This paper presents algorithmic components and corresponding software routines for extracting shoreline features from remote sensing imagery and LiDAR data. Conceptually, shoreline features are treated as boundary lin...This paper presents algorithmic components and corresponding software routines for extracting shoreline features from remote sensing imagery and LiDAR data. Conceptually, shoreline features are treated as boundary lines between land objects and water objects. Numerical algorithms have been identified and de-vised to segment and classify remote sensing imagery and LiDAR data into land and water pixels, to form and enhance land and water objects, and to trace and vectorize the boundaries between land and water ob-jects as shoreline features. A contouring routine is developed as an alternative method for extracting shore-line features from LiDAR data. While most of numerical algorithms are implemented using C++ program-ming language, some algorithms use available functions of ArcObjects in ArcGIS. Based on VB .NET and ArcObjects programming, a graphical user’s interface has been developed to integrate and organize shoreline extraction routines into a software package. This product represents the first comprehensive software tool dedicated for extracting shorelines from remotely sensed data. Radarsat SAR image, QuickBird multispectral image, and airborne LiDAR data have been used to demonstrate how these software routines can be utilized and combined to extract shoreline features from different types of input data sources: panchromatic or single band imagery, color or multi-spectral image, and LiDAR elevation data. Our software package is freely available for the public through the internet.展开更多
Although data for temporal spring river ice breakup are available for a number of Arctic rivers, there is a paucity of information related to the type of breakup. The Arctic Climate Impact Assessment (ACIA) of 2005 pr...Although data for temporal spring river ice breakup are available for a number of Arctic rivers, there is a paucity of information related to the type of breakup. The Arctic Climate Impact Assessment (ACIA) of 2005 predicted a transition from mechanical to thermal spring breakup of ice cover on arctic rivers, with this shift being greatest in exclusively Arctic watersheds where observed warming is most pronounced. We describe a rare instance of an entirely Arctic river with limited but well documented historical and recent data regarding the type of breakup. Time-series ground imagery of spring breakup from 1966, 1975, 1978, 2009, 2010 and 2012, incombination with interviews of local inhabitants, documents a shift from predominantly mechanical to predominantly thermal breakup after spring 1978 and by spring 2009 within the context of a locally and regionally warming Arctic. The resultant shift from predominantly mechanical to predominantly thermal breakup is predicted to result in significant changes to water, sediment, nutrient and organic carbon fluxes, as well as riparian ecology and human activities.展开更多
Building-level population data are of vital importance in disaster management,homeland security,and public health.Remotely sensed data,especially LiDAR data,which allow measures of three-dimensional morphological info...Building-level population data are of vital importance in disaster management,homeland security,and public health.Remotely sensed data,especially LiDAR data,which allow measures of three-dimensional morphological information,have been shown to be useful for fine-scale population estimations.However,studies using LiDAR data for population estimation have noted a nonstationary relationship between LiDAR-derived morphological indicators and populations due to the unbalanced characteristic of population distribution.In this article,we proposed a framework to estimate population at the building level by integrating POI data,nighttime light(NTL)data,and LiDAR data.Building objects were first derived using LiDAR data and aerial photographs.Then,three categories of building-level features,including geometric features,nighttime light intensity features,and POI features,were,respectively,extracted from LiDAR data,Luojia1-01 NTL data,and POI data.Finally,a welltrained random forest model was built to estimate the population of each individual building.Huangpu District in Shanghai,China,was chosen to validate the proposed method.A comparison between the estimation result and reference data shows that the proposed method achieved a good accuracy with R^(2)=0:65 at the building level and R^(2)=0:79 at the community level.The NTL radiance intensity was found to have a positive relationship with population in residential areas,while a negative relationship was found in office and commercial areas.Our study has shown that by integrating both the three-dimensional morphological information derived from LiDAR data and the human activity information extracted from POI and NTL data,the accuracy of building-level population estimation can be improved.展开更多
文摘This paper presents algorithmic components and corresponding software routines for extracting shoreline features from remote sensing imagery and LiDAR data. Conceptually, shoreline features are treated as boundary lines between land objects and water objects. Numerical algorithms have been identified and de-vised to segment and classify remote sensing imagery and LiDAR data into land and water pixels, to form and enhance land and water objects, and to trace and vectorize the boundaries between land and water ob-jects as shoreline features. A contouring routine is developed as an alternative method for extracting shore-line features from LiDAR data. While most of numerical algorithms are implemented using C++ program-ming language, some algorithms use available functions of ArcObjects in ArcGIS. Based on VB .NET and ArcObjects programming, a graphical user’s interface has been developed to integrate and organize shoreline extraction routines into a software package. This product represents the first comprehensive software tool dedicated for extracting shorelines from remotely sensed data. Radarsat SAR image, QuickBird multispectral image, and airborne LiDAR data have been used to demonstrate how these software routines can be utilized and combined to extract shoreline features from different types of input data sources: panchromatic or single band imagery, color or multi-spectral image, and LiDAR elevation data. Our software package is freely available for the public through the internet.
文摘Although data for temporal spring river ice breakup are available for a number of Arctic rivers, there is a paucity of information related to the type of breakup. The Arctic Climate Impact Assessment (ACIA) of 2005 predicted a transition from mechanical to thermal spring breakup of ice cover on arctic rivers, with this shift being greatest in exclusively Arctic watersheds where observed warming is most pronounced. We describe a rare instance of an entirely Arctic river with limited but well documented historical and recent data regarding the type of breakup. Time-series ground imagery of spring breakup from 1966, 1975, 1978, 2009, 2010 and 2012, incombination with interviews of local inhabitants, documents a shift from predominantly mechanical to predominantly thermal breakup after spring 1978 and by spring 2009 within the context of a locally and regionally warming Arctic. The resultant shift from predominantly mechanical to predominantly thermal breakup is predicted to result in significant changes to water, sediment, nutrient and organic carbon fluxes, as well as riparian ecology and human activities.
基金supported by the National Natural Science Foundation of China(grant numbers 41871331,41801343,and 42001357).
文摘Building-level population data are of vital importance in disaster management,homeland security,and public health.Remotely sensed data,especially LiDAR data,which allow measures of three-dimensional morphological information,have been shown to be useful for fine-scale population estimations.However,studies using LiDAR data for population estimation have noted a nonstationary relationship between LiDAR-derived morphological indicators and populations due to the unbalanced characteristic of population distribution.In this article,we proposed a framework to estimate population at the building level by integrating POI data,nighttime light(NTL)data,and LiDAR data.Building objects were first derived using LiDAR data and aerial photographs.Then,three categories of building-level features,including geometric features,nighttime light intensity features,and POI features,were,respectively,extracted from LiDAR data,Luojia1-01 NTL data,and POI data.Finally,a welltrained random forest model was built to estimate the population of each individual building.Huangpu District in Shanghai,China,was chosen to validate the proposed method.A comparison between the estimation result and reference data shows that the proposed method achieved a good accuracy with R^(2)=0:65 at the building level and R^(2)=0:79 at the community level.The NTL radiance intensity was found to have a positive relationship with population in residential areas,while a negative relationship was found in office and commercial areas.Our study has shown that by integrating both the three-dimensional morphological information derived from LiDAR data and the human activity information extracted from POI and NTL data,the accuracy of building-level population estimation can be improved.
