Automated classification of landform elements using object-based image analysis

This paper presents an automated classification system of landform elements based on object-oriented image analysis. First, several data layers are produced from Digital Terrain Models (DTM): elevation, profile curvature, plan curvature and slope gradient. Second, relatively homogenous objects are delineated at several levels through image segmentation. These object primatives are classified as landform elements using a relative classification model, built both on the surface shape and on the altitudinal position of objects. So far, slope aspect was not used in classification. The classification has nine classes: peaks and toe slopes (defined by the altitudinal position or the degree of dominance), steep slopes and flat/gentle slopes (defined by slope gradients), shoulders and negative contacts (defined by profile curvatures), head slopes, side slopes and nose slopes (defined by plan curvatures). Classes are defined using flexible fuzzy membership functions. Results are visually analyzed by draping them over DTMs. Specific fuzzy classification options were used to obtain an assessment of output accuracy. Two implementations of the methodology are compared using (1) Romanian datasets and (2) Berchtesgaden National Park, Germany. The methodology has proven to be reproducible; readily adaptable for diverse landscapes and datasets; and useful in respect to providing additional information for geomorphological and landscape studies. A major advantage of this new methodology is its transferability, given that it uses only relative values and relative positions to neighboring objects. The methodology introduced in this paper can be used for almost any application where relationships between topographic features and other components of landscapes are to be assessed.     

An Application of Bayesian Inverse Methods to Vertical Deconvolution of Hydraulic Conductivity in a Heterogeneous Aquifer at Oak Ridge National Laboratory

A Bayesian inverse method is applied to two electromagnetic flowmeter tests conducted in fractured weathered shale at Oak Ridge National Laboratory. Traditional deconvolution of flowmeter tests is also performed using a deterministic first-difference approach; furthermore, ordinary kriging was applied on the first-difference results to provide an additional method yielding the best estimate and confidence intervals. Depth-averaged bulk hydraulic conductivity information was available from previous testing. The three methods deconvolute the vertical profile of lateral hydraulic conductivity. A linear generalized covariance function combined with a zoning approach was used to describe structure. Nonnegativity was enforced by using a power transformation. Data screening prior to calculations was critical to obtaining reasonable results, and the quantified uncertainty estimates obtained by the inverse method led to the discovery of questionable data at the end of the process. The best estimates obtained using the inverse method and kriging compared favorably with first-difference confirmatory calculations, and all three methods were consistent with the geology at the site.     

腾格里沙漠油蒿群落土壤水分与碳酸钙淀积关系分析

为了阐明干旱区土壤含水量和土壤碳酸钙含量之间的关系,选择腾格里沙漠油蒿群落为研究对象,对80m×80m样地内400个4m×4m样方的土壤水分和碳酸钙的分布状况及二者含量之间的关系、空间异质性进行了研究。结果表明:腾格里沙漠油蒿群落土壤含水量呈均匀分布,碳酸钙含量呈小斑块分布;土壤含水量与碳酸钙含量之间存在显著的线性正相关关系,表明碳酸钙含量随着土壤含水量的增加而增加;土壤碳酸钙含量和土壤含水量空间异质性之间呈显著单调递增的幂函数关系。可见,干旱区土壤含水量和碳酸钙含量之间具有密切的关系,土壤含水量越高,碳酸钙淋溶和淀积作用加强,土壤碳酸钙含量也就随之升高。因而,土壤含水量是影响土壤碳酸钙形成的一个重要因素。     

景观空间异质性对地表水质服务的影响研究——以河南省伊河流域为例

人类干扰使景观空间异质性发生了剧烈变化,进而对生态系统服务产生了严重影响。目前,针对景观空间异质性与生态系统服务的关联关系研究还较为薄弱。以河南省伊河流域为研究区域,以地表水质服务为研究对象,分析了地表水质服务的空间变化;通过以水质采样点为中心建立缓冲区,分析了不同尺度下的景观空间异质性特征,并与地表水质指标进行相关性研究,以探讨景观空间异质性与地表水质服务间的影响规律及响应机制。结果表明：① 研究区地表水质状况整体上为西南、东北部较差,中部较好;② 从景观组成来看,林地面积占比或斑块个数的增加有利于地表水质的提高;③ 类型水平上,林地的景观构型与化学需氧量的质量浓度在200 m半径缓冲区内显著相关（P<0.05）,城镇用地的景观构型对地表水质的影响则在2000 m半径缓冲区内更为明显;④ 景观水平上,总磷的质量浓度受到景观构型变化的强烈影响（P<0.05）,景观构型异质性越低,总磷的质量浓度就越高;⑤ 适当增加景观空间异质性可有效降低地表水质污染物的质量浓度,提高地表水质服务。研究结论可为伊河流域的空间开发及水环境保护政策的制定提供科学依据。     

建成环境对城市不同年龄群体活力的时空异质性影响研究

城市活力是城市空间质量的重要体现,对构建可持续发展的城市空间具有重要参考意义。该文以西宁市主城区为例,利用手机信令数据探索城市不同年龄群体活力的空间分布模式,采用多元线性逐步回归模型和多尺度地理加权回归(MGWR)模型定量研究建成环境对城市不同年龄群体活力的时空异质性影响。研究发现:1)根据不同年龄群体活动能力和需求的差异可将活力空间分布模式划分为“离核紧凑型”“离核延伸型”“聚核延伸型”“聚核紧凑型”4种群体活力分布模式。2)建成环境对群体活力影响的群体差异和时空异质性显著。区位条件普遍影响各群体活力且具有稳健的时间变化特征;道路交叉口密度有利于青年和老年群体活力的提升,且白天影响力高于夜晚;公交线路数量仅与老年群体白天活力呈正相关关系;白天时段建筑密度对少年和老年群体活力的积极影响高于夜晚;容积率对青年群体夜晚活力和老年群体白天活力的影响完全相反;相比功能混合度,具体配套设施密度对各群体活力影响更突出。研究结果可为城市精准设计和差异化设施布局提供借鉴,促进城市空间协调发展。     

利用矢量影像法进行土地利用变化自动检测

为解决土地利用矢量图与遥感影像的变化检测问题,提出了一种基于类别的矢量图与遥感影像变化检测方法。在矢量图约束下,对遥感影像进行影像分割获取像斑;提取像斑在遥感影像上的直方图特征,采用G统计量度量像斑之间的特征距离;利用像斑与其他相同类别像斑之间的特征距离,构建单波段上像斑的类别异质度,自适应加权组合各波段上像斑的类别异质度构建像斑的类别异质度;依据最大熵方法获取各地物类别对应的异质度阈值,以类别为单位对各像斑进行变化判别,获取变化检测结果。在QuickBird遥感影像上的试验验证了本文方法的有效性,实现了矢量图与遥感影像的自动变化检测。     

地形通行性能的研究

本文从计算机地形分析的角度，研究了地形通行性能分析的方法与步骤，对涉及的地形因素，采用调研和试验得到的数据，建立了数学模型。     

顾及空间异质性的大范围地面沉降时空预测

大速率、不均匀的地面沉降已经威胁到人类的生产活动,高精度的沉降预测结果对于地质灾害的精准防控具有重要意义。为掌握地面沉降的演化规律,利用现场观测数据或InSAR数据开展了多项预测研究。然而,由于空间异质性的存在,大范围地面沉降的准确预测仍然是一项挑战。在这项研究中,从数据驱动的角度提出了一种顾及空间异质性的大范围地面沉降时空预测方法 STLSTM (Spatio-temporal Long Short-Term Memory)。首先,通过聚类识别地理空间中的均质子区;然后,在每个子区中,一个特别的长短期记忆LSTM (Long ShortTerm Memory)网络被用来捕捉局部位置的非线性特征;最后,利用预训练的网络对未来时刻的地面沉降进行定量预测。在实验部分,哨兵1号影像数据被用来比较STLSTM与其他8种基准方法的性能,利用空间统计指标分析了模型的有效性。结果表明,STLSTM在152 s内达到了最高的预测精度（71.4%）,且能够有效弱化空间异质性对大区域沉降预测任务的影响。总之,这项研究将空间异质性处理策略融合到深度学习模型中,实现了高精度、高时效的大范围地面沉降时空预测。     

关键陆表参数遥感产品真实性检验方法研究进展

遥感产品真实性检验是定量遥感基础研究的重要环节,是客观评价遥感产品精度、稳定性和一致性的重要手段,对于提高遥感定量化水平、推动和扩展遥感产品的深化应用具有重要的意义。本文基于当前陆表参数遥感产品真实性检验研究的相关成果,归纳总结了陆表参数遥感产品真实性检验方法,并就其中的5种真实性检验方法,即基于地面单点测量检验、基于地面多点采样检验、基于高分辨率数据的检验、交叉检验和间接检验,阐述了各方法的特点、局限性、适用条件。文章在最后探讨了陆表关键参数遥感产品真实性检验评价指标和验证中影响精度评价的关键因素。本文对于遥感产品真实性检验工作的开展和真实性检验系统研发具有重要的指导意义。     

How much variation in land surface phenology can climate oscillation modes explain at the scale of mountain pastures in Kyrgyzstan?

Climate oscillation modes can shape weather across the globe due to atmospheric teleconnections. We built on the findings of a recent study to assess whether the impacts of teleconnections are detectable and significant in the early season dynamics of highland pastures across five rayons in Kyrgyzstan. Specifically, since land surface phenology (LSP) has already shown to be influenced by snow cover seasonality and terrain, we investigated here how much more explanatory and predictive power information about climatic oscillation modes might add to explain variation in LSP. We focused on seasonal values of five climate oscillation indices that influence vegetation dynamics in Central Asia. We characterized the phenology in highland pastures with metrics derived from LSP modeling using Landsat NDVI time series together with MODIS land surface temperature (LST) data: Peak Height (PH), the maximum modeled NDVI and Thermal Time to Peak (TTP), the quantity of accumulated growing degree-days based on LST required to reach PH. Next, we calculated two metrics of snow cover seasonality from MODIS snow cover composites: last date of snow (LDoS), and the number of snow covered dates (SCD). For terrain features, we derived elevation, slope, and TRASP index as linearization of aspect. First, we used Spearman’s rank correlation to assess the geographical differentiation of land surface phenology metrics responses to environmental variables. PH showed weak correlations with TTP (positive in western but negative in eastern rayons), and moderate relationships with LDoS and SCD only in one northeastern rayon. Slope was weakly related to PH, while TRASP showed a consistent moderate negative correlation with PH. A significant but weak negative correlation was found between PH and SCAND JJA, and a significant weak positive correlation with MEI MAM. TTP showed consistently strong negative relationships with LDoS, SCD, and elevation. Very weak positive correlations with TTP were found for EAWR DJF, AMO DJF, and MEI DJF in western rayons only. Second, we used Partial Least Squares regression to investigate the role of oscillation modes altogether. PLS modelling of TTP showed that thermal time accumulation could be explained mostly by elevation and snow cover metrics, leading to reduced models explaining 55 to 70% of observed variation in TTP. Variable selection indicated that NAO JJA, AMO JJA and SCAND MAM had significant relationships with TTP, but their input of predictive power was neglible. PLS models were able to explain up to 29% of variability in PH. SCAND JJA and MEI MAM were shown to be significant predictors, but adding them into models did not influence modeling performance. We concluded the impacts of climate oscillation anomalies were not detectable or significant in mountain pastures using LSP metrics at fine spatial resolution. Rather, at a 30 m resolution, the indirect effects of seasonal climatic oscillations are overridden by terrain influences (mostly elevation) and snow cover timing. Whether climate oscillation mode indices can provide some new and useful information about growing season conditions remains a provocative question, particularly in light of the multiple environmental challenges facing the agropastoralism livelihood in montane Central Asia.