Investigation of Tree Spectral Reflectance Characteristics Using a Mobile Terrestrial Line Spectrometer and Laser Scanner

Abstract

In mobile terrestrial hyperspectral imaging, individual trees often present large variations in spectral reflectance that may impact the relevant applications, but the related studies have been seldom reported. To fill this gap, this study was dedicated to investigating the spectral reflectance characteristics of individual trees with a Sensei mobile mapping system, which comprises a Specim line spectrometer and an Ibeo Lux laser scanner. The addition of the latter unit facilitates recording the structural characteristics of the target trees synchronously, and this is beneficial for revealing the characteristics of the spatial distributions of tree spectral reflectance with variations at different levels. Then, the parts of trees with relatively low-level variations can be extracted. At the same time, since it is difficult to manipulate the whole spectrum, the traditional concept of vegetation indices (VI) based on some particular spectral bands was taken into account here. Whether the assumed VIs capable of behaving consistently for the whole crown of each tree was also checked. The specific analyses were deployed based on four deciduous tree species and six kinds of VIs. The test showed that with the help of the laser scanner data, the parts of individual trees with relatively low-level variations can be located. Based on these parts, the relatively stable spectral reflectance characteristics for different tree species can be learnt.

Reflecting conifer phenology using mobile terrestrial LiDAR: A case study of Pinus sylvestris growing under the Mediterranean climate in Perth, Australia

“…Alternative measures need to be introduced in order to synchronously monitor the physiological attributes of conifers. For instance, one of the monitoring devices of this kind can be spectrometers that can reflect tree photosynthetic or transpiration strengths (Jin and Eklundh, 2015), and it has also been validated that spectrometers can be installed onto mobile mapping platforms (Lin et al, 2013). Consequently, it can be inferred that the integration of mobile terrestrial LiDAR and hyperspectral imaging will be an excellent tool in phenological studies.…”

Segmentation in dermatological hyperspectral images: dedicated methods

“…As it was mentioned in the “ Background ” section, numerous similar segmentation methods are known from the literature. Hence, the method utilizing emissivity curves of a given object (SKE method) is used in many applications such as specim spectral imaging [ 30 , 31 ], hyperspectral imaging system [ 5 , 18 ] or imageJ [ 32 ]. However, in none of such applications the time of preliminary segmentation was obtained taking into account the emissivity curve at the level of 2.3 ms. For example in [ 33 ] the authors obtain the time of analysis equal to 100 ms using Intel ® Core i5 CPU M460 @2.5 GHz 4 GB RAM.…”

Hyperspectral imaging in medicine: image pre‐processing problems and solutions in Matlab

“…The ENVI file record format is a known and widely available format for recording files , e.g. derived from hyperspectral cameras .…”