1. Introduction
Ecosystem services (ES) are the benefits that humans derive from the natural world. These benefits are generated by the ecological functions of ecosystems, which can provide products and services directly or indirectly through their processes, structures, and functions. There are four types of ecosystem services: provisioning, regulation, support, and cultural services [
1,
2]. The study of ecosystem services was propelled to the forefront of ecological economics research by the publication of Costanza et al.’s paper “Estimation of the value of global ecosystem services” in the journal
Nature (1997) [
3]. This research provides the foundation for ecological conservation, function zoning, asset accounting, and compensation decisions [
4,
5,
6]. Assessments of ecosystem services have increasingly been used worldwide as a framework for ecological restoration and conservation, watershed management, and sustainable development policy making [
7]. Such assessments also provide recommendations for decision making on the balance and sustainable development of regional ecosystems [
8,
9]. Given the impact of natural environmental changes and human activities on ecosystem services, it is urgent to investigate the internal causes of changes in these services, particularly in inland river basins located in ecological function areas [
10]. Such research will aid in developing effective strategies for ecological restoration and conservation in these areas.
Land use/cover (LULC) represents the closest connection between humans and nature, and it significantly impacts ecosystem structures and functions through biogeochemical cycle processes [
11]. As such, LULC is a major driver of ecosystem service value (ESV) change and is decisive in maintaining ecosystem service functions [
12,
13]. Understanding the spatial relationship between LULC and ecosystem services is essential for effective regional ecosystem management and sustainable development. Most current studies in this area focus on estimating and changing the value of ecosystem services, which serves as an important benchmark for sustainable environmental development [
14]. Extensive research on ecosystem services has been conducted by Chinese scholars [
10]. In 2001, Xie Gaodi and other scholars revised the “Table of Equivalent Factors for Ecosystem Service Value” to align it with the Chinese context, using Costanza’s estimation method. This table was further updated and corrected by Xie et al. in 2007 and 2015 [
6,
15]. Numerous scholars have widely utilized this equivalent factor table to investigate the connection between land use and the value of ecosystem services [
16,
17,
18]. However, despite its nonreliance on a model or redundant calculations, this equivalent method can lead to erroneous evaluation results due to its dependence on predetermined value coefficients [
10]. Therefore, it becomes essential to adjust the coefficient values based on the specific conditions of the research area.
In contemporary research, various aggregation models, such as CLUE-S, ANN, CA, and FLUS, are frequently employed to analyze the spatial–temporal dynamic evolution of the ESV based on land use changes [
10,
19,
20,
21,
22]. However, these models encounter limitations in identifying factors influencing land use changes, impeding the implementation of dynamic spatiotemporal simulations for multiple land use patches [
10,
20,
22]. The recently introduced advanced patch-generating land use simulation model (PLUS) builds upon and enhances the effective adaptive inertia competition and roulette competition mechanisms present in existing models for simulating future land use [
23]. This model integrates the random forest (RF) algorithm to ascertain the developmental potential of each land use type, yielding a more precise simulation of changes in the spatial distribution of land use [
24]. In comparison to commonly used models in the past, this model demonstrates higher simulation accuracy, and its outcomes can better support planning policies aimed at achieving sustainable development. The PLUS model has been applied in several studies for simulating the ESV based on LULC [
10,
22,
25], revealing that PLUS simulations achieve greater accuracy and computational efficiency. Furthermore, LULC-based ESV simulations play a pivotal role in decision making regarding sustainable regional ecological management.
The temporal and spatial dynamics of ESV are undoubtedly crucial, yet it is equally imperative to consider the potential factors influencing the ESV. To adeptly design, implement, and adjust management strategies for achieving sustainable development, a nuanced understanding of the interplay between ESV and landscape patterns is essential [
15]. Moreover, further exploration is warranted to comprehend how natural conditions, climate change, and human activities collectively drive changes in the ESV. Climate factors, vegetation cover, and human disturbance emerge as pivotal drivers of the ESV changes in key ecologically functional areas, necessitating their inclusion in the ESV estimation [
10,
26,
27]. However, existing research based on land-use data often overlooks the spatiotemporal simulation of ESV with co-influencing factors. Hence, there is a pressing need to enhance our understanding of the impact of ecological changes induced by potential factors on the ESV [
10]. In the key ecological functional area of Changbai Mountain, the relationship between ESV and the aforementioned potential factors remains unclear. Simultaneously, the exploration of potential influencing factors of the ESV in ecological functional areas contributes to global ESV assessment, offering research insights and case support for the broader assessment of global ESV. Through the study of these potential influencing factors, a more profound comprehension and safeguarding of ecosystem services can be achieved, thereby making a substantial contribution to sustainable development.
The Changbai Mountain Ecological Function Zone is an extremely important ecological barrier in Northeast China, undertaking critical functions such as soil and water conservation, biodiversity maintenance, and strategically implementing the concept of eco-civilization construction to achieve harmonious coexistence between humans and nature. This study focuses on the representative Tumenn River Basin, a typical watershed within the Changbai Mountain Ecological Function Zone. Guided by principles of ecological protection and sustainable development, it integrates regional development goals with implementation methods, utilizing the coupled PLUS model to optimize land use structure. This approach aims to coordinate economic and social development with ecological protection. This research delves into the transformation patterns of land use from 2000 to 2020, identifying potential driving factors influencing changes in the ESV. Additionally, it examines the correlation between ESV and landscape pattern indices to analyze the spatial–temporal trends of ESV changes in the study area. Moreover, drawing on “Jilin Province’s Land and Space Planning (2021–2035)” and “Yanbian Korean Autonomous Prefecture’s Land and Space Master Plan (2021–2035)” (hereinafter referred to as “the Plan”), the PLUS model is employed to establish multiple scenarios for spatial constraints on land use in 2030. This study explores the changes in spatial cold and hot spots of ESV in the Tumen River Basin in 2030 under the natural development scenario (S1) and target-oriented scenario (S2). This comprehensive analysis offers decision-making support for land and space planning and management, particularly in ecological protection or functional areas.
6. Conclusions
In this comprehensive study, we conducted an assessment of the ESV of the TRB by leveraging LULC data. Equivalence factors were modified with consideration for biomass and socioeconomic factors, leading to insights into the intricate relationships between the ESV and natural conditions, climate change, human activity, and LPIs. Finally, the PLUS model was subsequently employed to simulate the spatiotemporal evolution of the TRB’s ESV under two scenarios: the natural development scenario and the goal-oriented scenario for 2030. The key findings are outlined below:
(1) The ESV of the TRB demonstrated a consistent increase from CNY 3.54 × 1010 in 2000 to CNY 4.08 × 1010 in 2015, followed by gradual fluctuations but an overall upward trajectory. This rise was attributed to the corrected standard equivalent factor (Ea′), emphasizing the developmental progress of the ecological environment and society;
(2) Regulating service value (RSV) and supporting service value (SSV) emerged as pivotal ecosystem functions within the watershed, displaying temporal consistency. The changes in provisioning service value (PSV) and cultural service value (CSV) remained relatively modest across the four stages. Notably, forest land held the highest proportion of the ESV, constituting 94% of the total value, while water areas and wetlands exhibited higher unit area ESV compared to other land types;
(3) Pearson correlation coefficients between the ESV and landscape pattern indices (SI, SHDI, and AI) revealed values of −0.65, 0.72, and 0.60, respectively. Factor exploration indicated the significance of all variables, with the q-value ranking as follows: HAILS (0.678) > TEM (0.470) > NDVI (0.435) > DEM (0.348) > SOMC (0.305) > PRE (0.148);
(4) The PLUS simulation forecasted that forest land would continue to have the highest ESV in 2030. Utilizing the GM (1, 1) model for prediction, unit ESV values for different land types were estimated: cultivated land (3394.79 CNY/ha), grassland (10,367.71 CNY/ha), water area (107,954.26 CNY/ha), unused land (558.64 CNY/ha), and wetland (44,708.07 CNY/ha). The exploration of ESV spatial cold spots and hot spots under different conditions validated the scientific basis and necessity for implementing the Plan.
In summary, our research underscores the imperative and scientific foundation for implementing sustainable development and ecological conservation plans in the TRB region. This study provides valuable insights and recommendations for future implementation efforts.