journal article·2025-01-21Open access

Projecting Future Land Use Evolution and Its Effect on Spatiotemporal Patterns of Habitat Quality in China

Yiqing Chen, Fengyu Zhang, Jinyao Lin

Applied Sciences· Volume 15· Issue 3

Abstract

In recent years, irrational land development has caused significant habitat quality problems. Previous habitat quality studies have mainly concentrated on medium- and small-sized areas, and few studies have conducted a comprehensive long-term analysis of terrestrial habitat quality in large countries. Accordingly, this research aimed to identify the changes in land use and habitat quality in China during the last four decades. The InVEST method was employed for evaluating China’s habitat quality. This evaluation included both habitat degradation and habitat quality scores. Then, the FLUS and InVEST methods were combined to project future land use evolution in China through 2050 and assess its effect on habitat quality. Our study demonstrated a robust connection between habitat quality and the spatial distribution of land use classes, topography, and resource availability. Furthermore, over the past four decades, high-quality habitats in the country have been degrading and shrinking, while low-quality habitats have been expanding. The projection results indicate that the habitat problems in China will become increasingly severe over the coming decades. Our study suggests that the habitat quality in China should be improved by optimizing land use patterns, stabilizing areas with optimal habitat conditions, and restoring degraded habitats.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Land Use and Ecosystem Services

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China

Huangpu Research School of Guangzhou University, Guangzhou 510006, China

Funding

Humanities and Social Sciences Research Program of the Ministry of Education of China · Guangdong Basic and Applied Basic Research Foundation · Guangdong Philosophy and Social Science Foundation

References (100+)

Show source-provided references
1. Accounting for Multisectoral Dynamics in Supporting Equitable Adaptation Planning: A Case Study on the Rice Agriculture in the Vietnam Mekong Delta10.1029/2020EF001939 ↗
2. High-spatiotemporal-resolution mapping of global urban change from 1985 to 201510.1038/s41893-020-0521-x ↗
3. Modeling different urban growth patterns based on the evolution of urban form: A case study from Huangpi, Central China10.1016/j.apgeog.2015.11.012 ↗
4. The varying driving forces of urban expansion in China: Insights from a spatial-temporal analysis10.1016/j.landurbplan.2018.03.004 ↗
5. Spatial governance of Beijing-Tianjin-Hebei urban agglomeration towards low-carbon transition10.1108/CAER-04-2022-0069 ↗
6. Land use and cover change (LUCC) impacts on Earth’s eco-environments: Research progress and prospects10.1016/j.asr.2022.09.054 ↗
7. The effects of factor generalization scales on the reproduction of dynamic urban growth10.1080/10095020.2022.2025748 ↗
8. Comparing the warming effects of different urban forms under projected climate change in China’s Guangdong-Hong Kong-Macau Greater Bay Area10.1016/j.uclim.2024.101824 ↗
9. Spatial regulation design of farmland landscape around cities in China: A case study of Changzhou City10.1016/j.cities.2019.102504 ↗
10. Identifying up-to-date urban land-use patterns with visual and semantic features based on multisource geospatial data10.1016/j.scs.2024.105184 ↗
11. A partitioning approach for regional sustainability based on economic development indicators and ecological values for China10.1016/j.jnc.2022.126179 ↗
12. Hotspots of land-use change in global biodiversity hotspots10.1016/j.resconrec.2021.105770 ↗
13. Long-term assessment of ecological risk dynamics in Wuhan, China: Multi-perspective spatiotemporal variation analysis10.1016/j.eiar.2023.107372 ↗
14. The occupation of cropland by global urban expansion from 1992 to 2016 and its implications10.1088/1748-9326/ab858c ↗
15. Diversity in global urban sprawl patterns revealed by Zipfian dynamics10.1080/2150704X.2022.2073794 ↗
16. Stochastic evaluation of restoration procedures on postmining land areas using a game theory approach10.1002/ldr.4163 ↗
17. Niu, J., Tang, W., Xu, F., Zhou, X., and Song, Y. (2016). Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis. ISPRS Int. J. Geo-Inf., 5.10.3390/ijgi5050066 ↗
18. Analyzing spatial patterns and influencing factors of different illegal land use types within ecological spaces: A case study of a fast-growing city10.1016/j.jclepro.2023.138883 ↗
19. Direct and indirect loss of natural habitat due to built-up area expansion: A model-based analysis for the city of Wuhan, China10.1016/j.landusepol.2017.12.048 ↗
20. Liao, J., Tang, L., and Shao, G. (2023). Coupling Random Forest, Allometric Scaling, and Cellular Automata to Predict the Evolution of LULC under Various Shared Socioeconomic Pathways. Remote Sens., 15.10.3390/rs15082142 ↗
21. Predicting the impacts of urban land change on LST and carbon storage using InVEST, CA-ANN and WOA-LSTM models in Guangzhou, China10.1007/s12145-022-00875-8 ↗
22. Li, M., Luo, H., Qin, Z., and Tong, Y. (2023). Spatial-Temporal Simulation of Carbon Storage Based on Land Use in Yangtze River Delta under SSP-RCP Scenarios. Land, 12.10.3390/land12020399 ↗
23. Tian, C., Pang, L., Yuan, Q., Deng, W., and Ren, P. (2024). Spatiotemporal Dynamics of Ecosystem Services and Their Trade-Offs and Synergies in Response to Natural and Social Factors: Evidence from Yibin, Upper Yangtze River. Land, 13.10.3390/land13071009 ↗
24. Investigating the effect of the cooling ecosystem service of urban green infrastructure on the mitigating of environmental heat load and energy efficiency in the metropolitan of Tabriz
25. Relationship between ecological spatial network and vegetation carbon use efficiency in the Yellow River Basin, China10.1080/15481603.2024.2318070 ↗
26. Cheng, W., Ma, C., Li, T., and Liu, Y. (2024). Construction of Ecological Security Patterns and Evaluation of Ecological Network Stability under Multi-Scenario Simulation: A Case Study in Desert–Oasis Area of the Yellow River Basin, China. Land, 13.10.2139/ssrn.4790092 ↗
27. What is the developmental level of outlying expansion patches? A study of 275 Chinese cities using geographical big data10.1016/j.cities.2019.102395 ↗
28. Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: A case study in Wuhan, China10.1016/j.compenvurbsys.2020.101569 ↗
29. SALT: A multifeature ensemble learning framework for mapping urban functional zones from VGI data and VHR images10.1016/j.compenvurbsys.2022.101921 ↗
30. Predicting land-use change: Intercomparison of different hybrid machine learning models10.1016/j.envsoft.2021.105207 ↗
31. Modeling the effects of urban expansion on regional carbon storage by coupling SLEUTH-3r model and InVEST model10.1111/1440-1703.1278 ↗
32. Spatial Sequential Modeling and Predication of Global Land Use and Land Cover Changes by Integrating a Global Change Assessment Model and Cellular Automata10.1029/2019EF001228 ↗
33. A hybrid parallel cellular automata model for urban growth simulation over GPU/CPU heterogeneous architectures10.1080/13658816.2015.1039538 ↗
34. Marques-Carvalho, R., Almeida, C.M.d., Escobar-Silva, E.V., Oliveira Alves, R.B.d., and Anjos Lacerda, C.S.d. (2023). Simulation and Prediction of Urban Land Use Change Considering Multiple Classes and Transitions by Means of Random Change Allocation Algorithms. Remote Sens., 15.10.3390/rs15010090 ↗
35. Wang, J., Zhang, J., Xiong, N., Liang, B., Wang, Z., and Cressey, E.L. (2022). Spatial and Temporal Variation, Simulation and Prediction of Land Use in Ecological Conservation Area of Western Beijing. Remote Sens., 14.10.3390/rs14061452 ↗
36. A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects10.1016/j.landurbplan.2017.09.019 ↗
37. Spatiotemporal impacts of climate change and human activities on blue and green water resources in northwest river basins of China10.1016/j.ecolind.2024.111823 ↗
38. Estimation of maize yield incorporating the synergistic effect of climatic and land use change in Jilin, China10.1007/s11442-023-2150-6 ↗
39. Evaluating the impacts of land use change on ecosystem service values under multiple scenarios in the Hunshandake region of China10.1016/j.scitotenv.2022.158067 ↗
40. Spatiotemporal dynamics of urban green space in Changchun: Changes, transformations, landscape patterns, and drivers10.1016/j.ecolind.2023.109958 ↗
41. An integrated model chain for diagnosing and predicting conflicts between production-living-ecological space in lake network regions: A case of the Dongting Lake region, China10.1016/j.ecolind.2024.112237 ↗
42. Iterative integration of deep learning in hybrid Earth surface system modelling10.1038/s43017-023-00452-7 ↗
43. Future scenarios impact on land use change and habitat quality in Lithuania10.1016/j.envres.2021.111101 ↗
44. Spatially Explicit Scenario Analysis of Habitat Quality in a Tropical Semi-arid Zone: Case Study of the Sokoto–Rima Basin10.1007/s41651-022-00106-0 ↗
45. Potential impact of future land use/cover dynamics on the habitat quality of the Yayo Coffee Forest Biosphere Reserve, southwestern Ethiopia10.1080/10106049.2023.2278327 ↗
46. Assessing and Modeling the Impacts of Wetland Land Cover Changes on Water Provision and Habitat Quality Ecosystem Services10.1007/s11053-020-09667-7 ↗
47. Global observation of urban expansion and land-cover dynamics using satellite big-data10.1016/j.scib.2020.10.022 ↗
48. An agent-based learning-embedded model (ABM-learning) for urban land use planning: A case study of residential land growth simulation in Shenzhen, China10.1016/j.landusepol.2020.104620 ↗
49. Wang, Q., Liu, D., Gao, F., Zheng, X., and Shang, Y. (2023). A Partitioned and Heterogeneous Land-Use Simulation Model by Integrating CA and Markov Model. Land, 12.10.3390/land12020409 ↗
50. Land-use change modeling with cellular automata using land natural evolution unit10.1016/j.catena.2023.106998 ↗
51. Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen, China10.1016/j.jtrangeo.2021.102974 ↗
52. Wang, S., Song, Q., Zhao, J., Lu, Z., and Zhang, H. (2023). Identification of Key Areas and Early-Warning Points for Ecological Protection and Restoration in the Yellow River Source Area Based on Ecological Security Pattern. Land, 12.10.3390/land12081643 ↗
53. Qi, J., Wang, Z., Cressey, E.L., Liang, B., and Wang, J. (2024). Considering the Joint Impact of Carbon Density Change and Land Use Change Is Crucial to Improving Ecosystem Carbon Stock Assessment in North China. Forests, 15.10.3390/f15010055 ↗
54. Ding, Q., Chen, Y., Bu, L., and Ye, Y. (2021). Multi-Scenario Analysis of Habitat Quality in the Yellow River Delta by Coupling FLUS with InVEST Model. Int. J. Environ. Res. Public Health, 18.10.3390/ijerph18052389 ↗
55. Li, M., Zhou, Y., Xiao, P., Tian, Y., Huang, H., and Xiao, L. (2021). Evolution of Habitat Quality and Its Topographic Gradient Effect in Northwest Hubei Province from 2000 to 2020 Based on the InVEST Model. Land, 10.10.3390/land10080857 ↗
56. Wu, L., Sun, C., and Fan, F. (2021). Estimating the Characteristic Spatiotemporal Variation in Habitat Quality Using the InVEST Model—A Case Study from Guangdong–Hong Kong–Macao Greater Bay Area. Remote Sens., 13.10.3390/rs13051008 ↗
57. Chen, C., Liu, J., and Bi, L. (2023). Spatial and Temporal Changes of Habitat Quality and Its Influential Factors in China Based on the InVEST Model. Forests, 14.10.3390/f14020374 ↗
58. Xiang, Q., Kan, A., Yu, X., Liu, F., Huang, H., Li, W., and Gao, R. (2023). Assessment of Topographic Effect on Habitat Quality in Mountainous Area Using InVEST Model. Land, 12.10.3390/land12010186 ↗
59. Wang, B., and Cheng, W. (2022). Effects of Land Use/Cover on Regional Habitat Quality under Different Geomorphic Types Based on InVEST Model. Remote Sens., 14.10.3390/rs14051279 ↗
60. Evaluating future habitat quality responding to land use change under different city compaction scenarios in Southern China10.1016/j.cities.2023.104410 ↗
61. Using ecological security pattern to identify priority protected areas: A case study in the Wuhan Metropolitan Area, China10.1016/j.ecolind.2023.110121 ↗
62. Berta Aneseyee, A., Noszczyk, T., Soromessa, T., and Elias, E. (2020). The InVEST Habitat Quality Model Associated with Land Use/Cover Changes: A Qualitative Case Study of the Winike Watershed in the Omo-Gibe Basin, Southwest Ethiopia. Remote Sens., 12.10.3390/rs12071103 ↗
63. Guan, X., Li, J., Yang, C., and Xing, W. (2023). Development Process, Quantitative Models, and Future Directions in Driving Analysis of Urban Expansion. ISPRS Int. J. Geo-Inf., 12.10.3390/ijgi12040174 ↗
64. Measuring urban spatial interaction in Wuhan Urban Agglomeration, Central China: A spatially explicit approach10.1016/j.scs.2017.04.014 ↗
65. Modeling urban land-use changes using a landscape-driven patch-based cellular automaton (LP-CA)10.1016/j.cities.2022.103906 ↗
66. Zhang, C., Wang, P., Xiong, P., Li, C., and Quan, B. (2021). Spatial Pattern Simulation of Land Use Based on FLUS Model under Ecological Protection: A Case Study of Hengyang City. Sustainability, 13.10.3390/su131810458 ↗
67. Huang, J., Lu, X., and Wang, Y. (2024). Spatio−Temporal Changes and Key Driving Factors of Urban Green Space Configuration on Land Surface Temperature. Forests, 15.10.3390/f15050812 ↗
68. Wang, H., Lin, C., Ou, S., Feng, Q., Guo, K., Xie, J., and Wei, X. (2024). Evolutionary Characteristics and Driving Forces of Green Space in Guangzhou from a Zoning Perspective. Forests, 15.10.3390/f15010135 ↗
69. Wu, J., Li, X., Luo, Y., and Zhang, D. (2021). Spatiotemporal effects of urban sprawl on habitat quality in the Pearl River Delta from 1990 to 2018. Sci. Rep., 11.10.1038/s41598-021-92916-3 ↗
70. Ecological and socioeconomic effects of China’s policies for ecosystem services10.1073/pnas.0706436105 ↗
71. Greener cities cost more green: Examining the impacts of different urban expansion patterns on NPP10.1016/j.buildenv.2022.109876 ↗
72. Modelling multi-regional urban growth with multilevel logistic cellular automata10.1016/j.compenvurbsys.2019.101457 ↗
73. The spatial spillover effect of fossil fuel energy trade on CO2 emissions10.1016/j.energy.2021.120038 ↗
74. Optimizing the ecological source area identification method and building ecological corridor using a genetic algorithm: A case study in Weihe River Basin, NW China10.1016/j.ecoinf.2024.102519 ↗
75. Assessing the scale effect of urban vertical patterns on urban waterlogging: An empirical study in Shenzhen10.1016/j.eiar.2024.107486 ↗
76. The impact of habitat loss and fragmentation on biodiversity in global protected areas10.1016/j.scitotenv.2024.173004 ↗
77. Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation10.1038/nature18326 ↗
78. Ecosystem services to support sustainable development: The modifiable areal unit problem in the transition between evaluation and management units10.1002/sd.3030 ↗
79. Flood evacuation simulations using cellular automata and multiagent systems -a human-environment relationship perspective10.1080/13658816.2019.1622015 ↗
80. Shrinkage and protection of inland lakes on the regional scale: A case study of Hubei Province, China10.1007/s10113-020-01594-w ↗
81. Understanding Land System Change Through Scenario-Based Simulations: A Case Study from the Drylands in Northern China10.1007/s00267-016-0802-3 ↗
82. Simulation of land use/land cover changes and urban expansion in Estonia by a hybrid ANN-CA-MCA model and utilizing spectral-textural indices10.1007/s10661-022-10266-7 ↗
83. Hydrological performance of extensive green roofs in response to different rain events in a subtropical monsoon climate10.1007/s11355-019-00380-z ↗
84. Lu, S., Xiao, Y., Lu, Y., and Lin, J. (2024). Spatialization of electricity consumption by combining high-resolution nighttime light remote sensing and urban functional zoning information. Geo-Spat. Inf. Sci.10.1080/10095020.2024.2356757 ↗
85. An assessment of the Ecological Conservation Redline: Unlocking priority areas for conservation10.1080/09640568.2022.2145939 ↗
86. Simulation of land-cover change in Jing-Jin-Ji region under different scenarios of SSP-RCP10.1007/s11442-022-1955-z ↗
87. Google Earth Engine for improved spatial planning in agricultural and forested lands: A method for projecting future ecological quality
88. Developing a game-theoretic interactive decision-making framework for urban land-use planning10.1016/j.habitatint.2023.102930 ↗
89. Fine-grained regional economic forecasting for a megacity using vector-based cellular automata
90. An adaptive agent-based optimization model for spatial planning: A case study of Anyue County, China10.1016/j.scs.2019.101733 ↗
91. Extracting knowledge from legacy maps to delineate eco-geographical regions10.1080/13658816.2020.1806284 ↗
92. A landscape metrics-based sample weighting approach for forecasting land cover change with deep learning models10.1080/10106049.2023.2240283 ↗
93. Hanoon, S.K., Abdullah, A.F., Shafri, H.Z.M., and Wayayok, A. (2023). Urban Growth Forecast Using Machine Learning Algorithms and GIS-Based Novel Techniques: A Case Study Focusing on Nasiriyah City, Southern Iraq. ISPRS Int. J. Geo-Inf., 12.10.3390/ijgi12020076 ↗
94. Yu, Y., He, J., Tang, W., and Li, C. (2018). Modeling Urban Collaborative Growth Dynamics Using a Multiscale Simulation Model for the Wuhan Urban Agglomeration Area, China. ISPRS Int. J. Geo-Inf., 7.10.3390/ijgi7050176 ↗
95. Influence of urban morphological characteristics on thermal environment10.1016/j.scs.2021.103045 ↗
96. The role of the private tree in bringing diversity and resilience to the urban forest10.1016/j.ufug.2023.127973 ↗
97. A hybrid Grey-Markov/ LUR model for PM10 concentration prediction under future urban scenarios10.1016/j.atmosenv.2018.06.014 ↗
98. Kirschner, V., Macků, K., Moravec, D., and Maňas, J. (2023). Measuring the relationships between various urban green spaces and local climate zones. Sci. Rep., 13.10.1038/s41598-023-36850-6 ↗
99. Guo, X., Zhang, H., Wu, Z., Zhao, J., and Zhang, Z. (2017). Comparison and Evaluation of Annual NDVI Time Series in China Derived from the NOAA AVHRR LTDR and Terra MODIS MOD13C1 Products. Sensors, 17.10.3390/s17061298 ↗
100. A two-step spatially explicit optimization approach of integrating ecosystem services (ES) into land use planning (LUP) to generate the optimally sustainable schemes10.1002/ldr.4624 ↗