基于FLUS-Markov模型的多情景景观生态风险评价与预测——以南疆克州为例

doi:10.13866/j.azr.2021.06.31

摘要/Abstract

摘要：

以新疆克孜勒苏柯尔克孜自治州（克州）为例,基于2005—2015年土地利用空间格局变化,利用FLUS-Markov复合模型预测2025年土地利用情况,采用Criteria Importance Though Intercrieria Correlation（CRITIC）权重法构建自然增长和生态保护情景下的景观生态风险指数,并采用自然断点法由低到高划分为5个等级（Risk Ⅰ、Risk Ⅱ、Risk Ⅲ、Risk Ⅳ和Risk Ⅴ）,以风险指数质心和标准差椭圆评价不同年份、多情景下景观生态风险时空格局和变化特征,探究影响其演化特征的驱动因素。结果表明：（1）在自然增长情景下,耕地、水域、建设用地面积不断增加,林地、草地、荒漠和裸地面积逐渐减小;生态保护情景下,草地相比于自然增长情景增加了51 km²。（2） 2005—2025年,克州景观生态风险整体呈现减小的趋势,生态保护情景相比于自然增长情景下的Risk Ⅰ、Risk Ⅱ和Risk Ⅳ面积分别增加34 km²、1240 km²和66 km²,Risk Ⅲ和Risk Ⅴ面积分别减少695 km²和645 km²。（3） 2005—2025年,自然增长情景下克州Risk Ⅰ、Risk Ⅱ、Risk Ⅳ和Risk Ⅴ呈扩散分布状态,Risk Ⅲ呈现紧凑收缩状态。（4）影响景观生态风险演化的主要因素是地形气候因子（解释力85%以上）,其次人口（解释力59%以上）也是重要的驱动因子,GDP对景观生态风险变化的贡献减小。

关键词: 土地利用, FLUS-Markov模型, 景观生态风险指数, 情景模拟, 驱动力, 新疆

Abstract:

In this study, the Kirgiz Autonomous Prefecture of Kizilsu in Xinjiang was set as an example to use an FLUS-Markov composite model based on changes in the land use spatial pattern from 2005 to 2015 for predicting the land use situation in 2025. The criteria importance though intercriteria correlation weight method was applied to construct the landscape ecological risk index under the two scenarios of natural growth and ecological protection. The natural risk index was also determined. The breakpoint method was divided into five levels (from low to high): risks I-V. Risk index centroid and standard deviation ellipse are used to evaluate the spatiotemporal pattern and changes in the characteristics of landscape ecological risk in different years and multiscenarios and to explore the driving factors affecting its evolution characteristics. Results show that (1) the area covered by cultivated land, water area, and construction land is increasing under a natural growth scenario, whereas the area spanning woodland, grassland, desert, and bare land is gradually decreasing. The grassland area under the ecological protection scenario increases by 51 km² compared with that under the natural growth scenario. (2) From 2005 to 2025, the overall landscape ecological risk of Kezhou decreased. Compared with the natural growth scenario, the areas under risks I, II, and IV in the ecological protection scenario increased by 34 km², 1240 km², and 66 km², respectively, and the areas under risks III and V decreased by 695 and 645 km², respectively. (3) From 2005 to 2025, risks I, II, IV, and V in Kezhou would be in a diffused distribution state, and risk III would be in a compact contraction state. (4) The main factors affecting the evolution of landscape ecological risk are topographic and climatic factors (interpretation over 85%). Another important driving factor is population (interpretation over 59%). The contribution of GDP to the changes in the landscape ecological risk is reduced.

Key words: land use, FLUS-Markov model, landscape ecological risk index, scenario simulation, driving force, Xinjiang

金梦婷,徐丽萍,徐权. 基于FLUS-Markov模型的多情景景观生态风险评价与预测——以南疆克州为例[J]. 干旱区研究, 2021, 38(6): 1793-1804.

JIN Mengting,XU Liping,XU Quan. FLUS-Markov model-based multiscenario evaluation and prediction of the landscape ecological risk in Kezhou, South Xinjiang[J]. Arid Zone Research, 2021, 38(6): 1793-1804.

图/表 10

图1

表1

表2

景观格局指数计算方法"

名称	景观指数	生态意义
景观破碎度指数(C_i)	边缘密度(ED) 面积加权的平均形状因子 (SHAPE_AM)	ED用于揭示景观或类型被边界分割的程度,是景观破碎化程度的直接反应,边界密度越高,反映景观破碎度越高。SHAPE_AM是度量景观空间格局复杂性的重要指标之一,对于自然斑块或自然景观的形状分析还有另一个很显著的生态意义,即常说的边缘效应。
景观分离度指数(N_i)	斑块结合指数 (COHESION) 聚集度指数 (AI) 集聚指数 (IJI)	COHESION指标描述的是景观里不同斑块类型的团聚程度或延展趋势,由于该指标包含空间信息,是描述景观格局的最重要的指数之一。AI来源于斑块类型水平上的邻近矩阵的计算,是反映景观聚集与分离程度的重要指标之一。IJI对那些受到某种自然条件严重制约的生态系统的分布特征反映显著。
景观优势度指数(D_i)	最大斑块指数 (LPI)	LPI有助于确定景观的模地或优势类型等,其值的大小决定着景观中的优势种、内部种的丰度等生态特征,其值的变化可以改变干扰的强度和频率,反映人类活动的方向和强弱。
景观结构指数(S_i)	S_i=a(ED)+b(SHAPE_AM) +c(COHESION) +d(AI) + e(IJI) + f(LPI)	反映不同景观所代表的生态系统受到干扰的损失程度。式中：a、b、c、d、e、f为相应景观指数的权重,为避免人为赋值的主观性,研究采用CRITIC权重法进行客观赋值。
景观脆弱度指数(F_i)	专家打分赋值获得	表示不同景观类型对外界干扰的敏感性,值越大,生态风险越大。景观脆弱度的大小与其在景观自然演替过程中所处的阶段有关,结合研究区特点,研究将景观类型按其脆弱度由高到低依次赋值：7裸地、6 荒漠、5 水域、4 耕地、3 草地、2 林地、1 建设用地。
景观损失度指数(R_i)	$R i = S i × F i$	表示不同景观类型所代表的生态系统受到外界干扰时,其自然属性损失的程度。
景观生态风险指数(ERI_i)	$ER I i = ∑ i = 1 n A ki A k R i$	表示一个评价单元内综合生态损失度的相对大小,即评价单元内生态风险大小。式中：ERI_i为第i个评价单元的景观生态风险指数;A_ki为第k个评价单元内景观类型i的面积;A_k为第k个评价单元的面积。

表2

表3

表4

图2

图3

表5

图4

表6

参考文献 40

[1]	彭建, 党威雄, 刘焱序, 等. 景观生态风险评价研究进展与展望[J]. 地理学报, 2015, 70(4):664-677. doi: 10.11821/dlxb201504013
	[ Peng Jian, Dang Weixiong, Liu Yanxu, et al. Review on landscape ecological risk assessment[J]. Acta Geographica Sinica, 2015, 70(4):664-677. ] doi: 10.11821/dlxb201504013
[2]	李青圃, 张正栋, 万露文, 等. 基于景观生态风险评价的宁江流域景观格局优化[J]. 地理学报, 2019, 74(7):1420-1437. doi: 10.11821/dlxb201907011
	[ Li Qingpu, Zhang Zhengdong, Wan Luwen, et al. Landscape pattern optimization in Ningjiang River Basin based on landscape ecological risk assessment[J]. Acta Geographica Sinica, 2019, 74(7):1420-1437. ] doi: 10.11821/dlxb201907011
[3]	曹祺文, 张曦文, 马洪坤, 等. 景观生态风险研究进展及基于生态系统服务的评价框架: ESRISK[J]. 地理学报, 2018, 73(5):843-855. doi: 10.11821/dlxb201805005
	[ Cao Qiwen, Zhang Xiwen, Ma Hongkun, et al. Review of landscape ecological risk and an assessment framework based on ecological services: ESRISK[J]. Acta Geographica Sinica, 2018, 73(5):843-855. ] doi: 10.11821/dlxb201805005
[4]	石玉琼, 王宁练, 李团胜, 等. 榆林市景观生态风险及其时空分异[J]. 干旱区研究, 2019, 36(2):494-504.
	[ Shi Yuqiong, Wang Ninglian, Li Tuansheng, et al. Landscape ecological risk and its spatiotemporal variation in Yulin[J]. Arid Zone Research, 2019, 36(2):494-504. ]
[5]	刘焱序, 王仰麟, 彭建, 等. 基于生态适应性循环三维框架的城市景观生态风险评价[J]. 地理学报, 2015, 70(7):1052-1067. doi: 10.11821/dlxb201507003
	[ Liu Yanxu, Wang Yanglin, Peng Jian, et al. Urban landscape ecological risk assessment based on the 3D framework of adaptive cycle[J]. Acta Geographica Sinica, 2015, 70(7):1052-1067. ] doi: 10.11821/dlxb201507003
[6]	Mo Wenbo, Wang Yong, Zhang Yingxue, et al. Impacts of road network expansion on landscape ecological risk in a megacity, China: A case study of Beijing[J]. Science of the Total Environment, 2017, 574:1000-1011. doi: 10.1016/j.scitotenv.2016.09.048
[7]	王晓峰, 延雨, 李月皓, 等. 银川市湿地景观演变及其驱动因素[J]. 干旱区研究, 2021, 38(3):855-866.
	[ Wang Xiaofeng, Yan Yu, Li Yuehao, et al. Wetland landscape evolution and its driving factors in Yinchuan[J]. Arid Zone Research, 2021, 38(3):855-866. ]
[8]	Li Jialin, Pu Ruiliang, Gong Hongbo, et al. Evolution characteristics of landscape ecological risk patterns in coastal zones in Zhejiang Province, China[J]. Sustainability-Basel, 2017, 9(4):584.
[9]	潘竟虎, 刘晓. 疏勒河流域景观生态风险评价与生态安全格局优化构建[J]. 生态学杂志, 2016, 35(3):791-799.
	[ Pan Jinghu, Liu Xiao. Landscape ecological risk assessment and landscape security pattern optimization in Shule River Basin[J]. Chinese Journal of Ecology, 2016, 35(3):791-799. ]
[10]	周亚军, 刘廷玺, 段利民, 等. 锡林河流域上游河谷湿地景观格局演变及其驱动力[J]. 干旱区研究, 2020, 37(3):580-590.
	[ Zhou Yajun, Liu Tingxi, Duan Limin, et al. Driving force analysis and landscape pattern evolution in the up stream valley of Xilin River Basin[J]. Arid Zone Research, 2020, 37(3):580-590. ]
[11]	Shi Hui, Yang Zhaoping, Han Fang, et al. Assessing landscape ecological risk for a world natural heritage site: A case study of Bayanbulak in China[J]. Polish Journal of Environmental Studies, 2015, 24(1):269-283. doi: 10.15244/pjoes/28685
[12]	许妍, 高俊峰, 高永年. 基于土地利用动态变化的太湖地区景观生态风险评价[J]. 湖泊科学, 2011, 23(4):642-648.
	[ Xu Yan, Gao Junfeng, Gao Yongnian. Landscape ecological risk assessment in the Taihu region based on land use change[J]. Journal of Lake Sciences, 2011, 23(4):642-648. ]
[13]	Liu Xiaoping, Liang Xun, Li Xia, et al. A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects[J]. Landscape and Urban Planning, 2017, 168:94-116. doi: 10.1016/j.landurbplan.2017.09.019
[14]	秦埼瑞, 李雪梅, 陈庆伟, 等. 基于FLUS模型的天山山区未来土地利用变化预估[J]. 干旱区研究, 2019, 36(5):1270-1279.
	[ Qin Qirui, Li Xuemei, Chen Qingwei, et al. Estimation of future land use change in the Tianshan Mountainous based on FLUS model[J]. Arid Zone Research, 2019, 36(5):1270-1279. ]
[15]	王明常, 郭鑫, 王凤艳, 等. 基于FLUS的长春市土地利用动态变化与预测分析[J]. 吉林大学学报(地球科学版), 2019, 49(6):1795-1804.
	[ Wang Mingchang, Guo Xin, Wang Fengyan, et al. Dynamic change and predictive analysis of land use types in Changchun City based on FLUS model[J]. Journal of Jilin University (Earth Science Edition), 2019, 49(6):1795-1804. ]
[16]	曹帅, 金晓斌, 杨绪红, 等. 耦合MOP与GeoSOS-FLUS模型的县级土地利用结构与布局复合优化[J]. 自然资源学报, 2019, 34(6):1171-1185.
	[ Cao Shuai, Jin Xiaobin, Yang Xuhong, et al. Coupled MOP and GeoSOS-FLUS models research on optimization of land use structure and layout in Jintan district[J]. Journal of Natural Resources, 2019, 34(6):1171-1185. ]
[17]	孙定钊, 梁友嘉. 基于改进Markov-CA模型的黄土高原土地利用多情景模拟[J]. 地球信息科学学报, 2021, 23(5):825-836. doi: 10.12082/dqxxkx.2021.200283
	[ Sun Dingzhao, Liang Youjia. Multi-scenario simulation of land use dynamic in the Loess Plateau using an improved Markov-CA model[J]. Journal of Geo-information Science, 2021, 23(5):825-836. ] doi: 10.12082/dqxxkx.2021.200283
[18]	李志明, 宋戈, 鲁帅, 等. 基于CA-Markov模型的哈尔滨市土地利用变化预测研究[J]. 中国农业资源与区划, 2017, 38(12):41-48.
	[ Li Zhiming, Song Ge, Lu Shuai, et al. Change and prediction of the land use in Harbin City based on CA-Markov model[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2017, 38(12):41-48. ]
[19]	朱文博, 张静静, 崔耀平, 等. 基于土地利用变化情景的生态系统碳储量评估——以太行山淇河流域为例[J]. 地理学报, 2019, 74(3):446-459. doi: 10.11821/dlxb201903004
	[ Zhu Wenbo, Zhang Jingjing, Cui Yaoping, et al. Assessment of territorial ecosystem carbon storage based on land use change scenario: A case study in Qihe River Basin[J]. Acta Geographica Sinica, 2019, 74(3):446-459. ] doi: 10.11821/dlxb201903004
[20]	刘春艳, 张科, 刘吉平. 1976—2013年三江平原景观生态风险变化及驱动力[J]. 生态学报, 2018, 38(11):3729-3740.
	[ Liu Chunyan, Zhang Ke, Liu Jiping. A long-term site study for the ecological risk migration of landscapes and its driving forces in the Sanjiang Plain from 1976 to 2013[J]. Acta Ecologica Sinica, 2018, 38(11):3729-3740. ]
[21]	娄妮, 王志杰, 何嵩涛. 基于景观格局的阿哈湖国家湿地公园景观生态风险评价[J]. 水土保持研究, 2020, 27(1):233-239.
	[ Lou Ni, Wang Zhijie, He Songtao. Assessment on ecological risk of Aha Lake national wetland park based on landscape pattern[J]. Research of Soil and Water Conservation, 2020, 27(1):233-239. ]
[22]	谢小平, 陈芝聪, 王芳, 等. 基于景观格局的太湖流域生态风险评估[J]. 应用生态学报, 2017, 28(10):3369-3377.
	[ Xie Xiaoping, Chen Zhicong, Wang Fang, et al. Ecological risk assessment of Taihu Lake basin based on landscape pattern[J]. Chinese Journal of Applied Ecology, 2017, 28(10):3369-3377. ]
[23]	张行, 陈海, 史琴琴, 等. 陕西省景观生态脆弱性时空演变及其影响因素[J]. 干旱区研究, 2020, 37(2):496-505.
	[ Zhang Hang, Chen Hai, Shi Qinqin, et al. Spatiotemporal evolution and driving factors of landscape ecological vulnerability in Shaanxi Province[J]. Arid Zone Research, 2020, 37(2):496-505. ]
[24]	吕乐婷, 张杰, 孙才志, 等. 基于土地利用变化的细河流域景观生态风险评估[J]. 生态学报, 2018, 38(16):5952-5960.
	[ Lyu Leting, Zhang Jie, Sun Caizhi, et al. Landscape ecological risk assessment of Xi river Basin based on land-use change[J]. Acta Ecologica Sinica, 2018, 38(16):5952-5960. ]
[25]	黄木易, 何翔. 近20年来巢湖流域景观生态风险评估与时空演化机制[J]. 湖泊科学, 2016, 28(4):785-793.
	[ Huang Muyi, He Xiang. Landscape ecological risk assessment and its mechanism in Chaohu Basin during the past almost 20 years[J]. Journal of Lake Sciences, 2016, 28(4):785-793. ]
[26]	刘世梁, 刘琦, 张兆苓, 等. 云南省红河流域景观生态风险及驱动力分析[J]. 生态学报, 2014, 34(13):3728-3734.
	[ Liu Shiliang, Liu Qi, Zhang Zhaoling, et al. Landscape ecological risk and driving force analysis in the Red River Basin[J]. Acta Ecologica Sinica, 2014, 34(13):3728-3734. ]
[27]	张月, 张飞, 周梅, 等. 干旱区内陆艾比湖区域景观生态风险评价及时空分异[J]. 应用生态学报, 2016, 27(1):233-242.
	[ Zhang Yue, Zhang Fei, Zhou Mei, et al. Landscape ecological risk assessment and its spatio-temporal variations in Ebinur Lake region of inland arid area[J]. Chinese Journal of Applied Ecology, 2016, 27(1):233-242. ]
[28]	王昆, 宋海洲. 三种客观权重赋权法的比较分析[J]. 技术经济与管理研究, 2003, 24(6):48-49.
	[ Wang Kun, Song Haizhou. A comparative analysis of three objective weighting methods[J]. Journal of Technical Economics & Management, 2003, 24(6):48-49. ]
[29]	李翔, 李学军. 南疆三地州自我发展能力的测度及实证分析[J]. 新疆社科论坛, 2014, 26(4):57-62.
	[ Li Xiang, Li Xuejun. Measurement and empirical analysis of self-development capacity of three southern Xinjiang prefectures[J]. Tribune of Social Sciences in Xinjiang, 2014, 26(4):57-62. ]
[30]	Pontius R G, Walker R, Yao-Kumah R, et al. Accuracy assessment for a simulation model of Amazonian deforestation[J]. Annals of the Association of American Geographers, 2007, 97(4):677-695. doi: 10.1111/j.1467-8306.2007.00577.x
[31]	赵林峰, 刘小平, 刘鹏华, 等. 基于地理分区与FLUS模型的城市扩张模拟与预警[J]. 地球信息科学学报, 2020, 22(3):517-530. doi: 10.12082/dqxxkx.2020.190477
	[ Zhao Linfeng, Liu Xiaoping, Liu Penghua, et al. Urban expansion simulation and early warning based on geospatial partition and FLUS model[J]. Journal of Geo-information Science, 2020, 22(3):517-530. ] doi: 10.12082/dqxxkx.2020.190477
[32]	陈海珍, 石铁柱, 邬国锋. 武汉市湖泊景观动态遥感分析(1973-2013年)[J]. 湖泊科学, 2015, 27(4):745-754.
	[ Chen Haizhen, Shi Tiezhu, Wu Guofeng. The dynamic analysis of lake landscape of Wuhan City in recent 40 years[J]. Journal of Lake Sciences, 2015, 27(4):745-754. ]
[33]	张彧瑞, 马金珠, 齐识. 人类活动和气候变化对石羊河流域水资源的影响——基于主客观综合赋权分析法[J]. 资源科学, 2012, 34(10):1922-1928.
	[ Zhang Yurui, Ma Jinzhu, Qi Shi. Human activities, climate change and water resources in the Shiyang Basin[J]. Resources Science, 2012, 34(10):1922-1928. ]
[34]	苏海民, 何爱霞. 基于RS和地统计学的福州市土地利用分析[J]. 自然资源学报, 2010, 25(1):91-99.
	[ Su Haiming, He Aixia. Analysis of land use based on RS and geostatistics in Fuzhou City[J]. Journal of Natural Resources, 2010, 25(1):91-99. ]
[35]	王芳, 陈芝聪, 谢小平. 太湖流域建设用地与耕地景观时空演变及驱动力[J]. 生态学报, 2018, 38(9):3300-3310.
	[ Wang Fang, Chen Zhicong, Xie Xiaoping. Analysis of spatial-temporal evolution and it's driving forces of construction land and cultivated landscape in Taihu Lake Basin[J]. Acta Ecologica Sinica, 2018, 38(9):3300-3310. ]
[36]	Achilleos G A. The inverse distance weighted interpolation method and error propagation mechanism-creating a DEM from an analogue topographical map[J]. Journal of Spatial Science, 2011, 56(2):283-304. doi: 10.1080/14498596.2011.623348
[37]	卢世俊. 乌鲁木齐城市空间扩展特征及驱动机制[J/OL]. 武汉大学学报(信息科学版), 2020. https://doi.org/10.13203/j.whugis20200119.
	[ Lu Shijun. Characteristics and driving mechanism of urban space expansion in Urumqi[J/OL]. Geomatics and Information Science of Wuhan University, 2020. https://doi.org/10.13203/j.whugis20200119. ]
[38]	王劲峰, 徐成东. 地理探测器: 原理与展望[J]. 地理学报, 2017, 72(1):116-134. doi: 10.11821/dlxb201701010
	[ Wang Jinfeng, Xu Chengdong. Geodetector: Principle and prospective[J]. Acta Geographica Sinica, 2017, 72(1):116-134. ] doi: 10.11821/dlxb201701010
[39]	李玮麒, 兰泽英, 陈德权, 等. 广州市土地利用多情景模拟及其生态风险时空响应[J]. 水土保持通报, 2020, 40(4):204-210.
	[ Li Weiqi, Lan Zeying, Chen Dequan, et al. Multi-scenario simulation of land use and its spatial-temporal response to ecological risk in Guangzho City[J]. Bulletin of Soil and Water Conservation, 2020, 40(4):204-210. ]
[40]	于涛, 沈浩, 仲嘉亮. 基于CA-Markov模型的新疆克州土地利用动态模拟研究[J]. 新疆环境保护, 2008, 30(1):11-14.
	[ Yu Tao, Shen Hao, Zhong Jialiang. Dynamic analogue research of land utilization of Kizilsu Kirgiz Autonomous Prefecture Xinjiang base on CA-Markov model[J]. Environmental Protection of Xinjiang, 2008, 30(1):11-14. ]

数据类型	数据内容	数据时间	数据分辨率	数据用途
土地利用数据	土地利用类型	2005年, 2015年	1 km×1 km	模型输入数据
地形数据	高程、坡度	2005年, 2015年	30 m×30 m	驱动因子数据
气候数据	气温、降水	2005年, 2015年	1 km×1 km
社会经济数据	人口、GDP	2005年, 2015年	1 km×1 km

	权重
	ED	SHAPE_AM	COHESION	AI	IJI	LPI
2005年	0.85	9.59	22.89	25.31	35.78	5.58
2015年	0.88	9.41	20.87	25.38	37.73	5.73
2025年自然增长情景	0.97	9.81	18.50	23.67	40.60	6.44
2025年生态保护情景	0.90	9.52	20.07	24.48	39.29	5.74

土地利用类型	面积/ km²
土地利用类型	2005年	2015年	2025年自然增长情景	2025年生态保护情景
耕地	1017	1220	1420	1265
林地	506	504	502	502
草地	37268	37190	37113	37164
水域	5534	5544	5554	5554
建设用地	48	65	81	81
荒漠	9358	9223	9091	9195
裸地	16055	16040	16025	16025

级别	2005年		2015年		2025年自然增长情景		2025年生态保护情景
级别	面积/km²	比例/%	面积/km²	比例/%	面积/km²	比例/%	面积/km²	比例/%
低生态风险Risk Ⅰ	343	2.26	317	2.09	317	2.09	351	2.32
较低生态风险Risk Ⅱ	3021	19.93	2643	17.44	2085	13.76	3325	21.94
中生态风险Risk Ⅲ	3640	24.02	4025	26.56	4619	30.48	3924	25.89
较高生态风险Risk Ⅳ	3583	23.64	3900	25.73	4982	32.87	5048	33.31
高生态风险Risk Ⅴ	4568	30.14	4270	28.18	3152	20.80	2507	16.54

年份	气温	降水	人口	国内生产总值	高程	坡度
2005	0.917	0.925	0.598	0.479	0.938	0.859
2015	0.909	0.894	0.636	0.131	0.941	0.857