顾及潮汐影响的中国红树林高分二号遥感制图
Mapping high-resolution mangrove forests in China using GF-2 imagery under the tide
- 2023年27卷第6期 页码：1320-1333
纸质出版日期： 2023-06-07
DOI： 10.11834/jrs.20221848
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摘要
遥感技术由于具有实时、准确、多尺度、可重复等优点广泛应用在红树林遥感制图研究中。目前，中国红树林遥感制图数据集中空间分辨率最高的产品是基于Sentinel 10 m数据生产的，此外，大多中国红树林遥感制图中忽略了潮汐的影响，导致红树林遥感制图结果不精准。本文利用国产高分二号数据源，顾及潮汐淹没的影响实现空间分辨率1 m的2020年中国红树林遥感制图。选取覆盖中国海岸线具有红树林分布的高分二号影像312景（24景高分一号影像补充缺失区域），对影像进行面向对象多尺度分割，采用红树林淹没指数作为顾及潮汐影响的表征参数，结合随机森林分类方法完成中国红树林高分遥感制图。研究结果表明：2020年中国红树林面积为29576.48 ha，95%的红树林主要分布在广西壮族自治区、广东省、海南省，其总体分类精度为92%，Kappa系数为0.89，尚未顾及潮汐影响的结果比顾及潮汐影响的结果少2531.24 ha。本文生产的红树林高分数据集可为中国红树林生态系统的监测、管理及评估提供高精度的数据支持，具有重要的实际应用价值。
Abstract
Remote sensing technology is widely used in mangrove forest mapping with the advantages of real-time mapping
accuracy
multiscalability
and repeatability. Until now
the mangrove forest dataset with the highest spatial resolution in China is produced by 10 m Sentinel data. In addition
most existing mangrove forest datasets in China ignore the influence of tide
leading to low spatial resolution and inaccurate mapping. On the basis of Chinese GF-2 images
this study aims to map Chinese mangrove forests in 2020 with a spatial resolution of 1 m under the tide. Specifically
312 scenes of GF-2 image covering China's coastline (24 scenes of GF-1 image covers the areas without GF-2 images) were selected. First
the selected images were segmented using the object-based multiscale method
and the submerged mangrove recognition index was used as a tidal influence indicator. Finally
high-resolution mapping of mangrove forests in China was conducted using the random forest classifier. Results show that the mangrove area in China in 2020 was 29
576.48 ha
95% of which was mainly distributed in the Guangxi
Guangdong
and Hainan Provinces. The overall classification accuracy was 92%
and the Kappa coefficient was 0.89. The mangrove area without tidal influence was 2
531.24 ha less than that with tidal influence. The high-resolution mangrove dataset generated in this study can provide high-precision data support for the monitoring
management
and evaluation of mangrove ecosystems in China
and it is valuable for practical application.
关键词
遥感高分二号红树林面向对象潮汐随机森林
Keywords
remote sensingGF-2mangrove forestsobject-orientedtiderandom forest classifier
references
Behera M D, Barnwal S, Paramanik S, Das P, Bhattyacharya B K, Jagadish B, Roy P S, Ghosh S M and Behera S K. 2021. Species-level classification and mapping of a mangrove forest using random forest—utilisation of AVIRIS-NG and sentinel data. Remote Sensing, 13(11): 2027 [DOI: 10.3390/RS13112027http://dx.doi.org/10.3390/RS13112027]
Blaschke T. 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1): 2-16 [DOI: 10.1016/j.isprsjprs.2009.06.004http://dx.doi.org/10.1016/j.isprsjprs.2009.06.004]
Brown M I, Pearce T, Leon J, Sidle R and Wilson R. 2018. Using remote sensing and traditional ecological knowledge (TEK) to understand mangrove change on the Maroochy River, Queensland, Australia. Applied Geography, 94: 71-83 [DOI: 10.1016/j.apgeog.2018.03.006http://dx.doi.org/10.1016/j.apgeog.2018.03.006]
Cao J J, Leng W C, Liu K, Liu L, He Z and Zhu Y H. 2018. Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sensing, 10(1): 89 [DOI: 10.3390/rs10010089http://dx.doi.org/10.3390/rs10010089]
Chen B Q, Xiao X M, Li X P, Pan L H, Doughty R, Ma J, Dong J W, Qin Y W, Zhao B, Wu Z X, Sun R, Lan G Y, Xie G S, Clinton N and Giri C. 2017. 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, 131: 104-120 [DOI: 10.1016/j.isprsjprs.2017.07.011http://dx.doi.org/10.1016/j.isprsjprs.2017.07.011]
Chen G, Weng Q H, Hay G J and He Y N. 2018. Geographic object-based image analysis (GEOBIA): emerging trends and future opportunities. GIScience and Remote Sensing, 55(2): 159-182 [DOI: 10.1080/15481603.2018.1426092http://dx.doi.org/10.1080/15481603.2018.1426092]
Conchedda G, Durieux L and Mayaux P. 2008. An object-based method for mapping and change analysis in mangrove ecosystems. ISPRS Journal of Photogrammetry and Remote Sensing, 63(5): 578-589 [DOI: 10.1016/j.isprsjprs.2008.04.002http://dx.doi.org/10.1016/j.isprsjprs.2008.04.002]
Diniz C, Cortinhas L, Nerino G, Rodrigues J, Sadeck L, Adami M and Souza-Filho P W M. 2019. Brazilian mangrove status: three decades of satellite data analysis. Remote Sensing, 11(7): 808 [DOI: 10.3390/rs11070808http://dx.doi.org/10.3390/rs11070808]
Elmahdy S I, Ali T A, Mohamed M M, Howari F M, Abouleish M and Simonet D. 2020. Spatiotemporal mapping and monitoring of mangrove forests changes from 1990 to 2019 in the Northern Emirates, UAE using random forest, kernel logistic regression and naive Bayes tree models. Frontiers in Environmental Science, 8: 102 [DOI: 10.3389/fenvs.2020.00102http://dx.doi.org/10.3389/fenvs.2020.00102]
Fan H Q and Wang W Q. 2017. Some thematic issues for mangrove conservation in China. Journal of Xiamen University (Natural Science), 56(3): 323-330
范航清, 王文卿. 2017. 中国红树林保育的若干重要问题. 厦门大学学报(自然科学版), 56(3): 323-330 [DOI: 10.6043/j.issn.0438-0479.201612003http://dx.doi.org/10.6043/j.issn.0438-0479.201612003]
Ghorbanian A, Zaghian S, Asiyabi R M, Amani M, Mohammadzadeh A and Jamali S. 2021. Mangrove ecosystem mapping using Sentinel-1 and Sentinel-2 satellite images and random forest algorithm in Google Earth Engine. Remote Sensing, 13(13): 2565 [DOI: 10.3390/RS13132565http://dx.doi.org/10.3390/RS13132565]
Gilani H, Naz H I, Arshad M, Nazim K, Akram U, Abrar A and Asif M. 2021. Evaluating mangrove conservation and sustainability through spatiotemporal (1990-2020) mangrove cover change analysis in Pakistan. Estuarine, Coastal and Shelf Science, 249: 107128 [DOI: 10.1016/j.ecss.2020.107128http://dx.doi.org/10.1016/j.ecss.2020.107128]
Giri C, Long J and Tieszen L. 2011. Mapping and monitoring Louisiana’s mangroves in the aftermath of the 2010 Gulf of Mexico Oil Spill. Journal of Coastal Research, 27(6): 1059-1064 [DOI: 10.2112/JCOASTRES-D-11-00028.1http://dx.doi.org/10.2112/JCOASTRES-D-11-00028.1]
Green E P, Clark C D, Mumby P J, Edwards A J and Ellis A C. 1998. Remote sensing techniques for mangrove mapping. International Journal of Remote Sensing, 19(5): 935-956 [DOI: 10.1080/014311698215801http://dx.doi.org/10.1080/014311698215801]
Hauser L T, Binh N A, Hoa P V, Quan N H and Timmermans J. 2020. Gap-free monitoring of annual mangrove forest dynamics in Ca Mau province, Vietnamese Mekong delta, using the Landsat-7-8 archives and post-classification temporal optimization. Remote Sensing, 12(22): 3729 [DOI: 10.3390/rs12223729http://dx.doi.org/10.3390/rs12223729]
Heumann B W. 2011. Satellite remote sensing of mangrove forests: recent advances and future opportunities. Progress in Physical Geography, 35(1): 87-108 [DOI: 10.1177/0309133310385371http://dx.doi.org/10.1177/0309133310385371]
Hossain M D and Chen D M. 2019. Segmentation for object-based image analysis (OBIA): a review of algorithms and challenges from remote sensing perspective. ISPRS Journal of Photogrammetry and Remote Sensing, 150: 115-134 [DOI: 10.1016/j.isprsjprs.2019.02.009http://dx.doi.org/10.1016/j.isprsjprs.2019.02.009]
Huang K, Meng X Z, Yang G, Sun W W. Spatio-temporal probability threshold method of remote sensing for mangroves mapping in China. National Remote Sensing Bulletin, 2022, 26(6): 1083-1095.
黄可, 孟祥珍, 杨刚, 孙伟伟. 中国红树林制图的遥感时空概率阈值方法. 遥感学报, 2022, 26(6): 1083-1095 [DOI:10.11834/jrs.20220449http://dx.doi.org/10.11834/jrs.20220449]
Jia K, Chen S M, Jiang W G. Long time-series remote sensing monitoring of mangrove forests in the Guangdong-Hong Kong-Macao Greater Bay Area, National Remote Sensing Bulletin, 2022, 26(6): 1096-1111
贾凯, 陈水森, 蒋卫国. 粤港澳大湾区红树林长时间序列遥感监测. 遥感学报, 2022, 26(6): 1096-1111 [DOI:10.11834/jrs.20221451http://dx.doi.org/10.11834/jrs.20221451]
Jia M M, Wang Z M, Mao D H, Huang C L and Lu C Y. 2021. Spatial-temporal changes of China’s mangrove forests over the past 50 years: an analysis towards the Sustainable Development Goals (SDGs). Chinese Science Bulletin, 66(30): 3886-3901
贾明明, 王宗明, 毛德华, 黄春林, 路春燕. 2021. 面向可持续发展目标的中国红树林近50年变化分析. 科学通报, 66(30): 3886-3901 [DOI: 10.1360/TB-2020-1412http://dx.doi.org/10.1360/TB-2020-1412]
Jia M M, Wang Z M, Wang C, Mao D H and Zhang Y Z. 2019. A new vegetation index to detect periodically submerged mangrove forest using single-tide sentinel-2 imagery. Remote Sensing, 11(17): 2043 [DOI: 10.3390/rs11172043http://dx.doi.org/10.3390/rs11172043]
Jiang Y F, Zhang L, Yan M, Qi J G, Fu T M, Fan S X and Chen B W. 2021. High-resolution mangrove forests classification with machine learning using Worldview and UAV hyperspectral data. Remote Sensing, 13(8): 1529 [DOI: 10.3390/RS13081529http://dx.doi.org/10.3390/RS13081529]
Kamal M, Phinn S and Johansen K. 2015. Object-based approach for multi-scale mangrove composition mapping using multi-resolution image datasets. Remote Sensing, 7(4): 4753-4783 [DOI: 10.3390/rs70404753http://dx.doi.org/10.3390/rs70404753]
Kirui K B, Kairo J G, Bosire J, Viergever K M, Rudra S, Huxham M and Briers R A. 2013. Mapping of mangrove forest land cover change along the Kenya coastline using Landsat imagery. Ocean and Coastal Management, 83: 19-24 [DOI: 10.1016/j.ocecoaman.2011.12.004http://dx.doi.org/10.1016/j.ocecoaman.2011.12.004]
Koedsin W and Vaiphasa C. 2013. Discrimination of tropical mangroves at the species level with EO-1 Hyperion data. Remote Sensing, 5(7): 3562-3582 [DOI: 10.3390/rs5073562http://dx.doi.org/10.3390/rs5073562]
Kong H and Liu J B. 2021. High spatial resolution remote sensing image classification based on pixel shape index method. Journal of Physics: Conference Series, 1952: 022054 [DOI: 10.1088/1742-6596/1952/2/022054http://dx.doi.org/10.1088/1742-6596/1952/2/022054]
Kuenzer C, Bluemel A, Gebhardt S, Quoc T V and Dech S. 2011. Remote sensing of mangrove ecosystems: a review. Remote Sensing, 3(5): 878-928. [DOI: 10.3390/rs3050878http://dx.doi.org/10.3390/rs3050878]
Li B. 2016. A multiple bands pixel shape index for classification of LiDAR and hyperspectral data. Geomatics Science and Technology, 4(4): 117-127
李波. 2016. 基于多波段像元形状指数的点云和高光谱数据分类研究. 测绘科学技术, 4(4):117-127 [DOI: 10.12677/gst.2016.44014http://dx.doi.org/10.12677/gst.2016.44014]
Li H Y, Jia M M, Zhang R, Ren Y X and Wen X. 2019a. Incorporating the plant phenological trajectory into mangrove species mapping with dense time series Sentinel-2 imagery and the Google Earth Engine platform. Remote Sensing, 11(21): 2479 [DOI: 10.3390/rs11212479http://dx.doi.org/10.3390/rs11212479]
Li T H, Zhao Z J and Han P. 2002. Detection and analysis of mangrove changes with multi-temporal remotely sensed imagery in the Shenzhen river estuary. Journal of Remote Sensing, 6(5): 364-369
李天宏, 赵智杰, 韩鹏. 2002. 深圳河河口红树林变化的多时相遥感分析. 遥感学报, 6(5): 364-369 [DOI: 10.11834/jrs.20020508http://dx.doi.org/10.11834/jrs.20020508]
Li W Z, El-Askary H, Qurban M A, Li J J, ManiKandan K P and Piechota T. 2019b. Using multi-indices approach to quantify mangrove changes over the Western Arabian Gulf along Saudi Arabia coast. Ecological Indicators, 102: 734-745 [DOI: 10.1016/j.ecolind.2019.03.047http://dx.doi.org/10.1016/j.ecolind.2019.03.047]
Lin P and Fu Q. 1995. Environmental Ecology and Economic Utilization of Mangroves in China. Beijing: Higher Education Press
林鹏, 傅勤. 1995. 中国红树林环境生态及经济利用. 北京: 高等教育出版社
Long J B and Giri C. 2011. Mapping the Philippines’ mangrove forests using Landsat imagery. Sensors, 11(3): 2972-2981 [DOI: 10.3390/S110302972http://dx.doi.org/10.3390/S110302972]
Maurya K, Mahajan S and Chaube N. 2021. Remote sensing techniques: mapping and monitoring of mangrove ecosystem—a review. Complex and Intelligent Systems, 7(6): 2797-2818 [DOI: 10.1007/S40747-021-00457-Zhttp://dx.doi.org/10.1007/S40747-021-00457-Z]
Monsef H A E and Smith S E. 2017. A new approach for estimating mangrove canopy cover using Landsat 8 imagery. Computers and Electronics in Agriculture, 135: 183-194 [DOI: 10.1016/j.compag.2017.02.007http://dx.doi.org/10.1016/j.compag.2017.02.007]
Neukermans G, Dahdouh‐Guebas F, Kairo J G and Koedam N. 2008. Mangrove species and stand mapping in Gazi Bay (Kenya) using Quickbird satellite imagery. Journal of Spatial Science, 53(1): 75-86 [DOI: 10.1080/14498596.2008.9635137http://dx.doi.org/10.1080/14498596.2008.9635137]
Pastor-Guzman J, Dash J and Atkinson P M. 2018. Remote sensing of mangrove forest phenology and its environmental drivers. Remote Sensing of Environment, 205: 71-84 [DOI: 10.1016/j.rse.2017.11.009http://dx.doi.org/10.1016/j.rse.2017.11.009]
Pimple U, Simonetti D, Sitthi A, Pungkul S, Leadprathom K, Skupek H, Som-ard J, Gond V and Towprayoon S. 2018. Google earth engine based three decadal Landsat imagery analysis for mapping of mangrove forests and its surroundings in the trat province of Thailand. Journal of Computer and Communications, 6(1): 247-264 [DOI: 10.4236/jcc.2018.61025http://dx.doi.org/10.4236/jcc.2018.61025]RogersK, LymburnerL, SalumR, BrookeB P and WoodroffeCD. 2017. Mapping of mangrove extent and zonation using high and low tide composites of Landsat data. Hydrobiologia, 803(1): 49-68 [DOI: 10.1007/s10750-017-3257-5http://dx.doi.org/10.1007/s10750-017-3257-5]
Sun W H, Chen B and Messinger D. 2014. Nearest-neighbor diffusion-based pan-sharpening algorithm for spectral images. Optical Engineering, 53(1): 013107 [DOI: 10.1117/1.OE.53.1.013107http://dx.doi.org/10.1117/1.OE.53.1.013107]
Tang H L, Liu K, Zhu Y H, Wang S G, Liu L and Song S. 2015. Mangrove community classification based on WorldView-2 image and SVM method. Acta Scientiarum Naturalium Universitatis Sunyatseni, 54(4): 102-111
唐焕丽, 刘凯, 朱远辉, 王树功, 柳林, 宋莎. 2015. 基于WorldView-2数据和支持向量机的红树林群落分类研究. 中山大学学报(自然科学版), 54(4): 102-111 [DOI: 10.13471/j.cnki.acta.snus.2015.04.020http://dx.doi.org/10.13471/j.cnki.acta.snus.2015.04.020]
Tuominen S and Pekkarinen A. 2005. Performance of different spectral and textural aerial photograph features in multi-source forest inventory. Remote Sensing of Environment, 94(2): 256-268 [DOI: 10.1016/j.rse.2004.10.001http://dx.doi.org/10.1016/j.rse.2004.10.001]
Vo Q T, Oppelt N, Leinenkugel P and Kuenzer C. 2013. Remote sensing in mapping mangrove ecosystems—an object-based approach. Remote Sensing, 5(1): 183-201 [DOI: 10.3390/rs5010183http://dx.doi.org/10.3390/rs5010183]
Wang D Z, Wan B, Qiu P H, Su Y J, Guo Q H and Wu X C. 2018. Artificial mangrove species mapping using pléiades-1: an evaluation of pixel-based and object-based classifications with selected machine learning algorithms. Remote Sensing, 10(2): 294 [DOI: 10.3390/rs10020294http://dx.doi.org/10.3390/rs10020294]
Wang H, Ren G B, Wu P Q, Liu A C, Pan L H, Ma Y M, Ma Y and Wang J J. 2020. Analysis on the remote sensing monitoring and landscape pattern change of mangrove in China from 1990 to 2019. Journal of Ocean Technology, 39(5): 1-12
王浩, 任广波, 吴培强, 刘爱超, 潘良浩, 马云梅, 马毅, 王锦锦. 2020. 1990-2019年中国红树林变迁遥感监测与景观格局变化分析. 海洋技术学报, 39(5): 1-12 [DOI: 10.3969/j.issn.1003-2029.2020.05.001http://dx.doi.org/10.3969/j.issn.1003-2029.2020.05.001]
Wang L, Jia M M, Yin D M and Tian J Y. 2019. A review of remote sensing for mangrove forests: 1956-2018. Remote Sensing of Environment, 231: 111223 [DOI: 10.1016/j.rse.2019.111223http://dx.doi.org/10.1016/j.rse.2019.111223]
Wang L, Shi C, Tian J Y, Song X N, Jia M M, Li X J, Liu X M, Zhong R F, Yin D M, Yang S S and Guo X X. 2018. Researches on mangrove forest monitoring methods based on multi-source remote sensing. Biodiversity Science, 26(8): 838-849
王乐, 时晨, 田金炎, 宋晓楠, 贾明明, 李小娟, 刘晓萌, 钟若飞, 殷大萌, 杨杉杉, 郭先仙. 2018. 基于多源遥感的红树林监测. 生物多样性, 26(8): 838-849 [DOI: 10.17520/biods.2018067http://dx.doi.org/10.17520/biods.2018067]
Wang L, Sousa W P, Gong P and Biging G S. 2004. Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama. Remote Sensing of Environment, 91(3/4): 432-440 [DOI: 10.1016/j.rse.2004.04.005http://dx.doi.org/10.1016/j.rse.2004.04.005]
Watkins B and Van Niekerk A. 2019. A comparison of object-based image analysis approaches for field boundary delineation using multi-temporal Sentinel-2 imagery. Computers and Electronics in Agriculture, 158: 294-302 [DOI: 10.1016/j.compag.2019.02.009http://dx.doi.org/10.1016/j.compag.2019.02.009]
Xia Q, Jia M M, HE T T, Xing X M and Zhu L J. 2021. Effect of tide level on submerged mangrove recognition index using multi-temporal remotely-sensed data. Ecological Indicators, 131: 108169 [DOI: 10.1016/j.ecolind.2021.108169].
Xia Q, Qin C Z, Li H, Huang C and Su F Z. 2018. Mapping mangrove forests based on multi-tidal high-resolution satellite imagery. Remote Sensing, 10(9): 1343 [DOI: 10.3390/rs10091343http://dx.doi.org/10.3390/rs10091343]
Xue C H, Qian S Y. Fusion of Landsat 8 and Sentinel-2 data for mangrove phenology information extraction and classification. National Remote Sensing Bulletin, 2022, 26(6): 1121-1142.
薛朝辉, 钱思羽. 融合Landsat 8与Sentinel-2数据的红树林物候信息提取与分类. 遥感学报, 2022, 26(6): 1121-1142 [DOI: 10.11834/jrs.20221448http://dx.doi.org/10.11834/jrs.20221448]
Yang S C, Lu W X, Zou Z and Li S. 2017. Mangrove wetlands: distribution, species composition and protection in China. Subtropical Plant Science, 46(4): 301-310
杨盛昌, 陆文勋, 邹祯, 李思. 2017. 中国红树林湿地: 分布、种类组成及其保护. 亚热带植物科学, 46(4): 301-310 [DOI: 10.3969/j.issn.1009-7791.2017.04.001http://dx.doi.org/10.3969/j.issn.1009-7791.2017.04.001]
Zhang D S, Cong L X, Wang Z Q, Chen H and Wang F. 2012. Object-oriented Zhangjiangkou mangrove communities classification using QuickBird imagery. Advanced Materials Research, 605-607: 2274-2278 [DOI: 10.4028/WWW.SCIENTIFIC.NET/AMR.605-607.2274http://dx.doi.org/10.4028/WWW.SCIENTIFIC.NET/AMR.605-607.2274]
Zhang X H and Tian Q J. 2013. A mangrove recognition index for remote sensing of mangrove forest from space. Research Communications, 105(8): 1149-1155
Zhang X H, Treitz P M, Chen D M, Quan C, Shi L X and Li X H. 2017. Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure. International Journal of Applied Earth Observation and Geoinformation, 62: 201-214 [DOI: 10.1016/j.jag.2017.06.010http://dx.doi.org/10.1016/j.jag.2017.06.010]
Zhao C P and Qin C Z. 2020. 10-m-resolution mangrove maps of China derived from multi-source and multi-temporal satellite observations. ISPRS Journal of Photogrammetry and Remote Sensing, 169: 389-405 [DOI: 10.1016/j.isprsjprs.2020.10.001http://dx.doi.org/10.1016/j.isprsjprs.2020.10.001]
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北京师范大学 地理科学学部
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