gansu province. （4） Climate change and human activities are two driving forces in vegetation restoration; more- over anthropogenic factors such as ＂Grain for Green Project＂ were the main causes leading to an increasing trend of NDVI on local scale. However, its influencing mechanism remains to be further investigated. （5） The Hurst Exponent of NDVI time series shows that the vegetation restoration was sustainable. It is expected that improvement in vegetation cover will expand to the most parts of the region.