26 October 2016 Evaluation of bispectral LIDAR data for urban vegetation mapping
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Abstract
Because of the large increase of urban population in the last decades, the question of sustainable development in urban areas is crucial. In this context, vegetation plays a significant role in urban planning, environmental protecting, and sustainable development policy making, heating and cooling requirements of buildings, displacement of animals dispersion, concentration of pollutants, and well-being. In numerous cities, vegetation is limited to public areas using GPS surveys or aerial remote sensing data.

Recently, very high-resolution sensors as Light Detection and Ranging (LiDAR) data have permitted significant improvements in vegetation mapping in urban areas. This paper presents an evaluation of a new generation of airborne LIDAR bi-spectral discrete point (Optech titan) for mapping and characterizing urban vegetation. The methodology is based on a four-step approach: 1) the analysis of the quality of data in order to estimate noise between the green and near-infrared LIDAR point clouds; 2) this enables to remove the topographic effects and 3) a first classification, devoted to the elimination of the non-vegetation class, is performed based on the intensity value of the two channels; finally, in 4), the tree coverage is classified into seven categories of strata combination. To this end specific descriptors related to the organization of the point clouds are used.

These first results show that compared to monospectral LiDAR data, bi-spectral LiDAR enables to improve significantly both the extraction and the characterization of urban objects. This reveals new perspectives for mapping and characterizing urban patterns and other complex structures.
Conference Presentation
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Jean Nabucet, Laurence Hubert-Moy, Thomas Corpetti, Patrick Launeau, Dimitri Lague, Cyril Michon, Herve Quenol, "Evaluation of bispectral LIDAR data for urban vegetation mapping", Proc. SPIE 10008, Remote Sensing Technologies and Applications in Urban Environments, 100080I (26 October 2016); doi: 10.1117/12.2241731; https://doi.org/10.1117/12.2241731
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