Dense wetland sample production at large scale by combining multi-source thematic datasets and visual interpretation

PENG Kaifeng; JIANG Weiguo; HOU Peng; LING Ziyan; NIU Zhenguo; MAO Dehua; HUANG Zhuo

doi:10.11834/jrs.20211152

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Dense wetland sample production at large scale by combining multi-source thematic datasets and visual interpretation
Vol. 28, Issue 2, Pages: 334-345(2024)
Published： 07 February 2024 ，
DOI： 10.11834/jrs.20211152

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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

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