地理学报 ›› 2021, Vol. 76 ›› Issue (7): 1693-1707.doi: 10.11821/dlxb202107009
收稿日期:
2020-06-30
修回日期:
2021-04-07
出版日期:
2021-07-25
发布日期:
2021-09-25
作者简介:
黄珏(1987-), 女, 湖南韶山人, 副教授, 研究方向为水环境遥感。E-mail: huangjue@sdust.edu.cn
基金资助:
HUANG Jue1(), LI Zhengmao1, ZHANG Ke1,2, JIANG Tao1
Received:
2020-06-30
Revised:
2021-04-07
Published:
2021-07-25
Online:
2021-09-25
Supported by:
摘要:
随着全球变暖和社会经济发展,中国湖泊富营养化情况时有发生,迫切需要对中国湖泊的浮游植物生物量进行有效监测。本文选择了中国756个面积超过10 km2的湖泊进行研究,基于Google Earth Engine(GEE)云端运算平台,反演2003—2018年间叶绿素a(chl-a)浓度数据,以此来分析研究各个湖泊的营养状态及其时空变化,探索了中国五大湖区内湖泊各季节与年均chl-a浓度时空分布特征与气象、社会经济及湖泊特征等影响因素之间的关系。结果表明: ① 中国湖泊的营养状态变化具有明显的季节性与地域性,研究时段内处于中营养状态的湖泊约占90%,春季时大多数位于东部平原湖区与东北平原与山区湖区的湖泊表现为贫营养状态,而青藏高原湖区与云贵高原湖区的湖泊在春季多呈现富营养状态。由各个湖泊年均chl-a浓度变化可以看出中国约82%的湖泊年均chl-a浓度的变化率小于0.5,呈现出轻微变化,18%的湖泊chl-a浓度呈现剧烈变化趋势。② 温度和降水对湖表chl-a浓度影响较大,超过70%湖泊的chl-a浓度与其表面温度和降水存在正相关性,其中大部分分布在中国北部与东部。缓冲区人口和草地占比、湖泊海拔和湖泊地理位置也对湖泊浮游植物生物量具有一定影响。
黄珏, 李正茂, 张珂, 江涛. 基于GEE的中国湖泊浮游植物生物量时空动态分析[J]. 地理学报, 2021, 76(7): 1693-1707.
HUANG Jue, LI Zhengmao, ZHANG Ke, JIANG Tao. Spatio-temporal dynamic analysis of phytoplankton biomass in Chinese lakes based on Google Earth Engine[J]. Acta Geographica Sinica, 2021, 76(7): 1693-1707.
[1] |
Bastviken D, Tranvik L J, Downing J A, et al. Freshwater methane emissions offset the continental carbon sink. Science, 2011,331(6013):50. DOI: 10.1126/science.1196808.
doi: 10.1126/science.1196808 pmid: 21212349 |
[2] | Yan Lijuan, Zheng Mianping, Wei Lejun. Change of the lakes in Tibetan Plateau and its response to climate in the past forty years. Earth Science Frontiers, 2016,23(4):310-323. |
[ 闫立娟, 郑绵平, 魏乐军. 近40年来青藏高原湖泊变迁及其对气候变化的响应. 地学前缘, 2016,23(4):310-323.] | |
[3] |
Mooij W M, S Hülsmann, Domis L, et al. The impact of climate change on lakes in the Netherlands: A review. Aquatic Ecology, 2005,39(4):381-400.
doi: 10.1007/s10452-005-9008-0 |
[4] |
Havens K, Jeppesen E. ecological responses of lakes to climate change. Water, 2018,10(7):917.
doi: 10.3390/w10070917 |
[5] |
Quayle W. C. Extreme responses to climate change in Antarctic lakes. Science, 2002,295(5555):645.
pmid: 11809962 |
[6] | Cao Jinling, Xu Qigong, Xi Beidou, et al. Regional heterogeneity of lake eutrophication effects in China. Environmental Science, 2012,33(6):1777-1783. |
[ 曹金玲, 许其功, 席北斗, 等. 我国湖泊富营养化效应区域差异性分析. 环境科学, 2012,33(6):1777-1783.] | |
[7] |
Qi Lingyan, Huang Jiacong, Gao Junfeng, et al. Spatial-temporal variation characteristics of chlorophyll-a concentration in Lake Hongze. Journal of Lake Sciences, 2016,28(3):583-591.
doi: 10.18307/2016.0314 |
[ 齐凌艳, 黄佳聪, 高俊峰, 等. 洪泽湖叶绿素a浓度的时空变化特征. 湖泊科学, 2016,28(3):583-591.] | |
[8] |
Zhang Y C, Ma R H, Zhang M, et al. Fourteen-year record (2000-2013) of the spatial and temporal dynamics of floating algae blooms in Lake Chaohu, observed from time series of MODIS images. Remote Sensing, 2015,7(8):10523-10542.
doi: 10.3390/rs70810523 |
[9] | Chong Dan, Li Haojie, Fan Shuo, et al. Inversion of chlorophyll-a concentration in nine plateau lakes in Yunnan based on MODIS data. Chinese Journal of Ecology, 2017,36(1):277-286. |
[ 种丹, 李浩杰, 范硕, 等. 基于MODIS数据的云南九大高原湖泊叶绿素a浓度反演. 生态学杂志. 2017,36(1):277-286.] | |
[10] |
Li J S, Wang S L, Wu Y H, et al. MODIS observations of water color of the largest 10 lakes in China between 2000 and 2012. International Journal of Digital Earth, 2016,9(8):788-805.
doi: 10.1080/17538947.2016.1139637 |
[11] | Wu Feng, Zhan Jinyan, Deng Xiangzheng, et al. Influencing factors of lake eutrophication in China: A case study in 22 lakes in China. Ecology and Environmental Sciences, 2012,21(1):94-100. |
[ 吴锋, 战金艳, 邓祥征, 等. 中国湖泊富营养化影响因素研究: 基于中国22个湖泊实证分析. 生态环境学报, 2012,21(1):94-100.] | |
[12] |
Kraemer B M, Mehner T, Adrian R. Reconciling the opposing effects of warming on phytoplankton biomass in 188 large lakes. Scientific Reports, 2017,7(1):10762. DOI: 10.1038/s41598-017-11167-3.
doi: 10.1038/s41598-017-11167-3 |
[13] |
Tamiminia H, Salehi B, Mahdianpari M, et al. Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 2020,164:152-170.
doi: 10.1016/j.isprsjprs.2020.04.001 |
[14] |
Chen B, Xiao X, Li X, et al. A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 2017,131:104-120.
doi: 10.1016/j.isprsjprs.2017.07.011 |
[15] | Long Shuang, Guo Zhengfei, Xu Li, et al. Spatiotemporal variations of fractional vegetation coverage in China based on Google Earth Engine. Remote Sensing Technology and Application, 2020,35(2):326-334. |
[ 龙爽, 郭正飞, 徐粒, 等. 基于Google Earth Engine 的中国植被覆盖度时空变化特征分析. 遥感技术与应用, 2020,35(2):326-334.] | |
[16] | Chen Qian, Li Xiaosong, Xiu Xiaomin, et al. Large scale shrub coverage mapping of sandy land at 30m resolution based on Google Earth Engine and machine learning. Acta Ecologica Sinica, 2019,39(11):4056-4069. |
[ 陈黔, 李晓松, 修晓敏, 等. 基于Google Earth Engine 与机器学习的大尺度30 m分辨率沙地灌木覆盖度估算. 生态学报, 2019,39(11):4056-4069.] | |
[17] |
Wang C, Jia M, Chen N, et al. Long-term surface water dynamics analysis based on Landsat imagery and the Google Earth Engine platform: A case study in the middle Yangtze River Basin. Remote Sensing, 2018,10(10):1635. DOI: 10.3390/rs10101635.
doi: 10.3390/rs10101635 |
[18] |
Deng Y, Jiang W, Tang Z, et al. Long-term changes of open-surface water bodies in the Yangtze River basin based on the Google Earth Engine cloud platform. Remote Sensing, 2019,11(19):2213. DOI: 10.3390/rs11192213.
doi: 10.3390/rs11192213 |
[19] | Chen Wei, Huang Huiping, Tian Yichen, et al. Monitoring and assessment of the eco-environment quality in the Sanjiangyuan region based on Google Earth Engine. Journal of Geo-information Science, 2019,21(9):1382-1391. |
[ 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. 地球信息科学学报, 2019,21(9):1382-1391.] | |
[20] | Jena R, Pradhan B. A model to detect forest change relating to mining using Google Earth Engine application in Belitung Island, Indonesia. 2019 6th International Conference on Space Science and Communication (IconSpace). IEEE, 2019. |
[21] | Li Yucheng, Zhang Jun, Xue Yufei, et al. Remote sensing image extraction for rubber forest distribution in the border regions of China, Laos and Myanmar based on Google Earth Engine platform. Transactions of the Chinese Society of Agricultural Engineering, 2020,36(8):174-181. |
[ 李宇宸, 张军, 薛宇飞, 等. 基于Google Earth Engine的中老缅交界区橡胶林分布遥感提取. 农业工程学报, 2020,36(8):174-181.] | |
[22] |
Oliphant A J, Thenkabail P S, Teluguntla P, et al. Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using a random forest classifier on the Google Earth Engine Cloud. International Journal of Applied Earth Observation and Geoinformation, 2019,81:110-124.
doi: 10.1016/j.jag.2018.11.014 |
[23] |
Sazib N, Mladenova I, Bolten J. Leveraging the Google Earth Engine for drought assessment using global soil moisture data. Remote sensing, 2018,10(8):1265. DOI: 10.3390/rs10081265.
doi: 10.3390/rs10081265 |
[24] |
Beaton A, Whaley R, Corston K, et al. Identifying historic river ice breakup timing using MODIS and Google Earth Engine in support of operational flood monitoring in Northern Ontario. Remote sensing of environment, 2019,224:352-364.
doi: 10.1016/j.rse.2019.02.011 |
[25] |
Yin Cai, Liu Miao, Sun Fengyun, et al. Influencing factors of non-point source pollution of watershed based on boosted regression tree algorithm. Chinese Journal of Applied Ecology, 2016,27(3):911-919.
doi: 10.13287/j.1001-9332.201603.020 pmid: 29726198 |
[ 尹才, 刘淼, 孙凤云, 等. 基于增强回归树的流域非点源污染影响因子分析. 应用生态学报, 2016,27(3):911-919.]
pmid: 29726198 |
|
[26] | Chen Lei, Guo Xi, Han Yi, et al. Research on spatio-temporal characteristics and driving factors of urban expansion in Nanchang city based on BRT model. Resources and Environment in the Yangtze Basin, 2020,29(2):322-333. |
[ 陈蕾, 郭熙, 韩逸, 等. 基于BRT模型的南昌市城市扩展时空特征及驱动因素研究. 长江流域资源与环境, 2020,29(2):322-333.] | |
[27] |
Song K S, Liu G, Wang Q, et al. Quantification of lake clarity in China using Landsat OLI imagery data. Remote Sensing of Environment, 2020,243:111800. DOI: 10.1016/j.rse.2020.111800.
doi: 10.1016/j.rse.2020.111800 |
[28] |
Ma Ronghua, Yang Guishan, Duan Hongtao, et al. China's lakes at present: Number, area and spatial distribution. Scientia Sinica: Terrae, 2011,41(3):394-401.
doi: 10.1360/zd-2011-41-3-394 |
[ 马荣华, 杨桂山, 段洪涛, 等. 中国湖泊的数量、面积与空间分布. 中国科学: 地球科学, 2011,41(3):394-401.] | |
[29] | Wang Sumin, Dou Hongshen. Records of Lakes in China. Beijing: Science Press, 1998: 398-399. |
[ 王苏民, 窦鸿身. 中国湖泊志. 北京: 科学出版社, 1998: 398-399.] | |
[30] |
Li Xiaofeng, Yao Xiaojun, Sun Meiping, et al. Spatial-temporal variations in lakes in northwest China from 2000 to 2014. Acta Ecologica Sinica, 2018,38(1):96-104.
doi: 10.1016/j.chnaes.2017.06.001 |
[ 李晓锋, 姚晓军, 孙美平, 等. 2000—2014 年我国西北地区湖泊面积的时空变化. 生态学报, 2018,38(1):96-104.] | |
[31] |
Long Yuannan, Yan Shixiong, Jiang Changbo, et al. A new method for extracting lake bathymetry using multi-temporal and multi-source remote sensing imagery: A case study of Dongting Lake. Acta Geographica Sinica, 2019,74(7):1467-1481.
doi: 10.11821/dlxb201907015 |
[ 隆院男, 闫世雄, 蒋昌波, 等. 基于多源遥感影像的洞庭湖地形提取方法. 地理学报, 2019,74(7):1467-1481.] | |
[32] | Zhang Xin, Wu Yanhong, Zhang Xin. Water level variation of inland lakes on the south-central Tibetan Plateau in 1972-2012. Acta Geographica Sinica, 2014,69(7):993-1001. |
[ 张鑫, 吴艳红, 张鑫. 1972—2012年青藏高原中南部内陆湖泊的水位变化. 地理学报, 2014,69(7):993-1001.] | |
[33] |
Chen J, Yi C, Wen Z. Systematic underestimation of MODIS global chlorophyll-a concentration estimation algorithm associating with scale effect. IEEE Sensors Journal, 2013,13(5):1656-1661.
doi: 10.1109/JSEN.2013.2239638 |
[34] | Campbell J W, Feng H. The empirical chlorophyll algorithm for MODIS: Testing the OC3M algorithm using NOMAD data//ocean color bio-optical algorithm mini-workshop. 2005: 27-29. |
[35] |
Yin Cai, Liu Miao, Sun Fengyun, et al. Influencing factors of non-point source pollution of watershed based on boosted regression tree algo- rithm. Chinese Journal of Applied Ecology, 2016,27(3):911-919.
doi: 10.13287/j.1001-9332.201603.020 pmid: 29726198 |
[ 尹才, 刘淼, 孙凤云, 等. 基于增强回归树的流域非点源污染影响因子分析. 应用生态学报, 2016,27(3):911-919.]
pmid: 29726198 |
|
[36] | Jiao Linlin, Chang Yu, Shen Dan, et al. Using boosted regression trees to analyze the factors affecting the spatial distribution pat- tern of wildfire in China. Chinese Journal of Ecology, 2015,34(8):2288-2296. |
[ 焦琳琳, 常禹, 申丹, 等. 利用增强回归树分析中国野火空间分布格局的影响因素. 生态学杂志, 2015,34(8):2288-2296.] | |
[37] |
Li Miao, Zang Shuying, Wu Changshan, et al. Spatial and temporal variation and its driving forces of urban impervious surface in urban-rural continuum of Harbin. Acta Geographica Sinica, 2017,72(1):105-115.
doi: 10.11821/dlxb201701009 |
[ 李苗, 臧淑英, 吴长山, 等. 哈尔滨市城乡结合部不透水面时空变化及驱动力分析. 地理学报, 2017,72(1):105-115.] | |
[38] |
Feng L, Hu C, Han X, et al. Long-term distribution patterns of chlorophyll-a concentration in China's largest freshwater lake: MERIS full-resolution observations with a practical approach. Remote Sensing, 2015,7(1):275-299.
doi: 10.3390/rs70100275 |
[39] |
Chen Xiaohua, Qian Xiaoyong, Li Xiaoping, et al. Long-term trend of eutrophication state of Lake Erhai in 1988-2013 and analyses of its socio-economic drivers. Journal of Lake Sciences, 2018,30(1):70-78.
doi: 10.18307/2018.0107 |
[ 陈小华, 钱晓雍, 李小平, 等. 洱海富营养化时间演变特征(1988—2013年)及社会经济驱动分析. 湖泊科学, 2018,30(1):70-78.] | |
[40] |
Tan W, Liu P, Liu Y, et al. A 30-year assessment of phytoplankton blooms in Erhai Lake using Landsat imagery: 1987 to 2016. Remote Sensing, 2017,9(12):1265. DOI: 10.3390/rs9121265.
doi: 10.3390/rs9121265 |
[41] | Wei Yaohong, Lin Zhengyao. Correlation research of climate between Qinghai-Xizang and Antarctic//The China Society on Tibetan Plateau. Symposium on Tibetan Plateau and Global Change, 1994: 119-128. |
[ 魏耀宏, 林振耀. 青藏与南极气候对比的初步研究//中国青藏高原研究会. 青藏高原与全球变化研讨会论文集, 1994: 119-128.] |
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