Abdulbasit A. Darem , Asma A. Alhashmi , Aloyoun M. Almadani , Ali K. Alanazi , Geraldine A. Sutantra
{"title":"利用遥感和地理信息系统编制北部边境地区土地利用和土地覆盖分类地图","authors":"Abdulbasit A. Darem , Asma A. Alhashmi , Aloyoun M. Almadani , Ali K. Alanazi , Geraldine A. Sutantra","doi":"10.1016/j.ejrs.2023.04.005","DOIUrl":null,"url":null,"abstract":"<div><p>The land use and land cover study (LULC) play an essential role in regional socio-economic development and natural resource management to develop sustainable development in vegetation changes, water quantity and quality, land resources, and coastal management. This study uses remote sensing data to investigate LULC in the Northern Border Region (NBR) in the Kingdom of Saudi Arabia. The purpose of this study is to obtain a better understanding of the patterns and drivers of changes in LULC in the NBR over the past three decades. Remote sensing data from Landsat imagery between 1990 and 2022 were used to classify LULC types, and a time series analysis was performed using Landsat imagery to detect changes over time. The classification finds four main classes: bare land, built-up area, rocks, and vegetation. The results indicate a significant increase in urban development. The outcomes revealed that most urbanization occurred in the outskirts of the cities, where previously there were bare soil lands. The main drivers of urbanization were population growth and economic development. These findings have important implications for city planning, the management of green spaces, and the sustainable development of cities. Maximum Likelihood classifier was used to perform the classification. The accuracy assessment demonstrated satisfactory results, with an overall accuracy of 92.6%. The study paves the way for further monitoring LULC changes in the NBR geographic location. The technique used was adequate to address the objectives of this study.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"26 2","pages":"Pages 341-350"},"PeriodicalIF":3.7000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Development of a map for land use and land cover classification of the Northern Border Region using remote sensing and GIS\",\"authors\":\"Abdulbasit A. Darem , Asma A. Alhashmi , Aloyoun M. Almadani , Ali K. Alanazi , Geraldine A. Sutantra\",\"doi\":\"10.1016/j.ejrs.2023.04.005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The land use and land cover study (LULC) play an essential role in regional socio-economic development and natural resource management to develop sustainable development in vegetation changes, water quantity and quality, land resources, and coastal management. This study uses remote sensing data to investigate LULC in the Northern Border Region (NBR) in the Kingdom of Saudi Arabia. The purpose of this study is to obtain a better understanding of the patterns and drivers of changes in LULC in the NBR over the past three decades. Remote sensing data from Landsat imagery between 1990 and 2022 were used to classify LULC types, and a time series analysis was performed using Landsat imagery to detect changes over time. The classification finds four main classes: bare land, built-up area, rocks, and vegetation. The results indicate a significant increase in urban development. The outcomes revealed that most urbanization occurred in the outskirts of the cities, where previously there were bare soil lands. The main drivers of urbanization were population growth and economic development. These findings have important implications for city planning, the management of green spaces, and the sustainable development of cities. Maximum Likelihood classifier was used to perform the classification. The accuracy assessment demonstrated satisfactory results, with an overall accuracy of 92.6%. The study paves the way for further monitoring LULC changes in the NBR geographic location. The technique used was adequate to address the objectives of this study.</p></div>\",\"PeriodicalId\":48539,\"journal\":{\"name\":\"Egyptian Journal of Remote Sensing and Space Sciences\",\"volume\":\"26 2\",\"pages\":\"Pages 341-350\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2023-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Egyptian Journal of Remote Sensing and Space Sciences\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1110982323000248\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Egyptian Journal of Remote Sensing and Space Sciences","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1110982323000248","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Development of a map for land use and land cover classification of the Northern Border Region using remote sensing and GIS
The land use and land cover study (LULC) play an essential role in regional socio-economic development and natural resource management to develop sustainable development in vegetation changes, water quantity and quality, land resources, and coastal management. This study uses remote sensing data to investigate LULC in the Northern Border Region (NBR) in the Kingdom of Saudi Arabia. The purpose of this study is to obtain a better understanding of the patterns and drivers of changes in LULC in the NBR over the past three decades. Remote sensing data from Landsat imagery between 1990 and 2022 were used to classify LULC types, and a time series analysis was performed using Landsat imagery to detect changes over time. The classification finds four main classes: bare land, built-up area, rocks, and vegetation. The results indicate a significant increase in urban development. The outcomes revealed that most urbanization occurred in the outskirts of the cities, where previously there were bare soil lands. The main drivers of urbanization were population growth and economic development. These findings have important implications for city planning, the management of green spaces, and the sustainable development of cities. Maximum Likelihood classifier was used to perform the classification. The accuracy assessment demonstrated satisfactory results, with an overall accuracy of 92.6%. The study paves the way for further monitoring LULC changes in the NBR geographic location. The technique used was adequate to address the objectives of this study.
期刊介绍:
The Egyptian Journal of Remote Sensing and Space Sciences (EJRS) encompasses a comprehensive range of topics within Remote Sensing, Geographic Information Systems (GIS), planetary geology, and space technology development, including theories, applications, and modeling. EJRS aims to disseminate high-quality, peer-reviewed research focusing on the advancement of remote sensing and GIS technologies and their practical applications for effective planning, sustainable development, and environmental resource conservation. The journal particularly welcomes innovative papers with broad scientific appeal.