Image data sharpening is a challenging field of remote sensing science, which has become more relevant as high spatial-resolution satellites and superspectral sensors have emerged. Although the spectral property is crucial for mineral mapping, spatial resolution is also important as it allows targeted minerals/rocks to be identified/interpreted in a spatial context. Therefore, improving the spatial context, while keeping the spectral property provided by the superspectral sensor, would bring great benefits for geological/mineralogical mapping especially in arid environments. In this paper, a new concept was tested using superspectral data (ASTER) and high spatial-resolution panchromatic data (WorldView-2) for image fusion. A modified Principal Component Analysis (PCA)-based sharpening method, which implements a histogram matching workflow that takes into account the real distribution of values, was employed to test whether the substitution of Principal Components (PC1–PC4) can bring a fused image which is spectrally more accurate. The new approach was compared to those most widely used—PCA sharpening and Gram–Schmidt sharpening (GS), both available in ENVI software (Version 5.2 and lower) as well as to the standard approach—sharpening Landsat 8 multispectral bands (MUL) using its own panchromatic (PAN) band. The visual assessment and the spectral quality indicators proved that the spectral performance of the proposed sharpening approach employing PC1 and PC2 improve the performance of the PCA algorithm, moreover, comparable or better results are achieved compared to the GS method. It was shown that, when using the PC1, the visible-near infrared (VNIR) part of the spectrum was preserved better, however, if the PC2 was used, the short-wave infrared (SWIR) part was preserved better. Furthermore, this approach improved the output spectral quality when fusing image data from different sensors (e.g., ASTER and WorldView-2) while keeping the proper albedo scaling when substituting the second PC.

Systematic quantification and monitoring of forest biophysical and biochemical variables is required to assess the response of ecosystems to climate change and gain a deeper understanding of the carbon cycle. Red-Edge Position (REP) is a hyperspectrally detectable parameter, which is sensitive to Chlorophyll (Chl) content. In the current study, REP was modelled for Norway spruce Forest canopy Reflectance and Transmittance (FRT) using Radiative Transfer Modelling (RTM) (resampled to HyMap and Sentinel-2 spectral resolution) as well as calculated from the real HyMap and simulated Sentinel-2 image data. Different REP extraction methods (PF, LE, 4PLI and its optimized versions for HyMap and Sentinel-2 spectral resolution) were assessed. The lowest differences in REP values calculated from image-extracted spectra and from the theoretical RTM simulations were found for the 4PLI method including its HyMap and Sentinel-2 optimized versions (4PLIH and 4PLIS). Despite its simplicity, the 4PLI REP extraction technique demonstrated its potential usefulness for estimating canopy chlorophyll (Chl × LAI) content using both airborne hyperspectral (HyMap) data as well as space-borne Sentinel-2 image data.

The study focuses on spatio-temporal changes in the physiological status of the Norway spruce forests located at the central and western parts of the Ore Mountains (northwestern part of the Czech Republic), which suffered from severe environmental pollution from the 1970s to the 1990s. The situation started improving after the pollution loads decreased significantly at the end of the 1990s. The general trends in forest recovery were studied using the tasseled cap transformation and disturbance index (DI) extracted from the 1985–2015 time series of Landsat data. In addition, 16 vegetation indices (VIs) extracted from airborne hyperspectral (HS) data acquired in 1998 using the Advanced Solid-State Array Spectroradiometer (ASAS) and in 2013 using the Airborne Prism Experiment (APEX) were used to study changes in forest health. The forest health status analysis of HS image data was performed at two levels of spatial resolution; at a tree level (original 2.0 m spatial resolution), as well as at a forest stand level (generalized to 6.0 m spatial resolution). The temporal changes were studied primarily using the VOG₁ vegetation index (VI) as it was showing high and stable sensitivity to forest damage for both spatial resolutions considered. In 1998, significant differences between the moderately to heavily damaged (central Ore Mountains) and initially damaged (western Ore Mountains) stands were detected for all the VIs tested. In 2013, the stands in the central Ore Mountains exhibited VI values much closer to the global mean, indicating an improvement in their health status. This result fully confirms the finding of the Landsat time series analysis. The greatest difference in Disturbance Index (DI) values between the central (1998: 0.37) and western Ore Mountains stands (1998: −1.21) could be seen at the end of the 1990s. Nonetheless, levelling of the physiological status of Norway spruce was observed for the central and western parts of the Ore Mountains in 2013 (mean DI values −1.04 (western) and −0.66 (central)). Although the differences between originally moderately-to-heavily damaged, and initially damaged stands generally levelled out by 2013, it is still possible to detect signs of the previous damage in some cases.