浏览全部资源
扫码关注微信
Published： 07 February 2022 ，
扫 描 看 全 文
宁晓刚，常文涛，王浩，张翰超，朱乾德.2022.联合GEE与多源遥感数据的黑龙江流域沼泽湿地信息提取.遥感学报，26（2）： 386-396
Ning X G，Chang W T，Wang H，Zhang H C and Zhu Q D. 2022. Extraction of marsh wetland in Heilongjiang Basin based on GEE and multi-source remote sensing data. National Remote Sensing Bulletin， 26（2）：386-396
湿地是地球上最重要的生态系统之一，在维持全球生态环境安全等方面发挥着举足轻重的作用。由于湿地独特的水文特征，传统的湿地监测需要耗费大量的人力和财力，对于大尺度的湿地信息提取更是困难重重。随着大数据和云计算的兴起，为大尺度和长时间序列的空间数据处理提供了契机。本文基于Google Earth Engine（GEE）云平台，使用Sentinel-1合成孔径雷达（SAR）数据、Sentinel-2光学数据以及地形数据，探讨了红边、雷达以及地形特征对大范围区域沼泽湿地提取的重要性，验证了利用JM距离寻找沼泽湿地提取最优特征组合的可行性，结合随机森林算法对2018年黑龙江流域沼泽湿地进行提取。研究表明：（1）Sentinel-2红边波段和Sentinel-1雷达波段以及地形数据有利于沼泽湿地信息提取，相比植被指数和水体指数沼泽的制图精度分别提高了7.56%，5.04%，4.48%；（2）利用JM距离得到的分离度表明，红边特征>其他光学特征>地形特征>雷达特征。进行特征优选后沼泽湿地的制图和用户精度分别提高了1.45%和3.02%，最终结合随机森林算法的总体精度为91.54%，沼泽的提取精度为88.55%。本研究利用GEE云平台和多源遥感数据以及机器学习算法，能够准确、快速、高效地提取大尺度范围沼泽湿地信息，具有较大的应用潜力。
Wetland is an important ecosystem on the planet and plays a pivotal role in maintaining global ecological environment security. Traditional wetland monitoring requires a lot of manpower and financial resources due to the unique hydrological characteristics of wetlands
and extracting large-scale wetland information is also difficult. Compared with traditional field surveys
remote sensing technology
which has the advantages of wide observation range
short update cycle
has played an important role in large-scale wetland information extraction. However
remote sensing monitoring of march wetland mainly uses optical images traditionally
which are severely affected by weather such as clouds and rain
making large-scale marsh wetland extraction challenging. The use of radar and terrain data can combine the spectral information and scattering mechanism
which has great potential for marsh wetland information extraction. Nevertheless
there are few studies that evaluate the differences of optical
SAR
and topographic features in importance for the extraction of marsh wetland information. The rise of big data and cloud computing has enabled large-scale and long time series spatial data processing. On the basis of the Google Earth Engine (GEE) cloud platform
this study uses Sentinel-1 synthetic aperture radar data
Sentinel-2 optical data
and terrain data to explore their importance to the extraction of marsh wetland at large scale
and verify the feasibility of JM distance to find the optimal feature combination to the extraction of marsh wetland. Random forest algorithm is also used to extract marsh wetlands in Heilongjiang Basin in 2018. In order to explore the importance of red edge
radar and topographic features and the best features conducive to marsh wetland extraction
six experimental schemes are designed. Scheme one uses the combination of spectral feature
vegetation index and water index. Scheme two uses the combination of spectral feature and red edge feature. Scheme three uses the combination of spectral feature and terrain feature. Scheme four uses the combination of spectral feature and radar feature. Scheme five uses the combination of spectral feature
vegetation index
water index
red edge feature
terrain feature and radar feature. Scheme six uses all features
which are optimized by JM distance. The research shows that (1) Sentinel-2 red edge bands and Sentinel-1 radar bands and terrain data are conducive to marsh wetland information extraction. Compared with vegetation indexes and water indexes
the producer accuracy of marsh increased by 7.56%
5.04%
and 4.48%. (2) The separation obtained using JM distance shows that the order is red-edge features > other optical features > terrain features > radar features. The marsh wetland in scheme six has the highest mapping accuracy and user accuracy. After using the JM distance to select features
the producer and user accuracies of the marsh wetland increased by 1.45% and 3.02%
respectively. The overall accuracy of the combined random forest algorithm was 91.54%
and the accuracy of marsh extraction was 88%. This study uses the GEE cloud platform
multisource remote sensing data
and machine learning algorithms to accurately
quickly
and efficiently extract large-scale marsh wetland information. This method has great application potential.
遥感Google Earth Engine沼泽湿地Sentinel-1Sentinel-2JM距离随机森林红边波段
remote sensingGoogle Earth EnginemarshSentinel-1Sentinel-2JM distancerandom forestred-edge bands
Amani M, Brisco B, Afshar M, Mirmazloumi S M, Mahdavi S, Mirzadeh S M J, Huang W M and Granger J. 2019. A generalized supervised classification scheme to produce provincial wetland inventory maps: an application of Google Earth Engine for big geo data processing. Big Earth Data, 3(4): 378-394 [DOI: 10.1080/20964471.2019.1690404http://dx.doi.org/10.1080/20964471.2019.1690404]
Belgiu M and Drăguţ L. 2016. Random forest in remote sensing: a review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114: 24-31 [DOI: 10.1016/j.isprsjprs.2016.01.011http://dx.doi.org/10.1016/j.isprsjprs.2016.01.011]
Breiman L. 2001. Random forests. Machine Learning, 45(1): 5-32 [DOI: 10.1023/A:1010933404324http://dx.doi.org/10.1023/A:1010933404324]
Chang W T, Wang H, Ning X G and Zhang H C. 2020. Extraction of Zhalong wetlands information based on images of sentinel-2 red-edge bands and sentinel-1 radar bands. Wetland Science, 18(1): 10-19
常文涛, 王浩, 宁晓刚, 张翰超. 2020. 融合Sentinel-2红边波段和Sentinel-1雷达波段影像的扎龙湿地信息提取. 湿地科学, 18(1): 10-19 [DOI: 10.13248/j.cnki.wetlandsci.2020.01.002http://dx.doi.org/10.13248/j.cnki.wetlandsci.2020.01.002]
Chatziantoniou A, Psomiadis E and Petropoulos G P. 2017. Co-Orbital Sentinel 1 and 2 for LULC mapping with emphasis on wetlands in a mediterranean setting based on machine learning. Remote Sensing, 9(12): 1259 [DOI: 10.3390/rs9121259http://dx.doi.org/10.3390/rs9121259]
Congalton R G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1): 35-46 [DOI: 10.1016/0034-4257(91)90048-Bhttp://dx.doi.org/10.1016/0034-4257(91)90048-B]
Cui M H. 2006. Status quo of wetlands of Heilongjiang River valley and the protection. Forest Inventory and Planning, 31(1): 75-78
崔茂欢. 2006. 黑龙江流域湿地现状及保护. 林业调查规划, 31(1): 75-78 [DOI: 10.3969/j.issn.1671-3168.2006.01.020http://dx.doi.org/10.3969/j.issn.1671-3168.2006.01.020]
Dubeau P, King D J, Unbushe D G and Rebelo L M. 2017. Mapping the Dabus wetlands, Ethiopia, using random forest classification of Landsat, PALSAR and topographic data. Remote Sensing, 9(10): 1056 [DOI: 10.3390/rs9101056http://dx.doi.org/10.3390/rs9101056]
Fang L, Wang W J, Jiang W G, Chen M, Wang Y, Jia K and Li Y S. 2017. Spatio-temporal variations of vegetation cover and its responses to climate change in the Heilongjiang Basin of China from 2000 to 2014. Scientia Geographica Sinica, 37(11): 1745-1754
方利, 王文杰, 蒋卫国, 陈民, 王永, 贾凯, 李延森. 2017. 2000～2014年黑龙江流域(中国)植被覆盖时空变化及其对气候变化的响应. 地理科学, 37(11): 1745-1754 [DOI: 10.13249/j.cnki.sgs.2017.11.017http://dx.doi.org/10.13249/j.cnki.sgs.2017.11.017]
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D and Moore R. 2017. Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202: 18-27 [DOI: 10.1016/j.rse.2017.06.031http://dx.doi.org/10.1016/j.rse.2017.06.031]
He Y, Huang C, Li H, Liu Q S, Liu G H, Zhou Z C and Zhang C C. 2019. Land-cover classification of random forest based on Sentinel-2A image feature optimization. Resources Science, 41(5): 992-1001
何云, 黄翀, 李贺, 刘庆生, 刘高焕, 周振超, 张晨晨. 2019. 基于Sentinel-2A影像特征优选的随机森林土地覆盖分类. 资源科学, 41(5): 992-1001 [DOI: 10.18402/resci.2019.05.15http://dx.doi.org/10.18402/resci.2019.05.15]
Hird J N, DeLancey E R, McDermid G J and Kariyeva J. 2017. Google Earth Engine, open-access satellite data, and machine learning in support of large-area probabilistic wetland mapping. Remote Sensing, 9(12): 1315 [DOI: 10.3390/rs9121315http://dx.doi.org/10.3390/rs9121315]
Limpitlaw D and Gens R. 2006. Dambo mapping for environmental monitoring using Landsat TM and SAR imagery: case study in the Zambian Copperbelt. International Journal of Remote Sensing, 27(21): 4839-4845 [DOI: 10.1080/01431160600835846http://dx.doi.org/10.1080/01431160600835846]
Lin C S, Wu C C, Tsogt K, Ouyang Y C and Chang C I. 2015. Effects of atmospheric correction and pansharpening on LULC classification accuracy using WorldView-2 imagery. Information Processing in Agriculture, 2(1): 25-36 [DOI: 10.1016/j.inpa.2015.01.003http://dx.doi.org/10.1016/j.inpa.2015.01.003]
Liu L, Zang S Y, Na X D and Pei X Y. 2013. Wetland mapping in the Zhalong natural reserve using optical and radar remotely sensed data and ancillary topographical data. Geography and Geo-Information Science, 29(1): 36-40
刘蕾, 臧淑英, 那晓东, 裴雪原. 2013. 兼容光学雷达影像及地形辅助数据的扎龙湿地遥感分类. 地理与地理信息科学, 29(1): 36-40 [DOI: 10.7702/dlydlxxkx20130108http://dx.doi.org/10.7702/dlydlxxkx20130108]
Mahdianpari M, Salehi B, Mohammadimanesh F, Homayouni S and Gill E. 2019. The first wetland inventory map of newfoundland at a spatial resolution of 10 m using sentinel-1 and sentinel-2 data on the Google earth engine cloud computing platform. Remote Sensing, 11(1): 43 [DOI: 10.3390/rs11010043http://dx.doi.org/10.3390/rs11010043]
Meng M, Tian H F, Wu M Q, Wang L and Niu Y. 2019. Evolution characteristics analysis of wetland landscape pattern based on Google Earth Engine platform: a case study on Baiyangdian. Journal of Yunnan University (Natural Science Edition), 41(2): 416-424
孟梦, 田海峰, 邬明权, 王力, 牛铮. 2019. 基于Google Earth Engine平台的湿地景观空间格局演变分析: 以白洋淀为例. 云南大学学报(自然科学版), 41(2): 416-424 [DOI: 10.7540/j.ynu.20170715http://dx.doi.org/10.7540/j.ynu.20170715]
Mitsch W J, Bernal B, Nahlik A M, Mander Ü, Zhang L, Anderson C J, Jørgensen S E and Brix H. 2013. Wetlands, carbon, and climate change. Landscape Ecology, 28(4): 583-597 [DOI: 10.1007/s10980-012-9758-8http://dx.doi.org/10.1007/s10980-012-9758-8]
Mitsch W J and Gosselink J G. 2000. The value of wetlands: importance of scale and landscape setting. Ecological Economics, 35(1): 25-33 [DOI: 10.1016/S0921-8009(00)00165-8http://dx.doi.org/10.1016/S0921-8009(00)00165-8]
Ozesmi S L and Bauer M E. 2002. Satellite remote sensing of wetlands. Wetlands Ecology and Management, 10(5): 381-402 [DOI: 10.1023/A:1020908432489http://dx.doi.org/10.1023/A:1020908432489]
Rodriguez-Galiano V F, Ghimire B, Rogan J, Chica-Olmo M and Rigol-Sanchez J P. 2012. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67: 93-104 [DOI: 10.1016/j.isprsjprs.2011.11.002http://dx.doi.org/10.1016/j.isprsjprs.2011.11.002]
Shalaby A and Tateishi R. 2007. Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Applied Geography, 27(1): 28-41 [DOI: 10.1016/j.apgeog.2006.09.004http://dx.doi.org/10.1016/j.apgeog.2006.09.004]
Shoko C and Mutanga O. 2017. Examining the strength of the newly-launched Sentinel 2 MSI sensor in detecting and discriminating subtle differences between C3 and C4 grass species. ISPRS Journal of Photogrammetry and Remote Sensing, 129: 32-40 [DOI: 10.1016/j.isprsjprs.2017.04.016http://dx.doi.org/10.1016/j.isprsjprs.2017.04.016]
Song C H, Woodcock C E, Seto K C, Lenney M P and Macomber S A. 2001. Classification and change detection using Landsat TM data: when and how to correct atmospheric effects?. Remote Sensing of Environment, 75(2): 230-244 [DOI: 10.1016/S0034-4257(00)00169-3http://dx.doi.org/10.1016/S0034-4257(00)00169-3]
Teluguntla P, Thenkabail P S, Oliphant A, Xiong J, Gumma M K, Congalton R G, Yadav K and Huete A. 2018. A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 144: 325-340 [DOI: 10.1016/j.isprsjprs.2018.07.017http://dx.doi.org/10.1016/j.isprsjprs.2018.07.017]
Wang A Q, Zhou D M and Gong H L. 2012. A research on method of wetland vegetation identification and classification based on radar backscatter characteristics. Remote Sensing Information, (2): 15-19
王安琪, 周德民, 宫辉力. 2012. 基于雷达后向散射特性进行湿地植被识别与分类的方法研究. 遥感信息, (2): 15-19 [DOI: 10.3969/j.issn.1000-3177.2012.02.003]
Whyte A, Ferentinos K P and Petropoulos G P. 2018. A new synergistic approach for monitoring wetlands using Sentinels -1 and 2 data with object-based machine learning algorithms. Environmental Modelling and Software, 104: 40-54 [DOI: 10.1016/j.envsoft.2018.01.023http://dx.doi.org/10.1016/j.envsoft.2018.01.023]
Yan B, Zhang X, Xia Z Q, Guo M C, Liu C X and Luo Y Y. 2019. Characteristics of precipitation variation in Heilongjiang River Basin. Journal of Yangtze River Scientific Research Institute, 36(7): 14-17, 22
鄢波, 张潇, 夏自强, 郭勉辰, 刘宸希, 罗云英. 2019. 黑龙江流域降水变化特征分析. 长江科学院院报, 36(7): 14-17, 22 [DOI: 10.11988/ckyyb.20171298http://dx.doi.org/10.11988/ckyyb.20171298]
Yan T T, Bian H F, Liao G X, Sheng L X, Zhang J S and Gao M H. 2014. Research status of methods for mapping forested wetlands based on remote sensing. Remote Sensing for Land and Resources, 26(2): 11-18
严婷婷, 边红枫, 廖桂项, 盛连喜, 张吉顺, 高明辉. 2014. 森林湿地遥感信息提取方法研究现状. 国土资源遥感, 26(2): 11-18 [DOI: 10.6046/gtzyyg.2014.02.03http://dx.doi.org/10.6046/gtzyyg.2014.02.03]
Yesou H, Pottier E, Mercier G, Grizonnet M, Haouet S, Giros A, Faivre R, Huber C and Michel J. 2016. Synergy of Sentinel-1 and Sentinel-2 imagery for wetland monitoring information extraction from continuous flow of sentinel images applied to water bodies and vegetation mapping and monitoring//2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Beijing: IEEE: 162-165 [DOI: 10.1109/IGARSS.2016.7729033http://dx.doi.org/10.1109/IGARSS.2016.7729033]
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 [DOI: 10.11975/j.issn.1002-6819.2015.13.025http://dx.doi.org/10.11975/j.issn.1002-6819.2015.13.025]
Zhang M, Zeng Y N and Zhu Y S. 2017. Wetland mapping of Donting Lake Basin based on time-series MODIS data and object-oriented method. Journal of Remote Sensing, 21(3): 479-492
张猛, 曾永年, 朱永森. 2017. 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. 遥感学报, 21(3): 479-492 [DOI: 10.11834/jrs.20176129http://dx.doi.org/10.11834/jrs.20176129]
相关文章
相关作者
相关机构
京公网安备11010802024621