Quantification of non-structural carbohydrates in different tissues of trees with diffuse reflectance spectroscopy: Is there a benefit from a fusion of near- and mid-infrared data?

Understanding the role of non-structural carbohydrates (NSC) in tree-level carbon cycling crucially depends on the availability of NSC data in a sufficient temporal resolution covering extreme conditions and seasonal peaks or declines. Chemical analytical methods should therefore get complemented by less extensive retrieval methods. To this end, we explored the potential of diffuse reflectance spectroscopy for estimating NSC contents at a set of 180 samples taken from leaves, roots, stems and branches of different tree species in different biogeographic regions. Multiple randomized partitioning in calibration and validation data were performed with near-infrared (NIR) and mid-infrared (MIR) as well as combined data. With derivative spectra, NIR markedly outperformed MIR data for NSC estimation; mean RMSE for outer validation samples equalled 2.58 (in % of dry matter) compared to 2.90, r2 was 0.64 compared to 0.52. We found complementary information related to NSC in both spectral domains, so that a combination with high-level data fusion (model averaging) increased accuracy (RMSE decreased to 2.19, r2 equalled 0.72). Spectral variable selection with the CARS algorithm further improved results slightly (RMSE = 1.97, r2 = 0.78). On the level of tissue types, we found a marked differentiation concerning the appropriateness of datasets and approaches. High-level data fusion was successful for leaves, NIR data (together with CARS) provided the best results for wooden tissues. This suggests further studies with a greater number of samples per tissue type but only for selected (main) tree species to finally judge the sensitivities of diffuse reflectance spectroscopy (NIR, MIR) for NSC retrieval.

Performance and Accuracy Comparisons of Classification Methods and Perspective Solutions for UAV-Based Near-Real-Time "Out of the Lab" Data Processing.

Today, integration into automated systems has become a priority in the development of remote sensing sensors carried on drones. For this purpose, the primary task is to achieve real-time data processing. Increasing sensor resolution, fast data capture and the simultaneous use of multiple sensors is one direction of development. However, this poses challenges on the data processing side due to the increasing amount of data. Our study intends to investigate how the running time and accuracy of commonly used image classification algorithms evolve using Altum Micasense multispectral and thermal acquisition data with GSD = 2 cm spatial resolution. The running times were examined for two PC configurations, with a 4 GB and 8 GB DRAM capacity, respectively, as these parameters are closer to the memory of NRT microcomputers and laptops, which can be applied "out of the lab". During the accuracy assessment, we compared the accuracy %, the Kappa index value and the area ratio of correct pixels. According to our results, in the case of plant cover, the Spectral Angles Mapper (SAM) method achieved the best accuracy among the validated classification solutions. In contrast, the Minimum Distance (MD) method achieved the best accuracy on water surface. In terms of temporality, the best results were obtained with the individually constructed decision tree classification. Thus, it is worth developing these two directions into real-time data processing solutions.

Comparison of Portable and Bench-Top Spectrometers for Mid-Infrared Diffuse Reflectance Measurements of Soils.

Mid-infrared (MIR) spectroscopy has received widespread interest as a method to complement traditional soil analysis. Recently available portable MIR spectrometers additionally offer potential for on-site applications, given sufficient spectral data quality. We therefore tested the performance of the Agilent 4300 Handheld FTIR (DRIFT spectra) in comparison to a Bruker Tensor 27 bench-top instrument in terms of (i) spectral quality and measurement noise quantified by wavelet analysis; (ii) accuracy of partial least squares (PLS) calibrations for soil organic carbon (SOC), total nitrogen (N), pH, clay and sand content with a repeated cross-validation analysis; and (iii) key spectral regions for these soil properties identified with a Monte Carlo spectral variable selection approach. Measurements and multivariate calibrations with the handheld device were as good as or slightly better than Bruker equipped with a DRIFT accessory, but not as accurate as with directional hemispherical reflectance (DHR) data collected with an integrating sphere. Variations in noise did not markedly affect the accuracy of multivariate PLS calibrations. Identified key spectral regions for PLS calibrations provided a good match between Agilent and Bruker DHR data, especially for SOC and N. Our findings suggest that portable FTIR instruments are a viable alternative for MIR measurements in the laboratory and offer great potential for on-site applications.