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.

Merging hyperspectral data from optical and thermal ranges allows a wider variety of minerals to be mapped and thus allows lithology to be mapped in a more complex way. In contrast, in most of the studies that have taken advantage of the data from the visible (VIS), near-infrared (NIR), shortwave infrared (SWIR) and longwave infrared (LWIR) spectral ranges, these different spectral ranges were analysed and interpreted separately. This limits the complexity of the final interpretation. In this study a presentation is made of how multiple absorption features, which are directly linked to the mineral composition and are present throughout the VIS, NIR, SWIR and LWIR ranges, can be automatically derived and, moreover, how these new datasets can be successfully used for mineral/lithology mapping. The biggest advantage of this approach is that it overcomes the issue of prior definition of endmembers, which is a requested routine employed in all widely used spectral mapping techniques. In this study, two different airborne image datasets were analysed, HyMap (VIS/NIR/SWIR image data) and Airborne Hyperspectral Scanner (AHS, LWIR image data). Both datasets were acquired over the Sokolov lignite open-cast mines in the Czech Republic. It is further demonstrated that even in this case, when the absorption feature information derived from multispectral LWIR data is integrated with the absorption feature information derived from hyperspectral VIS/NIR/SWIR data, an important improvement in terms of more complex mineral mapping is achieved.

Remote sensing is one of the modern methods that have significantly developed over the last two decades and, nowadays, it provides a new means for forest monitoring. High spatial and temporal resolutions are demanded for the accurate and timely monitoring of forests. In this study, multi-spectral Unmanned Aerial Vehicle (UAV) images were used to estimate canopy parameters (definition of crown extent, top, and height, as well as photosynthetic pigment contents). The UAV images in Green, Red, Red-Edge, and Near infrared (NIR) bands were acquired by Parrot Sequoia camera over selected sites in two small catchments (Czech Republic) covered dominantly by Norway spruce monocultures. Individual tree extents, together with tree tops and heights, were derived from the Canopy Height Model (CHM). In addition, the following were tested: (i) to what extent can the linear relationship be established between selected vegetation indexes (Normalized Difference Vegetation Index (NDVI) and NDVI_{red edge}) derived for individual trees and the corresponding ground truth (e.g., biochemically assessed needle photosynthetic pigment contents) and (ii) whether needle age selection as a ground truth and crown light conditions affect the validity of linear models. The results of the conducted statistical analysis show that the two vegetation indexes (NDVI and NDVI_{red edge}) tested here have the potential to assess photosynthetic pigments in Norway spruce forests at a semi-quantitative level; however, the needle-age selection as a ground truth was revealed to be a very important factor. The only usable results were obtained for linear models when using the second year needle pigment contents as a ground truth. On the other hand, the illumination conditions of the crown proved to have very little effect on the model’s validity. No study was found to directly compare these results conducted on coniferous forest stands. This shows that there is a further need for studies dealing with a quantitative estimation of the biochemical variables of nature coniferous forests when employing spectral data that were acquired by the UAV platform at a very high spatial resolution.

Remote sensing is one of the modern methods that have significantly developed over the last two decades and nowadays provides a new means for forest monitoring. High spatial and temporal resolutions are demanded for accurate and timely monitoring of forests. In this study multi-spectral Unmanned Aerial Vehicle (UAV) images were used to estimate canopy parameters (definition of crown extent, top and height as well as photosynthetic pigment contents). The UAV images in Green, Red, Red-Edge and NIR bands were acquired by Parrot Sequoia camera over selected sites in two small catchments (Czech Republic) covered dominantly by Norway spruce monocultures. Individual tree extents, together with tree tops and heights, were derived from the Canopy Height Model (CHM). In addition, the following were tested i) to what extent can the linear relationship be established between selected vegetation indexes (NDVI and NDVIred edge) derived for individual trees and the corresponding ground truth (e.g., biochemically assessed needle photosynthetic pigment contents), and ii) whether needle age selection as a ground truth and crown light conditions affect the validity of linear models. The results of the conducted statistical analysis show that the two vegetation indexes (NDVI and NDVIred edge) tested here have a potential to assess photosynthetic pigments in Norway spruce forests at a semi-quantitative level, however the needle-age selection as a ground truth was revealed to be a very important factor. The only usable results were obtained for linear models when using the 2nd year needle pigment contents as a ground truth. On the other hand, the illumination conditions of the crown proved to have very little effect on the model’s validity. No study was found to directly compare these results conducted on coniferous forest stands. This shows that there is a further need for studies dealing with a quantitative estimation of the biochemical variables of nature coniferous forests when employing spectral data acquired by the UAV platform at a very high spatial resolution.

During the last decade, remote sensing methods have developed significantly. The technological progress in development of new sensors and techniques opened up a large scope of new applications including near-field data collecting using Unmanned Aerial Vehicles (UAVs). State-of-the-art UAV technologies provide advantages including cost-effectiveness and temporal flexibility. For our case study, we acquired the high-resolution UAV data over the archaeological site near Černouček, the Czech Republic. This site was discovered at the beginning of 1990s as a result of low altitude aerial reconnaissance carried out by the Institute of Archaeology, Czech Academy of Sciences. Two ditched enclosures were identified due to vegetation marks in late spring and early summer, as higher moisture and presence of some chemical constituents in the secondary infill of the ditches give better conditions for plants above them. In 2017, new UAV data (Red, Green and Blue: RGB and Red and Near-infrared data: Red + NIR) were acquired over the Černouček site in June to find out whether there are some other objects hidden under ground. Using the RGB data digital elevation models were derived while the Red + NIR data were used to compute vegetation indices (VI), further spatial filtering allowing enhancing the local anomalies in the VI values was employed. As a result, several small objects were detected and suggested for the further investigations.