Ali Raza, Neyha Rubab Syed, Romana Fahmeed, Siham Acharki, Sajjad Hussain, Muhammad Zubair, Hussein Almohamad, Joseph Omeiza Alao, Md. Naimur Rahman, Hazem Ghassan Abdo
{"title":"巴基斯坦俾路支省(Balochistan)绿地(Nasirabad)地区土地利用/土地变化探测与地表温度变化","authors":"Ali Raza, Neyha Rubab Syed, Romana Fahmeed, Siham Acharki, Sajjad Hussain, Muhammad Zubair, Hussein Almohamad, Joseph Omeiza Alao, Md. Naimur Rahman, Hazem Ghassan Abdo","doi":"10.1007/s42452-023-05520-7","DOIUrl":null,"url":null,"abstract":"Abstract The current study determined the changes in Land Use/Land Change (LU/LC) and variation in land surface temperature (LST) in the Green Belt (Nasirabad district) area of Balochistan, Pakistan. To achieve this, we used GIS software (ArcMap 10.7.1) to analyze remote sensing data acquired from Landsat imagery taken in 1993, 2003, 2013, and 2023. A supervised classification technique using the maximum likelihood algorithm (MLC) was applied to generate a ground-truth LU/LC classification. Based on our findings, almost 415.28 km 2 (− 12.89%) of formerly undeveloped land has been transformed into urban neighborhoods and green spaces during the last three decades. Between 1993 and 2023, the study area gained 288.29 km 2 (8.94%) of vegetation and 136.10 km 2 (4.22%) of settled land. Minimum, maximum, and average LST changes were recorded as 7.50, − 5.06, and 1.22 °C for the whole thirty years. Overall, the analysis data showed that an increase in human settlements in the area investigated led to a rise in mean LST (1.22 °C). Finally, GIS and RS may be used together to track land usage over time, a crucial piece of data for eco-friendly planning. While the LU/LC and LST provide valuable insights into the rational and optimal use of land resources, the implications of policy remain constrained.","PeriodicalId":21821,"journal":{"name":"SN Applied Sciences","volume":"12 1","pages":"0"},"PeriodicalIF":2.8000,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Land use/land change detection and determination of land surface temperature variation in green belt (Nasirabad) district of Balochistan, Pakistan\",\"authors\":\"Ali Raza, Neyha Rubab Syed, Romana Fahmeed, Siham Acharki, Sajjad Hussain, Muhammad Zubair, Hussein Almohamad, Joseph Omeiza Alao, Md. Naimur Rahman, Hazem Ghassan Abdo\",\"doi\":\"10.1007/s42452-023-05520-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract The current study determined the changes in Land Use/Land Change (LU/LC) and variation in land surface temperature (LST) in the Green Belt (Nasirabad district) area of Balochistan, Pakistan. To achieve this, we used GIS software (ArcMap 10.7.1) to analyze remote sensing data acquired from Landsat imagery taken in 1993, 2003, 2013, and 2023. A supervised classification technique using the maximum likelihood algorithm (MLC) was applied to generate a ground-truth LU/LC classification. Based on our findings, almost 415.28 km 2 (− 12.89%) of formerly undeveloped land has been transformed into urban neighborhoods and green spaces during the last three decades. Between 1993 and 2023, the study area gained 288.29 km 2 (8.94%) of vegetation and 136.10 km 2 (4.22%) of settled land. Minimum, maximum, and average LST changes were recorded as 7.50, − 5.06, and 1.22 °C for the whole thirty years. Overall, the analysis data showed that an increase in human settlements in the area investigated led to a rise in mean LST (1.22 °C). Finally, GIS and RS may be used together to track land usage over time, a crucial piece of data for eco-friendly planning. While the LU/LC and LST provide valuable insights into the rational and optimal use of land resources, the implications of policy remain constrained.\",\"PeriodicalId\":21821,\"journal\":{\"name\":\"SN Applied Sciences\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2023-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SN Applied Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s42452-023-05520-7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SN Applied Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s42452-023-05520-7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Land use/land change detection and determination of land surface temperature variation in green belt (Nasirabad) district of Balochistan, Pakistan
Abstract The current study determined the changes in Land Use/Land Change (LU/LC) and variation in land surface temperature (LST) in the Green Belt (Nasirabad district) area of Balochistan, Pakistan. To achieve this, we used GIS software (ArcMap 10.7.1) to analyze remote sensing data acquired from Landsat imagery taken in 1993, 2003, 2013, and 2023. A supervised classification technique using the maximum likelihood algorithm (MLC) was applied to generate a ground-truth LU/LC classification. Based on our findings, almost 415.28 km 2 (− 12.89%) of formerly undeveloped land has been transformed into urban neighborhoods and green spaces during the last three decades. Between 1993 and 2023, the study area gained 288.29 km 2 (8.94%) of vegetation and 136.10 km 2 (4.22%) of settled land. Minimum, maximum, and average LST changes were recorded as 7.50, − 5.06, and 1.22 °C for the whole thirty years. Overall, the analysis data showed that an increase in human settlements in the area investigated led to a rise in mean LST (1.22 °C). Finally, GIS and RS may be used together to track land usage over time, a crucial piece of data for eco-friendly planning. While the LU/LC and LST provide valuable insights into the rational and optimal use of land resources, the implications of policy remain constrained.