喀斯特地区地表温度空间降尺度方法初探

喀斯特地区地形起伏大,常规的降尺度方法及所选择的因子对其不适用。该文根据喀斯特地区的特点,选取反射率、遥感指数及高程因子为尺度因子,通过随机森林模型建立MODIS第31、32波段辐射亮度数据和尺度因子之间的非线性关系,构建适合喀斯特地区的随机森林(Karst Random Forest,KRF)模型,成功将空间分辨率为1 km的热红外辐射亮度降至100 m,最后利用劈窗算法反演得到空间分辨率为100 m的地表温度。将KRF方法与仅考虑常规因子的多因子随机森林回归模型(MTVRF)和热锐化算法(TsHARP)对比,结果表明:1)在不同高差的喀斯特地区,KRF方法可较大程度提高地表温度降尺度精度,均方根误差(RMSE)在遵义市西北部和贵阳市以南地区分别为2.46 K和1.45 K,较MTVRF模型分别降低了0.1419 K和0.2928 K,较TsHARP算法分别降低了0.6204 K和0.6953 K,且在地形起伏度较低的喀斯特山区城市(贵阳市以南)表现更好;2)在喀斯特地区不同地类上,KRF方法效果也较好,其中植被区域最优,RMSE为1.41 K,破碎的裸土区域RMSE为1.84 K。研究显示,考虑特殊尺度因子的KRF方法可提高喀斯特地区地表温度的降尺度精度,为该地区以地表温度为基础的研究提供更精细可靠的地表温度产品。     

基于粒子滤波的行车轨迹路网匹配方法

行车轨迹是一种时间序列的地理空间位置采样数据,而传统的轨迹—路网匹配方法主要以全局或局部寻优的方式建立轨迹—路网匹配关系,影响了时空场景中数据的匹配计算过程的相对独立性。针对这个问题,本文基于粒子滤波（Particle Filter,PF）原理建立行车轨迹与道路网络之间的匹配关系。首先,沿轨迹中车辆运动方向在道路网络中搜索邻近道路节点,在与道路节点拓扑邻接的道路弧段上初始化随机生成粒子,根据轨迹中车辆运动模型将粒子沿所在道路弧段移动;然后,基于PF原理计算各时刻粒子运动状态及与行车轨迹采样点之间的距离误差,根据高斯概率密度函数计算粒子权重并利用随机重采样方法进行粒子重采样,迭代更新粒子运动状态;最后,计算与搜索到的道路节点拓扑邻接的每条道路弧段中累计粒子权重,通过各道路弧段累计权重计算轨迹—路网匹配关系。以行车轨迹进行实验表明,利用本文方法可以通过粒子时空变化反映采样点的移动,行车轨迹—路网匹配结果的正确率大于85%,能够实现行车轨迹和路网的准确匹配。     

Tropical land use land cover mapping in Pará (Brazil) using discriminative Markov random fields and multi-temporal TerraSAR-X data

Remote sensing satellite data offer the unique possibility to map land use land cover transformations by providing spatially explicit information. However, detection of short-term processes and land use patterns of high spatial–temporal variability is a challenging task.We present a novel framework using multi-temporal TerraSAR-X data and machine learning techniques, namely discriminative Markov random fields with spatio-temporal priors, and import vector machines, in order to advance the mapping of land cover characterized by short-term changes. Our study region covers a current deforestation frontier in the Brazilian state Pará with land cover dominated by primary forests, different types of pasture land and secondary vegetation, and land use dominated by short-term processes such as slash-and-burn activities. The data set comprises multi-temporal TerraSAR-X imagery acquired over the course of the 2014 dry season, as well as optical data (RapidEye, Landsat) for reference. Results show that land use land cover is reliably mapped, resulting in spatially adjusted overall accuracies of up to 79% in a five class setting, yet limitations for the differentiation of different pasture types remain.The proposed method is applicable on multi-temporal data sets, and constitutes a feasible approach to map land use land cover in regions that are affected by high-frequent temporal changes.     

南半球中高纬度区域不同类型云的辐射特性

利用CloudSat的2B-CLDCLASS-LIDAR云分类产品和2B-FLXHR-LIDAR辐射产品4 a（2007-2010年）的数据,定量分析了单层云（高云、中云、低云）和3种双层云（如:高云与中云共存、高云与低云共存以及中云与低云共存）在南半球中高纬度（40°-65°S）的云量、云辐射强迫和云辐射加热率。其中云辐射加热率定义为有云时的大气加热率廓线与晴空大气加热率廓线的差值。结果表明:研究区域盛行单层低云和单层中云,其云量分别为44.1%和10.3%。并且,中云重叠低云在双层云中云量也是最大（8.7%）。不同类型云的云量也显著影响着其云辐射强迫。单层低云在大气层顶、地表以及大气中的净云辐射强迫分别是-64.8、-56.5和-8.4 W/m2,其绝对值大于其他类型云。虽然单层的中云在大气层顶和地表的净辐射强迫也为负值,但其在大气中的净云辐射强迫为正值（2.3 W/m2）。最后,讨论了不同类型云对大气中辐射能量垂直分布的影响。所有类型云的短波（或长波）云辐射加热率都随高度升高表现为由负值转为正值（或由正值转为负值）。对于大部分云,其净云辐射加热率主要由长波云辐射加热率决定。这些研究结果旨在为模式中云重叠参数化方案在区域的适用性评估及改进提供观测依据。      

地球轨道旋回沉积节律研究进展——兼论轨道旋回的沉积学特征、年代学意义和研究方法

地球轨道旋回是古代沉积记录中常见的由地球轨道参数变动控制的沉积节律,它不仅自身表现显著的节律性,而且通过反馈体系强烈影响着沉积作用。同时,轨道旋回节律在不同的沉积背景下以不同的沉积特征显现和规则性叠加。地球轨道旋回赋有的规则频率变化特征又使其具有年代学意义和应用价值,它是天文地质学、地层学及沉积学研究的重点内容。根据国内外资料综述了这些方面的研究进展,并探讨了地球轨道旋回的研究方法。     

高分辨率青藏高原历史月降水数据重建

青藏高原对全球气候研究具有重要意义,而降水数据对水文、气象和生态等领域的研究也至关重要,且随着研究内容和尺度的变化,对高时空分辨率的历史降水数据的需求越发迫切。本文基于TRMM 3B43降水数据,采用随机森林算法,引入归一化植被指数(AVHRR NDVI)、高程(SRTM DEM)、坡度、坡向、经度、纬度6个地理因子,建立历史降水重建模型,获得1982-1997年分辨率为0.0833°的青藏高原年降水数据,然后根据比例系数法计算出月降水数据。为提高精度,利用站点数据对月降水数据进行校正。结果表明,该方法能简单有效地获得高时空分辨率的历史降水数据,决定系数R²大部分在0.4~0.9之间,平均值为0.6767,其中夏季效果最好,冬季效果最差;均方根误差RMSE和平均绝对误差MAE均在50 mm以下,RMSE均值为22.66 mm,MAE均值为15.97 mm;偏差Bias较小,基本在0.0~0.1之间。     

基于数值预报和随机森林算法的强对流天气分类预报技术

随机森林算法是当前得到较为广泛应用的机器学习方法之一,有着很高的预测精度,训练结果稳定,泛化能力强,解决多分类问题有明显优势。本文将随机森林算法应用于强对流的潜势预测和分类,分短时强降水、雷暴大风、冰雹和无强对流四种类别,基于2005—2016年NCEP 1°×1°再分析资料计算的对流指数和物理量,开展强对流天气的分类训练、0～12 h预报和检验,经2015—2016年独立测试样本检验表明,针对强对流发生站点的点对点检验,整体误判率为21. 9%,85次强对流过程基本无漏报,模型尤其适用于较大范围强对流天气。随机森林算法筛选的因子物理意义较为明确,和主观预报经验基本相符,模型准确率高,可用于日常业务。     

Classifying natural-language spatial relation terms with random forest algorithm

The exponential growth of natural language text data in social media has contributed a rich data source for geographic information. However, incorporating such data source for GIS analysis faces tremendous challenges as existing GIS data tend to be geometry based while natural language text data tend to rely on natural language spatial relation (NLSR) terms. To alleviate this problem, one critical step is to translate geometric configurations into NLSR terms, but existing methods to date (e.g. mean value or decision tree algorithm) are insufficient to obtain a precise translation. This study addresses this issue by adopting the random forest (RF) algorithm to automatically learn a robust mapping model from a large number of samples and to evaluate the importance of each variable for each NLSR term. Because the semantic similarity of the collected terms reduces the classification accuracy, different grouping schemes of NLSR terms are used, with their influences on classification results being evaluated. The experiment results demonstrate that the learned model can accurately transform geometric configurations into NLSR terms, and that recognizing different groups of terms require different sets of variables. More importantly, the results of variable importance evaluation indicate that the importance of topology types determined by the 9-intersection model is weaker than metric variables in defining NLSR terms, which contrasts to the assertion of ‘topology matters, metric refines’ in existing studies.     

基于随机森林模型的珠江三角洲30 m格网人口空间化

人口空间化是实现人口统计数据与其他环境资源空间数据融合分析的有效途径。本文选取夜间灯光数据、道路网数据、水域分布数据、建成区数据、数字高程模型和地形坡度数据作为影响珠江三角洲人口分布的变量因子,利用随机森林模型对珠江三角洲2010年人口数据进行了30 m格网空间化,并将模拟结果与三个公开数据集作精度对比,最后基于随机森林模型的变量因子重要性分析珠江三角洲人口空间分布的影响因素。结果表明：本文模拟整体精度达到82.32%,均优于WorldPop数据集以及中国公里网格人口数据集,接近GPW数据集,而且在人口密度中等区域模拟精度最高;通过对变量因子重要性进行度量,发现夜间灯光强度是珠江三角洲人口分布的最重要指示性指标,到水域的距离、到建成区的距离和路网密度对珠江三角洲人口分布均具有重要作用。利用随机森林模型结合多源信息能够实现高空间分辨率的人口空间化,可为精细化城市管理提供重要数据源,也可为相关政策决策制定提供支持。     

Grid-cell Aerosol Direct Shortwave Radiative Forcing Calculated Using the SBDART Model with MODIS and AERONET Observations:An Application in Winter and Summer in Eastern China

Taking winter and summer in eastern China as an example application, a grid-cell method of aerosol direct radiative forcing(ADRF) calculation is examined using the Santa Barbara DISORT Atmospheric Radiative Transfer(SBDART) model with inputs from MODIS and AERONET observations and reanalysis data. Results show that there are significant seasonal and regional differences in climatological mean aerosol optical parameters and ADRF. Higher aerosol optical depth(AOD)occurs in summer and two prominent high aerosol loading centers are observed. Higher single scattering albedo(SSA) in summer is likely associated with the weak absorbing secondary aerosols. SSA is higher in North China during summer but higher in South China during winter. Aerosols induce negative forcing at the top of the atmosphere(TOA) and surface during both winter and summer, which may be responsible for the decrease in temperature and the increase in relative humidity.Values of ADRF at the surface are four times stronger than those at the TOA. Both AOD and ADRF present strong interannual variations; however, their amplitudes are larger in summer. Moreover, patterns and trends of ADRF do not always correspond well to those of AOD. Differences in the spatial distributions of ADRF between strong and weak monsoon years are captured effectively. Generally, the present results justify that to calculate grid-cell ADRF at a large scale using the SBDART model with observational aerosol optical properties and reanalysis data is an effective approach.