Methods and workflow for spatial conservation prioritization using Zonation

Spatial conservation prioritization concerns the effective allocation of conservation action. Its stages include development of an ecologically based model of conservation value, data pre-processing, spatial prioritization analysis, and interpretation of results for conservation action. Here we investigate the details of each stage for analyses done using the Zonation prioritization framework. While there is much literature about analytical methods implemented in Zonation, there is only scattered information available about what happens before and after the computational analysis. Here we fill this information gap by summarizing the pre-analysis and post-analysis stages of the Zonation framework. Concerning the entire process, we summarize the full workflow and list examples of operational best-case, worst-case, and typical scenarios for each analysis stage. We discuss resources needed in different analysis stages. We also discuss benefits, disadvantages, and risks involved in the application of spatial prioritization from the perspective of different stakeholders. Concerning pre-analysis stages, we explain the development of the ecological model and discuss the setting of priority weights and connectivity responses. We also explain practical aspects of data pre-processing and the post-processing interpretation of results for different conservation objectives. This work facilitates well-informed design and application of Zonation analyses for the purpose of spatial conservation planning. It should be useful for both scientists working on conservation related research as well as for practitioners looking for useful tools for conservation resource allocation.     

Marxan with Zones: Software for optimal conservation based land- and sea-use zoning

Marxan is the most widely used conservation planning software in the world and is designed for solving complex conservation planning problems in landscapes and seascapes. In this paper we describe a substantial extension of Marxan called Marxan with Zones, a decision support tool that provides land-use zoning options in geographical regions for biodiversity conservation. We describe new functions designed to enhance the original Marxan software and expand on its utility as a decision support tool. The major new element in the decision problem is allowing any parcel of land or sea to be allocated to a specific zone, not just reserved or unreserved. Each zone then has the option of its own actions, objectives and constraints, with the flexibility to define the contribution of each zone to achieve targets for pre-specified features (e.g. species or habitats). The objective is to minimize the total cost of implementing the zoning plan while ensuring a variety of conservation and land-use objectives are achieved. We outline the capabilities, limitations and additional data requirements of this new software and perform a comparison with the original version of Marxan. We feature a number of case studies to demonstrate the functionality of the software and highlight its flexibility to address a range of complex spatial planning problems. These studies demonstrate the design of multiple-use marine parks in both Western Australia and California, and the zoning of forest use in East Kalimantan.     

用于函数优化的自适应类种子保留遗传算法

类种子保留遗传算法可以较好地处理维持进化种群多样性和保留重要个体的矛盾,但尚无有效方法确定其类控制参数.本文提出一种类控制参数随进化进程自适应变化的策略,其思想是:在进化前期类控制参数较大,将进化种群分成数目较少的粗类;随着进化的进行类控制参数自适应减小,将进化种群分成数目较多的细类.另外,个体的自适应变异充分利用了个体当前状态、本类种子和种群最优种子的信息.将该算法应用于5个基准数值函数优化问题,计算结果验证了本文算法在找到多个极值点的前提下有效地减少了计算量、提高了进化效率.     

Developing search strategies for detecting relevant experiments

Our goal is to analyze the optimality of search strategies for use in systematic reviews of software engineering experiments. Studies retrieval is an important problem in any evidence-based discipline. This question has not been examined for evidence-based software engineering as yet. We have run several searches exercising different terms denoting experiments to evaluate their recall and precision. Based on our evaluation, we propose using a high recall strategy when there are plenty of resources or the results need to be exhaustive. For any other case, we propose optimal, or even acceptable, search strategies. As a secondary goal, we have analysed trends and weaknesses in terminology used in articles reporting software engineering experiments. We have found that it is impossible for a search strategy to retrieve 100% of the experiments of interest (as happens in other experimental disciplines), because of the shortage of reporting standards in the community.     

Research synthesis in software engineering: A tertiary study

Context

Comparing and contrasting evidence from multiple studies is necessary to build knowledge and reach conclusions about the empirical support for a phenomenon. Therefore, research synthesis is at the center of the scientific enterprise in the software engineering discipline.

Objective

The objective of this article is to contribute to a better understanding of the challenges in synthesizing software engineering research and their implications for the progress of research and practice.

Method

A tertiary study of journal articles and full proceedings papers from the inception of evidence-based software engineering was performed to assess the types and methods of research synthesis in systematic reviews in software engineering.

Results

As many as half of the 49 reviews included in the study did not contain any synthesis. Of the studies that did contain synthesis, two thirds performed a narrative or a thematic synthesis. Only a few studies adequately demonstrated a robust, academic approach to research synthesis.

Conclusion

We concluded that, despite the focus on systematic reviews, there is limited attention paid to research synthesis in software engineering. This trend needs to change and a repertoire of synthesis methods needs to be an integral part of systematic reviews to increase their significance and utility for research and practice.     

Deforestation in Central Africa: Estimates at regional, national and landscape levels by advanced processing of systematically-distributed Landsat extracts

Accurate land cover change estimates are among the headline indicators set by the Convention on Biological Diversity to evaluate the progress toward its 2010 target concerning habitat conservation. Tropical deforestation is of prime interest since it threatens the terrestrial biomes hosting the highest levels of biodiversity. Local forest change dynamics, detected over very large extents, are necessary to derive regional and national figures for multilateral environmental agreements and sustainable forest management. Current deforestation estimates in Central Africa are derived either from coarse to medium resolution imagery or from wall-to-wall coverage of limited areas. Whereas the first approach cannot detect small forest changes widely spread across a landscape, operational costs limit the mapping extent in the second approach. This research developed and implemented a new cost-effective approach to derive area estimates of land cover change by combining a systematic regional sampling scheme based on high spatial resolution imagery with object-based unsupervised classification techniques. A multi-date segmentation is obtained by grouping pixels with similar land cover change trajectories which are then classified by unsupervised procedures. The interactive part of the processing chain is therefore limited to land cover class labelling of object clusters. The combination of automated image processing and interactive labelling renders this method cost-efficient. The approach was operationally applied to the entire Congo River basin to accurately estimate deforestation at regional, national and landscape levels. The survey was composed of 10 × 10 km sampling sites systematically-distributed every 0.5° over the whole forest domain of Central Africa, corresponding to a sampling rate of 3.3%. For each of the 571 sites, subsets were extracted from both Landsat TM and ETM+ imagery acquired in 1990 and 2000 respectively. Approximately 60% of the 390 cloud-free samples do not show any forest cover change. For the other 165 sites, the results are depicted by a change matrix for every sample site describing four land cover change processes: deforestation, reforestation, forest degradation and forest recovery. This unique exercise estimates the deforestation rate at 0.21% per year, while the forest degradation rate is close to 0.15% per year. However, these figures are less reliable for the coastal region where there is a lack of cloud-free imagery. The results also show that the Landscapes designated after 2000 as high priority conservation zones by the Congo Basin Forest Partnership had undergone significantly less deforestation and forest degradation between 1990 and 2000 than the rest of the Central African forest.     

Mapping understory vegetation using phenological characteristics derived from remotely sensed data

Understory vegetation is an important component in forest ecosystems not only because of its contributions to forest structure, function and species composition, but also due to its essential role in supporting wildlife species and ecosystem services. Therefore, understanding the spatio-temporal dynamics of understory vegetation is essential for management and conservation. Nevertheless, detailed information on the distribution of understory vegetation across large spatial extents is usually unavailable, due to the interference of overstory canopy on the remote detection of understory vegetation. While many efforts have been made to overcome this challenge, mapping understory vegetation across large spatial extents is still limited due to a lack of generality of the developed methods and limited availability of required remotely sensed data. In this study, we used understory bamboo in Wolong Nature Reserve, China as a case study to develop and test an effective and practical remote sensing approach for mapping understory vegetation. Using phenology metrics generated from a time series of Moderate Resolution Imaging Spectroradiometer data, we characterized the phenological features of forests with understory bamboo. Using maximum entropy modeling together with these phenology metrics, we successfully mapped the spatial distribution of understory bamboo (kappa: 0.59; AUC: 0.85). In addition, by incorporating elevation information we further mapped the distribution of two individual bamboo species, Bashania faberi and Fargesia robusta (kappa: 0.68 and 0.70; AUC: 0.91 and 0.92, respectively). Due to its generality, flexibility and extensibility, this approach constitutes an improvement to the remote detection of understory vegetation, making it suitable for mapping different understory species in different geographic settings. Both biodiversity conservation and wildlife habitat management may benefit from the detailed information on understory vegetation across large areas through the applications of this approach.     

基于直方图的空间查询选择率估计研究

空间查询优化是空间数据库中的关键问题之一,以查询代价估算为基础的查询优化技术是提高查询效率的一种重要方法,而估算代价的主要问题是估算查询结果(选择率)的大小。针对空间数据库中最常用的两种查询—空间选择和空间连接,阐述了几种主要用于查询选择率佑计的直方图算法,并对各算法的优缺点做了分析,最后对空间查询选择率估计的研究方向进行了展望。     

基于软件结构的最优测试工作量的分配方法

现代社会越来越依赖于稳定的软件系统的运行;除此之外,尽管这些软件系统的规模和复杂度越来越高,但是必须达到很高的系统稳定性,且必须降低进一步开发和维护所需要的资源。为了减轻软件开发的挑战性,需要一种有效的方法来引导软件系统中资源的分配。提出了一种根据每一组件对系统稳定性的贡献来确定每一组件所需分配的工作量的方法,从而在工作量最小的条件下达到一定的系统稳定性。假设组件对系统稳定性的影响主要包含两个方面的因素:系统结构和组件的可靠性。这种描述方法被称为基于结构的测试量分配优化方法,因为其在测试量分配过程中考虑了系统的结构。验证结果表明这种基于结构的优化方法比其他测试量分配策略更有效。     

Landscape metric performance in analyzing two decades of deforestation in the Amazon Basin of Rondonia, Brazil

Sixteen landscape metrics were evaluated with respect to the effects of spatial aggregation on six different years of Landsat data for a deforested area in Rondonia, Brazil. Spatial aggregation was performed by two methods. The first method involved varying the window size in texture mean co-occurrence filtering prior to classification. The second method involved aggregating the data post-classification by resampling with a majority filter. The Landscape Shape Index (LSI) and Square Pixel (SqP) metric showed the most predictable behavior of the shape complexity metrics having strong decreases with each increase in aggregation. The Edge Density (ED) and Patch Density (PD) metrics showed the most predictable behavior among the edge and patch metrics, decreasing with increasing aggregation. The Mean Nearest Neighbor (MNN) metric also behaved as expected but its results were less consistent than those of ED and PD. Many of the remaining metrics gave inconsistent and unpredictable results with respect to spatial aggregation.     

基于过程知识的软件项目计划及其控制

为使软件项目计划合理、计划执行控制有效,该文基于小组软件过程TSP(TeamSoftwareProcess),提出了一个软件过程知识支持下的具有决策、建议和预测能力的分布式软件项目计划与控制解决方案,与此同时,讨论了相关支持工具的体系结构实现及其实例应用场景。