Feature-location analyses for identification of urban tree species from very high resolution remote sensing data

2015 ◽  
Vol 29 ◽  
pp. 16-24 ◽  
Author(s):  
Jianhua Zhou ◽  
Jun Qin ◽  
kai Gao ◽  
Si Xu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Tree Species ◽  
Remote Sensing Data ◽  
Feature Location ◽  
Urban Tree ◽  
Sensing Data ◽  
Very High

scholarly journals Mapping the Urban Atmospheric Carbon Stock by LiDAR and WorldView-3 Data

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 692
Author(s):  
MD Abdul Mueed Choudhury ◽  
Ernesto Marcheggiani ◽  
Andrea Galli ◽  
Giuseppe Modica ◽  
Ben Somers
Keyword(s):  
Remote Sensing ◽  
Urban Area ◽  
Tree Species ◽  
Carbon Stock ◽  
Dominant Species ◽  
Remote Sensing Data ◽  
Data Sources ◽  
Lidar Data ◽  
Urban Tree ◽  
Sensing Data

Currently, the worsening impacts of urbanizations have been impelled to the importance of monitoring and management of existing urban trees, securing sustainable use of the available green spaces. Urban tree species identification and evaluation of their roles in atmospheric Carbon Stock (CS) are still among the prime concerns for city planners regarding initiating a convenient and easily adaptive urban green planning and management system. A detailed methodology on the urban tree carbon stock calibration and mapping was conducted in the urban area of Brussels, Belgium. A comparative analysis of the mapping outcomes was assessed to define the convenience and efficiency of two different remote sensing data sources, Light Detection and Ranging (LiDAR) and WorldView-3 (WV-3), in a unique urban area. The mapping results were validated against field estimated carbon stocks. At the initial stage, dominant tree species were identified and classified using the high-resolution WorldView3 image, leading to the final carbon stock mapping based on the dominant species. An object-based image analysis approach was employed to attain an overall accuracy (OA) of 71% during the classification of the dominant species. The field estimations of carbon stock for each plot were done utilizing an allometric model based on the field tree dendrometric data. Later based on the correlation among the field data and the variables (i.e., Normalized Difference Vegetation Index, NDVI and Crown Height Model, CHM) extracted from the available remote sensing data, the carbon stock mapping and validation had been done in a GIS environment. The calibrated NDVI and CHM had been used to compute possible carbon stock in either case of the WV-3 image and LiDAR data, respectively. A comparative discussion has been introduced to bring out the issues, especially for the developing countries, where WV-3 data could be a better solution over the hardly available LiDAR data. This study could assist city planners in understanding and deciding the applicability of remote sensing data sources based on their availability and the level of expediency, ensuring a sustainable urban green management system.

Riparian Vegetation Mapping for Hydraulic Roughness Estimation Using Very High Resolution Remote Sensing Data Fusion

2010 ◽  
Vol 136 (11) ◽  
pp. 855-867 ◽  
Author(s):  
Giovanni Forzieri ◽  
Gabriele Moser ◽  
Enrique R. Vivoni ◽  
Fabio Castelli ◽  
Francesco Canovaro
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Data Fusion ◽  
Riparian Vegetation ◽  
Remote Sensing Data ◽  
Vegetation Mapping ◽  
Hydraulic Roughness ◽  
Sensing Data ◽  
Very High

Mapping urban tree species using integrated airborne hyperspectral and LiDAR remote sensing data

2017 ◽  
Vol 200 ◽  
pp. 170-182 ◽  
Author(s):  
Luxia Liu ◽  
Nicholas C. Coops ◽  
Neal W. Aven ◽  
Yong Pang
Keyword(s):  
Remote Sensing ◽  
Tree Species ◽  
Remote Sensing Data ◽  
Urban Tree ◽  
Sensing Data

scholarly journals Application of very high resolution remote sensing data in studies of Antarctic penguins and greenhouse gases: A review

2017 ◽  
Vol 62 (31) ◽  
pp. 3605-3618
Author(s):  
XiangLan LI ◽  
Hong HE ◽  
Xiao CHENG ◽  
Jing ZHANG ◽  
GuoYing DONG ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Greenhouse Gases ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Very High
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