基于GWR模型的于田绿洲土壤表层盐分空间分异及其影响因子

以于田绿洲为研究靶区,利用24个采样点的土壤表层盐分数据,选取9个与土壤表层盐分密切相关的影响因子,结合空间自相关、传统回归分析和地理加权回归模型,分析表土盐分的空间分布特征及其影响因子的空间分异.结果表明:于田绿洲表土盐分在空间上并非随机分布,而是存在较强的空间依赖关系,空间自相关指数为0.479.地下水矿化度、地下水埋深、高程和温度是影响干旱区平原绿洲表土积盐的主要因子,这些因子具有空间异质性,选取的9个环境变量中除土壤pH值外,其他变量对表土盐分的影响强度均存在显著的空间分异.GWR模型对存在空间非平稳性数据的解释能力和估计精度都优于OLS模型,而且在模型估计参数的可视化上具有明显优势.     

基于GWR模型的东江水质空间分异与水生态功能分区验证

流域水生态功能分区研究是我国正在开展的一项重要工作,如何验证分区结果的合理性,是当前亟待解决的问题。采用地理加权回归(GWR)模型评估流域特征对东江水质的影响,验证水质及流域影响空间差异是否与一二级水生态功能分区结果吻合,并对比了GWR模型与普通最小二乘(OLS)模型性能,讨论了GWR在分区验证方面的应用价值及不足。结果显示:1)水质指标以及GWR模型局部解释率(Local R~2)均在一二级水生态功能分区间存在显著差异;2)相比OLS模型,GWR模型校正R~2更高,残差空间自相关指数Moran′s I更低。研究表明东江水生态功能分区结果能合理反映水陆耦合关系,有效解释水质空间差异。此外建议选择总氮(TN)而非溶解氧(DO)和总磷(TP)作为分区验证指标。GWR模型在分区结果验证中具有广泛应用前景。降低数据空间自相关影响及改善距离测度方法是未来GWR模型研究的难点问题。     

福州市土壤铬含量高光谱预测的GWR模型研究

通过系统分析不同光谱分辨率和光谱变换对土壤铬高光谱预测模型的不确定性影响,筛选出最优的光谱分辨率及光谱变量进行土壤铬含量预测的地理权重回归(GWR)模型构建,利用该模型进行福州市土壤铬含量预测,并将预测结果与普通最小二乘法回归(OLS)结果进行比较分析,探讨GWR模型在土壤铬高光谱预测中的适用性及局限性。结果表明:(1)在10 nm分辨率尺度下,以土壤全铬含量为因变量,反射率的二阶微分和反射率倒数的二阶微分为自变量构建的GWR模型对土壤铬预测的效果最好。GWR模型的R~2和调节R~2分别为0.821和0.716,较OLS模型分别提高了0.529和0.450,而AIC值为720.703,较OLS模型减少了22个单位,残差平方和仅为OLS模型的1/4,说明GWR模型的预测效果较OLS模型有了显著提高。(2)土壤铬预测模型的精度受光谱分辨率影响。对于OLS预测模型来说,3 nm分辨率的模型预测效果最好,而对于GWR预测模型来说,10nm分辨率的模型不仅预测效果最好,其相较于OLS模型的改善作用显著,为土壤铬含量GWR预测的最佳光谱分辨率。(3)光谱的一阶微分变换可以有效增强土壤铬的光谱特征,而其余的光谱变换对土壤铬的光谱特征则未起到增强作用,但可以很好地提高模型的预测效果。(4)研究得出土壤铬GWR模型预测的最佳光谱分辨率为10 nm,为EO-1 Hyperion影像的光谱分辨率,而且随着采样点的增加,GWR模型的预测效果趋于稳定,适合空间异质性大的区域尺度土壤铬预测。故该模型与高光谱影像结合,实现模型从实验室尺度向区域尺度的推广,为格网尺度土壤铬的空间预测提供可能。     

宁夏中宁枸杞林地粉壤土阳离子交换量空间分布预测方法对比

土壤阳离子交换量(CEC)是土壤施肥、改良的主要依据和土壤质量的评价指标,研究土壤CEC的空间分布及模型预测可为土壤养分监测、管理及精准农业实施提供科学依据。本研究以中宁枸杞林地粉壤土为对象,在自相关、交互相关等分析基础上,采用协同克里格(CoKriging)、普通最小二乘法(OLS)、地理加权回归(GWR)和随机森林(RF)模型对土壤CEC进行回归分析,比较了制图效果及模型预测精度。结果表明:中宁枸杞林地粉壤土CEC平均值为13.12 cmol·kg~(-1),属中等肥力;土壤CEC的空间分布具有自相关性,并与土壤pH、有机质、黏粒和电导率在不同滞后距离上存在不同的空间相互关系; RF模型预测图避免了CoKriging、OLS和GWR模型预测图中土壤CEC图斑边界两侧破碎程度大、突变明显的缺陷,使土壤CEC在空间变化上表现为自然、平缓的过渡; RF模型RMSE值分别比CoKriging、OLS和GWR模型减少33.82%、20.55%和19.81%,R~2分别提高8.84%、51.92%和7.69%。RF模型考虑了样点空间位置,明显提高了插值精度且制图效果更加平缓。     

基于不同空间模型的马尾松林生态系统碳密度研究

以江西省马尾松林生态系统为研究对象,基于样地调查及样品碳含量测定结果计算其碳密度,并选取立地、植被及气象等方面的15个因子,采用多元线性逐步回归方法筛选出对生态系统碳密度影响显著的因子,然后分别利用最小二乘模型(OLS)、空间误差模型(SEM)、空间滞后模型(SLM)和地理加权回归模型(GWR)构建生态系统碳密度与其影响因子之间的关系模型,筛选出最优的拟合模型。结果表明:对马尾松林生态系统碳密度影响显著的因子分别为海拔、坡度、土层厚度、胸径、年均温度和年均降水量。4种模型拟合结果均显示碳密度与坡度呈负相关,与海拔、土层厚度、胸径呈正相关。模型的决定系数(R~2)由大到小分别为GWR(0.8043)SEM(0.6371)SLM(0.6364)OLS(0.6321),模型均方误差(MSE)与赤池信息准则(AIC)最大的均为OLS模型,最小的均为GWR模型;残差检验表明GWR模型能有效降低模型残差的空间自相关性。综合分析得出GWR模型的拟合效果最优,更适用于江西省马尾松林生态系统碳密度的估测。     

基于GWR模型的土地覆盖与地表温度的关系——以京津唐城市群为例

基于Landsat TM土地覆盖分类数据和MODIS地表温度数据,探讨京津唐城市群不同土地覆盖的地表温度(7日),并采用常用的普通线性回归(OLS)和地理加权回归(GWR)方法分别拟合土地覆盖比例与地表温度的关系.结果表明: 研究区不同土地覆盖类型的地表温度差异明显,人工表面(40.92±3.49 ℃)和耕地(39.74±3.74 ℃)的平均温度较高,林地(34.43±4.16 ℃)和湿地(35.42±4.33 ℃)的平均温度较低;土地覆盖比例与地表温度显著相关,且两者之间的定量关系存在空间非稳定性,地理位置以及周围环境影响的差异是空间非稳定性产生的主要原因;GWR模型的拟合结果优于OLS模型(R_GWR²>R_OLS²),并且GWR模型可以量化土地覆盖比例与地表温度两者关系的空间非稳定性特征.     

降水量和温度对植被覆盖指数影响的空间非平稳性特征——以新疆伊犁河谷地区为例

多变量空间相关分析多基于时间序列数据,对数据时长与统计要求严格,空间非平稳性特征分析可以利用单期数据分析多变量之间的相关性。通过空间变系数回归模型分析了2006年和2011年的新疆伊犁地区降水量和温度对植被覆盖度指数影响的空间变化特征,利用局部线性地理加权回归(GWR)方法估计得到了回归系数曲面,揭示出变量间相互影响的空间异质性,同时利用线性回归最小二乘估计进行了对比。结果表明:(1)空间变系数回归模型可以用于变量间的空间相关分析;(2)局部线性GWR估计方法明显优于线性回归最小二乘估计;(3)拟合结果表明,伊犁地区降水量和温度对植被覆盖指数的影响具有显著的空间非平稳性特征;(4)模型估计误差是降水、气温之外的地形、地貌及人类活动等多种因素造成的,需进一步研究。方法可为具有空间非平稳性特征变量间空间相关性分析以及植被覆盖指数的空间模拟分布提供思路和方法。     

基于带宽优选地理加权回归模型的深圳市植被碳储量反演

植被碳储量估测是自然资源监测的重要内容,遥感技术结合地面样地进行反演可以获得区域范围内植被碳储量的空间连续分布,弥补了传统人工抽样调查估测的不足。然而,现有的参数和非参数遥感估测模型大多忽略了样地数据的变异与空间自相关关系。研究以Landsat 8 OLI影像为数据源提取遥感变量,结合植被碳储量实测调查数据,利用最小信息准则(AICc)、最大空间自相关距离(MSAD)和交叉验证(CV)分别确定最优带宽,组合Gaussian、Bi-square和Exponential核函数构建地理加权回归(GWR)模型估算深圳市植被碳储量,并与多元线性回归(MLR)进行比较,选择最优模型绘制深圳市植被碳储量空间分布图。研究结果表明,GWR模型整体精度优于MLR模型,GWR模型的决定系数(R~2)均高于MLR模型,且均方根误差(RMSE)和平均绝对误差(MAE)显著降低。带宽和核函数的选择对GWR模型估测结果产生了显著影响。以CV确定带宽、Exponential为核函数组合构建的GWR模型效果最佳,其R~2为0.697,RMSE为10.437 Mg C/hm~2,相比其它模型精度上升了13.87%—32....     

新疆猪毛菜属植物多样性地理分布格局及其环境解释

猪毛菜属(Salsola)是新疆干旱区分布最为丰富的被子植物属之一,是盐碱和荒漠区的先锋种和建群种,对西北干旱区植被恢复与建设具有巨大生态价值。基于新疆自然分布的33种猪毛菜属物种共741个分布数据,整合利用点格局法和物种分布模型法构建了物种丰富度(SR)、加权特有性指数(WE)和校正加权特有性指数(CWE)的分布格局。选取环境能量、水分可获得性、气候季节性、生境异质性、土壤条件和历史气候变化共6类19种生态因子,利用地理加权回归模型(GWR)探究了环境异质性对猪毛菜属物种丰富度的影响。结果显示：(1)基于现实点位模型和物种分布模型构建的物种丰富度具有一致性,均呈北高南低、西高东低的破碎化分布趋势,但物种分布模型的结果在空间上比点格局法更连续,物种丰富度的高值区主要分布于准噶尔盆地南缘、准噶尔西部山地、天山西端和天山南脉南缘;(2)加权特有性指数和校正加权特有性指数的分布格局与物种丰富度分布格局具有一定差异,其最大值集中分布于准噶尔盆地南缘、伊犁河谷和塔里木盆地西南缘;(3)GWR模型结果表明,海拔变幅、土壤酸碱度和最干月降水量是制约新疆分布的猪毛菜属丰富度和特有性分布的最重要因素。     

提高生态位模型转移能力来模拟入侵物种 的潜在分布

生态位模型利用物种分布点所关联的环境变量去推算物种的生态需求, 模拟物种的分布。在模拟入侵物种分布时, 经典生态位模型包括模型构建于物种本土分布地, 然后将其转移并投射至另一地理区域, 来模拟入侵物种的潜在分布。然而在模型运用时, 出现了模型的转移能力较低、模拟的结果与物种的实际分布不相符的情况, 由此得出了生态位漂移等不恰当的结论。提高生态位模型的转移能力, 可以准确地模拟入侵物种的潜在分布, 为入侵种的风险评估提供参考。作者以入侵种茶翅蝽(Halyomorpha halys)和互花米草(Spartina alterniflora)为例, 从模型的构建材料(即物种分布点和环境变量)入手, 全面阐述提高模型转移能力的策略。在构建模型之前, 需要充分了解入侵物种的生物学特性、种群平衡状态、本土地理分布范围及物种的生物历史地理等方面的知识。在模型构建环节上, 物种分布点不仅要充分覆盖物种的地理分布和生态空间的范围, 同时要降低物种采样点偏差; 环境变量的选择要充分考虑其对物种分布的限制作用、各环境变量之间的空间相关性, 以及不同地理种群间生态空间是否一致, 同时要降低环境变量的空间维度; 模型构建区域要真实地反映物种的地理分布范围, 并考虑种群的平衡状态。作者认为, 在生态位保守的前提下, 如果模型是构建在一个合理方案的基础上, 生态位模型的转移能力是可以保证的, 在以模型转移能力较低的现象来阐述生态位分化时需要引起注意。     

Application of a geographically-weighted regression analysis to estimate net primary production of Chinese forest ecosystems

Aim The objective of this paper is to obtain a net primary production (NPP) regression model based on the geographically weighted regression (GWR) method, which includes spatial non‐stationarity in the parameters estimated for forest ecosystems in China. Location We used data across China. Methods We examine the relationships between NPP of Chinese forest ecosystems and environmental variables, specifically altitude, temperature, precipitation and time‐integrated normalized difference vegetation index (TINDVI) based on the ordinary least squares (OLS) regression, the spatial lag model and GWR methods. Results The GWR method made significantly better predictions of NPP in simulations than did OLS, as indicated both by corrected Akaike Information Criterion (AIC_c) and R². GWR provided a value of 4891 for AIC_c and 0.66 for R², compared with 5036 and 0.58, respectively, by OLS. GWR has the potential to reveal local patterns in the spatial distribution of a parameter, which would be ignored by the OLS approach. Furthermore, OLS may provide a false general relationship between spatially non‐stationary variables. Spatial autocorrelation violates a basic assumption of the OLS method. The spatial lag model with the consideration of spatial autocorrelation had improved performance in the NPP simulation as compared with OLS (5001 for AIC_c and 0.60 for R²), but it was still not as good as that via the GWR method. Moreover, statistically significant positive spatial autocorrelation remained in the NPP residuals with the spatial lag model at small spatial scales, while no positive spatial autocorrelation across spatial scales can be found in the GWR residuals. Conclusions We conclude that the regression analysis for Chinese forest NPP with respect to environmental factors and based alternatively on OLS, the spatial lag model, and GWR methods indicated that there was a significant improvement in model performance of GWR over OLS and the spatial lag model.     

Spatial patterns of species richness in New World coral snakes and the metabolic theory of ecology

The metabolic theory of ecology (MTE) has attracted great interest because it proposes an explanation for species diversity gradients based on temperature-metabolism relationships of organisms. Here we analyse the spatial richness pattern of 73 coral snake species from the New World in the context of MTE. We first analysed the association between ln-transformed richness and environmental variables, including the inverse transformation of annual temperature (1/kT). We used eigenvector-based spatial filtering to remove the residual spatial autocorrelation in the data and geographically weighted regression to account for non-stationarity in data. In a model I regression (OLS), the observed slope between ln-richness and 1/kT was ?0.626 (r² = 0.413), but a model II regression generated a much steeper slope (?0.975). When we added additional environmental correlates and the spatial filters in the OLS model, the R² increased to 0.863 and the partial regression coefficient of 1/kT was ?0.676. The GWR detected highly significant non-stationarity, in data, and the median of local slopes of ln-richness against 1/kT was ?0.38. Our results expose several problems regarding the assumptions needed to test MTE: although the slope of OLS fell within that predicted by the theory and the dataset complied with the assumption of temperature-independence of average body size, the fact that coral snakes consist of a restricted taxonomic group and the non-stationarity of slopes across geographical space makes MTE invalid to explain richness in this case. Also, it is clear that other ecological and historical factors are important drivers of species richness patterns and must be taken into account both in theoretical modeling and data analysis.     

Extending geographically and temporally weighted regression to account for both spatiotemporal heterogeneity and seasonal variations in coastal seas

Space-time modelling has been successfully applied in numerous research projects and has been studied extensively in the field of geographical information science. However, the cyclical or seasonal variations in the temporal dimension of most spatiotemporal processes are rarely considered along with spatiotemporal nonstationarity. Seasonal variations are widespread and typical in marine environmental processes, and addressing both spatiotemporal heterogeneity and seasonal variations is particularly difficult in the turbid and optically complex coastal seas. By incorporating seasonal periodic effects into a geographically and temporally weighted regression (GTWR) model, we proposed a geographically and cycle-temporally weighted regression (GcTWR) model. To test its performance, modelling of chlorophyll-a, known as an important indicator of the coastal environment, is performed using the in situ data collected from 2012 to 2016 in the coastal sea of Zhejiang Province, China. GcTWR is compared with global ordinary least squares (OLS), geographically weighted regression (GWR), cycle-temporally weighted regression (cTWR), and GTWR models. In the results, the GcTWR model decreases absolute errors by 89.74%, 79.77%, 76.60% and 29.83% relative to the OLS, GWR, cTWR, and GTWR models, and presents a higher R² (0.9274) than the GWR (0.5911), cTWR (0.6465), and GTWR (0.8721) models. The estimation results further confirm that the seasonal influences in coastal areas are much more significant than the interannual effects, which accordingly demonstrates that extending the GTWR model to handle both spatiotemporal heterogeneity and seasonal variations are meaningful. In addition, a novel 3D visualization method is proposed to explore the spatiotemporal heterogeneity of the estimation results.     

Using water and energy variation to explain the botanical richness pattern of Theaceae species in southern China

BackgroundEnergy and water availability are essential for biodiversity maintenance. In addition to the independent effects of water and energy on biodiversity, recent studies clarified that the effects of interaction between water and energy availability were indispensable.MethodsIn this exercise, by combining the species presence information and the environmental predictors, we produced species distribution models at 20 × 20 arc-minute resolution for 193 Theaceae species. Initially, the ordinary least square (OLS) regression was used to examine the stationary relationships between Theaceae diversity and climate. The statistical effects of water and energy on species diversity were detected using Geographically Weighted Regression analysis (GWR). Furthermore, the contour plots were used to view the statistical effects of the water and energy interaction on species diversity.ResultsThe OLS results suggested that both energy and water availability are related to Theaceae species diversity. In GWR regression, the spatial variation of energy and water showed high explanatory power to the diversity pattern of Theaceae species. The patterns in the residuals of both OLS and GWR regression varied geographically. Therefore, the results of GWR regression were kept for further analysis. The value of diversity-water slopes decrease changed from positive to negative in extremely wet regions; In extremely dry conditions, the value of diversity-energy slopes decrease faster than other regions.ConclusionsOur results support the following findings: 1) the latitudinal distribution of Theaceae species was limited by thermal tolerance, which support the freezing-tolerance hypothesis in macro-ecology; 2) Theaceae species diversity are sensitive to the instability of precipitation, while the limitation from energy availability is weak; 3) the effects of water and energy on species diversity are strong in dry regions. Those findings can provide further implications for Theaceae species conservation under climate change scenarios.     

Exploring the Spatial Association between Social Deprivation and Cardiovascular Disease Mortality at the Neighborhood Level

Cardiovascular disease (CVD), the leading cause of death in the United States, is impacted by neighborhood-level factors including social deprivation. To measure the association between social deprivation and CVD mortality in Harris County, Texas, global (Ordinary Least Squares (OLS) and local (Geographically Weighted Regression (GWR)) models were built. The models explored the spatial variation in the relationship at a census-tract level while controlling for age, income by race, and education. A significant and spatially varying association (p < .01) was found between social deprivation and CVD mortality, when controlling for all other factors in the model. The GWR model provided a better model fit over the analogous OLS model (R² = .65 vs. .57), reinforcing the importance of geography and neighborhood of residence in the relationship between social deprivation and CVD mortality. Findings from the GWR model can be used to identify neighborhoods at greatest risk for poor health outcomes and to inform the placement of community-based interventions.     

Spatial autocorrelation and the selection of simultaneous autoregressive models

Aim Spatial autocorrelation is a frequent phenomenon in ecological data and can affect estimates of model coefficients and inference from statistical models. Here, we test the performance of three different simultaneous autoregressive (SAR) model types (spatial error = SAR_err, lagged = SAR_lag and mixed = SAR_mix) and common ordinary least squares (OLS) regression when accounting for spatial autocorrelation in species distribution data using four artificial data sets with known (but different) spatial autocorrelation structures. Methods We evaluate the performance of SAR models by examining spatial patterns in model residuals (with correlograms and residual maps), by comparing model parameter estimates with true values, and by assessing their type I error control with calibration curves. We calculate a total of 3240 SAR models and illustrate how the best models [in terms of minimum residual spatial autocorrelation (minRSA), maximum model fit (R²), or Akaike information criterion (AIC)] can be identified using model selection procedures. Results Our study shows that the performance of SAR models depends on model specification (i.e. model type, neighbourhood distance, coding styles of spatial weights matrices) and on the kind of spatial autocorrelation present. SAR model parameter estimates might not be more precise than those from OLS regressions in all cases. SAR_err models were the most reliable SAR models and performed well in all cases (independent of the kind of spatial autocorrelation induced and whether models were selected by minRSA, R² or AIC), whereas OLS, SAR_lag and SAR_mix models showed weak type I error control and/or unpredictable biases in parameter estimates. Main conclusions SAR_err models are recommended for use when dealing with spatially autocorrelated species distribution data. SAR_lag and SAR_mix might not always give better estimates of model coefficients than OLS, and can thus generate bias. Other spatial modelling techniques should be assessed comprehensively to test their predictive performance and accuracy for biogeographical and macroecological research.