4.2. Results of Mineralogical and Geochemical Analyses
Table 4 shows the mineral content from selected field samples. The sample ID is a combination of month and sequence of acquisition. We found a pH range from 2.4–2.7 for selected materials. The mineral composition of the region is dominated by sheet silicates, the mass fractions of their members kaolinite and muscovite in detected samples ranged from 39–65 wt %, obtained from XRD results. Around one quarter per sample was quartz, and the polymorph silicon variation cristobalite was detected, which can be a hint for the presence of opal.
We detected titan dioxide variations rutile, anatase, and titanite. These are typical components of sandy soil, possible indicators for heavy mineral-sands in the area, which can be found in river sediment beds. Most important, we find that goethite and jarosite dominate the AMD mineral fraction, as both are in differing amounts. Goethite dominated the classification results in the HSI maps. Former studies in the area reported similar observations, with goethite found in pH regions up to 6.5 [
21]. The authors concluded that the jarosite–goethite association shows a transition stage, for a metastable mineral converting to a stable form. We could not detect schwertmannite as an AMD proxy in the area, although we expected it in pH ranges from 3.0–4.5 [
10]. Only around 25% of the collected samples were found in this pH range, but for a few spectra, schwertmannite was detected by reflectance spectroscopy. However, XRD results were unambiguous regarding schwertmannite in these cases. It was not detected once, so we ruled it out. Some samples originating from the canyon area appeared to be jarosite in spectral investigations. Hand spectroscopy of some samples indicated a good goethite resemblance, but in pH zones of 2.7. We assumed goethite/jarosite in association, but XRD analyses of one of these structures revealed goethite or jarosite with major clay components. We found hematite only in small fractions. We perceived XRF iron values for these samples from 25–39%, showing that AMD minerals occur mainly on the top layer. With very low sulfur values (5–6%) and a Fe:S ratio of 5:1, we found jarosite as the minor constituent. We saw that the bulk of these samples, particular around the stream, was goethite and the XRD results of some local features are only true for the very small top-crust. These surface crusts/needles appear to be cemented clay-quartz grains with a very thin iron coating.
We did not identify the iron-sulfide pyrite, the progenitor mineral of the AMD cycle, by XRD. We deduce that this starting material for jarosite and further minerals should be completely degraded down to other minerals. Detection via VNIR-SWIR (Visible near infrared—short wave infrared) spectroscopy is not possible for all minerals identified (
Table 4). For example, quartz, feldspars, or pyrite show no characteristic absorption features in this range. Additionally, we assume that surfacing pyrite has long since degraded, and no surface material was left for detection by XRD.
We applied the handheld XRF in the field and on laboratory samples for a quick integration of basic elemental contents. The dominating elements we classified are aluminum, iron, sulfur, silicon, and titanium. Interesting for the study context are mainly iron and sulfur, as the sought-after AMD minerals are built of iron and sulfates. An indicator for jarosite is the 1.5 Fe:S ratio. One reference point is the correlation between Fe, S, and measured pH.
We observed high Si and Al content (from sheet silicates and quartz), but also increased sulfur and somewhat high iron values. The high correlation for Al and Si indicates a good fit for clay-type minerals. The highest iron content measured was 31 wt %. The results of XRD compared to XRF differ, because XRD samples were milled to be representative, and XRF scanning occurred on surface parts of the same samples. Furthermore, the XRD results are Rietveld-refined, to fit a library reflection peak profile on the measured profiles. We regard the XRD results, therefore, as calculated values.
The laboratory measurements with the handheld XRF introduced a signal attenuation. As the wet soil samples slurred the beryllium window of the XRF device, we applied a thin cling-foil between the sample and window. The X-ray passes through this polyethylene layer, so secondary radiation has to pass again through the foil. This resulted in an increased signal attenuation of secondary X-rays from the excited sample, as compared to vacuum or direct sample contact. Light elements up to the fourth periodic group are mainly affected by low-energy excitation and the X-ray response is severely reduced. We observed this effect during the scanning of the laboratory samples, especially aluminum and silicon, as light elements in compounds are affected. The detected X-ray response from excited shells has a lower energy for light compounds than for iron compounds.
We applied an XRF calibration with multiple reference standards to reduce this error. We utilized iron-specific laboratory standard samples for calibration [
45,
46]. A two-point calibration was conducted with these certified reference materials, where library wt % Fe was plotted against mean wt % Fe values. We deduced the calibration formula of
Fecorr = 1.0128
Feraw + 0.002718 to correct the iron readings. We achieved a correction of 2% for the iron content of XRF, however, the results should be treated as a gradient indicator.
We conducted a visually-based comparison of the continuum removed and normal reflectance spectra based on library data [
47] to sort and categorize the obtained field and laboratory spectra. We compared library and field spectra by the aid of the SAM. Exemplary for false positives is the mineral nontronite, which could not be detected by XRD, but showed a very high fit for a few corresponding field spectra. Nontronite as sheet-silicate is rare, sometimes found in alkaline soils, and was not reported in any other study regarding the Sokolov region [
34].
We chose assorted samples for specific minerals to act as proxies, according to the field spectrum, pH, mineral and iron content, surface color and location in the Litov area.
Figure 5A distributes typical field and drone-borne spectra. Exemplarily marked are absorption features, typical for OH-overtones, caused by vibrational absorption with minima between 1400–1900 nm. Around 2180–2220 nm, we observe Al-OH absorption. Between 2250–2380 nm, the visible absorption is caused by Fe-OH and Mg-OH bonds [
8,
26]. This feature is caused by metal-OH bending, which serves as strong diagnostic property for mineralogy, especially in sheet silicates [
1]. Due to the nature of the study zone, many of the probed surfaces were slightly moist, which contributes to the observed strong water absorption.
4.3. Field and UAS Spectra Combination, Resulting in the Endmember Selection
Figure 6 represents various reflectance spectra from the conducted campaigns, field and laboratory alike. Frame A displays the three principal surface materials observed in the Litov area, mostly leaves, hardpans or mineral crusts, and clays.
Figure 6B exhibits well-shaped mineral specimen representing prominent iron absorption visible, e.g., from 400–950 nm. These spectra were collected during the four campaigns in high proximity to the small river. Their SAM similarity is below 0.08 rad. The shoulders left to the 900 nm trough are varying, their peak location laying between 720 and 760 nm, with 720 nm indicating jarosite, whereas 750 nm suggests hematite and 760 nm points towards goethite. This difference between 720 nm and 760 nm is further used to discriminate between jarosite and goethite in the classified Rikola UAS-borne HSIs. The representative sample features were captured under laboratory conditions. Another diagnostic small absorption feature occurs around 436 nm for jarosite and 494 nm for goethite [
8,
26]. We compared the spectral field observations under repeatable settings, and scanned a wide range of field samples under laboratory conditions with the Rikola camera and the hand spectrometer. Naturally, surface conditions in the field can change within hours. We spread the solid samples evenly on cardboard trays, were store-dried, and scanned multiple times. A spectral library with the mean reflectance value per sample resulted. The iron absorption feature around 900 nm is prominently marked in most samples. The sample ‘‘May’’ of
Figure 6B has the distinctive jarosite absorption feature at 2264 nm [
48]. This feature, caused by Fe-OH vibration, is often found on pure powdered jarosite and mostly covered by broad OH-bending from topped clays. The XRD analysis of an adjacent sample (ID 2-014) contained 21% of jarosite, the highest measured value per sample. Spectral absorption features at 436 nm and 2264 nm are jarosite indicative and, therefore, substantiate it as an endmember.
We obtained the goethite endmember from a field sample surrounded by the local stream. We chose a hand-sized piece of solid crust, as it featured prominent enclosing sheets of hardened kaolinites and was covered by goethite on its surface (ID 4-023). Spectral measurements revealed a small 436 nm feature, but a steep incline at 494 nm, typically for goethite. Optical microscopy helped to discern the encrusted surface features, and showed the coalescence of the two different AMD minerals. The endmembers were compared with the obtained mean laboratory spectra from several ROIs per image. A further validation was conducted with USGS library spectra, targeting AMD minerals. USGS spectra Jarosite WS2501 and Goethite WS220 [
47] serve as comparable standards.
The classification, as well as the final endmember library in a wavelength range between 504–900 nm, are shown in
Figure 6C. The Rikola laboratory spectra displays the four designated endmember, applied on the UAS-borne HSIs (see upcoming section). The classification results from the lab reveals a high correlation between the observed material and library spectra.
4.4. The Rikola HSIs, Processed and Classified
HSIs resulting from the UAS Rikola are presented after processing. The HSIs can be mosaicked by the aforementioned toolbox [
14]. Although first investigations were applied on stitched mosaics, single scenes delivered more accurate and coherent classification results. Single scenes were topographically corrected and georeferenced. Another advantage is their small size and, therefore, faster processing times.
Figure 7 shows the overview of the Litov area as mosaic of different scenes. The false color RGB illustrates Rikola bands 17 (630 nm), 7 (551 nm), and 1 (504 nm) to create a natural looking image. Bright illuminations in the first few scenes on the right side were caused by a small oversaturation during the image acquisition.
The May campaign was flown manually, giving the advantage of more scenes from the same spot and a higher overlap in-between the single images, but consuming more time. The images with the highest quality, in regard of light conditions and noise, were selected from this dataset for further processing. The illumination during the very first campaign was mitigated by a considerable amount of clouds, and reduced daylight. The sprouting of vegetation and the thickening of the canopy from April to September is clearly visible in the images. NDVI investigations are possible, but have not been subject of this article.
The grade of detail possible with the UAS-borne HSIs is remarkable.
Figure 7 demonstrates the July mosaic his, overprinted with differently magnified HSI scenes from September. In the outcrop HSI with more detail, we can observe actual footsteps and tire carvings. Vegetation features, such as twigs and branches, are distinguishable. The pixel size of these HSIs varies between 3 and 4 cm. In the lower right corner, a 3D hypercube is shown for illustration, which indicates spectra of the canyon area. The right upper frame in image
Figure 7 is a false color scene from September. The RGB color-code are bands 50 (895 nm), 30 (735 nm), and 10 (575 nm). This band combination distributes vegetation as clearly distinguishable from the unusual displayed soil color. Changing band combinations can enable the user to observe, for example, different water depths. The brightness per band can differ to a certain degree.
4.5. Comparing the Spectral Classifications of UAS-Borne HSI
Examples of supervised classifications performed on single scenes are presented in this section. Only selected images are shown for practical reasons. We achieved compelling results by a combination of calculated band ratios and SAM classifications. Values below one are masked band ratios, as they contradict the purpose of mapping iron absorption. A decline of the reflectance values in a wavelength range from 720–880 nm (see
Table 1) represented the most noticeable spectral AMD features. The band ratios in
Figure 8 were calculated for 750/880 nm.
The legend in frame B is valid for C, as well. The red color class is representative for the goethite endmember, blue for jarosite as the main fraction, and green with sea green are the clay–rock mixtures. Most classifications indicate that the goethite endmember, together with clay endmember 1, do occur the most in almost every scene. The goethite class constantly reached smaller SAM values during classifications, meaning a better fit.
Frame D presents a classification result with a SAM angle of 0.08 rad and all four endmembers. A remarkable feature is the stream with clear iron absorption features. A distinction between the two iron minerals goethite and jarosite is possible. The clay–rock fraction can be observed in the northern part, and smaller areas are classified as clay. The shape of the captured stream is very similar to the band ratio in frame A, but the SAM image allows a closer look at the mineral distribution in the river bed.
Figure 8 compares mapping of the west beach and slope area. The band-ratio 720/880 nm (A and D) indicate iron absorption features in the spectrum but, in this case, to a much higher degree. The legend (frame A) shows that a ratio of four is reached, a high value in regard to other band-calculations, meaning a steep decrease from 720–880 nm. The light blue area is one, particularly low reflective submerged area with presumably high iron mineral content. Additionally, the stream is recognizable in frame A. SAM, as seen in frame B, does not map the same stream feature. As the classifications indicate, the SAM algorithm could not map the stream. One reason for this might be the high water absorption due to different water tables, but also a sensor shift in the Rikola around 650 nm. This device-specific property is unfortunate in our approach, as it creates artificial absorption and reflection features, in particular in the Rikola spectrum of April in
Figure 9.
The SAM classification on this scene captures the clay zone and coatings of the endmembers goethite and jarosite. During the field trip in May, the water table was observed to be slightly lower than in April. Possibly a thin coating of AMD minerals precipitated on the drained beach parts. The stream feature running through frames C and D is a small erosion rill with minimal water flow. The jarosite endmember with the highest jarosite content was taken in this rivulet. The rivulet was smaller than 5 cm in diameter, and surrounded by oxidized iron coatings, which can be seen in both frames. C and D in
Figure 7 illustrate a wall slope, which was carved by erosion.
The results depicted in
Figure 9 distribute four scenes from the same area, acquired in four time steps. Frames A–D respectively illustrate campaign months April, May, July, and September. The endmember legend is similar for the four frames. A noticeably difference is the stream, visible as red features in D and C, with differing amounts of pixels indicating jarosite in C, and increased goethite cover in D. The clay fraction is densely mapped in A and B, while in C and D there are broader gaps in between. Frames A and B indicate a channel seam, which contains endmember goethite. Distinguishable are the small erosion rills in the south of frame D. A larger part in the north is classified as clay-rock endmember (also referred to as sample 2-022), which is observed during field investigation. Possible reasons for the mapping differences are concluded in the following discussion section.