Science of the Total Environment 610–611 (2018) 17–23
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Editorial
Mapping the environment
1. Background Maps are our most common graphic representation and approximation of reality. They are fundamental for an integrated and deep understanding of the world that surround us. However the mapping of environmental variables is affected by spatial uncertainty; from this perspective maps of environmental variables can be considered an approximate representation of reality. Overall, maps have the power to show our place and perception of the world (Pickering, 2014). Maps can be used as political and military instruments of power. They can promote social change because they have important impacts on people's imagination and perception of mapped topics. Maps have been used for centuries for political and military purposes, such as planning and executing wars, claiming territories, the creation of nations, collecting taxes and identification of natural resources (Krupar, 2015), and even as propaganda (Harley, 1988). They have been useful to spatially display criminal activity (Spicer et al., 2016), human health issues (Hay et al., 2013; Keddem et al., 2015; Simarro et al., 2012), plant diseases (Bouwmeester et al., 2016), social activities (Tsou et al., 2013), elections forecast and results (Ondrejka, 2016; Pavia et al., 2008) and other social, environmental and economic phenomenon, such as archaeological sites (Wagner et al., 2013), population density (Gomes, 2017) and dynamics (Deville et al., 2014), social values and perceptions (Tyrvainen et al., 2007), population vulnerability (Frigerio and De Amicis, 2016), inequality (Salesses et al., 2013), food and nutrition security (Aliaga and Chaves-dos-Santos, 2014), well-being (Tian et al., 2015) education and poverty (Segun et al., 2012; Rafee Majid et al., 2017), land use intensity (Kuemmerle et al., 2013), land abandonment (Alcantara et al., 2012) and economic growth (Lenzen et al., 2012). Basically, all phenomenon that have a spatial dimension can be mapped and modelled. Objective mapping refers to final map(s) (in space and/or in time, 2D, 3D or 4D) that quantitatively represent, according to a digital representation, a spatio-temporal attribute (SA) related to environmental properties or processes being analysed as realistically and accurately as possible at the scale being utilized. The mapping of the SA of interest (SAI) should also take into consideration the spatial uncertainty that is inherent to the mapping process. From this perspective, in many circumstances, it is satisfactory to produce an exhaustive map of the SAI in an optimal sense: a kind of average scenario of the spatial distribution of the SAI among the possible equiprobable scenarios, with an attached measure of local uncertainty (Journel, 1989; Goovaerts, 1997). However, for some specific tasks, common for example in groundwater modelling (e.g., Koltermann and Gorelick, 1996; Eaton, 2006; Deutsch, 2002), there is the need to produce a consistent number of equiprobable
http://dx.doi.org/10.1016/j.scitotenv.2017.08.001 0048-9697/© 2017 Elsevier B.V. All rights reserved.
scenarios of the spatial distribution of the SAI (i.e., equiprobable maps) to analyse the impact of spatial uncertainty on decision making processes or in mathematical/numerical models using the produced scenarios. Under the above stated terms, the task of mapping an SAI in a specific spatio-temporal domain involves two main issues: 1) information retrieval related to the SAI; and 2) use of retrieved information for producing a spatially exhaustive map of the SAI. The two exposed issues encompass many scientific and technological aspects. This discussion is mainly focused on the second issue, which constitutes the core task of the objective mapping process and covers a wide set of methodologies and different perspectives; consequently, issue 1 will be outlined very briefly. Both issues are deeply connected with geographical information systems (GIS) (Burrough and McDonnel, 2000) and with their evolution toward geographical information services (Yue et al., 2015). The pivotal role of GIS in the context of mapping the environment is self-evident, just looking at one of the definitions (toolbox-based) of GIS given by Burrough and McDonnel (2000): “a powerful set of tools for collecting, storing, retrieving at will, transforming and displaying spatial data from the real world”. Moreover, GIS are now a common operative workingtool for researchers, not too much different from a spreadsheet or a word processor. The tendency of modern GIS to be oriented toward interoperability and offering services (Ferster and Coops, 2013) and mobile solutions is particularly beneficial in the processes related to environmental data collection and collation; this aspect is also relevant for conducting validation of the produced maps in the field. In particular, mobile GIS solutions coupled with technological development in the context of environmental parametrization empower the efficiency of field surveys and field interpretation, enforcing the adherence of digital mapping to environmental reality. From this perspective, the use of mobile GIS and mapping solutions in the context of geological and geomorphological surveys is emblematic (McCaffrey et al. 2005; Viles, 2016). Then, the fact that modern GIS solutions are giving more attention to 3D spatial data as well as to dynamic spatial data (i.e., spatiotemporal) is another aspect particularly beneficial for the task at hand (You and Lin, 2016; Yu and Gong, 2012; Goodchild et al., 2012). In fact, the analysis and forecasting of the evolution of environmental processes in time and space is becoming a very frequent task in many earth sciences disciplines (Alonzo et al., 2014; Poggio and Gimona, 2017). Finally, web-GIS coupled with BIGDATA analysis capabilities (Jeansoulin, 2016) have a relevant role in the distribution and retrieval of the increasing quantity of open environmental data that often corroborates the mapping of environmental properties.
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2. Maps and environmental understanding – a brief historical perspective The generation of maps to communicate spatial information has been taking place for a long time, with the oldest known maps dating to about 6200 B.C.E. (Friendly, 2008). The earliest maps were handdrawn or etched, suffered from a lack of standard methodologies, and were created before a good location system had been devised. Latitude and longitude were not used until 150 C.E., triangulation to determine mapping locations was not developed until 1533, cylindrical projection to display the globe on maps was invented in 1569, and cartography has only had broadly accepted, organized standards since about the 17th century (Harley, 1989, Friendly, 2008). These all represent important advances that were necessary to create accurate, reliable maps, and many of the advancements made in early mapping occurred through the development of increasingly accurate tools and techniques for locating position and creating map products (Friendly, 2008). Before environmental maps depicting the distribution of things such as climate, geology, soil, or vegetation could be created, it was necessary to have adequate base maps in place, therefore such maps lagged behind other cartographic products such as political boundary maps (Miller and Schaetzl, 2014). Maps of the distribution of environmental variables, which is the primary focus of this special issue, didn't begin appearing until the late 17th to late 18th centuries. The first known weather map did not appear until 1686, the first geological map appeared in 1778, the first topographic map in 1782, disease incidence was mapped for the first time in 1798, and by 1838 a physical atlas showing the distribution of plants, animals, climate, etc. had been published (Friendly, 2008). The first attempts at soil mapping occurred in Europe in the early 1700s (Brevik et al., 2016a), while the first modern soil map was produced in 1883 (Brevik and Miller, 2015). The study of these early maps is important as they serve as primary sources that allow us to understand the past status of our knowledge of various environmental subjects and how that knowledge has evolved over time (Edney, 2005; Brevik and Hartemink, 2013). The study of cartography is also important because poorly constructed maps have the ability to communicate misinformation. Information communicated through graytones, a popular way to communicate spatial information on natural resource maps, is particularly susceptible to distortion during the printing process; therefore careful planning and design of graytone images is very important. In extreme cases the values originally being communicated can be inverted during the printing process (Monmonier, 1996). Another issue is that maps can be purposefully manipulated to provide false information that advances a chosen agenda (Harley, 1988). One example of this is the generalization of information on derivative maps to favour one side or the other during an evaluation of the potential environmental impacts of a development project (Monmonier, 1996). Understanding the ways that information can be miscommunicated on maps, both inadvertently and on purpose, is an important part of being able to critically evaluate information transfer based on maps, and studying cartographic history provides great insights into ways miscommunication has happened in the past and how it may be avoided in the future. Beginning in the 1960s GIS, global positioning systems (GPS), and increasingly powerful computers made it much easier to generate maps, compare trends, and analyse spatial relationships than it had been in the past (Wegener, 1999). While there are many advantages to these advances, there are also some major disadvantages. For example, modern computer mapping programs allow people with little to no formal training in cartography to generate professional looking map products that, in some cases, have problems such as inappropriate projections or misleading symbols (Monmonier, 1996). The spatial modelling modules that are included with many commercial GIS programs also make it quite easy for individuals with inadequate training in spatial statistics to run spatial models that incorporate rather fundamental errors and, therefore, produce unreliable results (Fotheringham, 1999).
In addition, the use of GIS to run models that were not provided as a part of the commercial product has also led to questionable results (Wilson and Lorang, 1999). It is important to note that these weakness are not an argument against the use of GIS, but rather, represent an argument for appropriate training in cartography and modelling for those who will use GIS for such purposes. One common use of GIS has been to overlay and combine or compare layers of different information, for example, layers with geologic, hydrologic, precipitation, soils, topographic, and/or vegetative data. The idea of overlapping multiple layers to analyse spatial relationships is not a new one; prior to GIS scientists would place multiple maps printed on materials such as Mylar sheets on top of one another in an attempt to investigate spatial relationships (Marbut, 1951). However, when overlapping transparent sheets features rapidly blurred together as more layers were added (Aguirre, 2014). The desire to compare more spatial variables in a clear, legible way led to the first GIS (Tomlinson, 1962), and this has proven to be one of the strengths of GIS. The first GPS system that was available to the public was developed in the 1970s, but location inaccuracies of as much as 500 m could occur. This was because the system was established by the USA military and the signal was degraded to provide an advantage to the military during their operations. GPS was not truly useful as an environmental mapping tool until the signal degradation was removed, which occurred in 2000 (Hannay, 2009). In today's world even common devices such as cellular phones have GPS units that are capable of providing readings to within about 12–15 m of the user's actual location (Lee et al., 2015), while a differential global positioning system (DGPS) can provide accuracies as fine as about 0.10 m (Bakuła, 2010). Highly accurate GPS has led to great improvements in our ability to map and model environmental parameters as reliable locations can be determined rapidly and at low expense. This information can then be entered into a GIS and combined with other mapped or remotely sensed products. Remote and proximal sensing involve a wide range of non-invasive techniques used to gather information about the Earth, allow much greater coverage than traditional physical point sampling methods (Brevik et al., 2016b), and allow data collection from regions that are difficult to access or where sampled data is otherwise lacking (Anderson, 2016). Near-Earth sensors such as those mounted on tractors or that are hand held are referred to as proximal sensors, as opposed to remote sensors that gather data from satellites or airborne platforms (Mulla, 2013). These techniques usually measure radiation that is reflected or emitted from the Earth's surface or from vegetation. Aerial photography, a type of remote sensing, has been utilized since the 1930s, but the term “remote sensing” wasn't first used until 1960 (Jones and Vaughan, 2010). The first use of satellite data for meteorological research and forecasting occurred in the late 1960s, the first Earth Resources Technology satellite (later called LANDSAT) was launched in 1972, providing the first electronic detectors as opposed to cameras, and aerial thermal scanners have been used since the early 1970s (Jones and Vaughan, 2010). Sensors have been placed on satellites, manned aircraft, balloons, ships, ground platforms (Jones and Vaughan, 2010), drones, kites (Anderson, 2016), tractors or other ground-based vehicles, and can also be hand-held (Mulla, 2013). The development of GPS, GIS, and more powerful computers has greatly enhanced our ability to utilize proximal and remote sensing data (Jones and Vaughan, 2010). Recent advances have also made it much easier to use natural resource maps to improve on the mapping of other related natural resources. This cross use of maps has been practiced since at least the 1950s (Brevik and Miller, 2015). Applications have included the use of soil maps to create flood plain maps for land use planning (Witwer, 1966; Cain and Beatty, 1968; McCormack, 1971), soil maps to generate (Lindholm, 1993; Miller et al., 2008; Oehlke and Dolliver, 2011) or improve on geology (Rogers, 1953) and geomorphology (Brevik and Fenton, 1999; Schaetzl, 2001; Luehmann et al., 2013) maps, and the use of vegetation maps to improve on soil mapping (Ibáñez et al.,
Editorial
2016-in this issue). GIS is a very important tool in such mapping (Evans, 2002; Miller and Burras, 2015; Ibáñez et al., 2016-in this issue) and this practice has grown as GIS capabilities have improved. The use of one natural resource map to improve on another or to supply information about another has proven successful when there is a definite, understood relationship between the two resources and when the natural resource map being used to improve on the other was mapped at a more detailed scale than the resource map that is being improved. Such analyses need to be carefully conducted, because errors are more common in maps that are derived from previous maps than on maps derived from primary data (Monmonier, 1996). However, the integration of data from numerous fields will be vitally important to addressing the problems that face the world today (Grunwald et al., 2011; Bouma, 2015; Brevik and Sauer, 2015), and the ability to integrate data from related maps is one approach that has promise to help address those problems in an economically efficient fashion (Ibáñez et al., 2016-in this issue). For these reasons, further investigation into improved methods of mapping the environment, as well as ways to extract additional information from the maps we already have, are critical research needs.
3. Environmental information Environmental information retrieval is strongly influenced by hardware (sensors, miniaturization, communications, etc.) and software technological developments, which in the area of earth sciences have increased the capability to parametrize the geo-environment, both in the surface as well as in the sub-surface, at different spatial and temporal scales and resolutions to an astonishing degree. The technological improvements that affected both field and laboratory techniques, including proximal and remote sensing methodologies, GPS, GIS, and increased computer capabilities, increased the potential for environmental data collection (Bierkens, 2015; Eitel et al., 2016; Lettenmaier et al., 2015; Maghsoudi et al., 2013; Mecklenburg et al. 2016; Mulder et al., 2011; Ruddick et al., 2014; Simmons et al., 2016; Voigt et al., 2016). The wide range of methodologies and sensors that can be used for parametrizing the environment contribute to the diversification of environmental information sources and information characteristics. The characteristics of the information collected for mapping a specific SAI have a strong influence on the methodologies that can be adopted for the objective mapping. In a general setting, the environmental information available can be spatially fragmentary and heterogeneous. It can be spatially fragmentary because often the value of the SAI or other auxiliary SAs related to the SAI are only known for a limited set of locations in the spatial domain of interest. The heterogeneity of information refers to other important characteristics, such as typology, quality and support. First, the information can be in the form of direct measurements of the SAI (hard data), representable as a continuous or discrete variable (Deutsch, 2002; Goovaerts, 1997). Direct measurements can be characterized by different uncertainties and can refer to different spatial or spatiotemporal supports (Isaaks and Srivastava, 1989). Second, the information available can be in the form of direct measurements, spatially fragmentary or exhaustive measurements of auxiliary/secondary SAs related to the SAI, i.e., as proxies of the SAI or, from another perspective, as soft information (imprecise and/or fuzzy) related to the SAI (e.g. Copty and Rubin, 1995; Franssen et al., 1997; Poeter and McKenna, 1994; Posa and Rossi, 1990). Another type of information that should be considered in mapping procedures is expert knowledge (EK). EK can be related to the physical-chemical processes leading to a given spatial distribution of the SAI or to procedures related to data collection. With EK we can also generate qualitative information on the SAI (e.g., in a semantic way) that can be quantitatively exploited, e.g., by means of fuzzy sets and fuzzy logic or by means of Bayesian approaches (e.g., Allard et al., 2012; Demicco and Klir, 2004).
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4. Quantitative use of existing information Information availability is essential for mapping. This is common to almost all disciplines dealing with spatial and spatio-temporal data. It is not surprising that this issue has been, and still is, a fervent research topic in many disciplines such as ecology, environmental and engineering geology, geochemistry, hydrogeology, meteorology, oceanography, petroleum geology, remote sensing, soil science, etc. (Bond et al. 2007; Eaton, 2006; Goovaerts, 2001; Kanevsky and Maignan, 2004; McBratney et al., 2003; Myers, 1994; Rast et al., 2014). The core goal in mapping a specific SAI is to produce a realistic representation of its spatial or spatio-temporal distribution. The generic term “realistic” refers to two desired properties of the produced maps. First, the produced maps should have a low uncertainty, or putting it in another way, should predict accurately the value of the SAI in nonsampled locations. Second, the spatial patterns of the produced maps should be compatible with the related physico-chemical processes and factors (i.e., our expert knowledge) or with the spatial patterns coming from analogue spatial distributions of the SAI. These two desired properties can be achieved by means of different perspectives and methodologies. This paper will not provide an exhaustive and detailed report of the wide spectrum of methodologies and variants followed to create objective maps of a SAI from quantitative and qualitative data; our aim is to outline some of the main paths that are followed to achieve these results. Where possible, we stress where the different paths converge. Computational and mathematical approaches cover many sub-disciplines such as logic (boolean and fuzzy), spatial-statistics, time series analysis, data assimilation, inverse modelling, machine learning, pattern recognition, spectral-methods, statistical learning theory, numerical modelling, etc. A general trend common to the different approaches is the use of different sources of information (data fusion) in order to reduce the map uncertainty as much as possible (or reduce the differences between equiprobable scenarios) and improve adherence to real physico-chemical processes and mapped property distributions. In this discussion, we focus attention on mapping an SAI when the available information related to SAI is at least partially spatially fragmented. We avoid discussing the derivation of maps of the SAI (e.g., land cover type, geomorphological units, landslide hazard, etc.) only from existing exhaustive maps of other SAs. This topic is crucial in the field of remote sensing and is playing a pivotal role in the analysis of geo-environmental processes at all scales, from local to global (Bierkens, 2015; Mecklenburg et al., 2016; Mulder et al., 2011; Rast et al., 2014; Ruddick et al., 2014; Vereecken et al., 2016; Zare et al., 2016). The mapping of SAIs from existing exhaustive spatial maps of related SA is also common in geomorphology and engineering geology. In this context it is worth mentioning geomorphometry, where exhaustive data are represented by derivatives of digital terrain models (Kelner et al., 2016; Kim and Yu, 2009; Passalacqua et al., 2015). A well-beaten and natural track for mapping an SAI is the spatial-statistical perspective (Cressie 1993; Myers, 1994), where the problem is stated as a prediction (interpolation) problem and in some circumstances as a stochastic simulation problem. In this context, the geostatistical methodology (Chilès and Delfiner, 1999; Clark and Harper, 2002; Huijbregts and Journel, 1978; Goovaerts, 1997) is extremely well known and well-tested and furnishes ample theoretical and software implementations for the spatial analysis of SAs. It permits data fusion approaches to deal with continuous and discrete variables, to use imprecise data and spatiotemporal analysis (Diggle et al., 1998; Goovaerts, 1997; Hengl et al., 2004; McKee and Binns, 2016; Poeter and McKenna, 1994; Posa and Rossi 1990; Trevisani et al., 2017-in this issue). Approaches based on machine learning such as support vector machines, generalized additive models, and splines are becoming more and more popular for interpolation (e.g., Kanevsky and Maignan, 2004; Hastie et al., 2009; Hutchinson and Gessler, 1994). In disciplines such as hydrogeology and applied geophysics numerical inversion of physical-based numerical models is a way to produce
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maps of a given SAI. For example, in ground water modelling 3D fields of hydraulic conductivity can be derived by inverting a ground water model calibrated on piezometric heads and fluxes measured in the field as constraint (Franssen and Kinzelbach 2009; Linde et al., 2015; Zimmerman et al., 1998). The use of numerical models of physical-chemical processes calibrated with field data is another way to map an SAI. A special case is in the field of geo-modelling, where numerical models can model sedimentary processes producing a realistic 3D architecture based on field data and EK based evaluations (Copty and Rubin, 1995; Deutsch, 2002; Eaton, 2006; Hu and Chugunova, 2008; Koltermann and Gorelick, 1996; Linde et al., 2015; Weissmann et al., 1999). In this field it is worth citing approaches based on fuzzy logic, where expert knowledge is explicitly stated as a set of semantic rules (Demicco and Klir, 2004). In the description of dynamic systems and with the availability of a continuous flow of new environmental data, the approach of model calibration can be then extended to data-assimilation approaches, something common used in oceanography, atmospheric sciences, hydrology and hydrogeology (Franssen and Kinzelbach, 2009; Rast et al., 2014).
5. Novelty of the special issue and future needs The objective of this special issue is to bring to light the latest research and methods used in mapping the environment. The 26 works published in this special issue are from diverse regions of the globe, including Antarctica, South Africa, China, North America, the Mediterranean and Northern Europe. They are focused on mapping different aspects of the environment such as bedrock surface morphology (Trevisani et al., 2017-in this issue), landslides (Zezere et al., 2017-in this issue), soil moisture (Martinez-Murillo et al., 2017-in this issue), organic carbon, pH, texture, cation exchange capacity, and chemical properties (Bogunovic et al., 2017-in this issue; Mulder et al., 2016-in this issue; Ottoy et al., 2017-in this issue; Schillachi et al., 2017-in this issue), salinity (Scudiero et al., 2017-in this issue), permafrost distribution (DeLuigi et al., 2017-in this issue), glaciers (Fernandes et al., 2017in this issue), glacial and periglacial landforms (Palma et al., 2017-in this issue), biological crusts (Rodriguez-Caballero et al., 2017-in this issue), geomorphodiversity (Melelli et al., 2007-in this issue), vegetation formations (Ruiz-Fernandez et al., 2017-in this issue), paddy rice, (Qiu et al., 2017-in this issue), ecosystem services (Depellegrin et al., 2017-in this issue, Egarter Vigl et al., 2017-in this issue; Goldenberg et al., 2017-in this issue), flood probability (Kalantari et al., 2017-in this issue), landfill area (Manzo et al., 2017-in this issue), climate (Oliva et al., 2017-in this issue), biogeodiveristy (Ibáñez et al., 2016-in this issue), water soluble elements in snow and impurities (Niu et al., 2017a, 2017b-in this issue), water quality (Sener et al., 2017-in this issue) and digital photo classification (Pina et al., 2016-in this issue). Modern mapping makes extensive use of GIS and GPS capabilities, remote and proximal sensing, and spatial modelling across multiple fields (Doolittle and Brevik, 2014; Corbane et al., 2015; Seelan, 2015; Minasny and McBratney, 2016). A major future need in environmental mapping is spatially quantitative information that can be utilized by the wide variety of environmental models used to understand our world and the ways that human choices may impact it (Sanchez et al., 2009; Zhang et al., 2011; Brevik et al., 2016a). There is also a need to apply the spatial information contained in maps to new applications in areas that were not the original intent of the mapping (Brevik and Miller, 2015; Ibáñez et al., 2016-in this issue). On the remote sensing front, there is a need for fine spatial resolution data that covers the entire planet daily, information that may come from platforms such as the recently developed cubesat satellites (Anderson, 2016). The inclusion of high quality, miniaturized sensors in devices as common-place as mobile phones and tablets opens up new opportunities as well (Aitkenhead et al., 2014; Anderson, 2016), as does the GPS capability
of common and relatively inexpensive modern electronics (Beaudette and O'Geen, 2010). As remote and proximal sensing techniques see ever increasing use, there is a need to better understand the relationships between environmental covariates and the features being mapped with them as well as improved statistical models to relate covariates to the features being investigated (Dunstan et al., 2011; Khaledian et al., 2017) with better model validation and calibration (Malone et al., 2011; Horta et al., 2015). At the same time, it is important not to forget the importance of ground-truthing (field work) of remote and proximally sensed data (Seelan, 2015; Brevik et al., 2016a) as the models and predictions generated from proximal and remote sensing techniques are limited by the quality and quantity of field-based observations available to calibrate them with (Brevik et al., 2017). An important point to be stressed is that reliable mapping of the environment can be obtained only following a deep and holistic analysis of available environmental data. It is true that often the desired end product is a map of the studied environmental properties; however, as manifested by the research presented in this special issue, during an investigation it is possible to shed light on many aspects related to the studied environmental processes and factors with good data analysis; moreover, a careful exploratory data analysis is crucial to properly evaluate the reliability of the performed mapping. Finally, it is very important that the spatial data presented in maps be assessable and understandable to a wide range of end users. In the modern world scientific data is being used not only by specialists in the fields that generate that data, but also by related scientific fields, social science fields, government and private sector planners, and the general public. We have moved into a world of interdisciplinary and transdisciplinary applications of the knowledge we generate, and developing platforms that make that information available to all of these end users in formats they can utilize is a critical need (Bouma, 2014; Brevik et al., 2016a). Overall, we believe that this special issue is an advance in the current knowledge about mapping the environment. Acknowledgments The guest editors are thankful for the opportunity given by Science of the Total Environment to organize this special number. We appreciate the support and encouragement of the editor-in-chief Damia Barcelo in the organization of this work. We are also thankful for the high level of professionalism demonstrated by Jia Yang in the handling of technical aspects. The guest editors are very appreciative to the authors for the good contributions they submitted and the reviewers for the time they dedicated to improve the quality of the manuscripts. We would like to acknowledge the support of the projects Managing urban Biodiversity and Green Infrastructures to increase city resilience (UrbanGaia, ERALEARN 2020; Pereira), COST action ES1306 (Connecting European connectivity research; Pereira), and the National Science Foundation Grant Number IIA-1355466 (Brevik). References Aguirre, J.C., 2014. The Unlikely History of the Origins of Modern Maps. Smithsonian Magazine http://www.smithsonianmag.com/history/unlikely-history-originsmodernmaps-180951617/?no-ist Accessed 27 November 2016. Aitkenhead, M., Donnelly, D., Coull, M., Hastings, E., 2014. Innovations in environmental monitoring using mobile phone technology-a review. iJIM 8, 42–50. Alcantara, C., Kuemmerle, T., Prishchepov, A.V., Radeloff, V.C., 2012. Mapping abandoned agriculture with multi-temporal MODIS satellite data. Remote Sens. Environ. 124, 334–347. Aliaga, M.A., Chaves-dos-Santos, S.M., 2014. Food and nutrition security public initiatives from a human and socio-economic development perspective: mapping experiences within the 1996 world food summit signatories. Soc. Sci. Med. 104, 74–79. Allard, D., Comunian, A., Renard, P., 2012. Probability aggregation methods in geoscience. Math. Geosci. 44, 545–581. Alonzo, M., Bookhagen, B., Roberts, D.A., 2014. Urban tree species mapping using hyperspectral and lidar data fusion. Remote Sens. Environ. 148, 70–83. Anderson, K., 2016. Integrating multiple scales of remote sensing measurement - from satellites to kites. Prog. Phys. Geogr. 40, 187–195.
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Paulo Pereira Environmental Management Center, Mykolas Romeris University, Vilnius, Lithuania Corresponding author. E-mail address:
[email protected] Eric Brevik Department of Natural Sciences, Dickinson State University, Dickinson, ND, USA Sebastiano Trevisani University IUAV of Venice, Department of Architecture, Construction and Conservation, Venezia, Italy 30 July 2017 Available online xxxx
