Assessing natural and human‐induced river corridor changes using object‐based image analysis (Orljava River, Croatia)

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY Accounts of Chemical Research Pub Date : 2024-08-19 DOI:10.1002/rra.4369
Katarina Pavlek, Mateo Gašparović, Ronald E. Poeppl
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Abstract

This study employs object‐based image analysis to investigate land cover dynamics and channel changes in the managed corridor of the Orljava River following anthropogenic vegetation removal and a flood event. By classifying RGB and near‐infrared (NIR) images from the decade 2011–2021, five land cover classes within the river corridor were mapped: water, bare soil, sparse vegetation, dense vegetation, and shadows. A digital surface model generated from the images was used to differentiate between bare river channel units (river sediments) and bare soil in the floodplain, as well as to identify high vegetation, while agricultural land was classified manually. The study identified two main phases of river corridor changes, driven by a significant flood event in 2014. In the period 2011–2014, which includes the flood event, a substantial increase in the areas covered by water and river sediments was observed. The most notable decrease was in the bare soil class (90%), which primarily covered riverbanks and adjacent areas in 2011 after vegetation removal. In addition, the formation of large in‐channel bars due to sediment accumulation and significant channel migration was recorded. In the second phase (2014–2021), lower discharges facilitated gradual channel recovery, characterized by channel narrowing and an increase in dense and high vegetation. This study demonstrates the high reliability of object‐based classification in mapping river corridor units using optical photogrammetric data. Furthermore, it highlights the deficiencies in inadequate river management practices that led to considerable bank erosion and a loss of agricultural land.
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利用基于对象的图像分析评估自然和人为造成的河流走廊变化(克罗地亚奥尔雅瓦河)
本研究采用基于对象的图像分析方法,研究奥尔雅瓦河管理走廊在人为植被清除和洪水事件后的土地覆被动态和河道变化。通过对 2011-2021 年间的 RGB 和近红外(NIR)图像进行分类,绘制了河流走廊内的五个土地覆被类别:水、裸露土壤、稀疏植被、茂密植被和阴影。根据图像生成的数字地表模型用于区分裸露的河道单元(河道沉积物)和洪泛区的裸土,以及识别高植被,而农田则由人工分类。该研究确定了河道变化的两个主要阶段,这是由 2014 年的一次重大洪水事件驱动的。在包括洪水事件在内的 2011-2014 年期间,观察到被水和河流沉积物覆盖的面积大幅增加。减少最明显的是裸土等级(90%),2011 年植被清除后,裸土主要覆盖河岸和邻近地区。此外,由于泥沙淤积,河道内形成了大面积的栅栏,河道也发生了明显的迁移。在第二阶段(2014-2021 年),较低的排水量促进了河道的逐步恢复,其特点是河道变窄,高密度植被增加。这项研究表明,在使用光学摄影测量数据绘制河道走廊单元图时,基于对象的分类方法具有很高的可靠性。此外,它还强调了不适当的河流管理方法所存在的缺陷,这些缺陷导致了严重的河岸侵蚀和农田损失。
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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