Tea Cultivation Suitability Evaluation and Driving Force Analysis Based on AHP and Geodetector Results: A Case Study of Yingde in Guangdong, China

Abstract

Tea is an economically important crop. Evaluating the suitability of tea can better optimize the regional layout of the tea industry and provide a scientific basis for tea planting plans, which is also conducive to the sustainable development of the tea industry in the long run. Driving force analysis can be carried out to better understand the main influencing factors of tea growth. The main purpose of this study was to evaluate the suitability of tea planting in the study area, determine the prioritization of tea industry development in this area, and provide support for the government’s planning and decision making. This study used Sentinel image data to obtain the current land use data of the study area. The results show that the accuracy of tea plantation classification based on Sentinel images reached 86%, and the total accuracy reached 92%. Then, we selected 14 factors, including climate, soil, terrain, and human-related factors, using the analytic hierarchy process and spatial analysis technology to evaluate the suitability of tea cultivation in the study area and obtain a comprehensive potential distribution map of tea cultivation. The results show that the moderately suitable area (36.81%) accounted for the largest proportion of the tea plantation suitability evaluation, followed by the generally suitable area (31.40%), the highly suitable area (16.91%), and the unsuitable area (16.23%). Among these areas, the highly suitable area is in line with the distribution of tea cultivation at the Yingde municipal level. Finally, to better analyze the contribution of each factor to the suitability of tea, the factors were quantitatively evaluated by the Geodetector model. The most important factors affecting the tea cultivation suitability evaluation were temperature (0.492), precipitation (0.367), slope (0.302), and elevation (0.255). Natural factors influence the evaluation of the suitability of tea cultivation, and the influence of human factors is relatively minor. This study provides an important scientific basis for tea yield policy formulation, tea plantation site selection, and adaptation measures.

1. Introduction

Tea (Camellia sinensis L.O. Kuntze) is a perennial evergreen shrub and a high-value-added cash crop, and it is renowned for its nutritional, medicinal, antimicrobial, and anticancer properties worldwide [1,2]. China is the world’s leading tea-producing country, followed by India, Kenya, and Sri Lanka [3]. In the context of the Chinese government’s implementation of targeted poverty alleviation and rural revitalization, the tea sector has attracted the attention of government policymakers [4]. Under benefit policies, in recent years, the tea cultivation area of China has expanded from 108.89 million hm² in 2000 to 298.58 million hm² in 2018. The vigorous development of the tea sector has created numerous employment opportunities, increased the incomes of local farmers, and increased the fiscal revenues of local governments [5]. Although affected by the COVID-19 epidemic and low-temperature damage, China’s tea yield in 2020 was still rising. By 2020, the tea cultivation area in China reached 316.51 million hm², and the tea yield was 293 million tons. Tea exports in China totaled 348,800 tons, and their value was 2.038 billion dollars [6,7]. However, with the rapid development of China’s tea sector, some adverse effects have also been observed. On the one hand, the problem of tea overcapacity is severe with the continuous expansion of the tea planting area, especially under the influence of the COVID-19 epidemic [5]. This phenomenon has led to unstable tea prices and caused Chinese tea to lose its price advantage and competitiveness in the international market [8]. On the other hand, the excessive expansion of tea plantations has also had some disadvantageous effects on local ecological environments [9]. For example, forest areas and high slopes in hilly areas have been converted into tea plantations, which has resulted in soil erosion, landslides, and floods [10]. Such ecological disasters have been particularly prominent in newly built tea plantations. Due to the sparse vegetation and fragile ecosystem of new tea plantations, heavy rainfall can easily collapse tea plantations in areas with higher slopes [11]. In severe cases, entire tea plantations can be destroyed, causing severe damage to local ecological environments. Moreover, drastic changes in the global climate have led to frequent extreme weather disasters, such as drought and frost events, making some tea plantations unsuitable [12]. It is necessary to understand how the potential spatial distribution of tea plantations and the changing driving forces of the climate performance affect potential tea-growing areas, which could support tea production planning and management.

Research on the suitability of tea plantations is an essential means to avoid a blind expansion of tea plantations. It is also a meaningful way to understand the potential suitable distribution of tea plantations. Tea cultivation suitability evaluation is mainly performed by analyzing natural and human factors, such as climate, topography, soil conditions, and vegetation indices, to evaluate the potential and limitations of land use [3]. It serves as a foundation for land resource planning and management, and appropriate land use decision making can improve the efficiency of land use and the sustainable development of the environment [13]. However, in the early stages of tea plantation construction, there was no scientific or reasonable planning of tea plantations based on local conditions [14]. The development of the tea industry excessively pursues short-term benefits while ignoring the sustainable development of tea plantations. With advances in information and communication technology, land suitability evaluation has been performed using geographical information systems (GISs) and satellite remote sensing techniques [15]. In addition, essential criteria for sustainable tea production must be considered in the analytical hierarchy process (AHP) to prioritize experts’ opinions by the weight obtained from consistent GIS results [16]. Satellite remote sensing with a GIS-based AHP is a robust tool for developing spatial decision-making processes for tea cultivation suitability analysis [17]. According to previous studies, tea cultivation suitability evaluation has been applied in many countries. Li et al. used GISs and an improved land ecological suitability evaluation model to evaluate the land suitability of tea crops in southern China [18]. A land suitability assessment of tea production using remote sensing, a GIS, and expert opinion in the northeastern part of Bangladesh was performed by Das [3]. A land use assessment was conducted in Kenya on tea-growing areas, particularly in the Kirinyaga region, using the MaxEnt species distribution model [19]. A land suitability assessment was performed on tea and orange cultivation in Nghe, Vietnam, using land suitability evaluation (LSE) software and several ecological criteria [20]. Zhao et al. demonstrated the current and future distributions of tea species based on four SSP scenarios using the MaxEnt model in China [13]. These studies provide information regarding the constraints of land use for tea, opportunities for decision making, and the optimal utilization of land resources. Tea cultivation suitability evaluation mainly uses GISs combined with the AHP method. Some scholars have constructed an evaluation system suitable for local areas to evaluate suitability based on the local reality. Still, the system is difficult to migrate to other places. Although the evaluation method based on GISs and AHP is more traditional, the technique is universal. Meanwhile, there are few quantitative studies on the effects of different factors on tea cultivation suitability. In particular, more effort is needed to quantify the interactions between various elements. The Geodetector model (GDM) is a new spatial analysis model developed by Wang to detect the relationship between a geographic attribute and its explanatory factors [21]. The method is based on spatial heterogeneity theory and GIS spatial superposition technology and is designed to identify the correlations between factor and outcome variables effectively. The most significant advantage of this method is that it can effectively overcome the limitations of traditional statistical analysis methods in processing categorical variables [22]. The model has been applied to many areas, such as work on animal habitats, geological disasters, and vegetation planting [21,22,23]. The quantitative method helps to better elucidate the main constraints of tea cultivation so that they can be used as prioritized factors in the future selection of tea plantations to realize their sustainable development. Therefore, in critical tea-producing areas, tea cultivation suitability evaluation can learn the sustainable use of limited land resources, more accurately determine sites suitable for tea plantations, and improve tea yield and quality.

In the post-pandemic era, with the gradual recovery of the global economy, increasing consumption, and improvements in health-related concepts, demand for tea will continue to grow in China and internationally. In the long run, tea cultivation areas and yields will always be in a state of growth [7]. To better plan the tea sector development of important tea-producing regions, we studied the city of Yingde, which is located in a crucial tea-growing area in South China, as our research object. Yingde is a critical black-tea-producing hub known as the “Hometown of Chinese Black Tea”. Over 60 years, the tea plantation area in the study area increased from 357 hm² in 1959 to 9400 hm² in 2019, making it the largest county-level tea-producing area in Guangdong, China [24]. Yingde black tea has been exported to more than 70 countries and regions. The tea sector has become the mainstay of the study area, and the development of tea will directly affect the economic situation of the study area. The tea sector in the study area also faces some of the development process’s disadvantages. Therefore, considering the need for tea sector development, it is necessary to evaluate the suitability of tea cultivation. There has been no focus on assessing tea cultivation in the study area or its driving force analysis. Our paper focused on two aspects: (1) tea cultivation suitability evaluation based on remote sensing images and the AHP, and (2) GDM adoption for quantitative analysis of suitability factors. This research can help government decision makers formulate reasonable tea industry development strategies and achieve sustainable development of the tea industry.

2. Materials and Methods

2.1. Study Area

The whole land area of Yingde was selected as the study area (Figure 1). The city is located in the north-central part of Guangdong Province, south of the mountainous area of northern Guangdong and at the intersection of the three rivers, mainly including mountainous and hilly landforms. The climate in the study area is mild with abundant rainfall and a subtropical humid monsoon climate. To better determine the actual distribution of tea plantations in the study area, we conducted field surveys on the distribution of tea plantations in the study area in September 2020 and May 2021. In 2020, we acquired land use samples through the field survey. In 2021, we conducted field surveys of soil sample data from 68 tea plantations. Meanwhile, the Trimble GEO 7X RTK, with centimeter-scale accuracy, was used to collect the geographic location information of tea plantations, including longitude, latitude, and altitude data.

Topographic and soil nutrient data of tea plantations from the field investigation were used for statistical analysis. The results (

Table 1. Statistical table of investigation factors in tea plantations.

Factors	Min	Max	Mean	SD	CV
Elevation (m)	14 m	215 m	75 m	50 m	-
Slope	0	15	4	3	-
pH	4.01	6.72	4.95	0.58	11.78%
Soil organic matter (g/kg)	3.99	40.96	19.16	8.63	45.02%
Available nitrogen (mg/kg)	14.70	275.10	104.34	49.91	47.83%
Available phosphorus (mg/kg)	0.60	175.09	25.23	31.87	126.33%
Available potassium (mg/kg)	33	479	166	106	63.59%

) show that the elevation distribution of the recorded tea plantations ranged from 14 m to 215 m, with an average elevation of 75 m; slopes ranged from 0 to 15, with an average gradient of 4. The results (Table 1) regarding soil nutrients indicate that the average pH of the tea plantations was 4.95; organic matter was measured at 19.16 g/kg, and the average levels of available nitrogen, available phosphorus, and available potassium were 104.34, 25.23, and 166 mg/kg, respectively. The average levels of soil nutrients meet the standards for grade I tea plantations [25]. The results show that the soil nutrient status of tea plantations in the study area was good. In addition, we used the coefficient of variation (CV) to measure the degree of variation in the sample divided into three categories: high variability, CV > 35%; moderate variability, 15 < CV < 35%; and low variability, CV < 15% [26]. Regarding degrees of variation, pH had the lowest degree of variation (11.78%), indicating that the overall variation in the study area was minor. The variation in available phosphorus was the greatest (126.33%), indicating significant variations in the study area. The other factors were also high in variation. This shows that pH values in the study area are relatively stable while other soil factors vary greatly, mainly due to human factors such as fertilization. The variation in soil nutrients is expressed as available phosphorus > available potassium > available nitrogen > soil organic matter > pH value.

2.2. Data and Processing

Climate data were collected from the China Meteorological Data Network (http://data.cma.cn, accessed on 1 July 2021) and included annual average rainfall, average yearly temperature, and average yearly sunshine hours. The data were collected from 78 meteorological stations in the local and surrounding areas for 30 years, from 1980 to 2010. After preprocessing, the average value for each station in the study area was obtained. Raster data of the climate factors were obtained through the interpolation tool of ArcGIS software version 10.2. Topography data, including slope and aspect data, were extracted from the 30 m resolution DEM using ArcGIS software version 10.2. DEM data were downloaded from the geospatial data cloud (http://www.gscloud.cn, accessed on 6 June 2021).

Soil data were obtained through field sampling in May 2021, and 68 soil samples were collected. Five sampling points were randomly selected from each plot, and the soil was collected at depths of 0–20 cm with a soil drill; litter, rocks, and other impurities on the surface of the soil were removed, and all soil samples were mixed evenly in the same plot and placed in a plastic bag. The soil samples were air dried to measure soil nutrient factors, including phosphorus, potassium, nitrogen, organic matter, and pH. These factors were measured by a third-party institution (China National Rice Research Institute, Hangzhou, China) through physical and chemical experiments based on references [27]. Among them, pH and soil organic matter were measured by the potentiometric method and the potassium dichromate oxidation-volume method. Available phosphorus and available potassium contents were determined by sodium bicarbonate and ammonium acetate leaching–flame photometry methods. The alkaline hydrolysis diffusion method was used to determine the available nitrogen content. The inverse distance weighting (IDW) interpolation tool available in the ArcGIS 10.2 software generated a thematic map of the spatial distribution of soil nutrient factors.

Land use and land cover (LULC) data were derived from Sentinel-2A images obtained from the European Space Agency’s Copernicus Scientific Data Hub website (https://scihub.copernicus.eu/, accessed on 15 May 2021). The data were acquired on 29 April 2020, and 14 January 2021, with <3% cloud cover over the study area. The Sentinel-2A L1C image data were not calibrated or atmospherically corrected. First, the Sen2cor plug-in was used to perform atmospheric correction on the image data to obtain Sentinel-2A Level-2A data. Then, remote sensing data were processed by cloud removal, resampling, mosaic application, and cropping using SNAP8.0 processing software, and the image data of the research area with a resolution of 10 m were obtained. Additionally, 625 regions of interest in the study area were randomly selected as training samples. These samples were mainly obtained through field surveys and Google Earth images. Among the samples, 60% were used for training, and 40% were used for verification. Finally, the land use map of the study area for 2020 was obtained through the decision tree classification method. In addition, the normalized difference vegetation index (NDVI) distribution map of the study area was derived from image data through SNAP8.0 software. The tea output data of different towns were obtained from the Statistical Yearbook of Yingde in 2020.

2.3. Suitable Conditions for Tea Growth

The growth of tea trees is affected by many factors, such as the soil, and climatic and topographical conditions [28]. Soil is the fundamental factor governing tea cultivation, providing moisture and nutrients for the growth of tea trees. Soil nutrients are important indicators reflecting soil vitality and are an essential factor limiting soil productivity. Tea trees prefer acidic soil and are extremely sensitive to pH levels. Tea trees usually grow well in soils with lower pH levels ranging from 4.5 to 5.5 [3]. Tea tree growth will stagnate when the pH is too high or low. Soil organic matter is an essential indicator for measuring the fertility of tea plantation soil. The higher the soil organic matter content level, the more tea tree branches and leaves grow. Tea trees are economic crops used for their leaves, and nitrogen, phosphorus, and potassium are vital to tea development and quality. They also affect the quantity and quality of tea buds [29]. In our paper, the reclassification criteria of soil factors are mainly based on established standards, namely, the environmental requirement for the growing area of tea (NY/T 853-2004) [30].

Climatic conditions affect tea tree cultivation, the growth of tea trees, and the quality and yield of tea, especially rainfall and temperature [31]. Usually, tea plants require an average minimum rainfall of 1000 mm per year during the growing season, and 1800–2000 mm is optimal [31]. The average annual temperature suitable for tea growth is 19~23 °C. Tea tree branches grow slower when temperatures are higher than 30 °C or lower than 13 °C, and continuous high temperatures for many days also reduce tea yields [32]. Air humidity and light in a tea plantation also affect the accumulation of nutrients in fresh tea leaves. Due to data limitations and a lack of meteorological data at the tea plantation scale, only meteorological data at the county scale were used for interpolation, resulting in minor differences in climatic factors in the study area. Therefore, we used the natural discontinuity method to reclassify climate factors.

The growth of tea trees is also closely related to topographical conditions, especially in the microclimate of a tea plantation. The difference in elevation affects the microclimate of a tea plantation, which impacts the timing of tea harvesting and the accumulation of tea nutrients [33]. The aspect of the slope will affect sunlight exposure, and sunlight on southern slopes has a more significant effect than that on northern slopes and better supports the growth and development of tea trees; surface runoff in a tea plantation and erosion are also affected by the gradient [34,35]. Tea trees prefer damp conditions but not waterlogging; when a large amount of water accumulates, tea tree roots will quickly rot. Drainage conditions should be considered when tea trees are planted. Therefore, elevation, slope, and aspect need to be considered for tea cultivation to ensure planting in suitable areas. Our paper comprehensively considered the general law of the elevation distribution of tea plantations and the actual distribution of tea plantations in a field survey to determine altitude reclassification, as shown in

Table 2. Reclassification of the indicators for tea cultivation suitability evaluation.

Indicators	Highly Suitable (10)	Moderately Suitable (6)	Generally Suitable (3)	Unsuitable (1)
Precipitation (mm)	>2350	2200~2350	2100~2200	<2100
Temperature (°C)	>22	21.5~22	21~21.5	<21
Sunshine (h)	>1640	1620~1640	1580~1620	<1580
Elevation (m)	30~400	400~600	600~100	<30 $\cup$ >1000
Slope	5~25	<5	25~35	>35
Aspect	[South, southeast, southwest]	[East, west, northeast, northwest]	[North]	-
pH	4.5~5.5	5.5~6	4.0~4.5	6~6.8
Soil organic matter (g/kg)	>27	20~27	15~20	5~15
Nitrogen (mg/kg)	>150	110~150	60~110	<60
Phosphorus (mg/kg)	>30	20~30	10~20	<10
Potassium (mg/kg)	>200	120~200	80~120	<80
LULC	Tea plantation	Forest	Farmland	Building land/water body
NDVI	>0.6	0.4~0.6	0~0.4	<0
Tea yield (T)	>1000	600~1000	300~600	<300

. Slope and aspect reclassification are mainly based on references [29]. In addition, considering the current state of land use and human-related factors in the study area, it is instructive to evaluate the suitability of tea plantations. We used the LULC, NDVI, and tea yield measures as artificial factors. The LULC and NDVI reclassification criteria are mainly based on references [3]. Tea yields were primarily classified based on the actual distribution of tea yields in the study area using the natural discontinuity method.

According to the factors mentioned above affecting the growth of tea trees and data availability, the criteria selected for tea cultivation suitability evaluation in the study area were divided into four groups: climate, topography, soil, and human-related parameters. The first group includes climate parameters (annual average precipitation, annual average temperature, and annual average sunshine hours). The second group includes topographical parameters (elevation, slope, and aspect). The third group includes soil parameters (soil pH, soil organic matter, available nitrogen, available potassium, and available phosphorus). The fourth group includes vegetation and human-related parameters (LULC, NDVI, and tea yield). Based on the actual state of tea cultivation in the study area, the opinions of local experts, and the related literature and standards, the indicators affecting the growth of tea trees were divided into four classes: highly suitable, moderately suitable, generally suitable, and unsuitable; the reclassification standards are shown in Table 2.

2.4. Method

2.4.1. Decision Tree

Decision trees are a standard classification method in remote sensing image classification due to their simplicity, interpretability, and lower computational cost, and the possibility of a graphical display [36]. They consist of a series of segmentation conditions with a hierarchical structure. The basic idea is to gradually separate each object from the original image from the branch’s root. The key to a decision tree is to select the segmentation variable of each node and its segmentation threshold [37]. The vegetation index can reflect different plant spectral information and growth conditions through linear or nonlinear band combinations [38]. The NDVI is the best indicator of the plant growth status and vegetation spatial distribution density and is linearly related to the vegetation distribution density. The normalized difference water index (NDWI) is an effective indicator for distinguishing vegetation from non-vegetation [39]. We combined a spectral index (B8A), vegetation indices (NDVI, NDWI), and texture features (DEM) [40] to establish a decision tree classification model suitable for our paper, and its flowchart is shown in Figure 2.

2.4.2. Analytic Hierarchy Process

The analytic hierarchy process (AHP) is a decision-making method proposed by T.L. Saaty in the 1970s that is often used to evaluate suitability models to simplify complex problems, and it has strong applicability [41]. The method involves the following steps: (1) select factors and construct a hierarchy structure model; (2) according to the 1–9 scale criterion method, compare and score the relative importance of the two criteria and construct a judgment matrix; (3) calculate the weight of each element layer; (4) to test the consistency of the judgment matrix, use the consistency ratio as the index of the degree of judgment deviation. If the judgment matrix m is ≥3 orders, if CR < 0.1, it has an acceptable consistency; if CR ≥ 0.1, the judgment matrix is inconsistent and must be adjusted until it is consistent. The index’s weight was determined by the analytic hierarchy process in this study, and we used yaAHP software to measure the consistency index and weight value (

Table 3. The evaluation index of tea cultivation suitability evaluation and its weight value.

Target Layer	Feature Layer	Weight	Index Layer	Weight
Evaluation of suitability for tea cultivation in Yingde (A)	Climate factor B1	0.3679	Precipitation C11	0.1577
			Temperature C12	0.1577
			Sunshine C13	0.0526
	Terrain factor B2	0.3679	Elevation C21	0.1425
			Slope C22	0.1631
			Aspect C23	0.0622
	Soil factor B3	0.1686	pH C31	0.0717
			Soil organic matter C32	0.036
			Nitrogen C33	0.0203
			Potassium C34	0.0203
			Phosphorus C35	0.0203
	Other factor B4	0.0956	LULC C41	0.041
			NDVI C42	0.0137
			Tea production C43	0.041

). The result shows that CR = 0.0579 < 0.1, which indicates that the consistency ratio is acceptable. According to the classification criteria of all factors, all raster data were resampled. The spatial resolution of all factors was unified to a scale of 30 m × 30 m, and all data used in the study were projected into the CGS_WGS1984 projection system. Finally, we used the spatial analysis tool in ArcGIS 10.2 to predict the distribution of tea cultivation suitability according to the weight value of each factor obtained. The equation is as follows:

$$S={{\displaystyle \sum }}_{i=0}^n{w}_i\times u_i$$

(1)

where $S$ is the comprehensive score of the tea plantation suitability evaluation; $w_i$ is the weight of the $i$ factor; $u_i$ is the score of the $i$ factor; and $n$ is the number of evaluation factors.

2.4.3. Geodetector Model

The Geodetector model (GDM) is a statistical tool developed by Wang to detect spatial heterogeneity using four detection methods: factor, interaction, risk, and ecological detection [21]. In our study, we used factor, interaction, and ecological detection to analyze the spatial heterogeneity of factors affecting the suitability of tea cultivation.

The extent to which the influencing factors explained the suitability of tea cultivation was measured by the q-statistic of the factor detector. The equation is as follows:

$$\mathrm{q}=1-\frac{{{\displaystyle \sum }}_{k=1}^M{N}_k{\sigma }_k^2}{N{\sigma }^2}$$

(2)

where $k$ is the number of classifications of the influencing factor; $N_k$ represents the number of samples in subregion $k$; $N$ represents the total number of spatial units across the study area; and $\sigma$ and ${\sigma }_h$ represent the total variance and variance in the samples in subregion $k$, respectively. The q-statistic ranges from 0 to 1, and the greater the q-statistic, the stronger the explanatory power of the factor influencing the vegetation variation.

The interaction detector was used to analyze whether the interaction of any two influencing factors would increase or decrease the explanatory power of the vegetation variation. The evaluation criteria are shown in

Table 4. The basis for judging two-factor interaction patterns.

Basis of Judgment	Interaction
q(X1 ∩ X2) < Min(q(X1), q(X2))	Nonlinear weakening
Min(q(X1), q(X2)) < q(X1 ∩ X2) < Max(q(X1), q(X2))	Single nonlinear enhancement
q(X1 ∩ X2) > Max(q(X1), q(X2))	Double enhancement
q(X1 ∩ X2) = q(X1) + q(X2)	Independence
q(X1 ∩ X2) > q(X1) + q(X2)	Nonlinear enhancement

.

The presence of a significant difference between two factors in terms of their influence on vegetation change was examined by the F-statistic of the ecological detector. The equation is as follows:

$$\mathrm{F}=\frac{N_{{\mathrm{X}}_1}\left(N_{{\mathrm{X}}_2}-1\right){\mathrm{SSW}}_{{\mathrm{X}}_1}}{N_{{\mathrm{X}}_2}\left(N_{{\mathrm{X}}_1}-1\right){\mathrm{SSW}}_{{\mathrm{X}}_2}}$$

(3)

$${\mathrm{SSW}}_{{\mathrm{X}}_1} ={{\displaystyle \sum }}_{h=1}^{L1}{N}_h{\sigma }_h^2$$

(4)

$${\mathrm{SSW}}_{{\mathrm{X}}_2} ={{\displaystyle \sum }}_{h=1}^{L2}{N}_h{\sigma }_h^2$$

(5)

where $N_{{\mathrm{X}}_1}$ and $N_{{\mathrm{X}}_2}$ represent the sample size of the ${\mathrm{X}}_1$ and ${\mathrm{X}}_2$ factors, respectively; ${\mathrm{SSW}}_{{\mathrm{X}}_1}$ and ${\mathrm{SSW}}_{{\mathrm{X}}_2}$ represent the sum of variance in each class formed by the ${\mathrm{X}}_1$ and ${\mathrm{X}}_2$ factors; and L1 and L2 represent the number of classes for variables ${\mathrm{X}}_1$ and ${\mathrm{X}}_2$, respectively.

3. Results

3.1. Evaluation of Tea Plantation Classification Accuracy

We selected four indices to evaluate the classification accuracy of tea plantations, including overall accuracy (OA), the Kappa coefficient, producer accuracy (PA), and user accuracy (UA).

Table 5. Accuracy evaluation of land use classification in the study area.

Type	Percent	Area (km2)	Prod. Acc.	User Acc.	OA	K
Tea plantation	1.35%	76.04	71.54%	87.78%	92.22%	0.90
Forestland	73.4%	4135.52	95.32%	90.21%
Farmland	20.24%	1140.14	96.84%	89.56%
Waterbody	2.08%	116.97	97.13%	99.35%
Building land	2.93%	165.33	88.16%	92.87%

shows the accuracy evaluation results. According to the results, the study area’s overall land use classification accuracy was 92.22%, and the Kappa coefficient was 0.90. Concerning producer precision, the producer precision of forestland, farmland, and water systems was over 95%, that of construction land was 88.16%, and that of tea plantations was slightly lower at 71.54%. The user accuracy of the water system was close to 100%, that of forestland and construction land was more than 90%, that of farmland and tea plantations was 89.56% and 87.78%, respectively, and the overall level of user accuracy was high. The classification results of land use in the study area are shown in Figure 3, where the tea plantation area accounts for 1.35% of the area, spanning 76.04 km².

3.2. Individual Factor Analysis for Tea Cultivation Suitability Evaluation

The tea cultivation suitability zoning based on the individual factors of climate and topography in the study area is shown in Figure 4. Combined with those of

Table 6. Single-factor suitability class proportion statistics for tea cultivation (%).

Classes	${\mathbf{C}}_{11}$	${\mathbf{C}}_{12}$	${\mathbf{C}}_{13}$	${\mathbf{C}}_{21}$	${\mathbf{C}}_{22}$	${\mathbf{C}}_{23}$	${\mathbf{C}}_{31}$	${\mathbf{C}}_{32}$	${\mathbf{C}}_{33}$	${\mathbf{C}}_{34}$	${\mathbf{C}}_{35}$	${\mathbf{C}}_{41}$	${\mathbf{C}}_{42}$	${\mathbf{C}}_{43}$
Unsuitable	34.28	1.19	3.84	5.07	2.52		0.46	21.67	0.22	0.49	3.41	5.01	1.61	50.00
Generally suitable	57.33	42.05	48.17	5.16	8.95	12.45	0.69	33.30	59.43	11.22	48.57%	20.24	11.43	29.58
Moderately suitable	8.04	33.90	31.58	10.23	28.84	48.20	2.99	35.98	36.30	65.29	28.78%	73.40	12.12	13.28
Highly suitable	0.36	22.87	16.41	79.53	59.69	38.08	95.86	9.05	4.05	22.99	19.23%	1.35	74.85	7.14

, the results show that the average annual rainfall factor is mainly based on the suitable area, accounting for 57.33% of the total area, mainly in the central area; the proportion of the highly suitable area is 0.36%. Among the average annual temperature suitability zones, the generally suitable zone accounts for 42.05% and is mainly distributed in the eastern part of the study area; the highly suitable zone is distributed primarily in the urban area and the southwestern section of the study area, accounting for 22.87%. Average annual sunshine hours are mainly centered in the urban part of the study area, and suitability decreases from the center to the periphery. Among these, highly suitable areas account for 16.41%, moderately suitable areas account for 31.58%, and generally suitable areas account for 48.17%. In terms of altitude, 79.53% of the study area is located in a highly suitable area, and the whole study area is at a lower altitude suitable for the growth of tea trees. Among the suitable areas in terms of slope factors, highly suitable areas account for 59.69%, and generally suitable areas with a higher slope aspect proportion account for 48.82%.

The cultivation suitability zoning of soil and human factors in the study area is shown in Figure 5. Combined with those presented in Table 6, the results show that pH levels are ideal in the highly suitable area, accounting for 95.86%, and the whole study area is in the highly suitable area. In the organic matter suitability zone, the moderately suitable area accounts for 35.98%, and the main distribution is located in the western part of the study area. The generally suitable area accounts for 59.43% of available nitrogen suitability and is mainly found in the central and southern regions. Potassium is primarily located in the moderately suitable area, accounting for 65.29%, and is located in most areas of the study area. Available phosphorus is mainly found in the generally suitable area, accounting for 48.57%. Regarding the LULC factor, 73.4% of the study area is located in a moderately suitable area. In the NDVI factor suitable area, the highly suitable area accounts for 74.85%, and vegetation coverage is high, suitable for plant growth. A higher proportion of tea production is found in the generally suitable area, reaching 29.58%.

3.3. Overall Land Suitability for Tea in the Study Area

A comprehensive evaluation of the tea cultivation suitability of the study area is shown in Figure 6, and

Table 7. Potential land area in each suitability class.

Suitability Level	Pixel Counts	Area (%)	Area (km2)
Highly suitable	1,044,829	16.91%	952.69
Moderately suitable	2,274,514	36.81%	2073.94
Generally suitable	1,939,884	31.40%	1768.82
Unsuitable	1,002,528	16.23%	914.12

shows the tea cultivation area of each suitability class. Our research area comprises highly suitable, moderately suitable, generally suitable, and unsuitable regions. The distribution of the tea plantation suitability of the study area is mainly found in the moderately suitable area, distributed primarily in Lixi, Dadong, and Lianjiangkou in the south of the study area, covering an area of approximately 2073.94km² and accounting for 36.81% of the total area. The generally suitable area is the next most prominent area, covering an area of 1768.82 km² and accounting for 31.4% of the entire area. Highly suitable regions are mainly distributed in the towns of Yinghong, Shihuipu, Huanghua, and Jiulong in the western part, covering approximately 952.69 km² and accounting for 16.91% of the total area. Unsuitable areas mainly include built-up urban and township areas, river and lake systems, and high-altitude areas, covering an area of approximately 914.12 km² and accounting for 16.23% of the total area.

The statistics on potentially suitable areas for tea cultivation in different towns in the study area are shown in Figure 7. The highly suitable cultivation areas are mainly distributed in the towns of Shihuipu (75.47%), Huanghua (60.17%), Jiulong (66.01%), Hengshitang (51.34%), and Yinghong (47.19%). Towns with relatively high proportions of moderately suitable areas include Shuibian (74.54%), Dadong (73.98%), Lixi (67%), and Lianjiangkou (63.69%). The largest proportions of generally suitable areas are found in the towns of Qiaotou (57.53%), followed by Hengshishui (57.34%), Qingtang (54.68%), and Basha (47.99%). In addition, unsuitable cultivation areas are mainly distributed in built-up areas and high-elevation areas in Yingde.

3.4. Driving Force Analysis of Tea Cultivation Suitability

The results of the tea cultivation suitability assessment of the study area were taken as dependent variables, 14 factors were taken as independent variables, and 1000 m was selected as the basic grid scale through a repeated verification of the data to explore the impacts of all factors on tea cultivation suitability. The factor detector was used to calculate the q-statistics of each factor to determine the influence of each factor on the suitability evaluation of tea cultivation. The larger the q value, the stronger the impact of a factor on tea cultivation suitability. The results are shown in

Table 8. The q-statistics on factors influencing land suitability.

	${\mathbf{C}}_{11}$	${\mathbf{C}}_{12}$	${\mathbf{C}}_{13}$	${\mathbf{C}}_{21}$	${\mathbf{C}}_{22}$	${\mathbf{C}}_{23}$	${\mathbf{C}}_{31}$	${\mathbf{C}}_{32}$	${\mathbf{C}}_{33}$	${\mathbf{C}}_{34}$	${\mathbf{C}}_{35}$	${\mathbf{C}}_{41}$	${\mathbf{C}}_{42}$	${\mathbf{C}}_{43}$
q-statistic	0.367	0.492	0.039	0.255	0.302	0.016	0.002	0.065	0.004	0.063	0.051	0.024	0.015	0.036
p-value	0.000	0.000	0.000	0.000	0.000	0.000	1	0.000	0.987	0.000	0.000	0.000	0.009	0.000

. pH and available nitrogen p-values greater than 0.05 indicate no significance. The other factors significantly affect tea cultivation suitability (p < 0.05). Among the factors, temperature exhibits the most explanatory power (0.492), followed by precipitation (0.367), slope (0.302), and altitude (0.255), which contributed more than 0.255 to tea cultivation suitability. The q-statistics of the other factors are less than 0.1, having a relatively weak influence on tea cultivation suitability. Therefore, research and the construction of tea cultivation suitability of this study area are mainly affected by climate and terrain-related factors. In contrast, soil and vegetation factors have less effect.

The ecological detector determines whether there is a significant difference in the effects of two factors on tea cultivation suitability. The results (

Table 9. Statistical significance of the influences of pairwise factors on tea cultivation suitability assessment (95% confidence level).

	${\mathbf{C}}_{11}$	${\mathbf{C}}_{12}$	${\mathbf{C}}_{13}$	${\mathbf{C}}_{21}$	${\mathbf{C}}_{22}$	${\mathbf{C}}_{23}$	${\mathbf{C}}_{31}$	${\mathbf{C}}_{32}$	${\mathbf{C}}_{33}$	${\mathbf{C}}_{34}$	${\mathbf{C}}_{35}$	${\mathbf{C}}_{41}$	${\mathbf{C}}_{42}$	${\mathbf{C}}_{43}$
${\mathrm{C}}_{11}$
${\mathrm{C}}_{12}$	Y
${\mathrm{C}}_{13}$	Y	Y
${\mathrm{C}}_{21}$	Y	Y	Y
${\mathrm{C}}_{22}$	Y	Y	Y	Y
${\mathrm{C}}_{23}$	Y	Y	N	Y	Y
${\mathrm{C}}_{31}$	Y	Y	Y	Y	Y	N
${\mathrm{C}}_{32}$	Y	Y	N	Y	Y	Y	Y
${\mathrm{C}}_{33}$	Y	Y	Y	Y	Y	N	N	Y
${\mathrm{C}}_{34}$	Y	Y	N	Y	Y	Y	Y	N	Y
${\mathrm{C}}_{35}$	Y	Y	N	Y	Y	Y	Y	N	Y	N
${\mathrm{C}}_{41}$	Y	Y	N	Y	Y	N	N	Y	N	Y	N
${\mathrm{C}}_{42}$	Y	Y	N	Y	Y	N	N	Y	N	Y	Y	N
${\mathrm{C}}_{43}$	Y	Y	N	Y	Y	N	Y	N	N	N	N	N	N

Y means significant difference, and N means no significant difference.

) show that elements with higher q-statistics show substantial differences in their effects on tea cultivation suitability. Significant differences were found between the effects of precipitation and those of other factors on tea cultivation suitability. Temperature, altitude, and slope have similar characteristics to rainfall. However, we found no significant differences in the effects of sunshine, aspect, available nitrogen, and LULC on tea cultivation suitability. From these results (Table 9) and those of the factor detector, we conclude that precipitation, temperature, altitude, and slope have relatively strong impacts on tea cultivation suitability, while other factors have relatively small effects.

We used the interaction factor detector to calculate the effects of the interaction of any two factors on tea cultivation suitability. The results show that the interaction of the q-statistic of any pair of factors is greater than that of a single factor, indicating that the explanatory power is enhanced when pairs of factors interact. As shown in

Table 10. Interaction of each paired factor on tea cultivation suitability.

	$C_{11}$	$C_{12}$	$C_{13}$	$C_{21}$	$C_{22}$	$C_{23}$	$C_{31}$	$C_{32}$	$C_{33}$	$C_{34}$	$C_{35}$	$C_{41}$	$C_{42}$	$C_{43}$
${\mathrm{C}}_{11}$	0.367
${\mathrm{C}}_{12}$	0.520	0.492
${\mathrm{C}}_{13}$	0.385	0.506	0.039
${\mathrm{C}}_{21}$	0.563	0.681	0.304	0.255
${\mathrm{C}}_{22}$	0.615	0.732	0.351	0.510	0.302
${\mathrm{C}}_{23}$	0.387	0.518	0.057	0.273	0.322	0.016
${\mathrm{C}}_{31}$	0.370	0.498	0.045	0.260	0.306	0.019	0.002
${\mathrm{C}}_{32}$	0.429	0.512	0.164	0.322	0.355	0.081	0.070	0.065
${\mathrm{C}}_{33}$	0.404	0.500	0.063	0.260	0.309	0.020	0.008	0.073	0.004
${\mathrm{C}}_{34}$	0.402	0.513	0.125	0.303	0.362	0.082	0.072	0.143	0.080	0.063
${\mathrm{C}}_{35}$	0.440	0.504	0.141	0.305	0.344	0.068	0.058	0.099	0.074	0.096	0.051
${\mathrm{C}}_{41}$	0.397	0.526	0.069	0.287	0.315	0.042	0.028	0.087	0.032	0.099	0.075	0.024
${\mathrm{C}}_{42}$	0.389	0.517	0.059	0.285	0.310	0.033	0.019	0.080	0.023	0.085	0.065	0.026	0.015
${\mathrm{C}}_{43}$	0.378	0.502	0.096	0.290	0.345	0.054	0.046	0.143	0.075	0.093	0.117	0.073	0.061	0.036

Green means double enhancement; blue means nonlinear enhancement.

, 33 groups showed double enhancement, and 58 groups showed nonlinear enhancement. The interaction between temperature and other factors is more remarkable than 0.492, and the interaction between temperature and slope is the greatest, at 0.732. In addition, climate, slope, and elevation have strong interactions with other factors. This further indicates that temperature, climate, slope, and altitude are the leading factors affecting tea cultivation suitability. The interactions between soil, humans, and other factors were weak. In general, the interactions between all factors were more potent than the influence of every single factor on tea cultivation suitability.

4. Discussion

The evaluation of tea cultivation suitability is of great significance to the future development of the tea industry. In this paper, we used geospatial and remote sensing data and the AHP to determine the suitability of tea cultivation in the study area. Further, we used the GDM to analyze spatial driving forces behind the factors affecting the suitability of tea trees in the study area.

4.1. Analyses of Tea Plantation Classification

Land use data serve as the basis for evaluating the suitability of tea cultivation. The classification accuracy of land use data affects the results of final evaluations of the suitability of tea trees. Multispectral high-resolution Sentinel data have been used to obtain land use data for the study area through remote sensing interpretation, especially in large-scale studies. The most accurate tea plantation recognition results obtained from high-resolution imagery reported in the literature reached 95.51% (e.g., UAV image data). In comparison, the most accurate results obtained from medium-resolution imagery reached 88.2% (e.g., Sentinel image data) [42,43]. The extraction user accuracy of tea plantations in our study reached 87.78%. However, the producer accuracy of tea plantations in our study was 71.54%. The main reason for the lower accuracy is that tea plantations are divided into other land types, especially at the junction of tea plantations. This may be due to newly planted tea plantations in the study area. The smaller tea trees have a lower vegetation coverage, resulting in new tea plantations being wrongly classified as other land types, or being hard to distinguish [24]. In addition, due to the intercropping of tea plantations with other agroforestry practices in the study area, mixed tea plantations were mistakenly classified as agroforestry areas, resulting in low precision [27]. According to the Yingde Statistics Bureau, the tea plantation area in the study area covered 94 km² in 2019. Compared to the official statistical area, the extraction area of tea plantations identified in our paper was smaller. We only used two-phase image data to classify tea plantations, and the selected features were not obvious enough. In addition, our threshold setting range was relatively small in the decision tree method, such as for elevation. We selected a threshold range of 14–280 m according to the actual altitude distribution of tea plantations. Additionally, we ignored some tea plantations in high-altitude areas, resulting in a small actual classification area of tea plantations. Although the accuracy of our results is low, we attempted to set the threshold from the actual distribution of tea gardens in the study area. Our results are relatively reliable and provide reliable basic data for evaluating tea plantation cultivation suitability. We also introduced an NDVI reflecting the growth of crops and the actual tea yield in each town studied to better evaluate the suitability of tea plantations.

4.2. Analyses of Tea Plantation Driving Forces

The GDM model more intuitively and quantitatively measures the contributions of all indicators to the suitability of tea plantations. Climate is a crucial factor affecting the distribution and variation in the suitability of many crops [44]. In our study, three climatic factors, namely, precipitation, temperature, and sunshine, which are closely related to the growth of tea trees, were selected as evaluation factors for the suitability of tea cultivation. Table 8 shows that the explanatory factor strength of temperature on tea cultivation suitability was the greatest at 0.492, followed by precipitation at 0.367. Our result is consistent with recent studies confirming that temperature and precipitation have a more significant impact on the growth of tea trees. Zhao used the MaxEnt model to study the distribution of the suitability prediction of Chinese tea trees under different levels of climate change [13]. Additionally, the results show that annual temperature change was the most influential variable with a value of 51%; precipitation was the second most important variable with a value of 37%. Yi also verified that the temperature dramatically affects the suitability of tea cultivation [45]. Previous studies have also proven that the temperature can affect photosynthesis, transpiration, and reproductive growth in tea plantations and has an essential impact on the tea yield and quality [46,47]. Precipitation also dramatically affects the development of tea trees, with a value of 0.367. High precipitation can provide sufficient water for the growth of tea trees and ensure the growth of fresh tea leaves [48]. Especially in dry periods, the effect of precipitation factors on the suitability of tea leaves can reach 63.9%. Topographic factors are the second most important factors affecting the tea tree suitability distribution [49]. Among topographic factors, Table 8 shows that the explanatory factor strength of slope for tea cultivation suitability was the greatest at 0.302, followed by that of elevation at 0.255. From the results, the slope is another critical factor affecting the suitability of tea cultivation. This result is consistent with Khormali’s findings, which showed that the slope can affect the drainage systems of tea plantations and that poor drainage conditions directly lead to a decrease in tea yield [35].

Table 10 shows that the contribution of the interaction between two factors is more significant than that of a single factor, indicating that the interaction of factors results in mutual and nonlinear enhancement with no independence or weakening relationship. This proves that the evaluation of tea planting suitability is influenced by interacting factors. For example, elevation indirectly affects tea cultivation and the tea tree spatial distribution through temperature, precipitation, and soil nutrients [50]. Rainfall and temperature vary with altitude, where at higher altitudes, temperature changes affect plants more than at low altitudes, especially for tea trees [51]. At the same time, the influence of the aspect on the suitability of tea plants is much weaker than that of the altitude and slope. Still, there is a close relationship between aspects and soil properties, which also has a specific impact on the suitability of tea plantations [35]. In our paper, the q-statistics on soil and vegetation factors were small, and their influence on the suitability of tea trees was also minor. Still, they also have an enhancing effect when interacting with other factors, indicating that the interaction of multiple factors drives tea cultivation suitability.

4.3. Analyses of Tea Plantation Suitability Evaluation

The AHP suitability evaluation model is an essential means to simulate suitability evaluations of tea. The GDM is used to analyze the influence of each factor on the suitability of tea tree growth. Figure 6 shows that highly suitable areas for tea plantations in Yingde are mainly distributed in the northern and the western regions, which roughly mimics the actual distribution of tea plantations in Yingde. It is worth noting that although the evaluation results of the AHP model are highly accurate, this does not necessarily mean that the actual distribution of suitable areas is entirely consistent [52]. In addition, the growth of tea is affected not only by the climate, topography, and soil but also by socioeconomic development, human intervention, policies, and other human activities. However, due to data limitations, human-related factors and the economic value of the tea itself are not considered, and not all suitable areas are of a better quality or confer a greater economic benefit. In our paper, we only used two-phase remote sensing image data to extract the classification of tea plantations, resulting in a low classification accuracy of tea plantations. Our article mainly used GIS and AHP methods to evaluate tea cultivation suitability. The results are subject to a certain degree of subjectivity, leading to a specific difference between the evaluation results and the actual distribution of tea plantations. Although there are many uncertainties in the model, the model is still a vital evaluation tool for guiding tea cultivation.

5. Conclusions

In this study, we evaluated the cultivation suitability of tea plantations based on multiple variables, such as climate, soil, topography, and vegetation indices, using GIS, RS, and AHP methods. We also used the GDM to conduct a qualitative analysis of the driving forces behind tea tree suitability. The latest LULC data were obtained from Sentinel-2A image data; the data show that the tea plantation area in the study area covers 76.04 km², and the user precision level was 87.78%. Fourteen factors were selected to construct an index system of tea suitability evaluation in the study area. The slope and temperature weight values were higher at 0.1631 and 0.1577, respectively. The weighted overlay using the AHP demonstrated that land covering an area of 952.69 km² (16.91%) was highly suitable. The majority of the area (2073.94 km²) was moderately suitable land, which accounted for 36.81%. Further, 1768.82 km² (31.40%) of generally suitable land and 914.12 km² of the land, estimated at 16.23%, were not suitable for tea cultivation. GDM analysis showed that temperature offered the most explanatory power (0.492), followed by precipitation (0.367), slope (0.302), and altitude (0.255), contributing more than 0.255 to tea cultivation suitability, indicating that the suitability evaluation of tea plantations was mainly affected by natural factors. The present tea suitability evaluation of the studied area is of great significance to the sustainable development of the local tea industry. Therefore, land suitability assessment can profoundly impact the understanding of the future land use and production trends of tea in the study area. In the future, we will conduct a classification study of tea plantations based on multi-phase image data to obtain a higher-precision spatial distribution map of tea plantations. Meanwhile, we will carry out more objective evaluation research for tea cultivation suitability evaluation, and the evaluation results will be more instructive. We will also carry out smaller-scale research about tea cultivation suitability evaluation in the future, which can better guide tea farmers in grading tea and obtaining higher economic benefits.