Spaceborne Hyperspectral Data Research Articles

Next-gen regional fire risk mapping: Integrating hyperspectral imagery and National Forest Inventory data to identify hot-spot wildland-urban interfaces

The increasing threat of high-severity wildfires in Mediterranean Wildland-Urban Interface (WUI) areas demands to develop effective fire risk assessment and management strategies. Simultaneously, the newfound accessibility of spaceborne hyperspectral data represents a significant potential for generating fire severity assessments, whereas National Forest Inventories (NFI) offer a vast dataset related to vegetation and fuel loads, which is essential for shaping the planning and strategies of forest services. This research work aims to advance the state-of-the-art in WUI fire risk mapping in the western Mediterranean Basin by combining PRISMA spaceborne hyperspectral data and Spanish NFI data. The proposed methodology had three main stages: (i) fire severity assessment at local scale (a wildfire) by using PRISMA hyperspectral data and Multi-Endmember Spectral Mixture Analysis (MESMA) leveraging field-based measurements of the Composite Burn Index (70 plots); (ii) development of a high fire severity probability map at regional scale from the extrapolation of a Random Forest predictive model calibrated from fire severity estimates, NFI data and topo-climatic variables at local scale (overall accuracy = 92 %; Kappa = 0.8); and (iii) identification and characterization of zones that concentrate WUIs with high probability of high fire severity if a fire event occurs (hot-spot WUIs) by crossing the information from the previous regional high fire severity probability map and a WUI cartography developed at regional scale. Study area was Castilla y León Autonomous Region (larger Spanish region, 94,226 km2), where the second-largest extreme Spanish wildfire event (28,000 ha) occurred. We identified hot-spot WUIs so that stakeholders and decision-makers could (i) prioritize resources and interventions for effective fire management and mitigation, (ii) allocate resources for prevention, and (iii) plan evacuation measures to safeguard lives and property. This study contributes to the development of next-generation fire risk assessment methods that combine remote sensing technologies with comprehensive ground-level datasets.

First evaluation of fire severity retrieval from PRISMA hyperspectral data

The unprecedented availability of spaceborne hyperspectral data has great potential to provide fire severity estimates that align with post-fire management needs, overcoming complex logistics and data acquisition costs of airborne hyperspectral sensors, and the suboptimal sensitivity of broadband data to several post-fire ground components. We analyzed the feasibility of the PRISMA mission -one of the first spaceborne spectrometers operationally active- to assess fire severity by leveraging hyperspectral data dimensionality through the retrieval of sub-pixel components directly related to fire severity in the field. Multispectral data provided by Sentinel-2, commonly used in fire severity quantitative assessments, were used as a benchmark method. Multiple endmember spectral mixture analysis (MESMA) was used to retrieve fractional cover of char, photosynthetic vegetation (PV), as well as non-photosynthetic vegetation and soil (NPVS) from post-fire PRISMA Level 2D and Sentinel-2 Level 2A scenes in one of the largest wildfires ever recorded in the western Mediterranean Basin. Ground-truth data were obtained using the Composite Burn Index (CBI) to procure three field-measured severity metrics: vegetation, soil and site. The relationship between the CBI data on a continuum scale and the cover of char, PV and NPVS image fractions retrieved from PRISMA and Sentinel-2 was assessed through Random Forest regression (RFR). Likewise, Ordinal Forests (OF) algorithm was used to classify categorized CBI data (low, moderate and high fire severity). PRISMA-based RFR fire severity estimates at vegetation, soil and site levels (R2 = 0.64–0.79 and RMSE = 0.33–0.41) outperformed those of Sentinel-2 (R2 = 0.27–0.53 and RMSE = 0.54–0.60), and were in line with previous studies using airborne hyperspectral sensors at higher spatial resolution. Fire severity underestimation for high field CBI values was almost unnoticeable in the PRISMA estimates. Categorical fire severity, not currently estimated using hyperspectral data but with high interest in post-fire management, were accurately predicted by PRISMA-based OF classification, with consistent user's and producer's accuracy for each fire severity category. The high confusion between moderate and low/high fire severity categories, typical when unmixing broadband multispectral data, was overcome by the PRISMA-based classification scheme. Our results suggest that new spaceborne spectrometer missions can support reliable fire severity assessments equivalent to airborne spectrometers, but readily applicable to large-scale assessments of extreme wildfire events.

SAM-HIT: A Simulated Annealing Multispectral to Hyperspectral Imagery Data Transformation

Space-borne hyperspectral imagery data are known for their high spectral resolution in a number of narrow wavelength intervals, which makes these data useful for mineral mapping. However, the available free-of-charge hyperspectral scenes cover only narrow and scattered geographic areas. In contrast, multispectral imagery scenes have a nearly complete spatial coverage and wider wavelength intervals. The low spectral resolution of the multispectral data, however, limits their efficiency in the mineral mapping of small geological massifs or hydrothermal alteration halos. The present contribution presents a new transformation tool (SAM-HIT) to simulate the hyperspectral sensor responses in unscanned areas based on partially overlapping hyperspectral and multispectral scenes. Simulation or prediction of the pseudohyperspectral data is here accomplished by using the simulated annealing linear optimization algorithm, which allows the lowest possible mismatch between the original and predicted data. The high visual and numerical correlation of the resultant data confirms the reliability of the newly adopted transformation. Further, the application of the SAM-HIT to a well-exposed part of the Egyptian basement complex with available hyperspectral data showed high concordance and nearly identical band signatures, opening a new outlook for mineral exploration in vast areas by a nearly automated cost-free means.

Plant species richness prediction from DESIS hyperspectral data: A comparison study on feature extraction procedures and regression models

The diversity of terrestrial vascular plants plays a key role in maintaining the stability and productivity of ecosystems. Monitoring species compositional diversity across large spatial scales is challenging and time consuming. Airborne hyperspectral imaging has shown promise for measuring plant diversity remotely, but to operationalise these efforts over large regions we need to advance satellite-based alternatives. The advanced spectral and spatial specification of the recently launched DESIS (the DLR Earth Sensing Imaging Spectrometer) instrument provides a unique opportunity to test the potential for monitoring plant species diversity with spaceborne hyperspectral data. This study provides a quantitative assessment on the ability of DESIS hyperspectral data for predicting plant species richness in two different habitat types in southeast Australia. Spectral features were first extracted from the DESIS spectra, then regressed against on-ground estimates of plant species richness, with a two-fold cross validation scheme to assess the predictive performance. We tested and compared the effectiveness of Principal Component Analysis (PCA), Canonical Correlation Analysis (CCA), and Partial Least Squares analysis (PLS) for feature extraction, and Kernel Ridge Regression (KRR), Gaussian Process Regression (GPR), and Random Forest Regression (RFR) for species richness prediction. The best prediction results were r=0.76 and RMSE=5.89 for the Southern Tablelands region, and r=0.68 and RMSE=5.95 for the Snowy Mountains region. Relative importance analysis for the DESIS spectral bands showed that the red-edge, red, and blue spectral regions were more important for predicting plant species richness than the green bands and the near-infrared bands beyond red-edge. We also found that the DESIS hyperspectral data performed better than Sentinel-2 multispectral data in the prediction of plant species richness. Our results provide a quantitative reference for future studies exploring the potential of spaceborne hyperspectral data for plant biodiversity mapping.

CDFSL: Image Registration for Spaceborne Hyperspectral and Multispectral Data Having Large Spatial-Resolution Difference

Image registration aims to eliminate the geometric deviation between multi-source data with the same range, and to promote the collaborative application of data. In recent years, spaceborne hyperspectral (HS) and multispectral (MS) data have been widely used in Earth observation. However, the difference in the number of bands, spatial resolution, and spectral resolution puts forward higher requirements on the registration algorithm. The key to HS and MS image registration is to extract more common key points, weaken and eliminate the difference of radiation and spatial texture information to build superior descriptors, and achieve high-precision matching of key points. This paper introduces a new robust HS and MS registration method based on common deep feature subspaces. We first construct the common deep feature subspaces extraction network to extract consistent edge features and common subspace images of the image pair. Then, Harris algorithm is used to extract key points from consistent edge features between images, which reduces the impact of spatial resolution differences between images. Besides, the SIFT descriptor and subspace images are used to describe key points, which reduces the impact of radiation differences between images. Finally, Euclidean distance is used for the initial matching of key points, and the affine matrix is calculated after the outliers are eliminated, and image registration is performed. We perform experiments on spaceborne HS and MS datasets of different spatial resolutions and comparisons with state-of-the-art methods. Experimental results show that our method can obtain satisfactory registration results and is robust.

Comparing Gaofen-5, Ground, and Huanjing-1A Spectra for the Monitoring of Soil Salinity with the BP Neural Network Improved by Particle Swarm Optimization

Most of the world’s saline soils are found in arid or semiarid areas, where salinization is becoming serious. Ground laboratory hyperspectral data (analytical spectral devices, ASD) as well as spaceborne hyperspectral data, including Gaofen-5 (GF-5) and Huanjing-1A (HJ-1A), provide convenient salinity monitoring. However, the difference among ASD, GF-5, and HJ-1A spectra in salinity monitoring remains unclear. So, we used ASD, GF-5, and HJ-1A spectra as data sources in Gaotai County of Hexi Corridor, which has been affected by salinization. For a more comprehensive comparison of the three spectra datum, four kinds of band screening methods, including Pearson correlation coefficient (PCC), principal component analysis (PCA), successive projections algorithm (SPA), and random forest (RF) were used to reduce the dimension of hyperspectral data. Particle swarm optimization (PSO) was used to improve the random initialization of weights and thresholds of the back propagation neural network (BPNN) model. The results showed that root mean square error (RMSE) and determination of the coefficients (R2) of models based on ASD and HJ-1A spectra were basically similar. ASD spectra (RMSE = 4 mS·cm−1, R2 = 0.82) and HJ-1A (RMSE = 2.98 mS·cm−1, R2 = 0.93) performed better than GF-5 spectra (RMSE = 6.45 mS·cm−1, R2 = 0.67) in some cases. The good modelling result of HJ-1A and GF-5 data confirmed that spaceborne hyperspectral imagery has great potential in salinity mapping. Then, we used HJ-1A and GF-5 hyperspectral imagery to map soil salinity. The results of GF-5 and HJ-1A showed that extremely and highly saline soil mainly occurred in grassland and the southern part of arable land in Gaotai County. Other lands mainly featured non-saline and slightly saline soil. This can provide a reference for salinity monitoring research.

Using airborne and DESIS imaging spectroscopy to map plant diversity across the largest contiguous tract of tallgrass prairie on earth

Grassland ecosystems are under threat globally, primarily due to land-use and land-cover changes that have adversely affected their biodiversity. Given the negative ecological impacts of biodiversity loss in grasslands, there is an urgent need for developing an operational biodiversity monitoring system that functions in these ecosystems. In this paper, we assessed the capability of airborne and spaceborne imaging spectroscopy (also known as hyperspectral imaging) to capture plant α-diversity in a large naturally-assembled grassland while considering the impact of common management practices, specifically prescribed fire. We collected a robust in-situ plant diversity data set, including species composition and percent cover from 2500 sampling points with different burn ages, from recently-burned to transitional and pre-prescribed fire at the Joseph H. Williams Tallgrass Prairie Preserve in Oklahoma, USA. We expressed in-situ plant α-diversity using the first three Hill numbers, including species richness (number of observed species in a plant community), exponential Shannon entropy index (hereafter Shannon diversity; effective number of common species, where species are weighed proportional to their percent cover), and inverse Simpson concentration index (hereafter Simpson diversity; effective number of dominant species, where more weight is given to dominant species) at four different plot sizes, including 60 m × 60 m, 120 m × 120 m, 180 m × 180 m, and 240 m × 240 m. We collected full-range airborne hyperspectral data with fine spatial resolution (1 m) and visible and near-infrared spaceborne hyperspectral data from DESIS sensor with coarse spatial resolution (30 m), and used the spectral diversity hypothesis—i.e., that the variability in spectral data is largely driven by plant diversity—to estimate α-diversity remotely. In recently-burned plots and those at the transitional stage, both airborne and spaceborne data were capable of capturing Simpson diversity—a metric that calculates the effective number of dominant species by emphasizing abundant species and discounting rare species—but not species richness or Shannon diversity. Further, neither airborne nor spaceborne hyperspectral data sets were capable of capturing plant α-diversity of 60 m × 60 m or 120 m × 120 m plots. Based on these results, three main findings emerged: (1) management practices influence grassland biodiversity patterns that can be remotely detected, (2) both fine- and coarse-resolution remotely-sensed data can detect the effective number of dominant species (e.g., Simpson diversity), and (3) attention should be given to site-specific plant diversity field data collection to appropriately interpret remote sensing results. Findings of this study indicate the feasibility of estimating Simpson diversity in naturally-assembled grasslands using forthcoming spaceborne imagers such as National Aeronautics and Space Administration's Surface Biology and Geology mission.

Atmospheric Compensation of PRISMA Data by Means of a Learning Based Approach

Atmospheric compensation (AC) allows the retrieval of the reflectance from the measured at-sensor radiance and is a fundamental and critical task for the quantitative exploitation of hyperspectral data. Recently, a learning-based (LB) approach, named LBAC, has been proposed for the AC of airborne hyperspectral data in the visible and near-infrared (VNIR) spectral range. LBAC makes use of a parametric regression function whose parameters are learned by a strategy based on synthetic data that accounts for (1) a physics-based model for the radiative transfer, (2) the variability of the surface reflectance spectra, and (3) the effects of random noise and spectral miscalibration errors. In this work we extend LBAC with respect to two different aspects: (1) the platform for data acquisition and (2) the spectral range covered by the sensor. Particularly, we propose the extension of LBAC to spaceborne hyperspectral sensors operating in the VNIR and short-wave infrared (SWIR) portion of the electromagnetic spectrum. We specifically refer to the sensor of the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission, and the recent Earth Observation mission of the Italian Space Agency that offers a great opportunity to improve the knowledge on the scientific and commercial applications of spaceborne hyperspectral data. In addition, we introduce a curve fitting-based procedure for the estimation of column water vapor content of the atmosphere that directly exploits the reflectance data provided by LBAC. Results obtained on four different PRISMA hyperspectral images are presented and discussed.

Characterization of soil salinity and its impact on wheat crop using space-borne hyperspectral data

Facing the risk of soil salinization worldwide, there has been a growing interest in identifying rapid and inexpensive tools for soil salinity assessment. Remote sensing has shown great advantages in the field in recent decades. In present research, Hyperion Hyperspectral remote sensing data (EO-1) was used for characterization and mapping of salt-affected soils, to generate crop inventory map and to evaluate soil salinity impact on wheat crop growth in part of Mathura district of Uttar Pradesh representing Indo-Gangetic plain. Narrow bands can discriminate critical spectral differentials in detail and can assess the salinity hazard over crop. A detailed field survey was carried out in the study area in order to identify the salt-affected soils and to collect soil samples, groundwater table depth, chlorophyll content, LAI to characterize impact of soil salinity over crop. Various wheat crop spectra were collected for calculation of narrow band indices to discriminate various stress conditions. Spectral angle mapper (SAM) was used to generate crop inventory map with various types of crops. The same technique (SAM) was used to map various categories of salt affected soils represented by spectral endmembers of normal, slightly, moderately and highly salt-affected soils. The results showed that various severity classes of salt-affected soils could be reliably mapped using spectral angle mapper (SAM) analysis with an overall accuracy of 74.24 %. Empirical relationships developed between crop & soil parameters and vegetation indices using SMLR could show its possibility with an R2 of 0.52 and 0.41 to predict LAI and CCI, respectively. Validation results showed the RMSE of 0.8 and 5.2 to predict LAI and CCI. Partial least square regression (PLSR) statistical model (using spectroradiometer derived narrow band indices) was developed to assess different stress level with varying crop and soil parameters.

The Potential of Multispectral Vegetation Indices Feature Space for Quantitatively Estimating the Photosynthetic, Non-Photosynthetic Vegetation and Bare Soil Fractions in Northern China

Non-photosynthetic vegetation (<small>NPV</small>) is widely distributed in the arid and semi-arid area, especially in the sandy areas. The hyperspectral-based cellulose absorption index (<small>CAI</small>) is an accepted method for estimating the cover fractions of <small>NPV</small>. However, the spaceborne hyperspectral data currently available to us are very limited. In this study, we tried to identify one or more combinations based on the multispectral vegetation indices feature space model to quantitatively estimate the <small>PV</small>, <small>NPV</small> and bare soil fractions of the Otindag Sandy Land in northern China. Three frequently-used green vegetation indices, <small>NDVI</small>, <small>EVI</small> and <small>OSAVI</small>, and nine multispectral-based indices sensitive to <small>NPV</small> were used to examine the spatial patterns based on the field measured endmember spectra and non-growing and growing season Landsat-8 <small>OLI</small> image reflectance spectra. The capabilities of these different combinations were tested in this study area using mosaicked Landsat-8 <small>OLI</small> imagery. The results show that the feature space of different combinations based on the field measured spectra and image reflectance spectra has good consistency. The separability of feature space determines the availability of this model. The normalized difference senescent vegetation index (<small>NDSVI</small>) and brightness index (BI) were found to have greater potential to combine with the three selected green vegetation indices for simultaneous estimation of the fractional cover of PV, <small>NPV</small>, and bare soil in the Otindag Sandy Land because of their clear and separable feature space. We obtained the best and medium-precision estimates for <small>NDVI-NDSVI</small> (f<small>PV</small>: <small>RMSE</small>=0.26; f<small>NPV</small>: <small>RMSE</small>=0.17) and <small>OSAVI-BI</small> (f<small>PV</small>: <small>RMSE</small>=0.27; f<small>NPV</small>: <small>RMSE</small>=0.25) for 104 field observations.

Revised normalized difference nitrogen index (NDNI) for estimating canopy nitrogen concentration in wetlands

Nitrogen is an important element for plant growth, photosynthesis and productivity. It has been presented to have a close correlation with wetland purification and estuarine eutrophication. The quantification of nitrogen concentration in wetland plants from hyperspectral data can provide valuable information to help develop approaches to research wetland ecosystem functioning and biophysical processes. This paper developed a revised nitrogen index (revised NDNI) on the basis of NDNI and SAVI to estimate canopy nitrogen concentration of wetland reed from ground-measured and spaceborne hyperspectral data while reducing wetland background effects. The SAVI was revised through replacing the red band by the 1510nm band and a soil self-adjustable factor to increase vegetation sensitivity and diminish background noise. The revised NDNI integrated advantages of NDNI that was sensitive to canopy nitrogen concentration and revised SAVI that was used as the denominator in revised NDNI to reduce contributions of wetland background reflectance. The results showed that the revised NDNI was more robust than NDNI for nitrogen predictions of reeds, with a relatively higher accuracy (R2=0.84, 0.88, 0.78). It was more insensitive to the variations of LAI than the compared indices, with a lower correlation coefficient (R2=0.27). These results demonstrated that the revised NDNI was both sensitive to canopy nitrogen status of reeds and resistant to the background effects. A validation was performed using Hyperion image over two experimental fields in the study area. The results of canopy nitrogen concentration estimations exhibited a good correlation with ground-based measurements (R2=0.79). The revised NDNI was proved to be more suitable for canopy nitrogen concentration prediction of aquatic vegetation (such as Phragmites australis) in the context of wetlands.

Multi- and hyperspectral spaceborne remote sensing of the Aggeneys base metal sulphide mineral deposit sites in the Lower Orange River region, South Africa

New tools and algorithms for geological remote sensing are developed and verified at test sites throughout the world in preparation of the German hyperspectral satellite Mission (EnMAP), which is an Environmental Mapping and Analysis Program. The Aggeneys Cu-Pb-Zn deposit, situated in the arid north western part of South Africa, represents a unique field laboratory for testing these new tools. Here spaceborne hyperspectral data covering the Swartberg, Big Syncline and Gamsberg area were collected by the Hyperion sensor. New synergies between multispectral and hyperspectral spaceborne data can be demonstrated, such as the Iron Feature Depth index (IFD), which has recently been proposed for mine waste mapping in the North West Province of South Africa and for gossan detection at Haib River in South Namibia. The work presented here explores the potential of the IFD for gossan mapping and characterization at Gamsberg and Big Syncline, from EO-1 ALI and Landsat-8 OLI data together with mineral maps from expert systems such as the United States Geological Survey (USGS) Material Identification and Characterization Algorithm (MICA), and first results from EnMAPs EnGeoMAP algorithm. Field spectroscopic measurements and field sampling were carried out to validate and calibrate the results from the expert systems and the IFD. This ground truthing is a necessary complementary step to link the results from the expert systems and the IFD to in-situ field spectroscopy. Future mineral exploration initiatives may benefit from the techniques described here, because they can significantly narrow the expensive, exploration activities such as hyperspectral airborne data, field activities and drilling, by identifying the most promising mineral anomalies in an area from the spaceborne data.

Potential of Space-Borne Hyperspectral Data for Biomass Quantification in an Arid Environment: Advantages and Limitations

In spite of considerable efforts to monitor global vegetation, biomass quantification in drylands is still a major challenge due to low spectral resolution and considerable background effects. Hence, this study examines the potential of the space-borne hyperspectral Hyperion sensor compared to the multispectral Landsat OLI sensor in predicting dwarf shrub biomass in an arid region characterized by challenging conditions for satellite-based analysis: The Eastern Pamirs of Tajikistan. We calculated vegetation indices for all available wavelengths of both sensors, correlated these indices with field-mapped biomass while considering the multiple comparison problem, and assessed the predictive performance of single-variable linear models constructed with data from each of the sensors. Results showed an increased performance of the hyperspectral sensor and the particular suitability of indices capturing the short-wave infrared spectral region in dwarf shrub biomass prediction. Performance was considerably poorer in the area with less vegetation cover. Furthermore, spatial transferability of vegetation indices was not feasible in this region, underlining the importance of repeated model building. This study indicates that upcoming space-borne hyperspectral sensors increase the performance of biomass prediction in the world’s arid environments.

Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass

Estimates of forest biomass are needed for various technical and scientific applications, ranging from carbon and bioenergy policies to sustainable forest management. As local measurements are costly, there is a great interest in obtaining reliable estimates over large areas from remote sensing data. Currently, such estimates are obtained with a variety of data sources, statistical methods and prediction standards, and there is no agreement on what are best practices for this task.To improve our understanding of how these different methods affect prediction quality, we first conducted a systematic review of the available literature to identify the most common sensor types and prediction methods. Based on the review, we identified sample size of the reference points on the ground, prediction method (stepwise linear regression, support vector machines, random forest, Gaussian processes and k-nearest neighbor), and sensor type as the main differences that could potentially affect predictive quality. We then compared those factors in two case study areas in Germany and Chile, for which airborne discrete return Light Detection And Ranging (LiDAR) and airborne hyperspectral as well as airborne discrete return LiDAR and spaceborne hyperspectral data were available. For each factor combination, we calculated Pearson's coefficient of correlation between observations and predictions (r2) and root mean squared error (RMSE) for bootstrapped estimates using k-fold cross-validation with a varying number of folds. Finally, Analysis of Variance (ANOVA) was used to quantify the influence of the factors on the predictive error of the biomass models.Our results confirm previous findings that predictor data (sensor) type is the most important factor for the accuracy of biomass estimates, with LiDAR being preferable to hyperspectral data. In contrast to some previous studies, complementing LiDAR with hyperspectral data did not improve predictive accuracy. Also the prediction method had a substantial effect on accuracy and was generally more important than the sample size. In most cases, random forest performed best and stepwise linear models worst, judging from r2 and RMSE under cross-validation. Additional results suggested that r2 may deliver unrealistically large values when the hold-out sample during the cross-validation is too small.In conclusion, our literature review revealed that different methods for biomass estimation are currently used, with no general agreement on best practices. In our case studies, we found substantial accuracy differences between those methods, with LiDAR data, in combination with a random forest algorithm and a large number of reference sample units on the ground yielding the lowest error for biomass predictions. The comparatively high importance of the statistical prediction method seems particularly relevant, as they suggest that choosing the appropriate statistical method may be more effective than obtaining additional field data for obtaining good biomass estimates. Considering the costs of improving accuracy of global and regional biomass estimates by ground measurements, it seems sensible to invest in further comparative studies, preferably with a wider range of sites and including also RADAR sensors, to establish robust best-practice recommendations for obtaining regional and global biomass estimates from remote-sensing data.

Prediction of the presence of topsoil nitrogen from spaceborne hyperspectral data

Conventional methods of soil nitrogen extraction are time consuming, expensive and tedious. Remote sensing and Geographical Information System technologies can be used for the rapid and efficient prediction of the presence of soil nitrogen. However, studies are limited by and large to fields of larger and homogeneous units. This research concentrates on the prediction of topsoil nitrogen from harvested, scattered and small-sized agricultural fields of India using hyperspectral data. Spaceborne hyperspectral Hyperion data are used for the prediction of the presence of nitrogen. Multivariate partial least square regression method was used to predict the presence of nitrogen from reflectance. Reflectance data were pretreated using moving average and Savitzky–Golay filters which resulted in moderate prediction of R2 0.65 and 0.63 for calibration and validation, respectively. It can be inferred that Hyperion data can be effectively used for the prediction of the presence of soil nitrogen with a moderate level of accuracy even in case of scattered fields and fields of sizes approximately equal to the spatial resolution of the satellite.

Unsupervised multiple endmember spectral mixture analysis-based detection of opium poppy fields from an EO-1 Hyperion image in Helmand, Afghanistan

Since 1992, Afghanistan has gradually become the primary illicit opium producer in the entire world. To efficiently eradicate the opium poppy, it is crucial for the United Nations Office on Drugs and Crime (UNODC) and Ministry of Counter Narcotics of Afghanistan to monitor opium poppy cultivation timely. In situ detection of opium fields, however, is often expensive, time-consuming and dangerous in Afghanistan. To overcome the constraints of inaccessibility of opium fields, high-resolution (≤1m) images, like pan-sharpened IKONOS, have been applied in previous studies. Unfortunately, these high-resolution images are expensive when monitoring a large area. In contrast, EO-1 Hyperion imagery, the only source of spaceborne hyperspectral data, has a coarse resolution (30m), but it is free of charge. Moreover, Hyperion's large number of channels may increase the detection capability of subpixel size targets. Until now, however, little research has been found that identified opium fields from spaceborne or aerial hyperspectral images. Therefore, this study attempts to detect opium fields from a Hyperion image covering a study area in Southwest Afghanistan in a situation where training samples were not available. A proposed methodology based on unsupervised endmember-selection and multiple-endmember spectral mixture analysis can detect opium fields directly from the Hyperion image. The number of poppy pixels was overestimated by 12%.

Mineralogy and spectral reflectance of soils and tailings from historical gold mines, Nova Scotia

Gold was mined in 64 districts in southern Nova Scotia between 1861 and the early 1940s, followed by limited, intermittent production up to the present. There is extensive dispersion of arsenic- and mercury-bearing mine tailings in the receiving environment downstream from many of these sites. Elevated mercury concentrations, highest near old stamp mill foundations, occur because of the mercury amalgamation process used to extract gold until the 1940s. Arsenic, on the other hand, occurs naturally in arsenopyrite, which is associated with the gold-bearing quartz veins and host rocks. Tailings are composed of fine sand- to silt-sized quartz, feldspar, illite and chlorite, and represent the primary rock-forming minerals in the metasedimentary host rocks of the Cambro-Ordovician Meguma Supergroup. Carbonate and sulphide minerals occur in minor to trace amounts, along with secondary minerals such as scorodite (FeAsO 4 ·2H 2 O). The extent of tailings dispersal can be mapped through hyperspectral remote sensing methods, as these major mineral components provide an identifiable spectral signature through visible, near infrared and short-wave infrared regions. This paper examines the mineralogy of soils, tills and tailings in the Upper and Lower Seal Harbour gold districts of Nova Scotia. Ground-truthing of space-borne hyperspectral data demonstrates the potential for remote mapping of the spatial extent of these historical mine wastes.

Development of Agricultural Crops Spectral Library and Classification of Crops Using Hyperion Hyperspectral Data

The very high effectiveness of hyperspectral sensors in vegetation discrimination increases the applications of crop classification using hyperspectral data. However, for this capability to be exploitable, it is essential that a well-populated spectral library exists and is accessible in a user-friendly way by the user of this technology. To address this issue field hyperspectral measurement using field spectrometer have been collected and spectral library for various crops have been developed in this study. Spectral Angle Mapper (SAM) approach has been used for image classification and accuracy assessment has been carried out to test the end results. The reasonable good overall accuracy shows that the possible integration of field spectra (in-situ hyperspectral measurements) with pixel data (space-borne hyperspectral data).

Cloud Implementation of a Full Hyperspectral Unmixing Chain Within the NASA Web Coverage Processing Service for EO-1

The launch of the NASA Earth Observing 1 (EO-1) platform in November 2000 marked the establishment of spaceborne hyperspectral technology for land imaging. The Hyperion sensor onboard EO-1 operates in the 0.4-2.5 micrometer spectral range, with 10 nanometer spectral resolution and 30-meter spatial resolution. Spectral unmixing has been one of the most successful approaches to analyze Hyperion data since its launch. It estimates the abundance of spectrally pure constituents (endmembers) in each observation collected by the sensor. Due to the high spectral dimensionality of Hyperion data, unmixing is a very time-consuming operation. In this paper, we develop a cloud implementation of a full hyperspectral unmixing chain made up of the following steps: 1) dimensionality reduction; 2) automatic endmember identification; and 3) fully constrained abundance estimation. The unmixing chain will be available online within the Web Coverage Processing Service (WCPS), an image processing framework that can run on the cloud, as part of the NASA SensorWeb suite of web services. The proposed implementation has been demonstrated using the EO-1 Hyperion imagery. Our experimental results with a hyperspectral scene collected over the Okavango Basin in Botswana suggest the (present and future) potential of spectral unmixing for improved exploitation of spaceborne hyperspectral data. The integration of the unmixing chain in the WCPS framework as part of the NASA SensorWeb suite of web services is just the start of an international collaboration in which many more processing algorithms will be made available to the community through this service. This paper is not so much focused on the theory and results of unmixing (widely demonstrated in other contributions) but about the process and added value of the proposed contribution for ground processing on the cloud and onboard migration of those algorithms to support the generation of low-latency products for new airborne/spaceborne missions.

Hyperspectral satellite data (Hyperion) preprocessing—a case study on banded magnetite quartzite in Godumalai Hill, Salem, Tamil Nadu, India

This paper deals with many calibration methods involved in preprocessing of hyperspectral remote sensing data, while radiation calibration with respect to magnetite minerals without altering image reflectance. Radiometric calibration is the basis and important for both hyperspectral data applications and quantitative remote sensing. While at present there are many methods available for radiometric calibration in the spaceborne hyperspectral data, here, we used the highest spectral resolution Hyperion EO-1 satellite data using a variety of calibration models, which include the calibration model based on atmospheric radiation theory, conversion model as scene dependent such as flat field model, internal average reflectance model, logs residual modified model, and linear model based on the experience of the ground calibration, and so on, and hyperspectral data calibration study was implemented with magnetite mineral. After the above three models contrast, we have arrived at the conclusion that calibration model based on atmospheric radiation theory is the finest radiation calibration method for Hyperion satellite data preprocessing.