顾及潮汐影响的中国红树林高分二号遥感制图

夏清; 李建华; 代硕; 张涵; 邢学敏

doi:10.11834/jrs.20221848

大尺度湿地制图 | 浏览量 : 0 下载量: 2278 CSCD: 2

R-PDF
PDF
导出
分享
收藏
专辑

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
收稿：2022-01-04，

纸质出版：2023-06-07
DOI： 10.11834/jrs.20221848
稿件说明：

移动端阅览

夏清，李建华，代硕，张涵，邢学敏.2023.顾及潮汐影响的中国红树林高分二号遥感制图.遥感学报，27（6）： 1320-1333 DOI： 10.11834/jrs.20221848.

Xia Q，Li J H，Dai S，Zhang H and Xing X M. 2023. Mapping high-resolution mangrove forests in China using GF-2 imagery under the tide. National Remote Sensing Bulletin， 27（6）：1320-1333 DOI： 10.11834/jrs.20221848.

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

Keywords

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/RS13112027 http://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.2009.06.004 http://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.2018.03.006 http://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/rs10010089 http://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.2017.07.011 http://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: 10.1080/15481603.2018.1426092 http://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.2008.04.002 http://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/rs11070808 http://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.2020.00102 http://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.0438-0479.201612003 http://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/RS13132565 http://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.2020.107128 http://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-11-00028.1 http://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: 10.1080/014311698215801 http://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/rs12223729 http://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: 10.1177/0309133310385371 http://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.2019.02.009 http://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.20220449 http://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.20221451 http://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-2020-1412 http://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/rs11172043 http://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/RS13081529 http://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/rs70404753 http://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.2011.12.004 http://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/rs5073562 http://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: 10.1088/1742-6596/1952/2/022054 http://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/rs3050878 http://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.2016.44014 http://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/rs11212479 http://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.20020508 http://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.2019.03.047 http://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/S110302972 http://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-Z http://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.2017.02.007 http://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: 10.1080/14498596.2008.9635137 http://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.2017.11.009 http://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.2018.61025 http://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/s10750-017-3257-5 http://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.53.1.013107 http://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.2015.04.020 http://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.2004.10.001 http://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/rs5010183 http://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/rs10020294 http://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.1003-2029.2020.05.001 http://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.2019.111223 http://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.2018067 http://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.2004.04.005 http://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.2019.02.009 http://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/rs10091343 http://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.20221448 http://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.1009-7791.2017.04.001 http://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.605-607.2274 http://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.2017.06.010 http://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.2020.10.001 http://dx.doi.org/10.1016/j.isprsjprs.2020.10.001 ]

文章被引用时，请邮件提醒。

提交

结合深度学习和植被指数的滨海湿地高分二号遥感影像信息提取

粤港澳大湾区红树林长时间序列遥感监测

利用Sentinel-2影像超分辨率重建的红树林冠层氮含量反演

面向对象与卷积神经网络模型的GF-6 WFV影像作物分类