科技量值的品质评定—校准、测样两阶段矢量合成法计算测量不确定度

实现单位统一、量值准确可靠的检测,是科技工作成功的前提,务必使用法定计量单位,在硬件、软件受控满足法制计量要求条件下,获取优质可溯源的量值,完整的量值应为,A_(opt)±u(A_(opt))=(A-ΔA)±u(A-ΔA)=(A-ΔA)±u2j(A)+u2i(A);由此导出校准j、测量i两阶段矢量合成简化计算测量不确定度的方法;特别有利于有多种标样的实验室,以标样做加密质控样,随机插入批样中,由管理者对n(≥11)批质控样结果做出统计的系统误差ΔA和标准测量不确定度u(A),同时完成签发检测报告、考核检验员素质和常态化提供测量不确定度三大使命,履行对客户的检测值品质的承诺。     

The impact of uncertainties on cooperation over transboundary water: The case of Israeli–Palestinian negotiations

The effect of uncertainty on cooperation between the partners sharing the natural resources remains unknown. Uncertainty may strengthen cooperation between partners, as it is necessary to implement cooperative mitigation policies, however, it may also serve as a cause of friction between parties, as it may aggravate existing trust issues or power asymmetries. Given the potential for such contrary outcomes, we provide criteria to examine empirically how uncertainties in a transboundary setting seem to promote or impede cooperation. Taking Israeli–Palestinian Annapolis round and post-Annapolis negotiations as a case study, this work identifies the effect of uncertainties related to water on negotiation positions. Social and political uncertainties, which tend to be more associated with uncertainty regarding interpretation rather than a lack of information, play a much stronger role in water negotiations than do technical or physical uncertainties that often dominate in other resource issues. Many of the criteria used to assess the effect of uncertainty indicate that partners attempted to address uncertainties in an ostensibly cooperative manner, accepting negotiation venues and rules. However, confronting uncertainty stemming from interpretation of information often around social issues tends to result in additional uncertainties associated with delaying negotiations, spillover effects and power implications, each with negative implications in terms of cooperation. As such, mechanisms proposed to address these uncertainties also tend to be more disputed. The only type of mechanisms that did not appear to aggravate the effects of these uncertainties and perhaps the only that would be indicative of some type of cooperation, even if low level, are those that deal data and information exchange and research.     

Effects of national forest inventory plot location error on forest carbon stock estimation using k-nearest neighbor algorithm

This paper suggested simulation approaches for quantifying and reducing the effects of National Forest Inventory (NFI) plot location error on aboveground forest biomass and carbon stock estimation using the k-Nearest Neighbor (kNN) algorithm. Additionally, the effects of plot location error in pre-GPS and GPS NFI plots were compared. Two South Korean cities, Sejong and Daejeon, were chosen to represent the study area, for which four Landsat TM images were collected together with two NFI datasets established in both the pre-GPS and GPS eras. The effects of plot location error were investigated in two ways: systematic error simulation, and random error simulation. Systematic error simulation was conducted to determine the effect of plot location error due to mis-registration. All of the NFI plots were successively moved against the satellite image in 360° directions, and the systematic error patterns were analyzed on the basis of the changes of the Root Mean Square Error (RMSE) of kNN estimation. In the random error simulation, the inherent random location errors in NFI plots were quantified by Monte Carlo simulation. After removal of both the estimated systematic and random location errors from the NFI plots, the RMSE% were reduced by 11.7% and 17.7% for the two pre-GPS-era datasets, and by 5.5% and 8.0% for the two GPS-era datasets. The experimental results showed that the pre-GPS NFI plots were more subject to plot location error than were the GPS NFI plots. This study’s findings demonstrate a potential remedy for reducing NFI plot location errors which may improve the accuracy of carbon stock estimation in a practical manner, particularly in the case of pre-GPS NFI data.     

Gaussian processes uncertainty estimates in experimental Sentinel-2 LAI and leaf chlorophyll content retrieval

ESA’s upcoming Sentinel-2 (S2) Multispectral Instrument (MSI) foresees to provide continuity to land monitoring services by relying on optical payload with visible, near infrared and shortwave infrared sensors with high spectral, spatial and temporal resolution. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods, which ideally should provide uncertainty intervals for the predictions. Statistical learning regression algorithms are powerful candidats for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. In this paper, we focus on a new emerging technique in the field of Bayesian nonparametric modeling. We exploit Gaussian process regression (GPR) for retrieval, which is an accurate method that also provides uncertainty intervals along with the mean estimates. This distinct feature is not shared by other machine learning approaches. In view of implementing the regressor into operational monitoring applications, here the portability of locally trained GPR models was evaluated. Experimental data came from the ESA-led field campaign SPARC (Barrax, Spain). For various simulated S2 configurations (S2-10m, S2-20m and S2-60m) two important biophysical parameters were estimated: leaf chlorophyll content (LCC) and leaf area index (LAI). Local evaluation of an extended training dataset with more variation over bare soil sites led to improved LCC and LAI mapping with reduced uncertainties. GPR reached the 10% precision required by end users, with for LCC a NRMSE of 3.5–9.2% (r²: 0.95–0.99) and for LAI a NRMSE of 6.5–7.3% (r²: 0.95–0.96). The developed GPR models were subsequently applied to simulated Sentinel images over various sites. The associated uncertainty maps proved to be a good indicator for evaluating the robustness of the retrieval performance. The generally low uncertainty intervals over vegetated surfaces suggest that the locally trained GPR models are portable to other sites and conditions.     

Quantitative assessment of vegetation cover and soil degradation factors within terrain units for planning,monitoring and assessment of renaturation along oil and gas pipelines

This paper evaluates the renaturation activities applying the quantification of vegetation cover (VC), the site suitability analysis (SSA) based on the predefined criteria (slope steepness category (SSC), soil erodibility factor (K) and VC) and soil erosion model (SEM) results within the terrain units (TUs) along pipeline rights-of-way (RoW). Quantification of VC percentage is performed to assess the overall restored VC from 2005 to 2007. The results of the quantitative analysis in 2007 show that the total area of restored VC is 10.7 km², and 8.9 km² still needs to be restored to comply with the environmental acceptance criteria. As a result of SSA, TUs were prioritized by erosion vulnerability and this allowed to better understand the landscape behaviour in regards to erosion processes. SEM provided more detailed predictions of erosion classes falling into TUs. SEM identified 40% of erosion sites occurred from 2005 to 2010.     

Digital Terrain Modeling and Industrial Surface Metrology: Converging Realms

Digital terrain modeling has a micro- and nanoscale counterpart in surface metrology, the numerical characterization of industrial surfaces. Instrumentation in semiconductor manufacturing and other high-technology fields can now contour surface irregularities down to the atomic scale. Surface metrology has been revolutionized by its ability to manipulate square-grid height matrices that are analogous to the digital elevation models (DEMs) used in physical geography. Because the shaping of industrial surfaces is a spatial process, the same concepts of analytical cartography that represent ground-surface form in geography evolved independently in metrology. The surface topography of manufactured components, exemplified here by automobile-engine cylinders, is routinely modeled by variogram analysis, relief shading, and most other techniques of parameterization and visualization familiar to geography. This article introduces industrial surface-metrology, examines the field in the context of terrain modeling and geomorphology and notes their similarities and differences, and raises theoretical issues to be addressed in progressing toward a unified practice of surface morphometry.     

Uncertainties in estimates of cropland area in China: a comparison between an AVHRR-derived dataset and a Landsat TM-derived dataset

The large uncertainties in estimates of cropland area in China may have significant implications for major cross-cutting themes of global environmental change—food production and trade, water resources, and the carbon and nitrogen cycles. Many earlier studies have indicated significant under-reporting of cropland area in China from official agricultural census statistics datasets. Space-borne remote sensing analyses provide an alternative and independent approach for estimating cropland area in China. In this study, we report estimates of cropland area from the National Land Cover Dataset (NLCD-96) at the 1:100,000 scale, which was generated by a multi-year National Land Cover Project in China through visual interpretation and digitization of Landsat TM images acquired mostly in 1995 and 1996. We compared the NLCD-96 dataset to another land cover dataset at 1-km spatial resolution (the IGBP DIScover dataset version 2.0), which was generated from monthly Advanced Very High Resolution Radiometer (AVHRR)-derived Normalized Difference Vegetation Index (NDVI) from April, 1992 to March, 1993. The data comparison highlighted the limitation and uncertainty of cropland area estimates from the DIScover dataset.     

海洋渔业中的不确定性、风险及其效应

在海洋渔业中 ,存在着自然状态的不确定性 ,这会引起人的行为的不确定性 ,进而引起资源供给的不确定性。不确定性使渔民对其行为的预期存在风险 ,因而他们总是试图规避风险以减少不确定性带来的损失。人们的风险规避行为可能带来海洋渔业资源过度使用的效应     

Probabilistic constraints on structural lineament best fit plane precision obtained through numerical analysis

Understanding the orientation distribution of structural discontinuities using the limited information afforded by their trace in outcrop has considerable application, with such analysis often providing the basis for geological modelling. However, eigen analysis of 3D structural lineaments mapped at decimetre to regional scales indicates that discontinuity best fit plane estimates from such datasets tend to be unreliable. Here, the relationship between digitised lineament vertex geometry (coplanarity/collinearity) and the reliability of their estimated best fitting plane is investigated using Monte Carlo experiments. Lineaments are modelled as the intersection curve between two orthonormally oriented fractional Brownian surfaces representing the outcrop and discontinuity plane. Commensurate to increasing lineament vertex collinearity (K), systematic decay in estimated pole vector precision is observed from these experiments. Pole vector distributions are circumferentially constrained around the axis of rotation set by the end nodes of the synthetic lineaments, reducing the rotational degrees of freedom of the vertex set from three to one. Vectors on the unit circle formed perpendicular to this arbitrary axis of rotation conform to von Mises (circular normal) distributions tending towards uniform at extreme values of K. This latter observation suggests that whilst intrinsically unreliable, confidence limits can be placed upon orientation estimates from 3D structural lineaments digitised from remotely sensed data. A probabilistic framework is introduced which draws upon the statistical constraints obtained from our experiments to provide robust best fit plane estimates from digitised 3D structural lineaments.     

Empirical testing of the visualizations of climate change mitigation scenarios with citizens: A comparison among Germany,Poland, and France

While scenarios are used extensively for communication about climate change mitigation, little is known about the interpretation of these scenarios by citizens. We conducted a cross-country empirical evaluation of scenario visualizations for global mitigation, using online surveys in Germany (N = 379), Poland (N = 223), and France (N = 225). Each respondent received visualizations of the required changes in global carbon dioxide emissions and composition of electricity supply (fossil fuels, nuclear, and renewable sources) for limiting global warming to 1.5 °C. We evaluated the effects of respondents’ demographics, prior beliefs, numeracy, and graph literacy on the reading accuracy and knowledge gains from the visualizations. We also included an experimental between-groups design on visualization format, where four groups received different graph formats (steep or gradual graphs with depictions of uncertainty ranges or scenario ensembles) and the fifth group received a table. Results showed that higher education level, numeracy, and graph literacy increased reading accuracy in all countries, while age reduced them. Respondents with prior beliefs about climate change mitigation that matched the information in the visualizations had also higher reading accuracy and knowledge gains. While the effects of different visualization formats were comparatively minor, customizing formats according to demographic and country differences was used to reduce adverse effects from these differences. These results emphasize the need to design visualizations that match characteristics of the intended audience and could inform better communication of climate change mitigation scenarios to non-expert audience.