Dimitrios Gounaridis

Quantifying spatio-temporal patterns of forest fragmentation in Hymettus Mountain, Greece

The rapid land use/cover change (LUCC) and landscape fragmentation occurring around the world is largely attributed to human induced factors. Landscape fragmentation has become a central issue in landscape ecology and conservation policies due to its direct influence on biodiversity which consequently endangers the sustainability of ecological goods and ecosystem services. Thus, fragmentation monitoring and assessment is a critical issue in land use planning and sustainable environmental management in order to avoid any irreversible negative consequences. This research explores the application of methodologies that employ multi-temporal satellite imagery, combined with geographical information systems and landscape metrics, to assess forest fragmentation. The objective is to determine spatio-temporally the LUCCs focusing on the woody vegetation in Hymettus Mountain of Greece over the last decades. The study area, which has been designated as a Natura 2000 site, is situated near the city of Athens. It faces various perturbations triggered by socio-economic factors and the absence of an ongoing contextual appraisal for conservation. To quantify the LUCCs, nine Landsat images spanning 28 years are classified. Post classification comparison is applied to generate transition maps. Additionally, eight landscape metrics are calculated. The change detection results identify hot-spots of forest fragmentation where mitigation measures should be taken, so that further irreversible alteration of the ecosystem is prevented. The landscape metrics advocate that, during the last three decades, the woody vegetation have steadily been more fragmented. The primary direct causes are economic driven intense anthropogenic activities along with frequent wildland fires whereas the indirect cause is the absence of a sustainable environmental management and conservation strategy.

Land cover of Greece, 2010: a semi-automated classification using random forests

ABSTRACT Information about land cover (LC) and land use is fundamental in various areas of research regarding the Earths surface. However, field campaigns are costly and time consuming while existing data sets have strong limitations. Classification of LC by remote sensing, although considered a technically and methodologically challenging task, can facilitate mapping initiatives at various scales. This study suggests an efficient and robust methodology of LC classification with minimal user requirements. The study site is Greece which faces a lack of up to date LC maps at national scale. In this context we employed Landsat imagery, open source software and the random forest classification algorithm to produce a high resolution national LC map for 2010. The algorithm was trained semi-automatically, extracting information from available data sets. The results are promising, achieving an overall accuracy of 83%. The methodology presented minimizes many obstacles that lead to data deficiencies and can act as a baseline for future LC mapping initiatives.

Riparian areas in urban settings: two case studies from Greece

Riparian areas are significant ecosystems due to the numerous and substantial services they provide. These ecosystem services can range from wildlife habitat, water quality improvement, flood mitigation and recreational opportunities. Human developments, including cities and towns are frequently established in the riparian areas and degrade their functionality. Maintaining healthy riparian areas in urban settings that sustain their connectivity with the natural riparian areas should be of a great priority. Fragmented riparian areas lose many of the aforementioned ecosystem services. At the same time healthy riparian areas are aesthetically pleasing, creating a feeling of escape for the inhabitants, while also increasing the real estate value of the adjacent structures. In this study examples of healthy and degraded urban riparian areas from two Greek cities are presented. Overall, the proper restoration and sustainable management of these areas can improve the quality of life in urban settings and maintain the services they provide.

ASSESSING SOIL EROSION RISK FOR RHODES ISLAND, GREECE WITH A GIS-BASED MULTI-CRITERIA DECISION ANALYSIS

Reducing erosion in the Mediterranean region is a major priority because of its susceptibility and millennia long human inhabitation. The objective of this paper was to assess soil erosion risk on the semi-arid island of Rhodes, Greece. Rhodes has many protected areas that are part of the Natura 2000 network. To implement this assessment a Geographic Information System based Multi-criteria Decision Analysis was conducted. The Multi-criteria Decision Analysis was a combination of the Analytic Hierarchy Process and the Weighted Linear Combination. The criteria used, in the order of importance, were: I) Land-use, II) Slope, III) Normalized Difference Vegetation Index (NDVI), IV) Geology, V) Distance from Streams, VI) Fire Density, VII) Precipitation, VIII) Soil Quality, IX) Flow Length, X) Distance to Roads XI) Aspect and XII) Wetness. The final erosion risk map for the entire island had ranking values only of 3-6 although the scale ranged from 1-9. This indicates that there are no areas with extreme erosion risk. While less than 1% of the area had an erosion risk of 6, a significant percentage (15.4%) ranked as 5. Land managers should firstly implement best management practices in these areas (ranked as a 5 and 6). When comparing the Natura 2000 areas to the non Natura 2000 areas, the Natura 2000 had a larger percentage (~ 6%) of areas that ranked as 5, while the non Natura 2000 areas had a larger percentage (~ 6%) of areas that ranked as 4. Since the Natura 2000 areas are protected by the European Union, it was expected that they would be less vulnerable to erosion. The percentages of this study indicate that additional measures need to be implemented in these Natura 2000 areas to reduce soil erosion.

Incorporating Density in Spatiotemporal Land Use/Cover Change Patterns: The Case of Attica, Greece

This paper looks at the periodic land use/cover (LUC) changes that occurred in Attica, Greece from 1991 to 2016. During this period, land transformations were mostly related to the artificial LUC categories; therefore, the aim was to map LUC with a high thematic resolution aimed at these specific categories, according to their density and continuity. The classification was implemented using the Random Forests (RF) machine learning algorithm and the presented methodological framework involved a high degree of automation. The results revealed that the majority of the expansion of the built-up areas took place at the expense of agricultural land. Moreover, mapping and quantifying the LUC changes revealed three uneven phases of development, which reflect the socioeconomic circumstances of each period. The discontinuous low-density urban fabric started to increase rapidly around 2003, reaching 7% (from 2.5% in 1991), and this trend continued, reaching 12% in 2016. The continuous as well as the discontinuous dense urban fabric, almost doubled throughout the study period. Agricultural areas were dramatically reduced to almost half of what they were in 1991, while forests, scrubs, and other natural areas remained relatively stable, decreasing only by 3% in 25 years.

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.

Satellite data based approach for the estimation of anthropogenic heat flux over urban areas

Anthropogenic effects in urban areas influence the thermal conditions in the environment and cause an increase of the atmospheric temperature. The cities are sources of heat and pollution, affecting the thermal structure of the atmosphere above them which results to the urban heat island effect. In order to analyze the urban heat island mechanism, it is important to estimate the anthropogenic heat flux which has a considerable impact on the urban energy budget. The anthropogenic heat flux is the result of man-made activities (i.e. traffic, industrial processes, heating/cooling) and thermal releases from the human body. Many studies have underlined the importance of the Anthropogenic Heat Flux to the calculation of the urban energy budget and subsequently, the estimation of mesoscale meteorological fields over urban areas. Therefore, spatially disaggregated anthropogenic heat flux data, at local and city scales, are of major importance for mesoscale meteorological models. The main objectives of the present work are to improve the quality of such data used as input for mesoscale meteorological models simulations and to enhance the application potential of GIS and remote sensing in the fields of climatology and meteorology. For this reason, the Urban Energy Budget concept is proposed as the foundation for an accurate determination of the anthropogenic heat discharge as a residual term in the surface energy balance. The methodology is applied to the cities of Athens and Paris using the Landsat ETM+ remote sensing data. The results will help to improve our knowledge on Anthropogenic Heat Flux, while the potential for further improvement of the methodology is also discussed.

Assimilating Anthropogenic Heat Flux Estimated from Satellite Data in a Mesoscale Flow Model

The need for comprehensive prognostic meteorological models is paramount in various applications related to environmental assessment. The inclusion of urban land cover in the computational domain in mesoscale models introduces new challenges for accurately incorporating the complex interactions related to the dynamical and thermal effects of the urban canopy. Aiming to address these requirements, a new urban surface scheme was introduced in a mesoscale meteorological model incorporating parameterisations of the area-averaged effects of drag and turbulence production as well as an improved representation of the surface heat and moisture fluxes. In addition, an advanced data assimilation module was implemented for enabling the self-consistent estimation of anthropogenic heat fluxes on the basis of representative satellite data, as well as the introduction of resulting forcing in the surface energy budget. The enhanced version of the model was evaluated in two mesoscale applications covering the greater urban areas of Paris, France and Athens, Greece. The model was evaluated over the course of three periods of strong anticyclonic conditions, enabling a better assessment of the influence of urban effects. The results confirmed that the urban surface module enhancements led to a significant improvement of model performance. Finally, the assimilation of anthropogenic heat data in the model provided an improved capability of reproducing the observed spatial and temporal variation of surface temperature.