Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 99-105, 2016
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/99/2016/
doi:10.5194/isprs-archives-XLI-B7-99-2016
 
20 Jun 2016
LEAF AREA INDEX RETRIEVED FROM THERMAL HYPERSPECTRAL DATA
Elnaz Neinavaz, Andrew K. Skidmore, Roshanak Darvishzadeh, and Thomas A. Groen Department of Natural Resources Science, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE Enschede, The Netherlands
Keywords: Thermal, Emissivity spectra, Leaf area index (LAI), Canopy, Remote sensing, Spectrometer Abstract. Leaf area index (LAI) is an important essential biodiversity variable due to its role in many terrestrial ecosystem processes such as evapotranspiration, energy balance, and gas exchanges as well as plant growth potential. A novel approach presented here is the retrieval of LAI using thermal infrared (8–14 μm, TIR) measurements. Here, we evaluate LAI retrieval using TIR hyperspectral data. Canopy emissivity spectral measurements were recorded under controlled laboratory conditions using a MIDAC (M4401-F) illuminator Fourier Transform Infrared spectrometer for two plant species during which LAI was destructively measured. The accuracy of retrieval for LAI was then assessed using partial least square regression (PLSR) and narrow band index calculated in the form of normalized difference index from all possible combinations of wavebands. The obtained accuracy from the PLSR for LAI retrieval was relatively higher than narrow-band vegetation index (0.54 < R2 < 0.74). The results demonstrated that LAI may successfully be estimated from hyperspectral thermal data. The study highlights the potential of hyperspectral thermal data for retrieval of vegetation biophysical variables at the canopy level for the first time.
Conference paper (PDF, 863 KB)


Citation: Neinavaz, E., Skidmore, A. K., Darvishzadeh, R., and Groen, T. A.: LEAF AREA INDEX RETRIEVED FROM THERMAL HYPERSPECTRAL DATA, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 99-105, doi:10.5194/isprs-archives-XLI-B7-99-2016, 2016.

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