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
Landscape fragmentation is generally viewed as an indicator of environmental stresses or risks,but the fragmentation intensity assessment also depends on the scale of data and the definition of spatial unit.This study...Landscape fragmentation is generally viewed as an indicator of environmental stresses or risks,but the fragmentation intensity assessment also depends on the scale of data and the definition of spatial unit.This study aimed to explore the scale-dependence of forest fragmentation intensity along a moisture gradient in Yinshan Mountain of North China,and to estimate environmental sensitivity of forest fragmentation in this semi-arid landscape.We developed an automatic classification algorithm using simple linear iterative clustering(SLIC)and Gaussian mixture model(GMM),and extracted tree canopy patches from Google Earth images(GEI),with an accuracy of 89.2%in the study area.Then we convert the tree canopy patches to forest category according to definition of forest that tree density greater than 10%,and compared it with forest categories from global land use datasets,FROM-GLC10 and GlobeLand30,with spatial resolutions of 10 m and 30 m,respectively.We found that the FROM-GLC10 and GlobeLand30 datasets underestimated the forest area in Yinshan Mountain by 16.88%and 21.06%,respectively;and the ratio of open forest(OF,10%<tree coverage<40%)to closed forest(CF,tree coverage>40%)areas in the underestimated part was 2:1.The underestimations concentrated in warmer and drier areas occupied mostly by large coverage of OFs with severely fragmented canopies.Fragmentation intensity of canopies positively correlated with spring temperature while negatively correlated with summer precipitation and terrain slope.When summer precipitation was less than 300 mm or spring temperature higher than 4℃,canopy fragmentation intensity rose drastically,while the forest area percentage kept stable.Our study suggested that the spatial configuration,e.g.,sparseness,is more sensitive to drought stress than area percentage.This highlights the importance of data resolution and proper fragmentation measurements for forest patterns and environmental interpretation,which is the base of reliable ecosystem predictions with regard to the future climate scenarios.展开更多
Satellite image classification is crucial in various applications such as urban planning,environmental monitoring,and land use analysis.In this study,the authors present a comparative analysis of different supervised ...Satellite image classification is crucial in various applications such as urban planning,environmental monitoring,and land use analysis.In this study,the authors present a comparative analysis of different supervised and unsupervised learning methods for satellite image classification,focusing on a case study in Casablanca using Landsat 8 imagery.This research aims to identify the most effective machine-learning approach for accurately classifying land cover in an urban environment.The methodology used consists of the pre-processing of Landsat imagery data from Casablanca city,the authors extract relevant features and partition them into training and test sets,and then use random forest(RF),SVM(support vector machine),classification,and regression tree(CART),gradient tree boost(GTB),decision tree(DT),and minimum distance(MD)algorithms.Through a series of experiments,the authors evaluate the performance of each machine learning method in terms of accuracy,and Kappa coefficient.This work shows that random forest is the best-performing algorithm,with an accuracy of 95.42%and 0.94 Kappa coefficient.The authors discuss the factors of their performance,including data characteristics,accurate selection,and model influencing.展开更多
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.
基金the Natural Science Foundation of China(Grant No.41790425).
文摘Landscape fragmentation is generally viewed as an indicator of environmental stresses or risks,but the fragmentation intensity assessment also depends on the scale of data and the definition of spatial unit.This study aimed to explore the scale-dependence of forest fragmentation intensity along a moisture gradient in Yinshan Mountain of North China,and to estimate environmental sensitivity of forest fragmentation in this semi-arid landscape.We developed an automatic classification algorithm using simple linear iterative clustering(SLIC)and Gaussian mixture model(GMM),and extracted tree canopy patches from Google Earth images(GEI),with an accuracy of 89.2%in the study area.Then we convert the tree canopy patches to forest category according to definition of forest that tree density greater than 10%,and compared it with forest categories from global land use datasets,FROM-GLC10 and GlobeLand30,with spatial resolutions of 10 m and 30 m,respectively.We found that the FROM-GLC10 and GlobeLand30 datasets underestimated the forest area in Yinshan Mountain by 16.88%and 21.06%,respectively;and the ratio of open forest(OF,10%<tree coverage<40%)to closed forest(CF,tree coverage>40%)areas in the underestimated part was 2:1.The underestimations concentrated in warmer and drier areas occupied mostly by large coverage of OFs with severely fragmented canopies.Fragmentation intensity of canopies positively correlated with spring temperature while negatively correlated with summer precipitation and terrain slope.When summer precipitation was less than 300 mm or spring temperature higher than 4℃,canopy fragmentation intensity rose drastically,while the forest area percentage kept stable.Our study suggested that the spatial configuration,e.g.,sparseness,is more sensitive to drought stress than area percentage.This highlights the importance of data resolution and proper fragmentation measurements for forest patterns and environmental interpretation,which is the base of reliable ecosystem predictions with regard to the future climate scenarios.
文摘Satellite image classification is crucial in various applications such as urban planning,environmental monitoring,and land use analysis.In this study,the authors present a comparative analysis of different supervised and unsupervised learning methods for satellite image classification,focusing on a case study in Casablanca using Landsat 8 imagery.This research aims to identify the most effective machine-learning approach for accurately classifying land cover in an urban environment.The methodology used consists of the pre-processing of Landsat imagery data from Casablanca city,the authors extract relevant features and partition them into training and test sets,and then use random forest(RF),SVM(support vector machine),classification,and regression tree(CART),gradient tree boost(GTB),decision tree(DT),and minimum distance(MD)algorithms.Through a series of experiments,the authors evaluate the performance of each machine learning method in terms of accuracy,and Kappa coefficient.This work shows that random forest is the best-performing algorithm,with an accuracy of 95.42%and 0.94 Kappa coefficient.The authors discuss the factors of their performance,including data characteristics,accurate selection,and model influencing.
