Characterizing urban landscapes using fuzzy sets

Characterizing urban landscapes is important given the present and future projections of global population that favor urban growth. The definition of “urban” on a thematic map has proven to be problematic since urban areas are heterogeneous in terms of land use and land cover. Further, certain urban classes are inherently imprecise due to the difficulty in integrating various social and environmental inputs into a precise definition. Social components often include demographic patterns, transportation, building type and density while ecological components include soils, elevation, hydrology, climate, vegetation and tree cover. In this paper, we adopt a coupled human and natural system (CHANS) integrated scientific framework for characterizing urban landscapes. We implement the framework by adopting a fuzzy sets concept of “urban characterization” since fuzzy sets relate to classes of object with imprecise boundaries in which membership is a matter of degree. For dynamic mapping applications, user-defined classification schemes involving rules combining different social and ecological inputs can lead to a degree of quantification in class labeling varying from “highly urban” to “least urban”. A socio-economic perspective of urban may include threshold values for population and road network density while a more ecological perspective of urban may utilize the ratio of natural versus built area and percent forest cover. Threshold values are defined to derive the fuzzy rules of membership, in each case, and various combinations of rules offer a greater flexibility to characterize the many facets of the urban landscape. We illustrate the flexibility and utility of this fuzzy inference approach called the Fuzzy Urban Index for the Boston Metro region with five inputs and eighteen rules. The resulting classification map shows levels of fuzzy membership ranging from highly urban to least urban or rural in the Boston study region. We validate our approach using two experts assessing accuracy of the resulting fuzzy urban map. We discuss how our approach can be applied in other urban contexts with newly emerging descriptors of urban sustainability, urban ecology and urban metabolism.This research was partially supported by "Boston University Initiative on Cities Early Stage Urban Research Awards 2015-16" (Gopal & Phillips) and the Frederick S. Pardee Center for the Study of the Longer-Range Future at Boston University. We thank the anonymous reviewers for their careful reading of our manuscript and their many insightful comments and suggestions. (Boston University Initiative on Cities Early Stage Urban Research Awards; Frederick S. Pardee Center for the Study of the Longer-Range Future at Boston University)https://doi.org/10.1016/j.compenvurbsys.2016.02.002Published versio

Urban socioeconomic inequality and biodiversity often converge, but not always: A global meta-analysis

It is through urban biodiversity that the majority of humans experience nature on a daily basis. As cities expand globally, it is increasingly important to understand how biodiversity is shaped by human decisions, institutions, and environments. In some cities, research has documented convergence between high socioeconomic status (SES) and high species diversity. Yet, other studies show that residents with low SES live amid high biodiversity or that SES and biodiversity appear unrelated. This study examines the conditions linked to varying types of relationships between SES and biodiversity. We identified and coded 84 case studies from 34 cities in which researchers assessed SES-biodiversity relationships. We used fuzzy-set Qualitative Comparative Analysis (fsQCA) to evaluate combinations of study design and city-level conditions that explain why SES-biodiversity relationships vary city to city and between plants and animals. While the majority of cases demonstrated increased biodiversity in higher SES neighborhoods, we identified circumstances in which inequality in biodiversity distribution was ameliorated or negated by disturbance, urban form, social policy, or collective human preference. Overall, our meta-analysis highlights the contributions of residential and municipal decisions in differentially promoting biodiversity along socioeconomic lines, situated within each city’s environmental and political context. Through identifying conditions under which access to biodiversity is more or less unequal, we call attention to outstanding research questions and raise prospects for better promoting equitable access to biodiversity

Understanding Open Spaces in an Arid City

abstract: This doctoral dissertation research aims to develop a comprehensive definition of urban open spaces and to determine the extent of environmental, social and economic impacts of open spaces on cities and the people living there. The approach I take to define urban open space is to apply fuzzy set theory to conceptualize the physical characteristics of open spaces. In addition, a 'W-green index' is developed to quantify the scope of greenness in urban open spaces. Finally, I characterize the environmental impact of open spaces' greenness on the surface temperature, explore the social benefits through observing recreation and relaxation, and identify the relationship between housing price and open space be creating a hedonic model on nearby housing to quantify the economic impact. Fuzzy open space mapping helps to investigate the landscape characteristics of existing-recognized open spaces as well as other areas that can serve as open spaces. Research findings indicated that two fuzzy open space values are effective to the variability in different land-use types and between arid and humid cities. W-Green index quantifies the greenness for various types of open spaces. Most parks in Tempe, Arizona are grass-dominant with higher W-Green index, while natural landscapes are shrub-dominant with lower index. W-Green index has the advantage to explain vegetation composition and structural characteristics in open spaces. The outputs of comprehensive analyses show that the different qualities and types of open spaces, including size, greenness, equipment (facility), and surrounding areas, have different patterns in the reduction of surface temperature and the number of physical activities. The variance in housing prices through the distance to park was, however, not clear in this research. This dissertation project provides better insight into how to describe, plan, and prioritize the functions and types of urban open spaces need for sustainable living. This project builds a comprehensive framework for analyzing urban open spaces in an arid city. This dissertation helps expand the view for urban environment and play a key role in establishing a strategy and finding decision-makings.Dissertation/ThesisPh.D. Geography 201

It’s not big, it’s large: Mapping and characterizing urban landscapes of a different magnitude based on EO-data

The United Nation’s “World Urbanization Prospects” numeralise a migration process of a huge dimension – from rural to urban areas. While in 1975 only 37.7% of the world’s global population were urban dwellers, in 1990 already 43.0% and today little over 50% of all earth-dwellers are living in urban areas. For the year 2050 the expected number is even 67.2% (UN, 2011). This recent and prospective urbanization trend leads to new spatial dimensions of urban landscapes. One new trend is the spatial evolution of once polynuclei urban areas to so-called ‘mega-regions’. Because in literature clear definitions for the term ‘mega-region’ are missing or at least fuzzy and only qualitative we aim to derive quantitative physical spatial characteristics possibly defining mega-regions. For this purpose we use multi-temporal and multi-source satellite data to classify urbanized areas for an exemplary mega-region – the Hong Kong-Shenzhen-Guangzhou mega-region in Southern China – for the years 1975, 1990, 2000 and 2011. Furthermore, we suggest a set of spatial features potentially characteristic for the evolution of mega-regions. In particular we apply a multitude of spatial metrics at a defined spatial unit for the entire mega-region. The result is a novel spatial approach to capture, measure and analyze new dimensions and shapes of urban landscapes

Challenges in using land use and land cover data for global change studies

Land use and land cover data play a central role in climate change assessments. These data originate from different sources and inventory techniques. Each source of land use/cover data has its own domain of applicability and quality standards. Often data are selected without explicitly considering the suitability of the data for the specific application, the bias originating from data inventory and aggregation, and the effects of the uncertainty in the data on the results of the assessment. Uncertainties due to data selection and handling can be in the same order of magnitude as uncertainties related to the representation of the processes under investigation. While acknowledging the differences in data sources and the causes of inconsistencies, several methods have been developed to optimally extract information from the data and document the uncertainties. These methods include data integration, improved validation techniques and harmonization of classification systems. Based on the data needs of global change studies and the data availability, recommendations are formulated aimed at optimal use of current data and focused efforts for additional data collection. These include: improved documentation using classification systems for land use/cover data; careful selection of data given the specific application and the use of appropriate scaling and aggregation methods. In addition, the data availability may be improved by the combination of different data sources to optimize information content while collection of additional data must focus on validation of available data sets and improved coverage of regions and land cover types with a high level of uncertainty. Specific attention in data collection should be given to the representation of land management (systems) and mosaic landscape

A Novel Technique Based on the Combination of Labeled Co-Occurrence Matrix and Variogram for the Detection of Built-up Areas in High-Resolution SAR Images

Interests in synthetic aperture radar (SAR) data analysis is driven by the constantly increased spatial resolutions of the acquired images, where the geometries of scene objects can be better defined than in lower resolution data. This paper addresses the problem of the built-up areas extraction in high-resolution (HR) SAR images, which can provide a wealth of information to characterize urban environments. Strong backscattering behavior is one of the distinct characteristics of built-up areas in a SAR image. However, in practical applications, only a small portion of pixels characterizing the built-up areas appears bright. Thus, specific texture measures should be considered for identifying these areas. This paper presents a novel texture measure by combining the proposed labeled co-occurrence matrix technique with the specific spatial variability structure of the considered land-cover type in the fuzzy set theory. The spatial variability is analyzed by means of variogram, which reflects the spatial correlation or non-similarity associated with a particular terrain surface. The derived parameters from the variograms are used to establish fuzzy functions to characterize the built-up class and non built-up class, separately. The proposed technique was tested on TerraSAR-X images acquired of Nanjing (China) and Barcelona (Spain), and on a COSMO-SkyMed image acquired of Hangzhou (China). The obtained classification accuracies point out the effectiveness of the proposed technique in identifying and detecting built-up areas

Mapping the regional sustainable development

Different opinions about the idea of "sustainable development" (SD) are discussed and the structure of SD indexes is suggested. The last one is based on one of the main methodological statements of the modern geography. It tells, that the basement of the complicated territorial systems or complexes are nature, population, economy. There are distinguished two main types of the original status of the studied regions: - Regions, where the necessity to solve the acute ecological problems force to address to the questions of SD. It means that here it is observed the leading role of ecological factor. More often these are regions of ecological hazards, low developed territories with the raw materials and resources specialization. - Regions where ecological problems are stocked in the tight knot of contradictions with the social-economic and ethno-cultural problems, that means that the "leading" factor hardly could be marked out. It is obvious, that to these two situations must correspond the different orientation of the SD indexes. In first case the priority will be given to the exclusively ecological criterious, in second case - the complex system of indexes will be needed. There is suggested the structure of the system of the SD indexes for territories, which will be the specific framework on composing the model of SD. The maps, or the system of maps or atlas are the more flexible instrument than the system of indexes, because they represent the territorial "reference" and spatial image of the objects and events. Map creation coming from the announced principals must base on the certain indexes, added as the characteristic of SD for the certain region. This makes us to substantiate the certain system of the SD indexes for the regional level. Analysis shows, that in order to reach substantial completeness, it is useful to divide the indexes into 3 groups: - particular indexes of elements and components of the subsystems "nature" (characteristics of environmental conditions and resources), "population" (socio-demographic characteristics of different groups of the population), "economy" (characteristics of different branches); - problematic (structural) indexes, characterizing interlinks between ecological conditions and social-economic conditions; - general system indexes, giving the integrity to the whole set of indexes and characterizing as stability of every system, so as the degree of SD of the territory. Today authors is occupied in realization on the basis of the suggested concept some sheets of the SD atlas of Jaroslavl area (Russia). The concept of the SD atlas of the region is being realized in practice within the geographic ingormation system (GIS) "Terra". "Terra" is worked out on the order of the authorities of Jaroslavl area and now it is the first stage of creation of the integral territorial cadastre of Jaroslavl area as a part of the integral state cadastre of Russia. For today the multi-layer electronic maps of Jaroslavl area are realized within the GIS "Terra" (the basic scale is 1:100,000, for the territories of towns - 1:10,000). Along with that the data base is being created, corresponding to the certain layers of maps (forests, road infrastructure, settlements, administrative districts, building objects, etc.). The concrete working through the possibilities of the atlas creation and testing of the IGS "Terra" are carried out on creations of electronic versions of two maps, which thematic was chosen with account of the present data and theoretical approaches. These are the questions of antropogenic load on environment and general pollution of the territory.

Characterizing Land Use Change in Multidisciplinary Landscape-Level Analyses

Economists increasingly face opportunities to collaborate with ecologists on landscape-level analyses of socioeconomic and ecological processes. This often calls for developing empirical models to project land use change as input into ecological models. Providing ecologists with the land use information they desire can present many challenges regarding data, modeling, and econometrics. This paper provides an overview of the relatively recent adaptation of economics-based land use modeling methods toward greater spatial specificity desired in integrated research with ecologists. Practical issues presented by data, modeling, and econometrics are highlighted, followed by an example based on a multidisciplinary landscape-level analysis in Oregon's Coast Range mountains.Land Economics/Use,

Utilizing the Landsat spectral-temporal domain for improved mapping and monitoring of ecosystem state and dynamics

Just as the carbon dioxide observations that form the Keeling curve revolutionized the study of the global carbon cycle, free and open access to all available Landsat imagery is fundamentally changing how the Landsat record is being used to study ecosystems and ecological dynamics. This dissertation advances the use of Landsat time series for visualization, classification, and detection of changes in terrestrial ecological processes. More specifically, it includes new examples of how complex ecological patterns manifest in time series of Landsat observations, as well as novel approaches for detecting and quantifying these patterns. Exploration of the complexity of spectral-temporal patterns in the Landsat record reveals both seasonal variability and longer-term trajectories difficult to characterize using conventional bi-temporal or even annual observations. These examples provide empirical evidence of hypothetical ecosystem response functions proposed by Kennedy et al. (2014). Quantifying observed seasonal and phenological differences in the spectral reflectance of Massachusetts’ forest communities by combining existing harmonic curve fitting and phenology detection algorithms produces stable feature sets that consistently out-performed more traditional approaches for detailed forest type classification. This study addresses the current lack of species-level forest data at Landsat resolutions, demonstrating the advantages of spectral-temporal features as classification inputs. Development of a targeted change detection method using transformations of time series data improves spatial and temporal information on the occurrence of flood events in landscapes actively modified by recovering North American beaver (Castor canadensis) populations. These results indicate the utility of the Landsat record for the study of species-habitat relationships, even in complex wetland environments. Overall, this dissertation confirms the value of the Landsat archive as a continuous record of terrestrial ecosystem state and dynamics. Given the global coverage of remote sensing datasets, the time series visualization and analysis approaches presented here can be extended to other areas. These approaches will also be improved by more frequent collection of moderate resolution imagery, as planned by the Landsat and Sentinel-2 programs. In the modern era of global environmental change, use of the Landsat spectral-temporal domain presents new and exciting opportunities for the long-term large-scale study of ecosystem extent, composition, condition, and change