黑河流域2001~2017年植被变化特征及其可延续性评价

doi:10.11873/j.issn.1004-0323.2020.2.0335

遥感技术与应用

2020, Vol. 35

Issue (2): 335-344 DOI: 10.11873/j.issn.1004-0323.2020.2.0335

GEE专栏

黑河流域2001~2017年植被变化特征及其可延续性评价

谭美宝¹(

),冉有华^2,³(

),苏阳^2,³,李新^4,⁵,杜得彦⁶,廉耀康⁶

^1.兰州大学资源环境学院，甘肃兰州 730000
^2.中国科学院西北生态环境资源研究院，甘肃兰州 730000
^3.中国科学院大学，北京 100049
^4.中国科学院青藏高原研究所，北京 100101
^5.中国科学院青藏高原地球科学卓越创新中心，北京 100101
^6.黄河水利委员会黑河流域管理局黑河水资源与生态保护研究中心，甘肃兰州 730000

Characteristics and Sustainability Evaluation of Vegetation Change in Heihe River Basin during 2001 to 2017

Meibao Tan¹(

),Youhua Ran^2,³(

),Yang Su^2,³,Xin Li^4,⁵,Deyan Du⁶,Yaokang Lian⁶

^1.The College of Resource and Environment, Lanzhou University, Lanzhou 730000, China
^2.Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
^3.University of Chinese Academy of Sciences, Beijing, 100049, China
^4.Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
^5.CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, China
^6.Heihe Water Resources and Ecological Protection Research Center, Heihe River Bureau, Yellow River Conservancy Commission of the Ministry of Water Resources, Lanzhou 730000, China

全文: PDF(4281 KB) HTML

摘要：

植被的变化特征是流域生态监测的重要内容和流域综合管理决策的基础信息。基于谷歌地球引擎（Google Earth Engine,GEE），利用空间分辨率为250 m的MODIS-EVI（Enhanced Vegetation Index）产品，研究2001~2017年黑河流域植被的时空变化趋势及延续性特征。结合气温、降水与河流径流量观测数据，分析黑河流域上游、中下游绿洲与非绿洲区植被变化的影响因素。结果表明：近17年来黑河流域植被年最大EVI值年均增幅为0.003 9，年均新增植被面积为480.3 km²。受气温、降水、耕地开垦、水资源管理措施及与其密切相关的地下水等因素的不同影响，上中下游表现出不同的变化特征。无论是年最大EVI值还是植被面积，中游的增加趋势最为显著，绿洲区较非绿洲区增加趋势更为明显。这种变化趋势短期内可能延续，但长时间内存在较大风险。研究为快速监测植被变化提供了示范，揭示了干旱区植被监测中长势变化与类型变化的同等重要性，流域植被变化的区域协同性对合理分水、加强地表-地下水协同管理等流域综合管理提出了更高要求。

关键词： 遥感; 植被覆盖度; EVI; Google Earth Engine

Abstract:

Characteristics of vegetation variation play an important role in ecological monitoring and provide the basis for integrated river basin management decisions. In this study, the spatial-temporal trends in vegetation cover change and its sustainability in Heihe river basin during 2001~2017 were characterized, using MODIS-EVI time series data at a spatial resolution of 250 meters in Google Earth Engine(GEE) platform. Combined with temperature, precipitation and river runoff data, the factors affecting vegetation growth in Heihe River Basin were identified. The results show that: Over the last 17 years, the average annual increment of EVI in Heihe river basin was 0.003 9, and the annual expansion of vegetation area was 480.3 km². Vegetation in the upper, middle and lower reaches of Heihe river has changed in varying degrees affected by temperature, precipitation, reclamation of cultivated land, water resources management and related groundwater. Whether the annual maximum EVI value or vegetation area, the increase trend of vegetation in the middle reaches was the most significant, and the oasis area was more obvious than the non-oasis area. This trend is sustainable in the short term, but there is a greater risk for a long time scale. The study provides a demonstration for high-speed monitoring of vegetation changes, reflecting the equal importance of growth and type changes for monitoring vegetation in arid regions. The regional synergy of vegetation changes in river basin puts forward higher requirements for integrated river basin management, such as reasonable water separation and strengthening surface-groundwater collaborative management.

Key words: Remote Sensing Fractional vegetation cover EVI Google Earth Engine

收稿日期: 2019-01-07 出版日期: 2020-07-10

ZTFLH:

TP79

基金资助: 国家自然科学基金项目(41471359);中国科学院青年创新促进会项目(2016375)

通讯作者: 冉有华 E-mail: tanmb18@lzu.edu.cn;ranyh@lzb.ac.cn

作者简介: 谭美宝（1994-），女，吉林松原人，硕士研究生,主要从事环境遥感应用研究。E?mail:tanmb18@lzu.edu.cn

	服务
	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	谭美宝
	冉有华
	苏阳
	李新
	杜得彦
	廉耀康

引用本文:

谭美宝,冉有华,苏阳,李新,杜得彦,廉耀康. 黑河流域2001~2017年植被变化特征及其可延续性评价[J]. 遥感技术与应用, 2020, 35(2): 335-344.

Meibao Tan,Youhua Ran,Yang Su,Xin Li,Deyan Du,Yaokang Lian. Characteristics and Sustainability Evaluation of Vegetation Change in Heihe River Basin during 2001 to 2017. Remote Sensing Technology and Application, 2020, 35(2): 335-344.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.2.0335 或 http://www.rsta.ac.cn/CN/Y2020/V35/I2/335

图1 黑河流域位置示意图

图2 2001年与2017年黑河流域年最大EVI分布图

图3 黑河流域2001~2017年年最大EVI变化趋势分布图与判定系数R2分布图

表1 2001~2017年黑河流域上、中、下游植被覆盖分级变化趋势的面积百分比

图4 2001~2017年黑河流域上中下游植被年最大EVI值与覆盖面积的变化趋势

图5 2001~2017年黑河流域上中下游降水、年均气温、年均最高气温、年均最低气温与黑河干流径流量的变化趋势

表2 黑河流域各区域年最大化EVI均值、植被覆盖面积与气象、水文要素的相关系数

图6 黑河流域Hurst指数值分布图

1	Jiang Penghui, Zhao Ruifeng, Zhao Hailli, et al. Spatial-Temporal Evolution of Land Use/Cover Change in Middle Section of Heihe Basin Since 1975[J]. Journal of Ecology & Rural Environment, 2012，28（5）：473-479.
1	姜朋辉, 赵锐锋, 赵海莉, 等. 1975年以来黑河中游地区土地利用/覆被变化时空演变[J]. 生态与农村环境学报, 2012, 28(5):473-479.
2	Li Xin, Liu Shaomin, Ma Mingguo, et al. HiWATER: An Integrated Remote Sensing Experiment on Hydrological and Ecological Processes in Heihe River Basin[J]. Advances in Earth Science, 2012, 27(5):481-498.
2	李新, 刘绍民, 马明国, 等. 黑河流域生态—水文过程综合遥感观测联合试验总体设计[J]. 地球科学进展, 2012, 27(5):481-498.
3	Liu Jianguo, Yang Ting. Analysis on Water Sectors Performance and Its Impact Factors in Inland River Basin Context in Arid Regions: A Case Study in the Middle Reaches of Heihe River Basin[J]. Ecological Economy, 2017, 33(5):175-180.
3	刘建国, 杨婷. 干旱区内陆河流域水部门绩效及其影响因素分析——以黑河中游张掖市为例[J]. 生态经济（中文版）, 2017, 33(5):175-180.
4	Li X, Cheng G, Liu S, et al. Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific Objectives and Experimental Design[J]. Bulletin of American Meteorological Society, 2013, 94(8): 1145-1160.
5	Cheng G, Li X, Zhao W, et al. Integrated Study of the Water-Ecosystem-Economy in the Heihe River Basin[J]. National Science Review, 2014, 1(3): 413-428.
6	Li Xupu, Zhang Fuping, Wei Yongfen, et al. Research on Dynamic Changes of the Vegetation Coverage Levels in Heihe River Basin[J]. Areal Research and Development.
6	李旭谱, 张福平, 魏永芬, 等.黑河流域植被覆盖程度变化研究[J]. 地域研究与开发, 2013, 32(3):108-114.
7	Han Huibang, Ma Mingguo, Yan Ping. Periodicity Analysis of NDVI Time Series and Its Relationship with Climatic Factors in the Heihe River Basin in China[J]. Remote Sensing Technology and Application, 2011, 26(5):554-560.
7	韩辉邦, 马明国, 严平. 黑河流域 NDVI 周期性分析及其与气候因子的关系[J]. 遥感技术与应用, 2011, 26(5): 554-560.
8	Ma M, Frank V. Interannual Variability of Vegetation Cover in the Chinese Heihe River Basin and Its Relation to Meteorological Parameters[J]. International Journal of Remote Sensing, 2006, 27(16): 3473-3486.
9	Zhao X, Fu Z, Sun H, et al. Temporal and Spatial Variations of Vegetation Response to Dynamic Change of Meteorological Factors and Groundwater in the Heihe River Basin, China[J]. Journal of the Faculty of Agriculture, Kyushu University, 2017, 62(2): 503-511.
10	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.
11	Hao Binfei, Han Xujun, Ma Mingguo, et al. Research Process on the Application of Google Earth Engine in Geoscience and Environmental Sciences[J]. Remote Sensing Technology and Application, 2018, 33(4): 600-611.
11	郝斌飞, 韩旭军, 马明国, 等. Google Earth Engine在地球科学与环境科学中的应用研究进展[J]. 遥感技术与应用, 2018, 33(4):600 -611.
12	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.
13	Goldblatt R, You W, Hanson G, et al. Detecting the Boundaries of Urban Areas in India: A Dataset for Pixel-based Image Classification in Google Earth Engine[J]. Remote Sensing, 2016, 8(8): 634. doi:10.3390/rs8080634. doi: 10.3390/rs8080634
14	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 Sensing of Environment, 2016, 185:142-154.
15	Wang Genxu, Cheng Gguodong. Water Demand of Eco-system and Estimate Method in Arid Inland River Basins[J]. Journal of Desert Research, 2002, 22(2):129-134.
15	王根绪, 程国栋. 干旱内陆流域生态需水量及其估算─以黑河流域为例[J]. 中国沙漠, 2002, 22(2):129-134.
16	Liu Wei, Ma Jun, Xi Haiyang, et al. The Land Potential Productivity in Ejina Oasis in the Lower Reaches of the Heihe River: Dynamic Change and Driving Factors[J]. Journal of Glaciology and Geocryology, 2012, 34(6):1336-1345.
16	刘蔚, 马骏, 席海洋, 等. 黑河下游额济纳绿洲土地生产潜力的动态变化及影响因素分析[J]. 冰川冻土, 2012, 34(6):1336-1345.
17	Song Chunqiao, You Songcai, Ke Linhong, et al. Phenological Variation of Typical Vegetation Types in Northern Tibet and Its Response to Climate Changes[J]. Acta Ecologica Sinica, 2012, 32(4): 1045-1055.
17	宋春桥, 游松财, 柯灵红, 等. 藏北高原典型植被样区物候变化及其对气候变化的响应[J]. 生态学报, 2012, 32(4):1045-1055.
18	Cai B F, Yu R. Advance and Evaluation in the Long Time Series Vegetation Trends Research based on Remote Sensing[J]. Journal of Remote Sensing, 2009, 13(6): 1170-1186.
19	Hurst H E. Long Term Storage Capacity of Reservoirs[J]. American Society of Civil Engineers Transactions, 1951, 116(776): 770-808.
20	Mandelbrot B B., Wallis J R. Robustness of the Rescaled Range R/S in the Measurement of Noncyclic Long Run Statistical Dependence[J]. Water Resources Research, 1969, 5(5): 967-988.
21	Granero M A S, Segovia J E T, Pérez J G. Some Comments on Hurst Exponent and the Long Memory Processes on Capital Markets[J]. Physica A: Statistical Mechanics and Its Applications, 2008, 387(22): 5543-5551.
22	Jiang W, Yuan L, Wang W, et al. Spatio-temporal Analysis of Vegetation Variation in the Yellow River Basin[J]. Ecological Indicators, 2015, 51: 117-126.
23	Zhang Lifeng, Yan Haowen, Yang Shuwen, et al. Variations of Vegetation Coverage and Its Response to Terrain in Heihe River Basin[J]. Remote Sensing Information, 2018, 33(2): 46-52.
23	张立峰, 闫浩文, 杨树文, 等. 黑河流域植被覆盖变化及其对地形的响应[J]. 遥感信息, 2018, 33(2): 46-52.
24	Xi Haiyang, Feng Qi, Si Jianhua, et al. Response of NDVI to Groundwater Level Change in the Lower Reaches of the Heihe River, China[J]. Journal of Desert Research, 2013, 33(2): 574-582.
24	席海洋, 冯起, 司建华, 等. 黑河下游绿洲 NDVI 对地下水位变化的响应研究[J]. 中国沙漠, 2013, 33(2): 574-582.
25	Guo Qiaoling, Yang Yunsong, Chen Zhihui. Remote Sensing Monitoring Vegetation Cover Change in Ejina Oasis After Heihe River Water was Distributed[J]. Journal of Water Resources and Water Engineering, 2010, 21(5): 65-71.
25	郭巧玲, 杨云松, 陈志辉. 黑河分水后额济纳绿洲天然植被覆盖变化遥感监测[J]. 水资源与水工程学报, 2010, 21(5): 65-71.
26	Zhang Yichi, Yu Jingjie, Qiao Maoyun, et al. Effects of Eco-water Transfer on Changes of Vegetation in the Lower Heihe River Basin[J]. Journal of Hydraulic Engineering, 2011, 42(7): 757-765.
26	张一驰, 于静洁, 乔茂云, 等. 黑河流域生态输水对下游植被变化影响研究[J]. 水利学报, 2011, 42(7):757-765.
27	Guo Qiaoling, Feng Qi, Ren Shaofei, et al. The Impact of the Water Import on the Vegetation in the Heihe River[J]. China Rural Water and Hydropower, 2009(3): 36-43.
27	郭巧玲, 冯起, 任韶斐, 等. 黑河生态输水对流域植被的影响研究[J]. 中国农村水利水电, 2009(3):36-40.

[1]	程娟,肖青,闻建光,唐勇,游冬琴,卞尊健,吴胜标,郝大磊,钟守熠. 地物波谱数据库应用方法及遥感应用现状[J]. 遥感技术与应用, 2020, 35(2): 267-286.
[2]	柴旭荣,李明,周义,王金风,田庆春. 影像的土地覆被快速分类[J]. 遥感技术与应用, 2020, 35(2): 315-325.
[3]	龙爽,郭正飞,徐粒,周华真,方伟华,许映军. 基于Google Earth Engine的中国植被覆盖度时空变化特征分析[J]. 遥感技术与应用, 2020, 35(2): 326-334.
[4]	李宜展, 李泽霞, 刘细文, 魏韧, 郭世杰, 董璐. 遥感科学数据应用态势分析[J]. 遥感技术与应用, 2020, 35(1): 1-12.
[5]	李雷,郑兴明,赵凯,李晓峰,王广蕊. 基于CCI土壤水分产品的干旱指数精度评价及其对东北地区粮食产量的影响[J]. 遥感技术与应用, 2020, 35(1): 111-119.
[6]	张新平,乔治,李皓,闫杰,张芳芳,赵栋锋,王得祥,康海斌,杨航,冯扬. 基于Landsat影像和不规则梯形方法遥感反演延安城市森林表层土壤水分[J]. 遥感技术与应用, 2020, 35(1): 120-131.
[7]	李志鹏,陈健. 基于GOCI卫星的大气细颗粒物PM2.5的遥感反演及其时空分布规律研究[J]. 遥感技术与应用, 2020, 35(1): 163-173.
[8]	边玲玲,王卷乐,郭兵,程凯,魏海硕. 基于特征空间的黄河三角洲垦利县土壤盐分遥感提取[J]. 遥感技术与应用, 2020, 35(1): 211-218.
[9]	王一帆,徐涵秋. 利用MODIS EVI时间序列数据分析福建省植被变化(2000~2017年)[J]. 遥感技术与应用, 2020, 35(1): 245-254.
[10]	于丽君,聂跃平,杨林,朱建峰,孙雨,刘芳,高华光. 新疆轮台奎玉克协海尔古城空间考古综合研究[J]. 遥感技术与应用, 2020, 35(1): 255-266.
[11]	陈家利,郑东海,庞国锦,李新. 基于SMAP亮温数据反演青藏高原玛曲区域土壤未冻水[J]. 遥感技术与应用, 2020, 35(1): 48-57.
[12]	陈勇强,杨娜,胡新,佟明远. SMOS与SMAP过境时段表层土壤水分的稳定性研究[J]. 遥感技术与应用, 2020, 35(1): 58-64.
[13]	肖林,车涛,戴礼云. 多源雪深数据在中国的空间特征评估[J]. 遥感技术与应用, 2019, 34(6): 1133-1145.
[14]	王树果,刘伟,梁亮. 基于Triple-Collocation方法的微波遥感土壤水分产品不确定性分析及数据融合[J]. 遥感技术与应用, 2019, 34(6): 1227-1234.
[15]	徐卫星,薛华柱,靳华安,李爱农. 融合遥感先验信息的叶面积指数反演[J]. 遥感技术与应用, 2019, 34(6): 1235-1244.

Viewed

Full text

Abstract

Cited

Shared

Discussed