1. INTRODUCTION The proposed Three Gorges Project, one of the biggest hydroelectric projects in the world, will dam the middle reaches of the Changjiang (Yangtze) River, the third longest river in the world, and form ...1. INTRODUCTION The proposed Three Gorges Project, one of the biggest hydroelectric projects in the world, will dam the middle reaches of the Changjiang (Yangtze) River, the third longest river in the world, and form a large reservoir. Its impacts on environment have attracted wide attention. Entrusted by National Scientific-Technical Commission, the Chinese Academy of Sciences (CAS) was in charge of a research project on this issuse from 1984 to 1989. Tho use of remote sensing played an important role in the project considering the study area is mountainous and not convenientlv located, which makes it difficult to conduct the research onlv using conventional means.展开更多
1. PREFACE Lingdingyang is a trumpet estuary. It accepts the runoff of the Dongjiang River, the Beijiang River, the Zhengjiang River and the Liusihe River. It also accepts a part of the runoff of the Xijiang River. It...1. PREFACE Lingdingyang is a trumpet estuary. It accepts the runoff of the Dongjiang River, the Beijiang River, the Zhengjiang River and the Liusihe River. It also accepts a part of the runoff of the Xijiang River. Its mean year runoff is 1.742×10" M^3. In resent ten years, industry and agriculture are developing rapidly in Guangzhou City, Dongguan City, Zhongshan City, Shunde County, Panyu County. Lingdingyang’s pollution is increesing. Water quality of lingdingyang is steadily deteriorated. In order to investigate the situation of water environment of Lingdingyang, we study its static environmental capacity of nitrogen and phosphorus. LANDSAT imageries are used in the study. The concentrations of nitrogen and phosphorous is detected by convention method.展开更多
The majority of the population and economic activity of the northern half of Vietnam is clustered in the Red River Delta and about half of the country’s rice production takes place here. There are significant problem...The majority of the population and economic activity of the northern half of Vietnam is clustered in the Red River Delta and about half of the country’s rice production takes place here. There are significant problems associated with its geographical position and the intensive exploitation of resources by an overabundant population (population density of 962 inhabitants/km2). Some thirty years after the economic liberalization and the opening of the country to international markets, agricultural land use patterns in the Red River Delta, particularly in the coastal area, have undergone many changes. Remote sensing is a particularly powerful tool in processing and providing spatial information for monitoring land use changes. The main methodological objective is to find a solution to process the many heterogeneous coastal land use parameters, so as to describe it in all its complexity, specifically by making use of the latest European satellite data (Sentinel-2). This complexity is due to local variations in ecological conditions, but also to anthropogenic factors that directly and indirectly influence land use dynamics. The methodological objective was to develop a new Geographic Object-based Image Analysis (GEOBIA) approach for mapping coastal areas using Sentinel-2 data and Landsat 8. By developing a new segmentation, accuracy measure, in this study was determined that segmentation accuracies decrease with increasing segmentation scales and that the negative impact of under-segmentation errors significantly increases at a large scale. An Estimation of Scale Parameter (ESP) tool was then used to determine the optimal segmentation parameter values. A popular machine learning algorithms (Random Forests-RFs) is used. For all classifications algorithm, an increase in overall accuracy was observed with the full synergistic combination of available data sets.展开更多
Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation.Although the popular and common sensors of land resource satellites provide free a...Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation.Although the popular and common sensors of land resource satellites provide free and valuable images to map the land cover,coastal areas often encounter significant cloud cover,especially in tropical areas,which makes the classification in those areas non-ideal.To solve this problem,we proposed a framework of combining medium-resolution optical images and synthetic aperture radar(SAR)data with the recently popular object-based image analysis(OBIA)method and used the Landsat Operational Land Imager(OLI)and Phased Array type L-band Synthetic Aperture Radar(PALSAR)images acquired in Singapore in 2017 as a case study.We designed experiments to confirm two critical factors of this framework:one is the segmentation scale that determines the average object size,and the other is the classification feature.Accuracy assessments of the land cover indicated that the optimal segmentation scale was between 40 and 80,and the features of the combination of OLI and SAR resulted in higher accuracy than any individual features,especially in areas with cloud cover.Based on the land cover generated by this framework,we assessed the vulnerability of the marine disasters of Singapore in 2008 and 2017 and found that the high-vulnerability areas mainly located in the southeast and increased by 118.97 km2 over the past decade.To clarify the disaster response plan for different geographical environments,we classified risk based on altitude and distance from shore.The newly increased high-vulnerability regions within 4 km offshore and below 30 m above sea level are at high risk;these regions may need to focus on strengthening disaster prevention construction.This study serves as a typical example of using remote sensing techniques for the vulnerability assessment of marine disasters,especially those in cloudy coastal areas.展开更多
长江流域是我国重要的生态屏障之一,重庆市作为长江上游最后一道关口,研究其生态质量发展对于有效保护长江流域生态环境具有重要指导意义。基于2011—2021年间的Landsat影像等数据,计算遥感生态指数(Remote sensing based ecological in...长江流域是我国重要的生态屏障之一,重庆市作为长江上游最后一道关口,研究其生态质量发展对于有效保护长江流域生态环境具有重要指导意义。基于2011—2021年间的Landsat影像等数据,计算遥感生态指数(Remote sensing based ecological index,RSEI),并采用Sen(Theil-Sen median)趋势分析法和MK(Mann-Kendall)检验研究其变化趋势以及利用Hurst指数模型分析RSEI的持续特征。利用空间转移矩阵和重心迁移模型研究其在空间上分布特征的变化情况,最后使用降水、风速、近地表气温、海拔等辅助数据为影响因素,结合地理探测器进一步探究RSEI变化驱动力,探讨重庆市2011—2021的RSEI空间分布及演变趋势。结果表明:(1)重庆市多年平均RSEI为0.593,使用等间距法将其划分的等级为差、较差、中等、良、优的面积占比分别为2.48%、8.28%、38.32%、41,87%、9.05%。从整体来看重庆市生态质量水平较高,重庆市年际RSEI以显著趋势波动增长。(2)RSEI等级为差的地区空间上主要集中于重庆西部;较差等级主要围绕差一级的周围;中等等级主要位于重庆市中西部;有超过一半的区域RSEI等级为良或优,分布在重庆市中北部地区。(3)利用Hurst指数与Sen氏趋势分析结果利用ArcGIS叠加分析,结果共计有总体53.3%的RSEI会保持增长的可持续性。(4)通过因子探测,本文发现以近地表气温、海拔为主的自然因素以及以土地利用为主的人为因素是影响重庆市RSEI空间分布的主要影响因素。(5)从RSEI空间分布变化来看2011—2021年重庆市生态环境质量主要发生了“较差→中等”、“较差→良”、“中等→良”、“良→优”这四个路径,整体上重庆市生态环境得到优化。展开更多
China' Mainland has a poor distribution of meteorological stations.Existing models’estimation accuracy for creating high-resolution surfaces of meteorological data is restricted for air temperature,and low for re...China' Mainland has a poor distribution of meteorological stations.Existing models’estimation accuracy for creating high-resolution surfaces of meteorological data is restricted for air temperature,and low for relative humidity and wind speed(few studies reported).This study compared the typical generalized additive model(GAM)and autoencoder-based residual neural network(hereafter,residual network for short)in terms of predicting three meteorological parameters,namely air temperature,relative humidity,and wind speed,using data from 824 monitoring stations across China’s mainland in 2015.The performance of the two models was assessed using a 10-fold cross-validation procedure.The air temperature models employ basic variables such as latitude,longitude,elevation,and the day of the year.The relative humidity models employ air temperature and ozone concentration as covariates,while the wind speed models use wind speed coarse-resolution reanalysis data as covariates,in addition to the fundamental variables.Spatial coordinates represent spatial variation,while the time index of the day captures time variation in our spatiotemporal models.In comparison to GAM,the residual network considerably improved prediction accuracy:on average,the coefficient of variation(CV)R2 of the three meteorological parameters rose by 0.21,CV root-mean square(RMSE)fell by 37%,and the relative humidity model improved the most.The accuracy of relative humidity models was considerably improved once the monthly index was included,demonstrating that varied amounts of temporal variables are crucial for relative humidity models.We also spoke about the benefits and drawbacks of using coarse resolution reanalysis data and closest neighbor values as variables.In comparison to classic GAMs,this study indicates that the residual network model may considerably increase the accuracy of national high spatial(1 km)and temporal(daily)resolution meteorological data.Our findings have implications for high-resolution and high-accuracy meteorological parameter mapping in China.展开更多
Casablanca, Morocco's economic capital continues today to fight against the proliferation of informal settle- ments affecting its urban fabric illustrated especially by the slums. Actually Casablanca represents 25...Casablanca, Morocco's economic capital continues today to fight against the proliferation of informal settle- ments affecting its urban fabric illustrated especially by the slums. Actually Casablanca represents 25% of the total slums of Morocco [1]. These are the habitats of all deprived of healthy sanitary conditions and judged precarious from the perspective humanitarian and below the acceptable. The majority of the inhabi- tants of these slums are from the rural exodus with insufficient income to meet the basic needs of daily life. Faced with this situation and to eradicate these habitats, the Moroccan government has launched since 2004 an entire program to create cities without slums (C.W.S.) to resettle or relocate families. Indeed the process control and monitoring of this program requires first identifying and detecting spatial habitats. To achieve these tasks, conventional methods such as information gathering, mapping, use of databases and statistics often have shown their limits and are sometimes outdated. It is within this framework and that of the great German Morocco project “Urban agriculture as an integrative factor of development that fits our project de- tection of slums in Casablanca. The use of satellite imagery, particulary the HSR, has the advantage of providing the physical coverage of urban land but it raises the difficulty of choosing the appropriate method to apply.This paper is actually to develop new approaches based mainly on object-oriented classification of high spatial resolution satellite images for the detection of slums.This approach has been developed for mapping the urban land through by integration of several types of information (spectral, spatial, contextual ...) (Hofmann, P ., 2001, Herold et al. 2002b;Van Der Sande et al., 2003, Benz et al., 2004, Nobrega et al., 2006). In order to refine the result of classification, we applied mathematical morphology and in particular the closing filter. The data from this classification (binary image), which then will be used in a spatial data- base (ArcGIS).展开更多
Turning Earth observation(EO)data consistently and systematically into valuable global information layers is an ongoing challenge for the EO community.Recently,the term‘big Earth data’emerged to describe massive EO ...Turning Earth observation(EO)data consistently and systematically into valuable global information layers is an ongoing challenge for the EO community.Recently,the term‘big Earth data’emerged to describe massive EO datasets that confronts analysts and their traditional workflows with a range of challenges.We argue that the altered circumstances must be actively intercepted by an evolution of EO to revolutionise their application in various domains.The disruptive element is that analysts and end-users increasingly rely on Web-based workflows.In this contribution we study selected systems and portals,put them in the context of challenges and opportunities and highlight selected shortcomings and possible future developments that we consider relevant for the imminent uptake of big Earth data.展开更多
文摘1. INTRODUCTION The proposed Three Gorges Project, one of the biggest hydroelectric projects in the world, will dam the middle reaches of the Changjiang (Yangtze) River, the third longest river in the world, and form a large reservoir. Its impacts on environment have attracted wide attention. Entrusted by National Scientific-Technical Commission, the Chinese Academy of Sciences (CAS) was in charge of a research project on this issuse from 1984 to 1989. Tho use of remote sensing played an important role in the project considering the study area is mountainous and not convenientlv located, which makes it difficult to conduct the research onlv using conventional means.
文摘1. PREFACE Lingdingyang is a trumpet estuary. It accepts the runoff of the Dongjiang River, the Beijiang River, the Zhengjiang River and the Liusihe River. It also accepts a part of the runoff of the Xijiang River. Its mean year runoff is 1.742×10" M^3. In resent ten years, industry and agriculture are developing rapidly in Guangzhou City, Dongguan City, Zhongshan City, Shunde County, Panyu County. Lingdingyang’s pollution is increesing. Water quality of lingdingyang is steadily deteriorated. In order to investigate the situation of water environment of Lingdingyang, we study its static environmental capacity of nitrogen and phosphorus. LANDSAT imageries are used in the study. The concentrations of nitrogen and phosphorous is detected by convention method.
文摘The majority of the population and economic activity of the northern half of Vietnam is clustered in the Red River Delta and about half of the country’s rice production takes place here. There are significant problems associated with its geographical position and the intensive exploitation of resources by an overabundant population (population density of 962 inhabitants/km2). Some thirty years after the economic liberalization and the opening of the country to international markets, agricultural land use patterns in the Red River Delta, particularly in the coastal area, have undergone many changes. Remote sensing is a particularly powerful tool in processing and providing spatial information for monitoring land use changes. The main methodological objective is to find a solution to process the many heterogeneous coastal land use parameters, so as to describe it in all its complexity, specifically by making use of the latest European satellite data (Sentinel-2). This complexity is due to local variations in ecological conditions, but also to anthropogenic factors that directly and indirectly influence land use dynamics. The methodological objective was to develop a new Geographic Object-based Image Analysis (GEOBIA) approach for mapping coastal areas using Sentinel-2 data and Landsat 8. By developing a new segmentation, accuracy measure, in this study was determined that segmentation accuracies decrease with increasing segmentation scales and that the negative impact of under-segmentation errors significantly increases at a large scale. An Estimation of Scale Parameter (ESP) tool was then used to determine the optimal segmentation parameter values. A popular machine learning algorithms (Random Forests-RFs) is used. For all classifications algorithm, an increase in overall accuracy was observed with the full synergistic combination of available data sets.
基金Supported by the National Key Research and Development Program of China(No.2016YFC1402003)the CAS Earth Big Data Science Project(No.XDA19060303)the Innovation Project of the State Key Laboratory of Resources and Environmental Information System(No.O88RAA01YA)
文摘Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation.Although the popular and common sensors of land resource satellites provide free and valuable images to map the land cover,coastal areas often encounter significant cloud cover,especially in tropical areas,which makes the classification in those areas non-ideal.To solve this problem,we proposed a framework of combining medium-resolution optical images and synthetic aperture radar(SAR)data with the recently popular object-based image analysis(OBIA)method and used the Landsat Operational Land Imager(OLI)and Phased Array type L-band Synthetic Aperture Radar(PALSAR)images acquired in Singapore in 2017 as a case study.We designed experiments to confirm two critical factors of this framework:one is the segmentation scale that determines the average object size,and the other is the classification feature.Accuracy assessments of the land cover indicated that the optimal segmentation scale was between 40 and 80,and the features of the combination of OLI and SAR resulted in higher accuracy than any individual features,especially in areas with cloud cover.Based on the land cover generated by this framework,we assessed the vulnerability of the marine disasters of Singapore in 2008 and 2017 and found that the high-vulnerability areas mainly located in the southeast and increased by 118.97 km2 over the past decade.To clarify the disaster response plan for different geographical environments,we classified risk based on altitude and distance from shore.The newly increased high-vulnerability regions within 4 km offshore and below 30 m above sea level are at high risk;these regions may need to focus on strengthening disaster prevention construction.This study serves as a typical example of using remote sensing techniques for the vulnerability assessment of marine disasters,especially those in cloudy coastal areas.
文摘China' Mainland has a poor distribution of meteorological stations.Existing models’estimation accuracy for creating high-resolution surfaces of meteorological data is restricted for air temperature,and low for relative humidity and wind speed(few studies reported).This study compared the typical generalized additive model(GAM)and autoencoder-based residual neural network(hereafter,residual network for short)in terms of predicting three meteorological parameters,namely air temperature,relative humidity,and wind speed,using data from 824 monitoring stations across China’s mainland in 2015.The performance of the two models was assessed using a 10-fold cross-validation procedure.The air temperature models employ basic variables such as latitude,longitude,elevation,and the day of the year.The relative humidity models employ air temperature and ozone concentration as covariates,while the wind speed models use wind speed coarse-resolution reanalysis data as covariates,in addition to the fundamental variables.Spatial coordinates represent spatial variation,while the time index of the day captures time variation in our spatiotemporal models.In comparison to GAM,the residual network considerably improved prediction accuracy:on average,the coefficient of variation(CV)R2 of the three meteorological parameters rose by 0.21,CV root-mean square(RMSE)fell by 37%,and the relative humidity model improved the most.The accuracy of relative humidity models was considerably improved once the monthly index was included,demonstrating that varied amounts of temporal variables are crucial for relative humidity models.We also spoke about the benefits and drawbacks of using coarse resolution reanalysis data and closest neighbor values as variables.In comparison to classic GAMs,this study indicates that the residual network model may considerably increase the accuracy of national high spatial(1 km)and temporal(daily)resolution meteorological data.Our findings have implications for high-resolution and high-accuracy meteorological parameter mapping in China.
文摘Casablanca, Morocco's economic capital continues today to fight against the proliferation of informal settle- ments affecting its urban fabric illustrated especially by the slums. Actually Casablanca represents 25% of the total slums of Morocco [1]. These are the habitats of all deprived of healthy sanitary conditions and judged precarious from the perspective humanitarian and below the acceptable. The majority of the inhabi- tants of these slums are from the rural exodus with insufficient income to meet the basic needs of daily life. Faced with this situation and to eradicate these habitats, the Moroccan government has launched since 2004 an entire program to create cities without slums (C.W.S.) to resettle or relocate families. Indeed the process control and monitoring of this program requires first identifying and detecting spatial habitats. To achieve these tasks, conventional methods such as information gathering, mapping, use of databases and statistics often have shown their limits and are sometimes outdated. It is within this framework and that of the great German Morocco project “Urban agriculture as an integrative factor of development that fits our project de- tection of slums in Casablanca. The use of satellite imagery, particulary the HSR, has the advantage of providing the physical coverage of urban land but it raises the difficulty of choosing the appropriate method to apply.This paper is actually to develop new approaches based mainly on object-oriented classification of high spatial resolution satellite images for the detection of slums.This approach has been developed for mapping the urban land through by integration of several types of information (spectral, spatial, contextual ...) (Hofmann, P ., 2001, Herold et al. 2002b;Van Der Sande et al., 2003, Benz et al., 2004, Nobrega et al., 2006). In order to refine the result of classification, we applied mathematical morphology and in particular the closing filter. The data from this classification (binary image), which then will be used in a spatial data- base (ArcGIS).
基金the Austrian Science Fund(FWF)through the Doctoral College GIScience(DK W1237-N23)Contributions of Dirk Tiede and Hannah Augustin were supported by the Austrian Research Promotion Agency(FFG)the Austrian Space Application Programme(ASAP)within the project Sen2Cube.at(project no.:866016).
文摘Turning Earth observation(EO)data consistently and systematically into valuable global information layers is an ongoing challenge for the EO community.Recently,the term‘big Earth data’emerged to describe massive EO datasets that confronts analysts and their traditional workflows with a range of challenges.We argue that the altered circumstances must be actively intercepted by an evolution of EO to revolutionise their application in various domains.The disruptive element is that analysts and end-users increasingly rely on Web-based workflows.In this contribution we study selected systems and portals,put them in the context of challenges and opportunities and highlight selected shortcomings and possible future developments that we consider relevant for the imminent uptake of big Earth data.
