Dynamic estimating of wetland vegetation cover based on linear spectral mixture and time phase transformation models

ABSTRACT

Globally, remote sensing is being used to monitor vegetation degradation in areas of concern. In recent years, drought and water shortages have caused significant degradation of the wetland vegetation in Zhalong Wetland of Heilongjiang province, China. This paper employed middle- and high-resolution Landsat images to construct a Linear Spectral Mixture Analysis of the wetland, with the end member extraction verified by feasibility analysis and with vegetation cover data extracted over nearly 30 years. By considering the problem of poor timing with middle- and high-resolution images, this paper proposes a phase-transform method that combines the time advantage of moderate-resolution spectroradiometer images with the spatial advantage of high-resolution Landsat imagery. Based on an intensity analysis model, the temporal and spatial characteristics of vegetation cover in the study area were analyzed using a time scale and the level of vegetation cover. The results show that (1) from 1985 to 2015, the vegetation cover showed an overall tendency to degrade, and (2) vegetation cover was extracted based on the phase transformation and linear spectral mixture models with an accuracy of 0.8628, which is higher than that of traditional remote sensing methods. Improving the prediction accuracy in vegetation transfer is of great theoretical value in relation to global climate change.     

Remote sensing with IDRISI Taiga: a beginner's guide,by Timothy A. Warner and David J.

ABSTRACT

We applied annual Moderate Resolution Imaging Spectroradiometer (MODIS) product Vegetation Continuous Fields (VCF) data for the detection of forest cover change (FCC) in Mexico over the period 2000–2010. We excluded the pixels with uncertain information and applied a moving average and low-pass filter to smooth the multi-temporal data to reduce the fluctuations in the forest cover for each pixel. We applied a linear regression model and created two scenarios based on the coefficient of determination and slope to determine whether a pixel had changed its land cover over the study period. This model was able to label detected changes as deforestation, degradation, reforestation, and regrowth, based on the initial and final values of forest cover. The results showed that there has been more forest gain (reforestation and regrowth) than forest loss (deforestation and degradation) during the study period. We verified these results by comparing with the biomass data derived from the Mexican National Forest and Soil Inventory (Inventario Nacional Forestal y de Suelos, abbreviated to INFyS). Our model provides an efficient method to assess FCC at national level, which can contribute to the development of a reference level of greenhouse gas emission as necessary for the implementation of the international policy for reduction of emissions from deforestation and forest degradation (REDD+).     

基于Landsat 8卫星影像的北京地区土地覆盖分类

目的 土地覆盖分类能为生态系统模型、水资源模型和气候模型等提供重要信息,遥感技术运用于土地覆盖分类具有诸多优势。作为区域性土地覆盖分类应用的重要数据源,Landsat 5/7的TM和ETM+等数据已逐渐失效,Landsat 8陆地成像仪(OLI)较TM和ETM+增加了新的特性,利用Landsat 8数据进行北京地区土地覆盖分类研究,探讨处理方法的适用性。方法 首先,确定研究区域内土地覆盖分类系统,并对Landsat 8多光谱数据进行预处理,包括大气校正、地形校正、影像拼接及裁剪;然后,利用灰度共生矩阵提取全色波段纹理信息,与多光谱数据进行融合;最后,使用支持向量机(SVM)进行分类,获得土地覆盖分类结果。结果 经过精度评价和分析发现,6S模型大气校正和C模型地形校正预处理提高了不同类别之间的可分性,多光谱数据结合全色波段纹理特征能有效提高部分地物的土地覆盖分类精度,总体精度提高2.8%。结论 相对于Landsat TM/ETM+数据,Landsat 8 OLI数据新增特性有利于土地覆盖分类精度的提高。本文方法适用于Landsat 8 OLI数据土地覆盖分类研究与应用,能够满足大区域土地覆盖分类应用需求。     

Cloud removal for hyperspectral remotely sensed images based on hyperspectral information fusion

ABSTRACT

Hyperspectral remote sensing plays an important role in a wide variety of fields. However, its specific application for land surface analysis has been constrained due to the different shapes of thick, opaque cloud cover. The reconstruction of missing information obscured by clouds in remote-sensing images is an area of active research. However, most of the available cloud-removal methods are not suitable for hyperspectral images, because they lose the spectral information which is very important for hyperspectral analysis. In this article, we developed a new spectral resolution enhancement method for cloud removal (SREM-CR) from hyperspectral images, with the help of an auxiliary cloud-free multispectral image acquired at different times. In the fixed hyperspectral image, spectra of the cloud cover pixels are reconstructed depending on the relationship between the original hyperspectral and multispectral images. The final resulting image has the same spectral resolution as the original hyperspectral image but without clouds. This approach was tested on two experiments, in which the results were compared by visual interpretation and statistical indices. Our method demonstrated good performance.     

Mapping and monitoring land cover in the Willandra Lakes World Heritage Region

Abstract

Landsat digital data is used to map land cover in the Willandra Lakes World Heritage Region in the semi-arid region of New South Wales. An unsupervised classification using a migrating means clustering algorithm followed by a maximum-likelihood classification procedure was found to be the most discriminating method of classifying land cover. The results of applying three change-detection routines to multitemporal data sets of selected sites within the region are presented and discussed.     

Exploring Statistics and Demography Resources on the Web

ABSTRACT

Despite the rapid proliferation of statistics and demography resources on the Web, there has been little attempt to document them. This study represents an attempt at identifying and evaluating some of the most important and useful resources currently available in these fields. The results suggest that the field of statistics is much better documented on the Web than demography. Large industrialized countries, particularly the United States, are much better represented than many smaller countries. Technical innovations that allow users to create customized data sites, analyze statistical data online, and graph results in elaborate three-dimensional models make the Web a far superior resource in many ways to traditional paper resources.     

Land use dynamics in the semi-arid region of Brazil (Quixaba,PE): Characterization by principal component analysis (PCA)

The changes caused by the pressure of human occupation can be observed by non-sustainable land use forms, in the Brazilian semi-arid region. The multitemporal analysis of changes in the caatinga land cover provides sufficient information about the dynamics of this typical land use. Within this frame, principal component analysis (PCA) was applied in combination with field survey data, which permitted estimation of point samples of new recovery/degradation. The area selected for this study is located in the central-southern semi-arid section of Pernambuco State, north-east Brazil. It covers an area of about 190 km 2 and is located between geographical coordinates 8° 00' to 8° 07' S and 39° 45' to 39° 53' W. Five classes of changed and unchanged multitemporal effects were discriminated. The results of this study were evaluated using combined Geographical Information System (GIS) and field survey techniques. This work was less time-consuming and economically significant for the time period 1983-96. An alternative PCA method was proposed based on the analysis of the results and errors in a confusion matrix. This alternative method is based on the controlled rotative analogy of the PCA transformation, where a smaller angle of rotation was applied.     

An evolutionary approach to identify logical components

ContextIdentifying suitable components during the software design phase is an important way to obtain more maintainable software. Many methods including Graph Partitioning, Clustering-based, CRUD-based, and FCA-based methods have been proposed to identify components at an early stage of software design. However, most of these methods use classical clustering techniques, which rely on expert judgment.ObjectiveIn this paper, we propose a novel method for component identification, called SBLCI (Search-Based Logical Component Identification), which is based on GA (genetic algorithm), and complies with an iterative scheme to obtain logical components.MethodSBLCI identifies logical components of a system from its analysis models using a customized GA, which considers cohesion and coupling metrics as its fitness function, and has four novel guided GA operators based on the cohesive component concept. In addition, SBLCI has an iterative scheme in which it initially identifies high-level components in the first iteration. Then, in the next iterations, it identifies low-level sub-components for each identified component in previous iterations.ResultsWe evaluated the effectiveness of SBLCI with three real-world cases. Results revealed that SBLCI is a better alternative for identifying logical components and sub-components in comparison with existing component identification methods.     

A visual analysis approach to validate the selection review of primary studies in systematic reviews

ContextSystematic Literature Reviews (SLRs) are an important component to identify and aggregate research evidence from different empirical studies. Despite its relevance, most of the process is conducted manually, implying additional effort when the Selection Review task is performed and leading to reading all studies under analysis more than once.ObjectiveWe propose an approach based on Visual Text Mining (VTM) techniques to assist the Selection Review task in SLR. It is implemented into a VTM tool (Revis), which is freely available for use.MethodWe have selected and implemented appropriate visualization techniques into our approach and validated and demonstrated its usefulness in performing real SLRs.ResultsThe results have shown that employment of VTM techniques can successfully assist in the Selection Review task, speeding up the entire SLR process in comparison to the conventional approach.ConclusionVTM techniques are valuable tools to be used in the context of selecting studies in the SLR process, prone to speed up some stages of SLRs.     

Spatiotemporal variability of snow cover on Tibet,China using MODIS remote-sensing data

ABSTRACT

Snow cover is an important component of the cryosphere, and the study on spatial and temporal variations of snow cover is essential for understanding the consequences and impacts of climate change and water resources management. In this study, the temporal variation of snow-covered area (SCA) and spatial variability of snow-cover frequency (SCF) on Tibet is analysed based on the Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra snow cover product (MOD10A2) from 2000 to 2015, and relationships with main climate variables are investigated. Results are as follows: (1) there is a very weak decreasing trend in annual mean SCA, and a slight increasing trend in autumn and winter and a slight decreasing trend in spring and more robust decreasing trend in summer for SCA are found. (2) The temporal variation of SCA is negatively correlated with temperature, whereas it is little correlated with corresponding precipitation. (3) The general trend of spatial SCF variation on Tibet, predominated by snow-cover variations in spring and autumn, tends to decrease in spring while it tends to increase in autumn. (4) The spatial variability of SCF is attributed to snow-cover variations in autumn and spring, which is more obvious in higher latitudes in autumn while it is more noticeable in lower-latitude southeastern plateau in spring. (5) The regions with higher variability of snow cover are main pastoral land and more prone to snow-related disaster in Tibet, becoming key zone of snow-cover monitoring and disaster prevention and mitigation.     

A metamodel to integrate business processes time perspective in BPMN 2.0

ContextBusiness Process Management (BPM) is becoming a strategic advantage for organizations to streamline their operations. Most business experts are betting for OMG Business Process Model and Notation (BPMN) as de-facto standard (ISO/IEC 19510:2013) and selected technology to model processes. The temporal dimension underlies in any kind of process however, technicians need to shape this perspective that must also coexist with task control flow aspects, as well as resource and case perspectives. BPMN poorly gathers temporary rules. This is why there are contributions that extend the standard to cover such dimension. BPMN is mainly an imperative language. There are research contributions showing time constraints in BPMN, such as (i) BPMN patterns to express each rule with a combination of artifacts, thus these approaches increase the use of imperative BPMN style, and (ii) new decorators to capture time rules semantics giving clearer and simpler comprehensible specifications. Nevertheless, these extensions cannot yet be found in the present standard.ObjectiveTo define a time rule taxonomy easily found in most business processes and look for an approach that applies each rule with current BPMN 2.0 standard in a declarative way.MethodA model-driven approach is used to propose a BPMN metamodel extension to address time-perspective.ResultsWe look at a declarative approach where new time specifications may overlie the main control flow of a BPMN process. This proposal is totally supported with current BPMN standard, giving a BPMN metamodel extension with OCL constraints. We also use AQUA-WS as a software project case study which is planned and managed with MS Project. We illustrate business process extraction from project plans.ConclusionThis paper suggests to handle business temporal rules with current BPMN standard, along with other business perspectives like resources and cases. This approach can be applied to reverse engineering processes from legacy databases.     

Methodological variations in the production of CORINE land cover and consequences for long-term land cover change studies. The case of Spain.

ABSTRACT

Land cover information at national or regional scale is essential for science, monitoring, reporting, and policy making. CORINE Land Cover (CLC) is the most consistent land cover map for the entire European territory, with four repetitions during the period 1985–2012. The long-term consistency of CLC maps is the most appreciated strength and it should be guaranteed. Beyond some common general guidelines, the mapping approaches used in individual countries differ and change over time, leading to inconsistencies that should be known and reported. Through a series of metrics over the Spanish CLC most recent layers (CLC2006 and CLC2012) and comparisons with eight other countries CLC statistics, we demonstrate that the methodological changes recently implemented have introduced some discrepancies with previous CLC versions. The most affected classes in Spain were transitional woodland-shrub, complex cultivation patterns, artificial, grasslands, and forests. Users should be aware of the important implications these discrepancies may have in land use and land cover change studies, trend analysis, and reports.     

结合调整差值变换的(K,N)有意义图像分存方案

目的 针对传统有意义分存方法存在的像素扩张和分发掩体图像视觉质量不高等问题,提出一种结合调整差值变换的(K,N)有意义图像分存方案,该方案可用于与掩体等大且同为自然图像的密图分存.方法 在分存阶段,首先用调整差值变换将密图转换为差值图和位置图;其次将差值图和位置图进行(K,N)分存,分别嵌入到掩体图像中,并使用密钥确定位置图分存信息的嵌入位置和根据位置图中不同的差值类型选择不同的差值嵌入方法;再次对密钥进行(K,N)分存,将子密钥和分发掩体对应的MD5值公布到第3方公信方作为认证码;最后将子密钥和分发掩体分发给参与者进行保管.在恢复阶段,首先核对参与者的子密钥和分发掩体对应的MD5值,若认证通过的人数小于K,则恢复失败;否则,使用认证通过的子密钥还原出密钥,然后根据密钥提取并恢复出位置图;其次根据位置图中的差值类型来提取和恢复出差值图;最后使用逆调整差值变换还原出最终密图.结果 同现有方法相比,所提策略不存在像素扩张且分发掩体图像视觉质量较高,具有较强的恶意参与者检测能力.结论 本文方法的掩体图像与密图等大且同为自然图像,同经典有意义图像分存方案相比,克服了像素扩张问题,嵌入信息后的掩体图像具有较高的视觉质量,使用第3方公信方存储的MD5值作为认证码,具有较强的恶意参与者识别能力.     

A component recommender for bug reports using Discriminative Probability Latent Semantic Analysis

ContextThe component field in a bug report provides important location information required by developers during bug fixes. Research has shown that incorrect component assignment for a bug report often causes problems and delays in bug fixes. A topic model technique, Latent Dirichlet Allocation (LDA), has been developed to create a component recommender for bug reports.ObjectiveWe seek to investigate a better way to use topic modeling in creating a component recommender.MethodThis paper presents a component recommender by using the proposed Discriminative Probability Latent Semantic Analysis (DPLSA) model and Jensen–Shannon divergence (DPLSA-JS). The proposed DPLSA model provides a novel method to initialize the word distributions for different topics. It uses the past assigned bug reports from the same component in the model training step. This results in a correlation between the learned topics and the components.ResultsWe evaluate the proposed approach over five open source projects, Mylyn, Gcc, Platform, Bugzilla and Firefox. The results show that the proposed approach on average outperforms the LDA-KL method by 30.08%, 19.60% and 14.13% for recall @1, recall @3 and recall @5, outperforms the LDA-SVM method by 31.56%, 17.80% and 8.78% for recall @1, recall @3 and recall @5, respectively.ConclusionOur method discovers that using comments in the DPLSA-JS recommender does not always make a contribution to the performance. The vocabulary size does matter in DPLSA-JS. Different projects need to adaptively set the vocabulary size according to an experimental method. In addition, the correspondence between the learned topics and components in DPLSA increases the discriminative power of the topics which is useful for the recommendation task.     

Land degradation and erosion risk mapping by fusion of spectrally-based information and digital geomorphometric attributes

The Guadalentin basin, located in the SE of Spain, has a semiarid climate and presents typical characteristics of Mediterranean landscapes vulnerable to land degradation processes and desertification risks. In such an environment, when the vegetation cover is low, the signal received by satellites is dominated by the spectral properties of soils. Changes in these properties can be interpreted in terms of varying soil surface conditions. These optical changes underline the major modifications affecting soil surface under land degradation processes. The present research uses remote sensing techniques to characterise land degradation based on two approaches: spectral mixture analysis and a set of indices describing the spectrum shape. It also presents an integrated approach for evaluating ecosystem vulnerability to land degradation, through the combined analysis of spectrally-derived land units and geomorphometric units. Specific objectives consist of evaluating the potential of extending the indices describing the spectrum shape to the short-wave infrared region, and of identifying landscape units according to their sensitivity to land degradation. Our results demonstrate that the spatial distribution of regional patterns of land degradation can be reliably mapped by using both indices describing the spectrum shape and spectral unmixing. The latter holds great potential for operational mapping of soil conditions and erosion features from optical images. Moreover, landscape-unit analysis shows that DEM (Digital Elevation Model) variables combined with spectral information are very useful for land degradation assessment. This approach allowed us to segment the landscape into different units according to their lithology and vegetation characteristics, as well as their susceptibility to water erosion.     

Internet Acceptable Usage Policies: Writing and Implementation

Abstract

The job of an acceptable use policy is to explain what the organization considers acceptable Internet and computer use and to protect both employees and the organization from the ramifications of illegal actions. This article describes how such policies are written, what they should cover, and how they are most effectively activated.     

Review of: “ Télédétection spatiale: Université d' été européenne Toulouse 1990”. By MICHEL VAUZELLE (Cépadues-Éditions. Toulouse. 1992) [Pp. 309.] Price FF300.

In this paper, a simple model is proposed for measuring the vegetation cover over soil surfaces from radar signals acquired in semi-arid regions. In such regions, vegetation is characterized by the presence of clumps which partially cover the soil surface. The proposed model describes the relationship between the percentage of covered surface and the measured radar signal. Model simulations over Tunisian test areas, where ground parameters are controlled, are performed and compared with actual ERS2 radar measurements. A very good agreement is found. The model is then used to derive a map of the vegetation cover density for the whole studied site (in Tunisia). The approach used here is based upon supervised classification with classes defined by inverting the model and taking into account ERS calibration error. Each of the four classes thus defined exhibits a good classification rate, greater than 85%. Finally, two important applications for natural resources management are presented: vegetation monitoring and soil moisture monitoring.     

Assessment of grassland degradation near Lake Qinghai,West China,using Landsat TM and in situ reflectance spectra data

The severity of grassland degradation near Lake Qinghai, West China was assessed from a Landsat Thematic Mapper (TM) image in conjunction with in situ samples of per cent grass cover and proportion (by weight) of unpalatable grasses (PUG) collected over 1?m² sampling plots. Spectral reflectance at each sampling plot was measured with a spectrometer and its location determined with a Global Positioning System (GPS) receiver. After radiometric calibration, the TM image was geometrically rectified. Ten vegetation indices were derived from TM bands 3 and 4, and from the spectral reflectance data at wavelengths corresponding most closely to those of TM3 and TM4. Regression analyses showed that NDVI and SAVI are the most reliable indicators of grass cover and PUG, respectively. Significant relationships between TM bands-derived indices and in situ sampled grass parameters were established only after the former had been calibrated with in situ reflectance spectra data. Through the established regression models the TM image was converted into maps of grass cover parameters. These maps were merged to form a degradation map at an accuracy of 91.7%. It was concluded that TM imagery, in conjunction with in situ grass samples and reflectance spectra data, enabled the efficient and accurate assessment of grassland degradation inside the study area.     

Sub-pixel estimation of urban land cover components with linear mixture model analysis and Landsat Thematic Mapper imagery

We examine the utility of linear mixture modelling in the sub-pixel analysis of Landsat Enhanced Thematic Mapper (ETM) imagery to estimate the three key land cover components in an urban/suburban setting: impervious surface, managed/unmanaged lawn and tree cover. The relative effectiveness of two different endmember sets was also compared. The interior endmember set consisted of the median pixel value of the training pixels of each land cover and the exterior endmember set was the extreme pixel value. As a means of accuracy assessment, the resulting land cover estimates were compared with independent estimates obtained from the visual interpretation of digital orthophotography and classified IKONOS imagery. Impervious surface estimates from the Landsat ETM showed a high degree of similarity (RMS error (RMSE) within approximately ±10 to 15%) to that obtained using high spatial resolution digital orthophotography and IKONOS imagery. The partition of the vegetation component into tree vs grass cover was more problematic due to the greater spectral similarity between these land cover types with RMSE of approximately ±12 to 22%. The interior endmember set appeared to provide better differentiation between grass and urban tree cover than the exterior endmember set. The ability to separate the grass vs tree components in urban vegetation is of major importance to the study of the urban/suburban ecosystems as well as watershed assessment.     

Maintenance of a Piercing Set for Intervals with Applications

   Abstract. We show how to maintain efficiently a minimum piercing set for a set S of intervals on the line, under insertions and deletions to/from S. A linear-size dynamic data structure is presented, which enables us to compute a new minimum piercing set following an insertion or deletion in time O(c( S) log |S|), where c (S) is the size of the new minimum piercing set. We also show how to maintain a piercing set for S of size at most (1+ɛ)c (S), for 0 < ɛ ≤ 1 , in