Image Endmembers Research Articles

Assessing tree crown fire damage integrating linear spectral mixture analysis and supervised machine learning on Sentinel-2 imagery

ABSTRACT Crown fire damage is a mixture of three principal fire-related components: charred material, scorched foliage, and unaltered green canopy. This study estimated the abundance of these physical alterations in two immediate post-fire Mediterranean forest contexts (Portugal and Italy) by applying linear spectral mixture analysis (LSMA) on Sentinel-2 imagery. The tree crowns fire damage was subsequently mapped, integrating fractional abundance information in a random forest (RF) algorithm, comparing the accuracy resulting from the adoption of generic or image spectral libraries as the primary investigative goal. Although image-derived endmembers resulted in more effectiveness in terms of fire-related components abundance quantification (LMSA-derived RMSE < 0.1), the F-scores always were ≥ 90% whether generic endmembers or image endmembers derived information was employed. The environmental heterogeneity of the two study areas affected the fire severity gradients, with a prevalence of the charred (PT) (45–46%) and green class (IT) (44–53%). Post-fire temporal monitoring was initialized by applying the proposed strategies, and the preliminary results showed a positive recovery trend in forest vegetation from the first year following the fire event, with a reduced charcoal predominance and an increasing proportion of green components.

Characterizing Tree Species in Northern Boreal Forests Using Multiple-Endmember Spectral Mixture Analysis and Multi-Temporal Satellite Imagery

Northern boreal forests are characterized by open stands whereby trees, understory background, and shadow are all significant components of the spectral response within a pixels’ spatial footprint. To overcome this mixed pixel problem, accurate spectral characterization of these (endmember) components is necessary for spectral mixture analysis (SMA) to generate forest classifications at the species level. Obtaining these endmember spectra in the field, however, can be difficult or impossible. This study examined whether image endmember spectra can be identified using forest inventory information to derive dominant tree species classifications. This was tested using multiple-endmember SMA (MESMA) and single- and multi-date Landsat imagery of a forested area in the Northwest Territories, Canada. Image classifications (n = 80) were generated based on 20 image-date combinations and four unmixing models. Accuracies of 80% and 82% were achieved for open and medium dense forest stands, respectively using multi-date imagery, which outperformed single-date imagery acquired at peak phenology. The overall accuracy is 72%; lower due to challenges in very open stands. The multi-date MESMA approach was robust for both compositionally pure and mixed stands. The approach merits further investigation, particularly within the context of the increasing availability of regional-scale satellite imagery enabling composite time-series and spectral-temporal image features.

Comparison of Image Endmember- and Object-Based Classification of Very-High-Spatial-Resolution Unmanned Aircraft System (UAS) Narrow-Band Images for Mapping Riparian Forests and Other Land Covers

Riparian forests are critical for carbon storage, biodiversity, and river water quality. There has been an increasing use of very-high-spatial-resolution (VHR) unmanned aircraft systems (UAS)-based remote sensing for riparian forest mapping. However, for improved riparian forest/zone monitoring, restoration, and management, an enhanced understanding of the accuracy of different classification methods for mapping riparian forests and other land covers at high thematic resolution is necessary. Research that compares classification efficacies of endmember- and object-based methods applied to VHR (e.g., UAS) images is limited. Using the Sequential Maximum Angle Convex Cone (SMACC) endmember extraction algorithm (EEA) jointly with the Spectral Angle Mapper (SAM) classifier, and a separate multiresolution segmentation/object-based classification method, we map riparian forests/land covers and compare the classification accuracies accrued via the application of these two approaches to narrow-band, VHR UAS orthoimages collected over two river reaches/riparian areas in Austria. We assess the effect of pixel size on classification accuracy, with 7 and 20 cm pixels, and evaluate performance across multiple dates. Our findings show that the object-based classification accuracies are markedly higher than those of the endmember-based approach, where the former generally have overall accuracies of &gt;85%. Poor endmember-based classification accuracies are likely due to the very small pixel sizes, as well as the large number of classes, and the relatively small number of bands used. Object-based classification in this context provides for effective riparian forest/zone monitoring and management.

Green Vegetation Cover Dynamics in a Heterogeneous Grassland: Spectral Unmixing of Landsat Time Series from 1999 to 2014

The ability to quantify green vegetation across space and over time is useful for studying grassland health and function and improving our understanding of the impact of land use and climate change on grasslands. Directly measuring the fraction of green vegetation cover is labor-intensive and thus only practical on relatively smaller experimental sites. Remote sensing vegetation indices, as a commonly-used method for large-area vegetation mapping, were found to produce inconsistent accuracies when mapping green vegetation in semi-arid grasslands, largely due to mixed pixels including both photosynthetic and non-photosynthetic material. The spectral mixture approach has the potential to map the fraction of green vegetation cover in a heterogeneous landscape, thanks to its ability to decompose a spectral signal from a mixed pixel into a set of fractional abundances. In this study, a time series of fractional green vegetation cover (FGVC) from 1999 to 2014 is estimated using the spectral mixture approach for a semi-arid mixed grassland, which represents a typical threatened, species-rich habitat in Central Canada. The shape of pixel clouds in each of the Landsat images is used to identify three major image endmembers (green vegetation, bare soil/litter, and water/shadow) for automated image spectral unmixing. The FGVC derived through the spectral mixture approach correlates highly with field observations (R2 = 0.86). Change in the FGVC over the study period was also mapped, and green vegetation in badlands and uplands is found to experience a slight increase, while vegetation in riparian zone shows a decrease. Only a small portion of the study area is undergoing significant changes, which is likely attributable to climate variability, bison reintroduction, and wildfire. The results of this study suggest that the automated spectral unmixing approach is promising, and the time series of medium-resolution images is capable of identifying changes in green vegetation cover in semi-arid grasslands. Further research should investigate driving forces for areas undergoing significant changes.

Unmixing water and mud: Characterizing diffuse boundaries of subtidal mud banks from individual satellite observations

Mapping of subtidal banks in mud-dominated coastal systems is crucial as they influence not only shoreline and ecosystem dynamics but also economic activities and livelihoods of local communities. Due to associated spatiotemporal variations in suspended particulate matter concentrations, subtidal mudbanks are often confined by diffuse and rapidly changing boundaries. To avoid inaccurate representations of these mudbanks in remote sensing images, it is necessary to unmix distinctive reflectance signals into representative landcover fractions. Yet, extracting mud fractions, in order to characterize such diffuse boundaries, is challenging because of the spectral similarity between subtidal- and intertidal features. Here we show that an unsupervised decision tree, used to derive spatially explicit and spectrally coherent image endmembers, facilitates robust linear spectral unmixing on an image-to-image basis, enabling the separation of these coastal features. We found that resulting abundance maps represent cross-shore gradients of vegetation, water and mud fractions present at the coast of Suriname. Furthermore, we confirmed that it is possible to separate land, water and an initial estimate of intertidal zones on individual images. Thus, spectral signatures of end-member candidates, determined from relevant index histograms within these initial estimates, are consistent. These results demonstrate that spectral information from well-defined spatial neighbourhoods facilitates the detection of diffuse boundaries of mudbanks with a spectral unmixing approach.

A COMPARATIVE ANALYSIS OF SPECTRAL INFORMATION EXTRACTION ALGORITHMS FOR TARGET DETECTION OF HYDROTHERMAL ALTERATION ZONES USING ASTER SATELLITE IMAGE DATA

Abstract. The focus of this paper is to evaluate the performance of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for target detection of hydrothermal alteration zones associated with geothermal (GT) springs as proxy for narrowing areas of interest. The study employed the Per-pixel Spectral Angle Mapper (SAM) and the Sub-pixel Linear Spectral Unmixing (LSU) algorithms for spectral information extraction by using the ASTER satellite image data. In both cases, image endmember spectra specifically for kaolinite, alunite, and illite and calcite zones were selected and extracted by using the Analytical Imaging and Geophysics (AIG)-developed processing methods. The results of the analysis show that both SAM and LSU discriminated targets of interest better when employing image spectra and poorly when using library spectra. However, the Per-pixel SAM is unsuitable for target detection and more suited where the objective of the investigation is to classify whole scene and not particular targets as in this case. The LSU was found to be effective for discriminating alterations associated with the thermal springs especially where image endmember spectra are employed for analysis, thus recommended for prefeasibility mapping of GT related resources.

Analyses of Namibian Seasonal Salt Pan Crust Dynamics and Climatic Drivers Using Landsat 8 Time-Series and Ground Data

Salt pans are highly dynamic environments that are difficult to study by in situ methods because of their harsh climatic conditions and large spatial areas. Remote sensing can help to elucidate their environmental dynamics and provide important constraints regarding their sedimentological, mineralogical, and hydrological evolution. This study utilizes spaceborne multitemporal multispectral optical data combined with spectral endmembers to document spatial distribution of surface crust types over time on the Omongwa pan located in the Namibian Kalahari. For this purpose, 49 surface samples were collected for spectral and mineralogical characterization during three field campaigns (2014–2016) reflecting different seasons and surface conditions of the salt pan. An approach was developed to allow the spatiotemporal analysis of the salt pan crust dynamics in a dense time-series consisting of 77 Landsat 8 cloud-free scenes between 2014 and 2017, covering at least three major wet–dry cycles. The established spectral analysis technique Sequential Maximum Angle Convex Cone (SMACC) extraction method was used to derive image endmembers from the Landsat time-series stack. Evaluation of the extracted endmember set revealed that the multispectral data allowed the differentiation of four endmembers associated with mineralogical mixtures of the crust’s composition in dry conditions and three endmembers associated with flooded or muddy pan conditions. The dry crust endmember spectra have been identified in relation to visible, near infrared, and short-wave infrared (VNIR–SWIR) spectroscopy and X-ray diffraction (XRD) analyses of the collected surface samples. According these results, the spectral endmembers are interpreted as efflorescent halite crust, mixed halite–gypsum crust, mixed calcite quartz sepiolite crust, and gypsum crust. For each Landsat scene the spatial distribution of these crust types was mapped with the Spectral Angle Mapper (SAM) method and significant spatiotemporal dynamics of the major surface crust types were observed. Further, the surface crust dynamics were analyzed in comparison with the pan’s moisture regime and other climatic parameters. The results show that the crust dynamics are mainly driven by flooding events in the wet season, but are also influenced by temperature and aeolian activity in the dry season. The approach utilized in this study combines the advantages of multitemporal satellite data for temporal event characterization with advantages from hyperspectral methods for the image and ground data analyses that allow improved mineralogical differentiation and characterization.

Alteration Hydrothermal Stage Division and its Application in Geological Prospecting Using CASI-SASI Airborne Hyperspectral Data: Taken the Baixianishan Area in Liuyuan Town of Gansu Province as an Example

The division of the hydrothermal period based on alteration minerals extracted from airborne hyperspectral data has been rarely researched. This work processed CASI-SASI airborne hyperspectral data, and optimized reference spectra from image endmembers based on the expert knowledge in the Baixianishan area. The mixed tuned matched filtering method was utilized to map alteration minerals, and the distribution of limonite, short-wavelength sericite, middle-wavelength sericite and long-wavelength sericite was obtained. Distribution patterns of limonite and three sericite subclasses were analyzed, and three hydrothermal stages were divided. It is considered that the second hydrothermal activity is closely related to the gold deposit. It is proposed that prospecting factors of the Baixianishan gold deposit are NEE-trending fault structure, the vein-like distribution of limonite and short-wavelength sericite. Based on the discuss above, a favorable prospecting section is found in the south of the Xiaobaishan area. It is indicated that the CASI-SASI airborne hyperspectral data can quickly and accurately provide more effective remote sensing criteria at the regional scale for both basic geological survey and mineral resources exploration, which has a wide application prospect in the geological field.

Sparsity measure based library aided unmixing of hyperspectral image

Availability of a large number of application-specific spectral libraries has generated a great deal of interest in semi-blind unmixing of the hyperspectral image in both remote sensing and signal processing community. This study presents a novel, semi-supervised, parameter-free algorithm which employs sparsity measures for library pruning. The overall algorithm includes sparsity criteria based library pruning and sparse inversion method for abundance computation. In the pruning process, each library element is removed from the spectral library and the corresponding sparse abundance matrix is computed. The library elements which lead to higher sparsity are adjudged as image endmembers, based on the assumption that elimination of actual image endmember enhances sparsity level. The authors also present a detailed exploration of standard sparsity measures. They calculate the abundance of the pruned library by maximising Gini index or pq-norm sparsity, which satisfies the desirable sparsity properties and is easier to compute. The abundance calculation task is solved using the adaptive direction method of multipliers. The experimental results on several real and synthetic image datasets demonstrate the computational efficiency and proficiency the authors’ method in the presence of noise and highly coherent spectral library.

MULTISPECTRAL AND HYPERSPECTRAL SATELLITE DATA FOR ALTERATION MAPPING USING PARTIAL-SUBPIXEL ALGORITHM IN AN UNEXPLORED REGION

Abstract. The focus of this paper is to comparatively evaluate the performance of ASTER and Hyperion data for target detection of hydrothermal alteration zones associated with geothermal (GT) springs in an unexplored savannah region. The study employed the partial subpixel unmixing Mixture Tuned Match Filtering algorithm for spectral information extraction using the multispectral and hyperspectral satellite data. In both cases, image endmember spectra specifically for kaolinite, alunite, and illite and calcite zones were selected and extracted by using the Analytical Imaging and Geophysics (AIG)-developed processing methods. The results show that the Hyperion, despite its distortions, can effectively discriminate associated alteration zones better than ASTER. Consequently, Hyperspectral data and analysis are thus recommended for use in similar unexplored regions for GT resource detection and monitoring.

Subpixel Component Analysis for Hyperspectral Image Classification

Land-cover classification with hyperspectral imagery has been an active topic in the remote sensing community. It aims at relating a unique class label to each pixel in the scene, so that it can be well defined by a given land cover type. In this paper, we explore the intrinsic characteristics of hyperspectral imagery from a subpixel-level perspective and propose a new subpixel component analysis (SCA) approach for feature extraction and land-cover classification. The core idea of SCA is that we extract a subpixel attribute component feature from the abundance maps. Compared with the abundance maps, the extracted subpixel feature image shows higher signal-to-noise level and clearer spatial distribution details. In order to deal with spectral variability, as well as obtain representative image endmember signatures and their corresponding abundance maps, we adopt a regional clustering-based spatial preprocessing (RCSPP) strategy for endmember identification, and a partial unmixing model based on mixture tuned matched filtering (MTMF) for abundance estimation. Furthermore, to highlight the spatial distribution details as well as eliminate the noise disturbance in the derived abundance maps, we perform sparse image decomposition on the obtained abundance maps, thus achieving a new subpixel feature representation for classification. Our experimental results reveal that the proposed SCA approach can obtain feature representation with explicit physical meaning, clear spatial distribution details, and better noise robustness, leading to state-of-the-art classification results.

Mapping impervious surface fractions using automated Fisher transformed unmixing

Spatial explicit monitoring of impervious surface fractions provides essential information for urban planning, urban disaster prevention and mitigation. Moderate spectral and spatial resolution satellite observation has become a tool to perform such monitoring across different spatial and temporal scales. Yet, one major challenge in urban remote sensing is the high spatial heterogeneity causing the presence of image pixels constituting a mixture of different land cover materials. Here we apply an automated unmixing procedure specifically designed to optimally account for the pronounced spectral variability (i.e. high within-class and low between-class variability) prevailing in urban settings. The approach is based on Fisher Discriminant Analysis (FDA), a data dimensionality reduction technique in which between-class variability is maximized and within-class variability is minimized while transforming spectra from the reflectance to the Fisher feature space. We integrated the FDA transformation in the widely-applied Multiple Endmember Spectral Mixture Analysis (MESMA) approach in an attempt to more effectively address the endmember variability problem. We made use of online spectral libraries to train the Fisher transformation parameters and steer the automated selection of image endmembers. Our Fisher transformed MESMA algorithm (F-MESMA) was tested on medium spatial resolution Landsat scenes and impervious fraction maps were generated for five major cities located in different regions of the world. The output of F-MESMA was compared to the output of state-of-the-art spectral mixture analysis approaches which were specifically designed to reduce endmember variability issues. We could demonstrate that compared to other spectral transformation approaches, the ratio of within- vs between-class variability of the endmembers was most strongly reduced after applying the Fisher transformation. As a direct consequence, across all five cities F-MESMA consistently provided the most accurate impervious surface fraction estimates (RMSEF-MESMA = 0.13 vs. RMSEalternative approaches = [0.16–0.17]).

Remote sensing satellite imagery for prospecting geothermal systems in an aseismic geologic setting: Yankari Park, Nigeria

The aim of this investigation is to evaluate the applicability of multispectral satellite imagery for hydrothermal alteration mineral mapping and thermal anomaly detection as a proxy for the characterization of subtle geothermal systems in an aseismic geologic setting. The Yankari Park, an area in northeastern Nigeria characterized by several surface manifestations of hotsprings and hydrothermally altered rock deposits was selected for this study. The study evaluates the effectiveness of the Independent Component Analysis and Mixture Tuned Matched Filtering (MTMF) algorithm for target detection of hydrothermal alteration zones and the Single Channel Algorithm for thermal anomaly detection associated with subtle geothermal systems using spectral bands of Landsat-8, ASTER and Landsat-7, respectively. The results indicated that the use of verified image endmember spectra gives more accurate results compared to that from library spectra by implementing MTMF. Results of mapping thermally anomalous pixels did not conform to known locations of the thermal springs, however, examining the spatial correlation of the anomaly areas with the major fault-fracture systems from the geological map of the study area indicates a close affinity between them and with previously reported thermal gradients within heat conserving sedimentary formations. This investigation has significant implications as it signifies the need for the integrative use of alteration mineral mapping and thermal anomaly detection for cost-effective prefeasibility stage mapping of geothermal systems in an aseismic geologic setting located in African plate and other regions around the world.

Validating (MNF) transform to determine the least inherent dimensionality of ASTER image data of some uranium localities at Central Eastern Desert, Egypt

One of the main determinations of geological remote sensing is to develop and improve different image processing algorithms and methods for mineral mapping and rock type discrimination. This paper investigates one of those sophisticated algorithms; the validity of Minimum Noise Fraction (MNF) components to improve the spectral separability of The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor image data of Terra satellite, and to develop a standard method of noise removal and image enhancement. Previous studies that used in image processing to eliminate MNF eigenvalues, eliminate values less than the unit. However, removing MNF eigenvalues more than this value and its effect on the data, has not been examined. Therefore, in this paper, we demonstrate that we can eliminate more than the unit up to 2 eigenvalue without a negative inherent effect on the origin data structure. A comparison study using different Image Quality Measures (IQMs) methods, has been accomplished on data distribution before and after MNF transformation of the original and atmospherically corrected data. Matched Filter (MF) classification was performed on data before and after MNF to map a chosen known alteration mineral (Kaolinite) by matching the image endmember spectra and its spectra from USGS library. By examining the spectral trends of specific mineral alteration type across these transformed channels, areas of interest that are unique by the related previous well-known uranium mineralization may become superficial apparent. This examination also showed how the use of MNF as a pre-processing technique could improve the capability to extract information from ASTER data of El-Missikat, El-Eridya and Kab Amiri areas in the Central Eastern desert as a case study district for uranium mineralization occurrences.

Fast Semi-Supervised Unmixing of Hyperspectral Image by Mutual Coherence Reduction and Recursive PCA

Dictionary pruning step is often employed prior to the sparse unmixing process to improve the performance of library aided unmixing. This paper presents a novel recursive PCA approach for dictionary pruning of linearly mixed hyperspectral data motivated by the low-rank structure of a linearly mixed hyperspectral image. Further, we propose a mutual coherence reduction method for pre-unmixing to enhance the performance of pruning. In the pruning step we, identify the actual image endmembers utilizing the low-rank constraint. We obtain an augmented version of the data by appending each image endmember and compute PCA reconstruction error, which is a convex surrogate of matrix rank. We identify the pruned library elements according to PCA reconstruction error ratio (PRER) and PCA reconstruction error difference (PRED) and employ a recursive formulation for repeated PCA computation. Our proposed formulation identifies the exact endmember set at an affordable computational requirement. Extensive simulated and real image experiments exhibit the efficacy of the proposed algorithm in terms of its accuracy, computational complexity and noise performance.

Mineral mapping using spaceborne Tiangong‐1 hyperspectral imagery and ASTER data: A case study of alteration detection in support of regional geological survey at Jintanzi‐Malianquan area, Beishan, Gansu Province, China

This is an extension of our previous study and an applicability test on the mapping capability of Tiangong‐1 data with more complicated geological conditions over large areas. The Jintanzi‐Malianquan area is located in a major Au‐Cu‐Ni‐Cr resource belts in China. In order to support the 1:50,000 regional geological survey, this study presents the mapping results of using spectral angle mapper method and image endmembers from spaceborne Tiangong‐1 Hyperspectral Imager (HSI) shortwave infrared and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Six alteration minerals (muscovite, kaolinite, chlorite, epidote, calcite, and dolomite) related to hydrothermal ore deposits are used in the analysis. By comparing the results from both datasets, it is confirmed Tiangong‐1 HSI data can detect six major minerals (muscovite, kaolinite, chlorite, epidote, calcite, and dolomite), while ASTER can only discriminate the first five minerals in this study area. Fifteen targets for mineral exploration are mapped from the remote sensing results. Eleven targets have been verified by existing geologic maps and field validation for muscovite and epidote alteration. The results of this study suggest that the Tiangong‐1 HSI data are well suited for quick spaceborne reconnaissance of alteration minerals to support routine geological survey in large areas at 20‐m resolution, which provides continuous mapping products for all terrains at an accuracy of better than 1:50,000 scale map.

Predicting forest height using the GOST, Landsat 7 ETM+, and airborne LiDAR for sloping terrains in the Greater Khingan Mountains of China

Sloping terrain of forests is an overlooked factor in many models simulating the canopy bidirectional reflectance distribution function, which limits the estimation accuracy of forest vertical structure parameters (e.g., forest height). The primary objective of this study was to predict forest height on sloping terrain over large areas with the Geometric-Optical Model for Sloping Terrains (GOST) using airborne Light Detection and Ranging (LiDAR) data and Landsat 7 imagery in the western Greater Khingan Mountains of China. The Sequential Maximum Angle Convex Cone (SMACC) algorithm was used to generate image endmembers and corresponding abundances in Landsat imagery. Then, LiDAR-derived forest metrics, topographical factors and SMACC abundances were used to calibrate and validate the GOST, which aimed to accurately decompose the SMACC mixed forest pixels into sunlit crown, sunlit background and shade components. Finally, the forest height of the study area was retrieved based on a back-propagation neural network and a look-up table. Results showed good performance for coniferous forests on all slopes and at all aspects, with significant coefficients of determination above 0.70 and root mean square errors (RMSEs) between 0.50 m and 1.00 m based on ground observed validation data. Higher RMSEs were found in areas with forest heights below 5 m and above 17 m. For 90% of the forested area, the average RMSE was 3.58 m. Our study demonstrates the tremendous potential of the GOST for quantitative mapping of forest height on sloping terrains with multispectral and LiDAR inputs.

Analyses of Recent Sediment Surface Dynamic of a Namibian Kalahari Salt Pan Based on Multitemporal Landsat and Hyperspectral Hyperion Data

This study combines spaceborne multitemporal and hyperspectral data to analyze the spatial distribution of surface evaporite minerals and changes in a semi-arid depositional environment associated with episodic flooding events, the Omongwa salt pan (Kalahari, Namibia). The dynamic of the surface crust is evaluated by a change-detection approach using the Iterative-reweighted Multivariate Alteration Detection (IR-MAD) based on the Landsat archive imagery from 1984 to 2015. The results show that the salt pan is a highly dynamic and heterogeneous landform. A change gradient is observed from very stable pan border to a highly dynamic central pan. On the basis of hyperspectral EO-1 Hyperion images, the current distribution of surface evaporite minerals is characterized using Spectral Mixture Analysis (SMA). Assessment of field and image endmembers revealed that the pan surface can be categorized into three major crust types based on diagnostic absorption features and mineralogical ground truth data. The mineralogical crust types are related to different zones of surface change as well as pan morphology that influences brine flow during the pan inundation and desiccation cycles. These combined information are used to spatially map depositional environments where the more dynamic halite crust concentrates in lower areas although stable gypsum and calcite/sepiolite crusts appear in higher elevated areas.

Enhanced Compositional Mapping through Integrated Full-Range Spectral Analysis

We developed a method to enhance compositional mapping from spectral remote sensing through the integration of visible to near infrared (VNIR, ~0.4–1 µm), shortwave infrared (SWIR, ~1–2.5 µm), and longwave infrared (LWIR, ~8–13 µm) data. Spectral information from the individual ranges was first analyzed independently and then the resulting compositional information in the form of image endmembers and apparent abundances was integrated using ISODATA cluster analysis. Independent VNIR, SWIR, and LWIR analyses of a study area near Mountain Pass, California identified image endmembers representing vegetation, manmade materials (e.g., metal, plastic), specific minerals (e.g., calcite, dolomite, hematite, muscovite, gypsum), and general lithology (e.g., sulfate-bearing, carbonate-bearing, and silica-rich units). Integration of these endmembers and their abundances produced a final full-range classification map incorporating much of the variation from all three spectral ranges. The integrated map and its 54 classes provide additional compositional information that is not evident in the VNIR, SWIR, or LWIR data alone, which allows for more complete and accurate compositional mapping. A supplemental examination of hyperspectral LWIR data and comparison with the multispectral LWIR data used in the integration illustrates its potential to further improve this approach.

Mapping changing distributions of dominant species in oil-contaminated salt marshes of Louisiana using imaging spectroscopy

Abstract The April 2010 Deepwater Horizon (DWH) oil spill was the largest coastal spill in U.S. history. Monitoring subsequent change in marsh plant community distributions is critical to assess ecosystem impacts and to establish future coastal management priorities. Strategically deployed airborne imaging spectrometers, like the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), offer the spectral and spatial resolution needed to differentiate plant species. However, obtaining satisfactory and consistent classification accuracies over time is a major challenge, particularly in dynamic intertidal landscapes. Here, we develop and evaluate an image classification system for a time series of AVIRIS data for mapping dominant species in a heavily oiled salt marsh ecosystem. Using field-referenced image endmembers and canonical discriminant analysis (CDA), we classified 21 AVIRIS images acquired during the fall of 2010, 2011 and 2012. Classification results were evaluated using ground surveys that were conducted contemporaneously to AVIRIS collection dates. We analyzed changes in dominant species cover from 2010 to 2012 for oiled and non-oiled shorelines. CDA discriminated dominant species with a high level of accuracy (overall accuracy = 82%, kappa = 0.78) and consistency over three imaging dates (overall 2010 = 82%, overall 2011 = 82%, overall 2012 = 88%). Marshes dominated by Spartina alterniflora were the most spatially abundant in shoreline zones (≤ 28 m from shore) for all three dates (2010 = 79%, 2011 = 61%, 2012 = 63%), followed by Juncus roemerianus (2010 = 11%, 2011 = 19%, 2012 = 17%) and Distichlis spicata (2010 = 4%, 2011 = 10%, 2012 = 7%). Marshes that were heavily contaminated with oil exhibited variable responses from 2010 to 2012. Marsh vegetation classes converted to a subtidal, open water class along oiled and non-oiled shorelines that were similarly situated in the landscape. However, marsh loss along oil-contaminated shorelines doubled that of non-oiled shorelines. Only S. alterniflora dominated marshes were extensively degraded, losing 15% (354,604 m 2 ) cover in oiled shoreline zones, suggesting that S. alterniflora marshes may be more vulnerable to shoreline erosion following hydrocarbon stress, due to their landscape position.