Abstract
Remote sensing images can be used to delineate variations in the area of lakes and to assess the influence of environmental changes and human activities. However, because lakes are dynamic, results obtained from individual images acquired on a single date are not representative and do not accurately reflect ongoing changes. In this study, we used 8-day moderate resolution imaging spectroradiometer (MODIS) composite data from 2000 to 2010 to map water surface changes over 629 lakes in China. We combined automatic extraction of training data and support vector machine classification to derive the spatial distribution of these large water bodies. The producer’s and user’s accuracies for MODIS images were 91.06 % and 89.81 %, respectively, when compared with interpretation results from 30 m resolution Landsat images taken on similar days. Area changes, variability, inundation intensity, and rainy seasons of the 629 lakes were analyzed based on this multi-temporal lake database. The total area of the 629 lakes increased over the study period, primarily as a result of the expansion of lake areas on the Tibetan Plateau. There were 12 lakes with a maximum area >1,000 km2, and six of these decreased in area from 2000 to 2010. The shrinkages of Poyang Lake and Dongting Lake were −54.76 and −25.08 km2/a, respectively. The area of lakes on Tibetan Plateau, in northern Xinjiang, northeastern Inner Mongolia, and northeastern China varied little, while lakes on the Yangtze Plain, in southern Inner Mongolia, and central Xinjiang fluctuated considerably. Inundation intensity increased for lakes on the Tibetan Plateau, in northern Xinjiang, Heilongjiang, and Jilin, while inundation extent decreased in central Xinjiang, southern Tibet, southern Inner Mongolia, Sichuan, and on the Yangtze Plain. This study is an attempt to develop high-frequency specific land cover maps to improve applicability of general land cover maps. The lake products serve as an important supplement to hydrologic data. The lake database enables the generation of new land surface process models, which could improve the precision of simulations, based on more accurate observations of dynamic lake systems.
Similar content being viewed by others
References
Feng L, Hu C, Chen X et al (2012) Assessment of inundation changes of Poyang Lake using MODIS observations between 2000 and 2010. Remote Sens Environ 121:80–92
Ke CQ (2004) A review of monitoring lake environment change by means of remote sensing. Trans Oceanol Limnol 4:81–86 (in Chinese)
Du Y, Xue H, Wu S et al (2011) Lake area changes in the middle Yangtze region of China over the 20th century. J Environ Manag 92:1248–1255
Ma RH, Duan HT, Hu CM et al (2010) A half-century of changes in China’s lakes: global warming or human influence? Geophys Res Lett 37:1–6
Bai J, Chen X, Li JL et al (2011) Changes of inland lake area in arid Central Asia during 1975–2007: a remote sensing analysis. J Lake Sci 23:80–88 (in Chinese)
Wu YH, Zhu LP, Ye QH et al (2008) The response of lake-glacier variations to climate change in Nam Co Catchment, central Tibetan Plateau, during 1970–2000. J Geophys Sci 18:77–189 (in Chinese)
Li XG, Jiang N, Zhu XH et al (2006) Study on lake surface area change of major lakes in the Taihu basin during the past 30 years. Trans Oceanol Limnol 4:17–24 (in Chinese)
Li H, Xiao PF, Feng XZ et al (2010) Lake changes in spatial evolution and area in source region of three rivers in the recent 30 years. J Lake Sci 22:862–873 (in Chinese)
Dronova I, Gong P, Wang L (2011) Object-based analysis and change detection of major wetland cover types and their classification uncertainty during the low water period at Poyang Lake, China. Remote Sens Environ 115:3220–3236
Wang L, Dronova I, Gong P et al (2010) A new time series vegetation-water index of phenological-hydrological trait across species and functional types for Poyang Lake wetland ecosystem. Remote Sens Environ 125:49–63
Hui F, Xu B, Huang H et al (2008) Modeling spatial-temporal change of Poyang Lake using multitemporal Landsat imagery. Int J Remote Sens 29:5767–5784
Wessels KJ, De Fries RS, Dempewolf J et al (2004) Mapping regional land cover with MODIS data for biological conservation: examples from the Great Yellowstone ecosystem, USA and Pará State, Brazil. Remote Sens Environ 92:67–83
Zhang M, Zhou Q, Chen Z et al (2008) Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data. Int J Appl Earth Obs 10:476–485
Ozdogan M, Gutman G (2008) Comparisons of land cover and LAI estimates derived from ETM+. Remote Sens Environ 112:3520–3537
Zhang X, Sun R, Zhang B et al (2008) Land cover classification of the North China Plain using MODIS_EVI time series. ISPRS J Photogramm 63:476–484
Friedl MA, Sulla-Menashe D, Tan B et al (2010) MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens Environ 114:168–182
Tottrup C (2007) Forest and land cover mapping in a tropical highland region. Photogramm Eng Remote Sens 73:1057–1065
Heiskanen J, Kivinen S (2008) Assessment of multispectral, temporal and angular MODIS data for tree cover mapping in the tundra-taiga transition zone. Remote Sens Environ 112:2367–2380
Shimabukuro YE, Duarte V, Arai E et al (2009) Fraction images derived from Terra Modis data for mapping burnt areas in Brazilian Amazonia. Int J Remote Sens 30(6):1537–1546
Liu S, Gong P (2012) Change of surface cover greenness in China between 2000 and 2010. Chin Sci Bull 57:2835–2845
Vermote EF, Kotchenova SY, Ray JP (2011) MODIS Surface Reflectance User’s Guide. http://modis-sr.ltdri.org
Rundquist DC, Lawson MP, Queen LP et al (1987) The relationship between summer-season rainfall events and lake-surface area. Water Res 23:493–508
McFeeters SK (1996) The use of the normalized difference water index (NDWI) in the delineation of open water features. Int J Remote Sens 17:1425–1432
Xu H (2006) Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sens 27:3025–3033
Work EA, Gilmer DS (1976) Utilization of satellite data for inventorying prairie ponds and lakes. Photogramm Eng Remote Sens 42:685–694
Sheng YW, Shah CA, Smith LC (2008) Automated Image registration for hydrologic change detection in the Lake-Rich Arctic. IEEE Geosci Remote Sens Lett 5:414–418
Sivanpillai R, Miller SN (2010) Improvements in mapping water bodies using ASTER data. Ecol Inform 5:73–78
Lu S, Wu BF, Yan N et al (2011) Water body mapping method with HJ-1A/B satellite imagery. Remote Sens Environ 13:428–434
Liu X, He LH, Zhou C (2008) Study on lake surface area change in the mid-lower reaches of the Yangtze River based on the remote sensing technique. J East Chin Norm Univ (Nat Sci) 4:124–129 (in Chinese)
Ian HW, Eibe F, Mark AH (2011) Data mining: practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, San Francisco
Knorn J, Rabe A, Radeloff VC et al (2009) Land cover mapping of large areas using chain classification of nerghboring landsat satellite images. Remote Sens Environ 113:957–964
Maulik U, Chakraborty D (2011) A self-trained ensemble with semisupervised SVM: an application to pixel classification of remote sensing imagery. Pattern Recogn 44:615–623
Vapnik V (1982) Estimation of dependences based on empirical data. Nauka, Moscow (in Russian). Springer, New York
Hsu CW, Chang CC, Lin CJ (2003) A practical guide to support vector classification. http://www.csie.ntu.edu.tw/~cjlin/papers.html
Foody GM, Mathur A (2004) Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification. Remote Sens Environ 93:107–117
Huang H, Gong P, Clinton N et al (2008) Reduction of atmospheric and topographic effect on Landsat TM data for forest classification. Int J Remote Sens 29:5623–5642
Mountrakis G, Im J, Ogole C (2011) Support vector machines in remote sensing: a review. ISPRS J Photogramm Remote Sens 66:247–259
Cao X, Chen J, Imura H et al (2009) A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data. Remote Sens Environ 113:2205–2209
Song M, Civco D (2004) Road extraction using SVM and image segmentation. Photogramm Eng Rem S 70:1365–1371
Warner TA, Nerry F (2009) Does single broadband or multispectral thermal data add information for classification of visible, near- and shortwave infrared imagery of urban areas? Int J Remote Sens 30:2155–2171
Wang SM, Dou HS (1998) China Lake Catalogue. Science Press, Beijing (in Chinese)
Sun FD, Sun WX, Chen J et al (2012) Comparison and improvement of methods for identifying water bodies in remotely sensed imagery. Int J Remote Sens 33:6854–6875
Huang C, Davis LS, Townshend JRG (2002) An assessment of support vector machines for land cover classification. Int J Remote Sens 23:725–749
Huang C, Song K, Kim S et al (2008) Use of a dark object concept and support vector machines to automate forest cover change analysis. Remote Sens Environ 112:970–985
Niu ZG, Zhang HY, Wang XW et al (2012) Mapping wetland changes in China between 1978 and 2008. Chin Sci Bull 57:2813–2823
Gong P, Niu ZG, Cheng X et al (2010) China’s wetland change (1990–2000) determined by remote sensing. Sci China Earth Sci 53:1036–1042
Ma RH, Kong FX, Duan HT et al (2008) Spatio-temporal distribution of cyanobacteria blooms based on satellite imageries in Lake Taihu, China. J Lake Sci 20:687–694 (in Chinese)
Ma RH, Yang GS, Duan HT et al (2011) China’s lakes at present: number, area and spatial distribution. Sci China Earth Sci 54:283–289
China Meteorological Yearbook (2000–2009). China Meteorological Press, Beijing (in Chinese)
Pu JC, Yao TD, Wang NL (2004) Fluctuations of the glaciers on the Qinghai-Tibetan Plateau during the past century. J Glaciol Geocryol 26:517–522 (in Chinese)
Zhang G, Xie H, Kang S et al (2011) Monitoring lake level changes on the Tibetan Plateau using ICESat altimetry data (2003-2009). Remote Sens Environ 115:1733–1742
Wan W, Xiao PF, Feng XZ et al (2010) Remote sensing analysis for changes of lakes in the southeast of Qiangtang area, Qinghai-Tibet Plateau in recent 30 years. J Lake Sci 22:874–881 (in Chinese)
Bates BC, Kundzewicz ZW, Wu S et al (2008) Climate change and water. Technical Paper of the Intergovernmental Panelon Climate Change. IPCC Secretariat, Geneva
Gong P, Wang J, Yu L et al (2013) Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data. Int J Remote Sens 34:2607–2654
Dai Y, Zeng X, Dickinson RE et al (2003) The common land model (CLM). BAM Meteorol Soc 84:1013–1023
Oleson KW, Bonan GB, Feddema J et al (2010) Technical description of an Urban parameterization for the community land model (CLMU). National Center for Atmospheric Research. http://nldr.library.ucar.edu/repository/collections/TECH-NOTE-000-000-000-847
Michishita R, Gong P, Xu B (2012) Spectral mixture analysis for bi-sensor wetland mapping using Landsat TM and Terra MODIS data. Int J Remote Sens 33:3373–3401
Yang GS, Ma RH, Zhang L et al (2010) Lake status, major problems and protection strategy in China. J Lake Sci 22:799–810 (in Chinese)
Nanjing Institute of Geography and Limnology (2007) Chinese Academy of Sciences. On the cause of cyanophyta bloom and pollution in water intake area and emergency measures in Meiliang Bay, Lake Taihu in 2007. J Lake Sci 19:357–358 (in Chinese)
Acknowledgments
This work was supported by the National High-tech R&D Program of China (2009AA12200101).
Author information
Authors and Affiliations
Corresponding author
About this article
Cite this article
Sun, F., Zhao, Y., Gong, P. et al. Monitoring dynamic changes of global land cover types: fluctuations of major lakes in China every 8 days during 2000–2010. Chin. Sci. Bull. 59, 171–189 (2014). https://doi.org/10.1007/s11434-013-0045-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11434-013-0045-0