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
For regional ecological management,it is important to evaluate the quality of ecosystems and analyze the underlying causes of ecological changes.Using the Google Earth Engine(GEE)platform,the remote sensing ecological...For regional ecological management,it is important to evaluate the quality of ecosystems and analyze the underlying causes of ecological changes.Using the Google Earth Engine(GEE)platform,the remote sensing ecological index(RSEI)was calculated for the Lijiang River Basin in Guangxi Zhuang Autonomous Region for 1991,2001,2011,and 2021.Spatial autocorrelation analysis was employed to investigate spatiotemporal variations in the ecological environmental quality of the Lijiang River Basin.Furthermore,geographic detectors were used to quantitatively analyze influencing factors and their interaction effects on ecological environmental quality.The results verified that:1)From 1991 to 2021,the ecological environmental quality of the Lijiang River Basin demonstrated significant improvement.The area with good and excellent ecological environmental quality in proportion increased by 19.69%(3406.57 km^(2)),while the area with fair and poor ecological environmental quality in proportion decreased by 10.76%(1860.36 km^(2)).2)Spatially,the ecological environmental quality of the Lijiang River Basin exhibited a pattern of low quality in the central region and high quality in the periphery.Specifically,poor ecological environmental quality characterized the Guilin urban area,Pingle County,and Lingchuan County.3)From 1991 to 2021,a significant positive spatial correlation was observed in ecological environmental quality of the Lijiang River Basin.Areas with high-high agglomeration were predominantly forests and grasslands,indicating good ecological environmental quality,whereas areas with low-low agglomeration were dominated by cultivated land and construction land,indicating poor ecological environmental quality.4)Annual average precipitation and temperature exerted the most significant influence on the ecological environmental quality of the basin,and their interactions with other factors had the great influence.This study aimed to enhance understanding of the evolution of the ecological environment in the Lijiang River Basin of Guangxi Zhuang Autonomous Region and provide scientific guidance for decision-making and management related to ecology in the region.展开更多
As population increases, urban expansion occurs leading to the need for more food production. However, expanding urban areas at the cost of agricultural land is common worldwide, especially in developing countries lik...As population increases, urban expansion occurs leading to the need for more food production. However, expanding urban areas at the cost of agricultural land is common worldwide, especially in developing countries like Bangladesh. This study examines the impact of urban growth on agricultural land in the Lower Turag Basin, Dhaka, Bangladesh, from 2000 to 2020, using Google Earth Engine (GEE) for satellite-based analysis. Rapid urban expansion has dramatically reduced agricultural areas, prompting concerns about food security and sustainable land use. Land use and land cover (LULC) were mapped and quantified across three-time points—2000, 2010, and 2020—highlighting significant declines in arable land, particularly in Dhaka North City Corporation and Savar Upazila. Results align with SDG Indicator 11.3.1, as the ratio of agricultural land consumption to population growth reveals a faster rate of land loss compared to population increase, with agricultural land per capita dropping by 76% over the last two decades. This study suggests the urgency for policies promoting sustainable urban development to preserve essential arable spaces. Thus, it emphasizes the importance of integrating satellite data for urban planning and supports data-driven decisions to balance urbanization and agricultural preservation.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.
基金supported by the Guangxi Natural Science Foundation(2020GXNSFAA297266)Doctoral Research Foundation of Guilin University of Technology(GUTQDJJ2007059)Guangxi Hidden Metallic Mineral Exploration Key Laboratory。
文摘For regional ecological management,it is important to evaluate the quality of ecosystems and analyze the underlying causes of ecological changes.Using the Google Earth Engine(GEE)platform,the remote sensing ecological index(RSEI)was calculated for the Lijiang River Basin in Guangxi Zhuang Autonomous Region for 1991,2001,2011,and 2021.Spatial autocorrelation analysis was employed to investigate spatiotemporal variations in the ecological environmental quality of the Lijiang River Basin.Furthermore,geographic detectors were used to quantitatively analyze influencing factors and their interaction effects on ecological environmental quality.The results verified that:1)From 1991 to 2021,the ecological environmental quality of the Lijiang River Basin demonstrated significant improvement.The area with good and excellent ecological environmental quality in proportion increased by 19.69%(3406.57 km^(2)),while the area with fair and poor ecological environmental quality in proportion decreased by 10.76%(1860.36 km^(2)).2)Spatially,the ecological environmental quality of the Lijiang River Basin exhibited a pattern of low quality in the central region and high quality in the periphery.Specifically,poor ecological environmental quality characterized the Guilin urban area,Pingle County,and Lingchuan County.3)From 1991 to 2021,a significant positive spatial correlation was observed in ecological environmental quality of the Lijiang River Basin.Areas with high-high agglomeration were predominantly forests and grasslands,indicating good ecological environmental quality,whereas areas with low-low agglomeration were dominated by cultivated land and construction land,indicating poor ecological environmental quality.4)Annual average precipitation and temperature exerted the most significant influence on the ecological environmental quality of the basin,and their interactions with other factors had the great influence.This study aimed to enhance understanding of the evolution of the ecological environment in the Lijiang River Basin of Guangxi Zhuang Autonomous Region and provide scientific guidance for decision-making and management related to ecology in the region.
文摘As population increases, urban expansion occurs leading to the need for more food production. However, expanding urban areas at the cost of agricultural land is common worldwide, especially in developing countries like Bangladesh. This study examines the impact of urban growth on agricultural land in the Lower Turag Basin, Dhaka, Bangladesh, from 2000 to 2020, using Google Earth Engine (GEE) for satellite-based analysis. Rapid urban expansion has dramatically reduced agricultural areas, prompting concerns about food security and sustainable land use. Land use and land cover (LULC) were mapped and quantified across three-time points—2000, 2010, and 2020—highlighting significant declines in arable land, particularly in Dhaka North City Corporation and Savar Upazila. Results align with SDG Indicator 11.3.1, as the ratio of agricultural land consumption to population growth reveals a faster rate of land loss compared to population increase, with agricultural land per capita dropping by 76% over the last two decades. This study suggests the urgency for policies promoting sustainable urban development to preserve essential arable spaces. Thus, it emphasizes the importance of integrating satellite data for urban planning and supports data-driven decisions to balance urbanization and agricultural preservation.
