Open access peer-reviewed chapter

Analyzing the Evolution of Land-Use Changes Related to Vegetation, in the Galicia Region, Spain: From 1990 to 2018

Written By

Sérgio Lousada and José Manuel Naranjo Gómez

Submitted: 30 April 2022 Reviewed: 21 June 2022 Published: 22 July 2022

DOI: 10.5772/intechopen.106015

From the Edited Volume

Vegetation Dynamics, Changing Ecosystems and Human Responsibility

Edited by Levente Hufnagel and Mohamed A. El-Esawi

Chapter metrics overview

130 Chapter Downloads

View Full Metrics

Abstract

Considering the complex dynamics, patterns, and particularities that the Galicia region present—e.g., the fragility, shown to achieve sustainable development and growth—a study that analyzes the Land-Use related to the vegetation of this region is seen as pivotal to identifying barriers and opportunities for long-term sustainable development. Using GIS (Geographic Information Systems), the present chapter enables us to identify the dynamics and patterns of the evolution of the Land-Use Changes related to vegetation in the Galicia Region from 1990 to 2018 (years 1990, 2000, 2012, and 2018 using CORINE (Coordination of Information on the Environment) data). This study permits us to reinforce that the Land-Use Changes related to vegetation in the Galicia Region have undergone multiple changes—marked by increasing and decreasing periods. Also, can be considered a surveying baseline for the comparative analysis of similar works for different Land-Use Changes related to vegetation trends in Europe or worldwide. Land-Use Changes related to vegetation studies are reliable tools to evaluate the human activities and footprint of proposed strategies and policies in a territory. This chapter also enables us to understand that the main actors should design development policies to protect, preserve and conserve these incomparable landscapes, environments, ecosystems, and the region as a whole.

Keywords

  • Galicia region
  • GIS tools
  • land-use changes
  • regional studies
  • sustainable planning
  • territorial planning and management

1. Introduction

Institutions and citizens have been paying more attention to issues of sustainable development in the last few years [1, 2]. The international agendas have also specified a set of short-term measures and goals for reducing the impact of human activities on natural systems. Public bodies, on the other hand, do not always have the resources to formulate policies and plans capable of reacting to the increasing strains that the territories are subjected to [3].

Sustainable development is defined as “dissemination that meets current demands without jeopardizing future generations’ ability to meet their own needs” [4]. This concept reflects the fact that sustainable development refers to a condition in which an input provides the best possible result without depleting natural resources. In accordance with the definition of sustainable development, there is a blueprint known as the Sustainable Development Goals (SDG) that directs people toward sustainable development [1].

The Sustainable Development Goals (SDGs) are a set of objectives that aim to create a more sustainable future by increasing wealth while also conserving the planet. These objectives place a premium on the long-term Outline Perspective Plan (OPP). The United Nations General Assembly launched the Sustainable Development Goals in 2015, and they are now implemented in all nations [1, 4].

As a result, achieving a balance between the economic, social, and environmental components is one of the most pressing concerns of our time. This equilibrium is particularly important in this setting because it directly affects both the acquisition and processing of natural resources. In the 1990s, the concept of sustainable development was first applied to mining planning and management [2, 5]. Over the last two decades, a lot of effort has gone into developing a sustainable approach to mining [2, 5].

Because of its potential to provide data that is accessible to a large audience, Geographic Information Systems (GIS) is an attractive tool for social workers. Administrators in the field of social work, for example, could utilize this technology to document the prerequisites for a new agency location. Furthermore, policymakers can offer the findings of a needs assessment or evaluative study, and academics can present the findings of a needs assessment or evaluative study [1, 6, 7, 8]. GIS can be traced back to a variety of technologies, processes, and procedures used in science, technology, and business, such as geodesy, mapping, geology, and seafaring; coordinate-time referencing of objects; processing and aggregation of photographic images from space for scientific and military purposes; and processing of geophysics and geodynamics data [1, 7]. GIS is defined by Burrough (1986) [8] as a set of tools for collecting, storing, retrieving, modifying, and displaying spatial data from the real world for a specific purpose.

As a result, rather than just presenting the results in tables, this study article uses maps to geo-visualize the data. GIS is not a new technology in today’s world; it has been around for decades. It is also well-known for its capacity to provide a spatial-based solution [1].

GIS analyses based on the Corine Land Cover (CLC) database developed by the Copernicus program of the European Spatial Agency [3, 9] have been developed to determine a cognitive reference framework that shows the spread of land consumption at a national level and allows comparing the spread of this phenomenon among the various European countries.

Information on the Environment Coordination CORINE Land Cover (CLC) is a European effort that supports the collection and interpretation of geospatial data. It was initiated in 1985 in all nations of the European Community (EC). It was created with the following goals in mind: (a) obtain and synchronize interdisciplinary data on the state of the environment; (b) focus on priority areas in each EU country; (c) coordinate and coordinate data organization and management at the local and international levels; and (d) ensure data compatibility [10].

The CLC database is a tool for carrying out complicated geographical analyses based on various land use categories. As a result, the hierarchical structure of CLC classes has three levels. The first level of land use and land cover (artificial areas, agricultural areas, forest and semi-natural areas, wetlands, and water bodies) encompasses the five primary types of land use and land cover. There are fifteen departments on the second floor. Finally, the third level has 44 components that state that individual-level three classes’ methodological scope is strictly defined [10, 11].

In context, the Geographic Information System (GIS) provides access to extensive land data sources and monitors land changes through high-resolution land cover assessments and change evaluations, particularly in urbanization regions [10, 12, 13]. Changes in human activities and urban ecological land cover can also be observed using these systems [13]. Furthermore, Urban Atlas (UA) has a wealth of other information, such as the classification of high-resolution satellite pictures (SPOT 2.5 m, ALOS 2.5 m, RapidEye 5 m), allowing for the separation of significant coverage classes. The lowest mapping unit is 0.25 hectares, which permits the development of land cover maps for only 305 large European cities with populations of more than 100,000 people and an estimated accuracy of 5 meters. Despite this, the UA only has 20 land cover classes, many fewer than the CLC [10].

Nowadays, Land-Use Changes studies are reliable tools to evaluate the human activities and footprint of proposed strategies and policies in a territory. The land is an important natural resource and a spatial carrier of human economic and social activities, and ecology. Land-use change reflects the impact of human activities on the natural environment, causing changes in surface structure (i.e., water bodies, climate, and ecology) and affecting the ecosystem service value [14]. The land is a non-renewable resource and while demand is constantly increasing, it is imperative to maintain a balance between demand and supply, needs and interests, or between contradictory uses, through Land-Use policies that achieve sustainable development and improve the quality of the environment [15, 16]. Very often, a poorly developed urban planning process leads to the changing of more natural land surfaces into artificial ones planned for human activities, therefore increasing social vulnerability. Therefore, the evaluation of the Land-Use Change process is important to the sustainable development of urban areas and to increase the resilience of territories and communities [10, 16]. On the other hand, Land-Use planning may also positively impact the environment by preserving natural resources, enhancing open space opportunities, or providing a significant reduction in traffic pollution [15].

Land-Use depends on numerous factors, including population, economic status, infrastructure, industrial activities, geographic conditions, land development policies, etc. [15, 17] and impacts numerous parameters, including flood risk, landslide probability, biodiversity, urban climate, hydrological processes, and pollution [15, 17, 18, 19, 20].

Given the increasing number of disasters over recent years, one of the most efficient and accessible methods for reducing the pressure posed by natural or technological risks is reducing the vulnerability level of communities exposed to a particular hazard [21, 22, 23, 24].

At all levels of government, there is a demand for instruments to enhance policy-making aimed at long-term planning. In this context, the Ecosystem Services (ES) approach [25, 26] provides a structured framework for developing more useful instruments for assessing environmental performance.

However, in the spatial planning process, the application of an ecosystem services approach to landscape analysis, sustainable planning, and decision making is largely inadequate [27, 28]. Many spatial landscape frameworks and environmental planning tools that incorporate the concept of ecosystem services have been created over the last decade [26, 29].

As a result, territorial planning and management strategy is a fundamental instrument for attributing wealth preconditions to the inhabitants, thereby fostering prosperity for future generations living on that territory, fostering the reduction of social imbalances and spatial inequalities, and serving as a stimulus for sustainable development [16, 30].

In the context of this study, the CLC data will be used to examine and evaluate the Land-Use Changes connected to vegetation in the Galicia Region between 1990 and 2018.

In this regard, we emphasize that the current study will contribute to science by enabling the collection of big data connected to Land-Use Changes associated to vegetation, as well as an overview of how they have evolved in the Galicia Region over the last three decades.

As a result of this research, we are able to give some principles and recommendations for future regional planning and management strategies and policies to be developed and implemented throughout the Galicia Region.

Advertisement

2. The Galicia Region: A brief overview

The Autonomous Community of Galicia (NUTS 2) is a Spanish region (NUTS 2) in the northwestern part of the country (Figure 1), with a total area of 29.574 km2 and administratively divided into four provinces with a total of 313 municipalities and 3793 parishes, with an average population density of 91.35 hab/km2 spread over more than 30,000 population centers, although the majority of its population is concentrated along the coast [31].

Figure 1.

Delimitation of the study area - Galicia region (Source: Authors by ESRI ArcGIS, 2020).

The altitude in Galicia ranges from sea level to nearly 2000 m and the topography includes plains as well as mountain areas with steep valleys [31, 32]. Galicia has two types of climate according to the Köppen–Geiger classification, the Csb (Mediterranean-Oceanica climate) and the Csa (Mediterranean climate) [33]. According to the Spanish official cartography, 69% of Galicia is covered by forestland (the forest terrain, according to the Galician forestry sector authorities, includes woodlands and shrublands) [31, 32]. The dominant tree species are three species of pine (Pinus pinaster, Pinus radiata, Pinus sylvestris), and two Eucalyptus species (Eucalyptus globulus and Eucalyptus nitens) and broadleaves (riparian species, Quercus robur, Quercus pyrenaica and Castanea sativa, among others). The 2015 analysis of the forestry sector indicates that 30% of the forestland was shrublands and rocky areas [32]. The forestry sector represents 3.5% of the Galician GDP and 50% of the timber cuts are Eucalyptus spp. followed by conifers [32]. Galician land forest is highly fragmented. According to official cadastral information, it is estimated that 162,188 ha are in cadastral parcels that are smaller than 0.5 ha [31, 32]; this accounts for approximately 40% of the land covered by the main productive tree species in Galicia.

Therefore, the study area is a region of extensive agroforestry tradition and high potential productivity [34], being approximately 61% (1.8 million ha) of its forest area territory [34]. With more than 1.4 million wooded hectares, and average growth of 12.3 million m3 per year of wood, Galicia contributed just over 9.7 million m3 in wood shorts in 2019 (almost half of the annual timber cuts in Spain), an annual rate of use increasable, under sustainability criteria, according to official statistics [31].

The Galicia Region has followed a path of dual productive specialization in forest and dairy production over the last half-century. It currently produces nearly half of the country’s timber and 40% of its dairy. As a result of artificial plantations and spontaneous vegetation invasion [35], the area covered by trees and other woody vegetation rose dramatically, resulting in a major increase in the amount and continuity of biomass present on the terrain. As a result, the region is distinguished by a large percentage of forest area, accounting for about 60% of the territory’s total area and 11% of Spain’s total forest area [36].

Severe wildfires occur every few years due to sporadic, short but possibly strong periods of drought during the summer. In 2017, almost 62,000 ha were burned, the majority of which (approximately 42,000 ha) occurred in just a few days in early October [37]. From 1968 to 2012, there were 249,387 wildfires in the region [38], resulting in the burning of almost 8000 km2 (about a fourth of the total regional area) in the last 25 years (29.574 km2). Different writers have identified a number of structural factors of fire igniting activity [39]. Traditional rural lifestyles are vanishing, as are tensions over land management and ownership, conflicts at the wildland-urban boundary, and socioeconomic conditions.

Property fragmentation is widely seen in the region as a significant impediment to the sustainable and economic management of forests and rangelands. According to current estimates, the region has almost 1.7 million proprietors (out of a total population of about 2.7 million) and over 11 million plots with an average size of 0.25 hectares [40]. Private owners own the majority of the land—they own more than two-thirds of the forest area—but the average size of a private holding is 1.5–2 ha per person [36]. Private properties, on the other hand, include common lands, which are legally recognized as a non-divisible kind of collective (albeit private) property. Community membership is limited by law, and it is open to everyone who lives in the same region as the community. As a result, communities are fluid entities: those who move in become owners, while those who leave lose their ownership rights. At 656,000 ha, common lands make up the final third (public property in the region is essentially non-existent), and are maintained by about 3000 local communities, with an average area of 200 ha per community [40].

Climatologically, Galicia Region has large differences between its coastal and inland areas. Average annual rainfall varies between 800 and 1000 mm in inland areas, and 1600 and 1900 in coastal areas. The annual mean temperature is 13°C, with remarkable differences between the coastal and continental temperatures; at the same elevation, in summer (winter), temperatures are on the order of 2°C higher (5°C lower) in the continental part. Thus, the lowest temperatures can be observed in the interior, where the highest mountains are located, with average minimum temperatures around 5°C. Summers are warm, particularly in the southeast of the area, with maximum temperatures exceeding 30°C [31, 41].

Galicia’s territory is heterogeneous, having densely populated sections mixed in with more sparsely populated areas. Within them, there are some cities and headwaters of the region that act as focal points of activity [42]. Demographically and economically dynamic areas coexist with those characterized by a lack of vitality, but even within them, there are some cities and headwaters of the region that act as focal points of activity.

Rural depopulation is a serious issue in Spain, particularly in Galicia, where it is regarded as a demographic and territorial phenomenon. Indeed, since 2008, the population of the region has decreased by 9.2 percent. In terms of the foreign population, prior to the economic slump, the rise in immigrants helped to alleviate rural depopulation [42].

Other important factors contributing to rural depopulation include an aging population or low population density that prevents economic development. Age and gender disparities, on the other hand, may be to blame [43]. Rural masculinization happens at a young age as a result of largely female migration and a lack of equal productive and reproductive work, leading to a search for a higher educational level and career prospects in metropolitan regions [42]. Aging, geographical isolation, a lack of spatial integration with other surrounding places, bad connections and transportation difficulties, a lack of adequate social services, and lower levels of human capital and employment prospects are all disadvantages associated with low density. All of this unavoidably leads to a drop in the economy [42, 44].

Loss of human resources, lack of territorial growth, and incapacity to maintain commercial operations have not only economic, but also patrimonial and environmental consequences [45]. The loss of livestock and conventional agricultural uses, in terms of environmental effects, is a danger factor for natural environment protection. This is due to the fact that landscape changes are uncontrollable, and forest land management in rural regions is largely confined to individual plots [42].

In the Galician mountains, extensive livestock used to have an impact on the forest ecosystem, favoring mosaics and lowering fuel [46]. As a result, the fall in extensive animal husbandry in Spain (approximately 30% between 2004 and 2015) is seen as a contributing cause to forest fires [42].

The environmental implications of progressive abandonment of rural regions, such as soil loss and exposure to erosive phenomena over wide areas, can be deemed unfavorable in the medium term [43]. Furthermore, there is a lack of forest land management, which increases the risk of fire. Traditional burning activities and the use of fire in mountain management in Galicia have been linked to an increase in fire occurrence [42, 47].

Advertisement

3. Methodology

The data used was two layers of information. These are public and open and can be used to replicate this work in another work area. The analyzed area is the Galicia region, in Spain.

Firstly, land-use related to vegetation data were obtained. The European Space Agency (EEA) offers through the CORINE Land Cover (Coordination of Information-CLC) project a geodatabase using polygonal graphic features that evoke land uses throughout the European Union, for the years 1990, 2000, 2006, 2012 and 2018 [48].

The scale used is 1:100,000 in the Geodesic Reference System corresponding to the European Terrestrial Reference System 1989 (ETRS89) and the Mapping System is Universal Transverse Mercator (UTM), with the minimum cartographic unit (MCU) being equal to 25 hectares. The accuracy obtained has been increasing over the years, since in 1990 it was less than 50 meters, in 2000, 2006 and 2012 it was less than 25 meters, and finally, in 2018 it is less than 10 meters. Also, the information contained in these polygons is hierarchical in three levels of information (Table 1).

Level 1Level 2Level 3
1. Artificial surfaces1.1. Urban fabric1.1.1. Continous urban fabric
1.1.2. Discontinuous urban fabric
1.2. Industrial, commercial and transport1.2.1.Industrial or commercial units
1.2.2.Road and rail networks and associated land
1.2.3.Port areas
1.2.4.Airports
1.3. Mine, dump and construction sites1.3.1.Mineral extraction sites
1.3.2.Dump sites
1.3.3.Construction sites
1.4. Artificial, non-agricultural vegetated areas1.4.1.Green urban areas
1.4.2.Sport and leisure facilities
2. Agricultural areas2.1. Arable land2.1.1. Non-irrigated arable land
2.1.2. Permanently irrigated land
2.1.3. Rice fields
2.2. Permanent crops2.2.1.Vineyards
2.2.2.Fruit trees and berry plantations
2.2.3.Olive groves
2.3. Pastures2.3.1.Pastures
2.4. Heterogeneous agricultural areas2.4.1.Annual crops associated with permanent crops
2.4.2.Complex cultivation
2.4.3.Land occupied by agriculture
3. Forests and semi-natural areas3.1. Forests3.1.1. Broad-leaved forest
3.1.2.Coniferous forest
3.1.3.Mixed forest
3.2.Shrub and/or herbaceous vegetation association3.2.1. Natural grassland
3.2.2.Moors and heathland
3.2.3.Scierophyllous vegetation
3.2.4.Transitional woodland shrub
3.3. Open spaces with little or no vegetation3.3.1.Beaches, dunes, and plains
3.3.2.Bare rock
3.3.3.Sparsely vegetated areas
3.3.4.Burnt areas
3.3.5.Glaciers and perpetual snow
4. Wetlands4.1. Inland wetlands4.1.1.Inland marshes
4.1.2.Peatbogs
4.2. Coastal wetlands4.2.1.Salt marshes
4.2.2.Salines
4.2.3.Intertidal flats
5. Water bodies5.1. Inland waters5.1.1.Water courses
5.1.2.Water bodies
5.2. Marine waters5.2.1.Coastal lagoons
5.2.2.Estuaries
5.2.3.Sea and ocean

Table 1.

CORINE Land Cover nomenclature (Source: [48]*).

For detailed information about the CLC Codes, the authors recommend the following source: www.eea.europa. eu/publications/COR0-landcover, accessed on 30 November 2021.


The second layer of information corresponds to the administrative delimitation of the Autonomous Community of Galicia. From the National Geographic Information Center in Spain (CNIG), as shown in Figure 2.

Figure 2.

Delimitation of the study area - Galicia region (Source: Authors by ESRI ArcGIS, 2020).

Subsequently, both layers of information were treated using ArcGIS 10.5 Geographic Information Systems (GIS) management software. Initially, all layers of information were transformed to the ETRS89-Azimuth Equiarea Coordinate Reference System of Lambert-2001, as this was adopted as official (ETRS-LAEA) [49]. Because ETRS-LAEA is based on the projection of equivalent areas in the territory. In this way, it serves as a reference for homogeneous units for all European countries. As a result, this coordinate system is used for the representation of analytical and statistical data.

Subsequently, the layer relating to the administrative divisions of the country, Spain has carried out a selection query through alphanumeric information and the Galicia region was selected. Subsequently, this single region was kept in a single layer of information. This layer of information was the limit of the scope of action of this work. The clip tool was then used, with Galicia’s boundary as the reference layer. This procedure was used for each of the years studied (1990, 2000, 2006, 2012 and 2018). In this way, land uses were obtained, but only those that were included in the region. Subsequently, geometric measurement of the area of each of the polygons was performed in hectares. This resulted in the number of hectares of each polygon representative of land uses according to the CLC nomenclature.

Once this information was obtained, the alphanumeric information recorded in each of the tables for the years analyzed was exported to a database that was managed by the Microsoft Access database management program belonging to Microsoft Office 365 software.

Selection queries were made to the database using Structured Query Language (SQL) to select according to the CLC nomenclature, and then another grouping query was added to the previous query, also using SQL. Finally, the hectares for each land use were obtained for the years 1990, 2000, 2006, 2012 and 2018.

However, to take into account not only numerical but also geographical results, thematic maps were also obtained for each of the years. In this way, it was possible to identify where the greatest variation in land uses related to vegetation occurred and where there was predominant land uses related to vegetation.

To an easy understanding of the used methodology and case study selection criteria, a scheme has been developed (Figure 3).

Figure 3.

Summary scheme of the used methodology and case study selection criteria (Source: Authors).

Advertisement

4. Results

The results come from the analysis of the land-use related to vegetation changes for the Galicia region in the years 1990, 2000, 2006, 2012 and 2018. The results will be exposed through the tables, and thematic cartography. This typology of results exposed allows for extracting the most relevant information and characterizing the evolution of land use based on the 15 land uses determined by CLC in level 2. The information is organized as presented in Table 2, in percentage.

Level 219902000200620122018
1.1.1.21%1.25%1.50%1.52%1.68%
1.2.0.16%0.22%0.34%0.37%0.45%
1.3.0.22%0.32%0.29%0.32%0.30%
1.4.0.01%0.02%0.05%0.05%0.06%
2.1.0.00%0.00%1.61%1.62%2.50%
2.2.0.22%0.24%0.26%0.26%0.18%
2.3.0.00%0.00%0.16%0.18%0.28%
2.4.41.07%41.02%28.14%28.14%27.53%
3.1.31.27%31.16%39.81%39.13%38.33%
3.2.23.99%23.97%25.78%26.55%26.85%
3.3.1.05%0.98%1.32%1.09%1.09%
4.1.0.00%0.00%0.01%0.01%0.00%
4.2.0.10%0.10%0.09%0.09%0.09%
5.1.0.51%0.55%0.50%0.53%0.52%
5.2.0.19%0.18%0.14%0.14%0.14%

Table 2.

Percentage of land uses according to level 2 of CLC nomenclature in the Galicia region (Source: authors).

Values in bold corresponding to Land-Use Changes related to vegetation.


In order to know what are the differences in area extension for every land use, the differences in percentage areas between years are calculated.

From the information in Table 3, it can be seen that the two greatest differences occur for land uses 2.4. and 3.1. between 2000 and 2006. Indeed, −12.88% for 2.4. corresponding to Heterogeneous agricultural areas and 3.1. corresponding to forests. For this reason, it was determined the percentage area for land uses, but in this case at the Level 3.

Level 22000-19902006-20002012-20062018-2012
1.1.0.04%0.25%0.02%0.17%
1.2.0.06%0.12%0.03%0.08%
1.3.0.10%−0.03%0.02%−0.02%
1.4.0.01%0.03%0.00%0.01%
2.1.0.00%1.60%0.01%0.88%
2.2.0.01%0.02%0.01%−0.08%
2.3.0.00%0.16%0.01%0.10%
2.4.−0.05%−12.88%0.00%−0.61%
3.1.−0.12%8.66%−0.68%−0.80%
3.2.−0.02%1.81%0.77%0.30%
3.3.−0.07%0.34%−0.23%0.00%
4.1.0.00%0.01%0.00%−0.01%
4.2.0.00%−0.01%0.00%0.00%
5.1.0.04%−0.06%0.03%−0.01%
5.2.0.00%−0.04%0.00%0.00%

Table 3.

Percentage difference of land uses according to level 2 of CLC nomenclature in the Galicia region (Source: authors).

Values in bold corresponding to Land-Use Changes related to vegetation - significant changes.


Table 4 shows that the highest percentage corresponds to 3.1.1. in 2006 whose value is 23.83% 2006, that is to say, about a quarter of Galicia is occupied by broad-leaved forest. Nonetheless, it is important to take into account that the percentage of this land use was very lower years before, 7.75% in 2000 and 7.63% in 1990. Consequently, a huge increase in the area occupied by broad-leaved forests occurred between 2000 and 2006. Although in a lower intensity and also between the same years, has occurred an increase of the land use 3.1.2. coniferous forest, from 2.78% in 2000 to 10.47% in 2006. On the contrary, a reduction of the area occupied occurred in the same years for the land uses 2.43. the land occupied by agriculture and 3.1.3. mixed forest.

Level 319902000200620122018
2.4.1.0.00%0.00%0.00%0.00%0.00%
2.4.2.23.51%23.57%22.10%22.10%21.28%
2.4.3.13.25%13.17%5.77%5.76%5.95%
3.1.1.7.63%7.75%23.83%23.68%23.42%
3.1.2.2.84%2.78%10.47%10.02%9.60%
3.1.3.17.52%17.38%5.12%5.06%4.90%

Table 4.

Percentage of land uses according to level 3 for 2.4. and 3.1. land uses according to CLC nomenclature in the Galicia region (Source: authors).

Values in bold corresponding to Land-Use Changes related to vegetation - significant changes.


In this regard, to know the highest differences in the percentage area of the land uses, again it was calculated the difference between the years analyzed, but at the level 3 and specifically for the land uses classified into 2.4. and 3.1.

According to Table 5, it seems that the increase in certain kinds of land uses such as 3.1.1. broad-leaved forest and 3.1.2. coniferous forest is compensated by the decrease of other land uses like 2.4.2. complex cultivation, 2.4.3. the land occupied by agriculture and 3.1.3. mixed forest. Nonetheless, it is advisable to execute more exhaustive study research to know it.

Level 32000-19902006-20002012-20062018-2012
2.4.1.0.00%0.00%0.00%0.00%
2.4.2.0.06%−1.47%0.00%−0.82%
2.4.3.−0.08%−7.40%0.00%0.18%
3.1.1.0.11%16.09%−0.16%−0.26%
3.1.2.−0.06%7.69%−0.45%−0.42%
3.1.3.−0.14%−12.25%−0.07%−0.16%

Table 5.

Percentage difference of land uses according to level 3 for 2.4. and 3.1. land uses according to CLC nomenclature in the Galicia region (Source: authors).

Values in bold corresponding to Land-Use Changes related to vegetation - significant changes.


In addition, using ArcGIS 10.5 Geographic Information Systems (GIS) management software, it was possible to more accurately represent the location of each area (thematic cartography) – i.e., according to their respective CLC nomenclature and temporal variance, Figures 410.

Figure 4.

Land uses according to level 2 of CLC nomenclature in the Galicia region in 1990 (Source: Authors by ESRI ArcGIS, 2020).

Figure 5.

Land uses according to level 2 of CLC nomenclature in the Galicia region in 2000 (Source: Authors by ESRI ArcGIS, 2020).

Figure 6.

Land uses according to level 2 of CLC nomenclature in the Galicia region in 2006 (Source: Authors by ESRI ArcGIS, 2020).

Figure 7.

Land uses according to level 2 of CLC nomenclature in the Galicia region in 2012 (Source: Authors by ESRI ArcGIS, 2020).

Figure 8.

Land uses according to level 2 of CLC nomenclature in the Galicia region in 2018 (Source: Authors by ESRI ArcGIS, 2020).

Figure 9.

Land uses according to level 3 for 2.4. and 3.1. land uses of CLC nomenclature in the Galicia region in 2006 (Source: Authors by ESRI ArcGIS, 2020).

Figure 10.

Land uses according to level 3 for 2.4. and 3.1. land uses of CLC nomenclature in the Galicia region in 2012 (Source: Authors by ESRI ArcGIS, 2020).

Because between 2006 and 2000 has occurred the highest difference for land uses was classified into the groups 2.4. and 3.1. thematic cartography was made for this land use at level 3 and in these years.

Although Figures 9 and 10 show the land uses for both years, it is really difficult to know where these changes have been produced. Because these land uses occupy most of the Galicia territory and they are very spread. However, it is possible to realize that in the north area 3.1.2. land uses disappear between the analyzed years. Besides, in the west area disappear land uses classified in 2.4. and 3.1., in favor of other land uses since more white areas appear. This effect is also observed in the southeast area.

Advertisement

5. Discussion and conclusions

In this section, we will address the results that come from the analysis of the land-use related to vegetation changes for the Galicia region in the years 1990, 2000, 2006, 2012 and 2018.

Therefore, the results presented through the tables and thematic cartography, in the previous section are related to the characterization of the evolution of land use based on the 44 uses of the soil determined by CLC. So, as we are analyzing the Land-Use Changes related to vegetation, we will give more importance to the CORINE Land Cover nomenclature associates, not neglecting the rest.

According to Table 5, it seems that the increase in certain kinds of land uses such as 3.1.1. broad-leaved forest and 3.1.2. coniferous forest is compensated by the decrease of other land uses like 2.4.2. complex cultivation, 2.4.3. the land occupied by agriculture and 3.1.3. mixed forest. Nonetheless, it is advisable to execute more exhaustive study research to know it.

The previously portrayed can be validated by the observation of the thematic cartography (Figures 46, 9and10).

Corroborating what has already been portrayed concerning the Galicia Region, namely climatology, the rural depopulation, are factors that contribute to the increase of the Land-Use Changes related to vegetation, namely those related to the forest [31, 41, 42].

This temporal evolution – not only at the parish level but also at the municipality level – has been influenced by the land tenure regime and, as expected, by the land management carried out. Thus, forest ownership characterized by either solely private or solely public management showed a higher incidence of more productive forest types than mixed management [34].

In addition, the demographic aspects linked to these territorial units have contributed, directly or indirectly, to these forestry changes. So, densely populated areas have increased their forestland toward woodlands for timber production, although the environmental component of sustainable forest management requires a special weight given the strong urban and population pressure. On the other hand, the area of productive forestry did not increase in highly depopulated areas (unlike other forestry regions) because the economic incentives were insufficient to promote a future owner’s interest [34].

The research of vegetation-related Land-Use Changes is critical for understanding regional trends and developments [50, 51]. It was feasible to discern changes in all CLC levels in the Galicia Region from 1990 to 2018 throughout this examination.

Thus, it was credible to establish that these Land-Use Changes related to vegetation suffered some changes, characterized by increasing and decreasing periods. Some of those decreasing values are disturbing and should have special attention by the government authorities to provide preservation and conservation of these unique Galician landscapes and environments.

The changes in the Land-Use related to vegetation could be understood as a direct manifestation of human activity over natural environments [52, 53]. Therefore, the natural factors and features—i.e., geomorphology, slope, relief, soil, and vegetation, among many others— are critical for the proper organization and distribution of the territory and their consequent land uses [52]. The lack of knowledge aligned with the existence of planning conducts to the destruction of the natural resources causing a relevant (negative) impact on the local communities [54].

Therefore, the study of the Land-Use Changes related to vegetation is seen as pivotal to understanding the dynamics and tendencies of these territories as well as to provide clues for the main actors to where the efforts toward sustainable development and growth should be placed.

In the final remarks, the Land-Use Changes related to vegetation could be understood as another tool for the knowledge of the territory—assessing the past and envisioning the future.

Advertisement

6. Limitations of the study and future research directions

Although this chapter provides some insight into the dynamics, trends, and specificities of Land-Use Changes associated to vegetation in the Galicia Region, more research is needed to uncover new variables and significant findings.

In these territories, regional policies and societal behaviors change frequently, necessitating close monitoring and new analyses of the directions and dynamics of Land-Use Changes associated to vegetation, as well as the management of sustainable development methods.

Furthermore, due to the employed CLC’s minimum cartographic unit (25 hectares), some Land-Use in the Galicia Region could not be reflected in this study if these aspects were not identified. This problem would most likely be solved if newer versions of the CLC program were used, specifically the most recent one with a better resolution.

Future research on these regions may also combine cartography with protected natural spaces, their various figures, and Land-Use Changes related to vegetation through time.

Advertisement

Acknowledgments

This publication has been possible thanks to funding granted by the “Consejería de Economía, Ciencia y Agenda Digital” (Ministry of Economy, Science and Digital Agenda) of Extremadura govern, and by the European Regional Development Fund of the European Union through the reference grants GR21135, Research Group on Environment and Spatial Planning.

Advertisement

Conflict of interest

“The authors declare no conflict of interest.”

References

  1. 1. Yaakub NF, Masron T, Marzuki A, Soda R. GIS-based spatial correlation analysis: Sustainable development and two generations of demographic changes. Sustainability. 2022;14:1490. DOI: 10.3390/su14031490
  2. 2. Assumma V, Bottero M, Caprioli C, Datola G, Mondini G. Evaluation of ecosystem services in mining basins: An application in the piedmont region (Italy). Sustainability. 2022;14:872. DOI: 10.3390/su14020872
  3. 3. Botticini F, Auzins A, Lacoere P, Lewis O, Tiboni M. Land take and value capture: Towards more efficient land use. Sustainability. 2022;14:778. DOI: 10.3390/su14020778
  4. 4. Mensah J. Sustainable development: Meaning, history, principles, pillars, and implications for human action: Literature review. Cogent Social Sciences. 2019;5:1653531. DOI: 10.1080/23311886.2019.1653531
  5. 5. Bottero MC, Polo Pérez I, Taddia G, Lo RS. A geodatabase for supporting planning and management of mining activities: The case of Piedmont Region. Environment and Earth Science. 2020;79:83. DOI: 10.1007/s12665-020-8815-x
  6. 6. Felke TP. Geographic information systems: Potential uses in social work education and practice. Journal of Evidence-Based Social Work. 2006;3:103-113. DOI: 10.1300/J394v03n03_08
  7. 7. Andreev DV. The use of GIS technology in modern conditions. IOP Conference Series: Earth and Environmental Science. 2020;421:042001. DOI: 10.1088/1755-1315/421/4/042001
  8. 8. Burrough PA. Principles of geographical information systems for land resources assessment. Geocarto International. 1986;1:54-54. DOI: 10.1080/10106048609354060
  9. 9. Decoville A. Can the 2050 zero land take objective of the EU be reliably monitored? A Comparative Study. Journal of Land Use Science. 2016;11(3):331-349. DOI: 10.1080/1747423X.2014.994567
  10. 10. Naranjo Gómez JM, Lousada S, Garrido Velarde J, Castanho RA, Loures L. Land-use changes in the canary archipelago using the CORINE data: A retrospective analysis. Landscape. 2020;9:232. DOI: 10.3390/land9070232
  11. 11. Benedetti A, Picchiani M, Del Frate F. Sentinel-1 and sentinel-2 data fusion for urban change detection. IGARSS 2018. 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018:1962-1965. DOI: 10.1109/IGARSS.2018.8517586
  12. 12. Melchiorri M, Florczyk AJ, Freire S, Schiavina M, Pesaresi M, Kemper T. Unveiling 25 years of planetary urbanization with remote sensing: Perspectives from the global human settlement layer. Remote Sensing. 2018;10:768. DOI: 10.3390/rs10050768
  13. 13. Washaya P, Balz T. Sar coherence change detection of urban areas affected by disasters using sentinel-1 imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2018;XLII–3:1857-1861. DOI: 10.5194/isprs-archives-XLII-3-1857-2018
  14. 14. Chen Q , Mao Y, Morrison AM. The influence of land use evolution on the visitor economy in wuhan from the perspective of ecological service value. Landscape. 2022;11:1. DOI: 10.3390/land11010001
  15. 15. Botezan CS, Radovici A, Ajtai I. The challenge of social vulnerability assessment in the context of land use changes for sustainable urban planning—case studies: Developing cities in Romania. Landscape. 2022;11:17. DOI: 10.3390/land11010017
  16. 16. Castanho RA, Lousada S, Naranjo Gómez JM, Escórcio P, Cabezas J. Loures LF-P and L. dynamics of the land use changes and the associated barriers and opportunities for sustainable development on peripheral and insular territories: The madeira Island (Portugal). IntechOpen. 2018. DOI: 10.5772/intechopen.80827
  17. 17. Grigorescu I, Mitrică B, Kucsicsa G, Popovici E-A, Dumitraşcu M, Cuculici R. Post-communist land use changes related to urban sprawl in the Romanian metropolitan areas. Human Geography – Journal of Studied Research Human Geography. 2012;6:35-46. DOI: 10.5719/hgeo.2012.61.35
  18. 18. Caldas AM, Pissarra TCT, Costa RCA, Neto FCR, Zanata M, Parahyba RDBV, et al. Flood vulnerability, environmental land use conflicts, and conservation of soil and water: A study in the batatais SP municipality, Brazil. Water. 2018;10:1357. DOI: 10.3390/w10101357
  19. 19. Zarzycki J, Korzeniak J, Perzanowska J. Impact of land use changes on the diversity and conservation status of the vegetation of mountain grasslands (Polish Carpathians). Landscape. 2022;11:252. DOI: 10.3390/land11020252
  20. 20. Xie S, Zhang W, Zhao Y, Tong D. Extracting land use change patterns of rural town settlements with sequence alignment method. Landscape. 2022;11:313. DOI: 10.3390/land11020313
  21. 21. Kundzewicz ZW, Kanae S, Seneviratne SI, Handmer J, Nicholls N, Peduzzi P, et al. Flood risk and climate change: Global and regional perspectives. Hydrological Sciences Journal. 2014;59:1-28. DOI: 10.1080/02626667.2013.857411
  22. 22. Armaş I. Social vulnerability and seismic risk perception. Case study: The historic center of the Bucharest municipality/Romania. Natural Hazards. 2008;47:397-410. DOI: 10.1007/s11069-008-9229-3
  23. 23. Barichivich J, Gloor E, Peylin P, Brienen RJW, Schöngart J, Espinoza JC, et al. Recent intensification of Amazon flooding extremes driven by strengthened walker circulation. Science Advances. 2018;4:eaat8785. DOI: 10.1126/sciadv.aat8785
  24. 24. Vieira I, Barreto V, Figueira C, Lousada S, Prada S. The use of detention basins to reduce flash flood hazard in small and steep volcanic watersheds – A simulation from Madeira Island. Journal of Flood Risk Management. 2018;11:S930-S942. DOI: 10.1111/jfr3.12285
  25. 25. Pilogallo A, Scorza F. Mapping regulation ecosystem services specialization in Italy. Journal of Urban Planning and Development. 2022;148:04021072. DOI: 10.1061/(ASCE)UP.1943-5444.0000801
  26. 26. leBrasseur R. Mapping green infrastructure based on multifunctional ecosystem services: A sustainable planning framework for Utah’s Wasatch front. Sustainability. 2022;14:825. DOI: 10.3390/su14020825
  27. 27. Forkink A. Benefits and challenges of using an Assessment of Ecosystem Services approach in land-use planning. Journal of Environmental Planning and Management. 2017;60:2071-2084. DOI: 10.1080/09640568.2016.1273098
  28. 28. Maes J, Jacobs S. Nature-based solutions for Europe’s sustainable development. Conservation Letters. 2017;10:121-124. DOI: 10.1111/conl.12216
  29. 29. Albert C, Aronson J, Fürst C, Opdam P. Integrating ecosystem services in landscape planning: requirements, approaches, and impacts. Landscape Ecology. 2014;29:1277-1285. DOI: 10.1007/s10980-014-0085-0
  30. 30. Vulevic A, Macura D, Djordjevic D, Castanho RA. Assessing accessibility and transport infrastructure inequities in administrative units in Serbia’s danube corridor based on multi-criteria analysis and Gis mapping tools. Transylvanian Review of Administrative Sciences. 2018;14:123-143. DOI: 10.24193/tras.53E.8
  31. 31. López-Rodríguez G, Rodríguez-Vicente V, Marey-Pérez MF. Study of forest productivity in the occurrence of forest fires in Galicia (Spain). Sustainability. 2021;13:8472. DOI: 10.3390/su13158472
  32. 32. Alonso L, Picos J, Armesto J. Forest land cover mapping at a regional scale using multi-temporal sentinel-2 imagery and RF models. Remote Sensing. 2021;13:2237. DOI: 10.3390/rs13122237
  33. 33. Guitián MR, Rego PR. Clasificaciones climáticas aplicadas a Galicia: Revisión desde una perspectiva biogeográfica. Recursos Rurais Review of Institute Biodiversidade Agrar E Desenvolv Rural IBADER. 2007;3:31-53
  34. 34. Marey Pérez M, Rodríguez Vicente V, Crecente MR. Using GIS to measure changes in the temporal and spatial dynamics of forestland: experiences from north-west Spain. International Journal of Forestry Research. 2006;79:409-423. DOI: 10.1093/forestry/cpl027
  35. 35. Corbelle Rico EJ, Tubío Sánchez JM. Productivismo y abandono: dos caras de la transición forestal en Galicia (España), 1966-2009. Bosque Valdivia. 2018;39:457-467. DOI: 10.4067/S0717-92002018000300457
  36. 36. Marey-Pérez M, Díaz-Varela E, Calvo-González A. Does higher owner participation increase conflicts over common land? An analysis of communal forests in Galicia (Spain). IForest - Biogeosciences For. 2015;8:533-543. DOI: 10.3832/ifor1060-008
  37. 37. Chas-Amil M-L, García-Martínez E, Touza J. Iberian Peninsula October 2017 wildfires: Burned area and population exposure in Galicia (NW of Spain). International Journal of Disaster Risk Reduction. 2020;48:101623. DOI: 10.1016/j.ijdrr.2020.101623
  38. 38. Boubeta M, Lombardía MJ, González-Manteiga W, Marey-Pérez MF, Boubeta M, Lombardía MJ, et al. Burned area prediction with semiparametric models. International Journal of Wildland Fire. 2016;25:669-678. DOI: 10.1071/WF15125
  39. 39. Boubeta M, Lombardía MJ, Marey-Pérez M, Morales D, Boubeta M, Lombardía MJ, et al. Poisson mixed models for predicting number of fires. International Journal of Wildland Fire. 2019;28:237-253. DOI: 10.1071/WF17037
  40. 40. Marey-Perez M, Loureiro X, Corbelle-Rico EJ, Fernández-Filgueira C. Different strategies for resilience to wildfires: The experience of collective land ownership in Galicia (Northwest Spain). Sustainability. 2021;13:4761. DOI: 10.3390/su13094761
  41. 41. Chas-Amil ML, Prestemon JP, McClean CJ, Touza J. Human-ignited wildfire patterns and responses to policy shifts. Applied Geography. 2015;56:164-176. DOI: 10.1016/j.apgeog.2014.11.025
  42. 42. de Diego J, Rúa A, Fernández M. Designing a model to display the relation between social vulnerability and anthropogenic risk of wildfires in Galicia, Spain. Urban Science. 2019;3:32. DOI: 10.3390/urbansci3010032
  43. 43. Barreal J, Loureiro ML. Modelling spatial patterns and temporal trends of wildfires in Galicia (NW Spain). For Systems. 2015;24:e022-e022. DOI: 10.5424/fs/2015242-05713
  44. 44. Bergstrand K, Mayer B, Brumback B, Zhang Y. Assessing the relationship between social vulnerability and community resilience to hazards. Social Indicators Research. 2015;122:391-409. DOI: 10.1007/s11205-014-0698-3
  45. 45. Barreiro JB, Hermosilla T. Socio-geographic analysis of the causes of the 2006’s wildfires in Galicia (Spain). For Systems. 2013;22:497-509. DOI: 10.5424/fs/2013223-04165
  46. 46. Barreal J, Loureiro ML, Picos J. Estudio de la causalidad de los incendios forestales en Galicia. Economia Agraria y Recursos Naturales - Agricultural and Resource Economics. 2012;12(1):99-114. DOI: 10.7201/earn.2012.01.04
  47. 47. Wigtil G, Hammer RB, Kline JD, Mockrin MH, Stewart SI, Roper D, et al. Places where wildfire potential and social vulnerability coincide in the coterminous United States. International Journal of Wildland Fire. 2016;25:896-908. DOI: 10.1071/WF15109
  48. 48. CORINE Land Cover — European Environment Agency n.d. Publications Office of the European Union. Luxembourg. Available online: https://www.eea.europa.eu/publications/COR0-landcover [Accessed: April 28, 2022]
  49. 49. Publications Office of the EU n.d. https://op.europa.eu/en/publication-detail/-/publication/96743011-0b4f-11ea-8c1f-01aa75ed71a1 [Accessed April 28, 2022]
  50. 50. Gómez N, Manuel J. Impacts on the social cohesion of mainland Spain’s future motorway and high-speed rail networks. Sustainability. 2016;8:624. DOI: 10.3390/su8070624
  51. 51. Vulevic A, Castanho RA, Naranjo Gómez JM, Loures L, Cabezas J, Fernández-Pozo L, et al. Accessibility dynamics and regional cross-border cooperation (CBC) perspectives in the portuguese—Spanish borderland. Sustainability. 2020;12:1978. DOI: 10.3390/su12051978
  52. 52. Gao P, Niu X, Wang B, Zheng Y. Restoration area based on GIS and RS of northern China. Scientific Reports. 2015;5:11038. DOI: 10.1038/srep11038
  53. 53. Bertrand N, Vanpeene-Bruhier S. Periurban landscapes in mountain areas. Journal of Alpine Research | Revue de géographie alpine. 2007; 95(4):69-80. DOI: 10.4000/rga.363
  54. 54. Loures L. Land use: Assessing the past, envisioning the future. BoD – Books on Demand. 2019

Written By

Sérgio Lousada and José Manuel Naranjo Gómez

Submitted: 30 April 2022 Reviewed: 21 June 2022 Published: 22 July 2022