Preliminary evidence for the influence of physiography and scale upon the autocorrelation function of remotely sensed data

Previously established results demonstrate that LANDSAT data are autocorrelated and can be described by a univariate linear stochastic process known as auto-regressive-integrated-moving-average model of degree 1, 0, 1 or ARIMA (1, 0, 1). This model has two coefficients of interest for interpretation phi(1) and theta(1). In a comparison of LANDSAT thematic mapper simulator (TMS) data and LANDSAT MSS data several results were established: (1) The form of the relatedness as described by this model is not dependent upon system look angle or pixel size. (2) The phi(1) coefficient increases with decreasing pixel size and increasing topographic complexity. (3) Changes in topography have a greater influence upon phi(1) than changes in land cover class. (4) The theta(1) seems to vary with the amount of atmospheric haze. These patterns of variation in phi(1) and theta(1) are potentially exploitable by the remote sensing community to yield stochastically independent sets of observations, characterize topography, and reduce the number of bytes needed to store remotely sensed data

Ecosystems are showing symptoms of resilience loss

Ecosystems around the world are at risk of critical transitions due to increasing anthropogenic pressures and climate change. Yet, it is unclear where the risks are higher or where in the world are ecosystems more vulnerable. Here I measure resilience of primary productivity proxies for marine and terrestrial ecosystems globally. Up to 29% of global terrestrial ecosystem, and 24% marine ones, show symptoms of resilience loss. These symptoms are shown in all biomes, but by large Arctic tundra and boreal forest are the most affected, as well as the Indian Ocean and Easter Pacific. Despite the results are likely an underestimation, they enable the identification of risk areas as well as the potential synchrony of some transitions. Mapping where ecosystems are likely to undergo critical transitions or long transients can help prioritize areas for management interventions and conservation. These results pave the way towards developing an ecological resilience observatory.Comment: 19 pages (including SM), 2 figures on main text, 10 SM figure

Evaluating the Consistency of the 1982–1999 NDVI Trends in the Iberian Peninsula across Four Time-series Derived from the AVHRR Sensor: LTDR, GIMMS, FASIR, and PAL-II

Successive efforts have processed the Advanced Very High Resolution Radiometer (AVHRR) sensor archive to produce Normalized Difference Vegetation Index (NDVI) datasets (i.e., PAL, FASIR, GIMMS, and LTDR) under different corrections and processing schemes. Since NDVI datasets are used to evaluate carbon gains, differences among them may affect nations’ carbon budgets in meeting international targets (such as the Kyoto Protocol). This study addresses the consistency across AVHRR NDVI datasets in the Iberian Peninsula (Spain and Portugal) by evaluating whether their 1982–1999 NDVI trends show similar spatial patterns. Significant trends were calculated with the seasonal Mann-Kendall trend test and their spatial consistency with partial Mantel tests. Over 23% of the Peninsula (N, E, and central mountain ranges) showed positive and significant NDVI trends across the four datasets and an additional 18% across three datasets. In 20% of Iberia (SW quadrant), the four datasets exhibited an absence of significant trends and an additional 22% across three datasets. Significant NDVI decreases were scarce (croplands in the Guadalquivir and Segura basins, La Mancha plains, and Valencia). Spatial consistency of significant trends across at least three datasets was observed in 83% of the Peninsula, but it decreased to 47% when comparing across the four datasets. FASIR, PAL, and LTDR were the most spatially similar datasets, while GIMMS was the most different. The different performance of each AVHRR dataset to detect significant NDVI trends (e.g., LTDR detected greater significant trends (both positive and negative) and in 32% more pixels than GIMMS) has great implications to evaluate carbon budgets. The lack of spatial consistency across NDVI datasets derived from the same AVHRR sensor archive, makes it advisable to evaluate carbon gains trends using several satellite datasets and, whether possible, independent/additional data sources to contrast

The influence of catchment characteristics on river flow variability

Hydrology is yet to fully understand the role that catchment characteristics have in determining a river’s response to precipitation variability. This thesis assesses the influence that catchment characteristics have on modulating a river’s response to changes in precipitation throughout the UK. Central to this aim is the concept of the precipitation- to-flow relationship (the transformation of precipitation into river flow), which is characterised using the Variogram, a way of indexing temporal dependence (i.e. the average relationship between river flow on a given day and river flow on the previous days). Firstly, 116 catchments were grouped into four clusters, based on the shape of their variogram, which significantly differed in their catchment characteristics demonstrating that catchment characteristics control how, on average, precipitation is transformed into river flow. Furthermore, over 70% of un-gauged catchments could be clustered correctly using information about their soil type, slope and the percentage of arable land. Secondly, a new method which identifies the changes in the variogram parameters over 5-year overlapping moving windows was developed to investigate temporal changes in the variogram parameters. This method was successfully demonstrated to detect changes in multiple aspects of artificially perturbed river flow time series (e.g. seasonality, linear changes and variability). On average >70% of the variability in the catchment variogram parameters was explained by the precipitation characteristics, although there was large variability between catchments. Finally, the influence that the catchment characteristics have on the temporal changes in the variogram parameters was analysed, demonstrating that rivers in relatively impermeable upland catchments have a relationship with precipitation which is closer to linear and less variable than lowland, permeable catchments. This thesis contributes significant new knowledge that can be used for both assessing how individual catchments are likely to respond to projected changes in precipitation and in informing data transfer to un-gauged catchments

The Effect of Land Cover/Land Use Changes on the Regional Climate of the USA High Plains

We present the detection of the signatures of land use/land cover (LULC) changes on the regional climate of the US High Plains. We used the normalized difference vegetation index (NDVI) as a proxy of LULC changes and atmospheric CO2 concentrations as a proxy of greenhouse gases. An enhanced signal processing procedure was developed to detect the signatures of LULC changes by integrating autoregression and moving average (ARMA) modeling and optimal fingerprinting technique. The results, which are representative of the average spatial signatures of climate response to LULC change forcing on the regional climate of the High Plains during the 26 years of the study period (1981–2006), show a significant cooling effect on the regional temperatures during the summer season. The cooling effect was attributed to probable evaporative cooling originating from the increasing extensive irrigation in the region. The external forcing of atmospheric CO2 was included in the study to suppress the radiative warming effect of greenhouse gases, thus, enhancing the LULC change signal. The results show that the greenhouse gas radiative warming effect in the region is significant, but weak, compared to the LULC change signal. The study demonstrates the regional climatic impact of anthropogenic induced atmospheric-biosphere interaction attributed to LULC change, which is an additional and important climate forcing in addition to greenhouse gas radiative forcing in High Plains region

Land use adaptation to climate change: Economic damages from land-falling hurricanes in the Atlantic and gulf States of the USA, 1900-2005

Global climate change, especially the phenomena of global warming, is expected to increase the intensity of land-falling hurricanes. Societal adaptation is needed to reduce vulnerability from increasingly intense hurricanes. This study quantifies the adaptation effects of potentially policy driven caps on housing densities and agricultural cover in coastal (and adjacent inland) areas vulnerable to hurricane damages in the Atlantic and Gulf Coastal regions of the U.S. Time series regressions, especially Prais-Winston and Autoregressive Moving Average (ARMA) models, are estimated to forecast the economic impacts of hurricanes of varying intensity, given that various patterns of land use emerge in the Atlantic and Gulf coastal states of the U.S. The Prais-Winston and ARMA models use observed time series data from 1900 to 2005 for inflation adjusted hurricane damages and socio-economic and land-use data in the coastal or inland regions where hurricanes caused those damages. The results from this study provide evidence that increases in housing density and agricultural cover cause significant rise in the de-trended inflation-adjusted damages. Further, higher intensity and frequency of land-falling hurricanes also significantly increase the economic damages. The evidence from this study implies that a medium to long term land use adaptation in the form of capping housing density and agricultural cover in the coastal (and adjacent inland) states can significantly reduce economic damages from intense hurricanes. Future studies must compare the benefits of such land use adaptation policies against the costs of development controls implied in housing density caps and agricultural land cover reductions. © 2012 by the authors

Landscape metrics and indices : an overview of their use in landscape research

The aim of this overview paper is to analyze the use of various landscape metrics and landscape indices for the characterization of landscape structure and various processes at both landscape and ecosystem level. We analyzed the appearance of the terms landscape metrics/indexes/indices in combination with seven main categories in the field of landscape ecology [1) use/selection and misuse of metrics, 2) biodiversity and habitat analysis; 3) water quality; 4) evaluation of the landscape pattern and its change; 5) urban landscape pattern, road network; 6) aesthetics of landscape; 7) management, planning and monitoring] in the titles, abstracts and/or key words of research papers published in international peer-reviewed scientific journals indexed by the Institute of Science Information (ISI) Web of Science (WoS) from 1994 to October 2008. Most of the landscape metrics and indices are used concerning biodiversity and habitat analysis, and also the evaluation of landscape pattern and its change (up to 25 articles per year). There are only a few articles on the relationships of landscape metrics/indices/indexes to social aspects and landscape perception

The Impact of Diversified Income and Agricultural Tax Reform on the Consumption of Chinese Rural Residents

Income from different sources has become an important guarantee to sustain family life and normal expenses in China. How different sources of income affect farmers’ expenditure levels and how they have influenced farmers’ consumption since the agricultural tax reform in China since the 20th century has become a question worth exploring. Panel data from 2016 to 2020 are utilized to examine rural residents’ consumption expenditures and their sources of disposable income by region to analyze the impact of different income sources on farmers’ consumption and the correlation between income before and after agricultural tax reform, and finally conducts an analysis of future scenarios based on a time series model. The research results show that the elasticity of income from different sources on rural households’ per capita consumption expenditure is different, among which wage income is the most elastic, while household business income has a relatively small effect on consumption expenditure, but through data analysis we can find that agricultural tax reform has a significant and continuous promotion effect on rural households’ per capita consumption expenditure. The continuous cycle of agricultural tax reform is long, and the transmission chain through less tax payment, expanded reproduction, harvest, realization, and then consumption is long, thus there is a certain lag in tax reduction effect. Income from such sources in the first three years has a large impact on the current period data, while the first four and five years have a relatively small impact on current business income

Revisiting Urban Dynamics through Social Urban Data:

The study of dynamic spatial and social phenomena in cities has evolved rapidly in the recent years, yielding new insights into urban dynamics. This evolution is strongly related to the emergence of new sources of data for cities (e.g. sensors, mobile phones, online social media etc.), which have potential to capture dimensions of social and geographic systems that are difficult to detect in traditional urban data (e.g. census data). However, as the available sources increase in number, the produced datasets increase in diversity. Besides heterogeneity, emerging social urban data are also characterized by multidimensionality. The latter means that the information they contain may simultaneously address spatial, social, temporal, and topical attributes of people and places. Therefore, integration and geospatial (statistical) analysis of multidimensional data remain a challenge. The question which, then, arises is how to integrate heterogeneous and multidimensional social urban data into the analysis of human activity dynamics in cities? To address the above challenge, this thesis proposes the design of a framework of novel methods and tools for the integration, visualization, and exploratory analysis of large-scale and heterogeneous social urban data to facilitate the understanding of urban dynamics. The research focuses particularly on the spatiotemporal dynamics of human activity in cities, as inferred from different sources of social urban data. The main objective is to provide new means to enable the incorporation of heterogeneous social urban data into city analytics, and to explore the influence of emerging data sources on the understanding of cities and their dynamics.  In mitigating the various heterogeneities, a methodology for the transformation of heterogeneous data for cities into multidimensional linked urban data is, therefore, designed. The methodology follows an ontology-based data integration approach and accommodates a variety of semantic (web) and linked data technologies. A use case of data interlinkage is used as a demonstrator of the proposed methodology. The use case employs nine real-world large-scale spatiotemporal data sets from three public transportation organizations, covering the entire public transport network of the city of Athens, Greece.  To further encourage the consumption of linked urban data by planners and policy-makers, a set of webbased tools for the visual representation of ontologies and linked data is designed and developed. The tools – comprising the OSMoSys framework – provide graphical user interfaces for the visual representation, browsing, and interactive exploration of both ontologies and linked urban data.   After introducing methods and tools for data integration, visual exploration of linked urban data, and derivation of various attributes of people and places from different social urban data, it is examined how they can all be combined into a single platform. To achieve this, a novel web-based system (coined SocialGlass) for the visualization and exploratory analysis of human activity dynamics is designed. The system combines data from various geo-enabled social media (i.e. Twitter, Instagram, Sina Weibo) and LBSNs (i.e. Foursquare), sensor networks (i.e. GPS trackers, Wi-Fi cameras), and conventional socioeconomic urban records, but also has the potential to employ custom datasets from other sources. A real-world case study is used as a demonstrator of the capacities of the proposed web-based system in the study of urban dynamics. The case study explores the potential impact of a city-scale event (i.e. the Amsterdam Light festival 2015) on the activity and movement patterns of different social categories (i.e. residents, non-residents, foreign tourists), as compared to their daily and hourly routines in the periods  before and after the event. The aim of the case study is twofold. First, to assess the potential and limitations of the proposed system and, second, to investigate how different sources of social urban data could influence the understanding of urban dynamics. The contribution of this doctoral thesis is the design and development of a framework of novel methods and tools that enables the fusion of heterogeneous multidimensional data for cities. The framework could foster planners, researchers, and policy makers to capitalize on the new possibilities given by emerging social urban data. Having a deep understanding of the spatiotemporal dynamics of cities and, especially of the activity and movement behavior of people, is expected to play a crucial role in addressing the challenges of rapid urbanization. Overall, the framework proposed by this research has potential to open avenues of quantitative explorations of urban dynamics, contributing to the development of a new science of cities

Revisiting Urban Dynamics through Social Urban Data

The study of dynamic spatial and social phenomena in cities has evolved rapidly in the recent years, yielding new insights into urban dynamics. This evolution is strongly related to the emergence of new sources of data for cities (e.g. sensors, mobile phones, online social media etc.), which have potential to capture dimensions of social and geographic systems that are difficult to detect in traditional urban data (e.g. census data). However, as the available sources increase in number, the produced datasets increase in diversity. Besides heterogeneity, emerging social urban data are also characterized by multidimensionality. The latter means that the information they contain may simultaneously address spatial, social, temporal, and topical attributes of people and places. Therefore, integration and geospatial (statistical) analysis of multidimensional data remain a challenge. The question which, then, arises is how to integrate heterogeneous and multidimensional social urban data into the analysis of human activity dynamics in cities?  To address the above challenge, this thesis proposes the design of a framework of novel methods and tools for the integration, visualization, and exploratory analysis of large-scale and heterogeneous social urban data to facilitate the understanding of urban dynamics. The research focuses particularly on the spatiotemporal dynamics of human activity in cities, as inferred from different sources of social urban data. The main objective is to provide new means to enable the incorporation of heterogeneous social urban data into city analytics, and to explore the influence of emerging data sources on the understanding of cities and their dynamics.  In mitigating the various heterogeneities, a methodology for the transformation of heterogeneous data for cities into multidimensional linked urban data is, therefore, designed. The methodology follows an ontology-based data integration approach and accommodates a variety of semantic (web) and linked data technologies. A use case of data interlinkage is used as a demonstrator of the proposed methodology. The use case employs nine real-world large-scale spatiotemporal data sets from three public transportation organizations, covering the entire public transport network of the city of Athens, Greece.  To further encourage the consumption of linked urban data by planners and policy-makers, a set of webbased tools for the visual representation of ontologies and linked data is designed and developed. The tools – comprising the OSMoSys framework – provide graphical user interfaces for the visual representation, browsing, and interactive exploration of both ontologies and linked urban data.  After introducing methods and tools for data integration, visual exploration of linked urban data, and derivation of various attributes of people and places from different social urban data, it is examined how they can all be combined into a single platform. To achieve this, a novel web-based system (coined SocialGlass) for the visualization and exploratory analysis of human activity dynamics is designed. The system combines data from various geo-enabled social media (i.e. Twitter, Instagram, Sina Weibo) and LBSNs (i.e. Foursquare), sensor networks (i.e. GPS trackers, Wi-Fi cameras), and conventional socioeconomic urban records, but also has the potential to employ custom datasets from other sources.  A real-world case study is used as a demonstrator of the capacities of the proposed web-based system in the study of urban dynamics. The case study explores the potential impact of a city-scale event (i.e. the Amsterdam Light festival 2015) on the activity and movement patterns of different social categories (i.e. residents, non-residents, foreign tourists), as compared to their daily and hourly routines in the periods  before and after the event. The aim of the case study is twofold. First, to assess the potential and limitations of the proposed system and, second, to investigate how different sources of social urban data could influence the understanding of urban dynamics.  The contribution of this doctoral thesis is the design and development of a framework of novel methods and tools that enables the fusion of heterogeneous multidimensional data for cities. The framework could foster planners, researchers, and policy makers to capitalize on the new possibilities given by emerging social urban data. Having a deep understanding of the spatiotemporal dynamics of cities and, especially of the activity and movement behavior of people, is expected to play a crucial role in addressing the challenges of rapid urbanization. Overall, the framework proposed by this research has potential to open avenues of quantitative explorations of urban dynamics, contributing to the development of a new science of cities