Spatial Pair-Copula Modeling of Grade in Ore Bodies: A Case Study

A real-world mining application of pair-copulas is presented to model the spatial distribution of metal grade in an ore body. Inaccurate estimation of metal grade in an ore reserve can lead to failure of a mining project. Conventional kriged models are the most commonly used models for estimating grade and other spatial variables. However, kriged models use the variogram or covariance function, which produces a single average value to represent the spatial dependence for a given distance. Kriged models also assume linear spatial dependence. In the application, spatial pair-copulas are used to appropriately model the non-linear spatial dependence present in the data. The spatial pair-copula model is adopted over other copula-based spatial models since it is better able to capture complex spatial dependence structures. The performance of the pair-copula model is shown to be favorable compared to a conventional lognormal kriged model.     

柴达木盆地植被覆盖度的分析与模拟——空间自相关方法的运用(英文)

Relationship between vegetation and environmental factors has always been a major topic in ecology, but it has also been an important way to reveal vegetation’s dynamic response to and feedback effects on climate change. For the special geographical location and climatic characteristics of the Qaidam Basin, with the support of traditional and remote sensing data, in this paper a vegetation coverage model was established. The quantitative prediction of vegetation coverage by five environmental factors was initially realized through multiple stepwise regression (MSR) models. However, there is significant multicollinearity among these five environmental factors, which reduces the performance of the MSR model. Then through the introduction of the Moran Index, an indicator that reflects the spatial auto-correlation of vegetation distribution, only two variables of average annual rainfall and local Moran Index were used in the final establishment of the vegetation coverage model. The results show that there is significant spatial autocorrelation in the distribution of vegetation. The role of spatial autocorrelation in the establishment of vegetation coverage model has not only improved the model fitting R2 from 0.608 to 0.656, but also removed the multicollinearity among independents.     

Developing a multi-network urbanization model: A case study of urban growth in Denver,Colorado

Urbanization is an important issue concerning diverse scientific and policy communities. Computational models quantifying locations and quantities of urban growth offer numerous environmental and socioeconomic benefits. Traditional urban growth models are based on a single-algorithm fitting procedure and thus restricted on their ability to capture spatial heterogeneity. Accordingly, a GIS-based modeling framework titled multi-network urbanization (MuNU) model is developed that integrates multiple neural networks. The MuNU model enables a filtering approach where input data patterns are automatically reallocated into appropriate neural networks with targeted accuracies. We hypothesize that observations classified by individual neural networks share greater homogeneity, and thus modeling accuracy will increase with the integration of multiple targeted algorithms. Land use and land cover data sets of two time snapshots (1977 and 1997) covering the Denver Metropolitan Area are used for model training and validation. Compared to a single-step algorithm – either a stepwise logistic regression or a single neural network – several improvements are evident in the visual output of the MuNU model. Statistical validations further quantify the superiority of the MuNU model and support our hypothesis of effective incorporation of spatial heterogeneity.     

基于机器学习模型的区域土壤重金属空间预测精度比较研究

为识别区域土壤重金属的空间变异特征并厘清其影响因素,本研究构建了多元线性回归（MLR）、弹性网络回归（ENR）、随机森林（RF）、随机梯度提升（SGB）、堆叠（stacking）集成模型、反向传播神经网络（BP-ANN）、基于模型平均的神经网络集成（avNNet）、线性核支持向量机（SVM-L）和高斯核支持向量机（SVM-R）共九种机器学习模型,利用山东省中部土壤重金属（Cd、Cu、Hg、Pb和Zn）和环境辅助变量数据,开展区域土壤重金属空间预测精度比较研究。结果表明：RF对五种重金属空间预测的决定系数（R²）介于0.263~0.448之间,平均绝对误差（MAE）和均方根误差（RMSE）分别小于8.408和10.636,预测值/实际值（P/O）均接近于1,对五种重金属的预测效果均较为理想,是研究区土壤重金属空间预测的最优模型;SVM-R整体预测性能仅次于RF,各项精度评价指标均相对稳健,可作为备选模型;其余七种模型的预测性能均明显低于RF和SVM-R。RF的空间预测结果显示,研究区五种重金属呈现出相似的空间分布格局,含量均由研究区东北部向西南部递减,包括东北部、北部和南部3个高值区,且高值区与当地工业–交通密集区的分布格局一致,反映出人类活动是研究区土壤重金属空间分异的主要影响因素。本研究可为区域土壤污染调查、评价和管控提供科学参考。     

土地利用变化的多尺度空间自相关分析——以内蒙古翁牛特旗为例

用传统统计学方法模拟和解释土地利用变化的前提条件是研究分析的数据在统计上必须独立且均匀分布。但是空间数据相互之间通常具有依赖性 (即空间自相关),某一变量的值随着测定距离的缩小而变得更相似或更为不同。由于经典线性回归方法未能抓住数据的空间自相关特征,而空间自相关包含一些有用的信息,为了克服这一缺点,利用Moran的I系数自相关图来描述研究区土地利用变化的空间自相关,并且建立了不仅考虑回归而且又考虑空间自相关的混合回归-空间自相关回归模型 (即空间滞后模型)。研究得到：① 研究区土地利用变化模型中不但自变量之间而且因变量之间存在空间正自相关,这表明土地利用变化数据的空间自相关很强;② Moran的I系数随着尺度的变粗而减小,这是由于数据平均时的滤波特性和Moran的I系数对距离的非线性特征造成的;③ 经典线性回归模型的残差也表现出正相关,这表明标准的多元线性回归模型未能考虑土地利用数据所存在的空间依赖性;④ 混合回归-空间自相关回归模型 (即空间滞后模型) 的残差未存在空间自相关,并且有更好的拟合度;⑤ 相对于经典线性回归模型,混合回归-空间自相关回归模型 (即空间滞后模型) 对于存在空间自相关性的数据来说有着统计上的合理性,而经典线性回归模型未能考虑这些因素。     

Conflation and aggregation of spatial data improve predictive models for species with limited habitats: A case of the threatened yellow-billed cuckoo in Arizona,USA

Riparian vegetation provides important wildlife habitat in the southwestern United States, but limited distributions and spatial complexity often leads to inaccurate representation in maps used to guide conservation. We test the use of data conflation and aggregation on multiple vegetation/land-cover maps to improve the accuracy of habitat models for the threatened western yellow-billed cuckoo (Coccyzus americanus occidentalis). We used species observations (n = 479) from a state-wide survey to develop habitat models from 1) three vegetation/land-cover maps produced at different geographic scales ranging from state to national, and 2) new aggregate maps defined by the spatial agreement of cover types, which were defined as high (agreement = all data sets), moderate (agreement ≥ 2), and low (no agreement required). Model accuracies, predicted habitat locations, and total area of predicted habitat varied considerably, illustrating the effects of input data quality on habitat predictions and resulting potential impacts on conservation planning. Habitat models based on aggregated and conflated data were more accurate and had higher model sensitivity than original vegetation/land-cover, but this accuracy came at the cost of reduced geographic extent of predicted habitat. Using the highest performing models, we assessed cuckoo habitat preference and distribution in Arizona and found that major watersheds containing high-probably habitat are fragmented by a wide swath of low-probability habitat. Focus on riparian restoration in these areas could provide more breeding habitat for the threatened cuckoo, offset potential future habitat losses in adjacent watershed, and increase regional connectivity for other threatened vertebrates that also use riparian corridors.     

基于定性和定量辅助变量的土壤有机质空间分布预测——以四川三台县为例

准确获取土壤性质的空间分布信息,是区域土壤资源优化利用和土壤环境保护的需要。以川中丘陵区三台县为案例区,运用人工神经网络模型,构建融合区域定性及定量辅助变量的空间预测方法,模拟三台县土壤有机质的空间分布格局。结果表明,研究区土壤有机质在4.20~47.60 g kg^-1之间,平均为17.97 g kg^-1;变异系数为36.89%,属中等程度变异。土壤有机质的块金值与基台值之比为0.742,变程为7.0 km,即空间自相关性较弱。不同土壤类型间有机质含量差异显著;土属的空间分布较土类能更好地揭示研究区土壤有机质含量空间分布格局的差异。除土壤类型因素的影响外,坡度、地形湿度及植被盖度是研究区土壤有机质空间变异的主要因子。融合土壤类型因素和地形植被因子的神经网络模型预测结果,比普通克里格法、回归克里格法以及神经网络结合普通克里格的方法,更符合研究区地学规律和实际情况;其预测结果的平均绝对误差、平均相对误差和均方根误差较其他3种方法均降低幅度显著。同时,该方法对极值有较好的预测能力。研究为复杂环境条件下准确获取区域土壤性质的空间分布信息提供了较可行的方法。     

基于数字相机图像的长白山森林物候模拟

植被物候作为全球植被和陆面过程模型的重要参数,对其状态的准确描述在很大程度上决定着模型的模拟精度。温带森林作为北半球中高纬度地区主要植被类型及全球重要碳源,研究其物候期的变化将提高对区域碳通量的估算精度。本文以长白山阔叶红松林为研究对象,探讨了数字相机图像在物种尺度物候模拟及群落尺度物候模型改进方面的作用,结果如下：（1）物种尺度上,利用数字相机能获取两种植被（红松,蒙古栎）较为准确的物候期（与人工观测数据比较,绝对误差〈3d）;（2）群落尺度上,基于数字相机图像获取的冠层状态数据提高了基于气象数据的物候模型（GSI：growingseasonindex）的模拟精度（R2=0．9）,尤其是秋季物候模拟,为进一步分析群落物候的环境控制因子提供了有力手段。研究表明：数字相机不仅能够提供精确地基于物种尺度的物候数据,还可为遥感物候数据的校正提供参考,同时为生态模型中物候模块的改进及降低区域尺度碳通量模拟不确定性提供了新的思路。     

Incorporation of multi-scale spatial autocorrelation in soil moisture–landscape modeling

Effects of spatial autocorrelation (SAC), or spatial structure, have often been neglected in the conventional models of pedogeomorphological processes. Based on soil, vegetation, and topographic data collected in a coastal dunefield in western Korea, this research developed three soil moisture–landscape models, each incorporating SAC at fine, broad, and multiple scales, respectively, into a non-spatial ordinary least squares (OLS) model. All of these spatially explicit models showed better performance than the OLS model, as consistently indicated by R², Akaike’s information criterion, and Moran’s I. In particular, the best model was proved to be the one using spatial eigenvector mapping, a technique that accounts for spatial structure at multiple scales simultaneously. After including SAC, predictor variables with greater inherent spatial structure underwent more reduction in their predictive power than those with less structure. This finding implies that the environmental variables pedogeomorphologists have perceived important in the conventional regression modeling may have a reduced predictive power in reality, in cases where they possess a significant amount of SAC. This research demonstrates that accounting for spatial structure not only helps to avoid the violation of statistical assumptions, but also allows a better understanding of dynamic soil hydrological processes occurring at different spatial scales.     

Mapping transit‐based access: integrating GIS,routes and schedules

Seabed sediment textural parameters such as mud, sand and gravel content can be useful surrogates for predicting patterns of benthic biodiversity. Multibeam sonar mapping can provide near-complete spatial coverage of high-resolution bathymetry and backscatter data that are useful in predicting sediment parameters. Multibeam acoustic data collected across a ～1000 km² area of the Carnarvon Shelf, Western Australia, were used in a predictive modelling approach to map eight seabed sediment parameters. Four machine learning models were used for the predictive modelling: boosted decision tree, random forest decision tree, support vector machine and generalised regression neural network. The results indicate overall satisfactory statistical performance, especially for %Mud, %Sand, Sorting, Skewness and Mean Grain Size. The study also demonstrates that predictive modelling using the combination of machine learning models has provided the ability to generate prediction uncertainty maps. However, the single models were shown to have overall better prediction performance than the combined models. Another important finding was that choosing an appropriate set of explanatory variables, through a manual feature selection process, was a critical step for optimising model performance. In addition, machine learning models were able to identify important explanatory variables, which are useful in identifying underlying environmental processes and checking predictions against the existing knowledge of the study area. The sediment prediction maps obtained in this study provide reliable coverage of key physical variables that will be incorporated into the analysis of covariance of physical and biological data for this area.     

Spatio-temporal regression kriging for modelling urban NO2 concentrations

ABSTRACT

Recently developed urban air quality sensor networks are used to monitor air pollutant concentrations at a fine spatial and temporal resolution. The measurements are however limited to point support. To obtain areal coverage in space and time, interpolation is required. A spatio-temporal regression kriging approach was applied to predict nitrogen dioxide (NO₂) concentrations at unobserved space-time locations in the city of Eindhoven, the Netherlands. Prediction maps were created at 25 m spatial resolution and hourly temporal resolution. In regression kriging, the trend is separately modelled from autocorrelation in the residuals. The trend part of the model, consisting of a set of spatial and temporal covariates, was able to explain 49.2% of the spatio-temporal variability in NO₂ concentrations in Eindhoven in November 2016. Spatio-temporal autocorrelation in the residuals was modelled by fitting a sum-metric spatio-temporal variogram model, adding smoothness to the prediction maps. The accuracy of the predictions was assessed using leave-one-out cross-validation, resulting in a Root Mean Square Error of 9.91 μg m^?3, a Mean Error of ?0.03 μg m^?3 and a Mean Absolute Error of 7.29 μg m^?3. The method allows for easy prediction and visualization of air pollutant concentrations and can be extended to a near real-time procedure.     

Modelling patterned ground distribution in Finnish Lapland: an integration of topographical, ground and remote sensing information

New data technologies and modelling methods have gained more attention in the field of periglacial geomorphology during the last decade. In this paper we present a new modelling approach that integrates topographical, ground and remote sensing information in predictive geomorphological mapping using generalized additive modelling (GAM) . First, we explored the roles of different environmental variable groups in determining the occurrence of non‐sorted and sorted patterned ground in a fell region of 100 km2 at the resolution of 1 ha in northern Finland. Second, we compared the predictive accuracy of ground‐topography‐ and remote‐sensing‐based models. The results indicate that non‐sorted patterned ground is more common at lower altitudes where the ground moisture and vegetation abundance is relatively high, whereas sorted patterned ground is dominant at higher altitudes with relatively high slope angle and sparse vegetation cover. All modelling results were from good to excellent in model evaluation data using the area under the curve (AUC) values, derived from receiver operating characteristic (ROC) plots. Generally, models built with remotely sensed data were better than ground‐topography‐based models and combination of all environmental variables improved the predictive ability of the models. This paper confirms the potential utility of remote sensing information for modelling patterned ground distribution in subarctic landscapes.     

基于土壤水分和气象要素的林火预报研究——以广东省为例

基于国内现行的森林火险气象指数和单因子火险贡献度模型,以及逻辑回归模型和随机森林模型,在林火预报中引入微波遥感土壤水分信息,使用MCD14DL火点数据集和地面气象观测资料对广东省不同时间尺度的林火发生概率进行预测。结果表明：逻辑回归模型和随机森林模型构建的林火预测模型显著优于现行的森林火险气象指数和单因子火险贡献度模型,预测精度提升约20%。其中,随机森林模型对林火频数的解释程度最高（两者相关系数为0.476）。此外,加入微波土壤水分信息后,相较原有的基于气象要素的林火预测模型,2种机器学习模型的预测精度均略有提升,体现了表层土壤水分信息在林火预报中的重要性。研究可为高效提取对地观测信息,以改进华南地区不同时间尺度的林火预报工作提供参考。     

Boosting for Mineral Prospectivity Modeling: A New GIS Toolbox

With an increasing demand for raw materials, predictive models that support successful mineral exploration targeting are of great importance. We evaluated different machine learning techniques with an emphasis on boosting algorithms and implemented them in an ArcGIS toolbox. Performance was tested on an exploration dataset from the Iberian Pyrite Belt (IPB) with respect to accuracy, performance, stability, and robustness. Boosting algorithms are ensemble methods used in supervised learning for regression and classification. They combine weak classifiers, i.e., classifiers that perform slightly better than random guessing to obtain robust classifiers. Each time a weak learner is added; the learning set is reweighted to give more importance to misclassified samples. Our test area, the IPB, is one of the oldest mining districts in the world and hosts giant volcanic-hosted massive sulfide (VMS) deposits. The spatial density of ore deposits, as well as the size and tonnage, makes the area unique, and due to the high data availability and number of known deposits, well-suited for testing machine learning algorithms. We combined several geophysical datasets, as well as layers derived from geological maps as predictors of the presence or absence of VMS deposits. Boosting algorithms such as BrownBoost and Adaboost were tested and compared to Logistic Regression (LR), Random Forests (RF) and Support Vector machines (SVM) in several experiments. We found performance results relatively similar, especially to BrownBoost, which slightly outperformed LR and SVM with respective accuracies of 0.96 compared to 0.89 and 0.93. Data augmentation by perturbing deposit location led to a 7% improvement in results. Variations in the split ratio of training and test data led to a reduction in the accuracy of the prediction result with relative stability occurring at a critical point at around 26 training samples out of 130 total samples. When lower numbers of training data were introduced accuracy dropped significantly. In comparison with other machine learning methods, Adaboost is user-friendly due to relatively short training and prediction times, the low likelihood of overfitting and the reduced number of hyperparameters for optimization. Boosting algorithms gave high predictive accuracies, making them a potential data-driven alternative for regional scale and/or brownfields mineral exploration.

     

Predictive mapping of soil total nitrogen at a regional scale: A comparison between geographically weighted regression and cokriging

Accurately mapping the spatial distribution of soil total nitrogen is important to precision agriculture and environmental management. Geostatistical methods have been frequently used for predictive mapping of soil properties. Recently, a local regression method, geographically weighted regression (GWR), got the attention of environmentalists as an alternative in spatial modeling of environmental attributes, due to its capability of incorporating various auxiliary variables with spatially varied correlation coefficients. The objective of this study is to compare GWR and ordinary cokriging (OCK) in predictive mapping of soil total nitrogen (TN) using multiple environmental variables. 353 soil Samples within the surface horizon of 0–20 cm in a study area were collected, and their TN contents were measured for calibrating and validating the GWR and OCK interpolations. The environmental variables finally chosen as auxiliary data include elevation, land use types, and soil types. Results indicate that, although OCK is slightly better than GWR in global accuracy of soil TN prediction (the adjusted R² for GWR and OCK are 0.5746 and 0.6858, respectively), the soil TN map interpolated by GWR shows many details reflecting the spatial variations of major auxiliary variables while OCK smoothes out almost all local details. Geographically weighted regression could account for both the spatial trend and local variations, whilst OCK had difficulties to capture local variations. It is concluded that GWR is a more promising spatial interpolation method compared to OCK in predicting soil TN and potentially other soil properties, if a suitable set of auxiliary variables are available and selected.     

潜在自然植被研究与展望

植被－环境关系是全球变化与陆地生态系统关系研究的关键,由于受人类几千年活动的影响,全球原始自然植被所剩极少,植被的破坏掩盖了植被与环境之间的关系。如何重演反映环境决定特点的潜在自然植被,是植被－环境关系研究的起点。本文对潜在自然植被的概念、研究的时空尺度进行了论述,指出潜在自然植被研究2个发展阶段的特点：（1）传统的以经验为主的定性研究;（2）以潜在自然植被预测模型为基础的定量研究。在此基础上,提出今后我国潜在自然植被研究的重点应放在：针对我国自然植被分布的特点,构建适用于我国特定区域的潜在自然植被预测模型,对不同时空尺度下的潜在自然植被类型及其地理分布进行模拟与制图;单纯将气候作为潜在自然植被预测模型自变量的方法正逐渐为综合的环境变量方法所取代,并强调了潜在自然植被研究必须考虑与大气环流模式的耦合。     

黄土高原祖厉河流域潜在植被分布模拟研究

植被空间分布格局深受环境影响, 分析植被与环境之间的关系一直是植物生态学的中心问题。论文从黄土高原自然植被恢复问题出发, 引出植被恢复的参照标准- 潜在自然植被。 依据气象站和雨量站观测资料, 建立气温降水与海拔高度和地理位置的统计方程, 以GIS 为技术支撑, 对两大环境变量进行空间化。通过野外观测取样, 结合气温降水的空间分布, 界定祖厉河流域不同草地覆盖度的生境空间。根据生境空间边界函数, 对整个流域内的潜在草地植被分布进行模拟。草地分布现状与潜在草地分布对比发现, 低覆盖草地由北向南扩展, 占潜在中覆盖草地面积的37.39%, 占潜在高覆盖草地面积的34.98%。中覆盖草地也在此向南部区域和北部山地扩展, 占潜在高覆盖草面积的11.51%。高覆盖草地收缩到南部和北部山地狭小地带, 仅占其应有区域的4.84%。     

Predictive land value modelling in Guatemala City using a geostatistical approach and Space Syntax

ABSTRACT

Spatial information of land values is fundamental for planners and policy makers. Individual appraisals are costly, explaining the need for predictive modelling. Recent work has investigated using Space Syntax to analyse urban access and explain land values. However, the spatial dependence of urban land markets has not been addressed in such studies. Further, the selection of meaningful variables is commonly conducted under non-spatialized modelling conditions. The objective of this paper is to construct a land value map using a geostatistical approach using Space Syntax and a spatialized variable selection. The methodology is applied in Guatemala City. We used an existing dataset of residential land value appraisals and accessibility metrics. Regression-kriging was used to conduct variable selection and derive a model for spatial prediction. The prediction accuracy is compared with a multivariate regression. The results show that a spatialized variable selection yields a more parsimonious model with higher prediction accuracy. New insights were found on how Space Syntax explains land value variability when also modelling the spatial dependence. Space Syntax can contribute with relevant spatialized information for predictive land value modelling purposes. Finally, the spatial modelling framework facilitates the production of spatial information of land values that is relevant for planning practice.     

基于3种地理加权回归方法的安徽省土壤pH空间预测制图对比研究

基于安徽省140个采样点的土壤pH数据,综合考虑土壤、地形、气候、生物等因子对土壤pH的影响,采用地理加权回归(Geographically Weighted Regression, GWR)、主成分地理加权回归(Principal Component Geographically Weighted Regression, PCA-GWR)和混合地理加权回归(Mixed Geographically Weighted Regression, M-GWR)3种模型对安徽省土壤pH空间分布进行建模预测,揭示环境因子对土壤pH的影响在空间上的差异,最后以多元线性回归模型(Multiple Linear Regression, MLR)为基准比较3种GWR模型的精度。研究表明：(1)安徽省土壤pH具有空间异质性,且集聚特征明显。(2) 3种GWR模型中M-GWR模型略优,GWR、PCA-GWR和M-GWR的建模集调整后决定系数(R_adj²)分别为0.59、0.62和0.63;对比MLR模型,3种GWR模型的R_adj^2<...     

基于改进植被指数土壤水分遥感反演

以渭-库绿洲为例,基于Landsat8 OLI遥感数据,考虑到短波红外特征与土壤水分有很好的关联,将短波红外波段引入可见光-近红外波段构成的传统植被指数中,旨在建立新的植被指数监测土壤水分。基于改进前后共8种植被指数,通过灰色关联分析（GRA）筛选出3种高关联度植被指数,再用偏最小二乘回归（PLSR）进行建模,然后用该模型对研究区土壤水分反演,并对其空间分布格局进一步分析。结果显示：（1）在传统植被指数的基础上引入信息量较大的短波红外,可大幅度降低植被指数间的VIF,消除其多重共线性。（2）通过GRA分析可知,改进后的植被指数与土壤水分之间的关联度均要高于传统植被指数。（3）通过GRA分析筛选出3种高关联度植被指数建立得到精度较高,稳定性较好的PLSR模型,并反演研究区土壤水分分布状况,土壤水分总体上至西向东,由北到南降低,然而土壤水分最小值主要分布在绿洲—荒漠交错带,使得交错带成为“生态裂谷”。研究表明：将短波红外波段引入到可见光-近红外植被指数中,建立的新植被指数可获得较好的土壤水分空间分布反演结果。