Stability characterization of the response of white storks foraging behavior to vegetation dynamics retrieved from Landsat time series

Agricultural activities cause rapid changes in vegetation development at local and regional scales. Those modifications affect the small-scale behavior of animals, like the foraging ground usage of breeding white storks. Only recently, a novel approach, that enables to quantify the relationship between mowing and harvesting activities and a prolonged foraging time of storks by combining remote sensing time series with GPS telemetry, has been proposed. This study examines the stability of this approach. We investigate two potential influencing factors: different vegetation indices and time lags over which vegetation dynamics were retrieved. Mostly independent from the vegetation index and time lag, we observed that storks spent large proportions of foraging time in areas characterized by a recent drop in vegetation indices, indicative for a preferred usage after harvesting and mowing events. This suggest that the proposed approach is relatively stable and hence, provides a reasonable basis to investigate the effects of anthropogenic vegetation alterations on animal behavior at small spatiotemporal scales

To be, or not to be ‘urban’? A multi-modal method for the differentiated measurement of the degree of urbanization

Today, 56.6% of the world’s population is urban and the trend is rising; in Germany, the urbanization process is almost complete at 80.3%. This is the ubiquitously used narrative. Surprisingly, the figures behind it are rarely questioned. The spatial statistics responsible for this meta-narrative are, however, prone to ambiguity. They suffer from a lack of systematic empirical justification, and from cross-national differences in cut-off values used to differentiate between urban and rural populations. In this study, we present an empirical approach that allows to systematically map urban and rural populations in a spatially and thematically differentiated manner using a multimodal method. On the one hand, we resort to the common approach of presenting the degree of urbanization in terms of population figures for administrative units. However, we do not only use the common national threshold value, but we project various national thresholds applied in different countries across the globe to classify multiple degrees of urbanization onto our study site Germany. On the other hand, we also calculate various degrees of urbanization at a higher spatial resolution using a regular grid. Beyond the common approach of calculating the degree of urbanization by population figures, we also apply at grid-level two additional variables: building density and the share of a certain building type. By systematically applying thresholds between minimum and maximum per variable, we trace the effects on the resulting degree of urbanization. These multiple perspectives lead us to propose that a range rather than a singular threshold allows us to estimate the degree of urbanization in a more differentiated way. To do so, we estimate the degree of urbanization for Germany on a probability-based basis. Therefore, we combine possible variants from the administrative approach using population figures and the grid-based approach using thresholds of population, building density and the share of a certain building type. Our results show that Germany can be considered urban by at least 50.0% up to possibly 68.1% of the population, which by no means comes near the reported 80.3%. We conclude that the results of the commonly used approach to quantify urban populations are not tenable in their clarity and should therefore be used only with caution. cacaution for political and societal decision-makin

Validation of the European Urban Atlas & Automatic Classification of Urban Structures utilizing Cartosat-1 nDSM Data

Ongoing urbanization and population growth affect physical, ecological and socio-economic processes within and beyond urban areas. In this context, the physical spatial structure of cities serves as a basis for understanding urban areas in their entirety in order to achieve a sustainable urban growth. Remote sensing is a cost-effective data source to derive information on the physical spatial structure of urban areas in large areal and temporal coverage. The European Urban Atlas dataset is generated mainly on the basis of remotely sensed data and provides information on landuse/landcover of cities with more than 100,000 inhabitants in Europe. However, as proved within this study, the classification scheme does only partially hold distinct information on building structures and thus, as a matter of fact, does not contain holistic information on the morphology of urban areas. Within this study, a methodology towards the delineation of urban structures utilizing Cartosat-1 nDSM data and Urban Atlas building block information was developed. The nDSM data proved to be substantial in terms of building height classification but fails to add information on the building density. The methodology was applied in another city context achieving constant results and to an artificial reference unit of square objects achieving worse but still satisfactory results. The latter overcomes spatial limitations and allows for deriving information on urban structures independently from additional spatial data sources, on continental and even global scale. A cross-city structural analysis revealed differences and analogies of the urban morphology of Paris and London depending on the distance to the respective city center

The Physical Density of the City—Deconstruction of the Delusive Density Measure with Evidence from Two European Megacities

Density is among the most important descriptive as well as normative measures in urban research. While its basic concept is generally understandable, approaches towards the density measure are manifold, diverse and of multidimensional complexity. This evolves from differing thematic, spatial and calculative specifications. Consequently, applied density measures are often used in a subjective, non-transparent, unspecific and thus non-comparable manner. In this paper, we aim at a systematic deconstruction of the measure density. Varying thematic, spatial and calculative dimensions show significant influence on the measure. With both quantitative and qualitative techniques of evaluation, we assess the particular influences on the measure density. To do so, we reduce our experiment setting to a mere physical perspective; that is, the quantitative measures building density, degree of soil sealing, floor space density and, more specifically, the density of generic structural classes such as open spaces and highest built-up density areas. Using up-to-date geodata derived from remote sensing and volunteered geographic information, we build upon high-quality spatial information products such as 3-D city models. Exemplified for the comparison of two European megacities, namely Paris and London, we reveal and systemize necessary variables to be clearly defined for meaningful conclusions using the density measure

Measuring morphological polycentricity - A comparative analysis of urban mass concentrations using remote sensing data

Polycentricity belongs to the most versatile and fuzzy concepts in urban geography. It basically points to the ex-istence of more than one center within a conurbation. Previous studies have mostly referred to the spatial distri-bution of employment density for (sub-) center identification. In contrast, our study draws on large area 3D building models derived from ubiquitous remote sensing data. We use stereoscopic Cartosat-1 digital surface models in combination with building footprints. These geoinformation reflect the spatial configuration of the built dimension and allow a physical approach to the concept of polycentricity. For (sub-) center identification we thoroughly analyze conceptually different kinds of threshold approaches (global, region-specific and dis-tance-based) applied to concentrations of urban masses. After evaluating the advantages and disadvantages of the threshold approaches applied, we combine these methods to overcome their individual shortcomings. Last but not least, we establish a framework consisting of mapping techniques and site- and non-site specific statistics to evaluate polycentricity at fine-grained spatial intra-urban scale. In general we find that urban mass concentra-tions are a reasonable proxy for commonly used employment density data. We address the polycentricity issue across four German city regions—Frankfurt, Cologne, Stuttgart and Munich—and we find all of them to still be morphologically dominated by their core cities. Nevertheless, our analysis reveals striking differences of the urban spatial structure highlighting a rather monocentric pattern in the Munich region on the one hand, and a polycentric-dispersed distribution of urban mass concentrations in the Stuttgart region on the other hand

Quantitative assessment and comparison of urban patterns in Germany and the United States

In this paper, we present methods to assess the homogeneity of the settlement landscape and to identify settlement clusters in terms of location and size. Due to various input data, methodologies, and spatial concepts, there are only a few international comparative research studies with quantitative, spatial approaches to date. Existing studies are mostly qualitative and empirically hardly replicable. Here, we introduce two novel methodological approaches to describe urban land patterns in a numerically stable, consistent and globally comparable manner. The first method evaluates the multi-scale homogeneity of the settlements and the respective density distributions in a purely non-parametric approach. The second method transfers the settlement patterns into a Gaussian Mixture Model via hierarchical multi-scale clustering in order to describe each cluster by the parameters of a 2D Gaussian distribution. In order to proof the robustness of both approaches, two different reference units are considered for the interpretation of the results: OECD functional urban areas and standardized subsets of 200 km × 200 km around the same central cities. We show that German urban areas are characterized by a higher heterogeneity within a smaller neighbourhood with an almost symmetric density distribution in comparison to urban areas in the U.S. Furthermore, German urban areas possess an increasing homogeneity with increasing size, i.e., the larger the urban agglomeration, the more uniform it appears. It is the contrary in the U.S. These findings are substantiated by the analysis of individual city centres. In Germany, many clusters of similar size define the urban region, whereas in the U.S. one large dominating cluster exceeds most neighbouring settlements significantly. The study is performed on the Global Urban Footprint Density, which assigns a density value to each urban pixel of about 30 m by 30 m. In this way, the approach is a blueprint to be extended to urban patterns of any spatial unit worldwide

Dynamics of intra-urban employment geographies: A comparative study of U.S. and German metropolitan areas

In this paper we analyze changes in the intra-urban spatial distribution of employment across six U.S. and German city regions between 2002 and 2015. Our methodological approach allows for a systematic and spatially consistent comparison of urban spatial structures across the two different countries. The empirical results show major national, regional, and sectoral differences in the spatial distribution of employment. In the German case studies traditional core cities play a more important role for the regional labor market than in the U.S. Only relatively small shares of metropolitan employment are concentrated in subcenters. While employment concentrations are spatially less persistent in the U.S. case study regions, we did not find any evidence of common or country-specific trends toward increased polycentricity or employment dispersal. Changes in the spatial concentration of employment seem to be highly context-specific and influenced by the individual geographic and institutional frameworks of the analyzed metropolitan areas

Assessing cumulative uncertainties of remote sensing time series and telemetry data in animal-environment studies

Context Remote sensing time series (hereafter called time series) and telemetry data are widely used to study animal-environment relationships. However, both data sources are subject to uncertainties that can cause erroneous conclusions. To date, only the uncertainty of telemetry data can be estimated, e.g. through movement modelling, while information on the uncertainty of time series is often lacking. Consequently, it remains challenging to assess if and how the results of animal-environment studies are affected by cumulative uncertainties of telemetry and time series data. Objectives To address this gap, we proposed an approach to approximate time series uncertainties. Coupled with movement modelling, this allows to determine whether the results of animal-environment studies are robust to the cumulative uncertainties of time series and telemetry data. We demonstrated the procedure with a study that used time series to distinguish periods of favourable/poor prey accessibility for white storks. Our objective was to test whether the storks’ preference for fields during periods of favourable prey accessibility could be validated despite the uncertainties. Methods We estimated the telemetry data uncertainties based on continuous-time movement modelling, and approximated time series uncertainties based on data subsampling. We used Monte Carlo simulations to propagate the uncertainties and to generate several estimates of the stork habitat use and levels of prey accessibility. These data were applied in two habitat selection analyses to derive probability distributions of the analyses results, allowing us to characterise the output uncertainties. Results We found that, after accounting for uncertainty, favourable and poor prey accessibility periods were well discriminated, with storks showing the expected degree of preference/avoidance for them. However, our uncertainty analysis also showed, that compared to croplands, grasslands required more temporal NDVI samples to reliably identify these periods. Furthermore, the NDVI itself did not appear to be a coherent predictor of stork habitat selection when uncertainties were accounted for. Conclusion Our findings highlight the importance of validating results by assessing and quantifying the effect of input data uncertainties in animal-environment studies. To our knowledge, the approach presented is the first to assess the cumulative uncertainty of time series and telemetry data, hopefully raising awareness of the consequences of input data uncertainties for future studies

Deriving urban mass concentrations using TanDEM-X and Sentinel-2 data for the assessment of morphological polycentricity

Polycentricity refers to urban regions with more than one center. These additional (sub-) centers, e.g. spatial concentrations of jobs, are characteristic for the transformation of monocentric towards polycentric urban patterns. Frequently assessed with socioeconomic data, the phenomenon is also reflected in the built morphology of urban landscapes. Only recently, a methodology for largescale morphological characterization of built-up structures in urban areas relying on TanDEM-X and Sentinel-2 data has been introduced. Thus, a new way to investigate morphologic polycentricity in and among cities is provided. Relying on this approach, we derive the distribution of urban mass concentrations in four city regions. We identify high urban mass concentrations - proxies for (sub-) centers - using a threshold approach. A comparison between the studied regions reveals that only one city tends to have a polycentric urban structure. Our study highlights a new and promising possibility to study the urban morphologic development at global scales