Keywords

1 Introduction

Global and local ecosystems are profoundly being changed to cater the growing population and economic development. Technological evolution of human society finds its grounds in the permanent transformation and use of landscape, increasing living standard and comfort. Since the dawn of human society, ecosystems have been under continuous pressure. In this context, the assessment of the human-induced modifications in the structure and functionality of ecosystems is vital in the elaboration of subsequent planning policies, directed to environmental protection.

Policies and strategies were drawn to stop the natural ecosystem degradation. The most ambitious initiative is included in the Convention for Biological Diversity, which specifically refers to vulnerable ecosystems such as protected area, wetlands or aquatic ecosystems (CBD) (UN 1992). In the same direction act the EU Water Framework Directive and the 7th Environmental Action Plan (EC 2013). There are also numerous international projects aiming to conserve biodiversity and reduce habitat loss and ecosystem degradation.

Mapping the distribution and quality of ecosystems (often described by the amount and diversity of ecosystem services) lately represents one of the main focuses in science and policy on a global level (Roussel et al. 2017). Initiatives like IPBES (Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services) and MAES (Mapping and Assessment of Ecosystems and their Services) tried to draw a framework applicable at global, regional, and local level, but there are currently still many issues that must be overcome.

Considering the chosen scale, the spatial representation of ecosystems implies specific challenges and restrictions. For wider scales, continental or global, the mapping and evaluation of the ecosystems reside on the mainland use classes and large structural units, leaving out important ecological characteristics. Therefore, it is recommended to represent ecosystems more thoroughly (Blasi et al. 2017), considering the significant factors that produce discontinuities, properties of the environment (soil type, hydrographical basin), or different spatial relations (home range, species migration). The limits of ecosystems may be established by considering the place where a number of these discontinuities converge.

Ecosystem mapping is mandatory for land use planning, environmental impact assessment, and conservation as they demand spatial outcomes for proper decision-making (Ershov et al. 2016). Maps that represent the relevant ecosystem for the existence of a population will be totally different than maps showing the impact of human activities over the same ecosystem. Since ecosystems are dynamic entities, their morphological and structural evolution through time is meaningful. Land use planning and decision-making are greatly improved by more precise and accurate spatial data regarding ecosystems (Blasi et al. 2017).

Geospatial technologies provide the means to work with multiple layers of information, showing distinctive characteristics of the environment, everything being at the same time accurately represented in space. Contemporary geospatial technologies are extremely well suited to represent ecosystems in their high complexity, not only through simple models. In order to differentiate between ecosystems, we can use key criteria, such as the density of relations, but in practice, apparent homogeneity is a more adequate alternative. If multiple characteristics are to be used at the same time to delimitate the spatial extension of an ecosystem, geospatial technologies can manage information in different layers, representing landforms, soil’s category, or vegetation groups but also to produce a composite map by joining the layers using different algorithms.

The chapter presents the main types of data required by geospatial technologies and the data sources for mapping ecosystems. We also provide different examples of how geospatial technologies could be used to emphasize specific features of ecosystems. We focused on emphasizing the differences that occur by using different geospatial techniques to assess similar features, the differences being dependent on the quality of raw data, or the processing solution used in the analysis.

2 Data Sources for Mapping Ecosystems

The input data for geospatial technologies are much diversified and may be provided by various sources, from remote sensing (imagery and data collected from space or airborne platforms) to databases created by specialists. The smartphone technology is proving to be a valuable tool for ecosystem data collection by the wide public through applications that use GPS to accurately map the information (Edsall et al. 2015). Data availability, accuracy, and resolution have greatly improved, and further progress is expected to satisfy increasing and more specific needs. Some of these sources provide data at a global scale, while others register detailed aspects of the ecosystems.

At the global level, the most widely used data are the ones provided by satellites systems. They have the advantage of spatial and temporal coverage; the images being generated at global level with a daily frequency. The most used sources for remote sensing are images produced by Landsat-MSS, TM, ETM+ and SPOT-HRV, Aster Terra (Turner 2010). The most important platform for this type of images is the US Geological Survey (https://earthexplorer.usgs.gov/). The main software solutions for image processing is ENVI 5.3, and ArcGis 10.x for spatial analysis. Raw satellite images should be processed through some filters to eliminate some geometrical distortions using software solutions such as ERDAS Imagine before being used for classification (Badar and Romshoo 2008).

For European land use and land change studies, the Corine Land Cover (CLC) database (available at http://www.copernicus.eu/) is widely used by researchers. As it records land cover type from 1990, 2000, 2006, and 2012, the database is a useful product in emphasizing landscape dynamics throughout the continent (Feranec et al. 2010, 2007). However, this dataset is not sensitive for changes that occur within an area of less than 25 ha as this is the minimum mapping unit. For the CLC 1990 and 2000, the satellite data were provided by LANDSAT-5, respectively LANDSAT-7 sensors and the proposed thematic accuracy of at most 85% was achieved only for the 2000 version. For 2006 and 2012 versions, satellite data were provided by SPOT 4/5 and IRS P6 LISS III respectively IRS P6 LISS III and RapidEye sensor and the thematic accuracy has not been checked yet. The Copernicus platform also offers a wide range of products designed for the analysis of specific ecosystems.

Besides the global and regional datasets there are national products that provide land use data generated either from military topographical maps or orthophoto maps. Often, these products are used to calibrate the global and regional datasets as they have a better resolution and accuracy (Pătru-Stupariu et al. 2015; Iojă et al. 2011).

For many years, the main challenge of geospatial data was related with resolution, since the products with 120, 60 or even 30 m resolution do not present enough detail to map distinct ecosystem features, like ecotones between different ecosystems (e.g., the shoreline, urban expansion in protected habitats). Currently, new technologies facilitated the acquisition of images with high spatial and spectral resolution and improved radiometric and temporal coverage. However, as Bishop et al. (2012) underline, sensor improvements rise issues related with data volume, storage capacity, memory and processing speeds, increased information variability, algorithm suitability, data integration, analysis, and visualization. Accuracy and effectiveness of data retrieved from online sources are other issues that permanently occur in spatial analysis. Some online platforms, like Open Street Map, are based on voluntary participation therefore the data quality is not fully validated.

3 Geospatial Technologies for Mapping the Distribution of Ecosystems

According to the American Association for the Advancement of Science (2017), geospatial technologies refer to a range of modern tools contributing to the geographic mapping and analysis of the Earth. The main categories of geospatial technologies are related with:

Remote Sensing—images and data which are collected from space or airborne cameras and sensor platforms and are subsequently processed to extract the distribution or quality of different ecosystems or elements like vegetation, water bodies or built areas (United States Geological Survey 2017).

Geographic Information System (GIS) represents an ensemble of software tools which can be used for mapping and analyzing data. The raw data processed through GIS solutions must be georeferenced which means every information has assigned a specific location on Earth.

Global Positioning System (GPS) is a global navigation satellite system owned by U.S. Department of Defense which provides geolocation (position on Earth) and time information for military and civil use. Currently, there operates another satellite system managed by the Russian Federation (GLONASS), while the European Union, China, and Japan are preparing to launch their own systems.

Internet Mapping Technologies comprise software programs like Google Earth and web features like Microsoft Virtual Earth which allow visualization and sharing of geospatial data.

The main characteristic of geospatial data is represented by the spatial reference information assigned to each location which can be represented by geographical coordinates, addresses or other types of spatial attributes.

Accurate and consistent mapping of the distribution of ecosystems must rely on proper definition and classification for the ecosystems themselves. The scope, time frame, and the scale of such classifications are also extremely important for the subsequent mapping of ecosystems distribution.

Scale is relevant when mapping the distribution of ecosystems from several perspectives. Classifications can be done either by subdivision (top-down approach) or by agglomeration (bottom-up approach). From a scale-based reasoning, maps can emphasize from the distribution of major ecosystems (e.g., biogeographical zones), down to the smallest relevant ecosystems (e.g., a pine forest or an oasis). The level of detail is directly dependent on the scale, but adequate geospatial technologies can present the ecosystems at varying scales, depending on the resolution of the input information. Scale is also relevant when trying to establish the limits of an ecosystem.

Mapping the distribution of different ecosystems is best achieved by tools specifically designed for them, since ecosystems are extremely different, with specific key characteristics that need to be properly evaluated (Pagella and Sinclair 2014). Mapping the distribution of forest ecosystems needs a totally different set of indicators than mapping the distribution of river ecosystems (Remmel and Perera 2017). Using the available geospatial techniques greatly improves the efficiency of the ecosystems’ distribution mapping, when not trying to differentiate between ecosystems with minor differences. The outcome usually consists of probability maps that need further confirmation through field evaluation.

4 Geospatial Technologies for Mapping the Quality of Ecosystems

Geographic Information System (GIS), Global Positioning System (GPS), and Internet Mapping Technologies are primarily used to identify the distribution of ecosystems, while Remote Sensing can also directly provide a great amount of information about their quality. Once identified the distribution, the first three solutions can be used to store different characteristics of the elected elements (in our case categories of ecosystems) and model them through spatial or geostatistical analysis (e.g., modeling the air pollution in relation with the ecosystem services provided by different categories of land cover (Salata et al. 2017) or geo-computation (techniques, including cellular automata, that permit the representation and prediction of complex phenomena (Bishop et al. 2015) like space and time dynamics of a geo-ecosystems in Mediterranean areas (Nainggolan et al. 2012))).

Remote sensing is widely used for mapping natural resources, analyzing land cover and land use dynamics, and assessing ecological, soil, geological, hydrological, and cryosphere systems (Bishop et al. 2015) all of these uses being related with ecosystem mapping.

Among the biophysical properties of vegetation that can be derived through remote sensing, there are some as green biomass, leaf area index, chlorophyll concentration, leaf moisture, biochemical, canopy structure, height, and basal area (Chen et al. 2003) that can be related with the quality of ecosystems (Table 3.1).

Table 3.1 Review of the most common aerial imagery products used for ecosystem assessments
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5 Using Geospatial Technologies for Mapping Distinct Ecosystems

5.1 Forest Ecosystems

Throughout history, forest ecosystems have changed their status, from being natural landscapes features to becoming ecosystems that require special management plans to keep providing the required ecosystem services (Pukkala 2013). The Food and Agriculture Organization of the United Nation (2012) considers forest as “land spanning with more than 0.5 ha with trees higher than 5 m and a canopy cover of more than 10%, or trees able to reach these thresholds in situ”. Crown cover percentage or the tree canopy cover (TCC) represents a central element in many definitions given to forest ecosystems. The minimum TCC for considering a landscape to be a forest ecosystem varies for the European countries from 5 to 30% as well as the minimum surface that varies from 0.5 to 2 ha (Kleinn 2001). Choosing a forest definition based upon to assess forest ecosystems can firmly influence the estimates of deforestation and forest degradation areas or the recognition of their drivers (Chazdon et al. 2016). A forest ecosystem must be considered as such, based on more than crown cover, area, or width; it should consider the biodiversity within and the amount of ecosystem services it provides (Thompson et al. 2016).

Forest ecosystems are subject for a complex policy making and management process (Fig. 3.1). The modern management of woodlands consists in assessing their potential to provide ecosystem services and the pressures they face dictate how to exploit the ecosystem services in a sustainable way, what conservation methods are the best for a certain ecosystem, and to establish a monitoring plan for it.

Fig. 3.1
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Forest ecosystem management in relation with the society’s needs

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The availability of geospatial technologies towards the large public has made forest management easier and more efficient and in the same time more complex because it has opened the door for other scholars, besides foresters to provide their know-how, making forestry an interdisciplinary domain (Cushing et al. 2008). These technologies have taken the woodland analyses and management from a local perspective to a national and even a global perspective, by enhancing the possibility to process huge amounts of data (Hansen et al. 2013). The valuable field observation made by foresters, biologist, or other researchers is now completed with data obtained from remote sensing and GIS techniques.

Geospatial technics have been used to assess the rainforest ecosystems and their resilience mostly towards land use change (Souza et al. 2013; Pinheiro et al. 2016), to emphasize deforestation patterns and trends (Hansen et al. 2010, 2013) due to selective logging and forest fires (Souza et al. 2013; Pinheiro et al. 2016) or to identify driving forces for forest degradation (Morales-Barquero et al. 2015), or just to map the degraded forests by processing several indicators (Pfeifer et al. 2016) or the fragmentation degree (Dong et al. 2014). In the same time, geospatial techniques were also used to map and quantify the amount of ecosystem services provided by forests and elaborating conservation plans for the areas of high natural value (Mura et al. 2015).

The contribution of remote sensing procedures and GIS-based solutions in forest ecosystem assessments has encouraged researchers to develop specific tools and indicators. The knowledge about the present challenges, the causes, and their effects over forest quality have increased, enhancing the decision and policy makers to take better measures to maintain and improve the forest ecosystems. Geospatial solutions stood at the roots of several products, commonly used by scholars and different stakeholders in achieving sustainability in regard to forest management. These products cover global, regional, and local scales providing alternatives for their users depending on their objectives.

Shimada et al. (2014) developed a product representing forest and non-forest cover at a global scale using ALOS PALSAR (Advanced Land Observing Satellite—Phased Array Type L-band Synthetic Aperture Radar) data. This product was made available for the wide public thorough JAXA (eorc.jaxa.jp) website. ALOS PALSAR data were used by numerous researchers, focused on regional or local studies such as mapping forest cover (Thapa et al. 2014), mapping forest degradation, and loss (Whittle et al. 2012) or estimating aboveground biomass (Cartus et al. 2012).

Another product that provides tree cover density at global scale is the one generated by using the methods described by Sexton et al. (2013) who developed a 30 m resolution dataset by rescaling MODIS (Moderate-Resolution Imaging Spectroradiometer) vegetation continuous fields (VCF) tree cover layer using LANDSAT images (available on Global Land Cover Facility website glcf.umd.edu/).

Probably the best-known product regrading global forest ecosystems is provided by Hansen et al. (2013) who offer a 30 m resolution datasets recording global forest loss and gain, available on Global Forest Watch online platform (globalforestwatch.org). The product was developed using LANDSAT and MODIS imagery and allows researchers and decision makers to understand the dynamic of forest areas between 2000 and 2012 and to identify the areas that need special attention in terms of conservation and sustainable management.

At European level, the Corine Land Cover dataset classifies forest land cover in broad-leaved forests (code: 311), coniferous forests (code: 312), and mixed forests (code: 313) (Bossard et al. 2000). The limitations related with spatial resolution of the dataset affect less the forest ecosystems than smaller ecosystems, and the errors generally affect the edges.

Through the Copernicus Programme another dataset was elaborated, exclusively for forest ecosystems such as the tree cover density (TCD) from which it derived the forest type (FTY) dataset for the year 2012, having a spatial resolution of 20 m. However, TCD includes land uses that are not considered forests according to the forestry definition such as orchards, parks, and alley trees and group of trees within urban areas (Langanke 2013).

The Joint Research Centre (JRC), European Commission’s science and knowledge service has provided forest cover maps for 1990, 2000 and 2006. For 2006, they have also provided forest type datasets. For 1990 and 2000, JRC used the LANDSAT sensors resampled to 25 m resolution and CLC were used as ancillary data and for 2006, they used SPOT-4 sensors instead of the LANDSAT sensor. The methods used to generate the 2000 forest cover map are described by Pekkarinen et al. (2009) where they state that the resulting forest/non-forest map was validated with three independent datasets. The forest cover maps and data are available for download on the European Commission online platform (forest.jrc.ec.europa.eu) along with information and data regarding tree species distribution and projected distribution for the future. JRC also provides data and information about European forests’ pattern and fragmentation, forest fires, forest in relation with climate change, forest ecosystem services, or land use change of forest ecosystems.

5.1.1 Case Studies

Different datasets providing information about forest ecosystem differ in terms of the same indicator values. For instance, we used several datasets and products to estimate the amount of forest surface for the Romanian Carpathians in accordance with FAO definition (Fig. 3.2). The data were selected to emphasize the forest vegetation cover in 2000 using the JRC and CLC database and the ones proposed by Hansen et al. (2013) and Sexton et al. (2013). The generated maps enhance differences among the four data sources. The JRC and CLC data are more similar as they were resulting using similar base maps and satellite imagery and the same goes for the Sexton and Hansen datasets. The differences in forest vegetation surfaces between the minimum and the maximum value are of 0.75 million ha. As we mentioned before, there is a clear border between what we consider forest and forest vegetation cover. Most of the products generated through remote sensing procedures reveal forest vegetation land cover, which is not the same as the areas considered forest by foresters and forest managers. This confusion may lead to biased conclusion when referring to deforestation degrees, illegal logging, or forest cover dynamics.

Fig. 3.2
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Forest vegetation cover in the Romanian Carpathians extracted from different data sources (2000)

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The discrepancy among the surface values provided by the datasets is generated by the resolution of the base maps used to generate these products, whether talking about satellite imagery, orthophoto maps, or high-resolution spatial representation (Table 3.2). However, a preliminary analysis can indicate a pattern regarding the forest vegetation dynamics in the Romanian Carpathians by using all the abovementioned products. By averaging the surface indicated by each product per year, we can draw a preliminary conclusion that forest vegetation cover had a decreasing trend in the last 30 years (Fig. 3.3).

Table 3.2 Forest vegetation surfaces from different data sources—forest vegetation in Romanian Carpathians
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Fig. 3.3
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Forest vegetation surface evolution in the Romanian Carpathians—forecast generated through the aggregation of different datasets

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Our aim was to provide a brief review of the available geospatial technologies most commonly used in assessing forest ecosystems and we used the available, scientifically endorsed products to emphasize the dynamics of forest vegetation cover in the Romanian Carpathians. The results have revealed significant differences between different databases, enforcing the need of complementary research method when assessing forest ecosystems. Geospatial technologies should be used along with other specific methods to establish the quality of forest ecosystems. Such examples are provided by several authors that combined field methods and geospatial technologies to assess forest ecosystem in different study areas in the Carpathians (Teodosiu and Bouriaud 2012; Pătru-Stupariu et al. 2015).

We emphasize that using geospatial technologies in forest ecosystem studies has proved to boost the knowledge of these ecosystems. However, the need to crosscheck the accuracy of the results driven by using these technics is crucial for describing certain phenomena occurring in forest ecosystems.

5.2 River Ecosystems

River ecosystems have a specific linear topology and their map representation width at certain scales must be exaggerated, in terms of proportionality with reality, to make them visible. To set the limit of an ecosystem, an arbitrary criterion which offers higher contrast is sometimes chosen as the delimitating factor, since the ecosystems do not have clear-cut boundaries.

In some methodologies, the specific criteria used for the ecosystem classification serve to delimitate areas with those ecosystem characteristics (Gao et al. 2015). The set of criteria defining an ecosystem as a classification category is used to find the territories with these characteristics and the proper ecosystem defined as such. Some of these characteristics are inextricably linked to the territory where they appear or are best described through spatial distribution, using maps.

Mapping river ecosystems reveal distinct challenges. Determining the linear pattern and flow direction are among the most important variables. The physical parameters and the composition of the ecosystems are continuously changing along their length. River ecosystems are dynamic since water is in a state of constant movement along the length of the river. Further, water is also changing its properties through interaction with all the other components of the river, such as the stream bed lithology or the vegetation. While along a river there are segments with different properties, resulting in different ecosystems, to establish where one ecosystem ends and another one begins is a more challenging task, since there always is a fading transition between such river ecosystems. Moreover, the water flow keeps transferring some of the energy and matter and thus some characteristics from one river segment to the following. Consequently, the delimitation between the ecosystems of a river must be performed in an arbitrary fashion, considering the variation of a chosen key indicator or through expert opinion.

The Water Framework Directive explicitly emphasizes that the member states shall submit to the Commission a map or maps, in a Geographical Information System (GIS) format, of the geographical location of the types consistent with the degree of differentiation required under systems of classification described in the Directive. The classification of the water bodies in accordance with these criteria can be greatly improved by using geospatial technologies. Some of the indicators can be directly determined or calculated using GIS (e.g., altitude, slope, distance from river source). Satellite images offer opportunities to determine other specific indicators (e.g., mean water depth).

The Water Framework Directive methodology includes not only modalities to classify water bodies but also indicators for evaluating the water ecosystems quality. These indicators quantify the general biological, hydro morphological, physical and chemical, and specific pollutants (synthetic and non-synthetic) properties of the water. Some data cannot be obtained through geospatial technologies and there is the need for field surveys and laboratory analysis. For example, among the quality elements for the river classification of the ecological status, the composition, abundance and age structure of fish fauna is one of the main indicators that cannot be determined but through field sampling. Still, this data can offer more insights if it is collected and treated as spatial data. Further processing is also greatly improved, as it is data presentation through maps.

5.2.1 Case Studies

Today, GIS techniques and remote sensing are commonly used in river management. Field data can easily be computed using modern geospatial technologies, emphasizing issues that should be looked upon. We provided an example of how several fundamental characteristics of a river basin can be mapped by using geospatial techniques.

Our case study is represented by the Olt river basin, the major inner river of Romania. We used the digital elevation model (DEM)—20 m resolution to calculate the slope degree and orientation in Olt hydrographic basin (Fig. 3.4). These two characteristics are very useful in river management as a high slope degree means faster water flows, potentially turning into torrential phenomena, leading to floods and material damages. The slope degree and orientation were calculated using ArcGIS 10.3. by using Slope and Aspect tools from Spatial Analyst toolbox. Using the data provided by this analysis the decision-making process regarding flood management and river management is streamlined.

Fig. 3.4
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Different characteristics for Olt hydrographic basin represented using geospatial techniques

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5.3 Lakes Ecosystems

When mapping lake ecosystems, as it is also the case of the river ecosystems, there should be made the distinction between the actual water ecosystem and the riparian ecosystem that is usually found in the proximity. The riparian ecosystem is strongly integrated with the water ecosystem it borders, influencing the functional processes and the structure of the aquatic communities, extending for 50 m or more from the water surface (Angradi et al. 2016).

Mapping and evaluating the spatial variability of the lake ecosystems can be achieved efficiently by using habitat distribution maps at different scales, from 1: 50000 to 1:5000 and satellite imagery. When using satellite imagery to establish the distributions and the extension of the lake ecosystems, filed validation is necessary.

Besides Corinne Land Cover, a suitable database for mapping lake ecosystems is Copernicus Pan-European High-Resolution Permanent Water Bodies, available on the Copernicus official web platform (land.copernicus.eu). It contains five land use categories, at 20 m resolution, Lambert projection, ETRS-8: forest, pastures, water bodies, wetland, and built area. Using this database to delimitate ecosystems needs a pixel-by-pixel validation stage based on satellite imagery in order to eliminate errors that might have been generated when the initial processing was performed (EEA 2016). The CLC dataset cannot record water bodies with surfaces below 0.25 ha, and for more detailed analysis, more accurate datasets are required.

CORINE database is not sensitive whether the lake ecosystem is natural or artificial, such as sewage or industrial water basins. The input data for Copernicus is IRS-P6/Resourcesat-2, SPOT 4 and 5 satellite images, further improved using IRS, SPOT and Landsat 7 ETM+ satellite images. Compared to the CORINE, the Copernicus dataset is much more detailed and the number of identified lakes is much larger (Fig. 3.5).

Fig. 3.5
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Lake ecosystems in South of Romania: CLC 2012, code 512 (a) and Copernicus databases (b)

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Evaluating the degradation of aquatic ecosystems through remote sensing and GIS techniques is proving to be a useful approach (Gao et al. 2016; Al-Fahdawi et al. 2015). Also, identifying and delimiting aquatic ecosystems, including the lakes, through remote sensing methods such as Multiband Spectral Relationship, Normalized Difference Water Index (NDWI), and Tasseled Cap transformation (Gao et al. 2016) is proving to be successful in many situations. To extract lake position and extension, Landsat-8 OLI imagery offers good results, by processing an initial binary image and further applying a Normalized Difference Water Index (NDWI) (McFeeters 1996), eliminating by supervised classification the adjacent surfaces (Gao et al. 2016).

A series of 30 m resolution satellite images that can also be used for lake ecosystem analysis are accessible by USGS Global Visualization Viewer (Turner 2010). These data provide good territorial coverage and are available in the form of different periods of time datasets. They offer information for areas with limited accessibility and allow for comparative analysis in time. Remote sensing and satellite imagery are cost and time saving, considering the amount of data produced (Al-Fahdawi et al. 2015). Spatial resolution is still a limitation in the case of the aquatic ecosystems (Hestir et al. 2015). The size of the image pixel, compared with the size of the covered habitat is extremely important (Hestir et al. 2015).

5.4 Urban Ecosystems

Urban ecosystems can be defined as an integrated ensemble of connected built (sharing built or paved infrastructures) and green infrastructures (Burkhard and Maes 2017). Geospatial technologies have a critical role in the analysis of urban ecosystems quality since they can be used for mapping, spatial analysis and modeling. The diversity of databases, available in the last decade, and the high quality of sensors, and improved image resolution offer the opportunities in urban ecosystem assessment and sustainable urban planning for the future.

As more land is being urbanized and natural landscapes are shrinking, sustainability in urban planning has become a common topic within the scientific community (Gavrilidis et al. 2017). The complexity of “urban ecosystems” entails advanced methods for assessing the relations and process within a city. Geospatial technics have proved to be useful for a wide range of urban studies related to planning, environmental, or social issues. Remote sensing analysis and GIS technics used in urban analysis enhanced the role of geographers in this field of research.

Remote sensing has a critical role to play in the analysis of the interactions that occur between people and urban environments that may help shape our understanding of humans and the principle environment in which they live. Improved sensors, more exacting resolutions, the rapid convergence of earth observation systems, geographical information systems, and spatial statistics have expanded the scale and scope of remote sensing applications across human geography, the social sciences, and in the real world of public policy (Gatrell and Jensen 2008). The availability of geospatial technologies made possible different approaches in assessing urban sprawl and acknowledge when this phenomenon occurs in the world’s cities. Bhatta et al. (2010) examined several methods based on remote sensing technics and their suitability for sprawl measurements, concluding that some of the methods borrowed from other research domains are not as efficient in urban sprawl assessments. Even though the topic regarding urban sprawl is highly theorized, the use of geospatial technologies has enhanced the measurements of the phenomenon (Grădinaru et al. 2017; Jaeger and Schwick 2014).

Geospatial technologies have been used to assess, measure, and map different processes and phenomena within the urban settlements and their surrounding such as air pollutants dispersion (Fishman et al. 2008) and sources of pollution (Wang et al. 2013), built-up dynamics (Gavrilidis et al. 2015), occurring environmental conflicts and functional incompatibilities (Iojă et al. 2014b; Niţă et al. 2013), assessing and mapping urban landscape and urban landscape features (Inostroza 2017; Gavrilidis et al. 2016), planning and assessing urban green infrastructures (Gavrilidis et al. 2017; Iojă et al. 2014a), or assessing and mapping of the ecosystem services generated by different urban features (Burkhard and Maes 2017; Cvejić et al. 2015). High-resolution satellite images have been very useful in extracting information about different urban land covers. These images have often been used to extract data about urban green areas or areas covered with vegetation. Mathieu et al. (2007) mapped the private gardens from Dunedin (New Zeeland) using the technique of object-oriented classification on multispectral Ikonos images. The authors were among the first researchers to reach this degree of detail analysis as previous studies focused over the large green urban areas. Since present days, satellite imagery resolution evolved simultaneously with the assessment methods.

Air quality assessment is usually done by using in situ measured data at ground level in monitoring networks, by satellite measurements or modeling, or by a combination of the measurements and modeling approaches. Satellite data due to their large space coverage provide information on the distribution of pollutant concentrations (Fishman et al. 2008), estimation of the pollutant emission (Streets et al. 2013) and for air quality applications (Duncan et al. 2014).

Urban green infrastructures are a modern concept that assesses urban green space in an integrated way, in accordance with the amount and variety of ecosystem services they provide. From emphasizing the diversity of urban green infrastructures (Badiu et al. 2014), through assessing the connectivity within an urban green infrastructure (Niță et al. 2018) and finally to plan increasing and improving an urban green space network based on the human needs (Gavrilidis et al. 2017; Cucu et al. 2011), geospatial technologies have provided an useful tool for researchers focusing on urban environments.

5.4.1 Case Studies

Multiple studies that assessed urban dynamics or urbanization processes included land use and land cover analysis. Gavrilidis et al. (2015) emphasized the influence that a raw resource can have over the urbanization process of a city by correlating the land use and land cover changes with the dynamics of the economic activities in the last 100 years. Several studies have used the land use and land cover dynamics for assessing the sprawling patterns of cities (Sperandelli et al. 2013), as the information used for establishing a certain land use or land cover type were extracted from various base maps such as old topographical maps or aerial images. However, these studies are relevant only to settle landscape change patterns and they cannot be used for detailed analysis. For detailed scale analysis, high-resolution maps or imagery are required and for assessing land use and land cover dynamics at a precise level, same scale base maps are required too (Table 3.3; Fig. 3.6).

Table 3.3 Green space and built-up dynamics between 2005 and 2010 in Ploiesti (Romania)—data extracted from aerial images
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Fig. 3.6
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Green areas and built-up areas land cover distribution in Ploiesti (Romania)—extracted from aerial imagery (2005 and 2010)

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As emphasized in the examples above, using geospatial technologies to spot a certain dynamic of the urban land cover patterns generates “three-dimensional” results, providing spatial, quantitative, and qualitative outcomes that further can be developed, offering more depth to the analysis of urban areas and their surroundings. The availability and use of geospatial technics in urban planning have opened the door for participatory planning, thus the stakeholders, local authorities, citizens, and researchers can express themselves towards the processes that occur in an urban landscape.

6 Conclusion

Mapping different processes, phenomena or status quos helps indicated the spatial location of an issue, making the intervention process easier. Geospatial technics made mapping more accurate and easier, being a tool used not also by geographers, but also by scholars from different domains. As all geospatial technics assumes geographical knowledge, this new technological age in which location tagging and mapping became widely used is the perfect time for skillful geographers to prove their inputs in other research domains.

However, the use of geospatial techniques in ecosystem assessments could become biased without field data and observation to confirm the processed findings. This could be considered an important challenge for geography and geographers in future decades as the need to achieve results very fast in order to respect multiple deadlines. Thus, field observation could become more and more neglected and the dependency on geospatial techniques to provide field observation can alter the reality and further the effects of the decision-making process. We conclude this chapter by underling that geospatial techniques are useful tools in modern analysis that provide useful outcomes, improving the management of ecosystems but the development of these technologies should not substitute the classic assessment techniques, especially field observation which represents the basis of geographic research.