Long-term hydrology and aquatic biogeochemistry data from H. J. Andrews Experimental Forest,Cascade Mountains,Oregon

The H. J. Andrews Experimental Forest (HJA) encompasses the 6400 ha Lookout Creek watershed in western Oregon, USA. Hydrologic, chemistry and precipitation data have been collected, curated, and archived for up to 70 years. The HJA was established in 1948 to study the effects of harvest of old-growth conifer forest and logging-road construction on water quality, quantity and vegetation succession. Over time, research questions have expanded to include terrestrial and aquatic species, communities and ecosystem dynamics. There are nine small experimental watersheds and 10 gaging stations in the HJA, including both reference and experimentally treated watersheds. Gaged watershed areas range from 8.5 to 6242 ha. All gaging stations record stage height, water conductivity, water temperature and above-stream air temperature. At nine of the gage sites, flow-proportional water samples are collected and composited over 3-week intervals for chemical analysis. Analysis of stream and precipitation chemistry began in 1968. Analytes include dissolved and particulate species of nitrogen and phosphorus, dissolved organic carbon, pH, specific conductance, suspended sediment, alkalinity, and major cations and anions. Supporting climate measurements began in the 1950s in association with the first small watershed experiments. Over time, and following the initiation of the Long Term Ecological Research (LTER) grant in 1980, infrastructure expanded to include a set of benchmark and secondary meteorological stations located in clearings spanning the elevation range within the Lookout Creek watershed, as well as a large number of forest understory temperature stations. Extensive metadata on sensor configurations, changes in methods over time, sensor accuracy and precision, and data quality control flags are associated with the HJA data.     

Characterization of sudden and sustained base flow jump hydrologic behaviour in the humid seasonal tropics of the Panama Canal Watershed

Base flows are important for tropical regions with pronounced dry seasons, which are facing increasing water demands. Base flow generation, however, is one of the most challenging hydrological processes to characterize in the tropics. In many years during the May–December wet season in the Panama Canal Watershed (PCW), base flows in rivers abruptly increase. This increase persists until the start of the December–April dry season. Understanding this unusual base flow jump (BFJ) behaviour is critical to improve water provisioning in the seasonal tropics, especially during droughts and extended dry seasons. This study developed an integrated approach combining piecewise regression on cumulative average base flow and sensitivity analysis to calculate the timing and magnitude of BFJ. Rainfall, forest cover, mean land surface slope, catchment area, and estimated subsurface storage were tested as predictors for the occurrence and magnitude of the BFJs in seven subcatchments of the PCW. Sensitivity analysis on correlated predictors allowed ranking of predictor contributions due to isolated and cross-correlation effects. Correlations between observed BFJs and BFJs predicted by watershed and rainfall-related predictors were 0.92 and 0.65 for BFJ timing and magnitude, respectively. Forest cover was the second most significant predictor after cumulative rainfall for jump magnitude, owing to larger subsurface storage and groundwater recharge in forests than pastures. Catchments in the mountainous eastern PCW always generated larger jumps due to their higher rainfall and greater forest cover than the western PCW catchments. The cross-correlations between predictors contributed to more than 50% of the jump variances. The results demonstrate the importance of rainfall gradient and catchment characteristics in affecting the sudden and sustained BFJs, which can help inform land management decisions intended to enhance water supplies in the tropics. This study underscores the need for more research to further understand the hydrological processes involved in the BFJ phenomenon, including better BFJ models and field characterizations, to help improve tropical ecosystem services under a changing environment.     

Travel times for snowmelt-dominated headwater catchments: Influences of wetlands and forest harvesting,and linkages to stream water quality

The time it takes water to travel through a catchment, from when it enters as rain and snow to when it leaves as streamflow, may influence stream water quality and catchment sensitivity to environmental change. Most studies that estimate travel times do so for only a few, often rain-dominated, catchments in a region and use relatively short data records (<10 years). A better understanding of how catchment travel times vary across a landscape may help diagnose inter-catchment differences in water quality and response to environmental change. We used comprehensive and long-term observations from the Turkey Lakes Watershed Study in central Ontario to estimate water travel times for 12 snowmelt-dominated headwater catchments, three of which were impacted by forest harvesting. Chloride, a commonly used water tracer, was measured in streams, rain, snowfall and as dry atmospheric deposition over a 31 year period. These data were used with a lumped convolution integral approach to estimate mean water travel times. We explored relationships between travel times and catchment characteristics such as catchment area, slope angle, flowpath length, runoff ratio and wetland coverage, as well as the impact of harvesting. Travel time estimates were then used to compare differences in stream water quality between catchments. Our results show that mean travel times can be variable for small geographic areas and are related to catchment characteristics, in particular flowpath length and wetland cover. In addition, forest harvesting appeared to decrease mean travel times. Estimated mean travel times had complex relationships with water quality patterns. Results suggest that biogeochemical processes, particularly those present in wetlands, may have a greater influence on water quality than catchment travel times.     

结合BM3D和多级非线性加权平均中值滤波的遥感影像混合噪声去噪方法

提出了一种基于三维块匹配（BM3D）和多级非线性加权平均中值滤波的遥感影像混合噪声去噪方法，使用coief3小波和sym2小波替换传统三维块匹配算法中的bior1.5和hear参数，将三维块匹配算法和多级非线性加权平均中值滤波相结合以提高算法对混合噪声的去噪能力。进行一个仿真对比试验和一个真实遥感影像去噪试验，试验表明，本文提出的方法在遥感影像混合噪声去噪方面效果要优于传统混合噪声去噪方法。     

利用CEEMD分析中国沿海GNSS站高程时序变化

利用完备经验模态分解方法（CEEMD）对我国沿海地区6个GNSS基准站（2010—2018）的高程时序数据进行了处理分析。结果表明：CEEMD在高程时间序列分析中具有一定的优越性，可准确分解出各GNSS站高程时序中存在的周、月、季节、年等变化周期项，其中周年运动是主要贡献项，各站高程时间序列的短周期变化与潮汐变化周期具有密切关联性；沿海GNSS站的地面沉降既具有区域的一致性，又存在区域间差异性，其中D区DBJO、DZJJ站呈现先下降后上升的趋势，N区NZUH、NWZU站呈下降趋势，B区的BZMW呈上升趋势，而同海区的BLHT站则呈显著的下降趋势。     

运用GD-PBIBD编码的矢量空间数据数字指纹算法

针对矢量空间数据叛逆者追踪难及编码效率低的问题，本文提出了一种运用GD-PBIBD码的指纹算法。首先运用GD-PBIBD构造指纹编码，将待嵌入指纹序列运用Logistic映射置乱，通过D-P算法提取矢量空间数据的特征点，对特征点实施DFT变换得到相位系数和幅度系数；然后运用QIM方法将指纹嵌入DFT变换域的幅度系数上；最后应用DFT逆变换得到含指纹的矢量空间数据。试验选取部分中国路网数据和某区域绿地数据，运用该算法嵌入指纹后，对其进行线性和非线性模拟攻击，成功提取指纹序列，利用汉明距离追踪出合谋者。该算法编码构造简单，在码长一定的情况下，较BIBD码可容纳更多的用户，效率高；算法抗单用户指纹攻击及多重攻击的稳健性较好，也能够抵抗多用户最小值和最大最小值攻击，追踪到所有叛逆者，可用于矢量空间数据版权保护，为矢量空间数据叛逆者追踪提供依据。     

基于像元二分模型的地表植被覆盖度估算模块化设计与实现

地表植被覆盖度是的一种应用广泛的定量遥感产品,在水文、生态、区域变化等方面都具有重要的意义。像元二分模型是应用最多的一种遥感估算地表覆盖度的方法。目前,用遥感的方法进行地表植被覆盖度估算没有完整、系统的工具,用户只能逐步进行操作,效率低下,鉴于上述情况,本文运用IDL交互式数据语言,基于ENVI二次开发了一个植被覆盖度估算程序,取得了一定的成效,对遥感定量产品的生产、应用具有一定意义。     

基于已知特征平面的移动测量系统安置参数检校

安置误差是影响移动测量系统激光点云精度的主要因素之一。当激光点云中存在特征平面时,安置误差会导致该平面与真实平面之间产生较大的偏差,如果存在多个空间上位置关系较好的特征平面,就能够全面反映出系统的安置误差。本文以此为依据,给出了一种基于已知特征平面的移动测量系统安置参数检校方法,并详细阐述了检校场的布设、数据获取及处理等内容。实验证明,该方法是一种简单、可靠的检校方法,具有较高的实用性。     

基于时相变化的晴空条件下Himawari-8 AHI火点检测

Himawari-8静止气象卫星具有高空间分辨率、高观测频次和高时效特点,对于火点检测具有很强优势。对Himawari-8卫星的3.9μm和11.2μm两通道亮温进行了连续时相变化研究,得出两通道的亮温在时间上的变化差值稳定且规律明显。根据两通道的亮温时相特征,考虑白天可见光对3.9μm通道的影响,并结合火点产生时引起的亮温变化特征,提出了适用于晴空条件下改进的火点检测算法。在多处进行了此算法的实验,例如2018-11-27 T 16:40(UTC时)河北张家口市桥东区一化工厂附近发生的严重爆炸起火事件以及2019-02-28澳大利亚西南部发生的火灾事件,均快速有效的检测到了火点。实验表明,改进的火点检测算法能很好的进行火点检测,并能解决晨昏交界、冰雪下垫面、常规火源点、太阳耀光等火点检测的难题。     

香坊老工业区搬迁改造监测分析

利用地理信息技术及遥感监测方法,监测香坊老工业区搬迁改造试点进展情况,主要针对搬迁改造过程中地表覆盖、用地性质、空气质量变化情况,开展综合统计分析,客观评价香坊老工业搬迁改造实施成效。