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Article

Impact of Land Use Change on the Spatial and Temporal Evolution of Ecosystem Service Values in South China Karst Areas

by
Yongkuan Chi
* and
Cheng He
State Engineering Technology Institute for Karst Desertification Control, School of Karst Science, Guizhou Normal University, Guiyang 550000, China
*
Author to whom correspondence should be addressed.
Forests 2023, 14(5), 893; https://doi.org/10.3390/f14050893
Submission received: 12 March 2023 / Revised: 20 April 2023 / Accepted: 24 April 2023 / Published: 26 April 2023
(This article belongs to the Section Forest Ecology and Management)
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Abstract

:
Resource depletion, land-use change, and population growth triggered by the industrial revolution represent serious issues shared globally that have altered the structure, processes, and functions of ecosystems and had significant impacts on human well-being and survival security. This paper assesses changes in ecosystem service values (ESVs) in karst areas based on the perspective of land-use change. Guizhou province, which is typical of the South China Karst (SCK) ecologically fragile areas, was taken as a research subject. The past, current, and future spatial-temporal evolution of karst ESVs were assessed, using equivalence factors and CA-Markov modelling methods. The results show that: (1) from 1980 to 2040, arable land, woodland, and grassland occupy the main land types in the study area; at the same time, the water and built-up areas show a steady growth trend, with dramatic land use shifts occurring in the western, middle, and northern parts. (2) The overall ESVs increased by US$ 31.12 million during the study period, showing a temporal change trend of first decreasing and then increasing; forest land, grassland, and arable land area shift are the main factors of ESVs change; soil conservation, biodiversity conservation, and gas regulation functions are enhanced and play a vital role in the single ESVs increase; ESVs show a basin-type spatial distribution pattern. (3) The sensitivity index is <1, the ESV is inelastic to the VC factor adjustment, and the results are true and valid. This paper found that using quantitative methods to predict land use change of the South China Karst can provide accurate assessment of the differences in supply and demand for natural and social ecosystem services (ES) in a region, clarifying the trade-offs and synergies of ecosystem service functions, contributing to the achievement of sustainable development, and providing a practical reference for global land landscape optimization and land resource planners.

1. Introduction

Global warming, explosive population growth, and energy depletion have intensified, threatening the virtuous cycle of natural ecosystems and human survival [1,2]. In October 2021, the United Nations Framework Convention on Climate Change (UNFCCC) clearly stated that countries had an inescapable responsibility for global ecological protection of the environment and that it was urgent to reconstruct the resilience and productivity of natural and social ecosystems [3]. Natural ecosystems provided food, water, and raw materials for industrial production, recreation, and culture, as well as the spiritual world and the physical basis for human. Meanwhile, ecological processes of material cycling, energy flow, and information transfer play an important role in purifying air, fresh water, and waste disposal [4,5]. Holdren and Ehrlich first defined the concept of ecosystem services, clearly indicating the importance of ecosystems to human survival [6]. However, the high-speed economic development that followed the industrial revolution at all costs has changed ecosystem structure, function, and processes. Costanza et al. [7] proposed a global classification and value assessment of ES based on biome types. The United Nations (UN) Millennium Ecosystem Assessment stated that 60% of current global ecosystem services suffer from varying degrees of degradation, mainly in the form of continued declines in water resources, erosion regulation, climate regulation, and biodiversity [8]. Since then, assessments on ESVs have been widely used, globally, nationally (China, Pakistan), in watersheds (the Transboundary Karnali River Basin), and regionally (Maryland, USA) [9,10,11,12], revealing that land-use change is the most common driver of ES decline at the global scale [13].
Land-use change is an important manifestation of human socio-economic activities and natural environmental change, significantly altering global geological great-cycles and biological micro-cycles and directly affecting regional ecosystem service capacity, offering the opportunity for useful for qualitative and quantitative analysis of the consequences of human–environment interactions [14]. The initial approach to ESVs in China was based on global value estimation [15], which was combined with the actual natural and social development of the region, and proposed an equivalence factor approach with reference to the value of cash crops in agricultural ecosystems [16]. The method was fast, convenient, and low-cost, and started a boom in the valuation of ecosystem services in China. However, this method does not reflect the temporal trends and spatial distribution patterns of ESVs. With technological and economic innovations, the advantages of remote sensing data coverage, wide time period, and high frequency of monitoring are widely used in spatial and temporal land use evolution analysis [17], e.g., to study individual functions, such as the Earth’s carbon cycle, biodiversity and soil erosion, or the spatial-temporal evolution in the total value of current and future ES in the region [18,19,20,21,22,23]. The analysis of land use or land cover (LUCC) change patterns in response to the transformation mechanisms of ESVs is beneficial to the sustainable development of regional socio-economies. In particular, the assessment of ESVs in ecologically fragile areas is urgent and is the basis for the rational planning of territorial resources, reversing the global degradation of ecosystems and improving biodiversity decision-making.
The South China Karst centered on Guizhou is the most typical and concentrated karst development and the most comprehensive geomorphological type of ecologically fragile area in the world, and its desertification phenomenon has become a serious global ecological disaster [24,25,26]. The severe soil erosion, frequent disasters, high population density, loss of biodiversity, and declining ecosystem functions in karst areas [27], combined with the persistence of human poverty and limited arable land resources, have led to the continuation of traditional crude agricultural culture, which exacerbates the expansion of rock desertification and seriously hinders the economic sustainable development of the region. In order to solve the above questions, this study aims to explore the spatial-temporal evolution of different land type transformations, ES, and their driving factors in karst areas. In this occasion, we assumption that: (i) as human activities increase and land types shift towards arable land and built-up areas, there is a declining trend in the value of ecosystem services; (ii) Karst desertification control projects and the return of farmland to forest and grassland projects are important factors influencing the spatial-temporal evolution of ESVs. Thus, in order to test our hypothesis, this paper uses equivalence factors and simulation prediction methods to analyze the response of land-use shifts to ESVs from past, present, and future stages of the South China Karst. This paper provides important guidance for the scientific and comprehensive rocky desertification control, ecological environmental protection, and restoration in the study area, in addition to data support for the formulation of ecological protection red lines, functional zones, and ecological compensation schemes.

2. Materials and Methods

2.1. Overview of the Study Area

Guizhou province (103°36′–109°35′ E, 24°03′–27°46′ N) is located in the eastern part of the Yungui Plateau (Figure 1). It is a karst mountainous region between the Sichuan basin and the hills of Guangxi and is the center of a typical tropical-subtropical karst landform region [28]. The terrain is higher in the west than in the east, with a geomorphology that is highly undulating and an altitude difference of 2799 m, and it is part of a humid subtropical monsoon climate zone, with an average annual air temperature of 16.3 °C, precipitation of about 1100–1300 mm, and sunshine count of 1189 h [29]. The zonal soils are predominantly loamy and the non-zonal soils are predominantly calcareous [30]. The vegetation types are broadleaved evergreen, mixed evergreen-deciduous, and coniferous woodland [31]. The area is characterized by seasonal water and heat imbalance, intense karst action, high population density and limited arable land resources, and frequent poverty.

2.2. Data Sources

In 2002, large-scale ecological management projects were undertaken on ecosystems in China [32]. In order to compare differences in the impact of human activities, the three sets (1980, 2000 and 2020) of land-use data used in this study were obtained from the Resource and Environment Science and Data Center, Institute of Geographic Sciences, and Natural Resources Research, CAS. The 1980 data are from the Landsat 4–5 TM satellite digital product (accessed on 19 December 2022) with 90.2% accuracy; the 2020 data are from the Landsat8 OLI_TIRS satellite digital product (accessed on 19 December 2022) with 91.01% accuracy. Data from Landsat8 OLI_TIRS satellite digital product (accessed on 21 December 2022) had 94.3% data accuracy, as shown in Figure 2, with a spatial resolution of (1 km × 1 km), and were classified into six types, namely arable land, woodland, grassland, water, built-up areas, and unused land, with a comprehensive evaluation accuracy of over 94.3% [33]. Socio-economic data on agricultural production, planted area, and prices were obtained from the China Agricultural Information Network ((http://www.agri.cn/)(accessed on 25 December 2022)), the China Statistical Yearbook ((http://www.stats.gov.cn/tjsj/ndsj/)(accessed on 25 December 2022)), and the Guizhou Statistical Yearbook ((http://stjj.guizhou.gov.cn/)(accessed on 25 December 2022)).

2.3. Research Methods

2.3.1. CA-Markov Model

A metacellular automaton is a dynamic system with instantaneous states and discrete spaces that can treat arbitrary spatial locations as individual metacells and simulate the spatial variation of various natural processes, including land use [34], with the following equation:
Y t + 1 = f   ( Y t , W )
where Y is a discrete set of metacells; t is the period; W is the neighborhood of metacells; and f is the metacell transformation rule for the space.
The Markov models are implemented by calculating a land use change matrix for periods t to t +1, and the area or state transfer probability of interconversion between land-use types [35] with formula is as follows:
S t + 1 = S x y × A S t
S x y = S 11     S 12 ⋯ S 1 n S 21     S 22 ⋯ S 2 n ⋮                 ⋮               ⋮             ⋮ S n 1       S n 2 ⋯ S n n
where S t is the land use area at the beginning of the study, S t + 1   is the land-use area at the end of the study, and t and t +1 represent different moments at the beginning and end of the study, respectively. S x y is state transfer matrix, where x is a row of the matrix, y is a column of the matrix, each row of the matrix represents the probability of transferring x land to y land type, and n stands for land type.
The main land use types are woodland, grassland, and arable land (Figure 3), with the three undergoing a relatively dramatic mutual shift, with a significant increase in built-up land area. To test the accuracy and feasibility of the CA-Markov model for future land use analysis, we first simulated the 2020 land use data in Idrisi 17.0 forecasting software, using the real land use of 1980 and 2000 as the base years and setting the number of iterations to 20. Subsequently, the analogue land use values for 2020 were compared with the real values for accuracy verification and analysis, and it was found that the simulated results had a Kappa index of 83.6%, which was highly reliable and met the requirements of the study. The real land use values from 2000 to 2020 were used for the analysis, simulating the land use changes in 2040 in the survey area, and the results are shown in Figure 3.

2.3.2. Land Use Dynamic Changes

The average annual rate of land use type changes is used to describe trends in area change for a single type and is important for presenting spatial-temporal differences in land use types [36], as communicated below [37]:
K = U b − U a U b × 1 T × 100 %
where U a is the area of a type at the beginning of the study and U b the area of a type at the end of the study; K is the average annual rate of change for that type of land; T represents the study period.
The land transfer matrix is a dynamic process of transformation in various land types under specific conditions. Under certain spatial-temporal conditions, the reclassification tool of AcrMap 10.2 software was used to derive six types: arable land, woodland, grassland, water, built-up areas, and unused land. The spatial analysis function is then used to carry out a land type transfer analysis, and the equation is as follows [38]:
N = 10 A + B
where N is the new unit of land transfer, A is the land at the start of the study, and B is the land at the end of the study.

2.3.3. Calculation of ESVs

The ESV coefficients take into account the actual ecological and economic conditions in China, referring to the equivalence factor scheme of Constanza et al. and Xie et al. [8,39]. In order to eliminate the effects of temporal and spatial fluctuations in prices and grain production, the food value per unit area (US$/hm2) of the overall agricultural products was calculated using the 2021 agricultural prices (rice, wheat, and maize) as the reference base for the ESV calculation. The national ESV for standard equivalence factor was obtained as US$ 318.98/hm2, with national statistical yearbook data showing an grain yield of 5694.56 kg/hm2 and a grain yield of 3930 kg/hm2 in Guizhou province, with a correction factor of 0.69 (the ratio of Guizhou province to the national grain yield), where it is treated as 0 due to built-up areas value. The ESVs per unit area is illustrated in Table 1. Overall, the formula was calculated as follows [40]:
E a = 1 7 W a v  
V C i j = E C i j × E a
E S V s = ∑ i = 1 n A i × ∑ j = 1 k V C i j
E S V f = A i × V C i f
where E a is the national standard equivalent ESVs, W a v   is the price of food per unit area in the study area (US$/hm2·a), V C i j is the value coefficient of ecosystem service type j of type i landscape, and E C i j is the value coefficient of ecosystem service function j of type i landscape after correction. A i is the area of land type i. E S V f represents the functional value of individual ES; VCif represents the functional value of f ES for land type i (US$/hm2·a).

2.3.4. Distribution of Spatial and Temporal Variation in ESVs

The extent of change (gain or loss) in ecosystem services is characterized in the text for each study unit. The change in the value of ecosystem services per unit area (CVESPUA) is used to express the degree of change in ESV gains and losses. The equation is as follows:
C V E S P U A = E S V i + E S V j M
where E S V i represents ESV at the beginning of the study, E S V j represents ESV at the end of the study, and M is the area of counties, cities, and regions.

2.3.5. Sensitivity Analysis

A sensitivity model is used to test the veracity of the ESVs coefficient by adjusting the value of VC up or down by 50% and analyzing the extent to which the ESVs dependent on VC changes over time [41]. The equation is as follows:
C S = E S V b − E S V a / E S V a V C b i − V C a i / V C a i
where CS is the sensitivity coefficient of ESVs coefficient; E S V a and E S V b are the ESVs of the study area before and after adjustment; V C a i and V C b i are the value coefficients of ecosystem type i before and after increasing or decreasing.

2.3.6. Statistical Analysis

The paper uses Idrisi 17.0 software for the preliminary land use projections, Arcmap software for the mid-term transfer matrix analysis processing and mapping, Excel 2010 for the analysis of area and ecosystem service value calculations, and origin 2021 for graphics production.

3. Results

3.1. Analysis of Land Use Change

3.1.1. Land Use Pattern Change

The sum of the area of the main land types in the study area, arable land, woodland, and grassland, as a proportion of the total area, was 99.49%, 99.42%, 97.96%, and 96.95%, respectively (Figure 4 and Table 2). The degree of dynamics of built-up areas and water is much higher than that of the other land types and both have positive values, reaching 32.96% and 16.81% respectively at their peak, indicating a sharp fluctuation in the trend of their area change. From 1980 to 2000, the degree of dynamics of arable land, grassland, and unused land was all >0, and the area of the three types of land in this study period showed a small upward trend; from 2000 to 2040, the degree of dynamics was all <0, and the overall area was in a steady decline, while the opposite was true for woodland. This indicates that the extent of the different landscape types in the study area does not keep rising or falling alone, but shows a fluctuating trend.

3.1.2. Land Use Transfer Distribution

As shown in Figure 5, from 1980 to 2040, a total of 27 land use shifts occurred in the study area, with an overall change area of 72,966.92 km2, accounting for 41.60% of the total study area. The areas where the most dramatic land use shifts occurred were the western, northern, and center parts of the study area, with the north mainly experiencing the conversion of arable land and woodland and grassland to others land types. The western part is mainly the conversion of forest land into other land types. The center part is the conversion of other land types into built-up areas.
Overall, the land use shift from 1980 to 2040 shows (Figure 6d) that the conversion of arable land to other types and the ratio of the overall areas occupying the study area are the largest, at 29,818.87 km2 and 17% respectively. The main transformation is into woodland, grassland, and built-up area types, with the area of woodland increasing by 22,127.58 km2, accounting for 74.21% of the total area of arable land transformed into other types. This was followed by woodland, which was converted to other types, with an area of 24,971.07 km2, accounting for 14% of the overall area of the region. The conversion was mainly to arable land and grassland, with a rapid increase of 16,337 km2 in the area of arable land, accounting for 65.42% of the total area converted from woodland to other land.
The phenomenon of conversion of other land into woodland is the most obvious. The conversion area and the ratio to the total area of the study area are 32,753.34 km2 and 18.67%, respectively, among which the conversion of grassland to woodland is the largest at 10,406.27 km2, accounting for 31.77% of the entire converted area of woodland. Secondly, the area and proportion of arable land are 22,786.82 km2 and 12.99% respectively. The area of woodland to arable land is 16,337 km2, and the conversion rate is 71.69%. The transfer of arable land and woodland is the most intense, which shows the mutual transfer of arable land, woodland, and grassland, and the important characteristics of built-up areas and water. In addition, Figure 6a–c show a continuous increase in the area of woodland, arable land, and grassland transferred out to water and arable throughout the study period 1980–2040.

3.2. Changes in ESVs

3.2.1. Trends in Temporal Change

Between 1980 and 2040, land use changes in the South China Karst led to a trend of decreasing and then increasing ESVs (Figure 7a). First, ESVs in the study area decreased from US$ 583.68 million in 1980 to US$ 580.80 million, a decrease of US$ 2.88 million and a rate of change of 0.498%. Since 2000, there has been an overall increasing trend from US$ 580.80 million to US$ 614.79 million in 2040, an increase of US$ 33.99 million, a growth rate of 5.85%. The overall ESVs show the greatest trend of change from 2000 to 2020, with an increase of US$ 20.08 million over the 20-year period, a growth rate of 3.45%, indicating that ESVs have enhanced human well-being during this period. Changes in ESVs are closely related to changes in land area (Figure 7b), and 1980–2000 is identified as the only period in which the increase in ESVs is lower than the decrease. The increase and decrease from 2000 to 2020 is the largest throughout the study period, which has dramatic fluctuations in the value of land use shifts and ESVs, indicating that rational land planning is conducive to optimizing ecosystem service functions and driving sound economic development.
The overall land use type shift directly influenced the change in ESV during the study period (Figure 8). Woodland, arable land, and grassland are the main types of contribution to ESVs, with contributions in the range of 77.53%–80.02%, 10.43%–12.96%, and 6.81%–8.79%, respectively. The ESVs of woodland and water showed a change trend of decreasing and then increasing, with woodland ESVs decreasing by US$ 44.63 million up to 2000 and then increasing, with a cumulative increase of US$ 41.03 million in 40 years, thanks to the decrease in ESVs on arable land and grassland. The trends in ESVs on cropland, grassland, and unused land are very similar, showing an increase followed by a decrease. The most significant decrease in ESVs was seen on cropland, with a decrease in the value of cropland ESVs of US$ 4.26 million from 1980 to 2000, in the 40 years since 2000. It is in a steady downward trend, with a cumulative decrease of US$ 111.56 million, with the largest change in ESVs of cropland from 2020 to 2040, with a cumulative decrease of US$ 58.54 million. The increase in ESVs for woodland and water is much higher than the decrease in ESVs for arable land, grassland, and unused land, with an overall increase in ESVs in the study. Overall, the increase in woodland and water is the biggest reason for the increase in ESVs, and the decrease in arable land and grassland is the main factor for the decrease in ESVs. A trade-off mechanistic relationship arises between them.
Ecosystem services are grouped into nine broad functional categories. As shown in Figure 9, of all the function types, soil formation and retention, biodiversity conservation, gas regulation, and water conservation contributed the most, while the least valuable was food production. Over the 60-year period, the increase in soil formation and retention, biodiversity conservation, gas regulation, and water conservation was US$ 93.48 million, US$ 42.68 million, US$ 42.22 million, and US$ 39.01 million. Food production, on the other hand, decreased by US$ 16.96 million, with declining arable land area and soil fertility resulting in declining food production. Dividing the study period into three sub-stages, the value of water conservation increased the most from 2000 to 2020, at US$ 58.28 million, while the value of food production decreased the most from 2020 to 2040, at US$ 8.64 million. The increase in several ES types was much higher than the decrease, indicating that the overall ES capacity of the study area is steadily increasing. This indicates that the overall ES capacity of the study is steadily increasing.

3.2.2. Trends in Temporal Change

The ESVs of various land types and ES functions in the study do not reflect the variation in their spatial distribution. We took the study area of southern China karst as a county to calculate the spatial distribution of ESVs in 1980 and 2040 and calculated the change in ESVs from 1980 to 2040 (CVESPUA). The natural breakpoint method of ArcGIS software was used to classify ESVs into six class ranges. The space distribution of ESVs in the study area (Figure 10a,b) shows that the ESVs in the center region are low; the ESVs in the surrounding areas are high, forming a basin-shaped spatial distribution pattern. Secondly, based on the ESVs change map of the 1980—2040CVESPUA (Figure 10c), it is found that the ESVs in the northern and center regions show a decreasing trend, mainly concentrated in urban centers or urban-rural areas.

3.3. Sensitivity Analysis

As shown in Figure 11, increasing or decreasing the ESV coefficient by 50% for different landscape types, where the maximum value of the sensitivity coefficient is for woodland, when the value coefficient for woodland is adjusted by ±1%, the total value range of the corresponding landscape type will be adjusted by ±(0.758996%–0.779107%). This indicates that the study area has the largest area of woodland types and that the ESV is in a state of high elasticity to VC, as the transfer in and out varies dramatically. The smallest value is for unused land, where the total value of unused land changes (0.000005%–0.000007%) when the value factor is adjusted by ±1%. The sensitivity index of woodland decreases over time; the opposite is true for arable land and water, indicating that woodland tends to be stable in the context of the same landscape type, while the sensitivity index of woodland tends to be stable, and that of cropland and water tends to change sharply. The overall sensitivity index is in descending order: woodland > arable land > grassland > water > unused land. The results show that the overall sensitivity index is <1, the ESV is inelastic to the VC coefficient adjustment, and are true and valid.

4. Discussion

4.1. Land Use Change Influencing Factors

From 1980 to 2040, the main land types in SCK were arable land, woodland, and grassland. The overall land use change in SCK showed an increase in woodland, water, and built-up areas, accompanied by a decrease in arable land, grassland, and unused land. From 1980 to 2000, arable land, grassland, and unused land showed an increasing trend, while woodland did the opposite. From 2000 onwards, woodland, water, and built-up areas gradually increased, while arable land, grassland, and unused land showed the opposite trend, which is basically similar to the results of global and Chinese land type changes [9,42]. Due to the contradiction between the food demand of a high-density population and limited arable land resources, unreasonable farmland reclamation and grassland grazing activities at the expense of forest land have not brought human beings out of the bottleneck of poverty. To get out of this dilemma, the International Union for Conservation of Nature (IUCN) began to unite countries to call for environmental protection issues [43] and reduce the vicious recycling of land resources. The results of the study show that the regions with sharp fluctuations in land use shifts are the northern, western, and central parts of the study area, with a sharp inter-conversion of forest land, arable land, and grassland, and an increasing area of built-up land and water, similar to the results of the study of Shangzhou District and Xiong’an New Area in China [41,44]. For example, in the context of a high population density, global warming, rural poverty, and urban pollution drive large scale migration [45], the demand of people for water, food, and comfortable space continues to change land use [46,47]. With technological innovation and rapid economic development, urbanization is the main reason for the increase in construction land in the central region. In recent years, the government has undertaken a series of ecological and environmental protection projects, such as the “Food Green Project”, the return of built-up land to woodland and grassland, the Western Development Strategy, and ecological compensation [27,32,48], which have led to land use changes in the western and northern parts of the study area. SCK is not only facing ecosystem decline, soil erosion, and geological disasters, but is also affected by urbanization, climate change, and rocky desertification control projects [49,50]. Therefore, rational territorial spatial planning can effectively drive sustainable local economic development and promote people’s well-being.

4.2. Spatial and Temporal Evolution of ESVs

4.2.1. Temporal Trends

The ESVs showed a temporal trend of decreasing and then increasing. In the meantime, the increase in ESVs in woodland and water was much higher than the decrease in cropland and grassland, and the change was the same as the pattern of area transfer for all land use, which was similar to the trend of ESVs change as found by Chen et al. [36]. This is attributed to the fact that since 2000, ecological management projects in China have effectively curbed the expansion of rocky desertification control, slowed soil erosion, increased vegetation productivity and service capacity, and driven up the ESVs [51,52]. As rocky desertification control projects advance, the lag in ES functions makes it difficult to sustain the current management effectiveness, and the transition period of its eco-industrial structural transformation and eco-product supply capacity enhancement slows the growth rate of ESVs [53,54]. Wang et al. also confirmed that the Grain for Green Program (GFGP) increases the area of woodland and water, leading to an increase in the ESVs. The GFGP has also been used to delineate functional areas of provisioning and develop ecological restoration policies based on the trade-offs and synergies between provisioning and other services [55]. The studies focusing on individual ecosystem service functions have shown that soil formation and protection, biodiversity conservation, and water conservation dominate and that balancing different service functions can effectively enhance the overall regional ecosystem services [56].

4.2.2. Spatial Distribution Patterns

The results show that ESVs show a pattern of spatial distribution with a gradual decrease outward from the regional center, similar to the findings of He et al. [57], as economic development drives population migration and urbanization, changing the original spatial distribution pattern of ESVs. At the same time, the national targeted poverty alleviation and special treatment of rocky desertification directly addresses the poverty and stone desertification problems in the study area, improving the regional economic development pattern and greatly alleviating the livelihood problems of farmers in vulnerable areas [58], increasing the areas with a high value of ESVs to some extent. However, this does not mean that the industrial structure and economic development in areas with high ESVs are benign, and future research should continue to strengthen the collaborative relationship between ecosystem services and poverty [59]. At the same time, more attention should be given to the optimization of landscape patterns and the delineation of functional zones for ecosystem services. For example, the optimization of landscape patterns (land use ratios, spatial and temporal configurations of different systems) and the delineation of different functional zones (ecological protection, food production, residential recreation, and commercial activity zones) in ecologically fragile areas can provide practical references for ecosystem service management [60,61]. Clarifying the transformation mechanism that Lucid waters and lush mountains are invaluable assets and balancing the contradiction between ecological and economic benefits can effectively improve ecosystem service capacity and people’s well-being.

4.3. Future Responses to Rocky Desertification Control Based on Ecosystem Services Assessment

In order to cope with the potential risks of reducing the effectiveness of karst control, lagging service functions, and returning to poverty in the SCK, the report of the 20th National Congress of China pointed out that future ecological environmental protection should focus on the integration of “mountain, water, forest, field, lake, grass and sand” and scientifically promote comprehensive desertification control, making it clear that measures to optimize the landscape and enhance ecosystem service capacity are urgent [62]. Therefore, based on the limited arable land resources in rocky desertification areas, it is recommended to change the traditional single “agriculture, forestry, animal husbandry, sideline and fishery” into an agro-forestry, agro-grass composite management model, with scientific design and planning to jointly promote ecological and economic development. For example, the shading of trees in the agroforestry model not only reduces coffee production, but also enhances the biodiversity of the habitat and the capacity of ES, such as carbon sequestration and oxygen release [63]. Intercropping in the agro-grass management model effectively maintains the productivity of (S. miltiorrhiza) as well as improves soil structural stability and water recycling efficiency [30]. Restoration measures, such as mixing legume and grass families, forage and crop rotation, and degraded grassland improvement, are not only beneficial to regional industrial development [64], but also play an important role in optimizing the regional ecosystem services of soil conservation and water harvesting. A variety of ecological restoration optimization composite measures are formed to jointly promote the restoration and development of ecologically fragile areas.
In this study, the spatial-temporal evolution of ESVs in the study area was only roughly estimated based on homogenized and generally accurate remote sensing data, without considering the effects of landscape fragmentation of karst landscapes, climate change, and eco-industrial restructuring in South China Karst, and only the interrelationship between land use change and ESVs was analyzed. Therefore, we suggest that, based on the assessment of ESVs, we should clarify the trade-offs and synergistic mechanisms between various ecosystems and service functions and reasonably regulate the influencing factors and landscape optimization, combining scientific composite management measures and a parallel mechanism of long-term monitoring.

5. Conclusions

From 1980 to 2040, land types in the desertification control in the South China Karst area changed dramatically, with arable land, woodland, and grassland transformed into built-up areas and water to varying degrees. ESVs showed a trend of first decreasing and then increasing during the study period, with forest land contributing the most to ESVs. With the promotion of the series of ecological management projects to return farmland to forest and grass in the western and northern regions, the local ecological environment has been improved. Meanwhile, the urbanization process in the center part of the region has accelerated, encroaching on the limited arable land resources, which has led to a basin-type clustering spatial distribution pattern of ESVs. This result verifies the hypothesis that human activities directly influence the direction of land use type transformation and indirectly change the spatial-temporal distribution pattern of ESVs. Moreover, it shows that a series of rocky desertification control projects and national strategic investments is conducive to enhancing ecosystem service capacity. Among the single functional types, soil formation and retention, biodiversity conservation, gas regulation and water conservation contribute the most, and land use change is the influencing factor for the change of food production and the value of water conservation.
At the same time, we point out that there are effective measures to improve the karst desertification control: (1) agroforestry complex management model; (2) agro-grass intercropping model; (3) mixing legume and grass families, forage and crop rotation, and degraded grassland improvement. These measures can improve the structure, function, and services of rocky desertification control ecosystems, promote regional economic development, and safeguard people’s well-being.

Author Contributions

Y.C. conceived, designed the research, wrote the manuscript and funding acquisition; C.H. data collection, software use, analyzed data, provided modification comments, and reviewed the final manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

The acknowledgements are for the supports by the acknowledgement is for the supports by the Natural Science Research Project of Education Department of Guizhou Province [Qianjiaohe KY Zi (2022) 157]; Academic New Seedling Fund Project of Guizhou Normal University (Qianshi Xinmiao B15).

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

We appreciate the anonymous reviewers for their invaluable comments and suggestions on this manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Forests 14 00893 g001 550
Figure 1. Zone map. Note (LPSC: Liupanshui city; ASC: Anshun city; BJC: Bijie city; GYC: Guiyang city; ZYC: Zunyi city; TRC: Tongren city; SeGMADAP: South-east guizhou miao and dong autonomous prefecture; SGZBMAP: Southern guizhou buyi and miao autonomous prefecture; SwGZAP: Southwest guizhou autonomous prefecture).
Figure 1. Zone map. Note (LPSC: Liupanshui city; ASC: Anshun city; BJC: Bijie city; GYC: Guiyang city; ZYC: Zunyi city; TRC: Tongren city; SeGMADAP: South-east guizhou miao and dong autonomous prefecture; SGZBMAP: Southern guizhou buyi and miao autonomous prefecture; SwGZAP: Southwest guizhou autonomous prefecture).
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Figure 2. Land use maps for different periods in the study area.
Figure 2. Land use maps for different periods in the study area.
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Figure 3. Study of actual versus projected land use.
Figure 3. Study of actual versus projected land use.
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Figure 4. For the period 1980–2040, (a) shows the change in land use area in the study area; (b) shows the percentage of different land classes.
Figure 4. For the period 1980–2040, (a) shows the change in land use area in the study area; (b) shows the percentage of different land classes.
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Figure 5. Regional map of land use transfer distribution in the study area 1980—2040. Note (No change represents a land use type that has not changed the study period; Arable Land—Built-up Areas represents a change in land type from arable to built-up areas).
Figure 5. Regional map of land use transfer distribution in the study area 1980—2040. Note (No change represents a land use type that has not changed the study period; Arable Land—Built-up Areas represents a change in land type from arable to built-up areas).
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Figure 6. Area of land transfer in Guizhou Province. Note ((a–d) represent land use transfer at different time periods respectively).
Figure 6. Area of land transfer in Guizhou Province. Note ((a–d) represent land use transfer at different time periods respectively).
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Figure 7. Changes in total ecosystem values (a) and phased increases and decreases in values (b), 1980–2040.
Figure 7. Changes in total ecosystem values (a) and phased increases and decreases in values (b), 1980–2040.
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Figure 8. Different types of ESVs and their contribution in the study area from 1980–2040. Note (AL = Arable Land, WO = Woodland, GL = Grassland, WT = Water, UL = Unused Land. The line graph in the figure is a linear fit to the degree of contribution and reflects the trend and range of the degree of contribution.).
Figure 8. Different types of ESVs and their contribution in the study area from 1980–2040. Note (AL = Arable Land, WO = Woodland, GL = Grassland, WT = Water, UL = Unused Land. The line graph in the figure is a linear fit to the degree of contribution and reflects the trend and range of the degree of contribution.).
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Figure 9. Changes in the ESVs based on the type of ES function in the study area, 1980—2040.
Figure 9. Changes in the ESVs based on the type of ES function in the study area, 1980—2040.
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Figure 10. Spatial distribution of ES values in the study area. Note (a is the spatial distribution of ESVs in 1980; b is the spatial distribution of ESVs in 2040; c is the spatial distribution of the increase or decrease in ESVs in the region after 60 years of time change.)
Figure 10. Spatial distribution of ES values in the study area. Note (a is the spatial distribution of ESVs in 1980; b is the spatial distribution of ESVs in 2040; c is the spatial distribution of the increase or decrease in ESVs in the region after 60 years of time change.)
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Figure 11. Map of sensitivity coefficients for the study area 1980–2040.
Figure 11. Map of sensitivity coefficients for the study area 1980–2040.
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Table
Table 1. ESVs coefficients per unit area in the study area (US$/hm2·a).
Table 1. ESVs coefficients per unit area in the study area (US$/hm2·a).
ES TypesArable LandWoodlandGrasslandWaterUnused Land
Gas regulation110.05770.33176.070.000.00
Climate regulation195.88594.25198.08101.240.00
Water conservation132.06704.30176.074485.506.60
Soil formation and retention321.34858.36429.182.204.40
Waste treatment364.13288.32288.324001.292.20
Biodiversity conservation156.27717.50239.90548.0374.83
Food production220.0922.0166.0322.012.20
Raw material22.01572.2411.002.200.00
Recreational culture2.20281.728.80955.202.20
Table
Table 2. Land-use change (%) in the study region from 1980 to 2020.
Table 2. Land-use change (%) in the study region from 1980 to 2020.
Land Use Type
TimeArable LandWoodlandGrasslandWaterBuilt-Up
Areas 		Unused Land
1980–20000.03−0.050.080.410.811.11
2000–2020−0.350.27−0.649.4316.16−1.48
2020–2040−0.420.19−0.301.992.73−0.65
1980–2040−0.720.41−0.8316.8132.96−1.25
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Chi, Y.; He, C. Impact of Land Use Change on the Spatial and Temporal Evolution of Ecosystem Service Values in South China Karst Areas. Forests 2023, 14, 893. https://doi.org/10.3390/f14050893

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Chi Y, He C. Impact of Land Use Change on the Spatial and Temporal Evolution of Ecosystem Service Values in South China Karst Areas. Forests. 2023; 14(5):893. https://doi.org/10.3390/f14050893

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Chi, Yongkuan, and Cheng He. 2023. "Impact of Land Use Change on the Spatial and Temporal Evolution of Ecosystem Service Values in South China Karst Areas" Forests 14, no. 5: 893. https://doi.org/10.3390/f14050893

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