Environmental Monitoring 1: Comparison
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Human activities and climate change constitute the contemporary catalyst for natural processes and their impacts, i.e., geo-environmental hazards. Globally, natural catastrophic phenomena and hazards, such as drought, soil erosion, quantitative and qualitative degradation of groundwater, frost, flooding, sea level rise, etc., are intensified by anthropogenic factors. Thus, they present rapid increase in intensity, frequency of occurrence, spatial density, and significant spread of the areas of occurrence. The impact of these phenomena is devastating to human life and to global economies, private holdings, infrastructure, etc., while in a wider context it has a very negative effect on the social, environmental, and economic status of the affected region. Geospatial technologies including Geographic Information Systems, Remote Sensing—Earth Observation as well as related spatial data analysis tools, models, databases, contribute nowadays significantly in predicting, preventing, researching, addressing, rehabilitating, and managing these phenomena and their effects. 

  • environmental monitoring
  • climate change
  • geohazards

1. Drought Monitoring

With its stability and durability, nature can introduce extreme changes in the variables and factors of human systems. Extreme changes or extreme events, such as earthquakes, floods, and droughts, often called natural hazards, can present insurmountable obstacles and difficulties in how human societies deal with them [1][2][3][4][5]. In this context, it is argued that droughts are one of many natural hazards that can affect domestic use and irrigation—in other words, the water supply of an area [6][7]. The current trend among broad technical and professional circles, administrators, politicians, and decision-makers and generally among common citizens is to regard drought as something transient, a random and remote risk that requires only an extraordinary mobilization [8][9]. However, the accumulated experience of scientific research and observations in recent decades shows that droughts are inevitable, as these phenomena appear to be inescapable and permanent elements of the global climate [8][10][11][12][13][14]. Drought issues can turn into a water crisis and will play a crucial role in the next years in defining both development and environmental policies on a world level [15][16][17]. As far as Europe is concerned, the droughts in Greece in 1989–1993, in Spain in 2003, in France in 2005, and again in Greece in 2007–2008, confirm this trend, as well as the urgent need to implement common strategies to the problem across Europe and not just in the Mediterranean countries [1][11][18][19][20][21][22][23][24]. The next figure (Figure 1) presents such a context [21].

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Figure 1. Example of Geographic Information Systems (GIS) technique for monitoring/simulate drought [21].

Drought and its consequences must be recognized and taken into account from the early stages of water resources planning and management efforts [25][26][27][28][29]. Accordingly, measures and efforts to tackle droughts should begin by studying the phenomenon’s dimensions [1][30][31][32]. Functional definitions allow the determination of the beginning and end, as well as the degree of severity of drought [1][11][33][34]. These definitions are categorized based on four different approaches: meteorological, hydrological, agricultural, and socioeconomic drought. GIS and Expert Systems (E.S.) can respond timely to identify and recognize drought events over large areas through satellite data, gridded datasets, and measured values from meteorological stations. However, the calculation of Drought Risk and Vulnerability Assessment require spatial data on specific time step (monthly or yearly). Thus, using geoinformation solutions improve the observation visualization of drought hazards. [5][10][11][12][14][20][35][36][37].

Drought is a temporary state (months/years), instead of aridity, which is a permanent climate feature [38][39]. Additionally, seasonal drought, i.e., a well-defined dry season, must be distinguished from the drought that occurs with different characteristics [5][6]. There is considerable confusion between scientists and policymakers about diversifying these terms. A typical example is the Greek territory, especially in the island regions, where the rainfall season begins in October and ends in April [19][21][22]. Thus, a large part of the islands appears barren, and drought is an inevitable climate feature. However, the seasonal drought on these islands is almost certain to occur over time due to the failure to meet water needs over a given time horizon. Drought usually occurs when rainfall in a region is less than average and is followed by large evaporation rates for prolonged periods [20][37][40][41][42]. The drought varies from other natural disasters because of its leisureliness and long duration. In most cases, drought is caused either by a decrease in rainfall or a shortage of water resources reserves. The concept of deficiency is relevant and determined by a specific water demand by sector or by specific activities. Nevertheless, drought analyses and drought assessments should include all the available data from the investigated study area. These results composing drought indices (indicators or indexes) were visualized on a GIS environment to portray spatial distribution. There are efforts and they use geo-statistical techniques to transform the point values form meteorological stations to a spatial distribution in a specific time frame, namely, kriging and co-kriging methods and Inverse Distance Weighting (IDW) are the most common approaches it is referred to as having a better fit for indices or climate parameters [1][4][7][10][14][43].

To address drought phenomena, the development of a strategy and a Master Plan of these phenomena is recommended as an effective means of improving the ability to assess and respond to state mechanisms for action. Unanimity between state agencies and private and public interest groups is also an important part of the process. Composite indicators and indices can help recognize a drought promptly and achieve this goal. Additionally, in combination with forecasting models, a short-term prediction of the phenomenon and its effects can be successfully made for decision-makers to be able to better prepare by reducing or minimizing the effects and reaction time to them. An important helper in this direction is the promotion and integration of contingency planning.

2. Soil Erosion Monitoring

Soil erosion is a complex and dynamic phenomenon that affects many areas around the world. It is one of the most serious problems of land degradation and a major source of environmental deterioration, as it is the largest environmental problem the world is facing after population growth. Indicatively, it is reported that the United States loses soil 10 times faster than the rate of natural replenishment, while the corresponding loss for China and India is 30–40 times faster [44].

Worldwide, the total area of land affected by water erosion is 10,940,000 km2, of which 7,510,000 km2 are significantly degraded [45]. The annual sediment transport to the ocean from rivers around the world has been estimated to be 15 to 30 billion tonnes [46][47]. In addition, soil erosion affects the geomorphological characteristics of an area in terms of soil fertility, agricultural productivity, water quality, water reservoir capacity, and the evolution of coastal areas in sedimentary environments [48][49][50].

The current evolution of the erosion phenomenon is directly related to global warming, which results in a more intense hydrological cycle in several regions, including more total rainfalls and more frequent events of high intensity rainfall (combined or not). These changes in rainfall, as well as changes in temperature, solar radiation, and atmospheric CO2 concentrations, have significant effects on soil erosion rates. In general, the processes associated with the effects of climate change on soil erosion by water are complex and include changes in the volume and intensity of rainfall, number of days of rainfall, ratio between rain and snow, production of biomass from plants, decomposition rates of vegetable residues, evapotranspiration rates, and land use changes that are necessary to adapt to a new climate regime [51].

In addition, degradation of agricultural land by soil erosion is also a global phenomenon leading to the loss of nutrient-rich surface soil, increased runoff in the more impermeable layer of the ground and reduced availability of water to plants [52]. About 85% of soil degradation worldwide is associated with soil erosion and causes up to 17% reduction in crop productivity [53].

Effective monitoring, modeling, and analysis of the soil erosion phenomenon can provide information on the current erosion state, trends, and allow the analysis of different scenarios [52]. Furthermore, these actions can be important sources of information for land management decisions through the development of alternative land use scenarios and the evaluation of their results [54]. Quantitative monitoring and evaluation [55] is often required to extract the extent and magnitude of soil erosion problems in order to develop sound regional management strategies along with field measurements.

Soil erosion monitoring and analysis models are classified [56] into three main categories: empirical, conceptual (partly empirical/mixed) and physical. Examples of the three categories include the empirical model USLE and its modifications, the conceptual ANSWERS (Areal Nonpoint Source Watershed Environment Response Simulation), CREAMS (Chemicals, Runoff and Erosion from Agricultural Management Systems), and MODANSW (Modified ANSWERS), as well as the physical models EUROSEM (European Soil Erosion Model) and MIKE SHE (“Systeme Hydrologique Europeen” or European Hydrological System). Since 1930s, soil experts and decision-makers have been developing and using erosion models to monitor and calculate soil loss [57]. Over 80 erosion models have been created over the last fifty years [58].

One of the most widely used empirical models for monitoring and estimating erosion is the Universal Soil Loss Equation (USLE) model developed by Wischmeier and Smith in 1965, based on soil erosion measurements in the USA [57]. It has been developed mainly for the assessment of soil erosion on arable land or on slightly sloping topographies and is still used in a large number of studies to estimate soil loss [52]. With the revised version RUSLE, which takes into account the upstream areas that contribute to the downstream surface runoff and is thus considered to have better predictability than USLE [59], and modified version MUSLE [57][60][61], USLE is still widely used in soil loss assessment studies and is the most common tool for large-scale soil erosion assessment and monitoring in Europe [62][63][64][65].

In recent years, with the development and significant evolutions in GIS and remote sensing, as well as the progress made in computing power, efforts to spatially model soil erosion have been intensified and greatly upgraded [66]. The widespread dissemination of GIS and the use of satellite data has greatly facilitated the development of erosion models, since they allow the use of multiple data sources, easy modification of the structure of erosion models and unconditional reshaping of their scale [67][68]. The use of conventional methods for monitoring and assessing the risk of soil erosion is costly and time consuming. The integration of existing soil erosion models, field data and data provided by remote sensing technologies through the use of GIS has proved to be particularly advantageous, while enabling the spatial distribution of erosion to be presented through hazard maps, which are necessary for the design of protective measures [50][52][65][69][70][71].

The importance of continuous monitoring of soil erosion and application of integrated river basin management practices has been enhanced by the independent use of remote sensing data, such as aerial photos, LiDAR, UAV, and satellite data such as Landsat (ETM +, TM, MSS), IRS-P6 LISS (III, IV), ASTER GDEM, GeoEye, QuickBird, WorldView, MODIS, Hyperion, etc., but also by their combined analysis and evaluation with other types of data, such as rain gauges, soil samples, topographic maps, etc., in GIS environment [72][73][74][75][76][77][78][79][80][81][82]. A typical example of the above is the soil erosion risk mapping on a monthly basis by Nigel et al. [83] for the Mauritius region via GIS, decision rules, rainfall, and soil data, and SPOT satellite imagery. They developed the MauSERM model, which produces high resolution soil erosion monitoring maps for the whole area of Mauritius every month.

The combined use of remote sensing data (and indicators derived by these like NDVI), GIS and soil loss assessment models, such as USLE, RUSLE, RUSLE3D (a modification of RUSLE for composite soil), USPED (Unit Stream Power-based Erosion Deposition), PESERA (Pan-European Soil Erosion Risk Assessment), etc., has proven to be extremely important and effective in monitoring and predicting soil erosion [52][84][85][86][87][88][89][90][91][92][93][94][95][96][97][98][99][100]. A good and recent example supporting the previous argument is the implementation by Karydas et al. [101] of the G2 soil erosion model in five study areas in south-eastern Europe and Cyprus aiming to estimate soil loss on a monthly basis. This model operates in a GIS environment utilizing a wealth of satellite data such as Sentinel-2, MERIS, PROBA-V and SPOT-VGT. Based also on the above, a very useful reference would be the work of Leh et al. [102], who created scenarios for predicting future land use allocation and soil erosion levels for a US catchment area for 2030. This research combined remote sensing, GIS and modeling techniques and created land use maps through analysis of Landsat satellite images every five years for a period of 20 years (1986–2006). The studies were based on these maps for the synthesis of future scenarios that predict an increase in urbanization with a consequent increase in erosion risk for the study area.

Steps forward in the development of the previous logic were the utilization of the aforementioned data, models and methods along with the use of other techniques such as Frequency Ratio (FR), Analytical Hierarchy Process (AHP), Logistic Regression (LR), Multiple Linear Regression (MLR), Potential Erosion Index (PEI), Sediment Delivery Ratio (SDR), the GIS model GWLF (Generalized Watershed Loading Function), the TLSD (Transport Limited Sediment Delivery) function, etc. [103][104][105][106][107]. A worth noting and recent example is the work of Macedo et al. [108], who developed the Environmental Fragility Index (EFI) through GIS procedures and its ability to monitor and predict sediment deposition was evaluated on 148 rivers in Brazil. The index takes into account geoclimatic factors as well as anthropogenic pressures and utilizes geospatial, rainfall and Landsat TM satellite data.

In conclusion, taking into consideration all previous references, GIS and Remote Sensing are proven very useful tools with great potential in monitoring and estimating soil erosion reliably. These can be based on either models as for example USLE/RUSLE [91][109][110][111][112][113], or in other methods like for example Multi-Criteria Evaluation Techniques—MCE [114][115][116][117][118][119][120][121], or in combinations of the above.

Finally, spatial information analysis is an ever-evolving approach capable of monitoring evaluating, managing and analyzing the complex problems, such as soil erosion, of river basins and lake basins. In recent years, GIS has proven to be a good alternative as they are better decision support tools in soil monitoring, planning, management and sustainable development.

3. Groundwater Monitoring

Groundwater is one of the most valuable natural resources and is related, directly, to human health, economic development, and ecological diversity [122]. They are a vital resource for reliable and affordable drinking water supply in urban and rural environments and play a fundamental role in human well-being, sustainable agriculture, and the conservation of aquatic and terrestrial ecosystems [123]. Groundwater quality is usually very good, as it is filtered naturally through the soil and is clear, colorless, and free of microbial contamination, requiring minimal treatment [124].

Effective groundwater management and monitoring is of particular importance in areas with hard rocky soil, which are deprived almost everywhere and permanently of surface water. In these areas, where ever-growing human populations live, finding water supply zones is increasingly difficult [125]. In addition, in many areas groundwater availability is limited, and aquifer development is mainly associated with fractured and disintegrated horizons and secondary porosity created [126]. Moreover, exposure to various pollutants can render groundwater unsuitable for consumption and can endanger human life, fauna, and the environment [127].

Assessing the impact of climate change on a country’s water resources is the most important assessment that has to be carried out before planning any long-term water potential project. Numerous studies have been conducted worldwide on the trends of river flows in relation to water filtration in the face of climate change and potential pressures on groundwater aquifers [128]. In recent years, researchers around the world, have conducted numerous studies to assess groundwater reserves and groundwater aquifers, as sustainable groundwater management is increasingly necessary. Investigation of groundwater presence and delineation of groundwater aquifers can be achieved by indirectly analyzing some directly observed soil characteristics, such as tectonics [129].

Unlike traditional methods, remote sensing (RS) technology, with the benefits of spatial, spectral, and temporal data availability covering large and inaccessible areas in a short period of time, has proved to be a very useful tool for evaluation, monitoring and the management of groundwater resources [130][131]. In addition, it is widely used to map land surface features (such as linear tectonic elements, lithology, etc.), as well as to monitor groundwater supply zones [129].

Hydrogeological interpretation of satellite data has proven to be a valuable research tool in areas where little geological and cartographic information is available (or not accurate) as well as inaccessible areas [130]. Since satellite sensors cannot directly detect groundwater, the presence of groundwater results from the interpretation of different surface features derived from satellite images, such as geology, soil geomorphology, soil characteristics (texture, roughness, etc.), land uses/land cover, surface water bodies, etc. [132][133]. In recent years, the widespread use of satellite data in combination with conventional maps and corrected terrain data has facilitated the extraction of baseline information [134][135][136][137][138][139].

Alongside remote sensing, GSPs have emerged as powerful tools for spatial data management and decision making in various fields, including engineering and the environment [140][141]. The application of GIS for the assessment of groundwater resources has been reported several years before [142][143][144].

GIS and remote sensing tools are widely used to manage various natural resources [145][146]. Since groundwater zoning and simulation require large amounts of interdisciplinary data, the combined use of remote sensing techniques and GIS has become a valuable tool and important studies have been conducted in recent years [122]. One such example is the integration of remote sensing with GIS to prepare various thematic levels, such as lithology, drainage density, curvature of the basin, rainfall, slope, soil and land uses, etc., in is defined as a weight of importance, a method that can successfully support the identification of potential groundwater areas [123].

In similar research efforts, the combined application of remote sensing and GIS in groundwater management and the delimitation of potential aquifer zones has been implemented by various researchers worldwide [129][147][148][149]. The first corresponding studies include the work of Gustafsson [150], who used GSPs to analyze SPOT satellite data to map potential groundwater presence.

4. Frost Monitoring

The prevalence of low air temperatures and frost conditions in an area is a major factor controlling vegetation zonation and biodiversity. Frost has been globally identified as a leading hazard, as it can occur in almost any location, outside the tropical zones, especially at high altitudes [151]. It has significant impact on agriculture, forestry, pasture as it affects most biotic processes such as plant phenology, growth, evapotranspiration, moisture requirements, carbon fixation and decomposition in natural and cultivated mountain ecosystems [152][153][154][155]. Moreover, frost can cause significant damage to infrastructure works and inhibit their smooth and safe operation due to road closures, or even endanger their stability [156][157][158]. The next figure (Figure 2) presents such a context [158].

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Figure 2. An example of integration of GIS and Earth Observation (EO) for frost monitoring [158].

Two criteria are often used for the recording of frost: the formation of ice crystals on surfaces (during field surveys) and meteorological measurements of air temperature [159]. As far as agriculture is concerned, frost can be described as a meteorological event when crops and other plants experience freezing injury due to occurrence of an air temperature less than 0 °C [151].

Meteorological measurements have high accuracy but have limited ability to describe the spatial heterogeneity of frost distribution [160]. Attempts to spatial interpolate air temperature measurements and frost often lead to unrepresentative spatial patterns, due to the low number and the irregular distribution of weather stations [161].

Nowadays, with the rapid technological and scientific development in Earth Observation (EO) technology, there is an upgraded potential in the study of the spatiotemporal distribution of frost. Especially in areas where temperature data are unavailable or expensive due to sparsity of meteorological stations or difficult access, remote sensing can be an important and valuable source of information [162]. Remote sensing data have been developed that provide ready to use products with information on night-time land surface temperature, through which frost can be recorded. The values of remotely sensed LST are determined from Planck’s Law using the emissivity of thermal infrared (TIR) bands. The quality of LST retrievals is affected by sensor characteristics, atmospheric conditions, variations in spectral emissivity, surface type heterogeneity, soil moisture, visualization geometry, and assumptions related to the split-window method [162].

A broadly used LST data source is the Moderate Resolution Imaging Spectrometer (MODIS) sensor, which has been providing information since 2000. The MODIS LST product (MOD11A1) has very high temporal resolution, with more than one revisit per day for some areas of the world, but its spatial resolution is course (1 km). The accuracy of the retrieved LST depends on the atmospheric and land surface conditions and is better than 1 °K in some cases [163].

An alternative source of night time LST is the Advanced Spaceborn Thermal Emission and Reflection Radiometer (ASTER) sensor. The accuracy of the ASTER LST product (AST-08–L.2) is calculated to be 0.3 °K and has a 90-m resolution for land areas, but its temporal resolution is lower, as its revisit time is at 16-day intervals [164].

A relatively recent thermal infrared sensor is the Landsat 8 Thermal Infrared Sensor (TIRS), with two adjacent thermal bands. Several approaches have been developed for the generation of LST from Landsat 8 data, such as the radiative transfer equation (RTE)-based method, the split-window (SW) algorithm and the single channel (SC) method. The highest estimated accuracy is accomplished with RTE, which is better than 1 °K [165].

Lastly, Sentinel-3 which has been developed by the European Space Agency and provides with LST data on a daily basis since 2017. The product SLSTR Level-2 LST product contains LST information with a spatial resolution of 1 km. The accuracy of the derived LST depends on atmosphere type and is estimated to be 0.8 °K for Polar Regions, 1.5 °K for mid-latitudes and 2 °K for equatorial latitudes [166].

In temperature fluctuation and frost monitoring studies, it is essential to have datasets with high spatial and temporal resolution. The limitations of the currently available EO data sets of either low temporal resolution, or low spatial resolution can be overcome by synthesizing high temporal resolution imagery with high spatial resolution imagery (Liu & Weng, 2012). The development of data fusion models such as Spatial and Temporal Adaptive Reflectance Fusion Model, has enabled the generation of ASTER-like daily land surface temperature with promising results [167][168][169][170].

5. Flood Monitoring

Natural disasters have significant impacts in many countries around the world, with a large number of deaths, damage to technical and infrastructure projects and population relocations. In addition, due to the significant impacts of climate change, these impacts are expected to increase in the coming years in many countries. Although technology and scientific knowledge have grown significantly today, natural disasters continue to have disastrous economic and environmental consequences, as well as many human casualties worldwide. The study and monitoring of these phenomena has been consistent in recent decades and has shown increasing trends [171]. In order to successfully monitor these phenomena and manage their impacts, it is very important to create hazard and risk maps for both the natural and the artificial environment [172][173].

Among the most dangerous and serious natural disasters, affecting more than 75 million people worldwide, are flood events [173]. In extreme flood events, it is important to quickly manage the magnitude of their impacts and land uses covered by water [174]. In this context, flood mapping and modeling are useful tools for monitoring, predicting, protecting, but also improving short and long term assistance to affected areas immediately after the event.

According to Lekkas [175], there are two types of floods, upstream and downstream. Upstream floods are observed in the higher parts of the drainage area and are generally the result of intense short-term rainfall in a small area. In contrast, downstream floods are caused by long-term storms that infiltrate the soil and cover a wide area. Most floods are the result of (i) the total amount and distribution of rainfall, (ii) the permeability of rock or soil, and (iii) the topography of an area. It has also been found that the distribution of land use, especially in small drainage basins, can significantly affect the size and frequency of floods. In addition, the risk of a flood in a watershed is determined by the following factors: land use, flood volume, intensity and frequency, elevation and duration of flood, season, weight of sediment deposited, and finally efficiency of monitoring, prediction, prevention, warning and emergency measures adaptation mechanisms [175].

New environmental challenges have put water resources monitoring and management at high academic and research interest. The local effects of climate change in recent decades, such as rising temperatures, decreasing rainfall (or increasing in other cases), desertification, etc., but also the occurrence of extreme phenomena such as storms, floods, landslides, and soil erosion threaten human life and infrastructure. The tendency to deal with this issue is rapidly increasing, due to the ever-increasing occurrence of such phenomena and the need for optimal monitoring and management, but also due to the technological boom that provides new tools and techniques. This constantly modifying and changing environmental regime has promoted the need for systematic research in related fields such as hydrology and/or hydraulics. Important goals of this endeavor are optimal methodological efficiency, comprehensive databases, and in particular state-of-the-art modeling developments, as well as understanding, monitoring and predicting an event or phenomenon of high importance nowadays. Modern geo-technologies (e.g., GIS and Remote Sensing) play a key role in this ongoing effort.

In the early 1980s, geographers argued that flood maps did not significantly influence people’s perception of floods and at the same time did not provide sufficient research planning. According to the Canadian Flood Disaster Reduction Program, although awareness of the effects of floods has increased among—after mapping—groups, it is not due to maps [176][177]. In an effort to improve this weakness many methods have been tried. Among these is the use of Remote Sensing (RS), which is not a new idea. In 1980, proving the previous argument, a research paper proposed the application of remote sensing for disaster monitoring and warning through various processes, such as flood mapping and assessment [178].

During the next decade, the use of Remote Sensing and GIS in flood monitoring and research became well established. Both passive and active sensors were used and tested, as can be understood by the two following references. Hubert-Moy et al. [179] suggested that the spatial analysis of Landsat Thematic Mapper (TM) is compatible for the study of floods in small catchments. The study also supported the fact that satellite image processing and flow analysis can simulate flood conditions even several days after the event’s peak, although the satellite’s repeatability is low. On the other hand, the usefulness of synthetic aperture radar (SAR) data was examined in a 1996 study. This study was able to compare satellite data on river floods with photographic recordings (taken from a small aircraft) aiming to demonstrate the accuracy of the SAR technique [180].

In 21st century GIS, Remote Sensing and other modern technologies play an important role in flood monitoring, analysis and mapping, but also in hydrological and/or hydraulic modeling, firmly established. Many scientists aimed to develop new ideas based on the above tools and techniques. Free software packages have been developed and distributed, huge global digital databases have been created and a multitude of research projects have been carried out. The evolution and revolution of hydrological and hydraulic modeling, as well as flood monitoring, analysis, and mapping, with modern technologies are flourishing, constantly finding new applications, satisfying ever-increasing demands—needs and inviting more and more young scientists to work in this field. The use of remote sensing data for monitoring and investigating floods and related phenomena (e.g., coastal impacts from sea level rise due to climate change) has increased rapidly in recent years. Various monitoring and research applications were based on RS data like LIDAR (Light Detection and Ranging) [181], RADARSAT SAR [182], NEXRAD III [183], EUMETSAT, SENTINEL [184][185][186], COSMO-SkyMed SAR [187], optical and microwave data US DMSP/Quikscat, RADARSAT, LANDSAT-5/7 (TM, ETM+), EOS-AM TERRA/MODIS, SRTM DEM and ASTER [188][189][190]. The continuous evolution and improvement of remote sensing data (coverage range, spatial resolution, data frequency, etc.) is expected to continuously increase their use and efficiency.

Furthermore, several scientists have attempted to exploit, or propose new, hydrological, hydraulic and other models along with remote sensing and GIS data and techniques, in order to investigate and monitor several issues directly or indirectly related to surface runoff and flooding [189][191][192][193][194][195]. At this point it is worth mentioning the development of the HYDROTEL hydrological model [196], the application of the hydrological model SLURP [197][198], the SWAT model (Soil and Water Assessment Tool) [199][200][201][202][203] and its evolution SWAT2000 [204], the HEC-HMS/RAS models [183][186][205][206][207][208][209][210][211], the naturally distributed WEP-L hydrological model [212], the MIKE11 model [213], spatially distributed hydrological model LIS-Flood model [214]. An example of the variety of applications, which combines hydrological and other models (e.g., SLEUTH) with Remote Sensing and GIS, is the study of urban sprawl [215][216].

The evolution of geographic–spatial analysis methods, and in particular of GIS, has been the forerunner and essence of change to the approach of flooding from purely mathematical–theoretical models and in situ research into a functional approach through spatial analysis, spatial models, geostatistics and other related methods, e.g., logistic regression, frequency analysis, etc. [185][217][218][219][220][221][222][223][224][225][226][227][228][229][230][231][232][233]. The next figure (Figure 3) provides such a context [230].

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Figure 3. An example of GIS-based model for exploitation of floods via reservoirs ((a). DEM, (b). Land cover map, (c). Geology map) [230].

In general, the development of new technologies for rainfall–runoff modeling and flood monitoring, analysis, and forecasting, either in combination with GIS and Remote Sensing or autonomously, also involves the use of advanced methods such as Artificial Neural Networks (ANN), which are a highly evolving area of research [234][235], the application of fuzzy logic algorithms [236], SVM (Support Vector Machine) models [237].

The constant evolution of technology, as can be understood from the literature review, provides ever-more modern tools and techniques for the assessment and monitoring of natural disasters, and in particular floods. The development of new models (and the evolution of older ones), Geographic Information Systems, Remote Sensing, Applied Spatial Analysis, Geostatistics, etc., are some of the capabilities currently available to researchers, engineers, etc. to approach disastrous phenomena, such as floods. The need to apply these tools, to modernize existing methods, as well as to continually evolve and create new ones, is further enhanced by the effects of climate change and modern environmental, social, and economic requirements and needs.

6. Sea Level Rise Monitoring

According to the IPCC, (2018), global warming is likely to reach 1.5 °C between 2030 and 2052. Ablain et al. [238], explained in detail the importance of sea level rise, i.e., a measure of the increase in ocean volume, as a clear indicator of climate change and one of its main effects, by using data from European Space Agency (ESA) Earth Observation (EO) mission such as the ERS-1&2 and Envisat missions, the TOPEX/Poseidon, the Jason-1&2 and Geosat Follow-on (GFO) missions. Sea-level rise over the next century is expected to contribute significantly to physical changes along shorelines, enhancing coastal hazards particularly on low-gradient coastal zones, developing geodetic technologies and especially the Global Positioning System (GPS) [239][240]. Model-based projections of global mean sea level rise (relative to 1986–2005) suggest an indicative range of 0.26 to 0.77 m by 2100 for 1.5 °C of global warming [241].

Global warming is causing global mean sea level to rise mainly by the melting of glaciers and ice sheets resulting to the super induced addition of water into the ocean as well as by an increase of water body volume due to thermal expansion [242]. Relative sea level changes induced by variations for water in the oceans and land movements can be detected by tide gauges measurements, while EO satellite altimetry/gravimetry, as well as GPS satellites can be applied to measure absolute sea level changes at a global scale [243].

Tide gauges have been used since ancient times to record sea level variations but a well-developed network of tide gauges at almost 1000 locations around the globe, started to appear by the end of the nineteenth century. Moreover, the existence of altimeter satellites such as TOPEX/POSEIDO creates the opportunity for even better measures [244][245][246]. Human activities are estimated to have caused global mean sea level rise of about 21–24 cm since 1880 and even though there is a network of tide gauges provides valuable information about sea level changes from a few seconds to centuries [247][248][249], these observations suffer from several limitations, i.e., their geographic distribution which is poor in open oceans or the southern hemisphere, the availability of records which is not contemporary for all stations and the effect of vertical land movements which is the one of the main difficulties to interpret tide gauge measurements [244][250].

On the other hand, satellite altimetry together with SAR altimetry is described by Cipollini et al. [251] as one of the workhorses of open-ocean operational oceanography and global sea level monitoring, providing for more than 28 years valuable data sea level date with accuracies of the order of just a few cm even for the most remote areas of the oceans. Major support has been provided to the international scientific community by several space agencies such as the National Aeronautics and Space Administration (NASA), the European Space Agency (ESA) and the Centre National d’Etudes Spatiales (CNES) in research and development of innovative techniques for coastal altimetry.

According to Ablain et al. [238] satellite altimetry data fit well with tide gauge measurements, however it is of great importance to be able to link the satellite altimeter measurements of sea level rise with the tide gauge measurements, by bridging the open-ocean measurements with those in close proximity to the coast in order to meet the scientific needs for better data resolution.

GIS has proved that when it is integrated with remote sensing data and tide gauge measurements, it has the potential to act as an important tool in monitoring the environmental and socio-economic effects of sea-level rise. GIS is increasingly used as a support tool in sea-level rise monitoring, as it allows homogenization and integration of all the available data into a geodatabase, in order to access information, perform spatial and geostatistical analysis and it has a strong potential to combine a broad range of complex variables and data in varying formats and to integrate physical, ecological, socioeconomic, and hazards information. Furthermore, owing to its numerous advantages such as editing and data automation, visualization, mapping and map-based tasks, spatial consultation, spatial analysis, and geostatistical analysis and its flexibility, GIS can be used in further planning applications and future scenarios. Over the years GIS has shown great potential in its application and problem-solving capabilities and a great number of researchers have applied GIS-based methodologies to study sea level rise globally and regionally.

References

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