Yilak Taye Bihon, Abdella Kemal Mohammed, Elias Gebeyehu Ayele
{"title":"谷歌地球引擎中基于随机森林的土地利用和土地覆盖时空分析——以上青尼罗大埃塞俄比亚复兴大坝流域和水库为例","authors":"Yilak Taye Bihon, Abdella Kemal Mohammed, Elias Gebeyehu Ayele","doi":"10.1016/j.envc.2025.101311","DOIUrl":null,"url":null,"abstract":"<div><div>Land use and land cover (LULC) changes have significant positive and negative impacts on the environment. The increasing water demand, the limited water resources, high soil erosion, and dynamic LULC and climate changes of the Grand Ethiopian Renaissance Dam (GERD) basin prompted this study to analyse LULC changes as an initial step toward further research. This study aims to analyse LULC changes in the GERD basin from 1986 to 2024 using Landsat images, as well as the GERD and Aswan High Dam (AHD) reservoir areas during GERD reservoir water filling with Sentinel-2 images (2019–2024). The classification was initially performed at 15 sub-basins using Random Forest machine learning in GEE and then mosaicked using ArcMap GIS. Approximately 39,000 points have been used for training and validation in 1986, 2000, 2014, and 2024 for the entire basin. The overall accuracy and Kappa coefficient for all sub-basins considering the auxiliary data sets were greater than 85% and 0.81, respectively. Eucalyptus trees, built-up areas, water, agricultural land, and bare land expanded by 4505%, 1537%, 56%, 36%, and 9%, while shrubs, forests, grassland, and wetlands shrank by 38%, 27%, 19%, and 10%, respectively, from 1986 to 2024. The GERD reservoir annual water filling based on the ALOS PALSAR 12.5 m Digital Elevation Model (DEM) indicated that the first filling started in 2020 rainy season (560 m, 300 km<sup>2</sup>, 4.82 billion m<sup>3</sup>), 2021 (580 m, 460 km<sup>2</sup>, 10.5 billion m<sup>3</sup>), 2022 (600 m, 840 km<sup>2</sup>, 22 billion m<sup>3</sup>), 2023 (620 m, 1350 km<sup>2</sup>, 43 billion m<sup>3</sup>), 2024 (637 m, 1765 km<sup>2</sup>, 69.3 billion m<sup>3</sup>). On 9 September 2025, the full supply level was reached and inaugurated. When the GERD reservoir filled with water, the AHD reservoir's excess water overflowed into the Toshka lakes due to the high flow of the Nile River since 2020. The forest and shrubs in the GERD reservoir area were cleared before filling to prevent greenhouse gas emissions, decayed deposition on dead storage, and water quality issues. Ensemble, coupling, and comparisons of tools, methods, and factors should be considered for better classification accuracy. Effective LULC management that enhances ecosystem services and alleviates water stress on the GERD basin requires cooperation from all responsible stakeholders.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":"21 ","pages":"Article 101311"},"PeriodicalIF":0.0000,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spatiotemporal analysis of land use and land cover using random forest in Google Earth engine: A case study of the Grand Ethiopian Renaissance Dam basin and reservoir, Upper Blue Nile, Ethiopia\",\"authors\":\"Yilak Taye Bihon, Abdella Kemal Mohammed, Elias Gebeyehu Ayele\",\"doi\":\"10.1016/j.envc.2025.101311\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Land use and land cover (LULC) changes have significant positive and negative impacts on the environment. The increasing water demand, the limited water resources, high soil erosion, and dynamic LULC and climate changes of the Grand Ethiopian Renaissance Dam (GERD) basin prompted this study to analyse LULC changes as an initial step toward further research. This study aims to analyse LULC changes in the GERD basin from 1986 to 2024 using Landsat images, as well as the GERD and Aswan High Dam (AHD) reservoir areas during GERD reservoir water filling with Sentinel-2 images (2019–2024). The classification was initially performed at 15 sub-basins using Random Forest machine learning in GEE and then mosaicked using ArcMap GIS. Approximately 39,000 points have been used for training and validation in 1986, 2000, 2014, and 2024 for the entire basin. The overall accuracy and Kappa coefficient for all sub-basins considering the auxiliary data sets were greater than 85% and 0.81, respectively. Eucalyptus trees, built-up areas, water, agricultural land, and bare land expanded by 4505%, 1537%, 56%, 36%, and 9%, while shrubs, forests, grassland, and wetlands shrank by 38%, 27%, 19%, and 10%, respectively, from 1986 to 2024. The GERD reservoir annual water filling based on the ALOS PALSAR 12.5 m Digital Elevation Model (DEM) indicated that the first filling started in 2020 rainy season (560 m, 300 km<sup>2</sup>, 4.82 billion m<sup>3</sup>), 2021 (580 m, 460 km<sup>2</sup>, 10.5 billion m<sup>3</sup>), 2022 (600 m, 840 km<sup>2</sup>, 22 billion m<sup>3</sup>), 2023 (620 m, 1350 km<sup>2</sup>, 43 billion m<sup>3</sup>), 2024 (637 m, 1765 km<sup>2</sup>, 69.3 billion m<sup>3</sup>). On 9 September 2025, the full supply level was reached and inaugurated. When the GERD reservoir filled with water, the AHD reservoir's excess water overflowed into the Toshka lakes due to the high flow of the Nile River since 2020. The forest and shrubs in the GERD reservoir area were cleared before filling to prevent greenhouse gas emissions, decayed deposition on dead storage, and water quality issues. Ensemble, coupling, and comparisons of tools, methods, and factors should be considered for better classification accuracy. Effective LULC management that enhances ecosystem services and alleviates water stress on the GERD basin requires cooperation from all responsible stakeholders.</div></div>\",\"PeriodicalId\":34794,\"journal\":{\"name\":\"Environmental Challenges\",\"volume\":\"21 \",\"pages\":\"Article 101311\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-09-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental Challenges\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2667010025002306\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Environmental Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Challenges","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2667010025002306","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Environmental Science","Score":null,"Total":0}
引用次数: 0
摘要
土地利用和土地覆被变化对环境有显著的正、负影响。大埃塞俄比亚复兴大坝(GERD)流域不断增加的用水需求、有限的水资源、严重的水土流失、动态的LULC变化和气候变化促使本研究对LULC变化进行分析,作为进一步研究的第一步。利用Landsat影像分析1986 - 2024年GERD流域的LULC变化,利用Sentinel-2影像分析2019-2024年GERD水库蓄水期间GERD和阿斯旺高坝(AHD)库区的LULC变化。首先使用GEE中的随机森林机器学习对15个子流域进行分类,然后使用ArcMap GIS进行拼接。在1986年、2000年、2014年和2024年,整个盆地大约使用了39,000个点进行训练和验证。考虑辅助数据集,各子流域的总体精度和Kappa系数分别大于85%和0.81。1986 - 2024年,桉树、建成区、水域、农用地和裸地分别增加了4505%、1537%、56%、36%和9%,灌木、森林、草地和湿地分别减少了38%、27%、19%和10%。基于ALOS PALSAR 12.5 m数字高程模型(DEM)的GERD水库年充水表明,第一次充水开始于2020年雨季(560 m, 300 km2, 48.2亿m3)、2021年(580 m, 460 km2, 105亿m3)、2022年(600 m, 840 km2, 220亿m3)、2023年(620 m, 1350 km2, 430亿m3)、2024年(637 m, 1765 km2, 693亿m3)。2025年9月9日,已达到全部供应水平并投入使用。当GERD水库充满水时,由于尼罗河自2020年以来的高流量,AHD水库的多余水溢出到Toshka湖泊。为了防止温室气体排放、死库腐积和水质问题,在蓄水前清理了GERD库区的森林和灌木。为了获得更好的分类精度,应该考虑工具、方法和因素的集成、耦合和比较。有效的LULC管理,加强生态系统服务和缓解GERD流域的水资源压力,需要所有负责任的利益相关者的合作。
Spatiotemporal analysis of land use and land cover using random forest in Google Earth engine: A case study of the Grand Ethiopian Renaissance Dam basin and reservoir, Upper Blue Nile, Ethiopia
Land use and land cover (LULC) changes have significant positive and negative impacts on the environment. The increasing water demand, the limited water resources, high soil erosion, and dynamic LULC and climate changes of the Grand Ethiopian Renaissance Dam (GERD) basin prompted this study to analyse LULC changes as an initial step toward further research. This study aims to analyse LULC changes in the GERD basin from 1986 to 2024 using Landsat images, as well as the GERD and Aswan High Dam (AHD) reservoir areas during GERD reservoir water filling with Sentinel-2 images (2019–2024). The classification was initially performed at 15 sub-basins using Random Forest machine learning in GEE and then mosaicked using ArcMap GIS. Approximately 39,000 points have been used for training and validation in 1986, 2000, 2014, and 2024 for the entire basin. The overall accuracy and Kappa coefficient for all sub-basins considering the auxiliary data sets were greater than 85% and 0.81, respectively. Eucalyptus trees, built-up areas, water, agricultural land, and bare land expanded by 4505%, 1537%, 56%, 36%, and 9%, while shrubs, forests, grassland, and wetlands shrank by 38%, 27%, 19%, and 10%, respectively, from 1986 to 2024. The GERD reservoir annual water filling based on the ALOS PALSAR 12.5 m Digital Elevation Model (DEM) indicated that the first filling started in 2020 rainy season (560 m, 300 km2, 4.82 billion m3), 2021 (580 m, 460 km2, 10.5 billion m3), 2022 (600 m, 840 km2, 22 billion m3), 2023 (620 m, 1350 km2, 43 billion m3), 2024 (637 m, 1765 km2, 69.3 billion m3). On 9 September 2025, the full supply level was reached and inaugurated. When the GERD reservoir filled with water, the AHD reservoir's excess water overflowed into the Toshka lakes due to the high flow of the Nile River since 2020. The forest and shrubs in the GERD reservoir area were cleared before filling to prevent greenhouse gas emissions, decayed deposition on dead storage, and water quality issues. Ensemble, coupling, and comparisons of tools, methods, and factors should be considered for better classification accuracy. Effective LULC management that enhances ecosystem services and alleviates water stress on the GERD basin requires cooperation from all responsible stakeholders.