The Natural Resource Management Planning Portal: Perspectives for NRM Planning and Reporting

Natural Resource Management (NRM) is often conducted as a partnership between government and citizens. In Australia, government agencies formulate policy and fund implementation that may be delivered on-ground by community groups (such as Landcare). Since the late 1980s, over AUS$8b of Commonwealth investment has been made in NRM. However, quantitative evidence of environmental improvements is lacking. The NRM Planning Portal has been developed to (1) provide an online spatial information system for sharing Landcare and agency data; and (2) to facilitate NRM priority setting at local and regional planning scales. While the project successfully federates Landcare NRM activity data, challenges included (1) unstructured, non-standardized data, meaning that quantitative reporting against strategic objectives is not currently possible, and (2) a lack of common understanding about the value proposition for adopting the portal approach. Demonstrating the benefit of technology adoption is a key lesson for digital NRM planning.     

Forest operations,tree species composition and decline in rainfall explain runoff changes in the Nacimiento experimental catchments,south central Chile

Few long-term studies have explored how intensively managed short rotation forest plantations interact with climate variability. We examine how prolonged severe drought and forest operations affect runoff in 11 experimental catchments on private corporate forest land near Nacimiento in south central Chile over the period 2008–2019. The catchments (7.7–414 ha) contain forest plantations of exotic fast-growing species (Pinus radiata, Eucalyptus spp.) at various stages of growth in a Mediterranean climate (mean long-term annual rainfall = 1381 mm). Since 2010, a drought, unprecedented in recent history, has reduced rainfall at Nacimiento by 20%, relative to the long-term mean. Pre-drought runoff ratios were <0.2 under 8-year-old Eucalyptus; >0.4 under 21-year-old Radiata pine and >0.8 where herbicide treatments had controlled vegetation for 2 years in 38% of the catchment area. Early in the study period, clearcutting of Radiata pine (85%–95% of catchment area) increased streamflow by 150 mm as compared with the year before harvest, while clearcutting and partial cuts of Eucalyptus did not increase streamflow. During 2008–2019, the combination of emerging drought and forestry treatments (replanting with Eucalyptus after clearcutting of Radiata pine and Eucalyptus) reduced streamflow by 400–500 mm, and regeneration of previously herbicide-treated vegetation combined with growth of Eucalyptus plantations reduced streamflow by 1125 mm (87% of mean annual precipitation 2010–2019). These results from one of the most comprehensive forest catchment studies in the world on private industrial forest land indicate that multiple decades of forest management have reduced deep soil moisture reservoirs. This effect has been exacerbated by drought and conversion from Radiata pine to Eucalyptus, apparently largely eliminating subsurface supply to streamflow. The findings reveal tradeoffs between wood production and water supply, provide lessons for adapting forest management to the projected future drier climate in Chile, and underscore the need for continued experimental work in managed forest plantations.     

GNSS多天线基线网单历元模糊度同步解算法

提出多天线基线网单历元模糊度同步解算法，其具体实现步骤为：1）根据宽巷组合模糊度易于固定的优点，采用附加已知基线长度约束法同步解算各基线的宽巷模糊度，得到dm~cm级精度的近似基线分量；2）将解算得到的各近似基线分量作为约束，同步解算各基线的基频模糊度，以获取mm级精度的基线矢量。该方法的关键在于检验各历元宽巷模糊度解算的正确性，以获取可靠的近似基线分量，为解算各基线的基频模糊度提供准确的基线先验信息。由于动态情况下各历元观测信息比较少，单纯依赖ratio检验不可靠，提出结合基线误差、单位权中误差、基线网模糊度闭合环及ratio值等对多组宽巷模糊度进行检验，避免ratio值设置不当导致模糊度检验中发生纳伪和弃真问题。实测数据结果证明，该处理方法使得模糊度解算的成功率提高1%~2%，可以获取移动平台更丰富的导航信息，提高其服务能力。     

基于ArcGIS Engine与ArcGIS Server的海域定级信息管理系统设计与开发

以Visual Studio 2012为平台,利用ArcGIS Engine强大的空间分析功能,以C#为开发语言结合第三方插件设计开发海域定级决策子系统,实现了不同用海方式海域的自动化定级,对海域定级基础数据、过程数据和结果数据进行综合管理,构建了一个具有一定实用价值的海域定级信息管理系统原型;此外,利用WebGIS将海域定级决策子系统分析生成的结果数据发布成服务,实现海域定级信息共享子系统。本研究为海域定级提供智能化平台,提高对海域定级及评估效率,有利于海洋可持续发展。     

A-FPN算法及其在遥感图像船舶检测中的应用

光学遥感图像船舶检测主要面临两个挑战:光学遥感图像背景复杂,船舶检测易受海浪、云雾及陆地建筑等多方面干扰;遥感图像分辨率低,船舶目标小,对于其分类与定位带来很大困难;针对上述问题,在FPN的基础上,提出一种融入显著性特征的卷积神经网络模型A-FPN (Attention-Based Feature Pyramid Networks)。首先,利用卷积提取图像特征金字塔;然后,利用顶层金字塔逐级构建显著特征层,抑制背景信息,通过金字塔顶层的细粒度特征提高浅层特征的表达能力,构建自上而下的多级显著特征映射结构;最后利用Softmax分类器进行多层级船舶检测。A-FPN模型利用显著性机制引导不同感受下的特征进行融合,提高了模型的分辨能力,对遥感图像处理领域具有重要应用价值。实验阶段,利用公开的遥感目标检测数据集NWPU VHR-10中的船舶样本进行测试,准确率为92.8%,表明A-FPN模型适用于遥感图像船舶检测。     

青海省新一代BDS高精度基准服务平台的构建

介绍了青海省新一代北斗卫星导航系统(BDS)高精度基准服务平台.该平台具备用户管理、数据处理、空间环境监控、高精度定位等功能,可以满足系统管理员、行政领导、单位用户和实时动态(RTK)测量用户在内的各种用户的多样化需求,并且遵循了管理标准化、可扩展性强、现势性好、实用性好、安全性强、具有前瞻性等建设原则.     

PTP协议在广电时间同步网中的应用研究

随着广电行业的飞速发展,以串行数字接口(SDI)基带信号为基础技术架构的传统电视信号已经很难满足未来技术发展和业务扩展的需求．得益于现代通信技术的迅速发展以及即将到来的万物互联的5G时代,广电行业也将采用IP化技术来构建新的网络系统．处在向IP化发展的过渡期,广电IP化目前还存在一些亟待解决的问题．为了解决广电系统网络中需要更高精度的同步信号来完成时间同步的问题,本文利用精确时间协议（PTP）时间同步技术在专用以太网中完成时间同步组网,搭建了测试环境并对测试数据进行了分析．测试结果表明,相较于传统的网络时间协议（NTP）时间同步技术,输出PTP信号的准确度能够达到亚微秒量级,完全满足广电行业IP化数字电视系统时间同步信号的高精度需求．      

张力腿平台筋腱支撑浮筒结构设计研究

在张力腿平台筋腱与海底桩基连接后,需要筋腱支撑浮筒为筋腱提供足够的浮力,保证筋腱在平台运抵工作海域之前不会发生倾覆。针对南海油田自主研发的张力腿平台筋腱实际尺寸及设计要求,设计了适用于1 061 mm(40英寸)筋腱、500米水深的张力腿平台筋腱支撑浮筒,并对筋腱支撑浮筒主要受力进行了分析计算,根据所计算的载荷情况对关键部件进行了有限元仿真分析,验证了结构强度的可靠性,并得到了结构的应力集中点与最大变形点,为筋腱支撑浮筒的结构设计提供了参考。     

Evaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification,visual interpretation and machine learning approaches

We evaluate three approaches to mapping vegetation using images collected by an unmanned aerial vehicle (UAV) to monitor rehabilitation activities in the Five Islands Nature Reserve, Wollongong (Australia). Between April 2017 and July 2018, four aerial surveys of Big Island were undertaken to map changes to island vegetation following helicopter herbicide sprays to eradicate weeds, including the creeper Coastal Morning Glory (Ipomoea cairica) and Kikuyu Grass (Cenchrus clandestinus). The spraying was followed by a large scale planting campaign to introduce native plants, such as tussocks of Spiny-headed Mat-rush (Lomandra longifolia). Three approaches to mapping vegetation were evaluated, including: (i) a pixel-based image classification algorithm applied to the composite spectral wavebands of the images collected, (ii) manual digitisation of vegetation directly from images based on visual interpretation, and (iii) the application of a machine learning algorithm, LeNet, based on a deep learning convolutional neural network (CNN) for detecting planted Lomandra tussocks. The uncertainty of each approach was assessed via comparison against an independently collected field dataset. Each of the vegetation mapping approaches had a comparable accuracy; for a selected weed management and planting area, the overall accuracies were 82 %, 91 % and 85 % respectively for the pixel based image classification, the visual interpretation / digitisation and the CNN machine learning algorithm. At the scale of the whole island, statistically significant differences in the performance of the three approaches to mapping Lomandra plants were detected via ANOVA. The manual digitisation took a longer time to perform than others. The three approaches resulted in markedly different vegetation maps characterised by different digital data formats, which offered fundamentally different types of information on vegetation character. We draw attention to the need to consider how different digital map products will be used for vegetation management (e.g. monitoring the health individual species or a broader profile of the community). Where individual plants are to be monitored over time, a feature-based approach that represents plants as vector points is appropriate. The CNN approach emerged as a promising technique in this regard as it leveraged spatial information from the UAV images within the architecture of the learning framework by enforcing a local connectivity pattern between neurons of adjacent layers to incorporate the spatial relationships between features that comprised the shape of the Lomandra tussocks detected.     

Census survey approach to quantifying īnanga spawning habitat for conservation and management

Here, we describe a methodology for quantifying the spawning habitat of īnanga (Galaxias maculatus), a protected native fish species. Our approach is demonstrated with a survey of the Heathcote/ōpāwaho following the Canterbury earthquakes that produced unexpected findings. Spawning habitat was detected over a 2.5?km reach and the area occupied by spawning sites (75m²) was much larger than in previous records (ca.?21m²). Sites dominated by the invasive Phalaris arundinaceae were found to support high egg numbers. Spawning has not previously been recorded on this species and it is identified in the literature as a threat to spawning habitat. Considerable spatio-temporal variation was also detected in the location of spawning sites and pattern of egg production. Together, these aspects illustrate the need for a comprehensive survey methodology to reliably quantify spawning habitat. The Heathcote/ōpāwaho example shows the utility of our census approach for achieving this, and supporting habitat conservation objectives.