COMPARISON OF VERY NEAR INFRARED (VNIR) WAVELENGTH FROM EO-1 HYPERION AND WORLDVIEW 2 IMAGES FOR SALTMARSH CLASSIFICATION

Saltmarsh is one of the important communities of wetlands. Due to a range of pressures, it has been declared as an EEC (Ecological Endangered Community) in Australia. In order to correctly identify different saltmarsh species, development of distinct spectral characteristics is essential to monitor this EEC. This research was conducted to classify saltmarsh species based on spectral characteristics in the VNIR wavelength of Hyperion Hyperspectral and Worldview 2 multispectral remote sensing data. Signal Noise Ratio (SNR) and Principal Component Analysis (PCA) were applied in Hyperion data to test data quality and to reduce data dimensionality respectively. FLAASH atmospheric correction was done to get surface reflectance data. Based on spectral and spatial information a supervised classification followed by Mapping Accuracy (%) was used to assess the classification result. SNR of Hyperion data was varied according to season and wavelength and it was higher for all land cover in VNIR wavelength. There was a significant difference between radiance and reflectance spectra. It was found that atmospheric correction improves the spectral information. Based on the PCA of 56 VNIR band of Hyperion, it was possible to segregate 16 bands that contain 99.83 % variability. Based on reference 16 bands were compared with 8 bands of Worldview 2 for classification accuracy. Overall Accuracy (OA) % for Worldview 2 was increased from 72 to 79 while for Hyperion, it was increased from 70.47 to 71.66 when bands were added orderly. Considering the significance test with z values and kappa statistics at 95% confidence level, Worldview 2 classification accuracy was higher than Hyperion data

Mapeamento de óxidos de ferro usando imagens landsat-8/OLI e EO-1/hyperion nos depósitos ferríferos da Serra Norte, província mineral de Carajás, Brasil

FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOMapping methods for iron oxides and clay minerals, using Landsat-8/Operational Land Imager (OLI) and Earth Observing 1 (EO-1)/Hyperion imagery integrated with airborne geophysical data, were applied in the N4, N5, and N4WS iron deposits, Serra Norte, Carajás, Brazil. Band ratios were achieved on Landsat-8/OLI imagery, allowing the recognition of the main minerals from iron deposits. The Landsat-8/OLI imagery showed a robust performance for iron oxide exploration, even in vegetated shrub areas. Feature extraction and Spectral Angle Mapper hyperspectral classification methods were carried out on EO-1/Hyperion imagery with good results for mapping high-grade iron ore, the hematite-goethite ratio, and clay minerals from regolith. The EO-1/Hyperion imagery proved an excellent tool for fast remote mineral mapping in open-pit areas, as well as mapping waste and tailing disposal facilities. An unsupervised classification was carried out on a data set consisting of EO-1/Hyperion visible near-infrared 74 bands, Landsat-8/OLI-derived Normalized Difference Vegetation Index, Laser Imaging Detection and Ranging-derived Digital Terrain Model, and high-resolution airborne geophysical data (gamma ray spectrometry, Tzz component of gradiometric gravimetry data). This multisource classification proved to be an adequate alternative for mapping iron oxides in vegetated shrub areas and to enhance the geology of the regolith and mineralized areas463331349FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOsem informação307177/2014-9Métodos de mapeamento para óxidos de ferro e argilas, aplicados em imagens Landsat-8/Operational Land Imager (OLI) e Earth Observing 1 (EO-1)/Hyperion e integrados com dados aerogeofísicos, foram testados nos depósitos de ferro de N4, N5 e N4WS, Serra Norte, Carajás, Brasil. Razões de banda foram aplicadas à imagem Landsat-8/OLI, identificando os principais minerais dos depósitos de ferro de N4 e N5. As imagens Landsat-8/OLI mostraram um bom desempenho para a exploração de óxido de ferro, mesmo em áreas vegetadas. Extração de feições espectrais e o método de classificação hiperespectral Spectral Angle Mapper foram aplicados na imagem EO-1/Hyperion com bons resultados para o mapeamento de minério de ferro de alto teor, bem como da proporção de hematita-goethita do minério e de argilas nos regolitos. A imagem EO-1/Hyperion provou ser uma excelente ferramenta para o mapeamento remoto de minerais em áreas de mina a céu aberto, bem como no mapeamento das pilhas de minério. Uma classificação não supervisionada foi aplicada a dados de 74 bandas do visível e infravermelho próximo do EO-1/Hyperion, índice Normalized Difference Vegetation Index derivado do Landsat-8/OLI, Modelo Digital do Terreno derivado do Laser Imaging Detection and Ranging, e dados aerogeofísicos (gamaespectrometria e componente Tzz do dado gravimétrico gradiométrico). Essa classificação de dados multifonte mostrou ser uma alternativa para mapeamento de óxidos de ferro em áreas vegetadas, bem como da geologia do regolito e das áreas mineralizada

EO-1 Data Quality and Sensor Stability with Changing Orbital Precession at the End of a 16 Year Mission

The Earth Observing One (EO-1) satellite has completed 16 years of Earth observations in early 2017. What started as a technology mission to test various new advancements turned into a science and application mission that extended many years beyond the satellites planned life expectancy. EO-1s primary instruments are spectral imagers: Hyperion, the only civilian full spectrum spectrometer (430-2400 nm) in orbit; and the Advanced Land Imager (ALI), the prototype for Landsat-8s pushbroom imaging technology. Both Hyperion and ALI instruments have continued to perform well, but in February 2011 the satellite ran out of the fuel necessary to maintain orbit, which initiated a change in precession rate that led to increasingly earlier equatorial crossing times during its last five years. The change from EO-1s original orbit, when it was formation flying with Landsat-7 at a 10:01am equatorial overpass time, to earlier overpass times results in image acquisitions with increasing solar zenith angles (SZAs). In this study, we take several approaches to characterize data quality as SZAs increased. Our results show that for both EO-1 sensors, atmospherically corrected reflectance products are within 5 to 10 of mean pre-drift products. No marked trend in decreasing quality in ALI or Hyperion is apparent through 2016, and these data remain a high quality resource through the end of the mission

Advances in Hyperspectral Image Classification Methods for Vegetation and Agricultural Cropland Studies

Hyperspectral data are becoming more widely available via sensors on airborne and unmanned aerial vehicle (UAV) platforms, as well as proximal platforms. While space-based hyperspectral data continue to be limited in availability, multiple spaceborne Earth-observing missions on traditional platforms are scheduled for launch, and companies are experimenting with small satellites for constellations to observe the Earth, as well as for planetary missions. Land cover mapping via classification is one of the most important applications of hyperspectral remote sensing and will increase in significance as time series of imagery are more readily available. However, while the narrow bands of hyperspectral data provide new opportunities for chemistry-based modeling and mapping, challenges remain. Hyperspectral data are high dimensional, and many bands are highly correlated or irrelevant for a given classification problem. For supervised classification methods, the quantity of training data is typically limited relative to the dimension of the input space. The resulting Hughes phenomenon, often referred to as the curse of dimensionality, increases potential for unstable parameter estimates, overfitting, and poor generalization of classifiers. This is particularly problematic for parametric approaches such as Gaussian maximum likelihoodbased classifiers that have been the backbone of pixel-based multispectral classification methods. This issue has motivated investigation of alternatives, including regularization of the class covariance matrices, ensembles of weak classifiers, development of feature selection and extraction methods, adoption of nonparametric classifiers, and exploration of methods to exploit unlabeled samples via semi-supervised and active learning. Data sets are also quite large, motivating computationally efficient algorithms and implementations. This chapter provides an overview of the recent advances in classification methods for mapping vegetation using hyperspectral data. Three data sets that are used in the hyperspectral classification literature (e.g., Botswana Hyperion satellite data and AVIRIS airborne data over both Kennedy Space Center and Indian Pines) are described in Section 3.2 and used to illustrate methods described in the chapter. An additional high-resolution hyperspectral data set acquired by a SpecTIR sensor on an airborne platform over the Indian Pines area is included to exemplify the use of new deep learning approaches, and a multiplatform example of airborne hyperspectral data is provided to demonstrate transfer learning in hyperspectral image classification. Classical approaches for supervised and unsupervised feature selection and extraction are reviewed in Section 3.3. In particular, nonlinearities exhibited in hyperspectral imagery have motivated development of nonlinear feature extraction methods in manifold learning, which are outlined in Section 3.3.1.4. Spatial context is also important in classification of both natural vegetation with complex textural patterns and large agricultural fields with significant local variability within fields. Approaches to exploit spatial features at both the pixel level (e.g., co-occurrencebased texture and extended morphological attribute profiles [EMAPs]) and integration of segmentation approaches (e.g., HSeg) are discussed in this context in Section 3.3.2. Recently, classification methods that leverage nonparametric methods originating in the machine learning community have grown in popularity. An overview of both widely used and newly emerging approaches, including support vector machines (SVMs), Gaussian mixture models, and deep learning based on convolutional neural networks is provided in Section 3.4. Strategies to exploit unlabeled samples, including active learning and metric learning, which combine feature extraction and augmentation of the pool of training samples in an active learning framework, are outlined in Section 3.5. Integration of image segmentation with classification to accommodate spatial coherence typically observed in vegetation is also explored, including as an integrated active learning system. Exploitation of multisensor strategies for augmenting the pool of training samples is investigated via a transfer learning framework in Section 3.5.1.2. Finally, we look to the future, considering opportunities soon to be provided by new paradigms, as hyperspectral sensing is becoming common at multiple scales from ground-based and airborne autonomous vehicles to manned aircraft and space-based platforms

Prediction of topsoil organic carbon using airborne and satellite hyperspectral imagery

The Airborne Hyperspectral Scanner (AHS) and the Hyperion satellite hyperspectral sensors were evaluated for their ability to predict topsoil organic carbon (C) in burned mountain areas of northwestern Spain slightly covered by heather vegetation. Predictive models that estimated total organic C (TOC) and oxidizable organic C (OC) content were calibrated using two datasets: a ground observation dataset with 39 topsoil samples collected in the field (for models built using AHS data), and a dataset with 200 TOC/OC observations predicted by AHS (for models built using Hyperion data). For both datasets, the prediction was performed by stepwise multiple linear regression (SMLR) using reflectances and spectral indices (SI) obtained from the images, and by the widely-used partial least squares regression (PLSR) method. SMLR provided a performance comparable to or even better than PLSR, while using a lower number of channels. SMLR models for the AHS were based on a maximum of eight indices, and showed a coefficient of determination in the leave-one-out cross-validation R2 = 0.60–0.62, while models for the Hyperion sensor showed R2 = 0.49–0.61, using a maximum of 20 indices. Although slightly worse models were obtained for the Hyperion sensor, which was attributed to its lower signal-to-noise ratio (SNR), the prediction of TOC/OC was consistent across both sensors. The relevant wavelengths for TOC/OC predictions were the red region of the spectrum (600–700 nm), and the short wave infrared region between ~2000–2250 nm. The use of SMLR and spectral indices based on reference channels at ~1000 nm was suitable to quantify topsoil C, and provided an alternative to the more complex PLSR method

Assessing The Biophysical Naturalness Of Grassland In Eastern North Dakota With Hyperspectral Imagery

Over the past two decades, non-native species within grassland communities have quickly developed due to human migration and commerce. Invasive species like Smooth Brome grass (Bromus inermis) and Kentucky Blue Grass (Poa pratensis), seriously threaten conservation of native grasslands. This study aims to discriminate between native grasslands and planted hayfields and conservation areas dominated by introduced grasses using hyperspectral imagery. Hyperspectral imageries from the Hyperion sensor on EO-1 were acquired in late spring and late summer on 2009 and 2010. Field spectra for widely distributed species as well as smooth brome grass and Kentucky blue grass were collected from the study sites throughout the growing season. Imagery was processed with an unmixing algorithm to estimate fractional cover of green and dry vegetation and bare soil. As the spectrum is significantly different through growing season, spectral libraries for the most common species are then built for both the early growing season and late growing season. After testing multiple methods, the Adaptive Coherence Estimator (ACE) was used for spectral matching analysis between the imagery and spectral libraries. Due in part to spectral similarity among key species, the results of spectral matching analysis were not definitive. Additional indexes, Level of Dominance and Band variance , were calculated to measure the predominance of spectral signatures in any area. A Texture co-occurrence analysis was also performed on both Level of Dominance and Band variance indexes to extract spatial characteristics. The results suggest that compared with disturbed area, native prairie tend to have generally lower Level of Dominance and Band variance as well as lower spatial dissimilarity. A final decision tree model was created to predict presence of native or introduced grassland. The model was more effective for identification of Mixed Native Grassland than for grassland dominated by a single species. The discrimination of native and introduced grassland was limited by the similarity of spectral signatures between forb-dominated native grasslands and brome-grass stands. However, saline native grasslands were distinguishable from brome grass

Spectral identification and quantification of salts in the Atacama Desert

This work was part-funded by a Research Incentive Grant from The Carnegie Trust (REF: 70335) and a Royal Society of Edinburgh Research Fellowship to C. Cousins. J, Harris acknowledges funding from STFC (consolidated grant ST/N000528/1).Salt minerals are an important natural resource. The ability to quickly and remotely identify and quantify salt deposits and salt contaminated soils and sands is therefore a priority goal for the various industries and agencies that utilise salts. The advent of global hyperspectral imagery from instruments such as Hyperion on NASA’s Earth-Observing 1 satellite has opened up a new source of data that can potentially be used for just this task. This study aims to assess the ability of Visible and Near Infrared (VNIR) spectroscopy to identify and quantify salt minerals through the use of spectral mixture analysis. The surface and near-surface soils of the Atacama Desert in Chile contain a variety of well-studied salts, which together with low cloud coverage, and high aridity, makes this region an ideal testbed for this technique. Two forms of spectral data ranging 0.35 – 2.5 μm were collected: laboratory spectra acquired using an ASD FieldSpec Pro instrument on samples from four locations in the Atacama desert known to have surface concentrations of sulfates, nitrates, chlorides and perchlorates; and images from the EO-1 satellite’s Hyperion instrument taken over the same four locations. Mineral identifications and abundances were confirmed using quantitative XRD of the physical samples. Spectral endmembers were extracted from within the laboratory and Hyperion spectral datasets and together with additional spectral library endmembers fed into a linear mixture model. The resulting identification and abundances from both dataset types were verified against the sample XRD values. Issues of spectral scale, SNR and how different mineral spectra interact are considered, and the utility of VNIR spectroscopy and Hyperion in particular for mapping specific salt concentrations in desert environments is established. Overall, SMA was successful at estimating abundances of sulfate minerals, particularly calcium sulfate, from both hyperspectral image and laboratory sample spectra, while abundance estimation of other salt phase spectral end-members was achieved with a higher degree of error.Publisher PD