Floods are among the most severe and frequent natural disasters,impacting numerous countries worldwide.This study investigates flood mapping methodologies utilizing Google Earth Engine(GEE)with Sentinel-1,Sentinel-2,a...Floods are among the most severe and frequent natural disasters,impacting numerous countries worldwide.This study investigates flood mapping methodologies utilizing Google Earth Engine(GEE)with Sentinel-1,Sentinel-2,and Landsat data,focusing on the January 2021 Tetouan flood in Morocco.Three approaches were assessed:Sentinel-1 thresholding and NDWI(Normalized Difference Water Index)methods applied to Sentinel-2 and Landsat imagery.The analysis revealed flooded areas of 891 hectares(Sentinel-1),814 hectares(Sentinel-2),and 1237 hectares(Landsat),validated against ArcGIS(Geographic Information System)results estimating 900 hectares.Sentinel-1 demonstrated superior accuracy with only a 9-hectare deviation and proved effective under cloudy conditions.Sentinel-2 provided a balance between spatial resolution and error levels,with moderate commission and omission errors.Landsat detected the largest flood extent but exhibited a slight overestimation.The study emphasizes the advantages of GEE’s cloud-based platform,which significantly reduced processing time,facilitating rapid flood extent mapping.This scalability and efficiency make GEE an invaluable tool for disaster management.The results underline the potential of these methodologies for accurate and timely flood monitoring,enabling informed decision-making in resilience planning and emergency response.Such advancements are critical for mitigating the impacts of flooding and supporting sustainable disaster management strategies in vulnerable regions worldwide.展开更多
Based on the Google Earth Engine cloud computing data platform,this study employed three algorithms including Support Vector Machine,Random Forest,and Classification and Regression Tree to classify the current status ...Based on the Google Earth Engine cloud computing data platform,this study employed three algorithms including Support Vector Machine,Random Forest,and Classification and Regression Tree to classify the current status of land covers in Hung Yen province of Vietnam using Landsat 8 OLI satellite images,a free data source with reasonable spatial and temporal resolution.The results of the study show that all three algorithms presented good classification for five basic types of land cover including Rice land,Water bodies,Perennial vegetation,Annual vegetation,Built-up areas as their overall accuracy and Kappa coefficient were greater than 80%and 0.8,respectively.Among the three algorithms,SVM achieved the highest accuracy as its overall accuracy was 86%and the Kappa coefficient was 0.88.Land cover classification based on the SVM algorithm shows that Built-up areas cover the largest area with nearly 31,495 ha,accounting for more than 33.8%of the total natural area,followed by Rice land and Perennial vegetation which cover an area of over 30,767 ha(33%)and 15,637 ha(16.8%),respectively.Water bodies and Annual vegetation cover the smallest areas with 8,820(9.5%)ha and 6,302 ha(6.8%),respectively.The results of this study can be used for land use management and planning as well as other natural resource and environmental management purposes in the province.展开更多
The article employs the wetlands of Ruoergai(i.e.,Zoige),Sichuan Province,as a case study to analyze changes over various time scales,utilizing Landsat data from 2004,2008,2012,2016,2020,and 2023.The study uses the GE...The article employs the wetlands of Ruoergai(i.e.,Zoige),Sichuan Province,as a case study to analyze changes over various time scales,utilizing Landsat data from 2004,2008,2012,2016,2020,and 2023.The study uses the GEE platform and a deep learning model,focusing on the long-term perspective.This analysis serves as a focal point for discussing sustainable development,offering ecological balance information and a realistic foundation.The paper systematically gathers remote sensing classification images resembling sample points on the GEE(Google Earth Engine)platform.Simultaneously,it develops a deep learning model for classifying land types in Ruoergai into six categories:river-wetland,lake-wetland,swamp-wetland,grassland,forest and shrubland.This classification is achieved by utilizing various bands of Landsat data as input features and assigning land cover as corresponding labels.A comparison of classification results in 2016 indicates that the approach integrating the GEE platform and the deep learning model enhances overall accuracy by 9%compared to the random forest method.Furthermore,the overall accuracy surpasses that of the support vector machine method by 16%,and the CART method by 23%.These results affirm that the combined GEE platform and deep learning model outperforms the random forest method in overall accuracy.The findings reveal a declining trend in the wetland area of Ruoergai from 2004 to 2012,with the area remaining relatively stable from 2012 to 2016.Subsequently,there is a significant increase from 2016 to 2023.These trends corroborate the positive outcomes of long-term environmental protection policies implemented by the Chinese government.Furthermore,they underscore the success and efforts exerted by both the government and society in the sustainable management of wetland ecosystems.This serves as an exemplary case for advancing the SDG 15.1 development goal.展开更多
土地利用/覆盖变化(Land Use and Land Cover Change,LUCC)对全球有着重要影响,其已对植被覆盖、地表温度(Land Surface Temperature,LST)、反照率以及其它陆表参数产生显著影响。三峡工程自建设以来,库区的土地利用变化逐渐受到外界关...土地利用/覆盖变化(Land Use and Land Cover Change,LUCC)对全球有着重要影响,其已对植被覆盖、地表温度(Land Surface Temperature,LST)、反照率以及其它陆表参数产生显著影响。三峡工程自建设以来,库区的土地利用变化逐渐受到外界关注。利用欧空局300 m的土地覆盖分类数据分析三峡库区2000~2015年的土地利用变化;依托先进的Google Earth Engine(GEE)平台,获取MODIS(Moderate Resolution Imaging Spectroradiometer,MODIS)NDVI(Normalized Difference Vegetation Index,NDVI)、LST和反照率数据,并分析三者的时空变化趋势;此外,探究季节性归一化植被指数(Seasonally Integrated Normalized Difference Vegetation Index,SINDVI)与LST和反照率的关系;并分析土地利用变化对SINDVI、LST和反照率的影响。结果表明:2000~2015年,三峡库区土地利用变化显著,耕地、草地、灌木地分别减少2.4%,0.05%和0.62%;林地、水域和人造地表分别增加1.98%,0.04%和1.06%。研究期间SINDVI增加2.89,LST下降0.224℃,反照率减少0.002。总体来看,三峡库区SINDVI的空间分布格局与LST和反照率的相反,且库区大部分区域SINDVI与LST和反照率呈负相关。另外,不同土地类型对SINDVI、LST和反照率影响不同。该文系统地研究了LUCC与上述关键陆表参数的定量关系,可为更好地管理该地区自然环境和土地资源提供科学的依据。展开更多
湖泊是重要的淡水资源,准确了解湖泊水体动态变化有利于水资源可持续利用和社会经济发展。本研究基于Google Earth Engine(GEE)平台,以Joint Research Centre(JRC)全球地表水数据集和Landsat遥感影像为数据源,分析了1984—2018年大型湖...湖泊是重要的淡水资源,准确了解湖泊水体动态变化有利于水资源可持续利用和社会经济发展。本研究基于Google Earth Engine(GEE)平台,以Joint Research Centre(JRC)全球地表水数据集和Landsat遥感影像为数据源,分析了1984—2018年大型湖泊——太湖水体的动态变化,并利用改进的归一化差异水体指数(MNDWI)研究了太湖面积变化趋势。结果表明:1984—2018年,太湖湖泊面积呈增加趋势,共增加45.31 km^2,湖泊面积呈夏季低、春冬季高的特点,东太湖是太湖面积发生变化的主要区域。与1984年相比,2018年太湖88.9%的水体未发生任何变化,0.3%的水体永久性消失。湖泊面积变化受自然和人为因素的共同影响,农业灌溉、渔业养殖、围湖垦殖、水利工程设施和土地利用类型转移等导致湖泊面积减少;年降水量增加和环境保护政策的实施是湖泊面积增加的主要原因。本研究结果可为实施水资源可持续管理提供参考,亦验证了基于GEE平台开展水体长期变化监测的可行性。展开更多
文摘Floods are among the most severe and frequent natural disasters,impacting numerous countries worldwide.This study investigates flood mapping methodologies utilizing Google Earth Engine(GEE)with Sentinel-1,Sentinel-2,and Landsat data,focusing on the January 2021 Tetouan flood in Morocco.Three approaches were assessed:Sentinel-1 thresholding and NDWI(Normalized Difference Water Index)methods applied to Sentinel-2 and Landsat imagery.The analysis revealed flooded areas of 891 hectares(Sentinel-1),814 hectares(Sentinel-2),and 1237 hectares(Landsat),validated against ArcGIS(Geographic Information System)results estimating 900 hectares.Sentinel-1 demonstrated superior accuracy with only a 9-hectare deviation and proved effective under cloudy conditions.Sentinel-2 provided a balance between spatial resolution and error levels,with moderate commission and omission errors.Landsat detected the largest flood extent but exhibited a slight overestimation.The study emphasizes the advantages of GEE’s cloud-based platform,which significantly reduced processing time,facilitating rapid flood extent mapping.This scalability and efficiency make GEE an invaluable tool for disaster management.The results underline the potential of these methodologies for accurate and timely flood monitoring,enabling informed decision-making in resilience planning and emergency response.Such advancements are critical for mitigating the impacts of flooding and supporting sustainable disaster management strategies in vulnerable regions worldwide.
文摘Based on the Google Earth Engine cloud computing data platform,this study employed three algorithms including Support Vector Machine,Random Forest,and Classification and Regression Tree to classify the current status of land covers in Hung Yen province of Vietnam using Landsat 8 OLI satellite images,a free data source with reasonable spatial and temporal resolution.The results of the study show that all three algorithms presented good classification for five basic types of land cover including Rice land,Water bodies,Perennial vegetation,Annual vegetation,Built-up areas as their overall accuracy and Kappa coefficient were greater than 80%and 0.8,respectively.Among the three algorithms,SVM achieved the highest accuracy as its overall accuracy was 86%and the Kappa coefficient was 0.88.Land cover classification based on the SVM algorithm shows that Built-up areas cover the largest area with nearly 31,495 ha,accounting for more than 33.8%of the total natural area,followed by Rice land and Perennial vegetation which cover an area of over 30,767 ha(33%)and 15,637 ha(16.8%),respectively.Water bodies and Annual vegetation cover the smallest areas with 8,820(9.5%)ha and 6,302 ha(6.8%),respectively.The results of this study can be used for land use management and planning as well as other natural resource and environmental management purposes in the province.
文摘The article employs the wetlands of Ruoergai(i.e.,Zoige),Sichuan Province,as a case study to analyze changes over various time scales,utilizing Landsat data from 2004,2008,2012,2016,2020,and 2023.The study uses the GEE platform and a deep learning model,focusing on the long-term perspective.This analysis serves as a focal point for discussing sustainable development,offering ecological balance information and a realistic foundation.The paper systematically gathers remote sensing classification images resembling sample points on the GEE(Google Earth Engine)platform.Simultaneously,it develops a deep learning model for classifying land types in Ruoergai into six categories:river-wetland,lake-wetland,swamp-wetland,grassland,forest and shrubland.This classification is achieved by utilizing various bands of Landsat data as input features and assigning land cover as corresponding labels.A comparison of classification results in 2016 indicates that the approach integrating the GEE platform and the deep learning model enhances overall accuracy by 9%compared to the random forest method.Furthermore,the overall accuracy surpasses that of the support vector machine method by 16%,and the CART method by 23%.These results affirm that the combined GEE platform and deep learning model outperforms the random forest method in overall accuracy.The findings reveal a declining trend in the wetland area of Ruoergai from 2004 to 2012,with the area remaining relatively stable from 2012 to 2016.Subsequently,there is a significant increase from 2016 to 2023.These trends corroborate the positive outcomes of long-term environmental protection policies implemented by the Chinese government.Furthermore,they underscore the success and efforts exerted by both the government and society in the sustainable management of wetland ecosystems.This serves as an exemplary case for advancing the SDG 15.1 development goal.
