地表温度扰动特性及其与建设用地扩张的关系

doi:10.12082/dqxxkx.2018.180186

地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (10): 1529-1540.doi: 10.12082/dqxxkx.2018.180186

所属专题：气候变化与地表过程

• 遥感科学与应用技术 • 上一篇

地表温度扰动特性及其与建设用地扩张的关系

郑慧祯¹(), 陈燕红^2,³, 丁威¹, 潘文斌¹, 蔡芫镔^1,^*()

1. 福州大学环境与资源学院,福州 350116
2. 福州大学至诚学院,福州 350002
3. 福建师范大学地理科学学院,福州 350007

收稿日期:2018-04-16 修回日期:2018-07-31 出版日期:2018-10-25 发布日期:2018-10-17
作者简介:
作者简介：郑慧祯（1993-）,女,硕士生,主要从事城市生态方面研究。E-mail: zhz_0718@163.com
基金资助:
福建省青年社科项目（FJ2016C033）;福建省中青年教师教育科研项目（JT180021）

Characteristics of Land Surface Temperature Disturbance and Its Relationship to Built-up Land Expansion

ZHENG Huizhen¹(), CHEN Yanhong^2,³, DING Wei¹, PAN Wenbin¹, CAI Yuanbin^1,^*()

1. College of Environment and Resources, Fuzhou University, Fuzhou 350116, China
2. Fuzhou University Zhicheng College, Fuzhou 350002, China
3. College of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China

Received:2018-04-16 Revised:2018-07-31 Online:2018-10-25 Published:2018-10-17
Contact: CAI Yuanbin
Supported by:
Fujian Prov-ince Youth Social Science Project, No.FJ2016C033;Fujian Province Young and Middle-Aged Teacher Education Research Project, No.JT180021.

摘要/Abstract

摘要：

河口地区是人口密集、经济繁荣的区域之一,同时也是生态环境脆弱区,易受到人类活动带来的负面影响,其中热环境恶化问题尤其突出。本文基于多源遥感影像数据,结合遥感技术和统计学方法,研究城市化进程中闽江河口地区20年来的地表温度变化特性;利用Moran's I指数分析研究区表面温度的空间集聚特性及其产生的尺度效应,并定量分析表面温度与不同景观类型的相关性。结果表明：① 1993-2013年,闽江河口地区建设用地面积大幅增加,经历了由慢至快,而后稳中有升的过程;城市扩张是通过大量大规模的边缘式扩张实现的,东部、西部和南部是扩张的3个主要方向。② 地表温度空间格局变化明显,次高温区和高温区显著增加,次低温区和中温区减少,低温区变化不大;高温区扩散方向与建设用地扩张方向基本一致。③ 地表温度存在明显的扰动现象,表现为靠近主城区剧烈,城郊和郊区相对和缓的特性,同时地表温度空间格局具有显著的空间聚集性。④ 建设用地扩张是地表温度增强的重要原因,林/草地和水域能有效缓解区域高温;耕地并未表现出显著的降温效果,但随着耕地比重的增加,地表温度逐渐趋于稳定。研究结果可为改善城市热环境、促进区域可持续发展提供有益参考。

关键词: 城市扩张, 热环境, 空间自相关, 尺度效应, 闽江河口

Abstract:

Estuarine region is one of the most densely populated and prosperous area around the world, and it is also an eco-environmental vulnerable area which is more fragile to human activities. The acceleration of urbanization have inevitably resulted in a series of ecological and environmental problems on estuarine region, the thermal environment is a severe part of them. Higher temperatures and extreme heat not only hamper air quality but also increase energy consumption for cooling, threatening the health of urban residents. Based on the multi-source remote sensing images, characteristics of land surface temperature under the urbanization in Minjiang River estuary area were analyzed by using remote sensing techniques and statistical methods. With the help of Moran's I index, spatial clustering characteristic and scale effect of LST were examined. Further, the correlations between LST and different landscapes were found in quantitative analysis. The results showed that: (1) Built-up land area increased sharply from 1993 to 2013, showing a slow-rapid-steady and increasing process. A large number of large-scale edge-expansion was the primary growth type, meanwhile urban sprawl was mainly in east, west and south directions. (2) The area of sub-high and high temperature zone increased markedly, while the sub-low and middle temperature zone reduced; and there was no significant change in low temperature zone. Moreover, the spatial distribution of the high temperature region was consistent with built-up land expansion. (3) The LST exhibited an obvious disturbance characteristic; the temperature near city center presented dramatic changes and the temperature fluctuation in suburb was relatively smoother than Fuzhou city proper. On the other hand, the LST had a significant spatial clustering characteristic, and the spatial pattern of LST had a scale effect. (4) The dominance of built-up land significantly strengthened surface temperature, while increasing the dominance of vegetation and water could cool temperature. Cropland displayed no sign of cooling effect, the LST tended to be stable as the percentage of cropland increased. The results of the study can provide a useful reference for improving urban thermal environment and developing sustainable cities in estuarine regions.

Key words: urban expansion, thermal environment, spatial autocorrelation, scale effect, Minjiang River estuary

郑慧祯, 陈燕红, 丁威, 潘文斌, 蔡芫镔. 地表温度扰动特性及其与建设用地扩张的关系[J]. 地球信息科学学报, 2018, 20(10): 1529-1540.DOI:10.12082/dqxxkx.2018.180186

ZHENG Huizhen,CHEN Yanhong,DING Wei,PAN Wenbin,CAI Yuanbin. Characteristics of Land Surface Temperature Disturbance and Its Relationship to Built-up Land Expansion[J]. Journal of Geo-information Science, 2018, 20(10): 1529-1540.DOI:10.12082/dqxxkx.2018.180186

图/表 14

图1

表1

图2

表2

表3

图3

表4

图4

图5

表5

图6

表6

图7

图8

参考文献 37

[1]	李双成,赵志强,王仰麟.中国城市化过程及其资源与生态环境效应机制[J].地理科学进展,2009,28(1):63-70. doi: 10.11820/dlkxjz.2009.01.009
	[ Li S C, Zhao Z Q, Wang Y L.Urbanization process and effects of natural resource and environment in China: Research trends and future directions[J]. Progress in Geography, 2009,28(1):63-70. ] doi: 10.11820/dlkxjz.2009.01.009
[2]	张坤民. 中国环境保护事业60年[J].中国人口资源与环境,2010,20(6):1-5. doi: 10.3969/j.issn.1002-2104.2010.06.001
	[ Zhang K M.China's environmental protection of past sixty years since 1949[J]. China Population Resources and Environment, 2010,20(6):1-5. ] doi: 10.3969/j.issn.1002-2104.2010.06.001
[3]	白杨,孟浩,王敏,等.上海市城市热岛景观格局演变特征研究[J].环境科学与技术,2013,36(3):196-201.
	[ Bai Y, Meng H, Wang M, et al.Spatial and temporal changes of urban thermal landscape pattern in Shanghai[J]. Environmental Science & Technology, 2013,36(3):196-201. ]
[4]	姚远,陈曦,钱静.城市地表热环境研究进展[J].生态学报,2018,38(3):1134-1147.
	[ Yao Y, Chen X, Qian J.Research progress on the thermal environment of the urban surfaces[J]. Acta Ecologica Sinica, 2018,38(3):1134-1147. ]
[5]	黄木易,岳文泽,何翔.巢湖流域地表热环境与景观变化相关分析及其尺度效应[J].中国环境科学,2017,37(8):3123-3133.
	[ Huang M Y, Yue W Z, He X.Correlation analysis between land surface thermal environment and landscape change and its scale effect in Chaohu Basin[J]. China Environmental Science, 2017,37(8):3123-3133. ]
[6]	Deng Y H, Wang S J, Bai X Y, et al.Relationship among land surface temperature and LUCC, NDVI in typical karst area[J]. Scientific Reports, 2018,8(1):641-653. doi: 10.1038/s41598-017-19088-x
[7]	Tran D X, Pla F, Latorre-Carmona P, et al.Characterizing the relationship between land use land cover change and land surface temperature[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017,124:119-132. doi: 10.1016/j.isprsjprs.2017.01.001
[8]	Estoque R C, Murayama Y, Myint S W.Effects of landscape composition and pattern on land surface temperature: An urban heat island study in the megacities of Southeast Asia[J]. Science of the Total Environment, 2017,577:349-359. doi: 10.1016/j.scitotenv.2016.10.195
[9]	Wu H, Ye L P, Shi W Z, et al.Assessing the effects of land use spatial structure on urban heat island using HJ-1B remote sensing imagery in Wuhan, China[J]. International Journal of Applied Earth Observation and Geoinformation, 2014,32(1):67-78. doi: 10.1016/j.jag.2014.03.019
[10]	Guo G H, Wu Z F, Xiao R B, et al.Impacts of urban biophysical composition on land surface temperature in urban heat island clusters[J]. Landscape and Urban Planning, 2015,135:1-10. doi: 10.1016/j.landurbplan.2014.11.007
[11]	李斌,王慧敏,秦明周,等.NDVI、NDMI与地表温度关系的对比研究[J].地理科学进展,2017,36(5):585-596. doi: 10.18306/dlkxjz.2017.05.006
	[ Li B, Wang H M, Qin M Z, et al.Comparative study on the correlations between NDVI, NDMI and LST[J]. Progress in Geography, 2017,36(5):585-596. ] doi: 10.18306/dlkxjz.2017.05.006
[12]	王耀斌,赵永华,韩磊,等.西安市景观格局与城市热岛效应的耦合关系[J].应用生态学报,2017,28(8):2621-2628.
	[ Wang Y B, Zhao Y B, Han L, et al.Coupling relationship of landscape pattern and urban heat island effect in Xi’an, China[J]. Chinese Journal of Applied Ecology, 2017,28(8):2621-2628. ]
[13]	刘焱序,彭建,王仰麟.城市热岛效应与景观格局的关联:从城市规模、景观组分到空间构型[J].生态学报,2017,37(23):7769-7780.
	[ Liu Y X, Peng J, Wang Y L.Relationship between urban heat island and landscape patterns: From city size and landscape composition to spatial configuration[J]. Acta Ecologica Sinica, 2017,37(23):7769-7780. ]
[14]	国家气象信息中心.中国地面国际交换站气候资料日值数据集[EB/OL]. .
	[ National Meteorological Information Center. Dataset of daily climate data from Chinese surface stations for global exchange. ]
[15]	福建省地方志编纂委员会.福建省志[EB/OL]. .
	[ Compilation Committee of Fujian Province Chorography. Fujian Province Chorography [EB/OL]. ]
[16]	周亮进. 闽江河口湿地景观格局动态研究[D].上海:华东师范大学,2007.
	[ Zhou L J.Dynamic of landscape pattern of wetlands in Min River estuary[D]. Shanghai: East China Normal University, 2007. ]
[17]	福州市统计局 .福州统计年鉴2017 [EB/OL]. .
	[ Fuzhou City Bureau of Statistics. Fuzhou Statistical Yearbook 2017 [EB/OL]. . ]
[18]	覃志豪, Zhang M H, Karnieli A, 等.用陆地卫星TM6数据演算地表温度的单窗算法[J].地理学报,2001,56(4):456-466. doi: 10.1142/S0252959901000401
	[ Qin Z H, Zhang M H, Karnieli A, et al.Mono-window algorithm for retrieving land surface temperature from Landsat TM6 data[J]. Acta Geographica Sinica, 2001,56(4):456-466. ] doi: 10.1142/S0252959901000401
[19]	Jiménez-Muñoz J C, Sobrino J A. A generalized single-channel method for retrieving land surface temperature from remote sensing data[J]. Journal of Geophysical Research: Atmospheres, 2003,108(D22):1-9.
[20]	丁凤,徐涵秋.基于Landsat TM的3种地表温度反演算法比较分析[J].福建师范大学学报(自然科学版),2008,24(1):91-96.
	[ Ding F, Xu H Q.Comparison of three algorithms for retrieving land surface temperature from Landsat TM thermal infrared band[J]. Journal of Fujian Normal University (Natural Science Edition), 2008,24(1):91-96. ]
[21]	王猛猛. 地表温度与近地表气温热红外遥感反演方法研究[D].北京:中国科学院遥感与数字地球研究所,2017.
	[ Wang M M.Methodology development for retrieving land surface temperature and near surface air temperature based on thermal infrared Remote Sensing[D].Beijing: Institute of Remote Sensing and Digital Earth), 2017. ]
[22]	徐涵秋. Landsat 8热红外数据定标参数的变化及其对地表温度反演的影响[J].遥感学报,2016,20(2):229-235.
	[ Xu H Q.Change of Landsat 8 TIRS calibration parameters and its effect on land surface temperature retrieval[J]. Journal of Remote Sensing, 2016,20(2):229-235. ]
[23]	覃志豪, Li W J, Zhang M H, 等.单窗算法的大气参数估计方法[J].国土资源遥感,2003,56(2):37-43. doi: 10.3969/j.issn.1001-070X.2003.02.010
	[ Qin Z H, Li W J, Zhang M H, et al.Estimating of the essential atmospheric parameters of Mono-window algorithm for land surface temperature retrieval from Landsat TM6[J].Remote Sensing for Land & Resources, 2003,56(2):37-43. ] doi: 10.3969/j.issn.1001-070X.2003.02.010
[24]	Sobrino J A, Jiménez-Muñoz J C, Paolini L. Land surface temperature retrieval from Landsat TM5[J]. Remote Sensing of Environment, 2004,90(4):434-440. doi: 10.1016/j.rse.2004.02.003
[25]	Sun R H, Chen A L, Chen L D, et al.Cooling effects of wetlands in an urban region: The case of Beijing[J]. Ecological Indicators, 2012,20:57-64. doi: 10.1016/j.ecolind.2012.02.006
[26]	Qiao Z, Tian G, Xiao L.Diurnal and seasonal impacts of urbanization on the urban thermal environment: A case study of Beijing using MODIS data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013,85:93-101. doi: 10.1016/j.isprsjprs.2013.08.010
[27]	韩瑞丹,张丽,郑艺,等.曼谷城市扩张生态环境效应研究[J].生态学报,2017,37(19):6322-6334.
	[ Han R D, Zhang L, Zheng Y, et al.Urban expansion and its ecological environmental effects in Bangkok, Thailand[J]. Acta Ecologica Sinica, 2017,37(19):6322-6334. ]
[28]	许雪爽,包安明,常存,等.新疆重点城市建设用地扩张与人地配置协调性分析[J].经济地理,2017,37(10):92-99.
	[ Xu X S, Bao A M, Chang C, et al.Characteristics of urban built-up expansion and coordination of man-land allocation in the major cities in Xinjiang[J]. Economic Geography, 2017,37(10):92-99. ]
[29]	陈忠升,陈亚宁,李卫红,等.和田河流域土地利用变化及其生态环境效应分析[J].干旱区资源与环境,2009,23(3):49-54.
	[ Chen Z S, Chen Y N, Li W H, et al.Analysis on land use changes and their ecological effect in Hotan River basin[J]. Journal of Arid Land Resources and Environment, 2009,23(3):49-54. ]
[30]	Liu X P, Li X, Chen Y M, et al.A new landscape index for quantifying urban expansion using multi-temporal remotely sensed data[J]. Landscape Ecology, 2010,25(5):671-682. doi: 10.1007/s10980-010-9454-5
[31]	郭冠华,吴志峰,刘晓南.城市热环境季相变异及与非渗透地表的定量关系分析——以广州市中心区为例[J].生态环境学报,2015,24(2):270-277.
	[ Guo G H, Wu Z F, Liu X N.Seasonal variations of urban heat environment and its relationship to impervious surface: A case study of Guangzhou core urban area[J]. Ecology and Environment Sciences, 2015,24(2):270-277. ]
[32]	邬建国. 景观生态学[M].北京:高等教育出版社,2001.
	[ Wu J G.Landscape Ecology[M]. Beijing: Higher Education Press, 2001. ]
[33]	王鹏龙,张建明,吕荣芳.基于空间自相关的兰州市热环境[J].生态学杂志,2014,33(4):1089-1095.
	[ Wang P L, Zhang J M, Lv R F.Urban thermal environment pattern with spatial autocorrelation in Lanzhou[J]. Chinese Journal of Ecology, 2014,33(4):1089-1095. ]
[34]	谢正峰,王倩.广州市土地利用程度的空间自相关分析[J].热带地理,2009,29(2):129-133. doi: 10.3969/j.issn.1001-5221.2009.02.005
	[ Xie Z F, Wang Q.Spatial autocorrelation analysis of land use intensity in Guangzhou city[J]. Tropical Geography, 2009,29(2):129-133. ] doi: 10.3969/j.issn.1001-5221.2009.02.005
[35]	徐双. 近十年长沙热环境空间格局演变研究[D].南京:南京大学,2015.
	[ Xu S.Spatiotemporal changes of thermal environment pattern in Changsha in the recent past decade[D]. Nanjing: Nanjing University, 2015. ]
[36]	孟丹,王明玉,李小娟,等.京沪穗三地近十年夜间热力景观格局演变对比研究[J].生态学报,2013,33(5):1545-1558. doi: 10.5846/stxb201209121284
	[ Meng D, Wang M Y, Li X J, et al.The dynamic change of the thermal environment landscape patterns in Beijing, Shanghai and Guangzhou in the recent past decade[J]. Acta Ecologica Sinica, 2013,33(5):1545-1558. ] doi: 10.5846/stxb201209121284
[37]	Peng J, Xie P, Liu Y X, et al.Urban thermal environment dynamics and associated landscape pattern factors: A case study in the Beijing metropolitan region[J]. Remote Sensing of Environment, 2016,173:145-155. doi: 10.1016/j.rse.2015.11.027

获取日期	获取时间（北京时间）	卫星	多光谱波段分辨率/m	热红外波段分辨率/m
1993-06-26	9:55:00	Landsat 5	30	120（30）
2000-06-29	10:09:14	Landsat 5	30	120（30）
2008-07-05	10:19:41	Landsat 5	30	120（30）
2013-08-04	10:32:31	Landsat 8	30	100（30）

土地利用/覆盖类型	面积/km²				总变化量/km²
土地利用/覆盖类型	1993年	2000年	2008年	2013年	1993-2013年
水域	196.29	178.82	168.27	166.90	-29.39
林/草地	803.06	809.58	749.37	700.08	-102.98
耕地	389.79	378.39	344.30	322.54	-67.25
建设用地	175.14	193.68	306.50	380.34	205.20
湿地	37.00	35.30	31.91	30.36	-6.64
裸地	3.10	8.61	4.03	4.16	1.06

	1993-2000年	2000-2008年	2008-2013年	1993-2013年
变化量/km²	18.54	112.82	73.84	205.20
年变化率AVR/%	1.51	7.28	4.82	5.86
扩张速度AI/（km²/a）	2.65	14.10	14.77	10.26
年均增长率AGR/%	1.45	5.91	4.41	3.95

扩张模式	1993-2000年		2000-2008年		2008-2013年
扩张模式	数量比例/%	面积比例/%	数量比例/%	面积比例/%	数量比例/%	面积比例/%
边缘式	51.12	61.01	40.14	69.75	37.20	68.41
填充式	27.86	29.51	30.76	21.78	24.72	17.20
飞地式	21.02	9.48	29.10	8.47	38.08	14.39

	低温区	次低温区	中温区	次高温区	高温区	1993年合计
低温区	285.26	86.86	37.55	24.55	12.50	446.72
次低温区	135.57	256.56	132.27	71.86	30.27	626.53
中温区	29.66	116.75	71.81	58.61	27.05	303.88
次高温区	9.30	25.43	26.57	54.84	42.36	158.50
高温区	0.59	0.44	1.35	19.84	46.55	68.77
2013年合计	460.38	486.04	269.55	229.70	158.73	1604.40
面积净变化	13.66	-140.49	-34.33	71.20	89.96	0.00

地表温度扰动特性及其与建设用地扩张的关系

Characteristics of Land Surface Temperature Disturbance and Its Relationship to Built-up Land Expansion

RichHTML

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 14

参考文献 37

相关文章 15

Metrics

本文评价

推荐阅读 0

	300 m×300 m	600 m×600 m	900 m×900 m	1200 m×1200 m	1500 m×1500 m
Moran's I	0.8967	0.8516	0.8215	0.7962	0.7702
Z-Score	170.0791	80.9949	52.2108	38.1601	29.5304

[1]	马丽莎, 刘殿锋, 刘耀林. 城市扩张与生态空间非线性动态耦合关系梯度分析模型[J]. 地球信息科学学报, 2023, 25(10): 1968-1985.
[2]	杨洋, 邵哲平, 赵强, 潘家财, 胡雨, 梅强. 基于厦门港的海上交通事故地理空间分布及风险预测研究[J]. 地球信息科学学报, 2022, 24(9): 1676-1687.
[3]	丁小花, 王琤, 席俊杰, 王朝, 岳程瑜, 张青峰. 黄土高原地貌类型界线划定研究进展[J]. 地球信息科学学报, 2022, 24(7): 1219-1233.
[4]	赵志远, 黄永刚, 吴升, 邬群勇, 汪艳霞. 基于时空热点分析的城市交通违法行为特征识别方法[J]. 地球信息科学学报, 2022, 24(7): 1312-1325.
[5]	耿佳辰, 沈石, 程昌秀. “十三五”时期黄河流域PM_2.5时空分布规律及多尺度社会经济影响机制分析[J]. 地球信息科学学报, 2022, 24(6): 1163-1175.
[6]	卢奕帆, 梁颖然, 卢思言, 肖钺, 何小钰, 林锦耀. 结合“珞珈一号”夜间灯光与城市功能分区的广州市碳排放空间分布模拟及其影响因素分析[J]. 地球信息科学学报, 2022, 24(6): 1176-1188.
[7]	张永凯, 杨春月, 张晚军, 毕潇梅. 黄河流域人口预期寿命的时空演化及影响因素分析[J]. 地球信息科学学报, 2022, 24(5): 902-913.
[8]	桂志鹏, 梅宇翱, 吴华意, 李锐. 顾及POI人口吸引力异质性的城市人口空间化方法[J]. 地球信息科学学报, 2022, 24(10): 1883-1897.
[9]	林中立, 徐涵秋. 基于局地气候分区体系的福州城市热环境研究[J]. 地球信息科学学报, 2022, 24(1): 189-200.
[10]	李玉, 张友水. 植被与不透水面的降温和增温效率分析方法[J]. 地球信息科学学报, 2021, 23(9): 1548-1558.
[11]	方红亮. 真实和有效叶面积指数及聚集指数的尺度效应[J]. 地球信息科学学报, 2021, 23(7): 1155-1168.
[12]	李颉, 郑步云, 王劲峰. 2008—2018年中国手足口病时空分异特征[J]. 地球信息科学学报, 2021, 23(3): 419-430.
[13]	方云皓, 顾康康. 基于多元数据的中国地理空间疫情风险评估探索——以2020年1月1日至4月11日COVID-19疫情数据为例[J]. 地球信息科学学报, 2021, 23(2): 284-296.
[14]	葛咏, 刘梦晓, 胡姗, 任周鹏. 时空统计学在贫困研究中的应用及展望[J]. 地球信息科学学报, 2021, 23(1): 58-74.
[15]	刘明杰, 徐卓揆, 郜允兵, 杨晶, 潘瑜春, 高秉博, 周艳兵, 周万鹏, 王凌. 基于机器学习的稀疏样本下的土壤有机质估算方法[J]. 地球信息科学学报, 2020, 22(9): 1799-1813.