多因素耦合下三峡库区土地利用未来情景模拟

doi:10.11821/dlxb201611009

摘要/Abstract

摘要：

模型模拟和情景变化分析是未来土地利用变化研究的核心内容,本文以2000年三峡库区土地利用现状为基期数据,利用Binary Logistic模型回归分析驱动因子与土地利用间的关系,利用CLUE-S模型对2010年土地利用进行模拟,校验并确定影响库区土地利用驱动因素的主要参数后,基于自然增长、粮食安全、移民建设和生态保护对2020年、2030年库区土地利用情景予以模拟。结果表明：① 通过Binary Logistic模型分析和检验,水田、旱地、林地、草地、建设用地和水域的ROC曲线下面积值均大于0.8,表明所选驱动因子对土地利用的解释能力较强,可用来估算土地利用概率分布;② 2010年各地类模拟结果经验证得Kappa系数分别为水田0.9、旱地0.92、林地0.97、草地0.84、建设用地0.85和水域0.77,总体上能满足模拟与预测需求;③ 多情景模拟显示库区不同土地利用类型在空间上的竞争关系,以及所带来的对库区粮食安全、移民建设、生态保护的影响,包括水田大量转换为旱地（“水改旱”）、耕地与林草地被建设占用、林草地开垦为耕地、陡坡耕地退为林草地等行为,需要在土地利用优化中平衡各方面的需求;④ 多因素、多情景模拟能为库区土地利用提供更为清晰的、可供抉择的政策调控思路。

关键词: 土地利用, CLUE-S模型, 驱动因子, 情景模拟, 三峡库区

Abstract:

Model simulation and scenarios change analysis are the core contents of the future land use change study. In this paper, land use status data of the Three Gorge Reservoir Area (TGRA) in 1990 were used as base data. The relationship between driving factors and land use was analyzed by using binary logistic stepwise regression analysis, based on which land use in 2010 was simulated by CLUE-S model. After the inspection and determination of main parameters impacting driving factors of land use in the TGRA, land use of the TGRA in 2030 was simulated based on four scenarios, including natural growth, food security, emigrant construction and ecological protection. The results were shown as follows: (1) The areas under ROC curves of land use types were both greater than 0.8 under the analysis and inspection of binary logistic model. These land use types include paddy field, dry land, forestland, grassland, construction land and water area. Therefore, it has a strong interpretation ability of driving factors on land use, which can be used in the estimation of land use probability distribution. (2) The Kappa coefficients, verified from the result of land use simulation in 2010, were showed of paddy field 0.9, dry land 0.95, forestland 0.97, grassland 0.84, construction land 0.85 and water area 0.77. So the results of simulation could meet the needs of future simulation and prediction. (3) The results of multi-scenarios simulation show a spatial competition relationship between different land use types, and an influence on food security, emigrant construction and ecological protection in the TGRA, including some land use change such as the large-scale conversion from paddy field to dry land, the occupation of cultivated land, rapid expansion of construction land from forestland and grassland, the reclamation of cultivated land to forestland and grassland, the conversion of forestland and grassland to cultivated land. Therefore, it is necessary to balance the needs of various aspects in land use optimization, so as to achieve the coordination between social economy and ecological environment.

Key words: land use, CLUE-S model, driving factor, scenario simulation, Three Gorge Reservoir Area

邓华, 邵景安, 王金亮, 高明, 魏朝富. 多因素耦合下三峡库区土地利用未来情景模拟[J]. 地理学报, 2016, 71(11): 1979-1997.

Hua DENG, Jing'an SHAO, Jinliang WANG, Ming GAO, Chaofu WEI. Land use driving forces and its future scenario simulation in the Three Gorges Reservoir Area using CLUE-S model[J]. Acta Geographica Sinica, 2016, 71(11): 1979-1997.

图/表 13

图1

表1

表2

三峡库区不同情景下土地利用需求"

情景类型	情景描述
自然增长	以2000-2010年土地利用变化率为未来土地利用变化速率,进行未来土地利用情景模拟。
粮食安全	在“保供增收”的目标下,要坚守耕地红线政策,避免因过度开发而占用耕地,满足粮食需求的增长。因此,设置粮食安全情景的主要目的在于遵循现实条件下控制库区耕地转出的数量和方向,优先保护原有优质连片耕地。在库区粮食自给前提下,受现实利益驱动,水田会不断地转为旱地;随着陡坡耕地撂荒的进一步加剧和新一轮退耕还林政策的实施,靠近山区的水田和旱地部分逐渐转变为林草地。这样,未来库区水田和旱地的面积定会出现某种程度的减少。同时,为提高灌溉保证率,水域面积可能增加。另外,根据渝鄂主体功能区划分结果,库区林地仍将得到有效保护,且大部分林区属限制开发区,为此库区林地基本不变。2010年以后,库区步入后移民时期,建设用地增速趋缓,但仍然会保持一定速度的增长。
移民建设	库区进入移民后时代以后,经济社会发展战略重点在于重新调整库区社会经济发展战略,建设国家级生态经济特区,重建完整的库区产业结构等。因此,移民情景的设置在于满足后移民时期经济、社会发展对建设用地的基本需求。在此情景下,地势相对平缓区的耕地面积定会明显减少,林地因远离城镇、退耕政策实施以及耕地撂荒而有所增加,草地则因被开发为耕地而小幅减少。另外,在库区水产养殖快速发展的促进下,水域面积增加较快。
生态保护	在生态建设的发展战略上,国家已将三峡水库定位为淡水资源战略储备库,将库区定位为国家环境保护区和重点生态功能保护区。因此,设置生态情景的主要目的在于严格保护林草地、水域等生态用地。耕地尤其是旱地因退耕还林、森林工程等生态保护措施的实施而发生明显减少,林草地显著增加;因农田水利设施建设、水产养殖发展,水域面积会相对增加,但幅度不大;禁止大规模开发建设政策的强力推进,建设用地的扩张速度小于移民建设情景。

表2

图2

表3

表4

图3

表5

图4

图5

图6

图7

图8

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情景类型*	水田	旱地	林地	草地	建设用地	水域
2010年模拟	0.6	0.45	0.77	0.35	0.83	0.58
自然增长	同2010年模拟
粮食安全	0.78	0.8	0.65	0.43	0.7	0.6
移民建设	0.54	0.63	0.75	0.5	0.88	0.58
生态保护	0.56	0.66	0.8	0.75	0.73	0.5

编码	水田			旱地			林地			草地			建设用地			水域
编码	Bata 系数		Exp (B)		Bata 系数		Exp (B)		Bata 系数		Exp (B)	Bata 系数	Exp (B)	Bata 系数	Exp (B)	Bata 系数	Exp (B)
sc1gr0	-0.0008	0.9992		-0.0005	0.9995		0.0003	1.0003		0.0003	1.0003		-0.0038	0.9962		-0.003	0.997
sc1gr1	-0.0452	0.9558		-	-		0.0217	1.0219		-	-		-	-		-	-
sc1gr2	0.001	1.001		-	-		-0.0007	0.999		-	-		-	-		-	-
sc1gr3	-0.001	0.999		-	-		-	-		-	-		-	-		-	-
sc1gr4	-	-		-	-		0.1485	1.1601		-	-		-	-		-	-
sc1gr5	-	-		-	-		-	-		-	-		-	-		-	-
sc1gr6	-	-		0.00001	1		-0.00001	0.99999		-	-		-0.0005	0.9995		-	-
sc1gr7	-	-		0.00001	1		-	-		-	-		-	-		-0.008	0.992
sc1gr8	-	-		-0.00001	1		-	-		-	-		-	-		-	-
sc1gr9	-7.95	0.0004		-	-		2.4797	11.9375		-29.5009	0		-	-		-	-
sc1gr10	0.0085	1.0086		-	-		-	-		0.0131	1.0132		-0.0094	0.9906		-	-
sc1gr11	-	-		-	-		-	-		-0.0001	0.9999		-	-		-	-
sc1gr12	-	-		-	-		0.0005	1.0005		-	-		-	-		-	-
sc1gr13	-	-		-	-		-	-		0.2594	1.2961		-	-		-	-
Lyfd0	0.738	2.0918		-	-		-	-		-	-		-	-		-	-
Lyfd1	-	-		1.4647	4.3261		-	-		0.2414	1.273		-	-		-	-
Lyfd2	-0.2814	0.7547		-	—		3.1686	23.7745		-	-		-	-		-	-
Lyfd3	-	-		0.0616	1.0636		-	-		1.0544	2.8702		-	-		-	-
Lyfd4	-	-		-	-		-	-		-	-		0.0952	1.0999		-	-
Lyfd5	-0.028	0.9723		-	-		-	-		-	-		-	-		0.114	1.121
常量	-1.258	0.2841		-2.8154	0.0599		-6.429	0.0016		-3.838	0.022		-2.269	0.103		0.851	2.342
ROC值	0.91		0.842			0.885		0.903			0.989		0.997

2000年 2010年	林地	草地	水域	建设用地	水田	旱地	转出总计
林地		15.94	76.88	28.8	0.97	21.88	144.47
草地	195.89		32.02	14.66	0.45	51.57	294.6
水域	0.02	0.12		0.81	0.04	0.94	1.91
建设用地	0.84	0.35	12.97		0	0	14.15
水田	33.09	1.21	26.98	107.35		0.66	169.3
旱地	242.78	121.26	51.41	162.42	0.02		577.9
转入总计	472.62	138.87	200.26	314.03	1.49	75.06	1202.33

检验指标	水田	旱地	林地	草地	建设用地	水域
现状面积(km²)	6238.25	13643.25	29083	7538.5	693	992.5
模拟面积(km²)	5556.5	14327	29094	7539.5	683.5	989.5
重叠面积(km²)	5382	13180.5	28682	6469.25	598.5	767
重叠率(%)	96.86	92	98.58	85.80	87.56	77.51
Kappa系数	0.90	0.92	0.97	0.84	0.85	0.77