Extraordinary heat during the 1930s US Dust Bowl and associated large-scale conditions

Unusually hot summer conditions occurred during the 1930s over the central United States and undoubtedly contributed to the severity of the Dust Bowl drought. We investigate local and large-scale conditions in association with the extraordinary heat and drought events, making use of novel datasets of observed climate extremes and climate reanalysis covering the past century. We show that the unprecedented summer heat during the Dust Bowl years was likely exacerbated by land-surface feedbacks associated with springtime precipitation deficits. The reanalysis results indicate that these deficits were associated with the coincidence of anomalously warm North Atlantic and Northeast Pacific surface waters and a shift in atmospheric pressure patterns leading to reduced flow of moist air into the central US. Thus, the combination of springtime ocean temperatures and atmospheric flow anomalies, leading to reduced precipitation, also holds potential for enhanced predictability of summer heat events. The results suggest that hot drought, more severe than experienced during the most recent 2011 and 2012 heat waves, is to be expected when ocean temperature anomalies like those observed in the 1930s occur in a world that has seen significant mean warming.     

Object-oriented and pixel-based classification approaches to classify tropical successional stages using airborne high–spatial resolution images

Airborne high–spatial resolution images were evaluated for mapping purposes in a complex Atlantic rainforest environment in southern Brazil. Two study sites, covered predominantly by secondary evergreen rainforest, were surveyed by airborne multispectral high-resolution imagery. These aerophotogrammetric images were acquired at four spectral bands (visible to near-infrared) with spatial resolution of 0.39 m. We evaluated different data input scenarios to suit the object-oriented classification approach. In addition to the four spectral bands, auxiliary products such as band ratios and digital elevation models were considered. Comparisons with traditional pixel-based classifiers were also performed. The results showed that the object-based classification approach yielded a better overall accuracy, ranging from 89% to 91%, than the pixel-based classifications, which ranged from 62% to 63%. The individual classification accuracy of forest-related classes, such as young successional forest stages, benefits the object-based approach. These classes have been reported in the literature as the most difficult to map in tropical environments. The results confirm the potential of object-based classification for mapping procedures and discrimination of successional forest stages and other related land use and land cover classes in complex Atlantic rainforest environments. The methodology is suggested for further SAAPI acquisitions in order to monitor such endangered environment as well as to support National Land and Environmental Management Protocols.     

A Spatial Data Infrastructure Approach for the Characterization of New Zealand's Groundwater Systems

The future information needs of stakeholders for hydrogeological and hydro‐climate data management and assessment in New Zealand may be met with an Open Geospatial Consortium (OGC) standards‐compliant publicly accessible web services framework which aims to provide integrated use of groundwater information and environmental observation data in general. The stages of the framework development described in this article are search and discovery as well as data collection and access with (meta)data services, which are developed in a community process. The concept and prototype implementation of OGC‐compliant web services for groundwater and hydro‐climate data include demonstration data services that present multiple distributed datasets of environmental observations. The results also iterate over the stakeholder community process and the refined profile of OGC services for environmental observation data sharing within the New Zealand Spatial Data Infrastructure (SDI) landscape, including datasets from the National Groundwater Monitoring Program and the New Zealand Climate Database along with datasets from affiliated regional councils at regional‐ and sub‐regional scales. With the definition of the New Zealand observation data profile we show that current state‐of‐the‐art standards do not necessarily need to be improved, but that the community has to agree upon how to use these standards in an iterative process.     

Exploration of spatiotemporal and semantic clusters of Twitter data using unsupervised neural networks

ABSTRACT

The investigation of human activity patterns from location-based social networks like Twitter is an established approach of how to infer relationships and latent information that characterize urban structures. Researchers from various disciplines have performed geospatial analysis on social media data despite the data’s high dimensionality, complexity and heterogeneity. However, user-generated datasets are of multi-scale nature, which results in limited applicability of commonly known geospatial analysis methods. Therefore in this paper, we propose a geographic, hierarchical self-organizing map (Geo-H-SOM) to analyze geospatial, temporal and semantic characteristics of georeferenced tweets. The results of our method, which we validate in a case study, demonstrate the ability to explore, abstract and cluster high-dimensional geospatial and semantic information from crowdsourced data.     

Fluid Borders: Rethinking Historical Geography and Fixed Map Boundaries in Contested Regions

This article introduces a quantitative methodology for analyzing contested map borders. The article applies the new analytical technique to a data set of thirty maps showing Bulgaria in ca. 800 CE, a disputed state and period in medieval historiography with relevance to modern national politics and territorial claims. Based on the data set, we generate a series of new maps that make explicit the fluid medieval boundaries and general disagreement among geographers and historiographers. Our analysis begins with a simple point-in-polygon procedure to create a majority map that depicts the points included within the borders of the Bulgarian polity in sixteen or more of the maps (>50 percent). The majority map is then combined with percentage maps, confidence interval map boundaries, and cluster maps. The confidence interval maps are created via a spatial bootstrapping procedure and measure the uncertainty in the majority map. The cluster maps are developed via a radial basis function and provide insight into the potential affectivity based on the cartographers' countries of origin. The final map reflects the general modern consensus of the borders of the Bulgarian polity around 800 CE. Besides its quantitative contribution to medieval and modern cartographic, historiographical, and political debates, this article has developed a widely applicable methodology for synthesizing map borders and territories in cases of cartographic disagreement.