A Comparative Study of Various Land Use and Land Cover Change Models to Predict Ecosystem Service Value

Abstract

Ecosystem services are closely related to human well-being and are vulnerable to high-intensity human land-use activities. Understanding the evolution of land use and land cover (LULC) changes and quantifying ecosystem service value (ESV) are significant for sustainable development. In this study, we used land use and land cover data and other data from 2000 to 2020 to analyze the evolution of land use and land cover and ESV in Tongliao, China. With the goal of exploring the characteristics of different cellular automata (CA)-based models, CA-Markov, Future Land Use Simulation (FLUS), and Patch-generating Land Use Simulation (PLUS) models were used to simulate future land use and land cover, and the results were verified and compared. Considering the impacts of policies for capital farmland (CF) and ecological protection red line (EPRL) in the context of territorial spatial planning, four scenarios (inertial development, S1; CF, S2; EPRL, S3; EPRL and CF, S4) were set. The results showed that from 2000 to 2020, farmland and built-up land increased the most (341.18 km2 and 220.56 km2), while grassland had the largest decrease (380.08 km2). The main mutual transitions were from grassland and farmland. The total ESV showed a decreasing trend (from 52,364.56 million yuan to 51,620.62 million yuan). The simulation results for 2035 under four scenarios were similar, where farmland would decrease the most (96.81 km2). The ESV in 2035 would decrease from 51,620.62 million yuan to 51,541.12 million. In addition, under scenarios for the impact of policy, the land showed a trend of scattered expansion. This study provides a scientific basis for making regional sustainable development policy decisions and implementing ecological environmental protection measures.

Spatiotemporal Evolution and Future of Carbon Storage in Resource-Based Chinese Province: A Case Study from Shanxi Using PLUS–InVEST Model Prediction

“…In the future, the PLUS model can be combined with other land-use models or ecosystem service assessment models to improve its performance and expand its range of applications [63]. Integrating these factors will enhance the precision of land-use change projections and broaden the model's applicability to ecosystem services and environmental management [64].…”

Multi-Scenario Land Use Change Simulation and Spatial Response of Ecosystem Service Value in Black Soil Region of Northeast China

“…The LEAS module is able to extract the part of the expansion of each type of land use between the two periods of land use change and use the random forest algorithm to dig into the factors of each type of land use expansion and drivers one by one to obtain the development probability of each type of land use and the contribution of each driver to the expansion of each type of land use in that time period. The CARS module combines random seed generation and a threshold decreasing mechanism, enabling the automatic generation of patches to be simulated dynamically within the constraints of development probability [42,56].…”

Modeling the Land Cover Change in Chesapeake Bay Area for Precision Conservation and Green Infrastructure Planning

“…Before leveraging the remote sensing dataset, research efforts have focused on reclassifying surveyed land use data and using the context-based model such as the SLEUTH model to understand the transition from agricultural to urban land use in rapidly growing cities [14]. With the increasing availability of aerial images and Lidar data, there is more research using the integration of data from multiple sensors results in an enhanced land cover product and then classifying the product for specific use [15][16][17]. These applications aim to understand the correlation between neighborhood characteristics and land cover, such as adult mosquito abundance data to inform critical public health concerns [18], urban heat [19,20], and tree cover and social equity [21].…”