Gila Notesco

Mineral Classification of Land Surface Using Multispectral LWIR and Hyperspectral SWIR Remote-Sensing Data. A Case Study over the Sokolov Lignite Open-Pit Mines, the Czech Republic

Remote-sensing techniques offer an efficient alternative for mapping mining environments and assessing the impacts of mining activities. Airborne multispectral data in the thermal region and hyperspectral data in the optical region, acquired with the Airborne Hyperspectral Scanner (AHS) sensor over the Sokolov lignite open-pit mines in the Czech Republic, were analyzed. The emissivity spectrum was calculated for each vegetation-free land pixel in the longwave infrared (LWIR)-region image using the surface-emitted radiation, and the reflectance spectrum was derived from the visible, near-infrared and shortwave-infrared (VNIR–SWIR)-region image using the solar radiation reflected from the surface, after applying atmospheric correction. The combination of calculated emissivity, with the ability to detect quartz, and SWIR reflectance spectra, detecting phyllosilicates and kaolinite in particular, enabled estimating the content of the dominant minerals in the exposed surface. The difference between the emissivity values at λ = 9.68 µm and 8.77 µm was found to be a useful index for estimating the relative amount of quartz in each land pixel in the LWIR image. The absorption depth at around 2.2 µm in the reflectance spectra was used to estimate the relative amount of kaolinite in each land pixel in the SWIR image. The resulting maps of the spatial distribution of quartz and kaolinite were found to be in accordance with the geological nature and origin of the exposed surfaces and demonstrated the benefit of using data from both thermal and optical spectral regions to map the abundance of the major minerals around the mines.

Modelling Diverse Soil Attributes with Visible to Longwave Infrared Spectroscopy Using PLSR Employed by an Automatic Modelling Engine

The study tested a data mining engine (PARACUDA®) to predict various soil attributes (BC, CEC, BS, pH, Corg, Pb, Hg, As, Zn and Cu) using reflectance data acquired for both optical and thermal infrared regions. The engine was designed to utilize large data in parallel and automatic processing to build and process hundreds of diverse models in a unified manner while avoiding bias and deviations caused by the operator(s). The system is able to systematically assess the effect of diverse preprocessing techniques; additionally, it analyses other parameters, such as different spectral resolutions and spectral coverages that affect soil properties. Accordingly, the system was used to extract models across both optical and thermal infrared spectral regions, which holds significant chromophores. In total, 2880 models were evaluated where each model was generated with a different preprocessing scheme of the input spectral data. The models were assessed using statistical parameters such as coefficient of determination (R2), square error of prediction (SEP), relative percentage difference (RPD) and by physical explanation (spectral assignments). It was found that the smoothing procedure is the most beneficial preprocessing stage, especially when combined with spectral derivation (1st or 2nd derivatives). Automatically and without the need of an operator, the data mining engine enabled the best prediction models to be found from all the combinations tested. Furthermore, the data mining approach used in this study and its processing scheme proved to be efficient tools for getting a better understanding of the geochemical properties of the samples studied (e.g., mineral associations).

Mapping the Spectral Soil Quality Index (SSQI) Using Airborne Imaging Spectroscopy

Soil quality (SQ) assessment has numerous applications for managing sustainable soil function. Airborne imaging spectroscopy (IS) is an advanced tool for studying natural and artificial materials, in general, and soil properties, in particular. The primary goal of this research was to prove and demonstrate the ability of IS to evaluate soil properties and quality across anthropogenically induced land-use changes. This aim was fulfilled by developing and implementing a spectral soil quality index (SSQI) using IS obtained by a laboratory and field spectrometer (point scale) as well as by airborne hyperspectral imaging (local scale), in two experimental sites located in Israel and Germany. In this regard, 13 soil physical, biological, and chemical properties and their derived soil quality index (SQI) were measured. Several mathematical/statistical procedures, consisting of a series of operations, including a principal component analysis (PCA), a partial least squares-regression (PLS-R), and a partial least squares-discriminate analysis (PLS-DA), were used. Correlations between the laboratory spectral values and the calculated SQI coefficient of determination (R2) and ratio of performance to deviation (RPD) were R2 = 0.84; RPD = 2.43 and R2 = 0.78; RPD = 2.10 in the Israeli and the German study sites, respectively. The PLS-DA model that was used to develop the SSQI showed high classification accuracy in both sites (from laboratory, field, and imaging spectroscopy). The correlations between the SSQI and the SQI were R2 = 0.71 and R2 = 0.7, in the Israeli and the German study sites, respectively. It is concluded that soil quality can be effectively monitored using the spectral-spatial information provided by the IS technology. IS-based classification of soils can provide the basis for a spatially explicit and quantitative approach for monitoring SQ and function at a local scale.

Mineral Classification of Makhtesh Ramon in Israel Using Hyperspectral Longwave Infrared (LWIR) Remote-Sensing Data

Hyperspectral remote-sensing techniques offer an efficient procedure for mineral mapping, with a unique hyperspectral remote-sensing fingerprint in the longwave infrared spectral region enabling identification of the most abundant minerals in the continental crust—quartz and feldspars. This ability was examined by acquiring airborne data with the AisaOWL sensor over the Makhtesh Ramon area in Israel. The at-sensor radiance measured from each pixel in a longwave infrared image represents the emissivity, expressing chemical and physical properties such as surface mineralogy, and the atmospheric contribution which is expressed differently during the day and at night. Therefore, identifying similar features in day and night radiance enabled identifying the major minerals in the surface—quartz, silicates (feldspars and clay minerals), gypsum and carbonates—and mapping their spatial distribution. Mineral identification was improved by applying the radiance of an in situ surface that is featureless for minerals but distinctive for the atmospheric contribution as a gain spectrum to each pixel in the image, reducing the atmospheric contribution and emphasizing the mineralogical features. The results were in agreement with the mineralogy of selected rock samples collected from the study area as derived from laboratory X-ray diffraction analysis. The resulting mineral map of the major minerals in the surface was in agreement with the geological map of the area.

Integration of Hyperspectral Shortwave and Longwave Infrared Remote-Sensing Data for Mineral Mapping of Makhtesh Ramon in Israel

Hyperspectral remote-sensing in the reflected infrared and thermal infrared regions offers a unique and efficient alternative for mineral mapping, as most minerals exhibit spectral features in these regions, mainly in the shortwave and longwave infrared. Airborne hyperspectral data in both spectral regions, acquired with the AisaFENIX and AisaOWL (Specim) sensors over Makhtesh Ramon in Israel, were analyzed. Calculating the reflectance and emissivity spectra of each pixel in the shortwave infrared and longwave infrared region images, respectively, and determining mineral indices enabled identifying the dominant minerals in this area—kaolinite, calcite, dolomite, quartz, feldspars and gypsum—and mapping their spatial distribution in the surface. The benefit of using hyperspectral data from both reflected infrared and thermal infrared regions to improve mineral identification was demonstrated.

An automated procedure for reducing atmospheric features and emphasizing surface emissivity in hyperspectral longwave infrared (LWIR) images

ABSTRACT The at-sensor radiance of a pixel in a longwave infrared (LWIR) image represents the surface temperature, the surface emissivity, which is similar during the day and at night, and the atmospheric contribution, which is expressed differently during the day and at night. Based on this, an automated procedure, which locates pixels for which each absorption feature in their radiance during the day appears as an emission feature at night, indicative of atmospheric contribution, was developed. The average day or night spectrum of these indicative pixels was applied as a gain factor spectrum to the entire day or night image, respectively, reducing the atmospheric contribution and emphasizing the surface spectral features, represented by emissivity, of each pixel in the image. The procedure was examined on LWIR hyperspectral data cubes acquired over two different areas, and enabled effective reduction of the atmospheric features in both area data sets.

Using Visible Spectral Information to Predict Long-Wave Infrared Spectral Emissivity: A Case Study over the Sokolov Area of the Czech Republic with an Airborne Hyperspectral Scanner Sensor

Remote-sensing platforms are often comprised of a cluster of different spectral range detectors or sensors to benefit from the spectral identification capabilities of each range. Missing data from these platforms, caused by problematic weather conditions, such as clouds, sensor failure, low temporal coverage or a narrow field of view (FOV), is one of the problems preventing proper monitoring of the Earth. One of the possible solutions is predicting a detector or sensor’s missing data using another detector/sensor. In this paper, we propose a new method of predicting spectral emissivity in the long-wave infrared (LWIR) spectral region using the visible (VIS) spectral region. The proposed method is suitable for two main scenarios of missing data: sensor malfunctions and narrow FOV. We demonstrate the usefulness and limitations of this prediction scheme using the airborne hyperspectral scanner (AHS) sensor, which consists of both VIS and LWIR spectral regions, in a case study over the Sokolov area, Czech Republic.

Mineral mapping of makhtesh ramon in israel using hyperspectral remote sensing day and night LWIR images

Hyperspectral remote sensing in the thermal infrared region has been acknowledged as an innovative tool for earth environmental studies that complements the optical spectral region. The current study focuses on mapping surface mineral content using day and night airborne data in the longwave infrared (LWIR) spectral region over a well-known mineralogical site in Israel. Data were acquired with the AisaOWL hyperspectral sensor over Makhtesh Ramon in the Negev desert in southern Israel. Major minerals could be identified by locating similarities in day and night atsensor radiance spectra. The analysis resulted in the classification of quartz, carbonates, gypsum, kaolinite and other silicates according to their observed spectral features in both day and night data.

Using Multispectral Remote Sensing in the TIR Region for Monitoring the Environment over Mining Area

Remote sensing techniques using VIS-NIR-SWIR-TIR sensors, offer a unique opportunity to collect necessary spatial parameters that play a key role for better assessments of mining related environmental impacts. The TIR HSR sensors data, as they are still not well investigated, may contribute to characterize the necessary parameters. An atmospheric correction of TIR (LWIR) data, taken with the AHS multispectral sensor over the Sokolov area in the Czech Republic, was performed. Surface kinetic temperature and emissivity values of the study area were calculated. Some important parameters such as Apparent Thermal Inertia and soil sand and clay content were derived from the corrected data set. The ongoing analysis of the TIR HSR sensors data, ground measurements data and laboratory studies will contribute an additional data layer to the mapping of mining related environmental impacts.