结合多源专题数据和目视解译的大区域密集湿地样本数据生产

彭凯锋; 蒋卫国; 侯鹏; 凌子燕; 牛振国; 毛德华; 黄卓

doi:10.11834/jrs.20211152

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结合多源专题数据和目视解译的大区域密集湿地样本数据生产
Dense wetland sample production at large scale by combining multi-source thematic datasets and visual interpretation
2024年28卷第2期页码：334-345
纸质出版日期： 2024-02-07 ，
DOI： 10.11834/jrs.20211152
稿件说明：

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彭凯锋，蒋卫国，侯鹏，凌子燕，牛振国，毛德华，黄卓.2024.结合多源专题数据和目视解译的大区域密集湿地样本数据生产.遥感学报，28（2）： 334-345

Peng K F，Jiang W G，Hou P，Ling Z Y，Niu Z G，Mao D H and Huang Z. 2024. Dense wetland sample production at large scale by combining multi-source thematic datasets and visual interpretation. National Remote Sensing Bulletin， 28（2）：334-345
彭凯锋，蒋卫国，侯鹏，凌子燕，牛振国，毛德华，黄卓.2024.结合多源专题数据和目视解译的大区域密集湿地样本数据生产.遥感学报，28（2）： 334-345 DOI： 10.11834/jrs.20211152.

Peng K F，Jiang W G，Hou P，Ling Z Y，Niu Z G，Mao D H and Huang Z. 2024. Dense wetland sample production at large scale by combining multi-source thematic datasets and visual interpretation. National Remote Sensing Bulletin， 28（2）：334-345 DOI： 10.11834/jrs.20211152.

摘要

样本数据是开展湿地制图的研究基础之一，对于数据生产和精度验证具有重要作用。针对湿地生态系统类型多样，大区域的全湿地类型样本生产困难的问题，本研究提出了一种准确、高效的大区域密集湿地样本生产框架。该框架主要包括两部分：首先，基于已有的湿地数据产品，使用规则筛选的方法直接生产稳定的湿地样本点，能够得到河流、湖泊、水库、滨海木本沼泽（红树林）、滩涂的5种湿地类型样本点；其次，基于多源专题数据进行规则筛选，生产潜在湿地样本点，并利用Google Earth Engine大数据云平台、Google Earth平台和Collect Earth平台进行目视解译，以确定潜在湿地样本点的类型属性。本文开展大洲尺度的全湿地类型样本生产，结果表明：本研究共生产了150688个湿地样本点，其中内陆湿地样本点为121412个，滨海湿地样本点为11563个，人工湿地样本点为17693个。13种湿地类型中，湖泊样本点占比最大（39.22%），潟湖样本点占比最小（0.19%）。本文生产了稳定、高质量的湿地样本点，样本数量充足，空间分布合理，能够为湿地分类器的训练和分类结果精度验证提供可靠的数据基础。

Abstract

Sample collection is one of key research foundations for wetland mapping. It plays an important role in classifier training and accuracy validation. Generally

wetland samples are produced by visual interpretation based on high-spatial-resolution images or automatic generation based on multi-source existing dataset. The visual interpretation is time and labor consuming and cannot meet the demand for large-scale wetland classification. The automatic sample-generation method is unsuitable to detailed-type wetland mapping due to the diversity of wetlands and classification-scheme inconsistency of existing wetland datasets. Thus

an efficient and accurate sampling method is in demand for large-scale and detailed-type wetland mapping.

In our study

we collected a series of auxiliary datasets and developed an efficient solution for continental-scale wetland sample generation by combining automatic sampling method and visual interpretation. In the first part

the samples of five wetland types can be automatically generated by rule filtering based on multi-source existing datasets. River

lake

and reservoir samples were created using the JRC Global Surface Water

Global River Widths from Landsat and HydroLAKES datasets. Coastal swamp (mangrove) samples were produced by using Global Mangrove Watch dataset. Tidal flat samples were generated using the Global Intertidal Change dataset. In the second part

by combining time series of MODIS NDVI images and existing auxiliary datasets

we first produced potential wetland samples for coarse wetland types (i.e.

vegetated wetland samples and inundated wetland samples). Then

we identified them by visual interpretation based on the Google Earth Engine platform

Google Earth software

and Collect Earth software. We applied our sample method in our study area

and produced continental-scale and detailed-type wetland samples.

Results indicated that the total wetland samples in our study area was 150688

among which 141412 points were inland wetland samples

11563 were coastal wetland samples

and 17693 were human-made wetland samples. Among the 13 wetland sub-categories

lake accounted for the largest proportion (39.22%) and primarily distributed the northern and central of study area

whereas lagoon accounted for the smallest proportion (0.19%)

mostly scattered in coastal region of the study area. Samples of river

reservoir

inland swamp

and inland marsh also shared a considerable amount

accounting for 16.93%

9.86%

7.16%

and 11.12% of total wetland samples

respectively. River samples were primarily distributed north and south of the study area

and reservoir samples were primarily scattered south of the study area. Meanwhile

inland swamp and inland marsh samples were mostly distributed northwest and south of the study area.

This study successfully produced stable and high-quality wetland samples at continental scale. The generated samples shared sufficient quantities and reasonable spatial distribution

which can lay a good foundation for classifier training and accuracy validation. Meanwhile

by combining the multi-source thematic datasets and multiple platform

our designed sample solution can make full use of the existing database and greatly reduce manual workload. It can also create high-quality samples for complex wetland types

such marsh

swamp and floodplain. Overall

the designed sample method in our study was efficient and reliable

which has significance for large-scale wetland mapping.

关键词

遥感，湿地，样本生产，多源专题数据，目视解译，GoogleEarthEngine，GoogleEarth，CollectEarth

Keywords

remote sensingwetlandsample productionmulti-source thematic datavisual interpretationGoogle Earth EngineGoogle EarthCollect Earth

references

Allen G H and Pavelsky T M. 2018. Global extent of rivers and streams. Science, 361(6402): 585-588 [DOI: 10.1126/science.aat0636http://dx.doi.org/10.1126/science.aat0636]

Bunting P, Rosenqvist A, Lucas R, Rebelo L M, Hilarides L, Thomas N, Hardy A, Itoh T, Shimada M and Finlayson C M. 2018. The global mangrove watch - a new 2010 global baseline of mangrove extent. Remote Sensing, 10(10): 1669 [DOI: 10.3390/rs10101669http://dx.doi.org/10.3390/rs10101669]

Calderón-Loor M, Hadjikakou M and Bryan B A. 2021. High-resolution wall-to-wall land-cover mapping and land change assessment for Australia from 1985 to 2015. Remote Sensing of Environment, 252: 112148 [DOI: 10.1016/j.rse.2020.112148http://dx.doi.org/10.1016/j.rse.2020.112148]

Chen J S, Han Y, Chen G and Zhang J. 2014. Land utilization mapping in Guangdong Province based on integration of optical and SAR remote sensing data. Acta Ecologica Sinica, 34(24): 7233-7242

陈劲松, 韩宇, 陈工, 张瑾. 2014. 基于多源遥感信息融合的广东省土地利用分类方法——以雷州半岛为例. 生态学报, 34(24): 7233-7242 [DOI: 10.5846/stxb201310162499http://dx.doi.org/10.5846/stxb201310162499]

Deng Y, Jiang W G, Tang Z H, Ling Z Y and Wu Z F. 2019. Long-term changes of open-surface water bodies in the Yangtze River Basin based on the Google Earth Engine cloud platform. Remote Sensing, 11(19): 2213 [DOI: 10.3390/rs11192213http://dx.doi.org/10.3390/rs11192213]

Finlayson C M. 2018. Ramsar convention typology of wetlands//Finlayson C M, Everard M, Irvine K, McInnes R J, Middleton B A, van Dam A S and Davidson N C, eds. The Wetland Book: I: Structure and Function, Management, and Methods. Dordrecht: Springer: 1529-1532 [DOI: 10.1007/978-90-481-9659-3_339http://dx.doi.org/10.1007/978-90-481-9659-3_339]

Giri C, Ochieng E, Tieszen L L, Zhu Z, Singh A, Loveland T, Masek J and Duke N. 2011. Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography, 20(1): 154-159 [DOI: 10.1111/j.1466-8238.2010.00584.xhttp://dx.doi.org/10.1111/j.1466-8238.2010.00584.x]

Gong P, Liu H, Zhang M N, Li C C, Wang J, Huang H B, Clinton N, Ji L Y, Li W Y, Bai Y Q, Chen B, Xu B, Zhu Z L, Yuan C, Suen H P, Guo J, Xu N, Li W J, Zhao Y Y, Yang J, Yu C Q, Wang X, Fu H H, Yu L, Dronova I, Hui F M, Cheng X, Shi X L, Xiao F J, Liu Q F and Song L C. 2019. Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Science Bulletin, 64(6): 370-373 [DOI: 10.1016/j.scib.2019.03.002http://dx.doi.org/10.1016/j.scib.2019.03.002]

Han Q Q and Niu Z G. 2020. Construction of the long-term global surface water extent dataset based on water-NDVI spatio-temporal parameter set. Remote Sensing, 12(17): 2675 [DOI: 10.3390/rs12172675http://dx.doi.org/10.3390/rs12172675]

Hansen M C, Potapov P V, Moore R, Hancher M, Turubanova S A, Tyukavina A, Thau D, Stehman S V, Goetz S J, Loveland T R, Kommareddy A, Egorov A, Chini L, Justice C O and Townshend J R G. 2013. High-resolution global maps of 21st-century forest cover change. Science, 342(6160): 850-853 [DOI: 10.1126/science.1244693http://dx.doi.org/10.1126/science.1244693]

He Z X, Zhang M, Wu B F and Xing Q. 2019. Extraction of summer crop in Jiangsu based on Google Earth Engine. Journal of Geo-Information Science, 21(5): 752-766

何昭欣, 张淼, 吴炳方, 邢强. 2019. Google Earth Engine支持下的江苏省夏收作物遥感提取. 地球信息科学学报, 21(5): 752-766

Hu S J, Niu Z G and Chen Y F. 2017. Global wetland datasets: a review. Wetlands, 37(5): 807-817 [DOI: 10.1007/s13157-017-0927-zhttp://dx.doi.org/10.1007/s13157-017-0927-z]

Huang H B, Wang J, Liu C X, Liang L, Li C C and Gong P. 2020. The migration of training samples towards dynamic global land cover mapping. ISPRS Journal of Photogrammetry and Remote Sensing, 161: 27-36 [DOI: 10.1016/j.isprsjprs.2020.01.010http://dx.doi.org/10.1016/j.isprsjprs.2020.01.010]

Jia M M, Mao D H, Wang Z M, Ren C Y, Zhu Q D, Li X C and Zhang Y Z. 2020. Tracking long-term floodplain wetland changes: a case study in the China side of the Amur River Basin. International Journal of Applied Earth Observation and Geoinformation, 92: 102185 [DOI: 10.1016/j.jag.2020.102185http://dx.doi.org/10.1016/j.jag.2020.102185]

Jun C, Ban Y F and Li S N. 2014. Open access to earth land-cover map. Nature, 514(7523): 434-434 [DOI: 10.1038/514434chttp://dx.doi.org/10.1038/514434c]

Lehner B and Döll P. 2004. Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology, 296(1/4): 1-22 [DOI: 10.1016/j.jhydrol.2004.03.028http://dx.doi.org/10.1016/j.jhydrol.2004.03.028]

Li C C, Gong P, Wang J, Zhu Z L, Biging G S, Yuan C, Hu T Y, Zhang H Y, Wang Q, Li X C, Liu X X, Xu Y D, Guo J, Liu C X, Hackman K O, Zhang M N, Cheng Y Q, Yu L, Yang J, Huang H B and Clinton N. 2017. The first all-season sample set for mapping global land cover with Landsat-8 data. Science Bulletin, 62(7): 508-515 [DOI: 10.1016/j.scib.2017.03.011http://dx.doi.org/10.1016/j.scib.2017.03.011]

Liu H and Gong P. 2021. 21st century daily seamless data cube reconstruction and seasonal to annual land cover and land use dynamics mapping-iMap (China) 1.0. National Remote Sensing Bulletin, 25(1): 126-147

刘涵, 宫鹏. 2021. 21世纪逐日无缝数据立方体构建方法及逐年逐季节土地覆盖和土地利用动态制图—中国智慧遥感制图iMap (China) 1.0. 遥感学报, 25(1): 126-147

Mahdavi S, Salehi B, Granger J, Amani M, Brisco B and Huang W M. 2018. Remote sensing for wetland classification: a comprehensive review. GIScience and Remote Sensing, 55(5): 623-658 [DOI: 10.1080/15481603.2017.1419602http://dx.doi.org/10.1080/15481603.2017.1419602]

Mao D H, Wang Z M, Du B J, Li L, Tian Y L, Jia M M, Zeng Y, Song K S, Jiang M and Wang Y Q. 2020. National wetland mapping in China: a new product resulting from object-based and hierarchical classification of Landsat 8 OLI images. ISPRS Journal of Photogrammetry and Remote Sensing, 164: 11-25 [DOI: 10.1016/j.isprsjprs.2020.03.020http://dx.doi.org/10.1016/j.isprsjprs.2020.03.020]

Messager M L, Lehner B, Grill G, Nedeva I and Schmitt O. 2016. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nature Communications, 7(1): 13603 [DOI: 10.1038/ncomms13603http://dx.doi.org/10.1038/ncomms13603]

Murray N J, Phinn S R, DeWitt M, Ferrari R, Johnston R, Lyons M B, Clinton N, Thau D and Fuller R A. 2019. The global distribution and trajectory of tidal flats. Nature, 565(7738): 222-225 [DOI: 10.1038/s41586-018-0805-8http://dx.doi.org/10.1038/s41586-018-0805-8]

Pekel J F, Cottam A, Gorelick N and Belward A S. 2016. High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633): 418-422 [DOI: 10.1038/nature20584http://dx.doi.org/10.1038/nature20584]

Peng K F, Jiang W G and Deng Y. 2019. Identification of wetland damage degree and analysis of its driving forces in Wuhan urban agglomeration. Journal of Natural Resources, 34(8): 1694-1707

彭凯锋, 蒋卫国, 邓越. 2019. 武汉城市圈湿地受损程度识别及驱动因素分析. 自然资源学报, 34(8): 1694-1707 [DOI: 10.31497/zrzyxb.20190810http://dx.doi.org/10.31497/zrzyxb.20190810]

Pickens A H, Hansen M C, Hancher M, Stehman S V, Tyukavina A, Potapov P, Marroquin B and Sherani Z. 2020. Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series. Remote Sensing of Environment, 243: 111792 [DOI: 10.1016/j.rse.2020.111792http://dx.doi.org/10.1016/j.rse.2020.111792]

Sun C, Li J L, Liu Y X, Liu Y C and Liu R Q. 2021. Plant species classification in salt marshes using phenological parameters derived from Sentinel-2 pixel-differential time-series. Remote Sensing of Environment, 256: 112320 [DOI: 10.1016/j.rse.2021.112320http://dx.doi.org/10.1016/j.rse.2021.112320]

Tian F Y, Wu B F, Zeng H W, He Z X, Zhang M and Bofana J. 2019. Identifying soybean cropped area with Sentinel-2 data and multi-layer neural network. Journal of Geo-Information Science, 21(6): 918-927

田富有, 吴炳方, 曾红伟, 何昭欣, 张淼, Bofana J. 2019. 基于多层神经网络与Sentinel-2数据的大豆种植区识别方法. 地球信息科学学报, 21(6): 918-927

Wang P, Wan R R and Yang G S. 2017. Advance in classification and biomass estimation of plants in wetlands based on multi-source remote sensing data. Wetland Science, 15(1): 114-124

王鹏, 万荣荣, 杨桂山. 2017. 基于多源遥感数据的湿地植物分类和生物量反演研究进展. 湿地科学, 15(1): 114-124 [DOI: 10.13248/j.cnki.wetlandsci.2017.01.017http://dx.doi.org/10.13248/j.cnki.wetlandsci.2017.01.017]

Weise K, Höfer R, Franke J, Guelmami A, Simonson W, Muro J, O’Connor B, Strauch A, Flink S, Eberle J, Mino E, Thulin S, Philipson P, Van Valkengoed E, Truckenbrodt J, Zander F, Sánchez A, Schröder C, Thonfeld F, Fitoka E, Scott E, Ling M, Schwarz M, Kunz I, Thürmer G, Plasmeijer A and Hilarides L. 2020. Wetland extent tools for SDG 6.6.1 reporting from the Satellite-based wetland observation service (SWOS). Remote Sensing of Environment, 247: 111892 [DOI: 10.1016/j.rse.2020.111892http://dx.doi.org/10.1016/j.rse.2020.111892]

Wessel P and Smith W H F. 1996. A global, self-consistent, hierarchical, high-resolution shoreline database. Journal of Geophysical Research: Solid Earth, 101(B4): 8741-8743 [DOI: 10.1029/96JB00104http://dx.doi.org/10.1029/96JB00104]

Wu N, Shi R H, Zhuo W, Zhang C, Zhou B C, Xia Z L, Tao Z, Gao W and Tian B. 2021. A classification of tidal flat wetland vegetation combining phenological features with Google Earth Engine. Remote Sensing, 13(3): 443 [DOI: 10.3390/rs13030443http://dx.doi.org/10.3390/rs13030443]

Xie S, Liu L Y, Zhang X, Yang J N, Chen X D and Gao Y. 2019. Automatic land-cover mapping using Landsat time-series data based on Google Earth Engine. Remote Sensing, 11(24): 3023 [DOI: 10.3390/rs11243023http://dx.doi.org/10.3390/rs11243023]

Xu H Z Y, Wei Y C, Liu C, Li X and Fang H. 2019. A scheme for the long-term monitoring of impervious-relevant land disturbances using high frequency Landsat archives and the Google Earth Engine. Remote Sensing, 11(16): 1891 [DOI: 10.3390/rs11161891http://dx.doi.org/10.3390/rs11161891]

Xu P P, Herold M, Tsendbazar N E and Clevers J G P W. 2020. Towards a comprehensive and consistent global aquatic land cover characterization framework addressing multiple user needs. Remote Sensing of Environment, 250: 112034 [DOI: 10.1016/j.rse.2020.112034http://dx.doi.org/10.1016/j.rse.2020.112034]

Xue B, Xiao X, Li J Z, Xie X and Lu C P. 2019. Spatio-temporal evolution of water areas and croplands in the three provinces of Northeast China based on remote sensing data. Chinese Journal of Ecology, 38(5): 1444-1452

薛冰, 肖骁, 李京忠, 谢潇, 逯承鹏. 2019. 基于遥感数据的东北三省水域与农田用地时空演变. 生态学杂志, 38(5): 1444-1452 [DOI: 10.13292/j.1000-4890.201905.002http://dx.doi.org/10.13292/j.1000-4890.201905.002]

Yan W, Zhou W, Yi L L and Tian X. 2019. Research progress of remote sensing classification and change monitoring on forest types. Remote Sensing Technology and Application, 34(3): 445-454

颜伟, 周雯, 易利龙, 田昕. 2019. 森林类型遥感分类及变化监测研究进展. 遥感技术与应用, 34(3): 445-454 [DOI: 10.11873/j.issn.1004-0323.2019.3.0445http://dx.doi.org/10.11873/j.issn.1004-0323.2019.3.0445]

Zhang D J, Pan Y Z, Zhang J S, Hu T G, Zhao J H, Li N and Chen Q. 2020. A generalized approach based on convolutional neural networks for large area cropland mapping at very high resolution. Remote Sensing of Environment, 247: 111912 [DOI: 10.1016/j.rse.2020.111912http://dx.doi.org/10.1016/j.rse.2020.111912]

Zhang L, Gong Z N, Wang Q W, Jin D D and Wang X. 2019. Wetland mapping of yellow river delta wetlands based on multi-feature optimization of Sentinel-2 images. Journal of Remote Sensing, 23(2): 313-326

张磊, 宫兆宁, 王启为, 金点点, 汪星. 2019. Sentinel-2影像多特征优选的黄河三角洲湿地信息提取. 遥感学报, 23(2): 313-326 [DOI: 10.11834/jrs.20198083http://dx.doi.org/10.11834/jrs.20198083]

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