The Application of Geographic Information System in Urban Forest Ecological Compensation and Sustainable Development Evaluation

The Application of Geographic Information System in Urban Forest Ecological Compensation and Sustainable Development Evaluation

To evaluate the comprehensive benefit of the ecological compensation of urban forests in Tai’an city, an evaluation system based on the analytic hierarchy process and geographic information system was established. Based on the comprehensive benefit evaluation of urban forests in the research area, corresponding calculation standards and methods for ecological compensation were proposed. Finally, by analyzing the problems of urban forests and ecological compensation in the area, strategies for ecological compensation and sustainable development are proposed.

2.1. Construction of an Urban Forest Information Database Based on GIS

Tai’an City is a prefecture level city located in the central part of Shandong Province. It borders the provincial capital Jinan to the north, Jining to the south, Linyi to the east, and the Yellow River to the west, with a total area of 7762 km2. Tai’an City is named after Mount Taishan. It is built near the mountain and integrates the mountain and the city. The study area is located in the margin of Mount Taishan and in the east of North China Plain, covering an area of 571.39 km2 [14]. Mount Taishan is a typical area where the double layered structure of the basement and caprock of the North China platform outcrops well. The terrain of Mount Taishan is significantly different, and the terrain fluctuates greatly. The main peak Yuhuangding is 1545 m above sea level, and within a horizontal distance of less than 10 km, its relative height difference with the piedmont plain is more than 1300 m. Mount Taishan has obvious geomorphic boundaries, various geomorphic types, and well-developed erosion geomorphology [15]. The research area is located in the northern warm temperate climate zone, and due to the influence of terrain, there are significant differences in vertical gradient, horizontal distribution, and climate characteristics. The annual average humidity is 63%, and the annual average relative humidity range is 39%.The southwest wind and the northeast wind prevail at the top of Mount Taishan and in the urban area, respectively, with an annual average wind speed of 6.5 m/s and 2.5 m/s, respectively. This area belongs to the North China flora, and due to the influence of the Yellow Sea and Bohai Sea, it has abundant rainfall and is a transitional zone between dry and wet conditions. The vegetation is rich, divided into forests, shrubs, shrubland meadows, meadows, and other types, with a forest coverage rate of over 80% [16]. The abundant water source and complex terrain provide good conditions for various animals to forage and inhabit [17,18]. GIS technology is used to interpret remote sensing images of the area. On this basis, the Clip command is utilized to trim the study area and correct its atmospheric and radiation effects [19]. The comprehensive information of urban forests in the study area is denoted in Figure 1.
The building in Figure 1 presents a regular gray image with clear edges and shadows. Through research on the study area and analysis of relevant topographic maps, it is found that the distribution of the five ecosystem types in the area is relatively uniform. On this basis, the comprehensive information of urban forests obtained is interpreted and classified using remote-sensing images. Considering the needs of urban residents, they are segmented to meet the corresponding urban forest evaluation standards. The urban forest classification information of the study area is shown in Figure 2.
The urban forest area in Figure 2 can be interpreted into five types, namely industrial management forest, ecological public welfare forest, landscape and recreational forest, affiliated courtyard forest, and road greening forest. By supervised classification of its remote-sensing images, quantitative information of urban forests is obtained. And after processing the interpretation results, the forest map database and attribute library of the city in the area are obtained. Quantitative calculations are conducted on different types of urban forest landscape indicators using Fragstats 4.2 software, and the data are collected to obtain the classified patch landscape of urban forests. At the same time, data quantification processing is conducted on the overall landscape index of the urban forest [20]. The landscape classification and indicators of the urban forest classification patch level in the study area are indicated in Figure 3.
After interpreting the remote-sensing images of the study area through a GIS (Latest version, Redlands, CA, USA), the forest land, grassland, shrubs, and wetlands are extracted and integrated into urban forests, which are divided into five types as shown in Figure 3. Then, the corresponding forest information database is established to collect, integrate, and manage multi-dimensional data related to urban forests, so as to better understand and evaluate the status quo and changing trend of urban forests. It includes indicators such as the maximum patch area index, aggregation index, landscape shape index, patch density, amount of patches, percentage of patch type to landscape area, and patch type area, as shown in Figure 3. The patches of road greening forests are large and have a high connectivity. The amount of patches in the affiliated courtyard forest is relatively small, the landscape fragmentation is relatively small, and it has good morphological characteristics. The number of recreational forests is relatively small, mainly composed of large patches, with a low level of landscape fragmentation and uneven distribution of patches. Ecological public welfare forests account for the largest proportion in urban forests, with a moderate density and high fragmentation. The landscape morphology is relatively complex and has a good uniformity. The characteristic aspect of forest production is its low density and scattered patch distribution.

2.2. Construction of Comprehensive Benefit Evaluation Index System for Urban Forests

After obtaining comprehensive information on urban forests in the study area using GIS, a comprehensive benefit evaluation index system is constructed. When selecting evaluation indicators, it needs to fully consider the characteristics of urban forests. To fully showcase the entire urban forest landscape, four basic principles should be followed. Firstly, it needs to follow the scientific principle that the selected indicators should meet the requirements of urban forest development and objectively reflect the functional characteristics of the ecosystem. Secondly, it is necessary to meet the systematic principle, which comprehensively considers the ecological, economic, and social benefits of urban forests, as well as various factors such as structural characteristics and landscape patterns. It needs to conduct a comprehensive evaluation of the construction status of various indicators, while also reflecting the interrelationships between various subsystems, that is, select representative indicator factors to avoid overlapping evaluation content. Finally, it is necessary to follow the principle of operability to ensure that the operational process is practical and feasible, and to ensure that the data collection and calculation of evaluation indicators are feasible. Based on the above principles and combined with the comprehensive information of urban forests obtained from GIS, the indicator hierarchical structure model established is shown in Figure 4.
The study classifies and layers complex problems, and fully considering the current construction status of urban forests in the study area, a hierarchical model of the indicator system is constructed. The model is mainly divided into four layers as shown in Figure 4. After selecting supporting indicators that can provide support for the development status of the region, the Delphi expert evaluation method is used to invite relevant experts to score each indicator. Finally, by combining qualitative and quantitative methods, a comprehensive evaluation index system for urban forest quality in the region can be obtained, as shown in Figure 5.
In Figure 5, the study divides the comprehensive evaluation of urban forests into four indicator levels and selects 33 evaluation indicators. The target layer set U for this research evaluation is the urban forest landscape structure evaluation index (B1) and the urban forest comprehensive benefit evaluation index (B2). In the factor layer, the evaluation indicators for B1 include C1~C3, and the evaluation indicators for B2 include C4~C6. In the quasi measurement layer, the evaluation indicators for C1 are D1~D5. The evaluation indicators for C2 are D6~D10. The evaluation indicators for C3 are D11 to D16. The evaluation index set for C4 is D17, D18, D19, D20, D21, D22, and D23. The evaluation index set for C5 is D24, D25, D26, D27, and D28. The evaluation index set for C6 is D29, D30, D31, D32, and D33. In addition, the study will use the Delphi method and AHP to assign weights to evaluation indicators [21]. Experts and scholars in the field are invited to compare each indicator before assigning weights. Using mathematical statistics methods, it conducts statistical analysis on the results of expert evaluations. By constructing a comparison matrix and using the assignment method, the importance of each element within the same level is compared. On this basis, various elements are combined with the urban forest index evaluation system for subjective evaluation. And the 1–9 scale method is utilized to quantify it. The proportional scale of the relative importance of elements is shown in Table 1.
The normalization summation method is used to solve the eigenvector W and the maximum eigenvalue λ max . Firstly, it normalizes the column vectors of the judgment matrix, and the calculation expression is shown in Equation (1).

b i j = a i j i = 1 n a i j i = 1 , 2 , , n

Vectors in each row are added to obtain Equation (2).

V i = j = 1 n b i j i , j = 1 , 2 , , n

It normalizes the vector again to obtain the weight vector, as shown in Equation (3).

W i = v i i = 1 n V i i = 1 , 2 , 3 , , n

From Equations (1) to (3), the vector w 1 , w 2 , , w n T can be obtained, which is the weight vector. Finally, the maximum eigenvalue λ max of the judgment matrix is calculated, as denoted in Equation (4).

λ max = i = 1 n A W i n W i i , j = 1 , 2 , 3 , , n

In Equation (4), A W i represents the i element of A W , and n represents the order. Calculation by specialized mathematical software can ensure the accuracy of weighting. This study uses MATLAB software (R2022a version, Natick, MA, USA) to solve the maximum characteristic root λ max of the matrix S . In the actual calculation, some comparative averages may not fully achieve consistency, but to some extent, they can still be considered consistent. Equation (5) is the consistency testing formula [22].

C I = λ max n n 1 C R = C I R I

In Equation (5), R I means the average random consistency indicator. C R means the consistency ratio. When C R < 0.1 , consistency testing is acceptable, otherwise the judgment matrix will be corrected. The fuzzy comprehensive evaluation method is based on quantitative data as support and combined with the subjective consciousness of the evaluatorto conduct a comprehensive evaluation of the target from multiple perspectives. The steps are as follows: Firstly, determine the set of evaluation indicators. Secondly, determine the evaluation level. Next, determine the fuzzy relationship between each evaluation objective from U to V through Equation (6).

R = r 11 r 12 r 1 m r 21 r 22 r 2 m r n 1 r n 2 r n 3 r n m , 0 r i j 1

In Equation (6), r i j represents the frequency distribution of the i factor u i on the j comment v j . The factors in U usually satisfy r i j = 1 for the membership relationship of grade v j in V . Finally, combining the weight W of the indicator with the fuzzy relationship matrix R , the overall evaluation vector of the indicator is determined [23].

2.3. Construction of Evaluation Index System for Urban Forest Ecological Compensation Standards

The ecological compensation mechanism is an important guarantee for fully utilizing the ecological service function of urban forests. The research is based on the basic theory of ecological compensation and provides corresponding accounting methods, providing a theoretical basis for ecological compensation strategies in the region [24]. Figure 6 shows the overall framework of the ecological compensation mechanism.
In Figure 6, the subject of ecological compensation is Tai’an City, and the subject of compensation is the research area. Based on international practical experience in ecological compensation, diversified approaches to ecological compensation have been proposed. At present, the main means of ecological compensation are mainly government financial transfers, supplemented by other means. In addition, further progress is needed in marketization [25]. The ecosystem service function refers to the various benefits provided by the ecosystem to humans, including various supply services, such as carbon sequestration and oxygen release, air purification, and climate regulation services. Ecosystem services closely integrate the relationship between humans and nature, and the gross ecosystem product (GEP) denotes the sum of the values of various goods and services provided by ecosystems to humans. By establishing a GEP accounting system, it is possible to evaluate the gross domestic product of various ecosystems [26,27]. Based on the above theory, the urban forests’ comprehensive benefits are composed of ecological, economic, and social benefits. Figure 7 shows the evaluation index system for urban forest ecological compensation standards constructed through research.
In Figure 7, ecological benefits are mainly reflected in water source conservation, soil conservation, air purification, water quality purification, carbon sequestration and oxygen release, climate regulation, wind and sand fixation, and other aspects [28]. The connotation of water conservation capacity is mainly reflected in two aspects: regulating water quantity and purifying water quality. The main connotation of soil and water conservation capacity is water and fertilizer conservation. The air purification capacity is evaluated based on the absorption of gaseous pollutants, delayed TSP, PM10, and PM2.5. The meaning of water quality purification capacity is the removal amount of COD, the purification of total nitrogen, and the purification of total phosphorus [29]. The ability to fix carbon and release oxygen is measured by the fixation of carbon dioxide and the production of oxygen. The energy consumption of vegetation transpiration and surface evaporation is the main connotation of its climate control ability, and the ability to prevent wind and fix sand is measured by the amount of sediment deposition as an indicator of its sand blocking ability. The indicators of economic benefits include agriculture, forestry, animal husbandry, fisheries, and the value of water resources. The indicator connotation of the agricultural product value is the yield and output value of agricultural products. The indicator connotation of the forestry product value is the yield and output value of forestry products. The connotations of other indicators are respectively described in Figure 7. The distributed accounting model for urban forest ecological compensation standards is shown in Figure 8.
In Figure 8, the distributed measurement method first partitions heterogeneous urban forests and divides them into several levels. And according to different types of urban forests, they are divided into several secondary measurement units [30]. Secondly, the original forest is divided into two three-layer units, namely the natural forest and artificial forest. According to the age of the forest, it is divided into four levels of measurement units: young, medium-aged forest, near-mature forest, mature forest, and over-mature forest. On this basis, an ecosystem service function evaluation index system is established. In the calculation of ecological compensation standards, the calculation expression of the value of regulating water volume for water source conservation capacity is shown in Equation (7) [31].
In Equation (7), G T is used to evaluate the annual adjusted water volume of the forest stand, and C K is the buying and selling price of the water resource market. The calculation of the soil consolidation value in the soil retention capacity is shown in Equation (8) [32].

U g = G g × C t / ρ

In Equation (8), ρ is the soil capacity. G g is used to evaluate the annual soil conservation of forest stands. C t represents the cost of earthwork excavation and handling per unit volume. The calculation of the value of absorbing gas pollutants in the air purification capacity index is shown in Equation (9) [33].

U x = G s o 2 × K s o 2 + G f × K f + G N × K N

In Equation (9), G s o 2 represents the annual absorption of SO2 by the evaluated forest, and K s o 2 represents the management cost of SO2. G f represents the annual fluoride absorption of the evaluated forest, while K f represents the cost of fluoride treatment. G N represents the annual absorption of nitrogen oxides by the evaluated forest, and K N represents the cost of fluoride treatment. The calculation for the value of dust retention is shown in Equation (10) [34].

U z = U T S P + U P M 10 + U P M 2.5

In Equation (10), U T S P represents the value of the annual overdue TSP of the evaluated forest stand. U P M 10 represents the value of annual late payment PM10 in the assessed forest stand. U P M 2.5 represents the value of evaluating the annual overdue PM2.5 of the forest stand. The calculation of the water purification value is shown in Equation (11) [35].
In Equation (11), G d e represents the annual water purification amount of the evaluated forest, and K s represents the water purification cost. In terms of carbon sequestration and oxygen release capacity, the calculation of the carbon sequestration value is shown in Equation (12) [36].
In Equation (12), C c is the carbon sequestration price. G c represents the assessment of potential annual carbon sequestration in forest ecosystems. The calculation of the oxygen release value is shown in Equation (13) [37].
In Equation (13), G o is used to evaluate the annual oxygen release content of the forest. C o is the price of oxygen. In terms of climate regulation capacity, the calculation of the climate regulation value is shown in Equation (14) [38].
In Equation (14), G w is the annual climate regulation potential evaluation of the forest ecosystem. C w represents the cost of climate adjustment. The calculation of the wind and sand fixation value in the wind and sand fixation capacity index is shown in Equation (15) [39].

U w & s = K w & s × G w & s

In Equation (15), K w & s represents the cost of wind prevention and sand fixation. G w & s represents the evaluation of the quality of forest windbreak and sand fixation materials. In the indicators of cultural and educational functions, the calculation of the value of cultural and educational functions is shown in Equation (16) [40].
In Equation (16), U k represents the value of urban forest tourism, the leisure industry, and popular science education. In the indicators of sterilization and health functions, the calculation of the value of sterilization and health functions is shown in Equation (17).

U b h r = K s t e r i l i z e × G s t e r i l i z e

In Equation (17), K s t e r i l i z e represents the sterilization cost. G s t e r i l i z e represents the annual bactericidal capacity of the evaluated forest stand.

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