Influence of Transportation Accessibility on Urban-rural Income Disparity and Its Spatial Heterogeneity

Transportation accessibility has been treated as an important means of reducing the urban-rural income disparity.However,only a few studies have examined the effects of different types of transportation accessibility on urban-rural income disparity and their spatial heterogeneity.Based on data from 285 prefecture-level(and above)Chinese cities in 2000,2005,2010,2015,and 2020,this study uses spatial econometric models to examine how highway accessibility and railway accessibility influence the urban-rural income disparity and to identify their spatial heterogeneity.The result reveals that highway accessibility and railway accessibility have‘coreperiphery’ring-like circle structures.The urban-rural income disparity exhibits strong spatial clustering effects.Both highway accessibility and railway accessibility are negatively associated with urban-rural income disparity,and the former having a greater effect size.Moreover,there is a substitution effect between highway accessibility and railway accessibility in the whole sample.Furthermore,these associations differ in geographic regions.In the central region,highway accessibility is more important in reducing the urban-rural income disparity,but its effect is weakened with the increase of railway accessibility.In the western region,railway accessibility has a larger effect on narrowing the urban-rural income disparity,and this effect is strengthened by the increase of highway accessibility.We conclude that improving transportation accessibility is conducive to reducing the urban-rural income disparity but its effect is spatial heterogenetic.Highways and railways should be developed in a coordinated manner to promote an integrated transport network system.

Spatial Heterogeneity and Risk Factors of Dental Caries in 12-Year-Old Children in Shanxi Province,China

Objective This study aimed to explore the spatial heterogeneity and risk factors for dental caries in 12-year-old children in Shanxi province,China.Methods The data encompassed 3,721 participants from the two most recent oral health surveys conducted across 16 districts in Shanxi Province in 2015 and 2018.Eighteen specific variables were analyzed to examine the interplay between socioeconomic factors,medical resources and environmental conditions.The Geo-detector model was employed to assess the impacts and interactions of these ecological factors.Results Socioeconomic factors(Q=0.30,P<0.05)exhibited a more substantial impact compared to environmental(Q=0.19,P<0.05)and medical resource factors(Q=0.25,P<0.05).Notably,the urban population percentage(UPP)demonstrated the most significant explanatory power for the spatial heterogeneity in caries prevalence,as denoted by its highest q-value(q=0.51,P<0.05).Additionally,the spatial distribution’s heterogeneity of caries was significantly affected by SO2 concentration(q=0.39,P<0.05)and water fluoride levels(q=0.27,P<0.05)among environmental factors.Conclusion The prevalence of caries exhibited spatial heterogeneity,escalating from North to South in Shanxi Province,China,influenced by socioeconomic factors,medical resources,and environmental conditions to varying extents.

Quantifying the impact of earthquakes and geological factors on spatial heterogeneity of debris-flow prone areas:A case study in the Hengduan Mountains

Understanding the spatial heterogeneity of debris-flow-prone areas holds significant implications for regional risk management, particularly in seismically active regions with geological faults. Despite the significance of this knowledge, a comprehensive quantification of the influence of regional topographical and geological factors on the spatial heterogeneity of debris-flow-prone areas has been lacking. This study selected the Hengduan Mountains, an earthquake-prone region characterized by diverse surface conditions and complex landforms, as a representative study area. An improved units zoning and objective factors identification methodology was employed in earthquake and fault analysis to assess the impact of seismic activity and geological factors on spatial heterogeneity of debrisflow prone areas. Results showed that the application of GIS technology with hydrodynamic intensity and geographical units analysis can effectively analyze debris-flow prone areas. Meanwhile, earthquake and fault zones obviously increase the density of debrisflow prone catchments and make them unevenly distributed. The number of debris-flow prone areas shows a nonlinear variation with the gradual increase of geomorphic factor value. Specifically, the area with 1000 m-2500 m elevation difference, 25°-30° average slope, and 0.13-0.15 land use index is the most favorable conditions for debris-flow occurrence;The average annual rainfall from 600 to 1150 mm and landslides gradient from 16° to 35° are the main causal factors to trigger debris flow. Our study sheds light on the quantification of spatial heterogeneity in debris flow-prone areas in earthquake-prone regions, which can offer crucial support for post-debris flow risk management strategies.

Prediction of Porous Media Fluid Flow with Spatial Heterogeneity Using Criss-Cross Physics-Informed Convolutional Neural Networks

Recent advances in deep neural networks have shed new light on physics,engineering,and scientific computing.Reconciling the data-centered viewpoint with physical simulation is one of the research hotspots.The physicsinformedneural network(PINN)is currently the most general framework,which is more popular due to theconvenience of constructing NNs and excellent generalization ability.The automatic differentiation(AD)-basedPINN model is suitable for the homogeneous scientific problem;however,it is unclear how AD can enforce fluxcontinuity across boundaries between cells of different properties where spatial heterogeneity is represented bygrid cells with different physical properties.In this work,we propose a criss-cross physics-informed convolutionalneural network(CC-PINN)learning architecture,aiming to learn the solution of parametric PDEs with spatialheterogeneity of physical properties.To achieve the seamless enforcement of flux continuity and integration ofphysicalmeaning into CNN,a predefined 2D convolutional layer is proposed to accurately express transmissibilitybetween adjacent cells.The efficacy of the proposedmethodwas evaluated through predictions of several petroleumreservoir problems with spatial heterogeneity and compared against state-of-the-art(PINN)through numericalanalysis as a benchmark,which demonstrated the superiority of the proposed method over the PINN.

Spatial Heterogeneity Modeling Using Machine Learning Based on a Hybrid of Random Forest and Convolutional Neural Network (CNN)

Spatial heterogeneity refers to the variation or differences in characteristics or features across different locations or areas in space. Spatial data refers to information that explicitly or indirectly belongs to a particular geographic region or location, also known as geo-spatial data or geographic information. Focusing on spatial heterogeneity, we present a hybrid machine learning model combining two competitive algorithms: the Random Forest Regressor and CNN. The model is fine-tuned using cross validation for hyper-parameter adjustment and performance evaluation, ensuring robustness and generalization. Our approach integrates Global Moran’s I for examining global autocorrelation, and local Moran’s I for assessing local spatial autocorrelation in the residuals. To validate our approach, we implemented the hybrid model on a real-world dataset and compared its performance with that of the traditional machine learning models. Results indicate superior performance with an R-squared of 0.90, outperforming RF 0.84 and CNN 0.74. This study contributed to a detailed understanding of spatial variations in data considering the geographical information (Longitude & Latitude) present in the dataset. Our results, also assessed using the Root Mean Squared Error (RMSE), indicated that the hybrid yielded lower errors, showing a deviation of 53.65% from the RF model and 63.24% from the CNN model. Additionally, the global Moran’s I index was observed to be 0.10. This study underscores that the hybrid was able to predict correctly the house prices both in clusters and in dispersed areas.

Formation mechanism of inherent spatial heterogeneity of microstructure and mechanical properties of NiTi SMA prepared by laser directed energy deposition

Ni51Ti49 at.%bulk was additively manufactured by laser-directed energy deposition(DED)to reveal the microstructure evolution,phase distribution,and mechanical properties.It is found that the localized remelting,reheating,and heat accumulation during DED leads to the spatial heterogeneous distribution of columnar crystal and equiaxed crystal,a gradient distribution of Ni4Ti3 precipitates along the building direction,and preferential formation of Ni4Ti3 precipitates in the columnar zone.The austenite transformation finish temperature(Af)varies from-12.65℃(Z=33 mm)to 60.35℃(Z=10 mm),corresponding to tensile yield strength(σ0.2)changed from 120±30 MPa to 570±20 MPa,and functional properties changed from shape memory effect to superelasticity at room temperature.The sample in the Z=20.4 mm height has the best plasticity of 9.6%and the best recoverable strain of 4.2%.This work provided insights and guidelines for the spatial characterization of DEDed NiTi.

Detection of Multi-dimensional Driving Forces of Public Environmental Concern in China:Based on Spatial Heterogeneity Perspectives

Public environmental concern(PEC)is an important bottom-up force in building an environmentally sustainable society.Guided by attitude theory,this paper innovatively constructed a PEC evaluation index system,while introducing entropy weighted-TOPSIS(Technique for Order Preference by Similarity to Ideal Solution)to realize the assessment of PEC.Exploratory spatial data analysis was used to portray the spatio-temporal evolution patterns of PEC in 362 Chinese cities at prefecture-level and above from 2011 to 2018.Furthermore,the Geodetector model was performed to identify the multi-dimensional determinants of PEC from the perspective of spatial heterogeneity.The results indicated that:1)PEC in China exhibited a fluctuating upward trend,consistent with the spatial distribution law of‘Heihe-Tengchong Line’and‘Bole-Taipei Line’;2)the driving effect of each factor varied dynamically,but in general,economic development level,population size,industrial wastewater,and education level were the dominant driving factors explaining the spatial variation of PEC;3)risk detection revealed that four factors,government environmental regulations,PM_(2.5),vegetation coverage,and natural resource endowment,had nonlinear effects on PEC;4)the interactions between factors all demonstrated an enhancement in explaining the spatial differentiation of PEC.PEC was driven by the comprehensive interaction of four-dimensional factors of economy,society,pollutant emissions,and ecology.Among them,population agglomeration accompanied by a high level of regional economy and information technology can explain the increase in PEC to the greatest extent.

Eff ect of prescribed burning on the small-scale spatial heterogeneity of soil microbial biomass in Pinus koraiensis and Quercus mongolica forests of China

Prescribed burning can alter soil microbial activity and spatially redistribute soil nutrient elements.However,no systematic,in-depth studies have investigated the impact of prescribed burning on the spatial patterns of soil microbial biomass in temperate forest ecosystems in Northeast China.The present study investigated the impacts of prescribed burning on the small-scale spatial heterogeneity of microbial biomass carbon(MBC)and microbial biomass nitrogen(MBN)in the upper(0–10 cm)and lower(10–20 cm)soil layers in Pinus koraiensis and Quercus mongolica forests and explored the factors that infl uence spatial variations of these variables after prescribed burning.Our results showed that,MBC declined by approximately 30%in the 10–20 cm soil layer in the Q.mongolica forest,where there were no signifi cant eff ects on the soil MBC and MBN contents of the P.koraiensis forest(p>0.05)after prescribed burning.Compared to the MBC of the Q.mongolica forest before the prescribed burn,MBC spatial dependence in the upper and lower soil layers was approximately 7%and 2%higher,respectively.After the prescribed burn,MBN spatial dependence in the upper and lower soil layers in the P.koraiensis forest was approximately 1%and 13%lower,respectively,than that before the burn,and the MBC spatial variability in the 0–10 cm soil layer in the two forest types was explained by the soil moisture content(SMC),whereas the MBN spatial variability in the 0–10 cm soil layer in the two forests was explained by the soil pH and nitrate nitrogen(NO_(3)^(–)-N),respectively.In the lower soil layer(10–20 cm)of the Q.mongolica forest,elevation and ammonium nitrogen(NH 4+-N)were the main factors aff ecting the spatial variability of MBC and MBN,respectively.In the 10–20 cm soil layer of the P.koraiensis forest,NO_(3)^(–)-N and slope were the main factors aff ecting the spatial variability of MBC and MBN,respectively,after the burn.The spatial distributions of MBC and MBN in the two forests were largely structured with higher spatial autocorrelation(relative structural variance C/[C 0+C]>0.75).However,the factors infl uencing the spatial variability of MBC and MBN in the two forest types were not consistent between the upper and lower soil layers with prescribed burning.These fi ndings have important implications for developing sustainable management and conservation policies for forest ecosystems.

Identifying Spatial Heterogeneity in the Effects of High-Tech Firm Density on Housing Prices:Evidence from Guangdong-Hong Kong-Macao Greater Bay Area,China

Innovation capitalization is a new concept in innovation geography research.Extant research on a city scale has proven that innovation is an important factor affecting housing prices and verified that innovation has a capitalization effect.However,few studies investigate the spatial heterogeneity of innovation capitalization.Thus,case verification at the urban agglomeration scale is needed.Therefore,this study proposes a theoretical framework for the spatial heterogeneity of innovation capitalization at the urban agglomeration scale.Examining the Guangdong-Hong Kong-Macao Greater Bay Area(GHMGBA),China as a case study,the study investigated the spatial heterogeneity of the influence of high-tech firms,representing innovation,on housing prices.This work verified the spatial heterogeneity of innovation capitalization.The study constructed a data set influencing housing prices,comprising 11 factors in 5 categories(high-tech firms,convenience of living facilities,built environment,the natural environment,and the fundamentals of the districts)for 419 subdistricts in the GHMGBA.On the global scale,the study finds that high-tech firms have a significant and positive influence on housing prices,with the housing price increasing by 0.0156%when high-tech firm density increases by 1%.Furthermore,a semi-geographically weighted regression(SGWR)analysis shows that the influence of high-tech firms on housing prices has spatial heterogeneity.The areas where high-tech firms have a significant and positive influence on housing prices are mainly in the GuangzhouFoshan metropolitan area,western Shenzhen-Dongguan,north-central Zhongshan-Nansha district,and Guangzhou—all areas with densely distributed high-tech firms.These results confirm the spatial heterogeneity of innovation capitalization and the need for further discussion of its scale and spatial limitations.The study offers implications for relevant GHMGBA administrative authorities for spatially differentiated development strategies and housing policies that consider the role of innovation in successful urban development.

The particular species determining spatial heterogeneity in shady and terrace on the Qinghai-Tibetan Plateau,China

Spatial patterns of plant species and patchy community are important properties in grasslands.However,research regarding spatial patterns of formed patches with various species has not fully advanced until now.Our purpose is to clarify differences in spatial pattern formed by species and community constructed under shady and terrace habitats.The three common Kobresia-Carex patches(Size 1,0.6–0.9 m^(2);Size 2,3.0–3.8 m^(2) and Size 3,6.5–8.8 m^(2))were selected in shady and terrace on the Qinghai-Tibetan Plateau,and corresponding quadrats of 1m1m,2m2m and 3m3m were placed for S1,S2 and S3 patches,respectively.The surveyed quadrats were divided into 20cm20cm large cells(L-cells),and further divided into four 10cm10cm small cells(S-cells).We used the binary occurrence system(presence/absence data)to record occurrences of all species in S-cells.The analysis shows that the power law model was well able to determine the spatial distribution pattern of species or patchy community in shady and terrace.All species and patches show aggregated distribution in shady and terrace habitats.In the shady habitat,the relative spatial heterogeneity(ε)of individual plant species was lowest at presence frequency(P)of 0.1–0.3,whereas in the terrace habitatεwas lowest at P<0.1,andεincreased monotonically with increasing P.For most dominant species,P andεvalues were higher in terrace than those in shady.We concluded that the dominant species largely determine spatial heterogeneity of the Kobresia-Carex patches,while companion and rare species have weak influence on the community-level heterogeneity in shady and terrace habitats.

Spatial heterogeneity of urban land-cover landscape in Guangzhou from 1990 to 2005

Urbanization has boon the most important process that changed land cover landscape in Guangzhou since reformation, especially since 1990. It is essential for monitoring and assessing ecological consequences of urbanization to understand landscape quantitative characteristics and its changes. Based on four land-cover type maps interpreted from remote sensing TM images of 1990, 1995, 2000 and 2005, combining gradient analysis with landscape metrics, the quantified spatial pattern and its dynamics of urbanization in Guangzhou was got. Three landscape metrics were computed within different regional areas including the whole study area, two transects along two highways （one N-S and the other W-E） and radiation zones with equal distance outwards the city center were set. Buffer zones for transects N-S and W-E were outlined along highways. The following questions should be answered in this paper： What responses were implied with changing spatial grain size or extent for landscape pattern analysis？ Could gradient progress of urbanization be characterized by landscape pattern analysis？ Did landscape metrics reveal urban expanding gradually？ Were there directional differences in land cover landscape pattern during urbanizing development？ The results gave some affirmative answers. Landscape pattern exhibited obviously scale-dependent to grain size and extent. The landscape metrics with gradient analysis could quantitatively approach spatial pattern of urbanization. A precise location for urbanized area, like city center and sub-center, could be identified by multiple landscape metrics. Multiple adjunctive centers occurred as indicated by analysis of radiation zones around the city center. Directional differences of landscape pattern along the two transects （N-S and W-E） came into being. For example, fragmentation of landscape in the transect W-E was obviously higher than that in the transect N-S. All in all, some interesting and important ecological implications were revealed under landscape patterns of two transects or radiation zones, and that was the important step to link pattern with processes in urban ecological studies and the basis to improve urban environment.

A Method for Spatial Heterogeneity Evaluation on Landscape Pattern of Farmland Shelterbelt Networks:A Case Study in Midwest of Jilin Province,China

On the basis of landscape ecology, combining the Spot 5 high resolution satellite imagery with GIS, a method evaluating the spatial heterogeneity of shelterbelts distribution at landscape scale is put forward in this paper. The distance coefficients of reasonable and existing landscape indexes of farmland shelterbelt networks were com-puted, and then through the classification of the distance coefficients, and the establishment of evaluation rules, the spatial heterogeneity of farmland shelterbelts was evaluated. The method can improve the evaluating system of previ-ous studies on shelterbelts distribution, resolve the disadvantages of lacking spatiality of overall evaluation, and make the evaluation results have more directive significance for shelterbelt management. Based on this method, spatial het-erogeneity of shelterbelt networks was evaluated in the midwest of Jilin Province, China. The results show that the re-gions with fewer shelterbelts and no closed network account for 34.7% of the total area, but only 4.9% of the area has relative reasonable pattern of shelterbelt networks. Many problems exist in the distribution pattern of shelterbelts, therefore, much attention should be paid to construct farmland shelterbelts in the study area.

Impact factors identification of spatial heterogeneity of herbaceous plant diversity on five southern islands of Miaodao Archipelago in North China

Herbaceous plants are widely distributed on islands and where they exhibit spatial heterogeneity.Accurately identifying the impact factors that drive spatial heterogeneity can reveal typical island biodiversity patterns.Five southern islands in the Miaodao Archipelago,North China were studied herein.The spatial distribution of herbaceous plant diversity on these islands was analyzed,and the impact factors and their degree of impact on spatial heterogeneity were identified using CCA ordination and ANOVA.The results reveal 114 herbaceous plant species,belonging to 94 genera from 34 families in the50 plots sampled.The total species numbers on different islands were significantly positively correlated with island area,and the average a diversity was correlated with human activities,while the(3 diversity among islands was more affected by island area than mutual distances.Spatial heterogeneity within islands indicated that the diversities were generally high in areas with higher altitude,slope,total nitrogen,total carbon,and canopy density,and lower moisture content,pH,total phosphorus,total potassium,and aspect.Among the environmental factors,pH,canopy density,total K,total P,moisture content,altitude,and slope had significant gross effects,but only canopy density exhibited a significant net effect.Terrain affected diversity by restricting plantation,plantation in turn influenced soil properties and the two together affected diversity.Therefore,plantation was ultimately the fundamental driving factor for spatial heterogeneity in herbaceous plant diversity on the five islands.

Vulnerability of Water Resources and Its Spatial Heterogeneity in Haihe River Basin, China

To manage water resources effectively, a multiscale assessment of the vulnerability of water resources on the basis of political boundaries and watersheds is necessary. This study addressed issues on the vulnerability of water resources and provided a multiscale comparison of spatial heterogeneity under a climate change background. Using improved quantitative evaluation methods of vulnerabil- ity, the Theil index and the Shannon-Weaver index, we evaluated the vulnerability of water resources and its spatial heterogeneity in the Haihe River Basin in four scales, namely, second-class water resource regions （Class II WRRs）, third-class water resource regions （Class III WRRs）, Province-Class II WRRs, and Province-Class III WRRs. Results show that vulnerability enhances from the north to south in the different scales, and shows obvious spatial heterogeneity instead of moving toward convergence in multiscale assessment results. Among the Class II WRRs, the Tuhai-Majia River is the most vulnerable area, and the vulnerability of the Luanhe River is lower than that of the north of the Haihe River Basin, which in turn is lower than that of the south of the Haihe River Basin. In the scales of Class III WRRs and Province-Class III WRRs, the vulnerability shows obvious spatial heterogeneity and diversity measured by the Theil index and the Shannon-Weaver index. Multiscale vulnerability assessment results based on political boundaries and the watersheds of the Haihe River Basin innovatively provided in this paper are important and useful to characterize the real spatial pattern of the vulnerability of water resources and improve water resource management.

Spatial heterogeneity of soil chemical properties between Haloxylon persicum and Haloxylon ammodendron populations

Spatial heterogeneity is a ubiquitous feature in natural ecosystems, especially in arid regions. Different species and their discontinuous distribution, accompanied by varied topographic characteristics, result in soil resources distributed differently in different locations, and present significant spatial heterogeneity in desert ecosystems. In this study, conventional and geostatistical methods were used to identify the heterogeneity of soil chemical properties in two desert populations, Haloxylon persicum Bunge ex Boss., which dominates on the slopes and tops of sand dunes and Haloxylon ammodendron (C. A. Mey.) Bunge, which inhabits interdunes in the Gurbantunggut Desert of Xinjiang, China. The results showed that soil pH, electrical conductivity (EC), soil organic carbon (SOC), available nitrogen (AN) and available phosphorus (AP) were significantly higher in H. ammodendron populations than that in H. persicum. The coefficient of variation (CV) indicated that (1) most parameters presented a moderate degree of variability (10% 【 CV 【 100%) except pH in both plots, (2) the variability of soil pH, EC and AP in H. ammodendron populations was higher than that in H. persicum populations, and (3) SOC and AN in H. ammodendron populations were lower than that in H. persicum populations. Geostatistical analysis revealed a strong spatial dependence (C0/(C0+C) 【 25%) within the distance of ranges for all tested parameters in both plots. The Kriging-interpolated figures showed that the soil spatial distribution was correlated with the vegetation distribution, individual size of plants, and the topographic features, especially with the plants nearest to sampling points and the topographic features. In each plot, soil EC, SOC, AN and AP presented similar distributions, and fertile islands and salt islands occurred in both plots but did not affect every individual plant, since the sampling distance was larger than the size of such fertile islands. The results of topographic effects on soil heterogeneity suggested significant differences between the interdunes and dune-tops. Different topographic characteristics (physical factors) between plots result in the differences in SOC, AN and AP, while the heterogeneity of soil pH and EC arise from plant species and their distribution (biotic factor). Such biotic and physical factors did not occur in isolation, but worked together on soil heterogeneity, and played important parts in improving the soil properties. Hence these factors were ecologically valuable in the highly resource-stressed arid study area.

Impact of the Built Environment on the Spatial Heterogeneity of Regional Innovation Productivity:Evidence from the Pearl River Delta,China

With the global economy increasingly dependent on innovation,urban discourse has shifted to consider what kinds of spatial designs may best nurture innovation.We examined the relationship between the built environment and the spatial heterogeneity of regional innovation productivity(RIP)using the example of China's Pearl River Delta(PRD).Based on a spatial database of 522546 patent data from 2017,this study proposed an innovation-based built environment framework with the following five aspects:healthy en-vironment,daily interaction,mixed land use,commuting convenience,and technology atmosphere.Combining negative binomial regression and Geodetector to examine the impact of the built environment on RIP,the results show that the spatial distribution of innovation productivity in the PRD region is extremely uneven.The negative binomial regression results show that the built environment has a significant impact on the spatial differentiation of RIP,and,specifically,that healthy environment,mixed land use,commuting convenience,and technology atmosphere all demonstrate significant positive impacts.Meanwhile,the Geodetector results show that the built environment factor impacts the spatial heterogeneity of RIP to varying degrees,with technology atmosphere demonstrating the greatest impact intensity.We conclude that as regional development discourse shifts focus to the knowledge and innovation economy,the innovation-oriented design and updating of built environments will become extremely important to policymakers.

Spatial heterogeneity of soil nutrients in old growth forests of Korean pine

In this paper, we used geostatistics studied the spatial heterogeneity of total nitrogen and phosphorus on the top soil (0–10 cm) in old growth forests of Korean pine. There was a high degree of spatial heterogeneity of both nutrients which were dependent scales. The isotropic spatial dependent scale were 6.19 m (N%) and 11.10 m (P%). Both nutrients have anisotropic structures at sampled area. Spatial heterogeneity of autocorrelated was over 80%, and spatial autocorrelation was important in nutrient variations in space. This caused spatial patterns of total nitrogen and phosphorus in forest top soil.

Effect of spatial heterogeneity on natural regeneration of Manchurian ash

The spatial patterns of seedlings originating from natural regeneration are often heterogeneous since they are strongly influenced by microsite gradient. We supposed that the patterns of Manchurian ash (Fraxinus mandshurica Rupr.) seedlings, which were originated from natural seed rain, were also spatial heterogeneous in spite of relative homogeneous of planted forest. The tree seedling establishment and growth were monitored in the Forest-experimental-station of Northeast Forestry University during growing season from early May to late September in 1999. The emergence of seedlings began in middle May; but the peak was about in late May. Seedlings were counted in 635 grid cells in late June, there were about 16–30 individuals/m2, but almost all of them died off in late September. The scale and extent of seedling heterogeneity were assessed by semivariogram and fractal dimension. The study showed that over 70% of seedling pattern was spatially autocorrelated, and that the variation caused by random factors was in less than 30%. The spatial dependent scales, both isotropy and anisotropy, were 1.95–2.92 m and 1.83–6.40 m respectively in the research stands. Our hypothesis was supported although there was difference when samples were chose at both different spatial scale and different density stands.

Spatial heterogeneity of fine root biomass of Pinus massoniana forests in the Three Gorges Reservoir Area, China

Environmental heterogeneity is a constant presence in the natural world that significantly affects plant behavior at a variety of levels of complexity. In order to estimate the spatial pattern of fine root biomass in the Three Gorges Reservoir Area, the spatial heterogeneity of fine root biomass in the upper layer of soils （0-10 cm） in three Mas- son pine （Pinus massoniana） stands in the Three Gorges Reservoir Area, China, was studied in 30 m x 30 m plots with geostatistical analysis. The results indicate that 1） both the live and dead fine root biomass of stand 2 were less than those of other stands, 2） the spatial variation of fine roots in the three stands was caused together by structural and ran- dom factors with moderate spatial dependence and 3） the magnitude of spatial heterogeneity of live fine roots ranked as： stand 3 〉 stand 1 〉 stand 2, while that of dead fine roots was similar in the three stands. These findings suggested that the range of spatial autocorrelation for fine root biomass varied considerably in the Three Gorges Reservoir Area, while soil properties, such as soil bulk density, organic matter and total nitrogen, may exhibit great effect on the spatial distribution of fine roots. Finally, we express our hope to be able to carry out further research on the quantitative relation- ship between the spatial heterogeneous patterns of plant and soil properties.

Spatial heterogeneity of dead fuel moisture content in a Larix gmelinii forest in Inner Mongolia using geostatistics

Spatial heterogeneity of fuel moisture content determines the spread rate and direction of a forest fire.Research on the spatial heterogeneity of the moisture content of dead fuel of Larix gmelinii Rupr.showed that:(1)fuel moisture content in litter layer<semi-humus layer<humus layer,and the coefficient of variation decreased with sampling depth;(2)the sill value of the semi-humus layer was highest,the humus layer moderate,the litter layer the smallest,overall,the spatial heterogeneity of the semi-humus layer was the highest.The humus layer in the slant direction and three layers in a vertical direction showed strong spatial correlation with the lowest nugget coefficient of 0.0968;(3)the fuel moisture content of the humus layer showed strong spatial anisotropy;and,(4)estimating the total moisture content of the sampling site by stimulated sampling reasonable control of the sampling interval,and increasing the sampling intensity can reduce the error.When the sampling intensity is increased to more than 16 and the sampling interval 3 m,the standard error is<15%.The spatial heterogeneity of fuel moisture content is best revealed by increasing sampling density,sampling in different fire seasons,and in different slope directions and positions.The results can provide a scientific basis for forest fire prediction and prevention.