Dionissios Kalivas

Mapas de susceptibilidad de deslizamientos basados en GIS aplicados a la planificación natural y urbanística en Trikala, Grecia Central

Landslide susceptibility mapping is a practical tool in natural and urban planning; it can be applied for determining land use zones, in construction design and planning of a variety of projects. In this study, two different GIS based landslide susceptibility maps were generated in the mountainous part of the Trikala Prefecture in Thessaly, Central Greece. This was accomplished by using different methods for correlating factors, which have an effect on landslide occurrences. The instability factors taken into account were: lithology, tectonic features, slope gradients, road network, drainage network, land use and rainfall. A frequency distribution of the half number of the landslide events of the study area in each class of the instability factors was performed in order to rate the classes. Two models have been used to combine the instability factors and assess the overall landslide susceptibility, namely: the Weight Factor Model (WeF), which is a statistical method, and the Multiple Factor Model (MuF) that is a logical method. The produced maps were classified into four zones: Low, Moderate, High and Very High susceptible zones and validated using the other half number of the landslide events of the area. Evaluation of the results is optimized through a Landslide Models Indicator (La.M.I.).

Remote sensing and GIS analysis for mapping spatio-temporal changes of erosion and deposition of two Mediterranean river deltas: The case of the Axios and Aliakmonas rivers, Greece

Abstract Wetlands are among Earths most dynamic, diverse and varied habitats as the balance between land and water surfaces provide shelter to a unique mixture of plant and animal species. This study explores the changes in two Mediterranean wetland delta environments formed by the Axios and Aliakmonas rivers located in Greece, over a 25-year period (1984–2009). Direct photo-interpretation of four Landsat TM images acquired during the study period was performed. Furthermore, a sophisticated, semi-automatic image classification method based on support vector machines (SVMs) was developed to streamline the mapping process. Deposition and erosion magnitudes at different temporal scales during the study period were quantified using both approaches based on coastline surface area changes. Analysis using both methods was conducted in a geographical information systems (GIS) environment. Direct photo-interpretation, which formed our reference dataset, showed noticeable changes in the coastline deltas of both study areas, with erosion occurring mostly in the earlier periods (1990–2003) in both river deltas followed by deposition in more recent years (2003–2009), but at different magnitudes. Spatial patterns of coastline changes predicted from the SVMs showed similar trends. In absolute terms SVMs predictions of sediment erosion and deposition in the studied area were different in the order of 5–20% in comparison to photo-interpretation, evidencing the potential capability of this method in coastline changes monitoring. One of the main contributions of our work lies to the use of the SVMs classifier in coastal mapping of changes, since to our knowledge use of this technique has been under-explored in this application domain. Furthermore, this study provides important contribution to the understanding of Mediterranean river delta dynamics and their behaviours, and corroborates the usefulness of EO technology and GIS as an effective tool in policy decision making and successful landscape management. The latter is of considerable scientific and practical value to the wider community of interested users, given the continued open access to observations from this satellite radiometer globally.

Forest re-growth since 1945 in the Dadia forest nature reserve in northern Greece

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.

Conservation of Biodiversity in Managed Forests: Developing an Adaptive Decision Support System

Forest ecosystems provide several goods and services, but strategies for the conservation of biodiversity are missing in traditional forest management schemes. In this paper we develope a decision support system to optimize the conservation of biodiversity in managed forests, taking Dadia National Park as a case study area, a local Mediterranean hotspot of biodiversity in northeastern Greece. Using environmental niche factor analysis, we produced a series of spatially explicit habitat suitability models for vascular plants, amphibians, small birds and raptors and an overall model for total biodiversity. Further, we produced maps related to timber production and investigated potential conflicts between conservation of biodiversity and wood production. A decision support system based on a conflict assessment was created using three management scenarios. It enables the establishment of integrated management strategies and the assessment of their effects on biodiversity and timber production. Habitat suitability models for selected groups of organisms were found very effective to investigate the impact of the management on forests and wildlife. Further evaluation of key indicator taxa on these models could improve decision support systems and the sustainable management of forests.

Urban vegetation cover extraction from hyperspectral imagery and geographic information system spatial analysis techniques: case of Athens, Greece

Abstract The present study aimed at evaluating the performance of two different pixel-based classifiers [spectral angle mapper (SAM) and support vector machines (SVMs)] in discriminating different land-cover classes in a typical urban setting, focusing particularly on urban vegetation cover by utilizing hyperspectral (EO-1 Hyperion) data. As a case study, the city of Athens, Greece, was used. Validation of urban vegetation predictions was based on the error matrix statistics. Additionally, the final urban vegetation cover maps were compared at a municipality level against reference urban vegetation cover estimates derived from the digitization of very high-resolution imagery. To ensure consistency and comparability of the results, the same training and validation points dataset were used to compare the different classifiers. The results showed that SVMs outperformed SAM in terms of both classification and urban vegetation cover mapping with an overall accuracy of 86.53% and Kappa coefficient 0.823, whereas for SAM classification, the accuracy statistics obtained were 75.13% and 0.673, respectively. Our results confirmed the ability of both techniques, when combined with Hyperion imagery, to extract urban vegetation cover for the case of a densely populated city with complex urban features, such as Athens. Our findings offer significant information at the local scale as regards to the presence of open green spaces in the urban environment of Athens. Such information is vital for successful infrastructure development, urban landscape planning, and improvement of urban environment. More widely, this study also contributes significantly toward an objective assessment of Hyperion in detecting and mapping urban vegetation cover.

Autologistic regression and multicriteria evaluation models for the prediction of forest expansion

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.

Regional Mapping of Perennial Weeds in Cotton with the Use of Geostatistics

Abstract Perennial weeds constitute a serious problem in Greek cotton-growing areas, as they strongly competing against the crop and downgrade the final product. Monitoring weeds at a regional scale and relating their occurrence with abiotic factors will assist in the control of these species. Purple nutsedge, field bindweed, bermudagrass, and johnsongrass were studied in cotton crops for three consecutive growing seasons (2007 through 2009) in a large area of central Greece. Weed densities and uniformities per sampling site were assessed in relation to soil and climatic data. Abundance index (AI), which is highly dependent on abiotic factors, was also estimated, and revealed purple nutsedge to the most persistent and damaging species among the recorded weeds. Field bindweed showed the highest correlation with soil properties and especially with clay content. Furthermore, correlation analysis was used over the sampling years in order to assess the stability of weed occurrence in the sampling sites. Purple nutsedge, field bindweed, and bermudagrass proved to be stable in location and intensity. The weed density spatial distribution was evaluated by using local indicators of spatial autocorrelation (LISA) statistics, and was mapped by ordinary kriging and co-kriging interpolation methods. Only 1 to 3 spatial outliers were identified in each 1 of the 3 yr. Between the two interpolation methods co-kriging delivered better results for field bindweed and purple nutsedge, indicating that soil data could improve the estimation of weed occurrence. These co-kriging interpolated weed maps would be a very useful tool for decision makers in taking appropriate weed control measures. Nomenclature: Purple nutsedge, Cyperus rotundus L. CYPRO; field bindweed, Convolvulus arvensis L. CONAR; bermudagrass, Cynodon dactylon (L.) Pers. CYNDA; johnsongrass, Sorghum halepense (L.) Pers. SORHA; cotton, Gossypium hirsutum L. GOSHI.

Evaluation of three spatial interpolation methods to estimate forest volume in the municipal forest of the Greek island Skyros

Forest volume is of great interest to forest managers. Plot observations of forest volume are available from planning reports. For managers, however, what is relevant are forest volume surfaces. In this study, three interpolation methods were employed to construct continuous surfaces for the evaluation of forest volume at positions for which no measurements are available. For the Municipal Forest of Skyros Island, we compared spatial predictions derived from Inverse Distance Weighting (IDW), Block Kriging (BK), and Block Co-Kriging (BCK) interpolation methods, as applied to data from 120 sample plots. The existence of spatial autocorrelation in the data was identified using correlograms of Moran’s I index. Spatial outliers were identified and excluded from the analysis using the local Moran’s I index. Only slope, of the examined environmental factors, showed an acceptable correlation with forest volume and was used as an auxiliary variable for BCK. The performance of the three methods was evaluated, using an independent validation set and comparing these indices: mean error (ME) and root mean square error. Additionally, for BK and BCK, the indices, standardized ME, average standard error, and the standardized root-mean-square error were used. For the three interpolation methods, the BK method gave the more accurate results; BCK is the next more accurate method and IDW the third.

An Appraisal of the Potential of Landsat 8 in Estimating Chlorophyll-a, Ammonium Concentrations and Other Water Quality Indicators

In-situ monitoring of lake water quality in synergy with satellite remote sensing represents the latest scientific trend in many water quality monitoring programs worldwide. This study investigated the suitability of the Operational Land Imager (OLI) instrument onboard the Landsat 8 satellite platform in accurately estimating key water quality parameters such as chlorophyll-a and nutrient concentrations. As a case study the largest freshwater body of Greece (Trichonis Lake) was used. Two Landsat 8 images covering the study site were acquired on 30 October 2013 and 30 August 2014 respectively. Near concurrent in-situ observations from two water sampling campaigns were also acquired from 22 stations across the lake under study. In-situ measurements (nutrients and chlorophyll-a concentrations) were statistically correlated with various spectral band combinations derived from the Landsat imagery of year 2014. Subsequently, the most statistically promising predictive models were applied to the satellite image of 2013 and validation was conducted using in-situ data of 2013 as reference. Results showed a relatively variable statistical relationship between the in-situ and reflectances (R logchl-a: 0.58, R NH4: 0.26, R chl-a: 0.44). Correlation coefficient (R) values reported of up to 0.7 for ammonium concentrations and also up to 0.5 and up to 0.4 for chl-a concentration and chl-a concentrations respectively. These results represent a higher accuracy of Landsat 8 in comparison to its predecessors in the Landsat satellites series, as evidenced in the literature. Our findings suggest that Landsat 8 has a promising capability in estimating water quality components in an oligotrophic freshwater body characterized by a complete absence of any quantitative, temporal and spatial variance, as is the case of Trichonis lake. Yet, even with the presence of a lot of ground information as was the case in our study, a quantitatively accurate estimation of water quality constituents in coastal/inland waters remains a great challenge. The launch of sophisticated spaceborne sensing systems, such as that of Landsat 8, can assist in improving our ability to estimate freshwater lake properties from space.

Spatio-temporal monitoring of cotton cultivation using ground-based and airborne multispectral sensors in GIS environment

Multispectral sensor capability of capturing reflectance data at several spectral channels, together with the inherent reflectance responses of various soils and especially plant surfaces, has gained major interest in crop production. In present study, two multispectral sensing systems, a ground-based and an aerial-based, were applied for the multispatial and temporal monitoring of two cotton fields in central Greece. The ground-based system was Crop Circle ACS-430, while the aerial consisted of a consumer-level quadcopter (Phantom 2) and a modified Hero3+ Black digital camera. The purpose of the research was to monitor crop growth with the two systems and investigate possible interrelations between the derived well-known normalized difference vegetation index (NDVI). Five data collection campaigns were conducted during the cultivation period and concerned scanning soil and plants with the ground-based sensor and taking aerial photographs of the fields with the unmanned aerial system. According to the results, both systems successfully monitored cotton growth stages in terms of space and time. The mean values of NDVI changes through time as retrieved by the ground-based system were satisfactorily modelled by a second-order polynomial equation (R2 0.96 in Field 1 and 0.99 in Field 2). Further, they were highly correlated (r 0.90 in Field 1 and 0.74 in Field 2) with the according values calculated via the aerial-based system. The unmanned aerial system (UAS) can potentially substitute crop scouting as it concerns a time-effective, non-destructive and reliable way of soil and plant monitoring.