海岛型城市扩展的生态效应分析——以厦门岛为例

doi:10.13249/j.cnki.sgs.2015.07.867

摘要/Abstract

摘要：

利用Landsat卫星影像,采用新型遥感生态指数(RSEI)对海岛型城市厦门在不同时期的生态变化进行研究,以期揭示城市的快速发展对城市生态质量的影响。研究结果表明,1993~2009年,厦门岛的城市建筑用地虽经过大幅扩张,但并没有造成生态质量的大幅下滑,其RSEI生态指数值仅略为下降。分析表明,这主要是得益于厦门城市发展的科学规划。但是厦门岛西北部的生态较差,是今后进一步改善生态的重点地区。

关键词: 城市生态, 遥感, RSEI指数, 主成分分析, 厦门

Abstract:

Timely and precisely monitoring of ecological responses to urban′s fast expansion has become a very important issue for regional decision-makers. To meet this requirement, this article utilized a recently developed, remote sensing based ecological index (RSEI) to assess the urban ecological quality change in Xiamen, an island city located in Fujian Province, southeastern China, during the past two decades. The RSEI was constructed by integrating four important ecological indicators including greenness, wetness, dryness and heat.It can be represented respectively by four remote sensing indices or components, i.e., normalized difference vegetation index (NDVI), index-based built-up index (IBI), wetness component of the tasseled cap transformation (Wet), and land surface temperature (LST). The principal component analysis (PCA) was utilized to compress the four indicators into four PC components. Instead of using a simple, traditional weighted addition algorithm, the first component (PC1) was used to construct the RSEI because the PC1 is the best one among the PC components to represent the four indicators. Time-series Landsat Thematic Mapper (TM) images of 1993 and 2009 of Xiamen scenes, both acquired in summer season, were employed to compute RSEI and evaluate the ecological quality of the island. The successful application of the RSEI in Xiamen has reveled that, in spite of a fast urban expansion in the island during the study period from 1993 to 2009, the ecological quality of the island has not degraded substantially, because the RSEI value only declined slightly from 0.558 in 2003 to 0.534 in 2009. This is mainly owing to a scientific urban planning for the island city, which kept sufficient green spaces for the city and hence resulted in a much higher ratio of urban green spaces in 2009 than that in 2003. Nevertheless, low-grade ecological conditions have also been detected by the leveled RSEI map in the newly developed northwestern part of the island city, where the Xiamen Airport is located. The area, therefore, should be improved for the ecological quality in the near future. The RSEI-revealed results have also been compared with those using the index generated with a simple weighted addition algorithm. The result shows that the RSEI can explain the island city′s ecological status more reasonably than the latter.

Key words: urban ecology, remote sensing, RSEI, principal component analysis, Xiamen

中图分类号:

TP79

徐涵秋, 张好. 海岛型城市扩展的生态效应分析——以厦门岛为例[J]. 地理科学, 2015, 35(7): 867-872.

Han-qiu XU, Hao ZHANG. Ecological Response to Urban Expansion in An Island City: Xiamen, Southeastern China[J]. SCIENTIA GEOGRAPHICA SINICA, 2015, 35(7): 867-872.

图/表 4

图1

表1

表2

表3

参考文献 22

[1]	Ochoa-Gaona S, Kampichler C, de Jong B H J, et al. A multi-criterion index for the evaluation of local tropical forest conditions in Mexico[J]. Forest Ecology Management, 2010, 260: 618-627. doi: 10.1016/j.foreco.2010.05.018
[2]	Sullivan C A, Skeffington M S, Gormally M J, et al.The ecological status of grasslands on lowland farmlands in western Ireland and implications for grassland classification and nature value assessment[J]. Biological Conservation, 2010, 143: 1529-1539. doi: 10.1016/j.biocon.2010.03.035
[3]	Xu H Q, Ding F, Wen X L.Urban expansion and heat island dynamics in the Quanzhou region, China[J]. IEEE Journal Selected Topic in Applied Earth Observation and Remote Sensing, 2009, 2(2):74-79. doi: 10.1109/JSTARS.2009.2023088
[4]	Gupta K, Kumar P, Pathan S K, et al.Urban Neighborhood Green Index-A measure of green spaces in urban areas[J]. Landscape and Urban Planning, 2012, 105: 325-335. doi: 10.1016/j.landurbplan.2012.01.003
[5]	Ivits E, Cherlet M, Mehl W, et al.Estimating the ecological status and change of riparian zones in Andalusia assessed by multi-temporal AVHHR datasets[J]. Ecological Indicator, 2009, 9: 422-431. doi: 10.1016/j.ecolind.2008.05.013
[6]	Yuan F, Bauer M E.Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery[J]. Remote Sensing of Environment, 2007, 106: 375-386. doi: 10.1016/j.rse.2006.09.003
[7]	Xu Hanqiu.Analysis of impervious surface and its impact on urban heat environment using the normalized difference impervious surface Index (NDISI)[J]. Photogrammetric Engineering and Remote Sensing, 2010, 76(5): 557-565. doi: 10.14358/PERS.76.5.557
[8]	Nichol J.Remote sensing of urban heat islands by day and night[J]. Photogrammetric Engineering and Remote Sensing, 2005, 71(6): 613-621. doi: 10.14358/PERS.71.5.613
[9]	Imhoff M L, Zhang P, Wolfe R E, et al.Remote sensing of the urban heat island effect across biomes in the continental USA[J]. Remote Sensing of Environment, 2010, 114: 504-513. doi: 10.1109/IGARSS.2010.5653907
[10]	徐涵秋. 区域生态环境变化的遥感评价指数[J].中国环境科学,2013,33(5):889~897. doi: 10.3969/j.issn.1000-6923.2013.05.019
[11]	赵跃龙,张玲娟.脆弱生态环境定量评价方法的研究[J].地理科学,1999,19(1):73~79.
[12]	Crist E P.A TM tasseled cap equivalent transformation for reflectance factor data[J]. Remote Sensing of Environment, 1985, 17: 301-306. doi: 10.1016/0034-4257(85)90102-6
[13]	Goward S N, Xue Y K, Czajkowski K P.Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements——An exploration with the simplified simple biosphere model[J]. Remote Sensing of Environment, 2002, 79: 225-242.
[14]	Jiménez-Muñoz J C, Cristobal J, Sobrino J A, et al. Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47: 339-349. doi: 10.1109/TGRS.2008.2007125
[15]	Xu Hanqiu.A new index for delineating built-up land features in satellite imagery[J]. International Journal of Remote Sensing, 2008, 29(14):4269-4276. doi: 10.1080/01431160802039957
[16]	Williams M, Longstaff B, Buchanan C, et al.Development and evaluation of a spatially-explicit index of Chesapeake Bay health[J]. Marine Pollution Bulletin, 2009, 59: 14-25. doi: 10.1016/j.marpolbul.2008.11.018 pmid: 19117579
[17]	Chander G, Markham B L, Helder D L.Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors[J]. Remote Sensing of Environment, 2009, 113: 893-903.
[18]	Charvz P S Jr. Image-based atmospheric corrections—Revisited and revised[J]. Photogrammetric Engineering and Remote Sensing, 1996, 62(9): 1025-1036. doi: 10.1016/0031-0182(96)00019-3
[19]	徐涵秋. 基于影像的Landsat TM/ETM+数据正规化技术[J].武汉大学学报(信息科学版),2007,32(1):62~66. doi: 10.3321/j.issn:1671-8860.2007.01.016
[20]	陈静洁. 厦门市植被时空演变的遥感动态监测[D].福州:福州大学,2011.
[21]	杜丽萍. 厦门市城市空间动态变化的遥感研究[D].福州:福州大学,2011.
[22]	徐涵秋. 基于城市地表参数变化的城市热岛效应分析[J].生态学报,2011,31(14):3890~3901.

	1993年					2009年
	Wet	NDVI	IBI	LST	平均相关度	Wet	NDVI	IBI	LST	平均相关度
Wet	1	0.626	-0.852	-0.566	0.681	1	0.603	-0.782	-0.442	0.609
NDVI	0.626	1	-0.913	-0.639	0.726	0.603	1	-0.864	-0.517	0.661
IBI	-0.852	-0.913	1	0.657	0.807	-0.782	-0.864	1	0.498	0.715
LST	-0.566	-0.639	0.657	1	0.621	-0.442	-0.517	0.498	1	0.486
RSEI	0.79	0.931	-0.95	-0.836	0.877	0.697	0.875	-0.844	-0.85	0.817
EI_w	0.401	0.619	-0.537	0.163	0.43	0.299	0.469	-0.301	0.456	0.381

	1993						2009
	Wet	NDVI	IBI	LST	RSEI	EI_w	Wet	NDVI	IBI	LST	RSEI	EI_w
均值	0.587	0.719	0.490	0.450	0.558	0.539	0.669	0.680	0.503	0.458	0.534	0.439
标准差	0.093	0.139	0.139	0.126	0.197	0.112	0.067	0.121	0.069	0.134	0.166	0.100

RSEI 级	1993		2009
RSEI 级	面积（km²）	百分比（%）	面积（km²）	百分比（%）
1: 差 (0.0~0.2)	4.53	3.66	0.39	0.27
2: 较差(0.2~0.4)	31.50	25.49	27.14	18.69
3: 中等(0.4~0.6)	33.28	26.93	74.80	51.53
4: 良(0.6~0.8)	40.34	32.64	25.41	17.50
5: 优(0.8~1.0)	13.96	11.29	17.42	12.00
合计	123.61	100.00	145.15	100.00