{"title":"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)","authors":"Wiam Salhi, Ouissal Heddoun, Bouchra Honnit, Mohamed Nabil Saidi, Adil Kabbaj","doi":"10.1007/s12518-024-00549-8","DOIUrl":null,"url":null,"abstract":"<div><p>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%.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Characterizing land use-land cover changes in N’fis watershed, Western High Atlas, Morocco (1984–2022)\",\"authors\":\"Wiam Salhi, Ouissal Heddoun, Bouchra Honnit, Mohamed Nabil Saidi, Adil Kabbaj\",\"doi\":\"10.1007/s12518-024-00549-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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%.</p></div>\",\"PeriodicalId\":46286,\"journal\":{\"name\":\"Applied Geomatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-02-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Geomatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s12518-024-00549-8\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"REMOTE SENSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Geomatics","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s12518-024-00549-8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"REMOTE SENSING","Score":null,"Total":0}
Characterizing land use-land cover changes in N’fis watershed, Western High Atlas, Morocco (1984–2022)
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%.
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