
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12627-8
10.1016/j.heliyon.2024.e36596
e36596
Research Article
Determining the areas most suitable for urban land use while minimizing impact on natural areas. The case of the Machachi Valley, Ecuador
Ulloa-Espindola Rene rene.ulloae@alumnos.upm.es
a
Martín-Fernández Susana susana.martin@upm.es
b⁎
a ETSI Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, Ciudad Universitaria sn, 28040, Madrid, Spain
b Centro para la Conservación de la Biodiversidad y el Desarrollo Sostenible, ETSI de Montes, Forestal y del Medio Natural, Universidad Politécnica de Madrid, c/ José Antonio Nováis 10, 2804, Madrid, Spain
⁎ Corresponding author. susana.martin@upm.es
20 8 2024
15 9 2024
20 8 2024
10 17 e365968 4 2024
14 8 2024
19 8 2024
© 2024 The Authors. Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Due to the impact of urban growth on the environment, especially in developing countries, decision-making tools are needed to help locate the ideal areas for urban use. This study aims to identify the areas most suitable for urban use, minimizing their impact on forest land use in an area with high urban sprawl. As a new development, the study also considers the connectivity between forest-use patches and the loss of forest area as decision variables. The methodology used to determine these areas was AHP (Analytic Hierarchy Process) with GIS (Geography Information System). Twenty experts evaluated the criteria, considering forest conservation in addition to urban suitability. Socio-economic, physical, and environmental criteria and sub-criteria were scored with values from 1 to 5, with 5 indicating the maximum potential of the pixel to accommodate urban use. The study area was the Machachi Valley and the conurbation of Quito in Ecuador. The results indicate that the most suitable urban areas are located in the buffer that surrounds the initial urban use, and the land along the Pan-American Highway. The most vulnerable forest zone was in the south center of Quito and on the hillsides of the Pasochoa volcano. The results were also compared with maps that estimate the evolution of the land uses in this valley in the coming years. Based on this comparison, maintenance of current trends will result in a significant loss of native forest and fragmentation of forest patches in the lower valley. The information provided by maps of suitable urban use is a useful tool to protect the natural resources that land use policies should take into consideration.

Highlights

• Latin America faces a lack of land regulation and urban planning.

• Integration of GIS and AHP for locating urban land use considering forest conservation.

• Use of forest connectivity as a decision criterium.

• Identification of natural land use losses in Machachi Valley in the coming years.

Keywords

MCDM
Forest fragmentation
Environmental criteria
Native forest
Urban spread
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pmc1 Introduction

Urban growth is currently the main driver behind the changes in land use, converting it from agricultural and forestry land uses to artificial uses [[1], [2], [3]]. Also, by 2050, close to 2.5 billion more people will live in cities [4]. The main challenges posed by this growth lie in guaranteeing the sustainable availability of basic services, food security, energy and labor, while at the same time minimizing negative impacts on the environment [5], having land suitable for urban use, and improving the efficiency of urban and land planning so that the expansion of urban areas can keep pace with the population growth in cities [[6], [7], [8]].

Regarding natural resources, urban development and the expansion of sealed ground are increasingly affecting environmental quality and ecosystem patterns globally, regionally, and locally [9], changing the use of ecologically valuable lands and displacing agriculture into natural and forest zones [3,[10], [11], [12], [13]]. This affects human well-being [14], available ecosystem services [14,15], causes a loss of biodiversity, forest products, and agricultural land [3,[16], [17], [18]], diminishes air and water quality and the availability of surface and groundwater, changes the hydrologic balance in river basins [[19], [20], [21]], and fragments the natural landscape [16,22,23]. In light of these global challenges, the 2030 Agenda for Sustainable Development includes Goal 11, which is aimed at making cities and human settlements sustainable. The purposes of this goal include not only improving the conditions of cities but also studying the links between the cities and the rural environment, and the environmental impacts of urban development [4,24]. For this reason, the UN Habitat's New Urban Agenda [25] highlights the importance of territorial planning and urban planning to “promote sustainable land use, combine urban extensions with adequate densities and compactness to prevent and contain urban sprawl, as well as to prevent unnecessary land-use change and the loss of productive land and fragile and important ecosystems.”

This growth is especially aggressive in developing countries, where the lack of adequate territorial planning results in obvious visible environmental degradation, scarcity of public services, and the appearance of slums [4,26]. Specifically, in Latin America, 81 % of the population currently lives in cities [25]. Despite these weaknesses, Latin American countries conceive Territorial Planning (TP) as “a Governmental policy that defines a political-technical-administrative process that will determine: (1) how land use and occupation is organized; (2) the sustainable use of natural resources and biodiversity; (3) disaster prevention due to use, and (4) the management of economic imbalance and territorial fragmentation” [27].

In Ecuador, the surface areas of medium and large cities are growing faster than the urban population, with the cities of Quito and Guayaquil, and their urban and rural satellite localities [28] providing a clear example. Ecuador has aligned itself with this dynamic of evolution of TP in the region, but the drafting of territorial plans by the decentralized autonomous governments that are responsible for preparing the TP plans that correspond to their powers on three territorial levels: province, canton, and municipality [29], generates problems with land regulation, and urban development [28].

The choice of land-use locations involves the assessment of the land-use requirements and the suitability of the land for the use in question (urban, agriculture, forestry, etc.) [30].

The determination of urban land use suitability is especially challenging. An important part of this process is to determine the criteria that affect the suitability of the territory, and the weighting of each one [31]. Policymakers, professionals, and different stakeholders are involved in this type of planning. This means that their decisions need to address the sustainable development of urban areas and land-use change [[31], [32], [33]]. Urban planning theories and concepts such as compact city development, were developed to manage the harmony between the urban and natural ecosystems [34,35].

Multi-criteria decision-making methods (MCDM) have been used to solve management problems in a wide range of areas that require the integration of multiple criteria [36]. These methods help to resolve planning problems through their unbiased integration of planning objectives and the ordering of planning solutions, which make the decision-making process more rigorous and transparent [37,38]. These methods have the added advantage that they can involve one or more decision-makers by integrating their preferences, depending on the methodology that is applied [39].

In recent years, the combination of MCDM and Geographic Information Systems (GIS) has improved territory-evaluation processes, decision-making, and the efficiency of processing [40]. GIS-MCDM makes it possible to incorporate the preferences of experts from different disciplines into the evaluated territorial units [41], to provide insight into territorial land-use interactions and constraints [35,42] that limit the suitable areas in an assignment process [30,43,44], and to obtain accurate maps of territorial suitability [30,41].

This increase in studies that combine GIS and MCDM is especially notable in urban planning, agriculture, forest management, environment, and rural development [35,[45], [46], [47], [48], [49], [50], [51], [52], [53]]. Even though MCDM techniques have been only minimally applied at the local level, with most studies done at the national or regional levels [38], these methods do hold great potential, especially to identify future areas for urban use [38,54].

In this study, we focus on the Analytical Hierarchy Process (AHP) introduced by Saaty [55] which is currently one of the most frequently used multi-criteria decision-making tools, as demonstrated by the more than 35,400 documents on AHP published between 1980 and 2021 [56]. AHP is a weighting method that can combine quantitative and qualitative criteria to evaluate alternative scenarios and ultimately select the most preferable alternative [57], and it can be combined with GIS in multi-purpose spatial decision-making studies [58].

Earlier studies on the application of AHP to land management have covered physical planning [2,[59], [60], [61], [62]], urban planning [[63], [64], [65]], and, in agriculture [[66], [67], [68], [69]], land-use modelling and coverage changes [[70], [71], [72], [73]], and the determination of areas suitable for specific land uses [30,72,[74], [75], [76], [77], [78]].

There are also earlier studies about urban land use suitability that consider their compatibility with environmental factors. The focuses of these studies include groundwater contamination [42], water resource management [79], agriculture compatibility [80], the effect of uncontrolled population growth and urban landfill placement on environmental factors [81], the effect on wetlands [82], and the environmental effects of seven land-use types (aquaculture, arable land, horticulture, rangelands, industrial development, tourism, and ecotourism) [83]. However, we found that the scientific literature on MCDM does not include the study of the urban land-use suitability assessment considering forest protection constraints, even though there are 306 protected areas in 45 countries in Africa and Latin America with human settlements in the proximity that threaten ecosystem biodiversity [84]. Some of the proposed sustainable forest management solutions include the creation of buffers around forest zones [85] and maintaining or establishing connectivity between residual forest patches to conserve ecosystems in fragmented landscapes [86]. According to the scientific literature, these solutions are not used in urban land-use planning.

This study aims to identify the most suitable areas for urban use, minimizing their impact on forest land use in an area with high urban sprawl. In addition, as a new development, this study considers the connectivity between forest-use patches and the initial assessment of the criteria by experts, with AHP, with the dual purpose of conserving forest zones and assigning urban use.

2 Study area

The study area covers 881.61 km2 and is located south of the city of Quito (Quito canton), Rumiñahui canton, and the Machachi Valley, in the Mejía canton, province of Pichincha, at an elevation of more than 2945 m above sea level (see Fig. 1). The climate varies from humid tropical to high Andean climate.Fig. 1 Location of the study area.

Fig. 1

Quito's main urban problems are caused by the anomalous situation of most of the 200 peripheral cities, most of which are informal settlements with irregular constructions located in areas at high risk of natural and health disasters, in addition to the loss of agricultural and forestry areas that have been urbanized in the valleys to the east of the city (Tumbaco, Cumbayá, Valle de los Chillos and Valle de Machachi) [87]. Also, Quito's demographic, administrative and economic profile has generated urban corridors along the main road axes. The best example is the Pan-American Highway, which connects Quito, Alóag and Machachi and has also led to the destruction of the moorlands and native forest ecosystems (the Pasochoa volcano and the Toachi-Pilaton protected area, the foothills of the Ilalo volcano and Los Chillos Valley and the Atacazo and Rumiñahui mountains) that are highly vulnerable to water and wind erosion and territorial fragmentation [88]. This vulnerability puts the endemic species of fauna and flora that coexist in these Andean ecosystems at risk of extinction [89].

Regarding land-use distribution, Table 1 and Fig. 2 describe and show the land-use locations in the study area [90].Table 1 Land-use types in the study area.

Table 1Land use	Definition	Area (km2)	
Forest plantation	Anthropically established tree stand with one or more forest species.	23.64	
Areas with no vegetation cover	Areas generally devoid of vegetation that are not used for agriculture or forestry due to their soil, climatic, topographic or anthropic limitations, although they may have other uses.	3.88	
Shrub vegetation	Areas with a substantial component of non-tree native woody species. These include degraded areas in transition to dense canopy coverage.	66.13	
Moorland	High Andean vegetation is characterized by dominant non-arboreal species that includes fragments of native forest typical of the area.	172.36	
Agriculture and livestock	Agricultural areas and planted pastures.	401.80	
Native forest	Primary or secondary arboreal ecosystem regenerated by natural succession, characterized by the presence of trees of different native species in an irregular structure with one or more strata.	108.99	
Infrastructure	Road and transport networks, industry, and social infrastructure.	2.15	
Natural water bodies	Surface static or moving water.	0.62	
Herbaceous vegetation	Areas of spontaneously growing native herbaceous species receiving no special care and used for sporadic grazing, wildlife or protection purposes.	0.70	
Urban area	Areas mainly occupied by homes and public buildings.	101.34	

Fig. 2 Map of land uses in 2018.

Fig. 2

3 Methodology

The methodological process of this study was carried out in three stages. In stage one, the authors identified the criteria and sub-criteria for determining the most suitable areas for urban use that allow the conservation of forest use. In stage two, 20 experts participated in the AHP process to compare these criteria, and the sub-criteria were grouped by criteria. In stage three, the criteria and sub-criteria maps were integrated, applying the weights obtained in stage 2 (see Fig. 3).Fig. 3 Methodological steps to identify the most suitable urban areas.

Fig. 3

3.1 Stage 1: identification of criteria and sub-criteria

There are no universally accepted criteria for urban land-use planning [91]. Consequently, the procedure to choose the criteria and sub-criteria first identified the main land uses and analyzed the territorial planning systems at the municipal and regional levels. Then, we analyzed scientific and administrative publications related to territorial planning in the area. We decided to follow the procedure applied in similar studies by Mosadeghi et al. [38] in their work on the allocation of urban land use for the northeast Gold Coast located in Queensland (Australia), and Ullah and Mansourian [91], in their study of the feasibility of urban use in the area of influence of the city of Dhaka. Lastly, we created three broad criteria categories: physical, risk and impact, and socio-economic.

The variables were selected based on the following main characteristics: the morphology of the area, the climate and the highly particular hydrology that surrounds the area due to its location (ecological valley – the ground has a perennial upwelling of thermal and mineral waters), the risks and impacts for the population if urban areas were located on land with a high risk of natural disasters, and the risk to natural areas posed by a decrease in surface area and connectivity, as well as the socio-economic dynamics, since the peripheral areas already have infrastructure and services, which makes them more likely to accommodate urban use. The criteria and sub-criteria were represented with cartographic information obtained from the 1:25,000 Project for the generation of national geoinformation for soil management of the Military Geographic Institute of Ecuador in 2018 [92].

The physical criterion comprises three sub-criteria: slope, climatology and hydrology, and geomorphology criteria. The slope map was obtained from the altitude map. This determines the landslide risk of the population settlements, the quality of the urban development and the vulnerability of the ecological environment [33,93]. The climatology and hydrology map shows the water deficit in the study area. It was obtained from the precipitation, temperature, water deficit, and vegetation maps. The study area contains different microclimates with large temperature and rainfall differences that directly affect quality of life and water availability. Because of the valley's orientation and geographic position, the climate changes from humid tropical to high Andean [33]. Geomorphological cartography is valuable for managing and avoiding overexploitation of natural assets, such as water sources, rich soils, and gentle slopes, especially in volcanic areas like the study area [94].

The risk and impact criterion comprises the sub-criteria “Landslides”, “Loss of forest connectivity”, and “Loss of forest areas".

The “Landslide” sub-criterion determines the safest locations for urban settlements for this risk. In this zone, landslides may occur naturally due to the geomorphological characteristics and climate, as well as to the construction of urban areas themselves [93]. This map was obtained based on the army's map of landslide frequency and events [92].

The “Loss of forest connectivity” sub-criterion was included to analyze the connection between patches of forest vegetation that are currently isolated. As a result, areas that disconnect the forestry use will not be conducive to urban land use since this will surely increase the disconnection of this use even more (human beings are the main modifying agent of the vegetation cover). Dondina et al. [86] recommend the analysis of the land uses in a 300 m buffer around forest patches. Regarding this map, the authors first constructed a map based on the creation of an area of influence of 300 m around all forestry uses, in other words, uses that are classified as: “native forest”, “natural”, “moorland”, “plantation”, “shrub vegetation” and “herbaceous vegetation”. This map was then overlaid (overlap) on the other land uses: urban use, areas with no vegetation cover, native forest, infrastructure, and agricultural-livestock land to obtain the loss of forest connectivity map.

The last sub-criterion of this group, “Loss of forest areas”, determines the risk of change to agricultural, urban areas, infrastructure or another use that affects the benefits of forestry use. This map was generated from the changes from forestry use (“native forest”, “natural”, “moorland”, “plantation”, “shrub vegetation” and “herbaceous vegetation”) to urban use, infrastructure, and agricultural land, using the LUMs (land use map) of 2000 and 2018 as a reference.

Finally, the last criterion, socio-economic aspects, comprises “Area of urban influence”, “Distance to roads”, “Distance to services and infrastructure”, and “Areas of economic or cultural interest”. The “Area of urban influence” map determines the degree to which land uses are influenced by the presence of urban use. The influence is decisive for new urban or infrastructure settlements, because the closer it is to pre-existing infrastructure, the more likely the land use will change. This map is a reclassification of the land use map. Consequently, the land uses “urban areas” and “infrastructure” both correspond to urban land use, “agricultural land” and “areas with no vegetation cover” both correspond to no forest vegetation, and the forest-related land uses to the forest land use.

“Distance to roads” and “Distance to services and infrastructure” are determining factors for urban land use because areas that are closest to these elements are preferred for land use change [35]. The related maps were obtained from the map of the 200-m buffer from the road network and infrastructure. This distance was chosen because significant densities of urban settlements are recorded in the first 200 m from roads or infrastructure in the study area.

The “Areas of economic or cultural interest” sub-criterion was considered since areas closer to the urban land use are more likely to have access to these locations and as a result, are more likely to change the land use. The map was obtained from the “Literacy and education level” map developed by Military Geographic Institute of Ecuador [92].

On the other hand, as Leake and Malczewski [95] recommended, we considered criteria and sub-criteria that are measurable, operational for the decision problem, not redundant, and considered the fewest possible sub-criteria. One of the main concerns of our study was the value of the map data to indicate urban sustainability considering forest protection. To achieve this, we did a purpose reclassification of the initial values of the maps. The values of the pixels had two meanings: urban suitability and forest protection. A reclassification value of “1″ indicates that the pixel is adequate for urban use (minimum value) and that forest areas are protected. A value of “5″ is assigned to categories adequate for urban use (see Table 2). Categories classified as “Unknown” were assigned the value “0". Reclassification of maps was done with ArcGIS Pro ® v2.5 software. The reclassification was carried out in May 2023 in a workshop by four PhD participants with expertise in forest management and territory and urban planning. This reclassification was based on the reclassifications developed by Luan et al. [35] in their work regarding the assessment of land-use suitability for urban development. Luan et al. [35] assigned a new value to the pixels of the maps suitable for urban land use. A similar score was used in previous works with this multifunctional approach to reclassify land uses and land covers into multifunctional land uses [96].Table 2 Reclassification of sub-criteria to a 1–5 scale.

Table 2Sub-criterion	Initial classification	New Classification	
Slope	very gentle	5	
gentle	4	
moderate	3	
steep, very steep, extremely steep	1	
not applicable	0	
Climatology and Hydrology	30–40 mm or presence of watercourses	3	
20–30 mm	2	
10–20 mm	1	
0–10 mm	1	
Unknown	0	
Geomorphology	Volcanic relief, lava, pyroclastic flows, glacial valleys.	1	
Alluvial colluvium, lacustrine hilly relief.	2	
Medium hills, Plains, ancient colluvium	3	
Low hills, plains of volcanic deposits.	4	
Very low hilly volcanic relief and river valley	5	
Landslides	null	5	
low	4	
moderate	2	
high	1	
Loss of forest connectivity	urban area without overlaps	5	
agricultural use without overlaps	3	
agricultural use with overlap	2	
urban area with overlap	1	
forestry use	1	
Loss of forest areas	native forest	1	
natural forest	1	
moorlands	1	
forest plantation	1	
shrub vegetation	1	
herbaceous vegetation	1	
agricultural land	0	
populated area	0	
area with no vegetation cover	0	
infrastructure	0	
Area of urban influence	populated area	5	
infrastructure	5	
agricultural land	3	
area with no vegetation cover	3	
native forest	1	
natural	1	
moorlands	1	
forest plantation	1	
shrub vegetation	1	
herbaceous vegetation	1	
Distance to roads	urban area	3	
forestry use	1	
Distance to services and infrastructure	urban area and infrastructure	5	
forestry use	1	
Areas of economic or cultural interest	high	5	
moderate	1	
unknown	0	
Source: Authors' work

In the scores assigned to slope, the higher values are less steep. In terms of climatology and hydrology, the areas with less water availability are less suitable for urban land use. In the case of the geomorphology map, the smoother the values, the higher the score assigned. The risk level determines the score of the landslide map, so the higher the probability value, the lower the score. The values of loss of forest connectivity, distance to roads, and services and infrastructure are based on buffer analysis. The “areas of economic or cultural interest” factor is binary. If this value is high, its new value is 5; otherwise, it is 1. Loss of forest areas only comprises forest land uses, so the only value is 1. The same approach was applied to the area of urban influence factor, where urban land use values were changed to 5, forest land use values to 1, and the rest to 3.

3.2 Stage 2: identification of areas most suitable for urban use using multi-criteria evaluation by Analytical Hierarchy Process (AHP)

To identify each expert's preferences, the multi-criteria decision-making method Analytical Hierarchy Process (AHP) [55] was applied during the summer of 2022. According to Saaty [55]: “The purpose of the method is to enable the decision-maker to structure a multi-criteria problem by building a hierarchical model”. In this case, the structure had two levels: criteria and sub-criteria. By putting the criteria in order, their capacity to host urban land use can be defined, breaking it down into three or four sub-criteria.

Pairwise comparisons were then performed between the criteria. A pairwise comparison matrix can be used to convert subjective assessments into relatively important overall scores or weights. The comparisons are done by asking, “How important is criterion Ci about criterion Cj?” A comparison matrix, Cnxn, was constructed for every participant. A comparison matrix, Cnxn, has three basic properties, namely positivity (Cij > 0, for all i, j); homogeneity (Cij = 1, if criteria i and j are considered equally important: specifically, Cii = 1 for each i) and reciprocity (Cji = 1/Cij for all i, j). From this perspective, only 1/2n(n − 1) comparisons need to be made in the comparison matrix.

Every entry Ci,j is a number that represents the comparison between criteria i and j, according to the following scale, Saaty [97]:

1 = Both criteria equally important.

3 = One criterion very slightly more important than the other.

5 = One criterion moderately more important than the other.

7 = Demonstrated importance of one criterion over the other.

9 = Extreme of absolute importance of one criterion over the other.

Values express intermediate preference between two contiguous odd values.

The comparisons were made individually by 20 experts from Spain and Ecuador from the public administration, private companies, and academia, with training profiles and/or research experience in different areas of knowledge. These areas included: (1) territorial planning, (2) land-use management, (3) forest management, (4) natural hazards, (5) civil engineering, (6) urban management and (7) national heritage management. The experts received written information about the study and were asked to provide their consent before completing the AHP questionnaire. Data were collected anonymously, and no conclusions could be drawn regarding the experts’ identities. Participation in the study was voluntary.

The experts answered a questionnaire to compare the criteria and sub-criteria to obtain their weights. We used a Google form to facilitate this participation process. Fig. 4 shows an example of one expert's answers to the Physical sub-criteria comparison.Fig. 4 Example of one expert's answers to the comparison of the Physical criteria.

Fig. 4

The value assigned to the preference in the fourth column of Fig. 4 is Cij in the preference matrix if the criterion preferred is A, and Cji otherwise.

After the matrices were obtained for all participants, we applied the eigenvector method associated with the maximum eigenvalue of each comparison matrix [55] to calculate the weights for the criteria from each matrix. Lastly, we calculated the consistency of the decision maker's preference. Consistency implies transitivity in preference, reflected in each matrix. We applied the Consistency Ratio CR = CI/RI, to measure this transitivity, where CI, the Consistency Index is CI = (lmax − n)/(n − 1) and RI, the Random Index, is RI = 1.98(n − 2)/n, lmax is the maximum eigenvalue of the matrix and n the number of rows of the matrix. If CRis less than 0.05, if n = 3 or 0.09, if n = 4, then the preference is consistent, and the estimate is accepted. Every expert had to fill out 4 comparison matrices. After the final weights were obtained, the average values were calculated.

The results obtained from each expert were averaged to calculate the final relative weight of each criterion and sub-criterion.

3.3 Stage 3: AHP integration and algebra of maps of criteria and sub-criteria

The purpose of this phase was to give an integrated value to each point in the territory that expresses the merit of that point to be assigned urban use. After obtaining the weighting of the criteria and integrating the results from the 20 experts, the AHP results were applied. To do this, equations (1), (2), (3) were formulated to calculate the resulting maps integrating for each criterion and the overall integration map.

Physical Sub-criterion (SF):(1) SF=Cp×[Slopemap]+Cgf×[Geomorphologymap]+Cch×[ClimatologyandHidrologymap]

where: Cp, Cpg and Cch are the weights calculated with AHP, corresponding to each sub-criterion.

Risk and impacts sub-criterion (SRI):(2) SRI=Cdz×[Landslidemap]+Ccf×[Forestconnectivitymap]+Cpf×[Lossofforestareamap]

where: Cdz, Ccf and Cpf are the weights calculated with AHP, corresponding to each sub-criterion.

Socioeconomic aspects sub-criterion (SEC):(3) SEC=Ciu×[Urbaninfluenceareamap]+Cdc×[Distancetoroadmap]+Cdi×[Distancetoserviceandinfrastructuremap]+Cec×[Economicorculturalinterestareamap]

where: Ciu, Cdc, Cdi and Cec are the calculated coefficients of AHP, corresponding to each sub-criterion.

The map of areas most suitable for urban use (ZUAHP) is the summation of all the criteria multiplied by each of their weights:(4) ZUAHP=Csf*SSF+Csri*SRI+Csec*SEC

All the resulting maps are pixel maps that reflect susceptibility to urban use on a scale of “0 to 5". The map algebra operation was done with ArcGIS Pro ® v2.5 software.

Lastly, the results were also compared with the urban sprawl previously estimated for the next 20 years in the study by Ulloa-Espindola and Martín-Fernández [33].

4 Results

The following maps show the criteria and sub-criteria considered, classified on a scale of 1–5, as indicated in the legends, (value 0 in the case of “No Data”). These maps were integrated with the weights obtained in stage 2 of the Methodology, to generate the map of the areas most suitable for urban use. Fig. 5 shows the physical sub-criteria, which indicates that in the “Slope” sub-criteria, 25 % of the study area (scores 4 and 5) is suitable for urban use because the areas are flatter. Based on the “Climatology and Hydrology” map, only 10 % of the study area is most adequate for urban use because this area has a significant hydrological network. The case of the “Geomorphology” sub-criterion is similar; approximately 10 % of the area is suitable for urban use. Also, the areas around existing urban areas are moderately suitable for urban use (value 3).Fig. 5 Physical sub-criteria considered in the AHP methodology.

Fig. 5

Fig. 6 shows the sub-criteria of the “Risks and Impacts” criterion. Regarding, the “Landslides” sub-criterion, the area currently occupied by urban use and its surrounding area with relatively low slopes are suitable for this use (scores of 4 and 5), representing 62 % of the study area. For the “Loss of forest connectivity” and “Area of urban influence” sub-criteria, the suitable areas are very similar to the existing area of urban occupation [90] since this connectivity is limited by urban area and infrastructure. However, the area not adequate for urban use is larger in the agricultural area of the Machachi Valley because of the increase in the areas of influence of forestry use with a value of 2 on the “Loss of connectivity” map. It should be noted that the scale of the study did not include isolated buildings. “Losses of forest areas” included the areas with “native forest”, “natural”, “plantation”, “shrub vegetation” or “herbaceous vegetation” land-use types, where forest vegetation of any class could potentially be lost. Consequently, based on this sub-criterion, conversion to urban use should be avoided in all these uses.Fig. 6 Sub-criteria of risks and impacts considered in the AHP methodology.

Fig. 6

Notable differences were observed regarding “Socioeconomic aspects”. For example, based on “Distance to roads”, approximately 50 % of the territory is suitable for urban use, due to the presence of the Pan-American Highway and the road network. 30 % of the area is considered adequate for urban use based on “Distance to services and infrastructure”. For “Areas of economic or cultural interest”, 85 % of the territory is suitable for urban use (Fig. 7). Since there are no restrictions, these three sub-criteria potentially occupy broad areas of the study area.Fig. 7 Sub-criteria of socio-economic aspects considered in the AHP methodology.

Fig. 7

Regarding the integration of criteria and sub-criteria by applying the AHP method, the values of the average weights obtained from the comparisons and evaluations by experts are in Table 3.Table 3 Average weights of the criteria and sub-criteria by the AHP method.

Table 3Criterion	Average value	Sub-criterion	Average value	
Physical (Csf)	0.15	Slope (Cp)	0.40	
Climatology and Hydrology (Cch)	0.37	
Geomorphology (Cgf)	0.23	
Risks and impacts (Csri)	0.63	Landslides (Cdz)	0.39	
Loss of forest connectivity (Ccf)	0.30	
Loss of forest areas (Cpf)	0.31	
Socio-economic
(Csec)	0.22	Area of urban influence (Ciu)	0.25	
Distance to roads (Cdc)	0.18	
Distance to services and infrastructure (CDI)	0.35	
Areas of economic or cultural interest (Cec)	0.22	

The data in this table indicates that the risks and impacts of urban use are the most important factors that must be considered when preparing urban and land-use change plans. In this case, they also integrate the risk of loss of forest resources.

Regarding the weighting of sub-criteria, the Physical sub-criteria, “Slope” and “Climatology and Hydrology”, were the most important when determining urban uses, indicating that consideration of “Climatology and Hydrology” together is one of the limitations of the study. In terms of Risks and Impacts, the sub-criterion “Landslides” was considered somewhat more important when determining urban areas, due to the risk to the safety of the population and the presence in the study area of large zones of illegal constructions in these risk areas. Lastly, of the socio-economic aspects, the sub-criterion “Distance to services and infrastructures” was given the greatest importance when allocating urban use.

In the comparison process, the consistency ratios were all less than 0.05 (Urban land use comparison matrix ratio: 1.96 %, Physical matrix ratio: 2.67 %, Risks and impact matrix ratio: 2.39 %, and Socio-economic matrix ratio: 3.12 %, sub-criteria comparison matrix ratios vary from 2 % to 3 %) indicating that the values of the obtained weights are acceptable [98].

Fig. 8, Fig. 9, Fig. 10 show the maps generated by equations (1), (2), (3), which correspond to the criteria Physical, Risks and Impacts and Socio-Economic Aspects, respectively. Fig. 11 shows the integration of all the sub-criteria, and therefore the map of the most suitable areas for urban use based on equation number (4).Fig. 8 Map calculated with Physical sub-criteria.

Fig. 8

Fig. 9 Integrated map of the Risks and Impacts sub-criteria.

Fig. 9

Fig. 10 Map calculated with Socioeconomic Aspects sub-criteria.

Fig. 10

Fig. 11 Map of integration of the physical criteria, risks and impacts and socioeconomic aspects and area with urban use in 2018 and 2038.

Fig. 11

This map indicates that the steep slopes of the study area and the extensive river network mean that there are no very suitable areas (score of less than 4) for urban use from the conservation perspective. According to the experts, the most suitable areas are in South Quito and the central part of the Machachi Valley.

In terms of Risks and Impacts, once again, there is no suitable area for urban use; the dark brown areas have a high risk of landslide, (sub-criterion with the greatest weight), the dispersion of forest areas means that to achieve their connectivity and expand forestry use, urban planning should take these aspects into account, even if it limits the area to be built and is a driver to increase its efficiency. In this study, forest connectivity is considered solely as structural connectivity that measures the spatial distribution of the different forest areas in the study area, without considering wildlife behavior [99]. Although the valley area is more fragmented, the agricultural land and the rural gardens and green areas themselves do improve connectivity for some species, but these aspects were not considered in this study.

The map in Fig. 10 shows that based on the criterion “Socioeconomic aspects”, there are areas that are highly suitable for urban use throughout the valley, since they are already urban areas that are close to infrastructure and roads. This includes infrastructure and small developed areas in the mountain ranges. When Fig. 10 is compared with the Land Use Map (Fig. 2), socioeconomic aspects strengthen urban development in forest areas of the valley and hydrological basins.

The suitability of land uses in territories has frequently been evaluated to achieve the planning objectives of a region [100,101]. Fig. 11 shows the integrated map of the criteria used in the AHP analysis, together with the existing urban area of 2018 (Fig. 11, dark blue), and the results of the study by Ulloa-Espindola and Martín-Fernández [33]. This study estimates the change in land uses in the surrounding areas of Quito and the Machachi Valley, every 5 years until 2038. Fig. 11 shows the urban area estimated to be built from 2018 to 2038 in red. These areas were obtained by applying the Dinamica EGO cellular automata and multivariable software [33]. The predictive variables were seven physical and climatic factors and eight social factors. The kappa values obtained after comparing the estimated and real 2018 land-use maps were Kno 0.77 and Klocation 0.77. The rest of the scenario estimation (2023, 2028, and 2038) was calculated with the same drivers, probabilities and patterns as the 2018 land-use map [33].

Based on the results of the AHP analysis, the most suitable area for urban use (score 5) covers 80.5 km2, (see Table 4). However, there was 101.34 km2 of urban use in 2018, and in 15 years, this is expected to exceed the ideal by more than 52 %.Table 4 Urban use areas in km2 for each level of urban use suitability in 2018 and in future scenarios.

Table 4Future LUMs	Levels of suitability for urban use (5: most suitable for urban use; 1: not suitable for urban use at all)	
1	2	3	4	5	Total	
Actual LUM 2018	0.11	0.95	5.94	15.44	78.90	101.34	
5-year scenario	0.16	1.23	6.33	20.75	79.43	107.90	
10-year scenario	0.09	1.29	6.96	26.49	79.85	114.68	
20-year scenario	0.20	1.55	8.95	36.50	80.50	127.71	

On the other hand, the map shows how the Pan-American Highway attracts urban use and drives the fragmentation of forest connectivity between both slopes of the valley (See Fig. 7, Fig. 8, Fig. 11).

The situation will worsen over the next 20 years, with urban growth occurring in areas with low suitability for urban use (See Table 4). This is especially true in the Pasochoa volcano. The map in Fig. 11 shows how the areas of urban growth by 2038 put the Pasochoa volcano, the interior of Quito, and eastern Machachi (circled areas) at risk. The presence of the Pan-American Highway, the proximity of Tambillo to Quito, and the legislation itself determine this urban growth, [102,103]. The total figures for urban spatial growth are compared at the different levels of AHP and analyzed for different time scenarios in Table 4.

5 Discussion

In the face of unstoppable growth in Quito and large Latin American cities in general, realistic and inclusive planning is necessary to make that growth socially and environmentally sustainable. In environments like the one surrounding Quito, where natural spaces are of great value, this work has the advantage of considering the protection of forests as an underlying objective when designating the areas most suitable for urban use.

The method used combines AHP and GIS and is frequently used in decision-making to determine land suitability and optimal locations [76,77]. The process combines expert knowledge and literature sources to identify the factors to apply from the available datasets [104]. The criteria and sub-criteria applied show clear coincidences with previous work where the criteria are also grouped into physical, socioeconomic and prohibitive factors (or Risk and impact factors) that express vulnerability due to the occurrence of natural risks as a limiting factor when planning urban areas [35,38]. We considered consistency difficult to achieve in models with more than nine criteria when experts needed to make n(n-1)/2 comparisons [105]. For this reason, there are three groups of sub-criteria in this study, one for each criterion, whose sizes are three or four and made it possible to obtain consistent results.

Lastly, the reclassification of the sub-criteria on the 1–5 scale has been used with good results in earlier studies by Munier and Hontoria and Mishra et al. [105,106]. Our approach to assigning a new score to the values of the factors is similar to the one used by Luan et al. [35]: a higher score indicates a higher degree of suitability and a lower score, the presence of constraint.

To protect forest use, the factors “Loss of forest areas” and “Loss of connectivity” were considered. Including connectivity and forest loss is a way to identify, protect, and provide corridors to strengthen adaptation to Climate Change and minimize biodiversity losses [107].

While the “Loss of forest areas” factor appears in other works as a constraint when assigning other uses, such as agricultural use [104] or urban use [77], we found no previous work in which “Loss of connectivity” was used as a risk factor when analyzing urban use suitability [108,109]. Luan et al. [35] propose an intermediate approach that considers the distance to forest use as a criterion to protect this use, but it is less restrictive than the analysis of connectivity that is prioritized against the change of use to urban. When the study area is restricted to protected natural spaces, there are previous works that consider connectivity against the expansion of other uses, such as agricultural land [[109], [110], [111]].

The third sub-criterion in the “Risk and impact” group is the exposure to geological hazards, specifically landslides, that affect the vicinity of the city of Quito and the entire valley. And it would affect both the inhabitants and other land uses such as agriculture [35]. The “Risks and Impacts” criterion has the largest weight (0.69). In addition, the sub-criteria weights are balanced within the group, since their values are between 0.30 and 0.39, which makes their influence in the final integrated map clear.

However, studies that try to avoid the risks to which the most vulnerable land uses are subjected, such as Andean forests, have the disadvantage that when identifying the criteria, or sub-criteria, they are very dependent on the specific problem, making generalized extrapolation difficult [112].

On the other hand, roads and infrastructure are the main drivers of the transformation of forestry and agricultural uses [113]. Roads are a long-term latent driver of deforestation, especially in landscapes with a long history of human occupation like Quito [35,113,114]. We found some similarities in the weighting of the criteria with other previous works with the same objective for the same criteria. In our case, the weight of “Distance to roads”, 0.18, is similar to the value assigned in the study by Morales and De Vries [41], which assigns a weight of 0.21 to this criterion, with both studies giving the least importance to the “Geomorphology” criterion. In this study, “Distance to services and infrastructure” is weighted quite heavily (0.35), as opposed to the lower weight in de Vries's study of 0.20. In their study about urban land use planning, Mosadeghi et al. [38] assign an AHP weight of 0.38 to the criterion “Existence of infrastructure”. Nevertheless, the wide variety of approaches in urban land use suitability studies with AHP means that it is not always easy to compare results.

In terms of the accuracy of the consistency of the comparisons, similar levels are observed in the studies of Seyedmohammadi et al. and Vijith and Dodge-Wan [76,115].

Despite AHP's strengths, it does have some limitations, such as the lack of methodological support for the issue of uncertainty, and the lack of capacity to obtain network connections or to measure the interactions among criteria [116]. This is a limitation of this study, which requires the identification of spatial boundaries and the use of MCDM techniques, such as fuzzy AHP or ANP would be ideal [38,117,118].

As shown in Fig. 11, in 2018 in South Quito and the vicinity of Tambillo, and Machachi, urban use had already expanded in areas with moderate suitability for this type of use. Also, the forest and the hydrological network areas of the Pasochoa volcano would not be suitable for urban use (scores between 1 and 3). If the trend in land-use changes is maintained over time, as automata cellular results indicate (see vulnerable areas in Fig. 11), this will result in a lack of protection of biodiversity and water quality because changes in use to agriculture or urban development cause large losses of these factors, as well as changes to the hydrological regimes of the river basins and increased erosion due to runoff [119,120]. Previous works have also integrated the AHP method and cellular automata to define urban land use growth [[121], [122], [123]]. They apply the AHP method to determine the weights of the drivers used to obtain the dynamic simulation of the evolution of urban land use. This application of AHP makes it possible to introduce constraints such as ecological factors into the simulation. However, our approach is to highlight which forest areas are vulnerable when we compare suitable urban areas to the future location of the urban land use, and if the current land use policies and territory plans are not properly applied.

In summary, the results show the importance of determining a priori the most suitable areas for urban use, as an aid when developing urban planning to prevent the random growth of emerging areas, damage to hydrological basins and areas of native forest, and the appearance of new urban settlements in risk areas. In particular, the final map that was generated (Fig. 11) is a very useful tool for developing plans and defining policies [124]. However, in transition zones or zones with the highest risk of forest loss, since the scale is more local, decision-making methods such as the one proposed by Seyedmohamed et al. [76] which combines matter-element, AHP and GIS, can help to better define the zones with the highest risk of the loss of forest use, and better define the planning of land use individually for each transition unit, which would be an interesting focus for future research.

6 Conclusions

Spatial multitemporal analysis of land-use dynamics is vital to understanding the causes and effects of the occupation of the different uses and activities in a territory, especially territories that have special bio-geographic characteristics and latent environmental vulnerabilities. The area covered by this study, in addition to fulfilling these characteristics, is experiencing uncontrolled expansive urban development, which is one of the phenomena that has the greatest impact on the harmony of land uses. For this reason, the analyses used in this study are relevant to the established goals and provide a tool to support the design of sustainable land use plans and programs.

The combination of AHP techniques, Geographic Information Systems (GIS) and expert criteria results in a strategic, comprehensive, and practical method to produce results that are coherent and consistent with the reality of the study area. In addition, the results are comparable with results produced using other techniques that study the evolution of land use, because they are presented using spatial information.

The scoring by experts complements and supports the basic information that was used, further specifying the factors that energize urban use and forestry uses that are affected by the interaction between them. The multi-criteria scores by the experts, combined with GIS, increase the rigor of the assessments and generate results that communicate, validate and specify the analyses and interpretations.

All the results confirm that the evolution of urban spatial expansion and the effect of urban conurbation between the city of Quito and the towns of Aloag and Machachi included in the study area, exceeds the appropriate locations for it, compromising forest protection uses. The proposed future scenarios are unfavorable for the environmental axes because the strategic location of the Machachi Valley is highly influenced by multidimensional factors that occur simultaneously (national road connection, urban conurbation, climate and hydrology, rich soils for production, economy and industry) and that intensify the impacts of uncontrolled urban development, combined with the land-use dynamic, to the detriment of the forest cover and environmental sustainability of the sector.

This research helps promote studies and evaluations with a spatial focus to safeguard the environmental, economic, and social balance of the study area, which is vulnerable along different axes due to the progressive negative impact of land uses and especially aggressive urban development.

Funding

This research received no external funding.

Ethics approval and consent to participate

Participants received written information about the study and were asked to provide their consent before completing the AHP questionnaire. Data were collected anonymously, and no conclusions could be drawn regarding the experts’ identities. Participation in the study was voluntary.

Consent for publication

All of the authors consented to publish this manuscript.

Data availability

Data associated with the study has not been deposited into a publicly available repository.

Data will be made available on request.

CRediT authorship contribution statement

Rene Ulloa-Espindola: Writing – review & editing, Writing – original draft, Validation, Investigation, Formal analysis, Data curation, Conceptualization. Susana Martín-Fernández: Writing – review & editing, Writing – original draft, Validation, Methodology, Investigation, Formal analysis, Conceptualization.

Declaration of competing interest

None.
==== Refs
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