Volume II-5/W2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5/W2, 205-210, 2013
https://doi.org/10.5194/isprsannals-II-5-W2-205-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5/W2, 205-210, 2013
https://doi.org/10.5194/isprsannals-II-5-W2-205-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

  16 Oct 2013

16 Oct 2013

Nitrogen concentration estimation with hyperspectral LiDAR

O. Nevalainen1, T. Hakala1, J. Suomalainen1,2, and S. Kaasalainen1 O. Nevalainen et al.
  • 1Department of Photogrammetry and Remote Sensing, Finnish Geodetic Institute, Geodeetinrinne 2, 02431, Masala, Finland
  • 2Geo-Information Science and Remote Sensing, Wageningen University, 6700 AA Wageningen, the Netherlands

Keywords: Remote sensing, Hyperspectral, LiDAR, Laser scanning, Nitrogen estimation

Abstract. Agricultural lands have strong impact on global carbon dynamics and nitrogen availability. Monitoring changes in agricultural lands require more efficient and accurate methods. The first prototype of a full waveform hyperspectral Light Detection and Ranging (LiDAR) instrument has been developed at the Finnish Geodetic Institute (FGI). The instrument efficiently combines the benefits of passive and active remote sensing sensors. It is able to produce 3D point clouds with spectral information included for every point which offers great potential in the field of remote sensing of environment. This study investigates the performance of the hyperspectral LiDAR instrument in nitrogen estimation.

The investigation was conducted by finding vegetation indices sensitive to nitrogen concentration using hyperspectral LiDAR data and validating their performance in nitrogen estimation. The nitrogen estimation was performed by calculating 28 published vegetation indices to ten oat samples grown in different fertilization conditions. Reference data was acquired by laboratory nitrogen concentration analysis. The performance of the indices in nitrogen estimation was determined by linear regression and leave-one-out cross-validation.

The results indicate that the hyperspectral LiDAR instrument holds a good capability to estimate plant biochemical parameters such as nitrogen concentration. The instrument holds much potential in various environmental applications and provides a significant improvement to the remote sensing of environment.