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RISS 인기검색어
- 검색키워드
- 저자 : Pijanowski, Bryan (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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Mapping Vegetation Volume in Urban Environments by Fusing LiDAR and Multispectral Data
Jung, Jinha,Pijanowski, Bryan The Korean Society of Remote Sensing 2012 大韓遠隔探査學會誌 Vol.28 No.6
Urban forests provide great ecosystem services to population in metropolitan areas even though they occupy little green space in a huge gray landscape. Unfortunately, urbanization inherently results in threatening the green infrastructure, and the recent urbanization trends drew great attention of scientists and policy makers on how to preserve or restore green infrastructure in metropolitan area. For this reason, mapping the spatial distribution of the green infrastructure is important in urban environments since the resulting map helps us identify hot green spots and set up long term plan on how to preserve or restore green infrastructure in urban environments. As a preliminary step for mapping green infrastructure utilizing multi-source remote sensing data in urban environments, the objective of this study is to map vegetation volume by fusing LiDAR and multispectral data in urban environments. Multispectral imageries are used to identify the two dimensional distribution of green infrastructure, while LiDAR data are utilized to characterize the vertical structure of the identified green structure. Vegetation volume was calculated over the metropolitan Chicago city area, and the vegetation volume was summarized over 16 NLCD classes. The experimental results indicated that vegetation volume varies greatly even in the same land cover class, and traditional land cover map based above ground biomass estimation approach may introduce bias in the estimation results.
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Mapping Vegetation Volume in Urban Environments by Fusing LiDAR and Multispectral Data
Jin Ha Jung,Bryan Pijanowski 大韓遠隔探査學會 2012 大韓遠隔探査學會誌 Vol.28 No.6
Urban forests provide great ecosystem services to population in metropolitan areas even though they occupy little green space in a huge gray landscape. Unfortunately, urbanization inherently results in threatening the green infrastructure, and the recent urbanization trends drew great attention of scientists and policy makers on how to preserve or restore green infrastructure in metropolitan area. For this reason, mapping the spatial distribution of the green infrastructure is important in urban environments since the resulting map helps us identify hot green spots and set up long term plan on how to preserve or restore green infrastructure in urban environments. As a preliminary step for mapping green infrastructure utilizing multi-source remote sensing data in urban environments, the objective of this study is to map vegetation volume by fusing LiDAR and multispectral data in urban environments. Multispectral imageries are used to identify the two dimensional distribution of green infrastructure, while LiDAR data are utilized to characterize the vertical structure of the identified green structure. Vegetation volume was calculated over the metropolitan Chicago city area, and the vegetation volume was summarized over 16 NLCD classes. The experimental results indicated that vegetation volume varies greatly even in the same land cover class, and traditional land cover map based above ground biomass estimation approach may introduce bias in the estimation results.
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Jinha Jung,Bryan C. Pijanowski 한국지질과학협의회 2015 Geosciences Journal Vol.19 No.4
LiDAR is an active remote sensing technique with a unique capability to capture three-dimensional information of the earth’s surface even in heavily vegetated areas, and it is proven to be useful in many research applications. Although it is becoming the remote sensing platform of choice for planning and natural resource agencies that require three-dimensional information, the enormous data that are generated and the lack of available software analysis packages make LiDAR still unavailable to a typical user of spatial data. LiDARHub is a free and open source platform for web-based management, visualization and analysis of LiDAR data that enables development of online tools for LiDAR data processing in a web browser. The framework provides a foundation to develop online tools for LiDAR data processing and tools can be shared. The framework is also flexible so that the developed tools can be easily ported to High Performance Computing (HPC) environments that speed up the computationally extensive LiDAR data processing. Two example LiDARHub tools are presented as case studies to demonstrate potential software development scenarios. The developed tools provide easy to use user interface and hide complex computation so that users can take advantage of the LiDAR technology with only a web browser. The LiDARHub allows not only the sharing of large volume of LiDAR data but also developing online LiDAR processing platform for a large audience.
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