Characterizing Vegetation Canopy Structure Using Airborne Remote Sensing Data

Debsunder Dutta, Kunxuan Wang, Esther Lee, Allison Goodwell, Dong Kook Woo, Derek Wagner, Praveen Kumar

Research output: Contribution to journalArticle

Abstract

Vegetation canopy structure plays an important role in the partitioning of incident solar radiation, photosynthesis, transpiration, and other scalar fluxes. The vertical foliage distribution of the plant canopy is represented by leaf area density (LAD), which is defined as the one-sided leaf area per unit volume. Airborne light detection and ranging (LiDAR) offers the possibility to characterize the 3-D variation of LAD over space, which still remains a challenge to estimate. Moreover, the low density of point cloud data generally offered by airborne LiDAR may be insufficient for accurate LAD estimation in dense overlapping forest canopies. We develop a method for the estimation of the LAD profile using a combination of airborne LiDAR and hyperspectral data using a feature-based data fusion approach. After identifying vegetation species using hyperspectral data, point cloud LiDAR data is used in a 'tree-shaped' voxel approach to characterize the LAD of trees in a riparian forest setting. We also propose a set of relationships on simple geometry of overlap for the construction of tree shaped voxels. In a forest setting with overlapping canopies, the results indicate that the tree-shaped voxels are better able to attribute the LAD to the upper and middle parts of the overall canopy as well as individual tall and short trees compared with traditional cylindrical voxels.

Original languageEnglish (US)
Article number7762140
Pages (from-to)1160-1178
Number of pages19
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume55
Issue number2
DOIs
StatePublished - Feb 1 2017

Fingerprint

leaf area
canopy
vegetation
Transpiration
Incident solar radiation
Photosynthesis
Data fusion
Remote sensing
Fluxes
Geometry
riparian forest
forest canopy
transpiration
foliage
solar radiation
photosynthesis
partitioning
remote sensing
geometry

Keywords

  • Hyperspectral
  • leaf area density (LAD)
  • leaf area index (LAI)
  • light detection and ranging (LiDAR)
  • tree species
  • voxel

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

Cite this

Dutta, D., Wang, K., Lee, E., Goodwell, A., Woo, D. K., Wagner, D., & Kumar, P. (2017). Characterizing Vegetation Canopy Structure Using Airborne Remote Sensing Data. IEEE Transactions on Geoscience and Remote Sensing, 55(2), 1160-1178. [7762140]. DOI: 10.1109/TGRS.2016.2620478

Characterizing Vegetation Canopy Structure Using Airborne Remote Sensing Data. / Dutta, Debsunder; Wang, Kunxuan; Lee, Esther; Goodwell, Allison; Woo, Dong Kook; Wagner, Derek; Kumar, Praveen.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, No. 2, 7762140, 01.02.2017, p. 1160-1178.

Research output: Contribution to journalArticle

Dutta, D, Wang, K, Lee, E, Goodwell, A, Woo, DK, Wagner, D & Kumar, P 2017, 'Characterizing Vegetation Canopy Structure Using Airborne Remote Sensing Data' IEEE Transactions on Geoscience and Remote Sensing, vol 55, no. 2, 7762140, pp. 1160-1178. DOI: 10.1109/TGRS.2016.2620478
Dutta D, Wang K, Lee E, Goodwell A, Woo DK, Wagner D et al. Characterizing Vegetation Canopy Structure Using Airborne Remote Sensing Data. IEEE Transactions on Geoscience and Remote Sensing. 2017 Feb 1;55(2):1160-1178. 7762140. Available from, DOI: 10.1109/TGRS.2016.2620478

Dutta, Debsunder; Wang, Kunxuan; Lee, Esther; Goodwell, Allison; Woo, Dong Kook; Wagner, Derek; Kumar, Praveen / Characterizing Vegetation Canopy Structure Using Airborne Remote Sensing Data.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, No. 2, 7762140, 01.02.2017, p. 1160-1178.

Research output: Contribution to journalArticle

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