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生态学杂志 ›› 2020, Vol. 39 ›› Issue (9): 3174-3184.doi: 10.13292/j.1000-4890.202009.015

• 技术与方法 • 上一篇    

基于多时相无人机遥感影像优化河口湿地景观分类

张舒昱1,李兆富1*,徐锋1,潘剑君1,姜小三1,张文敏2   

  1. 1南京农业大学资源与环境科学学院, 南京 210095; 2南京师范大学地理科学学院, 南京 210023)
  • 出版日期:2020-09-10 发布日期:2021-03-10

Optimization of estuary wetland landscape classification based on multi-temporal UAV images.

ZHANG Shu-yu1, LI Zhao-fu1*, XU Feng1, PANG Jian-jun1, JIANG Xiao-san1, ZHANG Wen-min2   

  1. (1College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China;2College of Geography, Nanjing Normal University, Nanjing 210023, China).
  • Online:2020-09-10 Published:2021-03-10

摘要: 河口湿地具有丰富的生物多样性和高度异质化的景观格局。针对河口湿地景观的复杂性,采用传统的基于单幅遥感影像的分类方法并不能得到较好的分类结果。本研究采用多时相无人机遥感影像参与分类,以优化河口湿地景观自动分类结果。选择天目湖上游平桥河河口湿地为研究区,选取4个季节的无人机影像为基础数据源,采用面向对象与决策树相结合的分类方法,针对不同季节组合的影像进行分类。结果表明:采用多时相无人机影像能显著提升分类效果,且参与分类的时相越多,效果越好;单季影像中,春季是最适合进行景观分类的季节,分类总体精度为62.7%,Kappa系数为0.59;当4个季节获取的影像同时参与分类时,分类总体精度为91.7%,Kappa系数为0.90;参与分类的时相光谱特征差异越大,分类效果提升越明显。本研究可为河口湿地景观分类提供技术支持,并提出了一种利用可见光无人机遥感影像进行湿地景观分类的新思路。

关键词: 无人机, 河口湿地, 多时相遥感影像, 面向对象, 景观分类

Abstract: Estuary wetland is rich in biodiversity and highly heterogeneous in landscape pattern. Because of the complexity of estuary wetland landscape, traditional classification methods based on single remote sensing images cannot obtain high precision classification results. In this study, multitemporal UAV (unmanned aerial vehicle) remote sensing images were used to optimize the automatic classification results of estuary wetland landscape. The Pingqiao River estuary wetland in the upper reaches of Tianmu Lake was selected as a study case with four season UAV images. The classification method of object based and decision tree was optimized to classify the images from different combinations of four seasons. The results showed that the adoption of multitemporal UAV images significantly improved the classification precision. The more time of UAV images involved in classification, precision will be better. Spring was the most suitable season for landscape classification among the four seasons, with the overall accuracy of 62.7% and Kappa coefficient of 0.59. When the images from all four seasons were used in classification, the overall accuracy increased to 91.7% and the Kappa coefficient was 0.90. The greater the difference in the multitemporal spectral characteristics in classification, the more obvious the classification precision enhancement was. Our results provide technical support for classification of estuary wetland landscape, and put forward a new idea for wetland landscape classification by using visible-light UAV remote sensing images.

Key words: unmanned aerial vehicle (UAV), estuary wetland, multi-temporal remote sensing image, object-based, landscape classification.