Scholarlink

<p>Understanding and quantifying the dynamics of Land use/land cover (LULC) is crucial for effective watershed management, particularly in regions experiencing rapid economic development. The present study assessed the spatio-temporal dynamics of LULC in the Weito River Catchment (WRC) by classifying satellite images from 1986 (LANDSAT-5), 2003 (LANDSAT-7), and 2023 (LANDSAT-9) using maximum likelihood classification algorithm. The prediction was made for 2043 with the Markov chain model. Remote Sensing and Cellular Automata—Markov Modelling (CA-Markov Modelling) approaches were used for classification, change detection and analysis. The catchment area was classified into six LULC categories: Agriculture, Bareland, Forest, Grassland, Settlement, and Shrubland. Accuracy assessments using the Kappa Index revealed classification accuracies of 89, 91, and 94% for the years 1986, 2003, and 2023, respectively. The findings indicated a 134% expansion in Agriculture, while Forest, Grassland, and Shrubland declined by 36, 84, and 29%, respectively, in the historical period between 1986 and 2023. In the projected analysis, Agriculture is expected to increase by 13%, while Forest, Grassland, and Shrubland are expected to decline by 8, 79, and 21%, respectively, by 2043. Agricultural expansion, population growth and charcoal production are the major drivers behind LULC dynamics in the catchment. The implications of LULC dynamics of the catchment was manifested on water resource, local livelihoods, ecological wellbeing, climate adaptation strategies, and land policy interventions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Land use and land cover dynamics of the Weito River catchment in the Ethiopian rift using remote sensing and CA Markov modelling approaches

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月1日

查看全文
  • Tamene Tadele,
  • Yohannes Degu,
  • Abraham Mechal

摘要

Understanding and quantifying the dynamics of Land use/land cover (LULC) is crucial for effective watershed management, particularly in regions experiencing rapid economic development. The present study assessed the spatio-temporal dynamics of LULC in the Weito River Catchment (WRC) by classifying satellite images from 1986 (LANDSAT-5), 2003 (LANDSAT-7), and 2023 (LANDSAT-9) using maximum likelihood classification algorithm. The prediction was made for 2043 with the Markov chain model. Remote Sensing and Cellular Automata—Markov Modelling (CA-Markov Modelling) approaches were used for classification, change detection and analysis. The catchment area was classified into six LULC categories: Agriculture, Bareland, Forest, Grassland, Settlement, and Shrubland. Accuracy assessments using the Kappa Index revealed classification accuracies of 89, 91, and 94% for the years 1986, 2003, and 2023, respectively. The findings indicated a 134% expansion in Agriculture, while Forest, Grassland, and Shrubland declined by 36, 84, and 29%, respectively, in the historical period between 1986 and 2023. In the projected analysis, Agriculture is expected to increase by 13%, while Forest, Grassland, and Shrubland are expected to decline by 8, 79, and 21%, respectively, by 2043. Agricultural expansion, population growth and charcoal production are the major drivers behind LULC dynamics in the catchment. The implications of LULC dynamics of the catchment was manifested on water resource, local livelihoods, ecological wellbeing, climate adaptation strategies, and land policy interventions.