Classification and change detection of vegetation in the Ruoergai Wetland using optical and SAR remote sensing data

MING Yisen; LIU Qihang; BAI He; HUANG Chang

doi:10.11834/jrs.20221767

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Classification and change detection of vegetation in the Ruoergai Wetland using optical and SAR remote sensing data
Vol. 27, Issue 6, Pages: 1414-1425(2023)
Received：03 December 2021，

Published：07 June 2023
DOI： 10.11834/jrs.20221767
稿件说明：

移动端阅览

明義森，刘启航，柏荷，黄昌.2023.利用光学和SAR遥感数据的若尔盖湿地植被分类与变化监测.遥感学报，27（6）： 1414-1425 DOI： 10.11834/jrs.20221767.

Ming Y S，Liu Q H，Bai H and Huang C. 2023. Classification and change detection of vegetation in the Ruoergai Wetland using optical and SAR remote sensing data. National Remote Sensing Bulletin， 27（6）：1414-1425 DOI： 10.11834/jrs.20221767.

摘要

湿地植被在固碳过程中扮演重要角色。作为高寒湿地生态系统的典型代表，若尔盖湿地的植被分类与变化监测对于研究其碳汇功能具有重要意义。光学遥感和微波遥感在植被监测中各有其优缺点，因此本研究提出结合Sentinel-2光学数据和Sentinel-1合成孔径雷达SAR（Synthetic Aperture Radar）数据的若尔盖湿地植被分类与变化监测方法。基于动态时间归整算法DTW（Dynamic Time Warping）提取Sentinel-1 SAR数据的时间序列物候特征，结合Sentinel-2多光谱数据的光谱特征，以2020年实地获取的植被样本及使用样本迁移得到的2017年样本，通过随机森林算法分别对两个时期的若尔盖湿地植被进行分类并对其变化进行分析。研究表明：（1）结合Sentinel-1和Sentinel-2数据，充分发挥各自多时相、多光谱的优势对若尔盖湿地植被进行分类得到可靠的分类结果，总精度达到了97.43%，Kappa系数为0.96。（2）基于样本迁移原理，本研究通过将2020年实地采集的样本迁移至2017年，解决了历史时期实地样本不可得的问题，并针对SAR数据的特点提出基于DTW的样本迁移方法，顺利实现了2017年的植被分类过程。（3）通过对2017年—2020年植被变化进行分析，发现近年来若尔盖湿地植被总体变化不大，演变类别以恢复演替为主，约占研究区面积的7%。

Abstract

Wetland vegetation plays an important role in the process of carbon sequestration. As a typical alpine wetland ecosystem

the Ruoergai wetland has been attracting increasing attention due to its carbon sink function

which makes the classification and change detection of its vegetation coverage crucial.

This study aims to present a method for mapping the vegetation of the Ruoergai wetland and monitor its change by integrating Sentinel-2 optical data and Sentinel-1 Synthetic Aperture Radar (SAR) data

taking advantage of their respective advantages.

We utilize the spectral characteristics of Sentinel-2 MSI data and adopt the dynamic time warping algorithm to extract the time-series phenological characteristics of Sentinel-1 SAR data; in this manner

different wetland vegetations can be differentiated easily. The random forest algorithm was used to combine both data for classifying wetland vegetation types. In-site samples obtained by UAV in 2020 are used and migrated to 2017 to train and validate the classification results.

The classification results have an overall accuracy of 97.43% and a Kappa coefficient of 0.96. Vegetation in the Ruoerge wetlands remains overall unchanged from 2017 to 2020

with the changed area exhibiting a recovery trend (~7% of the total area).

On the basis of the principle of sample migration

this study solved the problem in which the field samples were not available in the historical period. By combining Sentinel-1 and Sentinel-2 data

their respective multitemporal and multispectral advantages were fully exploited to obtain reliable classification results.

关键词

Keywords

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