Geoinformation Technologies in Support of Environmental Hazards Monitoring under Climate Change: An Extensive Review

A review of the effects of environmental hazards on humans, their remediation for sustainable development, and risk assessment

In the race for economic development and prosperity, our earth is becoming more polluted with each passing day. Technological advances in agriculture and rapid industrialization have drastically polluted the two pillars of natural resources, land and water. Toxic chemicals and microbial contaminants/agents created by natural and anthropogenic activities are rapidly becoming environmental hazards (EH) with increased potential to affect the natural environment and human health. This review has attempted to describe the various agents (chemical, biological, and physical) responsible for environmental contamination, remediation methods, and risk assessment techniques (RA). The main focus is on finding ways to mitigate the harmful effects of EHs through the simultaneous application of remediation methods and RA for sustainable development. It is recommended to apply the combination of different remediation methods using RA techniques to promote recycling and reuse of different resources for sustainable development. The report advocates for the development of site-specific, farmer-driven, sequential, and plant-based remediation strategies along with policy support for effective decontamination. This review also focuses on the fact that the lack of knowledge about environmental health is directly related to public health risks and, therefore, focuses on promoting awareness of effective ways to reduce anthropological burden and pollution and on providing valuable data that can be used in environmental monitoring assessments and lead to sustainable development.

Exploring the spatial patterns of soil salinity and organic carbon in agricultural areas of Lesvos Island, Greece, using geoinformation technologies

The salt-affected soils national map of Greece was recently made available within the initiative of the Global Soil Partnership (GSP) of Food and Agriculture Organization of the United Nations FAO. The present study explores the development of higher resolution soil property maps included in this national scale product adopting a modified version of the FAO methodology and a logistic regression (LR) method based on ground and satellite data. Furthermore, it also investigates the correlation between saline soils and soil organic carbon (SOC) using geospatial analysis methods. The island of Lesvos in Greece has been selected as a case study. A probabilistic model for saline soils in the agricultural land of Lesvos is produced by exploiting geoinformation technologies. As a result, the spatial distribution of saline soils in the croplands of Lesvos was obtained. Indicatively, areas with p > 0.80 for the occurrence of saline soils accounting for ∼20% of a total area of 169.51 km² of the croplands in Lesvos. The Nagelkerke R² coefficient showed that the probabilistic model interprets 11.3% of the variance of the dependent variable from the independent factors. The model accuracy was assessed adopting the receiver operating characteristic (ROC) curve, which showed a reasonable adaptability with area under curve (equal to 0.73). The methodological approach proposed herein can support decision-making on agricultural land protection and planning activities which are key priority today due to environmental instability, food security, and climate change.

Applying Convolutional Neural Network to Predict Soil Erosion: A Case Study of Coastal Areas

The development of ecological restoration projects is unsatisfactory, and soil erosion is still a problem in ecologically restored areas. Traditional soil erosion studies are mostly based on satellite remote sensing data and traditional soil erosion models, which cannot accurately characterize the soil erosion conditions in ecological restoration areas (mainly plantation forests). This paper uses high-resolution unmanned aerial vehicle (UAV) images as the base data, which could improve the accuracy of the study. Considering that traditional soil erosion models cannot accurately express the complex relationships between erosion factors, this paper applies convolutional neural network (CNN) models to identify the soil erosion intensity in ecological restoration areas, which can solve the problem of nonlinear mapping of soil erosion. In this study area, compared with the traditional method, the accuracy of soil erosion identification by applying the CNN model improved by 25.57%, which is better than baseline methods. In addition, based on research results, this paper analyses the relationship between land use type, vegetation cover, and slope and soil erosion. This study makes five recommendations for the prevention and control of soil erosion in the ecological restoration area, which provides a scientific basis and decision reference for subsequent ecological restoration decisions.

Evaluating the Degradation of Natural Resources in the Mediterranean Environment Using the Water and Land Resources Degradation Index, the Case of Crete Island

Natural resources degradation poses multiple challenges particularly to environmental and economic processes. It is usually difficult to identify the degree of degradation and the critical vulnerability values in the affected systems. Thus, among other tools, indices (composite indicators) may also describe these complex systems or phenomena. In this approach, the Water and Land Resources Degradation Index was applied to the fifth largest Mediterranean island, Crete, for the 1999–2014 period. The Water and Land Resources Degradation Index uses 11 water and soil resources related indicators: Aridity Index, Water Demand, Drought Impacts, Drought Resistance Water Resources Infrastructure, Land Use Intensity, Soil Parent Material, Plant Cover, Rainfall, Slope, and Soil Texture. The aim is to identify the sensitive areas to degradation due to anthropogenic interventions and natural processes, as well as their vulnerability status. The results for Crete Island indicate that prolonged water resources shortages due to low average precipitation values or high water demand (especially in the agricultural sector), may significantly affect Water and Land degradation processes. Hence, Water and Land Resources Degradation Index could serve as an extra tool to assist policymakers to improve their decisions to combat Natural Resources degradation.

A GIS-Cellular Automata-Based Model for Coupling Urban Sprawl and Flood Susceptibility Assessment

In Urban Planning (UP), it is necessary to take under serious consideration the inhibitors of the spread of a settlement in a specific direction. This means that all those parameters for which serious problems may arise in the future should be considered. Among these parameters are geo-hazards, such as floods, landslides, mud movement, etc. This study deals with UP taking into account the possibility of widespread flooding in settlement expansion areas. There is a large flooding history in Greece, which is accompanied by a significant number of disasters in different types of land use/land cover, with a large financial cost of compensation and/or rehabilitation. The study area is the drainage basin of Erasinos River in the Attica Region, where many and frequent flood events have been recorded. The main goal of this study is to determine the flood susceptibility of the study area, taking into account possible factors that are decisive in flood occurrence. Furthermore, the flood susceptibility is also determined, taking into account the scenarios of precipitation and the urban sprawl scenario in the area of reference. The study of flood events uses the Analytic Hierarchy Process (AHP) model and the urban sprawl model SLEUTH, which calibrates historical urban growth, using open and cost-free data and software. Eventually, flood susceptibility maps were overlaid with future urban areas to find the vulnerable areas. Following, three scenarios of flood susceptibility with the corresponding susceptibility maps and vulnerability maps, which measure the flood susceptibility of the current and future urban space of the study area, are presented. The results have shown significant peaks in the moderate class of flood susceptibility, while, in the third scenario, high values of flood susceptibility seem to appear. The proposed methodology and specifically the output maps can serve as a decision support tool to assist urban planners and hazard managers in making informed decisions towards sustainable urban planning.

Integration of GIS-Based Multicriteria Decision Analysis and Analytic Hierarchy Process for Flood-Hazard Assessment in the Megalo Rema River Catchment (East Attica, Greece)

This study deals with the flood-hazard assessment and mapping in the catchment of Megalo Rema (East Attica, Greece). Flood-hazard zones were identified utilizing Multi-Criteria Decision Analysis (MCDA) integrated with Geographic Information System (GIS). Five factors were considered as the most influential parameters for the water course when high storm-water runoff exceeds drainage system capacity and were taken into account. These factors include slope, elevation, distance from stream channels, geological formations in terms of their hydro-lithological behavior and land cover. To obtain the final weights for each factor, rules of the Analytic Hierarchy Process (AHP) were applied. The final flood-hazard assessment and mapping of the study area were produced through Weighted Linear Combination (WLC) procedures. The final map showed that approximately 26.3 km2, which corresponds to 22.7% of the total area of the catchment, belongs to the high flood risk zone, while approximately 25 km2, corresponding to ~15% of the catchment, is of very high flood risk. The highly and very highly prone to flooding areas are located mostly at the southern and western parts of the catchment. Furthermore, the areas on both sides of the channel along the lower reaches of the main stream are of high and very high risk. The highly and very highly prone to flooding areas are relatively low-lying, gently sloping and extensively urbanized, and host the densely populated settlements of Rafina-Pikermi, Penteli, Pallini, Peania, Spata, Glika Nera, Gerakas and Anthousa. The accuracy of the flood-hazard map was verified by correlating flood events of the last 30 years, the Hydrologic Engineering Center’s River Analysis System (HEC–RAS) simulation and quantitative geomorphological analysis with the flood-hazard level. The results of our approach provide decision makers with important information for land-use planning at a regional scale, determining safe and unsafe areas for urban development.

A Model-Based Tool for Assessing the Impact of Land Use Change Scenarios on Flood Risk in Small-Scale River Systems—Part 2: Scenario-Based Flood Characteristics for the Planned State of Land Use

Land use changes can significantly influence the water balance, and thus especially the development of flood-triggering runoff peaks. Hence, it is advisable to assess possible changes already at the level of municipal planning. Since many different actors are usually involved in spatial planning, it is useful to provide a shared platform where stakeholders can access the same information to analyze and evaluate flood hazards. Therefore, a GIS routine for the prediction of soil sealing induced runoff peaks and resulting potential flooding in the watercourse was developed, which is embedded in a GIS based decision support system (GIS-DSS). The so-called storm water routine (SWR) is founded on preprocessed flood characteristics, calculated by means of hydrological/hydraulic models (described in part 1). The potential impact of land use change is assessed purely in GIS as flow difference which is routed through the river system. To validate this simplified method, a process model was set up with an exemplary land use change and its results were compared with the GIS-based results. For 16 of the 18 rainfall scenarios tested, the SWR provided very good to good agreement with the detailed model. For short and highly dynamic rain events the SWR approach is less reliable. Several supplements like the integration of LID are conceivable.

Random Forests with Bagging and Genetic Algorithms Coupled with Least Trimmed Squares Regression for Soil Moisture Deficit Using SMOS Satellite Soil Moisture

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.