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

Abstract

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

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.

Figures/Tables 10

Fig. 1

Tab. 1

Tab. 2

Calculation methods of landscape pattern indices"

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。

Tab. 2

Tab. 3

Tab. 4

Fig. 2

Fig. 3

Tab. 5

Fig. 4

Tab. 6

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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

FLUS-Markov model-based multiscenario evaluation and prediction of the landscape ecological risk in Kezhou, South Xinjiang

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