Typical imaging parameters of current satellites.
\r\n\tThis book will be a self-contained collection of scholarly papers targeting an audience of practicing researchers, academics, PhD students and other scientists. The contents of the book will be written by multiple authors and edited by experts in the field.
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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"70789",title:"Deep Learning Training and Benchmarks for Earth Observation Images: Data Sets, Features, and Procedures",doi:"10.5772/intechopen.90910",slug:"deep-learning-training-and-benchmarks-for-earth-observation-images-data-sets-features-and-procedures",body:'This chapter introduces the basic properties, features, and models for very specific Earth observation (EO) cases recorded by very high-resolution (VHR) multispectral, Synthetic Aperture Radar (SAR), and multi-temporal observations. Further, we describe and discuss procedures and machine learning-based tools to generate large semantic training and benchmarking data sets. The particularities of relative data set biases and cross-data set generalization are reviewed, and an algorithmic analysis frame is introduced. Finally, we review and analyze several examples of EO benchmarking data sets.
In the following, we describe what has to be taken into account when we want to benchmark the classification results of satellite images, in particular the classification capabilities, throughputs, and accuracies offered by modern machine learning and artificial intelligence approaches.
Our underlying goal is the identification and understanding of the semantic content of satellite images and their application-oriented interpretation from a user perspective. In order to determine the actual performance of automated image classification routines, we need to find and select test data and to analyze the performance of our classification and interpretation routines in an automated environment.
A particular point to be understood is what type of data exists for remote sensing images that we want to classify. We are faced with long processing chains for the scientific analysis of image data, starting with uncalibrated
In addition, we have to mention that free and open-access satellite products have revolutionized the role of remote sensing in Earth system studies. In our case, the data being used are based on multispectral (i.e., multi-color) sensors such as Landsat with 7 bands, Sentinel-2 [4] with 13 bands, Sentinel-3 with 21 bands, and MODIS with 36 bands but also SAR sensors such as Sentinel-1 [6], TerraSAR-X [26] or RADARSAT. For a better understanding of their imaging potential, we will describe the most important parameters of these images. For multispectral sensors, there exists several well-known and publicly available land cover benchmarking data sets comprising typical remote sensing image patches, while comparable SAR benchmarking data sets are very scarce and dedicated.
The main aspects being treated are:
ML paradigms to support the semantic annotation of very large data sets, that is, using hybrid methods integrating Support Vector Machines (SVMs), Bayesian, and Deep Neural Networks (DNNs) algorithms in active learning paradigms by using initially small and controllable training data sets, and progressively growing the volume of labeled data by transfer learning.
Proposing solutions to the semantic aspects of the spatial annotations for different sensor resolutions and spatial scales.
Discussing the implications of the sensory and semantic gaps.
In this chapter, we assume that we can rely on already processed data with sufficient calibration accuracy and accurate annotation allowing us to understand all imaging parameters and their accuracy. We also assume that we can profit from reliably documented image data and that we can continue with data analytics for image understanding and high-level interpretation without any further precautions.
The latter steps have to be organized systematically in order to guarantee reliable results. A common strategy is to split these tasks into three phases, namely initial basic software functionality testing; second, training and optimizing of the software parameters by means of selected reference data, and finally, benchmarking of the overall software functionality such as processing speed and attainable results. This systematic approach leads to quantifiable and comparable results as described in the following sections.
During the last years, the field of deep learning had an explosive expansion in many domains with predominance in computer vision, speech recognition, and text analysis. For example, during 2019, more than 500 articles per month have been published in the field of deep learning. Thus, any reports on the state of the art hardly can follow this development. In Ref. [1], published in January 2019, more than 330 references were analyzed reviewing the theoretical and architectural aspects for Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), including Long Short-Term Memories (LSTMs) and Gated Recurrent Units (GRUs), Auto-Encoders (AEs), Deep Belief Networks (DBNs), Generative Adversarial Networks (GANs), and Deep Reinforcement Learning (DRL). The review paper [1] also summarizes 20 deep learning frameworks, two standard development kits, 49 benchmark data sets in all domains, from which three are dedicated to hyperspectral remote sensing. In addition, Ball et al. [2] describe the landscape of deep learning from all perspectives, theory, tools, applications, and challenges as of 2017. This article analyzes 419 references. A more recent overview from April 2019 [3] summarizes more than 170 references reporting on applications of deep learning in remote sensing.
Typical remote sensing images acquired by aircraft or satellite platforms can be characterized based on the operational capabilities of these platforms (such as their flight path, their capabilities for instrument pointing, and the on-board data storage and data downlink capacities), the type of instruments and their sensors (such as optical images with distinctive spectral bands [4, 5] or radar images such as synthetic aperture radars [6]), and opportunities for the repetitive acquisition of geographically overlapping image time series (for instance, for vegetation monitoring to predict optimal crop harvesting dates).
Current images can provide raw data with more than eight bits per sample, can perform initial data processing and annotation already on board, and can downlink compressed data with error correcting codes. After downlinking the image data to ground stations, the received data will be stored and processed by dedicated computing facilities. A common remote sensing strategy is to perform a systematic level-by-level processing (generating so-called products that comprise image data together with metadata documenting relevant image acquisition and processing parameters).
A common conventional approach is to follow a unified concept, where Level-0 products contain unprocessed but re-ordered detector data; Level-1 data represent radiometrically calibrated intensity images, while Level-2 data are geometrically corrected and map-projected data. Level-3 data are higher level products such as semantic maps or overlapping time-series data. In general, users have access to different product levels and can access and download selected products from databases via image catalogs and so-called quick-look (also called thumb-nail) images.
Some additional products have to be generated interactively by the users. Typical examples are image content classifications and trend analyses following mathematical approaches. Today, these interactive steps migrate from purely interactive and simple tools to commonly accepted machine learning tools. At the moment, the majority of machine learning tools use “deep” learning approaches; here, the problem is decomposed into several layers to find a good representation of image content categories [7]. These aspects will be dealt with in more detail in Section 4.
What we have to outline first are some important parameters of remote sensing images. One critical point of typical remote sensing images is their enormous size calling for big data environments with powerful processors and large data stores. A second important point is the geometrical and radiometrical resolution of the image pixels, resulting in different target types that can be identified and discriminated during classification. While the typical pixel-to-pixel spacing of air-borne cameras corresponds to centimeters on the ground, space-borne instruments with high resolution flown on low polar orbits mostly lie in the range of half a meter to a few meters. In contrast, imaging from more distant geostationary or geosynchronous orbits results in low-resolution images. As for the number of brightness levels of each pixel, modern cameras often provide more than eight bits of resolution. Table 1 shows some typical parameters of current satellites with imaging instruments.
High-resolution imaging instruments | Optical cameras and spectrometers | SAR instruments |
---|---|---|
Image size (typ. lines × columns) | 104 × 104 pixels | 104 × 104 pixels |
Bands (typ.) | 300 to 1000 nm and infrared bands | C-band, X-band, L-band, etc. |
Spatial resolution (typ.) | Sub-meter to tens of m | Meters to several meters |
Target areas (typ.) | Land, ocean, ice, atmosphere | Land, ocean, ice |
Special modes (typ.) | Dynamical targeting stereo views fusion of bands | V and H polarization scan modes interferometry |
Pixel types (typ.) | Detector counts reflectances | Complex-valued “detected data” (amplitudes or intensities) |
Important parameters (typ.) | Number of overlapping bands | Viewing/incidence angle polar or geo. orbit |
Typical imaging parameters of current satellites.
Further, the pixels of an image can be complemented by additional information obtained by feature extraction and automated object identification (used as image content descriptors) as well as publicly available information from auxiliary external databases (e.g., geographical references or geophysical parameters). These data allow the provision of accurate quantitative results in physical units; however, one has to be aware of the fact that while many phenomena become visible, some internal relationships may remain invisible without dedicated additional investigations. Table 1 shows some typical parameters of current satellite images.
In addition to the standard image products as described above, any additional automated or interactive analysis and interpretation of remote sensing images calls for intelligent strategies how to quickly select distinct and representative images, how to generate image time series, to extract features, to identify objects, to recognize hitherto hidden relationships and correlations, to exploit statistical descriptive models describing additional relationships, and to apply techniques for the annotation and visualization of global/local image properties (that have to be stored and administered in databases).
While typical traditional image content analysis tools either use full images, sequences of small image patches, collections of mid-size image segments or countless individual pixels together with routines from already established toolboxes (e.g., Orfeo [9]), or advanced machine learning approaches exploiting innovative machine learning strategies, as for instance, transfer learning [8] or the use of adversarial networks [10]. However, any use of advanced approaches requires the preparation and conduction of tests that allow a benchmarking of the new software routines, notably methods and tools to generate and analyze data for testing, training, verification, and final benchmarking. These testing activities have to be supported by efficient visualization tools.
As can be seen from Table 2, there exist already quite a number of traditional image content analysis tools. Some of them generate pre-processed images for subsequent analysis by human image interpreters, while others allow the identification and extraction of objects. However, these tools do not yet exploit the most recent automated machine learning techniques.
Clipping of outliers and de-noising |
Color coding of brightness levels |
Histogram manipulation (e.g., stretching) |
Normalization and contrast enhancement |
Box-car filtering (e.g., high-pass filtering, smoothing) |
Transformations and filtering of coefficients |
Analysis of pixel statistics and use of computer vision algorithms (e.g., histograms of gradients, local binary patterns, speeded-up robust features) |
Feature extraction and classification (edges, corners, ridges, texture, color, interest points, shapes) |
Extraction of content-oriented regions and objects |
Typical capabilities of traditional image content analysis tools.
Currently, we see a lot of public interest in machine learning (ML), artificial intelligence (AI), and data science (DS). We have to make sure what we mean by these buzzwords:
ML is often used if we describe technical developments where a computer system is trained and used to find and classify objects in data sets. A prominent example is the identification and interpretation of traffic signs for automated driving, typically use cases where a computer system is coupled with a camera and other sensors, and the traffic signs have to be recognized independent of different illumination and weather conditions, a vast range of potential driving speeds, varying distances and perspectives, other cars moving within the field of view of the camera, supplementary information provided by text panels or adjacent traffic signs, and constraints to be observed such as the maximum reasonable processing time. In essence, we can consider these applications as a reduction of many image pixels into single features (from a given list of cases and options) or a combination of features (e.g., max speed of 30 mph except on weekends). In most cases, the ML software is tested and trained by many typical examples as well as counterexamples.
AI combines the full functionality of ML with additional decision-making and reaction capabilities. This additional decision-making can be implemented by continuous understanding of the current overall situation, the extraction of reactions from given rule sets (supported by continuously updated parameters), and the handling of unexpected emergency cases. In the case of autonomous driving, one can think of a lane change on a motorway after a reason for a lane change has been found, and from a number of alternative reactions, when a lane change appears as the best reaction. Then the current situation has to be checked when a lane change becomes possible, and a sequence of subsequent actions is executed.
DS as a scientific and technical discipline of its own shall provide all guiding principles that are needed from end-to-end system design up to data analytics and image understanding—including the system layout and verification, the selection of components and tools, the implementation and installation of the components and their verification, and the benchmarking of the full functionality. In the case of remote sensing applications, we also have to include all aspects of sensor calibration, comparisons with the findings of other researchers via Internet, and traceable scientific data interpretation.
As our applications mostly use cases dealing with remote sensing images, we can limit ourselves to the main ML paradigms that support the semantic annotation of very large data sets. Based on the current state-of-the art developments, we consider that there are three currently important fundamental and internationally accepted image classification approaches for remote sensing applications and two additional learning principles useful for satellite images:
One of the most critical points for satellite image classification is the dependence of the classification results on the resolution (pixel spacing) of the images. Experiences gained by many authors demonstrate that the identified classes and their local assignment within image patches are strongly resolution-dependent as higher resolution will often lead to a higher number of visible and identified semantic categories. Thus, the performance of any semantic interpretation of images must be considered as a data-dependent metric: this potential difficulty should prevent us from blind-folded direct performance comparisons.
Another similar point to be mentioned is the risk of sensory and semantic gaps encountered during image classification. Sensory gaps result from cases where a sensing instrument cannot measure the full range of potential cases with all their physical effects and details that could exist in a real-world scene and that we cannot record and identify with uniform confidence. A similar potential pitfall for image understanding can result from semantic gaps. For instance, during interactive labeling by test persons, different people could assign different categories to image patches due to their educational background, professional experiences, etc. For further details, see [15].
The number of available approaches, algorithms, and tools is growing continuously. Some examples have become very widespread in academia such as Caffe [16], TensorFlow [17], and PyTorch [18]. In contrast to these established solutions, a large number of fresh publications are submitted every day. As an example, the ArXiv preprint repository [19] collects in its “computer science” and “statistics” directories hundreds of new machine learning papers per day.
Many experiments with image classification systems have shown that traditional single-level (“shallow”) algorithms are less performant than multi-level (“deep”) concepts where distinct filtering operations are applied on each level, and the results of the previous levels can be used on each deeper level; the final result will be obtained by combining the specific results of each separate level. The reason for the better performance of multi-level algorithms is that one can apply distinct filters specifically tailored to each level. Typical examples are multi-resolution filters that detect image characteristics on several scales: when we look at satellite images of urban settlements, then a business district normally has larger high-rise buildings and broader streets than a residential suburb with interspersed low-rise buildings and individual gardens.
From a high-level perspective, we can say that learning works best with deep learning approaches exploiting dedicated “network” structures. Here, we understand networks as design structures of the data flows and the arrangement of pixel handling steps governing the processing of our images. This concept also supports more intricate label assignment concepts such as primary labels defining the main category of an image patch supplemented by secondary labels that provide additional information about “mixed classes” or supplementary spatial details of a given image patch.
In the meantime, some types of networks have emerged that have proven their robustness in the case of satellite images to be annotated semantically. In the following, we list four types of networks that have proven their usefulness for satellite image interpretation:
Besides the network types listed above, we also need an overall algorithmic architecture embedding the networks. For our applications, a “U” approach has proven to be a useful concept for satellite image content analysis. A “U” approach contains a descending branch followed by an ascending branch and is conceived for handling a progressively shrinking number of elements until a final core element (a main category) is found, followed by stepwise complementary semantic information. Further details can be found in [21].
In our experience, most general remote sensing applications can be solved efficiently by CNNs or similar approaches. However, quite a number of innovative alternatives have been proposed during the last years, for example, common auto-encoders, recursive approaches for time series, and adversarial networks for fast learning with only a few examples. In our case, we suggest to use CNNs for non-critical satellite image applications, while highly complicated or time-critical applications could call for innovative approaches as already described above.
When we train a classification network and verify its performance, the main goal is to train the system for correct category assignments resp. semantic annotations (labels), that is, to add supplementary information to each satellite image patch that we analyze.
The semantic annotations can either be learned in a preparatory phase or be taken from catalogs of already existing categories. If we aim at long-term analyses of satellite images, a good approach is to use the same catalogs during the entire lifetime of the analysis or to re-run the entire system with updated catalogs.
The easiest approach is to select typical examples for each category and to assign the given labels to all new image data. However, if we follow this straightforward approach, we will probably encounter some difficulties when image patches with unexpected content arrive. A first remedy is to add an additional “unknown” category and to assign this label to all image patches that do not fit well to one of the given categories. Further, experience with machine learning systems has shown that good classification results can also be reached when we systematically select positive as well as negative examples (i.e., counterexamples) for each category leading to a comprehensive coverage and understanding of each category. This process can be accomplished manually by knowledgeable operators (i.e., image interpretation experts) [22]. Another approach is data augmentation: If we do not have sufficient examples of a necessary category, one can create additional realistic data by simply flipping or rotating already available images.
This simple example leads us to systematic methods for a database creation. One has to find a comprehensive and fairly balanced set of examples that covers the expected total variety of cases. Thus, we avoid so-called database biases [23]. In addition, one has to make sure that the inclusion of additional examples does not lead to overfitting or excessive runtimes. This can be accomplished by setting up a validation testbed where these potential pitfalls can be tested, trained, and where the final performance of the created database structure can be verified. One has to be aware of the fact that database access times may strongly depend on the available computer systems, their interconnections, and the selected type of database.
These approaches led to a number of publicly available databases with label annotations for civilian remote sensing data. There are several semantically annotated databases based on optical (most often multispectral) data, while there are only a few databases based on SAR data. Some advanced remote sensing database examples are [25, 26, 27]. Of course, their general applicability and transferability depend on the actual image resolution, the imaging geometry, and the noise content of the images. Current state-of-the-art systems are being assessed based on end-to-end tests covering also inter alia practical aspects such as the runtime depending on the database design and the selected test images, the amount and organization of available labels, the correctness of the obtained annotations, and the overall implementation and validation effort.
As for remote sensing images, there exist already several semantically annotated collections of typical high-resolution satellite images—a number of collections of optical images and a few collections of SAR images. However, these collections often seem to be potpourris of interesting snapshots rather than systematically selected samples based on regionally typical target classes and their visibility as a function of different instrument types. The situation is aggravated by the current lack of systematically selected benchmarking data that could be used as well-known reference data for quality and performance assessments such as classification tasks or throughput testing.
These deficiencies have to be solved in the near future as more and more high-resolution images become publicly available, while the end-users already expect reliable automated image classification and content understanding results for more and more high-level applications. We can expect that the progress in deep learning will also lead to much progress in many other fields of image processing, even beyond the field of remote sensing; thus, remote sensing should be aware of what is published by the image processing and environmental protection communities at large.
While high-resolution imaging has made much progress for many remote sensing applications, standardized image classification benchmarking still deserves more progress. On the one hand, several benchmarking concepts and tools could still be gleaned from other disciplines; on the other hand, an optimal solution of test cases for SAR image interpretation still needs more progress in basic approaches of how to verify actual image classification results and the identification of dubious cases.
We appreciate the cooperation with Politehnica University of Bucharest (UPB) in Romania and our project partners from the European H2020 projects CANDELA (under grant agreement No. 776193) and ExtremeEarth (under grant agreement No. 825258).
Eco-cultural tourism stems from ecotourism, which has been supported by the United Nations since 2012 [1] in order to eradicate poverty, protect the environment and incorporate sustainable development. It recognises the importance of preserving and protecting biodiversity and natural areas, and of using them in a sustainable manner. It fosters entrepreneurship and development among local communities, and encourages tourists to preserve and respect natural and cultural heritage.
\nThis development has been possible after numerous recommendations from the World Tourism Organisation (UNWTO). Member States were urged to adopt incentive ecotourism policies, emphasising the positive effects of the creation of revenue, employment and education. Since the start of the nineties, ecotourism has been worked on at regional conferences and global summits, and 2002 was named the International Year of Ecotourism.
\nThe majority of ecotourism-related terms emerged from English-speaking media [2]: natural tourism, rural tourism, wilderness tourism, alternative tourism, environmental tourism, anthropological tourism, low impact tourism, travel with Mother Nature, adventure travel, jungle tourism, cultural tourism, ecotravel, green tourism, science tourism, soft adventure tourism, bio tourism, ethnic tourism, appropriate tourism, sustainable tourism, ecotripping, socially responsible tourism, ecoventures, nature vacations, wilderness tourism, and ethical travel. As a whole, they are related to responsible, respectful and honest tourism. It’s educational because it offers information before, during and after the trip. It’s interactive because it entails life experiences involving real contact and participation. It’s democratic because the benefits it creates are equally distributed; it encourages the participation of rural communities and supports resource conservation.
\nThe growth of ecotourism began in the eighties, notably because of the economic benefits of natural tourism. What is characteristic of ecotourism is that it is a tool for sustainable development [3] and it’s categorised as an activity that mixes the excitement of travelling with caring for the environment. Its main goals are sustainability, preservation and participation of the local community.
\nWith the modern tourist’s demands and new trends, ecotourism is moving on to its next stage and is asserting itself as eco-cultural tourism. It binds searching for contact with nature with valuing cultural authenticity, environmental sustainability, new experiences and a higher number of trips with a shorter stay in each place. That is to say, it’s created a symbiosis between natural tourism and cultural tourism, supported by sustainability and limitations on use, by small-scale activities and a high-quality tourism experience.
\nEco-cultural tourism is a new form of tourism where the main goal is not to generate wealth, expropriate land and exploit resources of inhabitants and countries in order to benefit big companies. It’s established itself within a new concept of tourism, both in socioeconomic terms and as a practical activity, that combines the conservation of natural and cultural sites with economic and recreational benefits through sustainable development, and improves the conditions of the recipient communities. The main motivation is observation, appreciation and contemplation as an experience within the natural and cultural environment.
\n\n\nTable 1\n shows the characteristics of eco-cultural tourism from its inception, together with the synergy of the United Nations resolution that emphasises poverty eradication and environmental protection through ecotourism. It’s a small-scale and fundamentally emotional form of tourism that promotes sociocultural and traditional values and environmental awareness, and offers benefits both to rural and urban areas. It requires a platform and type of tourism planning that encourages local sustainable development, involving all tourism sectors. It’s currently unique due to how it responds to problems related to climate change and imbalances in economic globalisation that have led to dead end conventional and/or traditional tourism. Tourism Development Projects offer a new path for tourism.
\n\n It’s small-scale tourism, with small businesses, with a specific and limited quality production and it’s fundamentally based on experiences | \n
\n | \n
\n | \n
\n | \n
Characteristics of eco-cultural tourism.
Source: own compilation.
Once eco-cultural tourism has been contextualised and treated together with its goals, a Planning Model with alternative tourism and the development of Tourism Projects is incorporated. In addition to this, a comparative methodology will be used to analyse global and local tourism in countries from Asia, Europe and South America. This will be guided by the following hypothesis: eco-cultural tourism requires local sustainable planning, endorsed by all tourism agents, and led by the load capacity and economic and environmental balance (green economies and ideologies). In this way, eco-cultural tourism will respond to the problems of poverty and climate change through Tourism Projects, directed by government policies and administrations.
\nPlanning for alternative tourism and creating Tourism Development Projects in eco-cultural tourism is carried out in unique spaces that, generally, have received little impact from conventional and/or traditional tourism. They are concrete, different or special spaces that are distinguishable from others. They are easier to design, manage and carry out [4]. The tourism development projects are based on planning models that involve the participation of all agents: representatives, politicians, businessmen and women, neighbourhood associations, indigenous communities, environmental groups, etc. The entire population of the natural and cultural area is involved in economic and social sustainable development and can obtain income, employment, and material and cultural wellbeing. Therefore, all agents organise their own tourism model based on the available resources, the existing heritage and the characteristics of the inhabiting communities.
\n\n\nTable 2\n shows a Planning and Sustainable Development Model for Eco-cultural Tourism comprising three phases, that can be applied to natural, cultural and urban areas, and that includes the following types of tourism: interior tourism, ecotourism, cultural tourism, rural tourism, natural tourism, domestic tourism, national tourism, experiential tourism, adventure tourism, agrotourism and green tourism. The first phase known as the PRELIMINARY STUDY of the whole area, is derived from a comprehensive study that seeks to obtain specific data (indicators, surveys, interviews) and carry out an analysis and report in accordance with the Factors or Resources, Attractors and Support Systems model (FAS) [5]. This tool allows you to order and classify the main elements that make up a tourism destination. It’s divided into factors or resources, attractors (tourism products) and support systems.
\nNatural, cultural and urban areas | \nPlanning, sustainable development and the promotion of nature and cultural heritage | \n
Original natural, cultural and urban area | \n1st Phase: preliminary study Data (indicators, surveys, interviews), analysis and report according to the World Tourism Organisation (UNWTO) FAS model. | \n
Natural, cultural and urban area to be planned | \n2nd phase: planning and carrying out the tourism project Specifying and planning goals according to the model: Strengths, weaknesses, opportunities, threats (SWOT) Developing the Tourism Project based on sustainable development and the load capacity | \n
Natural, cultural and urban area consolidated | \n3rd phase: implementing and evaluating the tourism project Implementing and managing the Tourism Project (Tourism and Entertainment Policies) | \n
Planning and sustainable development model for eco-cultural tourism.
Source: own compilation.
The
The
\n
This data and their indicators and analyses carried out by specialised technicians and specialists, are complemented by researching tourist populations that visit these destinations, their characteristics and preferences, their economic status, their destination images and attitudes towards the environment and biodiversity. A social investigation is carried out on these populations, and the planning and sustainable development model proceeds with the analysis of the following [6]:
Tourist populations that visit the local place of origin.
Preferences regarding cultural, social and religious traits.
Economic and consumption situation.
Perceptions regarding artistic and cultural heritage in the destination images of tourist populations.
Attitudes towards the environmental surroundings and biodiversity.
To be more specific, the aspects studied within the investigation [7] through national surveys can be useful for the PRELIMINARY STUDY of the natural and cultural area of origin. For example: tourist movement, tourists’ profile, why people feel encouraged to choose a specific tourist destination, information habits, purchase and consumption of the real or potential demand, image offer, competitors, tourist expense and the effect of tourism on the area’s economy. The reality obtained gives us a general map through which we can find out which resources are available in the area, and the general characteristics of its population and tourists.
\nThe second phase is PLANNING AND CREATING THE TOURISM PROJECT. This involves the detailed analysis of the general map of results from the PRELIMINARY STUDY in order to organise the planning and creation of the Tourism Project’s goals through the following model: Strengths, Weaknesses, Opportunities, Threats (SWOT) [8]. Some relevant
The development of the Tourism Project must be based on sustainable development and the load capacity. By doing so, the project will help to maximise the strengths of the area to the fullest, improve the identified weaknesses as far as possible, keep the potential threats under control, and reap rewards from the opportunities the cultural and environmental settings offers. Both public and private administrations of the town must agree on a local plan for the project. Such a plan is intended to develop specific programmes and find sources of funding, as well as to facilitate coordination with other programmes and activities from other departments in order to achieve ongoing, day to day management. This is what a tourist destination that is included in a local plan [4] (p. 61) needs in order to succeed:
Attractive natural and cultural resources.
Environmental protection in the places to be visited, around the tourist centres and the access routes.
Good weather conditions.
Easy access from outside and within the destination areas.
Tourist safety and protection: legal assistance, health care, complaints.
Value for money, especially when compared with other rival destinations that are in the same region or offer similar products.
Information and promotion of the tourist destination. Population awareness regarding the benefits arising from tourism. Kindness and hospitality.
Range of leisure activities: cultural tours to eco-museums, walks along historical roads, sports and birdwatching.
Authenticity of the tourist experience: how many distinguishing features the place has.
Clear and distinguishable positioning contrasted with other competitive destinations.
Possible complementary relationship with other destinations nearby that are already well-known in the marketplace: exploit synergies.
Last phase: IMPLEMENTATION AND ASSESSMENT OF THE TOURISM PROJECT. At this point, the principles and pillars of sustainability are applied, as well as the purposes of the tourism project, which must be done over temporary phases so that it is possible to make improvements, to turn down alternatives that have not worked out, and to formulate different strategies that help strengthen the project. In this context, the state, policies on tourism and leisure, and the sustainable development of eco-cultural tourism in the cultural and natural area are important. The Marrakech Process [9] recommends sustainable policy management. It also states that the policies should be led by local authorities, indigenous and local communities, any international organisation linked to the plan, the private sector, non-governmental organisations (NGOs) and consumers.
\nThe concept of tourism as a factor of sustainable development has been addressed by the UNWTO [10] in Article 3 of its Global Code of Ethics. It expressed the duty for all tourism development practitioners to safeguard the environment and natural resources, taking into consideration continuous and sustainable economic growth, so that all needs and aims of the present and future generations can be fairly met.
\nThe model of sustainable tourism curbed, to some extent, the almost unstoppable devastation created by a model of extensive growth that had deeply exhausted natural resources. Undoubtedly, the essence of eco-cultural tourism takes root in the principles of environmental planning, which is the preservation of any natural area and historic, cultural and archaeological sites. \nTable 3\n shows the case report of eco-cultural tourism in Asia, Europe and South America with the global and local dimension.
\nASIA: China, Malaysia, Thailand [11, 12, 13, 14, 15] Global dimension, natural, urban and rural areas: countries with emerging economies with high economic growth and rapid urbanisation. Developed/consolidated and developing tourist destinations: impacted by environmental pollution. Tourism policies with Ecotourism, green energy and economies. | \n
EUROPE: Spain [16, 17, 18, 19, 20, 21] Local dimension, natural rural and urban areas: A country with a developed European economy and outstanding natural and cultural heritage. Developed/consolidated tourism destinations: Tourism policies developed and promoted for recovery and not extinction. | \n
SOUTH AMERICA: Peru, Argentina and Bolivia [22, 23, 24] Local dimension, natural, rural and urban areas: Countries whose developing economies have natural and cultural attractions and activities that focus on sustainable tourism. Unspoiled/developing tourism destinations: Tourism projects with proposals for sustainability, quality, continuity, balance and community intervention. | \n
Case report of eco-cultural tourism in Asia, Europe and South America.
Source: own compilation.
Asian countries including China, Malaysia and Thailand are a modern example of countries with emerging economies, which have experienced significant economic growth and rapid urbanisation. Scientific studies have addressed, from a global dimension, the impacts of tourism and its destinations on logistics and economic dynamics, especially the effect the tourism industry has had on transport, especially by air in terms of travel and access to places of recreation. The latest research by Rehman Khan, Zhang, Kumar, Zayadskas, Streimikiene on measuring the impact of renewable energy, public health expenditure, logistics, and environmental performance on sustainable economic growth [11], is part of the Association of Southeast Asian Nations (ASEAN). They study secondary data from the World Bank’s website with structural equation models, concluding that the use of renewable energy will improve environmental sustainability and the creation of a better national image. The promotion of sustainable economic growth should be based on adequate planning that formulates policies in line with sustainability.
\nIndeed, environmental policies and sustainable development have been the priority axes in literature reviews and existing theoretical models relating green logistics and the economy to environmental sustainability and general social factors. This has been pointed out by Rehman Khan, Zhang, Kumar, Zayadskas, Streimikiene in their research. Measuring the impact of renewable energy, public health expenditure, logistics, and environmental performance on sustainable economic growth [12]. In this case, future research trends will have to be redirected to the micro-level, which means local and precise levels.
\nWithin the same context, the relationship between tourism and globalisation in environmental degradation is investigated in China [13], where a quantum distributed delay model is used. They show that economic growth stimulates environmental degradation and suggest a comprehensive policy framework that could achieve Sustainable Development Goal No. 7 (Affordable and clean energy), No. 8 (Decent work and economic growth) and No. 13 (Climate Action). Reorientation of policies to ensure sustainable development appears to be diffuse in the existing literature, if not entirely absent. The transportation and accommodation associated with tourism has an impact on carbon emissions and is an issue that must be addressed by reducing environmental degradation, promoting renewable energy, boosting tourism, and creating employment opportunities in both the public and private sectors. Technologies should not replace all manual labour. From this point, a new perspective towards ecotourism, with all its variants, is opened, and serves as a general platform for the proper running of these policies.
\nAlongside this, the quantum auto-regressive distributed delay model (QARDL) [14] is being studied in Malaysia, in order to analyse the impact of economic growth, tourism, transport and globalisation on carbon dioxide (CO2) emissions. The results of this study suggest that ecotourism is beneficial for economic growth in underdeveloped areas, in favour of protection and development. The long-term vision is that governments should encourage the development of low-carbon ecotourism and achieve green development of both tourism and the economy. All in all, the aim is to promote green consumption through policies and the ethical consideration between the local communities, the relevant agencies and the tourists. Such a mechanism will ensure responsibility in the behaviours towards the natural environment of the tourist destinations of the country.
\nFinally, the last Asian country listed in \nTable 3\n, Thailand [15], analyses all the elements mentioned in the previous research, adding the negative influence of high crime levels on tourism. It concludes the only solution to protect the tourism industry involves clean energy promotion by the government, and the application of biofuel use in the transport sectors to improve environmental sustainability.
\nSpain represents one of the developed economies in Europe with the most remarkable natural and cultural heritage. Destinations are consolidated and there are tourist policies in most established destinations, together with promotion for recovery and not extinction. One of the most significant activities corresponds to wilderness tourism, which usually begins to thrive after all stages of general tourism development, in an effort to satisfy a desire for outdoor recreation and appreciation of nature in large areas. It is related to development and sustainable tourism, so it refers to natural habitats and its biodiversity. In this context, natural parks, conservation areas, ecotourism, agricultural tourism, the rural environment and the involvement of the communities in the area of implementation are incredibly important. [25]. This is why three consolidated projects have been chosen, projects of three national parks in Spain out of fifteen that are today included in the list [26].
\n\n\nFigure 1\n presents a panoramic view of Ordesa National Park (Huesca, Spain) [16]. In the visitor’s guide, there is a map showing the location of the park in the north of the peninsula, located in the heart of the Pyrenees in the autonomous community of Aragon. A media guide (with pictures of the landscapes) can also be found. In the fact sheet of the informative brochure, there are useful addresses and recommendations for visiting and appreciating this park. The sheet also highlights that the place has been designated as a World Heritage Site, Global Geopark and Biosphere Reserve by UNESCO, and that it has also been awarded the European Diploma by the Council of Europe. There is accurate information about cultural aspects (customs and traditions), flora, fauna, and information about relevant visitor centres and information points. At Ordesa National Park, there is a plan for the recovery of bearded vultures and for the tracking of the chamois. It’s working towards improving the quality of visits, reducing the impact created of vehicles and permitting access to the park at any time during the fixed opening times. This has been known as the Visitor Access Plan in the Ordesa Valley since 1998.
\nOrdesa y Monte Perdido National Park (Huesca, Spain). Source:
\n\nFigure 2\n presents the Tablas de Daimiel National Park [17], a wetland that is considered virtually unique in Europe, and that is the only example of floodplains in an ecosystem, which was once characteristic of the inland plains of the peninsula. This ecosystem originates from the flooding that occurs in a plain when the Guadiana and Cigüela rivers jointly overflow at a point where they both discharge groundwater coming from a large aquifer. The preservation of this ecosystem has ensured the survival of the bird fauna, and has created a specific area for aquatic birds. The park is internationally acknowledged for being a Biosphere Reserve since 1981 and a Wetland of International Importance by the Ramsar Convention since 1982. It has a wide range of aquatic birds and, among the flora, the aquatic plants that comprise the substrate of the Tablas de Daimiel are of special importance. For the promotion of the park, a visitor’s guide, park guide and media guide (with pictures of the landscapes) are available.
\nTablas de Daimiel National Park (Ciudad Real, Spain). Source:
\n\nFigure 3\n shows the Timanfaya National Park, located in the western central area of Lanzarote, one of the Canary Islands (Spain) [18]. It was designated a Biosphere Reserve in 1993. It has undergone continuous volcanic processes, specifically in 1730, 1736 and 1824; with eruptions that completely changed the previous island’s morphology. Some natural values are volcanology (especially the preservation and beauty of the Montañas del Fuego or the Macizo de Timanfaya), the geomorphological forms, the scarce vegetation and the unique weather conditions. Visitors can get acquainted with these natural values while doing tours in small groups led by qualified guides.
\nTimanfaya National Park (Lanzarote, Spain). Source:
The park management team also organises activities for students, with the purpose of teaching them about nature and culture and to introduce them to the park’s flora and fauna, cultural and historical values, agricultural activities, restoration of native crops, fishery, scientific activities and environmental volunteering. In the media guide, wonderful pictures of natural landscapes are shown, with unique volcanoes that make this a valuable and a high-quality tourist visit.
\nProjects with proposals are another kind of case report, classified as intermediate level, so that in the future the eco-tourism destinations can become consolidated projects.
Finally, the
The panoramic and urban settings together make up the cultural landscapes. This specifically refers to historical towns, where the cultural tourists come and combine the feeling of the trip during their stay with the contemplative experience of the historic heritage. The protection of landscapes and heritage values led to the introduction of landscape conservation in urbanism and spatial planning during the 80s and 90s, and encouraged the valuing of landscapes as an enriching element by means of tourism [28]. Cultural landscapes are, in short, the result of people’s interaction with the natural environment over time, the result of which is a territory perceived and valued for its cultural qualities, which express a process and the support of a community’s identity. It represents a dynamic, complex reality which is difficult to manage [29].
\nResearch on the perception and assessment of the
With regards to the interests of the cultural tourist during the visit (\nTable 4\n), visiting panoramic viewpoints of the town is highlighted in first place (4.29), second, visiting monuments and seeing museums (4.20) and, in third place, taking guided tours around the different viewpoints of the town, from outside and inside (4.01). In the end, the activities that generate most satisfaction for the cultural tourist during the visit can be classified into the five activities set out below: the views of the whole town and its interiors (4.45), visiting monuments (4.38), interacting with people from the town (4.21), viewing natural landscapes (4.04), and the atmosphere of the streets and public squares (4.01).
\nInterests of the cultural tourist during the visit to the historical towns | \n\n\n | \nMode | \n
---|---|---|
-Visit panoramic viewpoint of the town. | \n4.29 | \n5 | \n
-Visit monuments and see museums. | \n4.20 | \n5 | \n
-Take guided tours of the different viewpoint of the town, from outside and inside. | \n4.01 | \n5 | \n
\n | \n\n | \n | \n
-Views of the whole city and its interiors. | \n4.45 | \n5 | \n
-Visiting monuments. | \n4.38 | \n5 | \n
-Interacting with people from the town. | \n4.21 | \n5 | \n
-Viewing natural landscapes. | \n4.04 | \n5 | \n
-Atmosphere of streets and public squares. | \n4.01 | \n5 | \n
Interests of the cultural tourist and activities during the visit to historical towns.
Source: own elaboration [20.]
Our last block in \nTable 3\n relates to South American countries with developing economies, natural and cultural attractions, and activities determined by sustainable tourism. In most cases, their destinations are unspoiled/developing, with tourism projects based on sustainability, three of which have been selected:
\n
The
\n
Eco-cultural tourism is aimed at meeting the needs and problems that traditional and/or conventional tourism has set aside during its development phase and economic success. It is a central core piece around which alternative forms of tourism will develop, and acts as a counterweight in the resolution of the imbalances and inequalities created. It’s returning to the original form of tourism, a beginning
The first steps of eco-cultural tourism are found in the ecotourism which was born out of the United Nations in 2012 to eradicate poverty—with the participation of the locals—, protect the environment and incorporate sustainable development. The 80s showed remarkable economic benefits in areas such as natural tourism, and comes together at different points with sustainable tourism and community tourism. Dangi and Jamal [30] carefully examined the chronology of sustainable community tourism, which resulted in the existence of integration approaches at a local-global level with community development. They also determined that good governance, guided by clearly defined ethical principles and justice, closed the local-global gap and guided the development and management of tourism.
\nThese new improvements, along with the demands of the modern tourist, have led to ecotourism consolidating itself as eco-cultural tourism, which promotes sociocultural and traditional values, environmental awareness, and benefits to rural and urban communities. Additionally, it opens up possibilities to develop economies at various levels for public and private enterprises, and offers the enjoyment of a unique high-quality experience. The responses given to climate change issues and the imbalances from economic globalisation are made possible through the planning of local sustainable development, comprising all tourism sectors involved in the creation of a Tourism Project, an Application Phase and the Evaluation of the structured project.
\nThe methodology used in the study of eco-cultural tourism, uses the global and local levels of tourist destinations located in countries with emerging economies (Asia: China, Malaysia, Thailand), developed countries (Europe: Spain) and developing nations (South America: Peru, Argentina, Bolivia). This highlights the need for a local level planning, which is endorsed by all tourism agents and has an accessible economic and environmental load capacity and balance. Promoting planned tourist areas will be possible with the support of government policies, aimed at progressively implementing clean energies, promoting the growth of undeveloped areas and including citizens in the evolution of tourism economies. Our initial hypothesis is confirmed and the path towards the logistic change of sustainable development is aimed at the protection and conservation of natural and cultural heritage.
\nI give thanks to the InetchOpen editorial for inviting me to participate write a book, directed and supervised by Sayd Abdul Rehman Khan, on tourism that reflects the current situation of the sector in today’s world and during this period marked by the COVID-19 pandemic.
\nIntechOpen publishes different types of publications
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