Characterizing land use-land cover changes in N’fis watershed, Western High Atlas, Morocco (1984–2022)

IF 2.3 Q2 REMOTE SENSING
Wiam Salhi, Ouissal Heddoun, Bouchra Honnit, Mohamed Nabil Saidi, Adil Kabbaj
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引用次数: 0

Abstract

The examination of changes in land use and land cover (LULC) holds a pivotal role in advancing our comprehension of underlying processes and mechanisms. The advancement of sophisticated earth observation programs has opened unprecedented opportunities to meticulously observe geographical areas, courtesy of the vast array of satellite imagery available across time. However, effectively analyzing this wealth of data to process LULC information remains a significant challenge within remote sensing. Recent times have witnessed the introduction of diverse techniques for scrutinizing satellite images, encompassing remote sensing technologies and machine/deep learning (M/DL) methods. This research endeavors to explore the transformation of LULC within the N’fis watershed, situated in the Western High Atlas region of Morocco, covering the timeline from 1984 to 2022. By harnessing remote sensing technologies, we have traced alterations in dams, forests, agriculture, and soil over this duration. Moreover, we have conducted comparisons among multiple machine and deep learning (M/DL) models to simulate and forecast LULC changes specifically for the year 2030. Our study outcomes manifest remarkable accuracy in LULC classification, consistently ranging between 91% and 97% for most years, with the kappa coefficient maintaining a range between 89% and 95%. Regarding predictive analysis, the Random Forest (RF) model emerges as the most precise, displaying an accuracy rate of 91%.

Abstract Image

摩洛哥西高阿特拉斯 N'fis 流域土地利用-土地覆被变化特征(1984-2022 年)
对土地利用和土地覆被变化(LULC)的研究在促进我们对基本过程和机制的理解方面发挥着举足轻重的作用。先进的地球观测计划为我们提供了前所未有的机会,利用大量的卫星图像对地理区域进行细致的观测。然而,如何有效地分析这些丰富的数据以处理土地利用、土地利用的变化(LULC)信息,仍然是遥感领域的一项重大挑战。近来,人们引进了各种技术来仔细检查卫星图像,其中包括遥感技术和机器/深度学习(M/DL)方法。本研究致力于探索摩洛哥西高阿特拉斯地区 N'fis 流域内土地利用、土地利用变化的情况,时间跨度为 1984 年至 2022 年。通过利用遥感技术,我们追踪了这段时期内水坝、森林、农业和土壤的变化。此外,我们还对多个机器学习和深度学习(M/DL)模型进行了比较,以模拟和预测 2030 年的土地利用、土地利用的变化。我们的研究结果表明,LULC 分类的准确率非常高,大多数年份的准确率始终在 91% 到 97% 之间,卡帕系数保持在 89% 到 95% 之间。在预测分析方面,随机森林(RF)模型最为精确,准确率达到 91%。
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来源期刊
Applied Geomatics
Applied Geomatics REMOTE SENSING-
CiteScore
5.40
自引率
3.70%
发文量
61
期刊介绍: Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences. The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology. Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements
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