JGIS  Vol.5 No.4 , August 2013
Urban Vegetation Mapping from Fused Hyperspectral Image and LiDAR Data with Application to Monitor Urban Tree Heights
Author(s) Fatwa Ramdani*
ABSTRACT

Urban vegetations have infinite proven benefits for urban inhabitants including providing shade, improving air quality, and enhancing the look and feel of communities. But creating a complete inventory is a time consuming and resource intensive process. The extraction of urban vegetation is a challenging task, especially to monitor the urban tree heights. In this study we present an efficient extraction method for mapping and monitoring urban tree heights using fused hyperspectral image and LiDAR data. Endmember distribution mapping using the spectral angle mapper technique is employed in this study. High convenience results achieved using fused hyperspectral and LiDAR data from this semiautomatics technique. This method could enable urban community organizations or local governments to map and monitor urbans tree height and its spatial distribution.


Cite this paper
F. Ramdani, "Urban Vegetation Mapping from Fused Hyperspectral Image and LiDAR Data with Application to Monitor Urban Tree Heights," Journal of Geographic Information System, Vol. 5 No. 4, 2013, pp. 404-408. doi: 10.4236/jgis.2013.54038.
References
[1]   J. Rogan and D. M. Chen, “Remote Sensing Technology for Mapping and Monitoring Land-Cover and Land-Use Change,” Progress in Planning, Vol. 61, No. 4, 2004, pp. 301-325. doi:10.1016/S0305-9006(03)00066-7

[2]   F. Ramdani, “Extraction of Urban Vegetation in Highly Dense Urban Environment with Application to Measure Inhabitants’ Satisfaction of Urban Green Space,” Journal of Geographic Information System, Vol. 5, No. 2, 2013, pp. 117-122. doi:10.4236/jgis.2013.52012

[3]   J. R. Jensen, “Remote Sensing of the Environment: An Earth Resource Perspective,” 2nd Edition, Pearson Prentice Hall, Upper Saddle River, 2007.

[4]   J. Reitberg, P. Krzystek and U. Stilla, “Analysis of Full Waveform LIDAR Data for the Classification of Deciduous and Coniferous Trees,” International Journal of Remote Sensing, Vol. 29, No. 5, 2008, pp. 1407-1431. doi:10.1080/01431160701736448

[5]   M. Awrangjeb, M. Ravanbakhsh and C. S. Fraser, “Automatic Detection of Residential Buildings Using LIDAR Data and Multispectral Imagery,” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 65, No. 5, 2010, pp. 457-467. doi:10.1016/j.isprsjprs.2010.06.001

[6]   Y. H. Chen, W. Su, J. Li and Z. P. Sun, “Hierarchical Object Oriented Classification Using Very High Resolution Imagery and LIDAR Data over Urban Areas,” Advances in Space Research, Vol. 43, No. 7, 2009, pp. 1101-1110. doi:10.1016/j.asr.2008.11.008

[7]   L. Guo, N. Chehata, C. Mallet and S. Boukir, “Relevance of Airborne Lidar and Multispectral Image Data for Urban Scene Classification Using Random Forests,” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 66, No. 1, 2011, pp. 56-66. doi:10.1016/j.isprsjprs.2010.08.007

[8]   X. L. Meng, L. Wang, J. L. Silvan-Cardenas and N. Currit, “A Multi-Directional Ground Filtering Algorithm for Airborne LIDAR,” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 64, No. 1, 2009, pp. 117-124. doi:10.1016/j.isprsjprs.2008.09.001

[9]   IEEE GRSS Data Fusion Contest, 2013. http://www.grss-ieee.org/community/technical-committees/data-fusion/

[10]   A. A. Green, M. Berman, P. Switzer and M. D. Craig, “A Transformation for Ordering Multispectral Data in Terms of Image Quality with Implications for Noise Removal,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 26, No. 1, 1988, pp. 65-74. doi:10.1109/36.3001

[11]   J. W. Boardman, F. A. Kruse and R. O. Green, “Mapping Target Signatures via Partial Unmixing of AVIRIS Data: In Summaries,” Fifth JPL Airborne Earth Science Workshop, JPL Publication 95-1, Vol. 1, 1995, pp. 23-26.

[12]   Exelisvis, “ENVI Software Classic Help”.

[13]   F. A. Kruse, A. B. Lefkoff, J. B. Boardman, K. B. Heidebrecht, A. T. Shapiro, P. J. Barloon, and A. F. H. Goetz, “The Spectral Image Processing System (SIPS)—Interactive Visualization and Analysis of Imaging Spectrometer Data,” Remote Sensing of the Environment, Vol. 44, No. 2-3, 1993, pp. 145-163. doi:10.1016/0034-4257(93)90013-N

[14]   D. A. Zimble, D. L. Evans, G. C. Carlson, R. C. Parker, S. C. Grado and P. D. Gerard, “Characterising Vertical Forest Structure Using Small-Footprint Airborne LiDAR,” Remote sensing of Environment, Vol. 87, No. 2-3, 2003, pp. 171-182. doi:10.1016/S0034-4257(03)00139-1

[15]   C. Hug, A. Ullrich and A. Grimm, “LiteMapper-5600—A Waveform-Digitizing LIDAR Terrain and Vegetation Mapping System,” Proceedings of the ISPRS Working Group VIII/2 Laser-Scanners for Forest and Landscape Assessment, Freiburg, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 36, 2004.

[16]   C. Mallet, U. Soergel and F. Bretar, “Analysis of FullWaveform LiDAR Data for Classification of Urban Areas,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. 37, Beijing, 2008. http://www.isprs.org/proceedings/XXXVII/congress/3_pdf/13.pdf

 
 
Top