高寒湿地分类的遥感特征优选研究

霍轩琳; 牛振国; 张波; 刘林崧; 李霞

doi:10.11834/jrs.20222080

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高寒湿地分类的遥感特征优选研究
Remote sensing feature selection for alpine wetland classification
2023年27卷第4期页码：1045-1060
纸质出版日期： 2023-04-07 ，
DOI： 10.11834/jrs.20222080

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霍轩琳，牛振国，张波，刘林崧，李霞.2023.高寒湿地分类的遥感特征优选研究.遥感学报，27（4）： 1045-1060

Huo X L，Niu Z G， Zhang B， Liu L S and Li X. 2023. Remote sensing feature selection for alpine wetland classification. National Remote Sensing Bulletin， 27（4）：1045-1060
霍轩琳，牛振国，张波，刘林崧，李霞.2023.高寒湿地分类的遥感特征优选研究.遥感学报，27（4）： 1045-1060 DOI： 10.11834/jrs.20222080.

Huo X L，Niu Z G， Zhang B， Liu L S and Li X. 2023. Remote sensing feature selection for alpine wetland classification. National Remote Sensing Bulletin， 27（4）：1045-1060 DOI： 10.11834/jrs.20222080.

摘要

高寒湿地是青藏高原重要的地表覆盖类型之一，对于水源涵养、调节气候、维护生物多样性等起着关键作用，准确及时获知高寒湿地的时空分布对于湿地的保护和管理十分必要。遥感分类特征优选对湿地制图具有关键性的作用。虽然像光谱特征、纹理特征、地形特征等均在已有研究中有涉及，但鲜有研究聚焦光谱指数特征，深入探讨其数理统计特征和特征优选方法。本研究以甘肃首曲高寒湿地保护区为研究区，基于Sentinel-2数据得到各分类特征（光谱、植被指数、红边指数和水体指数），采用Filter和Wrapper特征选择方法包括Jeffries-Matusita距离、光谱角距离（SAD）、欧氏距离（ED）、RF-RFE算法和Relief-F算法对上述特征进行优选，并利用Filter方法的Z检验进行量化评价。研究表明：（1）所有参与分类的类别中，河流与裸地最容易区分，其次为草原与沼泽，沼泽化草甸与草甸较为难分。对沼泽、沼泽化草甸、草甸、草原邻近两类可尝试MCARI2、NDWI、DVI、EVI、EWI、IRECI、MCARI、TCARI、UGWI指数进行区分；（2）就不同指数特征对湿地信息提取贡献程度而言，水体指数特征>植被指数特征>红边指数特征；（3）从特征优选方法角度看，Filter方法中的ED距离算法与 Relief-F算法表现突出；（4）最终选出适于高寒湿地信息提取的指数有RDVI、NDVI、MSR、RVI、VIgreen、RNDWI、NDWI、NDWI_B、MNDWI、EWI、CIre；（5）从不同分类特征的数理统计指标看，中值特征的分类结果最好，其次是平均值特征。本研究为湿地信息提取在特征变量优选方面提供了一种可迁移且普适性高的方法和思路。

Abstract

Alpine wetlands are an important surface cover type on the Qinghai―Tibet Plateau because they play a key role in water conservation

climate regulation

and biodiversity maintenance. Accurate and timely knowledge of the temporal and spatial distribution of alpine wetlands is necessary for wetland protection and management. The selection of remote sensing classification features is crucial in wetland mapping. Although spectral

texture

and topographic features have been investigated

studies focusing on spectral index features and their mathematical statistical features and feature selection methods are limited. This study aims to classify alpine wetlands from the aspects of mathematical statistical features

alpine wetland types

feature selection methods

and selected feature sets combined with random forest classification algorithm using Sentinel-2 image data and taking the Shouqu Alpine Wetland Reserve as the research site. An in-depth and comprehensive analysis on the spectral index characteristics of alpine wetlands is performed to optimize the classification characteristics of alpine wetlands.

The Gansu Shouqu Alpine Wetland Reserve was used as the research area

and classification characteristics (spectrum

vegetation index

red edge index

and water body index) were obtained on the basis of Sentinel-2 data. Filter and wrapper feature selection methods

including Jeffries–Matusita distance

Spectral Angular Distance (SAD)

Euclidean Distance (ED)

RF-RFE algorithm

and Relief-F algorithm are utilized to optimize these features. Meanwhile

Z test is applied for quantitative evaluation.

The following conclusions can be drawn from this study. (1) Among the categories of alpine wetlands involved in the classification

rivers and bare land are the easiest to distinguish

followed by grasslands and swamps and then swampy meadows and meadows. MCARI2

NDWI

DVI

EVI

EWI

IRECI

MCARI

TCARI

and UGWI indices can be used to differentiate among adjacent swamps

swampy meadows

meadows

and grasslands. (2) The order of contribution of different index characteristics to wetland information extraction in terms of degree is water body index characteristics > vegetation index characteristics > red edge index characteristics. (3) ED and Relief-F algorithms in the filter method demonstrate excellent performance from the perspective of feature optimization methods. (4) A suitable alpine wetland information extraction method is selected using the indices RDVI

NDVI

MSR

RVI

VIgreen

RNDWI

NDWI

NDWI_B

MNDWI

EWI

and CIre. (5) The mathematical statistics of different classification features indicated that the median feature obtains the best classification result

followed by the average value feature.

We provide detailed results from feature optimization methods

wetland classification optimization index

statistical feature evaluation

and categories involved in alpine wetland classification using multi-dimensional analysis. To the best of our knowledge

this study provides a novel transferable and universal method for the selection of characteristic variables for wetland information extraction.

关键词

遥感湿地分类高寒湿地特征优选青藏高原Sentinel-2

Keywords

remote sensingwetland classificationalpine wetlandfeature selectionQinghai-Tibet PlateauSentinel-2

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