基于源汇指数的沈阳热岛效应

基于2001和2010年Landsat 遥感影像,利用GIS技术识别沈阳城市热岛源区和汇区,利用地表温度(LST)、源区和汇区面积比率指数（CI）和热岛强度指数（LI）,评价分析了沈阳土地利用发展布局模式对热岛效应的影响.结果表明: 2001-2010年,沈阳三环内土地利用类型变化较大,导致热岛源、汇区面积变化明显,且主要发生在二环和三环.2001年,一环内热岛源、汇区面积比例分别为94.3%和5.7%,三环内分别为64.0%和36.0%;2010年,其比例在一环内分别为93.4%和6.6%,三环内分别为70.2%和29.8%,说明10年来“摊饼式”土地利用布局决定了沈阳热岛效应的“摊饼式”布局.研究期间,沈阳地表温度从一环至三环均呈递减趋势,热岛效应强度在2001年以单一中心为主,至2010年发展为多中心态势,热岛效应强度等级有所降低.从一环至三环,CI绝对值均呈增加趋势,LI值均小于1,说明期间研究区土地利用布局变化对改善区域热岛效应没有明显作用.     

成都市热岛效应及其与下垫面的关系

城市热岛效应是一种常见的城市气候,影响着城市规划及人民的生产与生活。本文以成都市中心城区为研究区,通过采用Landsat 8 TIRS数据,运用劈窗算法反演成都市中心城区4个时间点的地表温度,并对热岛效应的时空变化及其与下垫面的关系进行了分析。结果表明:劈窗算法在无同步气象观测资料校正大气透射率的情况下精度达到1.6 K;成都市中心城区明显存在热岛效应,其面积约占中心城区面积的30%,且春夏季热岛效应的空间差异性较大;三环外热岛效应较三环内明显,武侯区和双流区之间始终存在一个温度高值区;夏季归一化差异植被指数(NDVI)与地表温度存在显著的负相关关系,这说明可以通过种植绿色植物来缓解城市热岛效应;夏季地表温度与改进的归一化差异水体指数(MNDWI)的相关性不如春季和冬季,因此,夏季用水体来缓解热岛效应可能收效甚微;归一化建筑指数(NDBI)与温度在4个时间点上均呈正相关,这说明人类活动是造成城市热岛效应的原因之一,NDBI是研究城市热岛效应的有效指标。     

基于城市土地利用类型的地表温度与植被指数的关系

以沈阳市三环作为研究区域,基于QuickBird、Landsat/TM遥感影像和GIS空间分析技术,获取城市土地利用信息,陆地表面温度(LST)以及归一化植被指数(NDVI),定量分析了LST与NDVI在不同城市土地利用类型之间的差异及空间关系.结果表明:LST和NDVI具有明显相反的变化趋势,城市不同土地利用类型的LST与NDVI平均值也具有显著的差异性;多重比较发现,LST和NDVI在两两土地利用类型之间的差异不同;LST与NDVI相关性的显著程度受空间尺度的影响,LST与NDVI的相关性随空间尺度变化呈现出先增加后降低再逐渐增加的趋势.研究结果可为城市规划以及城市绿地系统规划中缓解城市热岛效应提供科学依据.     

京津冀城市群热岛定量评估

在采用城乡二分法估算区域范围内多个城市地表热岛强度(Surface Urban Heat Island,SUHI)时,如何选择城镇化影响最小的周边乡村背景是一个技术难点,提出了一种基于地形、土地利用、植被覆盖和城市夜间灯光指数来确定乡村背景的SUHI估算方法,并建立了基于SUHI和热岛比例指数(Urban Heat Island Proportion Index,UHPI)的城市热岛强度定量评估方法。利用上述方法,基于长时间序列MODIS和NOAA卫星资料,开展了京津冀城市群11个平原城市热岛时空变化分析与评估,并开展了社会经济驱动因子对城市热岛大小的影响评估研究,同时结合未来京津冀一体化发展提出相应参考建议。研究结果表明:(1)建立的SUHI估算方法能有效监测城市群热岛年/季和昼/夜变化,近5年(2010—2014)年均SUHI≥3℃的热岛总面积1926km~2,但在热岛最强的夏季白天可达7386 km~2(占行政区域面积的5.8%);排名前四的分别是北京(2351 km~2)、天津(1883km~2)、唐山(889 km~2)和石家庄(611 km~2),显示出超大、特大城市及资源性城市贡献了大部分城市群热岛面积;各中心城区平均SUHI和UHPI分别为3.0℃和0.61,热岛评估达到较严重等级以上的城市占到73%,表明当前城市群整体热岛处于严峻现状;(2)1994、2004年和2014年夏季白天城市群强热岛面积分别为190、1975、4539 km~2,各中心城区平均SUHI分别为1.2、2.6、3.2℃,UHPI分别为0.29、0.58和0.69,热岛评估等级分别为"一般"、"较严重"和"严重"等级,反映了20年来京津冀城市群热岛迅速增强增大事实;(3)各城市年均热岛面积增加2—86 km~2/a,强热岛面积增加主要发生在超大城市,北京、天津强热岛区之间的最短空间距离从1994年的94 km逐步缩减到2014年的52 km,未来存在形成"京津区域热岛群"的可能,建议在京津之间建立"绿色生态屏障"来消除这种可能性;(4)城镇人口数、国内生产总值和用电量都极大地影响着京津冀城市热岛大小,拟合模型决定系数R2分别为0.9097、0.912和0.9661,意味着在未来京津冀一体化城市发展中可采取控制城市人口规模、减少能源消耗等措施减缓热岛效应。     

南京地区大口径闪烁仪与涡动相关仪感热通量观测对比

湍流是地表与大气间物质与能量交换的主要形式,因而准确观测湍流通量历来是城市边界层研究的重要问题。本研究基于架设在南京信息工程大学内的大口径闪烁仪(large aperture scintillometer,LAS)和涡动相关仪(eddy covariance,EC)的同步观测,对比了LAS测得的感热通量和EC测得的感热通量的差异,结合归一化植被指数(NDVI)和归一化建筑指数(NDBI),分析了下垫面不均匀性对于两种仪器测得感热通量的影响。结果表明:城市地区LAS与EC具有较好的相关性(R2=0.76),拟合线斜率为0.95;白天,LAS的感热通量大于EC的感热通量,二者差值为18.8~39.4 W·m^-2;夜间,二者均在零值附近波动,差值为4.8~28.7 W·m^-2;月尺度上两种仪器的差值8月最大,其次为7月、4月,6月最小;差异产生的主要原因是风向造成的通量源区不同;通量源区内的NDVI值越大,感热通量与净辐射之比越小,二者呈显著负相关(k=-0.34,P<0.05);NDBI值越大,感热通量与净辐射之比越大,二者呈显著正相关(k=1.15,P<0.05)。     

北京市植物物候对热岛效应的响应

都市的快速发展致使城市热岛效应日渐加剧。城市绿化对缓解热岛效应有一定的作用,但同时其物候也受到了影响。本文基于遥感影像数据,通过对照北京市热岛区与非热岛区、不同热岛强度等级的植物物候差别,研究热岛效应对植物物候的影响。采用Landsat 8影像对北京市地表温度进行反演,结合空间分析进行热岛效应分区分级识别;通过MOD13A1的归一化植被指数(NDVI)数据,采用Dallimer平均值法和动态阈值法提取植被生长季始期(SOG)、生长季末期(EOG)、生长季长度(LOG)等重要植被物候参数,研究植被物候在热岛区与非热岛区、不同热岛强度等级之间的差异。结果表明:北京市夏季与冬季相比,热岛效应更为显著;利用Dallimer平均值法提取的北京市热岛区SOG比非热岛区提前4 d,EOG延迟9 d,LOG延长13 d;利用动态阈值法提取的SOG提早10 d,EOG推迟4d,LOG延长14 d,动态阈值法提取的物候信息与观测的物候信息更为接近;随着热岛强度等级提高,SOG提前,EOG推迟,LOG延长。本研究丰富了我国华北地区植被物候对热岛效应的响应机制,同时对研究全球变暖对生态系统的影响具有一定价值。     

近20年藏北地区AVHRR NDVI与气候因子的关系

利用藏北那曲地区1981~2001年NOAA/AVHRR的旬合成NDVI资料和6个站的逐日气象资料,分析了(归一化指数NDVI)的年内和年际变化规律以及NDVI与8个气候要素的相关关系,主要结论:影响NDVI年变化最显著的气候因子是温度,其中水汽压与NDVI的相关程度明显大于降水量;日照时数与NDVI呈负相关;NDVI与日照时数的滞后时间约0~10d,与风速没有滞后现象,与潜在蒸散、温度、水汽压和降水约20~40d;影响NDVI年际变化最显著的气候因子是潜在蒸散量。     

东北地区植被NDVI对不同时间尺度SPEI的响应

植被在陆地生态系统中起至关重要的作用。近年来干旱频率逐渐增加,对植被的生长发育产生严重的影响。本研究基于1982—2015年的归一化植被指数(normalized difference vegetation index,NDVI)和不同时间尺度的标准化降水蒸散指数(standardized precipitation evapotranspiration index,SPEI)的相关性来评估东北地区植被生长状况对干旱的响应。结果表明:年尺度和季尺度内NDVI和SPEI的最大相关系数值在研究区内西部内蒙古草原地区最大,东部长白山脉地区次之,中东部东北平原地区最小,且NDVI与SPEI的相关系数在12个月以上的时间尺度较大;生长季(4—10月)和夏季(6—8月)的NDVI对SPEI响应最为敏感,春季(4—5月)、秋季(9—10月)响应依次减弱;其中,5—7月的NDVI与SPEI-24的显著相关像元比例较高,而8—10月的NDVI则与SPEI-06的显著相关像元比例较高;不同的植被类型对干旱响应不同,森林NDVI在不同月份与不同尺度的SPEI大体上呈负相关,草原和农作物则主要以正相关为主。     

西安城市热环境格局的动态演变

以TM和ETM+热红外数据为基础,采用单窗算法,研究了西安1995-2006年间热岛效应景观分布及其动态变化特征.研究表明:西安城市热岛效应明显,热场空间分布格局与城市的建成区基本一致;随着城市化进程的加快,西安市热环境格局表现出异质性加剧的趋势;东西和南北方向上,温度剖面线与土地利用与覆被关系密切,以建设用地地表温度最高、水域最小,其大小关系依次为建设用地＞未利用地＞耕地＞草地＞林地＞水域.相关性分析表明,地表温度与归一化裸露指数( NDBI)呈正相关,与归一化植被指数(NDVI)呈负相关.     

基于GEE的长江经济带城市群热岛特征及影响因素

遥感技术能够客观、定量地评价地表城市热岛强度的时空变化，分析城市热岛的影响因素，为城市生态建设提供有效的科学参考。本研究基于GEE(Google Earth Engine)平台，以长时间序列的MODIS卫星资料为基础数据源，使用城乡二分法估算长江经济带2003—2019年的城市地表热岛强度，再利用城市热场变异指数评价生态环境，并利用地理探测器进行城市热岛成因分析。结果表明：长江经济带5个城市群2003—2019年全年平均气温为22.78℃,白天为23.91℃,夜间为7.59℃,平均温度整体上均呈上升趋势，分别为0.02、0.02和0.006℃·a^-1。长江经济带5个城市群17年无热岛占比最高，弱热岛和弱冷岛占比次之，强热岛占比最少，且城市热岛全年、白天和夜间都呈现下降趋势。长江经济带整体的生态评价为优。地理探测器因子探测表明，DEM、土地覆盖类型、O₃是长三角城市群热岛效应的主控因子；蒸散发量、紫外线气溶胶吸收指数、人口密度是长江中游城市群热岛效应的主控因子；归一化植被指数、夜间灯光指数、土地覆盖类型是成渝城市群热岛效应的主控因子；归一化植被...     

Partial least squares for functional joint models with applications to the Alzheimer's disease neuroimaging initiative study

Many biomedical studies have identified important imaging biomarkers that are associated with both repeated clinical measures and a survival outcome. The functional joint model (FJM) framework, proposed by Li and Luo in 2017, investigates the association between repeated clinical measures and survival data, while adjusting for both high-dimensional images and low-dimensional covariates based on the functional principal component analysis (FPCA). In this paper, we propose a novel algorithm for the estimation of FJM based on the functional partial least squares (FPLS). Our numerical studies demonstrate that, compared to FPCA, the proposed FPLS algorithm can yield more accurate and robust estimation and prediction performance in many important scenarios. We apply the proposed FPLS algorithm to a neuroimaging study. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.     

Managing data locally to answer questions globally: The role of collaborative science in ecology

Ecologists are increasingly asking large‐scale and/or broad‐scope questions that require vast datasets. In response, various top‐down efforts and incentives have been implemented to encourage data sharing and integration. However, despite general consensus on the critical need for more open ecological data, several roadblocks still discourage compliance and participation in these projects; as a result, ecological data remain largely unavailable. Grassroots initiatives (i.e. efforts initiated and led by cohesive groups of scientists focused on specific goals) have thus far been overlooked as a powerful means to meet these challenges. These bottom‐up collaborative data integration projects can play a crucial role in making high quality datasets available because they tackle the heterogeneity of ecological data at a scale where it is still manageable, all the while offering the support and structure to do so. These initiatives foster best practices in data management and provide tangible rewards to researchers who choose to invest time in sound data stewardship. By maintaining proximity between data generators and data users, grassroots initiatives improve data interpretation and ensure high‐quality data integration while providing fair acknowledgement to data generators. We encourage researchers to formalize existing collaborations and to engage in local activities that improve the availability and distribution of ecological data. By fostering communication and interaction among scientists, we are convinced that grassroots initiatives can significantly support the development of global‐scale data repositories. In doing so, these projects help address important ecological questions and support policy decisions.     

Is DIA proteomics data FAIR? Current data sharing practices,available bioinformatics infrastructure and recommendations for the future

Data independent acquisition (DIA) proteomics techniques have matured enormously in recent years, thanks to multiple technical developments in, for example, instrumentation and data analysis approaches. However, there are many improvements that are still possible for DIA data in the area of the FAIR (Findability, Accessibility, Interoperability and Reusability) data principles. These include more tailored data sharing practices and open data standards since public databases and data standards for proteomics were mostly designed with DDA data in mind. Here we first describe the current state of the art in the context of FAIR data for proteomics in general, and for DIA approaches in particular. For improving the current situation for DIA data, we make the following recommendations for the future: (i) development of an open data standard for spectral libraries; (ii) make mandatory the availability of the spectral libraries used in DIA experiments in ProteomeXchange resources; (iii) improve the support for DIA data in the data standards developed by the Proteomics Standards Initiative; and (iv) improve the support for DIA datasets in ProteomeXchange resources, including more tailored metadata requirements.     

Integrating vegetation field surveys with remotely sensed data

Summary   This paper explores data compatibility issues arising from the assessment of remnant native vegetation condition using satellite remote sensing and field-based data. Space-borne passive remote sensing is increasingly used as a way of providing a total sample and synoptic overview of the spectral and spatial characteristics of native vegetation canopies at a regional scale. However, integrating field-collected data often not designed for integration with remotely sensed data can lead to data compatibility issues. Subsequent problems associated with the integration of unsuited datasets can contribute to data uncertainty and result in inconclusive findings. It is these types of problems (and potential solutions) that form the basis of this paper. In other words, how can field surveys be designed to support and improve compatibility with remotely sensed total surveys? Key criteria were identified for consideration when designing field-based surveys of native vegetation condition (and other similar applications) with the intent to incorporate remotely sensed data. The criteria include recommendations for the siting of plots, the need for reference location plots, the number of sample sites and plot size and distribution, within a study area. The difficulties associated with successfully integrating these data are illustrated using real examples taken from a study of the vegetation in the Little River Catchment, New South Wales, Australia.     

The Wilcoxon signed rank test for paired comparisons of clustered data

The Wilcoxon signed rank test is a frequently used nonparametric test for paired data (e.g., consisting of pre- and posttreatment measurements) based on independent units of analysis. This test cannot be used for paired comparisons arising from clustered data (e.g., if paired comparisons are available for each of two eyes of an individual). To incorporate clustering, a generalization of the randomization test formulation for the signed rank test is proposed, where the unit of randomization is at the cluster level (e.g., person), while the individual paired units of analysis are at the subunit within cluster level (e.g., eye within person). An adjusted variance estimate of the signed rank test statistic is then derived, which can be used for either balanced (same number of subunits per cluster) or unbalanced (different number of subunits per cluster) data, with an exchangeable correlation structure, with or without tied values. The resulting test statistic is shown to be asymptotically normal as the number of clusters becomes large, if the cluster size is bounded. Simulation studies are performed based on simulating correlated ranked data from a signed log-normal distribution. These studies indicate appropriate type I error for data sets with > or =20 clusters and a superior power profile compared with either the ordinary signed rank test based on the average cluster difference score or the multivariate signed rank test of Puri and Sen. Finally, the methods are illustrated with two data sets, (i) an ophthalmologic data set involving a comparison of electroretinogram (ERG) data in retinitis pigmentosa (RP) patients before and after undergoing an experimental surgical procedure, and (ii) a nutritional data set based on a randomized prospective study of nutritional supplements in RP patients where vitamin E intake outside of study capsules is compared before and after randomization to monitor compliance with nutritional protocols.     

Approaches for Addressing Life Cycle Assessment Data Gaps for Bio‐based Products

There is an increasing need for life cycle data for bio‐based products, which becomes particularly evident with the recent drive for greenhouse gas reporting and carbon footprinting studies. Meeting this need is challenging given that many bio‐products have not yet been studied by life cycle assessment (LCA), and those that have are specific and limited to certain geographic regions. In an attempt to bridge data gaps for bio‐based products, LCA practitioners can use either proxy data sets (e.g., use existing environmental data for apples to represent pears) or extrapolated data (e.g., derive new data for pears by modifying data for apples considering pear‐specific production characteristics). This article explores the challenges and consequences of using these two approaches. Several case studies are used to illustrate the trade‐offs between uncertainty and the ease of application, with carbon footprinting as an example. As shown, the use of proxy data sets is the quickest and easiest solution for bridging data gaps but also has the highest uncertainty. In contrast, data extrapolation methods may require extensive expert knowledge and are thus harder to use but give more robust results in bridging data gaps. They can also provide a sound basis for understanding variability in bio‐based product data. If resources (time, budget, and expertise) are limited, the use of averaged proxy data may be an acceptable compromise for initial or screening assessments. Overall, the article highlights the need for further research on the development and validation of different approaches to bridging data gaps for bio‐based products.     

Data Fusion Methods for Human Health Risk Assessment: Review and Application

The improved accessibility to data that can be used in human health risk assessment (HHRA) necessitates advanced methods to optimally incorporate them in HHRA analyses. This article investigates the application of data fusion methods to handling multiple sources of data in HHRA and its components. This application can be performed at two levels, first, as an integrative framework that incorporates various pieces of information with knowledge bases to build an improved knowledge about an entity and its behavior, and second, in a more specific manner, to combine multiple values for a state of a certain feature or variable (e.g., toxicity) into a single estimation. This work first reviews data fusion formalisms in terms of architectures and techniques that correspond to each of the two mentioned levels. Then, by handling several data fusion problems related to HHRA components, it illustrates the benefits and challenges in their application.     

From information to understanding: the role of model organism databases in comparative and functional genomics

Data integration is key to functional and comparative genomics because integration allows diverse data types to be evaluated in new contexts. To achieve data integration in a scalable and sensible way, semantic standards are needed, both for naming things (standardized nomenclatures, use of key words) and also for knowledge representation. The Mouse Genome Informatics database and other model organism databases help to close the gap between information and understanding of biological processes because these resources enforce well-defined nomenclature and knowledge representation standards. Model organism databases have a critical role to play in ensuring that diverse kinds of data, especially genome-scale data sets and information, remain useful to the biological community in the long-term. The efforts of model organism database groups ensure not only that organism-specific data are integrated, curated and accessible but also that the information is structured in such a way that comparison of biological knowledge across model organisms is facilitated.