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KaiFeng PENG, WeiGuo JIANG, Peng HOU, et al. Dense wetland sample production at large scale by combining multi-source thematic datasets and visual interpretation. [J/OL]. National Remote Sensing Bulletin 1-13(2021)
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150 688个湿地样本点，其中内陆湿地样本点为121 412个，滨海湿地样本点为11 563个，人工湿地样本点为17 693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。
Sample collection is one of key research foundation for wetland mapping, which play 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, which cannot meet the demand for large-scale wetland classification. The automatic sample generation method is not suitable to detailed-type wetland mapping, due to the diversity of wetlands and classification scheme inconsistency of existing wetland datasets. Thus, the 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 5 wetland types could be automatically generated by rules filtering based on multi-source existing datasets. The river, lake and reservoir samples were created by using JRC Global Surface Water (JRC-GSW), Global River Widths from Landsat (GRWL) and HydroLAKES datasets. The coastal swamp (mangrove) samples were produced by using Global Mangrove Watch (GMW) dataset. The tidal flat samples were generated by using the Global Intertidal Change dataset. In the second part, 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 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.The results indicated that the total wetland samples in our study area were 150 688, of which 141 412 points were the inland wetland samples, 11 563 points were coastal wetland samples, and 17 693 points were human-made wetland samples. Among the 13 wetland sub-categories, the lake accounted for the largest proportion (39.22%) and mainly distributed the northern and central of study area, while the lagoon accounted for the smallest proportion (0.19%), mostly scattered in coastal region of the study area. Meanwhile, the samples of river, reservoir, inland swamp and inland marsh also shared considerable amount, accounting for 16.93%, 9.86%, 7.16% and 11.12% of total wetland samples, respectively. The river samples mainly distributed in northern and southern of study area, the reservoir samples mainly scattered in southern of study area, while the inland swamp and inland marsh samples mostly distributed northwest and southern of 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 could lay good foundation for classifier training and accuracy validation. Meanwhile, through combining the multi-source thematic datasets and multiple platform, our designed sample solution could not only make full use of the existing database and greatly reduce manual workload, but also created 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 was of significance for large-scale wetland mapping.
wetlandsample productionmulti-source thematic datavisual interpretationGoogle earth EngineGoogle EarthCollect Earth
Allen G H and Pavelsky T M. 2018. Global extent of rivers and streams. Science, 361(6402): 585-588.
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.
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, 2021, 252: 112148.
Chen J, Ban Y F, Li S N. 2014. Open access to Earth land-cover map. Nature, 514(7523): 434-434.
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.
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.
Finlayson C. 2018. Ramsar convention typology of wetlands[M]//The wetland book I: Structure and function, management and methods. Springer: 1529-1532.
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 Y, Suen P H, Guo J, Xu N, Li W J, Zhao Y Y, Yang J, Yu CQ, 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.
Giri C, Ochieng E, Tieszen L L, Zhu Z, Singh A, Loveland T, Masek J, 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.
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 Sets. Remote Sensing, 12(17): 2675.
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(15): 850–53.
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(05): 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(05): 752-766.
Hu S J, Niu Z G and Chen Y F. 2017. Global Wetland Datasets: a Review. Wetlands, 37(5), 807–817.
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.
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.
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.
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.
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(01): 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 & Remote Sensing, 55(5): 623-658.
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.
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): 1-11.
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, 565:222-225.
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.
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(08): 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(08): 1694-1707.
Pickens A H, Hansen M C, Hancher M, Stehman S V, Tyukavina A, Po‐tapov 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
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.
Tian Y F, Wu B F, Zeng H W, He Z X, Zhang M and José B. 2019. Identifying Soybean Cropped Area with Sentinel-2 Data and Multi-Layer Neural Network. Journal of Geo-Information Science, 21(06): 918-927.
田富有, 吴炳方, 曾红伟, 何昭欣, 张淼, José Bofana. 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(06): 918-927.
Wang P, Wan R R and Yang G S. 2017. Advance in Classification and Biomass Estimation of Plants inWetlands based on Multi-source Remote Sensing Data. Wetland Science, 15(01): 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(01): 114-124.
Weise K, Höfer R, Franke J, Guelmami A, Simonson W, Muro J, Connor B O, Strauch A, Flink S, Eberle J, Mino E, Thulin S, Philipson P, Valkengoed E V, 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.
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.
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.
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, 2019, 11(24): 3023.
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.
Xu P P, Herold M, Tsendbazar N E and Clevers J G. 2020. Towards a comprehensive and consistent global aquatic land cover characterization framework addressing multiple user needs. Remote Sensing of Environment, 250: 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(05): 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(05): 1444-1452.
Yan W, Zhou W, Yi L L, Tian X. 2019. Research Progress of Remote Sensing Classification and Change Monitoring on Forest Types. Remote Sensing Technology and Application, 34(03): 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(03): 445-454.]
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.
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. 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,2019,23(02):313-326.