Ningde Wang , Iram Naz , Rana Waqar Aslam , Abdul Quddoos , Walid Soufan , Danish Raza , Tibra Ishaq , Bilal Ahmed
{"title":"利用机器学习算法和遥感数据了解牧场转变的时空动态","authors":"Ningde Wang , Iram Naz , Rana Waqar Aslam , Abdul Quddoos , Walid Soufan , Danish Raza , Tibra Ishaq , Bilal Ahmed","doi":"10.1016/j.rama.2024.02.008","DOIUrl":null,"url":null,"abstract":"<div><p>Rangelands globally face escalating threats from overgrazing, land conversion, and climate change. This study investigates spatio-temporal rangeland degradation patterns in Pakistan's Bhakkar District, a semiarid region dependent on fragile pastoral ecosystems, over the past four decades at 10-yr intervals (1990, 2000, 2010, 2020). Remote sensing offers a valuable tool for monitoring these vast yet understudied dryland environments. We employed Landsat satellite data and machine learning algorithms to map land cover change and analyze vegetation health indicators. The random forest classifier achieved high accuracy (94%) in delineating six land cover categories–water, built-up, forest, cropland, rangeland, and barren land. Classified rangeland area declined by over 25%, largely due to agricultural expansion. Vegetation indices showed mixed trends, with decreases in enhanced vegetation index but marginal improvement in normalized difference vegetation index. Meanwhile, rising land surface temperatures pointed to increased aridity. These concerning changes underscore the urgent need for conservation policies tailored to community needs through participatory engagement. Rangeland degradation threatens the livelihoods and welfare of pastoral communities reliant on these ecosystems. Integrated solutions centered on adaptation and resilience can promote sustainability in Bhakkar's marginal dryland environments. This study demonstrates the power of satellite monitoring coupled with social research for implementing holistic strategies to address the globally prevalent threat of rangeland disruption.</p></div>","PeriodicalId":49634,"journal":{"name":"Rangeland Ecology & Management","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spatio-Temporal Dynamics of Rangeland Transformation using machine learning algorithms and Remote Sensing data\",\"authors\":\"Ningde Wang , Iram Naz , Rana Waqar Aslam , Abdul Quddoos , Walid Soufan , Danish Raza , Tibra Ishaq , Bilal Ahmed\",\"doi\":\"10.1016/j.rama.2024.02.008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Rangelands globally face escalating threats from overgrazing, land conversion, and climate change. This study investigates spatio-temporal rangeland degradation patterns in Pakistan's Bhakkar District, a semiarid region dependent on fragile pastoral ecosystems, over the past four decades at 10-yr intervals (1990, 2000, 2010, 2020). Remote sensing offers a valuable tool for monitoring these vast yet understudied dryland environments. We employed Landsat satellite data and machine learning algorithms to map land cover change and analyze vegetation health indicators. The random forest classifier achieved high accuracy (94%) in delineating six land cover categories–water, built-up, forest, cropland, rangeland, and barren land. Classified rangeland area declined by over 25%, largely due to agricultural expansion. Vegetation indices showed mixed trends, with decreases in enhanced vegetation index but marginal improvement in normalized difference vegetation index. Meanwhile, rising land surface temperatures pointed to increased aridity. These concerning changes underscore the urgent need for conservation policies tailored to community needs through participatory engagement. Rangeland degradation threatens the livelihoods and welfare of pastoral communities reliant on these ecosystems. Integrated solutions centered on adaptation and resilience can promote sustainability in Bhakkar's marginal dryland environments. This study demonstrates the power of satellite monitoring coupled with social research for implementing holistic strategies to address the globally prevalent threat of rangeland disruption.</p></div>\",\"PeriodicalId\":49634,\"journal\":{\"name\":\"Rangeland Ecology & Management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-03-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Rangeland Ecology & Management\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S155074242400040X\",\"RegionNum\":3,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Rangeland Ecology & Management","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S155074242400040X","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECOLOGY","Score":null,"Total":0}
Spatio-Temporal Dynamics of Rangeland Transformation using machine learning algorithms and Remote Sensing data
Rangelands globally face escalating threats from overgrazing, land conversion, and climate change. This study investigates spatio-temporal rangeland degradation patterns in Pakistan's Bhakkar District, a semiarid region dependent on fragile pastoral ecosystems, over the past four decades at 10-yr intervals (1990, 2000, 2010, 2020). Remote sensing offers a valuable tool for monitoring these vast yet understudied dryland environments. We employed Landsat satellite data and machine learning algorithms to map land cover change and analyze vegetation health indicators. The random forest classifier achieved high accuracy (94%) in delineating six land cover categories–water, built-up, forest, cropland, rangeland, and barren land. Classified rangeland area declined by over 25%, largely due to agricultural expansion. Vegetation indices showed mixed trends, with decreases in enhanced vegetation index but marginal improvement in normalized difference vegetation index. Meanwhile, rising land surface temperatures pointed to increased aridity. These concerning changes underscore the urgent need for conservation policies tailored to community needs through participatory engagement. Rangeland degradation threatens the livelihoods and welfare of pastoral communities reliant on these ecosystems. Integrated solutions centered on adaptation and resilience can promote sustainability in Bhakkar's marginal dryland environments. This study demonstrates the power of satellite monitoring coupled with social research for implementing holistic strategies to address the globally prevalent threat of rangeland disruption.
期刊介绍:
Rangeland Ecology & Management publishes all topics-including ecology, management, socioeconomic and policy-pertaining to global rangelands. The journal''s mission is to inform academics, ecosystem managers and policy makers of science-based information to promote sound rangeland stewardship. Author submissions are published in five manuscript categories: original research papers, high-profile forum topics, concept syntheses, as well as research and technical notes.
Rangelands represent approximately 50% of the Earth''s land area and provision multiple ecosystem services for large human populations. This expansive and diverse land area functions as coupled human-ecological systems. Knowledge of both social and biophysical system components and their interactions represent the foundation for informed rangeland stewardship. Rangeland Ecology & Management uniquely integrates information from multiple system components to address current and pending challenges confronting global rangelands.