基于多源遥感数据和GEE平台的博斯腾湖面积变化及影响因素分析

doi:10.12082/dqxxkx.2021.200361

地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (6): 1131-1153.doi: 10.12082/dqxxkx.2021.200361

基于多源遥感数据和GEE平台的博斯腾湖面积变化及影响因素分析

彭妍菲¹(), 李忠勤¹^,²^,³^,^*(), 姚晓军¹, 牟建新², 韩伟孝⁴^,⁵, 王盼盼¹

1.西北师范大学地理与环境科学学院,兰州 730070
2.中国科学院西北生态环境资源研究院冰冻圈科学国家重点实验室/天山冰川观测试验站,兰州 730000
3.石河子大学理学院,石河子 832000
4.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,兰州 730000
5.中国科学院大学,北京 100049

收稿日期:2020-06-30 修回日期:2020-09-19 出版日期:2021-06-25 发布日期:2021-08-25
通讯作者: *李忠勤（1962— ）,男,甘肃兰州人,博导,研究员,主要从事冰川与环境方面研究。E-mail: lizq@lzb.ac.cn
*李忠勤（1962— ）,男,甘肃兰州人,博导,研究员,主要从事冰川与环境方面研究。E-mail: lizq@lzb.ac.cn
作者简介:彭妍菲（1997— ）,女,甘肃兰州人,硕士生,主要从事冰冻圈遥感研究。E-mail: yanfei7026@163.com
基金资助:
第二次青藏高原综合科学考察研究(2019QZKK0201);中国科学院战略性先导科技专项(A类XDA20060201、XDA20020102);国家自然科学基金项目国际合作(41761134093);国家自然科学基金项目(41471058);冰冻圈科学国家重点实验室自主课题资助(SKLCS-ZZ-2020)

Area Change and Cause Analysis of Bosten Lake based on Multi-source Remote Sensing Data and GEE Platform

PENG Yanfei¹(), LI Zhongqin¹^,²^,³^,^*(), YAO Xiaojun¹, MOU Jianxin², HAN Weixiao⁴^,⁵, WANG Panpan¹

1. College of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, China
2. State Key Laboratory of Cryospheric Science / Tianshan Glaciological Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
3. College of sciences, Shihezi University, Shihezi 832000,China
4. Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
5. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2020-06-30 Revised:2020-09-19 Online:2021-06-25 Published:2021-08-25
Contact: LI Zhongqin
Supported by:
Second Tibetan Plateau Scientific Expedition and Research(2019QZKK0201);Strategic Priority Research Program of Chinese Academy of Sciences(Class A)(A类XDA20060201、XDA20020102);National Natural Science Foundation of China(41761134093);National Natural Science Foundation of China(41471058);The State Key Laboratory of Cryospheric founding(SKLCS-ZZ-2020)

摘要/Abstract

摘要：

作为典型的干旱区内陆湖泊,博斯腾湖的面积变化趋势与当地自然和人文环境的变迁密不可分。本文结合GIS与RS技术,利用Landsat影像和MODIS数据共2289景及JRC GSW水体掩膜产品,基于Google Earth Engine（GEE）平台采用指数法得出2000—2019年博斯腾湖面积年际和年内变化趋势,并采用2019年Sentinel-2影像进行结果对比分析,同时通过2000—2018年焉耆、库尔勒和巴音布鲁克气象站日值数据和人类活动分析其变化原因。得出如下结论：① 本结果中基于海量遥感数据提取面积的结果表明,GEE可以充分应用高时间分辨率遥感数据进行湖泊年际尤其是年内面积变化分析。相比于Landsat-5/7/8影像与MOD09GQ数据,由于Sentinel-2影像的时空分辨率优势,基于其所得的湖岸线可显示出较多细节。② 2000—2013年博斯腾湖面积共减少181.66 km²,变化速率为13.98 km/a;2013—2019年,湖泊共增加133.13 km²,变化速率为22.19 km²/a;③ 博斯腾湖面积一般在每年的3—6月呈上升趋势,且在当年6—9月保持峰值,面积在10—12月减小;④ 博斯腾湖面积年际变化与其流域内焉耆、库尔勒、巴音布鲁克气象站的降水、蒸发及积温因素变化的相关性未达到显著水平,而年内变化与上述气候要素相关性较高。

关键词: 博斯腾湖, 面积变化, 遥感提取, 湖岸线, Landsat, MODIS, Sentinel-2, Google Earth Engine

Abstract:

Bosten Lake is a typical inland lake in the arid zone. The change in the lake area is strongly related to local natural and cultural environmental changes. Based on the GIS and RS technologies, this paper combines Landsat imagery and MODIS data, including a total of 2289 scenes, with JRC GSW water mask products to characterize the interannual and intraannual changes of the area of Bosten Lake from 2000 to 2019 through the Google Earth Engine (GEE) platform using index methods. We use the 2019 Sentinel-2 images to compare and analyze the results. To quantify the the causes of the changes, we analyzed the human activities and daily meteorological data of Yanqi, Korla and Bayanbuluk meteorological stations during 2000-2018. Results show that: (1) the GEE is efficient for integrating multi-temporal high-resolution remote sensing data to analyze the temporal change of lake area, especially the intraannual change. Compared with Landsat-5/7/8 and MOD09GQ data, the lake shoreline extracted based on Sentinel-2 images shows more details due to their high temporal and spatial resolution; (2) during 2000-2013, the total lake area decreases by 181.66 km² with a decreasing rate of 13.98km²/a; while during 2013-2019, the lake area increases by 133.13 km² with a increasing rate of 22.19 km²/a; (3) Intraannually, the lake area shows an upward trend from Mar. to Jun., keeps peak until September, and decreases from Oct. to Dec. and (4) the interannual change of Bosten Lake area has no significant correlations with the changes of evaporation, precipitation, and accumulated temperature within the watershed. While the intraannual change of Bosten Lake area shows strong correlations with those meteorological varabiles.

Key words: Boston Lake, area Change, remote sensing extraction, shoreline, Landsat, MODIS, Sentinel-2, Google Earth Engine

彭妍菲, 李忠勤, 姚晓军, 牟建新, 韩伟孝, 王盼盼. 基于多源遥感数据和GEE平台的博斯腾湖面积变化及影响因素分析[J]. 地球信息科学学报, 2021, 23(6): 1131-1153.DOI:10.12082/dqxxkx.2021.200361

PENG Yanfei, LI Zhongqin, YAO Xiaojun, MOU Jianxin, HAN Weixiao, WANG Panpan. Area Change and Cause Analysis of Bosten Lake based on Multi-source Remote Sensing Data and GEE Platform[J]. Journal of Geo-information Science, 2021, 23(6): 1131-1153.DOI:10.12082/dqxxkx.2021.200361

图/表 21

图1

表1

图2

图3

图4

图5

图6

图7

图8

图9

图10

图11

表2

图12

图13

表3

图14

表4

表5

表6

图15

参考文献 86

[1]	Mason I, Guzkowska M, Rapley C, et al. The response of lake levels and areas to climatic change[J]. Climatic change, 1994,27(2):161-197. doi: 10.1007/BF01093590
[2]	Bai J, Chen X, Li J L, et al. Changes in the area of inland lakes in arid regions of central Asia during the past 30 years[J]. Environmental Monitoring and Assessment, 2011,178(1/2/3/4):247-256. doi: 10.1007/s10661-010-1686-y
[3]	张家凤. 开都河-孔雀河流域水资源优化配置研究[D]. 乌鲁木齐:新疆农业大学, 2012.
	[ Zhang J F. Study on optimized allocation of water resources in Kaidu River-Kongque river basin[D]. Urumqi: Xinjiang Agricultural University, 2012. ]
[4]	张建平. 博斯腾湖流域生态环境现状及治理对策浅析[J]. 环境科技, 2010,23(S2):76-79.
	[ Zhang J P. Eco-environment situation of Bosten Lake and its countermeasures[J]. Environmental Science and Technology, 2010,23(S2):76-79. ]
[5]	Huang C, Chen Y, Zhang S, et al. Detecting, extracting, and monitoring surface water from space using optical sensors: A review[J]. Reviews of Geophysics, 2018,56(2):333-360. doi: 10.1029/2018RG000598
[6]	Frazier P S, Page K J. Water body detection and delineation with Landsat TM data[J]. Photogrammetric Engineering and Remote Sensing, 2000,66(12):1461-1468.
[7]	Thomas R F, Kingsford R T, Lu Y, et al. Landsat mapping of annual inundation (1979-2006) of the Macquarie Marshes in semi-arid Australia[J]. International Journal of Remote Sensing, 2011,32(16):4545-4569. doi: 10.1080/01431161.2010.489064
[8]	Woodhouse I H. Introduction to microwave remote sensing[M]. Florida: CRC press, 2017: 98-100.
[9]	Schumann G J-P, Moller D K. Microwave remote sensing of flood inundation[J]. Physics and Chemistry of the Earth, Parts A/B/C, 2015,83:84-95.
[10]	Huang C, Chen Y, Wu J P, et al. An evaluation of Suomi NPP-VIIRS data for surface water detection[J]. Remote Sensing Letters, 2015,6(2):155-164. doi: 10.1080/2150704X.2015.1017664
[11]	Domenikiotis C, Loukas A, Dalezios N. The use of NOAA/AVHRR satellite data for monitoring and assessment of forest fires and floods[J]. Natural Hazards and Earth System Sciences, 2003,3(1/2):115-128.
[12]	Jain S K, Saraf A K, Goswami A, et al. Flood inundation mapping using NOAA AVHRR data[J]. Water Resources Management, 2006,20(6):949-959. doi: 10.1007/s11269-006-9016-4
[13]	Chen Y, Huang C, Ticehurst C, et al. An evaluation of MODIS daily and 8-day composite products for floodplain and wetland inundation mapping[J]. Wetlands, 2013,33(5):823-835. doi: 10.1007/s13157-013-0439-4
[14]	Brakenridge R, Anderson E. Modis-based flood detection, mapping and measurement: The potential for operational hydrological applications[C]// Transboundary Floods: Reducing Risks Through Flood Management, 2006.
[15]	Andreoli R, Yesou H, Li J, et al. Inland lake monitoring using low and medium resolution ENVISAT ASAR and optical data: Case study of Poyang Lake (Jiangxi, PR China)[C]// 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2007: 4578-4581.
[16]	Xie H, Luo X, Xu X, et al. Evaluation of Landsat 8 OLI imagery for unsupervised inland water extraction[J]. International Journal of Remote Sensing, 2016,37(8):1826-1844. doi: 10.1080/01431161.2016.1168948
[17]	Acharya T D, Lee D H, Yang I T, et al. Identification of water bodies in a Landsat 8 OLI image using a J48 decision tree[J]. Sensors, 2016,16(7):1075. doi: 10.3390/s16071075
[18]	Singh K, Ghosh M, Sharma S R. Wsb-DA: Water surface boundary detection algorithm using Landsat 8 OLI data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015,9(1):363-368. doi: 10.1109/JSTARS.4609443
[19]	Fisher A, Danaher T. A water index for SPOT5 HRG satellite imagery, New South Wales, Australia, determined by linear discriminant analysis[J]. Remote Sensing, 2013,5(11):5907-5925. doi: 10.3390/rs5115907
[20]	Davranche A, Lefebvre G, Poulin B. Wetland monitoring using classification trees and SPOT-5 seasonal time series[J]. Remote sensing of environment, 2010,114(3):552-562. doi: 10.1016/j.rse.2009.10.009
[21]	Yang X, Zhao S, Qin X, et al. Mapping of urban surface water bodies from Sentinel-2 MSI imagery at 10 m resolution via NDWI-based image sharpening[J]. Remote Sensing, 2017,9(6):596. doi: 10.3390/rs9060596
[22]	Sidle R C, Ziegler A D, Vogler J B. Contemporary changes in open water surface area of Lake Inle, Myanmar[J]. Sustainability Science, 2007,2(1):55-65. doi: 10.1007/s11625-006-0020-7
[23]	Klemenjak S, Waske B, Valero S, et al. Unsupervised river detection in RapidEye data[C]// 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012: 6860-6863.
[24]	Malinowski R, Groom G, Schwanghart W, et al. Detection and delineation of localized flooding from WorldView-2 multispectral data[J]. Remote sensing, 2015,7(11):14853-14875. doi: 10.3390/rs71114853
[25]	Yao F, Wang C, Dong D, et al. High-resolution mapping of urban surface water using ZY-3 multi-spectral imagery[J]. Remote Sensing, 2015,7(9):12336-12355. doi: 10.3390/rs70912336
[26]	Xu K Q, Zhang J Q, Watanabe M, et al. Estimating river discharge from very high-resolution satellite data: a case study in the Yangtze River, China[J]. Hydrological Processes, 2004,18(10):1927-1939. doi: 10.1002/(ISSN)1099-1085
[27]	Zhang H, Li J, Xiang N, et al. An automatic method of monitoring water bodies based on GF-1 data[C]// Ocean Remote Sensing and Monitoring from Space. International Society for Optics and Photonics, 2014,9261:926103.
[28]	Zhu Z, Wang S, Woodcock C E. Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images[J]. Remote Sensing of Environment, 2015,159:269-277. doi: 10.1016/j.rse.2014.12.014
[29]	Immitzer M, Vuolo F, Atzberger C. First experience with Sentinel-2 data for crop and tree species classifications in central Europe[J]. Remote Sensing, 2016,8(3):166. doi: 10.3390/rs8030166
[30]	Smith L C. Satellite remote sensing of river inundation area, stage, and discharge: A review[J]. Hydrological processes, 1997,11(10):1427-1439. doi: 10.1002/(ISSN)1099-1085
[31]	Ozesmi S L, Bauer M E. Satellite remote sensing of wetlands[J]. Wetlands Ecology and Management, 2002,10(5):381-402. doi: 10.1023/A:1020908432489
[32]	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):1425-1432. doi: 10.1080/01431169608948714
[33]	Xu H Q. 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):3025-3033. doi: 10.1080/01431160600589179
[34]	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. doi: 10.1016/j.rse.2013.08.029
[35]	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. doi: 10.1016/j.isprsjprs.2013.01.010
[36]	Mueller N, Lewis A, Roberts D, et al. Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia[J]. Remote Sensing of Environment, 2016,174:341-352. doi: 10.1016/j.rse.2015.11.003
[37]	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(7633):418-422. doi: 10.1038/nature20584
[38]	Frazier P, Page K, Louis J, et al. Relating wetland inundation to river flow using Landsat TM data[J]. International Journal of Remote Sensing, 2003,24(19):3755-3770. doi: 10.1080/0143116021000023916
[39]	Dong J, Xiao X, Menarguez M A, et al. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine[J]. Remote Sens Environ, 2016,185:142-154. doi: 10.1016/j.rse.2016.02.016
[40]	Hansen M C, Potapov P V, Moore R, et al. High-resolution global maps of 21st-century forest cover change[J]. Science, 2013,342(6160):850-853. doi: 10.1126/science.1244693
[41]	Nemani R, Votava P, Michaelis A, et al. Collaborative supercomputing for global change science[J]. Eos, Transactions American Geophysical Union, 2011,92(13):109-110.
[42]	万洪秀, 孙占东, 王润. 博斯腾湖湿地生态脆弱性评价研究[J]. 干旱区地理, 2006,29(2):248-254.
	[ Wan H X, Sun Z D, Wang R. Study on the evaluation of ecological frangibility of the wetlands in the Bosten Lake Region[J]. Arid Land Geography, 2006,29(2):248-254. ]
[43]	王亚俊, 李宇安, 王彦国, 等. 20世纪50年代以来博斯腾湖水盐变化及趋势[J]. 干旱区研究, 2005(3):355-360.
	[ Wang Y J, Li Y A, Wang Y G, et al. Study on the change of inflow and salt content of the Bosten Lake, Xinjiang since the 1950s[J]. Arid Zone Research, 2005(3):355-360. ]
[44]	高华中, 姚亦锋. 近50年来人类活动对博斯腾湖水位影响的量化研究[J]. 地理科学, 2005,25(3):305-309.
	[ Gao H Z, Yao Y F. Quantitative effect of human activities on water level change of Bosten lake in recent 50 years[J]. Scientia Geographica Sinica, 2005,25(3):305-309. ]
[45]	徐海量, 陈亚宁, 李卫红. 博斯腾湖湖水污染现状分析[J]. 干旱区资源与环境, 2003,17(3):95-97.
	[ Xu H L, Chen Y N, Li W H. Analysis on the pollution situation of Boston lake[J]. Journal of Arid Land Resources and Environment, 2003,17(3):95-97. ]
[46]	王润, Ernst Giese, 高前兆. 近期博斯腾湖水位变化及其原因分析[J]. 冰川冻土, 2003,25(1):60-64.
	[ Wang R, Giese E, Gao Q Z. The recent change of water level in the Bosten lake and analysis of its causes[J]. Journal of Glaciology and Geocryology, 2003,25(1):60-64. ]
[47]	张一琼, 海米提·依米提, 魏彬, 等. 1972—2011年博斯腾湖面积变化遥感分析[J]. 安徽农业科学, 2015,43(12):245-249.
	[ Zhang Y Q, Hamiti Imiti, Wei B, et al. The Remote sensing analysis of Bosten Lake Area change during 1972-2011[J]. Journal of Anhui Agricultural Sciences, 2015,43(12):245-249. ]
[48]	白洁, 陈曦, 李均力, 等. 1975—2007年中亚干旱区内陆湖泊面积变化遥感分析[J]. 湖泊科学, 2011,23(1):80-88.
	[ Bai J, Chen X, Li J L, et al. Changes of inland lake area in arid Central Asia during 1975-2007: A remote-sensing analysis[J]. Journal of Lake Science, 2011,23(1):80-88. ]
[49]	Wulder M A, Masek J G, Cohen W B, et al. Opening the archive: How free data has enabled the science and monitoring promise of Landsat[J]. Remote Sensing of Environment, 2012,122:2-10. doi: 10.1016/j.rse.2012.01.010
[50]	Cohen W B, Goward S N. Landsat's role in ecological applications of remote sensing[J]. BioScience, 2004,54(6):535-545. doi: 10.1641/0006-3568(2004)054[0535:LRIEAO]2.0.CO;2
[51]	Davis S M, Landgrebe D A, Phillips T L, et al. Remote sensing: the quantitative approach[J]. IEEE Computer Architecture Letters, 1981,3(6):713-714.
[52]	Vaughn Ihlen. Landsat 7(L7) Data Users Handbook-Version 2.0[EB/OL]. [2020-7-8]. https://prd-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/atoms/files/LSDS-1927_L7_Data_Users_Handbook-v2.pdf.
[53]	Justice C O, Vermote E, Townshend J R, et al. The Moderate Resolution Imaging Spectroradiometer (MODIS): Land remote sensing for global change research[J]. IEEE transactions on geoscience and remote sensing, 1998,36(4):1228-1249. doi: 10.1109/36.701075
[54]	Zhao H, Yang Z W, Di L P, et al. Evaluation of temporal resolution effect in remote sensing based crop phenology detection studies[C]// Computer and Computing Technologies in Agriculture V, 2012: 2011:135-150.
[55]	E. F Vermote, J. C Roger, J. P Ray. MODIS Surface Reflectance User's Guide(Collection 6)[EB/OL]. [2020-7-8] http://modis-sr.ltdri.org/guide/MOD09_UserGuide_v1.4.pdf.
[56]	Drusch M, Del Bello U, Carlier S, et al. Sentinel-2: ESA's optical high-resolution mission for GMES operational services[J]. Remote sensing of Environment, 2012,120:25-36. doi: 10.1016/j.rse.2011.11.026
[57]	ESA Sentinel-2 Team. GMES Sentinel-2 Mission Requirements Document[EB/OL]. [2020-7-8] https://earth.esa.int/pub/ESA_DOC/GMES_Sentinel2_MRD_issue_2.0_update.pdf.
[58]	Smith A R. Color gamut transform pairs[J]. ACM Siggraph Computer Graphics, 1978,12(3):12-19. doi: 10.1145/965139.807361
[59]	Joint Research Centre Global Surface Water-Data Users Guide(v2)[EB/OL]. [2019] https://global-surface-water.appspot.com/download.
[60]	Ji L, Zhang L, Wylie B. Analysis of dynamic thresholds for the normalized difference water index[J]. Photogrammetric Engineering & Remote Sensing, 2009,75(11):1307-1317.
[61]	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. doi: 10.1016/S0034-4257(96)00067-3
[62]	Soti V, Tran A, Bailly J S, et al. Assessing optical earth observation systems for mapping and monitoring temporary ponds in arid areas[J]. International Journal of Applied Earth Observation and Geoinformation, 2009,11(5):344-351. doi: 10.1016/j.jag.2009.05.005
[63]	Campos J C, Sillero N, Brito J C. Normalized difference water indexes have dissimilar performances in detecting seasonal and permanent water in the Sahara-Sahel transition zone[J]. Journal of Hydrology, 2012,464/465:438-446. doi: 10.1016/j.jhydrol.2012.07.042
[64]	Deus D, Gloaguen R. Remote sensing analysis of lake dynamics in semi-arid regions: implication for water resource management. Lake Manyara, East African Rift, Northern Tanzania[J]. Water, 2013,5(2):698-727. doi: 10.3390/w5020698
[65]	Tucker C J, Pinzon J E, Brown M E, et al. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data[J]. International Journal of Remote Sensing, 2005,26(20):4485-4498. doi: 10.1080/01431160500168686
[66]	Ma M, Wang X, Veroustraete F, et al. Change in area of Ebinur Lake during the 1998-2005 period[J]. International Journal of Remote Sensing, 2007,28(24):5523-5533. doi: 10.1080/01431160601009698
[67]	Chandrasekar K, Sesha Sai M V R, Roy P S, et al. Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS Vegetation Index product[J]. International Journal of Remote Sensing, 2010,31(15):3987-4005. doi: 10.1080/01431160802575653
[68]	常庆瑞, 蒋平安, 周勇. 遥感技术导论[M]. 北京: 科学出版社, 2004.
	[ Chang Q R, Jiang P A, Zhou Y. Introduction to Remote Sensing Technology[M]. Beijing: Science Press, 2004. ]
[69]	Lunetta R S, Knight J F, Ediriwickrema J, et al. Land-cover change detection using multi-temporal MODIS NDVI data[J]. Remote sensing of environment, 2006,105(2):142-154. doi: 10.1016/j.rse.2006.06.018
[70]	王志辉, 易善桢. 不同指数模型法在水体遥感提取中的比较研究[J]. 科学技术与工程, 2007,7(4):534-537.
	[ Wang Z H, Yi S Z. Comparison and research on the different index models used in water extraction by remote sensing[J]. Science Technology and Engineering, 2007,7(4):534-537. ]
[71]	杨忠恩, 骆剑承, 徐鹏炜. 利用NOAA-AVHRR资料提取水体信息的初步研究[J]. 遥感技术与应用, 1995,10(1):25-29.
	[ Yang Z E, Luo J C, Xu P W. Studies on applying NOAA-AVHRR data to making water area[J]. Remote Sensing Technology and Application, 1995,10(1):25-29. ]
[72]	刘凯, 彭力恒, 李想, 等. 基于Google Earth Engine的红树林年际变化监测研究[J]. 地球信息科学学报, 2019,21(5):731-739. doi: 10.12082/dqxxkx.2019.180354
	[ Liu K, Peng L H, Li X, et al. Monitoring the inter-annual change of mangroves based on the google earth engine[J]. Journal of Geo-information Science, 2019,21(5):731-739. ]
[73]	谭深, 吴炳方, 张鑫. 基于Google Earth Engine与多源遥感数据的海南水稻分类研究[J]. 地球信息科学学报, 2019,21(6):937-947. doi: 10.12082/dqxxkx.2019.180423.
	[ Tan S, Wu B F, Zhang X. Mapping paddy rice in the Hainan Province using both google earth engine and remote sensing images[J]. Journal of Geo-information Science, 2019,21(6):937-947. ]
[74]	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. doi: 10.1016/j.rse.2017.06.031
[75]	Du Y, Xue H P, Wu S J, et al. Lake area changes in the middle Yangtze region of China over the 20^th century [J]. Journal of Environmental Management, 2011,92(4):1248-1255. doi: 10.1016/j.jenvman.2010.12.007
[76]	Allen R G, Pereira L S, Raes D, et al. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56[J]. Food and Agriculture Organization of the United Nations, 1998,300(9):D05109.
[77]	林乃峰, 沈渭寿, 张慧, 等. 近35a西藏那曲地区湖泊动态遥感与气候因素关联度分析[J]. 生态与农村环境学报, 2012,28(3):231-237.
	[ Lin N F, Shen W S, Zhang H, et al. Correlation degree analysis of meteorological elements and dynamic remote sensing of alpine lakes in NaquRegion of Tibet in the past 35 years[J]. Rural Eco-Environment, 2012,28(3):231-237. ]
[78]	Chan J W, Tong T K. Multi-criteria material selections and end-of-life product strategy: Grey relational analysis approach[J]. Materials & Design, 2007,28(5):1539-1546. doi: 10.1016/j.matdes.2006.02.016
[79]	王国庆, 张建云, 刘九夫, 等. 气候变化和人类活动对河川径流影响的定量分析[J]. 中国水利, 2008(2):55-58.
	[ Wang G Q, Zhang J Y, Liu J F, et al. Quantitative assessment for climate change and human activities impact on river runoff[J]. China Water Resources, 2008(2):55-58. ]
[80]	迪拉娜·尼加提, 海米提·依米提, 麦麦提吐尔逊·艾则孜, 等. 基于3S的焉耆盆地生态服务价值对土地利用变化的响应[J]. 安徽农业科学, 2013,41(10):4571-4575.
	[The response of ecosystem services value to land use change in karashahar basin based on 3S technology[J]. Journal of Anhui Agricultural Sciences, 2013,41(10):4571-4575. ]
[81]	尚河英, 尹忠东. 塔里木河流域生态输水效益分析[J]. 水利科技与经济, 2014,20(2):17-20.
	[ Shang H Y, Yin Z D. An analysis on the Tarim river basin ecological water conveyance[J]. Water Conservancy Science and Technology and Economy, 2014,20(2):17-20. ]
[82]	赵奕. 博斯腾湖综合治理及生态修复工程前后水环境质量评价研究[D]. 乌鲁木齐:新疆大学, 2017.
	[ Zhao Y. Study on water environmental quality assessment before and after comprehensive treatment and ecological engineering remediation of the Lake Bosten[D]. Urumqi: Xinjiang University, 2017. ]
[83]	邓燕. 博斯腾湖生态输水对地下水的影响研究[J]. 黑龙江水利科技, 2019,47(1):10-13.
	[Study on influence of ecological water conveyance on groundwater in Bosten lake[J]. Heilongjiang Science and Technology of Water Conservancy, 2019,47(1):10-13. ]
[84]	岳桢干. 欧洲Sentinel-2A卫星即将大显身手—“哥白尼”对地观测计划简介(上)[J]. 红外, 2015,36(8):32-36.
	[ Yue Z G. The Sentinel-2A satellite will display skills to thefull-"Copernicus" on earth observation program[J]. Infrared, 2015,36(8):32-36. ]
[85]	杜保佳, 张晶, 王宗明, 等. 应用Sentinel-2A NDVI时间序列和面向对象决策树方法的农作物分类[J]. 地球信息科学学报, 2019,21(5):740-751. doi: 10.12082/dqxxkx.2019.180412
	[ Du B J, Zhang J, Wang Z M, et al. Crop mapping based on Sentinel-2A NDVI time series using object-oriented classification and decision tree model[J]. Journal of Geo-information Science, 2019,21(5):740-751. ]
[86]	陈炜, 黄慧萍, 田亦陈, 等. 基于Google Earth Engine平台的三江源地区生态质量动态监测与分析[J]. 地球信息科学学报, 2019,21(9):1382-1391. doi: 10.12082/dqxxkx.2019.190095
	[ Chen W, Huang H P, Tian Y C, et al. Monitoring and assessment of the eco-environment quality in the Sanjiangyuan regionbased on Google Earth Engine[J]. Journal of Geo-information Science, 2019,21(9):1382-1391. ]

数据源	时间	条带号		数据量/幅
Landsat-5 TM T1_TOA	2000—2011年	WRS_PATH	WRS_ROW
Landsat-5 TM T1_TOA	2000—2011年	142,143	31	84
Landsat-7 ETM+T1_TOA	2000—2003年、2012年	142,143	31	28
Landsat-8 OLI T1_TOA	2013—2019年	142,143	31	78
Landsat-8 OLI T1_SR	2019年	142,143	31	9
Sentinel-2 SR	2019年	SENING_ORBIT_NUMBER	MGRS_TILE
		19	45TVG, 45TWG	11 15
		19	45TVG, 45TWG	11 15
MOD09GQ	2000—2019年	-	-	2090
JRC GSW	2000—2015	-	-	-

	年积温/℃			年降水量/mm			年蒸发量/mm
	最大值	最小值	平均值	最大值	最小值	平均值	最大值	最小值	平均值
平原区	4762.5	4295.1	4569.9	145.3	21.8	67.6	1876.7	1360.3	1528.9
山区	1612.9	1340.2	1467.4	372.0	212.4	303.3	2044.8	1748.4	1872.4
相差	3149.6	2954.9	3102.5	-226.7	-190.6	-235.7	-168.1	-388.1	-343.5

	月均积温/℃	月均降水量/mm	月均蒸发量/mm
平原区	382.5	5.6	125.6
山区	123.4	25.1	156.0
相差	259.1	-19.5	-30.0

	年积温		年降水量		年蒸发量
	平原区	山区	平原区	山区	平原区	山区
Pearson	-0.3824	-0.3351	-0.0340	-0.0270	-0.2528	-0.0863
Kendall	-0.0703	-0.3072	-0.0177	0.0327	-0.2581	-0.0850
Spearman	-0.1474	-0.3622	0.0210	0.0898	-0.3624	-0.0836

	积温		降水量		蒸发量
	平原区	山区	平原区	山区	平原区	山区
年气象要素与年均面积	0.5668	0.2911	0.0102	0.0318	0.3348	0.0849
月气象要素与月均面积	0.6347	0.0094	0.4118	0.5770	0.7780	0.6930

基于多源遥感数据和GEE平台的博斯腾湖面积变化及影响因素分析

Area Change and Cause Analysis of Bosten Lake based on Multi-source Remote Sensing Data and GEE Platform

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 21

参考文献 86

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	段艳慧, 赵学胜, 彭舒. 基于信息熵的GlobeLand 30和WorldCover耕地破碎区一致性分析[J]. 地球信息科学学报, 2023, 25(5): 1027-1036.
[2]	于方圆, 曹家玮, 李发源, 李思进. 顾及对象特征的地面式光伏电站提取及减碳效益评估[J]. 地球信息科学学报, 2023, 25(3): 529-545.
[3]	甘聪聪, 邱炳文, 张建阳, 姚铖鑫, 叶智燕, 黄姮, 黄莹泽, 彭玉凤, 林艺真, 林多多, 苏中豪. 基于Sentinel-1/2动态耦合移栽期特征的水稻种植模式识别[J]. 地球信息科学学报, 2023, 25(1): 153-162.
[4]	聂祥琴, 陈瀚阅, 牛铮, 张黎明, 刘炜, 邢世和, 范协裕, 李家国. 基于时序影像的农业活动因子提取与闽西耕地SOC数字制图[J]. 地球信息科学学报, 2022, 24(9): 1835-1852.
[5]	刘佳, 伍宇明, 高星, 司文涛. 基于GEE和U-net模型的同震滑坡识别方法[J]. 地球信息科学学报, 2022, 24(7): 1275-1285.
[6]	王晓蕾, 石守海. 基于GEE的黄河流域植被时空变化及其地形效应研究[J]. 地球信息科学学报, 2022, 24(6): 1087-1098.
[7]	姚锦一, 王卷乐, 严欣荣, 魏海硕, Altansukh Ochir, Davaadorj Davaasuren. 基于深度神经网络的蒙古国色楞格河流域水体信息提取[J]. 地球信息科学学报, 2022, 24(5): 1009-1017.
[8]	谌稳, 孙立群, 李晴岚, 陈晨, 李家叶. 一种基于图论重构MODIS EVI时间序列数据集的新方法[J]. 地球信息科学学报, 2022, 24(4): 738-749.
[9]	于法川, 祝善友, 张桂欣, 朱佳恒, 张南, 徐永明. 复杂山区地形条件下ERA5再分析地表气温降尺度方法[J]. 地球信息科学学报, 2022, 24(4): 750-765.
[10]	陈点点, 陈芸芝, 冯险峰, 武爽. 基于超参数优化CatBoost算法的河流悬浮物浓度遥感反演[J]. 地球信息科学学报, 2022, 24(4): 780-791.
[11]	章敏, 吴文挺, 汪小钦, 孙玉. 基于潮汐动态淹没过程的长江口潮滩地形信息反演研究[J]. 地球信息科学学报, 2022, 24(3): 583-596.
[12]	刘恒孜, 吕宁, 姜侯, 姚凌. 基于DCT-PLS算法的MODIS LST缺值填补方法研究[J]. 地球信息科学学报, 2022, 24(2): 378-390.
[13]	詹琪琪, 赵伟, 杨梦娇, 付浩, 李昕娟, 熊东红. 雅鲁藏布江中部流域土地沙化遥感识别[J]. 地球信息科学学报, 2022, 24(2): 391-404.
[14]	段伟芳, 温小乐, 徐涵秋, 邓文慧. 基于光学与雷达影像变化检测的2020年鄱阳湖洪灾评估与分析[J]. 地球信息科学学报, 2022, 24(12): 2435-2447.
[15]	白磊, 张帆, 尚明, 师春香, 孙帅, 刘丽珺, 文元桥, 苏传程. 基于格点数据的1961—2018年中国多种积温时空变化研究[J]. 地球信息科学学报, 2021, 23(8): 1446-1460.

	月积温		月降水量		月蒸发量
	平原区	山区	平原区	山区	平原区	山区
Pearson	0.6099	0.1831	0.4453	0.5350	0.5516	0.6384
Kendall	0.2778	-0.0476	0.4944	0.5111	0.3333	0.2889
Spearman	0.4167	-0.1071	0.5958	0.6849	0.4424	0.4667