A geospatial web portal for sharing and analyzing greenhouse gas data derived from satellite remote sensing images

Greenhouse gas data collected by different institutions throughout the world have significant scientific values for global climate change studies. Due to the diversity of data formats and different specifications of data access interfaces, most of those data should be first downloaded onto a local machine before they can be used. To overcome this limitation, we present a geospatial web portal for sharing and analyzing greenhouse gas data derived from remote sensing images. As a proof-of-concept, a prototype has also been designed and implemented. The work：flow of the web portal contains four processes： data access, data analysis, results visualiza- tion, and results output. A large volume of greenhouse gas data have been collected, described, and indexed in the portal, and a variety of data analysis services, such as calculating the temporal variation of regionally averaged column CO2 values and analyzing the latitudinal variations of globally averaged column CO2 values, are integrated into this portal. With the integrated geospatial data and services, researchers can collect and analyze greenhouse gas data online, and can preview and download the analysis results directly from the web portal. The geospatial web portal has been implemented as a web application, and we also used a study case to illustrate this framework.

A New Method for Building-Level Population Estimation by Integrating LiDAR, Nighttime Light, and POI Data

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.