Forest Land Quality Evaluation and the Protection Zoning of Subtropical Humid Evergreen Broadleaf Forest Region Based on the PSO-TOPSIS Model and the Local Indicator of Spatial Association: A Case Study of Hefeng County, Hubei Province, China

Abstract

Forest land is the carrier for growing forests. It is of great significance to evaluate the forest land quality scientifically and delineate forestland protection zones reasonably for realizing better forest land management, promoting ecological civilization construction, and coping with global climate change. In this study, taking Hefeng County, Hubei Province, a subtropical humid evergreen broad-leaved forest region in China, as the study area, 14 indicators were selected from four dimensions—climatic conditions, terrain, soil conditions, and socioeconomics—to construct a forest land quality evaluation index system. Based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model, we introduced the Particle Swarm Optimization (PSO) algorithm to design the evaluation model to evaluate the forest land quality and analyze the distribution of forest land quality in Hefeng. Further, we used the Local Indicator of Spatial Association (LISA) to explore the spatial distribution of forest land quality and delineate the forest land protection zones. The results showed the following: (1) the overall quality of forest land was high, with some variability between regions. The range of Forest Land Quality Index (FLQI) in Hefeng was 0.4091–0.8601, with a mean value of 0.6337. The forest land quality grades were mainly first and second grade, with the higher-grade forest land mainly distributed in the central and southeastern low mountain regions of Zouma, Wuli, and Yanzi. The lower-grade forest land was mainly distributed in the northwestern middle and high mountain regions of Zhongying, Taiping, and Rongmei. (2) The global spatial autocorrelation index of forest land quality in Hefeng County was 0.7562, indicating that the forest land quality in the county had a strong spatial similarity. The spatial distribution of similarity types high-high (HH) and low-low (LL) was more clustered, while the spatial distribution of dissimilarity types high-low (HL) and low-high (LH) was generally dispersed. (3) Based on the LISA of forest land quality, forest land protection zones were divided into three types: key protection zones (KPZs), active protection zones (APZs), and general protection zones (GPZs). The forest land protection zoning basically coincided with the forest land quality. Combining the characteristics of self-correlated types in different forestland protection zones, corresponding management and protection measures were proposed. This showed that the PSO-TOPSIS model can be effectively used for forest land quality evaluation. At the same time, the spatial attributes of forest land were incorporated into the development of forest land protection zoning scheme, which expands the method of forest land protection zoning, and can provide a scientific basis and methodological reference for the reasonable formulation of forest land use planning in Hefeng County, while also serving as a reference for similar regions and countries.

1. Introduction

Forest land is the carrier on which forests grow, with which they form an important terrestrial ecological barrier, accounting for 30% of the Earth’s surface land area and approximate 4 billion ha, and is an important natural resource for sustaining human survival and social development [1,2,3]. In addition to a variety of ecological services such as water connotation, soil and water conservation, air purification, and carbon storage, forest land is also a main raw material for manufacturing industries and has an important ecological and economic benefit [4,5,6,7]. About 25% of the global population depends on forests for food and work [8]. In addition, forest land is also home to nearly 80% of the world’s terrestrial species, and such a rich diversity of species makes them important in maintaining a global ecosystem balance and responding to global environmental change [9]. To emphasize the importance of forest land, the United Nations General Assembly considered the adoption of the United Nations Strategic Plan for Forests (2017–2030) in 2017, which proposes a global action plan for each country to manage various forests land resources sustainably and to improve forest land quality effectively [10].

China is a vast country with a complex and diverse terrain. The high latitudinal difference between the north and south boundaries and the high western and low eastern terrain have created a rich and diverse climate type and physical geography in China, thus nurturing forest land resources with a wide variety of biological species and vegetation types. According to the report on the 9th Inventory of China’s Forest Resources (2014–2018) [11], China has a forest land area of 324 million ha, a forest cover of 22.96%, a forest stock of 17.56 billion m³, a total forest vegetation biomass of 18.80 billion tons, and a carbon stock of 9.19 billion tons. Although the total area of forest land in China is among the highest in the world, the per capita area of forest land is only 0.61 ha, which is less than 1/3 of the per capita area of forest land in the world [12]. In addition, since China’s reform and opening up in 1978, with the rapid socioeconomic development and population increase in China, much forest land has been transformed into arable land and construction land, resulting in a drastic decrease in forest land area. To strengthen the protection of forest land and improve the efficiency of forest land utilization, China’s State Council adopted the Outline of National Forest Land Protection and Utilization Plan (2010–2020) in 2010, which emphasized the importance of forest land in maintaining the ecological environment, promoting the ecological civilization construction and addressing global climate change [13]. As a result, the area of forest land in China has increased in the last 10 years with reference to the China Forest Resources Report (2014–2018) [11]. With the rapid socioeconomic development, the demand for forest land resources is increasing and the damage to forest land is becoming increasingly severe, causing a decline in the forest land quality. Additionally, climate change and forest fires are also important factors that cause damage to forest land resources resulting in a decline in forest land quality [14,15].

Since the 21st century, under the influence of the global greenhouse effect, continued climate warming has led to a significant increase in the frequency of forest fires and fire area [16,17]. China is a country with large forest land resources, which also is one of the countries with the highest risk of forest fires. Based on the China Forest Fire Protection Industry Current Research and Future Development Trend Analysis Report (2020–2026), in the past 10 years, the area of forest fires in China reached 225,625 ha [18], accounting for 0.07% of the total forest area in China [11], mainly in the northeast and southwest forest areas [19], which not only causes substantial losses to the society and economy, but also results in the degradation of the forest land ecological environment, which directly threatens the sustainable development of forestry and national ecological security [20]. In addition, climate change affects forest soil carbon and nitrogen cycling processes, mainly in terms of its effects on forest land soil respiration, soil carbon and nitrogen pools, and soil methane and nitrous oxide emissions, thus affecting forest land soil quality [21]. It is thus clear that there is a coupling between forest land degradation and global climate change, and effectively addressing forest land degradation is the key to cope with global climate change. Therefore, a comprehensive understanding of forest land quality, curbing land degradation, and enhancing forest land quality plays a vital role in improving climate change and ensuring national ecological security.

Forest land quality is a reflection of the state and condition of the land [22]. Forest land quality is a combination of multiple qualities and is influenced by the type of forest land and its combined characteristics [23]. Therefore, forest land quality evaluation should be based on terrain, soil, and other natural environmental factors closely related to the growth of forest vegetation and relevant management conditions to evaluate forest land quality comprehensively [24]. Scholars have often used terrain, climate, and soil fertility as the major indicators to assess the quality of forest land in the past [25]. Bonilla-Bedoya et al. [26] studied the effect of land-use change on the physicochemical quality of forest soils in the Western Amazonian landscape. Lu F.Z. et al. [27] selected terrain and soil fertility indicators and used the integrated index model to assess the quality of forest land. Wang Y.F. et al. [28] used Delphi to select indicators from soil fertility and wood biomass, which were used to construct a forest land quality evaluation indicator system. With the development of the social economy, people’s understanding of forest land quality has changed, and the evaluation of forest land quality should not merely consider soil fertility, but also land productive potential, land suitability, and ecological safety [29]. At the same time, the selection of indicators differs with different evaluation purposes. In the term of assessment of forest land productive potential, soil physicochemical indicators are mainly selected to reflect basic soil fertility, soil environment, and soil health [30]. When evaluating forest land suitability, climate and terrain factors will be used to select suitable tree species in addition to soil fertility factors that directly affect the productivity of forest land [31]. Additionally, the intensive application of computer technology and “3S” technology, which refers to global positioning systems (GPS), remote sensing (RS), and geographic information systems (GIS), has led to the development of more methods for farmland quality, ranging from simple qualitative description to quantitative analysis. Currently, the main methods commonly used for forest land quality evaluation are entropy weight (EW) [32], fuzzy assessment [33], the analytic hierarchy process (AHP) [34], and gray correlation analysis [35]. These methods all have the characteristics of simple models, strong data compatibility, and wide application. The EW objectively assigns weights based on the physical characteristics of the data, but does not introduce human cognitive discriminations of evaluation indicators [36]. The fuzzy assessment and gray correlation analysis are less stable in dealing with high-dimensional data, and it is difficult to dig deeper into nonlinear information [37,38]. The AHP is more subjective and less sensitive in determining index weights [39]. In recent years, many new algorithms have emerged to integrate different methods to achieve a combination of advantages, reduce the limitations of data analysis on evaluation methods, and improve the objectivity of evaluation results. Among them, the Particle Swarm Optimization (PSO) algorithm and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm have been used in many aspects such as risk decision analysis, land suitability evaluation, and land ecological safety evaluation [40,41,42,43] and have achieved good results. Han G.Y. et al. [44] combined the PSO algorithm and the TOPSIS model to construct an improved TOPSIS model based on the weight of the PSO for the corporate collaborative innovation partner selection model. Ali Bagherzadeh [45] evaluated the suitability of arable land for irrigated alfalfa production in the Joveyn plain of Northeastern Iran using a parameter-based neural network and TOPSIS model. Chen M. et al. [46] applied the EW and TOPSIS model to dynamically evaluate the sustainable land use level in Chengdu of China.

Forest land is a special type of land use, and the quality of forest land is affected by the combination of various influencing factors, not in a purely linear relationship, while PSO and TOPSIS are suitable for the processing of nonlinear information problems with a multifactor influence [44]. Therefore, it is feasible to combine the PSO algorithm and the TOPSIS model for forest land quality evaluation. Nilsson H. et al. [47] studied participatory forest planning in the municipality of Vilhelmina in Northern Sweden, where AHP was used to set weights for objectives based on stakeholder preferences and TOPSIS was used to generate an overall ranking of alternatives. Cai X.T. et al. [48] used the improved TOPSIS model, the hierarchical dynamic attitude model, the Markov chain model, GIS spatial analysis technology, and the barrier degree model to analyze the spatial and temporal patterns, and barrier factors, of forest ecological security in China during 2005–2015. Among a variety of optimization algorithms, the PSO has the unique advantage of a fast computational speed and better global search capability for solving large-scale mathematical optimization problems through memory and feedback mechanisms, with faster convergence than evolutionary algorithms and genetic algorithms, simple parameter settings, a strong local search capability, and a lower likelihood of falling into a local optimum [41]. Zhang Z.D. et al. [49] combined the subjective and objective weights obtained from the PSO­AHP model and rough set theory to obtain the combined weights based on the linear weighting idea and evaluated the irrigation water use efficiency of agricultural land using the fuzzy comprehensive evaluation model.

At present, the evaluation of forest land quality mainly revolves around the evaluation content, indicator system, and evaluation methods that have been intensively explored, which has enriched the system of forest land quality evaluation; however, there is a lack of study on the differential characteristics of forest land quality in spatial distribution. Hinsley S.A. et al. [50] used airborne laser scanning to study the distribution of forest land structure and bird habitat quality. Ford M.M. et al. [51] assessed the potential of forest rangelands in Minnesota and their spatial distribution by comparing the yields of unmanaged forest land grazing, forest rangeland, and open range systems. Fu X. et al. [52] integrated multitree species to study the spatial distribution of forest land quality and production potential in forest areas. Xing S.H. and Wei H. [53] conducted a zoning study of regional forest land productivity with the help of a dynamic cluster analysis model. In general, the current studies on the spatial characterization of forest land quality tend to describe qualitatively, lacking quantitative analysis, and fail to fully consider the impact of spatial attribute characteristics of forest land quality on forest land conservation. As a spatial entity, the quality of forest land is necessarily influenced by spatial factors, in addition to its natural properties and socioeconomics, and thus has certain spatial distribution characteristics [54]. The delineation of previous forest land protection zones mainly focuses on differences in forest land quality [22,53], ignoring the spatial characteristics of forest land. In addition, forest land protection zoning is a systematic project, and its delineation is not only the result of quality ranking, but also a process of spatial positioning and boundary dropping. Therefore, spatial factors need to be considered in the delineation of forest land protection zones.

Subtropical broadleaf evergreen forest is a forest vegetation type with broadleaf evergreen trees as the dominant species growing in a warm and humid subtropical climate. The subtropical broadleaf evergreen forests in China are an important part of the world’s broadleaf evergreen forests and play an important role in maintaining global ecological balance and sustainable human development [55]. As a mountainous region, Hefeng County in Hubei Province of China is an important ecological function reserve in China’s subtropical humid evergreen broad-leaved forest region, with forest land accounting for 88.70% of the county’s total land area. In this study, taking Hefeng County as the study area, we selected 14 indicators including average annual temperature, average annual precipitation, ≥10 °C accumulated temperature, wetness index, elevation, slope, soil organic matter, soil layer thickness, soil type, soil texture, soil pH, land degradation, traffic location, and forest disaster grade to construct a forest land quality comprehensive evaluation indicator system. Based on the TOPSIS model, we introduced the PSO algorithm to design the evaluation model to evaluate the forest land quality and analyze the distribution of forest land quality grades in Hefeng County. We then used the Local Indicator of Spatial Association (LISA) to explore the spatial aggregation characteristics of forest land quality at the village scale by taking the forest land quality index as a spatial variable. The delineation scheme of forest land protection zones was formulated based on the spatial autocorrelation types, and different zoning management measures were proposed, which can provide a scientific basis and methodological reference for the reasonable delineation of forest land protection zones and meet the real needs of forest land differentiated protection and management in Hefeng, while also serving as a reference for similar regions and countries. This study not only introduced the PSO-TOPSIS model, aiming to explore new ways to evaluate forest land quality, but also incorporated spatial attributes of forest land quality into the development of forest land protection zoning schemes, expanding the means of forest land protection zoning, which can provide a scientific basis and methodological reference for the formulation of forest land rational development and utilization policies, in addition to serving as a reference for similar studies.

2. Materials and Methods

2.1. Study Area

Hefeng County, which belongs to Enshi Miao and Tujia Autonomous Prefecture of Hubei Province in China, is located between 109°45′–110°38′ E and 29°38′–30°14′ W. This county, which has nine towns and an area of 286,800 ha, is located in the Southwestern Hubei Province in China, bordering Hunan Province in China (Figure 1a). Hefeng belongs to a subtropical continental monsoon climate with abundant sunshine, four distinct seasons, suitable temperature, and abundant precipitation. The territory of this county is characterized by long stretches of mountains and gullies, with the terrain gradually decreasing from the northwest to southeast, dominated by high mountains (Figure 1b). Hefeng belongs to the subtropical humid evergreen broad-leaved forest region and is rich in forest land resources, with an area of 25.44 million ha, accounting for 88.70% of the total land area. According to the 2018 national economic and social development statistics of Hefeng, the forest coverage rate of the county reached 72.7%. Hefeng, that has an overall sensitive ecological environment with severe soil erosion, is an important ecological function reserve in China [56]. In the past 20 years, with the acceleration of the local urbanization process, much forest land has been converted to arable land and construction land, resulting in a decrease in the amount of forest land [57]. In addition, the quality of forest land of Hefeng county has been severely degraded, mainly due to rapid socioeconomic development, dramatic population growth, and increased market demand. The frequent occurrence of forest fires has exacerbated forest land degradation, resulting in severe soil erosion and loss of biodiversity, which has become a bottleneck affecting the sustainable use of forest land and coordinated economic and social development [58]. Therefore, it is of great significance to evaluate forestland quality scientifically and delineate forest land protection zones reasonably in Hefeng for fine forest land management and to guarantee ecological security of the national land in the local area, and it can also provide a reference for similar countries and regions.

2.2. Data Collection and Processing

The basic data involved in this study mainly includes the following: (1) the current land use map and administrative division map were obtained from the Land Use Change Survey Database in 2018 (1:10,000) provided by the Natural Resources Bureau of Hefeng. (2) The meteorological data of average annual precipitation, average annual temperature, ≥10 °C accumulated temperature, which is the sum of the daily average temperature during the continuous period of ≥10 °C in a year, and the wetness index in the study area were obtained from month-by-month observations in 31 meteorological stations distributed in Hubei Province of China from 1949 to 2018, from the National Weather Science Data Center (http://data.cma.cn/, accessed on 10 April 2020). (3) Slope was extracted using a 30 m resolution digital elevation model (DEM) from the Geospatial Data Cloud Platform (http://www.gscloud.cn/, accessed on 22 April 2020). (4) Soil type, soil layer thickness, soil texture, land degradation, traffic location, and forest disaster grade were obtained from the Forest Resources Planning and Design Survey Database in 2018 (1:10,000) provided by the Forestry Bureau of Hefeng. (5) Soil organic matter and soil pH (1 km resolution) were obtained from the National Earth System Science Data Center Soil subcenter (http://soil.geodata.cn/index.html, accessed on 8 May 2020). (6) Socioeconomics and other statistical data were from the 2018 Statistical Yearbook and related agricultural statistics in Hefeng.

We used ArcGIS 10.2 software (Environmental Systems Research Institute, Redlands, CA, USA) for the projection transformation and vectorization of each evaluation indicator.

2.3. Methods

2.3.1. Evaluation Indicator System

The process of evaluation index system construction is shown in Figure 2. First, the evaluation unit was delineated. The evaluation unit is an independent unit with relatively consistent natural and socioeconomics attributes. The same evaluation unit has similar attributes, while the different unit has significant differences [59]. The patch formed by overlaying the current land use map, soil map, and administrative unit map is often used as the land evaluation unit [60,61]. To facilitate the investigation and management of forest resources, a survey unit based on the forest land subcompartment has been established in the forest resource planning and design survey in China. According to previous research [27,28], the forest land subcompartment was used as the evaluation unit of forest land quality in this study. A total of 80,024 forest land subcompartments with an area of 254,000 ha were extracted from the Forest Resources Planning and Design Survey Database of Hefeng in 2018 (1:10,000).

Second, the evaluation indicators were selected and standardized. The study of an indicator system is the basis for land evaluation [62]. The factors affecting the quality of forest land are complex, involving natural, ecological environment, and socioeconomics. Natural factors mainly include climate, soil, hydrology, and terrain, which are the basis for the development of forestry and are the basic indicators reflecting the quality of forest land. Socioeconomics factors generally affect the quality of forest land by influencing its management level, mainly including land use, land management, location, and disaster, which are also important factors affecting the quality of forest land. Climate is the basis for the photosynthesis of vegetation and has a strong influence on the quality of forest land. According to previous research, temperature and precipitation are generally selected to evaluate the climatic production potential of forest land [63]. In addition, forest land quality evaluation indicators have a time-scale effect, and different indicators have different scales for the time change. According to the response time of soil physical and chemical properties, the response time of the soil type is greater than 1000 years, the response time of soil layer thickness is between 100 and 1000 years, and the response time of soil texture is located between 10 and 100 years [64]. Soil organic matter is the main source of vegetation nutrients, and it is also therefore an important indicator of soil fertility [65]. Soil pH is closely related to fertilizer uptake efficiency, and most vegetation grows best when it is between 6.5 and 7.5 [66]. Land degradation is the main factor limiting the improvement of forest land quality [28]. Therefore, soil organic matter, soil type, soil texture, soil layer thickness, soil pH, and land degradation were selected as indicators of soil environment in forest land. The terrain is closely related to forest land quality and determines the spatial distribution of forest land quality. Elevation and slope were selected to evaluate the suitability of forest land [67]. Socioeconomic conditions affect the level of forest land management, and the transportation location and forest disaster grade were selected to assess the economic quality of forest land [24].

Based on the above analysis, the principles of comprehensiveness, dominance, and differentiation [46] were followed in this study, referring to the evaluation system established in the Technical Regulations for Defining Forest Land Border in Forest Land Planning on Protection and Utilization developed by the National Forestry Administration of China [68]. Fourteen indicators of average annual temperature, average annual precipitation, ≥10 °C accumulated temperature, wetness index, elevation, slope, soil type, soil layer thickness, soil organic matter, soil texture, soil pH, land degradation, traffic location, and forest disaster grade were selected from four dimensions—climatic conditions, terrain, soil conditions, and socioeconomics—to construct a forest land quality evaluation indicator system. To eliminate the influence of the evaluation indicator scale, the indicators were graded according to their influence on the quality of the forest land and their scores are assigned. The indicator score is assigned based on its indicator grade. The higher the score, the higher the quality of forest land. Indicators were standardized concerning the Technical Regulations for Defining Forest Land Border in Forest Land Planning on Protection and Utilization [68], Technical Regulations for Continuous Forest Inventory [69], Regulation for Gradation on Agriculture Land Quality [70], and Cultivated Land Quality Grade [71]. The evaluation indicator system of forest land quality is shown in

Table 1. Forest land quality evaluation indicator grading standards and their scores.

Criteria Layer	Indicator Layer	Weight	Score
			10	8	6	4	2
Climatic conditions	Average annual temperature/°C	0.12	>16.0	14.0–16.0	12.0–14.0	10.0–12.0	<10.0
	Average annual precipitation/mm	0.11	>2000	1600–2000	1200–1600	800–1200	<800
	≥10 °C accumulated temperature/°C	0.08	>7000	6000–7000	6000–5000	5000–4000	<4000
	Wetness index	0.11	>120	100–120	80–100	60–80	<60
Terrain	Elevation/m	0.04	<200	200–800	800–1400	1400–2000	>2000
	Slope/°	0.02	<5	5–14	15–24	25–34	≥35
Soil conditions	Soil type	0.01	Black soil, brown coniferous soil, brown loam, black calcareous soil, brown soil, dark brown loam	Tide soil, gray forest soil, gray-brown soil, dry red soil, yellow loam, yellow-brown soil	Drifting ash soil, chestnut calcium soil, chestnut brown soil, yellow cotton soil, brick red soil, red soil, volcanic ash soil, yellow-brown loam	Fresh soil, desert soil, sandy black soil, limestone soil, peat soil, purple soil, red soil, white pulp soil, silt soil, mountain meadow soil, forested meadow soil	Coarse bone soil, grass felt soil, gray-brown desert soil, red clay soil, cracked soil
	Soil layer thickness/cm	0.04	>8		40–80		<40
	Soil texture	0.06	Loamy soil		Loamy sandy soil, sandy loam		Clay, sandy clay
	Soil organic matter/(g/kg)	0.12	>40	30–40	20–30	10–20	<10
	Soil pH	0.08	6.0–7.9	5.5–6.0; 7.9–8.5	5.0–5.5; 8.5–9.0	4.5–5.0	<4.5; ≥9.0
	Land degradation	0.02	None	Light	Moderate	Intense	High intensity
Socioeconomics	Traffic location	0.09	First grade	Second grade	Third grade	Fourth grade	Fifth grade
	Forest disaster grade	0.10	None	Light	Medium	Heavy

.

Third, evaluation indicator weights were calculated. The PSO algorithm is an evolutionary computational technique proposed by Kennedy J. and Eberhart R. in 1995 [72]. The basic principle of PSO is to move individuals in the population to good positions according to the size of their adaptation to the environment, which is not only fast in computation but also has a strong global optimal search capability for nonlinear problems [41]. In this study, we constructed a nonlinear programming model of evaluation indicator weights by aiming at minimizing the sum of the distances between the weights of evaluation indicators and their maximum and minimum values. We then calculated the weight of each indicator by PSO, which can enter into mining the nonlinear information implied by the data. The calculation steps are as follows.

(1)

The original evaluation indicators are normalized to obtain the judgment matrix X.

$$\{\begin{array}{c}X={(X_{ij})}_{m\times n}\\ X_{ij}=\frac{x_{ij}}{{\displaystyle \sum _{i=1}^m{x}_{ij}}}\hfill \end{array}$$

(1)

where X_ij is the ratio of the indicator value of the ith evaluation unit under the jth indicator, m is the number of evaluation units, n is the number of evaluation indicators, and x_ij is the indicator value of the ith evaluation unit under the jth indicator.

(2)

Based on the judgment matrix X, the weight adaptation equation is constructed.

Let the optimal object be $H={(1,1,\dots ,1)}^T$ and the inferior object be $L={(0,0,\dots ,0)}^T$. The objective is then as follows.

$$\min f(w)={\displaystyle \sum _{j=1}^n{f}_j(w)}={\displaystyle \sum _{j=1}^n{\displaystyle \sum _{i=1}^m{w}_j^2\left[\frac{{(1-X_{ij})}^2+X_{ij}^2}{m\times n}\right]}} s.t\{\begin{array}{c}{\displaystyle \sum _{j=1}^n{w}_j=1}\\ w_j\ge 0\end{array}$$

(2)

where minf(w) is the planning equation of the weight, f_j(w) is the planning equation of the weight of the jth indicator, and w_j is the weight of the jth indicator.

To calculate the weights using the PSO, the planning Equation (2) is transformed into an adaptation equation.

$$P(w)=E{({\displaystyle \sum _{j=1}^n{w}_j-1})}^2+F{\displaystyle \sum _{j=1}^n{\displaystyle \sum _{i=1}^m{w}_j^2\left[\frac{{(1-X_{ij})}^2+X_{ij}^2}{m\times n}\right]}}$$

(3)

where E and F are penalty factors, whose values vary depending on the judgment matrix R.

(3)

The PSO is applied to solve the weights.

$$\{\begin{array}{l}{v}_{id}=w\ast v_{id}+c_1\ast rand()\ast (p_{id}-x_{id})+c_2\ast Rand()\ast (p_{gd}-x_{id})\hfill \\ x_{id}=x_{id}+v_{id}\hfill \end{array}$$

(4)

where x_id is the ith particle, v_id is the velocity of particle i, p_id is the best-adapted value, p_gd is the indicator number of the best-adapted value, w is the inertia weight, c₁ and c₂ are acceleration constants, rand() and Rand() are random values in the range [0,1], and d is the dimensionality of the search space.

In this study, the PSO was used to solve the weights of each evaluation indicator using Python 3.8, which was designed by Rossum G.V. in the early 1990s [73] (Table 1). Its parameters in Python were set as follows: the number of iterations was 1000, the number of populations was 200, the inertia weight was 1.0, the spatial dimension was 14, the acceleration constant c₁ = c₂ = 1.8, and the incremental threshold of the optimal fitness value was 0.002.

Finally, the forest land quality index was calculated. TOPSIS is a comprehensive evaluation method first proposed by Hwang C.L. and Yoon K. in 1981 [43]. Based on the normalized original evaluation indicator matrix, the cosine is used to calculate the optimal and inferior solutions of each evaluation indicator. The Euclidean distance between the evaluation indicator and the optimal and inferior solutions is used as the criterion for the evaluation solutions [47]. TOPSIS is widely used in land evaluation in various parts of world [42,46,74,75]. Jozi S.A. and Majd N.M. [76] used the TOPSIS and AHP models to evaluate ecological land capacity evaluation of Dehloran County. Luo W.B. and Tong Z. [77] used the TOPSIS model with information entropy weights to construct a rural land consolidation performance evaluation index system between 31 Chinese provinces from 2003 to 2007. Therefore, in this study, we used the improved TOPSIS model, which introduced weights [46] to evaluate the forest land quality and its calculation process is as follows.

(1)

Standardization of evaluation indicators. The extreme value method is usually applied to standardize the evaluation indicators to determine the status of the original values of each evaluation indicator in the weights.

(2)

Weighted judgment matrix construction. The indicator weight matrix U calculated based on the PSO is involved in the construction of the judgment matrix, i.e., the weighted judgment matrix ($P={(p_{ij})}_{m\times n}$) is obtained by multiplying the matrix X with its weight U.

$$P=\left[\begin{array}{cccc}{p}_{11}& p_{12}& \dots & p_{1n}\\ p_{21}& p_{22}& \dots & p_{2n}\\ \dots & \dots & \dots & \dots \\ p_{m1}& p_{m1}& & p_{mn}\end{array}\right]=\left[\begin{array}{cccc}{x}_{11}.u_1& x_{11}.u_1& \dots & x_{11}.u_1\\ x_{21}.u_2& x_{21}.u_2& \dots & x_{2n}.u_2\\ \dots & \dots & \dots & \dots \\ x_{m1}.u_m& x_{m1}.u_m& \dots & x_{mn}.u_m\end{array}\right]$$

(5)

(3)

The optimal solution P⁺ and the inferior solution P⁻ are sought.

$$\begin{array}{l}{P}^{+}=\left\{\max p_{ij}|\mathrm{j}=1,2,\dots ,n\right\}=\left\{p_1^{+},p_2^{+}\dots p_n^{+}\right\}\\ P^{-}=\left\{\max p_{ij}|j=1,2,\dots ,n\right\}=\left\{p_1^{-},p_2^{-}\dots p_n^{-}\right\}\end{array}$$

(6)

(4)

Calculate the distance. Calculate the R⁺ and R⁻ distances of each evaluation unit from the optimal solution, respectively.

$$\begin{array}{l}{R}_i^{+}=\sqrt{{\displaystyle \sum _{j=1}^n{(p_{ij}-p_i^{+})}^2}},i=1,2,\dots m\\ R_i^{-}=\sqrt{{\displaystyle \sum _{j=1}^n{(p_{ij}-p_i^{-})}^2}},i=1,2,\dots m\\ \end{array}$$

(7)

(5)

Calculate the closeness of each evaluation unit to the optimal solution Ci.

$$C_i=\frac{R_i^{-}}{R_i^{+}+R_i^{-}}$$

(8)

where larger C_i means better quality of the ith evaluation unit and C_i takes values in the range of [0,1]. When C_i is 0, it means the worst quality of forest land, and when C_i is 1, it means the best quality of forest land.

2.3.2. Spatial Autocorrelation

(1)

Local Indicator of Spatial Association

The first law of geography states that everything is correlated with each other and the closer things are to each other, the higher the correlation [78]. That is, geographic entities show certain correlations in their spatial distribution under the influence of both spatial attraction and spatial diffusion effects [79]. Spatial autocorrelation provides an effective method for exploring the spatial correlation patterns of geographic entities [80]. Spatial autocorrelation is a method to study the correlation between regional variables and their neighboring variables in terms of spatial location, by detecting the dependence of a location variant on its neighboring locations to determine whether it is spatial autocorrelation [81]. In spatial autocorrelation, one of the more commonly used parameters is the global Moran’s I index, which is calculated as follows:

$$I={\displaystyle \sum _{i=1}^n{\displaystyle \sum _{i=1}^n{W}_{ij}(X_i-\overline{X})(X_j-\overline{X})/S^2}}{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{i=1}^n{W}_{ij}}}$$

(9)

where n is the number of samples of variable X, X_i, and X_j are the actual measurements of the samples at positions i and j, respectively, S² is the variance, $\overline{X}$ is the mean, and W_ij is the value of the spatial weight matrix.

In 1995, Anselin L. [82] developed the concept of the Local Indicator of Spatial Association (LISA), which was a decomposed form of global spatial autocorrelation, reflecting the degree of correlation between a spatial unit and its neighboring spatial units on the value of indicators [80], and its expression is as follows:

$$\begin{array}{l}{I}_i=A_i{\displaystyle \sum _j{W}_{ij}{A}_j}\\ A_i=\raisebox{1ex}{$(x_i-\overline{x})$}\!\left/ \!\raisebox{-1ex}{$\sqrt{\frac{1}{n}{\displaystyle \sum _{i=1}^n{(x_i-\overline{x})}^2}}$}\right.\end{array}$$

(10)

where A_i is the normalized value of the observed values of indicator i of the spatial unit. X_i and $\overline{X}$_i are the same as in Equation (9).

The spatial pattern can be visualized by combining the local spatial autocorrelation index with the Moran scatter plot map [81], which is divided into four quadrants, representing four different types of autocorrelations [83]. The first and third quadrant points represent spatial similarity. The first quadrant points show high indicator values with high indicator neighbors (high-high), while the third quadrant points show low indicator values with low indicator neighbors (low-low). The second and fourth quadrant points indicate spatial dissimilarity. The second quadrant points show low indicator values surrounded by high indicator neighbors (low-high). In contrast, the fourth quadrant points show the high indicator values surrounded by low indicator neighbors (high-low).

(2)

Spatial Unit and Spatial Variable

The use of a subcompartment of forest land as an evaluation unit is to analyze the quality of forest land at a microscopic scale. Considering the large study area and the small size of the forest land subcompartment, it is difficult to identify the results of local spatial autocorrelation. In addition, forest land protection zoning is a systematic project, focusing more on medium- and macro-scale protection and utilization. Therefore, in this study, from the practice of forest land protection, the administrative village was taken as the spatial unit, and the village forest land quality index was used as a spatial variable to explore the zoning scheme of forest land conservation at the village scale, coupling the integrated influence of forest land natural, socioeconomics, and spatial attributes in forest land protection.

Based on the evaluation of the forest land subcompartment quality, the quality of forest land at the village scale is obtained by taking the ratio of the area of each forest land subcompartment to the total area of forest land subcompartment in the village as the weights, and its calculation is as follows.

$$F_m=\frac{F_{im}\times s_{im}}{{\displaystyle \sum s_{im}}}$$

(11)

where F_m is the average forest land quality index of the mth village; F_im is the forest land quality index of the ith forest land subcompartment in the mth village; S_im is the area of the ith forest land subcompartment in the mth village.

Meanwhile, the LISA and Moran scatter plot map of this study were implemented with the help of ArcGIS 10.2 (Environmental Systems Research Institute, Redlands, CA, USA) and GeoDa 1.14 (The University of Chicago, Chicago, IL, USA).

3. Results

3.1. Forest Land Quality Evaluation Results

3.1.1. Spatial Distribution of Evaluation Index

From the calculation results, the range of Forest Land Quality Index (FLQI) was 0.4091–0.8601, and the county average index was 0.6337, indicating that the overall forest land quality was high. The spatial distribution of the FLQI (Figure 3) showed that the FLQI is higher in the central and southeastern parts of the county and lower in the northwestern parts. According to the statistical characteristics of FLQI of towns (

Table 2. Statistical characteristics of forest land quality index (FLQI) of towns.

Name of Towns	Mean Value	Standard Deviation	Maximum Value	Minimum Value	Coefficient of Variation
Rongmei	0.61	0.08	0.80	0.41	13.34
Taiping	0.61	0.08	0.81	0.42	12.39
Tielu	0.61	0.07	0.85	0.42	11.34
Wuyang	0.64	0.06	0.77	0.44	9.61
Wuli	0.63	0.07	0.81	0.42	11.46
Xiaping	0.63	0.08	0.86	0.44	13.43
Yanzi	0.65	0.07	0.86	0.42	10.21
Zhongying	0.62	0.07	0.83	0.47	11.69
Zouma	0.66	0.07	0.81	0.44	10.48

), there was some variability in the mean values of FLQI in different towns. According to ANOVA, the p-value was 0.004 at the significance level of 0.05. It is thus clear that p < 0.05 indicates that the variability of FLQI between towns was relatively significant in general. The mean values of the FLQI in Zouma and Yanzi are relatively large, while the mean values of the FLQI in Rongmei, Tielu, and Taiping are relatively small. From the coefficient of variation of the FLQI, the FLQI in Xiaping fluctuated the most, while that in Wuyang fluctuated the least.

3.1.2. Spatial Distribution of Evaluation Grades

To explore the variability of quality among forest land subcompartment, according to the natural breakpoint method, the quality of forest land was divided into four grades: first grade, second grade, third grade, and fourth grade, with areas of 80,692, 68,372, 58,043, and 46,928 ha, respectively. The largest area of first-grade forest land was 31.76%, and the smallest area of fourth-grade forest land was 18.47%. In terms of the distribution of forest land quality grades (Figure 4), the first-grade forest land was the most widely distributed, mainly concentrated in the southeastern low mountain areas. The natural environment of the region was excellent. Its climate was suitable, the terrain was relatively flat, and the ability of soil and water conservation was strong. In addition, its soil conditions were good. Its soil type was mainly brown loam with a loamy texture, thick soil layer, and high soil nutrient content. In addition, convenient transportation conditions in this area effectively enhanced the management of forest land. Therefore, the excellent natural and socioeconomic conditions of this area ensured the stability and high quality of the forest land.

Second-grade forest land was also more commonly distributed in Hefeng, mainly located in the southeastern medium mountain areas, adjacent to first-grade forest land (Figure 4). The natural conditions in this area were also superior, with a more suitable climate, a small slope, a soil type mainly of yellow-brown loam, a soil layer thickness above 40 cm, a soil organic matter content between 30 and 40 g/kg, and a pH between 5.5 and 6.0. The forest land quality in this region has been degraded to a certain extent due to the terrain, which can be improved through soil improvement to increase the water and fertilizer retention capacity of the soil.

Third-grade forest land also occupied a high proportion of the area, mainly in the northwestern medium and high mountain areas, adjacent to second-grade forest land (Figure 4). The slope was larger, the elevation was generally above 1400 m, and the soil and water conservation capacity were poor in this region. In addition, the climate in the region was highly volatile, with frequent extreme weather events [84]. The thickness of the soil layer was thinner, the soil was more infertile, land degradation was more severe, the frequency of forest fires was higher [85], and the traffic conditions were unfavorable due to the terrain in this region. Therefore, the quality of forest land in this region was low, and the main limiting factors were terrain, climate, and soil. For this type of forest land, soil development and utilization management must be strengthened to prevent soil erosion.

Fourth-grade forest land had the least area and was mainly located in the northwestern high mountain areas, adjacent to third-grade forest land (Figure 4). The climate in the region was highly volatile, with frequent extreme temperature and precipitation events [84]. This region was at high altitude, generally above 2000 m, with large slopes, the most severe soil erosion, insufficient soil nutrients, and severe soil acidification. The main limiting factors for the quality of forest land in this region were climate, terrain, and soil. In addition, forest fires were frequent in the region, and the ecological degradation of forest land was more severe, which had a greater impact on the quality of forest land [85]. Strict forest land management and protection measures must be taken to reduce the frequency of forest disasters, prevent the ecological degradation of forest land, improve soil fertility, and reduce soil acidification.

In general, on the regional macro scale, forest land quality was highest in the low mountain areas of Hefeng County, followed by the medium mountain areas, medium and high mountain areas, and high mountain areas. It can be seen that the quality of forest land was closely related to the terrain, which determined the spatial pattern of forest land quality. In addition, on the town mesoscale, the elevation and the slope reflecting important characteristics of the terrain have important impacts on climate and soil physical and chemical properties. The elevation has a great influence on accumulated temperature, effective soil nutrients, and soil pH. As the elevation increases, the accumulated temperature gradually decreases, soil nutrients have a certain degree of decline, and soil pH shows an obvious upward trend, which has a greater impact on the growth of forest trees and decreases the quality of forest land [86]. The slope has a great influence on the soil thickness, soil water and fertilizer conditions, and the growth of trees [27]. The smaller the slope is, the more favorable the growth of forest trees is, and the higher the quality of the forest land is. Furthermore, factors such as climate change, soil fertility, transportation location, and forest fires also have a great influence on the quality of forest land.

Thus, on the town mesoscale, there is some variation in forest land quality (

Table 3. Area statistics of forest land quality grades in towns.

Towns	First Grade		Second Grade		Third Grade		Fourth Grade
	Area/′000 ha	Proportion/%	Area/′000 ha	Proportion/%	Area/′000 ha	Proportion/%	Area/′000 ha	Proportion/%
Rongmei	6	7.41	6	8.82	8	13.79	8	17.02
Taiping	6	7.41	7	10.29	9	15.52	8	17.02
Tielu	4	4.94	8	11.76	7	12.07	4	8.51
Wuyang	4	4.94	6	8.82	4	6.90	2	4.26
Wuli	10	12.35	9	13.24	7	12.07	7	14.89
Xiaping	5	6.17	4	5.88	3	5.17	3	6.38
Yanzi	13	16.05	10	14.71	5	8.62	3	6.38
Zhongying	10	12.35	8	11.76	8	13.79	8	17.02
Zouma	23	28.40	10	14.71	7	12.07	4	8.51

). The first-grade forest land was mainly distributed in Zouma, Yanzi, Wuli, and Zhongying with an area of 28.40%, 16.05%, 12.35%, and 12.35%, respectively, and a total area of 69.15%. The second-grade forest land was mainly concentrated in Yanzi, Zouma, and Wuli, accounting for 14.71%, 14.71%, and 13.24% of the area, respectively, with a total area of 42.66%. The third-grade forest land was mainly distributed in Taiping, Zhongying, and Rongmei, with an area of 15.52%, 13.79%, and 13.79%, respectively. Fourth-grade forest land was mainly distributed in Zhongying, Taiping, and Rongmei, accounting for 17.02%, 17.02%, and 17.02% of the area, respectively. It can be seen that the overall quality of forest land in Zouma, Yanzi, and Wuli was higher, with a more suitable climate, a low slope, and convenient transportation, and the soil can better retain water and fertilizer. The quality of forest land in Zhongying, Rongmei, and Taiping was generally low. The climate was poor, the slope was large, the soil erosion was more severe, the soil fertility was poor, the land degradation was severe, and forest disasters were more severe in this region. In addition, it can be seen that, even in areas with the same landform type, there is some variation in forest land quality. For example, both Rongmei and Zouma in Hefeng County had lower elevation (Figure 1), but compared with Zouma, the forest land quality in Rongmei was lower (Figure 4), which was due to the fact that Rongmei is the center town of socioeconomic development in Hefeng County, with a more developed social economy and a high intensity of forest land utilization, leading to soil erosion, forest disasters, and forest land degradation that are more severe.

3.2. Spatial Autocorrelation Results

Taking the administrative village as a spatial unit and the village-scale FLQI as a spatial variable, the global spatial autocorrelation index of forest land quality in Hefeng was calculated to be 0.7562, indicating that the county forest land quality had a strong similarity in spatial distribution and a certain spatial aggregation characteristic. The LISA types of forest land quality (Figure 5a) were mapped, reflecting the spatially clustered areas of forest land quality.

From the results of LISA, there were five LISA types: high-high (HH), high-low (HL), low-high (LH), low-low (LL), and nonsignificant (NS) in village-scale forest land quality in the county. In terms of number, the spatial similarity type (HH and LL) accounted for 48.51% of the total number of administrative villages, the spatial dissimilarity type (HL and LH) accounted for 35.15%, and the NS accounted for 16.34%. In terms of spatial distribution, the spatial distribution of similarity types (HH and LL) was more clustered, while the spatial distribution of dissimilarity types (HL and LH) was generally dispersed. The HH areas were mainly distributed in Zouma, Wuli, and Yanzi, where, in the county ranked first, the climate was suitable, the slope was small, the soil water and fertilizer retention ability was strong, the forest resources were rich, and the forest coverage rate was above 70%. Therefore, the quality of the forest land in this region was high. The LL areas were mainly concentrated in Rongmei and Taiping, where, in the center of the county’s economic development, forest land resources were relatively small, and the degree of fragmentation was high. As a result, forest land quality in this region was generally low due to severe disturbance by human activities. The HL areas were mainly concentrated in Zhongying and Wuyang. Due to the influence of the low quality of forest land in the surrounding towns (including Rongmei and Taiping), the forest land quality in this region was medium. The LH areas were mainly concentrated in southeastern Zouma and northern Yanzi, which were mostly distributed around the HH areas. The forest land quality was medium due to the influence of high-quality forest land in Zouma. The NS areas were mainly distributed in northern Tielu and Xiaping, where the quality of forest land was randomly distributed, with both high grades close to the HH areas and low grades located within the LL areas.

3.3. Forest Land Protection Zoning

3.3.1. Forest Land Protection Zoning Standards and Protection Measures

Theories and practices related to regional development show that there are diffusion and polarization effects between regions that can expand or reduce spatial differences between regions [87]. In general, the spatial similarity types HH and LL are a reflection of the spatial diffusion effect, while the spatial dissimilarity types LH and HL are a reflection of the spatial polarization effect [88]. Under the interaction of the two effects, based on the results of LISA, and combining the characteristics of the five spatial autocorrelation types, the county’s forest land protection zones can be divided into three types: key protected zones (KPZs), active protected zones (APZs), and general protected zones (GPZs) (Figure 5b). The protection zoning standards are shown in

Table 4. Forest land protection zoning standards.

Protection Zoning Type	Local Indicator of Spatial Association (LISA) Type	Typical Area	Characteristics
Key protection zone (KPZ)	HH	Zouma, Wuli, and Yanzi	The forest land quality was high and its spatial distribution was aggregated.
Active protection zone (APZ)	LH, HL, and NS (medium)	Zhongying and Wuyang	The forest land quality was medium.
General protection zone (GPZ)	LL and NS (low)	Rongmei, Taiping, and Tielu	The forest land quality was low, but its spatial distribution was aggregated.

.

The HH area is one of an aggregation of patches with a high FLQI, which generally has high forest land quality and entails strong aggregation characteristics in spatial distribution. Therefore, it is designated as an KPZ. In terms of protection measures, it is necessary to actively maintain and improve the quality of existing forest land, strengthen the intensity of protection, strictly protect the good natural ecological and environmental conditions of forest land, greatly reduce the damage to forest land resources from human activities, and strengthen the impact of spatial diffusion effects.

In the HL area, the high-quality forest land is concentrated in the center, while its surroundings are mostly surrounded by the low-quality forest land. Under the influence of the spatial polarization effect, the high-quality forest land is easily assimilated by the low-quality forest land, thus evolving into an LL area. Therefore, we should actively strengthen the protection of the central high-quality forest land, reduce the impact of the surrounding low-quality forest land on it, and gradually expand the range of protection. In the contrast, in the LH area, the high-quality forest land is distributed around the low-quality forest land. We should actively improve the protection and improvement of the central low-quality forest land, and emphasize the diffusion effect of the surrounding high-quality forest land to promote their evolution to an HH. On the other hand, non-forestry construction on the low-quality forest land should be avoided to minimize the impact of its polarization effect on the surrounding high-quality forest land. Therefore, the HL and LH are suitable for designation as APZs.

The LL area is one of an aggregation of patches with low FLQI, which generally has low forest land quality and entails strong aggregation characteristics in spatial distribution. Therefore, the LL area is suitable for designation as a GPZ. Its improvement should be treated differently, and comprehensive, prioritized, and gradual forest land protection measures should be considered. In addition, if the forest land is to be deagriculturalized, the LL area is an ideal choice.

In addition, according to the statistical results, in the NS area, there is both high-quality forest land close to HH and low-quality forest land. Therefore, the NS areas were further classified into two types (medium and low) by the natural breakpoint method: an APZ and a GPZ, respectively. Some conditional protection measures were adopted in conjunction with forest land protection and ecological civilization construction.

3.3.2. Analysis of the Results of Forest Land Protection Zoning

To further verify the consistency between the results of forest land protection zoning and the results of forest land quality evaluation, the zoning types and forest land quality grades were compared and analyzed (

Table 5. Area statistics of forest land quality grades compared with protection zones.

Protection Type		First Grade		Second Grade		Third Grade		Fourth Grade		Total
		Area/′000 ha	Proportion (n)/%	Area/′000 ha	Proportion (n)/%	Area/′000 ha	Proportion (n)/%	Area/′000 ha	Proportion (n)/%
KPZs	Area/′000 ha	46	58.23	22	27.85	8	10.13	3	3.80	79
	Proportion (m)/%	56.79		32.35		13.79		6.38		31.10
APZs	Area/′000 ha	29	29.29	34	34.34	23	23.23	13	13.13	99
	Proportion (m)/%	35.80		50.00		39.66		27.66		38.98
GPZs	Area/′000 ha	6	7.89	12	15.79	27	35.53	31	40.79	76
	Proportion (m)/%	7.41		17.65		46.55		65.96		29.92
Total		81	31.89	68	26.77	58	22.83	47	18.50	254

). First-grade forest land in the KPZs accounted for 56.79% of the total area of first-grade forest land in the county, and first- and second-grade forest land cumulatively accounted for 86.08% of the total area of KPZs. The second- and third-grade forest land in the APZs accounted for 50.00% and 39.66% of the total area of second- and third-grade forest land in the county, respectively. The first- and second-grade forest land cumulatively accounted for 63.63% of the total area of the APZs. The third- and fourth-grade forest land in the GPZs accounted for 46.55% and 65.96% of the total area of third- and fourth-grade forest land in the county, respectively. The third- and fourth-grade forest land cumulatively accounted for 76.32% of the total area of the GPZs. It can be seen that the KPZs covered most of the high-grade forest land in the county, while the GPZs covered most of the low-grade forest land in the county, and most of the medium-grade forest land was assigned as APZs, indicating that the forest land protection zoning scheme based on the LISA of forest land quality was basically consistent with the forest land quality evaluation results.

4. Discussion

4.1. Forest Land Quality Indicator System Construction

Whether the construction of the forest land quality evaluation indicator system is reasonable or not is directly related to the accuracy of evaluation results. How to reasonably determine the indicator system according to the purpose of an evaluation is the basic work of forest land quality evaluation. Mo K. [89] constructed a forest land quality evaluation indicator system from three aspects: soil conditions, terrain, and forest conditions. Deng W.Q. [90] established a comprehensive evaluation system of forest land quality from soil conditions and meteorological conditions. There is a close relationship between forest land quality and factors such as soil fertility, terrain, and management [91]. Due to the diversity of factors affecting forest land quality and regional variability, the current forest land quality evaluation indicator system is still not unified [92]. Additionally, the existing indicator system takes more into account factors such as soil fertility and management that affect the quality of forest land and ignores the impact of factors such as climate change and forest fires on land ecological degradation [89]. Hubei Province, which is situated in the middle reaches of the Yangtze River and belongs to the east–west, north–south climate transition zone, is a sensitive area of climate change. According to the Historical Information on Natural Disasters in Hubei Province, it is found that the frequency of droughts and floods in Hubei Province has increased since the 20th century. Gao X. et al. [93] also pointed out that the main areas of extreme temperature in Hubei Province in the last 20 years were concentrated in the north and west. Wu C.H. et al. [94] selected extreme precipitation indicators in Hubei in the last 10 years and found that extreme precipitation was mainly distributed in the western mountainous region and the southern part of Jianghan Plain. It can be seen that the study area, Hefeng County, is located in the western part of Hubei Province, which is one of the most significant areas of climate change in Hubei Province. In addition, it has been shown that climate change has a significant impact on forest fires and forest soil carbon and nitrogen cycling processes, which in turn affects forest land quality [95]. Therefore, it is particularly important to establish a system of indicators that considers climatic conditions and forest fires. Xiong C.S. et al. [54] considered the inclusion of forest fires in the forest land quality evaluation indicator system and constructed a comprehensive evaluation indicator system for forest land quality in terms of terrain, soil conditions, forest condition, traffic location, and forest fires to evaluate the quality of forest land. In order to further reveal the influence of climate change and forest fires on forest land quality, in this study, we combined the characteristics of the study area and selected 14 indicators of average annual temperature, average annual precipitation, ≥10 °C accumulated temperature, wetness index, elevation, slope, soil type, soil layer thickness, soil texture, soil organic matter, soil pH, land degradation, traffic location, and forest disaster grade from four dimensions—climatic conditions, terrain, soil conditions, and socioeconomics—to construct a forest land quality comprehensive evaluation indicator system. According to the evaluation results, the overall quality of forest land in Hefeng was high, but there were large differences between regions, and the low-quality forest land was mainly distributed in the middle and high mountain regions, and the limiting factors were mainly terrain, climate change, and forest fires, which was consistent with existing studies [96,97]. In the selection of climate change and forest fire indicators, due to the lack of data in the study area, only a few indicators such as average annual temperature, average annual precipitation, ≥10 °C accumulated temperature, wetness index, and forest disaster grade were used instead, which are not comprehensive enough for the description of climate change and forest fires on forest land quality and need to be further explored. In addition, forest land quality evaluation is a complex system project, and the evaluation indicator system and evaluation criteria established in this study are subject to further in-depth study and validation due to the limitation of data accessibility. As technology develops, further research could combine remote sensing data and observed data to find more suitable and sensible indicators for evaluating the forest land quality and exploring its spatial difference at the county scale.

4.2. Forest Land Quality Evaluation Model Construction Based on the PSO-TOPSIS Model

The quality of the evaluation method directly affects the accuracy of the forest land quality evaluation results. Ozkaya G. and Erdin E. [98] conducted a comparative analysis of forest and air quality in 30 countries using TOPSIS and VIKOR models. In this study, the PSO algorithm was introduced to address the shortcomings of the TOPSIS model, and a PSO-TOPSIS model that can be applied to forest land quality evaluation was designed. For the determination of indicator weights in the TOPSIS model, a nonlinear programming model on weights was established by using the PSO algorithm to determine the weight of each indicator by taking the minimum sum of distances to the optimal and inferior objects as the criterion. This weighting method was highly logical and avoided the subjectivity of determining the weights, making the solved weights more objective. In addition, it has a fairly fast approximation of the optimal solution and can effectively optimize the parameters of the system with a strong local search capability [49]. The TOPSIS model is one of the comprehensive evaluation methods for multiobjective decision making with limited solutions. The calculation of the forest land quality index by an improved TOPSIS model eliminated the influence of different indicator scales after the normalized processing of raw data. It can make full use of the information of the original data, reflect the actual situation objectively, obtain the comprehensive evaluation results of forest land quality, and reveal the key influencing factors of forest land quality. Therefore, the PSO-TOPSIS comprehensive evaluation model constructed in this study not only enriches the evaluation method of forest land quality, but also provides a certain reference for further improving the quality evaluation system, which can provide a scientific basis for formulating and regulating the market transfer price of forest land and compensation for occupied and expropriated forest land. Additionally, the evaluation model constructed in this study could be used as a reference for similar countries and regions; however, forest land quality evaluation is influenced by various factors such as climate, terrain, and soil and has significant inheritance and variability characteristics [51]. Due to the limitation of the research conditions, in this study, we only analyzed the spatial distribution characteristics of the current forest land quality evaluation, which does not apply to the dynamic monitoring analysis of forest land quality. Therefore, further improvement of the forest land quality evaluation model to improve the evaluation accuracy will be the focus and difficulty of the subsequent research.

4.3. Forest Land Protection Zoning Based on the LISA

Based on the LISA of forest land quality index, it can delineate forest land protection zones more scientifically and reasonably. The spatial distribution of forest land quality shows certain spatial aggregation variability characteristics. Specifically, the evaluation indicators of forest land quality will show spatially different or similar changes with increasing or decreasing distance [80], which in turn will form certain aggregated distribution characteristics in space. Wei S. C. et al. [99] used a combination of Moran scatter plot and LISA to explore the spatial structural characteristics and aggregation patterns of cropland quality in Guangning County of China using the cropland quality index as a spatial variable, and proposed a cropland protection zoning scheme. In this study, we used forest land quality index as a spatial variable and used LISA to explore the spatial aggregation pattern of forest land quality at the village scale, and delineated the forest land protection zones. We proposed a zoning protection strategy, which enriched the methods of forest land protection zoning and provided a new idea for the differentiated protection and fine management of forest land. In addition, the theory of territorial differentiation and the theory of coordinated development of integrated regions are important theoretical bases for zoning studies [100]. Further study of forest land zoning needs to be combined with zoning theory to explore the coordination of population, economy, urban construction, and environment in forest land utilization, to better realize the sustainable use of forest land and the coordinated development of society. How to select and identify the key factors of forest land protection zoning to achieve regional sustainable development, and how to combine the natural ecological and human elements of forest land use for forest land zoning need further exploration.

5. Conclusions

Based on the scientific evaluation of forest land quality in Hefeng based on the PSO-TOPSIS model, combining the natural conditions, socioeconomics, and spatial distribution characteristics of forest land quality, we used the LISA to explore the law of spatial aggregation of forest land quality at the village scale taking the forest land quality index as a spatial variable. A forest land protection zoning scheme was proposed accordingly.

By evaluating the quality of forest land, this study found that the range of the forest land quality index in Hefeng was 0.4091–0.8601, with a mean value of 0.6337, indicating that the overall forest land quality was high, but there were some differences among towns. In addition, there were differences between areas with different grades of forest land quality, mainly the first and second grades. The high-grade forest land was mainly distributed in the central and southeastern low mountain areas of Zouma, Wuli, and Yanzi, and the constraints were mainly terrain and traffic location. The low-grade forest land was mainly gathered in the northwestern high mountains of Zhongying, Taiping, and Rongmei, which was mainly affected by terrain, climate, soil fertility, and forest disasters.

Based on the evaluation of forest land quality, using spatial autocorrelation analysis, this study showed that the global spatial autocorrelation index of forest land in Hefeng with village forest land quality as a spatial variable was 0.7562, indicating that forest land quality in the county had a strong similar correlation in spatial distribution. There were five LISA types: high-high (HH), high-low (HL), low-high (LH), low-low (LL), and nonsignificant (NS). The spatial distribution of similarity types HH and LL was more clustered, while the spatial distribution of dissimilarity types HH and LL was generally dispersed. Based on the LISA results of forest land quality, this study proposed a scheme of forest land protection zoning. In response to the spatial aggregation characteristics of forest land quality, forest land protection was divided into three types: key protection zones (HH), active protection zones (HL, LH, and NS (medium)), and general protection zones (LL and NS (low)). The research results showed that the zoning scheme basically coincided with the forest land quality evaluation results. Combining the characteristics of different protection zones of self-correlated types, corresponding management and protection measures were proposed.

This study established the PSO-TOPSIS model to explore new methods of evaluating forest land quality, and the spatial attributes of forest land quality were incorporated into the development of forest land protection zoning schemes, which expanded the means of this type of zoning. The quantity of forest land still needs to be considered in the implementation of forest land zoning protection. Determining how effective management and construction can be implemented in the later stages of forest land protection and how a zoning–scale–management trinity can be actualized in a forest land protection system requires further in-depth research.