Delimiting Ecological Space and Simulating Spatial-temporal Changes in Its Ecosystem Service Functions based on a Dynamic Perspective: A Case Study on Qionglai City of Sichuan Province, China

doi:10.5814/j.issn.1674-764x.2022.06.017

Abstract

Abstract:

Delimiting ecological space scientifically and making reasonable predictions of the spatial-temporal trend of changes in the dominant ecosystem service functions (ESFs) are the basis of constructing an ecological protection pattern of territorial space, which has important theoretical significance and application value. At present, most research on the identification, functional partitioning and pattern reconstruction of ecological space refers to the current ESFs and their structural information, which ignores the spatial-temporal dynamic nature of the comprehensive and dominant ESFs, and does not seriously consider the change simulation in the dominant ESFs of the future ecological space. This affects the rationality of constructing an ecological space protection pattern to some extent. In this study, we propose an ecological space delimitation method based on the dynamic change characteristics of the ESFs, realize the identification of the ecological space range in Qionglai City and solve the problem of ignoring the spatial-temporal changes of ESFs in current research. On this basis, we also apply the Markov-CA model to integrate the spatial-temporal change characteristics of the dominant ESFs, successfully realize the simulation of the spatial-temporal changes in the dominant ESFs in Qionglai City's ecological space in 2025, find a suitable method for simulating ecological spatial-temporal changes and also provide a basis for constructing a reasonable ecological space protection pattern. This study finds that the comprehensive quantity of ESF and its annual rate of change in Qionglai City show obvious dynamics, which confirms the necessity of considering the dynamic characteristics of ESFs when identifying ecological space. The areas of ecological space in Qionglai city represent 98307 ha by using the ecological space identification method proposed in this study, which is consistent with the ecological spatial distribution in the local ecological civilization construction plan. This confirms the reliability of the ecological space identification method based on the dynamic characteristics of the ESFs. The results also show that the dominant ESFs in Qionglai City represented strong non-stationary characteristics during 2003-2019, which showed that we should fully consider the influence of the dynamics in the dominant ESFs on the future ESF pattern during the process of constructing the ecological spatial protection pattern. The Markov-CA model realized the simulation of spatial-temporal changes in the dominant ESFs with a high precision Kappa coefficient of above 0.95, which illustrated the feasibility of using this model to simulate the future dominant ESF spatial pattern. The simulation results showed that the dominant ESFs in Qionglai will still undergo mutual conversions during 2019-2025 due to the effect of the their non-stationary nature. The ecological space will still maintain the three dominant ESFs of primary product production, climate regulation and hydrological regulation in 2025, but their areas will change to 32793 ha, 52490 ha and 13024 ha, respectively. This study can serve as a scientific reference for the delimitation of the ecological conservation redline, ecological function regionalization and the construction of an ecological spatial protection pattern.

Key words: ecological space, dominant ecosystem service functions (ESFs), dynamicity, spatial-temporal change simulation, Markov-CA model

OU Dinghua, WU Nengjun, LI Yuanxi, MA Qing, ZHENG Siyuan, LI Shiqi, YU Dongrui, TANG Haolun, GAO Xuesong. Delimiting Ecological Space and Simulating Spatial-temporal Changes in Its Ecosystem Service Functions based on a Dynamic Perspective: A Case Study on Qionglai City of Sichuan Province, China[J]. Journal of Resources and Ecology, 2022, 13(6): 1128-1142.

Figures/Tables 11

References 37

[1]	Al-Ahmadi K, See L D, Heppenstall A, et al. 2009. Calibration of a fuzzy cellular automata model of urban dynamics in Saudi Arabia. Ecological Complexity, 6(2): 80-101. DOI URL
[2]	Al-sharif A A A, Pradhan B. 2014. Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS. Arabian Journal of Geosciences, 7(10): 4291-4301. DOI URL
[3]	Chen Y, Yue W Z, Zhang L, et al. 2020. Theoretical thinking on ecological space zoning from the perspective of territorial space planning. China Land Science, 34(8): 1-9. (in Chinese?)
[4]	Cicchetti D V, Feinstein A R. 1990. High agreement but low Kappa: II. Resolving the paradoxes. Journal of Clinical Epidemiology, 43(6): 551-558. PMID
[5]	Costanza R, d'Arge R, de Groot R, et al. 1997. The value of the world's ecosystem services and natural capital. Nature, 387(6630): 253-260. DOI URL
[6]	Costanza R, de Groot R, Sutton P, et al. 2014. Changes in the global value of ecosystem services. Global Environmental Change, 26(1): 152-158. DOI URL
[7]	Daily G C. 1999. Developing a scientific basis for managing earth's life support systems. Ecology & Society, 3(2): 45-49.
[8]	Daily G C, Soderqvist T, Aniyar S, et al. 2000. The value of nature and the nature of value. Science, 289(5478): 395-396.
[9]	Feinstein A R, Cicchetti D V. 1990. High agreement but low Kappa: I. The problems of two paradoxes. Journal of Clinical Epidemiology, 43(6): 543-549. DOI PMID
[10]	Feng Y J, Liu Y, Tong X H, et al. 2011. Modeling dynamic urban growth using cellular automata and particle swarm optimization rules. Landscape and Urban Planning, 102(3): 188-196. DOI URL
[11]	Gao J, Yu Z W, Wang L C, et al. 2019. Suitability of regional development based on ecosystem service benefits and losses: A case study of the Yangtze River Delta urban agglomeration, China. Ecological Indicators, 107: 105579. 1-105579.12.
[12]	Gao J X, Xu D L, Qiao Q, et al. 2020. Pattern construction of natural ecological space and planning theory exploration. Acta Ecologica Sinica, 40(3): 749-755. (in Chinese)
[13]	Guan X K, Zhang F R, Wang X L, et al. 2013. Spatial evolution of urban ecological land and its distribution optimization in Beijing. Areal Research and Development, 32(3): 119-124. (in Chinese)
[14]	Halmy M W A, Gessler P E, Hicke J A, et al. 2015. Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Applied Geography, 63: 101-112. DOI URL
[15]	Kucsicsa G, Popovici E.A, Bălteanu D, et al. 2019. Future land use/cover changes in Romania: Regional simulations based on CLUE-S model and CORINE land cover database. Landscape and Ecological Engineering, 15(1): 75-90.
[16]	Kumar P, Rosenberger J M, Iqbal G M D. 2016. Mixed integer linear programming approaches for land use planning that limit urban sprawl. Computers & Industrial Engineering, 102: 33-43. DOI URL
[17]	Li M Y, Tian F H, Dong Y Z. 2016. Space identification of ecological importance and protection on urban ecological land in Yanji-Longjing-Tumen Area. Scientia Geographical Sinica, 36(12): 1870-1876. (in Chinese)
[18]	Ma C G, Zhou M. 2018. A GIS-based interval fuzzy linear programming for optimal land resource allocation at a city scale. Social Indicators Research, 135(1): 143-166. DOI URL
[19]	Moreno N, Menard A, Marceau D J. 2008. VecGCA: A vector-based geographic cellular automata model allowing geometric transformations of objects. Environment & Planning B: Planning & Design, 35(4): 647-665.
[20]	Pahlavani P, Omaran H A, Bigdeli B. 2017. A multiple land use change model based on artificial neural network, Markov chain, and multi objective land allocation. Earth Observation and Geomatics Engineering, 1(2): 82-99.
[21]	Sang L L, Zhang C, Yang J Y, et al. 2011. Simulation of land use spatial pattern of towns and villages based on CA-Markov model. Mathematical and Computer Modelling, 54(3-4): 938-943. DOI URL
[22]	Shi Y, Wang R S, Huang J L, et al. 2012. An analysis of the spatial and temporal changes in Chinese terrestrial ecosystem service functions. Chinese Science Bulletin, 57(17): 2120-2131. DOI URL
[23]	Wang S D, Wang X C, Zhang H B. 2015. Simulation on optimized allocation of land resource based on DE-CA model. Ecological Modelling, 314: 135-144. DOI URL
[24]	Wang W J, Guo H C, Chuai X W, et al. 2014. The impact of land use change on the temporospatial variations of ecosystems services value in China and an optimized land use solution. Environmental Science & Policy, 44: 62-72.
[25]	Wang X, Ma B W, Li D, et al. 2020. Multi-scenario simulation and prediction of ecological space in Hubei Province based on FLUS model. Journal of Natural Resources, 35(1): 230-242. (in Chinese) DOI URL
[26]	Xie G D, Lu C X, Leng Y F, et al. 2003. Value evaluation of ecological assets of Qinghai-Tibet Plateau. Journal of Natural Resources, 18(2): 189-196. (in Chinese)
[27]	Xie G D, Zhang C X, Zhang L M, et al. 2015. Improvement of the evaluation method for ecosystem service value based on per unit area. Journal of Natural Resources, 30(8): 1243-1254. (in Chinese)
[28]	Xie G D, Zhen L, Lu C X, et al. 2008. A method of ecosystem services based on expert knowledge. Natural Resources, 23(5): 911-919. (in Chinese)
[29]	Xie G D, Zhen L, Lu C X, et al. 2010. Applying value transfer method for Eco-service valuation in China. Journal of Resources and Ecology, 1(1): 51-59.
[30]	Xie H L, Yao G, He Y F, et al. 2018. Study on spatial identification of critical ecological space based on GIS: A case study of Poyang Lake Ecological Economic Zone. Acta Ecologica Sinica, 38(16): 5926-5937. (in Chinese)
[31]	Xu H L, Gao W L, Chen S, et al. 2020. Identification of urban ecological space in South Jiangsu based on GIS: A case study of Suzhou. Chinese. Journal of Ecology, 39(2): 614-624. (in Chinese)
[32]	Yang X, Zheng X Q, Chen R. 2014. A land use change model: Integrating landscape pattern indexes and Markov-CA. Ecological Modelling, 283: 1-7. DOI URL
[33]	Yang X, Zheng X Q, Lv L N. 2012. A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata. Ecological Modelling, 233: 11-19. DOI URL
[34]	Yao N, Ma L Y, Yang J, et al. 2015. Changes of ecological spaces in Beijing's Plain areas between 1992 and 2013. Chinese Journal of Ecology, 34(5): 1427-1434. (in Chinese)
[35]	Zhang Y P, Chang Q, Guo X D. 2016. Management-oriented ecological land's conception and multi-dimensional classification system in China. Acta Ecologica Sinica, 36(12): 3655-3665. (in Chinese)
[36]	Zhao T Q, Ouyang Z Y, Jia L Q, et al. 2004. Ecosystem services and their valuation of China grassland. Acta Ecologica Sinica, 24(6): 1101-1110. (in Chinese)
[37]	Zhou R, Zhang H, Ye X Y, et al. 2016. The delimitation of urban growth boundaries using the CLUE-S land-use change model: Study on Xinzhuang Town, Changshu City, China. Sustainability, 8(11): 1182. DOI: 10.3390/su8111182. DOI URL

Ecosystem type	Land use type
Cropland	Paddy filed, irrigated cropland, rainfed cropland
Forest land	Woodland, shrubbery land, other woodlands, fruit plantation, tea plantation, other orchards
Grassland	Other grasslands
Wetland	Inland mudflat
Water body	River, pond, reservoir, ditches
Desert	Sand, barren land
Settlement	Mining land, land for scenic site facilities, railway, highway, rural road, city, organic town, village, hydraulic structure, land for agricultural facilities

Ecosystem type	Land use type
Cropland	Paddy filed, irrigated cropland, rainfed cropland
Forest land	Woodland, shrubbery land, other woodlands, fruit plantation, tea plantation, other orchards
Grassland	Other grasslands
Wetland	Inland mudflat
Water body	River, pond, reservoir, ditches
Desert	Sand, barren land
Settlement	Mining land, land for scenic site facilities, railway, highway, rural road, city, organic town, village, hydraulic structure, land for agricultural facilities

Ecosystem type	Provision service	Regulation service				Support service		Cultural service
Ecosystem type	PPP	GR	CR	EP	HR	SC	BIO	AL
Farmland	1.35	0.89	0.47	0.14	0.19	0.68	0.17	0.08
Forest	0.77	1.76	5.27	1.57	4.09	2.31	1.95	0.86
Grassland	0.58	1.21	3.19	1.05	2.53	1.58	1.34	0.59
Wetland	1.01	1.90	3.60	3.60	26.82	2.49	7.87	4.73
Desert	0.00	0.02	0.00	0.10	0.03	0.02	0.02	0.01
Waters	1.03	0.77	2.29	5.55	110.53	1.00	2.55	1.89
Settlement	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00

Ecosystem type	Provision service	Regulation service				Support service		Cultural service
Ecosystem type	PPP	GR	CR	EP	HR	SC	BIO	AL
Farmland	1.35	0.89	0.47	0.14	0.19	0.68	0.17	0.08
Forest	0.77	1.76	5.27	1.57	4.09	2.31	1.95	0.86
Grassland	0.58	1.21	3.19	1.05	2.53	1.58	1.34	0.59
Wetland	1.01	1.90	3.60	3.60	26.82	2.49	7.87	4.73
Desert	0.00	0.02	0.00	0.10	0.03	0.02	0.02	0.01
Waters	1.03	0.77	2.29	5.55	110.53	1.00	2.55	1.89
Settlement	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00

Function type	2003‒2007			2007‒2013			2013‒2019
Function type	PPP	CR	HR	PPP	CR	HR	PPP	CR	HR
PPP (ha)	29083	200	59	29084	598	65	29205	606	126
CR (ha)	195	55201	22	194	54815	14	450	54386	24
HR (ha)	49	15	13483	64	5	13468	92	31	13387