多普勒雷达图像逆风区的自动监测识别

在多普勒雷达降水回波径向速度场中及时准确地发现逆风区,对灾害天气预报预警具有重要意义.根据逆风区在雷达径向速度图中的物理图像特征,采用数字图像处理和分析方法实现了逆风区自动监测识别.首先,以雷达图像色标为依据,采用阈值法分别获取正、负速度区域二值图像,再对2幅图像分别进行形态学运算,然后将上述4幅图像做交叉逻辑运算,得到逆风区监测识别结果和相关参数.通过在2005-2011年长沙雷达站47幅根据实况进行人工标注后的多普勒雷达径向速度图像上进行实验,表明该方法对逆风区可以进行快速准确识别,与人工标注结果比较准确率可达89%,满足实际应用需要.     

内蒙古河套灌区春玉米作物系数试验研究

作物系数曲线是估算作物生长季耗水量变化的重要参数。基于2013年4—9月内蒙古巴彦淖尔市临河区田间水分试验和1994—2013年气象站观测资料,利用水量平衡法反求春玉米作物系数,分析生长季内的变化规律, 建立动态模拟方程,并与联合国粮农组织 (FAO) 分段直线法结果进行比较, 提出胁迫条件下作物系数的叶面积修正方法。结果表明:玉米作物系数随发育进程可用三项式曲线描述,变化趋势与产量水平无关, 但随产量增高而变幅增大;以出苗后相对积温为时间变量建立模拟方程效果较好,决定系数 (R2) 均在0.92以上;模拟计算出各站点最大 (1.30~1.48) 和平均 (0.831~0.919) 作物系数,与FAO分段直线法计算的典型值和区间值基本一致,生长中期平均相对误差为3.4%~7.2%;提出利用相对叶面积指数修正作物系数的计算方法;通过2014年实例检验,土壤水分模拟值与实测值的平均相对误差为6.3%,相对误差小于15%的占95.8%。     

Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data

A sufficient number of satellite acquisitions in a growing season are essential for deriving agronomic indicators, such as green leaf area index (GLAI), to be assimilated into crop models for crop productivity estimation. However, for most high resolution orbital optical satellites, it is often difficult to obtain images frequently due to their long revisit cycles and unfavorable weather conditions. Data fusion algorithms, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), have been developed to generate synthetic data with high spatial and temporal resolution to address this issue. In this study, we evaluated the approach of assimilating GLAI into the Simple Algorithm for Yield Estimation model (SAFY) for winter wheat biomass estimation. GLAI was estimated using the two-band Enhanced Vegetation Index (EVI2) derived from data acquired by the Operational Land Imager (OLI) onboard the Landsat-8 and a fusion dataset generated by blending the Moderate-Resolution Imaging Spectroradiometer (MODIS) data and the OLI data using the STARFM and ESTARFM models. The fusion dataset had the temporal resolution of the MODIS data and the spatial resolution of the OLI data. Key parameters of the SAFY model were optimised through assimilation of the estimated GLAI into the crop model using the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm. A good agreement was achieved between the estimated and field measured biomass by assimilating the GLAI derived from the OLI data (GLAI_L) alone (R² = 0.77 and RMSE = 231 g m⁻²). Assimilation of GLAI derived from the fusion dataset (GLAI_F) resulted in a R² of 0.71 and RMSE of 193 g m⁻² while assimilating the combination of GLAI_L and GLAI_F led to further improvements (R² = 0.76 and RMSE = 176 g m⁻²). Our results demonstrated the potential of using the fusion algorithms to improve crop growth monitoring and crop productivity estimation when the number of high resolution remote sensing data acquisitions is limited.     

Retrieval of effective leaf area index (LAIe) and leaf area density (LAD) profile at individual tree level using high density multi-return airborne LiDAR

As an important canopy structure indicator, leaf area index (LAI) proved to be of considerable implications for forest ecosystem and ecological studies, and efficient techniques for accurate LAI acquisitions have long been highlighted. Airborne light detection and ranging (LiDAR), often termed as airborne laser scanning (ALS), once was extensively investigated for this task but showed limited performance due to its low sampling density. Now, ALS systems exhibit more competing capacities such as high density and multi-return sampling, and hence, people began to ask the questions like—“can ALS now work better on the task of LAI prediction?” As a re-examination, this study investigated the feasibility of LAI retrievals at the individual tree level based on high density and multi-return ALS, by directly considering the vertical distributions of laser points lying within each tree crown instead of by proposing feature variables such as quantiles involving laser point distribution modes at the plot level. The examination was operated in the case of four tree species (i.e. Picea abies, Pinus sylvestris, Populus tremula and Quercus robur) in a mixed forest, with their LAI-related reference data collected by using static terrestrial laser scanning (TLS). In light of the differences between ALS- and TLS-based LAI characterizations, the methods of voxelization of 3D scattered laser points, effective LAI (LAIe) that does not distinguish branches from canopies and unified cumulative LAI (ucLAI) that is often used to characterize the vertical profiles of crown leaf area densities (LADs) was used; then, the relationships between the ALS- and TLS-derived LAIes were determined, and so did ucLAIs. Tests indicated that the tree-level LAIes for the four tree species can be estimated based on the used airborne LiDAR (R² = 0.07, 0.26, 0.43 and 0.21, respectively) and their ucLAIs can also be derived. Overall, this study has validated the usage of the contemporary high density multi-return airborne LiDARs for LAIe and LAD profile retrievals at the individual tree level, and the contribution are of high potential for advancing forest ecosystem modeling and ecological understanding.     

一种精确估计区域北斗接收机硬件延迟的方法

提出了一种精确估计区域北斗接收机硬件延迟(DCB)的方法。该方法不需要传统复杂的电离层模型,在已知一个参考站接收机硬件延迟的条件下,利用正常情况下电离层延迟量和卫星-接收机几何距离强相关这一特点,采用站间单差法来精确估计区域内BDS接收机的硬件延迟。试验结果表明,该方法单站估计的单站北斗接收机连续30d的硬件延迟RMS在0.3ns左右。通过GEO卫星双频观测值扣除已知卫星DCB和本文方法估计的接收机DCB,计算对应穿刺点一天的VTEC并和GIM格网内插结果并进行比对分析,二者大小和变化趋势均符合较好,进一步验证了本文提出的方法具有可靠性。     

城区地表形变差分TomoSAR监测方法

压缩感知技术(CS)的差分TomoSAR技术解决了中高分辨率SAR数据在城区出现的叠掩问题,实现了城区地表形变信息的重构,但是该方法仅利用了目标的稀疏特性并没有考虑目标的结构特性,对具有这两种特性的目标进行重构时其性能较差。针对这一问题,本文采用联合Khatri-Rao子空间和块压缩感知(KRS-BCS),提出了一种差分SAR层析成像方法。该方法依据目标的结构特性和重构观测矩阵具有的Khatri-Rao积性质,将稀疏结构目标的差分TomoSAR问题转化为Khatri-Rao子空间下的BCS问题,然后对目标进行块稀疏的l1/l2范数最优化求解,最后通过理论分析和仿真试验对分辨能力和重构估计性能进行了定性和定量评价,仿真结果表明本文所采用的KRS-BCS方法不仅保持了高分辨率的优点,而且有效地降低了虚假目标出现的概率,大幅度提高了散射点准确重构概率,切实可行地解决了CS方法的不足。应用实例研究中,利用34景Envisat卫星ASAR时间序列影像对日本千叶县茂原市城区进行地表形变监测,并以一等水准点和实时测量的GPS站点观测数据作为参考形变结果进行验证,试验结果表明采用KRS-BCS方法反演的结果与参考形变结果保持了良好的一致性且形变速率整体偏差也较小,实现了较高精度的城区地表形变估计。     

顾及邻域居民地群组相似性的道路网匹配方法

现有多源同比例尺道路网匹配方法中,大多只利用道路自身特征进行匹配,而较少顾及道路周边要素对匹配过程的影响和约束,从而影响了道路网匹配效果的进一步提高,特别是对系统误差改正后仍存在一定位置或旋转偏差的道路数据进行匹配时,这种影响尤为明显。本文借鉴人类对陌生环境的空间认知特点,提出了一种顾及邻域居民地群组相似性的道路网匹配方法。该方法通过构建城市骨架线网确定与道路相邻的居民地群组,进而计算居民地群组空间关系和几何特征相似度来获得对应道路的匹配结果。其特点在于:对存在位置或旋转偏差的道路数据匹配,以其邻域空间内居民地群组的整体相似性指标来带动道路自身匹配,实际上是增加了周边居民地群组对道路匹配过程的约束,更具鲁棒性。试验及对比分析表明,本方法能够较好地解决系统误差改正后仍存在较大位置和旋转偏差的道路数据间的匹配问题,提高匹配的正确率。     

小尺度土地利用变化模型在岩溶山区的应用

针对当前土地利用模拟技术在岩溶地区应用较少的现状,该文提出了利用CLUE-S模型对岩溶地区土地利用变化进行模拟。此模型全面考虑自然和人文因子,通过迭代方法综合空间分析和非空间分析,较好地模拟小尺度范围内土地利用变化,具有综合性、开放性、空间性、竞争效率性等特点。研究结果表明:该模型不仅能够准确地反映未来研究区土地利用变化趋势,而且能较好地筛选影响未来土地利用变化主要影响因子,进行不同情景土地利用变化模拟,对岩溶地区土地利用规划和管理具有参考价值。     

甘肃永靖黑方台4·29罗家坡黄土滑坡的特征

2015年4月29日上午,甘肃省永靖县盐锅峡镇黑方台滑坡高频发区的党川村罗家坡同一斜坡处连续发生了2次大规模黄土滑坡,总体积约65×10~4m~3,最大滑距630 m,摧毁14户居民房屋和3家工厂.通过现场详细调查、取样试验、1∶500地形测量、滑坡影像、视频等资料分析,对灌溉引发的罗家坡黄土滑坡的特征、滑动过程、形成机理进行了探讨.结果表明:第1次滑坡经过近2 a变形过程,整体突然失稳,高速远程滑动;第2次滑坡变形时间仅3 h,分块逐步滑动,滑动历时长而过程复杂,总体为低速远程滑动.高陡的地形和强度低、水敏感性强的土体是滑坡发生的基础,黄土台塬区长期农业灌溉是引发因素,大量水体入渗形成了20余米厚的饱和软弱基座,使抗剪强度降低,导致斜坡失稳滑动.黄土滑坡高速远程滑动的主要原因是滑坡剪出口位置高,滑动势能大,释放条件好,剪出口下部陡坡段为主要加速段;前方有开阔的滑动空间且有一定坡度、平缓的滑道;滑体底部饱和软弱黏性土在滑道上持续产生超孔隙水压力、液化等低摩阻效应,是远程滑动的润滑剂.同时,两次滑体间还存在冲击加速和能量传递作用.     

板桥-合水地区长63储层成岩相孔隙结构特征及优质储层分布

利用铸体薄片、扫描电镜、常规压汞、恒速压汞及相渗等资料,研究了板桥-合水地区长6₃储层成岩相类型及其微观孔隙结构特征,总结了不同成岩相的测井响应特征,确定了纵向及平面分布规律。结果表明优质成岩相的展布受沉积相、成岩作用与孔隙结构共同控制,尤其是喉道分布才是控制砂岩储层渗透性的主要因素。水云母胶结-残余粒间孔相和水云母胶结-长石溶蚀相储层主要分布于半深湖相重力流复合水道浊流沉积中,细-微细喉道发育,孔喉连通性好,渗流能力最好,油水呈相对均匀的渗流,孔隙内的油气极易通过喉道被开采出来;绿泥石胶结相、水云母胶结弱溶蚀相储层位于分支水道边缘,呈孤岛状分布,孔喉半径小但分布均匀,渗流能力中等-差;碳酸盐胶结相、碳酸盐+水云母胶结相储层主要发育在分支水道间,孔隙结构发育程度差,喉道细小且孔喉连通性差,富集于孔隙中的油气难以通过小喉道,采收率低。