To conduct a comprehensive analysis of the current status of water environment quality in Yilong Lake,a systematic study was undertaken to characterize the evolution of water quality.This study utilized monthly data o...To conduct a comprehensive analysis of the current status of water environment quality in Yilong Lake,a systematic study was undertaken to characterize the evolution of water quality.This study utilized monthly data on water quality indicators collected from three monitoring sections of Yilong Lake between 2016 and 2023,employing the Mann-Kendall trend test and ArcGIS spatial interpolation technique.The results indicated that the five-day biochemical oxygen demand(BOD5),total nitrogen(TN),and chlorophyll a(Chla)exhibited an overall increasing trend,whereas other indicators demonstrated a decreasing trend.The permanganate index(PI),chemical oxygen demand(COD),TN,and Chla were observed in the following order:east of the lake>middle of the lake>west of the lake.In contrast,the BOD5 and total phosphorus(TP)were ranked as west of the lake>east of the lake>middle of the lake.Additionally,ammonia nitrogen(NH3-N)was found to be in the order of east of the lake>west of the lake>middle of the lake,while transparency was ranked as west of the lake>middle of the lake>east of the lake.Urban domestic sewage,effluent from industrial parks,domestic waste generated by rural residents’production and daily activities,agricultural waste,wastewater from decentralized farming,domestic sewage,and point source discharges from the soybean processing industry are the primary contributors to the exceedance of water quality standards.The enhancement of a precise pollution control system,along with the regulation of pollution sources and the interception of pollutants,can significantly diminish the pollution load entering the lake.This approach is essential for the protection and restoration of river and lake ecosystems,thereby facilitating the gradual recovery of their ecological functions.Additionally,the implementation of ecological water replenishment and the recycling of water resources can improve the capacity of the water environment.Furthermore,bolstering scientific and technological support,as well as comprehensive supervision and assurance measures,is crucial to ensuring that water quality remains stable and adheres to established standards.展开更多
A novel spatial interpolation method based on integrated radial basis function artificial neural networks （IRBFANNs） is proposed to provide accurate and stable predictions of heavy metals concentrations in soil at u...A novel spatial interpolation method based on integrated radial basis function artificial neural networks （IRBFANNs） is proposed to provide accurate and stable predictions of heavy metals concentrations in soil at un- sampled sites in a mountain region. The IRBFANNs hybridize the advantages of the artificial neural networks and the neural networks integration approach. Three experimental projects under different sampling densities are carried out to study the performance of the proposed IRBFANNs-based interpolation method. This novel method is compared with six peer spatial interpolation methods based on the root mean square error and visual evaluation of the distribution maps of Mn elements. The experimental results show that the proposed method performs better in accuracy and stability. Moreover, the proposed method can provide more details in the spatial distribution maps than the compared interpolation methods in the cases of sparse sampling density.展开更多
Climate research relies heavily on good quality instrumental data; for modeling efforts gridded data are needed. So far, relatively little effort has been made to create gridded climate data for China. This is especia...Climate research relies heavily on good quality instrumental data; for modeling efforts gridded data are needed. So far, relatively little effort has been made to create gridded climate data for China. This is especially true for high-resolution daily data. This work, focuses on identifying an accurate method to produce gridded daily precipitation in China based on the observed data at 753 stations for the period 1951-2005. Five interpolation methods, including ordinary nearest neighbor, local polynomial, radial basis function, inverse distance weighting, and ordinary kriging, have been used and compared. Cross-validation shows that the ordinary kriging based on seasonal semi-variograms gives the best performance, closely followed by the inverse distance weighting with a power of 2. Finally the ordinary kriging is chosen to interpolate the station data to a 18 km× 18 km grid system covering the whole country. Precipitation for each 0.5°×0.5° latitude-longitude block is then obtained by averaging the values at the grid nodes within the block. Owing to the higher station density in the eastern part of the country, the interpolation errors are much smaller than those in the west （west of 100°E）. Excluding 145 stations in the western region, the daily, monthly, and annual relative mean absolute errors of the interpolation for the remaining 608 stations are 74%, 29%, and 16%, respectively. The interpolated daily precipitation has been made available on the internet for the scientific community.展开更多
Spatial interpolation is a common tool used in the study of fishery ecology, especially for the construction of ecosystem models. To develop an appropriate interpolation method of determining fishery resources density...Spatial interpolation is a common tool used in the study of fishery ecology, especially for the construction of ecosystem models. To develop an appropriate interpolation method of determining fishery resources density in the Yellow Sea, we tested four frequently used methods, including inverse distance weighted interpolation(IDW), global polynomial interpolation(GPI), local polynomial interpolation(LPI) and ordinary kriging(OK).A cross-validation diagnostic was used to analyze the efficacy of interpolation, and a visual examination was conducted to evaluate the spatial performance of the different methods. The results showed that the original data were not normally distributed. A log transformation was then used to make the data fit a normal distribution. During four survey periods, an exponential model was shown to be the best semivariogram model in August and October 2014, while data from January and May 2015 exhibited the pure nugget effect.Using a paired-samples t test, no significant differences(P>0.05) between predicted and observed data were found in all four of the interpolation methods during the four survey periods. Results of the cross-validation diagnostic demonstrated that OK performed the best in August 2014, while IDW performed better during the other three survey periods. The GPI and LPI methods had relatively poor interpolation results compared to IDW and OK. With respect to the spatial distribution, OK was balanced and was not as disconnected as IDW nor as overly smooth as GPI and LPI, although OK still produced a few 'bull's-eye' patterns in some areas.However, the degree of autocorrelation sometimes limits the application of OK. Thus, OK is highly recommended if data are spatially autocorrelated. With respect to feasibility and accuracy, we recommend IDW to be used as a routine interpolation method. IDW is more accurate than GPI and LPI and has a combination of desirable properties, such as easy accessibility and rapid processing.展开更多
Understanding the topographic context preceding the development of erosive landforms is of major relevance in geomorphic research, as topography is an important factor on both water and mass movement-related erosion, ...Understanding the topographic context preceding the development of erosive landforms is of major relevance in geomorphic research, as topography is an important factor on both water and mass movement-related erosion, and knowledge of the original surface is a condition for quantifying the volume of eroded material. Although any reconstruction implies assuming that the resulting surface reflects the original topography, past works have been dominated by linear interpolation methods, incapable of generating curved surfaces in areas with no data or values out- side the range of variation of inputs. In spite of these limitations, impossibility of validation has led to the assumption of surface representativity never being challenged. In this paper, a validation-based method is applied in order to define the optimal interpolation technique for reconstructing pre-erosion topography in a given study area. In spite of the absence of the original surface, different techniques can be nonetheless evaluated by quantifying their ca- pacity to reproduce known topography in unincised locations within the same geomorphic contexts of existing erosive landforms. A linear method （Triangulated Irregular Network, TIN） and 23 parameterizations of three distinct Spline interpolation techniques were compared using 50 test areas in a context of research on large gully dynamics in the South of Portugal. Results show that almost all Spline methods produced smaller errors than the TIN, and that the latter produced a mean absolute error 61.4% higher than the best Spline method, clearly establishing both the better adjustment of Splines to the geomorphic context considered and the limitations of linear approaches. The proposed method can easily be applied to different interpolation techniques and topographic contexts, enabling better calculations of eroded volumes and denudation rates as well as the investigation of controls by antecedent topographic form over erosive processes.展开更多
Spatial interpolation has been frequently encountered in earth sciences and engineering.A reasonable appraisal of subsurface heterogeneity plays a significant role in planning,risk assessment and decision making for g...Spatial interpolation has been frequently encountered in earth sciences and engineering.A reasonable appraisal of subsurface heterogeneity plays a significant role in planning,risk assessment and decision making for geotechnical practice.Geostatistics is commonly used to interpolate spatially varying properties at un-sampled locations from scatter measurements.However,successful application of classic geostatistical models requires prior characterization of spatial auto-correlation structures,which poses a great challenge for unexperienced engineers,particularly when only limited measurements are available.Data-driven machine learning methods,such as radial basis function network(RBFN),require minimal human intervention and provide effective alternatives for spatial interpolation of non-stationary and non-Gaussian data,particularly when measurements are sparse.Conventional RBFN,however,is direction independent(i.e.isotropic)and cannot quantify prediction uncertainty in spatial interpolation.In this study,an ensemble RBFN method is proposed that not only allows geotechnical anisotropy to be properly incorporated,but also quantifies uncertainty in spatial interpolation.The proposed method is illustrated using numerical examples of cone penetration test(CPT)data,which involve interpolation of a 2D CPT cross-section from limited continuous 1D CPT soundings in the vertical direction.In addition,a comparative study is performed to benchmark the proposed ensemble RBFN with two other non-parametric data-driven approaches,namely,Multiple Point Statistics(MPS)and Bayesian Compressive Sensing(BCS).The results reveal that the proposed ensemble RBFN provides a better estimation of spatial patterns and associated prediction uncertainty at un-sampled locations when a reasonable amount of data is available as input.Moreover,the prediction accuracy of all the three methods improves as the number of measurements increases,and vice versa.It is also found that BCS prediction is less sensitive to the number of measurement data and outperforms RBFN and MPS when only limited point observations are available.展开更多
Spatial-temporal distribution of marine fishes is strongly influenced by environmental factors.To obtain a more continuous distribution of these variables usually measured by stationary sampling designs,spatial interp...Spatial-temporal distribution of marine fishes is strongly influenced by environmental factors.To obtain a more continuous distribution of these variables usually measured by stationary sampling designs,spatial interpolation methods(SIMs)is usually used.However,different SIMs may obtain varied estimation values with significant differences,thus affecting the prediction of fish spatial distribution.In this study,different SIMs were used to obtain continuous environmental variables(water depth,water temperature,salinity,dissolved oxygen(DO),p H,chlorophyll a and chemical oxygen demand(COD))in the Changjiang River Estuary(CRE),including inverse distance weighted(IDW)interpolation,ordinary Kriging(OK)(semivariogram model:exponential(OKE),Gaussian(OKG)and spherical(OKS))and radial basis function(RBF)(regularized spline function(RS)and tension spline function(TS)).The accuracy and effect of SIMs were cross-validated,and two-stage generalized additive model(GAM)was used to predict the distribution of Coilia nasus from 2012 to 2014 in CRE.DO and COD were removed before model prediction due to their autocorrelation coefficient based on variance inflation factors analysis.Results showed that the estimated values of environmental variables obtained by the different SIMs differed(i.e.,mean values,range etc.).Cross-validation revealed that the most suitable SIMs of water depth and chlorophyll a was IDW,water temperature and salinity was RS,and p H was OKG.Further,different interpolation results affected the predicted spatial distribution of Coilia nasus in the CRE.The mean values of the predicted abundance were similar,but the differences between and among the maximum value were large.Studies showed that different SIMs can affect estimated values of the environmental variables in the CRE(especially salinity).These variations further suggest that the most applicable SIMs to each variable will also differ.Thus,it is necessary to take these potential impacts into consideration when studying the relationship between the spatial distribution of fishes and environmental changes in the CRE.展开更多
Quality-controlled and serially complete daily air temperature data are essential to evaluating and modelling the influences of climate change on the permafrost in cold regions. Due to malfunctions and location chang...Quality-controlled and serially complete daily air temperature data are essential to evaluating and modelling the influences of climate change on the permafrost in cold regions. Due to malfunctions and location changes of observing stations, temporal gaps （i.e., missing data） are common in collected datasets. The objective of this study was to assess the efficacy of Kriging spatial interpolation for estimating missing data to fill the temporal gaps in daily air temperature data in northeast China. A cross-validation experiment was conducted. Daily air temperature series from 1960 to 2012 at each station were estimated by using the universal Kriging （UK） and Kriging with an external drift （KED）, as appropriate, as if all the ob-servations at a given station were completely missing. The temporal and spatial variation patterns of estimation uncertainties were also checked. Results showed that Kriging spatial interpolation was generally desirable for estimating missing data in daily air temperature, and in this study KED performed slightly better than UK. At most stations the correlation coefficients （R2） between the observed and estimated daily series were 〉0.98, and root mean square errors （RMSEs） of the estimated daily mean （Tmean）, maximum （Tmax）, and minimum （Tmin） of air temperature were 〈3 ℃. However, the estimation quality was strongly affected by seasonality and had spatial variation. In general, estimation uncertainties were small in summer and large in winter. On average, the RMSE in winter was approximately 1 ℃ higher than that in summer. In addition, estimation uncertainties in mountainous areas with complex terrain were significantly larger than those in plain areas.展开更多
[ Objectivel The research aimed to study prediction model for spatial distribution of the average temperature based on GIS. [ Method l Average temperature over the years as research object, based on Ordinary Kriging ...[ Objectivel The research aimed to study prediction model for spatial distribution of the average temperature based on GIS. [ Method l Average temperature over the years as research object, based on Ordinary Kriging （OK）, Inverse Distance Weight （ IDW）, SPLINE and Mixed In- terpolation （MLR）, monthly temperature data from 1979 to 2008 at 18 long-term meteorological observation stations in Hainan Island were conduc- ted spatial grid treatment. Via contrasts and analyses on different interpolation methods, the optimum interpolation method for average temperature over the years in Hainan Island was selected. [ Resuitl By error analyses of the four interpolation methods for average temperature in recent 30 years in Hainan Island, it was found that accuracy was MLR 〉 IDW 〉 OK 〉 SPLINE. Spatial interpolation effect of MLR was the best for average temperature in Hainan Island. Spatial distribution of the average temperature in Halnan Island had obvious south-high-north-low latitudinal zonality and vertical zonality of gradually declining as altitude rise. In addition, temperature along coast was slightly higher than that in inland. Lapse rate of the temperature in each month in Hainan Island was 0.38 -0.85℃/100 m, and lapse rate of the annual average temperature was about 0.74 ℃/ 100 m. In different areas, lapse rate of the temperature as altitude was different at different time. [ Condusion] The research provided basis for ob- taining continuous distribution situation of the agricultural meteorological factor and establishing accurate prediction model of the spatial distribution in Hainan Island.展开更多
This paper proposes a low-complexity spatial-domain Error Concealment (EC) algorithm for recovering consecutive blocks error in still images or Intra-coded (I) frames of video sequences. The proposed algorithm works w...This paper proposes a low-complexity spatial-domain Error Concealment (EC) algorithm for recovering consecutive blocks error in still images or Intra-coded (I) frames of video sequences. The proposed algorithm works with the following steps. Firstly the Sobel operator is performed on the top and bottom adjacent pixels to detect the most likely edge direction of current block area. After that one-Dimensional (1D) matching is used on the available block boundaries. Displacement between edge direction candidate and most likely edge direction is taken into consideration as an important factor to improve stability of 1D boundary matching. Then the corrupted pixels are recovered by linear weighting interpolation along the estimated edge direction. Finally the interpolated values are merged to get last recovered picture. Simulation results demonstrate that the proposed algorithms obtain good subjective quality and higher Peak Signal-to-Noise Ratio (PSNR) than the methods in literatures for most images.展开更多
[ Objective ] The research aimed to study the best spatial interpolation method of the meteorological factor in Northeast China. [ Method ] Based on geostatistical analysis tool of the Arclnfo GIS software, several sp...[ Objective ] The research aimed to study the best spatial interpolation method of the meteorological factor in Northeast China. [ Method ] Based on geostatistical analysis tool of the Arclnfo GIS software, several spatial interpolation methods were used to estimate the meteorological fac- tore （annual rainfall and monthly average temperature） in Northeast China, such as inverse distance weighted （IDW）, radial basis function （RBF） and Kriging. Then, the best interpolation method of one meteorological factor was selected. [ Result] For monthly average temperature, Kriging method was better than others. For annual rainfall, precision of the evaluated value with RBF method was higher than that of the IDW and Kriging methods. [Conclusion] There was obvious regional difference of the meteorological factor in Northeast China. Monthly average temperature in south was higher than that in north, and annual rainfall in southeast was more than that in northwest in Northeast China.展开更多
Soil carbon to nitrogen(C/N) ratio is one of the most important variables reflecting soil quality and ecological function,and an indicator for assessing carbon and nitrogen nutrition balance of soils.Its variation ref...Soil carbon to nitrogen(C/N) ratio is one of the most important variables reflecting soil quality and ecological function,and an indicator for assessing carbon and nitrogen nutrition balance of soils.Its variation reflects the carbon and nitrogen cycling of soils.In order to explore the spatial variability of soil C/N ratio and its controlling factors of the Ili River valley in Xinjiang Uygur Autonomous Region,Northwest China,the traditional statistical methods,including correlation analysis,geostatistic alanalys and multiple regression analysis were used.The statistical results showed that the soil C/N ratio varied from 7.00 to 23.11,with a mean value of 10.92,and the coefficient of variation was 31.3%.Correlation analysis showed that longitude,altitude,precipitation,soil water,organic carbon,and total nitrogen were positively correlated with the soil C/N ratio(P < 0.01),whereas negative correlations were found between the soil C/N ratio and latitude,temperature,soil bulk density and soil p H.Ordinary Cokriging interpolation showed that r and ME were 0.73 and 0.57,respectively,indicating that the prediction accuracy was high.The spatial autocorrelation of the soil C/N ratio was 6.4 km,and the nugget effect of the soil C/N ratio was 10% with a patchy distribution,in which the area with high value(12.00–20.41) accounted for 22.6% of the total area.Land uses changed the soil C/N ratio with the order of cultivated land > grass land > forest land > garden.Multiple regression analysis showed that geographical and climatic factors,and soil physical and chemical properties could independently explain 26.8%and 55.4% of the spatial features of soil C/N ratio,while human activities could independently explain 5.4% of the spatial features only.The spatial distribution of soil C/N ratio in the study has important reference value for managing soil carbon and nitrogen,and for improving ecological function to similar regions.展开更多
Population is an important strategic resource for national development, a fundamental element of socio-economic development. The coordinated development of population and economy is an effective way to achieve rapid e...Population is an important strategic resource for national development, a fundamental element of socio-economic development. The coordinated development of population and economy is an effective way to achieve rapid economic growth. Based on the population statistics data of counties (districts) in Henan Province, China, from 2006 to 2021. The paper firstly uses the logistic population growth mathematical model to calculate the resident population growth rate of counties (districts), then utilizes the hotspot analysis and spatial semi-variogram analysis, to research the spatial distribution characteristics of the resident population growth rate in Henan Province. The research results show that the evolution of the regional resident population in the province basically conforms to the logistic natural growth model. The resident population growth rate shows the characteristics of high in the north and low in the south, high in the center and low in the surrounding regions. The resident population growth rate is positively correlated with the level of economic development;the urban built-up areas, especially the new regions in urban planning, have a fast growth rate of resident population, which has a significant siphon effect on the population of surrounding regions. The hotspots of resident population growth rate in the province are mainly distributed in the urban built-up areas and surrounding regions of Zhengzhou, Luoyang, and Xinxiang, accounting for about 3.51% of the total area of the province. The cold spots are mainly distributed in the eastern part of the province, forming zonal distribution, which spans across Shangqiu City, Zhoukou City, and Zhumadian City, accounting for about 8.61% of the total area of the province. The area with negative growth of resident population accounts for approximately 53.47% of the total province. The spatial distribution of the growth rate of the resident population in the whole province basically conforms to the spherical model, with a small dispersion degree and a short range. In the range, there is a high degree of variability in resident population growth rate.展开更多
The patial interpolation of borehole data is an important means of stratigraphic structure to construct a three-dimensional model of coal strata,and the reasonable selection of an effective spatial interpolation metho...The patial interpolation of borehole data is an important means of stratigraphic structure to construct a three-dimensional model of coal strata,and the reasonable selection of an effective spatial interpolation method will directly affect the accuracy of three-dimensional modeling of the strata.To select an effective spatial interpolation method and improve the accuracy of 3D modeling of formations,four interpolation methods(the inverse distance weight interpolation algorithm,the local polynomial interpolation algorithm,the radial basis neural network interpolation algorithm and the kriging interpolation algorithm)were compared and analyzed.In particular,the methods of interpolation algorithm,interpolation surface,sample test error,and cross-validation error were used.The experiment of 13-1 seam coal in the Huainan mining area showed the spatial surface interpolation effect of the radial basis neural network interpolation algorithm(RBF)compared with the inverse distance weight interpolation algorithm(IDW),local polynomial interpolation algorithm(LPI)and kriging algorithm.The three interpolation methods have higher accuracy and are more suitable for surface interpolation of coal seams,which is of great significance for improving the accuracy of subsequent 3D modeling of coal seams.展开更多
The current distribution of forest tree species is a result of natural or human mediated historical and contemporary processes. Knowledge of the spatial distribution of the diversity and divergence of populations is c...The current distribution of forest tree species is a result of natural or human mediated historical and contemporary processes. Knowledge of the spatial distribution of the diversity and divergence of populations is crucial for managing and conserving genetic resources in forest tree species. By combining tools from population genetics, landscape ecology and spatial statistics, landscape genetics thus represents a powerful method for evaluating the geographic patterns of genetic resources at the population level. In this study, we explore the possibility of combining genetic diversity data, spatial statistic tools and GIS technologies to map the genetic divergence and diversity of 31 Castanea sativa populations collected in Spain, Italy, Greece, and Turkey. The IDW technique was used to interpolate the diversity values and divergence indices as expected hetereozygosity (He), allelic richness (Rs), private allelic richness (PRs), and membership values (Q) of each population to different clusters. Genetic diversity maps and a synthetic map of the spatial genetic structure of European chestnut populations were produced. Spatial coincidences between landscape elements and statistically significant genetic discontinuities between populations were investigated. Evidence is provided of the significance of cartographic outputs produced in the study and on their usefulness in managing genetic resources.展开更多
The use of spatial interpolation methods of data is becoming increasingly common in geophysical analysis, for that reason, currently, several software already contain many of these methods, allowing more detailed stud...The use of spatial interpolation methods of data is becoming increasingly common in geophysical analysis, for that reason, currently, several software already contain many of these methods, allowing more detailed studies. In the present work four interpolation methods are evaluated, for the crustal thickness data of Brazil tectonic provinces, with the intention of making Moho’s map of the regions. The methods used were IDW, Natural Neighbor, Spline and Kriging. We compiled 257 data that constituted a geographic database implemented in the template Postgree PostGIS and were processed using the tools of interpolation located in the Spatyal Analyst Tools program ArcGIS?9 ESRI. Traditional methods, IDW, Natural Neighbor and Spline, generate artifacts in their results, the effects of aim, not consistent with the behavior of crust. Such anomalies are generated because of mathematical formulation methods added to data compiled gravimetry. The analysis results of geostatistical Kriging are more refined and consistent, showing no specific anormalities, i.e., the crustal thickness variation (thinning and thickening) is introduced gradually. Initial our estimates were separated in four specific blocks. With the approval of new networks (BRASIS, RSISNE and RSIS), the crustal thickness database for Brazil may be amended or supplemented so that new models may be generated more consistently, complementing studies of regional tectonics evolution and seismicity.展开更多
The accuracy between ordinary kriging and regression kriging was compared based on the combined consideration of sample size,spatial structure,and auxiliary variables(terrain indices and electromagnetic induction surv...The accuracy between ordinary kriging and regression kriging was compared based on the combined consideration of sample size,spatial structure,and auxiliary variables(terrain indices and electromagnetic induction surveys) for a variety of soil properties in two contrasting landscapes(agricultural vs.forested).When spatial structure could not be well captured by point-based observations(e.g.,when the ratio of sample spacing over correlation range was > 0.5),or when a strong relationship existed between target soil properties and auxiliary variables(e.g.,their R2 was > 0.6),regression kriging(RK) was more accurate for interpolating soil properties in both landscapes studied.Otherwise,ordinary kriging(OK) was better.Soil depth and wetness condition did not appear to affect the selection of kriging for soil moisture interpolation,because they did not significantly change the ratio of sample spacing over correlation range and the relationship with the auxiliary variables.Because of a smaller ratio of elevation change over total study area(E/A = 1.2) and multiple parent materials in the agricultural land,OK was generally more accurate in that landscape.In contrast,a larger E/A ratio of 6.8 and a single parent material led to RK being preferable in the steep-sloped forested catchment.The results from this study can be useful for selecting kriging for various soil properties and landscapes.展开更多
The immense and towering Tibetan Plateau acts as a heating source and, thus, deeply shapes the climate of the Eurasian continent and even the whole world. However, due to the scarcity of meteorological observation sta...The immense and towering Tibetan Plateau acts as a heating source and, thus, deeply shapes the climate of the Eurasian continent and even the whole world. However, due to the scarcity of meteorological observation stations and very limited climatic data, little is quantitatively known about the heating effect and temperature pattern of the Tibetan Plateau. This paper collected time series of MODIS land surface temperature （LST） data, together with meteorological data of 137 stations and ASTER GDEM data for 2001-2007, to estimate and map the spatial distribution of monthly mean air temperatures in the Tibetan Plateau and its neighboring areas. Time series analysis and both ordinary linear regression （OLS） and geographical weighted regression （GWR） of monthly mean air temperature （Ta） with monthly mean land surface temperature （Ts） were conducted. Regression analysis shows that recorded Ta is rather closely related to Ts, and that the GWR estimation with MODIS Ts and altitude as independent variables, has a much better result with adjusted R 2 〉 0.91 and RMSE = 1.13-1.53℃ than OLS estimation. For more than 80% of the stations, the Ta thus retrieved from Ts has residuals lower than 2℃. Analysis of the spatio-temporal pattern of retrieved Ta data showed that the mean temperature in July （the warmest month） at altitudes of 4500 m can reach 10℃. This may help explain why the highest timberline in the Northern Hemisphere is on the Tibetan Plateau.展开更多
High accuracy surface modeling （HASM） is a method which can be applied to soil property interpolation. In this paper, we present a method of HASM combined geographic information for soil property interpolation （HAS...High accuracy surface modeling （HASM） is a method which can be applied to soil property interpolation. In this paper, we present a method of HASM combined geographic information for soil property interpolation （HASM-SP） to improve the accuracy. Based on soil types, land use types and parent rocks, HASM-SP was applied to interpolate soil available P, Li, pH, alkali-hydrolyzable N, total K and Cr in a typical red soil hilly region. To evaluate the performance of HASM-SP, we compared its performance with that of ordinary kriging （OK）, ordinary kriging combined geographic information （OK-Geo） and stratified kriging （SK）. The results showed that the methods combined with geographic information including HASM-SP and OK-Geo obtained a lower estimation bias. HASM-SP also showed less MAEs and RMSEs when it was compared with the other three methods （OK-Geo, OK and SK）. Much more details were presented in the HASM-SP maps for soil properties due to the combination of different types of geographic information which gave abrupt boundary for the spatial varia- tion of soil properties. Therefore, HASM-SP can not only reduce prediction errors but also can be accordant with the distribution of geographic information, which make the spatial simula- tion of soil property more reasonable. HASM-SP has not only enriched the theory of high accuracy surface modeling of soil property, but also provided a scientific method for the ap- plication in resource management and environment planning.展开更多
As soil cation exchange capacity （CEC） is a vital indicator of soil quality and pollutant sequestration capacity,a study was conducted to evaluate cokriging of CEC with the principal components derived from soil phy...As soil cation exchange capacity （CEC） is a vital indicator of soil quality and pollutant sequestration capacity,a study was conducted to evaluate cokriging of CEC with the principal components derived from soil physico-chemical properties.In Qingdao,China,107 soil samples were collected.Soil CEC was estimated by using 86 soil samples for prediction and 21 soil samples for test.The first two principal components （PC1 and PC2） together explained 60.2% of the total variance of soil physico-chemical properties.The PC1 was highly correlated with CEC （r=0.76,P0.01）,whereas there was no significant correlation between CEC and PC2 （r=0.03）.The PC1 was then used as an auxiliary variable for the prediction of soil CEC.Mean error （ME） and root mean square error （RMSE） of kriging for the test dataset were-1.76 and 3.67 cmolc kg-1,and ME and RMSE of cokriging for the test dataset were-1.47 and 2.95 cmolc kg-1,respectively.The cross-validation R2 for the prediction dataset was 0.24 for kriging and 0.39 for cokriging.The results show that cokriging with PC1 is more reliable than kriging for spatial interpolation.In addition,principal components have the highest potential for cokriging predictions when the principal components have good correlations with the primary variables.展开更多
文摘To conduct a comprehensive analysis of the current status of water environment quality in Yilong Lake,a systematic study was undertaken to characterize the evolution of water quality.This study utilized monthly data on water quality indicators collected from three monitoring sections of Yilong Lake between 2016 and 2023,employing the Mann-Kendall trend test and ArcGIS spatial interpolation technique.The results indicated that the five-day biochemical oxygen demand(BOD5),total nitrogen(TN),and chlorophyll a(Chla)exhibited an overall increasing trend,whereas other indicators demonstrated a decreasing trend.The permanganate index(PI),chemical oxygen demand(COD),TN,and Chla were observed in the following order:east of the lake>middle of the lake>west of the lake.In contrast,the BOD5 and total phosphorus(TP)were ranked as west of the lake>east of the lake>middle of the lake.Additionally,ammonia nitrogen(NH3-N)was found to be in the order of east of the lake>west of the lake>middle of the lake,while transparency was ranked as west of the lake>middle of the lake>east of the lake.Urban domestic sewage,effluent from industrial parks,domestic waste generated by rural residents’production and daily activities,agricultural waste,wastewater from decentralized farming,domestic sewage,and point source discharges from the soybean processing industry are the primary contributors to the exceedance of water quality standards.The enhancement of a precise pollution control system,along with the regulation of pollution sources and the interception of pollutants,can significantly diminish the pollution load entering the lake.This approach is essential for the protection and restoration of river and lake ecosystems,thereby facilitating the gradual recovery of their ecological functions.Additionally,the implementation of ecological water replenishment and the recycling of water resources can improve the capacity of the water environment.Furthermore,bolstering scientific and technological support,as well as comprehensive supervision and assurance measures,is crucial to ensuring that water quality remains stable and adheres to established standards.
基金The National Natural Science Foundation of China(No.61261007,61062005)the Key Program of Yunnan Natural Science Foundation(No.2013FA008)
文摘A novel spatial interpolation method based on integrated radial basis function artificial neural networks （IRBFANNs） is proposed to provide accurate and stable predictions of heavy metals concentrations in soil at un- sampled sites in a mountain region. The IRBFANNs hybridize the advantages of the artificial neural networks and the neural networks integration approach. Three experimental projects under different sampling densities are carried out to study the performance of the proposed IRBFANNs-based interpolation method. This novel method is compared with six peer spatial interpolation methods based on the root mean square error and visual evaluation of the distribution maps of Mn elements. The experimental results show that the proposed method performs better in accuracy and stability. Moreover, the proposed method can provide more details in the spatial distribution maps than the compared interpolation methods in the cases of sparse sampling density.
基金supported by the Swedish Foundation for International Cooperation in Research and High Education through a grant to D.L.Chen.C.-H.Ho is supported by CATER 2006-4204
文摘Climate research relies heavily on good quality instrumental data; for modeling efforts gridded data are needed. So far, relatively little effort has been made to create gridded climate data for China. This is especially true for high-resolution daily data. This work, focuses on identifying an accurate method to produce gridded daily precipitation in China based on the observed data at 753 stations for the period 1951-2005. Five interpolation methods, including ordinary nearest neighbor, local polynomial, radial basis function, inverse distance weighting, and ordinary kriging, have been used and compared. Cross-validation shows that the ordinary kriging based on seasonal semi-variograms gives the best performance, closely followed by the inverse distance weighting with a power of 2. Finally the ordinary kriging is chosen to interpolate the station data to a 18 km× 18 km grid system covering the whole country. Precipitation for each 0.5°×0.5° latitude-longitude block is then obtained by averaging the values at the grid nodes within the block. Owing to the higher station density in the eastern part of the country, the interpolation errors are much smaller than those in the west （west of 100°E）. Excluding 145 stations in the western region, the daily, monthly, and annual relative mean absolute errors of the interpolation for the remaining 608 stations are 74%, 29%, and 16%, respectively. The interpolated daily precipitation has been made available on the internet for the scientific community.
基金The National Basic Research Program of China under contract No.2015CB453303the National Natural Science Foundation of China under contract No.U1405234+1 种基金the Aoshan Science&Technology Innovation Program under contract No.2015ASKJ02-05the Special Fund of the Taishan Scholar Project
文摘Spatial interpolation is a common tool used in the study of fishery ecology, especially for the construction of ecosystem models. To develop an appropriate interpolation method of determining fishery resources density in the Yellow Sea, we tested four frequently used methods, including inverse distance weighted interpolation(IDW), global polynomial interpolation(GPI), local polynomial interpolation(LPI) and ordinary kriging(OK).A cross-validation diagnostic was used to analyze the efficacy of interpolation, and a visual examination was conducted to evaluate the spatial performance of the different methods. The results showed that the original data were not normally distributed. A log transformation was then used to make the data fit a normal distribution. During four survey periods, an exponential model was shown to be the best semivariogram model in August and October 2014, while data from January and May 2015 exhibited the pure nugget effect.Using a paired-samples t test, no significant differences(P>0.05) between predicted and observed data were found in all four of the interpolation methods during the four survey periods. Results of the cross-validation diagnostic demonstrated that OK performed the best in August 2014, while IDW performed better during the other three survey periods. The GPI and LPI methods had relatively poor interpolation results compared to IDW and OK. With respect to the spatial distribution, OK was balanced and was not as disconnected as IDW nor as overly smooth as GPI and LPI, although OK still produced a few 'bull's-eye' patterns in some areas.However, the degree of autocorrelation sometimes limits the application of OK. Thus, OK is highly recommended if data are spatially autocorrelated. With respect to feasibility and accuracy, we recommend IDW to be used as a routine interpolation method. IDW is more accurate than GPI and LPI and has a combination of desirable properties, such as easy accessibility and rapid processing.
基金a research grant attributed to the first author by the Portuguese Foundation for Science and Technology(Ref.SFRH/BD/46949/2008)
文摘Understanding the topographic context preceding the development of erosive landforms is of major relevance in geomorphic research, as topography is an important factor on both water and mass movement-related erosion, and knowledge of the original surface is a condition for quantifying the volume of eroded material. Although any reconstruction implies assuming that the resulting surface reflects the original topography, past works have been dominated by linear interpolation methods, incapable of generating curved surfaces in areas with no data or values out- side the range of variation of inputs. In spite of these limitations, impossibility of validation has led to the assumption of surface representativity never being challenged. In this paper, a validation-based method is applied in order to define the optimal interpolation technique for reconstructing pre-erosion topography in a given study area. In spite of the absence of the original surface, different techniques can be nonetheless evaluated by quantifying their ca- pacity to reproduce known topography in unincised locations within the same geomorphic contexts of existing erosive landforms. A linear method （Triangulated Irregular Network, TIN） and 23 parameterizations of three distinct Spline interpolation techniques were compared using 50 test areas in a context of research on large gully dynamics in the South of Portugal. Results show that almost all Spline methods produced smaller errors than the TIN, and that the latter produced a mean absolute error 61.4% higher than the best Spline method, clearly establishing both the better adjustment of Splines to the geomorphic context considered and the limitations of linear approaches. The proposed method can easily be applied to different interpolation techniques and topographic contexts, enabling better calculations of eroded volumes and denudation rates as well as the investigation of controls by antecedent topographic form over erosive processes.
基金supported by grants from the Research Grants Council of Hong Kong Special Administrative Region,China(Project No.City U 11213119 and T22-603/15N)The financial support is gratefully acknowledgedfinancial support from the Hong Kong Ph.D.Fellowship Scheme funded by the Research Grants Council of Hong Kong,China。
文摘Spatial interpolation has been frequently encountered in earth sciences and engineering.A reasonable appraisal of subsurface heterogeneity plays a significant role in planning,risk assessment and decision making for geotechnical practice.Geostatistics is commonly used to interpolate spatially varying properties at un-sampled locations from scatter measurements.However,successful application of classic geostatistical models requires prior characterization of spatial auto-correlation structures,which poses a great challenge for unexperienced engineers,particularly when only limited measurements are available.Data-driven machine learning methods,such as radial basis function network(RBFN),require minimal human intervention and provide effective alternatives for spatial interpolation of non-stationary and non-Gaussian data,particularly when measurements are sparse.Conventional RBFN,however,is direction independent(i.e.isotropic)and cannot quantify prediction uncertainty in spatial interpolation.In this study,an ensemble RBFN method is proposed that not only allows geotechnical anisotropy to be properly incorporated,but also quantifies uncertainty in spatial interpolation.The proposed method is illustrated using numerical examples of cone penetration test(CPT)data,which involve interpolation of a 2D CPT cross-section from limited continuous 1D CPT soundings in the vertical direction.In addition,a comparative study is performed to benchmark the proposed ensemble RBFN with two other non-parametric data-driven approaches,namely,Multiple Point Statistics(MPS)and Bayesian Compressive Sensing(BCS).The results reveal that the proposed ensemble RBFN provides a better estimation of spatial patterns and associated prediction uncertainty at un-sampled locations when a reasonable amount of data is available as input.Moreover,the prediction accuracy of all the three methods improves as the number of measurements increases,and vice versa.It is also found that BCS prediction is less sensitive to the number of measurement data and outperforms RBFN and MPS when only limited point observations are available.
基金The Shanghai Municipal Science and Technology Commission Local Capacity Construction Project under contract No.18050502000the Monitoring and Evaluation of National Sea Ranch Demonstration Area Project in Changjiang River Estuary under contract No.171015the National Natural Science Foundation of China under contract No.41906074。
文摘Spatial-temporal distribution of marine fishes is strongly influenced by environmental factors.To obtain a more continuous distribution of these variables usually measured by stationary sampling designs,spatial interpolation methods(SIMs)is usually used.However,different SIMs may obtain varied estimation values with significant differences,thus affecting the prediction of fish spatial distribution.In this study,different SIMs were used to obtain continuous environmental variables(water depth,water temperature,salinity,dissolved oxygen(DO),p H,chlorophyll a and chemical oxygen demand(COD))in the Changjiang River Estuary(CRE),including inverse distance weighted(IDW)interpolation,ordinary Kriging(OK)(semivariogram model:exponential(OKE),Gaussian(OKG)and spherical(OKS))and radial basis function(RBF)(regularized spline function(RS)and tension spline function(TS)).The accuracy and effect of SIMs were cross-validated,and two-stage generalized additive model(GAM)was used to predict the distribution of Coilia nasus from 2012 to 2014 in CRE.DO and COD were removed before model prediction due to their autocorrelation coefficient based on variance inflation factors analysis.Results showed that the estimated values of environmental variables obtained by the different SIMs differed(i.e.,mean values,range etc.).Cross-validation revealed that the most suitable SIMs of water depth and chlorophyll a was IDW,water temperature and salinity was RS,and p H was OKG.Further,different interpolation results affected the predicted spatial distribution of Coilia nasus in the CRE.The mean values of the predicted abundance were similar,but the differences between and among the maximum value were large.Studies showed that different SIMs can affect estimated values of the environmental variables in the CRE(especially salinity).These variations further suggest that the most applicable SIMs to each variable will also differ.Thus,it is necessary to take these potential impacts into consideration when studying the relationship between the spatial distribution of fishes and environmental changes in the CRE.
基金funded by the Chinese National Fund Projects (Nos. 41401028, 41201066)by the State Key Laboratory of Frozen Soils Engineering (Project No. SKLFSE201201)
文摘Quality-controlled and serially complete daily air temperature data are essential to evaluating and modelling the influences of climate change on the permafrost in cold regions. Due to malfunctions and location changes of observing stations, temporal gaps （i.e., missing data） are common in collected datasets. The objective of this study was to assess the efficacy of Kriging spatial interpolation for estimating missing data to fill the temporal gaps in daily air temperature data in northeast China. A cross-validation experiment was conducted. Daily air temperature series from 1960 to 2012 at each station were estimated by using the universal Kriging （UK） and Kriging with an external drift （KED）, as appropriate, as if all the ob-servations at a given station were completely missing. The temporal and spatial variation patterns of estimation uncertainties were also checked. Results showed that Kriging spatial interpolation was generally desirable for estimating missing data in daily air temperature, and in this study KED performed slightly better than UK. At most stations the correlation coefficients （R2） between the observed and estimated daily series were 〉0.98, and root mean square errors （RMSEs） of the estimated daily mean （Tmean）, maximum （Tmax）, and minimum （Tmin） of air temperature were 〈3 ℃. However, the estimation quality was strongly affected by seasonality and had spatial variation. In general, estimation uncertainties were small in summer and large in winter. On average, the RMSE in winter was approximately 1 ℃ higher than that in summer. In addition, estimation uncertainties in mountainous areas with complex terrain were significantly larger than those in plain areas.
基金Supported by "Project 211" Construction Item,Hainan UniversityBasic Science Research Business Expense,Rubber Research Institute ,CATAS[YWFZX09-03(N)]Special Item of the Modern Agricultural Industrial Technology System Construction(CARS-34)
文摘[ Objectivel The research aimed to study prediction model for spatial distribution of the average temperature based on GIS. [ Method l Average temperature over the years as research object, based on Ordinary Kriging （OK）, Inverse Distance Weight （ IDW）, SPLINE and Mixed In- terpolation （MLR）, monthly temperature data from 1979 to 2008 at 18 long-term meteorological observation stations in Hainan Island were conduc- ted spatial grid treatment. Via contrasts and analyses on different interpolation methods, the optimum interpolation method for average temperature over the years in Hainan Island was selected. [ Resuitl By error analyses of the four interpolation methods for average temperature in recent 30 years in Hainan Island, it was found that accuracy was MLR 〉 IDW 〉 OK 〉 SPLINE. Spatial interpolation effect of MLR was the best for average temperature in Hainan Island. Spatial distribution of the average temperature in Halnan Island had obvious south-high-north-low latitudinal zonality and vertical zonality of gradually declining as altitude rise. In addition, temperature along coast was slightly higher than that in inland. Lapse rate of the temperature in each month in Hainan Island was 0.38 -0.85℃/100 m, and lapse rate of the annual average temperature was about 0.74 ℃/ 100 m. In different areas, lapse rate of the temperature as altitude was different at different time. [ Condusion] The research provided basis for ob- taining continuous distribution situation of the agricultural meteorological factor and establishing accurate prediction model of the spatial distribution in Hainan Island.
基金Supported by Doctor’s Foundation in Natural Science of Hebei Province of China (No.B2004129).
文摘This paper proposes a low-complexity spatial-domain Error Concealment (EC) algorithm for recovering consecutive blocks error in still images or Intra-coded (I) frames of video sequences. The proposed algorithm works with the following steps. Firstly the Sobel operator is performed on the top and bottom adjacent pixels to detect the most likely edge direction of current block area. After that one-Dimensional (1D) matching is used on the available block boundaries. Displacement between edge direction candidate and most likely edge direction is taken into consideration as an important factor to improve stability of 1D boundary matching. Then the corrupted pixels are recovered by linear weighting interpolation along the estimated edge direction. Finally the interpolated values are merged to get last recovered picture. Simulation results demonstrate that the proposed algorithms obtain good subjective quality and higher Peak Signal-to-Noise Ratio (PSNR) than the methods in literatures for most images.
文摘[ Objective ] The research aimed to study the best spatial interpolation method of the meteorological factor in Northeast China. [ Method ] Based on geostatistical analysis tool of the Arclnfo GIS software, several spatial interpolation methods were used to estimate the meteorological fac- tore （annual rainfall and monthly average temperature） in Northeast China, such as inverse distance weighted （IDW）, radial basis function （RBF） and Kriging. Then, the best interpolation method of one meteorological factor was selected. [ Result] For monthly average temperature, Kriging method was better than others. For annual rainfall, precision of the evaluated value with RBF method was higher than that of the IDW and Kriging methods. [Conclusion] There was obvious regional difference of the meteorological factor in Northeast China. Monthly average temperature in south was higher than that in north, and annual rainfall in southeast was more than that in northwest in Northeast China.
基金Under the auspices of National Science and Technology Support Program of China(No.2014BAC15B03)the West Light Funds of Chinese Academy of Sciences(No.YB201302)
文摘Soil carbon to nitrogen(C/N) ratio is one of the most important variables reflecting soil quality and ecological function,and an indicator for assessing carbon and nitrogen nutrition balance of soils.Its variation reflects the carbon and nitrogen cycling of soils.In order to explore the spatial variability of soil C/N ratio and its controlling factors of the Ili River valley in Xinjiang Uygur Autonomous Region,Northwest China,the traditional statistical methods,including correlation analysis,geostatistic alanalys and multiple regression analysis were used.The statistical results showed that the soil C/N ratio varied from 7.00 to 23.11,with a mean value of 10.92,and the coefficient of variation was 31.3%.Correlation analysis showed that longitude,altitude,precipitation,soil water,organic carbon,and total nitrogen were positively correlated with the soil C/N ratio(P < 0.01),whereas negative correlations were found between the soil C/N ratio and latitude,temperature,soil bulk density and soil p H.Ordinary Cokriging interpolation showed that r and ME were 0.73 and 0.57,respectively,indicating that the prediction accuracy was high.The spatial autocorrelation of the soil C/N ratio was 6.4 km,and the nugget effect of the soil C/N ratio was 10% with a patchy distribution,in which the area with high value(12.00–20.41) accounted for 22.6% of the total area.Land uses changed the soil C/N ratio with the order of cultivated land > grass land > forest land > garden.Multiple regression analysis showed that geographical and climatic factors,and soil physical and chemical properties could independently explain 26.8%and 55.4% of the spatial features of soil C/N ratio,while human activities could independently explain 5.4% of the spatial features only.The spatial distribution of soil C/N ratio in the study has important reference value for managing soil carbon and nitrogen,and for improving ecological function to similar regions.
文摘Population is an important strategic resource for national development, a fundamental element of socio-economic development. The coordinated development of population and economy is an effective way to achieve rapid economic growth. Based on the population statistics data of counties (districts) in Henan Province, China, from 2006 to 2021. The paper firstly uses the logistic population growth mathematical model to calculate the resident population growth rate of counties (districts), then utilizes the hotspot analysis and spatial semi-variogram analysis, to research the spatial distribution characteristics of the resident population growth rate in Henan Province. The research results show that the evolution of the regional resident population in the province basically conforms to the logistic natural growth model. The resident population growth rate shows the characteristics of high in the north and low in the south, high in the center and low in the surrounding regions. The resident population growth rate is positively correlated with the level of economic development;the urban built-up areas, especially the new regions in urban planning, have a fast growth rate of resident population, which has a significant siphon effect on the population of surrounding regions. The hotspots of resident population growth rate in the province are mainly distributed in the urban built-up areas and surrounding regions of Zhengzhou, Luoyang, and Xinxiang, accounting for about 3.51% of the total area of the province. The cold spots are mainly distributed in the eastern part of the province, forming zonal distribution, which spans across Shangqiu City, Zhoukou City, and Zhumadian City, accounting for about 8.61% of the total area of the province. The area with negative growth of resident population accounts for approximately 53.47% of the total province. The spatial distribution of the growth rate of the resident population in the whole province basically conforms to the spherical model, with a small dispersion degree and a short range. In the range, there is a high degree of variability in resident population growth rate.
文摘The patial interpolation of borehole data is an important means of stratigraphic structure to construct a three-dimensional model of coal strata,and the reasonable selection of an effective spatial interpolation method will directly affect the accuracy of three-dimensional modeling of the strata.To select an effective spatial interpolation method and improve the accuracy of 3D modeling of formations,four interpolation methods(the inverse distance weight interpolation algorithm,the local polynomial interpolation algorithm,the radial basis neural network interpolation algorithm and the kriging interpolation algorithm)were compared and analyzed.In particular,the methods of interpolation algorithm,interpolation surface,sample test error,and cross-validation error were used.The experiment of 13-1 seam coal in the Huainan mining area showed the spatial surface interpolation effect of the radial basis neural network interpolation algorithm(RBF)compared with the inverse distance weight interpolation algorithm(IDW),local polynomial interpolation algorithm(LPI)and kriging algorithm.The three interpolation methods have higher accuracy and are more suitable for surface interpolation of coal seams,which is of great significance for improving the accuracy of subsequent 3D modeling of coal seams.
文摘The current distribution of forest tree species is a result of natural or human mediated historical and contemporary processes. Knowledge of the spatial distribution of the diversity and divergence of populations is crucial for managing and conserving genetic resources in forest tree species. By combining tools from population genetics, landscape ecology and spatial statistics, landscape genetics thus represents a powerful method for evaluating the geographic patterns of genetic resources at the population level. In this study, we explore the possibility of combining genetic diversity data, spatial statistic tools and GIS technologies to map the genetic divergence and diversity of 31 Castanea sativa populations collected in Spain, Italy, Greece, and Turkey. The IDW technique was used to interpolate the diversity values and divergence indices as expected hetereozygosity (He), allelic richness (Rs), private allelic richness (PRs), and membership values (Q) of each population to different clusters. Genetic diversity maps and a synthetic map of the spatial genetic structure of European chestnut populations were produced. Spatial coincidences between landscape elements and statistically significant genetic discontinuities between populations were investigated. Evidence is provided of the significance of cartographic outputs produced in the study and on their usefulness in managing genetic resources.
基金supported CNPq/Instituto do Milenio grant 420222/05-7CNPq/INCT 573713/2008-1.
文摘The use of spatial interpolation methods of data is becoming increasingly common in geophysical analysis, for that reason, currently, several software already contain many of these methods, allowing more detailed studies. In the present work four interpolation methods are evaluated, for the crustal thickness data of Brazil tectonic provinces, with the intention of making Moho’s map of the regions. The methods used were IDW, Natural Neighbor, Spline and Kriging. We compiled 257 data that constituted a geographic database implemented in the template Postgree PostGIS and were processed using the tools of interpolation located in the Spatyal Analyst Tools program ArcGIS?9 ESRI. Traditional methods, IDW, Natural Neighbor and Spline, generate artifacts in their results, the effects of aim, not consistent with the behavior of crust. Such anomalies are generated because of mathematical formulation methods added to data compiled gravimetry. The analysis results of geostatistical Kriging are more refined and consistent, showing no specific anormalities, i.e., the crustal thickness variation (thinning and thickening) is introduced gradually. Initial our estimates were separated in four specific blocks. With the approval of new networks (BRASIS, RSISNE and RSIS), the crustal thickness database for Brazil may be amended or supplemented so that new models may be generated more consistently, complementing studies of regional tectonics evolution and seismicity.
基金Supported by the United States Department of Agriculture National Research Initiative Grant(No.2002-35102-12547)
文摘The accuracy between ordinary kriging and regression kriging was compared based on the combined consideration of sample size,spatial structure,and auxiliary variables(terrain indices and electromagnetic induction surveys) for a variety of soil properties in two contrasting landscapes(agricultural vs.forested).When spatial structure could not be well captured by point-based observations(e.g.,when the ratio of sample spacing over correlation range was > 0.5),or when a strong relationship existed between target soil properties and auxiliary variables(e.g.,their R2 was > 0.6),regression kriging(RK) was more accurate for interpolating soil properties in both landscapes studied.Otherwise,ordinary kriging(OK) was better.Soil depth and wetness condition did not appear to affect the selection of kriging for soil moisture interpolation,because they did not significantly change the ratio of sample spacing over correlation range and the relationship with the auxiliary variables.Because of a smaller ratio of elevation change over total study area(E/A = 1.2) and multiple parent materials in the agricultural land,OK was generally more accurate in that landscape.In contrast,a larger E/A ratio of 6.8 and a single parent material led to RK being preferable in the steep-sloped forested catchment.The results from this study can be useful for selecting kriging for various soil properties and landscapes.
基金National Natural Science Foundation of China,No.41030528No.41001278
文摘The immense and towering Tibetan Plateau acts as a heating source and, thus, deeply shapes the climate of the Eurasian continent and even the whole world. However, due to the scarcity of meteorological observation stations and very limited climatic data, little is quantitatively known about the heating effect and temperature pattern of the Tibetan Plateau. This paper collected time series of MODIS land surface temperature （LST） data, together with meteorological data of 137 stations and ASTER GDEM data for 2001-2007, to estimate and map the spatial distribution of monthly mean air temperatures in the Tibetan Plateau and its neighboring areas. Time series analysis and both ordinary linear regression （OLS） and geographical weighted regression （GWR） of monthly mean air temperature （Ta） with monthly mean land surface temperature （Ts） were conducted. Regression analysis shows that recorded Ta is rather closely related to Ts, and that the GWR estimation with MODIS Ts and altitude as independent variables, has a much better result with adjusted R 2 〉 0.91 and RMSE = 1.13-1.53℃ than OLS estimation. For more than 80% of the stations, the Ta thus retrieved from Ts has residuals lower than 2℃. Analysis of the spatio-temporal pattern of retrieved Ta data showed that the mean temperature in July （the warmest month） at altitudes of 4500 m can reach 10℃. This may help explain why the highest timberline in the Northern Hemisphere is on the Tibetan Plateau.
基金Foundation： National Natural Science Foundation of China, No.41001057 China National Science Fund for Distinguished Young Scholars, No.40825003 Project Supported by State Key Laboratory of Earth Surface Processes and Resource Ecology, No.2011-KF-06
文摘High accuracy surface modeling （HASM） is a method which can be applied to soil property interpolation. In this paper, we present a method of HASM combined geographic information for soil property interpolation （HASM-SP） to improve the accuracy. Based on soil types, land use types and parent rocks, HASM-SP was applied to interpolate soil available P, Li, pH, alkali-hydrolyzable N, total K and Cr in a typical red soil hilly region. To evaluate the performance of HASM-SP, we compared its performance with that of ordinary kriging （OK）, ordinary kriging combined geographic information （OK-Geo） and stratified kriging （SK）. The results showed that the methods combined with geographic information including HASM-SP and OK-Geo obtained a lower estimation bias. HASM-SP also showed less MAEs and RMSEs when it was compared with the other three methods （OK-Geo, OK and SK）. Much more details were presented in the HASM-SP maps for soil properties due to the combination of different types of geographic information which gave abrupt boundary for the spatial varia- tion of soil properties. Therefore, HASM-SP can not only reduce prediction errors but also can be accordant with the distribution of geographic information, which make the spatial simula- tion of soil property more reasonable. HASM-SP has not only enriched the theory of high accuracy surface modeling of soil property, but also provided a scientific method for the ap- plication in resource management and environment planning.
基金funded by the National Natural Science Foundation of China (40771095,40725010 and 41030746)the Water Conservancy Science and Technology Foundation of Qingdao City,China (2006003)
文摘As soil cation exchange capacity （CEC） is a vital indicator of soil quality and pollutant sequestration capacity,a study was conducted to evaluate cokriging of CEC with the principal components derived from soil physico-chemical properties.In Qingdao,China,107 soil samples were collected.Soil CEC was estimated by using 86 soil samples for prediction and 21 soil samples for test.The first two principal components （PC1 and PC2） together explained 60.2% of the total variance of soil physico-chemical properties.The PC1 was highly correlated with CEC （r=0.76,P0.01）,whereas there was no significant correlation between CEC and PC2 （r=0.03）.The PC1 was then used as an auxiliary variable for the prediction of soil CEC.Mean error （ME） and root mean square error （RMSE） of kriging for the test dataset were-1.76 and 3.67 cmolc kg-1,and ME and RMSE of cokriging for the test dataset were-1.47 and 2.95 cmolc kg-1,respectively.The cross-validation R2 for the prediction dataset was 0.24 for kriging and 0.39 for cokriging.The results show that cokriging with PC1 is more reliable than kriging for spatial interpolation.In addition,principal components have the highest potential for cokriging predictions when the principal components have good correlations with the primary variables.
