2.1. Study Area and Data Processing
Nanjing (31°14′ N~32°37′ N, 118°22′ E~119°14′ E), standing on the bank of the Yangtze River and guarding the Beijing–Shanghai artery, is not only the central city in the north wing of the Yangtze River Delta but also an important gateway that radiates to the development of the midwestern area. It is known as “the gateway to the south and east, the throat to the north and south ”, and there are 6 municipal districts, including Nanjing urban area, Jiangning District, Lishui District, Pukou District, Luhe District, and Gaochun District. In 2018, Nanjing’s GDP reached 1282.04 billion yuan, an increase of 8 percent, ranking the third among 16 cities with a GDP exceeding one trillion yuan. At the end of the year, the urban population was about 6.96 million, with a permanent urbanization rate of 82.50%. The per capita disposable income of urban and rural residents in the city was 52,916 yuan, an increase of 9.1 percent. The income ratio of urban and rural residents was 2.35:1, and the income gap continued to narrow.
Its geomorphic feature belongs to the Nanjing–Zhenjiang–Yangzhou hilly area, dominated by low mountains, hills, and downlands (60.8%), as well as plains, depressions, rivers, and lakes accounting for about 39.2%. The soil types are mainly divided into zonal soil and cultivated soil. Its soil distribution shows a certain regularity with topographic fluctuation and hydrological conditions, and can be divided into three categories: low mountain and hilly area, hillock area, and plain area. Nanjing has complex vegetation types, mainly including coniferous forest, deciduous broad-leaved forest, deciduous and evergreen broad-leaved mixed forest, bamboo forest, shrub, grass, and aquatic vegetation. The region is rich in water resources, and its water area covers 11% of the land area. Water area ecological land has a high ESV and is of great significance to the maintenance of biodiversity, the dominant functions of which include industrial water, irrigation, domestic water, fishery water, and so on. With the rapid development of the social economy and urbanization in Nanjing, urban construction land expands rapidly to the west, south, and east around the urban area of Nanjing.
Nanjing City covers nearly 6587 km
2, where 93 ecological protection areas are delineated, divided into 12 different types with a total area of 1455.04 km
2 (excluding the overlap). ERAs are managed at different levels, which can be divided into primary and secondary control areas. The primary control areas are the core of the ERAs, implementing the strictest control measures and strictly prohibiting all forms of development and construction activities. The secondary control areas focus on ecological protection, implement differentiated control measures, and prohibit the development and construction activities that damage the dominant ecological function. Among them, the primary control area covers 372.61 km
2 and the secondary control area covers 1082.43 km
2, accounting for 5.66% and 16.43% of the land area of the city, respectively. Part of the ERAs located outside the boundary of Nanjing was removed in GIS and then the sum area ultimately decreases to about 1440.71 km
2. Using GIS and vector data of the ECRL planning to calculate the distribution and schematic diagram of the ERAs in Nanjing, we rendered
Figure 1.
Spatial and socioeconomic data from multiple sources were incorporated in the analysis of spatial patterns and ESV evolution over time. The ECRL planning vector data come from the Nanjing Eco-environment Bureau, and the DEM data (SRTM 90 m) were from the Resource and Environment Science and Data Center. The land-use data in 2005, 2010, 2015, and 2018 were interpreted from Landsat TM/ETM and Landsat 8 images (1 km × 1 km) with the pretreatment of geometric correction, radiation correction, and atmospheric correction (United States Geological Survey, Reston, VA, USA). A first-level classification system (farmland, woodland, grassland, water area, construction land, and unused land) was adopted in this paper processed with image clipping and reclassification in ArcGis (Esri, Redlands, CA, USA) and Envi (Exelis Visual Information Solutions, Boulder, CO, USA) tools (
Figure 2). The areas planted wheat, paddy, and corn, and the grain yield per unit area and average price were from 2005–2018. The data were obtained from the Nanjing Statistical Yearbook and Compilation of Cost–Benefit Data of the National Agricultural Products [
33,
34]. Furthermore, the national grain consumer price index (CPI) was from the National Bureau of Statistics [
35].
2.2. Ecosystem Service Valuation
ESs refer to the necessary ecological products and services provided by the ecosystem directly or indirectly to meet the needs of human survival, health, and well-being [
36]. There is no unified evaluation system for ESs valuation, as the equivalent factor method and functional value method are two widely used ESs valuation methods. Based on distinguishing different ES functions, the former constructs the equivalent value of the different ESs, and then combined with the distribution area of each ecosystem to evaluate [
26,
36,
37,
38]. Besides, the value calculated by the latter depends on the amount and unit price of the functional quantity, and it is difficult to unify the evaluation method and parameter standard of each service value in this method [
39,
40]. In comparison with the functional value method, the equivalence factor method is more intuitive and requires less data, especially suitable for the assessment of ESV at regional scales [
26,
41]. The following formula was used to calculate the total value of the ecosystem services with all land-use types in the ecological and non-ecological redline scale of Nanjing:
, where
is the ESV coefficient per land-use type, and
is the coverage area per land-use type. Moreover, this paper also intends to make the corresponding adjustment planning for future territorial spatial structure by analyzing the impact of the PLES pattern evolution on ESV in the ERAs. The formula
was applied to calculate the gains and losses of ESV based on the transition from the initial-stage land-use type
into the final-stage land-use type
, where
and
stand for the ESV coefficient of the ith and jth land-use type, respectively;
presents the area where the
ith land-use type is converted to the
jth land-use type.
2.3. Quantification of ESs for Land-Use Types
To calculate the regional ESV depending on land coverage change over time, we used the ESs equivalent value per unit area table revised and supplemented to reclassify the land-use types [
38]. The method divided ESs into four categories: supply services, regulation services, cultural services, and support services, with reference to the Millennium Ecosystem Assessment (MA) [
42]. Based on the classification of China’s terrestrial ecosystem by Xie et al. in 2015 (
Supplementary Table S1) and the land-use characteristics in the study area, the land-use types in Nanjing were divided into six categories: farmland, woodland, grassland, water area, construction land, and unused land. Among them, the land-use type classification was done by following several principles: the equivalent value of farmland was taken as the mean value of the paddy field and dryland; woodland equivalent value was the average value of coniferous, needle-broadleaf, broadleaf, and shrub; the ESs equivalent value of grassland, water area, and unused land corresponded to the shrubbery, water system, and bare land, respectively; and then the ESV of the construction land was considered as zero [
36]. The revised ecosystem service equivalent value per unit area is shown in
Table 1.
2.4. Evaluation of PLES Structure Change
In recent years, the optimization of domestic territorial space tends to the planning framework system led by PLES [
43]. In this paper, the territorial space is divided into production space (farmland), living space (construction land), and ecological space (woodland, grassland, water area, and unused land) [
23,
44]. Among them, firstly, the production space principal offers agricultural products and services. Secondly, the leading function of the living space is to provide human habitation, consumption, leisure, and entertainment. Thirdly, the ecological space mainly provides ecological products and services, and plays an important role in the regulation, maintenance, and protection of regional ecological security [
43]. As the demarcated area of the ERAs and NERAs is fixed, according to the four phases of land-use data interpreted by remote sensing, the distribution and structure evolution characteristics of PLES in the study area from 2005 to 2018 were analyzed. Combining GIS tools to reclassify the land-use types of the ERAs and NERAs, we obtained the spatial distribution and structure change in PLES in the corresponding year.
2.5. Accounting and Revision of the Unit Equivalence Factor Value
Drawing on the 2016–2019 Nanjing Statistical Yearbook, it is known that the total output of wheat, rice, and corn in 2005, 2010, 2015, and 2018 accounted for more than 94% of the total grain output. As such, we regarded the abovementioned crops as the main grain products in the study area. Based on “the value of an equivalence factor is equal to 1/7 of the market value of grain per unit area in the current year” [
37], we computed the economic value of the standard unit equivalence factor, in combination with the national average market price of the grain crops, which was manifested in the following formula:
, where
is the economic value of the food production service provided by the farmland ecosystem per unit area (yuan/ha), namely, the ESV coefficient before correction;
represents the main food crops, including wheat, rice, and corn;
is the planting area of regional crops (ha);
is the national average price (yuan·kg
−1);
is the yield per unit area of grain (kg/ha); and
is the total area of food crops (ha). Data on the yield per unit area, average market price, and sown area of wheat, rice, and corn in Nanjing from 2005 to 2018 are shown in
Table 2.
To eliminate the influence of natural factors and inflation on grain price fluctuations, this paper introduced the revised ESV coefficient of the national grain fixed-base consumer price index (CPI) [
45], and the modified results are shown in
Table 3.