基于地理加权回归的青藏高原季节冻土区土壤有机碳空间分布研究

青藏高原土壤有机碳储量（soil organic carbon stocks, SOCS）对于区域生态环境演替具有重要作用, 但是其空间分布数据还比较缺乏, 特别是季节冻土区的数据较少。基于378个土壤剖面数据, 结合与土壤有机碳（soil organic carbon, SOC）相关的地形、 气候以及植被等环境因子, 使用地理加权回归（geographically weighted regression, GWR）模型模拟了青藏高原季节冻土区0 ~ 30 cm、 0 ~ 50 cm、 0 ~ 100 cm和0 ~ 200 cm深度的SOC总量和空间分布。结果表明： 青藏高原季节冻土区SOCS自东南向西北递减, 表层0 ~ 200 cm的SOC总量约15.37 Pg; 季节冻土区不同植被类型SOC从大到小依次为森林、 灌丛、 高寒草甸、 高寒草原和高寒荒漠; 各土壤类型中棕壤、 黑钙土和泥炭土的SOC最大, 而棕钙土、 棕漠土、 灰棕漠土、 风沙土、 石质土、 盐土、 冷钙土、 寒漠土以及冷漠土的SOC最小。研究结果给出了青藏高原季节冻土区SOC的总量、 空间分布及规律, 可为相关地球模式的发展提供基础数据。     

属性数据与空间数据连接对土壤有机碳储量估算的影响

土壤有机碳(Soil Organic Carbon, SOC) 库在陆地生态系统中具有重要作用。在基于GIS利用土壤类型法估算SOC储量时,由于土壤图比例尺的限制,属性数据与空间数据的连接会导致对SOC储量的估算结果产生很大不确定性。利用中国滇黔桂地区（云南省、贵州省和广西壮族自治区）798个土壤剖面及1∶[KG-*2]50万土壤图在不同的制图单元水平上,分别采用算术平均值法、中值法和面积加权平均值法（以土种志中的面积为权重）估算了该地区1 m深度SOC储量,并与基于土壤学专业知识连接方法（Pedological Knowledge Based Method, PKB）的估算结果进行了比较。结果表明：面积加权平均值法比中值法和算术平均值法的估算结果更为准确,而且在土类、亚类和土属水平上应用面积加权平均值法对SOC储量的估算结果差异不大。在采用较大比例尺、较为详细的土壤图时,PKB法较理想;反之,则面积加权平均值法较好。     

Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway)

Statistical models are one of the most preferred methods among many landslide susceptibility assessment methods. As landslide occurrences and influencing factors have spatial variations, global models like neural network or logistic regression (LR) ignore spatial dependence or autocorrelation characteristics of data between the observations in susceptibility assessment. However, to assess the probability of landslide within a specified period of time and within a given area, it is important to understand the spatial correlation between landslide occurrences and influencing factors. By including these relations, the predictive ability of the developed model increases. In this respect, spatial regression (SR) and geographically weighted regression (GWR) techniques, which consider spatial variability in the parameters, are proposed in this study for landslide hazard assessment to provide better realistic representations of landslide susceptibility. The proposed model was implemented to a case study area from More and Romsdal region of Norway. Topographic (morphometric) parameters (slope angle, slope aspect, curvature, plan, and profile curvatures), geological parameters (geological formations, tectonic uplift, and lineaments), land cover parameter (vegetation coverage), and triggering factor (precipitation) were considered as landslide influencing factors. These influencing factors together with past rock avalanche inventory in the study region were considered to obtain landslide susceptibility maps by using SR and LR models. The comparisons of susceptibility maps obtained from SR and LR show that SR models have higher predictive performance. In addition, the performances of SR and LR models at the local scale were investigated by finding the differences between GWR and SR and GWR and LR maps. These maps which can be named as comparison maps help to understand how the models estimate the coefficients at local scale. In this way, the regions where SR and LR models over or under estimate the landslide hazard potential were identified.     

Predictive geochemical mapping using environmental correlation

The distribution of chemical elements at and near the Earth's surface, the so-called critical zone, is complex and reflects the geochemistry and mineralogy of the original substrate modified by environmental factors that include physical, chemical and biological processes over time.Geochemical data typically is illustrated in the form of plan view maps or vertical cross-sections, where the composition of regolith, soil, bedrock or any other material is represented. These are primarily point observations that frequently are interpolated to produce rasters of element distributions. Here we propose the application of environmental or covariate regression modelling to predict and better understand the controls on major and trace element geochemistry within the regolith. Available environmental covariate datasets (raster or vector) representing factors influencing regolith or soil composition are intersected with the geochemical point data in a spatial statistical correlation model to develop a system of multiple linear correlations. The spatial resolution of the environmental covariates, which typically is much finer (e.g. ∼90 m pixel) than that of geochemical surveys (e.g. 1 sample per 10-10,000 km²), carries over to the predictions. Therefore the derived predictive models of element concentrations take the form of continuous geochemical landscape representations that are potentially much more informative than geostatistical interpolations.Environmental correlation is applied to the Sir Samuel 1:250,000 scale map sheet in Western Australia to produce distribution models of individual elements describing the geochemical composition of the regolith and exposed bedrock. As an example we model the distribution of two elements – chromium and sodium. We show that the environmental correlation approach generates high resolution predictive maps that are statistically more accurate and effective than ordinary kriging and inverse distance weighting interpolation methods. Furthermore, insights can be gained into the landscape processes controlling element concentration, distribution and mobility from analysis of the covariates used in the model. This modelling approach can be extended to groups of elements (indices), element ratios, isotopes or mineralogy over a range of scales and in a variety of environments.     

Examining the rainfall–topography relationship using non-stationary modelling technique in semi-arid Aseer region,Saudi Arabia

The mountainous region of Aseer, corresponding to the Afromontane phytogeographic region, is an eco-sensitive zone and has complex relationship between topography and rainfall. The region is located inland of the red sea escarpment edge in the west. Therefore, rainfall can occur during any month of the year in the mountain of the high Aseer region when moist air forces up the escarpment from the red sea. Monitoring the rainfall data and its topographical elevation variable in Aseer region is an essential requirement for feasible and accurate rainfall-based data for different applications, such as hydrological and ecological resource management in rugged terrain and remote areas. The relationship of elevation and rainfall are spatially non-stationary, non-linear, scale dependent, and often modelled by conventional regression models. Therefore, a local modelling technique, geographically weighted regression (GWR), was applied to deal with non-stationary, non-linear, scale-dependent problems. The GWR using topoclimatic data (elevation and rainfall) to analyse the cumulative rainfall data for rainy months (March to June) of the 4 years estimated from CHIRPS (Climate Hazards Group InfraRed Precipitation with Stations) product for Aseer region. The bandwidth (scale-size) of the Aseer region rainfall–elevation relationship has stabilised at round off 12 km. By selecting the suitable bandwidth, the spatial pattern of the rainfall–elevation relationship was significantly enhanced by using the GWR than the traditional ordinary least squares (OLS) regression model. GWR local modelling techniques estimated well in terms of accuracy, predictive power and decreased residual autocorrelation. Additionally, GWR assesses the significance of local statistic at each location and identified the location of spatial clusters with local regression coefficients significantly improved as compared with global OLS model, thereby highlighting local variations. Therefore, the GWR, local modelling approach managed to produce more accurate estimates by taking into account local characteristics.     

Standing different ground: the spatial heterogeneity of territorial disputes

Recent developments in spatial analysis and spatial data have allowed researchers to investigate various geographical factors in the quantitative analysis of conflict and war (Ward in Polit Geogr 21(2):155–158, 2002). Despite the importance of territory in interstate conflict, there has been a limited interest in the application of spatial analysis to the study of territorial conflict. Using geographically weighted regression (GWR) we evaluated the existing explanations of territorial conflict provided by a global scale analysis that assumes a spatial consistency in the explanatory variables. Specifically, we revisited Paul Huth’s foundational work by using GWR to examine the spatial pattern in the sign and significance of the variables. The result of GWR shows that the escalation of territorial conflict cannot be fully explained by one universal model. There is a high level of spatial variation in the regression parameters and the explanatory power of the model varies over space. A k-means cluster analysis was implemented for a further investigation of the regional pattern of the underlying causes of territorial disputes. The result of our GWR suggests the necessity and possibility to pursue a local or regional scale approach to the study of territorial conflict, an approach that challenges an epistemology of seeking a single explanation for the causes of conflict that neglects regional context. The spatial heterogeneity in the causes of territorial conflict escalation we find is framed within a narrative of the intertwined processes of colonialism, Cold War legacies, and competition for resources.     

Prediction of spatial soil organic carbon distribution using Sentinel-2A and field inventory data in Sariska Tiger Reserve

Dynamic and vigorous top soil is the source for healthy flora, fauna, and humans, and soil organic matters are the underpinning for healthy and productive soils. Organic components in the soil play significant role in stimulating soil productivity processes and vegetation development. This article deals with the scientific demand for estimating soil organic carbon (SOC) in forest using geospatial techniques. We assessed distribution of SOC using field and satellite data in Sariska Tiger Reserve located in the Aravalli Hill Range, India. This study utilized the visible and near-infrared reflectance data of Sentinel-2A satellite. Three predictor variables namely Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, and Renormalized Difference Vegetation Index were derived to examine the relationship between soil and SOC and to identify the biophysical characteristic of soil. Relationship between SOC (ground and predicted) and leaf area index (LAI) measured through satellite data was examined through regression analysis. Coefficient of correlation (R ²) was found to be 0.95 (p value < 0.05) for predicted SOC and satellite measured LAI. Thus, LAI can effectively be used for extracting SOC using remote sensing data. Soil organic carbon stock map generated through Kriging model for Landsat 8 OLI data demonstrated variation in spatial SOC stocks distribution. The model with 89% accuracy has proved to be an effective tool for predicting spatial distribution of SOC stocks in the study area. Thus, optical remote sensing data have immense potential for predicting SOC at larger scale.     

Addressing issues of spatial spillover effects and non-stationarity in analysis of residential burglary crime

The aim of this paper is to address two critical but largely neglected issues in the spatial analysis of urban crime which are spatial spillover effects of crime penetrating neighborhood boundaries and non-stationarity regarding the relationships between contextual factors and neighborhood crime. We use a GIS-based spatial approach to normalize the estimate of burglary crime at block group level and use the geographically weighted regression (GWR) to investigate the correlates of neighborhood crime. Results suggest that the use of normalized measure of neighborhood crime helps better reveal the spatial patterns of burglary crime and the use of GWR accounts for the spatial variations of relationships between contextual factors and crime. In particular, the normalized measure of crime has implications for improving the measurement accuracy of the risk of crime across urban neighborhoods and can be applied to the spatial analysis of other socioeconomic issues such as housing foreclosures and environmental hazards which are also plagued by the spatial spillover issue when geographically contiguous data are analyzed.     

贵州茂兰喀斯特地区土壤全钙含量空间估算模型与迁移分析

文章在世界自然遗产地贵州荔波茂兰保护区采集土壤全钙数据,分析采用地理加权回归（GWR）方法进行空间分析的有效性,筛选识别影响土壤全钙空间分布的主要因子,建立喀斯特地区土壤全钙含量空间分布计算模型,获取研究土壤全钙空间分布基础数据。通过土壤流失方程（USLE）计算土壤侵蚀状况,对比分析土壤全钙与土壤侵蚀空间关联,揭示土壤全钙的空间迁移规律。结果表明:（1）在岩性一致条件下,相对高差和坡度是影响土壤全钙空间分布的主导因子;（2）GWR模型的预测精度优于全局回归的（OLS）,相关系数分别是0.41和0.39;（3）通过土壤全钙含量空间估算模型,计算得到研究区土壤全钙空间分布特征,土壤全钙为0 ~37.68 gkg-1。研究结论说明,在湿润气候的喀斯特地区,尽管植被覆盖度大,但土壤全钙空间分布仍然深受成土母质影响,喀斯特峰林土壤侵蚀强度大,土壤全钙含量高,物质迁移以流失为主,峰丛洼地土壤侵蚀强度小,土壤全钙含量低,物质迁移以淋溶流失为主。      

土壤有机碳的主导影响因子及其研究进展

土壤有机碳库是全球碳循环的重要组成部分，其积累和分解的变化直接影响全球的碳平衡。理解土壤有机碳蓄积过程对生物、物理和人为因素的响应，把握关键的控制因子是准确预测土壤有机碳在全球变化情景下对大气CO 2的源／汇方向及准确评估碳收支的关键。综述了土壤有机碳主导影响因子的研究进展，并针对陆地碳循环特点，提出未来土壤有机碳研究应加强土壤有机碳过程与状态的定量化、土壤有机碳分解对环境因子的敏感性、氮沉降对土壤有机碳的影响、土壤有机碳对气候变率的响应及其反馈作用，以及土壤有机碳动态的综合模拟 5个方面的研究，为准确评估陆地碳收支提供依据。     

广南县幅岩溶区与非岩溶区土壤碳氮磷生态化学计量比空间变异分析

借助1∶25万云南省广南县幅土壤地球化学调查数据,并利用单因素方差分析、多重比较法以及地统计学方法,对岩溶区和非岩溶区土壤碳氮磷生态化学计量特征及其空间分布进行了对比分析。结果显示:广南县幅岩溶区土壤中有机碳（SOC）、全氮（TN）、全磷（TP）含量显著高于非岩溶区,而碳氮比（C∶N）、碳磷比（C∶P）、氮磷比（N∶P）显著低于非岩溶区;无论是岩溶区还是非岩溶区,表层（0～20 cm）SOC、TN、C∶N、C∶P、N∶P均显著高于深层（＞100 cm）。克里格空间插值结果表明,研究区表层土壤中SOC、TN、TP含量具有东高西低的特征,而C∶N、C∶P、N∶P具有低值区集中于东部、高值区散布在西部的空间分布格局。成土母质和土壤类型等自然因素严重制约了研究区土壤碳氮磷的空间变异,同时土地利用变化等人为因素也起到了不可忽视的作用。      

Analysis of long- and short-term temporal variations of the diffuse CO2 emission from Timanfaya volcano,Lanzarote, Canary Islands

Reported herein are the results of eight soil CO₂ efflux surveys performed from 2006 to 2011 at Timanfaya Volcanic Field (TVF), Lanzarote Island with the aim of evaluating the long- and short-term temporal variations of the diffuse CO₂ emission. Soil CO₂ efflux values ranged from non-detectable up to 34.2 g m⁻² d⁻¹, with the highest values measured in September 2008. Conditional sequential Gaussian simulations (sGs) were applied to construct soil CO₂ efflux distribution maps and to estimate the total CO₂ output from the studied area at the TVF. Soil CO₂ efflux maps showed a high spatial and temporal variability. Total CO₂ emission rates ranged between 41 and 518 t d⁻¹, February 2011 (winter) being the season when maximum diffuse CO₂ emission rates were observed. To investigate the influence of external variables on the soil CO₂ efflux, a geochemical station (LZT01) was installed at TVF to measure continuously the soil CO₂ efflux between July 2010 and March 2012 Since external factors such as barometric pressure, rainfall, soil water content, soil and air temperatures, and wind speed influence strongly the observed soil CO₂ effluxes, multiple regression analysis was applied to the time series recorded by the automatic geochemical station LZT01 to remove the contribution of these external factors. The influence of meteorological variables on soil CO₂ efflux oscillations accounts for 13% of total variance, with barometric pressure, rainfall and/or soil water content having the most influence in the control of the soil CO₂ efflux. These observations along with the results from the eight soil gas surveys performed at TVF indicate that the short and long-term trends in the diffuse CO₂ degassing are mainly controlled by environmental factors.     

1986—2019年黑龙江省松嫩平原表层土壤有机碳变化及固碳潜力估算

土壤有机碳(SOC)是评价土壤肥力和固碳能力的重要指标。因此,研究土壤有机碳的变化,对准确评价区域土壤固碳潜力,实现土壤资源的可持续利用具有重要的意义。利用黑龙江省第二次土壤普查数据和2019年实测土壤数据,运用GIS空间分析方法,分析了1986—2019年黑龙江省松嫩平原表层(0~20 cm)土壤有机碳密度(SOCD)的时空变化特征,运用土壤类型法估算了土壤有机碳储量,运用平衡法估算了土壤固碳潜力。结果表明:30多年来表层SOCD平均减少1.06 kg/m²,SOCD减少的地区主要分布在黑龙江省松嫩平原中部和东南部地区;表层SOC储量减少约143.99 Tg,SOC储量减少较多的土壤类型是草甸土、黑钙土和黑土,三者减少量占总SOC储量减少量的84.55%;当前黑龙江省松嫩平原表层土壤固碳潜力为-2.08 Tg,其中暗棕壤、白浆土、黑土为正潜力,其余土壤类型为负潜力。建议通过增施有机化肥、秸秆还田、推广免耕少耕等方法措施,以提高松嫩平原土壤固碳潜力。     

基于GWR和改进CLUE—S模型的区域土地利用格局模拟研究——以浙江省桐庐县为例

本文以浙江省桐庐县为研究区,在CLUE．S模型空间分析模块的回归建模中,考虑驱动因子对土地利用格局影响的空间不稳定性,用GWRLogistic模型取代全局最小二乘法（OLS）Logistic[D]lJ~模型来建立土地利用格局与其驱动因子之间的回归模型,实现了对CLUE—S模型的改进,并进行土地利用格局模拟的实证研究与模型比较。研究结果表明,运用GWRLogistic对CLUE—S模型进行改进,不但可以获得更高的模拟准确率,而且可以获得各驱动因子对土地利用格局影响的空间分异特征。同时,运用改进的模型定量地分析影响区域土地利用变化的内在机制,进行土地利用格局的动态模拟,预测土地利用未来变化的趋势,可以为桐庐县及其类似地区的土地利用规划决策提供科学依据。     

Visualization of geochemical data on maps at the Geological Survey of Finland

Software for presentation of geochemical data on maps has been integrated into a user-friendly package, ALKEMIA, at the Geological Survey of Finland (GSF). ALKEMIA contains procedures for generating dot maps, colour surface maps, shaded relief maps, and vector images, and for interpolating and smoothing irregularly gridded data. Combining dots and colour surfaces provides a means of visually integrating up to three variables on a map, revealing the spatial relationships between them. The methods employed are described in detail, particularly the moving weighted median, which has been widely used at the GSF for interpolating and smoothing geochemical and other numerical data. Several geochemical and oncological atlases and series of maps have been produced with these methods.     

GaRiRO: Gradient and residual integrated rank ordering of stations in rainfall monitoring network

This paper presents a new method that integrates gradient and residual values for rank ordering of stations in a monitoring network (GaRiRO). The innovation is derived from the fact that the parameter (dependent variable) gauged through the monitoring network is modelled using independent variables that influence its measured quantity. And the dependent variable exhibit non-stationary spatial gradient with respect to the independent variables, particularly in complex terrain. GaRiRO technique was developed to prioritize the rain gauge stations for optimizing the existing network and selection of the best locations for relocation or installation of gauges. Although initially aimed to assist hydrologists with a ranking scheme for rain gauge stations, it can be applied to any environmental, meteorological or hydro-meteorological monitoring network. The new procedure is based on deriving gradient and residual value at each station by modeling the spatial relationship of dependent-independent variable using geographically weighted regression (GWR) technique. For the prospective stations with no record, the gradient value is estimated using GWR model and the residual value is derived from the residual map generated by applying kriging technique on the residual derived at all gauged locations. The method combines the decision factor with analytical strength of GIS for prioritizing the stations which results in limited number of trials for installation or relocation of gauges to yield optimized network configuration.     

土壤碳及其在地球表层系统碳循环中的意义

本文回顾了近１０年来国内外对土壤碳研究的主要进展,分析了土壤碳的移动性及其影响因素,着重针对陆地系统碳汇饱和问题介绍了土壤碳对大气CO²源汇效应的碳转移过程及其在地球表层系统碳循环中的作用,指出应加强对土壤碳转移及其对全球变化的响应、土壤碳固存对大气CO²调控的机制和动态的研究,以便为缓解陆地系统碳汇饱和提供科学依据。     

基于MORPAS的证据权重法在百顺地区铀成矿预测中的应用

以比例尺均为1∶50000的地质矿产图,土壤地球化学图,航放U含量、U/K等值线图建立空间数据库;经空间统计分析,应用MORPAS系统的证据权重法筛选证据层,以印支期岩体、200 m半径构造线缓冲区、岩浆岩组合熵等21个证据层作为预测变量;以广东省百顺地区为研究区,根据证据权重法所求得的后验概率圈出多个找矿远景区。     

Global challenges and adaptations in management practices to preserve soil carbon pool with changing climate

Increasing CO₂ levels and its consequent effects have been prominent with climate change. Three out of ten transgressed planetary boundaries reflect our planet’s status at tipping point. Soil Organic Carbon (SOC) which helps soil supply water and nutrients to plants through roots is inherently related to various ecological systems and needs urgent attention. Although the total SOC globally is more than the total carbon in biosphere and atmosphere, the vulnerability of SOC due to anthropogenic activities is unavoidable. The environmental factors affecting sequestration of SOC, soil fertility, crop production, accelerated SOC removal with rising temperatures, green-house gases emissions and climate change are interrelated. Thus, it is impossible to understand and estimate the various scenarios of impacts on SOC pool with ever-changing ecosystems and related processes in soil environment completely. Based on currently predicted climate change scenarios, if deforestation is controlled and reestablishment is achieved, tropical forests can trap atmospheric CO₂ in the cheapest way and function as the largest sink on earth. The agricultural management practices (AMPs), which have been practiced in the last two decades and found helpful are suitable. However, some innovative adaptations such as crop modelling, selecting types of residue to change microbial communities, practices of grassland-grazing and low-C-emission AMPs are also necessary. To achieve the millennium development goals, we must accomplish food security, which relates all 17 sustainable development goals (SDGs) also relays agricultural systems, soil systems, ecosystem services, soil fertility and how best we nurture SOC pool with supportive AMPs.     

Is there a relationship between economic indicators and road fatalities in Texas? A multiscale geographically weighted regression analysis

GeoJournal - To assess spatial heterogeneity in geographic data, geographically weighted regression (GWR) has been widely used. This study used an advanced version of GWR, multiscale geographically...