Abstract
Biochemicals, such as chlorophyll (Chl) and nitrogen (N), are closely related to photosynthesis process of vegetation. Their accurate estimation is an important topic in remote sensing of vegetation. Previous studies mainly focused on Chl-N content inversion in leaf and canopy level, and few cared about their 3-D distributions, which was also an important indicator for the growth status of vegetation (GSV). Hyperspectral LiDAR (HSL) is a novel active remote sensing technology, which has target-sensitive band with hyperspectra resolution. Its 3-D point cloud data simultaneously contains rich spectral and precise geometrical characteristics of the target. This work aims to apply HSL data on 3-D Chl-N content mapping in vegetation through constructing HSL-based spectral indices (SIs). Except for following the SI forms of previous works, the normalized differential vegetation index and ratio index (RI) with four broadbands in an HSL spectral space were successively proposed to invert Chl-N content for the whole vegetation based on the artificial neural network (ANN) method. These four broadbands were transformed based on the relative spectral response curve of detector and the feature weights (FWs) of multiwavelength, respectively. Results show that most HSL-based ANN models can accurately invert Chl-N content with a mean R2 of >0.75, and some that fusing broadband data with convolution transformation, namely the FW-based RI, can even obtain a model R2 of 0.84 for N content inversion. Thus, HSL can be efficiently applied to 3-D Chl-N content mapping of vegetation and has great potential in GSV monitoring.
Highlights
CHLOROPHYLL and nitrogen (Chl-N) contents are significant indicators for photosynthetic process of vegetation which is closely related to the status of vegetation growth (GSV) [1]
Magney et al [9] conducted a terrestrial laser scanner (TLS) to assess the suitability of green laser return intensity (GLRI, 532 nm) in understanding the leaf photoprotective mechanisms. They found that the relationship between photoprotective non-photochemical quenching and GLRI for individual leaves exhibited a coefficient of determination (R2) of 0.52 to 0.78, and they stated that the GLRI-based TLS had great prospects in ascertaining foliar 3D physiological information
We found that a three-order polynomial function could well fit Hyperspectral LiDAR (HSL) intensity at a distance range of 5.6-5.9 m (Figs. 6a and 6c)
Summary
CHLOROPHYLL and nitrogen (Chl-N) contents are significant indicators for photosynthetic process of vegetation which is closely related to the status of vegetation growth (GSV) [1]. As an active remote sensing technology, LiDAR is independent from solar illumination and can separate soil background noise by obtaining vertical information of vegetation [8]. Magney et al [9] conducted a terrestrial laser scanner (TLS) to assess the suitability of green laser return intensity (GLRI, 532 nm) in understanding the leaf photoprotective mechanisms. They found that the relationship between photoprotective non-photochemical quenching and GLRI for individual leaves exhibited a coefficient of determination (R2) of 0.52 (wheat) to 0.78 (sunflower), and they stated that the GLRI-based TLS had great prospects in ascertaining foliar 3D physiological information. Eitel et al [11] used a GLRI-based TLS to measure N nutrition index for wheat in tillering and jointing stages, and they demonstrated that TLS could provide useful information for improving N management during early season growth
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