Airborne Hyperspectral Sensor Research Articles

Evaluation of thermal infrared hyperspectral imagery for the detection of onshore methane plumes: Significance for hydrocarbon exploration and monitoring

Methane (CH4) is the main constituent of natural gas. Fugitive CH4 emissions partially stem from geological reservoirs (seepages) and leaks in pipelines and petroleum production plants. Airborne hyperspectral sensors with enough spectral and spatial resolution and high signal-to-noise ratio can potentially detect these emissions. Here, a field experiment performed with controlled release CH4 sources was conducted in the Rocky Mountain Oilfield Testing Center (RMOTC), Casper, WY (USA). These sources were configured to deliver diverse emission types (surface and subsurface) and rates (20–1450 scf/hr), simulating natural (seepages) and anthropogenic (pipeline) CH4 leaks. The Aerospace Corporation’s SEBASS (Spatially-Enhanced Broadband Array Spectrograph System) sensor acquired hyperspectral thermal infrared data over the experimental site with 128 bands spanning the 7.6μm–13.5μm range. The data was acquired with a spatial resolution of 0.5m at 1500ft and 0.84m at 2500ft above ground level. Radiance images were pre-processed with an adaptation of the In-Scene Atmospheric Compensation algorithm and converted to emissivity through the Emissivity Normalization algorithm. The data was processed with a Matched Filter. Results allowed the separation between endmembers related to the spectral signature of CH4 from the background. Pixels containing CH4 signatures (absorption bands at 7.69μm and 7.88μm) were highlighted and the gas plumes mapped with high definition in the imagery. The dispersion of the mapped plumes is consistent with the wind direction measured independently during the experiment. Variations in the dimension of mapped gas plumes were proportional to the emission rate of each CH4 source. Spectral analysis of the signatures within the plumes shows that CH4 spectral absorption features are sharper and deeper in pixels located near the emitting source, revealing regions with higher gas density and assisting in locating CH4 sources in the field accurately. These results indicate that thermal infrared hyperspectral imaging can support the oil industry profusely, by revealing new petroleum plays through direct detection of gaseous hydrocarbon seepages, serving as tools to monitor leaks along pipelines and oil processing plants, while simultaneously refining estimates of CH4 emissions.

A regression approach to the mapping of bio-physical characteristics of surface sediment using in situ and airborne hyperspectral acquisitions

Remote sensing has been successfully utilized to distinguish and quantify sediment properties in the intertidal environment. Classification approaches of imagery are popular and powerful yet can lead to site- and case-specific results. Such specificity creates challenges for temporal studies. Thus, this paper investigates the use of regression models to quantify sediment properties instead of classifying them. Two regression approaches, namely multiple regression (MR) and support vector regression (SVR), are used in this study for the retrieval of bio-physical variables of intertidal surface sediment of the IJzermonding, a Belgian nature reserve. In the regression analysis, mud content, chlorophyll a concentration, organic matter content, and soil moisture are estimated using radiometric variables of two airborne sensors, namely airborne hyperspectral sensor (AHS) and airborne prism experiment (APEX) and and using field hyperspectral acquisitions by analytical spectral device (ASD). The performance of the two regression approaches is best for the estimation of moisture content. SVR attains the highest accuracy without feature reduction while MR achieves good results when feature reduction is carried out. Sediment property maps are successfully obtained using the models and hyperspectral imagery where SVR used with all bands achieves the best performance. The study also involves the extraction of weights identifying the contribution of each band of the images in the quantification of each sediment property when MR and principal component analysis are used.

An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles

Multitemporal hyperspectral remote sensing data have the potential to detect altered areas on the earth’s surface. However, dissimilar radiometric and geometric properties between the multitemporal data due to the acquisition time or position of the sensors should be resolved to enable hyperspectral imagery for detecting changes in natural and human-impacted areas. In addition, data noise in the hyperspectral imagery spectrum decreases the change-detection accuracy when general change-detection algorithms are applied to hyperspectral images. To address these problems, we present an unsupervised change-detection algorithm based on statistical analyses of spectral profiles; the profiles are generated from a synthetic image fusion method for multitemporal hyperspectral images. This method aims to minimize the noise between the spectra corresponding to the locations of identical positions by increasing the change-detection rate and decreasing the false-alarm rate without reducing the dimensionality of the original hyperspectral data. Using a quantitative comparison of an actual dataset acquired by airborne hyperspectral sensors, we demonstrate that the proposed method provides superb change-detection results relative to the state-of-the-art unsupervised change-detection algorithms.

Evaluation of the spectral characteristics of five hyperspectral and multispectral sensors for soil organic carbon estimation in burned areas

Frequent wildfires in the north-west region of Spain affect soil organic matter. Soil properties can be estimated both spatially and temporally using remote sensing. A wide range of satellite and airborne hyperspectral and multispectral sensors are currently available. The spectral resolution varies substantially among sensors, making it difficult to identify the most suitable sensors and spectral regions for a specific application. This study aims to identify the sensors and wavelengths with the greatest potential for topsoil organic C mapping. Total (TOC) and oxidisable organic carbon (OC) content were measured in 89 soil samples collected in a mountain region of north-western Spain. Reflectance spectra of the samples in the spectral region 400–2450 nm were resampled to the bands of five sensors: Compact Airborne Spectrographic Imager (CASI), Airborne Hyperspectral Scanner (AHS), Hyperion, Landsat 5 Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS). Calibration models obtained using partial least-squares regression proved to be effective for hyperspectral sensors and also for the multispectral sensor MODIS (R2 = 0.75–0.89), which suggests that hyperspectral capability is not required to accurately predict topsoil organic C. Models based on Landsat performed well, but with an error ~30–45% greater than that obtained for the hyperspectral sensors and MODIS.

Hyperspectral Sensors as a Management Tool to Prevent the Invasion of the Exotic Cordgrass Spartina densiflora in the Doñana Wetlands

We test the use of hyperspectral sensors for the early detection of the invasive dense-flowered cordgrass (Spartina densiflora Brongn.) in the Guadalquivir River marshes, Southwestern Spain. We flew in tandem a CASI-1500 (368–1052 nm) and an AHS (430–13,000 nm) airborne sensors in an area with presence of S. densiflora. We simplified the processing of hyperspectral data (no atmospheric correction and no data-reduction techniques) to test if these treatments were necessary for accurate S. densiflora detection in the area. We tested several statistical signal detection algorithms implemented in ENVI software as spectral target detection techniques (matched filtering, constrained energy minimization, orthogonal subspace projection, target-constrained interference minimized filter, and adaptive coherence estimator) and compared them to the well-known spectral angle mapper, using spectra extracted from ground-truth locations in the images. The target S. densiflora was easy to detect in the marshes by all algorithms in images of both sensors. The best methods (adaptive coherence estimator and target-constrained interference minimized filter) on the best sensor (AHS) produced 100% discrimination (Kappa = 1, AUC = 1) at the study site and only some decline in performance when extrapolated to a new nearby area. AHS outperformed CASI in spite of having a coarser spatial resolution (4-m vs. 1-m) and lower spectral resolution in the visible and near-infrared range, but had a better signal to noise ratio. The larger spectral range of AHS in the short-wave and thermal infrared was of no particular advantage. Our conclusions are that it is possible to use hyperspectral sensors to map the early spread S. densiflora in the Guadalquivir River marshes. AHS is the most suitable airborne hyperspectral sensor for this task and the signal processing techniques target-constrained interference minimized filter (TCIMF) and adaptive coherence estimator (ACE) are the best performing target detection techniques that can be employed operationally with a simplified processing of hyperspectral images.

Detection of asbestos-containing materials in agro-ecosystem by the use of airborne hyperspectral CASI-1500 sensor including the limited use of two UAVs equipped with RGB cameras

ABSTRACTThis study aims to identify asbestos-containing materials (ACM) through the use of innovative technology such as aerial hyperspectral sensors. The development of operational methodologies and ad hoc processing were also applied for the purpose of this study. The activity was part of the ICT Living Labs DroMEP project carried out by Water Research Institute of the National Research Council (IRSA-CNR) and Servizi di Informazione Territoriale S.r.l. (SIT Srl). This was funded by the Apulia Region to support the growth and development of specialized SMEs in offering digital content and services. Uncontrolled abandoned wastes pose a threat to the human health and ecosystem. The presence of harmful or dangerous substances released without any control can become a dangerous source of pollution. Many areas of the Apulia region generally, in southern Italy, are subjected to this type of phenomena because most often, these areas are not easily accessible to Authorities for the control and management of the territory. Land monitoring and characterization operations would be carried out in a very long time and would require significant financial resources and considerable effort if done by conventional methods. The project activities include the testing and integration of several technologies already available, but not engineered for specific purposes. The work has been focused on the development of a methodology with a defined and high reliability capable of identifying the presence of ACM in various piles of rubbish abandoned in agro-ecosystems. The developed methodology analyses the spectral behaviour of ACM highlighting and emphasizing certain features through the use of a procedure based on an if–then–else control structure. It also allows the selection of the most effective features to combine that significantly reduces the number of false positives.

Spectral monitoring of wheat canopy under uncontrolled conditions for decision making purposes

The increase in the supply of devices for acquiring spectral images has enabled its widespread adoption by farmers. These devices combine technical quality and ease of use, but the abandonment of experimental stations has led to the loss of the scientific supervision, which is necessary for the fulfilment of operating conditions. The aim of this study is to quantify the quality of the spectral images taken under uncontrolled conditions. With this objective, workflows in real plots were simulated for three years. Surveys were carried out by a spectral camera mounted on a ground platform without additional systems to control lighting, sun-target-sensor geometry and interferences. Images were processed using the empirical linear method and a set of five references, so that the goodness of fit is the first quality estimator. Over 97% of images reach a coefficient of determination equal to or above 0.75 at all wavelengths. The images were also compared with the spectral signatures obtained by a field radiometer and with images acquired by an airborne hyperspectral sensor. This allows us to quantify the error in determining the crop reflectance and to assess the effect of the uncontrolled conditions. The median error in comparison with data from the field radiometer is 0.01, 0.02 and 0.03 (green, red and near-infrared respectively) and with the airborne hyperspectral sensor the error is 0.01 or lower. The geometry is the key factor but it can be controlled and it is possible to use successfully the current methodologies for the agronomic interpretation. The monitoring quality is comparable, from a practical point of view, with more sophisticated instruments, but there is a need to find a solution to a bad timely operation of the camera for efficient operation of the workflow.

Automated lithological mapping using airborne hyperspectral thermal infrared data: A case study from Anchorage Island, Antarctica

The thermal infrared portion of the electromagnetic spectrum has considerable potential for mineral and lithological mapping of the most abundant rock-forming silicates that do not display diagnostic features at visible and shortwave infrared wavelengths. Lithological mapping using visible and shortwave infrared hyperspectral data is well developed and established processing chains are available, however there is a paucity of such methodologies for hyperspectral thermal infrared data. Here we present a new fully automated processing chain for deriving lithological maps from hyperspectral thermal infrared data and test its applicability using the first ever airborne hyperspectral thermal data collected in the Antarctic. A combined airborne hyperspectral survey, targeted geological field mapping campaign and detailed mineralogical and geochemical datasets are applied to small test site in West Antarctica where the geological relationships are representative of continental margin arcs. The challenging environmental conditions and cold temperatures in the Antarctic meant that the data have a significantly lower signal to noise ratio than is usually attained from airborne hyperspectral sensors. We applied preprocessing techniques to improve the signal to noise ratio and convert the radiance images to ground leaving emissivity. Following preprocessing we developed and applied a fully automated processing chain to the hyperspectral imagery, which consists of the following six steps: (1) superpixel segmentation, (2) determine the number of endmembers, (3) extract endmembers from superpixels, (4) apply fully constrained linear unmixing, (5) generate a predictive classification map, and (6) automatically label the predictive classes to generate a lithological map. The results show that the image processing chain was successful, despite the low signal to noise ratio of the imagery; reconstruction of the hyperspectral image from the endmembers and their fractional abundances yielded a root mean square error of 0.58%. The results are encouraging with the thermal imagery allowing clear distinction between granitoid types. However, the distinction of fine grained, intermediate composition dykes is not possible due to the close geochemical similarity with the country rock.

THE ENMAP CONTEST: DEVELOPING AND COMPARING CLASSIFICATION APPROACHES FOR THE ENVIRONMENTAL MAPPING AND ANALYSIS PROGRAMME – DATASET AND FIRST RESULTS

Abstract. The Environmental Mapping and Analysis Programme EnMAP is a hyperspectral satellite mission, supposed to be launched into space in the near future. EnMAP is designed to be revolutionary in terms of spectral resolution and signal-to-noise ratio. Nevertheless, it will provide a relatively high spatial resolution also. In order to exploit the capacities of this future mission, its data have been simulated by other authors in previous work. EnMAP will differ from other spaceborne and airborne hyperspectral sensors. Thus, the assumption that the standard classification algorithms from other sensors will perform best for EnMAP as well cannot by upheld since proof. Unfortunately, until today, relatively few studies have been published to investigate classification algorithms for EnMAP. Thus, the authors of this study, who have provided some insights into classifying simulated EnMAP data before, aim to encourage future studies by opening the EnMAP contest. The EnMAP contest consists in a benchmark dataset provided for algorithm development, which is presented herein. For demonstrative purposes, this report also represents two classification results which have already been realized. It furthermore provides a roadmap for other scientists interested in taking part in the EnMAP contest.

Tagging and Mapping of Mixed Dipterocarp Mountain Forest at Species Level using an Airborne Hyperspectral Imager

Forest inventories describe the quantity and quality of trees and other organisms of the forest and the characteristics of the land on which the forest grows. Thus, to manage the resource of the forest systematically, the forest has to be precisely identified and classified before implementing decisions. A study on the spatial distribution of mountain mixed hill dipterocarp forest tree species in Gunung Stong Forest Reserve, Kelantan, Malaysia was conducted using an airborne hyperspectral imaging technique to identify, tag and map tree distribution at species level for future sustainable harvest and management planning. The general objective of this study is to assess the capability and usefulness UPM-AISA airborne hyperspectral sensor in Pre-Felling forest inventory while the specific objectives are to identify, map and tag individual trees in Gunung Stong Forest Reserve, Kelantan. A Sobel filter was used to enhance the image followed by a Spectral Angle Mapper (SAM) analysis to classify the individual tree species within the study plot. A digital map of tree tagging was produced with the tree species identified were Kelat (Syzgium spp), Keledang (Artrocarpos spp), Mengkulang (Heritiera spp), Tempinis (Streblus elongatus), Keranji (Dialium spp), Tulang Daing (Callerya atropurea), Meranti Sarang Punai (Shorea parvifolia), Kembang Semangkuk Jantung (Scaphium macropodum), Bintangor (Calophyllum spp), Nyatoh Minyak (Sapotaceae spp), Mersawa (Anisoptera spp), Resak (Cotylelobium spp), Sepetir (Sindora spp), Temponek (Artocarpus rigidus) and Petaling. The mapping accuracy of 89.66% was attained in the 1 ha study plot. Tree tagging using airborne remote sensing has a great potential in the Pre-F inventory and should be integrated with a GIS database management for future decision in management, development and utilization of sustainable forest resource.

Hyperion과 ALI 영상의 융합을 위한 블록 기반의 융합기법 평가

영상융합 기법은 고해상도 영상을 이용하여 저해상도 영상의 공간해상도를 증대시키는 방법이다. 본 논문에서는 EO-1 위성에 탑재된 ALI 센서와 Hyperion 센서로부터 취득된 고해상도 흑백영상, 저해상도 다중분광 영상 및 초분광 영상을 활용한 초분광 영상의 융합기법에 대한 연구를 수행하였다. 특히, 초분광 영상과 다중분광 영상의 특성을 고려하여 초분광 영상의 블록을 구성하여 ALI 및 Hyperion 영상에 적용하고, 이에 따른 영상융합 기법의 성능을 평가하고자 하였다. 실험결과, 고해상도 흑백영상만을 사용한 융합결과와 비교하여 저해상도 다중분광 영상을 활용한 블록기반의 융합기법이 공간해상도를 효율적으로 향상시킬 수 있음을 확인하였으며, 제안된 융합기법이 기존의 블록기반 융합기법과 비교하여 분광왜곡을 최소화시킬 수 있음을 확인하였다. 이를 통해, 향후 발사될 다양한 초분광 위성 및 항공기 초분광 센서의 활용을 증대시킬 수 있을 것으로 판단된다. An Image fusion, or Pansharpening is a methodology of increasing the spatial resolution of image with low-spatial resolution using high-spatial resolution images. In this paper, we have performed an image fusion of hyperspectral imagery by using panchromatic image with high-spatial resolution, multispectral and hyperspectral images with low-spatial resolution, which had been acquired by ALI and Hyperion of EO-1 satellite sensors. The study has been mainly focused on evaluating performance of fusion process following to the image fusion methodology of the block association, which had applied to ALI and Hyperion dataset by considering spectral characteristics between multispectral and hyperspectral images. The results from experiments have been identified that the proposed algorithm efficiently improved the spatial resolution and minimized spectral distortion comparing with results from a fusion of the only panchromatic and hyperspectral images and the existing block-based fusion method. Through the study in a proposed algorithm, we could concluded in that those applications of airborne hyperspectral sensors and various hyperspectral satellite sensors will be launched at future by enlarge its usages.

Prediction of soil properties using imaging spectroscopy: Considering fractional vegetation cover to improve accuracy

Spectroscopic techniques have become attractive to assess soil properties because they are fast, require little labor and may reduce the amount of laboratory waste produced when compared to conventional methods. Imaging spectroscopy (IS) can have further advantages compared to laboratory or field proximal spectroscopic approaches such as providing spatially continuous information with a high density. However, the accuracy of IS derived predictions decreases when the spectral mixture of soil with other targets occurs. This paper evaluates the use of spectral data obtained by an airborne hyperspectral sensor (ProSpecTIR-VS – Aisa dual sensor) for prediction of physical and chemical properties of Brazilian highly weathered soils (i.e., Oxisols). A methodology to assess the soil spectral mixture is adapted and a progressive spectral dataset selection procedure, based on bare soil fractional cover, is proposed and tested. Satisfactory performances are obtained specially for the quantification of clay, sand and CEC using airborne sensor data (R2 of 0.77, 0.79 and 0.54; RPD of 2.14, 2.22 and 1.50, respectively), after spectral data selection is performed; although results obtained for laboratory data are more accurate (R2 of 0.92, 0.85 and 0.75; RPD of 3.52, 2.62 and 2.04, for clay, sand and CEC, respectively). Most importantly, predictions based on airborne-derived spectra for which the bare soil fractional cover is not taken into account show considerable lower accuracy, for example for clay, sand and CEC (RPD of 1.52, 1.64 and 1.16, respectively). Therefore, hyperspectral remotely sensed data can be used to predict topsoil properties of highly weathered soils, although spectral mixture of bare soil with vegetation must be considered in order to achieve an improved prediction accuracy.

Combining data mining algorithm and object-based image analysis for detailed urban mapping of hyperspectral images

Image classification of roofing types, road pavements, and natural features can assist land-cover maps in further examining the effects of such features on health, pollution, and the microclimate in urban settings. Airborne hyperspectral sensors with high spectral and spatial resolutions can be employed for detailed characterization of urban areas. This study aims to develop a procedure that is instrumental for automated knowledge discovery and mapping of urban surface materials from a large feature space of hyperspectral images. Two different images over Universiti Putra Malaysia (UPM) and Kuala Lumpur (KL), Malaysia, were captured by using hyperspectral sensors with 20 and 128 bands. The images were used to explore the combined performance of a data mining (DM) algorithm and object-based image analysis (OBIA). A large number of attributes were discovered with the C4.5 DM algorithm, which also generated the classification model as a decision tree. The UPM and KL classified images achieved 93.42 and 88.36% overall accuracy. The high accuracy of object-based classification can be linked to the knowledge discovery produced by the DM algorithm. This algorithm increased the productivity of OBIA, expedited the process of attribute selection, and resulted in an easy-to-use representation of a knowledge model from a decision tree structure.

Remote Estimation of Nutrients for a Drinking Water Source Through Adaptive Modeling

Morse Reservoir, a major water supply for the Indianapolis metropolitan area, IN, USA, experiences nuisance cyanobacterial blooms due to agricultural and point source nutrient loadings. Hyperspectral remote sensing data from both in situ and airborne AISA measurements were applied to an adaptive model based on Genetic Algorithms-Partial Least Squares (GA-PLS) by relating the spectral signal with total nitrogen (TN) and phosphorus (TP) concentrations. Results indicate that GA-PLS relating in situ spectral reflectance to the nutrients yielded high coefficients of determination (TN: R 2 = 0.88; TP: R 2 = 0.91) between measured and estimated TN (RMSE = 0.07 mg/L; Range: 0.6–1.88 mg/L), and TP (RMSE = 0.017; Range: 0.023–0.314 mg/L). The GA-PLS model also yielded high performance with AISA imaging data, showing close correlation between measured and estimated values (TN: RMSE = 0.11 mg/L; TP: RMSE = 0.02 mg/L). An analysis of in situ data indicated that TN and TP were highly correlated with chlorophyll-a and suspended matter in the water column, setting a basis for remotely sensed estimates of TN and TP. Spatial correlation of TN, TP with chlorophyll-a and suspended matters further confirmed that remote quantification of nutrients for inland waters is based on the strong association of optically active constituents with nutrients. Based on these results, in situ and airborne hyperspectral remote sensors can provide both quantitative and qualitative information on the distribution and concentration of nutrients in Morse Reservoir. Our modeling approach combined with hyperspectral remote sensing is applicable to other productive waters, where algal blooms are triggered by nutrients.

Soil organic carbon assessment by field and airborne spectrometry in bare croplands: accounting for soil surface roughness

Visible, Near and Short Wave Infrared (VNSWIR) diffuse reflectance spectroscopy (350nm to 2500nm) has been proven to be an efficient tool to determine the Soil Organic Carbon (SOC) content. SOC assessment (SOCa) is usually done by using calibration samples and multivariate models. However one of the major constraints of this technique, when used in field conditions is the spatial variation in surface soil properties (soil water content, roughness, vegetation residue) which induces a spectral variability not directly related to SOC and hence reduces the SOCa accuracy. This study focuses on the impact of soil roughness on SOCa by outdoor VIS-NIR-SWIR spectroscopy and is based on the assumption that soil roughness effect can be approximated by its related shadowing effect.A new method for identifying and correcting the effect of soil shadow on reflectance spectra measured with an Analytical Spectral Devices (ASD) spectroradiometer and an Airborne Hyperspectral Sensor (AHS-160) on freshly tilled fields in the Grand Duchy of Luxembourg was elaborated and tested. This method is based on the shooting of soil vertical photographs in the visible spectrum and the derivation of a shadow correction factor resulting from the comparison of “reflectance” of shadowed and illuminated soil areas.Moreover, the study of laboratory ASD reflectance of shadowed soil samples showed that the influence of shadow on reflectance varies according to wavelength. Consequently a correction factor in the entire [350–2500nm] spectral range was computed to translate this differential influence.Our results showed that SOCa was improved by 27% for field spectral data and by 25% for airborne spectral data by correcting the effect of soil relative shadow. However, compared to simple mathematical treatment of the spectra (first derivative, etc.) able to remove variation in soil albedo due to roughness, the proposed method, leads only to slightly more accurate SOCa.

Evaluating Light Availability, Seagrass Biomass, and Productivity Using Hyperspectral Airborne Remote Sensing in Saint Joseph’s Bay, Florida

Seagrasses provide a number of critical ecosystem services, including habitat for numerous species, sediment stabilization, and shoreline protection. Ariel photography is a useful tool to estimate the areal extent of seagrasses, but recent innovations in radiometrically calibrated sensors and algorithm development have allowed identification of benthic types and retrieval of absolute density. This study demonstrates the quantitative ability of a high spatial resolution (1 m) airborne hyperspectral sensor (3.2 nm bandwidth) in the complex coastal waters of Saint Joseph’s Bay (SJB). Several benthic types were distinguished, including submerged and floating aquatic vegetation, benthic red algae, bare sand, and optically deep water. A total of 23.6 km2 of benthic vegetation was detected, indicating no dramatic change in vegetation area over the past 30 years. SJB supported high seagrass density at depths shallower than 2 m with an average leaf area index of 2.0 ± 0.6 m2 m−2. Annual seagrass production in the bay was 13,570 t C year−1 and represented 41 % of total marine primary production. The effects of coarser spatial resolution were investigated and found to reduce biomass retrievals, underestimate productivity, and alter patch size statistics. Although data requirements for this approach are considerable, water column optical modeling may reduce the in situ requirements and facilitate the transition of this technique to routine monitoring efforts. The ability to quantify not just areal extent but also productivity of a seagrass meadow in optically complex coastal waters can provide information on the capacity of these environments to support marine food webs.

Analysis of Vegetation and Soil Patterns using Hyperspectral Remote Sensing, EMI, and Gamma‐Ray Measurements

The identification of spatial and temporal patterns of soil properties and moisture structures is an important challenge in environmental and soil monitoring as well as for soil landscape model approaches. This work examines the use of hyperspectral remote sensing techniques for quantifying geophysical parameters from the hyperspectral reflectance of the vegetation canopy. These can be used as proxies of the underlying soil and soil water conditions. Different spectral index derivatives, single band reflectance, and spectral indices from the airborne hyperspectral sensor AISA were quantified and tested in univariate and multivariate regression models for their correlation with geophysical measurements with electromagnetic induction (EMI) and gamma‐ray spectrometry. The best univariate models for predicting electrical conductivity based on spectral information were based on the vertical dipole of an EM38DD with an R2 = 0.54 with the spectral index Normalized Pigments Reflectance Index (NPCI) as well as for the horizontal dipole of an EM38DD with an R2 = 0.65 with the spectral index NPCI. For predicting soil characteristics measured with gamma‐ray spectrometry we received the best model results for γTh with an R2 = 0.55 with the spectral index NPCI and γK with an R2 = 0.44 with the spectral index Triangular Vegetation Index (TVI) and NPCI. The combination of variables including the geographical elevation was tested as the input for a multivariate regression analysis. For EMI and gamma‐ray measurements, the “elevation” was found to be the most predictive variable and an integration of spectral indices into the elevation‐based model led to only a slight improvement in the predictive power for EMI. An improvement could be made to explain the variance of gamma‐ray measurement signals by combining elevation and spectral information.

초분광 항공원격탐사 테스트베드 구축 및 시험자료 획득

분광 영상의 효과적인 테스트베드 구축은 초분광 영상의 다양한 활용을 위하여 선행되어야한다. 본 연구에서는 다양한 연구 분야에 적용할 수 있는 테스트베드의 구축 방법 및 효용성에 대한 기초 연구를 수행하였다. 이를 위하여, 기존의 국내 외 테스트베드 생성 방법을 분석하고, 이를 바탕으로 하여 항공기 기반 초분광 센서의 촬영을 위한 테스트베드를 설계하였다. 구축된 테스트베드를 촬영한 영상에서 기준자료를 생성시키기 위하여, 본 연구에서는 대리보정에 의한 전처리 기법을 적용하고, 이에 대한 효용성을 분석하였다. 실험결과, 대리보정은 타프를 이용하는 것이 가장 이상적이지만, 상황에 따라서 분광반사율이 일정하거나, 변화폭이 상대적으로 적은 물질을 이용하는 것이 가능하다는 것을 확인하였다. 본 연구에서 촬영한 테스트베드 자료는 국내 외의 초분광 영상 처리 연구에 참조자료로 사용될 수 있을 것으로 사료된다. The construction of hyperspectral test-bed dataset is essential for the effective performance of hyperspectral image for various applications. In this study, we analyzed the technical points for generating of optimal hyperspectral test-bed site for hyperspectral sensors and the efficiency of hyperspectral test-bed site. In this regard regions we analyzed existing construction techniques for generating test-bed site in domestic and foreign, and designed the test-bed site to acquire images from the airborne hyperspectral sensor. To produce a reference data from the image of constructed test-bed site, this study applied vicarious correction as a pre-processing and analyzed its efficiency. The result presented that it was ideal to use tarp for the vicarious correction, but it is possible to use the materials with constant spectral reflectance or with relatively low variance of spectral reflectance. The test-bed data taken in this study can be employed as the reference of domestic and foreign studies for hyperspectral image processing.

Mineralogical mapping of southern Namibia by application of continuum-removal MSAM method to the HyMap data

We applied the Modified Spectral Angle Mapper (MSAM) and continuum-removal methods to reflectance data obtained in the shortwave infrared regions by the airborne hyperspectral sensor, HyMap, to delineate distribution of the minerals related to hydrothermal alteration and pegmatite. The combination of these two methods was applied to remotely sensed hyperspectral data for the first time in this study. The effectiveness of this method, ‘continuum-removal MSAM’, was confirmed by comparing our mineral index maps with existing mineral maps of the Cuprite region, Nevada, USA. We then produced mineral index maps of southern Namibia using newly observed hyperspectral data. The existence of many pegmatite- and porphyry-type deposits is reported in this study area, where detailed mineralogical mapping based on remote-sensing data has not previously been conducted. Our mineral index maps are consistent with our field survey results, and also with X-ray diffraction analyses and spectral measurements of rock samples. In this study, the continuum-removal MSAM method successfully discriminated hydrothermal alteration minerals such as kaolinite and pyrophyllite, and pegmatite-related minerals such as high-aluminium muscovite and lepidolite in southern Namibia for the first time. The main advantages of our classification method are simplicity and high-precision identification of alteration minerals.

Hyperspectral Remote Sensing of Urban Areas

AbstractThe use of airborne hyperspectral sensors for urban analysis represents a significant advance in remote sensing. The greatest challenges to effectively using urban hyperspectral imagery include (i) managing the natural spectral complexity of urban fabrics, (ii) selecting optimal feature sets from an enormous number of candidate features, (iii) designing classifiers that are minimally affected by the curse of dimensionality, and (iv) reducing the computational burden of hyperspectral algorithms on large images. To address these challenges, there have been several promising methodological advances in urban hyperspectral analysis. Examples include multiclassifier systems, decision fusion processes, support vector machine algorithms, object oriented approaches to classification, and discrimination via morphological profiles. Unfortunately, few of these advances are leveraged in commercial software packages, thus limiting their practical utility to the practitioner.