Decadal variability in multitemporal satellite SPOT‐VEGETATION NDII data

In this letter the inner dynamics of 1998–2003 decadal (10‐day) series of satellite SPOT‐VEGETATION Normalized Difference of Infrared Index (NDII) data of forest ecosystems in southern Italy was explored. The Multiple Segmenting Method (MSM) and Detrended Fluctuation Analysis (DFA), which are able to detect deep dynamical features in short and nonstationary series, were applied. The obtained results suggest that the forest moisture content is characterized by persistent mechanisms leading to a behaviour more unstable to external perturbations.     

Enhancing traceability of persistent data access flows in process-driven SOAs

In process-driven, service-oriented architectures (SOAs), process activities can perform service operations, data transformations, or human tasks. Unfortunately, the process activities are usually tightly coupled. Thus, when the number of activities in the process grows, focusing on particular activities of the flow such as the service operations reading or writing persistent data is a time-consuming task. In particular, in order to solve structural problems concerning persistent data access such as deadlocks in data-intensive business processes, stakeholders need to understand the underlying persistent data access details of the activities i.e. physical storage schemes, and database connections. With our view-based model-driven approach, we provide a solution to generate flows of persistent data access activities (which we refer to as persistent data access flows). To the best of our knowledge these persistent data access flows are not used to solve structural problems in process-driven SOAs, yet. Moreover, our persistent data access flows can be flattened by diverse filer criteria e.g. by filtering all activities reading or writing from a specific database or table. Using our approach, we can enhance traceability and documentation of persistent data access in business processes. In a series of motivating scenarios from an industrial case study we present how our persistent data access flow concept can contribute to enhance productivity in service-oriented, process-driven environments. We qualitatively evaluate our concepts and prototypes, and finally, discuss the correctness and the complexity of the underlying algorithms.     

OPTIMAL TRACKING CONTROL FOR DISCRETE TIME‐DELAY SYSTEMS WITH PERSISTENT DISTURBANCES

Optimal tracking control (OTC) for discrete time‐delay systems affected by persistent disturbances with quadratic performance indexes is considered. Optimal tracking controller is designed based on a sensitivity approximation approach. By introducing a sensitivity parameter, we transform the original OTC problem into a series of difference equations without time‐advance on time‐delay terms. The obtained OTC law consists of analytic feedback and feedforward terms, and a compensation term, which is the sum of the infinite series of adjoint vectors. The compensation term can be obtained with an iterated formula for the adjoint vectors. A simulation example shows that the approximation approach is effective in tracking the reference input and robust with respect to exogenous persistent disturbances.     

ASTROMOD: a computer program integrating vegetation dynamics modelling,environmental modelling and spatial data visualisation in Microsoft Excel

This paper describes the development of a software framework for predicting vegetation change within the Astroni crater, a state nature reserve near Naples, Italy. ASTROMOD (ASTROni MODel), is designed to allow the analysis of environmental and management scenarios in the crater, thus aiding the reserve’s manager in effective decision making. ASTROMOD comprises a vegetation dynamics model developed for forest ecosystems, combined with models of environmental determinants. It integrates a user-friendly interface for visualising spatial data, a parameter database and a series of programming modules within Microsoft Excel. This approach is a significant departure from other spatial biophysical modelling approaches which require costly and complex software tools.     

Analysis of recent ground subsidence in the Sibari plain (Italy) by means of satellite SAR interferometry-based methods

This work attempts to map recent land subsidence on the Sibari plain in Calabria region (Southern Italy), by exploiting vertical velocity values and time series of persistent scatterer interferometry (PSI) synthetic aperture radar (SAR) data acquired in the period 1992–2011 by different satellite systems.

After reviewing other sources and rates of subsidence recorded by previous works since the beginning of the Holocene, this paper presents the spatial distribution of the current settlement rates showing that subsidence is due to the compressible alluvial sediments of the plain, but that in recent times it is mostly due also to human activities. In fact, according to the most recent monitoring period (2009–2011), downward motion rates are recorded as being only localized within site-specific industrial areas, such as the ‘Sibari Lakes’ nautical and residential centre and Corigliano Calabro Industrial Zone. The temporal analysis of persistent scatterers (PS) time series, carried out through the PS-Time program, adds further useful information, finally highlighting that, in these areas, subsidence has positively decreased or stabilized from the second semester of the year 2010.

This work shows that PSI measurements provide support in the quantitative evaluation of known subsidence process and that the analysis of time series allows effective detection of the temporal evolution trends of the phenomenon, such as changes or deceleration in displacement rates, not otherwise visible by merely considering the average velocity.     

A framework for discovering and classifying ubiquitous services in digital health ecosystems

A digital ecosystem is a widespread type of ubiquitous computing environment comprised of ubiquitous, geographically dispersed, and heterogeneous species, technologies and services. As a subdomain of the digital ecosystems, digital health ecosystems are crucial for the stability and sustainable development of the digital ecosystems. However, since the service information in the digital health ecosystems exhibits the same features as those in the digital ecosystems, it is difficult for a service consumer to precisely and quickly retrieve a service provider for a given health service request. Consequently, it is a matter of urgency that a technology is developed to discover and classify the health service information obtained from the digital health ecosystems. A survey of state-of-the-art semantic service discovery technologies reveals that no significant research effort has been made in this area. Hence, in this paper, we present a framework for discovering and classifying the vast amount of service information present in the digital health ecosystems. The framework incorporates the technology of semantic focused crawler and social classification. A series of experiments are conducted in order to respectively evaluate the framework and the employed mathematical model.     

Fire‐induced variability in satellite SPOT‐VGT NDVI vegetational data

Is it possible to quantitatively characterize the dynamic variability induced by fires on vegetation? To answer this question, we performed a fluctuation analysis of time series of satellite SPOT‐VEGETATION Normalized Difference Vegetation Index (NDVI) data from 1998 to 2003 in order to discriminate fire‐induced variability in the vegetation dynamics of forests in southern Italy. We used the Detrended Fluctuation Analysis (DFA), which permits the detection of persistent properties in non‐stationary signal fluctuations. We analysed two forest sites, one fire‐unaffected and the other fire‐affected. Our findings suggest that fires play an important role in the temporal evolution of forests, increasing the persistence of the vegetation dynamics.     

持久OOPL的存储管理模式

在持久环境（如持久ＯＯＰＬ和ＯＯＤＢＳ）中，涉及到两类不同的物理存储器：内存和磁盘，应开发隐藏物理细节的抽象存储器层次。虚拟对象存储器（ＶＯＳ）就是这种抽象存储器层次，在其上，持久对象和暂态对象的操作是同一的。作者利用双拷贝模型实现了一个ＶＯＳ管理系统。ＶＯＳ方法的优越性已通过ＸＤＰＣ＋＋的设计与实现得到了证明，ＸＤＰＣ＋＋是基于Ｃ＋＋的持久ＯＯＰＬ。     

新型非易失存储环境下事务型数据管理技术研究

为适应底层存储架构的变化,上层数据库系统已经经历了多轮的演化与变革。在大数据环境下,以非易失、大容量、低延迟、按字节寻址等为特征的新型非易失存储器件（NVM）的出现势必对数据库系统带来重大影响,相关的存储与事务处理技术是其中值得关注的重要环节。首先,概述了事务型数据库系统随存储环境发展的历史与趋势;其次,对影响上层数据管理系统设计的非易失性存储技术,以及面向大数据应用领域与硬件环境优化的事务技术进行综述与分析;最后,对非易失存储环境下事务型数据库面临的挑战与研究趋势进行了展望。     

Earth observations for terrestrial biodiversity and ecosystems

Earth observations, comprising satellite, aerial, and in situ systems, are increasingly recognized as critical observations for monitoring the Earth system and systems. Earth observation data are especially needed to fulfil the requirements of a host of international treaties and conventions, and to provide data and information to support conservation and resource management. The Group on Earth Observations, GEO was established to implement the Global Earth Observing Systems of Systems, GEOSS, which includes in its mandate the protection of ecosystems — Improving the management and protection of terrestrial, coastal, and marine ecosystems, and understanding, monitoring, and conserving biodiversity. This Special Issue focuses on Earth observations for terrestrial ecosystems and biodiversity. As such, it is a sampler of remote sensing assessments of the status and trends of biodiversity (species), and ecosystems.     

结合残差时空注意力机制的海面温度预测算法

海面温度(SST)与全球气候变化、海洋灾害、海洋生态系统密切相关,因此准确地预测SST是一个重要课题。现有区域型SST预测方法将SST时间序列处理为二维矩阵序列并作为模型输入,每个矩阵对应着特定时刻的区域SST,通过提取时空特征来实现其预测,但未充分考虑不同时空特征在时间维度和空间维度上对SST影响的不均衡性,限制了预测精度地提高。为了解决该问题,提出了一种结合时间注意力机制和空间注意力机制的区域SST预测方法(CRA-ConvLSTM),使得模型动态关注不同时刻的时间特征和区域内不同点的空间特征,赋予不同的影响权重,进而提高SST预测精度。具体来说,首先将输入的区域SST时间序列通过卷积神经网络(CNN）编码为多层特征向量,提取局部特征;然后构建了残差时间注意力模块,自适应地学习不同时刻的注意力权重,提取时间维度上的关键特征,并设计了残差空间注意力模块,提取区域内不同点在空间维度上的关键特征,此外,将注意力机制结合残差结构避免了网络中信息量过少导致的性能下降问题;最后通过卷积长短时记忆神经网络(ConvLSTM)将特征向量映射为SST预测结果。实验结果显示,该模型的均方根误差(RMS...     

Monitoring forest conditions in a protected Mediterranean coastal area by the analysis of multiyear NDVI data

The operational utilization of remote sensing techniques for monitoring terrestrial ecosystems is often constrained by problems of under-sampling in space and time, particularly in heterogeneous and unstable Mediterranean environments. The current work deals with the use of the NOAA-AVHRR and Landsat-TM/ETM+ images to produce long-term NDVI data series characterising coniferous and broadleaved forests in a protected coastal area in Tuscany (Central Italy). Two methods to extract NDVI values of relatively small vegetated areas from NOAA-AVHRR data were first evaluated by comparison to estimates from higher resolution Landsat-TM/ETM+images. The optimal method was then applied to multitemporal AVHRR data series to derive 10-day NDVI profiles of coniferous and broadleaved forests over a 15-year period (1986-2000). Trend analyses performed on these data series showed that notable NDVI decreases occurred during the study period, particularly for the coniferous forest in summer and early fall. Further analysis carried out on local meteorological measurements led to identify the likely causes of these negative trends in contemporaneous winter rainfall decreases which were significantly correlated with the found NDVI variations.     

Indicators of Northern Eurasia's land‐cover change trends from SPOT‐VEGETATION time‐series analysis 1998–2005

The Boreal and Tundra ecosystems of the mid to high latitudes are sensitive indicators of environmental impacts from both climate change and direct human activities. This study uses inter‐annual and inter‐seasonal SPOT‐VGT mosaics for recent years from 1998 to 2005 covering the entire boreal ecosystems of northern Eurasia. Linear trends could be detected in the NDVI and NDWI time series that differ by season, land‐cover type, and latitude. Significant positive NDVI trends are described for spring and related negative trends for NDWI over the boreal forest zone. They indicate an earlier onset of the vegetation green‐up. Similar vegetation dynamics can be described for autumn. The tundra ecosystems of the northern Eurasia latitudes exhibit trends of negative NDVI and positive NDWI. This may be explained by earlier snowmelt and increasing amounts of surface water from positive temperature anomalies. The non‐ambiguous coarse‐scale indicators require further detailed studies to identify driving factors and amount for positive feedbacks in boreal ecosystems.     

Investigation of polynya dynamics in the northern Bering Sea using greyscale morphology image-processing techniques

Arctic coastal polynyas, defined here as persistent openings in the winter sea ice pack, are significant areas for ocean–atmosphere heat exchange, high winter sea ice production with resulting brine rejection and the dependence of local ecosystems. It is therefore critical to accurately quantify polynya dynamics to understand their spatial and temporal variability, particularly in the context of recent dramatic Arctic sea ice declines. In this study, the so-called erosion algorithm (a greyscale morphology image-processing technique) is applied to satellite-derived sea ice concentrations in the northern Bering Sea to investigate polynya dynamics throughout this region. Greyscale morphology allows for the estimation of sea ice extent with a defined error tolerance through the removal of regions with low sea ice concentrations in the marginal ice zone. Furthermore, since polynyas are the primary source of water within the sea ice pack, the presence of water here can therefore be utilized to define the areal extent of polynyas. We utilized AMSR-E (Advanced Microwave Scanning Radiometer-EOS) sea ice concentrations during January–April from 2003 to 2008 in the northern Bering Sea to extract daily time series of water area within polynyas by a water integration method after data are preprocessed using greyscale morphology techniques. These results compare well with those calculated by more traditional methods utilizing sea ice concentration thresholds and show the great utility of greyscale morphology techniques as a preprocessing method (which eliminates artificial determination of polynya areal extents and enables automation of the overall image-processing routine). In addition, based on the results of our algorithms we investigated the potential driving forces (e.g. offshore wind velocity) of polynya development in the northern Bering Sea as well as calculate the spatial and interannual variability of heat fluxes across these water surfaces owing to polynya formation.     

Hierarchical image segmentation based on similarity of NDVI time series

Although a variety of hierarchical image segmentation procedures for remote sensing imagery have been published, none of them specifically integrates remote sensing time series in spatial or hierarchical segmentation concepts. However, this integration is important for the analysis of ecosystems which are hierarchical in nature, with different ecological processes occurring at different spatial and temporal scales. Therefore, the objective of this paper is to introduce a multi-temporal hierarchical image segmentation (MTHIS) methodology to generate a hierarchical set of segments based on spatial similarity of remote sensing time series. MTHIS employs the similarity of the fast Fourier transform (FFT) components of multi-seasonal time series to group pixels with similar temporal behavior into hierarchical segments at different scales. Use of the FFT allows the distinction between noise and vegetation related signals and increases the computational efficiency. The MTHIS methodology is demonstrated on the area of South Africa in an MTHIS protocol for Normalized Difference Vegetation Index (NDVI) time series. Firstly, the FFT components that express the major spatio-temporal variation in the NDVI time series, the average and annual term, are selected and the segmentation is performed based on these components. Secondly, the results are visualized by means of a boundary stability image that confirms the accuracy of the algorithm to spatially group pixels at different scale levels. Finally, the segmentation optimum is determined based on discrepancy measures which illustrate the correspondence of the applied MTHIS output with landcover-landuse maps describing the actual vegetation. In future research, MTHIS can be used to analyze the spatial and hierarchical structure of any type of remote sensing time series and their relation to ecosystem processes.     

持久化内存文件系统的磨损攻击与防御机制

近来出现诸多以非易失性存储器（non-volatile memory,简称NVM）作为存储设备的新型持久化内存文件系统,充分发掘NVM的低延迟和可按字节寻址等优点,优化文件访问的I/O栈和一致性机制,极大提升文件系统的性能.然而,现有持久化内存文件系统都没有考虑NVM写耐受度低的缺陷,极易导致NVM被磨损穿（wear out）.针对NVM写耐受度低的缺点,探索多种利用基本文件操作对NVM造成磨损攻击的方式,并在真实持久化内存文件系统PMFS中以实验证明磨损攻击的严重性.为有效防御针对NVM的磨损攻击,提出了持久化内存文件系统磨损防御机制（persistent in-memory file system wear defense technique,简称PFWD）,包括索引节点元数据虚拟化技术、超级块迁移技术、文件数据页磨损均衡技术和文件索引结构迁移技术,保护文件系统中所有可能被磨损攻击利用的数据结构.实验结果证明所提出的PFWD技术能有效地防御病毒发动对NVM的磨损攻击,提高了存储系统的稳定性.     

Developing a continental-scale measure of gross primary production by combining MODIS and AmeriFlux data through Support Vector Machine approach

Remote sensing is a potentially powerful technology with which to extrapolate eddy covariance-based gross primary production (GPP) to continental scales. In support of this concept, we used meteorological and flux data from the AmeriFlux network and Support Vector Machine (SVM), an inductive machine learning technique, to develop and apply a predictive GPP model for the conterminous U.S. In the following four-step process, we first trained the SVM to predict flux-based GPP from 33 AmeriFlux sites between 2000 and 2003 using three remotely-sensed variables (land surface temperature, enhanced vegetation index (EVI), and land cover) and one ground-measured variable (incident shortwave radiation). Second, we evaluated model performance by predicting GPP for 24 available AmeriFlux sites in 2004. In this independent evaluation, the SVM predicted GPP with a root mean squared error (RMSE) of 1.87 gC/m²/day and an R² of 0.71. Based on annual total GPP at 15 AmeriFlux sites for which the number of 8-day averages in 2004 was no less than 67% (30 out of a possible 45), annual SVM GPP prediction error was 32.1% for non-forest ecosystems and 22.2% for forest ecosystems, while the standard Moderate Resolution Imaging Spectroradiometer GPP product (MOD17) had an error of 50.3% for non-forest ecosystems and 21.5% for forest ecosystems, suggesting that the regionally tuned SVM performed better than the standard global MOD17 GPP for non-forest ecosystems but had similar performance for forest ecosystems. The most important explanatory factor for GPP prediction was EVI, removal of which increased GPP RMSE by 0.85 gC/m²/day in a cross-validation experiment. Third, using the SVM driven by remote sensing data including incident shortwave radiation, we predicted 2004 conterminous U.S. GPP and found that results were consistent with expected spatial and temporal patterns. Finally, as an illustration of SVM GPP for ecological applications, we estimated maximum light use efficiency (e_max), one of the most important factors for standard light use efficiency models, for the conterminous U.S. by integrating the 2004 SVM GPP with the MOD17 GPP algorithm. We found that e_max varied from ∼ 0.86 gC/MJ in grasslands to ∼ 1.56 gC/MJ in deciduous forests, while MOD17 e_max was 0.68 gC/MJ for grasslands and 1.16 gC/MJ for deciduous forests, suggesting that refinements of MOD17 e_max may be beneficial.     

Alightweight class library for extended persistent object management in C++

When applications must keep their data structures persistent, additional effort beyond transient modeling is necessary to realize suitable persistent storage. The most convenient approach is the use of a persistent programming language (PPL), which offers internal mechanisms for storing and loading data transparently for the application programmer. An alternative to a PPL is a universal programming language that has been extended by persistence concepts for any data typesThis paper introduces an extended generic mechanism that abstracts necessary functionality for realizing persistence of any C++ object structure. First, we discuss general problems of persistent storage and the motivation for this approach. The design aspects and the reasons for special features are described in detail with the data and implementation insides. Finally, we sketch the persistent data structure and present the integration of the library in three different example programs.     

面向森林生态学研究的Web处理服务系统

森林是陆地生态系统的主体,森林生态保护与恢复是生态文明建设的重要组成部分.面对森林生态模型单机化、重复开发部署的现状,本文设计并实现一个面向森林生态学研究的Web处理服务系统.该系统根据开放地理空间信息联盟（Open Geospatial Consortium,OGC）制定的空间数据共享及互操作的接口规范,基于WebService技术,实现了面向森林生态学研究的多模型整合,并将模型计算结果进行展示,减少了重复开发和部署工作,提高了数据与服务的共享性.     

Revisiting Persistent Indexing Structures on Intel Optane DC Persistent Memory

Persistent indexing structures are proposed in response to emerging non-volatile memory(NVM)to provide high performance yet durable indexes.However,due to the lack of real NVM hardware,many prior persistent indexing structures were evaluated via emulation,which varies a lot across different setups and differs from the real deployment.Recently,Intel has released its Optane DC Persistent Memory Module(PMM),which is the first production-ready NVM.In this paper,we revisit popular persistent indexing structures on PMM and conduct comprehensive evaluations to study the performance differences among persistent indexing structures,including persistent hash tables and persistent trees.According to the evaluation results,we find that Cacheline-Conscious Extendible Hashing(CCEH)achieves the best performance among all evaluated persistent hash tables,and Failure-Atomic ShifT B+-Tree(FAST)and Write Optimal Radix Tree(WORT)perform better than other trees.Besides,we find that the insertion performance of hash tables is heavily influenced by data locality,while the insertion latency of trees is dominated by the flush instructions.We also uncover that no existing emulation methods accurately simulate PMM for all the studied data structures.Finally,we provide three suggestions on how to fully utilize PMM for better performance,including using clflushopt/clwb with sfence instead of clflush,flushing continuous data in a batch,and avoiding data access immediately after it is flushed to PMM.