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The need for information on soil moisture at large scale to facilitate a sustainable intensification of agricultural land and to ensure food security due to increasing populations cannot be oversta...

Soil Moisture Deficit (SMD) is a key indicator of soil water content changes and is valuable to a variety of applications, such as weather and climate, natural disasters, agricultural water management, etc. Soil Moisture and Ocean Salinity (SMOS) is a dedicated mission focused on soil moisture retrieval and can be utilized for SMD estimation. In this study, the use of soil moisture derived from SMOS has been provided for the estimation of SMD at a catchment scale. Several approaches for the estimation of SMD are implemented herein, using algorithms such as Random Forests (RF) and Genetic Algorithms coupled with Least Trimmed Squares (GALTS) regression. The results show that for SMD estimation, the RF algorithm performed best as compared to the GALTS, with Root Mean Square Errors (RMSEs) of 0.021 and 0.024, respectively. All in all, our study findings can provide important assistance towards developing the accuracy and applicability of remote sensing-based products for operational use.

The National Map of Saline – Alkaline Soils of Greece was recently developed within the initiative of the European Soil Partnership (ESP) of FAO. The technique combines between other MODIS satellite imagery, spatial interpolation methods and ground surveying to derive at 1 km spatial resolution maps of soil’s salinity (SS) and soil organic carbon (SOC).The present study investigates for the first time the development of higher resolution maps of these soil properties adopting the aforementioned methodology. Furthermore, this study attempted to estimate the Carbon sequestration (SOC) using Remote Sensing and geostatistic methods of spatial analysis, a concern that is eminent today due to its effect on climate change mitigation.As a case study the island of Mytilene in Greece is used, for which detailed information on soil properties as well as climatic, geomorphological, geological and soil data was available from previous studies. An MCDA (Multiple Criteria Decision Analysis) method was applied in a GIS environment using Landsat satellite imagery for the composition of a Saline - Alkaline map. Between the key soil parameters estimated spatially included the Electrical Conductivity (EC), Exchangeable Sodium Percentage (ESP) and pH. Geospatial data analysis methods were implemented to visualize all the derived parameters related for the study area and to analyze the final products in the spatial domain.Finding suggests that climate change and soil directly affect one another. The impact of environmental and climate change in addition to unsustainable agricultural practices seems to be linked to salinity increase, soil erosion and loss of organic matter. In addition, when land degradation as well as erosion and loss of vegetation occur, SOC emissions increase. Under these conditions, soil cannot absorb enough amounts of CO2, especially when soil salinization and sodicity exists; inputs are further limited due to declines in vegetation health. The role of geoinformation technologies in support of sustainable agricultural production under the pressure of both climate change and anthropogenic activities is also discussed within the present study framework. KEYWORDS: geoinformation, soil, pH, salinity, soil organic carbon, geostatistics, earth observation, GIS, Greece

Urbanisation is a global phenomenon with an important impact on the quality of human life. Europe has been widely affected by urbanisation. One of the main characteristics of urban growth is sprawl, a negative form of urban expansion, which affects large cities and most types of urban landscapes. Spatial indicators are applied to CORINE Urban Morphological Zones (UMZ) changes in order to measure urban sprawl between 1990–2000 and 2000–2006 in 24 European countries. The indicators calculate urban morphological properties such as shape, aggregation, compactness and dispersion. The results revealed that the urban areas (UMZ) increased by 146% during 1990–2006 and the urbanisation becomes more circle-shaped and less complex where mostly sprawl occurs. Moreover, urban form becomes less clumped or aggregated. Therefore, due to accelerating rates of urban sprawl, European urban planning should intensify appropriate initiatives to avoid negative impacts on human life.

Sustainable development is a key component in urban studies. Earth Observation (EO) can play a valuable role in sustainable urban development and planning, since it represents a powerful data source with the potential to provide a number of relevant urban sustainability indicators. To this end, in this paper we propose a conceptual list of EO-based indicators capable of supporting urban planning and management. Three cities with different typologies, namely Basel, Switzerland; Tel Aviv, Israel; and Tyumen, Russia were selected as case studies. The EO-based indicators are defined to effectively record the physical properties of the urban environment in a diverse range of environmental sectors such as energy efficiency, air pollution and public health, water, transportation and vulnerability to hazards. The results assess the potential of EO to support the development of a set of urban environmental indicators towards sustainable urban planning and management.

Analysis of urban sprawl is an issue that has been continuously attracting attention in the planning and research community. Τhis paper presents the results of an analysis of the growth of the city of Rethymno during the 1997–2010 time period. Rethymno is a city in the island of Crete in Greece with population of about 35,000 people, in which developed land has expanded at a rate that is double the growth of the population during the study period. A qualitative analysis was first performed to identify growth patterns in the different parts of the city, how these are related to planning regulations and the extent of cohesiveness of the development. A logistic regression model was estimated using various variables influencing the expansion of the built up area. Variables such as slope, distance from main roads, distance from the University, distance from coastline, as well as variables describing the proximity to other developed areas were used as independent variables in the logistic regressions. Planning constraints with respect zoning were also considered. The accuracy/goodness of fit of the simulation results were also tested using Receiver Operating Characteristic (ROC) curve. The results revealed high (performance) accuracy, which can support the applicability of the proposed method in urban sprawl modeling. Once the equations were estimated they were applied using data from 2010 to identify future trends of urbanization. The methodology adopted in this study can result in a tool that can be of use to urban planning authorities in identifying areas of future urban growth and therefore, adopt zoning policies encouraging or discouraging growth in these areas according to the sustainability objectives of the local community.

Teaching remote sensing in higher education has been traditionally restricted in lecture and computer-aided laboratory activities. This paper presents and evaluates an engaging inquiry-based educational experiment. The experiment was incorporated in an introductory remote sensing undergraduate course to bridge the gap between theory and application of relevant technology. During this semester-long experiment, titled ‘ESF goes to space’, students designed, built, launched and successfully acquired imagery from the upper stratospheric parts (approximately 27 000 m). Replication guidelines are presented and a post-evaluation discusses benefits and limitations for students, instructors and university.

Land use changes are complex ecological processes driven by the interaction of biophysical and human related factors. The prediction of forest land use changes is important for sustainable forest management and biodiversity conservation. This study investigates the modelling process of the spatial dynamics of a forest ecosystem in north eastern Greece. For the prediction of forest expansion, based on land use data of the study area, a deterministic approach using logistic regression and heuristic methods of multi-criteria evaluation is adopted. The set of factors driving forest expansion are: the slope, the distance to roads, the distance to urban areas, the distance to forest, the soil depth, the soil erosion and the influence from the land uses of the neighbourhood. The spatial autocorrelation of driving factors is addressed using an autologistic regression model. The multicriteria evaluation approach is developed using weighted linear combination (WLC) and ordered weighted averaging (OWA) methods. In WLC method the relative importance of each factor was estimated using the analytical hierarchy process. In the OWA method, decision strategies are generated using a selection of relative linguistic quantifiers, which allow different Risk in decisions. The accuracy of the models produced was tested with real data for the year 2001 using the ROC validation method. All the methods produced satisfactory results. Autologistic regression showed slightly better performance than multicriteria evaluation methods due to higher degree of objectivity in defining the importance of driving factors for forest expansion.

Human population continues to aggregate in urban centers. This inevitably increases the urban footprint with significant consequences for biodiversity, climate, and environmental resources. Urban growth prediction models have been extensively studied with the overarching goal to assist in sustainable management of urban centers. Despite the extensive body of research, these models are not frequently included in the decision making process. This review aims on bringing this gap by analyzing results from a survey investigating developer and user perceptions from the modeling and planning communities, respectively. An overview of existing models, including advantages and limitations, is also provided. A total of 156 manuscripts is identified. Analysis of aggregated statistics indicates that cellular automata are the prevailing modeling technique, present in the majority of published works. There is also a strong preference for local or regional studies, a choice possibly related to data availability. The survey found a strong recognition of the models’ potential in decision making, but also limited agreement that these models actually reach that potential in practice. Collaboration between planning and modeling communities is deemed essential for transitioning models into practice. Data availability is considered a stronger restraining factor by respondents with limited algorithmic experience, which may indicate that model input data are becoming more specialized, thus significantly limiting wide-spread applicability. This review assesses developer and user perceptions and critically discusses existing urban growth prediction models, acting as a reference for future model development. Specific guidelines are provided to facilitate transition of this relatively mature science into decision making activities

Urbanization changes have been widely examined and numerous urban growth models have been proposed. We introduce an alternative urban growth model specifically designed to incorporate spatial heterogeneity in urban growth models. Instead of applying a single method to the entire study area, we segment the study area into different regions and apply targeted algorithms in each subregion. The working hypothesis is that the integration of appropriately selected region-specific models will outperform a globally applied model as it will incorporate further spatial heterogeneity. We examine urban land use changes in Denver, Colorado. Two land use maps from different time snapshots (1977 and 1997) are used to detect the urban land use changes, and 23 explanatory factors are produced to model urbanization. The proposed Spatially Heterogeneous Expert Based (SHEB) model tested decision trees as the underlying modeling algorithm, applying them in different subregions. In this paper the segmentation tested is the division of the entire area into interior and exterior urban areas. Interior urban areas are those situated within dense urbanized structures, while exterior urban areas are outside of these structures. Obtained results on this model regionalization technique indicate that targeted local models produce improved results in terms of Kappa, accuracy percentage and multi-scale performance. The model superiority is also confirmed by model pairwise comparisons using t-tests. The segmentation criterion of interior/exterior selection may not only capture specific characteristics on spatial and morphological properties, but also socioeconomic factors which may implicitly be present in these spatial representations. The usage of interior and exterior subregions in the present study acts as a proof of concept. Other spatial heterogeneity indicators, for example landscape, socioeconomic and political boundaries could act as the basis for improved local segmentations.

Extensive urbanization and growth of population density have acquired a paramount interest towards a sustainable urban development. Earth Observation (EO) is an important source of information required for urban planning and management. The availability of EO data provides the immense opportunity for urban environmental indicators development easily derived by remote sensors. In this study, the state of the art methods were employed to develop urban planning and management relevant indicators that can be evaluated by using EO data. The importance of this approach lies on providing alternatives for improving urban planning and management, without consuming time and resources in collecting field or archived data. The evaluated urban indicators were integrated into a Web‐based Information System that was developed for online exploitation. The results for three case studies are therefore available online and can be used by urban planners and stakeholders in supporting their planning decisions.

The Dadia forest complex, in the Evros prefecture, in north eastern Greece was designated as a nature reserve in 1980 in order to protect the black vulture (Aegypius monachus) and other raptors. In this paper, the impacts of the protection on the forest growth were assessed using geographic information system (GIS) technologies. The major requirement for almost all research needed for sustainable forest management is extensive and intensive monitoring. GIS is a convenient tool for integrating remotely sensed data and various other kinds geo-referenced data. Detailed spatial and temporal change patterns of the land uses in the area were quantified by interpreting aerial photographs of the years 1945 and 1973 and a satellite image of 2001. The results showed that the rate of forest growth is significantly larger during the second period (1973–2001) than during the earlier one (1945–1973). This is mostly due to the introduction, in 1980, of a protection regime, including two high-protection core areas, and a buffer zone. From 1945 to 2001 the openings which are important as hunting biotopes for raptors were significantly decreased. Apart from the protection of the area that was played an important role in the forest changes the effects of various landscape parameters (elevation, slope, distance from the roads and urban areas, aspect, soil depth, geology, erosion and forest density) on these changes were examined.

In order to implement robust climate change adaption and mitigation strategies in cities fine spatial scale information on building stock is required. However, for many cities such information is rarely available. In response, we present a methodology that allows topographic building footprints to be classified to the level of residential spatial topological-building types and corresponding period of construction. The approach developed employs spatial structure and topology to first recognise residential spatial topological types of Detached , Semi-Detached or Terrace . Thereafter, morphological and spatial metrics are employed with multinomial logistic regression to assign buildings to particular periods of construction for use within city-scale impact assessment studies. Overall the system developed performs well for the classification of residential building exemplars for the city of Manchester UK, with an overall accuracy of 83.4%, although with less satisfactory results for the Detached period of construction (76.6%) but excellent accuracies for the Semi-Detached residential buildings (93.0%).