Conflation and aggregation of spatial data improve predictive models for species with limited habitats: A case of the threatened yellow-billed cuckoo in Arizona,USA

Riparian vegetation provides important wildlife habitat in the southwestern United States, but limited distributions and spatial complexity often leads to inaccurate representation in maps used to guide conservation. We test the use of data conflation and aggregation on multiple vegetation/land-cover maps to improve the accuracy of habitat models for the threatened western yellow-billed cuckoo (Coccyzus americanus occidentalis). We used species observations (n = 479) from a state-wide survey to develop habitat models from 1) three vegetation/land-cover maps produced at different geographic scales ranging from state to national, and 2) new aggregate maps defined by the spatial agreement of cover types, which were defined as high (agreement = all data sets), moderate (agreement ≥ 2), and low (no agreement required). Model accuracies, predicted habitat locations, and total area of predicted habitat varied considerably, illustrating the effects of input data quality on habitat predictions and resulting potential impacts on conservation planning. Habitat models based on aggregated and conflated data were more accurate and had higher model sensitivity than original vegetation/land-cover, but this accuracy came at the cost of reduced geographic extent of predicted habitat. Using the highest performing models, we assessed cuckoo habitat preference and distribution in Arizona and found that major watersheds containing high-probably habitat are fragmented by a wide swath of low-probability habitat. Focus on riparian restoration in these areas could provide more breeding habitat for the threatened cuckoo, offset potential future habitat losses in adjacent watershed, and increase regional connectivity for other threatened vertebrates that also use riparian corridors.     

Computing least air pollution exposure routes

Personalized routing counts on traveler’s preferences which are usually based on different criteria, such as shortest, fastest, least traffic, or less expensive (e.g., less fuel cost, toll free). However, people are increasingly becoming concerned about the adverse health effects of exposure to air pollution in chosen routes. Exposures to elevated air pollution concentrations particularly endanger children, pregnant women, elderly people, and people with asthma and other respiratory conditions. Choosing routes with least air pollution exposure (APE) is seen as one approach to minimize the level of pollution exposed, which is a major public health issue. Routing algorithms use weights on segments of road networks to find optimum routes. While existing weights are commonly distance and time, among a few others, there is currently no weight based on APE to compute least APE routes. In this paper, we present a weight function that computes weight based on APE. Two different approaches, geostatistical and non-geostatistical, were used to compute APE weight. Each approach was evaluated, and the results indicate that the APE weight is suitable for computing least APE routes.     

Evaluating the fitness for use of spatial data sets to promote quality in ecological assessment and monitoring

This article proposes and illustrates a practical methodological framework to evaluate the fitness for use of spatial data sets for environmental and ecological applications, focusing on user requirements for specified application contexts. The methodology is based on the use of metadata to analyze similarity between the data characteristics and the user’s needs or expectations for several quality indicators. Additionally, the concept of ‘critical factors’ is introduced in this framework, allowing users to define which quality indicators have greater importance given their own requirements or expectations and the specified application contexts. The proposed methodology further allows integrating and interconnecting the spatial data quality (SDQ) evaluation methodology with metadata geoportals in WebGIS platforms, facilitating its operation by users from non-spatial disciplines and with often limited expertise on this subject. Examples of the evaluation of fitness for use for specific application contexts within the project BIO_SOS (‘Biodiversity Multi-SOurce Monitoring System: From Space To Species’ FP7 project) are presented. By providing a prompt and straightforward evaluation tool, the proposed methodology can encourage the implementation of SDQ evaluation routines in ecological assessment and monitoring programs, promoting a more adequate use of geospatial data and ultimately contributing to well-supported policy and management decisions.     

Tagging-the-triangle algorithm for partitioning features with inconsistent boundaries

In spatial data sets, gaps or overlaps among features are frequently found in spatial tessellations due to the non-abutting edges with adjacent features. These non-abutting edges in loose tessellations are also called inconsistent boundaries or slivers; polygons containing at least one inconsistent boundary are called inconsistent polygons or sliver polygons. The existing algorithms to solve topological inconsistencies in sliver polygons suffer from one or more of three major issues, namely determination of tolerances, excessive CPU processing time for large data sets and loss of vertex history. In this article, we introduce a new algorithm that mitigates these three issues. Our algorithm efficiently searches the features with inconsistent polygons in a given spatial data set and logically partitions them among adjacent features. The proposed algorithm employs the constrained Delaunay triangulation technique to generate labelled triangles from which inconsistent polygons with gaps and overlaps are identified using label counts. These inconsistent polygons are then partitioned using the straight skeleton method. Moreover, each of these partitioned gaps or overlaps is distributed among the adjacent features to improve the topological consistency of the spatial data sets. We experimentally verified our algorithm using the real land cadastre data set. The comparison results show that the proposed algorithm is four times faster than the existing algorithm for data sets with 200,000 edges.     

Geospatial spreadsheets with microscale air quality visualization and synchronization for supporting multiple-scenario visual collaboration

A major obstacle of the interdisciplinary communications for decision-making is that each participant prefers the solution from their own area because current collaboration systems cannot effectively mediate among many alternatives. If a visual collaboration system could display multiple alternatives in a single visual presentation, users would be able to compare alternatives with only a few variables, where all other parameters are fixed, at least from a visual perspective. In this study, we developed a visualization tool to support a microscale air quality (MSAQ) management group, the members of which often need to compare the effects of many alternative control methods. The main achievements of this study are: (1) three-dimensional (3D) visualization of the MSAQ using a 3D city model and (2) development of a geospatial spreadsheet (GS) to facilitate visual comparisons, thereby improving decision-making by making it easy to compare differences based on realistic visual display items. A questionnaire-based survey showed that use of the GS could generate a shared perspective and provide a spatiotemporal context for the participants, thereby making it easy to compare, mediate and reach decisions when confronted with many alternatives.     

高光谱遥感土地质量指标信息提取研究进展

高光谱遥感技术已成为提取土地质量指标的前沿技术之一。该文回顾了国内外高光谱遥感技术在土地质量指标提取中的研究进展,总结归纳了作为土地生态系统载体的土壤属性质量指标和反映生态系统结构功能的质量指标中的土壤有机质、全氮、土壤水分、铁氧化物、土壤重金属、土壤质地、土壤盐分、植被覆盖度、生物量、指示种10个指标的提取、敏感波段选择与反演方法,指出研究中存在的问题,最后展望了高光谱遥感技术在土地质量指标提取研究中的应用前景。     

能见度气象监测数据质量检查方法初探

运用河北省境内103套交通气象监测站逐分钟的能见度气象监测数据,结合气象台站自动站和人工观测资料,探讨了能见度气象监测数据的质量问题和质量检查方法。归纳出3类能见度监测数据的质量问题,提出允许值检查、时间一致性检查、内部一致性检查、空间一致性检查、人为识别法等几种可用于能见度气象监测数据的质量检查方法,同时给出了雾和强降雨造成的低能见度数据质量检查和控制指标。     

不确定度在多波束测深数据质量评估中的应用

在海道测量中,由于无法对测量数据进行多余和重复观测,因而不能精确测定各种误差,同时也几乎没有测量成果质量的控制指标,这正是多波束测深数据质量评估所面临的现实且急需解决的难题。基于国际海道测量规范S-44(5th)的要求,研究了不确定度在多波束数测深数据质量评估中的应用。通过实例分析可知,测量结果的可用性在很大程度上取决于其不确定度的大小,不确定度越小,说明测量结果质量越高,越具有可靠性。因此,将不确定度充分合理地应用于多波束测深数据处理和评估是一种最为理想的途径。     

海洋测量成果质量控制的探讨

结合实际测量过程中出现的问题,对提高海洋测量成果质量的几个关键环节进行了思考,从标准规范、规章制度到人员素质等三个方面提出了相应的建议,对于提高技术人员的基本素质、严格执行标准规范和规章制度、全面提高测量成果质量具有一定的参考意义。     

自组织数据地图在大辽河河口水质分析中的应用

自组织数据地图(SOM,Self-organizing Map)具有强大的数据压缩和提炼功能,应用它可以对水质监测数据进行处理,找到水质分析的新方法。本文以大辽河河口水质监测数据为例,应用MATLAB中SOM工具箱,采用批处理SOM算法,利用MATLAB语言编写出水质分析程序。该程序通过SOM的训练对数据特征进行分析和显示,并进一步分析SOM训练结果,将监测数据分成4类,最后实现数据跟踪及新的监测数据的自动归类。该方法可以将水质监测数据的特征可视化,便于直观、深入地分析水质状况,为识别水质特征、跟踪水质变化和应对措施提供决策支持。