Ground-based Hyperspectral Imaging Research Articles

Classification of Garlic (Allium sativum L.) Crops by Fertilizer Differences Using Ground-Based Hyperspectral Imaging System

In contemporary agriculture, enhancing the efficient production of crops and optimizing resource utilization have become paramount objectives. Garlic growth and quality are influenced by various factors, with fertilizers playing a pivotal role in shaping both aspects. This study aimed to develop classification models for distinguishing garlic fertilizer application differences by employing statistical and machine learning techniques, such as partial least squares (PLS), based on data acquired from a ground-based hyperspectral imaging system in the agricultural sector. The garlic variety chosen for this study was Hongsan, and the fertilizer application plots were segmented into three distinct sections. Data were acquired within the VIS/NIR wavelength range using hyperspectral imaging. Following data acquisition, the standard normal variate (SNV) pre-processing technique was applied to enhance the dataset. To identify the optimal wavelengths, various techniques such as sequential forward selection (SFS), successive projections algorithm (SPA), variable importance in projection (VIP), and interval partial least squares (iPLS) were employed, resulting in the selection of 12 optimal wavelengths. For the fertilizer application difference model, six integrated vegetation indices were chosen for comparison with existing growth indicators. Using the same methodology, the model construction showed accuracies of 90.7% for PLS. Thus, the proposed model suggests that efficient regulation of garlic fertilizer application can be achieved by utilizing statistical and machine learning techniques.

Measurements of Methane Emissions from a Biofertilizer Storage Tank Using Ground-Based Hyperspectral Imaging and Flux Chambers.

Open storages of organic material represent potentially large sources of the greenhouse gas methane (CH4), an emissions source that will likely become more common as a part of societal efforts toward sustainability. Hence, monitoring and minimizing CH4 emissions from such facilities are key, but effective assessment of emissions without disturbing the flux is challenging. We demonstrate the capacity of using a novel high-resolution hyperspectral camera to perform sensitive CH4 flux assessments at such facilities, using as a test case a biofertilizer storage tank for residual material from a biogas plant. The camera and simultaneous conventional flux chamber measurements showed emissions of 6.0 ± 1.3 and 13 ± 5.7 kg of CH4 h-1, respectively. The camera measurements covered the whole tank surface of 1104 m2, and the chamber results were extrapolated from measurements over 5 m2. This corresponds to 0.7-1.4% of the total CH4 production at the biogas plant (1330 N m3 h-1 corresponding to 950 kg h-1). The camera could assess the entire tank emission in minutes without disturbing normal operations at the plant and revealed additional unknown emissions from the inlet to the tank (17 g of CH4 h-1) and during the loading of the biofertilizer into trucks (3.1 kg of CH4 h-1 during loading events). This study illustrates the importance of adequate measurement capacity to map methane fluxes and to verify that methane emission mitigation efforts are effective. Given the high methane emissions observed, it is important to reduce methane emissions from open storage of organic material, for example by improved digestion in the biogas reactor, precooling of sludge before storage, or building gastight storage tanks with sealed covers. We conclude that hyperspectral, ground-based remote sensing is a promising approach for greenhouse gas monitoring and mitigation.

A Gated Content-Oriented Residual Dense Network for Hyperspectral Image Super-Resolution

Limited by the existing imagery sensors, a hyperspectral image (HSI) is characterized by its high spectral resolution but low spatial resolution. HSI super-resolution (SR) aims to enhance the spatial resolution of the HSIs without modifying the equipment and has become a hot issue for HSI processing. In this paper, inspired by two important observations, a gated content-oriented residual dense network (GCoRDN) is designed for the HSI SR. To be specific, based on the observation that the structure and texture exhibit different sensitivities to the spatial degradation, a content-oriented network with two branches is designed. Meanwhile, a weight-sharing strategy is merged in the network to preserve the consistency in the structure and the texture. In addition, based on the observation of the super-resolved results, a gating mechanism is applied as a form of post-processing to further enhance the SR performance. Experimental results and data analysis on both ground-based HSIs and airborne HSIs have demonstrated the effectiveness of the proposed method.

Ground-Based Hyperspectral Image Surveillance System for Explosive Detection: Methods, Experiments, and Comparisons

Ground-Based Hyperspectral Image Surveillance System for Explosive Detection: Methods, Experiments, and Comparisons

Ground-based visible–near infrared hyperspectral imaging for monitoring cliff weathering of a volcanic island in Dokdo, South Korea

Coastal cliffs undergo erosion and weathering more rapidly under the influence of strong waves and sea winds, leading to stability and environmental conservation issues. Ground-based hyperspectral imaging is useful for the identification and geological interpretation of minerals or rocks in vertical outcrops that are difficult to confirm from an aerial view or through in situ investigation for safety reasons. High spatial and spectral resolutions of visible–near infrared (VNIR) sensors can be advantageous for detecting weathering in cliffs made of volcanic rocks; however, their potential is not well known. In this study, two classification techniques, mixture-tuned matched filtering (MTMF) and support vector machine (SVM), were applied to VNIR hyperspectral data of the cliff face of a volcanic island in Dokdo, South Korea, and the classification results were compared. Results show that SVM is superior to MTMF for the classification of volcanic rocks and weathering minerals. The distinction between volcanic rocks with similar compositions and textures deteriorated using both methods. The shading of the surface owing due to unevenness and stratification also affected the accuracy of classification. This study shows that ground-based VNIR hyperspectral image analysis is a powerful and an effective approach to predict possible geomorphological changes and safety on volcanic islands, as it can explore the weathering of sea cliffs and highlight potentially vulnerable locations.

The Impacts of Air Quality on Vegetation Health in Dense Urban Environments: A Ground-Based Hyperspectral Imaging Approach

We examine the impact of changes in ozone (O3), particulate matter (PM2.5), temperature, and humidity on the health of vegetation in dense urban environments, using a very high-resolution, ground-based Visible and Near-Infrared (VNIR, 0.4–1.0 μm with a spectral resolution of 0.75 nm) hyperspectral camera deployed by the Urban Observatory (UO) in New York City. Images were captured at 15 min intervals from 08h00 to 18h00 for 30 days between 3 May and 6 June 2016 with each image containing a mix of dense built structures, sky, and vegetation. Vegetation pixels were identified using unsupervised k-means clustering of the pixel spectra and the time dependence of the reflection spectrum of a patch of vegetation at roughly 1 km from the sensor that was measured across the study period. To avoid illumination and atmospheric variability, we introduce a method that measures the ratio of vegetation pixel spectra to the spectrum of a nearby building surface at each time step relative to that ratio at a fixed time. This “Compound Ratio” exploits the (assumed) static nature of the building reflectance to isolate the variability of vegetation reflectance. Two approaches are used to quantify the health of vegetation at each time step: (a) a solar-induced fluorescence indicator (SIFi) calculated as the simple ratio of the amplitude of the Compound Ratio at 0.75 μm and 0.9 μm, and (b) Principal Component Analysis (PCA) decomposition designed to capture more global spectral features. The time dependence of these vegetation health indicators is compared to that of O3, PM2.5, temperature, and humidity values from a distributed and publicly available in situ air quality sensor network. Assuming a linear relationship between vegetation health indicators and air quality indicators, we find that changes in both SIF indicator values and PC amplitudes show a strong correlation (r2 value of 40% and 47%, respectively) with changes in air quality, especially in comparison with nearby buildings used as controls (r2 value of 1% and 4%, respectively, and with all molecular correlations consistent with zero to within 3σ uncertainty). Using the SIF indicator, O3 and temperature exhibit a positive correlation with changes in photosynthetic rate in vegetation, while PM2.5 and humidity exhibit a negative correlation. We estimate full covariant uncertainties on the coefficients using a Markov Chain Monte Carlo (MCMC) approach and demonstrate that these correlations remain statistically significant even when controlling for the effects of diurnal sun-sensor geometry and temperature variability. This work highlights the importance of quantifying the effects of various air quality parameters on vegetation health in urban environments in order to uncover the complexity, covariance, and interdependence of the numerous factors involved.

A comparison of ground-based hyperspectral imaging and red-edge multispectral imaging for façade material classification

This paper compares the feasibility of classifying building façade materials in situ via a low-cost multispectral imaging camera with 5 bands compared to a high-cost, short-wave infrared hyperspectral unit with 240 bands. The sensors were used to classify five common façade materials: brick, mortar, stone, painted window frames, and rendering. The data were subjected to a Partial Least Squares Discriminant Analysis by selecting 95 training pixels and 65 validation pixels for each material. Across all materials, the hyperspectral imaging demonstrated a moderate advantage over multispectral imaging (with a correct classification rate of 99% versus 80%) for the selected data and stronger qualitative matching to the entire façade. While the hyperspectral data were consistently superior, the multispectral data still produced valuable results, thereby demonstrating potential as a fast, easy, and much cheaper technology compared to hyperspectral units for in situ façade materials classification.

Hyperspectral fluorescence imaging: Robust detection of petroleum in porous sedimentary rock formations

Examining hand samples can be a necessary step for geologic studies, and effective mapping of such samples can be achieved through the high spectral and spatial resolutions of ground-based hyperspectral imaging (HSI) at the millimeter to centimeter scale. We have developed a simple approach to crude oil identification and characterization — feasible in 16 h — based on hyperspectral data collected under ultraviolet (UV) lighting and normalized with respect to the fluorescence patterns of the Spectralon diffuse reflectance material. The samples under consideration were extracted from a core acquired from an Early Cretaceous bituminous sandstone formation in the Athabasca Basin located near Fort McMurray, Alberta, Canada. This basin contains the largest natural bitumen deposit in the world, where surface mining operations currently are viable only for approximately 20% of the estimated 164 billion barrels of total recoverable oil reserves. This deposit is unique in that its tar sands are water-wet, which facilitates the separation of bitumen from the sandstone via water-based gravity separation. However, large amounts of water are still required for oil recovery; therefore, a fast and reliable way to mark portions of the deposit where ample petroleum has accumulated and assess its extractability based on its physical characteristics prior to mining can be helpful for optimizing resource usage. For this reason, we test and visually develop the ability of three classification methods — the spectral angle mapper, support vector machine, and supervised neural network — to distinguish among bitumen, Spectralon, and a nonfluorescent slate background based on the emission of visible light in response to absorbing UV light of different wavelengths. We also adopt spectral indices useful for indicating concentrated bitumen in tar sands. Errors inherent to the methodology are discussed along with ways to mitigate them. After accounting for these, HSI can be a valuable asset alongside other techniques used for production economics evaluation.

Adaptive Nonnegative Sparse Representation for Hyperspectral Image Super-Resolution

As the hyperspectral images (HSIs) usually have a low spatial resolution, HSI super-resolution has recently attracted more and more attention to enhance the spatial resolution of HSIs. A common method is to fuse the low-resolution (LR) HSI with a multispectral image (MSI) whose spatial resolution is higher than the HSI. In this article, we proposed a novel adaptive nonnegative sparse representation-based model to fuse an HSI and its corresponding MSI. First, basing the linear spectral unmixing, the nonnegative structured sparse representation model estimates the sparse codes of the desired high-resolution HSI from both the LR-HSI and the MSI. Then, the adaptive sparse representation can balance the relationship between the sparsity and collaboration by generating a suitable coefficient. Finally, in order to obtain more accurate results, we alternately optimize the spectral basis and coefficients rather than keeping the spectral basis fixed. The alternating direction method of multipliers is applied to solve the proposed optimization problem. The experimental results on both ground-based HSIs and real remote sensing HSIs show the superiority of our proposed approach to some other state-of-the-art HSI super-resolution methods.

지상기반 초분광 영상을 이용한 탄산염 광산 노두 광물 분포도 작성

지상기반 초분광 영상 기술을 이용해 경기광업과 가사리 탄산염암 노두를 대상으로 광물 탐지 성능을 평가하고 광물 분포도를 작성하였다. 연속체 제거 기법은 CO₃, Al-OH에 의한 광물 고유의 흡수 특징을 쉽고 빠르게 탐지할 수 있었지만 세분화된 광물 분류는 어려웠다. 반면 다중 범위 분광특징근사 기법은 백운석, 방해석, 마그네사이트와 같은 탄산염 광물들 뿐만 아니라 고령석, 일라이트와 같은 층상 규산염 광물들을 세부적으로 분류할 수 있었다. 현장 관찰과 암석시료에 대한 X선 회절 분석 및 분광측정 결과들은 초분광 영상을 이용한 탄산염 광물 분류 결과에 대한 높은 정확도를 보여준다. 초분광 영상으로부터 획득되는 분광학적 특징들은 탄산염 광물의 유기물 함량, 입자크기, 분급, 다공성과 같은 물리적, 화학적 특성과 관련이 있기 때문에 초분광 영상기술은 탄산염 노두를 해석하는데 있어 현장 관찰과 화학분석과 같은 전통적 탐사기법과 함께 상호보완적으로 활용될 수 있을 것이다. 지상기반 초분광 영상 기술은 특히 직접적인 조사가 어려운 높은 수직 노두에 대해 광물 정보를 얻거나 층서 및 구조 분석과 관련된 지질매핑이나 해석을 할 때 효과적으로 사용 될 수 있을 것으로 보인다.

Detection of Grapevine Leafroll-Associated Virus 1 and 3 in White and Red Grapevine Cultivars Using Hyperspectral Imaging

Grapevine leafroll disease (GLD) is considered one of the most widespread grapevine virus diseases, causing severe economic losses worldwide. To date, six grapevine leafroll-associated viruses (GLRaVs) are known as causal agents of the disease, of which GLRaV-1 and -3 induce the strongest symptoms. Due to the lack of efficient curative treatments in the vineyard, identification of infected plants and subsequent uprooting is crucial to reduce the spread of this disease. Ground-based hyperspectral imaging (400–2500 nm) was used in this study in order to identify white and red grapevine plants infected with GLRaV-1 or -3. Disease detection models have been successfully developed for greenhouse plants discriminating symptomatic, asymptomatic, and healthy plants. Furthermore, field tests conducted over three consecutive years showed high detection rates for symptomatic white and red cultivars, respectively. The most important detection wavelengths were used to simulate a multispectral system that achieved classification accuracies comparable to the hyperspectral approach. Although differentiation of asymptomatic and healthy field-grown grapevines showed promising results further investigations are needed to improve classification accuracy. Symptoms caused by GLRaV-1 and -3 could be differentiated.

Quantitative analysis of facies variation using ground-based lidar and hyperspectral imaging in Mississippian limestone outcrop near Jane, Missouri

Ground-based hyperspectral imaging is useful for geologic mapping because of its high spectral and spatial resolutions at a millimeter to centimeter scale. We have used hyperspectral and terrestrial laser scanner (TLS) data collected in close range to a roadcut near Jane, Missouri, that contains a subvertical outcrop of Lower Mississippian limestone. The outcrop consists of the Compton, Northview, and Pierson Formations, which we evaluated for facies heterogeneity. The sequence near Jane, Missouri, was deposited in shelf margin with high-frequency sea-level fluctuations. These fluctuations introduced lithologic and geometric heterogeneity to the facies, and debris flows brought in carbonate mounds referred to as outrunner blocks. These are important to interpret accurately because of their equivocal depositional origin, which is highly debated by previous workers. We combined hyperspectral data with TLS for an integrated spatial analysis of geometric and compositional variations in facies by accurate, point cloud-registered mineralogical mapping. We mapped several carbonate facies based on spectral signatures of calcite, silt, and clay particles and distinguished pure limestone outrunner blocks from surrounding mud-prone limestone facies with various proportions of silt and clay (a total of approximately 60%). By tracing the classified facies from combined hyperspectral and TLS imagery, we produced a lithostratigraphic framework, which indicates rapid changes in lithology and the presence of shale baffles that vary the character of the Compton through Pierson interval and contribute to heterogeneity in this outcrop. The data suggest a lower energy depositional environment and support the hypothesis of transported outrunner blocks in a distally steepened ramp system. The information that we have evaluated in our study could help to explain reservoir heterogeneity in equivalent carbonate fields.

Satellite-ground integrated destriping network: A new perspective for EO-1 Hyperion and Chinese hyperspectral satellite datasets

From the EO-1 Hyperion imaging spectrometer to the newly launched Chinese satellite hyperspectral imagers, stripe noise is a ubiquitous phenomenon that seriously degrades the data quality and usability. Although previous efforts have achieved inspiring results, hyperspectral image (HSI) destriping remains a challenging task, as the stripe degradations are sometimes more complicated than the predefined assumptions, i.e., the preselected reference, filter, or handcrafted priors. With the rapid advances in deep learning technologies, convolutional neural networks (CNNs) provide a new potential to learn essential priors in an automatic manner. However, the training of CNNs is highly reliant on a large high-quality standard dataset, which is difficult to acquire for hyperspectral spaceborne sensors. In this paper, an innovative approach termed the satellite-ground integrated destriping network (SGIDN) is proposed for HSIs. Rather than using self-training, a satellite-ground integrated strategy is proposed, for the first time, to mitigate the data dependency, so that a large set of striped-clean pairs is generated from the ground-based HSIs. Considering the varied stripes among different bands, a unique CNN architecture design, including the combination of 3D convolution and 2D convolution, residual learning, and supplementary gradient channels, is integrated to capture the intrinsic spectral-spatial features in the HSIs and the unidirectional property of stripe noise. Compared with the traditional methods, SGIDN can be flexibly extended to specific HSI destriping tasks, e.g., coexisting horizontal and vertical stripes, and generalizes well to different hyperspectral satellite sensors. Given the same study area (Shanghai, China), three HSIs acquired by the EO-1 Hyperion imaging spectrometer, the Chinese HJ-1A HSI sensor, and the wide-range hyperspectral imager onboard the Chinese SPARK spectral micro-nano satellite are adopted to assess the proposed SGIDN model. Both simulated and real-data experiments confirm that SGIDN can consistently outperform the benchmark methods, with a higher degree of efficiency. Moreover, the land-cover mapping results further demonstrate the necessity of destriping and the suitability of the destriped results for use in further applications.

Retrieval of NO2 Column Amounts from Ground-Based Hyperspectral Imaging Sensor Measurements

Total column amounts of NO2 (TCN) were estimated from ground-based hyperspectral imaging sensor (HIS) measurements in a polluted urban area (Seoul, Korea) by applying the radiance ratio fitting method with five wavelength pairs from 400 to 460 nm. We quantified the uncertainty of the retrieved TCN based on several factors. The estimated TCN uncertainty was up to 0.09 Dobson unit (DU), equivalent to 2.687 × 1020 molecules m−2) given a 1° error for the observation geometries, including the solar zenith angle, viewing zenith angle, and relative azimuth angle. About 0.1 DU (6.8%) was estimated for an aerosol optical depth (AOD) uncertainty of 0.01. In addition, the uncertainty due to the NO2 vertical profile was 14% to 22%. Compared with the co-located Pandora spectrophotometer measurements, the HIS captured the temporal variation of the TCN during the intensive observation period. The correlation between the TCN from the HIS and Pandora also showed good agreement, with a slight positive bias (bias: 0.6 DU, root mean square error: 0.7 DU).

Ground-Based Hyperspectral Image Surveillance Systems for Explosive Detection: Part II—Radiance to Reflectance Conversions

The usage of white references for reflectance conversion in a captured scene for potential ground-based hyperspectral image surveillance systems suffers from security, as it exposes the location of the system, and could be expensive for a broad application due to the high prices of standard Spectralons. In Part I of this two-part article, the state-of-the-art of ground-based hyperspectral imaging systems for explosive trace detection was discussed along with the future challenges for generic surveillance systems. The part II of the article proposes different approaches for reflection conversion to compensate the usage of white references at every capturing instant during surveillance, which is one of the main challenges of these systems. As an initial approach, recording the radiance of white reference only at the initialization of the system is tested by capturing an experimental setup in different days and times throughout a duration of two months. The correlations between the incident lights obtained from white references at different times, days, and orientations are compared. The effects of using a prerecorded white radiance for the reflectance conversion in the current scene are investigated. Second, a practical approach for reflectance calculation is tested by employing cheap-price reference objects having smooth characteristics in short-wave infrared spectrum, instead of expensive Spectralons. Finally, a novel reflectance conversion method is developed by using the reflectance of the static background materials in the scene rather than standard references by considering the stationary characteristics of surveillance systems. The proposed approaches are compared and verified on the baseline detection methods.

Ground-Based Hyperspectral Image Surveillance Systems for Explosive Detection: Part I—State of the Art and Challenges

Existing ground-based hyperspectral imaging (HSI) systems provide a good potential for standoff detection of explosives and their traces. The current technology, however, has still essential challenges to achieve a generic surveillance system for dynamic scenes with moving vehicles. In this regard, part I of this two-part article presents a state-of-the-art of existing HSI systems for explosive detection and discusses the future challenges for such a surveillance system. Considering the utilization of a light source and the operating spectral region, the presented overview classifies the related HSI systems as active and passive systems in the long-wave and short-wave infrared spectra, and investigates the methods in each class with respect to the targeted explosives, illumination and capturing devices, target detection algorithms, and performance evaluation methodologies. The investigation has revealed the major challenges for a generic surveillance system as 1) a thorough experimental performance validation with respect to time, date, and orientation, 2) sufficient acquisition speeds to capture moving vehicles, 3) registration and regulation of different spectral bands captured at different positions of the movement in a dynamic scene, 4) lower false positive rates required for the dynamic scenes compared to static control points, and 5) white reference compensations for the reflectance conversion in a continuous and secure surveillance system. The companion part II article then provides different solutions for reflection conversion to eliminate the dependencies to the white references for an economic and sustainable system.

Hyperspectral imaging and neural networks to classify herbicide-resistant weeds

A segment of the field of precision agriculture is being developed to accurately and quickly map the location of herbicide-resistant and herbicide-susceptible weeds using advanced optics and computer algorithms. In our previous paper, we classified herbicide-susceptible and herbicide-resistant kochia [ Bassia scoparia (L.) Schrad.] using ground-based hyperspectral imaging and a support vector machine learning algorithm, achieving classification accuracies of up to 80%. In our current work, we imaged kochia along with marestail (also called horseweed) [ Conyza canadensis (L.) Cronquist] and common lambsquarters ( Chenopodium album L.) and the crops barley, corn, dry pea, garbanzo, lentils, pinto bean, safflower, and sugar beet, all of which were grown at the Southern Agricultural Research Center in Huntley, Montana. These plants were imaged using both ground-based and drone-based hyperspectral imagers and were classified using a neural network machine learning algorithm. Depending on what plants were imaged, the age of the plants, and lighting conditions, the classification accuracies ranged from 77% to 99% for spectra acquired on our ground-based imaging platform and from 25% to 79% on our drone-based platform. These accuracies were generally highest when imaging younger plants.

Integrated Hyperspectral and Geochemical Study of Sediment-Hosted Disseminated Gold at the Goldstrike District, Utah

The Goldstrike district in southwest Utah is similar to Carlin-type gold deposits in Nevada that are characterized by sediment-hosted disseminated gold. Suitable structural and stratigraphic conditions facilitated precipitation of gold in arsenian pyrite grains from ascending gold-bearing fluids. This study used ground-based hyperspectral imaging to study a core drilled in the Goldstrike district covering the basal Claron Formation and Callville Limestone. Spectral modeling of absorptions at 2340, 2200, and 500 nm allowed the extraction of calcite, clay minerals, and ferric iron abundances and identification of lithology. This study integrated remote sensing and geochemistry data and identified an optimum stratigraphic combination of limestone above and siliciclastic rocks below in the basal Claron Formation, as well as decarbonatization, argillization, and pyrite oxidation in the Callville Limestone, that are related with gold mineralization. This study shows an example of utilizing ground-based hyperspectral imaging in geological characterization, which can be broadly applied in the determination of mining interests and classification of ore grades. The utilization of this new terrestrial remote sensing technique has great potentials in resource exploration and exploitation.

DISCRIMINATION AND CHARACTERIZATION OF PROMINENT DESERTIC VEGETATIONS USING HYPERSPECTRAL IMAGING DATA

Abstract. Imaging Hyperspectral data are advent as potential solutions in modeling, discrimination and mapping of vegetation species. Hyperspectral remote sensing provides valuable information about vegetation type, leaf area index, chlorophyll, and leaf nutrient concentration. Estimation of these vegetation parameters has been made possible by calculating various vegetation indices (VIs), usually by ratioing, differencing, ratioing differences and combinations of suitable spectral band. This paper presents a ground-based hyperspectral imaging system for characterizing vegetation spectral features. In this study, a ground-based hyperspectral imaging data (AISA VNIR 400–960 nm, Spectral Resolution @ 2.5 nm) was used for spectral vegetation discrimination and characterization of natural desertic tree species. This study assessed the utility of hyperspectral imagery of 240 narrow bands in discrimination and classification of desert tree species in Jodhpur region using ENVI software. Vegetation indices derived from hyperspectral images used in the Analysis for tree species classification discrimination study. Prominent occurring two desertic tree species, viz., Neem and Babul in Jodhpur region could be effectively discriminated. Study demonstrated the potential utility of narrow spectral bands of Hyperspectral Imaging data in discriminating vegetation species in a desertic terrain.

Mineral Classification of Soils Using Hyperspectral Longwave Infrared (LWIR) Ground-Based Data

Soil mineralogy is an important factor affecting chemical and physical processes in the soil. Most common minerals in soils—quartz, clay minerals and carbonates—present fundamental spectral features in the longwave infrared (LWIR) region. The current study presents a procedure for determining the soil mineralogy from the surface emissivity spectrum. Ground-based hyperspectral LWIR images of 90 Israeli soil samples were acquired with the Telops Hyper-Cam sensor, and the emissivity spectrum of each sample was calculated. Mineral-related emissivity features were identified and used to create indicants and indices to determine the content of quartz, clay minerals, and carbonates in the soil in a semi-quantitative manner—from more to less abundant minerals. The resultant mineral content was in good agreement with the mineralogy derived from chemical analyses.