A Random Forest-Cellular Automata modelling approach to explore future land use/cover change in Attica (Greece), under different socio-economic realities and scales

This paper explores potential future land use/cover (LUC) dynamics in the Attica region, Greece, under three distinct economic performance scenarios. During the last decades, Attica underwent a significant and predominantly unregulated process of urban growth, due to a substantial increase in housing demand coupled with limited land use planning controls. However, the recent financial crisis affected urban growth trends considerably. This paper uses the observed LUC trends between 1991 and 2016 to sketch three divergent future scenarios of economic development. The observed LUC trends are then analysed using 27 dynamic, biophysical, socio-economic, terrain and proximity-based factors, to generate transition potential maps, implementing a Random Forests (RF) regression modelling approach. Scenarios are projected to 2040 by implementing a spatially explicit Cellular Automata (CA) model. The resulting maps are subjected to a multiple resolution sensitivity analysis to assess the effect of spatial resolution of the input data to the model outputs. Findings show that, under the current setting of an underdeveloped land use planning apparatus, a long-term scenario of high economic growth will increase built-up surfaces in the region by almost 24%, accompanied by a notable decrease in natural areas and cropland. Interestingly, in the case that the currently negative economic growth rates persist, artificial surfaces in the region are still expected to increase by approximately 7.5% by 2040

Air quality and urban sustainable development: the application of machine learning tools

[EN] Air quality has an efect on a population¿s quality of life. As a dimension of sustainable urban development, governments have been concerned about this indicator. This is refected in the references consulted that have demonstrated progress in forecasting pollution events to issue early warnings using conventional tools which, as a result of the new era of big data, are becoming obsolete. There are a limited number of studies with applications of machine learning tools to characterize and forecast behavior of the environmental, social and economic dimensions of sustainable development as they pertain to air quality. This article presents an analysis of studies that developed machine learning models to forecast sustainable development and air quality. Additionally, this paper sets out to present research that studied the relationship between air quality and urban sustainable development to identify the reliability and possible applications in diferent urban contexts of these machine learning tools. To that end, a systematic review was carried out, revealing that machine learning tools have been primarily used for clustering and classifying variables and indicators according to the problem analyzed, while tools such as artifcial neural networks and support vector machines are the most widely used to predict diferent types of events. The nonlinear nature and synergy of the dimensions of sustainable development are of great interest for the application of machine learning tools.Molina-Gómez, NI.; Díaz-Arévalo, JL.; López Jiménez, PA. (2021). Air quality and urban sustainable development: the application of machine learning tools. 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Factors shaping alien plant species richness spatial patterns across Natura 2000 Special Areas of Conservation of Greece

This paper aims to determine the main factors that shape the spatial patterns of alien plant species occurrence across Natura 2000 Special Areas of Conservation (SACs) in Greece, and quantify their influence. A series of spatial analysis techniques for the development of a spatial database of the factors involved, followed by a boosted negative binomial Generalised Additive Model for location scale and shape, were implemented. Native plant species richness, topography and hydrography, human population density, and a spatial preference to the northern-western sites are the key factors that explain the variation in the occurrence of alien plant species. Native plant species richness and human population density have a positive effect on alien plant species presence, while topography aspects, such as elevation and slope, and the distance from the hydrographical network a negative one. All factors are indirectly linked to propagule pressure emphasizing the importance of human activities for the efforts on managing protected areas. © 2017 Elsevier B.V

Multiflow Transmitter With Full Format and Rate Flexibility for Next Generation Networks

We extend our proof-of-concept demonstration of a novel multiflow transmitter for next generation optical metro networks. The multiflow concept is based on the combination of spectrum and polarization sliceability, and its implementation on the combination of a polymer photonic integration platform with high-speed IQ modulators. In this paper, we replace the static scheme of our previous demonstration for the definition of the optical flows and the generation of the driving signals, and we unveil the true potential of the transmitter in terms of programmability and network flexibility. Using a software-defined optics (SDO) platform for the configuration of the digital and optical parts of the transmitter, and the configuration of the optical switch inside the node, we demonstrate operation with flexible selection of the number and type of the optical flows, and flexible selection of the modulation format, symbol rate, emission wavelength, and destination of each flow. We focus on 16 specific cases accommodating one or two optical flows with modulation format up to 64-quadrature amplitude modulation, and symbol rate up to 25 Gbaud. Through transmission experiments over 100 km of standard single-mode fiber, we validate the possibility of the transmitter to interchange its configuration within this range of operation cases with bit-error rate performance below the forward error correction limit. Future plans for transmitter miniaturization and extension of our SDO platform in order to interface with the software-defined networking hierarchy of true networks are also outlined

Scattered or polycentric? Untangling urban growth in three southern European metropolitan regions through exploratory spatial data analysis

The present study illustrates an exploratory spatial data analysis (ESDA) aimed at investigating changes in the distribution of built-up areas in three southern European metropolitan regions (Barcelona, Rome and Athens). An approach based on global Moran's indexes of spatial autocorrelations was proposed to assess similarities in the spatial organization of the three regions, based on land-use data for 1960 and 2010. Compact monocentric, scattered low-density and mixed polycentric structures were compared in the three regions using local Moran's indexes computed at two different scales, "urban" (5 km radius) and "regional" (20 km radius). The proposed approach identifies emerging trends in scattered monocentric or polycentric development. Our results outline the trend toward scattered urban expansion for the three cities, with signs of a modest shift toward polycentrism in Barcelona. ESDA provides basic information needed for policies promoting spatially balanced, sustainable development in originally compact and economically segmented regions

Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing

Quantifying and monitoring the spatial and temporal dynamics of the global land cover is critical for better understanding many of the Earth's land surface processes. However, the lack of regularly updated, continental-scale, and high spatial resolution (30 m) land cover data limit our ability to better understand the spatial extent and the temporal dynamics of land surface changes. Despite the free availability of high spatial resolution Landsat satellite data, continental-scale land cover mapping using high resolution Landsat satellite data was not feasible until now due to the need for high-performance computing to store, process, and analyze this large volume of high resolution satellite data. In this study, we present an approach to quantify continental land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources