Abstract
Abstract. Novel miniaturized multi- and hyperspectral imaging sensors on board of unmanned aerial vehicles have recently shown great potential in various environmental monitoring and measuring tasks such as precision agriculture and forest management. These systems can be used to collect dense 3D point clouds and spectral information over small areas such as single forest stands or sample plots. Accurate radiometric processing and atmospheric correction is required when data sets from different dates and sensors, collected in varying illumination conditions, are combined. Performance of novel radiometric block adjustment method, developed at Finnish Geospatial Research Institute, is evaluated with multitemporal hyperspectral data set of seedling stands collected during spring and summer 2016. Illumination conditions during campaigns varied from bright to overcast. We use two different methods to produce homogenous image mosaics and hyperspectral point clouds: image-wise relative correction and image-wise relative correction with BRDF. Radiometric datasets are converted to reflectance using reference panels and changes in reflectance spectra is analysed. Tested methods improved image mosaic homogeneity by 5 % to 25 %. Results show that the evaluated method can produce consistent reflectance mosaics and reflectance spectra shape between different areas and dates.
Highlights
Drones equipped with various sensors can be used for collecting dense point clouds and spectral information over small forested areas, such as single stands or sample plots
Remote sensing methods traditionally used in forest inventories, such as aerial imaging or airborne laser scanning (White et al 2016) have not been able to characterize seedling stands with sufficient accuracy for operational forest management
The fundamental research question in our project is to study what is the potential of high-resolution hyperspectral and photogrammetric datasets collected using low-cost drones in automatic determination of the tree species, TPI and mean tree height of seedling stands that are the key attributes for determining the management actions, such as precommercial thinnings or re-plantings
Summary
Drones equipped with various sensors can be used for collecting dense point clouds and spectral information over small forested areas, such as single stands or sample plots. If this data could be processed and interpreted automatically, drones could be used for supporting large-area inventories or stand-wise assessments by replacing part of the required field work. The fundamental research question in our project is to study what is the potential of high-resolution hyperspectral and photogrammetric datasets collected using low-cost drones in automatic determination of the tree species, TPI and mean tree height of seedling stands that are the key attributes for determining the management actions, such as precommercial thinnings or re-plantings.
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