秦淮河流域近30年不透水面景观格局时空演变研究

doi:10.3724/SP.J.1047.2017.00238

摘要/Abstract

摘要：

不透水面作为表征城市化的重要指标,具有极其重要的生态学意义。本文选取多时相Landsat影像,以秦淮河流域为研究区,通过旋转森林算法得到9年土地利用覆盖图,并综合不透水率动态变化分析、土地利用变化轨迹分析、景观格局指数分析探究大流域尺度在近30年间不透水面景观格局的演变过程,旨在揭示其在城市化背景下的时空演变规律。研究结果表明：近30年来,秦淮河流域城市化进程推动下的景观格局变化显著,不透水面积增长近4倍,景观优势度大幅提升;城市化格局演变在2001-2003年前后具有明显差异,前期城市扩张以南京城区与江宁区为主,后期南京城区不透水面扩张大大减缓,而溧水区与句容市扩张速度大幅提升;城市化建设后期,流域内不透水面斑块形状复杂度降低、散布与并列情况减少、斑块的连通性、聚集度逐步增加,其中连通性水平较高的地区主要集中在南京城区和江宁区。

关键词: Landsat, 大流域尺度, 城市扩张, 景观格局, 不透水面

Abstract:

Urbanization has made natural and semi-natural landscape to be gradually replaced by impervious surface, which has caused significant reduction of surface permeability in urban region. Along with these changes, profound transformations have occurred in hydrological processes, water environment, urban thermal environment, and ecological service system. Impervious surface is an important indicator of characterizing the urban expansion, which has extremely important ecological implications. We chose the multi-temporal Landsat images as our data sources, and took Qinhuai River Basin as the research area in this study. Rotation forest algorithm, which belongs to the category of ensemble learning that synthesizes the advantages of different classifiers and effectively addresses the limits of the information provided by a single image, was used to produce the nine-year land cover maps of Qinhuai River Basin. Focusing on the large watershed scale, we explored the changing process of the urban landscape pattern in the research area during the past 30 years. Impervious surface coverage dynamic analysis was used to reveal the changes of impervious surface area. Land cover change trajectory analysis was used to explore the resources of impervious surface and transformation process of land cover. Landscape metrics analysis was used to quantify the spatial and temporal changes of impervious surface pattern. We aimed to reveal the spatial-temporal changing characteristics of urban landscape configuration against the background of urbanization. The results showed that the landscape pattern has changed significantly during the past 30 years. Overall, the impervious area has increased by nearly four times. The dominance of the impervious surface has increased greatly. The analysis suggested that the turning point of urban expansion is between 2001 and 2003. Urban expansion mainly occurred in Nanjing city and Jiangning district before the turning point, while the impervious surface expansion rate of Nanjing city has greatly decreased after that. At the same time, there is a sharp rise in the expansion rate of Lishui and Jurong districts. The impervious surface within the 2001-2003 period had the highest spatial heterogeneity, which then decreased significantly after the turning point until 2015. The shape of the impervious patches has becoming simpler at the latter stage, and the impervious surface has turned from a dispersed distribution into a distribution pattern with higher connectivity. Besides, the area with high level of connectivity is mainly distributed in Nanjing city and Jiangning district.

Key words: Landsat, big watershed scale, urban expansion, landscape pattern, impervious surface

宋明明, 都金康, 郑文龙, 李成蹊, 卞国栋. 秦淮河流域近30年不透水面景观格局时空演变研究[J]. 地球信息科学学报, 2017, 19(2): 238-247.DOI:10.3724/SP.J.1047.2017.00238

SONG Mingming,DU Jinkang,ZHENG Wenlong,LI Chengxi,BIAN Guodong. Quantifying the Spatial-Temporal Changes of Impervious Surface Landscape Pattern from1988 to 2015 in Qinhuai River Basin[J]. Journal of Geo-information Science, 2017, 19(2): 238-247.DOI:10.3724/SP.J.1047.2017.00238

图/表 9

图1

表1

表2

表3

景观格局指数生态含义"

格局指数	计算公式	解释
斑块数量NP	$n k$	n_k表示地类k的斑块总数量,常用来描述景观的异质性,其值的大小与景观的破碎度有很好的正相关
斑块数量年均增长率 Annual Growth Rate of NP	$n b n a - 1$	n_b表示后期斑块数量,n_a表示前期斑块数量,n为时间跨度（年）,解释斑块数量年平均增长的速度（%）
平均斑块尺寸MPS	$A n k × 106$	地类k总面积A与斑块数量之比,转化为km²。代表一种平均状况
最大斑块指数LPI	$max (a ij) A$ （100）	0<LPI≤100,a_ij表示第i行,第j列斑块的面积,A为总面积（m²）,决定景观的优势种
景观形状指数LSI	$0.25 E A$	以正方形为标准,LSI ≥1,只有一个正方形斑块时LSI =1,形状越不规则,LSI越大
散布与并列指标IJI	$- ∑ k = 1 m (e ik ∑ k = 1 m e ik) Ln (e ik ∑ k = 1 m e ik)] Ln (m - 1 100$	IJI在斑块类型级别上等于与某斑块类型i相邻的各斑块类型的邻接边长除以斑块i的总边长再乘以该值的自然对数之后的和的负值,除以斑块类型数减1的自然对数,最后乘以100是为了转化为百分比的形式
聚集度指数AI	$[g ij max → g ij] (100)$	AI基于同类型斑块像元间公共边界长度来计算,当该类型中像元间的公共边界达到最大时,具有最大的聚合指数
香农多样性指数SHDI	$- ∑ i = 1 n [P i Ln (P i)]$	当景观中只有一种类型的板块时SHDI =0,当斑块类型增加或者各斑块所占比例趋于相近时,SHDI也相应增大
香农均度指数SHEI	$- ∑ i = 1 m [p i Ln (P i)] LnM$	SHEI等于香农多样性指数除以给定景观丰度下的最大可能多样性（各斑块类型均等分布）

表3

表4

表5

表6

图2

图3

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年份	数据类型	遥感影像采集时间	分辨率/m
1988	Landsat5 TM	1988-07-05	30
1994	Landsat5 TM	1994-07-06	30
2001	Landsat7 ETM+	2001-07-17	30
2003	Landsat5 TM	2003-02-05	30
2006	Landsat5 TM	2006-05-20	30
2009	Landsat7 ETM+	2009-05-04	30
2011	Landsat7 ETM+	2011-07-29	30
2013	Landsat8 OLI	2013-08-11	30
2015	Landsat7 ETM+	2015-05-05	30

	年份
	1988	1994	2001	2003	2006	2009	2011	2013	2015
总体精度	0.93	0.90	0.91	0.92	0.93	0.93	0.92	0.90	0.93
Kappa系数	0.86	0.83	0.85	0.88	0.87	0.85	0.85	0.88	0.89
不透水面F值	0.90	0.89	0.90	0.91	0.92	0.86	0.92	0.89	0.92

时间	全流域/%	南京/%	江宁/%	溧水/%	句容/%
1988	3.92	10.69	3.72	2.89	3.77
1994	4.43	18.60	5.08	3.23	2.50
2001	8.59	32.74	9.53	6.11	5.60
2003	9.01	39.41	11.16	5.49	4.44
2006	11.12	48.14	14.32	6.67	4.98
2009	13.51	50.23	17.88	8.83	6.25
2011	16.31	59.38	21.22	12.51	7.22
2013	17.48	60.84	22.64	11.46	9.93
2015	19.09	59.52	25.17	15.22	9.09
1988-2001	5.71	9.09	7.60	4.36	1.09
2001-2015	6.45	3.50	7.01	8.88	6.15

转变模式	面积/km²	百分比/%	模式内主要转变轨迹	面积/km²	百分比/%
稳定不变	1467.44	56.77	耕地-耕地-耕地	1232.40	83.98
稳定不变	1467.44	56.77	林地-林地-林地	162.58	11.08
前期变化	318.91	12.34	耕地-建筑-建筑	133.36	41.82
前期变化	318.91	12.34	林地-耕地-耕地	62.57	19.62
后期变化	460.97	17.83	耕地-耕地-建筑	256.29	55.60
后期变化	460.97	17.83	林地-林地-耕地	60.87	13.20
连续变化	135.81	5.25	耕地-耕地-裸地	56.01	12.15
			耕地-裸地-建筑	44.80	32.98
			林地-耕地-建筑	19.42	14.30
重复变化	201.67	7.80	耕地-裸地-耕地	55.97	27.76
重复变化	201.67	7.80	耕地-林地-耕地	61.20	30.34

	年份
	1988	1994	2001	2003	2006	2009	2011	2013	2015
NP	7929	18 857	20 498	25 121	21 467	17 346	12 569	16 375	15 908
LSI	97.24	145.36	161.65	173.51	164.08	151.18	121.62	148.66	147.26
LPI	0.08	0.19	1.85	3.33	4.35	6.53	7.34	7.44	8.14
IJI	37.97	47.36	54.65	53.73	41.97	43.16	45.08	37.47	34.39
AI	71.22	59.25	67.56	66.02	71.10	75.85	82.35	77.61	80.23
SHDI	0.73	0.83	0.97	1.01	0.96	1.00	0.98	1.02	1.05
SHEI	0.46	0.52	0.60	0.63	0.60	0.62	0.61	0.63	0.65