A GRASS GIS Scripting Framework for Monitoring Changes in the Ephemeral Salt Lakes of Chotts Melrhir and Merouane, Algeria

IF 3.8 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Applied System Innovation Pub Date : 2023-06-25 DOI:10.3390/asi6040061
Polina Lemenkova
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引用次数: 2

Abstract

Automated classification of satellite images is a challenging task that enables the use of remote sensing data for environmental modeling of Earth’s landscapes. In this document, we implement a GRASS GIS-based framework for discriminating land cover types to identify changes in the endorheic basins of the ephemeral salt lakes Chott Melrhir and Chott Merouane, Algeria; we employ embedded algorithms for image processing. This study presents a dataset of the nine Landsat 8–9 OLI/TIRS satellite images obtained from the USGS for a 9-year period, from 2014 to 2022. The images were analyzed to detect changes in water levels in ephemeral lakes that experience temporal fluctuations; these lakes are dry most of the time and are fed with water during rainy periods. The unsupervised classification of images was performed using GRASS GIS algorithms through several modules: ‘i.cluster’ was used to generate image classes; ‘i.maxlik’ was used for classification using the maximal likelihood discriminant analysis, and auxiliary modules, such as ‘i.group’, ‘r.support’, ‘r.import’, etc., were used. This document includes technical descriptions of the scripts used for image processing with detailed comments on the functionalities of the GRASS GIS modules. The results include the identified variations in the ephemeral salt lakes within the Algerian part of the Sahara over a 9-year period (2014–2022), using a time series of Landsat OLI/TIRS multispectral images that were classified using GRASS GIS. The main strengths of the GRASS GIS framework are the high speed, accuracy, and effectiveness of the programming codes for image processing in environmental monitoring. The presented GitHub repository, which contains scripts used for the satellite image analysis, serves as a reference for the interpretation of remote sensing data for the environmental monitoring of arid and semi-arid areas of Africa.
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用于监测阿尔及利亚Chotts-Melrhir和Merouane短暂盐湖变化的GRASS GIS脚本框架
卫星图像的自动分类是一项具有挑战性的任务,它使遥感数据能够用于地球景观的环境建模。在本文件中,我们实现了一个基于GRASS GIS的框架,用于区分土地覆盖类型,以确定阿尔及利亚Chott Melrhir和Chott Merouane短暂盐湖的内陆盆地的变化;我们采用嵌入式算法进行图像处理。本研究提供了从美国地质调查局获得的9张陆地卫星8–9 OLI/TIRS卫星图像的数据集,这些图像为期9年,从2014年到2022年。对图像进行分析,以检测经历时间波动的短暂湖泊的水位变化;这些湖泊大部分时间都是干燥的,在雨季有水供应。使用GRASS GIS算法通过几个模块对图像进行无监督分类:“i.cluster”用于生成图像类别“i.maxlik”用于使用最大似然判别分析进行分类,并使用辅助模块,如“i.group”、“r.support”、“r.import”等。本文件包括用于图像处理的脚本的技术说明,以及对GRASS GIS模块功能的详细评论。这些结果包括使用GRASS GIS分类的Landsat OLI/TIRS多光谱图像的时间序列,在9年期间(2014-2012年)撒哈拉阿尔及利亚部分的短暂盐湖中确定的变化。GRASS GIS框架的主要优点是环境监测中图像处理程序代码的高速性、准确性和有效性。所提供的GitHub存储库包含用于卫星图像分析的脚本,可作为解释非洲干旱和半干旱地区环境监测遥感数据的参考。
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来源期刊
Applied System Innovation
Applied System Innovation Mathematics-Applied Mathematics
CiteScore
7.90
自引率
5.30%
发文量
102
审稿时长
11 weeks
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