基于地理加权回归模型探究环境异质性对秦岭大熊猫空间利用的影响

环境异质性对野生动物分布的影响具有明显的空间不均匀性。传统分析中多采用经典线性回归模型来量化野生动物分布与环境变量之间的关系,难以准确反映物种-环境关系的空间异质特征。地理加权回归(GWR)是近年来提出的一种新的空间分析方法,通过将空间结构嵌入线性回归模型中,以此来探测空间关系的非均匀性。以秦岭大熊猫为例,应用GWR模型分析大熊猫空间分布与环境异质性特征之间的潜在关系,并同经典的全局最小二乘回归法(OLS)进行比较。结果表明,GWR模型的AIC、R~2和校正R~2均显著优于OLS模型,GWR模型的局部回归系数估计能够更加深刻地揭示大熊猫空间分布与环境变量间的复杂空间关系,且GWR模型能够为物种的科学保护提供更加有效的理论支撑。因此,GWR模型可为探究物种-环境关系的空间异质特征提供一种新的方法,在物种栖息地选择与利用研究中具有一定的应用前景。     

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....     

景观生态视角下草地生态质量的空间差异及其影响因素——以内蒙古四子王旗为例

科学评价北方荒漠草原的生态质量,有助于草地景观的生态修复与荒漠化防治。以内蒙古四子王旗为例,采用景观生态视角,通过构建生态质量评价体系,结合综合指数法和自然间断法,对研究区内130个行政村域的草地生态质量进行综合评价及类别划定;通过引入地理加权回归模型并对比最小二乘回归模型,评估外源压力对草地生态质量的影响。结果表明:(1)2016年四子王旗村域草地生态质量的指数变化幅度为0.003—0.765,呈现出北高南低的区域差异;在空间分布上,高高集聚、低低集聚和低高集聚的空间特征反映出村域草地生态质量的非均衡特征及空间依赖;(2)对比模型估计参数,地理加权回归模型具有解释草地生态质量空间异质性和处理数据非平稳性的优势,预测精度优于最小二乘回归模型;(3)6种变量对草地生态质量的回归系数表明,经济压力和环境压力的影响效应明显高于人口压力和资源压力。与草地生态质量变化呈正相关的因素依次为:生态用地比、人均耕地面积和草地载畜量,而与至城镇中心距离、劳动人口比和人均GDP则依次呈现出负相关关系。研究结果可为草地生态保护与治理提供参考。     

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.     

基于地理加权回归克里格的日平均气温插值

气温是大量农业、水文、气候、生态模型的输入变量.在地形复杂的区域,考虑气温与环境变量的线性回归关系和残差的自相关性的方法(如回归克里格法,regression Kriging,RK)是目前气温插值的主要方法.但此类方法多使用基于普通最小二乘的全局回归技术,没有顾及回归关系的空间非平稳性.地理加权回归克里格(geographically weighted regression-Kriging,GWRK)是一种既能顾及回归关系的空间非平稳性、又能考虑残差的自相关性的一种插值方法.本文用RK和GWRK对海南岛2013年12月18日的日平均气温进行插值并进行比较研究.依相关性分析和逐步回归分析的结果,采用RK1(以海拔为辅助变量)、GWRK1(以海拔为辅助变量)、RK2(以纬度、海拔、海陆距离为辅助变量)和GWRK2(以海拔、海陆距离为辅助变量)4种模型进行研究,并用80个验证站评估4种模型的精度.结果表明:GWRK1模型的最大正误差、最大负误差、平均绝对误差、均方根误差均最接近于0.从最大正误差、平均绝对误差、均方根误差3个指标看,考虑更多辅助变量的RK2、GWRK2模型反而不及只考虑海拔的RK1、GWRK1模型,表明RK2、GWRK2模型中辅助变量之间的相关性对插值结果有较大影响.     

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。     

基于地理加权回归克里格模型的帽儿山地区森林碳储量空间分布

森林碳储量对于全球气候变化具有重要影响,以往的模型估算未考虑到模型残差的空间相关性和碳储量数据的非平稳性,影响模型的预测精度.本研究基于东北林业大学帽儿山实验林场的ETM+遥感影像数据和193块固定样地,利用地理加权克里格回归(GWRK)建立森林碳储量与遥感和地形因子的回归模型,同时对比最小二乘模型(OLS)、地理加权回归模型(GWR)的预测精度.结果表明: 对于帽儿山地区的森林碳储量估算,GWRK的平均绝对误差(MAE)、均方根误差(RMSE)低于OLS模型和GWR模型,GWRK模型的平均误差(ME)低于GWR模型,与OLS模型相近.GWRK模型的预测精度为83.2%,较OLS模型(73.7%)和GWR模型(77.3%)分别提高6%和10%,拟合精度明显提高,说明GWRK模型是森林碳储量估算的有效方法.利用GWRK模型预测的研究区森林碳储量平均值为70.31 t·hm^-2,在海拔较高的地区,森林碳储量值相对较高,说明海拔对其有较大影响.     

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.     

南丰蜜桔可溶性固形物近红外检测的多元线性回归变量筛选

文章采用反向区间偏最小二乘法结合连续投影算法,筛选南丰蜜桔近红外检测的多元线性回归变量。对南丰蜜桔近红外光谱进行多元散射校正后,利用反向间隔偏最小二乘法,从500～1750 nm中初选出7个光谱区间,用于多元线性回归变量筛选。利用通过遗传算法和连续投影算法筛选出的变量建立了多元线性回归模型。经比较发现,利用反向区间偏最小二乘法结合连续投影算法筛选出的变量建立的多元线性回归模型,预测结果最优,模型预测相关系数为0.937,模型预测均方根误差为0.613 oBrix。结果表明,反向区间偏最小二乘法结合连续投影算法,可以有效地筛选近红外光谱的多元线性回归变量,提高南丰蜜桔可溶性固形物模型的预测精度。     

Application of geographically weighted regression in estimating the effect of climate and site conditions on vegetation distribution in Haihe Catchment,China

Climate and topography are the two key factors influencing vegetation pattern, distribution, and plant growth. Traditionally, studies on the relationship between vegetation and climate rely largely on field data from limited samples. Now, digital elevation model (DEM) and remote sensing data readily provide huge amounts of spatial data on site-specific conditions like elevation, aspect, and climate, while recent development of geographically weighted regression (GWR) analysis facilitates efficient spatial evaluation of interactions among vegetation and site conditions. Using Haihe Catchment as a case study, GWR is applied in establishing spatial relations among leaf area index (LAI; a critical vegetation index from Moderate Resolution Imaging Spectroradiometer (MODIS)) and interpolated climate variables and site conditions including elevation, aspect, and Topographic Wetness Index (TWI). This study suggests that the GWR solution to spatial effect of climate and site conditions on vegetation is much better than ordinary least squares (OLS). In most of the study area, effects of elevation, aspect change from south to north, and precipitation on LAI are positive, while temperature, TWI, and potential evapotranspiration have a negative influence. Spatially, models perform better in places with large spatial variations in LAI—primarily driven by strong spatial variations in temperature and precipitation. On the contrary, the effect of topographic and climatic factors on vegetation is weak in regions with small spatial variations in LAI. This study shows that overall water availability is a determining factor for spatial variations in vegetation.     

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.     

气候和放牧对锡林郭勒地区植被覆盖变化的影响

基于锡林郭勒盟15个气象站点1981-2007年的逐月气温、降水量数据及各旗县的牲畜头数,在ArcGIS软件的支持下,分析气候干燥度和牲畜密度的空间分布,结合1981-2007年的逐旬归一化植被指数（NDVI）数据,对研究区植被覆盖变化的驱动因素进行分析.结果表明： 研究期间,锡林郭勒盟气候干燥度与植被覆盖状况之间存在良好的线性回归关系;NDVI与牲畜密度之间存在良好的二项式回归关系,随着NDVI值的升高,牲畜密度先增加后降低;植被覆盖状况与干燥度和牲畜密度呈复线性相关关系,其中,NDVI与干燥度呈正相关,与牲畜密度呈负相关,且干燥度对NDVI的影响远大于牲畜密度对NDVI的影响.     

Vegetation dynamics and responses to recent climate change in Xinjiang using leaf area index as an indicator

There is a strong signal showing that the climate in Xinjiang, China has changed from warm-dry to warm-wet since the early 1980s, leading to an increase in vegetation cover. Based on a regression analysis and Hurst index method, this study investigated the spatial–temporal characteristics and interrelationships of the vegetation dynamics and climate variability in Xinjiang Province using the leaf area index (LAI) and a gridded meteorological dataset for the period 1982–2012. Further analysis focused on the discrimination between climatic change and human-induced effects on the vegetation dynamics, and several conclusions were drawn. (1) Vegetation dynamics differ in mountain and plains regions, with a significant increasing trend of vegetation cover in oases and decreasing trend of vegetation growth in the Tienshan and Altay Mountain. The Hurst exponent results indicated that the vegetation dynamic trend was consistent, with a sustainable area percentage of 51.18%, unsustainable area percentage of 4.04%, and stable and non-vegetated area ratio of 44.78%. (2) The warm-dry to warm-wet climatic pattern in Xinjiang Province since the 1980s mainly appeared in the western part of the Tienshan region and North Xinjiang. Temperatures increased in all seasons over the majority of Xinjiang, and precipitation showed a significant increasing trend in the mountainous regions in spring, summer and autumn, whereas the rate of precipitation change was higher in the plains region in winter compared with that in other seasons. (3) A correlation occurs between the climate variables (precipitation and temperature) and mean LAI, and this correlation varies at the seasonal and regional scales, with coniferous forest, meadow and grassland more correlated with precipitation in spring and summer and not correlated with temperature, which indicated that precipitation was the dominant factor affecting the growth of mountain vegetation. The mean LAI of vegetation in the plains exhibited significant correlation with precipitation in winter and temperature in spring and summer. (4) A residual analysis showed a human-induced change that was superimposed on the climate trend and exhibited two effects: vegetation regeneration in oases throughout Xinjiang and desertification in the meadow located in the mountainous area of the western Tienshan Mountains and Altay Mountains. (5) Grassland is the most sensitive vegetation type to short-term climatic fluctuations and is the land-use type that has been most severely degraded by human activity; thus, local governments should take full advantage of this climatic warm-wet shift and focus on protecting vegetation to improve this fragile arid environment.     

1982—2015年新疆地区植被生长对气温的响应

基于1982-2015年归一化植被指数(NDVI)数据集、植被类型和气象数据,采用滑动偏相关分析、线性趋势分析和GIS空间分析方法,揭示了新疆地区生长季植被对气温响应的变化特征.结果表明:研究期间,在整个生长季,新疆地区植被活动对气温变化的响应强度呈现明显的降低趋势;季节尺度上,这种响应关系的变化趋势在夏、秋两季较为明显,春季植被活动对气温变化响应的变化趋势与之相反.在整个生长季,不同类型植被对气温变化的响应呈现减弱态势;在春季,草地和森林对气温变化的响应呈现显著增强趋势,而灌丛和荒漠对气温变化的响应趋势正好相反;在夏季,4种植被(草地、灌丛、荒漠、森林)对气温变化的响应均呈现显著降低趋势;在秋季,4种植被对气温变化的响应均没有显著的统计学特征.新疆地区生长季气温对植被的影响力减弱具有区域的普遍性特征,这可能与研究区降雨和太阳辐射活动变化的有关.     

青藏高原草地退化特征及其与气候因子的关系

了解草地退化的分布、特征、变化趋势及持续性,揭示草地退化机理,可为有效管理和保护草地提供重要的科学依据。本研究选择草地覆盖度作为草地退化的遥感监测指标,建立了草地退化遥感监测和评价指标体系,对青藏高原草地退化现状(2016—2020年)进行了评价,利用线性回归和Hurst指数分析了长时间序列尺度上(1982—2020年)草地覆盖度变化的趋势及持续性,并且基于草地覆盖度与气候因子的偏相关分析,研究了气候因子对草地退化的影响。结果表明: 2016—2020年,平均草地退化面积达24.3%,主要表现为轻度退化和中度退化,主要分布在低海拔和高植被覆盖地区。1982—2020年,草地覆盖度在青藏高原北部、西部和西南部地区呈增加趋势,在东部和中部地区呈减少趋势。98.1%的地区草地覆盖度的Hurst指数小于0.5,草地覆盖度变化表现出反持续性。草地覆盖度与降水量的偏相关系数(0.096)整体高于其与温度的偏相关系数(-0.033),温度占主导地位的面积占比为16.0%,主要分布在青藏高原的中部和东南部,降水量占主导地位的面积占比为12.2%,主要分布在青藏高原东北部和西部。     

Spatial nonstationarity and scale-dependency in the relationship between species richness and environmental determinants for the sub-Saharan endemic avifauna

Aim This article aims to test for and explore spatial nonstationarity in the relationship between avian species richness and a set of explanatory variables to further the understanding of species diversity variation. Location Sub‐Saharan Africa. Methods Geographically weighted regression was used to study the relationship between species richness of the endemic avifauna of sub‐Saharan Africa and a set of perceived environmental determinants, comprising the variables of temperature, precipitation and normalized difference vegetation index. Results The relationships between species richness and the explanatory variables were found to be significantly spatially variable and scale‐dependent. At local scales > 90% of the variation was explained, but this declined at coarser scales, with the greatest sensitivity to scale variation evident for narrow ranging species. The complex spatial pattern in regression model parameter estimates also gave rise to a spatial variation in scale effects. Main conclusions Relationships between environmental variables are generally assumed to be spatially stationary and conventional, global, regression techniques are therefore used in their modelling. This assumption was not satisfied in this study, with the relationships varying significantly in space. In such circumstances the average impression provided by a global model may not accurately represent conditions locally. Spatial nonstationarity in the relationship has important implications, especially for studies of species diversity patterns and their scaling.