Per-Pixel Versus Object-Based Classification of Urban Land Cover Extraction Using High Spatial Resolution Imagery

In using traditional digital classification algorithms, a researcher typically encounters serious issues in identifying urban land cover classes employing high resolution data. A normal approach is to use spectral information alone and ignore spatial information and a group of pixels that need to be considered together as an object. We used QuickBird image data over a central region in the city of Phoenix, Arizona to examine if an object-based classifier can accurately identify urban classes. To demonstrate if spectral information alone is practical in urban classification, we used spectra of the selected classes from randomly selected points to examine if they can be effectively discriminated. The overall accuracy based on spectral information alone reached only about 63.33%. We employed five different classification procedures with the object-based paradigm that separates spatially and spectrally similar pixels at different scales. The classifiers to assign land covers to segmented objects used in the study include membership functions and the nearest neighbor classifier. The object-based classifier achieved a high overall accuracy (90.40%), whereas the most commonly used decision rule, namely maximum likelihood classifier, produced a lower overall accuracy (67.60%). This study demonstrates that the object-based classifier is a significantly better approach than the classical per- pixel classifiers. Further, this study reviews application of different parameters for segmentation and classification, combined use of composite and original bands, selection of different scale levels, and choice of classifiers. Strengths and weaknesses of the object-based prototype are presented and we provide suggestions to avoid or minimize uncertainties and limitations associated with the approach.

Extreme summer heat in Phoenix, Arizona (USA) under global climate change (2041-2070)

Summer extreme heat events in the arid Phoenix, Arizona (USA) metropolitan region for the period 2041-2070 are projected based on the ensemble of ten climate models from the North American Regional Climate Change Assessment Program for the SRES A2 greenhouse gas emissions scenario by the Intergovernmental Panel on Climate Change. Extreme heat events are identified by measures related to two thresholds of the maximum daily air temperature distribution for the historical reference period 1971-2000. Comparing this reference period to the model ensemble-mean, the frequency of extreme heat events is projected to increase by a factor of six to 1.9 events per summer and the average number of event days per year is projected to increase by a factor of 14. The inter-model range for the average number of EHE days per summer is larger for the projected climate, 10.6 to 42.2 days, than for simulations of the past climate simulations (1.5 to 2.4 days).Projektionen für extreme Hitzeereignisse in den Sommermonaten in Phoenix, Arizona (USA) wurden für den Zeitraum 2041-2070 unter der Annahme des SRES A2 Treibhausgasemissionsszenarios des Intergovernmental Panel on Climate Change erstellt. Dafür wurden Simulationsergebnisse eines Ensembles von zehn Klimamodellen ausgewertet, die im Rahmen des North American Regional Climate Change Assessment Programmes angewendet wurden. Die extremen Hitzeereignisse wurden mit Hilfe von Kriterien bestimmt, die auf Schwellenwerttemperaturen für die maximalen täglichen Lufttemperaturen für die Referenzperiode 1971-2000 beruhen. In der Zukunft versechsfacht sich im Ensemblemittel die Häufigkeit der Extremereignisse (~ 1.9 Ereignisse pro Sommer) im Vergleich zum Referenzzeitraum mit einer vierzehnfachen mittleren Anzahl der Hitzetage pro Sommer. Dabei vergrößert sich die Schwankungsbreite der Modelle bezüglich der mittleren Anzahl der Hitzetage pro Sommer in der Zukunft gegenüber der der Vergangenheit von 1.5-2.4 auf 10.6-42.2

Extreme summer heat in Phoenix, Arizona (USA) under global climate change (2041-2070)

Summer extreme heat events in the arid Phoenix, Arizona (USA) metropolitan region for the period 2041-2070 are projected based on the ensemble of ten climate models from the North American Regional Climate Change Assessment Program for the SRES A2 greenhouse gas emissions scenario by the Intergovernmental Panel on Climate Change. Extreme heat events are identified by measures related to two thresholds of the maximum daily air temperature distribution for the historical reference period 1971-2000. Comparing this reference period to the model ensemble-mean, the frequency of extreme heat events is projected to increase by a factor of six to 1.9 events per summer and the average number of event days per year is projected to increase by a factor of 14. The inter-model range for the average number of EHE days per summer is larger for the projected climate, 10.6 to 42.2 days, than for simulations of the past climate simulations (1.5 to 2.4 days)

Contribution of land use changes to near-surface air temperatures during recent summer extreme heat events in the Phoenix Metropolitan Area

The impact of 1973–2005 land use–land cover (LULC) changes on near-surface air temperatures during four recent summer extreme heat events (EHEs) are investigated for the arid Phoenix, Arizona, metropolitan area using the Weather Research and Forecasting Model (WRF) in conjunction with the Noah Urban Canopy Model. WRF simulations were carried out for each EHE using LULC for the years 1973, 1985, 1998, and 2005. Comparison of measured near-surface air temperatures and wind speeds for 18 surface stations in the region show a good agreement between observed and simulated data for all simulation periods. The results indicate consistent significant contributions of urban development and accompanying LULC changes to extreme temperatures for the four EHEs. Simulations suggest new urban developments caused an intensification and expansion of the area experiencing extreme temperatures but mainly influenced nighttime temperatures with an increase of up to 10 K. Nighttime temperatures in the existing urban core showed changes of up to 2 K with the ongoing LULC changes. Daytime temperatures were not significantly affected where urban development replaced desert land (increase by 1 K); however, maximum temperatures increased by 2–4 K when irrigated agricultural land was converted to suburban development. According to the model simulations, urban landscaping irrigation contributed to cooling by 0.5–1 K in maximum daytime as well as minimum nighttime 2-m air temperatures in most parts of the urban region. Furthermore, urban development led to a reduction of the already relatively weak nighttime winds and therefore a reduction in advection of cooler air into the city

Trade-offs and responsiveness of the single-layer urban canopy parametrization in WRF: an offline evaluation using the MOSCEM optimization algorithm and field observations

For an increasing number of applications, mesoscale modelling systems now aim to better represent urban areas. The complexity of processes resolved by urban parametrization schemes varies with the application. The concept of fitness-for-purpose is therefore critical for both the choice of parametrizations and the way in which the scheme should be evaluated. A systematic and objective model response analysis procedure (Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm) is used to assess the fitness of the single-layer urban canopy parametrization implemented in the Weather Research and Forecasting (WRF) model. The scheme is evaluated regarding its ability to simulate observed surface energy fluxes and the sensitivity to input parameters. Recent amendments are described, focussing on features which improve its applicability to numerical weather prediction, such as a reduced and physically more meaningful list of input parameters. The study shows a high sensitivity of the scheme to parameters characterizing roof properties in contrast to a low response to road-related ones. Problems in partitioning of energy between turbulent sensible and latent heat fluxes are also emphasized. Some initial guidelines to prioritize efforts to obtain urban land-cover class characteristics in WRF are provided. Copyright © 2010 Royal Meteorological Society and Crown Copyright

The Integrated WRF/Urban Modeling System: Development, Evaluation, and Applications to Urban Environmental Problems

To bridge the gaps between traditional mesoscale modeling and microscale modeling, the National Center for Atmospheric Research (NCAR), in collaboration with other agencies and research groups, has developed an integrated urban modeling system coupled to the Weather Research and Forecasting (WRF) model as a community tool to address urban environmental issues. The core of this WRF/urban modeling system consists of: 1) three methods with different degrees of freedom to parameterize urban surface processes, ranging from a simple bulk parameterization to a sophisticated multi-layer urban canopy model with an indoor outdoor exchange sub-model that directly interacts with the atmospheric boundary layer, 2) coupling to fine-scale Computational Fluid Dynamic (CFD) Reynolds-averaged Navier–Stokes (RANS) and Large-Eddy Simulation (LES) models for Transport and Dispersion (T&D) applications, 3) procedures to incorporate high-resolution urban land-use, building morphology, and anthropogenic heating data using the National Urban Database and Access Portal Tool (NUDAPT), and 4) an urbanized high-resolution land-data assimilation system (u-HRLDAS). This paper provides an overview of this modeling system; addresses the daunting challenges of initializing the coupled WRF/urban model and of specifying the potentially vast number of parameters required to execute the WRF/urban model; explores the model sensitivity to these urban parameters; and evaluates the ability of WRF/urban to capture urban heat islands, complex boundary layer structures aloft, and urban plume T&D for several major metropolitan regions. Recent applications of this modeling system illustrate its promising utility, as a regional climate-modeling tool, to investigate impacts of future urbanization on regional meteorological conditions and on air quality under future climate change scenarios

The Influence of green areas and roof albedos on air temperatures during Extreme Heat Events in Berlin, Germany

The mesoscale atmospheric model COSMO-CLM (CCLM) with the Double Canyon Effect Parametrization Scheme (DCEP) is applied to investigate possible adaption measures to extreme heat events (EHEs) for the city of Berlin, Germany. The emphasis is on the effects of a modified urban vegetation cover and roof albedo on near-surface air temperatures. Five EHEs with a duration of 5 days or more are identified for the period 2000 to 2009. A reference simulation is carried out for each EHE with current vegetation cover, roof albedo and urban canopy parameters (UCPs), and is evaluated with temperature observations from weather stations in Berlin and its surroundings. The derivation of the UCPs from an impervious surface map and a 3-D building data set is detailed. Characteristics of the simulated urban heat island for each EHE are analysed in terms of these UCPs. In addition, six sensitivity runs are examined with a modified vegetation cover of each urban grid cell by -25%, 5% and 15%, with a roof albedo increased to 0.40 and 0.65, and with a combination of the largest vegetation cover and roof albedo, respectively. At the weather stations' grid cells, the results show a maximum of the average diurnal change in air temperature during each EHE of 0.82 K and -0.48 K for the -25% and 15% vegetation covers, -0.50 K for the roof albedos of 0.65, and -0.63 K for the combined vegetation and albedo case. The largest effects on the air temperature are detected during midday

Achieving FAIR Data Principles at the Environmental Data Initiative, the US-LTER Data Repository

The Environmental Data Initiative (EDI) is a continuation and expansion of the original United Stated Long-Term Ecological Research Program (US-LTER) data repository which went into production in 2013. Building on decades of data management experience in LTER, EDI is addressing the challenge of publishing a diverse corpus of research data (Servilla et al. 2016). EDI’s accomplishments span all aspects of the data curation and publication lifecycle, including repository cyberinfrastructure, outreach and training, and enhancements to data documentation methodologies used by the environmental and ecological research communities. EDI is managing almost 43,000 unique data packages and their revisions from a community of nearly 2,300 individual data authors, most of which are contributed by LTER sites, and are openly accessible and documented with rich science metadata in the Ecological Metadata Language (EML) standard. Here we will present how EDI achieves FAIR data principles (Wilkinson et al. 2016, Stall et al. 2017), and report data use metrics as a measure of success. The FAIR principles serve as benchmarks for EDI’s operation and management: the data we curate are Findable because they reside in an open repository, with unique and persistent digital object identifiers (DOIs) and standard metadata indexed as a searchable resource; they are Accessible through industry standard protocols and are, in most cases, under an open-access license (access control is available if required); Interoperability is achieved by archiving data in commonly used file formats, and both metadata and data are machine readable and accessible; rich, high quality science metadata, with automated congruence and completeness checking, render data fit for Reuse in multiple contexts and environments, along with easily generated data provenance to document their lineage. The success of this approach is proven by the number and spatial and temporal extent of recent re-analyses and synthesis efforts of these data. Although formal data citations are not yet common practice, a Google Scholar search reveals over 400 journal articles crediting data re-use through an EDI DOI. However, despite improved data availability, researchers still report that the largest time investment in synthesis projects is discovering, cleaning and combining primary datasets until all data are completely understood and converted to a similar format. Starting with long-term biodiversity observation data EDI is addressing this issue by implementing a pre-harmonization of thematically similar data sets. Positioned between the data author’s specific data format and larger biodiversity data stores or synthesis projects, this approach allows uniform access without the loss of ancillary information. This pre-harmonization step may be accomplished by data managers because the dataset still contains all original information without any aggregation or science question specific decisions for data omission or cleaning. The data are still distributed into distinct datasets allowing for asynchronous updating of long-term observations. The addition of specific and standardized metadata makes them easily discoverable