Above ground biomass (AGB) is a parameter commonly used for assessment of grassland systems. Destructive AGB measurements, although accurate, are time consuming and are not easily undertaken on a repeat basis or over large areas. Structure-from-Motion (SfM) photogrammetry and Terrestrial Laser Scanning (TLS) are two technologies that have the potential to yield precise 3D structural measurements of vegetation quite rapidly. Recent advances have led to the successful application of TLS and SfM in woody biomass estimation, but application in natural grassland systems remains largely untested. The potential of these techniques for AGB estimation is examined considering 11 grass plots with a range of biomass in South Dakota, USA. Volume metrics extracted from the TLS and SfM 3D point clouds, and also conventional disc pasture meter settling heights, were compared to destructively harvested AGB total (grass and litter) and AGB grass plot measurements. Although the disc pasture meter was the most rapid method, it was less effective in AGB estimation (AGB grass r 2 = 0.42, AGB total r 2 = 0.32) than the TLS (AGB grass r 2 = 0.46, AGB total r 2 = 0.57) or SfM (AGB grass r 2 = 0.54, AGB total r 2 = 0.72) which both demonstrated their utility for rapid AGB estimation of grass systems.
Thank you very much for your time and efforts. We regret that our previous response was not fully satisfactory. We have now attached the results for the models based on the combination of two libraries at the end of the response sheet and provide our reasoning for not including the additional analysis into the manuscript. We really hope our argumentation helps to accept the paper in its present form.We have uploaded a clean and a track-change version of the manuscript (minor correction of typos only). We further provide a response sheet to the comments from the reviewer.
We evaluated the effectiveness of different approaches to compensate for across-track brightness gradients within a hyperspectral image mosaic comprised of multiple flight lines in the San Francisco Bay Area. We calculated the spectral consistency of adjacent flight lines and conducted regression-based unmixing of woody-and non-woody vegetation fractions to assess the comparative benefits of the methods. Results showed that a class-wise empirical approach produced the most spectrally consistent, nearly seamless image mosaics and led to accurate vegetation fraction maps (mean absolute error = 12.6%). Overall, a class-wise empirical approach is recommended as a simple, flexible and transferable technique to compensate for brightness gradients over a global empirical approach, brightness normalization or continuum removal.
The EnMAP-Box is a free and open source QGIS plugin. It integrates the strength of Python-based image processing and machine learning with graphical interfaces for handling hyperspectral images and spectral libraries in a GIS environment.
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