Rainwater harvesting (RWH) systems have been developed to compensate for shortage in the water supply worldwide. Such systems are not very common in arid areas, particularly in the Gulf Region, due to the scarcity of ...Rainwater harvesting (RWH) systems have been developed to compensate for shortage in the water supply worldwide. Such systems are not very common in arid areas, particularly in the Gulf Region, due to the scarcity of rainfall and their reduced efficiency in covering water demand and reducing water consumption rates. In spite of this, RWH systems have the potential to reduce urban flood risks, particularly in densely populated areas. This study aimed to assess the potential use of RWH systems as urban flood mitigation measures in arid areas. Their utility in the retention of stormwater runoff and the reduction of water depth and extent were evaluated. The study was conducted in a residential area in Bahrain that experienced waterlogging after heavy rainfall events. The water demand patterns of housing units were analyzed, and the daily water balance for RWH tanks was evaluated. The effect of the implementation of RWH systems on the flood volume was evaluated with a two-dimensional hydrodynamic model. Flood simulations were conducted in several rainfall scenarios with different probabilities of occurrence. The results showed significant reductions in the flood depth and flood extent, but these effects were highly dependent on the rainfall intensity of the event. RWH systems are effective flood mitigation measures, particularly in urban arid regions short of proper stormwater control infrastructure, and they enhance the resilience of the built environment to urban floods.展开更多
In order to meet the application requirements of autonomous vehicles, this paper proposes a simultaneous localization and mapping (SLAM) algorithm, which uses a VoxelGrid filter to down sample the point cloud data, ...In order to meet the application requirements of autonomous vehicles, this paper proposes a simultaneous localization and mapping (SLAM) algorithm, which uses a VoxelGrid filter to down sample the point cloud data, with the combination of iterative closest points (ICP) algorithm and Gaussian model for particles updating, the matching between the local map and the global map to quantify particles' importance weight. The crude estimation by using ICP algorithm can find the high probability area of autonomous vehicles' poses, which would decrease particle numbers, increase algorithm speed and restrain particles' impoverishment. The calculation of particles' importance weight based on matching of attribute between grid maps is simple and practicable. Experiments carried out with the autonomous vehicle platform validate the effectiveness of our approaches.展开更多
Impervious surface area(ISA)is an important parameter for many environmental or socioeconomic relevant studies.The unique characteristics of remote sensing data made it the primary data source for ISA mapping at vario...Impervious surface area(ISA)is an important parameter for many environmental or socioeconomic relevant studies.The unique characteristics of remote sensing data made it the primary data source for ISA mapping at various scales.This paper summarizes general ISA mapping procedure and major techniques and discusses impacts of scale issues on selection of remote sensing data and corresponding algorithms.Previous studies have indicated that ISA mapping remains a challenge,especially in urban–rural frontiers and in covering a large area.Effectively employing rich spatial information in high spatial resolution imagery through texture and objectbased methods is valuable.Data fusion of multi-resolution images and spectral mixture analysis are common approaches to reduce the mixed pixel problem in medium spatial resolution images such as Landsat.Coarse spatial resolution images such as MODIS and DMSP-OLS are valuable for national and global ISA mapping but more research is needed to effectively integrate multisource/scale data for improving mapping performance.Development of an optimal procedure corresponding to specific study areas and purposes is required to generate accurate ISA mapping results.展开更多
Mapping built land cover at unprecedented detail has been facilitated by increasing availability of global high-resolution imagery and image processing methods.These advances in urban feature extraction and built-area...Mapping built land cover at unprecedented detail has been facilitated by increasing availability of global high-resolution imagery and image processing methods.These advances in urban feature extraction and built-area detection can refine the mapping of human population densities,especially in lower income countries where rapid urbanization and changing population is accompanied by frequently out-of-date or inaccurate census data.However,in these contexts it is unclear how best to use built-area data to disaggregate areal,count-based census data.Here we tested two methods using remotely sensed,built-area land cover data to disaggregate population data.These included simple,areal weighting and more complex statistical models with other ancillary information.Outcomes were assessed across eleven countries,representing different world regions varying in population densities,types of built infrastructure,and environmental characteristics.We found that for seven of 11 countries a Random Forest-based,machine learning approach outperforms simple,binary dasymetric disaggregation into remotely-sensed built areas.For these more complex models there was little evidence to support using any single built land cover input over the rest,and in most cases using more than one built-area data product resulted in higher predictive capacity.We discuss these results and implications for future population modeling approaches.展开更多
Vegetation mapping using field surveys is expensive. Distribution modelling, based on sample surveys, might overcome this challenge. We tested if models trained from sample surveys could be used to predict the distrib...Vegetation mapping using field surveys is expensive. Distribution modelling, based on sample surveys, might overcome this challenge. We tested if models trained from sample surveys could be used to predict the distribution of vegetation types in neighbourhood areas, and how reliable the spatial transferability was. We also tested whether we should use ecological dissimilarity or spatial distance to foresee modelling performance. Maximum entropy models were run for three vegetation types based on a vegetation map within a mountain range. Environmental variables were selected backwards, model complexity was kept low. The models are based on points from a small part of each study site, transferred into the entire sites, and then tested for performance. Environmental distance was tested using principle component analysis. All models had high uncorrected AUC values. The ability to predict presences correctly was low. The ability to predict absences correctly was high. The ability to transfer the distribution model depended on environmental distance, not spatial distance.展开更多
文摘Rainwater harvesting (RWH) systems have been developed to compensate for shortage in the water supply worldwide. Such systems are not very common in arid areas, particularly in the Gulf Region, due to the scarcity of rainfall and their reduced efficiency in covering water demand and reducing water consumption rates. In spite of this, RWH systems have the potential to reduce urban flood risks, particularly in densely populated areas. This study aimed to assess the potential use of RWH systems as urban flood mitigation measures in arid areas. Their utility in the retention of stormwater runoff and the reduction of water depth and extent were evaluated. The study was conducted in a residential area in Bahrain that experienced waterlogging after heavy rainfall events. The water demand patterns of housing units were analyzed, and the daily water balance for RWH tanks was evaluated. The effect of the implementation of RWH systems on the flood volume was evaluated with a two-dimensional hydrodynamic model. Flood simulations were conducted in several rainfall scenarios with different probabilities of occurrence. The results showed significant reductions in the flood depth and flood extent, but these effects were highly dependent on the rainfall intensity of the event. RWH systems are effective flood mitigation measures, particularly in urban arid regions short of proper stormwater control infrastructure, and they enhance the resilience of the built environment to urban floods.
基金Supported by the Major Research Plan of the National Natural Science Foundation of China(91120003)Surface Project of the National Natural Science Foundation of China(61173076)
文摘In order to meet the application requirements of autonomous vehicles, this paper proposes a simultaneous localization and mapping (SLAM) algorithm, which uses a VoxelGrid filter to down sample the point cloud data, with the combination of iterative closest points (ICP) algorithm and Gaussian model for particles updating, the matching between the local map and the global map to quantify particles' importance weight. The crude estimation by using ICP algorithm can find the high probability area of autonomous vehicles' poses, which would decrease particle numbers, increase algorithm speed and restrain particles' impoverishment. The calculation of particles' importance weight based on matching of attribute between grid maps is simple and practicable. Experiments carried out with the autonomous vehicle platform validate the effectiveness of our approaches.
基金The authors acknowledge supports from the Zhejiang A&F University’s Research and Development Fund-talent startup project(2013FR052)Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration,School of Environmental and Resource Sciences,Zhejiang A&F University and Center for Global Change and Earth Observations,Michigan State University.
文摘Impervious surface area(ISA)is an important parameter for many environmental or socioeconomic relevant studies.The unique characteristics of remote sensing data made it the primary data source for ISA mapping at various scales.This paper summarizes general ISA mapping procedure and major techniques and discusses impacts of scale issues on selection of remote sensing data and corresponding algorithms.Previous studies have indicated that ISA mapping remains a challenge,especially in urban–rural frontiers and in covering a large area.Effectively employing rich spatial information in high spatial resolution imagery through texture and objectbased methods is valuable.Data fusion of multi-resolution images and spectral mixture analysis are common approaches to reduce the mixed pixel problem in medium spatial resolution images such as Landsat.Coarse spatial resolution images such as MODIS and DMSP-OLS are valuable for national and global ISA mapping but more research is needed to effectively integrate multisource/scale data for improving mapping performance.Development of an optimal procedure corresponding to specific study areas and purposes is required to generate accurate ISA mapping results.
基金FRS,AEG,JNN,AK,and AS are funded by the Bill&Melinda Gates Foundation(OPP1134076)AJT is supported by funding from U.S.National Institutes of Health/National Institute of Allergy and Infectious Diseases(U19AI089674)+1 种基金the Bill&Melinda Gates Foundation(OPP1106427,OPP1032350,OPP1134076)the Clinton Health Access Initiative,National Institutes of Health,and a Wellcome Trust Sustaining Health Grant(106866/Z/15/Z).
文摘Mapping built land cover at unprecedented detail has been facilitated by increasing availability of global high-resolution imagery and image processing methods.These advances in urban feature extraction and built-area detection can refine the mapping of human population densities,especially in lower income countries where rapid urbanization and changing population is accompanied by frequently out-of-date or inaccurate census data.However,in these contexts it is unclear how best to use built-area data to disaggregate areal,count-based census data.Here we tested two methods using remotely sensed,built-area land cover data to disaggregate population data.These included simple,areal weighting and more complex statistical models with other ancillary information.Outcomes were assessed across eleven countries,representing different world regions varying in population densities,types of built infrastructure,and environmental characteristics.We found that for seven of 11 countries a Random Forest-based,machine learning approach outperforms simple,binary dasymetric disaggregation into remotely-sensed built areas.For these more complex models there was little evidence to support using any single built land cover input over the rest,and in most cases using more than one built-area data product resulted in higher predictive capacity.We discuss these results and implications for future population modeling approaches.
文摘Vegetation mapping using field surveys is expensive. Distribution modelling, based on sample surveys, might overcome this challenge. We tested if models trained from sample surveys could be used to predict the distribution of vegetation types in neighbourhood areas, and how reliable the spatial transferability was. We also tested whether we should use ecological dissimilarity or spatial distance to foresee modelling performance. Maximum entropy models were run for three vegetation types based on a vegetation map within a mountain range. Environmental variables were selected backwards, model complexity was kept low. The models are based on points from a small part of each study site, transferred into the entire sites, and then tested for performance. Environmental distance was tested using principle component analysis. All models had high uncorrected AUC values. The ability to predict presences correctly was low. The ability to predict absences correctly was high. The ability to transfer the distribution model depended on environmental distance, not spatial distance.