Subway construction is one of the effective means to boost economic and social development, as well as relieve urban traffic pressure. However, the surface deformation in the process of its operation and maintenance has become one of the most intractable problems faced by urban construction, planning and management departments. This study delved into the surface deformation evolution and underlying mechanisms along the subways in Zhengzhou, China. Leveraging a dataset comprising 88 Sentinel-1A ascending orbit images, we applied a hybrid approach of PS-InSAR and SBAS-InSAR inversion methods to extract both the temporal deformation field and deformation velocity field within the study area. The study interpreted the spatiotemporal evolution of surface deformation within the metro corridor buffer zones. Additionally, through the integration of well-logging hydrological data and geological borehole data, we comprehensively elucidated the mechanisms driving surface uplift in Zhengzhou central urban area. With the intention of accurately analyzing the evolution over two settlement funnels, we implemented a Bayesian estimation algorithm to denoise the subsidence curves extracted from MT-InSAR deformation field at the Shamen metro station. Then, the subsidence velocity features for the subsidence troughs were analyzed at this station. According to the theory of effective stress, the calculation model of soil element consolidation compression was established, and the methodology calculating soil strata compression within the water-level drop funnel was proposed. Based on the measurement data, the reliability of the proposed calculation model and methodology was verified, and the mechanism of surface settlement in typical subway station is revealed. The results demonstrated that significant subsidence zones along the metro corridor predominantly cluster in the eastern, northwestern, and southwestern sectors of the central urban area. Notably, the most substantial subsidence occurs in the Huiji District (Line 2) to the northwest and the Jinshui District (Lines 1 and 5) to the east, with maximum subsidence rates of 16 mm/a and 12 mm/a, respectively. Conversely, uplift areas are primarily situated at the convergence of four administrative districts (Erqi District, Zhongyuan District, Jinshui District, and Guancheng District), affecting Lines 1, 2, and 5 of the metro, with a peak uplift of 67 mm. This uplift area closely aligns with the underground water prohibitive extraction zone. Prior to groundwater prohibitive extraction, it indicated seasonal fluctuations for underground water level, which subsequently exhibited a notable rise following conservation measures. There exists a growing dose feedback function relationship between surface uplift and well-logging level in the prohibitive water- extraction zone. The root mean square error (RMSE) and relative root mean square error (RRMSE) are +/- 3 mm and 6.7 %, respectively. The theoretical calculation values afford a better fit with the measured values, which approves the effectiveness of the proposed calculation model. The principal reason behind the dual subsidence funnels observed on the surface at the Shamen metro station, Zhengzhou China is attributed to groundwater discharge during metro operation, with the subsidence rate at the maximum point conforming to a Logistic time function curve. The insights gained from this study hold practical significance for not only mitigating potential risks but safeguarding urban public security in the process of subway operation.

Slope deformation is one of the focal issues of concern during the normal operation and maintenance of the South-North Water Transfer Middle Route Project. To study the slope deformation evolution in the deep excavation section at the head of the canal, we applied 88 views of Sentinel-1A ascending image data from 2017 to 2019 and MT-InSAR(Multi-temporal InSAR) deformation monitoring technology to obtain long-time series deformation rates and cumulative deformation fields over the slope in the study area. Based on the analysis of the time-series monitoring data of the deformation field sample points, a LSTM (Long Short Term Memory Network) slope deformation predictive model was constructed to predict the slope deformation for the next 12 months at 12 sample points of the deep excavation slope. The impact of rainfall on slope deformation was investigated, and the reliability of the LSTM model was verified by using the measured data. The results show that the average annual deformation rate of the slope ranges from 10mm/a to 25mm/a, the maximum cumulative deformation is about 60 mm, and the slope of the excavated section is generally in an uplifted state. The rainfall-induced repeated uplift or subsidence of the canal slopes together with the peak deformation was closely related to the amount of rainfall during the wet season, and the longer the duration of the wet season, the more obvious the crest. Among the12 sample sites, the minimum and maximum deformation predicted using the LSTM model were 51.7 mm and 73.9 mm respectively, with the lowest correlation coefficient of 0.994 and the highest of 0.999. The maximum and minimum values of RMSE (Root Mean Square Error) were 4.4 mm and 3.6 mm respectively, indicating reliable prediction results. The results of the study can provide reference for the prevention and control of geological hazards in the South-North Water Transfer Canal.

针对稀疏匹配点无法满足三维重建工作需要及传统密集匹配算法面对明暗变换影像匹配无力等问题,本文提出了一种结合马氏距离与梯度描述符的密集匹配方案.该方案首先利用初始可靠同名点建立同名三角网;然后以各三角形的对应中点作为加密匹配基元,以描述符与马氏距离作为两种影响因素,建立得分计算公式;最后以超过该得分阈值者作为匹配点,遍历所有三角形,更新三角网重复上述步骤,直至没有新的匹配点产生.利用网络公开数据集进行验证,试验结果表明,本文提出的密集匹配方案较好地解决了传统算法面对明暗变换影像适应性较差的问题,同时对多种变换影像有着较好的适应性与稳定性.

In the underground environment, it is difficult to obtain spatial three-dimensional data because of occlusion and its complexity. Mobile light detection and ranging (LiDAR) measurement technology has the ability to obtain three-dimensional spatial information quickly and accurately, but in the underground environment, because of the lack of global navigation satellite system (GNSS) signal in the integrated navigation system, the measurement accuracy decreases with the increase of time. In this paper, an extended Kalman filter-based loose mode is constructed using the real coordinates of the center of gravity of the target and the measurement information of the laser foothold. It provides an additional space-time reference for the integrated navigation system of the mobile LiDAR system and restricts the errors. The simulation results show that the proposed method can effectively improve the measurement accuracy of mobile LiDAR system and enhance the 3D spatial perception ability of underground space.

In the operation and maintenance of the South–North Water Transfer Project, monitoring and predicting the canal slope deformation quickly and efficiently is one of the urgent problems to be solved. To predict the slope deformation of the deep excavated canal section at the head of the canal. We propose a new idea of adopting the joint prediction of MT-InSAR and Fbprophet. Firstly, MT-InSAR monitoring technology was used to invert channel deformation using 88 Sentinel-1A orbit-raising image data with a time baseline from 2017 to 2019. The time-series deformation of nine monitoring points was also extracted, and it was found that the time-series curves of the cumulative deformation of the channel slope showed fluctuations. The Fbprophet algorithm was then used to train the prediction model in Python to predict the channel slope deformation over the next 365 days. Finally, the prediction results were compared with the MT-InSAR monitoring values to analyze the prediction accuracy and applicability of the Fbprophet algorithm for the slope deformation monitoring of the South–North Water Transfer Project. The results show that: the deformation rate of the slope of the deep excavation section is in the range of 10 mm/a to 25 mm/a, the maximum accumulated deformation is about 60 mm, and the slope of the excavation canal shows a lifting phenomenon; among the nine monitoring points, the minimum and maximum predicted values of deformation using the machine learning prediction model trained in this paper were 56 mm and 73 mm, respectively; comparing the predicted and monitored values, their correlation coefficients were 0.998 at the highest and 0.988 at the lowest, and the minimum and maximum values of RMSE (RootMean Square Error) were 0.72 mm and 2.87 mm, respectively. It shows that the prediction model trained by the Fbprophet algorithm in this paper applies to the prediction of slope deformation in the deep excavation section, and our prediction results can provide a data reference for disaster prevention and the sustainable development of the South–North Water Transfer Project.

针对GNSS时空分析过程中地壳异常形变信息量级小、难以发现的问题,该文利用独立成分分析探测川滇地区2011-2017年异常形变的时空影响.计算各分量的贡献值,结合空间响应,确定GNSS坐标时间序列的共模误差,进而利用功率谱分析其物理特征.结果显示,N、E、U3个方向的共模误差均为分形白噪声,且具有58.07 d的共同周期.此外,在"次要"分量中探测到3个异常信号,持续时间为8～12个月,最大位移超过6mm.异常信号的影响范围主要为云南地区、龙门山断裂带西南段以及岷江断裂带.地壳形变的时空分析结果有助于探索川滇地区共模误差的地球物理意义,为动态分析川滇地震的时空影响提供了数据参考.

China’s “plain–mountain transition zone” (hereinafter referred to as the “transition zone”) has experienced rapid and diverse urbanization processes. Assessing the dynamic characteristics of urbanization is particularly important for sustainable development of the transition zone. Nighttime light (NTL) data have been widely used to monitor urbanization. Based on the prolonged artificial nighttime-light dataset of China (PANDA) from 1984 to 2020, we partitioned the nighttime light of the study area into four types (low, medium, high, and extremely high) by adjusting the threshold of the brightness gradient (BG) method. The spatiotemporal characteristics of urbanization in 426 districts and counties of 71 prefecture-level cities in the transition zone were analyzed. Our results indicated that the middle region of the transition zone (Yanshan Mountains and Taihang Mountains) experienced the fastest urbanization development, and the urban expansion speed broke through the topographic limitation of the plain–mountain. However, the rapid development of urbanization in the middle plains resulted in the nighttime lighting area (NTLA) tending to become saturated, which caused an unsustainable potential crisis in urban development in this area. Urbanization was mainly manifested in the transition of the low nighttime lighting type (NTLT) to the medium NTLT or higher NTLT. The northern region of the transition zone (Greater Khingan Mountains) experienced the slowest urbanization development, with the lowest nighttime lighting density (NTLD) in the northern mountainous area, where the urbanization was mainly manifested by the expansion of the low NTLT. The urbanization development of the southern region in the transition zone (Wushan and Xuefeng Mountains) was at a medium level, and the urbanization of the plain in the southern region was also better than that of the mountainous area. Urbanization was mainly manifested in the expansion of the low NTLT, supplemented by the transition from the low NTLT to high NTLT. Whether in the north, middle, or south of the transition zone, the plain–mountain topographic variations caused a gap in urbanization, making the urbanization development of the mountains and plains unbalanced.

为揭示贺兰山1989-2017年的生态质量变化与气候和地形的关系,利用Landsat数据,基于遥感生态指数(RSEI),结合SRTM DEM数据、中国气象数据和植被类型空间分布数据,对贺兰山山地生态系统进行研究.结果 表明:将WET、NDVI、SI、LST耦合得到的RSEI可综合反映生态质量,其中NDVI荷载值最大;1989-2017年RSEI均值总体呈上升趋势,增长速率为0.0058,变化范围为0.2849～0.3671,RSEI改善面积是退化面积的14.6倍;RSEI的优、良等级主要分布在林区,且生态环境变异系数小;RSEI中、下和差等级主要分布在荒漠和草原覆盖区,且为生态环境改善的主要区域;RSEI与气温有9.29％的相关关系区域通过了显著性检验(P＜0.05),与降水有12.51％的相关关系区域通过了显著性检验(P＜0.05),RSEI对降水的响应大于气温;在海拔2500～ 3000 m、坡度35°～40°时,贺兰山生态质量最好;贺兰山生态环境虽存在缓慢变好趋势,但整体生态质量仍极端脆弱,生态保护工作任重道远.

为了快速、高效、无损监测板栗树的红蜘蛛病虫害,以实地采集的板栗树局部感染明显叶片、感染轻重不均匀叶片、恢复中的感染叶片及不同感染程度叶片为研究对象,利用UHD185型高光谱相机和数码相机获取各种叶片的高光谱图像和RGB图像,以RGB图像为参考,选择各种叶片的感兴趣区,在高光谱图像上提取感兴趣区的光谱曲线,通过微分运算提取光谱曲线的绿峰、红谷、低位、红边、高位、高肩6种光谱特征及特征波长,利用大量实测数据分析板栗树叶各个光谱特征及特征波长随红蜘蛛病虫害危害程度的叶片级变化规律,得到识别红蜘蛛病虫害最佳的光谱特征.利用无人机(Unmanned aerial vehicle,UAV)搭载UHD185型相机,获取了实验区高光谱影像.结果 表明,监测板栗树红蜘蛛病虫害危害程度的最佳光谱特征为红边和低位,其与红蜘蛛病虫害的决定系数均超过0.6,当发生轻度红蜘蛛病虫害时,红边波长和低位波长出现"蓝移",说明无人机高光谱遥感系统具有早期发现红蜘蛛病虫害的能力,可为板栗树红蜘蛛病虫害的及时治理提供科学依据.

以农作物、生态观赏林、经济果树林为研究对象,从数据采集、处理、分析等方面对基于高光谱技术的病虫害监测方法进行梳理、总结,并提出高光谱技术应用在农林果木方面的不足和展望,为以后的植物分类识别、病虫害监测等研究提供参考.

为获取芦山地震前后川滇地区地壳的形变特征,利用陆态网2009-2013年和2014-2016年两期全球卫星导航系统(GNSS)水平速度场资料分别计算并对比分析了地震发生前后主应变率场、最大剪应变率场、面膨胀率场及基线长度的变化情况.结果显示芦山地震之前龙门山断裂带主要以压缩应变为主,面压缩应变和最大剪应变均处于高值区,震后能量部分释放,压缩形变程度减弱,但其西南方向的安宁河断裂带和鲜水河断裂带南段出现明显的压缩应变高值区,同时南汀河断裂带附近也呈现较明显的压缩应变.岷江断裂带附近区域的压缩应变虽然减弱,但仍然没有改变它的应变状态,这可能促使了2017年九寨沟Ms7.0地震的发生.

岁月如歌,初心不改.作为一所经河南省政府批准建立的测绘类高职院校,自2017 年开始招生以来,河南测绘职业学院始终坚持社会主义办学方向,坚持立德树人根本任务,践行"修德砺能、善思笃行"的校训,坚持知行合一、德育为先、能力为本的办学理念,不断深化教育教学改革,初步行成了"一主线、一产业、一模式、一工程、一队伍、一金课"的办学格局,即始终坚持"产教融合、工学结合"这一办学主线;构建专业集群、扩展产业链条;坚持"学做教"教学模式,实施理实一体化教学;坚持学生全面发展,落实"五个一"工程;加强教师团队建设,推进"双师双能型"教师队伍建设;加强课程改革,打造思政金课.学院紧紧围绕该办学格局,努力奋进,为培养和造就德、智、体、美、劳全面发展的社会主义事业接班人做了大量工作并取得了显著成效.

为了揭示宁夏贺兰山自然保护区长时序植被初级净生产力(NPP)变化与气候相关性,基于MOD17A3H NPP数据、SRTM DEM数据和中国气象数据,定量分析了2004-2015年宁夏贺兰山自然保护区植被NPP时空变化特征及其与气候因子的响应.结果 表明:(1) 2004-2015年研究区植被平均NPP为97.91 9 C/(m2·a),增长速率为0.28g C/(m2·a),12 a间植被总NPP变化范围为0.32～0.50 Tg C/a.(2)植被NPP具有较强的空间分异性.水平地带性表现为NPP总量整体北部优于南部(0.042 5 Tg C＞0.031 6 Tg C),东部优于西部(0.014 5 Tg C＞0.007 3 Tg C);垂直地带性表现为NPP值呈现出针叶林—阔叶林—灌丛—草原—荒漠的垂直景观结构变化,NPP高值集中分布在海拔2 500m以上的高海拔地区.(3)研究区气候呈冷湿化趋势,气温变化率为-0.01℃/a,降水变化率为2.77 mm/a.植被NPP变化与年降水量呈显著正相关(r =0.646 8,p＜0.05),其中草原和灌丛区的植被NPP对降水量响应最强.宁夏贺兰山自然保护区生态结构虽存在缓慢变好趋势,但森林生态系统仍极端脆弱,生态保护工作任重道远.

为了充分利用日益增多的多时态土地利用/覆被矢量数据,依据不同属性全方位挖掘提取其中隐含的要素变化转移信息,进而为土地资源科学管理与统筹规划提供技术支持和决策依据,首先提出了广义转移矩阵的概念,并进行了形式化定义,然后设计了一种基于矢量数据的广义转移矩阵自动计算生成方法,详细阐述了数据预处理、匹配关系建立、要素变化检测、矩阵设置与生成4个实施步骤中涉及的主要问题及解决策略.试验结果表明:所提方法能够大幅提高转移矩阵矢量化计算生成的准确度和灵活性,有效克服人工手动处理计算效率低、周期长、易出错、难检查的不足,以及常用栅格化计算方法精度低、结果单一、不能准确全面反映土地利用/覆盖要素在不同属性上的变化转移情况的局限.

针对地表质量负荷对京津地区 GNSS坐标时间序列噪声特性的影响,选取中国大陆构造环境监测网络 8 个GNSS基准站 2012-2014年的坐标时间序列,利用 CATS软件计算大气压、非潮汐海洋、积雪和土壤湿度等质量负载改正前后 GNSS坐标时间序列的谱指数、最优噪声模型、速度的变化.发现地表质量负载对 GNSS坐标时间序列的噪声特性产生了明显影响.结果显示,京津地区 GNSS坐标序列包含白噪声和有色噪声,且最优噪声模型具有多样性.扣除质量负载后 N 、U分量的噪声模型变化明显,主要表现为 FN+WN和PL+WN.而 N 、E分量的谱指数分别趋近于 FN和 WN.质量负载改正后基准站 U方向的线性速度变化较大,且北京地区变化量大于天津地区.研究结果为提高 GNSS数据解算精度、精细分析地壳形变提供参考.

Transient aseismic creep of the fault frequently induces earthquakes with high magnitude and destructiveness. In allusion to the challenges of slow sliding speed and difficult to detect, an automatic detection method of transient aseismic creep information for the fault was proposed, which based on the abnormal fluctuations of GNSS continuous coordinate time series. First, independent component analysis was used to improve the signal-to-noise ratio (SNR); then the relative strength index and kurtosis value of the fluctuations of the coordinate time series were calculated; finally a creep signal probability was converted through the cumulative distribution function, so as to the fault creep event was detected. In this paper, a 500-day GNSS surface displacement time series was simulated that included a 25-day transient creep signal. The experimental results show that the creep information of the fault can be effectively detected when the signal strength was at least equivalent to the noise level. After calculating the GNSS data for three consecutive years in the Akutan Zone, onecreep signalwas detected and it might be an aseismic creep signal related to the strong movement of the volcanic. In accordance with the processing results of seven-year GNSS data from 18 stations of the Crustal Movement Observation Network of China in Sichuan province, four abnormal signals was found. The analysis results indicated that these signals may be closely related to the abnormal displacement caused by stress accumulation and fault creep caused by the earthquake.

Independent component analysis (ICA) is a blind source signal separation method which can effectively estimate high-order information and thus can effectively extract the common mode errors (CMEs) of a regional global navigation satellite system (GNSS) observation network. In this paper, ICA is used for the weighted filtering (WICA) and the extraction of CMEs of a regional GNSS observation network with the root mean square error (RMSE) of daily solution taken as the weighting factor. Through an analysis of the observed data from 19 valid stations of the Crustal Movement Observation Network of China (CMONOC) in North China, it is shown that the coordinate series precision of 13, 16 and 12 stations in the N, E and U directions, respectively, after filtering by WICA is higher than that by the traditional ICA method. The average correlation coefficient of the coordinate time series for each station after filtering is obviously decreased. Two simulation experiments are designed to extract known CMEs. It is shown that CMEs can be recovered better by WICA and that the standard deviations of most stations after filtering are smaller than those by ICA. The results from the real data and simulation experiments suggest that the RMSE of coordinate series be considered in spatio-temporal filtering.

In view of the current highway slope risk assessment mostly to judge the risk level,rarely go deep into the specific point of the problem;put forward a method for evaluating risk of highway slope based on AHP-Fuzzy and laser point cloud data.In this method,first of all,the highway is divided into several sections according to the geological condition,secondly,the AHP-fuzzy comprehensive evaluation method is used to carry out a overall assessment and determine the dangerous slope,thirdly,using vehicle LiDAR system to obtain two period laser point cloud data of each dangerous slope,and building two-period DEM model for the dangerous slope.meanwhile,the two-period model were analyzed and compared by the method of chromatography analysis for local evaluation and find out the specific risk,finally,according to expert opinion to determine the risk of the type of disaster,and put forward the corresponding prevention and control recommendations.Taking a highway in Chongqing city as an example,a new method is used to evaluate it,results show that,the study area 4,8,10 sections of a large risk,prone to landslides and the proposed timely prevention and control measures.Compared with the traditional method,this method can not only determine the dangerous slope,but also find out the concrete danger point,which opens up a new way for the research of the highway slope disaster prevention and reduction.

针对时空滤波提取GNSS连续坐标时间序列中的共模误差对探测地壳微形变信息至关重要的问题,该文探索了小尺度区域内叠加滤波、主成分分析、独立分量分析等时空滤波方法的特性,基于华北地区13个GNSS测站连续3年的坐标时间序列进行时空滤波,并对比分析不同方法的滤波效果.结果显示,3种方法滤波后均不同程度地降低了坐标时间序列的离散度;区域叠加滤波与主成分分析提取的共模误差空间响应一致,滤波后的标准差基本相同,表明提取共模误差的性质相同,都以二阶统计量为主要信息;独立分量分析滤波后坐标时间序列的标准差较高,这与区域叠加滤波和主成分分析过度滤波有关.

针对由无人机倾斜影像匹配所生成的高密度三维彩色点云,文中提出一种基于随机森林的点云分类方法.在提取彩色点云几何特征和光谱特征的基础上,首先采用变量重要性评分策略进行特征的重要性评估,进而确定一组用于分类的最优特征子集,最后采用随机森林算法将点云分为建筑物、树木以及低矮植被三类.实验结果表明,该方法可以将匹配点云中提取的几何特征和光谱特征有机融合,并在减少冗余特征的基础上,有效提高倾斜影像匹配点云的分类精度和效率.