基于长时序
  <strong>Landsat 5/8</strong>多波段遥感影像的青海湖面积变化研究

doi:10.11867/j.issn.1001-8166.2019.4.0346

地球科学进展 ›› 2019, Vol. 34 ›› Issue (4): 346 -355. doi: 10.11867/j.issn.1001-8166.2019.4.0346

地理与地理信息科学下一篇

基于长时序 Landsat 5/8多波段遥感影像的青海湖面积变化研究

韩伟孝 ^1, ²(

),黄春林 ¹(

),王昀琛 ^1, ²,顾娟 ³

1. 中国科学院西北生态环境资源研究院，甘肃省遥感重点实验室，甘肃兰州 730000
2. 中国科学院大学，北京 100049
3. 兰州大学西部环境教育部重点实验室，甘肃兰州 730000

收稿日期:2018-11-16 修回日期:2018-12-06 出版日期:2019-04-10
通讯作者: 黄春林 E-mail:hwx1012010362@163.com;huangcl@lzb.ac.cn
基金资助:
中国科学院战略性先导 A 类专项“地球大数据科学工程”(编号: XDA19040500);兰州大学西部环境教育部重点实验室开放基金“青海湖水体信息遥感提取”(lzujbky-2017-kl01)

Study on the Area Variation of Qinghai Lake Based on Long-Term Landsat 5/8 Multi-Band Remote Sensing Imagery

Weixiao Han ^1, ²( ),Chunlin Huang ¹( ),Yunchen Wang ^1, ²,Juan Gu ³

1. Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Key Laboratory of Western China's Environmental Systems, Ministry of Education, Lanzhou University, Lanzhou 730000, China

Received:2018-11-16 Revised:2018-12-06 Online:2019-04-10 Published:2019-05-27
Contact: Chunlin Huang E-mail:hwx1012010362@163.com;huangcl@lzb.ac.cn
About author: Han Weixiao (1990-), male, Lanzhou City, Gansu Province, Ph.D student. Research areas include remote sensing big data.| Han Weixiao (1990-), male, Lanzhou City, Gansu Province, Ph.D student. Research areas include remote sensing big data.
Supported by:
Project supported by the Strategic Priority Research Program of the Chinese Academy of Sciences " CASEarth Big Data Science Project"(No．XDA19040500);The Open Foundation of MOE Key Laboratory of Western China's Environmental System, Lanzhou University "Water information extraction of Qinghai Lake based on remote sensing images”(No.lzujbky-2017-kl01)

全文 ( 27 ) 下载PDF ( 2029 )

青海湖作为高海拔的内陆湖泊，其表面水体面积多年变化对寒旱区的气候变化和水循环至关重要。为了研究30年来青海湖湖泊面积变化规律，提取了1986—2017年（除去2012年）覆盖青海湖的459景Landsat 5/8影像，采用6种常用的水体提取方法分别提取了青海湖表面水体面积，并分析了不同方法的差异，最终分别对Landsat 5 TM和Landsat 8 OLI遥感影像采用改进的归一化差异水体指数（MNDWI）和水体指数2015（WI2015）方法获得1986—2017年青海湖表面水体面积的年变化，并分析其变化趋势。结果表明：1989—2003年青海湖面积减小了175.34 km²，年平均减小率为12.52 km²/a，2003—2017年青海湖面积增加了183.43 km²，年平均增加率为13.10 km²/a，整体上，1986—2017年青海湖面积增加了104.46 km²，年平均增加率为3.37 km²/a。

关键词: 水体指数, Google Earth Engine, 面积变化, Landsat, 青海湖

As a high-altitude inland lake, Qinghai Lake's annual change in surface water area is critical for climate change and water cycle in the cold and arid regions. In order to study the spatial and temporal variation of area of Qinghai Lake in the past 30 years, extract 459 images from Landsat 5/8 that covering Qinghai Lake from 1986 to 2017 (excluding 2012). Apply six common water extraction methods to extract the surface water area of ??Qinghai Lake and analyze the differences of methods. Finally, obtain the annual surface water area variation of Qinghai Lake from 1986 to 2017 by using MNDWI and WI2015 water extraction methods for Landsat5 TM and Landsat8 OLI images respectively, and analyze its variation trend. The results show that the area of ??Qinghai Lake decreased by 175.34 km² from 1989 to 2003, and the average annual reduction rate was 12.52 km²/a . The area of ??Qinghai Lake increased by 183.43 km² from 2003 to 2017, and the average annual increase rate was 13.10 km²/a. The area of ??Qinghai Lake increased by 104.46 km², and the average annual increase rate was 3.37 km²/a from 1986 to 2017.

Key words: Water Index, Google Earth Engine, Area variation, Landsat, Qinghai Lake.

中图分类号:

P343.3

图1 青海湖位置示意图

Fig. 1 Location of Qinghai Lake

图2 1986—2017年每年的Landsat 5/8影像数

Fig. 2 The annual number of Landsat 5/8 imagery during 1986-2017

表1 Landsat 5/8 影像相关信息

Table 1 The related information of Landsat 5/8 imagery

卫星

发射时间

数据起始时间

数据结束时间

时间段/a

行编号34影像

/景

行编号35影像

/景

影像合计

/景

平均每年影像

/景

合计

1986-05-01

2017-10-31

232

227

459

14.81

Landsat 5

1984-03-01

1986-05-01

2011-10-31

185

186

371

14.27

Landsat 8

2013-02-11

2013-05-01

2017-10-31

17.60

表1 Landsat 5/8 影像相关信息

Table 1 The related information of Landsat 5/8 imagery

行编号34影像

/景

行编号35影像

/景

影像合计

/景

平均每年影像

/景

表2 应用于 Landsat 5/8影像的 6种常用的水体指数

Table 2 Six water index methods applied in Landsat 5/8 imagery

指数	方程	影像数据
TCW^{[ 15 ]}	$T C W = 0.0315 ρ b 1 + 0.2021 ρ b 2 + 0.3102 ρ b 3 + 0.1594 ρ b 4 - 0.6806 ρ b 5 - 0.6109 ρ b 7$	Landsat 4/5 TM
NDWI^{[ 33 ]}	$N D W I = ρ b 2 - ρ b 4 ρ b 2 + ρ b 4$	Landsat MSS(Multispectral Scanner)
MNDWI^{[ 34 ]}	$M N D W I = ρ b 2 - ρ b 5 ρ b 2 + ρ b 5$	Landsat TM, Landsat 7 ETM+
AWEI^{[ 20 ]}	$A W E I n s h = 4 (ρ b 2 - ρ b 5) - (0.25 ρ b 4 + 2.75 ρ b 7)$ ； $A W E I s h = ρ b 1 + 2.5 ρ b 2 - 1.5 (ρ b 4 + ρ b 5) - 0.25 ρ b 7$	Landsat 5 TM
WI2015^{[ 19 ]}	$W I 2015 = 1.7204 + 171 ρ b 2 + 3 ρ b 3 - 70 ρ b 4 - 45 ρ b 5 - 71 ρ b 7$	Landsat 5 TM/7 ETM+/8 OLI
MBWI^{[ 18 ]}	$M B W I = 2 ρ b 3 - ρ b 4 - ρ b 5 - ρ b 6 - ρ b 7$	Landsat 8 OLI

表2 应用于 Landsat 5/8影像的 6种常用的水体指数

Table 2 Six water index methods applied in Landsat 5/8 imagery

指数	方程	影像数据
TCW^{[ 15 ]}	$T C W = 0.0315 ρ b 1 + 0.2021 ρ b 2 + 0.3102 ρ b 3 + 0.1594 ρ b 4 - 0.6806 ρ b 5 - 0.6109 ρ b 7$	Landsat 4/5 TM
NDWI^{[ 33 ]}	$N D W I = ρ b 2 - ρ b 4 ρ b 2 + ρ b 4$	Landsat MSS(Multispectral Scanner)
MNDWI^{[ 34 ]}	$M N D W I = ρ b 2 - ρ b 5 ρ b 2 + ρ b 5$	Landsat TM, Landsat 7 ETM+
AWEI^{[ 20 ]}	$A W E I n s h = 4 (ρ b 2 - ρ b 5) - (0.25 ρ b 4 + 2.75 ρ b 7)$ ； $A W E I s h = ρ b 1 + 2.5 ρ b 2 - 1.5 (ρ b 4 + ρ b 5) - 0.25 ρ b 7$	Landsat 5 TM
WI2015^{[ 19 ]}	$W I 2015 = 1.7204 + 171 ρ b 2 + 3 ρ b 3 - 70 ρ b 4 - 45 ρ b 5 - 71 ρ b 7$	Landsat 5 TM/7 ETM+/8 OLI
MBWI^{[ 18 ]}	$M B W I = 2 ρ b 3 - ρ b 4 - ρ b 5 - ρ b 6 - ρ b 7$	Landsat 8 OLI

表3 Landsat 5/8影像中波谱的波段信息

Table 3 Spectral bands information of Landsat 5/8 imagery

卫星	变量	蓝波段	绿波段	红波段	近红外	短波红外1	短波红外2
Landsat 5	波段名	B1	B2	B3	B4	B5	B7
	波谱范围/μm	0.45~0.52	0.52~0.60	0.63~0.69	0.76 ~ 0.90	1.55 ~1.75	2.08 ~2.35
	带宽/μm	0.07	0.08	0.06	0.14	0.20	0.27
Landsat 8	波段名	B2	B3	B4	B5	B6	B7
	波谱范围/μm	0.45~0.51	0.53~0.59	0.64~0.67	0.85~0.88	1.57~1.65	2.11~2.29
	带宽/μm	0.06	0.06	0.03	0.03	0.08	0.08

表3 Landsat 5/8影像中波谱的波段信息

Table 3 Spectral bands information of Landsat 5/8 imagery

卫星	变量	蓝波段	绿波段	红波段	近红外	短波红外1	短波红外2
Landsat 5	波段名	B1	B2	B3	B4	B5	B7
	波谱范围/μm	0.45~0.52	0.52~0.60	0.63~0.69	0.76 ~ 0.90	1.55 ~1.75	2.08 ~2.35
	带宽/μm	0.07	0.08	0.06	0.14	0.20	0.27
Landsat 8	波段名	B2	B3	B4	B5	B6	B7
	波谱范围/μm	0.45~0.51	0.53~0.59	0.64~0.67	0.85~0.88	1.57~1.65	2.11~2.29
	带宽/μm	0.06	0.06	0.03	0.03	0.08	0.08

表4 用于验证的 Landsat 5/8影像信息

Table 4 The selected Landsat 5/8 imagery for validation

卫星	序号	获取时间	卫星	序号	获取时间	云量/%
Landsat 5	1	1987-10-18	Landsat 8	1	2013-10-09	2.58
	2	1989-09-21		2	2015-07-27	2.33
	3	1990-06-20		3	2015-08-12	0.57
	4	1994-11-06		4	2015-09-13	1.71
	5	2001-07-04		5	2015-10-15	0.09
	6	2008-05-04		6	2016-07-29	0.81

表4 用于验证的 Landsat 5/8影像信息

Table 4 The selected Landsat 5/8 imagery for validation

卫星	序号	获取时间	卫星	序号	获取时间	云量/%
Landsat 5	1	1987-10-18	Landsat 8	1	2013-10-09	2.58
	2	1989-09-21		2	2015-07-27	2.33
	3	1990-06-20		3	2015-08-12	0.57
	4	1994-11-06		4	2015-09-13	1.71
	5	2001-07-04		5	2015-10-15	0.09
	6	2008-05-04		6	2016-07-29	0.81

图3 Landsat 5/8影像提取1986—2017年青海湖表面水体面积流程图

Fig. 3 Flowchart of extracting surface water area of Qinghai Lake using Landsat 5/8 imagery during 1986-2017

图4 利用GEE平台计算得到的1986—2017年青海湖表面面积

Fig. 4 Surface water area of Qinghai Lake using GEE during 1986-2017

图5 6种方法提取青海湖表面水体边界图

Fig. 5 Boundary of surface water of Qinghai Lake using six methods

图6 1986—2017年Landsat 5 TM和Landsat 8 OLI青海湖表面水体面积变化

Fig. 6 Surface water area of Qinghai Lake from Landsat 5 TM and Landsat 8 OLI imagery during 1986-2017

1	Yang Y , Liu Y , Zhou M , et al . Landsat 8 OLI image based terrestrial water extraction from heterogeneous backgrounds using a reflectance homogenization approach[J]. Remote Sensing of Environment, 2015, 171: 14-32.
2	Alsdorf D E , Lettenmaier D P . Tracking fresh water from space[J]. Science, 2003, 301(5 639): 1 491-1 494.
3	Br?nmark C , Hansson L A . Environmental issues in lakes and ponds: Current state and perspectives[J]. Environmental Conservation, 2002, 29(3): 290-307.
4	Tranvik L J , Downing J A , Cotner J B , et al . Lakes and reservoirs as regulators of carbon cycling and climate[J]. Limnology and Oceanography, 2009,54(6 part2): 2 298-2 314.
5	Tulbure M G , Broich M . Spatiotemporal dynamic of surface water bodies using Landsat time-series data from 1999 to 2011[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 79: 44-52.
6	Verpoorter C , Kutser T , Seekell D A , et al . A global inventory of lakes based on high‐resolution satellite imagery[J]. Geophysical Research Letters, 2014, 41(18): 6 396-6 402.
7	Lewis Jr W M . Global primary production of lakes: 19^th Baldi Memorial Lecture[J]. Inland Waters, 2011, 1(1): 1-28.
8	Li Yu , Liu Yuan . Long-term reconstructions and simulations of the hydrological cycle in the inland rivers, arid China: A case study of the Shiyang River drainage basin[J]. Advances in Earth Science, 2017, 32(7): 731-743.
	李育,刘媛 . 干旱区内流河流域长时间尺度水循环重建与模拟——以石羊河流域为例[J].地球科学进展,2017,32(7):731-743.
9	Seekell D A , Pace M L , Tranvik L J , et al . A fractal‐based approach to lake size‐distributions[J]. Geophysical Research Letters, 2013, 40(3): 517-521.
10	Downing J A , Prairie Y T , Cole J J , et al . The global abundance and size distribution of lakes, ponds, and impoundments[J]. Limnology and Oceanography, 2006, 51(5): 2 388-2 397.
11	Toure S I , Stow D A , Shi H , et al . Land cover and land use change analysis using multi-spatial resolution data and object-based image analysis[J]. Remote Sensing of Environment, 2018, 210: 259-268.
12	Camps-Valls G . Machine learning in remote sensing data processing[C]//Machine Learning for Signal Processing, MLSP 2009. IEEE International Workshop on IEEE, 2009: 1-6.
13	Pekel J F , Cottam A , Gorelick N , et al . High-resolution mapping of global surface water and its long-term changes[J]. Nature, 2016, 540(7 633): 418.
14	Jia K , Jiang W , Li J , et al . Spectral matching based on discrete particle swarm optimization: A new method for terrestrial water body extraction using multi-temporal Landsat 8 images[J]. Remote Sensing of Environment, 2018, 209: 1-18.
15	Crist E P . A TM tasseled cap equivalent transformation for reflectance factor data[J]. Remote Sensing of Environment, 1985, 17(3): 301-306.
16	Gao B C . NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space[J]. Remote Sensing of Environment, 1996, 58(3): 257-266.
17	Yue H , Liu Y , Wang H , et al . Analysis of dynamic change of Hongjiannao Lake based on MNDWI[C]// IOP Conference Series: Earth and Environmental Science. IOP Publishing, 2017, 57(1): 012005.
18	Wang X , Xie S , Zhang X , et al . A robust Multi-Band Water Index (MBWI) for automated extraction of surface water from Landsat 8 OLI imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2018, 68: 73-91.
19	Fisher A , Flood N , Danaher T . Comparing Landsat water index methods for automated water classification in eastern Australia[J]. Remote Sensing of Environment, 2016, 175: 167-182.
20	Feyisa G L , Meilby H , Fensholt R , et al . Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery[J]. Remote Sensing of Environment, 2014, 140: 23-35.
21	Xu Hanzeyu , Liu Chong , Wang Junbang , et al . Study on extraction of cirtrus orchard in Gannan Region based on Google Earth Engine platform[J]. Journal of Geo-information Science, 2018， 20(3): 396-404.
	徐晗泽宇,刘冲,王军邦,等 .Google Earth Engine平台支持下的赣南柑橘果园遥感提取研究[J].地球信息科学学报,2018,20(3):396-404.
22	Gorelick N , Hancher M , Dixon M , et al . Google Earth Engine: Planetary-scale geospatial analysis for everyone[J]. Remote Sensing of Environment, 2017, 202: 18-27.
23	Patel N N , Angiuli E , Gamba P , et al . Multitemporal settlement and population mapping from Landsat using Google Earth Engine[J]. International Journal of Applied Earth Observation and Geoinformation, 2015, 35: 199-208.
24	Liu X , Hu G , Chen Y , et al . High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform[J]. Remote Sensing of Environment, 2018, 209: 227-239.
25	Wang Shuai , Xu Hanqiu , Shi Tingting . Retireval of tasseled cap transformation coefficients for GF-1 WFV2 sensor data[J]. Advances in Earth Science, 2018, 33(6): 641-652.
	王帅,徐涵秋,施婷婷 .GF-1 WFV2传感器数据的缨帽变换系数反演[J].地球科学进展,2018,33(6):641-652.
26	Cai Y , Ke C Q , Duan Z . Monitoring ice variations in Qinghai Lake from 1979 to 2016 using passive microwave remote sensing data[J]. Science of the Total Environment, 2017, 607: 120-131.
27	Zhang G , Xie H , Duan S , et al . Water level variation of Lake Qinghai from satellite and in situ measurements under climate change[J]. Journal of Applied Remote Sensing, 2011, 5(1): 053532.
28	Yuan Y , Li D L , An D . Respons of water level in Qinghai Lake to climate change in the Qinghai-Xizang Plateau[J]. Plateau Meteorology, 2012, 31(1): 57-64.
29	Li X Y , Xu H Y , Sun Y L , et al . Lake-level change and water balance analysis at Lake Qinghai, west China during recent decades[J]. Water Resources Management, 2007, 21(9): 1 505-1 516.
30	Ding Y J , Liu F J . Estimating on water balance elements in the drainage basin of Qinghai Lake[J]. Arid Land Geography, 1993, 16(4): 25-30.
31	Yin Q , Yang Y . Remote sensing monitoring of Lake Qinghai based on EOS/MODIS data[J]. Journal of Lake Sciences, 2005, 17(4): 356-360.
32	Che T , Li X , Jin R . Monitoring the frozen duration of Qinghai Lake using satellite passive microwave remote sensing low frequency data[J]. Chinese Science Bulletin, 2009, 54(13): 2 294-2 299.
33	McFeeters S K . The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features[J]. International Journal of Remote Sensing, 1996, 17(7): 1 425-1 432.
34	Xu H . Modification of Normalised Difference Water Index (NDWI) to enhance open water features in remotely sensed imagery[J]. International Journal of Remote Sensing, 2006, 27(14): 3 025-3 033.

[1]	丁之勇, 鲁瑞洁, 刘畅, 段晨曦. 环青海湖地区气候变化特征及其季风环流因素[J]. 地球科学进展, 2018, 33(3): 281-293.

阅读次数

全文

摘要