面向对象方法的时间序列MODIS数据湿地信息提取——以洞庭湖流域为例
Wetland mapping of Donting Lake Basin based on time-series MODIS data and object-oriented method
- 2017年21卷第3期 页码:479-492
纸质出版日期: 2017-5 ,
录用日期: 2016-9-12
DOI: 10.11834/jrs.20176129
扫 描 看 全 文
浏览全部资源
扫码关注微信
纸质出版日期: 2017-5 ,
录用日期: 2016-9-12
扫 描 看 全 文
张猛, 曾永年, 朱永森. 面向对象方法的时间序列MODIS数据湿地信息提取——以洞庭湖流域为例[J]. 遥感学报, 2017,21(3):479-492.
Meng ZHANG, Yongnian ZENG, Yongsen ZHU. Wetland mapping of Donting Lake Basin based on time-series MODIS data and object-oriented method[J]. Journal of Remote Sensing, 2017,21(3):479-492.
以洞庭湖流域为研究区,对大范围湿地信息遥感提取方法进行了研究。先基于时间序列MODIS EVI及物候特征参数,通过J-M(Jeffries-Matusita distance)距离分析,构建了MODIS(250 m)最佳时序组合分类数据;其次,通过Johnson指数确定了最佳分割尺度,采用面向对象的遥感分类方法(Random tree分类器)提取了洞庭湖流域的湿地信息,并验证该方法的适用性。研究结果表明,基于时序数据与面向对象的Random tree分类的总体精度和Kappa系数分别为78.84%和0.71
较之基于像元的相同算法的总体分类精度和Kappa系数分别提高了5.79%和0.04。同时,基于面向对象方法的湿地整体的用户精度与生产者精度较基于像元方法分别提高了4.56%和6.21%,可有效提高大区域湿地信息提取的精度。
The mapping of large-scale wetlands involves time-series coarse spatial resolution remote sensing data and pixel-based methods
such as the decision tree and threshold techniques. However
few studies use low spatial-resolution images(such as moderate-resolution imaging spectroradiometer (MODIS)) and object-oriented methods to extract information from large-area wetlands. Although spatial resolution has some disadvantages
the coarse spatial resolution image has high time resolution
considerable spectral information
and low cost. Therefore
the high temporal characteristics of coarse image and object-oriented method can be used to extract wetland information over a large area
such as basins and continents. In this study
the object-oriented method and time-series MODIS Enhanced Vegetation Index (EVI) data are utilized to map the wetland of the Dongting Lake Basin. The time-series MODIS EVI images are smoothed using the double logistic function fitting method of TIMESAT software package
which is based on MATLAB. Meanwhile
the phenology indices are calculated from the time-series MODIS EVI data. Subsequently
the best combination of images and optimal segmentation scale are determined with the
J
Bh
distance and Johnson index. Wetland mapping is then verified using a random tree classifier based on the segmented images. In addition
validation data are derived from the visual interpretation of Landsat 8 images
Google Earth
and land-use data. To verify the classification effect of the object-oriented classification method on coarse spatial resolution images
the pixel-based method is also utilized to classify the best combination of images and is then compared with the upper method. The phenology of various ground cover types is obviously different
which indicates that they can be used to distinguish different land types
especially vegetation types. Given the image combination of the critical periods (DOY113
DOY145
DOY173
DOY193
DOY241
and DOY289) of vegetation growth and phenology (start of season and length of season)
we can determine the
J
Bh
(9.143) and
JM
distances to meet research needs. An optimal segmentation scale parameter (15 pixels of MODIS) is obtained using the Johnson index. Based on object-oriented classification method
the overall accuracy and Kappa coefficient of the random tree classifier are 78.84% and 0.71 respectively. Compared with the object-oriented method
a pixel-based classification method with random tree classifier achieves a lower overall accuracy and Kappa coefficient of 73.05% and 0.67
respectively. In traditional pixel-based analysis
the surrounding pixels contribute a substantial proportion of signals. The object-oriented method analysis utilizes objects instead of pixels
which effectively reduces signals from surrounding pixels by integrating neighborhood information. The object-oriented classification method also reduces the " saltand pepper” effect of mapping heterogeneous landscapes and enhances the accuracy of the analysis. However
large pixels of water still exist
thus causing existing mutual fault points (368 and 228). A total of 237 and 316 pixels of sedge and reed are classified to water and 113 and 128 pixels are classified to mudflat. However
683 and 502 pixels of paddy are misclassified into dry land and forest. We also obtained higher user accuracy for the whole wetland through the object-oriented classification technique than pixel-based classification method. Both the user and producer accuracies improved to approximately 4.56% and 6.21%
respectively. The user and producer accuracy of wetland categories were approximately 74.75%—88.03% and 78.68%—84.36%
respectively
based on the object-oriented method
which considerably increased compared with pixel-based method except for the user accuracy of the sedge wetland. On the one hand
this finding can be attributed to strong heterogeneity and mixed pixels. On the other hand
the influence of human activity
disturbance
crop prices
and national policy caused an increasingly broken patch of sedge field
thus causing misclassification. The combination of time-series MODIS EVI data and object-oriented method effectively extract wetland information on a watershed scale. It provides a new method and technique for wetland mapping on a large scale
even in a continental range.
时间序列MODIS面向对象分类Random tree湿地洞庭湖流域
time series dataMODIS EVI dataobject-oriented methodRandom treewetlandDongting Lake Basin
Chopra R, Verma V K and Sharma P K. 2001. Mapping, monitoring and conservation of Harike wetland ecosystem, Punjab, India, through remote sensing. International Journal of Remote Sensing, 22(1): 89–98
Corcoran J M, Knight J F and Gallant A L. 2013. Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in northern Minnesota. Remote Sensing, 5(7): 3212–3238
Dronova I, Peng G and 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 Sensing of Environment, 115(12): 3220–3236
Dronova I, Gong P, Clinton N E, Wang L, Fu W, Qi S H and Liu Y. 2012. Landscape analysis of wetland plant functional types: the effects of image segmentation scale, vegetation classes and classification methods. Remote Sensing of Environment, 127: 357–369
Ghioca-Robrecht D M, Johnston C A and Tulbure M G. 2008. Assessing the use of multiseason QuickBird imagery for mapping invasive species in a Lake Erie coastal Marsh. Wetlands, 28(4): 1028–1039
宫兆宁, 林川, 赵文吉, 崔天翔. 2014. WorldView-2影像的湿地典型挺水植物群落含水量估算研究-以北京野鸭湖湿地为例. 红外与毫米波学报, 33(5): 533–545
Gong Z N, Lin C, Zhao W J and Cui T X. 2014. Canopy water content estimation for typical emerged plant community using WorldView-2 imagery: a case study in Wild Duck Lake wetland, Beijing. Journal of Infrared and Millimeter, 33(5): 533–545
Han X X, Chen X L and Feng L. 2015. Four decades of winter wetland changes in Poyang Lake based on Landsat observations between 1973 and 2013. Remote Sensing of Environment, 156: 426–437
Hao P Y, Wang L, Niu Z, Aablikim A, Huang N, Xu S G and Chen F. 2014. The potential of time series merged from Landsat-5 TM and HJ-1 CCD for crop classification: a case study for Bole and Manas counties in Xinjiang, China. Remote Sensing, 6(8): 7610–7631
Johnson B and Xie Z X. 2011. Unsupervised image segmentation evaluation and refinement using a multi-scale approach. ISPRS Journal of Photogrammetry and Remote Sensing, 66(4): 473–483
Jönsson P and Eklundh L. 2004. TIMESAT-a program for analyzing time-series of satellite sensor data. Computers and Geosciences, 30(8): 833–845
康峻, 侯学会, 牛铮, 高帅, 贾坤. 2014. 基于拟合物候参数的植被遥感决策树分类. 农业工程学报, 30(9): 148–156
Kang J, Hou X H, Niu Z, Gao S and Jia K. 2014. Decision tree classification based on fitted phenology parameters from remotely sensed vegetation data. Transactions of the Chinese Society of Agricultural Engineering, 30(9): 148–156
Kayastha N, Thomas V, Galbraith J and Banskota A. 2012. Monitoring wetland change using inter-annual Landsat time-series data. Wetlands, 32(6): 1149–1162
Klemas V. 2013. Remote sensing of emergent and submerged wetlands: an overview. International Journal of Remote Sensing, 34(18): 6286–6320
Laba M, Blair B, Downs R, Monger B, Philpot W, Smith S, Sullivan P and Baveye P C. 2010. Use of textural measurements to map invasive wetland plants in the Hudson River National Estuarine Research Reserve with IKONOS satellite imagery. Remote Sensing of Environment, 114(4): 876–886
Landmann T, Schramm M, Colditz R R, Dietz A and Dech S. 2010. Wide area wetland mapping in semi-arid Africa using 250-Meter MODIS metrics and topographic variables. Remote Sensing, 2(7): 1751–1766
Lane C R, Liu H X, Autrey B C, Anenkhonov O A, Chepinoga V V and Wu Q S. 2014. Improved wetland classification using eight-band high resolution satellite imagery and a hybrid approach. Remote Sensing, 6(12): 12187–12216
雷璇, 杨波, 蒋卫国, 杨一鹏, Kuenzer C, 陈强. 2012. 东洞庭湿地植被格局变化及其影响因素. 地理研究, 31(3): 461–470
Lei X, Yang B, Jiang W G, Yang Y P, Kuenzer C and Chen Q. 2012. Vegetation pattern changes and their influencing factors in the East Dongting Lake wetland. Geographical Research, 31(3): 461–470
李向应, 秦大河, 效存德, 陈茹. 2011. 近期气候变化研究的一些最新进展. 科学通报, 56(36): 3029–3040
Li X Y, Qin D H, Xiao C D and Chen R. 2011. Progress regarding climate change during recent years. Chinese Science Bulletin, 56(36): 3029–3040
龙岳红, 秦建新, 贺新光, 杨准. 2015. 洞庭湖流域植被动态变化的小波多分辨率分析. 地理学报, 70(9): 1491–1502
Long Y H, Qin J X, He X G and Yang Z. 2015. Wavelet multi-resolution analysis of vegetation dynamic change in Dongting Lake Basin. Acta Geographica Sinica, 70(9): 1491–1502
吕铭志, 盛连喜, 张立. 2013. 中国典型湿地生态系统碳汇功能比较. 湿地科学, 11(1): 114–120
Lv M Z, Sheng L X and Zhang L. 2013. A review on carbon fluxes for typical wetlands in different climates of China. Wetland Science, 11(1): 114–120
牟晓杰, 刘兴土, 阎百兴, 崔保山. 2015. 中国滨海湿地分类系统. 湿地科学, 13(1): 19–26
Mou X J, Liu X T, Yan B X and Cui B S. 2015. Classification system of coastal wetlands in China. Wetland Sicence, 13(1): 19–26
Peerbhay K, Mutanga O, Lottering R and Ismail R. 2016. Mapping Solanum mauritianum plant invasions using WorldView-2 imagery and unsupervised random forests. Remote Sensing of Environment, 182: 39–48
Pengra B W, Johnston C A and Loveland T R. 2007. Mapping an invasive plant, Phragmites australis, in coastal wetlands using the EO-1 Hyperion hyperspectral sensor. Remote Sensing of Environment, 108(1): 74–81
Poulin B, Davranche A and Lefebvre G. 2010. Ecological assessment ofPhragmites australis wetlands using multi-season SPOT-5 scenes. Remote Sensing of Environment, 114(7): 1602–1609
Reschke J and Hüttich C. 2014. Continuous field mapping of Mediterranean wetlands using sub-pixel spectral signatures and multi-temporal Landsat data. International Journal of Applied Earth Observation and Geoinformation, 28: 220–229
Reschke J and Hüttich C. 2014. Continuous field mapping of Mediterranean wetlands using sub-pixel spectral signatures and multi-temporal Landsat data. International Journal of Applied Earth Observation and Geoinformation, 28: 220–229
Shi K, Zhang Y L, Zhu G W, Liu X H, Zhou Y Q, Xu H, Qin B Q, Liu G and Li Y M. 2015. Long-term remote monitoring of total suspended matter concentration in Lake Taihu using 250 m MODIS-Aqua data. Remote Sensing of Environment, 164: 43–56
孙佩军, 张锦水, 潘耀忠, 谢登峰, 袁周米琪. 2016. 构建时空融合模型进行水稻遥感识别. 遥感学报, 20(2): 328–343
Sun P J, Zhang J S, Pan Y Z, Xie D F and Yuan Z M Q. 2016. Temporal-spatial-fusion model for area extraction of paddy rice using multi-temporal remote sensing images. Journal of Remote Sensing, 20(2): 328–343
Szantoi Z, Escobedo F, Abd-Elrahman A, Smith S and Pearlstine L. 2013. Analyzing fine-scale wetland composition using high resolution imagery and texture features. International Journal of Applied Earth Observation and Geoinformation, 23: 204–212
Tian B, Zhou Y X, Thom R M, Diefenderfer H L and Yuan Q. 2015. Detecting wetland changes in Shanghai, China using FORMOSAT and Landsat TM imagery. Journal of Hydrology, 529: 1–10
Van Niel T G, Mcvicar T R and Datt B. 2005. On the relationship between training sample size and data dimensionality: Monte Carlo analysis of broadband multi-temporal classification. Remote Sensing of Environment, 98(4): 468–480
Wang L, Dronova I, Gong P, Yang W B, Li Y R and Liu Q. 2012. A new time series vegetation-water index of phenological-hydrological trait across species and functional types for Poyang Lake wetland ecosystem. Remote Sensing of Environment, 125: 49–63
吴健生, 刘建政, 黄秀兰, 彭建, 李慧坚. 2012. 基于面向对象分类的土地整理区农田灌排系统自动化识别. 农业工程学报, 28(8): 25–31
Wu J S, Liu J Z, Huang X L, Peng J and Li H J. 2012. Automatic identification of irrigation and drainage system in land reclamation area based on object-oriented classification. Transactions of the Chinese Society of Agricultural Engineering, 28(8): 25–31
谢登峰, 张锦水, 潘耀忠, 孙佩军, 袁周米琪. 2015. Landsat 8和MODIS融合构建高时空分辨率数据识别秋粮作物. 遥感学报, 19(5): 791–805
Xie D F, Zhang J S, Pan Y Z, Sun P J and Yuan Z M Q. 2015. Fusion of MODIS and Landsat 8 images to generate high spatial-temporal resolution data for mapping autumn crop distribution. Journal of Remote Sensing, 19(5): 791–805
杨元征, 常禹, 胡远满, 刘淼, 李月辉. 2011. 环境灾害遥感小卫星在辽河三角洲湿地景观制图中的应用. 应用生态学报, 22(6): 1552–1558
Yang Y Z, Chang Y, Hu Y M, Liu M and Li Y H. 2011. Application of small remote sensing satellite constellations for environmental hazards in wetland landscape mapping: taking Liaohe delta, Liaoning province of northeast China as a case. Chinese Journal of Applied Ecology, 22(6): 1552–1558
张猛, 曾永年. 2015. 基于多时相Landsat数据融合的洞庭湖区水稻面积提取. 农业工程学报, 31(13): 178–185
Zhang M and Zeng Y N. 2015. Mapping paddy fields of Dongting Lake area by fusing Landsat and MODIS data. Transactions of the Chinese Society of Agricultural Engineering, 31(13): 178–185
张树文, 颜凤芹, 于灵雪, 卜坤, 杨久春, 常丽萍. 2013. 湿地遥感研究进展. 地理科学, 33(11): 1406–1412
Zhang S W, Yan F Q, Yu L X, Bu K, Yang J C and Chang L P. 2013. Application of remote sensing technology to wetland research. Scientia Geographica Sinica, 33(11): 1406–1412
郑姚闽, 牛振国, 宫鹏, 戴永久, 上官微. 2013. 湿地碳计量方法及中国湿地有机碳库初步估计. 科学通报, 58(2): 170–180
Zheng Y M, Niu Z G, Gong P, Dai Y J and Shangguan W. 2013. Preliminary estimation of the organic carbon pool in China’s wetlands. Chinese Science Bulletin, 58(6): 662–670
周葆华, 尹剑, 金宝石, 朱磊. 2014. 30年来武昌湖湿地退化过程与原因. 地理学报, 69(11): 1697–1706
Zhou B H, Yin J, Jin B S and Zhu L. 2014. Degradation of Wuchang Lake wetland and its causes during 1980-2010. Acta Geographica Sinica, 69(11): 1697–1706
相关作者
相关机构