Updating Landsat time series of surface-reflectance composites and forest change products with new observations

The use of time series satellite data allows for the temporally dense, systematic, transparent, and synoptic capture of land dynamics over time. Subsequent to the opening of the Landsat archive, several time series approaches for characterizing landscape change have been developed, often representing a particular analytical time window. The information richness and widespread utility of these time series data have created a need to maintain the currency of time series information via the addition of new data, as it becomes available. When an existing time series is temporally extended, it is critical that previously generated change information remains consistent, thereby not altering reported change statistics or science outcomes based on that change information. In this research, we investigate the impacts and implications of adding additional years to an existing 29-year annual Landsat time series for forest change. To do so, we undertook a spatially explicit comparison of the 29 overlapping years of a time series representing 1984–2012, with a time series representing 1984–2016. Surface reflectance values, and presence, year, and type of change were compared. We found that the addition of years to extend the time series had minimal effect on the annual surface reflectance composites, with slight band-specific differences (r ≥ 0.1) in the final years of the original time series being updated. The area of stand replacing disturbances and determination of change year are virtually unchanged for the overlapping period between the two time-series products. Over the overlapping temporal period (1984–2012), the total area of change differs by 0.53%, equating to an annual difference in change area of 0.019%. Overall, the spatial and temporal agreement of the changes detected by both time series was 96%. Further, our findings suggest that the entire pre-existing historic time series does not need to be re-processed during the update process. Critically, given the time series change detection and update approach followed here, science outcomes or reports representing one temporal epoch can be considered stable and will not be altered when a time series is updated with newly available data.     

What does Gunshot Detection Technology tell us about gun violence?

PurposeEvaluate the sensitivity of Gunshot Detection Technology (GDT) relative to Calls for Service. Existing crime data sources have biases that do not present a complete picture of gun-related crime. GDT may offer a new metric for firearm crimes. However, few studies have assessed the accuracy of GDT and its relationship to other measures of violence.MethodsGDT and gun crime-related Calls for Service in Washington, DC during 2010 were studied. Using temporal comparisons for month, day of year, weekday, and hour of the day, spatial comparisons on a quarter-mile square grid, and a Poisson-Lognormal-CAR spatial regression model on a combined grid by time period dataset, we examined the sensitivity of GDT activations relative to gunshot-related calls for service.ResultsThe results showed that relative GDT sensitivity changed by time and by space. In particular, the relative sensitivity of GDT was much stronger in the evening and at nighttime than in the daytime. In terms of spatial variation, we found that GDT sensitivity decreased with distance from the nearest zone centroid. In addition, there were two small geographic areas in the study area in which the relative GDT sensitivity was lower than expected.ConclusionsGDT systems identify the frequency and locational accuracy of gunshot incidents, particularly at nighttime. This technology has the potential to improve data collection on gun use and violence and produce more accurate representations if the temporal and distance limitations of the technology are understood. GDT may improve gun detection and, thereby, improve police operations and public support for police.     

信息通讯技术对老年人的社区满意度影响路径——以南京市锁金社区为例

在中国老龄化进程加快以及智慧养老快速发展的背景下,如何运用信息通讯技术(ICT)提高老年人社区生活质量成为地理学与规划学等领域的关注热点。本文选取南京市首个智慧平台落户社区——锁金社区为实证调研对象,通过构建结构方程模型结合问卷调查,探究信息通讯技术对老年人的社区满意度影响路径,其中问卷内容主要包括个人特征属性、个人ICT使用情况、社会支持情况、社区满意度等。结果表明：老年人信息设备的拥有及使用越趋向多元化,其网络活动越频繁;受教育使用水平越高、使用互联网意愿更强烈的老年人其网络活动越频繁;信息通讯技术主要通过社交类网络活动促进老年人的家庭、邻里交往,提高老年人的社会支持,从而提升社区满意度;与社区建成环境相比,社会支持对于老年人的社区满意度影响更大。因此,合理运用信息通讯技术加强老年人与家人、邻里等的联系强度,是提高老年人社区生活质量的一条可行路径。研究结果对于智慧社区、智慧养老建设具有一定的借鉴意义。     

吉林省地震台网监控能力分析

本文简要介绍了吉林省地震台网布局,监测手段设置及仪器装备状况,重点对测震台网,地震前兆台网的监控能力进行分析,给出了台网和实际控制范围,对目前台网存在的问题以及今后的发展情况进行了讨论。     

顺义4.0级地震前兆异常特征与京区前兆监测网监测能力探讨

通过对北京地区现有定点地震前兆监测网进行清理,分析了现有定点专业前兆监测台站、观测点、群测点的数量及分布密度的现状。对１９９６年１２月１６日北京顺义ＭＳ４．０地震的短临异常特征进行了探讨,表明顺义ＭＳ４．０地震前在震中周围出现一个直径约３５ｋｍ的短临异常密集区域。从异常形态看,该区域中的异常大部分可能带有来自震源的信息。结合顺义地震短临异常点位分布特征,对北京地区前兆台网的密度问题进行了探讨。     

基于遥感技术的开都河下游绿洲区土壤盐渍化动态监测研究

以3期遥感图像为数据源,经图像处理,并结合野外考察,建立解译标志进行遥感解译。结果表明,开都河下游绿洲区的盐渍化土地面积呈先增加后减少趋势,盐渍化程度呈先加重后减轻趋势;盐渍化土壤主要分布在开都河沿岸低洼地和博斯腾湖湖滨地区及其北岸清水沟、曲惠渠、乌什塔拉渠两岸附近;盐渍化的形成与研究区地形地貌、地下水以及干旱的气候条件密切相关,人类农业活动对盐渍化土壤形成具有重要作用。     

Main progress and prospect for China's hydrogeological survey

This paper reviewed the main achievements of hydrogeological survey in China, summarized the significant progress of hydrogeological survey over the past decade, and forecasted the key responsibilities for hydrogeological survey in the p14th Five-year Planq. The significant progress includes: China established the 1: 50 000 standard hydrogeological survey system with Chinese characteristics and produced the new generation of high-quality hydrogeological maps; the national groundwater monitoring project was completed and accepted, which marks China taking the leading position in groundwater monitoring internationally; fruitful results were achieved in the national groundwater quality survey, and groundwater quality background values were basically identified and checked; hydrogeological and environmental geological survey was continuously promoted in karst areas and the ecological restoration of rocky desertification achieved remarkable results; China strengthened layer exploration techniques for groundwater, integrating the key and practical techniques of layer exploration and monitoring; the exploration of groundwater in the poverty-stricken regions and old revolutionary base areas were effectively promoted to strongly guarantee the poverty alleviation and drinking water safety; the mystery of desert groundwater was uncovered, making up for the shortage of 1: 250 000 hydrogeological survey in the Badain Jaran Desert; and more efforts were made to conduct survey on the water resources in the basin, and to finish the unified measurement of national-scale groundwater level.     

Optimum size and density of surface grid arrays for retrieving accurate shear-tensile fracturing of microearthquakes

Surface arrays became an important tool for monitoring the induced seismicity in hydraulic fracturing experiments and for assessing the impact of fluid injection on the fracturing process of microearthquakes. The layout of sensors plays a key role in this task because it controls the accuracy of event locations and retrieved seismic moment tensors. We simulate various configurations of grid sensor arrays characterized by a different number of sensors, array span, sensor spacing, depth of sources and various shear/tensile source mechanisms of events. The moment tensor inversion is carried out using synthetically calculated P-wave amplitudes with added random noise. A bias in the solutions is evaluated by errors in the double-couple percentage of inverted moment tensors because the double-couple errors inform us about the sensitivity of the network to detect the shear/tensile fracturing mode of induced microearthquakes. The results show that the accuracy of the double-couple percentage is mostly controlled by the offset-to-depth ratio R defined as the ratio of half of the network size to the event depth. The optimum value of R is in the range of 0.75–1.5 irrespective of the type of the focal mechanism. If 121 (11 × 11) sensors are distributed in a regular grid and recorded data are characterized by a 10% random noise, the double-couple error is less than 6%. This error increases, if R is not optimum or if the number of sensors is reduced. However, even sparse arrays with 49 (7 × 7) or 16 (4 × 4) sensors can yield a reasonable accuracy, provided the surface grid arrays are designed to have an optimum size.     

Estimation of groundwater storage from seismic data using deep learning

Convolutional neural networks can provide a potential framework to characterize groundwater storage from seismic data. Estimation of key components, such as the amount of groundwater stored in an aquifer and delineate water table level, from active-source seismic data are performed in this study. The data to train, validate and test the neural networks are obtained by solving wave propagation in a coupled poroviscoelastic–elastic media. A discontinuous Galerkin method is applied to model wave propagation, whereas a deep convolutional neural network is used for the parameter estimation problem. In the numerical experiment, the primary unknowns estimated are the amount of stored groundwater and water table level, while the remaining parameters, assumed to be of less of interest, are marginalized in the convolutional neural network-based solution. Results, obtained through synthetic data, illustrate the potential of deep learning methods to extract additional aquifer information from seismic data, which otherwise would be impossible based on a set of reflection seismic sections or velocity tomograms.     

Intra-survey reservoir fluctuations – implications for quantitative 4D seismic analysis

During the time taken for seismic data to be acquired, reservoir pressure may fluctuate as a consequence of field production and operational procedures and fluid fronts may move significantly. These variations prevent accurate quantitative measurement of the reservoir change using 4D seismic data. Modelling studies on the Norne field simulation model using acquisition data from ocean-bottom seismometer and towed streamer systems indicate that the pre-stack intra-survey reservoir fluctuations are important and cannot be neglected. Similarly, the time-lapse seismic image in the post-stack domain does not represent a difference between two states of the reservoir at a unique base and monitor time, but is a mixed version of reality that depends on the sequence and timing of seismic shooting. The outcome is a lack of accuracy in the measurement of reservoir changes using the resulting processed and stacked 4D seismic data. Even for perfect spatial repeatability between surveys, a spatially variant noise floor is still anticipated to remain. For our particular North Sea acquisition data, we find that towed streamer data are more affected than the ocean-bottom seismometer data. We think that this may be typical for towed streamers due to their restricted aperture compared to ocean-bottom seismometer acquisitions, even for a favourable time sequence of shooting and spatial repeatability. Importantly, the pressure signals on the near and far offset stacks commonly used in quantitative 4D seismic inversion are found to be inconsistent due to the acquisition timestamp. Saturation changes at the boundaries of fluid fronts appear to show a similar inconsistency across sub-stacks. We recommend that 4D data are shot in a consistent manner to optimize aerial time coverage, and that additionally, the timestamp of the acquisition should be used to optimize pre-stack quantitative reservoir analysis.